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Novel cardiovascular risk markers in cardiovascular diseases

Alghibiwi, Hanan (2021) Novel cardiovascular risk markers in cardiovascular diseases. PhD thesis, University of Glasgow.

Introduction:

Cardiovascular disease is the leading cause of death worldwide, and the global prevalence of cardiovascular disease increased every year. Cardiovascular disease (CVD) is a complex multifactorial disease. It is essential to understand the different biological and genetic factors that are associated with the development and progression of CVD in order to improve the diagnosis, prognosis and treatment of the condition and, thereby, reduce mortality and morbidity.

The use of cardiometabolic markers, including blood biomarkers such as troponin, and NT-proBNP, as well as non-invasive imaging markers, such as carotid intima media thickness and pulse wave arterial stiffness, might help to correctly identify patients with a CVD risk at an early stage. These markers may help practitioners understand and monitor disease progression, but whether they provide additional information to the conventional risk factors included in the risk prediction model or disease progression is not currently clear.

In contrast, other biomarkers could help provide insights into pathophysiological processes and help practitioners understand potential drug targets. MicroRNAs (miRNAs) are a family of small, noncoding RNA molecules that regulate gene expression by targeting specific messenger RNA. Dysregulation of specific miRNAs expression have been associated with cardiovascular diseases. Since the discovery of miRNAs in body fluids, including plasma, saliva and urine, a strong body of evidence has been published demonstrating the potential use of circulating miRNAs as markers for several diseases, including CVD.

This thesis studies the possible determinants for imaging markers of cardiovascular disease risk, including carotid intima media thickness and pulse wave arterial stiffness index and further investigates putative circulating miRNAs as novel biomarkers for cardiovascular diseases. The overarching aim is to provide insight into novel biomarkers for monitoring and treatment of cardiovascular disease progression.

The associations of circulating miRNA expression with markers of cardiometabolic diseases: CAMERA trial: Candidate circulating miRNAs were selected on the basis of the findings in the literature review that showed that their dysregulation was associated with a change of cardiometabolic markers such as cardiac enzyme and imaging markers. A cross-sectional analysis was conducted to investigate the associations of circulating miRNAs, miR30c, miR103, miR133a, miR122 and miR146a with cardiometabolic markers, using 60 paired stored plasma samples at baseline and after 18 months from the CAMERA trial. Significant associations were observed for selected circulating miRNAs with biomarkers of cardiovascular risk in a population with coronary heart disease and insulin resistance. A significant association was found between the expression of miR103 with cardiac biomarkers, including troponin T and NT-pro-BNP, and miR122 with carotid intima media thickness. These associations supported previous observations that indicated an association between the upregulated miR122 expression and adverse lipid profile (Willeit et al., 2017a), and the role of lipid in the progression of carotid intima media thickness that was shown in a study by (Huang et al., 2016). In addition, upregulated miR103 expression in response to cardiac necrosis was observed in a study done by Wang et al. (2015a). Therefore, further research is needed to understand the specific role of miR103 in cardiac necrosis.

The effect of metformin on the expression of circulating miRNA: The Carotid Atherosclerosis: MEtformin for insulin ResistAnce (CAMERA) trial : Metformin is the first line therapy for Type 2 diabetes. The effect of metformin on the risk of cardiovascular diseases had been studied in two randomised controlled trials: the UKPDS and the HOME trial. Those studies showed that metformin reduced the risk of CVD through surrogates in patients with diabetes. In this study, the effect of metformin on the expression of these circulating miRNAs was explored using both the baseline samples and the 18-month plasma samples from the CAMERA randomised control trial (RCT). Randomisation to metformin failed to show any effect on the expression of circulating miR30c, miR103, mi133a, miR122 and miR146a in a population without diabetes and with coronary heart disease, although the study was, perhaps, underpowered and the effect of metformin in the study in general was very modest. While no strong evidence for metformin having an effect on these miRNAs was observed, this was one of the first, if not the first, RCT to study miRNA biomarkers and provides a platform for future research in this area.

Association of cardiovascular risk factors with carotid intima media thickness and pulse wave arterial stiffness in UK Biobank; Cross sectional study: More established vascular biomarkers of CVD were studied for the purposes of this thesis, in a much larger study than previously possible, in order to understand whether measurement of the markers might be clinically useful. In this first study, using the UK Biobank (42,727 participants), the determinants of vascular imaging biomarkers (carotid intima media thickness and pulse wave arterial stiffness index) were examined using traditional cardiovascular risk factors including systolic and diastolic blood pressure, HbA1c, lipid markers and anthropometric measurements. A cross-sectional analysis was done to investigate the upstream determinants for carotid intima media thickness and pulse wave arterial stiffness. Generally, systolic blood pressure and age were the strongest independent risk factors for a high cIMT value. It was found that, for every one SD increase in age and systolic blood pressure, the mean cIMT increased by 0.357 and 0.115 SD, respectively. Systolic blood pressure and age were the strongest independent risk factors for high PWASI. It was found that, for every one SD increase in age and systolic blood pressure, the mean PWASI increased by 0.005 and 0.046 SD, respectively. Also, this study showed that the cIMT was higher in males than females, along with other cardiovascular risk factors. This study, therefore, highlights potentially important differences in what these biomarkers indicate about upstream cardiovascular risk. Although both appear to be influenced by established risk factors, in line with the findings of much of the rest of the literature, they are different. This means they may have different clinical utility in different settings.

Carotid intima media thickness and pulse wave arterial stiffness with association of cardiovascular events in UK Biobank: Prospective study: In this UK Biobank based study, the focus was on establishing whether carotid intima media thickness or pulse wave arterial stiffness index was associated with the incidence of cardiovascular diseases, stroke and CHD. In addition, we adjusted for the strongest established determinants based on findings of chapter 5 that include systolic blood pressure and age for cIMT, age and systolic blood pressure for PWASI.

After adjustment, one SD increase of mean cIMT was associated with 50 % increase risk of incidence of CVD events. The incidence of CVD events increased in highest quantile (mean cIMT >748µm) by 3 folds compared to persons in the lowest quantiles (mean cIMT <588µm). While, per one SD change in PWASI, the risk of CVD events decreased by 13% after adjustment with age and gender over a median 3 years of follow up in United Kingdom. Therefore, it appears cIMT is the more convincing biomarker that reflects CVD risk in the short term.

Overall conclusion:

This study has demonstrated the potential utility of circulating miRNAs as novel biomarkers for cardiovascular disease, providing new data for the most promising miRNA biomarkers, in the context of a large and well standardised study relative to much of the rest of the literature. Also, the study reports on the upstream risk factors that are important potential determinants of imaging biomarkers as well as on the association between these imaging markers and incidence of cardiovascular disease in the large UK Biobank population. The understanding of these biomarkers might help in the future to develop the way cardiovascular disease risk is managed in the clinical setting.

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Item Type:	Thesis (PhD)
Qualification Level:	Doctoral
Additional Information:	Supported by funding from King Saud University in Riyadh, Saudi Arabia.
Subjects:	>
Colleges/Schools:	>
Supervisor's Name:	Welsh, Dr. Paul and Logue, Dr. Jennifer
Date of Award:	2021
Depositing User:
Unique ID:	glathesis:2021-82615
Copyright:	Copyright of this thesis is held by the author.
Date Deposited:	04 Feb 2022 13:34
Last Modified:	08 Apr 2022 16:53
Thesis DOI:
URI:

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Cardiovascular risk factors and lifestyle behaviours in relation to longevity: a Mendelian randomization study

Affiliations.

1 Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
2 Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute and Amsterdam Cardiovascular Sciences Research Institute, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
3 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
4 Department of Nephrology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, the Netherlands.
5 MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
6 Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
7 Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
PMID: 33107078
PMCID: PMC7894570
DOI: 10.1111/joim.13196

Background: The American Heart Association introduced the Life's Simple 7 initiative to improve cardiovascular health by modifying cardiovascular risk factors and lifestyle behaviours. It is unclear whether these risk factors are causally associated with longevity.

Objectives: This study aimed to investigate causal associations of Life's Simple 7 modifiable risk factors, as well as sleep and education, with longevity using the two-sample Mendelian randomization design.

Methods: Instrumental variables for the modifiable risk factors were obtained from large-scale genome-wide association studies. Data on longevity beyond the 90 th survival percentile were extracted from a genome-wide association meta-analysis with 11,262 cases and 25,483 controls whose age at death or last contact was ≤ the 60 th survival percentile.

Results: Risk factors associated with a lower odds of longevity included the following: genetic liability to type 2 diabetes (OR 0.88; 95% CI: 0.84;0.92), genetically predicted systolic and diastolic blood pressure (per 1-mmHg increase: 0.96; 0.94;0.97 and 0.95; 0.93;0.97), body mass index (per 1-SD increase: 0.80; 0.74;0.86), low-density lipoprotein cholesterol (per 1-SD increase: 0.75; 0.65;0.86) and smoking initiation (0.75; 0.66;0.85). Genetically increased high-density lipoprotein cholesterol (per 1-SD increase: 1.23; 1.08;1.41) and educational level (per 1-SD increase: 1.64; 1.45;1.86) were associated with a higher odds of longevity. Fasting glucose and other lifestyle factors were not significantly associated with longevity.

Conclusion: Most of the Life's Simple 7 modifiable risk factors are causally related to longevity. Prevention strategies should focus on modifying these risk factors and reducing education inequalities to improve cardiovascular health and longevity.

Keywords: Mendelian randomization; cardiovascular risk factors; instrumental variable analysis; lifestyle; longevity.

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Pathways to longevity - but is it successful? Strandberg TE. Strandberg TE. J Intern Med. 2021 Feb;289(2):264-266. doi: 10.1111/joim.13211. Epub 2020 Dec 19. J Intern Med. 2021. PMID: 33340173 No abstract available.

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Original Research Article
Open access
Published: 08 February 2023

Knowledge, awareness, and presence of cardiovascular risk factors among college staff of a Nigerian University

Uchechukwu Martha Chukwuemeka 1 ,
Favour Chidera Okoro 1 ,
Uchenna Prosper Okonkwo ORCID: orcid.org/0000-0002-5201-651X 1 ,
Ifeoma Adaigwe Amaechi 1 ,
Anthony Chinedu Anakor 2 ,
Ifeoma Uchenna Onwuakagba 1 &
Christiana Nkiru Okafor 3

Bulletin of Faculty of Physical Therapy volume 28 , Article number: 8 ( 2023 ) Cite this article

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Metrics details

Cardiovascular diseases (CVDs) are the leading cause of global morbidity often overlooked. Much of the population risk of CVD is attributable to modifiable risk factors, and the gaps in knowledge of cardiovascular risk factors (CRF) are barriers to the effective prevention and treatment of CVDs.

To assess the knowledge, awareness, and CVD risk among the staff of the college of health science.

A cross-sectional study of 70 academic and non-academic staff who consented were given questionnaires for cardiovascular risk factor (CRF) knowledge level, cardiovascular risk awareness (CRA), international physical activity questionnaire (IPAQ), and international stress management association questionnaire (ISMAQ). Selected anthropometric indices, blood pressure, and fasting blood sugar (FBG) were also measured.

The mean knowledge level of CVDs was 23.21 ± 3.230, and the mean CRA was 42.61 ± 4.237. The study participants demonstrated moderate-to-high stress (48%), physical inactivity of 18.9%, overweight/obesity of 62.48%, abdominal obesity of 21.4%, hypertensive (systole and diastole) of 27.2%, hyperglycemic of 7.2%, and smokers of 7.2%. There was a significant relationship between the participants’ knowledge level and awareness of CVDs ( p < 0.003) and knowledge of CRFs also increased with an increase in educational level. Participants > 40 years had a 3–9% risk of having a CVD event within 10 years.

Conclusions

The knowledge and awareness of CRFs among the participants was high, and some exhibited risk factors. The staff of the university could improve their risk score by practicing health-promoting behaviors like increased physical activity, blood pressure control, and smoking cessation.

Cardiovascular diseases (CVDs) are the leading cause of death globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to heart attack and stroke. Over three-quarters of CVD deaths take place in low- and middle-income countries. Out of the 17 million premature deaths (under the age of 70) due to noncommunicable diseases in 2019, 38% were caused by CVDs [ 1 , 2 ]. Cardiovascular disease (CVD) is not an actual disease in itself; rather, it is a lifestyle disease that is defined as a heart and blood vessel disease, also known as heart disease [ 3 ]. The American Heart Association (2017) refers to cardiovascular diseases as several conditions including but not limited to heart disease, heart attack, stroke, heart failure, arrhythmia, and heart valve problems.

The leading CVD cause of death and disability in 2010 in sub-Saharan Africa (SSA) was a stroke, and CVD deaths in sub-Saharan Africa occur at younger ages on average than in the rest of the world [ 4 ]. Many prospective cohort studies have demonstrated that hypertension is a strong risk factor for total mortality and cardiovascular disease [ 5 ]. A previous report has shown that 38% of people suffering strokes are middle-aged (40-69), and the average age for a woman suffering a stroke has dropped from 75 to 73 and for men, it has dropped from 71 to 68 [ 6 ]. The annual incidence rate of stroke in Africa is up to 316 per 100,000 individuals, which is within the highest incidence rates in the world, and the prevalence rate of 1460 per 100,000 reported in one region of Nigeria, western Africa, is clearly among the highest in the world [ 7 ]. According to a meta-analysis of 20 observational studies, hypertensive subjects have poorer levels of health-related quality of life (HRQoL) than non-hypertensive subjects and this lower perception of HRQoL has been related to the frequent presence of comorbidities, with the side effects of antihypertensive drugs (headaches, dizziness, tinnitus, nausea) and, in particular, with the difficulty in being able to control BP through prescribed therapeutic procedures [ 8 ]. Disability and mortality attributable to CVDs and the traditional risk factors, including hypertension, obesity, diabetes mellitus, and dyslipidemia, continue to rise in several SSA countries, and in 2013, an estimated 1 million deaths were attributable to CVD in SSA, constituting 5.5% of all global CVD-related deaths and 11.3% of all deaths in Africa [ 9 ]. The CVD burden has major ramifications for SSA economies in terms of manpower loss and spiraling healthcare costs, and it also places additional pressure on an already constrained health system in many countries [ 9 ].

Cardiovascular epidemiological studies have demonstrated the association of many risk factors with the development of CVDs, which can be caused by both modifiable and non-modifiable predisposing risk factors and can be prevented mainly through health-promoting lifestyle interventions [ 3 , 10 ]. Non-modifiable risk factors are the factors that cannot be controlled and they include age, gender, genetics, and ethnicity [ 11 ]. A study revealed that age was a main non-modifiable risk factor of CVD and increasing age increases the chances of getting CVD but as a result of modernization associated with the modifiable risk factors for CVD, it now affects all age groups [ 3 ].

Much of the population’s risk of CVD is attributable to nine modifiable traditional risk factors, including smoking, history of hypertension or diabetes, obesity, unhealthy diet, lack of physical activity, excessive alcohol consumption, raised blood lipids, and psychosocial factors, which eight of these risk factors (excessive alcohol use, tobacco use, high blood pressure, high body mass index (BMI), high cholesterol, high blood glucose, dietary choices, and physical inactivity) account for 61% of CVD deaths globally [ 1 ]. The prevalence of CVD risk factors is dramatically increasing in low- and middle-income African countries, particularly in urban areas [ 12 ]. The Global Burden of Disease, Injuries, and Risk Factor Study is the first systematic and comprehensive attempt to map and quantify risk factors and diseases to identify emerging threats to population health and opportunities for prevention [ 1 , 13 ].

University staff is particularly susceptible to preventable risks for ill health including smoking, physical inactivity, poor nutrition, alcohol consumption, high blood cholesterol, and high blood pressure, and a study cited almost 50% of the university staff surveyed were at high to moderate risk for developing ill-health because they exhibited three or more of these risk factors [ 14 ]. Another study showed that the prevalence of non-communicable diseases (NCD) risk factors was found to be substantially high among university employees with unhealthy dietary patterns and physical inactivity being the most prevalent factors [ 15 ]. Cardiovascular disease risk factors cannot be ignored without worsening the health indices of Nigeria, rather these risks should be well researched and their preventive strategies well understood. The risk reduction practices include the avoidance of smoking, exercise, healthy eating, avoidance or moderation of alcohol, regular medical check-ups, and risk assessment scoring [ 16 ]. A sedentary lifestyle is a major risk for CVD, and university staff are more inclined to live a sedentary lifestyle. Therefore, it is important to find out how knowledgeable they are on the risk factors of CVDs as adequate awareness could help reduce the risk factors. The gaps in the knowledge of CVD conditions and their risk factors in the general population are important barriers to the effective prevention and treatment of CVDs and that is what the current study intends to bridge. The role of knowledge in health behaviors and sustained behavioral changes has been proposed by several models including the health belief model; these models postulate that knowledge of a disease condition influences a patient’s attitude and practice improves compliance with treatment and has been shown to lead to a reduction in prevalence and aversion of complications [ 17 ]. Success in the implementation of any health promotion program is dependent on context-specific information on knowledge, awareness, and perception of the targeted population. There is however a regional level scarcity of evidence on the knowledge and awareness levels of CVDs and its risk factors among the populations of SSA.

The study is a cross-sectional survey carried out among academic and non-academic staff of the Faculty of Health Sciences and Technology who were consecutively recruited. The study participants included individuals who were willing to participate and had no cardiac problems before the time of this study. Those individuals who were sick at the time of the study, taking antibiotics, insulin, lipid-lowering drugs, and contraceptives as well as those with a case of cardiovascular diseases such as stroke, hypertensive heart diseases, or heart failure before the time of the study or had an underlying cardiac disease at the time of the study were excluded from the study. The sample size was estimated using the GPower version 3.1.9. A sample size of 71 has 95% power to detect a difference at a moderate effect size of 0.4. The alpha level was set at 0.05.

