initial presentation of diabetes mellitus

Type 2 Diabetes Mellitus Clinical Presentation

• Author: Romesh Khardori, MD, PhD, FACP; Chief Editor: George T Griffing, MD  more...
• Sections Type 2 Diabetes Mellitus
• Pathophysiology
• Epidemiology
• Patient Education
• Physical Examination
• Approach Considerations
• Glucose Studies
• Glycated Hemoglobin Studies
• Urinary Albumin Studies
• Diabetes Testing in Asymptomatic Patients
• Tests to Differentiate Type 2 and Type 1 Diabetes
• Pharmacologic Therapy
• Management of Glycemia
• Dietary Modifications
• Activity Modifications
• Bariatric Surgery
• Laboratory Monitoring
• Monitoring for Diabetic Complications
• Management of Hypertension
• Management of Dyslipidemia
• Management of Coronary Heart Disease
• Management of Ophthalmologic Complications
• Management of Diabetic Neuropathy
• Management of Infections
• Management of Intercurrent Medical Illness
• Management of Critical Illness
• Pharmacologic Considerations in Surgery
• Prevention of Type 2 Diabetes Mellitus
• Stroke Prevention in Diabetes
• Consultations
• Medication Summary
• Antidiabetics, Biguanides
• Antidiabetics, Sulfonylureas
• Antidiabetics, Meglitinide Derivatives
• Antidiabetics, Alpha-Glucosidase Inhibitors
• Antidiabetics, Thiazolidinediones
• Antidiabetics, Glucagonlike Peptide-1 Agonists
• Dual GIP/GLP-1 Agonists
• Antidiabetics, Dipeptidyl Peptidase IV Inhibitors
• Antidiabetics, Amylinomimetics
• Selective Sodium-Glucose Transporter-2 Inhibitors
• Bile Acid Sequestrants
• Antidiabetics, Rapid-Acting Insulins
• Antidiabetics, Short-Acting Insulins
• Antidiabetics, Intermediate-Acting Insulins
• Antidiabetics, Long-Acting Insulins
• Dopamine Agonists
• Questions & Answers
• Media Gallery

The diagnosis of diabetes mellitus is readily entertained when a patient presents with classic symptoms (ie, polyuria, polydipsia, polyphagia, weight loss). Other symptoms that may suggest hyperglycemia include blurred vision, lower extremity paresthesias, or yeast infections, particularly balanitis in men. However, many patients with type 2 diabetes are asymptomatic, and their disease remains undiagnosed for many years.

In older studies, the typical patient with type 2 diabetes had diabetes for at least 4-7 years at the time of diagnosis. [ 135 ] Among patients with type 2 diabetes in the United Kingdom Prospective Diabetes Study, 25% had retinopathy; 9%, neuropathy; and 8%, nephropathy at the time of diagnosis. (For more information, see Diabetic Neuropathy .)

Patients with established diabetes

In patients with known type 2 diabetes, inquire about the duration of the patient's diabetes and about the care the patient is currently receiving for the disease. The duration of diabetes is significant because the chronic complications of diabetes are related to the length of time the patient has had the disease.

A focused diabetes history should also include the following questions:

Is the patient's diabetes generally well controlled (with near-normal blood glucose levels) - Patients with poorly controlled blood glucose levels heal more slowly and are at increased risk for infection and other complications

Does the patient have severe hypoglycemic reactions - If the patient has episodes of severe hypoglycemia and therefore is at risk of losing consciousness, this possibility must be addressed, especially if the patient drives or has significant underlying neuropathy or cardiovascular disease

Does the patient have diabetic nephropathy that might alter the use of medications or intravenous (IV) radiographic contrast material

Does the patient have macrovascular disease, such as coronary artery disease (CAD) that should be considered as a source of acute symptoms

Does the patient self-monitor his or her blood glucose levels - If so, note the frequency and range of values at each time of day

When was the patient's hemoglobin A1c (HbA1c; an indicator of long-term glucose control) last measured, and what was it

What is the patient’s immunization history - Eg, influenza, pneumococcal, hepatitis B, tetanus, herpes zoster

As circumstances dictate, additional questions may be warranted, as follows:

Does the patient give a history of recent polyuria, polydipsia, nocturia, or weight loss - These are symptoms of hyperglycemia

Has the patient had episodes of unexplained hypoglycemia - If so, when, how often, and how does the patient treat these episodes

Does the patient have hypoglycemia unawareness (ie, does the patient lack the adrenergic warning signs of hypoglycemia) - Hypoglycemia unawareness indicates an increased risk of subsequent episodes of hypoglycemia

Regarding retinopathy, when was the patient's last dilated eye examination, and what were the results

Regarding nephropathy, does the patient have known kidney disease; what were the dates and results of the last measurements of urine protein and serum creatinine levels

Does the patient have hypertension (defined as a blood pressure of 130/80 mm Hg or higher); what medications are taken

Does the patient have CAD

Regarding peripheral vascular disease, does the patient have claudication or a history of vascular bypass

Has the patient had a stroke or transient ischemic attack

What are the patient's most recent lipid levels; is the patient taking lipid-lowering medication

Does the patient have a history of neuropathy or are symptoms of peripheral neuropathy or autonomic neuropathy present (including impotence if the patient is male)

Does the patient have a history of foot ulcers or amputations; are any foot ulcers present

Are frequent infections a problem; at what site

Dawn phenomenon

The Dawn phenomenon, defined as a blood glucose increase of over 20 mg/dL occurring at the end of the night, appears to be common in type 2 diabetes. In a study of 248 noninsulin-treated patients with type 2 diabetes who underwent continuous glucose monitoring for 2 consecutive days, approximately half were found to have the dawn phenomenon. [ 136 , 137 ] Patients with the dawn phenomenon had HbA1c levels and 24-hour mean glucose values that were significantly higher than in other patients, the mean differences being 4.3 mmol/mol for HbA1c (0.39%) and 12.4 mg/dL for average 24-hour glucose concentrations. Mean 24-hour glucose did not significantly differ between patients treated with diet alone and those treated with oral antihyperglycemic agents (ie, oral antidiabetic drugs did not eliminate the dawn phenomenon). [ 136 , 137 ]

Early in the course of diabetes mellitus, the physical examination findings are likely to be unrevealing. Ultimately, however, end-organ damage may be observed. Potential findings are listed in the image below.

A diabetes-focused examination includes vital signs, funduscopic examination, limited vascular and neurologic examinations, and a foot assessment. Other organ systems should be examined as indicated by the patient's clinical situation.

Assessment of vital signs

Baseline and continuing measurement of vital signs is an important part of diabetes management. In addition to vital signs, measure height, weight, and waist and hip circumferences.

In many cases, blood pressure measurement will disclose hypertension, which is particularly common in patients with diabetes. Patients with established diabetes and autonomic neuropathy may have orthostatic hypotension. Orthostatic vital signs may be useful in assessing volume status and in suggesting the presence of an autonomic neuropathy.

If the respiratory rate and pattern suggest Kussmaul respiration, diabetic ketoacidosis (DKA) must be considered immediately, and appropriate tests ordered. DKA is more typical of type 1 diabetes, but it can occur in type 2.

Funduscopic examination

The funduscopic examination should include a careful view of the retina. The optic disc and the macula should be visualized. If hemorrhages or exudates are seen, the patient should be referred to an ophthalmologist as soon as possible. Examiners who are not ophthalmologists tend to underestimate the severity of retinopathy, especially if the patients' pupils are not dilated.

Whether patients develop diabetic retinopathy depends on the duration of their diabetes and on the level of glycemic control maintained. [ 138 , 139 ] Because the diagnosis of type 2 diabetes often is delayed, 20% of these patients have some degree of retinopathy at diagnosis. The following are the 5 stages in the progression of diabetic retinopathy:

Dilation of the retinal venules and formation of retinal capillary microaneurysms

Increased vascular permeability

Vascular occlusion and retinal ischemia

Proliferation of new blood vessels on the surface of the retina

Hemorrhage and contraction of the fibrovascular proliferation and the vitreous

The first 2 stages of diabetic retinopathy are known as background or nonproliferative retinopathy. Initially, the retinal venules dilate; then microaneurysms (tiny red dots on the retina that cause no visual impairment) appear. As the microaneurysms or retinal capillaries become more permeable, hard exudates appear, reflecting the leakage of plasma.

Larger retinal arteriolar and venular calibres have been associated with lower scores on memory tests but not with lower scores on other cognitive tests. [ 140 ] This association was strong in men. Impaired arteriolar autoregulation may be an underlying mechanism of memory decrements.

Rupture of intraretinal capillaries results in hemorrhage. If a superficial capillary ruptures, a flame-shaped hemorrhage appears. Hard exudates are often found in partial or complete rings (circinate pattern), which usually include multiple microaneurysms. These rings usually mark an area of edematous retina. The patient may not notice a change in visual acuity unless the center of the macula is involved.

Macular edema can cause visual loss; therefore, all patients with suspected macular edema must be referred to an ophthalmologist for evaluation and possible laser therapy. Laser therapy is effective in decreasing macular edema and preserving vision but is less effective in restoring lost vision. (For more information, see Macular Edema in Diabetes .)

Preproliferative and proliferative diabetic retinopathy are the next stages in the progression of the disease. Cotton-wool spots can be seen in preproliferative retinopathy. These represent retinal microinfarcts caused by capillary occlusion; they appear as patches that range from off-white to gray, and they have poorly defined margins.

Proliferative retinopathy is characterized by neovascularization, or the development of networks of fragile new vessels that often are seen on the optic disc or along the main vascular arcades. The vessels undergo cycles of proliferation and regression. During proliferation, fibrous adhesions develop between the vessels and the vitreous. Subsequent contraction of the adhesions can result in traction on the retina and retinal detachment. Contraction also tears the new vessels, which hemorrhage into the vitreous.

Patients with preproliferative or proliferative retinopathy must immediately be referred for ophthalmologic evaluation because laser therapy is effective in this condition, especially before actual hemorrhage occurs.

Often, the first hemorrhage is small and is noted by the patient as a fleeting, dark area, or "floater," in the field of vision. Because subsequent hemorrhages can be larger and more serious, the patient should be referred immediately to an ophthalmologist for possible laser therapy. Patients with retinal hemorrhage should be advised to limit their activity and keep their head upright (even while sleeping), so that the blood settles to the inferior portion of the retina, thus obscuring less central vision.

Patients with active proliferative diabetic retinopathy are at increased risk of retinal hemorrhage if they receive thrombolytic therapy; therefore, this condition is a relative contraindication to the use of thrombolytic agents.

One study has shown that individuals with gingival hemorrhaging have a high prevalence of retinal hemorrhage. [ 141 ] Much of this association is driven by hyperglycemia, making it possible to use gingival tissue to study the natural course of microvascular disease in patients with diabetes.

Foot examination

The dorsalis pedis and posterior tibialis pulses should be palpated and their presence or absence noted. This is particularly important in patients who have foot infections, because poor lower-extremity blood flow can slow healing and increase the risk of amputation.

Documenting lower-extremity sensory neuropathy is useful in patients who present with foot ulcers because decreased sensation limits the patient's ability to protect the feet and ankles. This can be assessed with the Semmes Weinstein monofilament or by assessment of reflexes, position, and/or vibration sensation.

If peripheral neuropathy is found, the patient should be made aware that foot care (including daily foot examination) is very important for preventing foot ulcers and avoiding lower-extremity amputation. (For more information, see Diabetic Foot and Diabetic Foot Infections .)

Differentiation of type 2 from type 1 diabetes

Type 2 diabetes mellitus can usually be differentiated from type 1 diabetes mellitus on the basis of history and physical examination findings and simple laboratory tests (see Workup for more information). Patients with type 2 diabetes are generally obese, and may have acanthosis nigricans and/or hirsutism in conjunction with thick necks and chubby cheeks.

Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care . 2003 Jan. 26 Suppl 1:S5-20. [QxMD MEDLINE Link] .

Parker ED, Lin J, Mahoney T, et al. Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care . 2024 Jan 1. 47 (1):26-43. [QxMD MEDLINE Link] . [Full Text] .

Unger RH, Orci L. Paracrinology of islets and the paracrinopathy of diabetes. Proc Natl Acad Sci U S A . 2010 Sep 14. 107(37):16009-12. [QxMD MEDLINE Link] . [Full Text] .

Philippe MF, Benabadji S, Barbot-Trystram L, Vadrot D, Boitard C, Larger E. Pancreatic volume and endocrine and exocrine functions in patients with diabetes. Pancreas . 2011 Apr. 40(3):359-63. [QxMD MEDLINE Link] .

Bacha F, Lee S, Gungor N, Arslanian SA. From pre-diabetes to type 2 diabetes in obese youth: pathophysiological characteristics along the spectrum of glucose dysregulation. Diabetes Care . 2010 Oct. 33(10):2225-31. [QxMD MEDLINE Link] . [Full Text] .

Hansen KB, Vilsboll T, Bagger JI, Holst JJ, Knop FK. Increased postprandial GIP and glucagon responses, but unaltered GLP-1 response after intervention with steroid hormone, relative physical inactivity, and high-calorie diet in healthy subjects. J Clin Endocrinol Metab . 2011 Feb. 96(2):447-53. [QxMD MEDLINE Link] .

Lawrence JM, Casagrande SS, Herman WH, et al. Genetics of Type 2 Diabetes. Diabetes in America . 2023 Dec 20. [QxMD MEDLINE Link] . [Full Text] .

Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P, et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet . 2010 Feb. 42(2):142-8. [QxMD MEDLINE Link] . [Full Text] .

Chiefari E, Tanyolac S, Paonessa F, Pullinger CR, Capula C, Iiritano S, et al. Functional variants of the HMGA1 gene and type 2 diabetes mellitus. JAMA . 2011 Mar 2. 305(9):903-12. [QxMD MEDLINE Link] .

Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med . 2011 Apr. 17(4):448-53. [QxMD MEDLINE Link] . [Full Text] .

Testa R, Olivieri F, Sirolla C, Spazzafumo L, Rippo MR, Marra M, et al. Leukocyte telomere length is associated with complications of type 2 diabetes mellitus. Diabet Med . 2011 Nov. 28(11):1388-94. [QxMD MEDLINE Link] .

Krssak M, Winhofer Y, Gobl C, Bischof M, Reiter G, Kautzky-Willer A, et al. Insulin resistance is not associated with myocardial steatosis in women. Diabetologia . 2011 Jul. 54(7):1871-8. [QxMD MEDLINE Link] .

Leiter LA, Lundman P, da Silva PM, Drexel H, Junger C, Gitt AK. Persistent lipid abnormalities in statin-treated patients with diabetes mellitus in Europe and Canada: results of the Dyslipidaemia International Study. Diabet Med . 2011 Nov. 28(11):1343-51. [QxMD MEDLINE Link] .

Stern MP. Do non-insulin-dependent diabetes mellitus and cardiovascular disease share common antecedents?. Ann Intern Med . 1996 Jan 1. 124(1 Pt 2):110-6. [QxMD MEDLINE Link] .

Haffner SM, D'Agostino R Jr, Mykkanen L, Tracy R, Howard B, Rewers M, et al. Insulin sensitivity in subjects with type 2 diabetes. Relationship to cardiovascular risk factors: the Insulin Resistance Atherosclerosis Study. Diabetes Care . 1999 Apr. 22(4):562-8. [QxMD MEDLINE Link] .

Busko M. Gray-matter atrophy may drive cognitive decline in diabetes. Medscape Medical News . August 22, 2013. [Full Text] .

Moran C, Phan TG, Chen J, et al. Brain atrophy in type 2 diabetes: regional distribution and influence on cognition. Diabetes Care . 2013 Aug 12. [QxMD MEDLINE Link] .

Brooks M. Depression accelerates cognitive decline in type 2 diabetes. Medscape Medical News . October 17, 2013. [Full Text] .

Sullivan MD, Katon WJ, Lovato LC, Miller ME, Murray AM, Horowitz KR, et al. Association of Depression With Accelerated Cognitive Decline Among Patients With Type 2 Diabetes in the ACCORD-MIND Trial. JAMA Psychiatry . 2013 Oct 1. 70(10):1041-7. [QxMD MEDLINE Link] .

McCall B. Causal Link Found for Type 2 Diabetes and Lung Complications. Medscape News UK . 2023 Apr 28. [Full Text] .

Garg S, Kim L, Whitaker M, et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 — COVID-NET, 14 States, March 1–30, 2020. MMWR . 2020 Apr 8. [Full Text] .

Stokes EK, Zambrano LD, Anderson KN, et al. Coronavirus Disease 2019 Case Surveillance — United States, January 22–May 30, 2020. MMWR Morb Mortal Wkly Rep . 2020 Jun 15. [Full Text] .

Franki R. Comorbidities Increase COVID-19 Deaths by Factor of 12. Medscape Medical News . 2020 Jun 17. [Full Text] .

Zoler ML. Cleaner data confirm severe COVID-19 link to diabetes, hypertension. MDedge Cardiology News . 2020 Jul 27. [Full Text] .

Barrera FJ, Shekhar S, Wurth R, et al. Prevalence of Diabetes and Hypertension and their Associated Risks for Poor Outcomes in Covid-19 Patients. J Endocr Soc . 2020 Jul 21. [Full Text] .

King J. Inflammatory markers may explain COVID-19, diabetes dynamic. MDedge . 2020 Apr 15. [Full Text] .

Guo W, Li M, Dong Y, et al. Diabetes is a risk factor for the progression and prognosis of COVID-19. Diabetes Metab Res Rev . 2020 Mar 31. e3319. [QxMD MEDLINE Link] . [Full Text] .

Ahlqvist E, Storm P, Karajamaki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol . 2018 Mar 1. [QxMD MEDLINE Link] .

Davenport L. Diabetes Consists of Five Types, Not Two, Say Researchers. Medscape Medical News . 2018 Mar 1. [Full Text] .

Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet . 2004 Jan 10. 363(9403):157-63. [QxMD MEDLINE Link] .

Wei GS, Coady SA, Goff DC Jr, et al. Blood pressure and the risk of developing diabetes in African Americans and Whites: ARIC, CARDIA, and the framingham heart study. Diabetes Care . 2011 Apr. 34(4):873-9. [QxMD MEDLINE Link] . [Full Text] .

Dabelea D, Pettitt DJ, Hanson RL, Imperatore G, Bennett PH, Knowler WC. Birth weight, type 2 diabetes, and insulin resistance in Pima Indian children and young adults. Diabetes Care . 1999 Jun. 22(6):944-50. [QxMD MEDLINE Link] .

Yarbrough DE, Barrett-Connor E, Kritz-Silverstein D, Wingard DL. Birth weight, adult weight, and girth as predictors of the metabolic syndrome in postmenopausal women: the Rancho Bernardo Study. Diabetes Care . 1998 Oct. 21(10):1652-8. [QxMD MEDLINE Link] .

Li Y, Qi Q, Workalemahu T, Hu FB, Qi L. Birth Weight, Genetic Susceptibility, and Adulthood Risk of Type 2 Diabetes. Diabetes Care . 2012 Aug 24. [QxMD MEDLINE Link] .

Slining MM, Kuzawa CW, Mayer-Davis EJ, Adair LS. Evaluating the indirect effect of infant weight velocity on insulin resistance in young adulthood: a birth cohort study from the Philippines. Am J Epidemiol . 2011 Mar 15. 173(6):640-8. [QxMD MEDLINE Link] . [Full Text] .

American Society for Metabolic an Bariatric Surgery. Type 2 Diabetes and Metabolic Surgery. Available at https://asmbs.org/resources/type-2-diabetes-and-metabolic-surgery-fact-sheet#:~:text=Obesity%20%E2%80%93%20medically%20defined%20as%20a,BMI%20of%20at%20least%2025). . October 2018; Accessed: March 6, 2021.

Wang J, Luben R, Khaw KT, Bingham S, Wareham NJ, Forouhi NG. Dietary energy density predicts the risk of incident type 2 diabetes: the European Prospective Investigation of Cancer (EPIC)-Norfolk Study. Diabetes Care . 2008 Nov. 31(11):2120-5. [QxMD MEDLINE Link] . [Full Text] .

Cameron NA, Petito LC, McCabe M, et al. Quantifying the Sex-Race/Ethnicity-Specific Burden of Obesity on Incident Diabetes Mellitus in the United States, 2001 to 2016: MESA and NHANES. J Am Heart Assoc . 2021 Feb 16. 10 (4):e018799. [QxMD MEDLINE Link] . [Full Text] .

Zoler ML. Obesity Pegged as Diabetes Cause in Almost Half of US Cases. Medscape Medical News . 2021 Feb 18. [Full Text] .

Hectors TL, Vanparys C, van der Ven K, Martens GA, Jorens PG, Van Gaal LF, et al. Environmental pollutants and type 2 diabetes: a review of mechanisms that can disrupt beta cell function. Diabetologia . 2011 Jun. 54(6):1273-90. [QxMD MEDLINE Link] .

Pauza AG, Thakkar P, Tasic T, et al. GLP1R Attenuates Sympathetic Response to High Glucose via Carotid Body Inhibition. Circ Res . 2022 Feb 1. CIRCRESAHA121319874. [QxMD MEDLINE Link] . [Full Text] .

Sweet pressure – scientists discover link between high blood pressure and diabetes. University of Bristol. Available at https://www.bristol.ac.uk/news/2022/february/blood-pressure-diabetes.html . February 1, 2022; Accessed: February 2, 2022.

Billings LK, Florez JC. The genetics of type 2 diabetes: what have we learned from GWAS? Ann N Y Acad Sci. 2010 Nov;1212:59-77. [Full Text] .

de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, Grant RW, et al. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care . 2011 Jan. 34(1):121-5. [QxMD MEDLINE Link] . [Full Text] .

Winckler W, Weedon MN, Graham RR, McCarroll SA, Purcell S, Almgren P, et al. Evaluation of common variants in the six known maturity-onset diabetes of the young (MODY) genes for association with type 2 diabetes. Diabetes . 2007 Mar. 56(3):685-93. [QxMD MEDLINE Link] .

Molven A, Ringdal M, Nordbo AM, Raeder H, Stoy J, Lipkind GM, et al. Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes. Diabetes . 2008 Apr. 57(4):1131-5. [QxMD MEDLINE Link] .

Hoffman LS, Fox TJ, Anastasopoulou C, Jialal I. Maturity Onset Diabetes in the Young. StatPearls . 2023 Aug 14. [QxMD MEDLINE Link] . [Full Text] .

Edghill EL, Bingham C, Ellard S, Hattersley AT. Mutations in hepatocyte nuclear factor-1beta and their related phenotypes. J Med Genet . 2006 Jan. 43(1):84-90. [QxMD MEDLINE Link] . [Full Text] .

Raeder H, Johansson S, Holm PI, Haldorsen IS, Mas E, Sbarra V, et al. Mutations in the CEL VNTR cause a syndrome of diabetes and pancreatic exocrine dysfunction. Nat Genet . 2006 Jan. 38(1):54-62. [QxMD MEDLINE Link] .

van den Ouweland JM, Lemkes HH, Ruitenbeek W, Sandkuijl LA, de Vijlder MF, Struyvenberg PA, et al. Mutation in mitochondrial tRNA(Leu)(UUR) gene in a large pedigree with maternally transmitted type II diabetes mellitus and deafness. Nat Genet . 1992 Aug. 1(5):368-71. [QxMD MEDLINE Link] .

Castellino AM. Genetically Lowered Birth Weight May Cause Type 2 Diabetes. Medscape Medical News . July 4, 2016. [Full Text] .

Wang T, Huang T, Li Y, Zheng Y, Manson JE, Hu FB, et al. Low birthweight and risk of type 2 diabetes: a Mendelian randomisation study. Diabetologia . 2016 Jun 23. [QxMD MEDLINE Link] .

Pan A, Lucas M, Sun Q, van Dam RM, Franco OH, Manson JE, et al. Bidirectional association between depression and type 2 diabetes mellitus in women. Arch Intern Med . 2010 Nov 22. 170(21):1884-91. [QxMD MEDLINE Link] . [Full Text] .

Nouwen A, Winkley K, Twisk J, Lloyd CE, Peyrot M, Ismail K, et al. Type 2 diabetes mellitus as a risk factor for the onset of depression: a systematic review and meta-analysis. Diabetologia . 2010 Dec. 53(12):2480-6. [QxMD MEDLINE Link] . [Full Text] .

Siuta MA, Robertson SD, Kocalis H, Saunders C, Gresch PJ, Khatri V, et al. Dysregulation of the norepinephrine transporter sustains cortical hypodopaminergia and schizophrenia-like behaviors in neuronal rictor null mice. PLoS Biol . 2010 Jun 8. 8(6):e1000393. [QxMD MEDLINE Link] . [Full Text] .

Feig DS, Shah BR, Lipscombe LL, Wu CF, Ray JG, Lowe J, et al. Preeclampsia as a risk factor for diabetes: a population-based cohort study. PLoS Med . 2013 Apr. 10(4):e1001425. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. New Global Registry Investigates COVID-19 and New-Onset Diabetes. Medscape Medical News . 2020 Jun 13. [Full Text] .

Xie Y, Al-Aly Z. Risks and burdens of incident diabetes in long COVID: a cohort study. Lancet Diabetes Endocrinol . 2022 Mar 21. [Full Text] .

Tucker ME. 'Profound Implications': COVID Ups Diabetes Risk 40% a Year Later. Medscape Medical News . 2022 Mar 23. [Full Text] .

Tang X, Uhl S, Zhang T, et al. SARS-CoV-2 infection induces beta cell transdifferentiation. Cell Metab . 2021 May 19. [QxMD MEDLINE Link] . [Full Text] .

Wu CT, Lidsky PV, Xiao Y, et al. SARS-CoV-2 infects human pancreatic β cells and elicits β cell impairment. Cell Metab . 2021 May 18. [QxMD MEDLINE Link] . [Full Text] .

Barrett CE, Koyama AK, Alvarez P, et al. Risk for Newly Diagnosed Diabetes >30 Days After SARS-CoV-2 Infection Among Persons Aged MMWR Morb Mortal Wkly Rep</i>. 2022 Jan 7. 71: [Full Text] .

Tucker ME. COVID-19 Associated With Increased Diabetes Risk in Youth. Medscape Medical News . 2022 Jan 10. [Full Text] .

Cromer SJ, Colling C, Schatoff D, et al. Newly diagnosed diabetes vs. pre-existing diabetes upon admission for COVID-19: Associated factors, short-term outcomes, and long-term glycemic phenotypes. J Diabetes Complications . 2022 Feb 4. 108145. [QxMD MEDLINE Link] . [Full Text] .

Centers for Disease Control and Prevention. National Diabetes Statistics Report. CDC. Available at https://www.cdc.gov/diabetes/data/statistics-report/index.html . Reviewed January 18, 2022; Accessed: January 28, 2022.

Tucker ME. More Than 1 in 10 People in US Have Diabetes, CDC Says. Medscape Medical News . 2022 Jan 26. [Full Text] .

Andes LJ, Cheng YJ, Rolka DB, Gregg EW, Imperatore G. Prevalence of Prediabetes Among Adolescents and Young Adults in the United States, 2005-2016. JAMA Pediatr . 2019 Dec 2. e194498. [QxMD MEDLINE Link] . [Full Text] .

Dunleavy BP. Prevalence of prediabetes high among U.S. teens, young adults. 2019 Dec 2. Available at https://www.upi.com/Health_News/2019/12/02/Prevalence-of-prediabetes-high-among-US-teens-young-adults/8591575297926/ .

Searing L. Over a quarter of 12-to-19-year-olds have prediabetes, research shows. The Washington Post. Available at https://www.washingtonpost.com/health/2022/04/05/prediabetes-youth/ . April 5, 2022; Accessed: April 6, 2022.

Liu J, Li Y, Zhang D, Yi SS, Liu J. Trends in Prediabetes Among Youths in the US From 1999 Through 2018. JAMA Pediatr . 2022 Mar 28. [QxMD MEDLINE Link] .

Hackethal V. 2 in 5 American Adults Will Develop Diabetes. Medscape Medical News. Available at https://www.medscape.com/viewarticle/829833 . Accessed: August 13, 2014.

Gregg EW, Zhuo X, Albright AL, et al. Trends in lifetime risk and years of life lost due to diabetes in the USA, 1985—2011: a modelling study. The Lancet Diabetes & Endocrinology. Available at https://www.thelancet.com/journals/landia/article/PIIS2213-8587(14)70161-5/fulltext . Accessed: August 13, 2014.

Ludwig J, Sanbonmatsu L, Gennetian L, Adam E, Duncan GJ, Katz LF, et al. Neighborhoods, obesity, and diabetes--a randomized social experiment. N Engl J Med . 2011 Oct 20. 365(16):1509-19. [QxMD MEDLINE Link] .

Tucker ME. IDF Atlas: 1 in 10 Adults Worldwide Now Has Diabetes. Medscape Medical News . 2021 Dec 7. [Full Text] .

Selvin E, Steffes MW, Ballantyne CM, Hoogeveen RC, Coresh J, Brancati FL. Racial differences in glycemic markers: a cross-sectional analysis of community-based data. Ann Intern Med . 2011 Mar 1. 154(5):303-9. [QxMD MEDLINE Link] . [Full Text] .

Albers JW, Herman WH, Pop-Busui R, Feldman EL, Martin CL, Cleary PA, et al. Effect of prior intensive insulin treatment during the Diabetes Control and Complications Trial (DCCT) on peripheral neuropathy in type 1 diabetes during the Epidemiology of Diabetes Interventions and Complications (EDIC) Study. Diabetes Care . 2010 May. 33(5):1090-6. [QxMD MEDLINE Link] . [Full Text] .

White NH, Sun W, Cleary PA, Tamborlane WV, Danis RP, Hainsworth DP, et al. Effect of prior intensive therapy in type 1 diabetes on 10-year progression of retinopathy in the DCCT/EDIC: comparison of adults and adolescents. Diabetes . 2010 May. 59(5):1244-53. [QxMD MEDLINE Link] . [Full Text] .

Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet . 1998 Sep 12. 352(9131):837-53. [QxMD MEDLINE Link] .

Perreault L, Pan Q, Mather KJ, Watson KE, Hamman RF, Kahn SE. Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study. Lancet . 2012 Jun 16. 379(9833):2243-51. [QxMD MEDLINE Link] .

Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med . 2008 Oct 9. 359(15):1577-89. [QxMD MEDLINE Link] .

Dluhy RG, McMahon GT. Intensive glycemic control in the ACCORD and ADVANCE trials. N Engl J Med . 2008 Jun 12. 358(24):2630-3. [QxMD MEDLINE Link] .

Skyler JS, Bergenstal R, Bonow RO, Buse J, Deedwania P, Gale EA, et al. Intensive glycemic control and the prevention of cardiovascular events: implications of the ACCORD, ADVANCE, and VA Diabetes Trials: a position statement of the American Diabetes Association and a Scientific Statement of the American College of Cardiology Foundation and the American Heart Association. J Am Coll Cardiol . 2009 Jan 20. 53(3):298-304. [QxMD MEDLINE Link] .

Duckworth W, Abraira C, Moritz T, Reda D, Emanuele N, Reaven PD, et al. Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med . 2009 Jan 8. 360(2):129-39. [QxMD MEDLINE Link] .

Griffin SJ, Borch-Johnsen K, Davies MJ, Khunti K, Rutten GE, Sandbek A, et al. Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trial. Lancet . 2011 Jul 9. 378(9786):156-67. [QxMD MEDLINE Link] . [Full Text] .

Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med . 2008 Feb 7. 358(6):580-91. [QxMD MEDLINE Link] .

Kerr D, Partridge H, Knott J, Thomas PW. HbA1c 3 months after diagnosis predicts premature mortality in patients with new onset type 2 diabetes. Diabet Med . 2011 Dec. 28(12):1520-4. [QxMD MEDLINE Link] .

Gruss C, Gutierrez C, Burhans WC, DePamphilis ML, Koller T, Sogo JM. Nucleosome assembly in mammalian cell extracts before and after DNA replication. EMBO J . 1990 Sep. 9(9):2911-22. [QxMD MEDLINE Link] . [Full Text] .

Zheng F, Yan L, Yang Z, Zhong B, Xie W. HbA 1c , diabetes and cognitive decline: the English Longitudinal Study of Ageing. Diabetologia . 2018 Jan 25. [QxMD MEDLINE Link] . [Full Text] .

Melville NA. HbA1c Levels in Diabetes Linked to Cognitive Decline. Medscape Medical News . 2018 Jan 30. [Full Text] .

Cano JF, Baena-Diez JM, Franch J, Vila J, Tello S, Sala J, et al. Long-term cardiovascular risk in type 2 diabetic compared with nondiabetic first acute myocardial infarction patients: a population-based cohort study in southern Europe. Diabetes Care . 2010 Sep. 33(9):2004-9. [QxMD MEDLINE Link] . [Full Text] .

Yamasaki Y, Nakajima K, Kusuoka H, Izumi T, Kashiwagi A, Kawamori R, et al. Prognostic value of gated myocardial perfusion imaging for asymptomatic patients with type 2 diabetes: the J-ACCESS 2 investigation. Diabetes Care . 2010 Nov. 33(11):2320-6. [QxMD MEDLINE Link] . [Full Text] .

Young LH, Wackers FJ, Chyun DA, Davey JA, Barrett EJ, Taillefer R, et al. Cardiac outcomes after screening for asymptomatic coronary artery disease in patients with type 2 diabetes: the DIAD study: a randomized controlled trial. JAMA . 2009 Apr 15. 301(15):1547-55. [QxMD MEDLINE Link] . [Full Text] .

Murthy VL, Naya M, Foster CR, Gaber M, Hainer J, Klein J, et al. Association Between Coronary Vascular Dysfunction and Cardiac Mortality in Patients with and without Diabetes Mellitus. Circulation . 2012 Aug 23. [QxMD MEDLINE Link] .

American Diabetes Association. Statistics about diabetes. ADA. Available at https://diabetes.org/about-diabetes/statistics/about-diabetes . Accessed: September 11, 2024.

U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2011. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Available at https://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf . Accessed: January 5, 2012.

National Institute of Diabetes and Digestive and Kidney Diseases. National Diabetes Statistics, 2011. National Diabetes Information Clearinghouse. Available at https://diabetes.niddk.nih.gov/dm/pubs/statistics/ . Accessed: January 5, 2012.

Wannamethee SG, Shaper AG, Whincup PH, Lennon L, Sattar N. Impact of diabetes on cardiovascular disease risk and all-cause mortality in older men: influence of age at onset, diabetes duration, and established and novel risk factors. Arch Intern Med . 2011 Mar 14. 171(5):404-10. [QxMD MEDLINE Link] .

Seshasai SR, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med . 2011 Mar 3. 364(9):829-41. [QxMD MEDLINE Link] .

Lind M, Olsson M, Rosengren A, Svensson AM, Bounias I, Gudbjornsdottir S. The relationship between glycaemic control and heart failure in 83,021 patients with type 2 diabetes. Diabetologia . 2012 Aug 16. [QxMD MEDLINE Link] .

Shah AS, Khoury PR, Dolan LM, Ippisch HM, Urbina EM, Daniels SR, et al. The effects of obesity and type 2 diabetes mellitus on cardiac structure and function in adolescents and young adults. Diabetologia . 2011 Apr. 54(4):722-30. [QxMD MEDLINE Link] .

Giovannucci E, Harlan DM, Archer MC, Bergenstal RM, Gapstur SM, Habel LA, et al. Diabetes and cancer: a consensus report. Diabetes Care . 2010 Jul. 33(7):1674-85. [QxMD MEDLINE Link] . [Full Text] .

Tseng CH. Diabetes and risk of bladder cancer: a study using the National Health Insurance database in Taiwan. Diabetologia . 2011 Aug. 54(8):2009-15. [QxMD MEDLINE Link] .

Colmers IN, Bowker SL, Majumdar SR, Johnson JA. Use of thiazolidinediones and the risk of bladder cancer among people with type 2 diabetes: a meta-analysis. CMAJ . 2012 Jul 3. [QxMD MEDLINE Link] .

Yin M, Zhou J, Gorak EJ, Quddus F. Metformin is associated with survival benefit in cancer patients with concurrent type 2 diabetes: a systematic review and meta-analysis. Oncologist . 2013 Nov 20. [QxMD MEDLINE Link] . [Full Text] .

Nelson R. Metformin boosts survival in diabetic cancer patients. Medscape Medical News . November 25, 2013. [Full Text] .

Lopez-de-Andres A, Perez-Farinos N, de Miguel-Diez J, et al. Type 2 diabetes and postoperative pneumonia: An observational, population-based study using the Spanish Hospital Discharge Database, 2001-2015. PLoS One . 2019. 14 (2):e0211230. [QxMD MEDLINE Link] . [Full Text] .

Chen Y, Yang D, Cheng B, et al. Clinical Characteristics and Outcomes of Patients With Diabetes and COVID-19 in Association With Glucose-Lowering Medication. Diabetes Care . 2020 May 14. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. 'The Story Unfolding Is Worrisome' for Diabetes and COVID-19. Medscape Medical News . 2020 May 26. [Full Text] .

Bode B, Garrett V, Messler J, et al. Glycemic Characteristics and Clinical Outcomes of COVID-19 Patients Hospitalized in the United States. J Diabetes Sci Technol . 2020. [Full Text] .

Tucker ME. Pay Attention to In-Hospital Glucose to Save Lives in COVID-19. Medscape Medical News . 2020 Apr 20. [Full Text] .

Barron E, Bakhai C, Kar P, et al. Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study. Lancet Diabetes Endocrinol . 2020 Aug 13. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. Newly Published Articles Inform on COVID-19 Risk by Diabetes Type. Medscape Medical News . 2020 Aug 17. [Full Text] .

Zhu L, She ZG, Cheng X, et al. Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes. Cell Metab . 2020 May 1. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. Largest Study to Date Links Glucose Control to COVID-19 Outcomes. Medscape Medical News . 2020 May 14. [Full Text] .

Holman N, Knighton P, Kar P, et al. Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: a population-based cohort study. Lancet Diabetes Endocrinol . 2020 Aug 13. [QxMD MEDLINE Link] . [Full Text] .

Busko M. Older, Sicker Diabetes Patients Have Worse COVID-19 Prognosis. Medscape Medical News . 2021 Apr 28. [Full Text] .

Schlesinger S, Neuenschwander M, Lang A, et al. Risk phenotypes of diabetes and association with COVID-19 severity and death: a living systematic review and meta-analysis. Diabetologia . 2021 Apr 28. [QxMD MEDLINE Link] . [Full Text] .

Wang S, Ma P, Zhang S, et al. Fasting blood glucose at admission is an independent predictor for 28-day mortality in patients with COVID-19 without previous diagnosis of diabetes: a multi-centre retrospective study. Diabetologia . 2020 Jul 10. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. Hyperglycemia predicts COVID-19 death even without diabetes. MDedge Internal Medicine . 2020 Jul 13. [Full Text] .

Wang W, Shen M, Tao Y, et al. Elevated glucose level leads to rapid COVID-19 progression and high fatality. BMC Pulm Med . 2021 Feb 24. 21 (1):64. [QxMD MEDLINE Link] . [Full Text] .

Carrasco-Sanchez FJ, Lopez-Carmona MD, Martinez-Marcos FJ, et al. Admission hyperglycaemia as a predictor of mortality in patients hospitalized with COVID-19 regardless of diabetes status: data from the Spanish SEMI-COVID-19 Registry. Ann Med . 2021 Dec. 53 (1):103-16. [QxMD MEDLINE Link] .