Before the commencement of this study, ethical approval was obtained from the Ethical Review Committee of the Faculty of Health Sciences, Nnamdi Azikiwe University, Awka. Also, the process and the objectives of the study were explained to the prospective participants, and a written consent document was obtained before the data collation instrument was given to them. Data were collected at the college premises ensuring adequate privacy. Each participant was given a questionnaire to provide data regarding age, marital status, education, knowledge, and awareness of cardiovascular disease risk factors, tobacco use, and alcohol consumption and educated on the necessity to perform a fasting blood sugar test which was carried out the next day before they had breakfast. Their CVD risk knowledge and awareness, BMI, fasting blood glucose, waist circumference, stress levels, physical activity levels, and blood pressure were assessed. Cardiovascular risk knowledge and awareness were assessed using the cardiovascular disease risk factors knowledge level (CARRF-KL) and cardiovascular risk factor (CRF) questionnaires to check the level of individual CVD risk awareness. Both questionnaires were scored by summing up all scores for all the items in the questionnaire.

CARRF-KL consisted of 28 items. The first 4 items were examining the factors like characteristics of CVD, prevention, and age, and 15 items (items 5, 6, 9–12, 14, 18–20, 23–25, 27, 28) were examining the risk factors and 9 items (items 7, 8, 13, 15, 16, 17, 21, 22, 26) were examining the outcome of changes in risk behaviors. All the items were presented in the form of complete true or false statements, requiring participants to respond with “Yes,” “No,” or “Don’t know.” Each correct answer was given a score of 1. Six of the statements in the scale were wrong (1, 11, 12, 16, 24, 26), and these were inversely encoded compared to the rest. The maximum total score was determined as 28. The CARRF-KL has been used in other studies with reliable test-retest reliability, internal consistency, and validity [ 18 ]. CRF was used to assess their attitude to risk factors, smoking habits, and alcohol consumption. The maximum obtainable score on the questionnaire is 48. Eight of the items are scored on a 2-point scale, while the remaining items were scored on a 5-point scale. For the two-point scale “yes” denotes “1” and “no” denotes “0” except for two items (CRA3c and CRA3f) where “yes” denotes “0” and “no” denotes “1,” whereas for the other 5-point scales “Extremely & Very correct” denotes “5,” “Very much & Correct” denotes “4,” “Much & Undecided” denotes “3,” “A little & Incorrect” denotes “2” and “Not at all & Very incorrect” denotes “1.” The CRA was categorized as high CRA if the CRA score is >35, moderate CRA if the CRA score is from 25 to 35, and low CRA if is <25 [ 19 ]. The level of participant smoking habit or tobacco consumption was assessed by asking the participant if they smoked or not if they consumed cigarettes, Indian hemp, pipes full of tobacco, snuff, etc. (one or more of the listed products), when they started smoking, how frequent they smoked and the number of each product consumed per day. Participant’s alcohol consumption was assessed by asking the participants if they took alcohol or not, how many units (bottle) of light (beer, long drinks, or equivalent), how many units (big glass) of moderate (wine, whisky, cognac or equivalent), and how many units (medium glass) of strong (vodka, whisky, cognac or equivalent) alcohol beverage they drank in a week and the frequency of having at least one unit of alcohol drink per week in the past 6 months. The International Stress Management Association Questionnaire (ISMAQ) is a 25-item questionnaire with a total obtainable stress score of 25. It is measured on a two-point scale of yes and no in which “yes” is designed as 1 and “No” as 0. Stress levels were categorized as “low stress” for a score of ≤3, “moderate stress” for a score between 4 and 10, and “high stress” if the stress score is >10. Self-reported PA levels of participants were assessed using the short form 7 days International Physical Activity Questionnaire (IPAQ-SF). The IPAQ-SF was scored by rating PA level as multiples of metabolic equivalents (METS) expressed as MET-min per week: vigorous (8 METS), moderate (4 METS), and walking (3.3 METS). The physical activity was classified into 3 groups: inactive—the lowest level of physical activity with less than 600 MET-minutes/week, minimally/moderately active—achieving a minimum of at least 600 MET-minutes/week, and vigorously/HEPA active—achieving a minimum of at least 3000 MET-minutes/week.

Weight was measured in kilograms using a weighing scale. The participant was asked to stand erect on the weighing scale, looking straight, and wearing light clothing. The reading was then taken when the pointer was stabilized. Height was measured in centimeters using a height meter. The participant stood erect, looking straight ahead, and barefoot. The measurement was taken from the vertex of the head. The BMI of the participants was calculated from their respective height in meters and weight in kilograms using the formula: weight (kg)/height (m 2 ). The systolic and diastolic blood pressure (BP) was measured in a sitting position using an automatic (Andon, KD-595) sphygmomanometer after the participants have rested for at least 5 min. Then, the cuff was wrapped around 2cm above the cubital fossa of the left arm supported at heart level. Consecutive BP values were taken and the average was recorded as the actual BP level. Blood sugar was assessed with the participants’ not eating 8–12h before the time of the test using the glucometer. The surface of one of the fingers was wiped clean with an alcohol swab. The glucometer was turned on and a test strip was inserted. A lancet was then used to pierce the tip of the wiped finger, and a drop of blood was placed on the test strip. The result was recorded in mg/dL with values >126mg/dL being recorded as diabetes. CVD risk was calculated using the WHO non-laboratory-based chart to assess each participant’s 10-year risk of having a CVD event. It compared each participant’s smoking level to their BMI, SBP, and age with the risk being higher for participants above 40 years and above. The risk level was graded using a color code to group the risk level with each color indicating a 10-year risk of a fatal or non-fatal CVD event. It was graded as green (<5%), yellow (5 %–< 10%), orange (10 %–< 20%), red (20 %–< 30%), and deep red (≥30%).

Statistical analysis

The data were analyzed using the IBM SPSS version 23. The data from this study were summarized using proportions, percentages, mean, and standard deviations as well as charts. The data were analyzed using Spearman rank correlation, Kruskal Wallis test, and Mann-Whitney U . The alpha level was set at <0.05.

Seventy participants (mean age 36.54 ± 9.961years) were involved in this study. Fifty percent of the study participants were males, about 30% (mostly males) were single, and 37.1% of the female participants were married. The mean values of the knowledge and awareness were CARRF-KL 23.21 ± 3.230 and CRA 42.61 ± 4.237. The physical characteristics, cardiovascular parameters, and risk factors of the participants are presented in Table 1 .

According to Table 2 , a higher proportion of the male participants recorded higher stress levels, higher systolic and diastolic blood pressure values, and higher blood glucose levels while the majority of the female participants recorded higher values for BMI, waist circumference, and waist-hip ratio. The majority of the participants were moderately active and a significant positive relationship ( r = 0.353) between CARRF-KL and CRA with a p value of 0.003 was also found. Figure 1 shows physical activity level in relation to BMI.

Bar chart of relationship of BMI with physical activity level

Table 3 showed the correlation between CARRF-KL and educational level, depicting that the participants who had a Ph.D. had the highest knowledge of cardiovascular risk factors with a mean rank of 49.29 while the participants with WASSCE had the lowest knowledge of cardiovascular risk factors with a mean rank of 29.19. The CRA in relation to educational level showed higher mean values for participants with a Master’s degree and the lowest for participants with a bachelor’s degree. The mean rank of the awareness of cardiovascular risk factors of smokers and non-smokers to cardiovascular diseases showed 33.90 and 35.62, respectively. Their knowledge of cardiovascular risk factors mean rank was recorded with the former being 38.30 and the latter 35.28. The majority of the participants (57.1%) were below the age of 40, but participants 40 and above had between 3 and 9% risk of having a CVD event within the next 10 years as seen in Table 4 .

General awareness of the cardiovascular disease, the knowledge of risk factors for developing cardiovascular diseases, physical activity level, stress, BMI, waist circumference, fasting blood sugar level, and blood pressure were assessed. The participants were made up of an equal number of male and female participants, and the result of this study corresponds with other literature [ 20 , 21 ]. The knowledge of cardiovascular risk factors was relatively good in this study population in contrast to some other studies among Staff of Ekiti State University with the majority (68.6%) of the study population having low knowledge [ 22 ] and among the staff of Ladoke Akintola University of Technology with a percentage of low, moderate, and high knowledge being recorded as 49.0%, 31.1%, and 19.9%, respectively [ 23 ]. The high knowledge reported in our study could be a result of the geographic location and that the population was the staff who worked in a college of health science and this could result in an increase in their level of knowledge of cardiovascular diseases. Aysel Badir’s study concurred with this postulation, as it reported that students who had graduated from vocational health schools had a higher mean knowledge score than those who graduated from high schools (the total mean CARRF-KL score was 22.47 ± 3.38 out of a maximum of 28 (female = 22.63 ± 3.31; male = 20.82 ± 3.57)) [ 24 ].

The awareness level of cardiovascular risk factors among this population in the current study was generally good, and this finding was supported by a study that showed that more than half of the respondents had high awareness of cardiovascular risk factors [ 25 , 26 ]. The majority of the participants knew about CVD and were able to describe and identify some CVD conditions although some wrongly identified diabetes and HIV/AIDS as a CVD. Smokers had a higher knowledge of cardiovascular risk factors and a lower awareness level of cardiovascular risk factors. In comparison, a reverse relationship existed for non-smokers who had better awareness of the cardiovascular risk factors and a lower knowledge of cardiovascular risk factors. It was reported in a study carried out on adults in the Muar community that both smokers and non-smokers were aware of the risk associated with smoking, but this awareness did not influence 58.3% of them to prevent or stop smoking [ 27 ]. Another study also showed that health students were aware of the health risks associated with tobacco use, although their knowledge of the health risk was not enough to dissuade them from smoking [ 28 ]. A study carried out among working-age people in the Ga-Rankuwa community in 2008 revealed that among its participants, 60% were knowledgeable about and aware of the inherent risks of smoking for cardiovascular disease [ 29 ].

Among those who consumed alcohol, those who drank 5 units of light alcohol had a higher knowledge of cardiovascular risk factors while those who drank four units of alcohol had higher awareness of cardiovascular risk factors than those who drank less or did not drink at all. Contrarily to the outcome of this research, a study carried out in 2006 reported that out of all the correspondents that participated, only 16% were able to identify alcohol as a major risk factor for stroke [ 30 ]. The reason for the difference in knowledge levels may not be because there is limited information on the effects of alcohol on cardiovascular health or that participants of our study have a higher knowledge level, but that alcohol has wider acceptability and use worldwide. What people may not know is what quantity of alcohol intake constitutes cardiovascular risk. Although there is limited literature on the knowledge and awareness of alcohol with regard to CVDs, a study has reported that most of the burden associated with alcohol use stems from regular heavier drinking; affirming that the effect of alcohol consumption on hypertension is almost entirely detrimental with the risk increasing as the consumption increases [ 31 ].

This study supported the assertion that an increasing level of knowledge of CVD and its risks comes with an increased level of education. Those who had a Ph.D. had the highest level of knowledge on cardiovascular disease risk factors while the knowledge steadily decreased in lower education levels with secondary education having the lowest level of knowledge [ 32 ]. This could be attributed to the fact that those with higher educational levels have a higher knowledge of cardiovascular risk factors as a result of increased exposure to information and a better understanding of how to prevent or control these risk factors. Participants who had a master’s had a higher level of awareness followed by those with secondary education, Ph.D., and Bachelor’s degree having the lowest level of awareness. This also concurred with results from studies that showed that lower educational levels increased cardiovascular risk factors among participants of their study and respondents with a higher level of education generally have better lifestyles and adherence to treatment [ 33 , 34 ]. Yuqiu and Wright, however, reported no clear trend was determined between the risk factors investigated with regard to educational level, but there was a positive trend between age and awareness [ 29 ].

The average BMI and waist circumference values reflected a generally overweight population when compared to the normal BMI value of the national heart, lung, and blood institute. Approximately equal numbers of males and females were overweight and the same number were obese. This agrees with the results gotten from a Malaysian study which also had equal percentages of male and female overweight participants in their study [ 35 ]. This study revealed that 34.7% of the participants had a waist-to-hip ratio that put them at moderate to high risk of cardiovascular diseases and a greater part of this percentage was made up of females which corresponded with the aforementioned Malaysian study reporting a higher prevalence of abdominal obesity among female respondents [ 35 ].

The mean values of the diastolic and systolic blood pressures gotten in this study conformed to the WHO standards for diastolic and systolic blood pressures and conforms to the results of a similar study showing that the average value of SBP and DBP (78.80 and 116.71, respectively) were relatively normal among the participants [ 36 ]. Among those with elevated blood pressure (SBP 5.7%; DBP 12.9%) in our study, males had a higher percentage of hypertensive participants. This also correlates with the results gotten from a study where approximately 75% of males were hypertensive ( p <0.001) [ 37 ]. Males also had a higher percentage of diastolic pre-hypertensive participants while the systolic pre-hypertensive count was relatively equal, thus further emphasizing the higher tendency of males to acquire diastolic hypertension. This difference was explained by Maranon and Reckelhoff [ 38 ] as a result of differences in androgen secretion in males and females.

Findings from this study revealed that the majority of the participants were involved in a moderately active lifestyle and this could be attributed to their increased knowledge level. This is in line with a study that reported academic staff have a high perception that physical activity enhances the quality of life and knowledge about the usefulness of physical activity in promoting good health [ 39 ]. A study carried out in Udupi district Karnataka recorded a larger percentage (85.9%) of the participants were moderately to vigorously active [ 40 ]. The tendency to engage in physical activity was higher among males than in females and this is supported by a couple of studies [ 41 , 42 ]. This disparity between gender and physical activity could be attributed to the fact that males are generally stronger and abler at accomplishing higher-intensity physical activity than females [ 43 ].

The majority of the participants were moderately active comprising more than 50% of the participants. Different studies state that there is an inverse relationship between PA and BMI, i.e., as physical activity increases the value of BMI decreases, leading to an increase in muscle mass and a decrease in fat mass [ 44 ], and participants who were physically active at high levels, had a higher rate of overweight/obese than underweight and normal BMI and this was associated to their misperception of body image [ 45 ]. Although in contrast, a previous study reported that underweight, overweight, and obese participants had poorer performance in the physical activity index than normal-weight participants. This difference from our results may be due to environmental or lifestyle factors [ 46 ].

Implication of findings

The implication of this study revolves around the need for the staff of the university to improve their risk scores by practicing health-promoting behaviors like increased physical activity, blood pressure control, and smoking cessation. This entails that without deliberate efforts to promote health behaviors people can slide into health challenges that are associated with modifiable risk factors. The findings have challenged stakeholders in the health industry to redouble their efforts in their various areas of practice to improve awareness creation among the populace to lessen the risk factors that bring about health issues. We will go back to the population to create awareness of health-enhancing behaviors as a way of appreciating them for participating in the current study. This, we believe, will help in strengthening their resolve to adopt health behavior strategies that will help them stay healthy. Also, awareness creation in the area of knowing the risk associated with a lack of awareness of CVD risk factors should be prioritized by the authors during the dissemination of the study outcome to the study population.

Limitation of study

The research design used in this study was a cross-sectional survey which may not guarantee the authenticity of the participant’s responses. Also, the small sample size may hinder the generalization of the outcome. Based on the two limitations the outcome should be interpreted with caution.

Availability of data and materials

The data is with the corresponding author and will be made available at a reasonable request.

Abbreviations

Low- and middle-income countries

Cardiovascular disease

Sub-Saharan Africa

Health-related quality of life

Blood pressure

Physical activity

Body mass index

Non-communicable disease

Cardiovascular Disease Risk Factors Knowledge Level

Cardiovascular risk factor

Systolic blood pressure

Diastolic blood pressure

World Health Organization

International Physical Activity Questionnaire Short-Form

International Stress Management Association Questionnaire

Metabolic equivalents

International business machine

Statistical Package of Social Sciences

West Africa Senior School Certificate

Human immunodeficiency virus

Acquired immunodeficiency syndrome

Doctor of philosophy

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Uchechukwu Martha Chukwuemeka, Favour Chidera Okoro, Uchenna Prosper Okonkwo, Ifeoma Adaigwe Amaechi & Ifeoma Uchenna Onwuakagba

Department of Physiology, Faculty of Basic Medical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

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Chukwuemeka, U.M., Okoro, F.C., Okonkwo, U.P. et al. Knowledge, awareness, and presence of cardiovascular risk factors among college staff of a Nigerian University. Bull Fac Phys Ther 28 , 8 (2023). https://doi.org/10.1186/s43161-023-00119-w

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Am J Prev Cardiol
v.5; 2021 Mar

Ten things to know about ten cardiovascular disease risk factors

Harold e. bays.

a Medical Director / President, Louisville Metabolic and Atherosclerosis Research Center, Louisville, KY USA

Pam R. Taub

b University of California San Diego Health, San Diego, CA USA

Elizabeth Epstein

Erin d. michos.

c Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Richard A. Ferraro

Alison l. bailey.

d Chief, Cardiology, Centennial Heart at Parkridge, Chattanooga, TN USA

Heval M. Kelli

e Northside Hospital Cardiovascular Institute, Lawrenceville, GA USA

Keith C. Ferdinand

f Professor of Medicine, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA USA

Melvin R. Echols

g Assistant Professor of Medicine, Department of Medicine, Cardiology Division, Morehouse School of Medicine, New Orleans, LA USA

Howard Weintraub

h NYU Grossman School of Medicine, NYU Center for the Prevention of Cardiovascular Disease, New York, NY USA

John Bostrom

Heather m. johnson.

i Christine E. Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital/Baptist Health South Florida, Clinical Affiliate Associate Professor, Florida Atlantic University, Boca Raton, FL USA

Kara K. Hoppe

j Assistant Professor, Division of Maternal Fetal Medicine, Department of Obstetrics & Gynecology, University of Wisconsin School of Medicine and Public Health, Madison, WI USA

Michael D. Shapiro

k Center for Prevention of Cardiovascular Disease, Section of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC USA

Charles A. German

l Section of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC USA

Salim S. Virani

m Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center and Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX USA

Aliza Hussain

n Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX USA

Christie M. Ballantyne

o Department of Medicine and Center for Cardiometabolic Disease Prevention, Baylor College of Medicine, Houston, TX USA

Ali M. Agha

Peter p. toth.

p CGH Medical Center, Sterling, IL USA

q Cicarrone center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD USA

Given rapid advancements in medical science, it is often challenging for the busy clinician to remain up-to-date on the fundamental and multifaceted aspects of preventive cardiology and maintain awareness of the latest guidelines applicable to cardiovascular disease (CVD) risk factors. The “American Society for Preventive Cardiology (ASPC) Top Ten CVD Risk Factors 2021 Update” is a summary document (updated yearly) regarding CVD risk factors. This “ASPC Top Ten CVD Risk Factors 2021 Update” summary document reflects the perspective of the section authors regarding ten things to know about ten sentinel CVD risk factors. It also includes quick access to sentinel references (applicable guidelines and select reviews) for each CVD risk factor section. The ten CVD risk factors include unhealthful nutrition, physical inactivity, dyslipidemia, hyperglycemia, high blood pressure, obesity, considerations of select populations (older age, race/ethnicity, and sex differences), thrombosis/smoking, kidney dysfunction and genetics/familial hypercholesterolemia. For the individual patient, other CVD risk factors may be relevant, beyond the CVD risk factors discussed here. However, it is the intent of the “ASPC Top Ten CVD Risk Factors 2021 Update” to provide a succinct overview of things to know about ten common CVD risk factors applicable to preventive cardiology.

What is already known?

• Since 2020, the “American Society for Preventive Cardiology (ASPC) Top Ten CVD Risk Factors” has summarized the clinical relevance of ten important CVD risk factors towards the goal of preventing CVD events. [1]
• Among factors that increase the risk of CVD include unhealthful nutrition, physical inactivity, dyslipidemia, hyperglycemia, high blood pressure, obesity, considerations of select patient populations (older age, race/ethnicity and sex differences), thrombosis/smoking, kidney dysfunction, and genetics/familial hypercholesterolemia.
• Diagnosing and treating multiple CVD risk factors help prevent or reduce the risk of CVD.

What is new?

• The “ASPC Top Ten CVD Risk Factors 2021 Update” summarizes ten important CVD risk factors from the perspective of section authors. This update reflects several new guidelines, and contains hundreds of new references, most of them from 2018–2020.
• Primary care clinicians (family practice, internal medicine, nurse practitioners, physician assistants, obstetrics/gynecology, etc.) may benefit from an overview summary of multiple CVD risk factor identification and management. Specialists may benefit because a specialist in one aspect of preventive cardiology may not necessarily have expertise in all aspects of preventive cardiology.
• In addition to the “Top Ten” things to remember for each of ten sentinel CVD risk factors summarized in tabular form, updated sentinel citations are listed in the applicable tables to reflect the latest science and provide in-depth resources (e.g., illustrative guidelines and other selected references).

1. Introduction

The “American Society for Preventive Cardiology (ASPC) Top Ten Cardiovascular Disease (CVD) Risk Factors 2021 Update” is intended to help both primary care clinicians and specialists be better informed about the ever-increasing pace of advances in CVD prevention. The “ASPC Top Ten CVD Risk Factors 2021 Update” summarizes ten things to know about ten important CVD risk factors, listed in tabular formats, and updated by section authors. These CVD risk factors include unhealthful nutrition, physical inactivity, dyslipidemia, hyperglycemia, high blood pressure, obesity, considerations of select populations, sex differences, and race/ethnicity, thrombosis/smoking, kidney dysfunction, and genetics/familial hypercholesterolemia. The intent is not to create a comprehensive discussion of all aspects of preventive cardiology. Instead, the intent is to focus on fundamental clinical considerations in preventive cardiology. For those wishing a more intensive discussion of any of these CVD risk factors, this “ASPC Top Ten CVD Risk Factors 2021 Update” also provides illustrative and updated guidelines and other selected references in the applicable tables, for the reader to access more detailed information.

The summary approach of the “ASPC Top Ten CVD Risk Factors 2021 Update” may benefit primary care clinicians (family practice, internal medicine, nurse practitioners, physician assistants, obstetrics/gynecology, etc.), who may welcome an overview of how CVD risk factors are best diagnosed and managed. Specialists may benefit, because a “specialist” in one aspect of preventive cardiology may not always have expertise in other basic aspects of preventive cardiology. Additionally, many (most) patients with CVD have multiple CVD risk factors, which requires a multifactorial approach. Patients with CVD, or who are at risk for CVD, benefit from global CVD risk reduction, with appropriate attention given to all applicable CVD risk factors. It may therefore be helpful for clinicians to have an overview of core principles applicable to the multiple CVD risk factors that often occur within the same patient who has CVD, or who is at risk for CVD. Finally, compared to prior versions, this version includes updates and different perspectives from different authors. Interested readers may elect to review prior versions of “ASPC Top Ten CVD Risk Factors” publications for different perspectives on these same topics, and to see how thinking may have evolved. [1]

2. Unhealthful nutrition

2.1. definition.

The primary components of medical nutrition therapy for CVD prevention include qualitative composition, energy content, and food consumption timing. ( Fig. 1 ) From 2015–2018, 17.1% of US adults ≥ 20 were on a “special diet” on a given day. More women were on a special diet than men, and more adults aged 40–59 and ≥ 60 were on a special diet than adults aged 20–39. The most common type of special diet reported among all adults was a weight loss or low-calorie diet. From 2007–2008 through 2017–2018, the percentage of adults on any special diet, weight loss or low-calorie diets, and low carbohydrate diets increased, while the percentage of adults on low-fat or low-cholesterol diets decreased. [2]

Thinking outside the plate: CVD prevention via a focus on eating pattern quality, quantity, energy density, timing, and patient engagement. [9] , [10] , [11] , [12]

The most healthful dietary strategy incorporates evidence-based nutrition and feeding patterns. Dietary patterns most associated with reduced CVD risk are those that: [3] , [4] , [5] , [6]

○ Vegetables, fruits, legumes, nuts, whole grains, seeds, and fish.
○ Foods rich in monounsaturated and polyunsaturated fatty acids such as fish, nuts, and non-tropical vegetable oils.
○ Soluble fiber.
○ Saturated fat (e.g., ultra-processed red meats and tropical oils).
○ Excessive sodium.
○ Cholesterol, especially in patients at high risk for CVD with known increases in cholesterol blood levels with increased cholesterol intake.
○ Ultra-processed carbohydrates and meats
○ Sugar-sweetened beverages.
○ Alcoholic beverages [ 7 , 8] .
○ Trans fats.

Positive caloric balance and increased body fat increase the risk of CVD. [3] One objective of healthful nutrition is to achieve a healthy body weight (see “Overweight and Obesity” section). This might best be achieved by medical nutrition therapy that incorporates qualitative dietary intake (e.g., avoiding ultra-processed foods, including sugar-sweetened foods), quantitative caloric restriction (e.g., avoiding energy dense foods), and possibly, temporal restriction of food. [13]

Food consumption can affect the microbiome. Lower microbiota diversity is associated with increased CVD risk. [14] Gut microbiota may generate short chain fatty acids, affect bile metabolism, and result in exposure to intestinal lipopolysaccharides that may stimulate proinflammatory signaling, potentially promoting obesity and metabolic disease. [ 5 , 14] Potentially pathogenic gut microbiota can generate trimethylamine-N-oxide (TMAO), which is a metabolite biomarker associated with increased atherosclerosis and thrombosis. [ 14 , 15] TMAO levels can potentially be reduced by replacing animal meats with plant-based foods. [16] Having said this, increased fish intake (generally thought healthful) can also increase TMAO, suggesting that concentration of TMAO alone cannot simply be interpreted as a marker of unhealthy food intake or unhealthy dietary pattern. [17]

Important to CVD prevention is that the microbiome can affect CVD drugs via metabolism, activation, deactivation, toxic metabolite production, modulation of transport, alteration in biliary excretion, with effects on the potential for therapeutic effect and drug toxicity. [18] No prospective CVD outcomes trial has yet demonstrated that altering the microbiome in humans reduces CVD risk or events.

2.2. Epidemiology

• Data from 2015–2016 suggests the prevalence of obesity (body mass index/BMI ≥ 30 kg/m 2 ) was ∼ 40% of United States (US) adults. [19] Projections suggest that most of today's children (∼ 60%) will develop obesity at the age of 35 years, and roughly half of the projected prevalence will occur during childhood. [20]
• Positive caloric balance may result in enlargement of adipocytes and adipose tissue, resulting in adiposopathy (i.e., adipose tissue intracellular and intercellular stromal dysfunction leading to pathogenic adipose tissue endocrine and immune responses) that directly and indirectly contribute to metabolic diseases – most being major risk factors for CVD. [ 5 , 21] Some of the most common adiposopathic metabolic consequences of obesity are major CVD risk factors such as type 2 diabetes mellitus (T2DM) and hypertension. [ 5 , 22] Over the past decades, the rates of T2DM and hypertension have dramatically increased. [22]
• Concomitant with the increased prevalence of obesity and metabolic CVD risk factors is the intake of energy dense foods with low nutritional value, eating dealignment with circadian rhythms, [23] and consumption of fast foods. [24]

Positive caloric balance and increased body fat increase the risk of CVD. [5] Atherosclerotic CVD (ASCVD) is rare among hunter-gatherers populations, whether the nutritional intake is higher in fat or lower in fat.[ 25 , 26] While sometimes higher, total energy expenditure among rural hunter-gathers may not always be substantially different from more industrialized populations. [ 26 , 27] However, hunter-gather populations not only have reduced prevalence of CVD risk factors, but also low risk for CVD. This may be partially related to their preferential consumption of whole foods and fiber, as well as their dependence on daylight for feeding and, therefore, eating patterns better aligned with natural circadian rhythms. Where hunter-gathers dramatically differ from more industrialized populations is BMI. The BMI of hunter-gather populations is typically < 20 kg/m 2 , [28] which is substantially below the BMI of many industrialized nations where CVD is the #1 cause of death among men and women. The reduced potential for adiposopathic consequences leading to CVD risk factors and CVD helps explain why hunter-gatherer populations have lower blood pressure, and a total cholesterol level of ∼ 100 mg/dL, compared to a total cholesterol level of ∼ 200 mg/dL in adult Americans, [29] and an overall reduced rate of CVD.

2.3. Diagnosis and treatment

Table 1 lists ten things to know about nutrition and CVD prevention.

Ten things to know about nutrition and cardiovascular disease (CVD) prevention.

Saturated fat intake may promote atherogenesis via increased low-density lipoprotein cholesterol (LDL-C) levels, increased apolipoprotein B levels, increased LDL particle number, increase inflammation, and endothelial dysfunction. , In isocaloric settings, CVD risk is reduced when saturated fats are replaced by unsaturated fats and when ultra-processed carbohydrates are replaced by fiber rich complex carbohydrates found in healthful whole foods such as vegetables and fruits. all CVD risk factors. Both dietary patterns prioritize vegetables, fruits, whole grains, fat-free or low-fat dairy products, fish, poultry, lean meats, nuts, seeds, legumes, and fiber. A vegetarian meal plan includes plant-based foods such as vegetables, fruits whole grains, legumes, seeds, and nuts. Some “vegetarian diets” allow for eggs and milk. Animal meats are discouraged. The Ornish Diet is illustrative of a highly fat-restricted nutritional intervention wherein macro and micronutrients are best eaten as natural whole food. The Ornish Diet includes vegetables, fruits, whole grains, legumes, and soy with limited amounts of green tea. , fatty acids. No long-term prospective clinical trial indicates that the ketogenic diet reduces CVD; however, ketogenic diets are often successful in promoting clinical weight loss in patients with overweight or obesity. The ketogenic diet may also lower postprandial glucose/insulin levels, lower blood pressure, lower triglyceride levels, and raise high density lipoprotein cholesterol (HDL-C) levels. Especially if the relatively high proportion of dietary fat with the ketogenic diet is composed of saturated fats, then LDL-C levels may increase, which may prompt consideration of replacing saturated fats with monounsaturated and/or polyunsaturated fats. , , , If the ketogenic diet is suspected to have promoted an increase in cholesterol intestinal absorption, then reduced dietary cholesterol and/or a cholesterol absorption inhibitor (e.g., ezetimibe) might be considered. , However, time-mediated caloric restriction is also a potential option, such as intermittent fasting, fasting-mimicking diets, and time restricted eating. Intermittent fasting [e.g., no food intake a certain number of days per week, such as alternate day fasting or fasting 2 days per week (5:2)] and fasting-mimicking diet (e.g., 5 days per week of low-calorie, low carbohydrate, proportionately higher fat nutritional intake), may facilitate weight reduction and improve CVD metabolic risk factors. Specifically, intermittent fasting may reduce overall caloric intake, reduce body weight, and improve metabolic parameters (e.g. improve insulin sensitivity, blood pressure, lipids, and inflammatory markers, even among patients with metabolic syndrome treated with statins and anti-hypertensive agents). This can often be achieved while preserving resting metabolic rate and lean body mass, , especially if accompanied by routine physical activity. , , , ( ) However, at least one prospective, randomized clinical trial demonstrated that compared to consistent meal timing (eating throughout the day, such as 3 meals per day and snacks), TRE had no significant effect on weight or metabolic markers, but did show a decrease in appendicular lean mass. Prioritizing early in the day eating may promote greater diet induced thermogenesis and relatively favorable effects on blood glucose and insulin concentrations compared to eating large evening meals. , In an isocaloric setting, greater meal frequency (grazing with multiple small meals and frequent snacks) may not afford clinically meaningful metabolic advantages over 3 standard meals per day. However, food energy density / total daily caloric intake, food quality, food consumption times, and food knowledge and education may all play a role in affecting major CVD risk factors (hyperglycemia, high blood sugar, high blood lipids). ( ) dietary supplements do not reduce CVD, which includes supplementation with vitamin D. , , Conversely, vitamin intake in the form of healthful whole food consumption (e.g., fruits and vegetables) are associated with reduced risk of CVD. A notable example is the consumption of dairy products containing micro- and macronutrients (e.g., proteins, calcium, magnesium, potassium, vitamins) that may reduce inflammation and reduce CVD risk. , The balanced nutrients within “whole food” or “full fat” dairy consumption may help explain why dairy intake is often reported to have a neutral or favorable effect on CVD risk, even when some of the fatty acids in dairy foods are saturated fats. , , , While healthful plant-based foods (whole grains, fruits, vegetables, nuts, legumes, oils, tea, and coffee) may reduce CVD risk, unhealthful plant-based intake (juices, sweetened beverages, ultra-refined grains, potatoes/fries, and sweets) may increase CVD risk, This (in addition to genetics and other factors) helps account for a relatively high rate of CVD among many vegetarians from India. and meta-analyses suggest supplements containing a combination of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) effectively may reduce CVD events. Yet no prospective, randomized clinical trial data has yet proven this to be true. Conversely, prospective clinical outcomes data do support prescription icosapent ethyl (purified EPA) as reducing CVD events in patients at high CVD risk with baseline hypertriglyceridemia. In contrast to the meta-analyses of non-prospective EPA and DHA supplement studies, early discontinuation due to futility of the prospective, randomized Outcomes Study to Assess STatin Residual Risk Reduction With EpaNova in HiGh CV Risk PatienTs With Hypertriglyceridemia (STRENGTH) trial of Epanova (a free fatty acid, omega-3 carboxylic acid prescription agent) suggests concentrated capsule EPA and DHA intake (beyond nutritional intake) does not reduce CVD events in patients with hypertriglyceridemia. , Barriers exist to healthful eating patterns, such as cost, convenience/preparation time, family taste preferences, and limitations of federal food assistance programs to low-income individuals. Another barrier includes a lack of education regarding the purchase and preparation of healthful foods, which may be facilitated by shared culinary medical appointments (i.e., including nutrition and cooking lessons with cardiac rehabilitation appointments). Methods to implement healthful nutrition include educating patients regarding evidenced-based meal plans and dietary practice guidelines, and referring patients to a dietitian nutritionist to implement medical nutrition therapy to help manage CVD risk factors and reduce CVD risk. ,

2019 A Clinician's Guide to Healthy Eating for Cardiovascular Disease Prevention
2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guideline
2018 Clinician's Guide for Trending Cardiovascular Nutrition Controversies: Part II
2017 Dietary Fats and Cardiovascular Disease: A Presidential Advisory From the American Heart Association

3. Physical inactivity

3.1. definition and physiology.