Tucker ME. Blood Glucose on Admission Predicts COVID-19 Severity in All. Medscape Medical News . 2020 Nov 30. [Full Text] .

Klonoff DC, Messler JC, Umpierrez GE, et al. Association Between Achieving Inpatient Glycemic Control and Clinical Outcomes in Hospitalized Patients With COVID-19: A Multicenter, Retrospective Hospital-Based Analysis. Diabetes Care . 2020 Dec 15. [QxMD MEDLINE Link] . [Full Text] .

Harding A. Glycemia in Early COVID-19 Hospitalization Linked to Mortality. Reuters Health Information . 2020 Dec 21. [Full Text] .

Sardu C, D'Onofrio N, Balestrieri ML, et al. Outcomes in Patients With Hyperglycemia Affected by Covid-19: Can We Do More on Glycemic Control?. Diabetes Care . 2020 May 19. [QxMD MEDLINE Link] . [Full Text] .

Cariou B, Hadjadj S, Wargny M, et al. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study. Diabetologia . 2020 May 29. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. 10% With Diabetes Hospitalized for COVID-19 Die Within a Week. Medscape Medical News . 2020 Jun 1. [Full Text] .

Wargny M, Potier L, Gourdy P, et al. Predictors of hospital discharge and mortality in patients with diabetes and COVID-19: updated results from the nationwide CORONADO study. Diabetologia . 2021 Feb 17. [QxMD MEDLINE Link] . [Full Text] .

Davenport L. 1 in 5 Diabetes Patients Hospitalized With COVID-19 Die in 28 Days. Medscape Medical News . 18 Feb 2021. [Full Text] .

Centers for Disease Control and Prevention. Coronavirus Disease 2019 (COVID-19): People of Any Age with Underlying Medical Conditions. CDC. Available at https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html . Updated June 25, 2020; Accessed: June 27, 2020.

Murphy HR, Steel SA, Roland JM, Morris D, Ball V, Campbell PJ, et al. Obstetric and perinatal outcomes in pregnancies complicated by Type 1 and Type 2 diabetes: influences of glycaemic control, obesity and social disadvantage. Diabet Med . 2011 Sep. 28(9):1060-7. [QxMD MEDLINE Link] . [Full Text] .

Sperl-Hillen J, Beaton S, Fernandes O, Von Worley A, Vazquez-Benitez G, Parker E, et al. Comparative effectiveness of patient education methods for type 2 diabetes: a randomized controlled trial. Arch Intern Med . 2011 Dec 12. 171(22):2001-10. [QxMD MEDLINE Link] .

Khunti K, Gray LJ, Skinner T, Carey ME, Realf K, Dallosso H, et al. Effectiveness of a diabetes education and self management programme (DESMOND) for people with newly diagnosed type 2 diabetes mellitus: three year follow-up of a cluster randomised controlled trial in primary care. BMJ . 2012 Apr 26. 344:e2333. [QxMD MEDLINE Link] . [Full Text] .

Duke SA, Colagiuri S, Colagiuri R. Individual patient education for people with type 2 diabetes mellitus. Cochrane Database Syst Rev . 2009 Jan 21. CD005268. [QxMD MEDLINE Link] .

Harris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis. Diabetes Care . 1992 Jul. 15(7):815-9. [QxMD MEDLINE Link] .

Nainggolan L. Dawn Phenomenon Affects Half of Type 2 Diabetes Patients. Medscape Medical News . Nov 7 2013. [Full Text] .

Monnier L, Colette C, Dejager S, et al. Magnitude of the dawn phenomenon and its impact on the overall glucose exposure in type 2 diabetes: is this of concern?. Diabetes Care . 2013 Oct 29. [QxMD MEDLINE Link] .

Mohamed Q, Gillies MC, Wong TY. Management of diabetic retinopathy: a systematic review. JAMA . 2007 Aug 22. 298(8):902-16. [QxMD MEDLINE Link] .

Frank RN. Diabetic retinopathy. N Engl J Med . 2004 Jan 1. 350(1):48-58. [QxMD MEDLINE Link] .

Ding J, Strachan MW, Fowkes FG, Wong TY, Macgillivray TJ, Patton N, et al. Association of retinal arteriolar dilatation with lower verbal memory: the Edinburgh Type 2 Diabetes Study. Diabetologia . 2011 Jul. 54(7):1653-62. [QxMD MEDLINE Link] .

Hujoel PP, Stott-Miller M. Retinal and gingival hemorrhaging and chronic hyperglycemia. Diabetes Care . 2011 Jan. 34(1):181-3. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Diagnosis and classification of diabetes mellitus. Diabetes Care . 2010 Jan. 33 Suppl 1:S62-9. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024. Diabetes Care . 2024 Jan 1. 47 (Suppl 1):S20-S42. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] American Association of Clinical Endocrinologists Statement on the Use of A1C for the Diagnosis of Diabetes. Available at https://www.aace.com/files/AACEpositionA1cfeb2010.pdf . Accessed: May 14 2012.

[Guideline] American Diabetes Association. Standards of medical care in diabetes--2012. Diabetes Care . 2012 Jan. 35 Suppl 1:S11-63. [QxMD MEDLINE Link] .

World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF consultation. World Health Organization, Geneva, 2006. Available at https://whqlibdoc.who.int/publications/2006/9241594934_eng.pdf .

Brambilla P, La Valle E, Falbo R, Limonta G, Signorini S, Cappellini F, et al. Normal fasting plasma glucose and risk of type 2 diabetes. Diabetes Care . 2011 Jun. 34(6):1372-4. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Kirkman MS, et al. Executive summary: guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem . 2011 Jun. 57(6):793-8. [QxMD MEDLINE Link] .

International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care . 2009 Jul. 32(7):1327-34. [QxMD MEDLINE Link] . [Full Text] .

Huang ES, Liu JY, Moffet HH, John PM, Karter AJ. Glycemic control, complications, and death in older diabetic patients: the diabetes and aging study. Diabetes Care . 2011 Jun. 34(6):1329-36. [QxMD MEDLINE Link] . [Full Text] .

Wang W, Lee ET, Howard BV, Fabsitz RR, Devereux RB, Welty TK. Fasting plasma glucose and hemoglobin A1c in identifying and predicting diabetes: the strong heart study. Diabetes Care . 2011 Feb. 34(2):363-8. [QxMD MEDLINE Link] . [Full Text] .

Brooks M. Hemoglobin A1c misses many cases of diabetes. Medscape . 2019 Mar 28. [Full Text] .

Nowicka P, Santoro N, Liu H, Lartaud D, Shaw MM, Goldberg R, et al. Utility of hemoglobin A(1c) for diagnosing prediabetes and diabetes in obese children and adolescents. Diabetes Care . 2011 Jun. 34(6):1306-11. [QxMD MEDLINE Link] . [Full Text] .

Vijayakumar P, Nelson RG, Hanson RL, Knowler WC, Sinha M. HbA1c and the Prediction of Type 2 Diabetes in Children and Adults. Diabetes Care . 2017 Jan. 40 (1):16-21. [QxMD MEDLINE Link] . [Full Text] .

Davenport L. HbA1c Predicts Diabetes Risk in Children and Adolescents. Medscape Medical News . 2017 Jan 4. [Full Text] .

Lu ZX, Walker KZ, O'Dea K, Sikaris KA, Shaw JE. A1C for screening and diagnosis of type 2 diabetes in routine clinical practice. Diabetes Care . 2010 Apr. 33(4):817-9. [QxMD MEDLINE Link] . [Full Text] .

Lerner N, Shani M, Vinker S. Predicting type 2 diabetes mellitus using haemoglobin A1c: A community-based historic cohort study. Eur J Gen Pract . 2013 Nov 29. [QxMD MEDLINE Link] .

McCall B. Simple saliva swab and early HbA1c test predict diabetes. Medscape Medical News . February 11, 2014. [Full Text] .

Gerstein HC, Islam S, Anand S, Almahmeed W, Damasceno A, Dans A, et al. Dysglycaemia and the risk of acute myocardial infarction in multiple ethnic groups: an analysis of 15,780 patients from the INTERHEART study. Diabetologia . 2010 Dec. 53(12):2509-17. [QxMD MEDLINE Link] .

Suzuki S, Koga M, Amamiya S, Nakao A, Wada K, Okuhara K, et al. Glycated albumin but not HbA1c reflects glycaemic control in patients with neonatal diabetes mellitus. Diabetologia . 2011 Sep. 54(9):2247-53. [QxMD MEDLINE Link] .

Wilson DM, Xing D, Cheng J, Beck RW, Hirsch I, Kollman C, et al. Persistence of individual variations in glycated hemoglobin: analysis of data from the Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Randomized Trial. Diabetes Care . 2011 Jun. 34(6):1315-7. [QxMD MEDLINE Link] . [Full Text] .

U.S. Preventive Services Task Force. Screening for Type 2 Diabetes Mellitus in Adults. Available at https://www.ahrq.gov/clinic/uspstf/uspsdiab.htm .

[Guideline] Tucker ME. Diabetes Care 2022: Screen More, Personalize, Use Technology. Medscape Medical News . 2021 Dec 20. [Full Text] .

[Guideline] American Diabetes Association. Introduction: Standards of Medical Care in Diabetes-2022. Diabetes Care . 2022 Jan 1. 45 (Supplement_1):S1-S2. [QxMD MEDLINE Link] . [Full Text] .

American Diabetes Association. Standards of Medical Care in Diabetes-2015: Abridged for Primary Care Providers. Clinical Diabetes . 2015. 33(2): [Full Text] .

Colayco DC, Niu F, McCombs JS, Cheetham TC. A1C and cardiovascular outcomes in type 2 diabetes: a nested case-control study. Diabetes Care . 2011 Jan. 34(1):77-83. [QxMD MEDLINE Link] . [Full Text] .

Gerstein HC, Miller ME, Genuth S, Ismail-Beigi F, Buse JB, Goff DC Jr, et al. Long-term effects of intensive glucose lowering on cardiovascular outcomes. N Engl J Med . 2011 Mar 3. 364(9):818-28. [QxMD MEDLINE Link] .

Ng JM, Cooke M, Bhandari S, Atkin SL, Kilpatrick ES. The effect of iron and erythropoietin treatment on the A1C of patients with diabetes and chronic kidney disease. Diabetes Care . 2010 Nov. 33(11):2310-3. [QxMD MEDLINE Link] . [Full Text] .

Morrison F, Shubina M, Turchin A. Encounter frequency and serum glucose level, blood pressure, and cholesterol level control in patients with diabetes mellitus. Arch Intern Med . 2011 Sep 26. 171(17):1542-50. [QxMD MEDLINE Link] .

Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). UK Prospective Diabetes Study (UKPDS) Group. Lancet . 1998 Sep 12. 352(9131):854-65. [QxMD MEDLINE Link] .

[Guideline] Busko M. HbA1c Below 8% in Diabetes for 'Most' Says ACP, But Others Disagree. Medscape Medical News . 2018 Mar 5. [Full Text] .

[Guideline] Qaseem A, Wilt TJ, Kansagara D, et al. Hemoglobin A1c Targets for Glycemic Control With Pharmacologic Therapy for Nonpregnant Adults With Type 2 Diabetes Mellitus: A Guidance Statement Update From the American College of Physicians. Ann Intern Med . 2018 Mar 6. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. Type 2 Diabetes 'Remission' Is a Reality, Say Major Organizations. Medscape Medical News . 2021 Sep 1. [Full Text] .

Riddle MC, Cefalu WT, Evans PH, et al. Consensus Report: Definition and Interpretation of Remission in Type 2 Diabetes. J Clin Endocrinol Metab . 2021 Aug 30. [QxMD MEDLINE Link] . [Full Text] .

Alfayez OM, Al Yami MS, Alshibani M, et al. Network meta-analysis of nine large cardiovascular outcome trials of new antidiabetic drugs. Prim Care Diabetes . 2019 Jan 31. [QxMD MEDLINE Link] .

Scarpello JH, Howlett HC. Metformin therapy and clinical uses. Diab Vasc Dis Res . 2008 Sep. 5(3):157-67. [QxMD MEDLINE Link] .

Bodmer M, Meier C, Krahenbuhl S, Jick SS, Meier CR. Metformin, sulfonylureas, or other antidiabetes drugs and the risk of lactic acidosis or hypoglycemia: a nested case-control analysis. Diabetes Care . 2008 Nov. 31(11):2086-91. [QxMD MEDLINE Link] . [Full Text] .

Sun L, Xie C, Wang G, et al. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin. Nat Med . 2018 Dec. 24 (12):1919-29. [QxMD MEDLINE Link] .

Melville NA. Metformin's Effect in Diabetes Linked to Gut Microbiota Changes. Medscape Medical News . 2018 Nov 29. [Full Text] .

Turner RC, Cull CA, Frighi V, Holman RR. Glycemic control with diet, sulfonylurea, metformin, or insulin in patients with type 2 diabetes mellitus: progressive requirement for multiple therapies (UKPDS 49). UK Prospective Diabetes Study (UKPDS) Group. JAMA . 1999 Jun 2. 281(21):2005-12. [QxMD MEDLINE Link] .

UKPDS 28: a randomized trial of efficacy of early addition of metformin in sulfonylurea-treated type 2 diabetes. U.K. Prospective Diabetes Study Group. Diabetes Care . 1998 Jan. 21(1):87-92. [QxMD MEDLINE Link] .

Qaseem A, Barry MJ, Humphrey LL, Forciea MA, Clinical Guidelines Committee of the American College of Physicians. Oral Pharmacologic Treatment of Type 2 Diabetes Mellitus: A Clinical Practice Guideline Update From the American College of Physicians. Ann Intern Med . 2017 Jan 3. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. ACP Updates Guidelines for Type 2 Diabetes Care. Medscape Medical News . 2017 Jan 3. [Full Text] .

Vashisht R, Jung K, Schuler A, et al. Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data Sciences and Informatics Initiative. JAMA Net Open . 2018 Aug 24. 1(4): [Full Text] .

Tucker ME. Big Data Confirm Type 2 Diabetes Treatment Approach. Medscape Medical News . 2018 Aug 31. [Full Text] .

Kooy A, de Jager J, Lehert P, Bets D, Wulffele MG, Donker AJ, et al. Long-term effects of metformin on metabolism and microvascular and macrovascular disease in patients with type 2 diabetes mellitus. Arch Intern Med . 2009 Mar 23. 169(6):616-25. [QxMD MEDLINE Link] .

Pradhan AD, Everett BM, Cook NR, Rifai N, Ridker PM. Effects of initiating insulin and metformin on glycemic control and inflammatory biomarkers among patients with type 2 diabetes: the LANCET randomized trial. JAMA . 2009 Sep 16. 302(11):1186-94. [QxMD MEDLINE Link] .

Andersson C, Olesen JB, Hansen PR, Weeke P, Norgaard ML, Jorgensen CH, et al. Metformin treatment is associated with a low risk of mortality in diabetic patients with heart failure: a retrospective nationwide cohort study. Diabetologia . 2010 Dec. 53(12):2546-53. [QxMD MEDLINE Link] .

Roussel R, Travert F, Pasquet B, Wilson PW, Smith SC Jr, Goto S, et al. Metformin use and mortality among patients with diabetes and atherothrombosis. Arch Intern Med . 2010 Nov 22. 170(21):1892-9. [QxMD MEDLINE Link] .

Gross JL, Kramer CK, Leitão CB, Hawkins N, Viana LV, Schaan BD, et al. Effect of antihyperglycemic agents added to metformin and a sulfonylurea on glycemic control and weight gain in type 2 diabetes: a network meta-analysis. Ann Intern Med . 2011 May 17. 154(10):672-9. [QxMD MEDLINE Link] .

Zeller M, Danchin N, Simon D, Vahanian A, Lorgis L, Cottin Y, et al. Impact of type of preadmission sulfonylureas on mortality and cardiovascular outcomes in diabetic patients with acute myocardial infarction. J Clin Endocrinol Metab . 2010 Nov. 95(11):4993-5002. [QxMD MEDLINE Link] .

Bellomo Damato A, Stefanelli G, Laviola L, Giorgino R, Giorgino F. Nateglinide provides tighter glycaemic control than glyburide in patients with Type 2 diabetes with prevalent postprandial hyperglycaemia. Diabet Med . 2011 May. 28(5):560-6. [QxMD MEDLINE Link] .

Retnakaran R, Qi Y, Harris SB, Hanley AJ, Zinman B. Changes over time in glycemic control, insulin sensitivity, and beta-cell function in response to low-dose metformin and thiazolidinedione combination therapy in patients with impaired glucose tolerance. Diabetes Care . 2011 Jul. 34(7):1601-4. [QxMD MEDLINE Link] . [Full Text] .

DeFronzo RA, Tripathy D, Schwenke DC, Banerji M, Bray GA, Buchanan TA, et al. Pioglitazone for diabetes prevention in impaired glucose tolerance. N Engl J Med . 2011 Mar 24. 364(12):1104-15. [QxMD MEDLINE Link] .

Gerstein HC, Yusuf S, Bosch J, Pogue J, Sheridan P, Dinccag N, et al. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial. Lancet . 2006 Sep 23. 368(9541):1096-105. [QxMD MEDLINE Link] .

Phung OJ, Sood NA, Sill BE, Coleman CI. Oral anti-diabetic drugs for the prevention of Type 2 diabetes. Diabet Med . 2011 Aug. 28(8):948-64. [QxMD MEDLINE Link] .

Charpentier G, Halimi S. Earlier triple therapy with pioglitazone in patients with type 2 diabetes. Diabetes Obes Metab . 2009 Sep. 11(9):844-54. [QxMD MEDLINE Link] .

Dormandy JA, Charbonnel B, Eckland DJ, Erdmann E, Massi-Benedetti M, Moules IK, et al. Secondary prevention of macrovascular events in patients with type 2 diabetes in the PROactive Study (PROspective pioglitAzone Clinical Trial In macroVascular Events): a randomised controlled trial. Lancet . 2005 Oct 8. 366(9493):1279-89. [QxMD MEDLINE Link] .

Rennings AJ, Russel FG, Li Y, Deen PM, Masereeuw R, Tack CJ, et al. Preserved response to diuretics in rosiglitazone-treated subjects with insulin resistance: a randomized double-blind placebo-controlled crossover study. Clin Pharmacol Ther . 2011 Apr. 89(4):587-94. [QxMD MEDLINE Link] .

Lewis JD, Ferrara A, Peng T, Hedderson M, Bilker WB, Quesenberry CP Jr, et al. Risk of bladder cancer among diabetic patients treated with pioglitazone: interim report of a longitudinal cohort study. Diabetes Care . 2011 Apr. 34(4):916-22. [QxMD MEDLINE Link] . [Full Text] .

Ferrara A, Lewis JD, Quesenberry CP Jr, Peng T, Strom BL, Van Den Eeden SK, et al. Cohort study of pioglitazone and cancer incidence in patients with diabetes. Diabetes Care . 2011 Apr. 34(4):923-9. [QxMD MEDLINE Link] . [Full Text] .

Piccinni C, Motola D, Marchesini G, Poluzzi E. Assessing the association of pioglitazone use and bladder cancer through drug adverse event reporting. Diabetes Care . 2011 Jun. 34(6):1369-71. [QxMD MEDLINE Link] . [Full Text] .

Loke YK, Singh S, Furberg CD. Long-term use of thiazolidinediones and fractures in type 2 diabetes: a meta-analysis. CMAJ . 2009 Jan 6. 180(1):32-9. [QxMD MEDLINE Link] . [Full Text] .

Quintanilla Rodriguez BS, Correa R. Rosiglitazone. StatPearls . 2023 Jul 3. [QxMD MEDLINE Link] . [Full Text] .

Bunck MC, Diamant M, Corner A, Eliasson B, Malloy JL, Shaginian RM, et al. One-year treatment with exenatide improves beta-cell function, compared with insulin glargine, in metformin-treated type 2 diabetic patients: a randomized, controlled trial. Diabetes Care . 2009 May. 32(5):762-8. [QxMD MEDLINE Link] . [Full Text] .

Buse JB, Bergenstal RM, Glass LC, Heilmann CR, Lewis MS, Kwan AY, et al. Use of twice-daily exenatide in Basal insulin-treated patients with type 2 diabetes: a randomized, controlled trial. Ann Intern Med . 2011 Jan 18. 154(2):103-12. [QxMD MEDLINE Link] .

Drucker DJ, Buse JB, Taylor K, Kendall DM, Trautmann M, Zhuang D, et al. Exenatide once weekly versus twice daily for the treatment of type 2 diabetes: a randomised, open-label, non-inferiority study. Lancet . 2008 Oct 4. 372(9645):1240-50. [QxMD MEDLINE Link] .

Pencek R, Blickensderfer A, Li Y, Brunell SC, Chen S. Exenatide once weekly for the treatment of type 2 diabetes: effectiveness and tolerability in patient subpopulations. Int J Clin Pract . 2012 Aug 24. [QxMD MEDLINE Link] .

Blevins T, Pullman J, Malloy J, Yan P, Taylor K, Schulteis C, et al. DURATION-5: exenatide once weekly resulted in greater improvements in glycemic control compared with exenatide twice daily in patients with type 2 diabetes. J Clin Endocrinol Metab . 2011 May. 96(5):1301-10. [QxMD MEDLINE Link] .

Douglas D. Exenatide More Effective Than Insulin Detemir: Study. Available at https://www.medscape.com/viewarticle/777411 . Accessed: January 15, 2013.

Davies M, Heller S, Sreenan S, Sapin H, Adetunji O, Tahbaz A, et al. Once-Weekly Exenatide Versus Once- or Twice-Daily Insulin Detemir: Randomized, open-label, clinical trial of efficacy and safety in patients with type 2 diabetes treated with metformin alone or in combination with sulfonylureas. Diabetes Care . 2012 Dec 28. [QxMD MEDLINE Link] .

Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med . 2016 Jul 28. 375 (4):311-22. [QxMD MEDLINE Link] . [Full Text] .

Tamborlane WV, Barrientos-Perez M, Fainberg U, et al. Liraglutide in Children and Adolescents with Type 2 Diabetes. N Engl J Med . 2019 Apr 28. [QxMD MEDLINE Link] .

US Food and Drug Administration. FDA approves Tanzeum to treat type 2 diabetes [press release]. April 15, 2014. Available at https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm393289.htm . Accessed: April 21, 2014.

Busko M. FDA approves weekly injectable diabetes drug: albiglutide. Medscape Medical News . April 15, 2014. [Full Text] .

Douglas D. Albiglutide Long-Acting Option for Diabetes Control. Medscape Medical News. Available at https://www.medscape.com/viewarticle/821700 . Accessed: March 17, 2014.

Pratley RE, Nauck MA, Barnett AH, et al. Once-weekly albiglutide versus once-daily liraglutide in patients with type 2 diabetes inadequately controlled on oral drugs (HARMONY 7): a randomised, open-label, multicentre, non-inferiority phase 3 study. Lancet Diabetes Endocrinol . 2014. Feb 6. [Epub ahead of print].

Tucker M. FDA Approves Once-Weekly Dulaglutide for Type 2 Diabetes. Medscape Medical News. Available at https://www.medscape.com/viewarticle/831969 . Accessed: September 26, 2014.

Wysham C, Blevins T, Arakaki R, Colon G, Garcia P, Atisso C, et al. Efficacy and safety of dulaglutide added onto pioglitazone and metformin versus exenatide in type 2 diabetes in a randomized controlled trial (AWARD-1). Diabetes Care . 2014 Aug. 37(8):2159-67. [QxMD MEDLINE Link] .

Frias JP, Bonora E, Nevarez Ruiz LA, et al. Efficacy and Safety of Dulaglutide 3mg and 4.5mg vs. Dulaglutide 1.5mg: 52-Week Results from AWARD-11 (Abst 357-OR). Presented at the 80th Scientific sessions American of the Diabetes Association. 2020 Jun 12-16. Chicago, IL. Available at https://diabetes.diabetesjournals.org/content/69/Supplement_1/357-OR .

Umpierrez G, Tofe Povedano S, Perez Manghi F, Shurzinske L, Pechtner V. Efficacy and safety of dulaglutide monotherapy versus metformin in type 2 diabetes in a randomized controlled trial (AWARD-3). Diabetes Care . 2014 Aug. 37(8):2168-76. [QxMD MEDLINE Link] .

Nauck M, Weinstock RS, Umpierrez GE, Guerci B, Skrivanek Z, Milicevic Z. Efficacy and safety of dulaglutide versus sitagliptin after 52 weeks in type 2 diabetes in a randomized controlled trial (AWARD-5). Diabetes Care . 2014 Aug. 37(8):2149-58. [QxMD MEDLINE Link] . [Full Text] .

Gerstein HC, Colhoun HM, Dagenais GR, et al. Dulaglutide and cardiovascular outcomes in type 2 diabetes (REWIND): a double-blind, randomised placebo-controlled trial. Lancet . 2019 Jul 13. 394 (10193):121-30. [QxMD MEDLINE Link] .

Ahren B, Galstyan G, Gautier JF, et al. Postprandial Glucagon Reductions Correlate to Reductions in Postprandial Glucose and Glycated Hemoglobin with Lixisenatide Treatment in Type 2 Diabetes Mellitus: A Post Hoc Analysis. Diabetes Ther . 2016 Jun 18. [QxMD MEDLINE Link] .

Yabe D, Ambos A, Cariou B, et al. Efficacy of lixisenatide in patients with type 2 diabetes: A post hoc analysis of patients with diverse β-cell function in the GetGoal-M and GetGoal-S trials. J Diabetes Complications . 2016 May 24. [QxMD MEDLINE Link] . [Full Text] .

Pfeffer MA, Claggett B, Diaz R, et al. Lixisenatide in Patients with Type 2 Diabetes and Acute Coronary Syndrome. N Engl J Med . 2015 Dec 3. 373 (23):2247-57. [QxMD MEDLINE Link] . [Full Text] .

Fonseca VA, Alvarado-Ruiz R, Raccah D, Boka G, Miossec P, Gerich JE. Efficacy and safety of the once-daily GLP-1 receptor agonist lixisenatide in monotherapy: a randomized, double-blind, placebo-controlled trial in patients with type 2 diabetes (GetGoal-Mono). Diabetes Care . 2012 Jun. 35(6):1225-31. [QxMD MEDLINE Link] . [Full Text] .

Marso SP, Bain SC, Consoli A, et al. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med . 2016 Nov 10. 375 (19):1834-44. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. FDA Approves CVD Benefit for Once-Weekly Semaglutide. Medscape Medical News . 2020 Jan 17. [Full Text] .

Ozempic (semaglutide SC) [package insert]. Plainsboro, NJ: Novo Nordisk. January 2020. Available at [Full Text] .

Rosenstock J, Allison D, Birkenfeld AL, et al. Effect of Additional Oral Semaglutide vs Sitagliptin on Glycated Hemoglobin in Adults With Type 2 Diabetes Uncontrolled With Metformin Alone or With Sulfonylurea: The PIONEER 3 Randomized Clinical Trial. JAMA . 2019 Apr 16. 321 (15):1466-80. [QxMD MEDLINE Link] . [Full Text] .

Rodbard HW, Rosenstock J, Canani LH, et al. Oral Semaglutide versus Empagliflozin in Patients with Type 2 Diabetes Uncontrolled on Metformin: The PIONEER 2 Trial. Diabetes Care . 2019 Sep 17. [QxMD MEDLINE Link] .

Pratley R, Amod A, Hoff ST, et al. Oral semaglutide versus subcutaneous liraglutide and placebo in type 2 diabetes (PIONEER 4): a randomised, double-blind, phase 3a trial. Lancet . 2019 Jul 6. 394 (10192):39-50. [QxMD MEDLINE Link] .

Rybelsus (semaglutide oral) [package insert]. Plainsboro, NJ: Novo Nordisk. January 2020. Available at [Full Text] .

Frías JP, Davies MJ, Rosenstock J, and the, SURPASS-2 Investigators. Tirzepatide versus Semaglutide Once Weekly in Patients with Type 2 Diabetes. N Engl J Med . 2021 Aug 5. 385 (6):503-515. [QxMD MEDLINE Link] . [Full Text] .

Aschner P, Katzeff HL, Guo H, Sunga S, Williams-Herman D, Kaufman KD, et al. Efficacy and safety of monotherapy of sitagliptin compared with metformin in patients with type 2 diabetes. Diabetes Obes Metab . 2010 Mar. 12(3):252-61. [QxMD MEDLINE Link] .

Vilsboll T, Rosenstock J, Yki-Jarvinen H, Cefalu WT, Chen Y, Luo E, et al. Efficacy and safety of sitagliptin when added to insulin therapy in patients with type 2 diabetes. Diabetes Obes Metab . 2010 Feb. 12(2):167-77. [QxMD MEDLINE Link] .

Perez-Monteverde A, Seck T, Xu L, Lee MA, Sisk CM, Williams-Herman DE, et al. Efficacy and safety of sitagliptin and the fixed-dose combination of sitagliptin and metformin vs. pioglitazone in drug-naïve patients with type 2 diabetes. Int J Clin Pract . 2011 Sep. 65(9):930-8. [QxMD MEDLINE Link] .

Owens DR, Swallow R, Dugi KA, Woerle HJ. Efficacy and safety of linagliptin in persons with type 2 diabetes inadequately controlled by a combination of metformin and sulphonylurea: a 24-week randomized study. Diabet Med . 2011 Nov. 28(11):1352-61. [QxMD MEDLINE Link] .

Willemen MJ, Mantel-Teeuwisse AK, Straus SM, Meyboom RH, Egberts TC, Leufkens HG. Use of dipeptidyl peptidase-4 inhibitors and the reporting of infections: a disproportionality analysis in the World Health Organization VigiBase. Diabetes Care . 2011 Feb. 34(2):369-74. [QxMD MEDLINE Link] . [Full Text] .

Monami M, Dicembrini I, Antenore A, Mannucci E. Dipeptidyl peptidase-4 inhibitors and bone fractures: a meta-analysis of randomized clinical trials. Diabetes Care . 2011 Nov. 34(11):2474-6. [QxMD MEDLINE Link] . [Full Text] .

Solerte SB, D'Addio F, Trevisan R, et al. Sitagliptin Treatment at the Time of Hospitalization Was Associated With Reduced Mortality in Patients With Type 2 Diabetes and COVID-19: A Multicenter, Case-Control, Retrospective, Observational Study. Diabetes Care . 2020 Sep 29. [QxMD MEDLINE Link] . [Full Text] .

Zoler ML. Fewer Deaths in Hospitalized COVID Diabetes Patients on Sitagliptin. Medscape Medical News . 2020 Oct 1. [Full Text] .

Nainggolan L. FDA approves canagliflozin, a first-in-class diabetes drug. March 29, 2013. Medscape Medical News. Available at https://www.medscape.com/viewarticle/781709 . Accessed: April 2, 2013.

US Food and Drug Administration. FDA approves Invokana to treat type 2 diabetes [press release]. March 29, 2013. Available at https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm345848.htm . Accessed: April 2, 2013.

Tucker M. FDA Approves Dapagliflozin (Farxiga) for Type 2 Diabetes Treatment. Medscape Medical News. Available at https://www.medscape.com/viewarticle/818858 . Accessed: January 13, 2014.

FDA News Release. FDA approves Farxiga to treat type 2 diabetes. U.S. Food and Drug Administration. Available at https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm380829.htm . Accessed: January 13, 2014.

Roden M, Weng J, Eilbracht J, et al. Empagliflozin monotherapy with sitagliptin as an active comparator in patients with type 2 diabetes: a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Diabetes Endocrinol . 2013 Nov. 1(3):208-19. [QxMD MEDLINE Link] .

Ridderstrale M, Andersen KR, Zeller C, Kim G, Woerle HJ, Broedl UC. Comparison of empagliflozin and glimepiride as add-on to metformin in patients with type 2 diabetes: a 104-week randomised, active-controlled, double-blind, phase 3 trial. Lancet Diabetes Endocrinol . 2014 Jun 16. [QxMD MEDLINE Link] .

Stenlof K, Cefalu WT, Kim KA, Alba M, Usiskin K, Tong C, et al. Efficacy and safety of canagliflozin monotherapy in subjects with type 2 diabetes mellitus inadequately controlled with diet and exercise. Diabetes Obes Metab . 2013 Apr. 15(4):372-82. [QxMD MEDLINE Link] . [Full Text] .

Clar C, Gill JA, Court R, Waugh N. Systematic review of SGLT2 receptor inhibitors in dual or triple therapy in type 2 diabetes. BMJ Open . 2012. 2(5): [QxMD MEDLINE Link] . [Full Text] .

Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. N Engl J Med . 2019 Jun 13. 380 (24):2295-306. [QxMD MEDLINE Link] .

Wilding JP, Woo V, Soler NG, Pahor A, Sugg J, Rohwedder K, et al. Long-term efficacy of dapagliflozin in patients with type 2 diabetes mellitus receiving high doses of insulin: a randomized trial. Ann Intern Med . 2012 Mar 20. 156(6):405-15. [QxMD MEDLINE Link] .

Nauck MA, Del Prato S, Meier JJ, Duran-Garcia S, Rohwedder K, Elze M, et al. Dapagliflozin versus glipizide as add-on therapy in patients with type 2 diabetes who have inadequate glycemic control with metformin: a randomized, 52-week, double-blind, active-controlled noninferiority trial. Diabetes Care . 2011 Sep. 34(9):2015-22. [QxMD MEDLINE Link] . [Full Text] .

Strojek K, Yoon KH, Hruba V, Elze M, Langkilde AM, Parikh S. Effect of dapagliflozin in patients with type 2 diabetes who have inadequate glycaemic control with glimepiride: a randomized, 24-week, double-blind, placebo-controlled trial. Diabetes Obes Metab . 2011 Oct. 13(10):928-38. [QxMD MEDLINE Link] .

Rosenstock J, Vico M, Wei L, Salsali A, List JF. Effects of dapagliflozin, an SGLT2 inhibitor, on HbA(1c), body weight, and hypoglycemia risk in patients with type 2 diabetes inadequately controlled on pioglitazone monotherapy. Diabetes Care . 2012 Jul. 35(7):1473-8. [QxMD MEDLINE Link] . [Full Text] .

Wiviott SD, Raz I, Bonaca MP, et al. Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med . 2019 Jan 24. 380 (4):347-57. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. FDA Approves Empagliflozin for Reducing CVD Death. Medscape Medical News . 2016 Dec 2. [Full Text] .

Peters AL. 'Incredibly Exciting': Diabetes Drug With CV Benefits. Medscape . 2016 Dec 21. [Full Text] .

Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. N Engl J Med . 2015 Nov 26. 373 (22):2117-28. [QxMD MEDLINE Link] . [Full Text] .

Terra SG, Focht K, Davies M, et al. Phase III, efficacy and safety study of ertugliflozin monotherapy in people with type 2 diabetes mellitus inadequately controlled with diet and exercise alone. Diabetes Obes Metab . 2017 May. 19 (5):721-8. [QxMD MEDLINE Link] .

Rosenstock J, Frias J, Pall D, et al. Effect of ertugliflozin on glucose control, body weight, blood pressure and bone density in type 2 diabetes mellitus inadequately controlled on metformin monotherapy (VERTIS MET). Diabetes Obes Metab . 2017 Aug 31. [QxMD MEDLINE Link] .

Dagogo-Jack S, Liu J, Eldor R, et al. Efficacy and safety of the addition of ertugliflozin in patients with type 2 diabetes mellitus inadequately controlled with metformin and sitagliptin: The VERTIS SITA2 placebo-controlled randomized study. Diabetes Obes Metab . 2017 Sep 17. [QxMD MEDLINE Link] . [Full Text] .

Zoler ML. FDA Approves Finerenone (Kerendia) for Slowing CKD in Type 2 Diabetes. Medscape Medical News . 2021 Jul 12. [Full Text] .

Bakris GL, Agarwal R, Anker SD, et al. Effect of Finerenone on Chronic Kidney Disease Outcomes in Type 2 Diabetes. N Engl J Med . 2020 Dec 3. 383 (23):2219-29. [QxMD MEDLINE Link] . [Full Text] .

US Food and Drug Administration. FDA Approves Drug to Reduce Risk of Serious Kidney and Heart Complications in Adults with Chronic Kidney Disease Associated with Type 2 Diabetes. FDA. Available at https://www.fda.gov/drugs/drug-safety-and-availability/fda-approves-drug-reduce-risk-serious-kidney-and-heart-complications-adults-chronic-kidney-disease . July 9, 2021; Accessed: July 15, 2021.

de la Pena A, Riddle M, Morrow LA, Jiang HH, Linnebjerg H, Scott A, et al. Pharmacokinetics and pharmacodynamics of high-dose human regular U-500 insulin versus human regular U-100 insulin in healthy obese subjects. Diabetes Care . 2011 Dec. 34(12):2496-501. [QxMD MEDLINE Link] . [Full Text] .

Agency for Healthcare Research and Quality. Comparative Effectiveness, Safety, and Indications of Insulin Analogues in Premixed Formulations for Adults With Type 2 Diabetes. AHRQ: Agency for Healthcare Research and Quality. Available at https://www.effectivehealthcare.ahrq.gov/index.cfm/search-for-guides-reviews-and-reports/?productid=108&pageaction=displayproduct. . Accessed: March 7, 2012.

Blair HA, Keating GM. Insulin Glargine 300 U/mL: A Review in Diabetes Mellitus. Drugs . 2016 Mar. 76 (3):363-74. [QxMD MEDLINE Link] .

Toujeo. US Food and Drug Administration. Available at https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/206538s006lbl.pdf . Accessed: 2018 Apr 25.

Davies MJ, Gross JL, Ono Y, Sasaki T, Bantwal G, Gall MA, et al. Efficacy and safety of insulin degludec given as part of basal-bolus treatment with mealtime insulin aspart in type 1 diabetes: a 26-week randomized, open-label, treat-to-target non-inferiority trial. Diabetes Obes Metab . 2014 Oct. 16 (10):922-30. [QxMD MEDLINE Link] . [Full Text] .

Zinman B, DeVries JH, Bode B, Russell-Jones D, Leiter LA, Moses A, et al. Efficacy and safety of insulin degludec three times a week versus insulin glargine once a day in insulin-naive patients with type 2 diabetes: results of two phase 3, 26 week, randomised, open-label, treat-to-target, non-inferiority trials. Lancet Diabetes Endocrinol . 2013 Oct. 1 (2):123-31. [QxMD MEDLINE Link] .

Marso SP, McGuire DK, Zinman B, et al. Efficacy and Safety of Degludec versus Glargine in Type 2 Diabetes. N Engl J Med . 2017 Aug 24. 377 (8):723-32. [QxMD MEDLINE Link] . [Full Text] .

Zinman B, Fulcher G, Rao PV, Thomas N, Endahl LA, Johansen T, et al. Insulin degludec, an ultra-long-acting basal insulin, once a day or three times a week versus insulin glargine once a day in patients with type 2 diabetes: a 16-week, randomised, open-label, phase 2 trial. Lancet . 2011 Mar 12. 377(9769):924-31. [QxMD MEDLINE Link] .

Afrezza (insulin inhaled) prescribing information [package insert]. Valencia CA, United States: MannKind Corporation. June 2014. Available at [Full Text] .

Fiasp (insulin aspart) [package insert]. 800 Scudders Mill Road, Plainsboro, NJ 08536: Novo Nordisk Inc. September 2017. Available at [Full Text] .