Physical activity is any bodily movement produced by skeletal muscles that requires energy expenditure. [ 68 , 69] The intensity of physical activity is defined in terms of metabolic equivalent units (METS). One MET is defined as the oxygen consumed while sitting at rest and is equal to 3.5 ml O 2 per kg body weight x minutes. [70] Light activity (e.g., slow walking) is 1.6–2.9 METS, moderate-intensity activity (e.g., moderate speed walking) is 3.0–5.9 METS and vigorous activity (e.g., moderate jogging) is ≥6 METS. As a frame of reference, patients who undergo cardiac stress testing and able to achieve ≥ 10 METS (e.g., high moderate to fast jogging) on a treadmill without ST-depression are generally at very-low risk for CVD. [71] Sedentary behavior refers to any waking activity with a low level of energy expenditure while sitting or lying down (1–1.5 METS).[ 72 , 73]

Physical exercise is a subcategory of physical activity that is “planned, structured, repetitive, and aims to improve or maintain one or more components of physical fitness.” [68] Physical activity also includes muscle activity during leisure time, for transportation, and as part of a person's work – often termed non-exercise activity thermogenesis (NEAT). [68] Among two individuals of similar size, NEAT can be the single greatest inter-individual difference in daily energy expenditure, with variances of up to 2000 kcal per day; [74] the energy expenditure due to NEAT physical activity often exceeds the daily energy expenditure due to physical exercise. [75] Physical inactivity increases the risk of CVD, [ 76 , 77] not unlike other risk factors such as cigarette smoking and dyslipidemia. [78]

3.2. Epidemiology

• In the US one in two adults live with a chronic disease. Only 50% of adults get sufficient physical activity to reduce the risk of many chronic diseases such as CVD [79]
• Roughly $117 billion in US healthcare costs yearly and 10% of premature mortality is associated with inadequate physical activity [79]
• Only 26% of US adult men and 19% of adult women obtain guideline-directed activity levels according to federal physical activity monitoring data. [80]
• Worldwide, approximately 3.9 million premature deaths annually might be prevented with adequate physical activity. [81]

3.3. Diagnosis and treatment

One example of clinically implementing physical activity is a physical exercise prescription that includes frequency, intensity, time spent, type, and enjoyment (FITTE). [ 5 , 82 , 83] Table 2 lists ten things to know about the diagnosis and treatment of physical inactivity and CVD prevention.

Ten things to know about physical inactivity and cardiovascular disease (CVD) prevention.

, , , Physical activity above these recommendations may provide additional benefit. , Evidence supports benefits of muscle strengthening exercises (resistance training) of major muscle groups 2 – 3 times per week. For patients unable to meet recommended physical activity goals, some moderate to vigorous physical activity (even less than recommended amounts) may help reduce CVD risk. A separate goal is to reduce sedentary behavior, even if replaced by light activity. , Mortality risk reduction can be achieved with even short periods of daily exercise. Once a physical activity treatment plan is crafted by the clinician based upon patient history and physical exam, and assessed through “physical activity vital signs,” it is common to find not all patients will adhere to physical activity guidance. For example, after 12 months from intervention, physical activity assessment and recommendations may result in 1 out of 12 sedentary adults meeting international recommended levels of physical activity (i.e., a number needed to treat of 12). ), diabetes mellitus, and well-controlled hypertension, resistance training ≥3 times/week may be beneficial to reduce CVD risk, improve insulin sensitivity, and reduce resting blood pressure. Specifically, while true it is best for patients to have blood pressure controlled before embarking on a strenuous resistance training program, it is also true resistance training in patients with prehypertension or medication-controlled hypertension may reduce systolic and diastolic blood pressure. , , Prior to recommending resistance or dynamic training, patients benefit from an evaluation of their medical status, as well as a review of the planned physical activity. , , , ] , Individuals with decreased physical activity, immobility, and/or muscle wasting medications will often have a decrease in muscle mass. Patients with sarcopenia (i.e., often found in older individuals) may have a BMI in the normal weight range, but high percent body fat and increase in visceral fat and android fat (i.e., abdominal subcutaneous adipose tissue plus visceral adipose tissue), which is a body composition profile associated with increased risk for CVD. , , , , Less than 5000 steps per day is considered sedentary; ≥10,000 steps per day is considered active. While ≥10,000 steps per day may be optimal, any amount of physical activity above baseline has CVD benefits. , , Brisk walking is a moderate intensity activity that most patients can do towards their recommended 150 minutes/week that confers CVD benefits similar to other types of moderate to vigorous activities. , ,

2020 Top 10 Things to Know about the Second Edition of the Physical Activity Guidelines for Americans
2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease: The Task Force on sports cardiology and exercise in patients with cardiovascular disease of the European Society of Cardiology (ESC)
2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines
2018 The Physical Activity Guidelines for Americans
2018 Physical Activity Guidelines Advisory Committee

4. Dyslipidemia

4.1. definition and physiology.

Lipids include fats, steroids, phospholipids, steroids, triglycerides, and cholesterol that are important cellular components of body tissues and organs. Lipids are carried in the blood by lipoproteins. Except for cholesterol carried by HDL particles (and in some cases, possibly chylomicrons), other lipoproteins that carry cholesterol are atherogenic. Atherogenic lipoproteins may become entrapped within the subendothelial space, where they may undergo oxidation and scavenging by arterial macrophages, resulting in foam cells, fatty streaks, and then atherosclerotic plaque formation. Progressive enlargement of the atherosclerotic plaque may produce chronic hemodynamically significant narrowing of the artery resulting in angina or claudication; acute plaque rupture may cause myocardial infarction and/or stroke.

Atherogenesis is promoted by increased numbers of atherogenic lipoproteins. Apolipoprotein B (apoB) levels and low-density lipoprotein (LDL) particle number are predictors of ASCVD risk and are superior to measuring the cholesterol carried by atherogenic lipoproteins (i.e., LDL) in predicting atherosclerotic CVD risk (i.e., LDL-C). This is especially true when atherogenic lipoprotein particle numbers are discordant with atherogenic lipoprotein cholesterol levels, [102] as may occur with diabetes mellitus or adiposopathic dyslipidemia. [ 5 , 103] However, largely because of convention, and because CVD outcomes trials of lipid-altering drugs have specified LDL-C as the primary lipid efficacy parameter, LDL-C remains the primary lipid treatment target in most dyslipidemia management guidelines.

Remnant lipoproteins are formed in the circulation via triglyceride-rich lipoproteins that undergo lipolysis by various lipases, such as chylomicrons and very-low-density lipoproteins (VLDL), leading to small VLDL and intermediate density lipoproteins (IDL). Lipoprotein remnant cholesterol is the cholesterol carried by lipoprotein remnants and is a marker of ASCVD risk. Remnant cholesterol is sometimes defined as blood cholesterol not contained in LDL and HDL particles. The methodology of measuring and reporting lipoprotein remnants vary, and often do not correlate well with one another. [104] Measurement of remnant lipoprotein cholesterol is not included in most major lipid management guidelines.

One molecule of apolipoprotein (apo) B is found on each atherogenic lipoprotein. The collection of all cholesterol carried by atherogenic lipoproteins (i.e., except HDL cholesterol) is termed non-HDL cholesterol (calculation of non-HDL cholesterol = total cholesterol – HDL cholesterol). Because apo B and non-HDL cholesterol better reflects ASCVD risk (compared to LDL-C alone), measurement of these biomarkers may provide additional useful information regarding risk for CVD events and are sometimes included in lipid management guidelines and societal recommendations. [ 105 , 106]

Finally, regarding definitions, lipid treatment “targets” are often defined as the lipid parameter being treated (e.g., LDL-C), lipid “goals” are the desired lipid parameter level, and lipid “threshold” is the level by which if exceeded, may prompt the addition or intensification of lipid-lowering therapy (e.g., LDL-C ≥ 70 mg/dL for patients at very high CVD risk). [ 96 , 97] While some prior lipid guidelines were interpreted as suggesting lipid “goals” were no longer clinically justified, [107] , [108] , [109] , many current inter-societal and international lipid guidelines have reaffirmed goals or thresholds in the management of patients with dyslipidemia. [ 110 , 111]

4.2. Epidemiology

According to the US Centers for Disease Control: [112]

• Data reported from 2015–2016 suggests that more than 12% of adults age 20 and older had total cholesterol higher than 240 mg/dL
• Only slightly more than half of US adults (55%, or 43 million) who could benefit, are taking cholesterol-lowering pharmacotherapy
• The number of US adults age 20 or older who have total cholesterol levels higher than 200 mg/dL is approximately 95 million, with nearly 29 million adult Americans having total cholesterol levels higher than 240 mg/dL

4.3. Diagnosis and treatment

Table 3 lists ten things to know about the diagnosis and treatment of dyslipidemia and CVD prevention.

Ten things to know about lipids and cardiovascular disease (CVD) prevention.

, However, compared to LDL-C, apolipoprotein B, non-HDL-C and LDL particle number may be better predictors of CVD risk in select populations, such as patients with diabetes mellitus, obesity, hypertriglyceridemia, non-fasting blood samples, and those with low LDL-C levels. , , that inputs CVD risk factors (i.e. < 5% = low risk; 5 – 7.4% = borderline risk; 7.5 – 19.9% = intermediate risk; ≥ 20% = high ten year CVD risk) or the MESA 10 year risk coronary heart disease (CHD) calculator found at which among its 12 risk factors includes coronary artery calcification race/ethnicity and family history. It is recommended that patients with ASCVD initially receive high intensity statin therapy (i.e., atorvastatin 40 – 80 mg per day, or rosuvastatin 20 – 40 mg per day). The objective of lipid-altering therapy with statins is to achieve a ≥ 50% reduction in LDL-C and achieve an LDL-C <70 mg/dL. In patients at very-high risk, achieving an LDL-C of < 55 mg/dL may also be appropriate, , with no apparent threshold below which further incremental risk reduction is not observed. Achieving lower levels of LDL-C often requires addition of other lipid-lowering drugs to statin therapy, such as ezetimibe, proprotein convertase subtilisin kexin (PCSK) 9 inhibitors, bempedoic acid, or bile acid binding resins. , No CVD outcomes trials have yet shown that reducing Lp(a) levels reduces the risk of CVD events. However, such trials are ongoing. Management of HeFH includes aggressive cholesterol lowering at an early age, usually involving statin therapy. In appropriate patients, “high intensity statins” (atorvastatin 40 - 80 mg or rosuvastatin 20 – 40 mg) may lower LDL-C ≥ 50%, and are often recommended as first-line therapy in patients with CVD or at high risk for CVD. , The most common clinical manifestation of statin intolerance is statin-associated muscle symptoms (SAMS), which may limit the dose or use of statins. , SAMS can sometimes be mitigated by rechallenging with the same statin at a lower dose, using different statins, or recommending statins be administered a few days per week, rather than daily. , , Occasionally, the maximally tolerated dose of a statin is no statin, requiring use of other lipid-altering drugs to achieve clinically desirable LDL-C levels. Bempedoic acid lowers LDL-C ∼ 18%, and when combined with ezetimibe in a fixed dose combination, lowers LDL-C ∼ 38%. A CVD outcome study with bempedoic acid is ongoing. , , . In Europe, it is noted the risk for hypertriglyceride-induced pancreatitis is clinically significant at a severely elevated triglyceride level of 10 mmol/L (880 mg/dL). In the US, very high triglyceride levels are typically defined as ≥ 500 mg/dL, that may not only increase CVD risk, but also increase the risk of hypertriglyceride-induced pancreatitis – sometimes resulting in recurrent bouts of hypertriglyceride-induced pancreatitis. , Omega-3 fatty acids lower triglycerides and non-HDL-C. Prescription icosapent ethyl is an eicosapentaenoic acid, ethyl ester agent that reduces the risk of multiple CVD endpoints in patients at high CVD risk having triglyceride levels ≥ 150 mg/dL. A CVD outcome study of a selective peroxisome proliferator-activated receptor alpha modulator (pemafibrate) is ongoing, with an entry criteria being patients with diabetes mellitus having hypertriglyceridemia and low HDL-C levels. ,

2020 Handelsman Y. Consensus Statement By The American Association Of Clinical Endocrinologists And American College Of Endocrinology On The Management Of Dyslipidemia And Prevention Of Cardiovascular Disease
2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines
2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk.
2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. .

5. Hyperglycemia

5.1. definition and physiology.

Diabetes mellitus is a pathologic condition characterized by high blood glucose. Type 1 diabetes results from an absolute deficiency of insulin secretion. The early stages of T2DM are often characterized by insulin resistance, that when accompanied by an inadequate insulin secretory response, results in hyperglycemia. Among patients with T2DM, the relative degree of insulin resistance and insulin secretion can substantially vary. [135] Diabetes mellitus can be diagnosed [136] with one of the following measurements:

• Hemoglobin A1c level ≥ 6.5%.
• Fasting plasma glucose ≥ 126 mg/dL on two successive measurements.
• Random glucose level of ≥ 200 mg/dL.
• Oral glucose tolerance test with 2 h glucose value ≥ 200 mg/dL.

Diabetes mellitus contributes to both microvascular disease (e.g., retinopathy, nephropathy, neuropathy) and macrovascular disease. Hyperglycemia may contribute to atherosclerosis via direct and indirect mechanisms. Direct adverse effects of elevated circulating glucose levels include endothelial dysfunction, oxidative stress, heightened systemic inflammation, activation of receptors of advanced glycosylated end products, increased LDL oxidation, and endothelial nitric oxide synthase (eNOS) dysfunction. Indirect adverse effects of elevated glucose levels include platelet hyperactivity. While insulin resistance (i.e., as might be mediated by mechanisms involving adiposopathic responses associated with obesity) often leads to hyperglycemia, hyperglycemia may conversely contribute to insulin resistance via glucotoxicity. [137] Normalizing hyperglycemia and reduced glucotoxicity is one proposed mechanism how sodium glucose co-transporter 2 inhibitors may increase peripheral insulin sensitivity. [138] Insulin resistance may increase non-esterified circulating free fatty acids and worsen dyslipidemia, (e.g., increased very low-density lipoprotein hepatic secretion, reduced HDL-C levels, and increased small, more dense LDL particles). [139]

Women with prior history of gestational diabetes are at increased risk for T2DM. [140] Many risk factors for CVD are also risk factors for gestational diabetes (e.g., increased body fat, physical inactivity, increased age, nonwhite race, hypertension, reduced HDL-C, triglycerides ≥ 250 mg/dL). A history of gestational diabetes mellitus doubles the risk for CVD. [141] Diagnosis of gestational diabetes mellitus (GDM) includes a 75 g oral glucose tolerance test (OGTT) performed at 24–28 weeks of gestation. GDM is diagnosed when fasting glucose levels are ≥ 92 mg/dL, or 2 h glucose levels ≥ 153 mg/dl. The diagnosis of GDM is also made when during an OGTT, the 1 h glucose levels is ≥ 180 mg/dL. [142]

5.2. Epidemiology

T2DM is associated with double the risk for death and a 10-fold increase in hospitalizations for coronary heart disease. [143] According to the US Centers for Disease Control: [144]

• About 30.3 million US adults have diabetes mellitus; 1 in 4 may be unaware.
• Diabetes mellitus is the 7 th leading cause of death in the US.
• Diabetes mellitus is the most common cause of kidney failure, lower-limb amputations, and adult onset blindness.
• In the last 20 years, the number of adults diagnosed with diabetes mellitus has more than doubled.

5.3. Diagnosis and treatment

Table 4 lists ten things to know about the diagnosis and treatment of diabetes mellitus and CVD prevention.

Ten things to know about diabetes mellitus and cardiovascular disease (CVD) prevention.

A confounder is that metformin (and comprehensive lifestyle management) was commonly used as background therapy for CVD outcomes trials of other anti-diabetes agents that have demonstrated reduction in CVD risk. Thus, metformin and weight management and physical activity often remain first-line therapies for patients with T2DM. Additional pharmacotherapy may include anti-diabetes mellitus agents known to have CVD outcomes benefits, [e.g., some sodium glucose transporter 2 (SGLT2) inhibitors and some glucagon like peptide-1 (GLP-1) receptor agonists], whose use should be considered independently of baseline hemoglobin A1c or individualized hemoglobin A1c goal. CVD outcomes trials in patients with T2DM support empagliflozin and canagliflozin as effective in reducing CVD events, and empagliflozin, canagliflozin, dapagliflozin, ertugliflozin as effective in preventing hospitalizations due to heart failure. In patients with ischemic CVD or heart failure treated with comprehensive lifestyle intervention and metformin, SGLT2 inhibitors having CVD benefits should be considered as next line therapy. , Also, the management of CVD is often complicated by kidney disease, with kidney disease being a risk factor for CVD In addition to their favorable CVD effects, SGLT2 inhibitors may reduce the progression of kidney disease. , Some GLP-1 receptor agonists have clinical trial evidence supporting a reduction in ischemic CVD (e.g., liraglutide, semaglutide, dulaglutide). In patients with ischemic CVD treated with comprehensive lifestyle intervention and metformin, GLP-1 receptor agonists having CVD benefits should be considered as next line therapy. , In patients with CVD, or at risk for CVD, sulfonylureas are among the last anti-diabetes mellitus agents to consider, except perhaps when cost is a major barrier to use of other anti-diabetes agents for glucose control. Dipeptidyl peptidase-4 inhibitors have a neutral effect on body weight and atherosclerotic CVD; saxagliptin may increase the risk of hospitalization for heart failure.

2020 Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes
2020 Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes
2020 Cardiorenal Protection With the Newer Antidiabetic Agents in Patients With
Diabetes and Chronic Kidney Disease A Scientific Statement From the American Heart Association
2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases.
2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease

6. High blood pressure

6.1. definition and physiology.

Hypertension (HTN) can be defined as arterial blood pressure readings that, when persistently elevated above ranges established by medical organizations, adversely affect patient health. African Americans have a higher prevalence of HTN than Caucasians, helping to account for a higher rate myocardial infarction, stroke, chronic and end-stage kidney disease (ESKD), and congestive heart failure among African Americans. [ 159 , 160]

A challenge with diagnosis of HTN is ensuring accurate measurement: [ 161 , 162]

• Patients should avoid caffeine, physical exercise, stress, and/or smoking for 30 min prior to blood pressure measurement.
• Patients should have an empty bladder, have clothing removed from the arm, be seated with feet flat on the floor, relaxed and quiet for 5 min prior to blood pressure measurement.
• Blood pressure should be obtained by properly validated and calibrated blood pressure measurement device, with proper cuff size, and taken by trained medical personnel.
• On first measurement date, blood pressure should be measured in both arms by repeated values separated by at least one minute, with a record of the values and respective arms (left and right).
• Longitudinally, future blood pressure measurement should be on the same arm previously recorded as having the highest blood pressure measurement.

6.2. Epidemiology

According to the US Centers for Disease Control: [163]

• Uncontrolled HTN rates are rising in the US, with nearly half of adults in the US (108 million, or 45%) having HTN defined as a systolic blood pressure ≥ 130 mm Hg or a diastolic blood pressure ≥ 80 mm Hg or are taking medication for hypertension.
• Approximately 1 in 4 adults (24%) with HTN have their blood pressure under control.
• At least half of adults (30 million) with blood pressure ≥140/90 mm Hg who should be taking medication to control their blood pressure are not prescribed or are not taking medication.

6.3. Diagnosis and treatment

Diagnosing HTN requires accurate assessment and measurement. In a medical office setting, blood pressure should be measured by a properly validated and calibrated BP measurement device, with proper cuff size, and taken by trained medical personnel. [ 6 , 164] Regarding blood pressure self-monitoring outside of a medical office setting (e.g., home, workplace), validated blood pressure measuring devices can be found at the US Blood Pressure Validated Listing (VDL™ at https://www.validatebp.org ), which is an American Medical Association web-based independent review initiative that determines blood pressure measuring devices available in the US that meet the Validated Device Listing Criteria. Most guidelines and scientific statements do not recommend the routine use of finger devices and wrist cuffs because of higher likelihood of efforts associated with incorrect positioning. [164]

Ambulatory blood pressure monitoring (ABPM) is often performed out of the office setting via a blood pressure cuff device that records blood pressure readings every 15–30 min intervals, typically for 24 to 48 h. Because of repeated blood pressure measurements over an extended time, ABPM is superior to a single office blood pressure measurement in the overall assessment of blood pressure, with implications regarding assessment of target organ damage and CVD risk. Some believe ABPM is the gold standard measurement for any patient with high blood pressure. Selected patients who may especially benefit from ABPM include patients with otherwise variable blood pressure readings or patients with suspected “white coat” or “masked” hypertension. [165]

Lowering blood pressure reduces CVD risk, reduces the progression of kidney disease, and reduces overall mortality among a range of patients otherwise at risk for CVD, including patients with and without high blood pressure. [ 161 , [166] , [167] , [168] , [169] , [170] ] Table 5 lists ten things to know about the diagnosis and treatment of HTN and CVD prevention.

Ten things to know about hypertension and cardiovascular disease (CVD) prevention.

, Self-monitoring of blood pressure can also help assess the effectiveness of HTN therapy. Conversely, older individuals may experience signs and symptoms of hypotension if blood pressure is treated too aggressively, (i.e., with signs and symptoms of hypotension and potential decrease in myocardial perfusion with reduction in diastolic blood pressure < 70 mmHg). , , Due to these effects and the results of CVD outcomes trials, the American College of Cardiology has recommended chlorthalidone as the preferred thiazide or thiazide-type diuretic. , Thiazide diuretics are often a first-line therapy for HTN. Loop diuretics (e.g., furosemide, torasemide, bumetanide, and azosemide may be preferred in patients with heart failure (especially torasemide) and when estimated glomerular filtration rate is < 30 ml/min. , , , Beta blockers reduce CVD in patients with reduced ejection fraction, are used to treat angina pectoris and cardiac dysrhythmias, and may reduce the risk of recurrent myocardial infarction after an acute myocardial infarction. However, the blood pressure lowering may be less than with other anti-hypertensive drug treatments. [ , ,

2020 Self-Measured Blood Pressure Monitoring at Home: A Joint Policy Statement From the American Heart Association and American Heart Association
2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease
2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines
2018 ESC/ESH Guidelines for the management of arterial hypertension
2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults

7. Overweight and obesity

7.1. definition and physiology.

Overweight is defined as BMI > 25 and < 30 kg/m 2 . Obesity is defined as BMI ≥ 30 kg/m 2 . An increase in BMI is associated with an increase in coronary artery calcium, carotid intimal medial thickness, left ventricular thickness, [ 182 , 183] and increased lifetime CVD risk, [ 182 , 184] substantially mediated by obesity-promoted CVD risk factors. [ 5 , 185] Among patients with increased muscle mass (“body builders”), their increase in BMI might erroneously suggest an increase in body fat, while in patients with decreased muscle mass (sarcopenia), BMI might underestimate body fat. [5]

Obesity can be subcategorized into different classes: [186]

• Class I (BMI 30–34.9 kg/m2)
• Class II (BMI 35–39.9 kg/m2)
• Class III (or “severe;” BMI ≥40 kg/m2)

The adverse biomechanical aspects of obesity (“fat mass disease”) often compromise cardiac function via pericardial mechanical restraint, impaired left ventricular expansion, impaired left ventricular filling, and diastolic heart failure. [5] Obesity can also lead to adipocyte and adipose tissue dysfunction (“sick fat”). Adiposopathy is defined as pathogenic adipose tissue anatomic/functional disturbances promoted by positive caloric balance in genetically and environmentally susceptible individuals that result in adverse endocrine and immune responses that may directly promote CVD, and may cause or worsen metabolic disease. [21] Adiposopathy is analogous to cardiomyopathy, myopathy, encephalopathy, ophthalmopathy, retinopathy, enteropathy, nephropathy, neuropathy, and dermopathy. Pathologic enlargement of heart cells and heart organ results in anatomic/functional abnormalities leading to “cardiomyopathy.” Pathogenic enlargement of fat cells and fat organ results in anatomic/functional abnormalities leading to “adiposopathy.” The dysfunction of adipose tissue (adiposopathy or “sick fat”) can be caused by positive caloric balance, physical inactivity, genetic predisposition, and environmental causes. Anatomic manifestations of adiposopathy include adipocyte hypertrophy, visceral, pericardial, perivascular, and other periorgan adiposity, growth of adipose tissue beyond its vascular supply, increased number of adipose tissue immune cells, and “ectopic fat deposition” in other body organs (e.g., liver, muscle, kidney). Pathophysiological manifestations of adiposopathy include impaired adipogenesis, pathological adipocyte organelle dysfunction (e.g., mitochondrial and endoplasmic reticulum “stress”), increased circulating free fatty acids, pathogenic adipose tissue endocrine responses (e.g., increased leptin, increased tumor necrosis factor-alpha, decreased adiponectin, and increased mineralocorticoids), and pathogenic adipose tissue immune responses (e.g., increased proinflammatory responses through increased tumor necrosis factor-alpha and decreased anti-inflammatory responses through decreased adiponectin). [ 5 , 26] Among the clinical manifestations of the adiposopathic consequences of obesity include hyperglycemia, high blood pressure, dyslipidemia, metabolic syndrome, and fatty liver, which are associated with increased CVD risk. [5]

In 2020, an illustrative clinical example of how obesity contributes to cardiopulmonary disease was the Severe Acute Respiratory Syndrome coronavirus (COVID-19) pandemic. In general, it was previously known that obesity increased the risk and severity of upper respiratory tract infections (URI). This was thought partially due to fat mass disease-mediated compromise of lung function with reduced tidal volume, reduced forced expiratory volume (FEV 1), sleep apnea, day and nighttime hypoxia, as well as general debilitation and immobility. [5]

Additionally, “sick fat disease” adiposopathic responses were known to predispose to infection and worse outcomes due to disruption of innate and acquired immunity and exaggerated pro-inflammatory responses. With the onset of the COVID-19 pandemic, patients with obesity were among the most frequently affected, and most adversely affected. This was made more challenging because many patients feared exposure to the virus and thus limited their urgent and chronic medical care for obesity, metabolic diseases, and cardiovascular disease management – further worsening outcomes - especially among patients with obesity, diabetes, hypertension, and CVD. [187] Additional challenges included limited access to providers and temporary closure of many cardiac rehabilitation programs. In response, an ASPC Scientific Statement recommended: (1) expansion of telehealth visits; (2) enhanced self-monitoring of clinical signs and symptoms; (3) strategies towards medication adherence; (4) better utilization of team-based care; and (5) more aggressive adherence to lifestyle recommendations – including therapies directed at obesity management. [187]

While the adiposopathic manifestations of obesity result in immunopathies and endocrinopathies that indirectly increase CVD risk, obesity may also result in adiposopathic consequences that directly increase CVD risk. Epicardial and visceral fat share the same mesodermal embryonic origin, both are associated with increased CVD risk, and both are highly correlated with increased coronary calcification. Epicardial adipose tissue can directly contribute to heart failure (e.g., especially heart failure with preserved ejection fraction or HFpEF), atherosclerosis, cardiac dysrhythmias, fatty infiltration of the heart, and increased coronary calcium potentially related to pathogenic adipose tissue surrounding the heart, as well as pathogenic paracrine and vasocrine signaling and transmission of inflammatory factors, fatty acids, and possibly transport of atherogenic lipoproteins (i.e., “outside to in” model of atherosclerosis) [5]

In short, worsening obesity directly correlates with worsening impact on the cardiovascular system, with mortality, nonfatal coronary heart disease, and congestive heart failure increased among patients with severe obesity versus those with lesser classes of obesity. [188] The adverse effect of obesity on CVD can be both indirect through obesity-mediated development of major CVD risk factors (e.g. T2DM, HTN, and dyslipidemia) or direct via fat mass effects or adiposopathic epicardial immune and endocrine effects. [ 5 , 189 , 190]

Percent body fat more accurately assesses body fat than BMI. However, while percent body fat analysis may provide diagnostic clarity, measures of percent body fat differ in their accuracy and reproducibility. Dual X-ray absorptiometry (DXA) is often considered a “gold standard” for body composition analysis. Currently, the cut-off points for percent body fat are largely based on subjective opinion. Conversely, much data supports waist circumference and assessment of android/visceral fat as correlating to CVD risk, because an increase in waist circumference reflects adiposopathic dysfunction, which both directly and indirectly increases the risk of CVD. [5]

Another measure of potential clinical benefit is the waist-to-hip ratio. An elevated waist-to-hip ratio (> 0.9 in men; > 0.83 in women) may be a better predictor of myocardial infarction than an elevated BMI. [ 191 , 192] However, not all analyses support clinically meaningful differences between BMI, waist circumference, waist to hip ratio and weight to height ratio. [193] Also, BMI, waist circumference, waist to hip ratio and weight to height ratio are not direct measures of android or visceral adiposity, which are anthropometric measures most associated with CVD risk. Differences in android and visceral fat accumulation helps explain the differences in CVD risk between men and women, [194] can be measured by DXA for initial assessment, and then followed longitudinally by DXA to determine response to obesity treatment. [5]

The metabolic syndrome [195] is an LDL-C-independent clustering of CVD risk factors that include 3 or more of the following:

• Elevated waist circumference [men ≥ 40 inches (102 cm); women ≥ 35 inches (88 cm)]. Different waist circumference diagnostic criteria may apply to different races or ethnicities (e.g., Asian men ≥ 40 cm; Asian women ≥ 80 cm) [196]
• Elevated triglycerides ≥ 150 mg/dL (1.7 mmol/L), or use of medications for high triglycerides
• Reduced HDL-C (men < 40 mg/dL (1.03 mmol/L); women < 50 mg/dL (1.29 mmol/L), or use of medications for low HDL-C
• Elevated blood pressure (≥ 130/85 mm Hg or use of medication for HTN)
• Elevated fasting glucose ≥ 100 mg/dL (5.6 mmol/L) or use of medication for hyperglycemia.

An increase in waist circumference is the only anatomic abnormality listed in defining metabolic syndrome and reflects the importance of adiposopathic endocrine and immune abnormalities leading to CVD risk factors and CVD itself. [95]

7.2. Epidemiology

According to the US Centers for Disease Control: [19]

• The prevalence and severity of obesity in US adults has significantly increased from 1999–2000 through 2017–2018 [19]
• In 2017–2018, the age-adjusted prevalence of obesity (BMI ≥ 30 kg/m 2 ) was ∼ 40% of US adults [19]
• In 2017-2018, non-Hispanic black adults (49.6%) - especially non-Hispanic black women (56.9%) - had the highest age-adjusted prevalence of obesity compared with other race and Hispanic-origin groups [19]
• In 2017-2018, the age-adjusted prevalence of severe obesity (BMI ≥ 40 kg/m 2 ) was 9.2% of US adults [19]
• Complications of obesity include heart disease and stroke
○ CVD risk factors (e.g., diabetes mellitus, HTN, dyslipidemia).
○ Cardiovascular hemodynamics and heart function.
○ Heart, heart cells, and structure (which can result in electrocardiogram tracing abnormalities).
○ Atherosclerosis and MI.
○ Adiposopathic immunopathies that promote CVD risk factors and CVD.
○ Adiposopathic endocrinopathies that promote CVD risk factors and CVD.
○ Thrombosis.

While current antiobesity drug treatments can improve CVD risk factors, their use is limited to only ∼ 1% of eligible patients. [197] No current anti-obesity drug has CVD outcomes data to support anti-obesity drugs reduce CVD events. However, CVD outcomes trials are ongoing to determine if existing or future anti-obesity drugs reduce CVD events. [ 5 , 198]

Bariatric surgery continues to evolve as a treatment for obesity. [5] Bariatric surgery not only reduces CVD risk factors (i.e., T2DM, HTN and dyslipidemia, [199] but also reduces the risk of MI, stroke, and all-cause mortality. [ 200 , 201] Similar to anti-obesity drugs, bariatric surgery is performed in less than 1% of appropriate patients for which it is indicated. [202] Among the few medically eligible patients who receive treatment with bariatric surgery, significant disparities exist according to race, income, education level, and insurance type. [203]

7.3. Diagnosis and treatment

Table 6 lists ten things to know about the diagnosis and treatment of increased body fat and CVD prevention.

Ten things to know about increased body fat and cardiovascular disease (CVD) prevention

, , Obesity directly increases the risk of CVD (e.g., via adiposopathic effects of epicardial fat), and indirectly increases the risk of CVD via the adiposopathic promotion of major CVD risk factors such as diabetes mellitus, high blood pressure, dyslipidemia, and thrombosis, as well as other conditions associated with increased CVD risk (e.g., sleep apnea, polycystic ovary disease, gestational diabetes, fatty liver). , , Both weight reduction, and weight loss maintenance often present challenges in patients with overweight or obesity. Given that obesity is a multifactorial disease, overweight and obesity are best managed utilizing a multifactorial approach including nutrition, physical activity, motivational interviewing, behavior modification, pharmacotherapy, and possibly bariatric surgery. [ , , In patients wherein the GLP-1 receptor agonist is being used to treat T2DM, liraglutide, semaglutide, and dulaglutide reduce the risk of CVD events. Anti-obesity agents (including GLP-1 receptor agonists alone or in combination with other molecular entities) are being evaluated in CVD outcomes trials. When accompanied by weight loss, many anti-obesity drugs reduce CVD risk factors (i.e., orlistat, liraglutide, naltrexone/bupropion, and phentermine/topiramate are not contraindicated in patients with CVD).

2021 Obesity Algorithm eBook, presented by the Obesity Medicine Association.
2020 Obesity in Adults: A Clinical Practice Guideline
2015 Pharmacological Management of Obesity: An Endocrine Society Clinical Practice Guideline
2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults

8. Considerations of selected populations (older age, race/ethnicity, sex differences)

8.1. definition and physiology, 8.1.1. older individuals.

Older individuals (i.e., > 75 years of age) vary considerably in their future risk for CVD and life expectancy. This variance in CVD risk and mortality is largely dependent on underlying co-morbidities and degree of frailty. [216] Given the paucity of evidenced-based data among older individuals for the primary prevention of CVD, treatment recommendations are best determined by shared decision-making utilizing a patient-centered approach. [ 110 , 216] Clinicians should tailor discussions to individual CVD risk factors, complexity of concurrent illnesses, considerations of the quality of life, and cost issues related to polypharmacy. [216]

8.1.2. Race

South Asians [217] may be at increased CVD risk largely due to increased prevalence of metabolic syndrome (even at a lower BMI), insulin resistance and adiposopathic dyslipidemia (sometimes called ‘‘atherogenic dyslipidemia”), which can be defined as elevated triglycerides, reduced HDL-C levels, increased LDL particle number, with an increased prevalence of smaller, more dense LDL particles, and increased lipoprotein(a), all which may increase CVD risk. [26] Asians may also have increased risk of thrombosis as evidenced by increased plasminogen activator inhibitor, fibrinogen, lipoprotein (a), and homocysteine. Asians may have other factors that increase CVD risk such as impaired cerebrovascular autoregulation and sympathovagal activity, increased arterial stiffness, and endothelial dysfunction, [218] although it is uncertain if endothelial function of Asian Indians are inherently attenuated in comparison to Caucasians. [219]

Having South Asia heritage is considered an ASCVD risk enhancing factor [110] The “Mediators of Atherosclerosis in South Asians Living in America (MASALA)” was a longitudinal cohort of South Asians in the United States. This study supported South Asians as experiencing a disproportionately high burden of prevalent and incident T2DM compared with members of other race/ethnic groups. [220] The same applies to ASCVD. After adjusting for ASCVD risk factors, South Asians may have greater coronary artery calcification progression than Chinese, black, and Latino men but similar change to that of whites. [221]

South Asians make up over 20% of the world population. South Asians can be defined as those with ethnic roots originating from the Indian subcontinent (e.g., India, Pakistan, Sri Lanka, Nepal, and Bangladesh). Having said this, the term “South Asian” represents a heterogeneous population, with differences in diet, culture, and lifestyle among different South Asian populations and religions. Nonetheless, multiple studies have confirmed that South Asians have a 3- to 5-fold increase in the risk for myocardial infarction and cardiovascular death as compared with other ethnic groups. [222]

African Americans have among the highest CVD rates of any US ethnic or racial group. African Americans often have more favorable selected lipid parameters compared with Caucasian Americans (e.g., higher HDL-C levels and lower triglyceride levels), and lower coronary artery calcium (CAC) than whites. Conversely, African Americans have a higher prevalence of HTN, left ventricular hypertrophy, obesity, T2DM, chronic kidney disease (CKD), and elevated lipoprotein (a) levels. [223]

Hispanic/Latino individuals often have elevated triglyceride and reduced HDL-C levels, and increased risk for insulin resistance. A ‘‘Hispanic Mortality Paradox’’ is sometimes described wherein the Hispanic/Latino population is reported as having a lower overall risk of mortality than non-Hispanic Whites and non-Hispanic Blacks (albeit higher risk of mortality than Asian Americans). [224] Nonetheless, CVD is the leading cause of death among Hispanics and the “Hispanic Paradox” may not apply to all Hispanic/Latino subpopulations. [225] Thus, to reduce CVD risk, Hispanic/Latino individuals should undergo diagnosis and treatment of CVD risk factors similar to other ethnicities / races. [226]

Native Americans are defined as members of indigenous peoples of North, Central, and South America, with American Indians and Alaskan Natives often residing in North America. [227] In 2018, American Indians / Alaska Natives were 50% more likely to be diagnosed with CVD compared to non-Hispanic Whites, which may be related to a higher prevalence of CVD risk factors such as obesity, diabetes mellitus, HTN, and higher rates of cigarette smoking. [227] Pima (Akimel O'odham or “river people”) Indians are a subset of American Indians located in southern Arizona and northern Mexico. Pima Indians have a high rate of CVD risk factors (e.g., high prevalence of obesity, insulin resistance, T2DM, higher triglyceride levels, reduced HDL-C levels, and higher prevalence of metabolic syndrome). [228] Older literature suggests incident CVD events among Pima Indians may not be as high as predicted. [229] This is, in part, because in some cases compared to Caucasians, untreated LDL-C levels may be lower among Pima men older than 30 and in women older than 25 years of age. [228] Despite a potential lower CVD risk compared to Caucasians, heart disease remains a major cause of mortality among Pima Indians, especially among those with concomitant renal failure. [230]

Women with CVD risk factors are at increased risk for CVD events, directionally similar to men. CVD is the leading cause of mortality among women. [231] CVD causes ∼ 4 times as many deaths in women compared to breast cancer. [232] Compared to men, women are at higher risk for bleeding after invasive cardiac procedures, and are more predisposed to autoimmune/inflammatory disease, and fibromuscular dysplasia, potentially predisposing to myocardial infarction in the absence of atherosclerotic obstructive coronary arteries - especially among younger women. [233] According to the 2018 American Heart Association, American College of Cardiology Guideline on the Management of Blood Cholesterol, premature menopause and hypertensive disorders of pregnancy (i.e., preeclampsia) are CVD risk enhancers. [110] Gestational diabetes and preterm delivery are also recognized as increasing lifetime CVD risk.