Nainggolan L. FDA Approves New Fast-Acting Insulin, Fiasp, for Diabetes in Adults. Medscape Medical News . 2017 Sep 29. [Full Text] .

US Food and Drug Administration. Early Communication About Safety of Lantus (Insulin Glargine). [Full Text] .

Suissa S, Azoulay L, Dell'Aniello S, Evans M, Vora J, Pollak M. Long-term effects of insulin glargine on the risk of breast cancer. Diabetologia . 2011 Sep. 54(9):2254-62. [QxMD MEDLINE Link] .

Johnson JA, Bowker SL, Richardson K, Marra CA. Time-varying incidence of cancer after the onset of type 2 diabetes: evidence of potential detection bias. Diabetologia . 2011 Sep. 54(9):2263-71. [QxMD MEDLINE Link] .

Stefansdottir G, Zoungas S, Chalmers J, Kengne AP, Knol MJ, Leufkens HG, et al. Intensive glucose control and risk of cancer in patients with type 2 diabetes. Diabetologia . 2011 Jul. 54(7):1608-14. [QxMD MEDLINE Link] .

Shyangdan DS, Royle P, Clar C, Sharma P, Waugh N, Snaith A. Glucagon-like peptide analogues for type 2 diabetes mellitus. Cochrane Database Syst Rev . 2011 Oct 5. CD006423. [QxMD MEDLINE Link] .

Handelsman Y, Goldberg RB, Garvey WT, Fonseca VA, Rosenstock J, Jones MR, et al. Colesevelam hydrochloride to treat hypercholesterolemia and improve glycemia in prediabetes: a randomized, prospective study. Endocr Pract . 2010 Jul-Aug. 16(4):617-28. [QxMD MEDLINE Link] .

Rosenstock J, Fonseca VA, Garvey WT, Goldberg RB, Handelsman Y, Abby SL, et al. Initial combination therapy with metformin and colesevelam for achievement of glycemic and lipid goals in early type 2 diabetes. Endocr Pract . 2010 Jul-Aug. 16(4):629-40. [QxMD MEDLINE Link] .

Sando KR, Taylor J. Bromocriptine: its place in type 2 diabetes Tx. J Fam Pract . 2011 Nov. 60(11):E1-5. [QxMD MEDLINE Link] .

Gaziano JM, Cincotta AH, O'Connor CM, Ezrokhi M, Rutty D, Ma ZJ, et al. Randomized clinical trial of quick-release bromocriptine among patients with type 2 diabetes on overall safety and cardiovascular outcomes. Diabetes Care . 2010 Jul. 33(7):1503-8. [QxMD MEDLINE Link] . [Full Text] .

Bolen S, Wilson L, Vassy J, et al. Comparative Effectiveness and Safety of Oral Diabetes Medications for Adults With Type 2 Diabetes [Internet]. Comparative Effectiveness Reviews . 2007 Jul. 8: [QxMD MEDLINE Link] . [Full Text] .

Bennett WL, Wilson LM, Bolen S, et al. Oral Diabetes Medications for Adults With Type 2 Diabetes: An Update [Internet]. Comparative Effectiveness Reviews . 2011 Mar. 27: [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. New AACE algorithm addresses all aspects of type 2 diabetes. Medscape Medical News . April 23, 2013. [Full Text] .

[Guideline] Garber AJ, Abrahamson MJ, Barzilay JI, Blonde L, Bloomgarden ZT, Bush MA, et al. AACE Comprehensive Diabetes Management Algorithm 2013. Endocr Pract . 2013 Mar-Apr. 19(2):327-36. [QxMD MEDLINE Link] .

Boule NG, Robert C, Bell GJ, Johnson ST, Bell RC, Lewanczuk RZ, et al. Metformin and exercise in type 2 diabetes: examining treatment modality interactions. Diabetes Care . 2011 Jul. 34(7):1469-74. [QxMD MEDLINE Link] . [Full Text] .

Brooks M. Metformin Cuts Dementia Risk in Type 2 Diabetes. Medscape Medical News. Available at https://www.medscape.com/viewarticle/807886 . Accessed: July 23, 2013.

Rodbard HW, Jellinger PS, Davidson JA, Einhorn D, Garber AJ, Grunberger G, et al. Statement by an American Association of Clinical Endocrinologists/American College of Endocrinology consensus panel on type 2 diabetes mellitus: an algorithm for glycemic control. Endocr Pract . 2009 Sep-Oct. 15(6):540-59. [QxMD MEDLINE Link] .

Qayyum R, Bolen S, Maruthur N, Feldman L, Wilson LM, Marinopoulos SS, et al. Systematic review: comparative effectiveness and safety of premixed insulin analogues in type 2 diabetes. Ann Intern Med . 2008 Oct 21. 149(8):549-59. [QxMD MEDLINE Link] .

Porcellati F, Lucidi P, Rossetti P, Candeloro P, Andreoli AM, Marzotti S, et al. Differential effects of adiposity on pharmacodynamics of basal insulins NPH, glargine, and detemir in type 2 diabetes mellitus. Diabetes Care . 2011 Dec. 34(12):2521-3. [QxMD MEDLINE Link] . [Full Text] .

Baldwin D, Zander J, Munoz C, Raghu P, Delange-Hudec S, Lee H, et al. A Randomized Trial of Two Weight-Based Doses of Insulin Glargine and Glulisine in Hospitalized Subjects With Type 2 Diabetes and Renal Insufficiency. Diabetes Care . 2012 Jun 14. [QxMD MEDLINE Link] .

Grunberger G, Abelseth JM, Bailey TS, Bode BW, Handelsman Y, Hellman R. Consensus statement by the american association of clinical endocrinologists/american college of endocrinology insulin pump management task force. Endocr Pract . 2014 May 1. 20(5):463-89. [QxMD MEDLINE Link] .

Fritsche A, Larbig M, Owens D, Haring HU. Comparison between a basal-bolus and a premixed insulin regimen in individuals with type 2 diabetes-results of the GINGER study. Diabetes Obes Metab . 2010 Feb. 12(2):115-23. [QxMD MEDLINE Link] .

Siegelaar SE, Kerr L, Jacober SJ, Devries JH. A decrease in glucose variability does not reduce cardiovascular event rates in type 2 diabetic patients after acute myocardial infarction: a reanalysis of the HEART2D study. Diabetes Care . 2011 Apr. 34(4):855-7. [QxMD MEDLINE Link] . [Full Text] .

Chen MJ, Jovanovic A, Taylor R. Utilizing the second-meal effect in type 2 diabetes: practical use of a soya-yogurt snack. Diabetes Care . 2010 Dec. 33(12):2552-4. [QxMD MEDLINE Link] . [Full Text] .

Qaseem A, Vijan S, Snow V, Cross JT, Weiss KB, Owens DK. Glycemic control and type 2 diabetes mellitus: the optimal hemoglobin A1c targets. A guidance statement from the American College of Physicians. Ann Intern Med . 2007 Sep 18. 147(6):417-22. [QxMD MEDLINE Link] .

Boussageon R, Bejan-Angoulvant T, Saadatian-Elahi M, Lafont S, Bergeonneau C, Kassaï B, et al. Effect of intensive glucose lowering treatment on all cause mortality, cardiovascular death, and microvascular events in type 2 diabetes: meta-analysis of randomised controlled trials. BMJ . 2011 Jul 26. 343:d4169. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. Diabetes in the Elderly Addressed in Consensus Report. Medscape Medical News. October 25, 2012. Accessed November 13, 2012.

Sue Kirkman M, Briscoe VJ, Clark N, et al. Diabetes in Older Adults: A Consensus Report. J Am Geriatr Soc . 2012 Oct 25. [QxMD MEDLINE Link] .

Klonoff DC, Buckingham B, Christiansen JS, Montori VM, Tamborlane WV, Vigersky RA, et al. Continuous glucose monitoring: an Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab . 2011 Oct. 96(10):2968-79. [QxMD MEDLINE Link] .

Kompala T, Neinstein A. A new era: increasing continuous glucose monitoring use in type 2 diabetes. Am J Manag Care . 2019 Mar. 25 (4 Spec No.):SP123-6. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. Noninvasive, Low Cost CGM for Type 2 Diabetes Coming in US and EU. Medscape Medical News . 2020 Dec 2. [Full Text] .

US Food and Drug Administration. FDA Clears First Over-the-Counter Continuous Glucose Monitor. FDA. Available at https://www.fda.gov/news-events/press-announcements/fda-clears-first-over-counter-continuous-glucose-monitor . March 5, 2024; Accessed: September 6, 2024.

Ahmedani MY, Ul Haque MS, Basit A, Fawwad A, Alvi SF. Ramadan Prospective Diabetes Study: the role of drug dosage and timing alteration, active glucose monitoring and patient education. Diabet Med . 2012 Jan 11. [QxMD MEDLINE Link] .

Wing RR, Lang W, Wadden TA, Safford M, Knowler WC, Bertoni AG, et al. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care . 2011 Jul. 34(7):1481-6. [QxMD MEDLINE Link] . [Full Text] .

Wing RR, Bolin P, Brancati FL, Bray GA, Clark JM, Coday M, et al. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med . 2013 Jul 11. 369(2):145-54. [QxMD MEDLINE Link] . [Full Text] .

Lazo M, Solga SF, Horska A, Bonekamp S, Diehl AM, Brancati FL, et al. Effect of a 12-month intensive lifestyle intervention on hepatic steatosis in adults with type 2 diabetes. Diabetes Care . 2010 Oct. 33(10):2156-63. [QxMD MEDLINE Link] . [Full Text] .

Lean MEJ, Leslie WS, Barnes AC, et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet . 2017 Dec 5. [Full Text] .

Nainggolan L. Liquid Diet, Gradual Reintroduction of Food, Prompts Diabetes Remission. Medscape . 2017 Dec 5. [Full Text] .

Esposito K, Maiorino MI, Ciotola M, Di Palo C, Scognamiglio P, Gicchino M, et al. Effects of a Mediterranean-style diet on the need for antihyperglycemic drug therapy in patients with newly diagnosed type 2 diabetes: a randomized trial. Ann Intern Med . 2009 Sep 1. 151(5):306-14. [QxMD MEDLINE Link] .

Larsen RN, Mann NJ, Maclean E, Shaw JE. The effect of high-protein, low-carbohydrate diets in the treatment of type 2 diabetes: a 12 month randomised controlled trial. Diabetologia . 2011 Apr. 54(4):731-40. [QxMD MEDLINE Link] .

Bassil M, Burgos S, Marliss EB, Morais JA, Chevalier S, Gougeon R. Hyperaminoacidaemia at postprandial levels does not modulate glucose metabolism in type 2 diabetes mellitus. Diabetologia . 2011 Jul. 54(7):1810-8. [QxMD MEDLINE Link] .

Mozaffarian D, Cao H, King IB, Lemaitre RN, Song X, Siscovick DS, et al. Trans-palmitoleic acid, metabolic risk factors, and new-onset diabetes in U.S. adults: a cohort study. Ann Intern Med . 2010 Dec 21. 153(12):790-9. [QxMD MEDLINE Link] . [Full Text] .

Uribarri J, Cai W, Ramdas M, Goodman S, Pyzik R, Chen X, et al. Restriction of advanced glycation end products improves insulin resistance in human type 2 diabetes: potential role of AGER1 and SIRT1. Diabetes Care . 2011 Jul. 34(7):1610-6. [QxMD MEDLINE Link] . [Full Text] .

Reeds DN, Patterson BW, Okunade A, Holloszy JO, Polonsky KS, Klein S. Ginseng and ginsenoside Re do not improve ß-cell function or insulin sensitivity in overweight and obese subjects with impaired glucose tolerance or diabetes. Diabetes Care . 2011 May. 34(5):1071-6. [QxMD MEDLINE Link] . [Full Text] .

Clerici C, Nardi E, Battezzati PM, Asciutti S, Castellani D, Corazzi N, et al. Novel soy germ pasta improves endothelial function, blood pressure, and oxidative stress in patients with type 2 diabetes. Diabetes Care . 2011 Sep. 34(9):1946-8. [QxMD MEDLINE Link] . [Full Text] .

n-3 Fatty Acids and Cardiovascular Outcomes in Patients with Dysglycemia. N Engl J Med . 2012 Jun 11. [QxMD MEDLINE Link] .

Umpierre D, Ribeiro PA, Kramer CK, Leitao CB, Zucatti AT, Azevedo MJ, et al. Physical activity advice only or structured exercise training and association with HbA1c levels in type 2 diabetes: a systematic review and meta-analysis. JAMA . 2011 May 4. 305(17):1790-9. [QxMD MEDLINE Link] .

Balducci S, Zanuso S, Nicolucci A, De Feo P, Cavallo S, Cardelli P, et al. Effect of an intensive exercise intervention strategy on modifiable cardiovascular risk factors in subjects with type 2 diabetes mellitus: a randomized controlled trial: the Italian Diabetes and Exercise Study (IDES). Arch Intern Med . 2010 Nov 8. 170(20):1794-803. [QxMD MEDLINE Link] .

Church TS, Blair SN, Cocreham S, Johannsen N, Johnson W, Kramer K, et al. Effects of aerobic and resistance training on hemoglobin A1c levels in patients with type 2 diabetes: a randomized controlled trial. JAMA . 2010 Nov 24. 304(20):2253-62. [QxMD MEDLINE Link] . [Full Text] .

Chudyk A, Petrella RJ. Effects of exercise on cardiovascular risk factors in type 2 diabetes: a meta-analysis. Diabetes Care . 2011 May. 34(5):1228-37. [QxMD MEDLINE Link] . [Full Text] .

Loimaala A, Groundstroem K, Rinne M, Nenonen A, Huhtala H, Parkkari J, et al. Effect of long-term endurance and strength training on metabolic control and arterial elasticity in patients with type 2 diabetes mellitus. Am J Cardiol . 2009 Apr 1. 103(7):972-7. [QxMD MEDLINE Link] .

Hegde SV, Adhikari P, Kotian S, Pinto VJ, D'Souza S, D'Souza V. Effect of 3-month yoga on oxidative stress in type 2 diabetes with or without complications: a controlled clinical trial. Diabetes Care . 2011 Oct. 34(10):2208-10. [QxMD MEDLINE Link] . [Full Text] .

Dixon JB, Zimmet P, Alberti KG, Rubino F. Bariatric surgery: an IDF statement for obese Type 2 diabetes. Diabet Med . 2011 Jun. 28(6):628-42. [QxMD MEDLINE Link] . [Full Text] .

Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med . 2002 Feb 7. 346(6):393-403. [QxMD MEDLINE Link] . [Full Text] .

Davenport L. 'Historic' Guidelines on Role of Bariatric Surgery in Diabetes. Medscape Medical News . May 25, 2016. [Full Text] .

Rubino F, Nathan DM, Eckel RH, et al. Metabolic Surgery in the Treatment Algorithm for Type 2 Diabetes: A Joint Statement by International Diabetes Organizations. Diabetes Care . 2016 Jun. 39 (6):861-77. [QxMD MEDLINE Link] . [Full Text] .

Courcoulas AP, Patti ME, Hu B, et al. Long-Term Outcomes of Medical Management vs Bariatric Surgery in Type 2 Diabetes. JAMA . 2024 Feb 27. 331 (8):654-64. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. Bariatric Surgery Tops Usual Care in Type 2 Diabetes. Medscape Medical News . 2024 Feb 27. [Full Text] .

Kashyap SR, Bhatt DL, Wolski K, Watanabe RM, Abdul-Ghani M, Abood B, et al. Metabolic Effects of Bariatric Surgery in Patients With Moderate Obesity and Type 2 Diabetes: Analysis of a randomized control trial comparing surgery with intensive medical treatment. Diabetes Care . 2013 Feb 25. [QxMD MEDLINE Link] .

Tucker ME. Bariatric Surgery: Many Can Come Off Insulin Long Term. Medscape . 2017 Nov 6. [Full Text] .

Cigolle CT, Lee PG, Langa KM, Lee YY, Tian Z, Blaum CS. Geriatric conditions develop in middle-aged adults with diabetes. J Gen Intern Med . 2011 Mar. 26(3):272-9. [QxMD MEDLINE Link] . [Full Text] .

Schernhammer E, Hansen J, Rugbjerg K, Wermuth L, Ritz B. Diabetes and the risk of developing Parkinson's disease in Denmark. Diabetes Care . 2011 May. 34(5):1102-8. [QxMD MEDLINE Link] . [Full Text] .

Cereda E, Barichella M, Pedrolli C, Klersy C, Cassani E, Caccialanza R, et al. Diabetes and risk of Parkinson's disease: a systematic review and meta-analysis. Diabetes Care . 2011 Dec. 34(12):2614-23. [QxMD MEDLINE Link] . [Full Text] .

Chen HF, Chen P, Li CY. Risk of malignant neoplasm of the pancreas in relation to diabetes: a population-based study in Taiwan. Diabetes Care . 2011 May. 34(5):1177-9. [QxMD MEDLINE Link] . [Full Text] .

Kanaya AM, Adler N, Moffet HH, Liu J, Schillinger D, Adams A, et al. Heterogeneity of diabetes outcomes among asians and pacific islanders in the US: the diabetes study of northern california (DISTANCE). Diabetes Care . 2011 Apr. 34(4):930-7. [QxMD MEDLINE Link] . [Full Text] .

Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. UK Prospective Diabetes Study Group. BMJ . 1998 Sep 12. 317(7160):703-13. [QxMD MEDLINE Link] . [Full Text] .

Hansson L, Zanchetti A, Carruthers SG, Dahlof B, Elmfeldt D, Julius S, et al. Effects of intensive blood-pressure lowering and low-dose aspirin in patients with hypertension: principal results of the Hypertension Optimal Treatment (HOT) randomised trial. HOT Study Group. Lancet . 1998 Jun 13. 351(9118):1755-62. [QxMD MEDLINE Link] .

[Guideline] Doyle-Delgado K, Chamberlain JJ, Shubrook JH, Skolnik N, Trujillo J. Pharmacologic Approaches to Glycemic Treatment of Type 2 Diabetes: Synopsis of the 2020 American Diabetes Association's Standards of Medical Care in Diabetes Clinical Guideline. Ann Intern Med . 2020 Sep 1. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] ElSayed NA, Aleppo G, Aroda VR, et al. Introduction and Methodology: Standards of Care in Diabetes-2023. Diabetes Care . 2023 Jan 1. 46 (Supplement_1):S1-S4. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. ADA Advises New BP, Lipid Targets for People With Diabetes. Medscape Medical News . 2022 Dec 13. [Full Text] .

Anderson RJ, Bahn GD, Moritz TE, Kaufman D, Abraira C, Duckworth W. Blood pressure and cardiovascular disease risk in the Veterans Affairs Diabetes Trial. Diabetes Care . 2011 Jan. 34(1):34-8. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] de Boer IH, Bangalore S, Benetos A, et al. Diabetes and Hypertension: A Position Statement by the American Diabetes Association. Diabetes Care . 2017 Sep. 40 (9):1273-84. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Jenkins K. ADA Updates Recommendations for Managing Hypertension in Diabetes. Medscape . 4 Sep 2017. [Full Text] .

FDA Drug Safety Communication: FDA review of cardiovascular risks for diabetics taking hypertension drug olmesartan not conclusive; label updates required. US Food and Drug Administration. Available at https://www.fda.gov/Drugs/DrugSafety/ucm402323.htm . Accessed: June 29, 2014.

O'Riordan M. No CV risk with olmesartan in diabetics, says FDA review. Medscape Medical News . June 24, 2014. [Full Text] .

Parving HH, Brenner BM, McMurray JJ, de Zeeuw D, Haffner SM, Solomon SD, et al. Cardiorenal End Points in a Trial of Aliskiren for Type 2 Diabetes. N Engl J Med . 2012 Nov 3. [QxMD MEDLINE Link] .

Hermida RC, Ayala DE, Mojon A, Fernandez JR. Influence of time of day of blood pressure-lowering treatment on cardiovascular risk in hypertensive patients with type 2 diabetes. Diabetes Care . 2011 Jun. 34(6):1270-6. [QxMD MEDLINE Link] . [Full Text] .

Management of dyslipidemia in adults with diabetes. Diabetes Care . 2000 Jan. 23 Suppl 1:S57-60. [QxMD MEDLINE Link] .

Bell DS, Bakris GL, McGill JB. Comparison of carvedilol and metoprolol on serum lipid concentration in diabetic hypertensive patients. Diabetes Obes Metab . 2009 Mar. 11(3):234-8. [QxMD MEDLINE Link] .

Aspirin therapy in diabetes. Diabetes Care . 2000 Jan. 23 Suppl 1:S61-2. [QxMD MEDLINE Link] .

Ogawa H, Nakayama M, Morimoto T, Uemura S, Kanauchi M, Doi N, et al. Low-dose aspirin for primary prevention of atherosclerotic events in patients with type 2 diabetes: a randomized controlled trial. JAMA . 2008 Nov 12. 300(18):2134-41. [QxMD MEDLINE Link] .

Saito Y, Morimoto T, Ogawa H, Nakayama M, Uemura S, Doi N, et al. Low-dose aspirin therapy in patients with type 2 diabetes and reduced glomerular filtration rate: subanalysis from the JPAD trial. Diabetes Care . 2011 Feb. 34(2):280-5. [QxMD MEDLINE Link] . [Full Text] .

Okada S, Morimoto T, Ogawa H, Kanauchi M, Nakayama M, Uemura S, et al. Differential effect of low-dose aspirin for primary prevention of atherosclerotic events in diabetes management: a subanalysis of the JPAD trial. Diabetes Care . 2011 Jun. 34(6):1277-83. [QxMD MEDLINE Link] . [Full Text] .

Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet . 1994 Nov 19. 344(8934):1383-9. [QxMD MEDLINE Link] .

MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial. Lancet . 2002 Jul 6. 360(9326):7-22. [QxMD MEDLINE Link] .

Sever PS, Dahlof B, Poulter NR, Wedel H, Beevers G, Caulfield M, et al. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial--Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet . 2003 Apr 5. 361(9364):1149-58. [QxMD MEDLINE Link] .

Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HA, Livingstone SJ, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet . 2004 Aug 21-27. 364(9435):685-96. [QxMD MEDLINE Link] .

Preiss D, Seshasai SR, Welsh P, Murphy SA, Ho JE, Waters DD, et al. Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a meta-analysis. JAMA . 2011 Jun 22. 305(24):2556-64. [QxMD MEDLINE Link] .

Tucker ME. Statins Up Type 2 Diabetes Risk, Overweight at Greatest Risk. Medscape Medical News . 2019 Mar 13. [Full Text] .

Ahmadizar F, OchoaRosales C, Glisic M, Franco OH, Muka T, Stricker BH. Associations of statin use with glycaemic traits and incident type 2 diabetes. Br J Clin Pharmacol . 2019 Mar 5. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. ADA endorses ACC/AHA statin guidelines, with caveats. Medscape Medical News. Available at https://www.medscape.com/viewarticle/837138 . Accessed: December 24, 2014.

Rubins HB, Robins SJ, Collins D, Fye CL, Anderson JW, Elam MB, et al. Gemfibrozil for the secondary prevention of coronary heart disease in men with low levels of high-density lipoprotein cholesterol. Veterans Affairs High-Density Lipoprotein Cholesterol Intervention Trial Study Group. N Engl J Med . 1999 Aug 5. 341(6):410-8. [QxMD MEDLINE Link] .

Frye RL, August P, Brooks MM, Hardison RM, Kelsey SF, MacGregor JM, et al. A randomized trial of therapies for type 2 diabetes and coronary artery disease. N Engl J Med . 2009 Jun 11. 360(24):2503-15. [QxMD MEDLINE Link] . [Full Text] .

Agardh E, Tababat-Khani P. Adopting 3-year screening intervals for sight-threatening retinal vascular lesions in type 2 diabetic subjects without retinopathy. Diabetes Care . 2011 Jun. 34(6):1318-9. [QxMD MEDLINE Link] . [Full Text] .

Sjolie AK, Klein R, Porta M, Orchard T, Fuller J, Parving HH, et al. Effect of candesartan on progression and regression of retinopathy in type 2 diabetes (DIRECT-Protect 2): a randomised placebo-controlled trial. Lancet . 2008 Oct 18. 372(9647):1385-93. [QxMD MEDLINE Link] .

Oshitari T, Asaumi N, Watanabe M, Kumagai K, Mitamura Y. Severe macular edema induced by pioglitazone in a patient with diabetic retinopathy: a case study. Vasc Health Risk Manag . 2008. 4(5):1137-40. [QxMD MEDLINE Link] . [Full Text] .

Food and Drug Administration. FDA Requires Boxed Warning and Risk Mitigation Strategy for Metoclopramide-Containing Drugs. U.S. Food and Drug Administration. Available at https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm149533.htm . Accessed: August 4, 2010.

Chou KL, Galetta SL, Liu GT, Volpe NJ, Bennett JL, Asbury AK, et al. Acute ocular motor mononeuropathies: prospective study of the roles of neuroimaging and clinical assessment. J Neurol Sci . 2004 Apr 15. 219(1-2):35-9. [QxMD MEDLINE Link] .

Queale WS, Seidler AJ, Brancati FL. Glycemic control and sliding scale insulin use in medical inpatients with diabetes mellitus. Arch Intern Med . 1997 Mar 10. 157(5):545-52. [QxMD MEDLINE Link] .

Sawin CT. Action without benefit. The sliding scale of insulin use. Arch Intern Med . 1997 Mar 10. 157(5):489. [QxMD MEDLINE Link] .

Wiener RS, Wiener DC, Larson RJ. Benefits and risks of tight glucose control in critically ill adults: a meta-analysis. JAMA . 2008 Aug 27. 300(8):933-44. [QxMD MEDLINE Link] .

Finfer S, Chittock DR, Su SY, Blair D, Foster D, Dhingra V, et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med . 2009 Mar 26. 360(13):1283-97. [QxMD MEDLINE Link] .

Van den Berghe G, Wilmer A, Milants I, Wouters PJ, Bouckaert B, Bruyninckx F, et al. Intensive insulin therapy in mixed medical/surgical intensive care units: benefit versus harm. Diabetes . 2006 Nov. 55(11):3151-9. [QxMD MEDLINE Link] .

Malmberg K. Prospective randomised study of intensive insulin treatment on long term survival after acute myocardial infarction in patients with diabetes mellitus. DIGAMI (Diabetes Mellitus, Insulin Glucose Infusion in Acute Myocardial Infarction) Study Group. BMJ . 1997 May 24. 314(7093):1512-5. [QxMD MEDLINE Link] . [Full Text] .

Malmberg K, Ryden L, Wedel H, Birkeland K, Bootsma A, Dickstein K, et al. Intense metabolic control by means of insulin in patients with diabetes mellitus and acute myocardial infarction (DIGAMI 2): effects on mortality and morbidity. Eur Heart J . 2005 Apr. 26(7):650-61. [QxMD MEDLINE Link] .

Mellbin LG, Malmberg K, Norhammar A, Wedel H, Ryden L. Prognostic implications of glucose-lowering treatment in patients with acute myocardial infarction and diabetes: experiences from an extended follow-up of the Diabetes Mellitus Insulin-Glucose Infusion in Acute Myocardial Infarction (DIGAMI) 2 Study. Diabetologia . 2011 Jun. 54(6):1308-17. [QxMD MEDLINE Link] .

Avanzini F, Marelli G, Donzelli W, Busi G, Carbone S, Bellato L, et al. Transition from intravenous to subcutaneous insulin: effectiveness and safety of a standardized protocol and predictors of outcome in patients with acute coronary syndrome. Diabetes Care . 2011 Jul. 34(7):1445-50. [QxMD MEDLINE Link] . [Full Text] .

Vanderwood KK, Hall TO, Harwell TS, Butcher MK, Helgerson SD. Implementing a state-based cardiovascular disease and diabetes prevention program. Diabetes Care . 2010 Dec. 33(12):2543-5. [QxMD MEDLINE Link] . [Full Text] .

Reis JP, Loria CM, Sorlie PD, Park Y, Hollenbeck A, Schatzkin A. Lifestyle factors and risk for new-onset diabetes: a population-based cohort study. Ann Intern Med . 2011 Sep 6. 155(5):292-9. [QxMD MEDLINE Link] .

Yeh HC, Duncan BB, Schmidt MI, Wang NY, Brancati FL. Smoking, smoking cessation, and risk for type 2 diabetes mellitus: a cohort study. Ann Intern Med . 2010 Jan 5. 152(1):10-7. [QxMD MEDLINE Link] .

Dong JY, Xun P, He K, Qin LQ. Magnesium intake and risk of type 2 diabetes: meta-analysis of prospective cohort studies. Diabetes Care . 2011 Sep. 34(9):2116-22. [QxMD MEDLINE Link] . [Full Text] .

Ibarrola-Jurado N, Salas-Salvado J, Martinez-Gonzalez MA, Bullo M. Dietary phylloquinone intake and risk of type 2 diabetes in elderly subjects at high risk of cardiovascular disease. Am J Clin Nutr . 2012 Nov. 96(5):1113-8. [QxMD MEDLINE Link] .

National Diabetes Information Clearinghouse. Insulin Resistance and Pre-diabetes. Available at https://diabetes.niddk.nih.gov/dm/pubs/insulinresistance/#medicines .

Xiang AH, Hodis HN, Kawakubo M, Peters RK, Kjos SL, Marroquin A, et al. Effect of pioglitazone on progression of subclinical atherosclerosis in non-diabetic premenopausal Hispanic women with prior gestational diabetes. Atherosclerosis . 2008 Jul. 199(1):207-14. [QxMD MEDLINE Link] . [Full Text] .

Chiasson JL. Acarbose for the prevention of diabetes, hypertension, and cardiovascular disease in subjects with impaired glucose tolerance: the Study to Prevent Non-Insulin-Dependent Diabetes Mellitus (STOP-NIDDM) Trial. Endocr Pract . 2006 Jan-Feb. 12 Suppl 1:25-30. [QxMD MEDLINE Link] .

[Guideline] Meschia JF, Bushnell C, Boden-Albala B, et al. Guidelines for the primary prevention of stroke: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke . 2014 Dec. 45 (12):3754-832. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Jenkins K. ADA Updates Recommendations for Managing Hypertension in Diabetes. Medscape . 2017 Sep 4. [Full Text] .

[Guideline] de Boer IH, Bangalore S, Benetos A, et al. Diabetes and Hypertension: A Position Statement by the American Diabetes Association. Diabetes Care . 2017 Sep. 40 (9):1273-1284. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] American Diabetes Association Professional Practice Committee. Summary of Revisions: Standards of Care in Diabetes-2024. Diabetes Care . 2024 Jan 1. 47 (Supplement_1):S5-S10. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Davenport L. ADA Releases Comprehensive Type 2 Diabetes Guidelines for Youth. Medscape Medical News . 2018 Nov 19. [Full Text] .

[Guideline] Davies MJ, D'Alessio DA, Fradkin J, et al. Management of Hyperglycemia in Type 2 Diabetes, 2018. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care . 2018 Oct 4. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Ivers NM, Jiang M, Alloo J, et al. Diabetes Canada 2018 clinical practice guidelines: Key messages for family physicians caring for patients living with type 2 diabetes. Can Fam Physician . 2019 Jan. 65 (1):14-24. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] LeRoith D, Biessels GJ, Braithwaite SS, et al. Treatment of Diabetes in Older Adults: An Endocrine Society* Clinical Practice Guideline. J Clin Endocrinol Metab . 2019 May 1. 104 (5):1520-74. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Tucker ME. New Endocrine Society Guidelines Address Diabetes in Older Adults. Medscape Medical News . 2019 Mar 23. [Full Text] .

[Guideline] Busko M. ESC Diabetes and CVD Guideline: 'Unprecedented' New Evidence. Medscape Medical News . 2019 Sep 2. [Full Text] .

[Guideline] Tucker ME. More Guidance on 'Vulnerable Subgroup' With Diabetes and COVID-19. Medscape Medical News . 2020 Apr 28. [Full Text] .

[Guideline] Bornstein SR, Rubino F, Khunti K, et al. Practical recommendations for the management of diabetes in patients with COVID-19. Lancet Diabetes Endocrinol . 2020 Apr 23. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Grunberger G, Sherr J, Allende M, et al. American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus. Endocr Pract . 27 (2021):505-37. [Full Text] .

Tucker ME. 'A Better Picture': First AACE Guidelines on Diabetes Technology. Medscape Medical News . 2021 May 31. [Full Text] .

[Guideline] Mannucci E, Candido R, Monache LD, et al. Italian guidelines for the treatment of type 2 diabetes. Acta Diabetol . 2022 May. 59 (5):579-622. [QxMD MEDLINE Link] . [Full Text] .

Keller DM. New EASD/ADA Position Paper Shifts Diabetes Treatment Goals. Medscape Medical News. Available at https://www.medscape.com/viewarticle/771989 . Accessed: October 15, 2012.

Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. Management of hyperglycaemia in type 2 diabetes: a patient-centered approach. Position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia . 2012 Jun. 55(6):1577-96. [QxMD MEDLINE Link] .

Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care . 2012 Jun. 35(6):1364-79. [QxMD MEDLINE Link] . [Full Text] .

Tucker ME. New diabetes guidelines ease systolic blood pressure target. December 20, 2012. Medscape Medical News. Available at https://www.medscape.com/viewarticle/776543 . Accessed: January 8, 2013.

[Guideline] American Diabetes Association Professional Practice Committee. American Diabetes Association clinical practice recommendations: 2013. Diabetes Care . January 2013. 36 (suppl 1):S1-S110. [Full Text] .

• Simplified scheme for the pathophysiology of type 2 diabetes mellitus.
• Prevalence of type 2 diabetes mellitus in various racial and ethnic groups in the United States (2007-2009 data).
• Prevalence of diabetes mellitus type 2 by age in the United States (2007 estimates).
• Possible physical examination findings in patients with type 2 diabetes mellitus.
• Diagnostic criteria (American Diabetes Association) for diabetes mellitus type 2.
• Major findings from the primary glucose study in the United Kingdom Prospective Diabetes Study (UKPDS).
• Results from metformin substudy in the United Kingdom Prospective Diabetes Study (UKPDS).
• Findings from the blood pressure substudy in the United Kingdom Prospective Diabetes Study (UKPDS).
• Laboratory monitoring guidelines for patients with type 2 diabetes mellitus.
• American Diabetes Association guidelines for low-density lipoprotein cholesterol in diabetes mellitus type 2.
• Treatment of type 2 diabetes mellitus.
• Types of insulin. Premixed insulins can be assumed to have a combination of the onset, peak, and duration of the individual components.
• Simplified scheme for using insulin in treating patients with type 2 diabetes mellitus.
• Simplified scheme of idealized blood glucose values and multiple dose insulin therapy in type 2 diabetes mellitus.

Contributor Information and Disclosures

Romesh Khardori, MD, PhD, FACP (Retired) Professor, Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Eastern Virginia Medical School Romesh Khardori, MD, PhD, FACP is a member of the following medical societies: American Association of Clinical Endocrinology , American College of Physicians , American Diabetes Association , Endocrine Society Disclosure: Nothing to disclose.

George T Griffing, MD Professor Emeritus of Medicine, St Louis University School of Medicine George T Griffing, MD is a member of the following medical societies: American Association for Physician Leadership , American Association for the Advancement of Science , American College of Medical Practice Executives , American College of Physicians , American Diabetes Association , American Federation for Medical Research , American Heart Association , Central Society for Clinical and Translational Research , Endocrine Society , International Society for Clinical Densitometry , Southern Society for Clinical Investigation Disclosure: Nothing to disclose.

Howard A Bessen, MD Professor of Medicine, Department of Emergency Medicine, University of California, Los Angeles, David Geffen School of Medicine; Program Director, Harbor-UCLA Medical Center

Howard A Bessen, MD is a member of the following medical societies: American College of Emergency Physicians

Disclosure: Nothing to disclose.

Barry E Brenner, MD, PhD, FACEP Professor of Emergency Medicine, Professor of Internal Medicine, Program Director, Emergency Medicine, Case Medical Center, University Hospitals, Case Western Reserve University School of Medicine

Barry E Brenner, MD, PhD, FACEP is a member of the following medical societies: Alpha Omega Alpha , American Academy of Emergency Medicine , American College of Chest Physicians , American College of Emergency Physicians , American College of Physicians , American Heart Association , American Thoracic Society , Arkansas Medical Society , New York Academy of Medicine , New York Academy ofSciences ,and Society for Academic Emergency Medicine

William L Isley, MD Senior Associate Consultant, Associate Professor of Medicine, Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Mayo Clinic of Rochester

William L Isley, MD is a member of the following medical societies: Alpha Omega Alpha , American College of Physicians , American Diabetes Association , American Federation for Medical Research , Endocrine Society , and Phi Beta Kappa

Kenneth Patrick L Ligaray, MD Fellow, Department of Endocrinology, Diabetes and Metabolism, St Louis University School of Medicine

Kenneth Patrick Ligaray, MD is a member of the following medical societies: American Association of Clinical Endocrinologists and Endocrine Society

Anne L Peters, MD, CDE Director of Clinical Diabetes Programs, Professor, Department of Medicine, University of Southern California, Keck School of Medicine, Los Angeles, California, Los Angeles County/University of Southern California Medical Center

Anne L Peters, MD, CDE is a member of the following medical societies: American College of Physicians and American Diabetes Association

Disclosure: Amylin Honoraria Speaking and teaching; AstraZeneca Consulting fee Consulting; Lilly Consulting fee Consulting; Takeda Consulting fee Consulting; Bristol Myers Squibb Honoraria Speaking and teaching; NovoNordisk Consulting fee Consulting; Medtronic Minimed Consulting fee Consulting; Dexcom Honoraria Speaking and teaching; Roche Honoraria Speaking and teaching

David S Schade, MD Chief, Division of Endocrinology and Metabolism, Professor, Department of Internal Medicine, University of New Mexico School of Medicine and Health Sciences Center

David S Schade, MD is a member of the following medical societies: American College of Physicians , American Diabetes Association , American Federation for Medical Research , Endocrine Society , New Mexico Medical Society , New York Academy of Sciences , and Society for Experimental Biology and Medicine

Don S Schalch, MD Professor Emeritus, Department of Internal Medicine, Division of Endocrinology, University of Wisconsin Hospitals and Clinics

Don S Schalch, MD is a member of the following medical societies: American Diabetes Association , American Federation for Medical Research , Central Society for Clinical Research , and Endocrine Society

Erik D Schraga, MD Staff Physician, Department of Emergency Medicine, Mills-Peninsula Emergency Medical Associates

Francisco Talavera, PharmD, PhD Adjunct Assistant Professor, University of Nebraska Medical Center College of Pharmacy; Editor-in-Chief, Medscape Drug Reference

Disclosure: Medscape Salary Employment

Scott R Votey, MD Director of Emergency Medicine Residency, Ronald Reagan UCLA Medical Center; Professor of Medicine/Emergency Medicine, University of California, Los Angeles, David Geffen School of Medicine

Scott R Votey, MD is a member of the following medical societies: Society for Academic Emergency Medicine

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• 2003/viewarticle/do-night-owls-have-higher-risk-type-2-diabetes-2023a1000qi1education Do Night Owls Have a Higher Risk for Type 2 Diabetes? 0.25 LOC / CME / MOC Credit / CE Credits education You are being redirected to Medscape Education Yes, take me there 0.25 LOC / CME / MOC Credit / CE Do Night Owls Have a Higher Risk for Type 2 Diabetes?