8.2. Epidemiology

• Due to insufficient data (many CVD outcomes trials excluded older patients), the treatment recommendations for primary CVD risk reduction in individuals > 75 years old often have less scientific support than treatment recommendations for younger age groups. Also, due to the population makeup of the supporting databases, CVD risk scores are only validated for individuals at or below 65, 75, or 80 years of age, depending upon the CVD risk assessment calculator. For example, the ACC/AHA ASCVD Risk Calculator includes an age range of 40–79 years. [234]
• Many CVD risk calculators do not take into full account the influence of race on CVD risk. The ACC/AHA ASCVD Heart Risk Calculator is limited to the races of “Other” and African Americans. [234] Conversely, the Multi-Ethnic Study of Atherosclerosis (MESA) 10-year CHD risk tool includes Caucasians, Chinese, African Americans, and Hispanics 45–85 years of age as data input, along with coronary artery calcification. [235]
• CVD is the leading cause of death for women and men of most racial and ethnic groups in the US, accounting for ∼20% of deaths per year. [236]
• African Americans ages 35–64 years are 50% more likely to have high blood pressure than whites. African Americans ages 18–49 are 2 times as likely to die from heart disease than whites. [237]
• Compared to Caucasians, Hispanics/Latinos have 35% less heart disease, but a 50% higher death rate from diabetes, 24% more poorly controlled high blood pressure, and 23% more obesity.
• Compared with US-born Hispanics/Latinos, foreign-born Hispanics/Latinos have about half as much heart disease; 29% less high blood pressure; and 45% more high total cholesterol. [238]
• Compared to Caucasian adults, American Indians/Alaska Native adults have a higher prevalence of CVD risk factors such as obesity, high blood pressure, and current cigarette smoking. In 2018, American Indians/Alaska Natives had a 50 percent greater risk for coronary heart disease compared to non-Hispanic Whites. [227]
• Heart disease is the leading cause of death for African American and Caucasian women in the US. Among American Indian and Alaska Native women, heart disease and cancer cause roughly the same number of deaths each year. [239]
• Age and sex are important risk factors for stroke. One in 5 US women between 55–75 years of age will have a stroke in her lifetime. Stroke kills twice as many women as breast cancer. [240] Greater longevity in women helps account for strokes occurring more frequently in women than men; however, women may also have sex-specific stroke risk factors (e.g., endogenous hormones, exogenous hormones, and pregnancy-related exposures). [241]

8.3. Diagnosis and treatment

Table 7 lists ten things to know about the diagnosis and treatment of patients of older age, different races/ethnicities, and women.

Ten things to know about select populations (older age, race/ethnicity, sex differences) and cardiovascular disease (CVD) prevention.

(d) Older individuals should avoid cigarette smoking which not only increases the risk of cancer, lung disease, and frailty, but also increases the risk of CVD and thrombosis. In patients with CVD treated with aspirin for anti-thrombotic effects, the benefits of continuing aspirin in older patients with CVD often exceed the risk of bleeding. Regarding primary prevention, the risk of bleeding in frail individuals over 80 years of age may exceed the potential benefits of preventing the first CVD event; and (e) Appropriate, patient-centered nutritional intervention and physical activity/exercise may not only have CVD benefits, but other CVD risk factor and anti-frailty health benefits in older individuals.[ , which may be especially important among African Americans. Guidelines for pharmacologic CVD prevention in African Americans are generally similar to other racial/ethnic groups, except regarding heart failure and HTN. In African Americans, diuretics and calcium channel blockers may be preferred over angiotensin converting enzyme inhibitors and beta-blockers. Important factors in effective CVD prevention among minorities are sustainable interventions that adequately address communication barriers, and that both acknowledge and address the impact of race/ethnic culture in discussions regarding behavioral and other treatment recommendations. Effective patient communication may or may not be influenced by the race/ethnicity of the provider. Clinician decision making may be influenced by integrating themes regarding race, patient-levels issues, system-level issues, bias and racism, patient values, and communication. On a patient level, practical interventions to potentially improve understanding and adherence to treatment among minorities may generally include instilling confidence in the minority patient communication abilities, and specifically facilitating the simple asking and answering of clinically applicable questions. [ , Any cardioprotective effect is mostly lost among women with T2DM. Women with T2DM have a three-fold increased risk of CVD, with a higher risk of heart failure, stroke, claudication, and CVD mortality compared to men with T2DM. While supporting CVD outcome data are more limited than men, statins appear to be equally effective for secondary CVD prevention in women, although women may have a greater likelihood of developing statin-associated diabetes mellitus and myalgias. PCOS increases CVD risk, largely because of accompanying cardiometabolic abnormalities such as insulin resistance, glucose intolerance, diabetes mellitus, HTN, dyslipidemia (increased triglycerides and decreased HDL-C), metabolic syndrome, increased C-reactive protein, increased coronary artery calcium scores, increased carotid intima-medial thickness, and endothelial dysfunction. As with other patients having increased CVD risk, women with PCOS should be aggressively treated with healthful nutrition and physical activity. Statin therapy may be indicated in many women with PCOS; however, statins may worsen insulin sensitivity in women with PCOS. Conversely, statin therapy may lower testosterone in women with PCOS, with variable reports regarding effects on menstrual regularity, spontaneous ovulation, hirsutism, or acne. [ , Statin therapy combined with metformin therapy in women with PCOS may not only lower cholesterol, triglyceride, and testosterone levels, but may also improve insulin resistance with improvement in menstrual regularity, hirsutism, acne, and spontaneous ovulation. While the degree of possible teratogenic effects are unclear, statins are contraindicated in women who are pregnant, or who may become pregnant. In women going through menopause, the loss of estrogens may have systemic effects such as worsening circulating lipids and lipoproteins and reduced central nervous system satiety effects of estrogens. Taken together with age-related increase in body fat, women undergoing menopause are at increased risk for insulin resistance, HTN, and dyslipidemia – increasing CVD risk. In some cases, hormone replacement therapy primarily used to treat menopausal symptoms may increase the risk of CVD among menopausal women. If menopausal hormone therapy is to be used in menopausal women, it should be at the lowest effective dose, administered early (within 5 years) of menopause, and should not be prescribed for the purpose of preventing CVD.

2020 The Use of Sex-Specific Factors in the Assessment of Women's Cardiovascular Risk [ ,
2020 US Department of Health and Human Services Office of Minority Health. Minority Population Profiles.
2017 American Heart Association Council on E, Prevention, Council on Cardiovascular Disease in the Y, Council on C, Stroke N, Council on Clinical C, Council on Functional G, Translational B, Stroke C. Cardiovascular Health in African Americans: A Scientific Statement From the American Heart Association.
2016 Cardiovascular Disease in Women: Clinical Perspectives
2014 American Heart Association Council on E, Prevention, American Heart Association Council on Clinical C, American Heart Association Council on C, Stroke N. Status of cardiovascular disease and stroke in Hispanics/Latinos in the United States: a science advisory from the American Heart Association

9. Thrombosis and smoking

9.1. definition and physiology.

Thrombosis is the intravascular (arterial or venous) coagulation of blood, resulting in a “blood clot” which may cause local or downstream obstruction of a vessel (thromboembolism). Atherosclerosis may lead to chronic luminal narrowing that obstructs on-demand blood flow, resulting in angina or claudication. Thromboembolic acute obstruction of a femoral vein may lead to an acute deep vein thrombosis; an acute obstruction of a coronary artery may lead to a myocardial infarction; and an acute obstruction to a carotid artery may lead to a stroke. [259]

Risk factors for thrombosis include older age, atrial fibrillation, cigarette smoking, prosthetic heart valves, blood clotting disorders, trauma/fractures, physical inactivity (including prolonged bed rest / immobility), obesity, diabetes mellitus, HTN, dyslipidemia, certain drug treatments (estrogens), pregnancy, and cancer. Finally, a prior CVD event increases the risk of a future CVD event, often involving a thromboembolic component. Thus, patients with an acute coronary syndrome benefit from well-managed anti-thrombotic therapy as secondary prevention to reduce the risk of future CVD events.

Tobacco cigarette smoking is a well-known, major contributor to CVD morbidity and mortality. [260] Tobacco cigarette smoking increases CVD risk via promoting thrombosis, inflammation, free radical formation, carbon monoxide-mediated increases in carboxyhemoglobin formation, increase in sympathetic activity (with increased myocardial oxygen demand and potential promotion of dysrhythmias), reduced nitric oxide with endothelial dysfunction, and oxidation of LDL-C. [260]

Vaping devices (electronic cigarettes or “e-cigarettes”) are battery-operated nicotine (as well as flavoring and other chemicals) delivery devices that generate an aerosol that is intended to be inhaled. Vitamin E acetate, an additive in some tetrahydrocannabinol (THC) - containing e-cigarette, or vaping, products, is strongly linked to “E-cigarette or Vaping product use-associated Lung Injury” (EVALI). Nicotine alone has the potential to adversely affect the cardiovascular system via an acute increase in the sympathetic nervous system, increase in blood pressure, decrease in coronary blood flow, increase in myocardial remodeling/fibrosis, promotion of dysrhythmias and promotion of thrombosis, with longer-term adverse effects on endothelial function, inflammation, lipid levels (reduced high density lipoprotein and increased LDL-C levels), blood pressure, and insulin resistance. [261]

9.2. Epidemiology

According to the US Centers for Disease Control: [262] , [263] , [264] , [265] , [266]

• Stroke is a leading cause of serious long-term disability, reducing mobility in more than half of stroke survivors age 65 and over.
• In the US, stroke is responsible for 1 out of 20 deaths.
• About 90% of all strokes are ischemic strokes.
• The risk of having a first stroke is nearly twice as high for blacks as for whites, and blacks have the highest rate of death due to stroke.
• Smoking is a leading cause of preventable death, accounting for 480,000 deaths a year.
• In 2018, 13.7% of all adults (34.2 million people) smoked cigarettes: 15.6% of men and 12.0% of women.
• Cigarette smoking has a dose-response relationship with stroke. [267]
• E-cigarettes are the most frequently used tobacco product among youths. Roughly 5% of middle school students and 20% of high school students report using e-cigarettes. [266]

9.3. Diagnosis and treatment

Table 8 lists ten things to know about the diagnosis and treatment of thrombosis and smoking and CVD prevention.

Ten things to know about thrombosis and smoking and cardiovascular disease (CVD) prevention

, , , , , , , , ] Aspirin may be beneficial in primary prevention for select patients with diabetes mellitus who are at high risk for CVD and who are at low risk for bleeding, but only after a patient-centered evaluation and discussion. [ , , Coronary artery calcium (CAC) assessment can help inform the clinical use of aspirin in primary prevention, with those having a CAC score of ≥ 100 Agatston Units (AU) having a favorable risk/benefit estimation from the use of aspirin, while those with zero CAC are estimated to have net harm from aspirin. Aspirin coated preparations may reduce gastrointestinal bleeding. The coated aspirin dose of 100 mg per day may help reduce CVD, death (and cancer), with lower doses being better tolerated (i.e., less bleeding) and higher doses having greater CVD risk reduction. Aspirin doses of 75 – 100 mg per day may offer the optimal benefit/risk ratio in chronic prevention of recurrent atherothrombosis in patients with an acute coronary syndrome (i.e., 81 mg “baby aspirin”). , , Aspirin platelet inhibition is fastest with chewable aspirin, which has a more rapid onset of action than soluble aspirin, which has a more rapid onset of action than whole solid aspirin, which has a more rapid onset of action than enteric-coated aspirin. After calling 9-1-1 for emergency phone help, patients undergoing an acute myocardial infarction are advised to chew one 325 mg aspirin slowly, preferably within 30 minutes of the onset of symptoms. Chronic administration of aspirin is recommended to prevent recurrent ischemic stroke. Administration of aspirin is recommended for acute stroke, due to the potential of worsening of a hemorrhagic stroke. [ , ) can help engage patients in a discussion about smoking cessation. The 5 A's include: (a) sk patients about tobacco use; (b) dvise smokers to quit tobacco; (c) ssess a smoker's readiness to quit; (d) ssist smokers to quit; (e) rrange follow-up. , , patients who smoke cigarettes may benefit from a Ask, Advise, and Refer (AAR) approach to a behavioral support program. Referral program utilization is enhanced with patient agreement to be contacted (Ask, Advise, and Contact or AAC) for a behavior support appointment, as opposed to simply being referred. If upon initial patient-centered discussions, the patient declines referral for behavior support, then this offer should be repeated on subsequent clinician encounters, as the willingness of the patient to quit smoking may change over time. , , However, most e-cigarettes contain nicotine, which is highly addictive and may increase the long-term risk of CVD. Those choosing to use e-cigarettes as an alternative to cigarettes should completely switch from cigarettes to e-cigarettes, and not use both products concomitantly. While reasonable to use e-cigarettes as a part of a bridging smoking cessation strategy in certain populations, the data on such an approach remain unclear. The FDA has not approved e-cigarettes as a smoking cessation aid, and more research is needed to better understand the long term health effects of e-cigarettes and their role in helping smokers to stop tobacco smoking. [ ,

2020 Centers for Disease Control. Smoking & Tobacco Use. Electronic cigarettes
2020 Smoking Cessation. A Report from the Surgeon General
2020 Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes-
2020 Heart Disease and Stroke Statistics-Update: A Report From the American Heart Association
2018 ACC Expert Consensus Decision Pathway on Tobacco Cessation Treatment

10. Kidney dysfunction

10.1. definition and physiology.

According to the “Kidney Disease: Improving Global Outcomes” (KDIGO) guidelines, [301] CKD is defined as greater than 3 months of a reduced estimated glomerular filtration rate (eGFR), beginning at a moderate reduction of < 60 mg/min/1.73 m 2 and/or increase in urine protein excretion [i.e., albuminuria, beginning at a moderate increase in the albumin creatinine ratio ≥ 30 mg/g (≥ 3 mg/mmol)]. [302] In addition to the accompanying major CVD risk factors that promote and/or worsen kidney function (e.g., high blood pressure, diabetes mellitus, cigarette smoking), CKD is an independent risk factor for CVD, likely due to endothelial dysfunction, accelerated atherosclerosis, [303] increased inflammation, vascular calcification and other vasculopathies. [304] Other non-traditional CVD risk factors often found in patients with CKD include left ventricular hypertrophy, anemia, abnormal calcium-phosphate metabolism, and elevated urate levels. [305]

Over 2/3 rd of patients over 65 years with CKD have concomitant CVD. [306] Both eGFR < 60 mg/min/1.73 m2 and albuminuria are independent predictors of CVD events and CVD mortality. [307] CVD is inversely related to eGFR. Generally, CKD and ESKD are associated with a 5–10 fold higher risk for developing CVD compared to aged matched controls. [308] Specifically, patients with CKD having eGFR 15–60 mg/min/1.73 m2 have about two to three times higher risk of CVD mortality, compared to patients without CKD. [ 307 , 309] As such, CKD is considered a “risk enhancing factor” that places patients at high risk for CVD. [110]

10.2. Epidemiology

According to the US Centers for Disease Control and The Heart Disease and Stroke Statistics 2020 Update from the American Heart Association: [ 310 , 311]

• 15% of US adults are estimated to have CKD.
• The prevalence of CVD increases with age, with about 1/3 of patients over 60 years of age having CVD.
• Most (9 in 10) adults with CKD do not know they have CKD.
• African Americans are about 3 times more likely than whites to develop end stage kidney disease (ESKD).
• In US adults aged 18 years or older, diabetes mellitus and high blood pressure are the main reported causes of ESKD and the prevalence of CKD is about 37% of adults with diabetes mellitus and 31% among adults with high blood pressure. [157]
• In US children and adolescents younger than 18 years, polycystic kidney disease and glomerulonephritis (inflammation of the kidneys) are the main causes of ESKD.
• CKD is often associated with low rates of standard preventive therapies directed towards CVD risk reduction (e.g., adequate control of glucose, blood pressure, and cholesterol). [312] For example, in an analysis of patients with CKD evaluated from 2003–2007, only 50% were taking statins, and 42% who had statins recommended were not taking them. [313] In summary, patients with CKD are often not treated with statins. When treated, patients with CKD rarely achieve LDL-C treatment goals. [314]

10.3. Diagnosis and treatment

Table 9 lists ten things to know about the diagnosis and treatment of kidney dysfunction and CVD prevention.