• 20021788533-overviewDiseases & Conditions Diseases & Conditions Type 2 Diabetes Mellitus and TCF7L2

PARITA PATEL, MD, AND ALLISON MACEROLLO, MD

Am Fam Physician. 2010;81(7):863-870

A more recent article on diabetes mellitus is available .

See related editorial on page 843 .

Author disclosure: Nothing to disclose.

Based on etiology, diabetes is classified as type 1 diabetes mellitus, type 2 diabetes mellitus, latent autoimmune diabetes, maturity-onset diabetes of youth, and miscellaneous causes. The diagnosis is based on measurement of A1C level, fasting or random blood glucose level, or oral glucose tolerance testing. Although there are conflicting guidelines, most agree that patients with hypertension or hyperlipidemia should be screened for diabetes. Diabetes risk calculators have a high negative predictive value and help define patients who are unlikely to have diabetes. Tests that may help establish the type of diabetes or the continued need for insulin include those reflective of beta cell function, such as C peptide levels, and markers of immune-mediated beta cell destruction (e.g., autoantibodies to islet cells, insulin, glutamic acid decarboxylase, tyrosine phosphatase [IA-2α and IA-2β]). Antibody testing is limited by availability, cost, and predictive value.

Prevention, timely diagnosis, and treatment are important in patients with diabetes mellitus. Many of the complications associated with diabetes, such as nephropathy, retinopathy, neuropathy, cardiovascular disease, stroke, and death, can be delayed or prevented with appropriate treatment of elevated blood pressure, lipids, and blood glucose. 1 – 4

In 1997, the American Diabetes Association (ADA) introduced an etiologically based classification system and diagnostic criteria for diabetes, 5 which were updated in 2010. 1 Type 2 diabetes accounts for approximately 90 to 95 percent of all persons with diabetes in the United States, and its prevalence is increasing in adults worldwide. 6 With the rise in childhood obesity, type 2 diabetes is increasingly being diagnosed in children and adolescents. 6

The risk of diabetes is increased in close relatives suggesting a genetic predisposition, although no direct genetic link has been identified. 7 Type 1 diabetes accounts for 5 to 10 percent of persons with diabetes 6 and is characterized by insulin deficiency that is typically an autoimmune-mediated condition.

Latent autoimmune diabetes in adults includes a heterogenous group of conditions that are phenotypically similar to type 2 diabetes, but patients have autoantibodies that are common with type 1 diabetes. Diagnostic criteria include age of 30 years or older; no insulin treatment for six months after diagnosis; and presence of autoantibodies to glutamic acid decarboxylase, islet cells, tyrosine phosphatase (IA-2α and IA-2β), or insulin.

Patients with maturity-onset diabetes of youth typically present before 25 years of age, have only impaired insulin secretion, and have a monogenetic defect that leads to an autosomal dominant inheritance pattern. These patients are placed in a subcategory of having genetic defects of beta cell. 8

The old terminology of prediabetes has now been replaced with “categories of increased risk for diabetes.” This includes persons with impaired fasting glucose, impaired glucose tolerance, or an A1C level of 5.7 to 6.4 percent. 1 , 9 , 10

Diagnostic Criteria and Testing

The 1997 ADA consensus guidelines lowered the blood glucose thresholds for the diagnosis of diabetes. 5 This increased the number of patients diagnosed at an earlier stage, although no studies have demonstrated a reduction in long-term complications. Data suggest that as many as 5.7 million persons in the United States have undiagnosed diabetes. 6 Table 1 compares specific diagnostic tests for diabetes. 11 – 14

TESTS TO DIAGNOSE DIABETES

Blood Glucose Measurements . The diagnosis of diabetes is based on one of three methods of blood glucose measurement ( Table 2 ) . 1 Diabetes can be diagnosed if the patient has a fasting blood glucose level of 126 mg per dL (7.0 mmol per L) or greater on two separate occasions. The limitations of this test include the need for an eight-hour fast before the blood draw, a 12 to 15 percent day-to-day variance in fasting blood glucose values, and a slightly lower sensitivity for predicting microvascular complications. 15 , 16

Diabetes can also be diagnosed with a random blood glucose level of 200 mg per dL (11.1 mmol per L) or greater if classic symptoms of diabetes (e.g., polyuria, polydipsia, weight loss, blurred vision, fatigue) are present. Lower random blood glucose values (140 to 180 mg per dL [7.8 to 10.0 mmol per L]) have a fairly high specificity of 92 to 98 percent; therefore, patients with these values should undergo more definitive testing. A low sensitivity of 39 to 55 percent limits the use of random blood glucose testing. 15

The oral glucose tolerance test is considered a first-line diagnostic test. Limitations include poor reproducibility and patient compliance because an eight-hour fast is needed before the 75-g glucose load, which is followed two hours later by a blood draw. 17 The criterion for diabetes is a serum blood glucose level of greater than 199 mg per dL (11.0 mmol per L).

In 2003, the ADA lowered the threshold for diagnosis of impaired fasting glucose to include a fasting glucose level between 100 and 125 mg per dL (5.6 and 6.9 mmol per L). Impaired glucose tolerance continues to be defined as a blood glucose level between 140 and 199 mg per dL (7.8 and 11.0 mmol per L) two hours after a 75-g load. Patients meeting either of these criteria are at significantly higher risk of progression to diabetes and should be counseled on effective strategies to lower their risk, such as weight loss and exercise. 1 , 9

A1C . A1C measurement has recently been endorsed by the ADA as a diagnostic and screening tool for diabetes. 1 One advantage of using A1C measurement is the ease of testing because it does not require fasting. An A1C level of greater than 6.5 percent on two separate occasions is considered diagnostic of diabetes. 18 Lack of standardization has historically deterred its use, but this test is now widely standardized in the United States. 19 A1C measurements for diagnosis of diabetes should be performed by a clinical laboratory because of the lack of standardization of point-of-care testing. Limitations of A1C testing include low sensitivity, possible racial disparities, and interference by anemia and some medications. 15

TESTS TO IDENTIFY TYPE OF DIABETES

Tests that can be used to establish the etiology of diabetes include those reflective of beta cell function (e.g., C peptide) and markers of immune-mediated beta cell destruction (e.g., insulin, islet cell, glutamic acid decarboxylase, IA-2α and IA-2β autoantibodies). Table 3 presents the characteristics of these tests. 20 – 27

C peptide is linked to insulin to form proinsulin and reflects the amount of endogenous insulin. Patients with type 1 diabetes have low C peptide levels because of low levels of endogenous insulin and beta cell function. Patients with type 2 diabetes typically have normal to high levels of C peptide, reflecting higher amounts of insulin but relative insensitivity to it. In a Swedish study of patients with clinically well-defined type 1 or 2 diabetes, 96 percent of patients with type 2 diabetes had random C peptide levels greater than 1.51 ng per mL (0.50 nmol per L), whereas 90 percent of patients with type 1 diabetes had values less than 1.51 ng per mL. 20 In the clinically undefined population, which is the group in which the test is most often used, the predictive value is likely lower.

Antibody testing is limited by availability, cost, and predictive value, especially in black and Asian patients. Prevalence of any antibody in white patients with type 1 diabetes is 85 to 90 percent, 5 whereas the prevalence in similar black or Hispanic patients is lower (19 percent in both groups in one study). 28 In persons with type 2 diabetes, the prevalence of islet cell antibody is 4 to 21 percent; glutamic acid decarboxylase antibody, 7 to 34 percent; IA-2, 1 to 2 percent; and any antibody, 11.6 percent. 24 , 25 , 29 In healthy persons, the prevalence of any antibody marker is 1 to 2 percent 30 ; thus, overlap of the presence of antibodies in various types of diabetes and patients limits the utility of individual tests.

As with any condition, a rationale for screening should first be established. Diabetes is a common disease that is associated with significant morbidity and mortality. It has an asymptomatic stage that may be present for up to seven years before diagnosis. The disease is treatable, and testing is acceptable and accessible to patients. Early treatment of diabetes that was identified primarily by symptoms improves microvascular outcomes. 31 However, it is not clear whether universal screening reduces diabetes-associated morbidity and mortality. Table 4 presents screening guidelines from several organizations. 1 , 8 , 32 – 38

TYPE 1 DIABETES

Screening for type 1 diabetes is not recommended because there is no accepted treatment for patients who are diagnosed in the asymptomatic phase. The Diabetes Prevention Trial identified a group of high-risk patients based on family history and positivity to islet cell antibodies. However, treatment did not prevent progression to type 1 diabetes in these patients. 39

TYPE 2 DIABETES

Medications and lifestyle interventions may reduce the risk of diabetes, although 20 to 30 percent of patients with type 2 diabetes already have complications at the time of presentation. 40 Although a recent analysis suggests that screening for and treating impaired glucose tolerance in persons at risk of diabetes may be cost-effective, the data on screening for type 2 diabetes are less certain. 41 It is unclear whether the early diagnosis of type 2 diabetes through screening programs, with subsequent intensive interventions, provides an incremental benefit in final health outcomes compared with initiating treatment after clinical diagnosis.

Guidelines differ regarding who should be screened for type 2 diabetes. The U.S. Preventive Services Task Force (USPSTF) recommends limiting screening to adults with a sustained blood pressure of greater than 135/80 mm Hg. 34 , 42 The American Academy of Family Physicians concurs, but specifically includes treated and untreated patients. 43 The Canadian Task Force on Preventive Health Care recommends screening all patients with hypertension or hyperlipidemia. 33 The ADA recommends screening a much broader patient population based on risk. 1

There are several questionnaires to predict a patient's risk of diabetes. The Diabetes Risk Calculator was developed using data from the National Health and Nutrition Examination Survey III and incorporates age, height, weight, waist circumference, ethnicity, blood pressure, exercise, history of gestational diabetes, and family history. 13 , 14 For diagnosis of diabetes, it has a positive predictive value (PPV) of 14 percent and a negative predictive value (NPV) of 99.3 percent. The tool is most valuable in helping define which patients are very unlikely to have diabetes. 13

GESTATIONAL DIABETES

Whether patients should be screened for gestational diabetes is unclear. The USPSTF states that there is insufficient evidence to recommend for or against screening. 34 The ADA and the American College of Obstetricians and Gynecologists recommend risk-based testing, although most women require testing based on these inclusive guidelines. 36 The Glucola test is the most commonly used screening test for gestational diabetes and includes glucose testing one hour after a 50-g oral glucose load. An abnormal Glucola test result (i.e., blood glucose level of 140 mg per dL or greater) should be confirmed with a 75-g or 100-g oral glucose tolerance test. Whether screening and subsequent treatment of gestational diabetes alter clinically important perinatal outcomes is unclear. Untreated gestational diabetes is associated with a higher incidence of macrosomia and shoulder dystocia. 44 A randomized controlled trial found that treatment led to a reduction in serious perinatal complications, with a number needed to treat of 34. Treatment did not reduce risk of cesarean delivery or admission to the neonatal intensive care unit, however. 44

New-Onset Symptomatic Hyperglycemia

Patients may initially present with diabetic ketoacidosis or hyperglycemic hyperosmolar state ( Table 5 ) , 45 both of which are initially managed with insulin because they are essentially insulin deficiency states. Both groups of patients may present with polyuria, polydipsia, and signs of dehydration. Diagnostic criteria of diabetic ketoacidosis include a blood glucose level greater than 250 mg per dL (13.9 mmol per L), pH of 7.3 or less, serum bicarbonate level less than 18 mEq per L (18 mmol per L), and moderate ketonemia. However, significant ketosis has also been shown to occur in up to one third of patients with hyperglycemic hyperosmolar state. 46

Although diabetic ketoacidosis typically occurs in persons with type 1 diabetes, more than one half of newly diagnosed black patients with unprovoked diabetic ketoacidosis are obese and many display classic features of type 2 diabetes—most importantly with a measurable insulin reserve. 47 Thus, the presentation does not definitively determine the type of diabetes a patient has. Presence of antibodies, particularly glutamic acid decarboxylase antibody, predicts a higher likelihood of lifelong insulin requirement. There is, however, an overlap of presence of antibodies in type 1 and type 2 diabetes, and among patients with type 2 diabetes who may not require insulin. 48

A Swedish population-based study showed that among the 9.3 percent of young adults with newly diagnosed diabetes that could not be classified as type 1 or type 2, the presence of glutamic acid decarboxylase antibody was associated with a need for insulin within three years (odds ratio = 18.8; 95% confidence interval, 1.8 to 191). 26 The PPV for insulin treatment was 92 percent in those with the antibody. It should be noted that among patients who were negative for antibodies, 51 percent also needed insulin within three years. In contrast, the United Kingdom Prospective Diabetes Study found that only 5.7 percent of patients without glutamic acid decarboxylase antibody eventually needed insulin therapy, giving the test an NPV of 94 percent. 25 With these conflicting data, clinical judgment using a patient's phenotype, history, presentation, and selective laboratory testing is the best way to manage patients with diabetes.

American Diabetes Association. Standards of medical care in diabetes–2010. Diabetes Care. 2010;33(suppl 1):S11-S61.

Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group [published correction appears in Lancet . 1999;354(9178):602]. Lancet. 1998;352(9131):837-853.

The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J Med. 1993;329(14):977-986.

Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577-1589.

Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 1997;20(7):1183-1197.

Centers for Disease Control and Prevention. 2007 national diabetes fact sheet. http://www.cdc.gov/diabetes/pubs/factsheet07.htm. Accessed July 8, 2009.

Tuomi T. Type 1 and type 2 diabetes: what do they have in common?. Diabetes. 2005;54(suppl 2):S40-S45.

American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(suppl 1):S62-S69.

Knowler WC, Barrett-Connor E, Fowler SE, et al.; for the Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403.

Tuomilehto J, Lindström J, Eriksson JG, et al.; for the Finnish Diabetes Prevention Study Group. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343-1350.

Tabaei BP, Herman WH. A multivariate logistic regression equation to screen for diabetes: development and validation. Diabetes Care. 2002;25(11):1999-2003.

Rohlfing CL, Wiedmeyer HM, Little RR, England JD, Tennill A, Goldstein DE. Defining the relationship between plasma glucose and HbA(1c): analysis of glucose profiles and HbA(1c) in the Diabetes Control and Complications Trial. Diabetes Care. 2002;25(2):275-278.

Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care. 2008;31(5):1040-1045.

Mochan E, Ebell M. Risk-assessment tools for detecting undiagnosed diabetes. Am Fam Physician. 2009;80(2):175-178.

Saudek CD, Herman WH, Sacks DB, Bergenstal RM, Edelman D, Davidson MB. A new look at screening and diagnosing diabetes mellitus. J Clin Endocrinol Metab. 2008;93(7):2447-2453.

Petersen PH, Jørgensen LG, Brandslund I, De Fine Olivarius N, Stahl M. Consequences of bias and imprecision in measurements of glucose and HbA1C for the diagnosis and prognosis of diabetes mellitus. Scand J Clin Lab Invest Suppl. 2005;240:51-60.

Ko GT, Chan JC, Woo J, et al. The reproducibility and usefulness of the oral glucose tolerance test in screening for diabetes and other cardiovascular risk factors. Ann Clin Biochem. 1998;35(pt 1):62-67.

International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32(7):1327-1334.

Little RR, Rohlfing CL, Wiedmeyer HM, Myers GL, Sacks DB, Goldstein DE for the NGSP Steering Committee. The National Glycohemoglobin Standardization Program: a five-year progress report. Clin Chem. 2001;47(11):1985-1992.

Berger B, Stenström G, Sundkvist G. Random C-peptide in the classification of diabetes. Scand J Clin Lab Invest. 2000;60(8):687-693.

Sacks DB, Bruns DE, Goldstein DE, Maclaren NK, McDonald JM, Parrott M. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem. 2002;48(3):436-472.

Sabbah E, Savola K, Kulmala P, et al. Diabetes-associated autoanti-bodies in relation to clinical characteristics and natural course in children with newly diagnosed type 1 diabetes. The Childhood Diabetes in Finland Study Group. J Clin Endocrinol Metab. 1999;84(5):1534-1539.

Tuomi T, Carlsson A, Li H, et al. Clinical and genetic characteristics of type 2 diabetes with and without GAD antibodies. Diabetes. 1999;48(1):150-157.

Turner R, Stratton I, Horton V, et al. UKPDS 25: autoantibodies to islet-cell cytoplasm and glutamic acid decarboxylase for prediction of insulin requirement in type 2 diabetes. UK Prospective Diabetes Study Group [published correction appears in Lancet . 1998;351(9099):376]. Lancet. 1997;350(9087):1288-1293.

Bottazzo GF, Bosi E, Cull CA, et al. IA-2 antibody prevalence and risk assessment of early insulin requirement in subjects presenting with type 2 diabetes (UKPDS 71) [published correction appears in Diabetologia . 2005;48(6):1240]. Diabetologia. 2005;48(4):703-708.

Törn C, Landin-Olsson M, Ostman J, et al. Glutamic acid decarboxylase antibodies (GADA) is the most important factor for prediction of insulin therapy within 3 years in young adult diabetic patients not classified as type 1 diabetes on clinical grounds. Diabetes Metab Res Rev. 2000;16(6):442-447.

Savola K, Bonifacio E, Sabbah E, et al. IA-2 antibodies—a sensitive marker of IDDM with clinical onset in childhood and adolescence. Childhood Diabetes in Finland Study Group. Diabetologia. 1998;41(4):424-429.

Avilés-Santa L, Maclaren N, Raskin P. The relationship between immune-mediated type 1 diabetes mellitus and ethnicity. J Diabetes Complications. 2004;18(1):1-9.

Davis TM, Wright AD, Mehta ZM, et al. Islet autoantibodies in clinically diagnosed type 2 diabetes: prevalence and relationship with metabolic control (UKPDS 70). Diabetologia. 2005;48(4):695-702.

Maclaren N, Lan M, Coutant R, et al. Only multiple autoantibodies to islet cells (ICA), insulin, GAD65, IA-2 and IA-2beta predict immune-mediated (type 1) diabetes in relatives. J Autoimmun. 1999;12(4):279-287.

Harris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM occurs at least 4–7 yr before clinical diagnosis. Diabetes Care. 1992;15(7):815-819.

American Association of Clinical Endocrinologists Medical Guidelines for Clinical Practice for the management of diabetes mellitus. http://www.aace.com/pub/pdf/guidelines/DMGuidelines2007.pdf .Accessed July 8, 2009.

Feig DS, Palda VA, Lipscombe L. Screening for type 2 diabetes mellitus to prevent vascular complications: updated recommendations from the Canadian Task Force on Preventive Health Care. CMAJ. 2005;172(2):177-180.

U.S. Preventive Services Task Force. Screening for type 2 diabetes mellitus in adults. Recommendation statement. June 2008. http://www.ahrq.gov/clinic/uspstf08/type2/type2rs.htm#clinical . Accessed July 8, 2009.

Committee on Obstetric Practice. ACOG Committee Opinion No. 435: postpartum screening for abnormal glucose tolerance in women who had gestational diabetes mellitus. Obstet Gynecol. 2009;113(6):1419-1421.

American College of Obstetricians and Gynecologists Committee on Practice Bulletins—Obstetrics. ACOG practice bulletin. Clinical management guidelines for obstetrician-gynecologists. Number 30, September 2001 (replaces technical bulletin number 200, December 1994). Gestational diabetes. Obstet Gynecol. 2001;98(3):525-538.

Canadian Task Force on Preventive Health Care. Summary table of recommendations. Sreening for gestational diabetes mellitus. http://www.ctfphc.org/Tables/Ch02tab.htm . Accessed January 18, 2010.

U.S. Preventive Services Task Force. Screening for gestational diabetes mellitus. Recommendation statement. May 2008. http://www.ahrq.gov/clinic/uspstf08/gestdiab/gdrs.htm . Accessed January 18, 2010.

Diabetes Prevention Trial—Type 1 Diabetes Study Group. Effects of insulin in relatives of patients with type 1 diabetes mellitus. N Engl J Med. 2002;346:1685-1691.

Glucose tolerance and mortality: comparison of WHO American Diabetes Association diagnostic criteria. The DECODE study group. European Diabetes Epidemiology Group. Diabetes Epidemiology: Collaborative analysis Of Diagnostic criteria in Europe. Lancet. 1999;354(9179):617-621.

Gillies CL, Lambert PC, Abrams KR, et al. Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis. BMJ. 2008;336(7654):1180-1185.

Screening for type 2 diabetes mellitus in adults: U.S. Preventive Services Task Force recommendation statement [published correction appears in Ann Intern Med . 2008;149(2):147]. Ann Intern Med. 2008;148(11):846-854.

American Academy of Family Physicians. Recommendations for clinical preventive services. https://www.aafp.org/patient-care/clinical-recommendations/a-z.html . Accessed July 8, 2009.

Crowther CA, Hiller JE, Moss JR, McPhee AJ, Jeffries WS, Robinson JS for the Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) Trial Group. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N Engl J Med. 2005;352(24):2477-2486.

Umpierrez GE, Murphy MB, Kitabchi AE. Diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome. Diabetes Spectrum. 2002;15(1):28-36.

Kitabchi AE, Umpierrez GE, Murphy MB, et al. Management of hyperglycemic crises in patients with diabetes. Diabetes Care. 2001;24(1):131-153.

Umpierrez GE, Casals MM, Gebhart SP, Mixon PS, Clark WS, Phillips LS. Diabetic ketoacidosis in obese African-Americans. Diabetes. 1995;44(7):790-795.

Palmer JP, Hampe CS, Chiu H, Goel A, Brooks-Worrell BM. Is latent auto-immune diabetes in adults distinct from type 1 diabetes or just type 1 diabetes at an older age?. Diabetes. 2005;54(suppl 2):S62-S67.

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• v.10(4); Oct-Dec 2020

Pathophysiology of diabetes: An overview

Mujeeb z. banday.

Department of Biochemistry, Government Medical College and Associated Shri Maharaja Hari Singh Hospital, Srinagar, Kashmir, India

Aga S. Sameer

1 Department of Basic Medical Sciences, College of Medicine, King Saud Bin Abdul Aziz University for Health Sciences, King Abdullah International Medical Research Centre, National Guard Health Affairs, Jeddah, Saudi Arabia

Saniya Nissar

Diabetes mellitus is a chronic heterogeneous metabolic disorder with complex pathogenesis. It is characterized by elevated blood glucose levels or hyperglycemia, which results from abnormalities in either insulin secretion or insulin action or both. Hyperglycemia manifests in various forms with a varied presentation and results in carbohydrate, fat, and protein metabolic dysfunctions. Long-term hyperglycemia often leads to various microvascular and macrovascular diabetic complications, which are mainly responsible for diabetes-associated morbidity and mortality. Hyperglycemia serves as the primary biomarker for the diagnosis of diabetes as well. In this review, we would be focusing on the classification of diabetes and its pathophysiology including that of its various types.

INTRODUCTION

Diabetes mellitus (DM), also known simply as diabetes is a complex metabolic disorder characterized by hyperglycemia, a physiologically abnormal condition represented by continued elevated blood glucose levels. Hyperglycemia results from anomalies in either insulin secretion or insulin action or both and manifests in a chronic and heterogeneous manner as carbohydrate, fat, and protein metabolic dysfunctions. Diabetes follows a progressive pattern with complex pathogenesis and varied presentation.[ 1 , 2 ]

Hyperglycemia and its associated carbohydrate, fat, and protein metabolic dysfunctions affect multiple organs of the body and disrupt their normal functioning. These disruptions progress gradually and arise mostly due to the adverse effects of hyperglycemia and its associated metabolic anomalies on the normal structure and functioning of micro- and macrovasculature, which lie at the core of organ structure, and function throughout the body. The structural and functional disruptions in organ system vasculature lead to micro- and macrovascular complications. Organ damage, dysfunction, and, ultimately, organ failure characterize these complications and affect body organs, which include, in particular, eyes, kidneys, heart, and nerves. Eye-related complications result in retinopathy with progression to blindness. Kidney-associated complications lead to nephropathy and potential renal failure. Heart-related complications include hypertension and coronary heart disease. Nerve-associated complications lead to neuropathy, which can be autonomic and/or peripheral. Cardiovascular, gastrointestinal, and genitourinary (including sexual) dysfunctions are characteristic manifestations of autonomic neuropathy, whereas foot infections including ulcers requiring amputations and Charcot joint (osteoarthropathy) are often associated with long-term peripheral neuropathy.[ 3 , 4 , 5 ] The cerebrovascular disease, peripheral arterial disease, and coronary heart disease, together termed as atherosclerotic cardiovascular disease, are of common occurrence in diabetes and constitute one of the major causes of diabetes-associated morbidity and mortality.[ 1 , 4 , 5 ]

Diabetes with its ever-increasing global prevalence has emerged as one of the most important and challenging health issues confronting the human population of the present world. The increase in the prevalence of diabetes in most regions across the globe has been parallel to the rapid economic development, leading to urbanization and adoption of modern lifestyle habits.[ 6 ] In the year 2019, the number of adult people aged 20–79 years with diabetes has been estimated to be about 463 million, which represents 9.3% of the total world adult population. By the year 2030, this number has been estimated to increase to 578 million, representing 10.2% of the total world adult population and further increase to 700 million by the year 2045, which represents 10.9% of the total world adult population. In the year 2019, the prevalence of diabetes among men and women has been estimated to be 9.6% and 9.0%, respectively, of the total respective gender world population.[ 7 ] Furthermore, in the year 2019, approximately 4.2 million adult people aged 20–99 years died due to diabetes, and its associated complications and health expenditure on diabetes estimated to at least 760 billion USD, which represents 10% of the total spending on adults. Diabetes during pregnancy has been estimated to have affected more than 20 million live births (1 in 6 live births) in the year 2019.[ 8 ]

CLASSIFICATION AND PATHOPHYSIOLOGY

DM is characterized by complex pathogenesis and varied presentation and any classification of this disorder, therefore, is arbitrary, but nevertheless useful, and is often influenced by the physiological conditions present at the time of assessment and diagnosis. The classification currently used is based on both the etiology and the pathogenesis of disease and is useful in the clinical assessment of disease and for deciding the required therapy. According to this classification, diabetes can be divided into four main types or categories: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), gestational diabetes mellitus (GDM), and diabetes caused or associated with certain specific conditions, pathologies, and/or disorders [ Figure 1 ].[ 1 , 9 ]

Four types of diabetes mellitus

Type 1 diabetes mellitus

T1DM, also known as type 1A DM or as per the previous nomenclature as insulin-dependent diabetes mellitus (IDDM) or juvenile-onset diabetes, constitutes about 5–10% of all the cases of diabetes. It is an autoimmune disorder characterized by T-cell-mediated destruction of pancreatic β-cells, which results in insulin deficiency and ultimately hyperglycemia.[ 10 , 11 ] The pathogenesis of this autoimmunity, though not yet fully understood, has been found to be influenced by both genetic and environmental factors. The rate of development of this pancreatic β-cell-specific autoimmunity and the disorder itself is rapid in most of the cases as in infants and children (juvenile onset) or may be gradual as in adults (late onset).

The variability in the rate at which the immune-mediated destruction of the pancreatic β-cells occurs often defines the eventual progression of this disease. In some cases, children and adolescents, the β-cell destruction and subsequent failure occur suddenly, which can lead to diabetic ketoacidosis (DKA), often described as the first manifestation of the disease. In others, the disease progression is very slow with a mild increase in fasting blood glucose levels, which assumes a severe hyperglycemic form with or without ketoacidosis, only in the presence of physiological stress conditions such as severe infections or onset of other disorders. In some other cases, which include adults, β-cells may retain some degree of function to secrete only that quantity of insulin, which is only sufficient to prevent ketoacidosis for many years. However, due to progressive insulin deficiency, these individuals become insulin-dependent with the emergence of severe hyperglycemia and subsequent ketoacidosis. Despite the variable progression of this type of diabetes, the affected individuals in the beginning or in the middle or even in the later stages of their life become severely or absolutely insulin-deficient and become dependent on insulin treatment for their survival. This severe or absolute insulin deficiency irrespective of its occurrence at any age manifests itself as low or undetectable levels of plasma C-peptide.[ 1 , 10 , 11 ]

T1DM is an autoimmune disorder characterized by several immune markers, in particular autoantibodies. These autoantibodies are associated with the immune-mediated β-cell destruction, characteristic of this disease. The autoantibodies include glutamic acid decarboxylase autoantibodies (GADAs) such as GAD65, islet cell autoantibodies (ICAs) to β-cell cytoplasmic proteins such as autoantibodies to islet cell antigen 512 (ICA512), autoantibodies to the tyrosine phosphatases, IA-2 and IA-2α, insulin autoantibodies (IAAs), and autoantibodies to islet-specific zinc transporter isoform 8 (ZnT8). At least one of these autoantibodies can be used for the clinical diagnosis of this disease but usually more of these immune markers have been observed in approximately 85–90% of patients with new-onset T1DM.[ 1 , 12 ] Of these autoantibodies, GAD65 is the most important and is present in about 80% of all T1DM individuals at the time of diagnosis, followed by ICAs present in 69–90% and IA-2α found in 54–75% of all T1DM individuals at clinical presentation.

The IAAs are important immune markers present in infants and young children who are prone to diabetes and its prevalence decreases as the age of onset of diabetes increases. The presence of IAAs in these individuals who have not been previously treated with insulin is an important indication of developing T1DM. IAAs are present in about 70% of all infants and young children at the time of diagnosis. The IAAs also play an important inhibitory role toward insulin function in patients on insulin therapy. Although not often clinically significant but nevertheless, this immune response has been observed with varying degrees of severity in at least 40% of patients on insulin treatment and therefore shows differential clinical manifestations.[ 13 ] These autoantibodies mostly consist of polyclonal immunoglobulin G (IgG) antibodies and differ in their affinities and binding capacities toward insulin. IAAs can either be high insulin affinity/low insulin-binding capacity or low insulin affinity/high insulin-binding capacity. The low insulin affinity/high insulin-binding capacity IAAs are responsible for clinical manifestations. At high titers, the binding of these antibodies to insulin prevents or delays its action and is responsible for characteristic hyperglycemia in the immediate postprandial period, which leads to significantly increased insulin requirements followed by unpredictable hypoglycemic episodes (postprandial hypoglycemia) observed later.[ 14 ]

These autoantibodies assume more clinical and diagnostic importance in some cases, particularly adults, with late-onset of this disease where the destruction of the pancreatic β-cells occurs at a very slow rate and often the disease masquerades as in T2DM. In such cases, these autoantibodies enable the correct diagnosis of this disorder as the T1DM, rather than the most common T2DM. This type of diabetes is often described as “Latent Autoimmune Diabetes in Adults (LADA),” also known as “slowly progressing insulin-dependent diabetes.”[ 15 ]

LADA is the most common form of adult-onset autoimmune diabetes and accounts for 2–12% of all diabetic cases in the adult population.[ 16 ] Of the autoantibodies, GADAs are the most important and sensitive markers for LADA followed by ICAs. However, the IAAs, autoantibodies to the tyrosine phosphatases—IA-2 and IA-2α, and autoantibodies to islet-specific zinc transporter isoform 8 (ZnT8) which are observed in patients with juvenile- or young-onset T1DM are detectable in only a small number of cases in LADA.[ 17 ] In a study on LADA (Action LADA study), GADAs were the only diabetes-specific autoantibodies detected in 68.6% of total screened subjects whereas IA-2α and ZnT8A represented the single-type autoantibody detections in 5% and 2.3% of all the screened study subjects. In the same study, more than one type of autoantibody was detected in 24.1% of study subjects.[ 18 ] LADA is also sometimes referred to as T2DM with ICAs.

Besides the characteristic immune-mediated pancreatic β-cell destruction, several other autoimmune disorders including myasthenia gravis, Addison’s disease (primary adrenal insufficiency), celiac sprue (celiac disease), pernicious anemia, vitiligo, Hashimoto’s thyroiditis, Graves’ disease, dermatomyositis, autoimmune gastritis, and autoimmune hepatitis have been observed with an increased incidence in patients with T1DM.[ 1 , 10 , 19 , 20 ] The autoimmune nature of this disease and its association with other autoimmune conditions mainly stem from the strong association of this disorder with human leukocyte antigen (HLA), its linkage to the DQA and DQB genes, and its direct influence by DRB genes. All of these are hotspot gene regions associated with immune response including autoimmunity. The genome-wide association studies have shown a strong association of this disease with HLA-DR3 and HLA-DR4 haplotypes and the exclusive association of DR4-DQB1I0302 haplotype with the autoimmune destruction of the β-cells. As with other diseases, these various HLA haplotypes can increase or decrease the susceptibility toward the T1DM.[ 21 , 22 , 23 ] However, several non-HLA genes or gene regions also influence the susceptibility to this disease. The most prominent among them is the insulin gene (INS) region, designated as IDDM2 located on chromosome 11p5.5. The variable number of tandem repeats in the promoter region of this gene region has been observed to influence the susceptibility toward this disease.[ 24 ] Besides IDDM2, CTLA-4, PTPN-22, and CD25 are other non-HLA genes associated with the disease.[ 25 ] The patients with this type of diabetes can be but are rarely obese at the time of assessment and diagnosis.[ 1 , 10 ]

Idiopathic diabetes

Idiopathic diabetes, also referred to as ICA-negative or type 1B diabetes, includes the forms of diabetes which are similar to T1DM in presentation but characterized by variable nonimmune β-cell dysfunction without any observed HLA association unlike T1DM and hence, sometimes it is also described as a separate type of DM. This type of diabetes exhibits a strong pattern of inheritance and has been observed in only a minority of patients, of Asian or African-Caribbean origin. The etiology of idiopathic diabetes remains largely unknown.

The disease is characterized by severe but varying degrees of insulin deficiency (insulinopenia) which can exhibit episodic patterns concomitant with varying degrees of severity and episodic DKA. These patients, therefore, may require insulin replacement therapy initially but the need for the therapy may not be absolute and may vary in accordance with the episodic patterns of insulinopenia and ketoacidosis characteristic of these forms of T1DM.[ 26 ]

Type 2 diabetes mellitus

T2DM, also known as non-insulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes, as per the previous nomenclature, constitutes about 90–95% of all the cases of diabetes. This type of diabetes is characterized by two main insulin-related anomalies: insulin resistance and β-cell dysfunction.[ 27 , 28 , 29 ] Insulin resistance results from disruption of various cellular pathways, which lead to a decreased response, or sensitivity of cells in the peripheral tissues, in particular the muscle, liver, and adipose tissue toward insulin. In the early stages of the disease, decreased insulin sensitivity triggers β-cells hyperfunction to achieve a compensatory increase in insulin secretion to maintain normoglycemia. The higher levels of circulating insulin (hyperinsulinemia), thus, prevent hyperglycemia. However, gradually, the increased insulin secretion by β-cells is not able to compensate sufficiently for the decrease in insulin sensitivity. Moreover, β-cell function begins to decline and β-cell dysfunction eventually leads to insulin deficiency. As a result, normoglycemia can no longer be maintained and hyperglycemia develops. Although insulin levels are decreased, the secretion of insulin in most cases is sufficient to prevent the occurrence of DKA.[ 29 ] But DKA may occur during severe stress conditions such as those associated with infections or other pathophysiological scenarios. DKA may also be precipitated by the use of certain drugs including sodium-glucose co-transporter-2 (SGLT2) inhibitors, corticosteroids, and atypical antipsychotics (second-generation antipsychotic drugs).[ 30 , 31 ] In absence of any severe physiological stress conditions, patients with T2DM often do not require any insulin therapy both at the time of disease onset and even after, throughout their lifetime.[ 27 , 28 , 29 ]

T2DM progresses very slowly and asymptomatically with even mild hyperglycemia developing over years and as such remains largely undiagnosed until the appearance of classic symptoms associated with severe hyperglycemia such as weight loss, growth impairment, blurred vision, polyuria, and polydipsia in the advanced stages of the disease. The pathogenesis/etiology of this form of diabetes is complex and involves multiple known and unknown factors, which in a conclusive manner can be described as a combination of genetic (polygenic) predispositions and strong environmental influences. T2DM has been more frequently associated with increasing age, obesity, family history of diabetes, physical inactivity, and adoption of modern lifestyles: with prior GDM in women and with pathophysiological conditions such as hypertension and dyslipidemia. It occurs more frequently in individuals belonging to certain racial or ethnic groups including Native Americans (American Indians), Asian Americans, African Americans, Hispanic, and Latino. The frequent occurrence of T2DM in the mentioned racial or ethnic groups and its observed strong association with first-degree blood relations point strongly toward the role of genetic factors in the etiology of this disease, but these factors are complex and remain largely unspecified. However, unlike T1DM, no association of this disease has been found with genes involved in the immune response including autoimmunity and consequently there is no immune-mediated pancreatic β-cell destruction.[ 32 , 33 ]

Obesity plays an important role in the homeostatic regulation of systemic glucose due to its influence on the development of insulin resistance through its effect on the sensitivity of tissues to insulin and as such most but not all patients with T2DM are overweight or obese.[ 34 ] The increased body fat content, a characteristic of obesity, is such an important risk factor for T2DM that not only the total amount but also the distribution of body fat itself defines the development of insulin resistance and subsequently hyperglycemia. The increased abdominal fat or visceral obesity has been frequently associated with this type of diabetes in comparison to increased gluteal/subcutaneous fat or peripheral obesity.[ 35 ] Due to its strong association with increased body fat content or obesity, the patients with T2DM often present with various cardiovascular risk factors such as hypertension and lipoprotein metabolic abnormalities characterized by elevated triglycerides and low levels of high-density lipoproteins (HDLs). Due to its lifelong duration and associated diverse metabolic derangements characteristic of hyperglycemia, T2DM, particularly in the middle and later decades, is frequently associated with the development of various microvascular and macrovascular complications. Figure 2 enlists some of the main risk factors of T2DM.

Some of the main risk factors of type 2 diabetes mellitus

Gestational diabetes mellitus

GDM is defined as any degree of glucose intolerance or diabetes diagnosed at the outset or during pregnancy, usually the second or third trimester. This definition earlier also included any undetected T2DM which may begin prior to or occur at the time of pregnancy onset. However, the latest recommendations of the International Association of the Diabetes and Pregnancy Study Groups exclude from this definition diabetes diagnosed at the pregnancy onset or afterward in high-risk women such as with obesity where any degree of glucose intolerance is described as previously undiagnosed overt diabetes rather than GDM. GDM is different from any preexisting diabetes in women undergoing pregnancies and usually resolves soon after childbirth or termination of pregnancy.[ 1 , 36 ]

During early pregnancy, both the fasting and post-prandial blood glucose levels are usually lower than normal but the blood glucose levels increase during the third trimester of pregnancy, and in cases where this blood glucose level reaches the diabetic levels, the condition is described as GDM. More than 90% of all the cases of diabetes and its complications that occur during pregnancy can be attributed to GDM. The incidence of GDM varies from 1% to 14% of all pregnancies and its prevalence is greatly influenced by the populations under study. GDM occurs more frequently in certain racial or ethnic groups than others and this influence of ethnicity on risk of GDM is very important and has long been established. The prevalence of GDM is highest among Asian Indians, higher in aboriginal Australians, Middle Eastern (Lebanese, Syrian, Iranian, Iraqi, or Afghanistan), Filipina, Pacific Islanders, and Chinese, Japanese, Korean, and Mexican women. The prevalence is lower in blacks and lowest among non-Hispanic white women.[ 37 , 38 ] The risk of GDM increases with age, obesity, previous pregnancy with large babies, and any previous history of impaired glucose tolerance or GDM.[ 39 , 40 ] Furthermore, GDM has been associated with an increased lifetime risk of developing T2DM. The regular and lifetime screening for any kind of glucose impairment is, therefore, highly recommended in order to ensure early diagnosis of T2DM in such individuals.[ 41 , 42 , 43 ]

Other types of diabetes

Besides T1DM, T2DM, and GDM, diabetes in various other forms, though in smaller percentages with respect to overall diabetic incidence scenario, has been found to be associated with some specific conditions including various pathologies and/or several disorders. The prominent among these types of diabetes include diabetes resulting from the monogenic defects in β-cell function and those due to genetic abnormalities in insulin action, endocrinopathies, exocrine pancreatic pathologies, and several other specific conditions.