Ten things to know about kidney disease and cardiovascular disease (CVD) prevention

increases the risk of death, CVD events, and hospitalizations Among patients with coronary heart disease, an eGFR < 30 mg/min/1.73 m substantially increases the risk of CVD mortality and all-cause mortality. CVD is a leading cause of death among patients with CKD, In patients without CKD (who are often younger), cancer and CVD are the two most common causes of death. Among patients with CKD, CVD is the most common cause of death, with increasing risk of CVD death inversely related to the eGFR. , , In patients with T2DM, both SGLT2 inhibitors and GLP-1 receptor agonists reduce CVD events. SGLT2 inhibitors may reduce the progression of renal disease by 45% in those with or without CVD. GLP-1 receptor agonists can reduce urinary albumin excretion, slow kidney disease progression, and reduce CV events. [ , While both reduce the risk of CVD, compared to GLP-1 receptor agonists, SGLT2 inhibitors have a more marked effect on preventing hospitalization for heart failure and reducing kidney disease progression. With the exception of thiazolidinediones and GLP-1 receptor agonists, virtually all anti-diabetes medication classes have representative drugs that require dosing adjustment, depending upon eGFR. Many anti-diabetes medications are not recommended and/or have lack of data regarding their safety and efficacy in patients with severe renal insufficiency. especially in the presence of proteinuria. [ , Preferred antihypertensive agents in patients with CKD (but not dialysis) include: (a) angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers ARBs); (b) diuretics; (c) dihydropyridine calcium channel blockers; and (d) mineralocorticoid receptor blockers. Preferred antihypertensive agents in patients undergoing dialysis include (a) beta adrenergic blockers (e.g., atenolol); (b) dihydropyridine calcium channel blockers; (c) angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers; (d) direct vasodilators. The benefit:risk ratio of ACE inhibitors and ARBs is unclear in patients with eGFR < 30 mg/min/1.73 m . This helps account for why, as a class, ACE inhibitors and ARBs are more commonly discontinued with eGFR < 30 mg/min/1.73 m , compared to patients with higher eGFR. Having said this, discontinuing ACE inhibitors or ARB's after hospitalization specifically for acute kidney injury may be associated with a higher risk of post discharge mortality. [ , In non-dialysis patients with eGFR < 30 mg/min/1.73 m , loop diuretics are preferred over thiazide diuretics. Torsemide generally has more predictable bioavailability compared to furosemide. Dialysis patients with some urine output may benefit from continued loop diuretics. In patients with renal insufficiency, dihydropyridine calcium channel blockers (amlodipine, felodipine, nicardipine, nifedipine) may be preferred over non dihydropyridine channel blockers (i.e., verapamil, diltiazem) due to potentially less drug interactions with common medications (e.g., statins) and less potential for atrioventricular conduction delays and heart block when used together with betablockers. Beta blockers in patients with ESKD may reduce the risk of heart failure, HTN, and cardiac dysrhythmias. Direct vasodilators (hydralazine and minoxidil) are usually one of the last line therapies for HTN and renal failure. Virtually all anti-hypertensive medications classes have representative drugs that require dosing adjustment, depending upon eGFR. however, the relative risk reduction in major vascular event risk diminishes as eGFR declines. , , Statin therapy may not reduce kidney failure, but may modestly reduce proteinuria and rate of eGFR decline. With the exception of atorvastatin, other statins (as well as many other lipid-altering drugs) require dosing adjustment in patients with CKD. Clinical trial evidence supports ezetimibe plus simvastatin combination as reducing the incidence of major atherosclerotic events in patients with a wide range of patients with advanced CKD. Moderate intensity statin (with or without ezetimibe) is recommended in adults with CKD not on dialysis, who have a 10-year ASCVD risk of 7.5% or higher. [ , While no dosing adjustment is needed for patients with mild or moderately impaired renal function, little to no data exists regarding the use of proprotein subtilisin/kexin 9 inhibitors in patients with severe CKD. Antiplatelet therapy in patients with CKD may reduce the risk of myocardial infarction, but increase the risk of bleeding. The risk of bleeding in patients with CKD is compounded with the use of dual antiplatelet therapy. , Regarding body weight, an obesity paradox is sometimes described in patients with CKD wherein those with increased adiposity have a survival advantage. Potential explanations include (a) CKD due to obesity may progress less aggressively compared to kidney disease due to other causes; (b) patients with CKD related to obesity may have less intense inflammation, circulatory cytokines, and endotoxin-lipoprotein interactions compared to other inflammatory causes of CKD; (c) obesity may allow for increased sequestration of uremic toxins in adipose tissue; (d) patients at lower BMI may be undernourished with protein-muscle-energy wasting; and (e) patients at lower BMI may have worsened hemodynamic stability. However, among patients with CKD, patients with obesity have a higher risk for CKD progression, with or without accompanying metabolic abnormalities. Bariatric surgery in patients with CKD may reduce eGFR decline, reduce incidence of kidney failure, and improve access for possible kidney transplantation.[ , As with CVD, routine physical activity reduces the risk of morbidity and mortality in patients with CKD. Additionally, patients with CKD with deteriorating renal function may likewise have a deterioration in their physical activity, cardiorespiratory fitness, and muscle mass, with full recovery not achieved even with renal transplant. The combination of physical inactivity, uremia, and possible decrease in protein intake contributes to loss of muscle mass. Regular physical activity has cardiometabolic benefits, as well as neuromuscular, cognitive, and renoprotective benefits. , albuminuria ≥ 300 mg per 24 hours, or rapid decline in eGFR.

2020 Heart Disease and Stroke Statistics – Update: A Report from the American Heart Association
2020 Cardiorenal Protection With the Newer Antidiabetes Agents in Patients with Diabetes and Chronic Kidney Disease: A Statement from the American Heart Association
2019 Clinical Pharmacology of Antihypertensive Therapy for the Treatment of Hypertension in CKD
2019 Chronic Kidney Disease Diagnosis and Management: A Review.
2019 Primary and Secondary Prevention of Cardiovascular Disease in Patients with Chronic Kidney Disease

11. Genetic abnormalities / familial hypercholesterolemia

11.1. definition and physiology.

Among the more common inherited causes of CVD in younger individuals include genetic abnormalities leading to vasculopathies, aneurysmal disorders, cardiomyopathies, and coagulopathies. [ 190 , 352] Genetic abnormalities can also lead to CVD risk factors such as diabetes mellitus [353] and hypertension. [354] Other genetic abnormalities leading to CVD includes inherited dysrhythmia syndromes and genetic dyslipidemias. [355]

Within the clinical practice of preventive cardiology, genetic dyslipidemia is the most common treatable cause of inherited premature coronary heart disease. [190] Laboratory diagnosis of inherited dyslipidemias may involve sequencing the entire human genome or custom sequencing of one or more genes. In some countries, it is common for patients with marked elevations in LDL-C levels to undergo genetic evaluation for Familial Hypercholesterolemia (FH) to identify pathogenic variants of the LDL receptor (LDLR, most common), apolipoprotein B (APOB), or proprotein convertase subtilisin/kexin type 9 (PCSK9). [ 356 , 357] However, in addition to laboratory genetic testing, the diagnosis of Familial Hypercholesterolemia can also be made clinically. In the US, FH is more commonly assessed via one or more clinical diagnostic criteria for FH such as The American Heart Association, Simon Broome, and/or Dutch Lipid Clinic Network criteria (see Tables 10a – 10c ). [358] , [359] , [360] , [361] , [362]

Simon Broome diagnostic criteria for Familial Hypercholesterolemia [ 360 , 365]

American Heart Association Clinical Criteria for the Diagnosis of Heterozygous FH [359]

Dutch Lipid Clinic Network diagnostic criteria for Familial Hypercholesterolemia [ 360 , 362 , 365]

	Points
Criteria
Family history
First-degree relative with known premature coronary and vascular disease, OR	1
First-degree relative with known LDL-C level above the 95th percentile
First-degree relative with tendinous xanthomata and/or arcus cornealis, OR	2
Children aged less than 18 years with LDL-C level above the 95th percentile

Patient with premature coronary artery disease	2
Patient with premature cerebral or peripheral vascular disease	1

Tendinous xanthomata	6
Arcus cornealis prior to age 45 years	4

LDL-C ≥ 330 mg/dL (≥ 8.5)	8
LDL-C 250 – 329 mg/dL (6.5–8.4)	5
LDL-C 190 – 249 mg/dL (5.0–6.4)	3
LDL-C 155 – 189 mg/dL (4.0–4.9)	1

Functional mutation in the gene	8

Definite Familial Hypercholesterolemia	>8
Probable Familial Hypercholesterolemia	6 – 8
Possible Familial Hypercholesterolemia	3 – 5
Unlikely Familial Hypercholesterolemia	<3

LDL-C = low - density lipoprotein cholesterol

DNA = Deoxynucleic acid

LDL-R = low - density lipoprotein receptor

Apo B = apolipoprotein B

PCSK9 = Proprotein convertase subtilisin/kexin type 9

Among patients without FH, an elevated lipoprotein (a) [Lp(a)] level is an independent CVD risk factor [117] and the most common monogenic cause of atherosclerotic CVD. The European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) Guidelines for the Management of Dyslipidemias suggest that Lp(a) measurement should be considered at least once in each adult person's lifetime to identify those with very high inherited Lp(a) levels >180 mg/dL (>430 nmol/L), who may have a lifetime risk of ASCVD equivalent to the risk associated with heterozygous familial hypercholesterolemia. [111] Measurement of Lp(a) is superior to genetic testing for an LPA variant, as current genetic testing for this variant is not a reliable predictor of elevated Lp(a) levels in all ethnic groups. In addition to identification of monogenic disorders, genetic testing may allow for the calculation of a “ polygenic risk score ” to complement clinical risk scores used to predict ASCVD events. [ 356 , 363 , 364] However, the role of these “ polygenic risk scores ” in primary and secondary prevention of CVD is still evolving.

11.2. Epidemiology

• In the US, heterozygous FH (as defined by the Dutch Lipid Clinic criteria) occurs in approximately 1:250 individuals, [366] with an increased rate among those having Lebanese, South African Afrikaner, South African (Ashkenazi) Jewish, South African Indian, French Canadian, Finland, Tunisia, and Denmark population backgrounds. [367]
• The risk of premature coronary heart disease (CHD) is increased by 20 fold among untreated FH patients [368] and CHD typically occurs before age 55 and 60 among women and men with FH respectively. [362]
• Myocardial infarction occurs about 20 years earlier among those with FH compared to those without FH, [369] and occurs in up to 1 in 7 of patients having acute coronary syndrome < 45 years of age. [370]
• Beyond atherosclerotic CVD, among the more common inherited causes of other forms of CVD among younger individuals include genetic abnormalities leading to vasculopathies, aneurysmal disorders, and coagulopathies. [371]

11.3. Diagnosis and treatment

Table 10d lists ten things to know about the diagnosis and treatment of genetics/familial hypercholesterolemia and CVD prevention.

Ten things to know about genetics/familial hypercholesterolemia and cardiovascular disease (CVD) prevention

Heterozygous Familial Hypercholesterolemia (HeFH) is most commonly an autosomal dominant genetic metabolic disorder resulting in marked elevations of LDL-C levels (i.e., typically ≥ 190 mg/dL in adults), a 10 – 17 fold increased risk of atherosclerotic CVD in untreated patients, and an 8 – 14 fold increase in patients treated with statins. The residual CVD risk among statin-treated patients suggests under-treatment with statins and other lipid-altering drugs, and/or delayed introduction of lipid-altering drugs. It is likely that patients with phenotypic FH who have negative genetic testing for FH may have an unidentified FH mutation. Thus, many clinicians choose to utilize clinical diagnostic criteria based upon AHA, Simon Broome, and/or Dutch Lipid Clinic Network criteria over genetic testing to diagnose FH (Tables a – c). [ , , , tendon xanthomas are the physical exam finding most strongly associated with FH, and the physical exam finding most included in FH diagnostic criteria (see Tables 10 b – c). Aortic stenosis is also often found in patients with FH, potentially detected by heart murmur upon auscultation of the heart, and whose onset and severity are dependent on lifetime exposure to increased LDL-C levels. Lipoprotein (a) is an additional lipid parameter that should be assessed in patients with HeFH. , , , , , , Measuring Lp(a) is superior to genetic testing for an variant, as current genetic testing for this variant is not a reliable predictor of elevated Lp(a) in all ethnic groups. Genetic testing may allow for the calculation of a “ ” to complement clinical risk scores used to predict ASCVD events. The role of these “ ” in primary and secondary prevention of CVD is still evolving. [ , ,

2020 Genetic Testing in Dyslipidemia: A Scientific Statement from the National Lipid Association
2018 Clinical Genetic Testing for Familial Hypercholesterolemia: JACC Scientific Expert Panel
2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines.
2018 Familial hypercholesterolemia treatments: Guidelines and new therapies.
2017 Cascade Screening for Familial Hypercholesterolemia and the Use of Genetic Testing

12. Conclusion

The “ASPC Top Ten CVD Risk Factors 2021 Update” summarizes ten things to know about ten CVD risk factors, accompanied by sentinel references for each section. The ten CVD risk factors include unhealthful nutrition, physical inactivity, dyslipidemia, hyperglycemia, high blood pressure, obesity, considerations of select populations (older age, race/ethnicity, and sex differences), thrombosis/smoking, kidney dysfunction and genetics/familial hypercholesterolemia. Primary care clinicians may benefit from a summary of the basics regarding diagnosis and management of CVD risk factors, which is fundamental to preventive cardiology. Specialists may benefit because not all specialists in one area of preventive cardiology will be a specialist in all aspects of preventive cardiology. Finally, the field of preventive cardiology is undergoing rapid growth. Those beginning in preventive cardiology may benefit from an overview of essentials in diagnosis and management of CVD risk factors. The “ASPC Top Ten CVD Risk Factors 2021 Update” represents a starting point for those interested in a multifactorial approach CVD prevention, with preventive cardiology best implemented via a team-based approach that depending on the situation, may include clinicians, nurses, dietitians, pharmacists, educators, front-desk personnel, social workers, community health workers, psychologists, exercise physiologists, and other health providers. [6]

Author contributions

Section authors reviewed and edited their respective sections. Dr. Peter Toth reviewed and edited the entire manuscript. All authors reviewed and approved responses to journal peer reviewer comments. Dr. Harold Bays served as medical writer, coordinated the input of authors, and submitted the manuscript.

Disclosures

Pam Taub MD reports being a consultant for Amgen, Esperion, Boehringer Ingelheim, Novo Nordisk, and Sanofi, is a shareholder in Epirum Bio and has research grants from NIH (R01 DK118278-01 and R01 HL136407) American Heart Association (SDG #15SDG2233005) and Department of Homeland Security/FEMA (EMW-2016-FP-00788). Elizabeth Epstein MD reports no disclosures. Erin D. Michos MD, MHS, FAHA, FACC, FASE, FASPC reports no disclosures. Richard Ferraro, MD, M.Ed reports no disclosures. Alison L. Bailey, MD FACC reports no disclosures. Heval Mohamed Kelli, MD reports no disclosures. Harold Edward Bays MD, FOMA, FTOS, FACC, FNLA, FASPC reports site receipt of research grants from 89Bio, Acasti, Akcea, Allergan, Alon Medtech/Epitomee, Amarin, Amgen, AstraZeneca, Axsome, Boehringer Ingelheim, Civi, Eli Lilly, Esperion, Evidera, Gan and Lee, Home Access, Janssen, Johnson and Johnson, Lexicon, Matinas, Merck, Metavant, Novartis, NovoNordisk, Pfizer, Regeneron, Sanofi, Selecta, TIMI, and Urovant. Dr. Harold Bays has served as a consultant/advisor for 89Bio, Amarin, Esperion, Matinas, and Gelesis, and speaker for Esperion. Keith C. Ferdinand, MD, FACC,FAHA, FNLA,FASPC reports no disclosures. Melvin R. Echols, MD, FACC serves as a consultant/advisor for Abbott. Howard Weintraub MD reports research grants from Amgen, Novartis, Akcea, NovoNordisk, consultant/advisory services for Novartis, Amgen and speaker for Esperion. John Bostrom MD reports no disclosures. Heather M. Johnson, MD, MS, MMM, FACC, FAHA reports receiving research grant support from NIH/NHLBI and Pfizer. Kara Hoppe, DO, MS reports receiving research grant support from NIH/NHLBI. Michael D. Shapiro DO, MCR reports being a member of the Scientific Advisory Board of Alexion, Amgen, Esperion, and Novartis. Charles Amir German MD, MS reports no disclosures. Salim S. Virani MD PhD reports research support from Department of Veterans Affairs, World Heart Federation, Tahir and Jooma Family, and receipt of honoraria from American College of Cardiology (Associate Editor for Innovations, ACC.org). Aliza Hussain MD reports no disclosures. Christie M. Ballantyne, MD reports receipt of grant/research support through his institution from Abbott Diagnostic, Akcea, Amgen, Esperion, Novartis, Regeneron, and Roche Diagnostic, and serves a consultant for Abbott Diagnostics, Akcea, Althera, Amarin, Amgen, Arrowhead, Astra Zeneca, Corvidia, Denka Seiken, Esperion, Gilead, Janssen, Matinas BioPharma Inc, New Amsterdam, Novartis, Novo Nordisk, Pfizer, Regeneron, Roche Diagnostic, and Sanofi-Synthelabo. Ali M. Agha, MD reports no disclosures. Peter P. Toth, MD, PhD reports being a consultant to Amarin, Amgen, bio89, Kowa, Novartis, Resverlogix, Theravance and speaker for Amarin, Amgen, Esperion, Merck, and Novo-Nordisk.