Diabetes caused due to the monogenic defects in β-cell function

Diabetes resulting from monogenic defects in β-cell function constitutes only 0.6–2% of all the cases of diabetes and mainly includes maturity-onset diabetes of the young (MODY) and neonatal diabetes, besides other but rare types.

Maturity-onset diabetes of the young

MODY is a genetically, metabolically, and clinically heterogeneous group of mostly non-insulin-dependent diabetes, resulting from mutations in several specific genes involved in pancreatic β-cell function, which affects glucose sensing and subsequent insulin secretion with no or minimal defects, if any, in insulin action. MODY, as the name suggests, has an early onset with glucose tolerance impairment and hyperglycemia occurring usually before the age of 25 years and is often misdiagnosed as T1DM or T2DM.[ 44 , 45 ] MODY accounts for less than 2% of all the cases of diabetes[ 46 ] and 1–6% of all the pediatric cases of diabetes.[ 47 ] MODY follows an autosomal dominant inheritance pattern and typically involves the vertical transmission of the disorder through at least three generations and exhibits a phenotype shared by all family members with diabetes.[ 48 ] To date, MODY has been associated with mutations in one of the 14 genes identified so far and these genes are mostly located on different chromosomes.[ 1 , 9 , 46 , 49 ] Figure 3 provides a graphical representation of various MODY subtypes along with their alternative names based on genes involved. The most common forms of this group of diabetes are designated as MODY2 and MODY3 which together account for more than 80% of all the cases of this type of diabetes.[ 50 , 51 ]

Types of maturity-onset diabetes of the young and their alternative names based on genes involved

MODY2 (GCK MODY ). MODY2 results from one or several of more than 200 loss-of-function mutations in the glucokinase (GCK) gene located on chromosome 7p13 and accounts for 15–25% of all MODY cases.[ 50 , 52 ] GCK gene codes for GCK enzyme, which catalyzes the first and rate-limiting step of glycolytic phosphorylation of glucose to glucose-6-phosphate at a rate proportional to the glucose concentration. This unique catalytic property allows the GCK enzyme to function as a sort of a glucose sensor and enables the β-cells to elicit an insulin secretion response appropriate to the existing concentrations of glucose.[ 53 ] The loss-of-function mutations characteristic of MODY2 disrupt this glucose-sensing function of the GCK enzyme such that only hyperglycemic but not normoglycemic levels can elicit a normal insulin secretion response from the β-cells. In MODY2, the fasting hyperglycemia remains mild but persistent and stable and the disorder is non-insulin-dependent. MODY2 is clinically nonprogressive with mild or no symptoms and is usually not associated with the development of microvascular and macrovascular complications. GCK gene is a mutational hotspot region and more than 600 mutations have been identified in the 10 exons of this gene, which have been associated with both hyperglycemia and hypoglycemia.[ 54 ]

MODY3 (HNF-1α MODY) and MODY1 (HNF-4α MODY ). MODY3 results from the loss-of-function mutations in hepatocyte nuclear factor (HNF)-1α gene located on chromosome 12q24, which codes for the transcription factor, HNF-1α (transcription factor MODY) and accounts for 30–50% of all MODY cases.[ 51 ] HNF-1α is expressed in the kidney, liver, intestine, and pancreatic β-cells and is involved in regulating the expression of several hepatic genes many of which are involved in glucose metabolism including glucose transporter 1 and 2 (GLUT1 and GLUT2). More than 400 mutations have been identified in HNF-1α gene.[ 55 ] MODY3 presents with a symptomatic rapid progression to overt diabetes reflected through a progressive impairment from glucose intolerance to severe hyperglycemia, often leading to T1DM and T2DM like microvascular and macrovascular complications. MODY3 has been associated with a decreased pancreatic β-cell mass due to an increased rate of β-cell apoptosis, particularly from the third decade of life onward, and therefore, MODY3 is characterized by a progressive decrease in insulin secretion.[ 56 ] Depending on the hyperglycemic severity and duration since onset, MODY3 may be or may not be insulin-dependent.

MODY1 accounts for around 5% of all MODY cases. MODY1 is caused due to the loss-of-function mutations in the transcription factor, HNF-4α gene located on chromosome 20q13. HNF-4α is mainly expressed in the liver and also in kidney and pancreatic β-cells and regulates the expression of genes involved in glucose transport, metabolism, and nutrient-induced insulin secretion and also triglyceride metabolism and lipoprotein biosynthesis.[ 57 ] HNF-4α mutation is characterized by a progressive defect and a decline in insulin secretion from infancy onward and resembles clinically with MODY3. It is associated with hyperinsulinemic hypoglycemia in the neonatal period, which begins to remit during infancy, and as such, the decline in insulin production starts in infancy but the emergence of hyperglycemia and subsequent full-blown diabetes occurs during adolescence.[ 47 , 58 ] HNF-4α mutation and hence MODY1 have been associated with decreased levels of apolipoprotein—apoAII, apoCIII, and apoB and HDLs and increased levels of low-density lipoproteins (LDLs).

Hyperglycemia associated with HNF-1α and HNF-4α mutations in MODY3 and MODY1, respectively, can be efficiently controlled through the treatment with low-dose sulfonylureas. These agents maintain efficacy or remain effective for many years and are preferred first-line of treatment in these patients compared to insulin and other therapies used in T1DM and T2DM. However, to ensure proper treatment, an early and accurate diagnosis is very important to avoid mislabeling these MODY types as T1DM or T2DM and prevent administration of inappropriate avoidable therapies in these patients.[ 59 , 60 ]

MODY5 (HNF-1β MODY ). MODY5 results from mutations in the transcription factor, HNF-1β gene located on chromosome 17q12 and accounts for around 5% of all cases of MODY.[ 61 , 62 ] HNF-1β is involved in the regulation of genes that are associated with various embryonic developmental processes, in particular, the genesis of various organs including the liver, pancreas, lungs, gut, kidney, and genitourinary tract.[ 63 ] MODY5 develops in early adolescence or adulthood. HNF-1β mutations that result in MODY5 often present as renal cysts, renal cysts and diabetes syndrome, renal dysplasia, hypoplastic glomerulonephritic kidney disease, urinary tract malformation,[ 64 , 65 ] and reduced birth weight.[ 66 ] Some of these conditions are evident from the 17th week of gestation[ 65 ] or are seen in infants or young children, independent of the hyperglycemic status.[ 47 ] Renal dysfunction,[ 65 ] liver dysfunction, and pancreatic abnormalities[ 67 ] are common as the disorder develops and end-stage renal disease develops in half of the patients with MODY5 by 45 years of age independent of diabetic kidney disease status.[ 65 ] Genitourinary tract malformations especially uterine abnormalities such as rudimentary uterus in addition to vaginal aplasia have also been reported in MODY5.[ 67 ] Insulin dependence develops relatively earlier due to liver and pancreatic abnormalities, in particular, hepatic insulin resistance and pancreatic hypoplasia.[ 47 , 68 ] Hyperuricemia, gout, low HDL levels, and elevated triglyceride levels are commonly observed in patients with MODY5.[ 61 , 69 ]

Other types of MODY . Relatively rare and less common types of MODY, which account for less than 1% of all MODY cases, include as follows:

• MODY4 (PDX-1/IPF-1 MODY ): MODY4 results from mutations in the transcription factor, pancreatic and duodenal homeobox-1(PDX-1), also known as insulin promoter factor (IPF)-1 gene located on chromosome 13q12.2.[ 70 ] PDX-1/IPF-1 is involved in the development of exocrine and endocrine pancreas and plays an important role in regulating the expression of insulin, glucagon, GLUT2, and GCK encoding genes.[ 71 , 72 ] Homozygous mutations in PDX-1/IPF-1 gene result in pancreas agenesis, hypoplasia, and pancreatic exocrine insufficiency and in permanent neonatal diabetes (PND) whereas heterozygous PDX-1/IPF-1 gene mutations cause β-cell impairment, which leads to defective insulin secretion and hyperglycemia.[ 73 , 74 ]
• MODY6 (NEUROD1 MODY ): MODY6 results from mutations in the transcription factor, neurogenic differentiation factor-1(NEUROD1) gene located on chromosome 2q31.[ 75 ] NEUROD1 belongs to the basic helix-loop-helix family of transcription factors and is involved in the regulation of several cell differentiation pathways associated with neuronal and pancreatic development, in particular, those involved in endocrine islet cells (islets of Langerhans) differentiation including the pancreatic β-cells. NEUROD1 gene mutations interfere with the maturation of β-cells and impair their glucose-sensing ability and as a result, their insulin secretion response. Homozygous NEUROD1 gene mutations lead to neonatal diabetes and are associated with neurological abnormalities whereas heterozygous mutations result in childhood- or adult-onset diabetes.[ 76 , 77 ]

The other types of MODY in this category include MODY7 (KLF11 MODY), which results from the mutations in Kruppel-like factor 11 (KLF11) gene located on chromosome 2p25[ 78 ] and MODY8 (CEL MODY), which arises from the mutations in carboxyl ester lipase (CEL) gene located on chromosome 9q34.[ 79 ] This category also includes MODY9 (PAX4 MODY), caused due to the mutations in PAX family transcription factor, Paired box gene 4 (PAX4) gene located on chromosome 7q32[ 80 ] and MODY10 (INS MODY), which results from the mutations in the INS located on chromosome 11p15[ 81 , 82 ]; also, MODY11 (BLK MODY), which arises due to the mutations in the human homolog of a B-lymphocyte-specific protein tyrosine kinase (BLK) gene located on chromosome 8p23.1.[ 83 ]

Furthermore, there is MODY12 (ABCC8-MODY), which results from the mutations in ATP-binding cassette transporter subfamily C member 8 (ABCC8) gene located on chromosome 11p15 and ABCC8 which encodes sulfonylurea receptor-1 (SUR1) protein, a subunit of ATP-sensitive potassium (KATP) channel. MODY12 is responsive to sulfonylureas.[ 84 ]

The remaining types include MODY13 (KCNJ11-MODY) and MODY14 (APPL1-MODY). MODY13 (KCNJ11-MODY) is caused due to the mutations in potassium inwardly rectifying channel subfamily J member 11 (KCNJ11) gene located on chromosome 11p15.1 which encodes β-cell inward rectifier, BIR (inwardly rectifying potassium channel Kir6.2), a subunit of ATP-sensitive potassium (KATP) channel.[ 85 , 86 ] MODY14 (APPL1-MODY) results from the mutations in Adaptor Protein, Phosphotyrosine Interacting With PH Domain and Leucine Zipper 1 (APPL1) or DCC-interacting protein 13-α (DIP13-α) gene located on chromosome 3p14.3.[ 87 ]

Neonatal diabetes mellitus

Neonatal diabetes mellitus (NDM), also known as early-onset or congenital diabetes, is the diabetes diagnosed during the first 6 months of life. It is a rare disorder with a global incidence rate of 1 per 500,000–300,000 (1:500,000–1:300,000)[ 88 , 89 ] live births; though a study in Italy has reported a higher incidence of 1 per 90,000 (1:90,000).[ 88 ] NDM is predominantly of genetic origin with 80–85% cases occurring due to monogenic defects and is characterized by severe uncontrolled hyperglycemia along with hypoinsulinemia and requires insulin replacement therapy.[ 89 ] The genetic abnormalities lead to β-cell dysfunction and decreased β-cell mass due to increased apoptotic or non-apoptotic β-cell death. These defects also result in developmental abnormalities of pancreas and/or its islets or in very rare cases their complete absence leading to decreased production and secretion of insulin or hypoinsulinemia and in the latter case an absolute insulin deficiency.[ 90 ] Neonatal diabetes is highly distinct from early-onset T1DM and differs from it both in the origin and pattern of inborn pancreatic disorder and mostly occurs during the first 6 months of life whereas T1DM mostly develops after 6 months of life. Based largely on the clinical features, NDM can assume either of these two forms: transient neonatal diabetes mellitus (TNDM) and permanent neonatal diabetes mellitus (PNDM).

TNDM is the more common form representing approximately 55–60% of all cases of neonatal diabetes. It usually resolves within 12–18 months after birth but in a majority of cases, NDM relapses during the later years of life from late childhood to early or late adulthood and presents itself as T2DM, indicating the presence of varying degrees of severity, but persistent β-cell dysfunction, which leads to possible inadequate insulin secretion and/or insulin resistance. Furthermore, the diabetes may also precipitate under stress conditions such as hormonal changes as observed in puberty or in certain diseases.[ 89 , 91 ] TNDM results most often from the abnormalities in chromosome 6 specifically involving the overexpression of imprinted and paternally expressed genes in the 6q24 region. This includes the HYMAI (hydatiform mole associated and imprinted) gene, zinc finger protein, ZAC gene, and pituitary adenylate cyclase activating polypeptide-1 (PACAP1) gene. A small percentage of TNDM cases arises from the mutations in the ATP binding cassette subfamily C member 8 (ABCC8) gene also known as sulfonylurea receptor-1 (SUR1) gene and rarely from the mutations in the potassium voltage-gated channel subfamily J member 11 (KCNJ11 or Kir6.2).[ 89 ] Both ABCC8 and KCNJ11genes are functionally linked together as these genes encode for the proteins that constitute the individual subunits of the β-cell K ATP channel. The K ATP channel is an eight-subunit ATP-sensitive potassium channel with two types of subunits: four regulatory subunits encoded by ABCC8 (SUR1) gene and four pore-forming subunits encoded by KCNJ11 (Kir6.2) gene. This channel regulates the secretion of insulin from the pancreatic β-cells, thus providing a direct link with normal glucose homeostasis and its dysregulation in diabetes.

PNDM is the less common form of NDM, which unlike TNDM does not go into remission and persists permanently. PNDM most commonly results from the heterozygous autosomal dominant mutations in the ABCC8 and the KCNJ11 genes encoding, respectively, the SUR1 and Kir6.2 subunits of the β-cell K ATP channel.[ 9 , 89 ] Several mutations identified in the INS also cause PNDM.[ 9 ] Besides, this type of neonatal diabetes is associated with several syndromes including the immune-dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome, and Wolcott–Rallison syndrome. IPEX syndrome is an autoimmune disorder that results from the mutations in the FOXP3 gene. The autoimmune disorders that characterize IPEX syndrome include autoimmune enteropathy (an autoimmune disorder of the intestines), dermatitis or eczema (an autoimmune disorder of the skin), and polyendocrinopathy (multiple autoimmune disorders of the endocrine glands, including pancreas and thyroid).[ 9 , 89 , 92 ] WRS is an autosomal recessive disorder, which results from the mutations in the EIF2AK3 gene.[ 9 , 89 , 93 ] The main characteristics of this disorder include multiple epiphyseo-metaphyseal dysplasia and hepatic dysfunction. Diabetes is a permanent feature associated with this syndrome and in consanguineous families; WRS has emerged as the most frequent cause of PNDM.[ 94 , 95 ] Clinically, it is not possible to predict whether a particular neonatal dysfunction of glucose homeostasis will eventually develop into TNDM or PNDM, which makes the correct diagnosis, and the assessment of the underlying cause of the disorder including the genetic factors involved, an important aspect in the management of this disorder.

Besides MODY and NDM, there are several other monogenic defects in β-cell function which result in DM. These include the point mutations in mitochondrial DNA such as 3243A-G mutation in the mitochondrial transfer RNA leucine-1 (MTTL1) gene, which leads to deafness and diabetes[ 96 ] and the autosomal dominant mutations, which result in a total inability or abnormal conversion of proinsulin to insulin.[ 97 ] Furthermore, it also includes the mutations that lead to the production of structurally abnormal insulin molecules with impaired receptor binding.

Diabetes caused due to genetic abnormalities in insulin action

Several genetic abnormalities in insulin action resulting either from insulin receptor functional impairment or decrease in the number of insulin receptors, caused mainly due to the mutations in insulin receptor (INSR) gene located on chromosome 19, have been identified. These abnormalities in insulin action lead to hyperinsulinemia or insulin resistance and subsequent mild to modest hyperglycemia or may even cause severe hyperglycemia characteristic of overt diabetes.[ 1 ] The various forms of diabetes resulting from the abnormalities in insulin action, often described as inherited severe insulin resistance syndromes, include type A insulin resistance syndrome, lipoatrophic diabetes, Donohue syndrome (leprechaunism), and Rabson–Mendenhall syndrome (RMS).

Type A insulin resistance syndrome results from mutations in INSR gene. This syndrome is associated with menstruation disorders (primary amenorrhea or oligomenorrhea) and specific forms of polycystic ovarian syndrome characterized by hirsutism due to hyperandrogenism and multiple enlarged cysts on the ovaries, in females[ 98 ]; also, acanthosis nigricans, a skin pigmentation disorder, most often in females than in males[ 99 ] and obesity,[ 100 ] most often in males than in females and severe insulin resistance.

Lipoatrophic diabetes is a monogenic but heterogeneous insulin resistance syndrome associated with lipoatrophy and lipodystrophy and characterized by paucity (insufficiency) of fat, insulin resistance, and dyslipidemia, more specifically, hypertriglyceridemia.[ 101 ] Lipoatrophic diabetes arises due to the mutations in several different genes, which manifests as different genetic syndromes. It may result from the mutations in Laminin A/C (LMNA) gene located on chromosome 1q21–22 and manifest as an autosomal dominant disorder known as familial partial lipoatrophy, also known as Dunnigan or Koberling-Dunnigan syndrome.[ 102 ] Lipoatrophic diabetes may also result from the mutations, either in the AGPAT2 gene or in the BSCL2 gene. AGPAT2 gene located on chromosome 9q34 encodes the enzyme 1-acyl-sn-glycerol-3-phosphate- d -acetyltransferase-2, which is involved in triglyceride synthesis. Berardinelli-Seip congenital lipodystrophy type-2 (BSCL2) gene, also known as γ3-linked gene (GNG3) or seipin gene, located on chromosome 11q13 encodes seipin, an endoplasmic reticulum-associated protein involved in lipid droplet biogenesis. Both these mutations manifest as an autosomal recessive disorder known as Congenital generalized lipoatrophy or Berardinelli-Seip syndrome.[ 103 , 104 ]

Furthermore, the mutations in insulin receptor gene may also lead to the Donohue syndrome (leprechaunism) and the RMS, both of which manifest in infancy, and diabetes in these syndromes is characterized by strong insulin resistance and severe hyperglycemia.[ 105 ]

Endocrinopathies

Several endocrinopathies resulting in or from abnormal functioning of various hormones can lead to diabetes. These include the endocrinopathies that involve the hyperactivity of those hormones which partly or fully antagonize the function of insulin such as Cushing syndrome, acromegaly, pheochromocytoma, glucagonoma, and hyperthyroidism, which result from hyperactivity of cortisol, growth hormone, norepinephrine (and epinephrine), glucagon, and thyroid hormones, respectively. Diabetes associated with these endocrine disorders usually occurs when a defect in insulin secretion and/or action is already present.[ 106 , 107 ] Some endocrinopathies induce diabetes through inhibition of insulin secretion and these include somatostatinoma, which leads to the excessive secretion of somatostatin and primary hyperaldosteronism[ 108 ] or Conn’s syndrome induced hypokalemia, which involves the hypersecretion and hyperactivity of the hormone, aldosterone.[ 109 ] Diabetes caused due to various endocrinopathies usually resolves when endocrinopathies are treated or managed.

Exocrine pancreatic pathologies

Several diseases of the exocrine pancreas have been found to cause diabetes but the contribution of these diseases to the overall incidence of diabetes is minimal with less than 0.5% of all the cases of diabetes resulting from the diseases of the exocrine pancreas. These include chronic pancreatitis (fibrocalculous pancreatopathy), trauma (pancreatectomy), infection, hereditary hemochromatosis, secondary hemochromatosis, cystic fibrosis, and pancreatic neoplasia (adenocarcinoma and glucagonoma).[ 110 , 111 , 112 ] All these pancreatic pathologies, with the exception of pancreatic neoplasia, lead to diabetes only when they are severe enough to cause extensive pancreatic damage, involving the endocrine pancreas, including the islets of Langerhans, which leads to a considerable reduction in the β-cell mass and impairment of β-cell function.[ 113 ] The pancreatic neoplasia-associated diabetes occurs even without any reduction in β-cell mass.[ 1 ]

Several infections caused by viruses are known to cause β-cell dysfunction, mainly through β-cell destruction, and lead to hyperglycemia, which gradually presents as overt diabetes. These include infections caused by cytomegalovirus, adenovirus, Coxsackie virus B, and mumps. Besides, congenital rubella syndrome, caused by rubella virus, has also been closely linked with diabetes, but this diabetes in most of the cases is associated with the presence of HLA and other immune markers, which are characteristic of T1DM.[ 1 , 114 , 115 ] Furthermore, insulin resistance has been associated with chronic hepatitis C virus infection and progression of fibrosis and a very high prevalence of T2DM has been reported among the individuals infected with the hepatitis C virus.[ 116 , 117 ]

Drug- or chemical-induced

Several drugs and chemicals are known to induce diabetes. These agents induce diabetes either through the impairment of insulin production or secretion, which mainly results from the destruction of β-cells or through a decrease in the sensitivity of tissues to insulin, which causes insulin resistance. Diabetes resulting from the drug- or chemical-induced increase in insulin resistance occurs only in susceptible individuals. Furthermore, these agents may worsen or increase the severity of hyperglycemia in individuals with already existing overt diabetes. The drugs and chemicals known to induce diabetes include glucocorticoids, diazoxide, thiazides, β 2 -receptor agonists (salbutamol and ritodrine), nonselective β-adrenergic antagonists, dilantin, hormones including growth hormone (in very high doses), thyroid hormone (thyroxine/triiodothyronine), somatostatin, estradiol, levonorgestrel, and glucagon. These also include γ-interferon, protease inhibitors (indinavir, nelfinavir, ritonavir, and saquinavir), nicotinic acid, and β-cell toxins including streptozocin (streptozotocin), cyclosporine, rodenticide vacor and pentamidine, and several antipsychotics.[ 118 , 119 ] Furthermore, immune checkpoint inhibitors, such as ipilimumab, nivolumab, and pembrolizumab, used in cancer immunotherapy for treatment of advanced-stage cancers, including head and neck cancer, renal cancer, urothelial cancers, non-small-cell lung carcinoma, and melanoma besides other cancers[ 120 , 121 , 122 , 123 , 124 , 125 ] have been reported to induce new-onset T1DM, through immune-mediated β-islet cell dysfunction.[ 125 , 126 ]

Other genetic syndromes associated with diabetes

There are many others, besides the already mentioned genetic syndromes, that are usually associated with an increased incidence of diabetes. These include Down’s syndrome, Turner’s syndrome, Wolfram’s syndrome, Klinefelter’s syndrome, Huntington’s chorea, Friedreich’s ataxia, myotonic dystrophy, Laurence-Moon-Biedl syndrome, Porphyria, and Prader-Willi syndrome among others.[ 127 ]

Uncommon forms of immune-mediated diabetes

The uncommon forms of immune-mediated diabetes are very rare in occurrence and mainly include diabetes associated with Moersch-Woltman syndrome (stiff-person syndrome [SPS]), anti-insulin receptor antibodies (AIRAs), and insulin autoimmune syndrome (IAS; Hirata’s disease).

Moersch-Woltman syndrome or the SPS is a very rare autoimmune disorder that affects the central nervous system and is characterized by progressive fluctuating rigidity of the axial muscles (muscles of the trunk and head), accompanied by painful muscle spasms. Patients with SPS generally present with high titers of GADAs and are frequently associated with various diseases including pernicious anemia, thyroiditis, vitiligo, and type 1-like diabetes. Although GADAs are detected in most of the individuals with T1DM alone; but the individuals with SPS with or without diabetes have 50–100 times more titers of GADAs.[ 128 ]

The AIRAs are often associated with various autoimmune diseases, including primary biliary cholangitis, systemic lupus erythematosus, and Hashimoto thyroiditis. AIRAs generally bind to insulin receptors on various insulin target tissues, which block the binding of insulin to these receptors and hence the subsequent signaling pathways. This leads to diabetes characterized by a rapidly progressive and extreme form of insulin resistance, earlier termed as type B insulin resistance. Alternatively, AIRAs once bound to target receptors may sometimes cause spontaneous hyperinsulinemic hypoglycemia by acting as insulin agonists. Diabetes associated with AIRAs is often characterized by acanthosis nigricans and impaired insulin degradation.[ 129 , 130 ]

IAS or Hirata’s disease is described as a condition, which is characterized by the presence of autoantibodies to the endogenous insulin (IAA) in the absence of any previous exposure to the exogenous insulin, absence of any pathological abnormalities of the pancreatic islets and presents as endogenous hyperinsulinemia hypoglycemia. Although, the predisposition to this condition is present from birth, but the overt disease most often presents itself during adulthood and can be triggered by exposure to certain drugs and viruses. IAS can be controlled through simple dietary management.[ 131 ]

Ketosis-prone diabetes mellitus

Ketosis-prone diabetes mellitus (KPD) describes another heterogeneous group of diabetes, which like T2DM, characteristically does not involve the immune-mediated destruction of pancreatic β-cells but unlike T2DM, this type presents with frequent episodes of DKA or unprovoked ketosis.[ 132 ] KPD occurs most frequently in African Americans and Africans in sub-Saharan Africa but has now been observed increasingly in Hispanic, Chinese, and Japanese populations.[ 132 , 133 , 134 , 135 ] One of the best described subtypes of this diabetes is Flatbush diabetes which along with characteristic episodic DKA is frequently associated with HLA-DR3 and/or HLA-DR4 haplotypes.[ 136 ] The patients with KPD show periodic but absolute requirement of insulin replacement therapy, concomitant with the episodes of DKA and outside of the frequent episodes of DKA, the diabetes can be controlled through simple diet management without insulin replacement therapy.

CONCLUSIONS

DM is a heterogeneous metabolic disease, represented by diverse forms, each with a distinct pathophysiological origin but often manifest as a disorder with overlapping and difficult-to-differentiate characteristics. The treatment and management of each of these diabetic types are distinct in some characteristics but share a great deal of similarity as well as is the case with the disorder itself. All this emphasizes the importance of correct and timely diagnosis of each of these diabetic types and the critical role of their pathophysiological understanding. This is vital to safeguard diabetic individuals from exposures to potential adverse effects of improper, ineffective, or avoidable pharmaceutical interventions, which often delays the desired prognosis and increases the duration of hyperglycemic exposures. The long-term hyperglycemia, in turn, has often been associated with increased risk of microvascular and macrovascular diabetic complications, which affect the quality of life and mainly contribute to the diabetes-associated morbidity and mortality. For diabetes in general, and in particular, the diabetes types resulting from genetic mutations or associated genetic anomalies, the correct and timely molecular diagnosis can help in disease risk analysis and help in disease prediction and timely identification of individuals at an increased risk to the disorder, in particular, the family members. The predictive molecular/genetic testing and preventive management can play a vital role in such cases. Furthermore, irrespective of the diabetes type, various lifestyle modifications and interventions such as extensive diet control, physical exercises, change of daily sedentary routine, and control of obesity are important in the prevention and the management of diabetes. The educational campaigns, which make the general population aware of the pathogenesis of this disease and the various controllable risk factors associated with it, are also a vital tool in the management and control of diabetes mellitus.

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Diagnostic tests for diabetes, type 1 diabetes, prediabetes and type 2 diabetes, cystic fibrosis–related diabetes, posttransplantation diabetes mellitus, monogenic diabetes syndromes, pancreatic diabetes or diabetes in the context of disease of the exocrine pancreas, gestational diabetes mellitus, 2. classification and diagnosis of diabetes: standards of medical care in diabetes—2021.

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American Diabetes Association; 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021 . Diabetes Care 1 January 2021; 44 (Supplement_1): S15–S33. https://doi.org/10.2337/dc21-S002

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The American Diabetes Association (ADA) “Standards of Medical Care in Diabetes” includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee ( https://doi.org/10.2337/dc21-SPPC ), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction ( https://doi.org/10.2337/dc21-SINT ). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC .

Diabetes can be classified into the following general categories:

Type 1 diabetes (due to autoimmune β-cell destruction, usually leading to absolute insulin deficiency, including latent autoimmune diabetes of adulthood)

Type 2 diabetes (due to a progressive loss of adequate β-cell insulin secretion frequently on the background of insulin resistance)

Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young), diseases of the exocrine pancreas (such as cystic fibrosis and pancreatitis), and drug- or chemical-induced diabetes (such as with glucocorticoid use, in the treatment of HIV/AIDS, or after organ transplantation)

Gestational diabetes mellitus (diabetes diagnosed in the second or third trimester of pregnancy that was not clearly overt diabetes prior to gestation)

This section reviews most common forms of diabetes but is not comprehensive. For additional information, see the American Diabetes Association (ADA) position statement “Diagnosis and Classification of Diabetes Mellitus” ( 1 ).

Type 1 diabetes and type 2 diabetes are heterogeneous diseases in which clinical presentation and disease progression may vary considerably. Classification is important for determining therapy, but some individuals cannot be clearly classified as having type 1 or type 2 diabetes at the time of diagnosis. The traditional paradigms of type 2 diabetes occurring only in adults and type 1 diabetes only in children are no longer accurate, as both diseases occur in both age-groups. Children with type 1 diabetes typically present with the hallmark symptoms of polyuria/polydipsia, and approximately one-third present with diabetic ketoacidosis (DKA) ( 2 ). The onset of type 1 diabetes may be more variable in adults; they may not present with the classic symptoms seen in children and may experience temporary remission from the need for insulin ( 3 – 5 ). Occasionally, patients with type 2 diabetes may present with DKA ( 6 ), particularly ethnic and racial minorities ( 7 ). It is important for the provider to realize that classification of diabetes type is not always straightforward at presentation and that misdiagnosis is common (e.g., adults with type 1 diabetes misdiagnosed as having type 2 diabetes; individuals with maturity-onset diabetes of the young [MODY] misdiagnosed as having type 1 diabetes, etc.). Although difficulties in distinguishing diabetes type may occur in all age-groups at onset, the diagnosis becomes more obvious over time in people with β-cell deficiency.

In both type 1 and type 2 diabetes, various genetic and environmental factors can result in the progressive loss of β-cell mass and/or function that manifests clinically as hyperglycemia. Once hyperglycemia occurs, patients with all forms of diabetes are at risk for developing the same chronic complications, although rates of progression may differ. The identification of individualized therapies for diabetes in the future will require better characterization of the many paths to β-cell demise or dysfunction ( 8 ). Across the globe many groups are working on combining clinical, pathophysiological, and genetic characteristics to more precisely define the subsets of diabetes currently clustered into the type 1 diabetes versus type 2 diabetes nomenclature with the goal of optimizing treatment approaches. Many of these studies show great promise and may soon be incorporated into the diabetes classification system ( 9 ).

Characterization of the underlying pathophysiology is more precisely developed in type 1 diabetes than in type 2 diabetes. It is now clear from studies of first-degree relatives of patients with type 1 diabetes that the persistent presence of two or more islet autoantibodies is a near certain predictor of clinical hyperglycemia and diabetes. The rate of progression is dependent on the age at first detection of autoantibody, number of autoantibodies, autoantibody specificity, and autoantibody titer. Glucose and A1C levels rise well before the clinical onset of diabetes, making diagnosis feasible well before the onset of DKA. Three distinct stages of type 1 diabetes can be identified ( Table 2.1 ) and serve as a framework for future research and regulatory decision-making ( 8 , 10 ). There is debate as to whether slowly progressive autoimmune diabetes with an adult onset should be termed latent autoimmune diabetes in adults (LADA) or type 1 diabetes. The clinical priority is awareness that slow autoimmune β-cell destruction can occur in adults leading to a long duration of marginal insulin secretory capacity. For the purpose of this classification, all forms of diabetes mediated by autoimmune β-cell destruction are included under the rubric of type 1 diabetes. Use of the term LADA is common and acceptable in clinical practice and has the practical impact of heightening awareness of a population of adults likely to develop overt autoimmune β-cell destruction ( 11 ), thus accelerating insulin initiation prior to deterioration of glucose control or development of DKA ( 4 , 12 ).

Staging of type 1 diabetes ( 8 , 10 )

FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; 2-h PG, 2-h plasma glucose.

The paths to β-cell demise and dysfunction are less well defined in type 2 diabetes, but deficient β-cell insulin secretion, frequently in the setting of insulin resistance, appears to be the common denominator. Type 2 diabetes is associated with insulin secretory defects related to inflammation and metabolic stress among other contributors, including genetic factors. Future classification schemes for diabetes will likely focus on the pathophysiology of the underlying β-cell dysfunction ( 8 , 9 , 13 – 15 ).

Diabetes may be diagnosed based on plasma glucose criteria, either the fasting plasma glucose (FPG) value or the 2-h plasma glucose (2-h PG) value during a 75-g oral glucose tolerance test (OGTT), or A1C criteria ( 16 ) ( Table 2.2 ).

Criteria for the diagnosis of diabetes

DCCT, Diabetes Control and Complications Trial; FPG, fasting plasma glucose; OGTT, oral glucose tolerance test; WHO, World Health Organization; 2-h PG, 2-h plasma glucose.

In the absence of unequivocal hyperglycemia, diagnosis requires two abnormal test results from the same sample or in two separate test samples.

Generally, FPG, 2-h PG during 75-g OGTT, and A1C are equally appropriate for diagnostic screening. It should be noted that the tests do not necessarily detect diabetes in the same individuals. The efficacy of interventions for primary prevention of type 2 diabetes ( 17 , 18 ) has mainly been demonstrated among individuals who have impaired glucose tolerance (IGT) with or without elevated fasting glucose, not for individuals with isolated impaired fasting glucose (IFG) or for those with prediabetes defined by A1C criteria.

The same tests may be used to screen for and diagnose diabetes and to detect individuals with prediabetes ( Table 2.2 and Table 2.5 ) ( 19 ). Diabetes may be identified anywhere along the spectrum of clinical scenarios—in seemingly low-risk individuals who happen to have glucose testing, in individuals tested based on diabetes risk assessment, and in symptomatic patients.

Fasting and 2-Hour Plasma Glucose

The FPG and 2-h PG may be used to diagnose diabetes ( Table 2.2 ). The concordance between the FPG and 2-h PG tests is imperfect, as is the concordance between A1C and either glucose-based test. Compared with FPG and A1C cut points, the 2-h PG value diagnoses more people with prediabetes and diabetes ( 20 ). In people in whom there is discordance between A1C values and glucose values, FPG and 2-h PG are more accurate ( 21 ).

Recommendations

2.1 To avoid misdiagnosis or missed diagnosis, the A1C test should be performed using a method that is certified by the NGSP and standardized to the Diabetes Control and Complications Trial (DCCT) assay. B

2.2 Marked discordance between measured A1C and plasma glucose levels should raise the possibility of A1C assay interference and consideration of using an assay without interference or plasma blood glucose criteria to diagnose diabetes. B

2.3 In conditions associated with an altered relationship between A1C and glycemia, such as hemoglobinopathies including sickle cell disease, pregnancy (second and third trimesters and the postpartum period), glucose-6-phosphate dehydrogenase deficiency, HIV, hemodialysis, recent blood loss or transfusion, or erythropoietin therapy, only plasma blood glucose criteria should be used to diagnose diabetes. (See other   conditions   altering   the   relationship   of   a1c   and   glycemia below for more information.) B

The A1C test should be performed using a method that is certified by the NGSP ( www.ngsp.org ) and standardized or traceable to the Diabetes Control and Complications Trial (DCCT) reference assay. Although point-of-care A1C assays may be NGSP certified and cleared by the U.S. Food and Drug Administration (FDA) for use in monitoring glycemic control in people with diabetes in both Clinical Laboratory Improvement Amendments (CLIA)-regulated and CLIA-waived settings, only those point-of-care A1C assays that are also cleared by the FDA for use in the diagnosis of diabetes should be used for this purpose, and only in the clinical settings for which they are cleared. As discussed in Section 6 “Glycemic Targets” ( https://doi.org/10.2337/dc21-S006 ), point-of-care A1C assays may be more generally applied for assessment of glycemic control in the clinic.

A1C has several advantages compared with FPG and OGTT, including greater convenience (fasting not required), greater preanalytical stability, and less day-to-day perturbations during stress, changes in diet, or illness. However, these advantages may be offset by the lower sensitivity of A1C at the designated cut point, greater cost, limited availability of A1C testing in certain regions of the developing world, and the imperfect correlation between A1C and average glucose in certain individuals. The A1C test, with a diagnostic threshold of ≥6.5% (48 mmol/mol), diagnoses only 30% of the diabetes cases identified collectively using A1C, FPG, or 2-h PG, according to National Health and Nutrition Examination Survey (NHANES) data ( 22 ).

When using A1C to diagnose diabetes, it is important to recognize that A1C is an indirect measure of average blood glucose levels and to take other factors into consideration that may impact hemoglobin glycation independently of glycemia, such as hemodialysis, pregnancy, HIV treatment ( 23 , 24 ), age, race/ethnicity, pregnancy status, genetic background, and anemia/hemoglobinopathies. (See other   conditions   altering   the   relationship   of   a1c   and   glycemia below for more information.)

The epidemiologic studies that formed the basis for recommending A1C to diagnose diabetes included only adult populations ( 22 ). However, recent ADA clinical guidance concluded that A1C, FPG, or 2-h PG can be used to test for prediabetes or type 2 diabetes in children and adolescents (see screening   and   testing   for   prediabetes   and   type   2 diabetes   in   children   and   adolescents below for additional information) ( 25 ).

Race/Ethnicity/Hemoglobinopathies

Hemoglobin variants can interfere with the measurement of A1C, although most assays in use in the U.S. are unaffected by the most common variants. Marked discrepancies between measured A1C and plasma glucose levels should prompt consideration that the A1C assay may not be reliable for that individual. For patients with a hemoglobin variant but normal red blood cell turnover, such as those with the sickle cell trait, an A1C assay without interference from hemoglobin variants should be used. An updated list of A1C assays with interferences is available at www.ngsp.org/interf.asp .

African Americans heterozygous for the common hemoglobin variant HbS may have, for any given level of mean glycemia, lower A1C by about 0.3% compared with those without the trait ( 26 ). Another genetic variant, X-linked glucose-6-phosphate dehydrogenase G202A, carried by 11% of African Americans, was associated with a decrease in A1C of about 0.8% in homozygous men and 0.7% in homozygous women compared with those without the variant ( 27 ).

Even in the absence of hemoglobin variants, A1C levels may vary with race/ethnicity independently of glycemia ( 28 – 30 ). For example, African Americans may have higher A1C levels than non-Hispanic Whites with similar fasting and postglucose load glucose levels ( 31 ). Though conflicting data exists, African Americans may also have higher levels of fructosamine and glycated albumin and lower levels of 1,5-anhydroglucitol, suggesting that their glycemic burden (particularly postprandially) may be higher ( 32 , 33 ). Similarly, A1C levels may be higher for a given mean glucose concentration when measured with continuous glucose monitoring ( 34 ). Despite these and other reported differences, the association of A1C with risk for complications appears to be similar in African Americans and non-Hispanic Whites ( 35 , 36 ).

Other Conditions Altering the Relationship of A1C and Glycemia

In conditions associated with increased red blood cell turnover, such as sickle cell disease, pregnancy (second and third trimesters), glucose-6-phosphate dehydrogenase deficiency ( 37 , 38 ), hemodialysis, recent blood loss or transfusion, or erythropoietin therapy, only plasma blood glucose criteria should be used to diagnose diabetes ( 39 ). A1C is less reliable than blood glucose measurement in other conditions such as the postpartum state ( 40 – 42 ), HIV treated with certain protease inhibitors (PIs) and nucleoside reverse transcriptase inhibitors (NRTIs) ( 23 ), and iron-deficient anemia ( 43 ).

Confirming the Diagnosis

Unless there is a clear clinical diagnosis (e.g., patient in a hyperglycemic crisis or with classic symptoms of hyperglycemia and a random plasma glucose ≥200 mg/dL [11.1 mmol/L]), diagnosis requires two abnormal test results, either from the same sample ( 44 ) or in two separate test samples. If using two separate test samples, it is recommended that the second test, which may either be a repeat of the initial test or a different test, be performed without delay. For example, if the A1C is 7.0% (53 mmol/mol) and a repeat result is 6.8% (51 mmol/mol), the diagnosis of diabetes is confirmed. If two different tests (such as A1C and FPG) are both above the diagnostic threshold when analyzed from the same sample or in two different test samples, this also confirms the diagnosis. On the other hand, if a patient has discordant results from two different tests, then the test result that is above the diagnostic cut point should be repeated, with careful consideration of the possibility of A1C assay interference. The diagnosis is made on the basis of the confirmed test. For example, if a patient meets the diabetes criterion of the A1C (two results ≥6.5% [48 mmol/mol]) but not FPG (<126 mg/dL [7.0 mmol/L]), that person should nevertheless be considered to have diabetes.

Each of the tests has preanalytic and analytic variability, so it is possible that a test yielding an abnormal result (i.e., above the diagnostic threshold), when repeated, will produce a value below the diagnostic cut point. This scenario is likely for FPG and 2-h PG if the glucose samples remain at room temperature and are not centrifuged promptly. Because of the potential for preanalytic variability, it is critical that samples for plasma glucose be spun and separated immediately after they are drawn. If patients have test results near the margins of the diagnostic threshold, the health care professional should discuss signs and symptoms with the patient and repeat the test in 3 – 6 months.

In a patient with classic symptoms, measurement of plasma glucose is sufficient to diagnose diabetes (symptoms of hyperglycemia or hyperglycemic crisis plus a random plasma glucose ≥200 mg/dL [11.1 mmol/L]). In these cases, knowing the plasma glucose level is critical because, in addition to confirming that symptoms are due to diabetes, it will inform management decisions. Some providers may also want to know the A1C to determine the chronicity of the hyperglycemia. The criteria to diagnose diabetes are listed in Table 2.2 .

2.4 Screening for type 1 diabetes risk with a panel of islet autoantibodies is currently recommended in the setting of a research trial or can be offered as an option for first-degree family members of a proband with type 1 diabetes. B

2.5 Persistence of autoantibodies is a risk factor for clinical diabetes and may serve as an indication for intervention in the setting of a clinical trial. B

Immune-Mediated Diabetes

This form, previously called “insulin-dependent diabetes” or “juvenile-onset diabetes,” accounts for 5 – 10% of diabetes and is due to cellular-mediated autoimmune destruction of the pancreatic β-cells. Autoimmune markers include islet cell autoantibodies and autoantibodies to GAD (GAD65), insulin, the tyrosine phosphatases IA-2 and IA-2β, and zinc transporter 8 (ZnT8). Numerous clinical studies are being conducted to test various methods of preventing type 1 diabetes in those with evidence of islet autoimmunity ( www.clinicaltrials.gov and www.trialnet.org/our-research/prevention-studies ) ( 12 , 45 – 49 ). Stage 1 of type 1 diabetes is defined by the presence of two or more of these autoimmune markers. The disease has strong HLA associations, with linkage to the DQA and DQB genes. These HLA-DR/DQ alleles can be either predisposing or protective ( Table 2.1 ). There are important genetic considerations, as most of the mutations that cause diabetes are dominantly inherited. The importance of genetic testing is in the genetic counseling that follows. Some mutations are associated with other conditions, which then may prompt additional screenings.

The rate of β-cell destruction is quite variable, being rapid in some individuals (mainly infants and children) and slow in others (mainly adults) ( 50 ). Children and adolescents may present with DKA as the first manifestation of the disease. Others have modest fasting hyperglycemia that can rapidly change to severe hyperglycemia and/or DKA with infection or other stress. Adults may retain sufficient β-cell function to prevent DKA for many years; such individuals may have remission or decreased insulin needs for months or years and eventually become dependent on insulin for survival and are at risk for DKA ( 3 – 5 , 51 , 52 ). At this latter stage of the disease, there is little or no insulin secretion, as manifested by low or undetectable levels of plasma C-peptide. Immune-mediated diabetes is the most common form of diabetes in childhood and adolescence, but it can occur at any age, even in the 8th and 9th decades of life.

Autoimmune destruction of β-cells has multiple genetic predispositions and is also related to environmental factors that are still poorly defined. Although patients are not typically obese when they present with type 1 diabetes, obesity is increasingly common in the general population, and there is evidence that it may also be a risk factor for type 1 diabetes. As such, obesity should not preclude the diagnosis. People with type 1 diabetes are also prone to other autoimmune disorders such as Hashimoto thyroiditis, Graves disease, celiac disease, Addison disease, vitiligo, autoimmune hepatitis, myasthenia gravis, and pernicious anemia (see Section 4 “Comprehensive Medical Evaluation and Assessment of Comorbidities,” https://doi.org/10.2337/dc21-S004 ).

Idiopathic Type 1 Diabetes

Some forms of type 1 diabetes have no known etiologies. These patients have permanent insulinopenia and are prone to DKA but have no evidence of β-cell autoimmunity. However, only a minority of patients with type 1 diabetes fall into this category. Individuals with autoantibody-negative type 1 diabetes of African or Asian ancestry may suffer from episodic DKA and exhibit varying degrees of insulin deficiency between episodes (possibly ketosis-prone diabetes). This form of diabetes is strongly inherited and is not HLA associated. An absolute requirement for insulin replacement therapy in affected patients may be intermittent. Future research is needed to determine the cause of β-cell destruction in this rare clinical scenario.

Screening for Type 1 Diabetes Risk

The incidence and prevalence of type 1 diabetes is increasing ( 53 ). Patients with type 1 diabetes often present with acute symptoms of diabetes and markedly elevated blood glucose levels, and approximately one-third are diagnosed with life-threatening DKA ( 2 ). Multiple studies indicate that measuring islet autoantibodies in individuals genetically at risk for type 1 diabetes (e.g., relatives of those with type 1 diabetes or individuals from the general population with type 1 diabetes–associated genetic factors) identifies individuals who may develop type 1 diabetes ( 10 ). Such testing, coupled with education about diabetes symptoms and close follow-up, may enable earlier identification of type 1 diabetes onset. A study reported the risk of progression to type 1 diabetes from the time of seroconversion to autoantibody positivity in three pediatric cohorts from Finland, Germany, and the U.S. Of the 585 children who developed more than two autoantibodies, nearly 70% developed type 1 diabetes within 10 years and 84% within 15 years ( 45 ). These findings are highly significant because while the German group was recruited from offspring of parents with type 1 diabetes, the Finnish and American groups were recruited from the general population. Remarkably, the findings in all three groups were the same, suggesting that the same sequence of events led to clinical disease in both “sporadic” and familial cases of type 1 diabetes. Indeed, the risk of type 1 diabetes increases as the number of relevant autoantibodies detected increases ( 48 , 54 , 55 ). In The Environmental Determinants of Diabetes in the Young (TEDDY) study, type 1 diabetes developed in 21% of 363 subjects with at least one autoantibody at 3 years of age ( 56 ).

There is currently a lack of accepted and clinically validated screening programs outside of the research setting; thus, widespread clinical testing of asymptomatic low-risk individuals is not currently recommended due to lack of approved therapeutic interventions. However, one should consider referring relatives of those with type 1 diabetes for islet autoantibody testing for risk assessment in the setting of a clinical research study (see www.trialnet.org ). Individuals who test positive should be counseled about the risk of developing diabetes, diabetes symptoms, and DKA prevention. Numerous clinical studies are being conducted to test various methods of preventing and treating stage 2 type 1 diabetes in those with evidence of autoimmunity with promising results (see www.clinicaltrials.gov and www.trialnet.org ).

2.6 Screening for prediabetes and type 2 diabetes with an informal assessment of risk factors or validated tools should be considered in asymptomatic adults. B

2.7 Testing for prediabetes and/or type 2 diabetes in asymptomatic people should be considered in adults of any age with overweight or obesity (BMI ≥25 kg/m 2 or ≥23 kg/m 2 in Asian Americans) and who have one or more additional risk factors for diabetes ( Table 2.3 ). B

2.8 Testing for prediabetes and/or type 2 diabetes should be considered in women with overweight or obesity planning pregnancy and/or who have one or more additional risk factor for diabetes ( Table 2.3 ). C

2.9 For all people, testing should begin at age 45 years. B

2.10 If tests are normal, repeat testing carried out at a minimum of 3-year intervals is reasonable, sooner with symptoms. C

2.11 To test for prediabetes and type 2 diabetes, fasting plasma glucose, 2-h plasma glucose during 75-g oral glucose tolerance test, and A1C are equally appropriate ( Table 2.2 and Table 2.5 ). B

2.12 In patients with prediabetes and type 2 diabetes, identify and treat other cardiovascular disease risk factors. A

2.13 Risk-based screening for prediabetes and/or type 2 diabetes should be considered after the onset of puberty or after 10 years of age, whichever occurs earlier, in children and adolescents with overweight (BMI ≥85th percentile) or obesity (BMI ≥95th percentile) and who have one or more risk factor for diabetes. (See Table 2.4 for evidence grading of risk factors.) B

2.14 Patients with HIV should be screened for diabetes and prediabetes with a fasting glucose test before starting antiretroviral therapy, at the time of switching antiretroviral therapy, and 3−6 months after starting or switching antiretroviral therapy. If initial screening results are normal, fasting glucose should be checked annually. E

Criteria for testing for diabetes or prediabetes in asymptomatic adults

CVD, cardiovascular disease; GDM, gestational diabetes mellitus; IFG, impaired fasting glucose; IGT, impaired glucose tolerance.

Risk-based screening for type 2 diabetes or prediabetes in asymptomatic children and adolescents in a clinical setting ( 202 )

GDM, gestational diabetes mellitus.

After the onset of puberty or after 10 years of age, whichever occurs earlier. If tests are normal, repeat testing at a minimum of 3-year intervals (or more frequently if BMI is increasing or risk factor profile deteriorating) is recommended. Reports of type 2 diabetes before age 10 years exist, and this can be considered with numerous risk factors.

Prediabetes

“Prediabetes” is the term used for individuals whose glucose levels do not meet the criteria for diabetes but are too high to be considered normal ( 35 , 36 ). Patients with prediabetes are defined by the presence of IFG and/or IGT and/or A1C 5.7 – 6.4% (39 – 47 mmol/mol) ( Table 2.5 ). Prediabetes should not be viewed as a clinical entity in its own right but rather as an increased risk for diabetes and cardiovascular disease (CVD). Criteria for testing for diabetes or prediabetes in asymptomatic adults is outlined in Table 2.3 . Prediabetes is associated with obesity (especially abdominal or visceral obesity), dyslipidemia with high triglycerides and/or low HDL cholesterol, and hypertension.

Criteria defining prediabetes *

FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; OGTT, oral glucose tolerance test; 2-h PG, 2-h plasma glucose.

For all three tests, risk is continuous, extending below the lower limit of the range and becoming disproportionately greater at the higher end of the range.

IFG is defined as FPG levels from 100 to 125 mg/dL (from 5.6 to 6.9 mmol/L) ( 57 , 58 ) and IGT as 2-h PG during 75-g OGTT levels from 140 to 199 mg/dL (from 7.8 to 11.0 mmol/L) ( 59 ). It should be noted that the World Health Organization (WHO) and numerous other diabetes organizations define the IFG cutoff at 110 mg/dL (6.1 mmol/L).

As with the glucose measures, several prospective studies that used A1C to predict the progression to diabetes as defined by A1C criteria demonstrated a strong, continuous association between A1C and subsequent diabetes. In a systematic review of 44,203 individuals from 16 cohort studies with a follow-up interval averaging 5.6 years (range 2.8 – 12 years), those with A1C between 5.5% and 6.0% (between 37 and 42 mmol/mol) had a substantially increased risk of diabetes (5-year incidence from 9% to 25%). Those with an A1C range of 6.0–6.5% (42 – 48 mmol/mol) had a 5-year risk of developing diabetes between 25% and 50% and a relative risk 20 times higher compared with A1C of 5.0% (31 mmol/mol) ( 60 ). In a community-based study of African American and non-Hispanic White adults without diabetes, baseline A1C was a stronger predictor of subsequent diabetes and cardiovascular events than fasting glucose ( 61 ). Other analyses suggest that A1C of 5.7% (39 mmol/mol) or higher is associated with a diabetes risk similar to that of the high-risk participants in the Diabetes Prevention Program (DPP) ( 62 ), and A1C at baseline was a strong predictor of the development of glucose-defined diabetes during the DPP and its follow-up ( 63 ). Hence, it is reasonable to consider an A1C range of 5.7 – 6.4% (39 – 47 mmol/mol) as identifying individuals with prediabetes. Similar to those with IFG and/or IGT, individuals with A1C of 5.7 – 6.4% (39 – 47 mmol/mol) should be informed of their increased risk for diabetes and CVD and counseled about effective strategies to lower their risks (see Section 3 “Prevention or Delay of Type 2 Diabetes,” https://doi.org/10.2337/dc21-S003 ). Similar to glucose measurements, the continuum of risk is curvilinear, so as A1C rises, the diabetes risk rises disproportionately ( 60 ). Aggressive interventions and vigilant follow-up should be pursued for those considered at very high risk (e.g., those with A1C >6.0% [42 mmol/mol]).

Table 2.5 summarizes the categories of prediabetes and Table 2.3 the criteria for prediabetes testing. The ADA diabetes risk test is an additional option for assessment to determine the appropriateness of testing for diabetes or prediabetes in asymptomatic adults ( Fig. 2.1 ) ( diabetes.org/socrisktest ). For additional background regarding risk factors and screening for prediabetes, see screening   and   testing   for   prediabetes   and   type   2 diabetes   in   asymptomatic   adults and also screening   and   testing   for   prediabetes   and   type   2 diabetes   in   children   and   adolescents below.

ADA risk test ( diabetes.org/socrisktest ).

Type 2 Diabetes

Type 2 diabetes, previously referred to as “noninsulin-dependent diabetes” or “adult-onset diabetes,” accounts for 90 – 95% of all diabetes. This form encompasses individuals who have relative (rather than absolute) insulin deficiency and have peripheral insulin resistance. At least initially, and often throughout their lifetime, these individuals may not need insulin treatment to survive.

There are various causes of type 2 diabetes. Although the specific etiologies are not known, autoimmune destruction of β-cells does not occur, and patients do not have any of the other known causes of diabetes. Most, but not all, patients with type 2 diabetes have overweight or obesity. Excess weight itself causes some degree of insulin resistance. Patients who do not have obesity or overweight by traditional weight criteria may have an increased percentage of body fat distributed predominantly in the abdominal region.

DKA seldom occurs spontaneously in type 2 diabetes; when seen, it usually arises in association with the stress of another illness such as infection, myocardial infarction, or with the use of certain drugs (e.g., corticosteroids, atypical antipsychotics, and sodium–glucose cotransporter 2 inhibitors) ( 64 , 65 ). Type 2 diabetes frequently goes undiagnosed for many years because hyperglycemia develops gradually and, at earlier stages, is often not severe enough for the patient to notice the classic diabetes symptoms caused by hyperglycemia. Nevertheless, even undiagnosed patients are at increased risk of developing macrovascular and microvascular complications.

Patients with type 2 diabetes may have insulin levels that appear normal or elevated, yet the failure to normalize blood glucose reflects a relative defect in glucose-stimulated insulin secretion. Thus, insulin secretion is defective in these patients and insufficient to compensate for insulin resistance. Insulin resistance may improve with weight reduction, exercise, and/or pharmacologic treatment of hyperglycemia but is seldom restored to normal. Recent interventions with intensive diet and exercise or surgical weight loss have led to diabetes remission ( 66 – 72 ) (see Section 8 “Obesity Management for the Treatment of Type 2 Diabetes,” https://doi.org/10.2337/dc21-S008 ).

The risk of developing type 2 diabetes increases with age, obesity, and lack of physical activity. It occurs more frequently in women with prior gestational diabetes mellitus (GDM), with hypertension or dyslipidemia, with polycystic ovary syndrome, and in certain racial/ethnic subgroups (African American, American Indian, Hispanic/Latino, and Asian American). It is often associated with a strong genetic predisposition or family history in first-degree relatives (more so than type 1 diabetes). However, the genetics of type 2 diabetes is poorly understood and under intense investigation in this era of precision medicine ( 13 ). In adults without traditional risk factors for type 2 diabetes and/or younger age, consider islet autoantibody testing (e.g., GAD65 autoantibodies) to exclude the diagnosis of type 1 diabetes.

Screening and Testing for Prediabetes and Type 2 Diabetes in Asymptomatic Adults

Screening for prediabetes and type 2 diabetes risk through an informal assessment of risk factors ( Table 2.3 ) or with an assessment tool, such as the ADA risk test ( Fig. 2.1 ) (online at diabetes.org/socrisktest ), is recommended to guide providers on whether performing a diagnostic test ( Table 2.2 ) is appropriate. Prediabetes and type 2 diabetes meet criteria for conditions in which early detection via screening is appropriate. Both conditions are common and impose significant clinical and public health burdens. There is often a long presymptomatic phase before the diagnosis of type 2 diabetes. Simple tests to detect preclinical disease are readily available. The duration of glycemic burden is a strong predictor of adverse outcomes. There are effective interventions that prevent progression from prediabetes to diabetes (see Section 3 “Prevention or Delay of Type 2 Diabetes,” https://doi.org/10.2337/dc21-S003 ) and reduce the risk of diabetes complications ( 73 ) (see Section 10 “Cardiovascular Disease and Risk Management,” https://doi.org/10.2337/dc21-S010 , and Section 11 “Microvascular Complications and Foot Care,” https://doi.org/10.2337/dc21-S011 ). In the most recent National Institutes of Health (NIH) Diabetes Prevention Program Outcomes Study (DPPOS) report, prevention of progression from prediabetes to diabetes ( 74 ) resulted in lower rates of developing retinopathy and nephropathy ( 75 ). Similar impact on diabetes complications was reported with screening, diagnosis, and comprehensive risk factor management in the U.K. Clinical Practice Research Datalink database ( 73 ). In that report, progression from prediabetes to diabetes augmented risk of complications.

Approximately one-quarter of people with diabetes in the U.S. and nearly half of Asian and Hispanic Americans with diabetes are undiagnosed ( 57 , 58 ). Although screening of asymptomatic individuals to identify those with prediabetes or diabetes might seem reasonable, rigorous clinical trials to prove the effectiveness of such screening have not been conducted and are unlikely to occur. Based on a population estimate, diabetes in women of childbearing age is underdiagnosed ( 76 ). Employing a probabilistic model, Peterson et al. ( 77 ) demonstrated cost and health benefits of preconception screening.

A large European randomized controlled trial compared the impact of screening for diabetes and intensive multifactorial intervention with that of screening and routine care ( 78 ). General practice patients between the ages of 40 and 69 years were screened for diabetes and randomly assigned by practice to intensive treatment of multiple risk factors or routine diabetes care. After 5.3 years of follow-up, CVD risk factors were modestly but significantly improved with intensive treatment compared with routine care, but the incidence of first CVD events or mortality was not significantly different between the groups ( 59 ). The excellent care provided to patients in the routine care group and the lack of an unscreened control arm limited the authors' ability to determine whether screening and early treatment improved outcomes compared with no screening and later treatment after clinical diagnoses. Computer simulation modeling studies suggest that major benefits are likely to accrue from the early diagnosis and treatment of hyperglycemia and cardiovascular risk factors in type 2 diabetes ( 79 ); moreover, screening, beginning at age 30 or 45 years and independent of risk factors, may be cost-effective (<$11,000 per quality-adjusted life year gained—2010 modeling data) ( 80 ). Cost-effectiveness of screening has been reinforced in cohort studies ( 81 , 82 ).

Additional considerations regarding testing for type 2 diabetes and prediabetes in asymptomatic patients include the following.

Age is a major risk factor for diabetes. Testing should begin at no later than age 45 years for all patients. Screening should be considered in adults of any age with overweight or obesity and one or more risk factors for diabetes.

BMI and Ethnicity

In general, BMI ≥25 kg/m 2 is a risk factor for diabetes. However, data suggest that the BMI cut point should be lower for the Asian American population ( 83 , 84 ). The BMI cut points fall consistently between 23 and 24 kg/m 2 (sensitivity of 80%) for nearly all Asian American subgroups (with levels slightly lower for Japanese Americans). This makes a rounded cut point of 23 kg/m 2 practical. An argument can be made to push the BMI cut point to lower than 23 kg/m 2 in favor of increased sensitivity; however, this would lead to an unacceptably low specificity (13.1%). Data from WHO also suggests that a BMI of ≥23 kg/m 2 should be used to define increased risk in Asian Americans ( 85 ). The finding that one-third to one-half of diabetes in Asian Americans is undiagnosed suggests that testing is not occurring at lower BMI thresholds ( 86 , 87 ).

Evidence also suggests that other populations may benefit from lower BMI cut points. For example, in a large multiethnic cohort study, for an equivalent incidence rate of diabetes, a BMI of 30 kg/m 2 in non-Hispanic Whites was equivalent to a BMI of 26 kg/m 2 in African Americans ( 88 ).

Medications

Certain medications, such as glucocorticoids, thiazide diuretics, some HIV medications ( 23 ), and atypical antipsychotics ( 66 ), are known to increase the risk of diabetes and should be considered when deciding whether to screen.

Individuals with HIV are at higher risk for developing prediabetes and diabetes on antiretroviral (ARV) therapies, so a screening protocol is recommended ( 89 ). The A1C test may underestimate glycemia in people with HIV; it is not recommended for diagnosis and may present challenges for monitoring ( 24 ). In those with prediabetes, weight loss through healthy nutrition and physical activity may reduce the progression toward diabetes. Among patients with HIV and diabetes, preventive health care using an approach used in patients without HIV is critical to reduce the risks of microvascular and macrovascular complications. Diabetes risk is increased with certain PIs and NRTIs. New-onset diabetes is estimated to occur in more than 5% of patients infected with HIV on PIs, whereas more than 15% may have prediabetes ( 90 ). PIs are associated with insulin resistance and may also lead to apoptosis of pancreatic β-cells. NRTIs also affect fat distribution (both lipohypertrophy and lipoatrophy), which is associated with insulin resistance. For patients with HIV and ARV-associated hyperglycemia, it may be appropriate to consider discontinuing the problematic ARV agents if safe and effective alternatives are available ( 91 ). Before making ARV substitutions, carefully consider the possible effect on HIV virological control and the potential adverse effects of new ARV agents. In some cases, antihyperglycemic agents may still be necessary.

Testing Interval

The appropriate interval between screening tests is not known ( 92 ). The rationale for the 3-year interval is that with this interval, the number of false-positive tests that require confirmatory testing will be reduced and individuals with false-negative tests will be retested before substantial time elapses and complications develop ( 92 ). In especially high-risk individuals, particularly with weight gain, shorter intervals between screening may be useful.

Community Screening

Ideally, testing should be carried out within a health care setting because of the need for follow-up and treatment. Community screening outside a health care setting is generally not recommended because people with positive tests may not seek, or have access to, appropriate follow-up testing and care. However, in specific situations where an adequate referral system is established beforehand for positive tests, community screening may be considered. Community testing may also be poorly targeted; i.e., it may fail to reach the groups most at risk and inappropriately test those at very low risk or even those who have already been diagnosed ( 93 ).

Screening in Dental Practices

Because periodontal disease is associated with diabetes, the utility of screening in a dental setting and referral to primary care as a means to improve the diagnosis of prediabetes and diabetes has been explored ( 94 – 96 ), with one study estimating that 30% of patients ≥30 years of age seen in general dental practices had dysglycemia ( 96 , 97 ). A similar study in 1,150 dental patients >40 years old in India reported 20.69% and 14.60% meeting criteria for prediabetes and diabetes using random blood glucose. Further research is needed to demonstrate the feasibility, effectiveness, and cost-effectiveness of screening in this setting.

Screening and Testing for Prediabetes and Type 2 Diabetes in Children and Adolescents

In the last decade, the incidence and prevalence of type 2 diabetes in children and adolescents has increased dramatically, especially in racial and ethnic minority populations ( 53 ). See Table 2.4 for recommendations on risk-based screening for type 2 diabetes or prediabetes in asymptomatic children and adolescents in a clinical setting ( 25 ). See Table 2.2 and Table 2.5 for the criteria for the diagnosis of diabetes and prediabetes, respectively, which apply to children, adolescents, and adults. See Section 13 “Children and Adolescents” ( https://doi.org/10.2337/dc21-S013 ) for additional information on type 2 diabetes in children and adolescents.

Some studies question the validity of A1C in the pediatric population, especially among certain ethnicities, and suggest OGTT or FPG as more suitable diagnostic tests ( 98 ). However, many of these studies do not recognize that diabetes diagnostic criteria are based on long-term health outcomes, and validations are not currently available in the pediatric population ( 99 ). The ADA acknowledges the limited data supporting A1C for diagnosing type 2 diabetes in children and adolescents. Although A1C is not recommended for diagnosis of diabetes in children with cystic fibrosis or symptoms suggestive of acute onset of type 1 diabetes and only A1C assays without interference are appropriate for children with hemoglobinopathies, the ADA continues to recommend A1C for diagnosis of type 2 diabetes in this cohort to decrease barriers to screening ( 100 , 101 ).

2.15 Annual screening for cystic fibrosis–related diabetes (CFRD) with an oral glucose tolerance test should begin by age 10 years in all patients with cystic fibrosis not previously diagnosed with CFRD. B

2.16 A1C is not recommended as a screening test for cystic fibrosis–related diabetes. B

2.17 Patients with cystic fibrosis–related diabetes should be treated with insulin to attain individualized glycemic goals. A

2.18 Beginning 5 years after the diagnosis of cystic fibrosis–related diabetes, annual monitoring for complications of diabetes is recommended. E

Cystic fibrosis–related diabetes (CFRD) is the most common comorbidity in people with cystic fibrosis, occurring in about 20% of adolescents and 40 – 50% of adults ( 102 ). Diabetes in this population, compared with individuals with type 1 or type 2 diabetes, is associated with worse nutritional status, more severe inflammatory lung disease, and greater mortality. Insulin insufficiency is the primary defect in CFRD. Genetically determined β-cell function and insulin resistance associated with infection and inflammation may also contribute to the development of CFRD. Milder abnormalities of glucose tolerance are even more common and occur at earlier ages than CFRD. Whether individuals with IGT should be treated with insulin replacement has not currently been determined. Although screening for diabetes before the age of 10 years can identify risk for progression to CFRD in those with abnormal glucose tolerance, no benefit has been established with respect to weight, height, BMI, or lung function. OGTT is the recommended screening test; however, recent publications suggest that an A1C cut point threshold of 5.5% (5.8% in a second study) would detect more than 90% of cases and reduce patient screening burden ( 103 , 104 ). Ongoing studies are underway to validate this approach. Regardless of age, weight loss or failure of expected weight gain is a risk for CFRD and should prompt screening ( 103 , 104 ). The Cystic Fibrosis Foundation Patient Registry ( 105 ) evaluated 3,553 cystic fibrosis patients and diagnosed 445 (13%) with CFRD. Early diagnosis and treatment of CFRD was associated with preservation of lung function. The European Cystic Fibrosis Society Patient Registry reported an increase in CFRD with age (increased 10% per decade), genotype, decreased lung function, and female sex ( 106 , 107 ). Continuous glucose monitoring or HOMA of β-cell function ( 108 ) may be more sensitive than OGTT to detect risk for progression to CFRD; however, evidence linking these results to long-term outcomes is lacking, and these tests are not recommended for screening outside of the research setting ( 109 ).

CFRD mortality has significantly decreased over time, and the gap in mortality between cystic fibrosis patients with and without diabetes has considerably narrowed ( 110 ). There are limited clinical trial data on therapy for CFRD. The largest study compared three regimens: premeal insulin aspart, repaglinide, or oral placebo in cystic fibrosis patients with diabetes or abnormal glucose tolerance. Participants all had weight loss in the year preceding treatment; however, in the insulin-treated group, this pattern was reversed, and patients gained 0.39 (± 0.21) BMI units (P = 0.02). The repaglinide-treated group had initial weight gain, but this was not sustained by 6 months. The placebo group continued to lose weight ( 110 ). Insulin remains the most widely used therapy for CFRD ( 111 ). The primary rationale for the use of insulin in patients with CFRD is to induce an anabolic state while promoting macronutrient retention and weight gain.

Additional resources for the clinical management of CFRD can be found in the position statement “Clinical Care Guidelines for Cystic Fibrosis–Related Diabetes: A Position Statement of the American Diabetes Association and a Clinical Practice Guideline of the Cystic Fibrosis Foundation, Endorsed by the Pediatric Endocrine Society” ( 112 ) and in the International Society for Pediatric and Adolescent Diabetes's 2014 clinical practice consensus guidelines ( 102 ).

2.19 Patients should be screened after organ transplantation for hyperglycemia, with a formal diagnosis of posttransplantation diabetes mellitus being best made once a patient is stable on an immunosuppressive regimen and in the absence of an acute infection. B

2.20 The oral glucose tolerance test is the preferred test to make a diagnosis of posttransplantation diabetes mellitus. B

2.21 Immunosuppressive regimens shown to provide the best outcomes for patient and graft survival should be used, irrespective of posttransplantation diabetes mellitus risk. E

Several terms are used in the literature to describe the presence of diabetes following organ transplantation ( 113 ). “New-onset diabetes after transplantation” (NODAT) is one such designation that describes individuals who develop new-onset diabetes following transplant. NODAT excludes patients with pretransplant diabetes that was undiagnosed as well as posttransplant hyperglycemia that resolves by the time of discharge ( 114 ). Another term, “posttransplantation diabetes mellitus” (PTDM) ( 114 , 115 ), describes the presence of diabetes in the posttransplant setting irrespective of the timing of diabetes onset.

Hyperglycemia is very common during the early posttransplant period, with ∼90% of kidney allograft recipients exhibiting hyperglycemia in the first few weeks following transplant ( 114 – 117 ). In most cases, such stress- or steroid-induced hyperglycemia resolves by the time of discharge ( 117 , 118 ). Although the use of immunosuppressive therapies is a major contributor to the development of PTDM, the risks of transplant rejection outweigh the risks of PTDM and the role of the diabetes care provider is to treat hyperglycemia appropriately regardless of the type of immunosuppression ( 114 ). Risk factors for PTDM include both general diabetes risks (such as age, family history of diabetes, etc.) as well as transplant-specific factors, such as use of immunosuppressant agents ( 119 ). Whereas posttransplantation hyperglycemia is an important risk factor for subsequent PTDM, a formal diagnosis of PTDM is optimally made once the patient is stable on maintenance immunosuppression and in the absence of acute infection ( 117 – 120 ). In a recent study of 152 heart transplant recipients, 38% had PTDM at 1 year. Risk factors for PTDM included elevated BMI, discharge from the hospital on insulin, and glucose values in the 24 h prior to hospital discharge ( 121 ). In an Iranian cohort, 19% had PTDM after heart and lung transplant ( 122 ). The OGTT is considered the gold standard test for the diagnosis of PTDM (1 year posttransplant) ( 114 , 115 , 123 , 124 ). However, screening patients using fasting glucose and/or A1C can identify high-risk patients requiring further assessment and may reduce the number of overall OGTTs required.

Few randomized controlled studies have reported on the short- and long-term use of antihyperglycemic agents in the setting of PTDM ( 119 , 125 , 126 ). Most studies have reported that transplant patients with hyperglycemia and PTDM after transplantation have higher rates of rejection, infection, and rehospitalization ( 117 , 119 , 127 ). Insulin therapy is the agent of choice for the management of hyperglycemia, PTDM, and preexisting diabetes and diabetes in the hospital setting. After discharge, patients with preexisting diabetes could go back on their pretransplant regimen if they were in good control before transplantation. Those with previously poor control or with persistent hyperglycemia should continue insulin with frequent home self-monitoring of blood glucose to determine when insulin dose reductions may be needed and when it may be appropriate to switch to noninsulin agents.

No studies to date have established which noninsulin agents are safest or most efficacious in PTDM. The choice of agent is usually made based on the side effect profile of the medication and possible interactions with the patient's immunosuppression regimen ( 119 ). Drug dose adjustments may be required because of decreases in the glomerular filtration rate, a relatively common complication in transplant patients. A small short-term pilot study reported that metformin was safe to use in renal transplant recipients ( 128 ), but its safety has not been determined in other types of organ transplant. Thiazolidinediones have been used successfully in patients with liver and kidney transplants, but side effects include fluid retention, heart failure, and osteopenia ( 129 , 130 ). Dipeptidyl peptidase 4 inhibitors do not interact with immunosuppressant drugs and have demonstrated safety in small clinical trials ( 131 , 132 ). Well-designed intervention trials examining the efficacy and safety of these and other antihyperglycemic agents in patients with PTDM are needed.

2.22 All children diagnosed with diabetes in the first 6 months of life should have immediate genetic testing for neonatal diabetes. A

2.23 Children and those diagnosed in early adulthood who have diabetes not characteristic of type 1 or type 2 diabetes that occurs in successive generations (suggestive of an autosomal dominant pattern of inheritance) should have genetic testing for maturity-onset diabetes of the young. A

2.24 In both instances, consultation with a center specializing in diabetes genetics is recommended to understand the significance of these mutations and how best to approach further evaluation, treatment, and genetic counseling. E

Monogenic defects that cause β-cell dysfunction, such as neonatal diabetes and MODY, represent a small fraction of patients with diabetes (<5%). Table 2.6 describes the most common causes of monogenic diabetes. For a comprehensive list of causes, see Genetic Diagnosis of Endocrine Disorders ( 133 ).

Most common causes of monogenic diabetes ( 133 )

AD, autosomal dominant; AR, autosomal recessive; IUGR, intrauterine growth restriction; OGTT, oral glucose tolerance test; UPD6, uniparental disomy of chromosome 6; 2-h PG, 2-h plasma glucose.

Neonatal Diabetes

Diabetes occurring under 6 months of age is termed “neonatal” or “congenital” diabetes, and about 80 – 85% of cases can be found to have an underlying monogenic cause ( 134 – 137 ). Neonatal diabetes occurs much less often after 6 months of age, whereas autoimmune type 1 diabetes rarely occurs before 6 months of age. Neonatal diabetes can either be transient or permanent. Transient diabetes is most often due to overexpression of genes on chromosome 6q24, is recurrent in about half of cases, and may be treatable with medications other than insulin. Permanent neonatal diabetes is most commonly due to autosomal dominant mutations in the genes encoding the Kir6.2 subunit ( KCNJ11 ) and SUR1 subunit ( ABCC8 ) of the β-cell K ATP channel. A recent report details a de novo mutation in EIF2B1 affecting eIF2 signaling associated with permanent neonatal diabetes and hepatic dysfunction, similar to Wolcott-Rallison syndrome but with few severe comorbidities ( 138 ). Correct diagnosis has critical implications because most patients with K ATP -related neonatal diabetes will exhibit improved glycemic control when treated with high-dose oral sulfonylureas instead of insulin. Insulin gene ( INS ) mutations are the second most common cause of permanent neonatal diabetes, and, while intensive insulin management is currently the preferred treatment strategy, there are important genetic counseling considerations, as most of the mutations that cause diabetes are dominantly inherited.

Maturity-Onset Diabetes of the Young

MODY is frequently characterized by onset of hyperglycemia at an early age (classically before age 25 years, although diagnosis may occur at older ages). MODY is characterized by impaired insulin secretion with minimal or no defects in insulin action (in the absence of coexistent obesity). It is inherited in an autosomal dominant pattern with abnormalities in at least 13 genes on different chromosomes identified to date. The most commonly reported forms are GCK-MODY (MODY2), HNF1A-MODY (MODY3), and HNF4A-MODY (MODY1).

For individuals with MODY, the treatment implications are considerable and warrant genetic testing ( 139 , 140 ). Clinically, patients with GCK-MODY exhibit mild, stable fasting hyperglycemia and do not require antihyperglycemic therapy except sometimes during pregnancy. Patients with HNF1A- or HNF4A-MODY usually respond well to low doses of sulfonylureas, which are considered first-line therapy. Mutations or deletions in HNF1B are associated with renal cysts and uterine malformations (renal cysts and diabetes [RCAD] syndrome). Other extremely rare forms of MODY have been reported to involve other transcription factor genes including PDX1 ( IPF1 ) and NEUROD1 .

Diagnosis of Monogenic Diabetes

A diagnosis of one of the three most common forms of MODY, including GCK-MODY, HNF1A-MODY, and HNF4A-MODY, allows for more cost-effective therapy (no therapy for GCK-MODY; sulfonylureas as first-line therapy for HNF1A-MODY and HNF4A-MODY). Additionally, diagnosis can lead to identification of other affected family members. Genetic screening is increasingly available and cost-effective ( 138 , 140 ).

A diagnosis of MODY should be considered in individuals who have atypical diabetes and multiple family members with diabetes not characteristic of type 1 or type 2 diabetes, although admittedly “atypical diabetes” is becoming increasingly difficult to precisely define in the absence of a definitive set of tests for either type of diabetes ( 135 – 137 , 139 – 145 ). In most cases, the presence of autoantibodies for type 1 diabetes precludes further testing for monogenic diabetes, but the presence of autoantibodies in patients with monogenic diabetes has been reported ( 146 ). Individuals in whom monogenic diabetes is suspected should be referred to a specialist for further evaluation if available, and consultation is available from several centers. Readily available commercial genetic testing following the criteria listed below now enables a cost-effective ( 147 ), often cost-saving, genetic diagnosis that is increasingly supported by health insurance. A biomarker screening pathway such as the combination of urinary C-peptide/creatinine ratio and antibody screening may aid in determining who should get genetic testing for MODY ( 148 ). It is critical to correctly diagnose one of the monogenic forms of diabetes because these patients may be incorrectly diagnosed with type 1 or type 2 diabetes, leading to suboptimal, even potentially harmful, treatment regimens and delays in diagnosing other family members ( 149 ). The correct diagnosis is especially critical for those with GCK-MODY mutations where multiple studies have shown that no complications ensue in the absence of glucose-lowering therapy ( 150 ). Genetic counseling is recommended to ensure that affected individuals understand the patterns of inheritance and the importance of a correct diagnosis.

The diagnosis of monogenic diabetes should be considered in children and adults diagnosed with diabetes in early adulthood with the following findings:

Diabetes diagnosed within the first 6 months of life (with occasional cases presenting later, mostly INS and ABCC8 mutations) ( 134 , 151 )

Diabetes without typical features of type 1 or type 2 diabetes (negative diabetes-associated autoantibodies, nonobese, lacking other metabolic features, especially with strong family history of diabetes)

Stable, mild fasting hyperglycemia (100 – 150 mg/dL [5.5 – 8.5 mmol/L]), stable A1C between 5.6% and 7.6% (between 38 and 60 mmol/mol), especially if nonobese

Pancreatic diabetes includes both structural and functional loss of glucose-normalizing insulin secretion in the context of exocrine pancreatic dysfunction and is commonly misdiagnosed as type 2 diabetes. Hyperglycemia due to general pancreatic dysfunction has been called “type 3c diabetes” and, more recently, diabetes in the context of disease of the exocrine pancreas has been termed pancreoprivic diabetes ( 1 ). The diverse set of etiologies includes pancreatitis (acute and chronic), trauma or pancreatectomy, neoplasia, cystic fibrosis (addressed elsewhere in this chapter), hemochromatosis, fibrocalculous pancreatopathy, rare genetic disorders ( 152 ), and idiopathic forms ( 1 ), which is the preferred terminology. A distinguishing feature is concurrent pancreatic exocrine insufficiency (according to the monoclonal fecal elastase 1 test or direct function tests), pathological pancreatic imaging (endoscopic ultrasound, MRI, computed tomography), and absence of type 1 diabetes–associated autoimmunity ( 153 – 157 ). There is loss of both insulin and glucagon secretion and often higher-than-expected insulin requirements. Risk for microvascular complications is similar to other forms of diabetes. In the context of pancreatectomy, islet autotransplantation can be done to retain insulin secretion ( 158 , 159 ). In some cases, autotransplant can lead to insulin independence. In others, it may decrease insulin requirements ( 160 ).

2.25 Test for undiagnosed prediabetes and diabetes at the first prenatal visit in those with risk factors using standard diagnostic criteria. B

2.26 Test for gestational diabetes mellitus at 24 – 28 weeks of gestation in pregnant women not previously found to have diabetes. A

2.27 Test women with gestational diabetes mellitus for prediabetes or diabetes at 4 – 12 weeks postpartum, using the 75-g oral glucose tolerance test and clinically appropriate nonpregnancy diagnostic criteria. B

2.28 Women with a history of gestational diabetes mellitus should have lifelong screening for the development of diabetes or prediabetes at least every 3 years. B

2.29 Women with a history of gestational diabetes mellitus found to have prediabetes should receive intensive lifestyle interventions and/or metformin to prevent diabetes. A

For many years, GDM was defined as any degree of glucose intolerance that was first recognized during pregnancy ( 60 ), regardless of the degree of hyperglycemia. This definition facilitated a uniform strategy for detection and classification of GDM, but this definition has serious limitations ( 161 ). First, the best available evidence reveals that many, perhaps most, cases of GDM represent preexisting hyperglycemia that is detected by routine screening in pregnancy, as routine screening is not widely performed in nonpregnant women of reproductive age. It is the severity of hyperglycemia that is clinically important with regard to both short- and long-term maternal and fetal risks. Universal preconception and/or first trimester screening is hampered by lack of data and consensus regarding appropriate diagnostic thresholds and outcomes and cost-effectiveness ( 162 , 163 ). A compelling argument for further work in this area is the fact that hyperglycemia that would be diagnostic of diabetes outside of pregnancy and is present at the time of conception is associated with an increased risk of congenital malformations that is not seen with lower glucose levels ( 164 , 165 ).

The ongoing epidemic of obesity and diabetes has led to more type 2 diabetes in women of reproductive age, with an increase in the number of pregnant women with undiagnosed type 2 diabetes in early pregnancy ( 166 – 169 ). Because of the number of pregnant women with undiagnosed type 2 diabetes, it is reasonable to test women with risk factors for type 2 diabetes ( 170 ) ( Table 2.3 ) at their initial prenatal visit, using standard diagnostic criteria ( Table 2.2 ). Women found to have diabetes by the standard diagnostic criteria used outside of pregnancy should be classified as having diabetes complicating pregnancy (most often type 2 diabetes, rarely type 1 diabetes or monogenic diabetes) and managed accordingly. Women who meet the lower glycemic criteria for GDM should be diagnosed with that condition and managed accordingly. Other women should be rescreened for GDM between 24 and 28 weeks of gestation (see Section 14 “Management of Diabetes in Pregnancy,” https://doi.org/10.2337/dc21-S014 ). The International Association of the Diabetes and Pregnancy Study Groups (IADPSG) GDM diagnostic criteria for the 75-g OGTT as well as the GDM screening and diagnostic criteria used in the two-step approach were not derived from data in the first half of pregnancy, so the diagnosis of GDM in early pregnancy by either FPG or OGTT values is not evidence based ( 171 ) and further work is needed.

GDM is often indicative of underlying β-cell dysfunction ( 172 ), which confers marked increased risk for later development of diabetes, generally but not always type 2 diabetes, in the mother after delivery ( 173 , 174 ). As effective prevention interventions are available ( 175 , 176 ), women diagnosed with GDM should receive lifelong screening for prediabetes to allow interventions to reduce diabetes risk and for type 2 diabetes to allow treatment at the earliest possible time ( 177 ).

GDM carries risks for the mother, fetus, and neonate. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study ( 178 ), a large-scale multinational cohort study completed by more than 23,000 pregnant women, demonstrated that risk of adverse maternal, fetal, and neonatal outcomes continuously increased as a function of maternal glycemia at 24 – 28 weeks of gestation, even within ranges previously considered normal for pregnancy. For most complications, there was no threshold for risk. These results have led to careful reconsideration of the diagnostic criteria for GDM.

GDM diagnosis ( Table 2.7 ) can be accomplished with either of two strategies:

The “one-step” 75-g OGTT derived from the IADPSG criteria, or

The older “two-step” approach with a 50-g (nonfasting) screen followed by a 100-g OGTT for those who screen positive, based on the work of Carpenter and Coustan's interpretation of the older OʼSullivan ( 179 ) criteria.

Screening for and diagnosis of GDM

GDM, gestational diabetes mellitus; GLT, glucose load test; OGTT, oral glucose tolerance test.

American College of Obstetricians and Gynecologists notes that one elevated value can be used for diagnosis ( 189 ).

Different diagnostic criteria will identify different degrees of maternal hyperglycemia and maternal/fetal risk, leading some experts to debate, and disagree on, optimal strategies for the diagnosis of GDM.

One-Step Strategy

The IADPSG defined diagnostic cut points for GDM as the average fasting, 1-h, and 2-h PG values during a 75-g OGTT in women at 24 – 28 weeks of gestation who participated in the HAPO study at which odds for adverse outcomes reached 1.75 times the estimated odds of these outcomes at the mean fasting, 1-h, and 2-h PG levels of the study population. This one-step strategy was anticipated to significantly increase the incidence of GDM (from 5 – 6% to 15–20%), primarily because only one abnormal value, not two, became sufficient to make the diagnosis ( 180 ). Many regional studies have investigated the impact of adopting the IADPSG criteria on prevalence and have seen a roughly one- to threefold increase ( 181 ). The anticipated increase in the incidence of GDM could have a substantial impact on costs and medical infrastructure needs and has the potential to “medicalize” pregnancies previously categorized as normal. A recent follow-up study of women participating in a blinded study of pregnancy OGTTs found that 11 years after their pregnancies, women who would have been diagnosed with GDM by the one-step approach, as compared with those without, were at 3.4-fold higher risk of developing prediabetes and type 2 diabetes and had children with a higher risk of obesity and increased body fat, suggesting that the larger group of women identified by the one-step approach would benefit from increased screening for diabetes and prediabetes that would accompany a history of GDM ( 182 , 183 ). The ADA recommends the IADPSG diagnostic criteria with the intent of optimizing gestational outcomes because these criteria are the only ones based on pregnancy outcomes rather than end points such as prediction of subsequent maternal diabetes.

The expected benefits of using IADPSG to the offspring are inferred from intervention trials that focused on women with lower levels of hyperglycemia than identified using older GDM diagnostic criteria. Those trials found modest benefits including reduced rates of large-for-gestational-age births and preeclampsia ( 184 , 185 ). It is important to note that 80 – 90% of women being treated for mild GDM in these two randomized controlled trials could be managed with lifestyle therapy alone. The OGTT glucose cutoffs in these two trials overlapped with the thresholds recommended by the IADPSG, and in one trial ( 185 ), the 2-h PG threshold (140 mg/dL [7.8 mmol/L]) was lower than the cutoff recommended by the IADPSG (153 mg/dL [8.5 mmol/L]). No randomized controlled trials of treating versus not treating GDM diagnosed by the IADPSG criteria but not the Carpenter-Coustan criteria have been published to date. Data are also lacking on how the treatment of lower levels of hyperglycemia affects a mother's future risk for the development of type 2 diabetes and her offspring's risk for obesity, diabetes, and other metabolic disorders. Additional well-designed clinical studies are needed to determine the optimal intensity of monitoring and treatment of women with GDM diagnosed by the one-step strategy ( 186 , 187 ).

Two-Step Strategy

In 2013, the NIH convened a consensus development conference to consider diagnostic criteria for diagnosing GDM ( 188 ). The 15-member panel had representatives from obstetrics and gynecology, maternal-fetal medicine, pediatrics, diabetes research, biostatistics, and other related fields. The panel recommended a two-step approach to screening that used a 1-h 50-g glucose load test (GLT) followed by a 3-h 100-g OGTT for those who screened positive. The American College of Obstetricians and Gynecologists (ACOG) recommends any of the commonly used thresholds of 130, 135, or 140 mg/dL for the 1-h 50-g GLT ( 189 ). A systematic review for the U.S. Preventive Services Task Force compared GLT cutoffs of 130 mg/dL (7.2 mmol/L) and 140 mg/dL (7.8 mmol/L) ( 190 ). The higher cutoff yielded sensitivity of 70–88% and specificity of 69 – 89%, while the lower cutoff was 88 – 99% sensitive and 66 – 77% specific. Data regarding a cutoff of 135 mg/dL are limited. As for other screening tests, choice of a cutoff is based upon the trade-off between sensitivity and specificity. The use of A1C at 24–28 weeks of gestation as a screening test for GDM does not function as well as the GLT ( 191 ).

Key factors cited by the NIH panel in their decision-making process were the lack of clinical trial data demonstrating the benefits of the one-step strategy and the potential negative consequences of identifying a large group of women with GDM, including medicalization of pregnancy with increased health care utilization and costs. Moreover, screening with a 50-g GLT does not require fasting and is therefore easier to accomplish for many women. Treatment of higher-threshold maternal hyperglycemia, as identified by the two-step approach, reduces rates of neonatal macrosomia, large-for-gestational-age births ( 192 ), and shoulder dystocia, without increasing small-for-gestational-age births. ACOG currently supports the two-step approach but notes that one elevated value, as opposed to two, may be used for the diagnosis of GDM ( 189 ). If this approach is implemented, the incidence of GDM by the two-step strategy will likely increase markedly. ACOG recommends either of two sets of diagnostic thresholds for the 3-h 100-g OGTT—Carpenter-Coustan or National Diabetes Data Group ( 193 , 194 ). Each is based on different mathematical conversions of the original recommended thresholds by O'Sullivan ( 179 ), which used whole blood and nonenzymatic methods for glucose determination. A secondary analysis of data from a randomized clinical trial of identification and treatment of mild GDM ( 195 ) demonstrated that treatment was similarly beneficial in patients meeting only the lower thresholds per Carpenter-Coustan ( 193 ) and in those meeting only the higher thresholds per National Diabetes Data Group ( 194 ). If the two-step approach is used, it would appear advantageous to use the Carpenter-Coustan lower diagnostic thresholds as shown in step 2 in Table 2.7 .

Future Considerations

The conflicting recommendations from expert groups underscore the fact that there are data to support each strategy. A cost-benefit estimation comparing the two strategies concluded that the one-step approach is cost-effective only if patients with GDM receive postdelivery counseling and care to prevent type 2 diabetes ( 196 ). The decision of which strategy to implement must therefore be made based on the relative values placed on factors that have yet to be measured (e.g., willingness to change practice based on correlation studies rather than intervention trial results, available infrastructure, and importance of cost considerations).

As the IADPSG criteria (“one-step strategy”) have been adopted internationally, further evidence has emerged to support improved pregnancy outcomes with cost savings ( 197 ), and IADPSG may be the preferred approach. Data comparing population-wide outcomes with one-step versus two-step approaches have been inconsistent to date ( 198 , 199 ). In addition, pregnancies complicated by GDM per the IADPSG criteria, but not recognized as such, have outcomes comparable to pregnancies with diagnosed GDM by the more stringent two-step criteria ( 200 , 201 ). There remains strong consensus that establishing a uniform approach to diagnosing GDM will benefit patients, caregivers, and policy makers. Longer-term outcome studies are currently underway.

Suggested citation: American Diabetes Association. 2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2021 . Diabetes Care 2021;44(Suppl. 1):S15−S33

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• Diabetes & Primary Care
• Vol:11 | No:06

Clinical presentations, diagnosis and prevention of diabetes

• 14 Dec 2009

Researchers, public health physicians and frontline clinicians, including GPs, are increasingly convinced that we are entering an epidemic (if not a pandemic) of diabetes mellitus. Rates of diabetes prevalence are increasing across the world, particularly in developing countries, and an increasing number of people are being diagnosed in primary care. This article explores the classification and diagnosis of diabetes, focusing on risk factors, pre-diabetes, and management and prevention strategies for type 2 diabetes in primary care.

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In 2007 it was estimated that 4.82% of the UK population have diabetes (2.45 million people) (Yorkshire and Humber Public Health Observatory, 2007). Data from the author’s own practice alone show a trebling in the prevalence of type 2 diabetes in the past 20 years, with a relentless year on year increase (Evans et al, 2008). With the diagnosis of diabetes comes an increased risk of cardiovascular disease (CVD) and three-quarters of people with diabetes will die from cardiovascular causes (Garber, 2003). 

Along with this rise in the prevalence of diabetes there is also a growing number of people in the UK with intermediate or borderline hyperglycaemia (often known as “pre-diabetes”). The challenge to primary care is therefore to encourage early diagnosis, intervention and, if possible, prevention of both of these disorders.

The questions, therefore, are: how do we define diabetes and pre-diabetes, and how can we prevent people developing these potentially life-threatening conditions?

Type 1 and type 2 diabetes Raised blood glucose (hyperglycaemia) has numerous health implications. Diabetes mellitus is “a group of metabolic diseases characterised by hyperglycaemia resulting from defects in insulin secretion, action or both”. This definition by the American Diabetes Association (ADA, 2009) illustrates the fact that diabetes is a syndrome with multiple causes. 

The vast majority of people with diabetes fall into two main groups: type 1 and type 2 (ADA, 2009). As described in an earlier module in the series, type 1 diabetes is caused by an absolute deficiency of insulin thought to be due to autoimmune destruction of pancreatic islet cells. Type 1 accounts for between 5% and 10% of all cases and is often seen in younger people, usually before the age of 40 (Diabetes UK, 2009). Type 2 diabetes, however, is far more common (90% of all cases) and is usually diagnosed in people over 45 years of age who are often obese or physically inactive (Diabetes UK, 2009). It is rapidly increasing in prevalence and is the driver for the current diabetes epidemic.

Type 2 diabetes is strongly dependent on ethnicity and is more common in south Asian or Afro-Caribbean populations. In these populations in the UK, people may develop type 2 diabetes at a younger age and at lower BMI levels than their Caucasian counterparts. Unlike type 1 diabetes, type 2 is characterised by a relative insulin deficiency and is often associated with insulin resistance and features of the so-called metabolic syndrome – an increase in waist circumference and raised blood pressure, low HDL-cholesterol, raised plasma triglycerides or a raised blood glucose (Alberti et al, 2005).

Type 2 diabetes usually develops after a long prodromal period of several years of gradually increasing blood glucose levels (Harris et al, 1992), and most people pass through a period of pre-diabetes before their hyperglycaemia reaches the diabetes threshold. Recent data from the Whitehall II study (Tabák et al, 2009) showed that before diagnosis with type 2 diabetes, study participants had a slow increase in their blood glucose levels over the 13 years of the study, but that blood glucose levels then rose rapidly in the 2–3 years preceding diagnosis. 

People with type 2 diabetes often do not need insulin for a period of time after diagnosis (hence the previous term “non-insulin dependent”). In addition, type 2 diabetes is often asymptomatic until blood glucose levels rise (Evans et al, 2003). 

Whatever the cause of the hyperglycaemia, however, be it type 1 or 2 diabetes, the symptoms include polyuria, polydipsia, weight loss, tiredness, blurred vision and susceptibility to infections. Long-term complications can be disabling, even fatal, and include neuropathy, retinopathy, CVD, sexual dysfunction and a significant impact on the individual’s quality of life and social functioning. However, even at diagnosis of type 2 diabetes, around 25% of people may already have complications (UK Prospective Diabetes Study Group, 1998). 

Rarer causes of diabetes Type 2 diabetes is generally considered to be a polygenic disorder. Monogenic causes of diabetes are seen less frequently (1–2% of all cases) (Murphy et al, 2008), but nevertheless can present to GPs. For example, it is thought that each GP practice has at least one person whose diabetes is due to maturity-onset diabetes of the young (MODY), although this is unlikely to have been recognised as such. 

MODY is a monogenic autosomal dominant condition often causing hyperglycaemia in people under the age of 20, and hence is likely to be diagnosed as either type 1 or early type 2 diabetes. The chromosomal defects and functional deficiencies have now been determined, and the most common form involves a mutation in one of the liver transcription factors known as hepatocyte nuclear factor (HNF-1 α ). People with MODY usually present with early-onset diabetes aged 15–30 years, are not insulin-dependent and usually not obese. There is usually a strong family history of diabetes, often with family members developing the condition before the age of 25.

MODY is important to the primary care team for several reasons, including the need to screen other family members and offer genetic counselling, the need to define the precise sub-type of MODY by genetic testing, and the need for specialist referral to ensure the right diagnosis is made. Treatment options are often dependent on the individual’s genetic sub-type (e.g. the use of low-dose sulphonylureas in people with the HNF-1 α subtype) (Murphy et al, 2008).

Another monogenic cause of diabetes in middle-aged adults is maternally inherited diabetes and deafness (MIDD). People with the condition have hyperglycaemia and a maternal history of diabetes as well as young-onset bilateral sensori-neural hearing loss. A mitochondrial mutation has been identified (m.3243A>G) (Fischel-Ghodsian, 2001).

When a more unusual form of diabetes is suspected, e.g. younger onset, a strong family history or a lack of the usual insulin resistance features, then discussion with your local specialist about the possibility of monogenic diabetes, the need for genetic testing and possible referral may be helpful. A very practical and educational website is www.diabetesgenes.org.

Diagnosing diabetes Diabetes can and should be diagnosed in primary care without specialist referral unless the individual’s condition is potentially life-threatening, such as diabetic ketoacidosis, or hyperglycaemia is severe and requiring immediate insulin treatment.

Currently, both the World Health Organization and International Diabetes Federation (WHO and IDF, 2006) and the ADA (2009) recommend that the diagnosis of diabetes (and pre-diabetes states) is based on a blood glucose measurement ( Table 1 ). Unless people have hyperglycaemic symptoms then this blood glucose estimation should be repeated; either repeated fasting plasma measures (after at least an 8-hour fast) or an oral glucose tolerance test (OGTT) (75 g of anhydrous glucose which equates to 410 ml of Lucozade Energy Original) are commonly used in primary care.

Traditionally, the OGTT has been promoted as the gold standard for the diagnosis of diabetes and has been used extensively in epidemiological studies. However, the recommended use of repeated fasting plasma glucose (FPG) estimations, which are cheap and more convenient for both doctor and patient, may well have moved UK primary care teams away from the OGTT. The use of OGTT is therefore debatable as it is intensive in terms of patient time, nurse time, and has surprisingly poor repeatability. A proportion of general practices do not therefore use it as a diagnostic tool. However, OGTT should be considered in people with impaired fasting glucose (IFG), 30% of whom will have diabetes if challenged with a glucose load (WHO and IDF, 2006).

Currently, there is also debate regarding the introduction of HbA 1c as the diagnostic test for diabetes. HbA 1c is the predominant form of glycated haemoglobin, present in red blood cells, which reflects the average plasma glucose concentration over the preceding 2–3 months, and is expressed as a percentage of HbA (International Expert Committee [IEC], 2009), and hence would give a better overall glycaemic picture. The new NHS Health Check Programme (2009) advocates the use of HbA 1c with a cut-off of >6.5% (>48 mmol/mol) as diagnostic of diabetes. The use of HbA 1c may therefore rapidly gain in popularity. It is more convenient (as it does not require a fasting specimen), is reliable and correlates well with long-term complications, hence its use in people once they are diagnosed with diabetes. International recommendations promoting the use of HbA 1c in diagnosis were recently published (IEC, 2009), and national bodies across the world are currently considering whether to implement HbA 1c as the diagnostic test for diabetes. 

It should be noted that the diagnostic cut-offs for the development of diabetes specified in Table 1 are derived from plasma glucose levels associated with increased risk of retinopathy, as well as the population distribution of plasma glucose (WHO and IDF, 2006).

Risk factors for diabetes  The most important risk factor for type 2 diabetes is obesity. There are, however, other modifiable and non-modifiable risk factors ( Table 2 ). These are used as risk indicators to identify those at higher risk of type 2 diabetes in several clinical settings, for example in risk-screening questionnaires such as FINDRISC (Finnish Type 2 Diabetes Risk Score; Lindström and Tuomilehto, 2003); in opportunistic screening in GP surgeries (Evans et al, 2008); in risk calculations using routinely collected data held in GP databases such as the QDScore (Hippisley-Cox et al, 2009); and in the new NHS Health Check Programme (2009) to identify those who should have a glucose test.

Pre-diabetes Another area of debate is the diagnosis of the intermediate hyperglycaemic states collectively known as pre-diabetes. All these conditions have in common the fact that blood glucose levels are raised yet are not above the threshold that is diagnostic of type 2 diabetes. The two most important features of pre-diabetes in primary care are the increased risk of CVD, which is two to three times that of normoglycaemic individuals (Coutinho et al, 1999), and the increased risk of progression to type 2 diabetes. Hence the potential for prevention of both diabetes and CVD in this high-risk group.

The term “pre-diabetes” has been considered by some as being potentially misleading, as a large proportion of people with pre-diabetes do not progress to diabetes. Other terms such as non-diabetic hyperglycaemia, intermediate hyperglycaemia and impaired glucose regulation are therefore gaining popularity. Risk factors for pre-diabetes are generally considered to be the same as those for type 2 diabetes as both conditions share the common pathology of insulin resistance. 

The terminology is complicated, but currently two states are recognised: IFG diagnosed on repeated fasting blood glucose (FBG) measurements and impaired glucose tolerance (IGT) diagnosed on an OGTT ( Table 1 ). There is some debate, however, about the level of FPG in IFG. The ADA (2009) recommend that IFG includes an FPG of 5.6–6.9 mmol/L rather than the stricter criterion of 6.1–6.9 mmol/L in the WHO and IDF (2006) recommendations. A person may have either IFG or IGT (in isolation) or both (i.e. an FPG of 6.1–6.9 mmol/L and a 2-hour glucose ≥7.8 mmol/L and

People with pre-diabetes are asymptomatic. Nevertheless, some features of the metabolic syndrome may often be present. Also, a number of associated conditions, such as peripheral neuropathy (Singleton et al, 2005) and carpal tunnel syndrome (Gulliford et al, 2006), are increasingly being recognised. Despite these associations, people with pre-diabetes are usually diagnosed by screening.

Both IFG and IGT are increasingly prevalent. For example, it is estimated that 5.1% of the UK population aged 20–79 may have IGT (IDF, 2003). Pre-diabetes carries an increased risk of progression to type 2 diabetes, although this can vary dependent on ethnicity and other factors such as initial level of glycaemia (Unwin et al, 2002). On average, around 5% of people with IGT progress to type 2 diabetes annually (Santaguida et al, 2005). It is widely accepted that people with these conditions are at greater risk of both type 2 diabetes and CVD (Coutinho et al, 1999), and interventions designed to prevent diabetes have, in the main, been targeted at this population.  

Education of people with pre-diabetes  Previous work in developing a pragmatic screening programme using the GP database identified a large proportion of people with pre-diabetes (Greaves et al, 2004). 

Studies had previously shown that individuals and healthcare professionals alike were confused about the implications of the diagnosis of pre-diabetes (Wylie et al, 2002; Whitford et al, 2003; Williams et al, 2004). The author and colleagues therefore developed an educational package for people with pre-diabetes and their healthcare professionals. This package, known as WAKEUP (Ways of Addressing Knowledge Education and Understanding in Prediabetes), was found to be acceptable both to people with pre-diabetes and healthcare professionals (Evans et al, 2006). 

Managing pre-diabetes Although generic guidance was given to GPs and practice nurses, the qualitative data from healthcare professionals in the WAKEUP study revealed a need for robust practice systems to facilitate effective management and follow-up of individuals with pre-diabetes (Evans et al, 2006). Key messages in the WAKEUP study that should be conveyed to people with pre-diabetes were identified ( Table 3 ). Similar qualitative work undertaken by Troughton et al (2008) has also shown that this population expected structured follow-up after their diagnosis.

It should not be forgotten that people with pre-diabetes need appropriate lifestyle advice regarding smoking, alcohol, and possible prescription of lipid-lowering drugs, such as statins, and also blood pressure medication if appropriate. For these reasons an annual review in primary care would seem reasonable with these cardiovascular risk factors being addressed, and also an FBG test (or even OGTT) undertaken to assess any progression towards diabetes.

Primary prevention of type 2 diabetes  As the transition from normoglycaemia through impaired glucose regulation to type 2 diabetes takes several years, it is logical to intervene and aim to prevent or delay the onset of diabetes. This can be at individual or population level. The best evidence regarding prevention exists in high-risk individuals, although several countries such as Finland have a national population programme to prevent diabetes that involves all stakeholders.

There is now substantial evidence from large-scale randomised trials in various populations across the world that progression to diabetes can be prevented or delayed in high-risk groups both by behavioural (Tuomilehto et al, 2001; Knowler et al, 2002; Ramachandran et al, 2006) and pharmacological interventions (Chiasson et al, 2002; Knowler et al, 2002; Lindström and Tuomilehto, 2003; Torgerson et al, 2004; Gerstein et al, 2006).

Lifestyle A meta-analysis has shown that lifestyle interventions can produce a 50% relative risk reduction in the incidence of type 2 diabetes at 1 year (Yamaoka and Tango, 2005). Typically these interventions are in high-risk individuals, such as those with pre-diabetes (usually IGT), and interventions are targeted at halting or slowing beta-cell dysfunction. 

The majority of behavioural interventions are relatively intensive and designed to increase an individual’s physical activity levels and encourage weight loss and dietary change. Relatively modest changes in lifestyle, such as a 5% reduction in weight or an increase in moderate physical activity to 4 hours a week, can have important benefits in reducing the risk of diabetes. 

In the Finnish Diabetes Prevention Study (DPS; Tuomilehto et al, 2001) a clear “dose–response” curve was observed, such that the greater the number of behavioural changes (the success score), the lower the risk of diabetes in an individual ( Figure 1 ). It was also noted that the beneficial effects observed in the Finnish DPS persisted when the participants were followed-up a median of 3 years after the intervention had finished (Lindström et al, 2006). 

Lifestyle interventions of course have other general benefits for the individual. However, the majority of these interventions are not feasible or affordable in a resource-limited NHS, and there is therefore a need to develop, pilot and evaluate a pragmatic intervention that could be delivered in primary care or in the community. It is possible that this could be based on motivational interviewing (MI), and early results with MI in promoting weight loss in obese people through lay facilitators are encouraging (Greaves et al, 2008). 

The need for a pragmatic intervention is now more urgent as the NHS Health Check Programme begins. A large number of people with pre-diabetes will undoubtedly be identified and will need intervention. These interventions will also need to be culturally sensitive in the light of the large number of people from ethnic communities in the UK with pre-diabetes. 

Pharmacological interventions As well as lifestyle interventions, drugs have also been shown to reduce progression to type 2 diabetes, including metformin (Knowler et al, 2002; Ramachandran et al, 2006), acarbose (Chiasson et al, 2002), orlistat (Torgerson et al, 2004) as well as troglitazone – although later withdrawn (Azen et al, 1998) – and rosiglitazone (Gerstein et al, 2006). 

A meta-analysis by Gillies et al (2008) showed that drug interventions were both less effective and less cost-effective than lifestyle. The IDF (Alberti et al, 2007) recommends drug therapy as second-line after lifestyle intervention for diabetes prevention, yet, unfortunately, no pharmaceutical agent is licensed for diabetes prevention in the UK. 

There is also debate about whether these drugs simply mask progression to diabetes by lowering blood glucose, which then rises in the subsequent wash-out period once treatment has finished. On balance, however, it is generally thought that diabetes prevention through lifestyle or drugs is cost-effective and should be actively promoted in clinical practice (Gillies et al, 2008).  

Practitioner behaviour  In UK primary care there is a considerable gap between the theory of diabetes prevention and its active implementation. Several qualitative and questionnaire studies have shown that GPs and primary care staff are confused by the whole area of pre-diabetes and its diagnosis and wanted more information and guidance (Wylie et al, 2002; Whitford et al, 2003; Williams et al, 2004). GPs also expressed a variety of attitudes towards pre-diabetes, ranging from enthusiastically embracing its management to diagnostic nihilism (Fearn-Smith et al, 2007). 

In the biggest database study to date (Holt et al, 2008), it was demonstrated that GPs were missing opportunities to diagnose both pre-diabetes and diabetes in their registered patients. For example, borderline blood glucose results were not being followed-up with either a repeat test or OGTT. Better education of healthcare professionals is therefore needed. Box 1 gives a case study highlighting some common problems encountered in primary care.

Screening for diabetes and pre-diabetes  Although population screening is not thought to be appropriate (Wareham and Griffin, 2001), targeted or selective screening for both diabetes and pre-diabetes is now considered to be both effective and cost-effective (Waugh et al, 2008). Most authorities advise two-stage screening. First, individuals at higher risk of diabetes are identified using GP data or a questionnaire, such as FINDRISC (Lindström and Tuomilehto, 2003), and then a blood glucose test such as an FBG, an OGTT or an HbA 1c test is used.

NICE guidance on preventing type 2 diabetes will not be available until June 2011, although European guidance from the IMAGE (Development and Implementation of a European Guideline and Training Standards for Diabetes Prevention) project will be available in early 2010 ( http://www.image-project.eu/ ). 

In the new NHS Health Check Programme, all people aged 40–74 years who are not on a disease register will be called in for a face-to-face check and assessment of their vascular risk. Those who are overweight or obese or have a raised blood pressure will also be screened for diabetes. Managing this exercise and its implications will be a major challenge to all practitioners in primary care who wish to prevent type 2 diabetes and its complications.

Conclusion The prevalence of type 2 diabetes is rapidly increasing in the UK, although primary care teams should be aware of the rarer types of diabetes (e.g. MODY or MIDD) as well as type 1 diabetes. 

The risk factors for type 2 diabetes and pre-diabetes are well recognised and primary care teams are in an ideal position to screen for both conditions (either opportunistically or systematically). Finally, it is now clear that type 2 diabetes can be prevented or delayed by lifestyle or pharmacological interventions in those at highest risk.

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Alberti KG, Zimmet P, Shaw J; IDF Epidemiology Task Force Consensus Group (2005) The metabolic syndrome – a new worldwide definition.  Lancet   366 : 1059–62 Alberti KG, Zimmet P, Shaw J (2007) International Diabetes Federation: a consensus on Type 2 diabetes prevention. Diabet Med   24 : 451–63 American Diabetes Association (2009) Diagnosis and classification of diabetes mellitus.  Diabetes Care   32 : S62–7 Azen SP, Peters RK, Berkowitz K et al (1998) TRIPOD (TRoglitazone In the Prevention Of Diabetes): a randomized, placebo-controlled trial of troglitazone in women with prior gestational diabetes mellitus.  Control Clin Trials  19 : 217–31 Chiasson JL, Josse RG, Gomis R et al (2002) Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial.  Lancet   359 : 2072–7 Coutinho M, Gerstein HC, Wang Y, Yusuf S (1999) The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years.  Diabetes Care 22 : 233–40 Diabetes UK (2009) Diabetes in the UK 2009: Key Statistics on Diabetes. Diabetes UK, London Evans PH, Luthra M, Powell R et al (2003) Diagnosis of type 2 diabetes in primary care.  British Journal of Diabetes and Vascular Disease   3 : 342–4  Evans PH, Winder R, Greaves C et al (2006) Ways of addressing knowledge, education and understanding in pre-diabetes: the WAKEUP study. Abstract.  Diabet Med   23 (Suppl2): 132–3 Evans P, Langley P, Gray DP (2008) Diagnosing type 2 diabetes before patients complain of diabetic symptoms – clinical opportunistic screening in a single general practice.  Fam Pract   25 : 376–81 Fearn-Smith JDG, Evans PH, Harding G, Campbell JL (2007) Attitudes of GPs to the diagnosis and management of impaired glucose tolerance: The practitioners’ attitudes to hyperglycaemia (PAtH) questionnaire.  Primary Care Diabetes   1 : 35–41 Fischel-Ghodsian N (2001) Mitochondrial DNA mutations and diabetes: another step toward individualized medicine.  Ann Intern Med   134 : 777–9 Garber AJ (2003) Cardiovascular complications of diabetes: prevention and management.  Clin Cornerstone   5 : 22–37 Gerstein HC, Yusuf S, Bosch J et al (2006) Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial.  Lancet   368 : 1096–105 Gillies CL, Lambert PC, Abrams KR et al (2008) Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis.  Br Med J   336 : 1180–5 Greaves CJ, Stead JW, Hattersley A et al (2004) A simple pragmatic system for detecting new cases of type 2 diabetes and impaired fasting glycaemia in primary care.  Fam Pract   21 : 57–62 Greaves CJ, Middlebrooke A, O’Loughlin L et al (2008) Motivational interviewing for modifying diabetes risk: a randomised controlled trial.  Br J Gen Pract   58 : 535–40 Gulliford MC, Latinovic R, Charlton J, Hughes RA (2006) Increased incidence of carpal tunnel syndrome up to 10 years before diagnosis of diabetes.  Diabetes Care   29 : 1929–30 Harris MI, Klein R, Wellborn TA, Knuiman MW (1992) Onset of NIDDM occurs at least 4–7 years before clinical diagnosis. Diabetes Care   15 : 815–19 Hippisley-Cox J, Coupland C, Robson J et al (2009) Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore.  Br Med J   338 : b880 Holt TA, Stables D, Hippisley-Cox J et al (2008) Identifying undiagnosed diabetes: cross-sectional survey of 3.6 million patients’ electronic records.  Br J Gen Pract   58 : 192–6 International Diabetes Federation (2003)  Diabetes Atlas.  2nd ed. IDF, Brussels International Expert Committee (2009) Report on the role of the A1C assay in the diagnosis of diabetes.  Diabetes Care   32 : 1327–34 Knowler WC, Barrett-Connor E, Fowler SE et al (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.  N Engl J Med  346 : 393–403 Lindström J, Tuomilehto J (2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk.  Diabetes Care  26 : 725–31 Lindström J, Ilanne-Parikka P, Peltonen M et al (2006) Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study.  Lancet   368 : 1673–9 Murphy R, Ellard S, Hattersley AT (2008) Clinical implications of a molecular genetic classification of monogenic beta-cell diabetes.  Nat Clin Pract Endocrinol Metab   4 : 200–13 NHS Health Check Programme (2009)  NHS Health Check: Vascular Risk Assessment and Management Best Practice Guidance.  Department of Health, London Ramachandran A, Snehalatha C, Mary S et al (2006) The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia   49 : 289–97 Santaguida PL, Balion C, Hunt D et al (2005) Diagnosis, prognosis, and treatment of impaired glucose tolerance and impaired fasting glucose.  Evid Rep Technol Assess (Summ)  Aug: 1–11 Singleton JR, Smith AG, Russell J, Feldman EL (2005) Polyneuropathy with impaired glucose tolerance: implications for diagnosis and therapy.  Curr Treat Options Neurol   7 : 33–42 Tabák AG, Jokela M, Akbaraly TN et al (2009) Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study.  Lancet   373 : 2215–21 Torgerson JS, Hauptman J, Boldrin MN, Sjöström L (2004) XENical in the prevention of diabetes in obese subjects (XENDOS) study: a randomized study of orlistat as an adjunct to lifestyle changes for the prevention of type 2 diabetes in obese patients.  Diabetes Care   27 : 155–61 Troughton J, Jarvis J, Skinner C et al (2008) Waiting for diabetes: perceptions of people with pre-diabetes: a qualitative study.  Patient Educ Couns   72 : 88–93 Tuomilehto J, Lindström J, Eriksson JG et al (2001) Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.  N Engl J Med   344 : 1343–50 UK Prospective Diabetes Study (UKPDS) Group (1998) Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33).  Lancet   352 : 837–53 Unwin N, Shaw J, Zimmet P, Alberti KG (2002) Impaired glucose tolerance and impaired fasting glycaemia: the current status on definition and intervention.  Diabet Med   19 : 708–23 Wareham NJ, Griffin SJ (2001) Should we screen for type 2 diabetes? Evaluation against National Screening Committee criteria.  BMJ   322 : 986–8 Waugh N, Scotland G, McNamee P et al (2008) Screening for type 2 diabetes: literature review and economic modelling. Health Technol Assess   11 : iii–iv, ix–xi, 1–125 Whitford DL, Lamont SS, Crosland A (2003) Screening for type 2 diabetes: is it worthwhile? Views of general practitioners and practice nurses.  Diabet Med   20 : 155–8 Williams R, Rapport F, Elwyn G et al (2004) The prevention of type 2 diabetes: general practitioner and practice nurse opinions.  Br J Gen Pract  54 : 531–5 World Health Organization, International Diabetes Federation (2006)  Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycaemia. Report of a WHO/IDF Consultation.  WHO, Geneva Wylie G, Hungin AP, Neely J (2002) Impaired glucose tolerance: qualitative and quantitative study of general practitioners’ knowledge and perceptions.  BMJ   324 : 1190 Yamaoka K, Tango T (2005) Efficacy of lifestyle education to prevent type 2 diabetes: a meta-analysis of randomized controlled trials.  Diabetes Care  28 : 2780–6 Yorkshire and Humber Public Health Observatory (2007)  Diabetes Key Facts Supplement 2007.  YHPHO, York

Editorial: The importance of getting the correct diabetes diagnosis

Pcds committee elections (2024): call for candidates, conference over coffee: diabetes and obesity within multiple long-term conditions, q&a: lipid management – part 3: triglycerides and use of non-statin drugs, lada – assessing diabetes in a non-overweight younger person, challenges and opportunities in reducing risk of diabetes-related cardiovascular disease: making every contact count, interactive case study: mody – a strong family history of diabetes.

Jane Diggle tackles the challenges of getting the diabetes diagnosis right.

How to stand for election to the PCDS Committee.

The interactions between diabetes, obesity and long-term conditions, including cardiovascular disease, chronic kidney disease and cancer.

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The Standards of Care in Diabetes ( Standards of Care ) includes all of current clinical practice recommendations of the American Diabetes Association (ADA) and is intended to provide clinicians, researchers, policy makers, and other individuals with the components of diabetes care, general treatment goals, and tools to evaluate the quality of care.

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Diabetes Mellitus Clinical Presentation

During prediabetes and the early stages of diabetes, the majority of patients are asymptomatic. As a result, obtaining a diagnosis may be delayed for many years if routine screening measures for diabetes, such as laboratory work, are not performed during regular healthcare visits. Typically, patients with type 1 diabetes mellitus (T1DM) present with symptomatic hyperglycemia and sometimes with diabetic ketoacidosis (DKA). DKA is defined as an acute metabolic complication of diabetes distinguished by hyperglycemia, hyperketonemia, and metabolic acidosis. DKA occurs primarily in T1DM and is less common in T2DM; it can present with nausea, vomiting, and abdominal pain and cause cerebral edema, coma, and death. DKA may be the initial presentation in an estimated 25% of adults with T1DM. The most common symptoms associated with T1DM are polyuria, polydipsia, and polyphagia, along with lethargy, nausea, and blurred vision, all of which result from the hyperglycemia. The onset of symptoms may be abrupt. Polyuria is caused by osmotic diuresis secondary to hyperglycemia. In young children, severe nocturnal enuresis secondary to polyuria could be a warning sign of diabetes onset. In T1DM, thirst is a response to the hyperosmolar state and dehydration. The American Diabetes Association notes that adults with new-onset T1DM may present with a short duration of illness of 1 to 4 weeks or more, a gradually progressing process that can be misinterpreted as type 2 diabetes mellitus (T2DM). Patients with T1DM may also present with fatigue, weakness, and weight loss despite normal appetite. Patients with T2DM may present with symptomatic hyperglycemia but are frequently asymptomatic for diabetes, and T2DM is often discovered during routine checkups and laboratory testing. Classic symptoms of T2DM include polyuria, polydipsia, polyphagia, and weight loss. Other symptoms that may suggest hyperglycemia include blurred vision, lower-extremity paresthesia, and delayed wound healing. In some patients, initial symptoms may actually be indicative of diabetic complications (such as neuropathy, retinopathy, skin acanthosis, and recurring Candida infections), signifying that the T2DM has been present for some time. This highlights the need for routine screening, especially in high-risk patient populations. In some patients, a hyperosmolar hyperglycemic state occurs initially, particularly during a period of extreme stress or when glucose metabolism is further impaired by use of certain pharmacologic agents, such as corticosteroids. Since early diagnosis and clinical intervention are essential for preventing and/or reducing the complications associated with poorly controlled diabetes, patients should be reminded to maintain routine healthcare and seek medical care if they are experiencing any symptoms associated with hyperglycemia. The content contained in this article is for informational purposes only. The content is not intended to be a substitute for professional advice. Reliance on any information provided in this article is solely at your own risk.

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[Diabetes mellitus: clinical presentation and differential diagnosis of hyperglycemia in childhood and adolescence]

Affiliation.

• 1 Servicio de Endocrinología, Hospital Infantil Universitario Niño Jesús, Madrid, España.
• PMID: 22857943
• DOI: 10.1016/j.anpedi.2012.06.013

Diabetes mellitus is one of the most common chronic diseases in childhood. Despite being a clinical and etiopathogenically heterogeneous disorder, type 1 autoimmune diabetes accounts for more than 95% of cases in children. Recent advances have meant that a growing number of patients have been assigned to other subtypes of diabetes. In such cases, the correct diagnosis is facilitated by the fact that many of these rare causes of diabetes are associated with specific clinical syndromes or may present at a certain age. Many of them are also subsidiaries of molecular diagnosis. The aim of this review is to update the current knowledge in this field of pediatric diabetes, in an attempt to determine the most accurate diagnosis and its implications on appropriate treatment and prognosis.

Copyright © 2012 Asociación Española de Pediatría. Published by Elsevier Espana. All rights reserved.

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• [Diagnosis of diabetes mellitus in children]. Tubiana-Rufi N. Tubiana-Rufi N. Rev Prat. 1996 Mar 1;46(5):552-5. Rev Prat. 1996. PMID: 8815519 Review. French.
• [Diagnosis and classification of diabetes mellitus]. Rossi G; American Diabetes Association. Rossi G, et al. Recenti Prog Med. 2010 Jul-Aug;101(7-8):274-6. Recenti Prog Med. 2010. PMID: 20842952 Italian. No abstract available.
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• Published: 14 September 2024

Type 2 diabetes mellitus negatively affects the functional performance of 6-min step test in chronic heart failure: a 3-year follow-up study

• Aldair Darlan Santos-de-Araújo 1 ,
• Daniela Bassi-Dibai 2 ,
• Izadora Moraes Dourado 1 ,
• Cássia da Luz Goulart 3 ,
• Renan Shida Marinho 4 ,
• Jaqueline de Almeida Mantovani 1 ,
• Gabriela Silva de Souza 1 ,
• Polliana Batista dos Santos 5 ,
• Meliza Goi Roscani 6 ,
• Shane A. Phillips 7 &
• Audrey Borghi-Silva 1  

Diabetology & Metabolic Syndrome volume  16 , Article number:  229 ( 2024 ) Cite this article

Metrics details

Type 2 diabetes mellitus (T2DM) and chronic heart failure (CHF) present a decrease in functional capacity due to the intrinsic nature of both pathologies. It is not known about the potential impact of T2DM on functional capacity when assessed by 6-min step test (6MST) and its effect as a prognostic marker for fatal and non-fatal events in patients with CHF.

to evaluate the coexistence of T2DM and CHF in functional capacity through 6MST when compared to CHF non-T2DM, as well as to investigate the different cardiovascular responses to 6MST and the risk of mortality, decompensation of CHF and acute myocardial infarction (AMI) over 36 months.

This is a prospective cohort study with 36 months of follow-up in individuals with T2DM and CHF. All participants completed a clinical assessment, followed by pulmonary function testing, echocardiography, and 6MST. The 6MST was performed on a 20 cm high step and cardiovascular responses were collected: heart rate, systemic blood pressure, oxygen saturation, BORG dyspnea and fatigue. The risk of mortality, acute myocardial infarction and decompensation of CHF was evaluated.

Eighty-six participants were included. The CHF-T2DM group had a significantly lower functional capacity than the CHF non-T2DM group (p < 0.05). Forced Expiratory Volume in one second (L), ejection fraction (%), gender and T2DM influence and are predictors of functional capacity (p < 0.05; adjusted R squared: 0.419). CHF-T2DM group presented a higher risk of mortality and acute myocardial infarction over the 36 months of follow-up (p < 0.05), but not to the risk of decompensation (p > 0.05).

T2DM negatively affects the functional performance of 6MST in patients with CHF. Gender, ejection fraction (%), FEV1 (L) and T2DM itself negatively influence exercise performance.

Introduction

Common, highly prevalent, closely related and frequently associated, CHF and T2DM have a bidirectional relationship, that is, the origin and evolution of each pathology can be mutually influenced [ 1 , 2 , 3 , 4 ]. The interactions between both diseases is widely known [ 5 , 6 ], however, the treatment approach continues to be a challenge, from screening to optimizing therapeutic decision-making that addresses aspects of rehabilitation of these individuals due to the high rate of morbidity and mortality and the heterogeneous clinical presentation of both conditions [ 1 , 6 , 7 ].

In both diseases, exercise capacity has been adopted as an important outcome and the main guidelines and international campaigns have drawn attention to the importance of this assessment and the inclusion of this outcome in therapeutic optimization [ 8 , 9 , 10 , 11 ]. Since the results that reflect exercise capacity have discriminative prognostic value for mortality risk, risk of unfavorable outcomes, the assessment of treatment efficacy in both conditions is highly desirable [ 12 , 13 , 14 ].

When it comes to exercise capacity, cardiopulmonary exercise testing (CPET) has established itself as the most effective tool for assessing this outcome [ 10 , 15 ], however, the arsenal of equipment used and the need for a team that involves trained professionals led scientists to develop options that could reflect the CPET's ability to exercise in a more economically accessible and simple, but not replaceable [ 16 , 17 ]. The 6-min step test (6MST) has gained notoriety and clinical and scientific popularity due to its practicality and simple, low-cost and easily available alternative option, especially in environments where the most sophisticated resources and equipment for achieving the gold standard are not available, in addition to having an important correlation with CPET [ 16 , 18 ]. In both individuals with CHF [ 16 ] and individuals with T2DM [ 18 ], the reliability and validity of the 6MST has already been scientifically proven and the test has strong concurrent validity when compared to the CPET.

Undoubtedly, both disease negatively impact on exercise capacity, affecting the cardiovascular, respiratory and metabolic dynamic for the supply of oxygen to peripheral muscles [ 19 , 20 ]. However, although the coexistence of T2DM and CHF has been growingly reported, description of fatal and nonfatal events and its relation with functional (in)capacity considering the presence of both conditions is still scarce. Therefore, the objective of this investigation is to evaluate whether individuals with CHF with T2DM have worse functional capacity when compared to a group with CHF without T2DM. Secondarily, we aimed to investigate the different cardiovascular responses presented in both groups and the risk of mortality, decompensation of heart failure and acute myocardial infarction over 36 months. Our hypothesis is, based on all the previously described aspects, that T2DM not only negatively affects the functional performance of individuals with CHF, but also presents itself as an independent factor for reducing functional capacity in the 6MST, worse cardiovascular responses and presents greater mortality, decompensation of CHF and acute myocardial infarction risk.

Methodology

Study design.

This is a prospective longitudinal investigation with a follow-up of 3 years (36 months) carried out by the Cardiopulmonary Physiotherapy Laboratory (LACAP) of the Federal University of São Carlos—UFSCar, located in São Carlos, SP, Brazil. The participant recruitment process took place between December 2017 and November 2020. The university's ethics committee previously approved the development of the investigation under protocol number 5.188.654 and the research followed the principles of the Declaration of Helsinki. The STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) guideline was used to conduct the study [ 21 ]. All participants were informed about the research objectives and gave their informed consent before being evaluated .

Participants

The Cardiology Outpatient Clinics of the Medical Specialties Center (CEME) and the São Carlos University Hospital (HU-UFSCar) were used to actively search for participants eligible for the investigation. We included patients over 40 years of age, confirmed diagnosis of heart failure with left ventricular ejection fraction below 50% by echocardiography, with or without clinical diagnosis of T2DM, clinical stability and absence of medication changes in the last 3 months. Those aged over 80 years, diagnosed with heart failure with preserved ejection fraction, history of cardiovascular events in the last 6 months, decompensation of the disease in the last 3 months, presence of any implantable cardiac pacemaker, unstable angina, diagnosis of any neoplasms, uncontrolled systemic arterial hypertension, cognitive impairment or lack of understanding of the study proposal were excluded of study.

Initial assessment

Initially, a prior anamnesis was carried out using an assessment form developed by the laboratory and researchers involved so that personal information, associated pathologies and medications used were collected. The medical records of the included patients were also used as a tool to search for important information.

Anthropometric variables

To estimate the height of participants, a stadiometer (Welmy R-110, Santa Bárbara do Oeste, São Paulo, Brazil) was used. Body mass in kilograms (kg), body fat mass (kg), body fat percentage (%) and skeletal muscle mass (kg) were determined through bioelectrical impedance analysis, using the InBody 720 device. Participants were instructed to fast for at least 4 h, wear light clothing, remove all metallic objects in contact with the body, urinate before the exam, avoid drinking alcoholic beverages for 12 h and not perform strenuous physical exercise the day before the evaluation. During the examination, participants were positioned in an upright position, barefoot, with their shoulders slightly abducted and their elbows flexed at approximately 15°, as recommended by the manufacturer (BIOSPACE, 2004). The Body Mass Index (BMI) was calculated by dividing body mass (kg) by height squared in meters (kg/m 2 ). The BMI classification was established as follows: low weight (15–19.9 kg/m 2 ); normal weight (20–24.9 kg/m 2 ); overweight (25–29.9 kg/m 2 ); obesity I (30–34.9 kg/m 2 ); obesity II (35–39.9 kg/m 2 ); and obesity III (≥ 40 kg/m 2 )[ 22 ].

Minnesota Questionnaire

Previously validated for the Brazilian population [ 23 ], this questionnaire consists of 21 questions relating to the limitations associated with heart failure considering the last month. The answers to each question range from 0 to 5, where 0 represents no limitations and 5 the maximum limitation. These questions involve a physical dimension (1–7, 12 and 13), which are highly related to dyspnea, fatigue; emotional dimension (17- 21); and other issues (number 8, 9, 10, 11, 14, 15 and 16) which, together with the previous dimensions, form the total score.

New York Heart Association—NYHA

The New York Heart Association (NYHA) functional classification was used to assess the severity of functional limitations resulting from the CHF condition based on the symptoms experienced by the participant during physical activity. It allows stratifying the degree of limitation imposed by it: class I—absence of symptoms during daily activities, with limitation in efforts similar to that expected in healthy individuals; class II—symptoms triggered by daily activities; class III—symptoms triggered by activities less intense than everyday activities; class IV—symptoms at rest [ 24 ].

Pulmonary function—spirometry

The assessment of lung function was conducted using spirometry (Masterscreen Body, Mijnhardt/Jäger, Würzburg, Germany) by a previously trained researcher, following conventional techniques and the acceptability and reproducibility guidelines of the American Thoracic and European Respiratory Societies (ATS/ERS). At least three slow and forced maneuvers considered acceptable and reproducible were performed, as recommended, and repeated 20 min after administration of 400 µg of Albuterol Sulfate. Participants with overlapping chronic obstructive pulmonary disease were diagnosed according to the GOLD criteria (post-bronchodilator forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) ratio < 0.70) [ 25 , 26 ].

Transthoracic echocardiogram

The transthoracic echocardiogram was performed by a cardiologist, using an ultrasound device with a 3 MHz transducer (Phillips, HD11 XE, Bothell, Washington, United States) according to recommendations [ 27 ]. The end systolic and diastolic diameter of the left ventricle, early diastolic mitral filling velocities (E wave), early diastolic velocity of the mitral annulus (E' wave) and left ventricular ejection fraction (LFEV) were obtained using the Simpson method [ 28 ].

6-min step test—6MST

The 6MST has been previously validated for individuals with CHF [ 16 ]. Prior to the test, upon arrival at the laboratory, participants were informed about the nature and dynamics of the test so that any doubts regarding carrying it out could be clarified. Then, they underwent a period of 4 min of rest (2 min sitting and 2 min standing) so that vital signs could be collected (resting heart rate [HR], peripheral oxygen saturation [SpO 2 ] and blood pressure systemic) in addition to perceived exertion for dyspnea and fatigue of the lower limbs using the BORG 10 scale in each position. At the end of the 4 min, they were instructed to go up and down a single step with a height of 20 cm (cm) in a self-paced manner, being allowed to slow down, if necessary, and even interrupt the test to rest. Verbal encouragement commands were used for each minute of testing and the time remaining until completion. The step numbers were counted from the beginning to the end of the 6-min time and recorded. Vital signs collected prior to the test, as well as feelings of lower limb fatigue and dyspnea were obtained immediately at the end of the test and the 6th min of recovery.

Despite being considered a test of a submaximal nature, some criteria for interrupting the exam were adopted so that the integrity of the patient's health was guaranteed: reaching 85% of maximum HR, arterial oxygen saturation ≤ 87%, systolic blood pressure (SBP) greater than 170 mmHg and DBP greater than 110 mmHg, BORG score greater than 7 for dyspnea and lower limb fatigue, anginal pain > 2, dizziness, vertigo and nausea. The prediction of functional performance of participants in the 6MST for the Brazilian population was made using the equations proposed by Arcuri et al.[ 29 ] 6MST = 209 – (1.05 × age) for men and 6MST = 174 – (1.05 × age) for women, where age is expressed in years; and Albuquerque et al.[ 30 ] 6MST = 106 + (17.02 × [0:woman; 1:man]) + (− 1.24 × age) + (0.8 × height) + (− 0.39 × weight) where 6MST is expressed in number of steps; age, in years; height, in cm; and weight, in kg.

Participants follow-up

Information on mortality, AMI and acute decompensated heart failure was collected through periodic telephone calls every 6 months and/or through hospital records from the date of the patient's initial evaluation in the laboratory. According to the European Society of Cardiology [ 28 ], acute decompensation of heart failure was understood as a rapid or gradual clinical presentation of the signs and symptoms of heart failure at rest, severe enough to cause unplanned office visits, emergency room visits or hospitalization requiring urgent assessment and subsequent initiation or intensification of treatment that includes therapies or procedures.

Statistical analyses

Data are presented as mean and standard deviation or absolute values and percentages of occurrence when appropriate. The Kolmogorov–Smirnov test was used to verify the normality of the data. For the analysis between the groups test T for independent samples was used when the data presented a normal distribution. When the data presented a non-parametric distribution, the Mann–Whitney test was used. The χ 2 test was used to compare categorical variables. Kaplan–Meier analysis was used to test the risk of all-cause mortality, acute decompensated heart failure, and acute myocardial infarction over 36 months of follow-up. Differences between curves were evaluated using the Log-rank test, Breslow and Tarone-Ware.

The covariates included in the present analysis constitute a broad spectrum of factors associated with unfavorable outcomes (mortality, decompensation of heart failure and AMI). Univariate linear regression analyses were performed to verify the association between the independent variables and the dependent variable (steps in the 6MST) [ 31 ]. For the multiple linear regression model, variables that presented a p-value < 0.20 in the univariate analysis were selected as covariates [ 32 ]. Comparisons of 6MST performance and cardiovascular responses between groups were expressed as mean, standard deviation (SD), mean difference (MD), and effect size calculated using Cohen’s d, with the categorization based on the values established by Cohen [ 33 ]. The effect size was calculated based on the Cohen d, according to the website: < https://www.psychometrica.de/effect_size.html >. It was considered the following interpretation of the d value: 0.2 (weak), 0.5 (moderate) and > 0.8 (large effect size) [ 33 ]. Raincloud plots were produced using the JASP 0.18.2 software [ 34 ] for data visualization of the step test performance and predictive values < https://jasp-stats.org/ >. All analyzes were performed using GraphPad Software, Inc. (2019). GraphPad Prism (versão 8.0.1). San Diego, CA < https://www.graphpad.com >. The probability of type 1 error occurrence was established at 5% for all tests (p < 0.05).

Initially, one hundred and twenty-one participants were recruited, however thirty-five were not included. Finally, eighty-six participants were included: 34 CHF-T2DM group and 52 in CHF non-T2DM group (Fig.  1 ). Information about the characteristics of the sample included in the study can be viewed in Table  1 . The groups did not differ in terms of age (years), sex distribution and height (m). However, the CHF-T2DM group had higher body weight and BMI when compared to the CHF non-T2DM and, consequently, a greater number of participants with obesity (65%) (class I [40%], class II [18%] and class III [5%]). Additionally, this same group had a higher prevalence of coronary artery disease (12%), dyslipidemia (72%), use of beta-blockers (85%) and lower LVEF (%). No statistically significant differences were observed in the outcomes of quality of life (Minnesotta questionnaire), functional classification (NYHA) and lung function (spirometry). In total, 14 individuals died over the 36 months of follow-up (9 in the CHF-T2DM group and 5 in the CHF non-T2DM). Furthermore, 13 individuals progressed to acute decompensation of heart failure and 10 to AMI.

In Fig.  2 , when we evaluated functional performance comparing to 6MST in both groups, we observed that the CHF-T2DM group had a significantly lower functional capacity than the CHF non-T2DM group (60 ± 29 versus 87 ± 31; Cohen’s d = 0.875) and that they achieved an average percentage of 45 ± 20 versus 64 ± 22 when considering the prediction equation by Arcuri et al., and 43 ± 19 versus 60 ± 19 when considering the prediction equation by Albuquerque et al. Regarding cardiovascular responses (Table  2 ), we only found a lower heart rate chronotropic response by heart rate in beats per minute in the CHF-T2DM (bpm) at peak exercise (97 ± 26 versus 108 ± 21; Cohen’s d: 0.476).

Raincloud plots for functional capacity by 6MST and predicted values in CHF non-T2DM and CHF-T2DM. CHF non-T2DM chronic heart failure without type 2 diabetes mellitus, CHF-T2DM chronic heart failure with type 2 diabetes mellitus, % percentage, n number in absolute value, p < 0.05: statistical significance

The univariate linear regression model (Table  3 ) revealed that FEV1 (L), ejection fraction (%), gender and T2DM influence and are predictors of approximately 42% functional capacity (p < 0.05; adjusted R squared: 0.419). Secondarily, when we analyzed the Kaplan–Meier curves, we observed that the CHF-T2DM presented a higher risk of mortality (Fig.  3 ) and acute myocardial infarction (Fig.  4 ) over the 36 months of follow-up (p < 0.05 to Log-rank, Brelow and Tarone-ware), however, regarding the risk of heart failure decompensation (Fig.  5 ), there was no statistically significant difference between the groups (p > 0.05 to Log-rank, Brelow and Tarone-ware).

Kaplan–Meier curve for mortality over a period of 36 months. CHF chronic heart failure, %: percentage

Kaplan–Meier curve for acute myocardial infarction over a period of 36 months. CHF chronic heart failure, % percentage

Kaplan–Meier curve for acute decompensation over a period of 36 months. CHF chronic heart failure, % percentage

The main results of this investigation are associated with some important aspects: (1) for the first time, the impact of T2DM on CHF was investigated considering the performance and cardiovascular variables of 6MST; (2) we confirmed our hypothesis that the association of T2DM and CHF presents worse functional capacity compared to the CHF non-T2DM group; (3) secondarily, we observed a higher risk of mortality and AMI in the CHF-T2DM over 36 months of follow-up.

The heterogeneous presentation of CHF, that is, concomitant with other risk factors that contribute to the increase in unfavorable outcomes, with consequent development of disabling functional limitations [ 28 , 35 ]. Particularly, in individuals affected by CHF, the decrease in functional capacity is linked to multifactorial mechanisms that involve, above all, early anaerobic metabolism resulting from a combination of reduced blood flow in skeletal muscle, decreased aerobic enzymes in skeletal muscle, morphological and functional changes of musculoskeletal fibers and inefficiency of the cardiovascular and respiratory system [ 36 , 37 , 38 ]. T2DM, in turn, presents peculiar characteristics that compromise exercise capacity in this population, mainly associated with ineffective glucose uptake, mitochondrial imbalance and the transition from oxidative to glycolytic fiber type [ 39 , 40 ].

Paradoxically to the physiological limitations mentioned above, the effort required to perform the 6MST requires vertical displacement and the involvement of large muscle groups that demand greater cardiovascular stress when compared, for example, to the 6-min walk test, leading to an increase extraction oxygen [ 16 , 29 ]. Considering oxygen uptake, it is nothing new that, individually, both diseases present a decrease in functional capacity when evaluated by field and laboratory tests. In individuals with CHF, whether with a reduced ejection fraction or with its preservation, different methods that reflect this outcome indicate functional impairment over time [ 41 , 42 ]. The same reasoning can be observed in patients with T2DM [ 40 , 43 ].

Previously, and in an unprecedented way, an investigation proved that in individuals with CHF the addition of T2DM is associated with a reduction in the distance covered during the 6-min walk test (6MWT) in addition to being an independent determinant of worse performance in the group with coexistence of both pathologies [ 44 ]. Still considering the phenotypic nature of CHF presentation, recently, a group of researchers observed that T2DM demonstrated to be the strongest predictor of limited exercise capacity in CHF and preserved ejection fraction when also assessed by the 6MWT [ 45 ].

In healthy individuals, variables such as weight (kg), height (cm), age (years) and gender influence 6MST performance and explained at least 42% of the variability in functional capacity [ 30 ]. Parallel to this, our results point to an influence of gender, T2DM, ejection fraction (%) and FEV1 (L) and, undoubtedly, we need to recognize how much each variable makes sense in our regression model since current literature has demonstrated the influence of each of them on exercise capacity. The influence of gender is associated with the nature of the physiological difference that men and women present in the cardiovascular, respiratory and musculoskeletal systems, both in healthy individuals and in individuals affected by heart failure [ 46 , 47 ]. In turn, airflow limitation, more specifically when assessed by FEV1 (L), contributes to functional performance in this population also being compromised. The contribution of lung function to exercise capacity in patients with CHF has been previously discussed and accounts for approximately 30% of maximal exercise capacity during CPET [ 48 ].

Our sample presented some important characteristics that deserve discussion. At first, we must keep in mind that the majority of people affected by T2DM are overweight or obese [ 3 ]. Although there is a paradoxical relationship, that is, inversely proportional, between weight gain and 6MST performance, in practical terms, when we talk about T2DM it is practically utopian to disregard overweight or obesity in this population due to the close relationship between these two outcomes [ 30 ]. We minimally understand the importance of controlling the variable that reflects obesity in both groups so that this bias is minimized, but this may reflect a small portion of the population affected by CHF-T2DM since the coexistence of both pathologies is highly prevalent [ 49 ] and that obesity is strongly connected to T2DM [ 3 ]. Nevertheless, the impact of T2DM culminates in structural and functional changes in the heart muscle that lead to exercise intolerance in patients with CHF and, not surprisingly, the CHF-T2DM group showed a lower left ventricular ejection fraction that may be a reflection of coexistence of both diseases [ 20 ].

When we considered the characterization of our sample using the NYHA scale, we observed that most participants were categorized as NYHA I and II. Curiously, there was a significant risk of mortality and AMI in our sample, revealing a true paradox, as these functional classes typically reflect better functional capacity. Since our patients were followed during a pandemic period, we hypothesize several possible explanations for these unfavorable outcomes: (a) COVID-19 infection and its deleterious effects, leading to an increased risk of AMI and mortality [ 50 ]; (b) the impact of lockdown on increasing the risks associated with these outcomes [ 51 ]; (c) limited discrimination of the NYHA classification [ 52 ].

Clinically, our results contribute not only to recognizing the impact of T2DM in individuals with CHF on 6MST performance, but mainly so that the results can be used in more precise therapies that consider the nature of the coexistence of both pathologies once the evaluation of this outcome. It is routinely used for prognostic, diagnostic, pharmacological optimization, monitoring of disease progression and investigation of functional decline.

Limitations

This is a study with some limitations that deserve to be described. It was not possible to characterize the sample according to metabolic outcomes such as fasting blood glucose or glycated hemoglobin and we also do not have information about the time of diagnosis of T2DM.

T2DM negatively affects the functional performance of 6MST in patients with CHF. Sex, ejection fraction (%), FEV1 (L) and T2DM itself negatively influence this outcome and must be considered within the evaluation.

Availability of data and materials

The set of data generated and/or analyzed during the present study are available through the corresponding author upon reasonable request.

Elendu C, Amaechi DC, Elendu TC, Ashna M, Ross-Comptis J, Ansong SO, et al. Heart failure and diabetes: understanding the bidirectional relationship. Medicine (Baltimore). 2023;102:E34906.

Article   PubMed   Google Scholar  

Palazzuoli A, Iacoviello M. Diabetes leading to heart failure and heart failure leading to diabetes: epidemiological and clinical evidence. Heart Fail Rev. 2023;28:585.

Article   CAS   PubMed   Google Scholar  

Theofilis P, Oikonomou E, Tsioufis K, Tousoulis D. Diabetes mellitus and heart failure: epidemiology, pathophysiologic mechanisms, and the role of SGLT2 inhibitors. Life. 2023;13:497.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sugandh F, Chandio M, Raveena F, Kumar L, Karishma F, Khuwaja S, et al. Advances in the management of diabetes mellitus: a focus on personalized medicine. Cureus. 2023;15:e43697.

PubMed   PubMed Central   Google Scholar  

Thomas MC. Type 2 diabetes and heart failure: challenges and solutions. Curr Cardiol Rev. 2016;12:249.

Htay T, Soe K, Lopez-Perez A, Doan AHA, Romagosa MA, Aung KK. Mortality and cardiovascular disease in type 1 and type 2 diabetes. Curr Cardiol Rep. 2019;21:1407.

Article   Google Scholar  

Bytyçi I, Bajraktari G. Mortality in heart failure patients. Anatol J Cardiol. 2015;15:63.

Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, et al. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care. 2016;39:2065.

Article   PubMed   PubMed Central   Google Scholar  

Flotyńska J, Szybiak W, Naskręt D, Zozulińska-Ziółkiewicz D, Grzelka-Woźniak A, Uruska A. Methods of Assessment of Physical Capacity in People with Diabetes Mellitus Type 1. Curr Diabetes Rev . 2024;20.

Herdy AH, Ritt LEF, Stein R, de Araújo CGS, Milani M, Meneghelo RS, et al. Cardiopulmonary exercise test: background, applicability and interpretation. Arq Bras Cardiol. 2016;107:467.

CAS   PubMed   PubMed Central   Google Scholar  

Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2022;79:e263-421.

Huang WM, Chang HC, Chen CN, Huang CJ, Yu WC, Cheng HM, et al. Symptom-limited exercise capacity is associated with long-term survival. Medicine (Baltimore). 2023;102:E34948.

Zisman-Ilani Y, Fasing K, Weiner M, Rubin DJ. Exercise capacity is associated with hospital readmission among patients with diabetes. BMJ Open Diabetes Res Care. 2020;8:1771.

Nojima H, Yoneda M, Watanabe H, Yamane K, Kitahara Y, Sekikawa K, et al. Association between aerobic capacity and the improvement in glycemic control after the exercise training in type 2 diabetes. Diabetol Metab Syndr. 2017;9:63.

Myers J, Arena R, Cahalin LP, Labate V, Guazzi M. Cardiopulmonary exercise testing in heart failure. Curr Probl Cardiol. 2015;40:322–72.

Marinho RS, Jürgensen SP, Arcuri JF, Goulart CL, Dos Santos PB, Roscani MG, et al. Reliability and validity of six-minute step test in patients with heart failure. Brazilian J Med Biol Res. 2021;54:e10514.

Article   CAS   Google Scholar  

Giannitsi S, Bougiakli M, Bechlioulis A, Kotsia A, Michalis LK, Naka KK. 6-minute walking test: a useful tool in the management of heart failure patients. Ther Adv Cardiovasc Dis. 2019;13:1–10.

Lee MC. Validity of the 6-minute walk test and step test for evaluation of cardio respiratory fitness in patients with type 2 diabetes mellitus. J Exerc Nutr Biochem. 2018;22:49.

Poole DC, Richardson RS, Haykowsky MJ, Hirai DM, Musch TI. Exercise limitations in heart failure with reduced and preserved ejection fraction. J Appl Physiol. 2018;124:208.

Nesti L, Pugliese NR, Sciuto P, Natali A. Type 2 diabetes and reduced exercise tolerance: a review of the literature through an integrated physiology approach. Cardiovasc Diabetol. 2020;19:134.

Malta M, Cardoso LO, Bastos FI, Magnanini MMF, da Silva CMFP. STROBE initiative: guidelines on reporting observational studies. Rev Saude Publica. 2010;44:559–65.

Nuttall FQ. Body mass index: obesity, BMI, and health: a critical review. Nutr Today. 2015;50:117.

Carvalho VO, Guimarães GV, Carrara D, Bacal F, Bocchi EA. Validação da versão em português do Minnesota Living with Heart Failure Questionnaire. Arq Bras Cardiol. 2009;93:39–44.

Rohde LEP, Montera MW, Bocchi EA, Clausell NO, de Albuquerque DC, Rassi S, et al. Diretriz brasileira de insuficiência cardíaca crônica e aguda. Arq Bras Cardiol. 2018;111:436–539.

PubMed   Google Scholar  

De Castro Pereira CA, Sato T, Rodrigues SC. New reference values for forced spirometry in white adults in Brazil. J Bras Pneumol. 2007;33:397–406.

Google Scholar  

Graham BL, Steenbruggen I, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, et al. Standardization of spirometry 2019 update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med. 2019;200:e70.

Mitchell C, Rahko PS, Blauwet LA, Canaday B, Finstuen JA, Foster MC, et al. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of Echocardiography. J Am Soc Echocardiogr. 2019;32:1–64.

McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC. Rev Esp Cardiol (Engl Ed). 2022;75:523.

Arcuri JF, Borghi-Silva A, Labadessa IG, Sentanin AC, Candolo C, Di Lorenzo VAP. Validity and reliability of the 6-minute step test in healthy individuals: a cross-sectional study. Clin J Sport Med. 2016;26:69–75.

Salles Albuquerque V, Dal Corso S, Pereira do Amaral D, Medina Dutrade Oliveira T, Fonseca Souza G, Naara Silva de Souza R, et al. Normative values and reference equation for the six-minute step test to evaluate functional exercise capacity: a multicenter study. J Bras Pneumol. 2022. https://doi.org/10.36416/1806-3756/e20210511 .

Anderson TW, Theodore W. An introduction to multivariate statistical analysis. 2003;721. Available from: https://www.wiley.com/en-us/An+Introduction+to+Multivariate+Statistical+Analysis%2C+3rd+Edition-p-9780471360919 .

Maldonado G, Greenland S. Simulation study of confounder-selection strategies. Am J Epidemiol. 1993;138:923–36. https://doi.org/10.1093/oxfordjournals.aje.a116813 .

Cohen J. Statistical power analysis for the behavioral sciences. Stat Power Anal Behav Sci. 2013. https://doi.org/10.4324/9780203771587 .

JASP—Free and User-Friendly Statistical Software (version 0.18.2). Available from: https://jasp-stats.org/ .

Roh J, Hill JA, Singh A, Valero-Muñoz M, Sam F. Heart failure with preserved ejection fraction: heterogeneous syndrome, diverse preclinical models. Circ Res. 2022;130:1906–25.

Sullivan MJ, Hawthorne MH. Exercise intolerance in patients with chronic heart failure. Prog Cardiovasc Dis. 1995;38:1–22.

Tucker WJ, Haykowsky MJ, Seo Y, Stehling E, Forman DE. Impaired exercise tolerance in heart failure: role of skeletal muscle morphology and function. Curr Heart Fail Rep. 2018;15:323–31.

Zizola C, Schulze PC. Metabolic and structural impairment of skeletal muscle in heart failure. Heart Fail Rev. 2013;18:623–30. https://doi.org/10.1007/s10741-012-9353-8 .

Bassi-Dibai D, Santos-de-Araújo AD, Dibai-Filho AV, de Azevedo LFS, Goulart CDL, Luz GCP, et al. Rehabilitation of individuals with diabetes mellitus: focus on diabetic myopathy. Front Endocrinol (Lausanne). 2022;13:869921.

Bilak JM, Gulsin GS, McCann GP. Cardiovascular and systemic determinants of exercise capacity in people with type 2 diabetes mellitus. Ther Adv Endocrinol Metab. 2021;12.

Meyer K, Westbrook S, Schwaibold M, Hajric R, Lehmann M, Roskamm H. Cardiopulmonary determinants of functional capacity in patients with chronic heart failure compared with normals. Clin Cardiol. 1996;19:944–8.

Fuentes-Abolafio IJ, Stubbs B, Pérez-Belmonte LM, Bernal-López MR, Gómez-Huelgas R, Cuesta-Vargas AI. Physical functional performance and prognosis in patients with heart failure: a systematic review and meta-analysis. BMC Cardiovasc Disord. 2020;20:512.

Kuziemski K, Słomiński W, Jassem E. Impact of diabetes mellitus on functional exercise capacity and pulmonary functions in patients with diabetes and healthy persons. BMC Endocr Disord. 2019;19:2.

Ingle L, Reddy P, Clark AL, Cleland JGF. Diabetes lowers six-minute walk test performance in heart failure. J Am Coll Cardiol. 2006;47:1909–10.

Berisha-Muharremi V, Henein MY, Dini FL, Haliti E, Bytyçi I, Ibrahimi P, et al. Diabetes is the strongest predictor of limited exercise capacity in chronic heart failure and preserved ejection fraction (HFpEF). Front Cardiovasc Med. 2022;9:883615.

Rozenbaum Z, Granot Y, Sadeh B, Havakuk O, Arnold JH, Shimiaie J, et al. Sex differences in heart failure patients assessed by combined echocardiographic and cardiopulmonary exercise testing. Front Cardiovasc Med. 2023;10:1098395.

Herdy AH, Uhlendorf D. Reference values for cardiopulmonary exercise testing for sedentary and active men and women. Arq Bras Cardiol. 2011;96:54–9.

Dimopoulou I, Tsintzas OK, Daganou M, Cokkinos DV, Tzelepis GE. Contribution of lung function to exercise capacity in patients with chronic heart failure. Respiration. 1999;66:144–9.

Echouffo-Tcheugui JB, Xu H, DeVore AD, Schulte PJ, Butler J, Yancy CW, et al. Temporal trends and factors associated with diabetes mellitus among patients hospitalized with heart failure: findings from get with the guidelines-heart failure registry. Am Heart J. 2016;182:9–20.

Zuin M, Rigatelli G, Battisti V, Costola G, Roncon L, Bilato C. Increased risk of acute myocardial infarction after COVID-19 recovery: a systematic review and meta-analysis. Int J Cardiol. 2023;372:138.

Chagué F, Boulin M, Eicher JC, Bichat F, Saint Jalmes M, Cransac-Miet A, et al. Impact of lockdown on patients with congestive heart failure during the coronavirus disease 2019 pandemic. ESC Hear Fail. 2020;7:4420.

Caraballo C, Desai NR, Mulder H, Alhanti B, Wilson FP, Fiuzat M, et al. Clinical implications of the New York Heart Association Classification. J Am Hear Assoc Cardiovasc Cerebrovasc Dis. 2019;8:e014240.

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Acknowledgements

To the patients who participated in the research. To the Coordination for the Improvement of Higher Education Personnel (CAPES), National Council for Scientific and Technological Development (CNPq) and São Paulo Research Foundation (FAPESP) for maintaining the postgraduate programs in Brazil. University Hospital of Federal University of São Carlos—SP-Brazil (HU-UFSCar) Brazilian Company of Hospital Services (EBSERH). Professor Ph.D. Audrey Borghi-Silva is CNPq Research Productivity Scholarship—Level 1B.

This work was supported by São Paulo Research Foundation (FAPESP Process 15/26501-1).

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Study design: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Conceptualization: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Methodology: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Data collection: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Data analysis and interpretation: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. Initial manuscript writing: ADS, DB, IMD, CLG, RSM, JAM, GSS, PBS, MGR, SAP, AB. All the authors read and approved the final manuscript.

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Correspondence to Audrey Borghi-Silva .

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Santos-de-Araújo, A.D., Bassi-Dibai, D., Dourado, I.M. et al. Type 2 diabetes mellitus negatively affects the functional performance of 6-min step test in chronic heart failure: a 3-year follow-up study. Diabetol Metab Syndr 16 , 229 (2024). https://doi.org/10.1186/s13098-024-01464-z

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DOI : https://doi.org/10.1186/s13098-024-01464-z

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