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Cardiovascular risk factors and physical activity for the prevention of cardiovascular diseases in the elderly.

1. Introduction

2. pathophysiological processes encountered in cardiovascular diseases of the elderly, 3. diabetes mellitus and cardiovascular disease in elderly patients, 4. obesity and cardiovascular disease in elderly patients, 5. specific interventions for particular clinical settings, 5.1. stroke prevention, 5.2. prevention of coronary artery disease, 5.3. prevention of peripheral artery disease, 6. physical activity in the elderly and the prevention of cardiovascular diseases, 6.1. mechanisms and effects, 6.2. types of physical activities studied in the elderly, 6.3. physical activity, insulin sensitivity and glycemic control, 6.4. physical activity and blood pressure values, 7. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Ciumărnean, L.; Milaciu, M.V.; Negrean, V.; Orășan, O.H.; Vesa, S.C.; Sălăgean, O.; Iluţ, S.; Vlaicu, S.I. Cardiovascular Risk Factors and Physical Activity for the Prevention of Cardiovascular Diseases in the Elderly. Int. J. Environ. Res. Public Health 2022 , 19 , 207. https://doi.org/10.3390/ijerph19010207

Ciumărnean L, Milaciu MV, Negrean V, Orășan OH, Vesa SC, Sălăgean O, Iluţ S, Vlaicu SI. Cardiovascular Risk Factors and Physical Activity for the Prevention of Cardiovascular Diseases in the Elderly. International Journal of Environmental Research and Public Health . 2022; 19(1):207. https://doi.org/10.3390/ijerph19010207

Ciumărnean, Lorena, Mircea Vasile Milaciu, Vasile Negrean, Olga Hilda Orășan, Stefan Cristian Vesa, Octavia Sălăgean, Silvina Iluţ, and Sonia Irina Vlaicu. 2022. "Cardiovascular Risk Factors and Physical Activity for the Prevention of Cardiovascular Diseases in the Elderly" International Journal of Environmental Research and Public Health 19, no. 1: 207. https://doi.org/10.3390/ijerph19010207

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Open Access

Peer-reviewed

Research Article

Patients’ knowledge on cardiovascular risk factors and associated lifestyle behaviour in Ethiopia in 2018: A cross-sectional study

Roles Conceptualization, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Adelaide Nursing School, The University of Adelaide, Adelaide, Australia, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliation Adelaide Nursing School, The University of Adelaide, Adelaide, Australia

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliations Royal Adelaide Hospital, College of Nursing and Health Sciences, Flinders University and Centre for Heart Rhythm Disorders, Adelaide, Australia, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden

Lemma B. Negesa,
Judy Magarey,
Philippa Rasmussen,
Jeroen M. L. Hendriks

Published: June 4, 2020
https://doi.org/10.1371/journal.pone.0234198
Reader Comments

Cardiovascular disease (CVD) is posing a major public health challenge globally. Evidence reports significant gaps in knowledge of cardiovascular risk factors among patients with CVD. Despite the growing burden of cardiovascular disease in developing countries, there is limited data available to improve the awareness of this area, which is crucial for the implementation of prevention programs.

A cross-sectional survey was conducted in two referral hospitals in Eastern Ethiopia from June-September 2018. Outpatients with a confirmed diagnosis cardiovascular conditions were eligible for participation in the study. A convenience sampling technique was used. The primary outcome of the study was knowledge of cardiovascular risk factors among patients with cardiovascular disease. The knowledge of cardiovascular disease risk factors was measured using a validated instrument (heart disease fact questionnaire). A score less than 70% was defined as suboptimal knowledge. Multivariable linear regression was used to examine the relationship between knowledge of cardiovascular risk factors and explanatory variables.

A total of 287 patients were enrolled in the study. Mean age was 47±11yrs and 56.4% of patients were females. More than half of patients (54%) had good knowledge on cardiovascular risk factors (scored>70%), whilst 46% demonstrated suboptimal knowledge levels in this area. Urban residency was associated with higher cardiovascular risk factors knowledge scores, whereas, never married and no formal education or lower education were identified as predictors of lower knowledge scores. There was no statistically significant association between knowledge of cardiovascular risk factors and actual cumulative risk behaviour.

Almost half of CVD patients in Ethiopia have suboptimal knowledge regarding cardiovascular risk factors. Residence, education level and marital status were associated with knowledge of cardiovascular risk factors. Implementation of innovative interventions and structured, nurse-led lifestyle counselling would be required to effectively guide patients in developing lifestyle modification and achieve sustainable behaviour change.

Citation: Negesa LB, Magarey J, Rasmussen P, Hendriks JML (2020) Patients’ knowledge on cardiovascular risk factors and associated lifestyle behaviour in Ethiopia in 2018: A cross-sectional study. PLoS ONE 15(6): e0234198. https://doi.org/10.1371/journal.pone.0234198

Editor: Baltica Cabieses, Universidad del Desarrollo, CHILE

Received: February 4, 2020; Accepted: May 20, 2020; Published: June 4, 2020

Copyright: © 2020 Negesa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because of ethical issues. Data are available from the University of Adelaide, Adelaide Nursing School (contact via [email protected] ) for researchers who meet the criteria for access to confidential data.

Funding: The study was supported by University of Adelaide.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Cardiovascular disease (CVD) remains a global major cause of death [ 1 ] and represents a significant disease burden in populations around the world. The global burden of disease studies reported an estimated 422.7 million cases of CVD, causing 17.92 million deaths worldwide in 2015 [ 1 ]. Developing countries are facing a high burden of CVD whilst awareness of disease and associated risk factors is limited [ 2 , 3 ]. Those living in poverty and especially those in low-income countries are significantly more impacted by CVD [ 4 ]. Moreover, findings show that the prevalence of CVD is increasing and posing a public health challenge in developing countries [ 1 , 5 ]. High blood pressure is of major influence in the increasing CVD burden in these countries [ 1 ]. For most patients with hypertension it is uncontrolled which causes further cardiovascular (CV) complications [ 6 ]. Hypertension affects more than 1.3 billion people worldwide and one third of adults have the condition [ 7 , 8 ]. The number of adults with hypertension in 2025 is predicted to increase by about 60% [ 9 ]. Moreover, the total number of individuals with hypertension is increasing rapidly to epidemic levels with a projected 125.5 million individuals affected by 2025 in Sub-Saharan Africa [ 10 ].

From an epidemiologic view on disease prevalence, Ethiopia is in epidemiologic transition from predominantly infectious diseases to chronic diseases. CVD is a major public health challenge in Ethiopia. The overall prevalence of hypertension among the Ethiopian population is 19.6%, and is higher among the urban population (23.7%) [ 11 ]. In 2015, ischemic heart disease was the first leading causes of age standardised death rates and fourth leading causes of age standardized disability adjusted life years with rates of 141.9 and 2535.7 per 100,000 population respectively [ 12 ]. The increasing prevalence of CVD in developing countries is related to unhealthy lifestyle behaviours. Except few region based studies, evidence on CV risk behaviours is scarce in Ethiopia. Findings from the Southern part of the country show that 10.8% of CV patients smoke cigarettes, 12.1% drink alcohol and 73.9% don’t do any physical activity [ 13 ]. A study performed in the capital of Ethiopia reported 68.6% of hypertensive patients don’t exercise, 14.1% smoke cigarette, 25.2% drink alcohol and 30.9% don’t adhere to healthy diet [ 14 ].

According to the health belief model, knowledge regarding health behaviour is a strong modifying factor for healthy lifestyle, however it should be combined with other factors such as good perceptions, positive health attitudes and many other conditions such as socioeconomic factors [ 15 ]. Studies also have revealed knowledge of specific risk factors is associated with healthy behaviour, however, knowledge alone does not motivate behavioural change [ 16 – 19 ]. The Heart disease fact questionnaire which was designed and validated by Wagner et al. (2005) and has been commonly used for the assessment of knowledge of CV risk factors knowledge [ 20 ].

There is limited research regarding the knowledge of CV risk factors in developing countries [ 3 , 21 – 23 ]. The majority of adults in Sub-Saharan Africa fail to name even one CV risk factor, [ 22 ] and in Nigeria almost 50% have poor knowledge about CV risk factors [ 3 ]. In Cameroon, this knowledge level is also suboptimal, such that 36% of adults are unaware of CV risk factors [ 21 ]. Nevertheless, in South Africa, most adults are aware that cigarette smoking and excessive alcohol consumption are risk factors for CVD [ 24 ]. The level of education and place of residence have a significant influence on health literacy. It has been reported that higher education levels correlate with a better knowledge of CVD, less number of risk factors and changes in health related behaviour [ 22 , 25 ].

Gaps in evidence on CVD and risk factors form a barrier to effective prevention of cardiovascular conditions. Thus, evidence on patients’ knowledge of CV risk factors is paramount in primary and secondary prevention of CVD [ 26 ]. However, research to reduce the existing evidence gap and the increasing burden of CV risk behaviours in developing countries is scarce. Few studies conducted so far in Ethiopia focussed at describing the high burden of CVD, none of the studies explored CV patients’ knowledge of CV risk factors. Evidence on patients’ knowledge of CV risk factors has vital importance for evidence based health policy and help to design customised interventions. Therefore, the purpose of this study was to assess knowledge of cardiovascular risk factors and associated factors among patients with CVD.

Design, settings and sampling

A cross-sectional survey was conducted in two main referral hospitals in East- Ethiopia, Hiwot Fana Specialised University Hospital and Dilchora Referral Hospital. This study was conducted in chronic follow up units of the two hospitals. The chronic follow up unit provides regular outpatient care for patients with chronic conditions such as hypertension, heart failure, myocardial infarction and diabetes mellitus. The clinic specifically focusses on providing follow up services which include treatment of CVD and counselling of patients to achieve healthy lifestyle behaviours. During the study period (June to September 2018), a total of 820 patients with CVD attended the follow up care in the two participating hospitals.

Patients with a confirmed diagnosis of hypertension, heart failure, or myocardial infarction, in the age range between 18-64yrs were eligible for participation in the study. Patients with congenital heart disorders, rheumatic heart disease, infectious heart disease and inflammatory heart disease were excluded. Mentally ill patients and those with a disability (hearing and talking impairment) which would hinder their ability to participate in the study were also excluded.

The sample size was determined using single population proportion formula with the following assumptions: 95% confidence level, 1.96 (Zα/2), 50% proportion, 5% degree of precision (d), and N (820) total CVD patients attending chronic follow up units of the two hospitals. Based on this assumption and using finite correction, the sample size was 261, and predicting a 10% nonresponse rate, the final sample size was 287. The total 287 calculated sample was allocated for the two hospitals proportional to their total number of patients attending each chronic follow up unit. A convenience sampling was used to select study participants.

Participants were given overview of the study by nurse or physician who were working in follow up unit, then, they were referred to poster information which was posted outside the follow up unit. The poster information contained title of the study, researchers name, eligibility criteria and contact address (mobile phone and email) of data collector. Voluntary participants contacted data collector through phone address or the data collector approached the patients and provided additional information using participant information sheet upon their exit from follow up unit. Recruitment of the patients took place from June to September 2018.

Ethical considerations

Ethical approval was obtained from the Human Research Ethics Review Committee, University of Adelaide, Australia, and the Institutional Health Research Ethics Review Committee, Haramaya University, Ethiopia before commencing the study. Informed and written consent was obtained from each participant prior to participation in the study.

Data collection and tools

Data were collected using three validated tools, the World Health Organisation (WHO) STEPs instrument, International physical activity questionnaire and the Heart Disease Fact Questions. The WHO STEPs instrument follows a stepwise approach to chronic disease risk factor surveillance in individuals aged 18–64 years [ 27 ]. Ethiopian Public Health Institute adapted the WHO STEPs instrument to Ethiopian context by including khat chewing and the use of local alcohol and cigarette products in the risk behaviour assessment. Locally adapted version of WHO STEPs instrument was translated and used to assess sociodemographic variables and CV risk behaviours including cigarette smoking, alcohol consumption, khat chewing and fruit and vegetable consumption. The international physical activity questionnaire was used to assess physical activity [ 28 ].

The primary outcome of the study was knowledge of cardiovascular risk factors among patients with cardiovascular disease. The ‘Heart Disease Fact Questionnaire’ (HDFQ) was used to assess the patient’s knowledge of CV risk factors. The HDFQ showed good content and face validity, and demonstrated adequate internal consistency, with Kuder–Richardson-20 formula of 0.77 [ 20 ]. The English version of both the international physical activity questionnaire and the HDFQ were translated into local languages and were back translated into English by language experts to check reliability of the translations. Two nurses who have bachelor qualifications conducted data collection through face to face interviews with patients.

Current smoking, khat chewing and alcohol drinking were defined as use within the last 30 days. Inadequate consumption of fruit and vegetables was defined as consumption of less than five servings (equivalent to 400g) of fruit and vegetables per day [ 27 ]. Physical activity (PA) level was measured by computing Metabolic Equivalent (MET)-minutes per week for vigorous intensity PA, moderate intensity PA and walking. Vigorous intensity PA was defined as requiring a large amount of effort (>6 METs) and causes rapid breathing and a substantial increase in heart rate. Moderate intensity PA was defined as requiring a moderate amount of effort (3–6 METs) and noticeably acceleration in heart rate. Low level PA was defined as attaining less than 600 MET-minutes per week [ 29 ].

Actual cumulative risk behaviour was obtained from the five lifestyle risk behaviours assessed among the patients, (smoking, alcohol drinking, khat chewing, inadequate consumtion of fruit and vegetables and physical inacativity), with a maximum score of 5 (all risk behaviours present) and a minimum score of zero (none of the risk behaviours present).

The patient’s knowledge of CV disease risk was measured using the HDFQ [ 20 ] on a two point scale with “0” = wrong answer and “1” = correct answer. Then, it was scored by adding the correct scores of all the items for each participant. A higher score was used to indicate a better knowledge of CV risk factors. The score out of 100 was categorised as good/optimal knowledge (score ≥70%), fair knowledge (score between 50% and 69%) and poor level of knowledge (score <50%). A score < 70% was categorised as suboptimal knowledge [ 3 ].

Statistical analysis

The data was entered on Epidata version 3.0 and were checked for completeness and consistency. Statistical analysis was performed by using IBM SPSS statistics version 25. The univariate analysis was reported as proportion, percentage, and frequency, and continuous data were reported as mean and standard deviation. A normality test was done for continuous variables age and knowledge of CV risk factors. A linear regression model was used to assess association between knowledge of CV risk factors and independent variables. First, associations between knowledge and predictors were analysed by means of bivariate linear regression to identify factors associated with the dependent variable. Then, those variables with a P -value < 0.2 on bivariate linear regression were included in a multivariable linear regression model to test for significant associations. The magnitude of the association between different independent variables in relation to the dependent variable was measured using estimates and 95% confidence intervals, and P -values < 0.05 were considered to be statistically significant.

Characteristics of the participants

A total of 287 patients diagnosed with CVD who attended the chronic follow up care were enrolled in the study; 115 patients from Hiwot Fana Specialised University Hospital and 172 patients from Dilchora Referral Hospital. Mean age was 47 years (±11 SD) and 56.4% of patients were of the female gender. The majority (70.7%) of the patients were diagnosed with hypertension. More than half of the patients had a low level of education. The sociodemographic characteristics of the participants are depicted in Table 1 .

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https://doi.org/10.1371/journal.pone.0234198.t001

Knowledge of cardiovascular risk factors

The mean percentage HDFQ score was 70.5% (±15.3). Overall, 155 patients (54%) had optimal knowledge of risk factors (scored ≥70%), whereas, the remaining 132 patients (46%) had sub-optimal knowledge ( Fig 1 ). The majority of patients demonstrated significant knowledge about facts that age, 228 (79.4%), smoking 280 (97.6%), being overweight 262 (91.3%) and high blood pressure 235 (81.9%) are risk factors for cardiovascular disease. At the same time patients had deficient knowledge about the fact that family history of heart disease 249 (86.8%) and diabetes 184 (64.1%) are also risk factors. Almost one fifth 55 (19.2%) did not understand that keeping blood pressure under control reduces the risk of developing cardiovascular disease, 52 (18.1%) were unable to identify eating fatty food affects blood cholesterol level, and 115 (40.1%) assume only exercising at a gym or in an exercise class lower a chance of developing cardiovascular disease. Table 2 shows the percentage of patients who answered the heart disease fact questions correctly.

https://doi.org/10.1371/journal.pone.0234198.g001

https://doi.org/10.1371/journal.pone.0234198.t002

Actual cumulative risk behaviour and knowledge of cardiovascular disease risk factors association

Through our previous study [ 30 ], we have assessed five CV risk behaviours, i.e. smoking, alcohol drinking, khat chewing, fruit and vegetable intake and physical activity. None of the patients met the WHO recommendation for fruit and vegetable consumption (more than five serving daily), 148 (51.6%) were physically inactive (attained less than 600 MET-min per week), 57 (19.9%) were current khat chewers, 54 (18.8%) were current alcohol drinkers and 3 (1%) were current smokers. Almost one-third 86 (30%) them had one risk behaviour, more than half 149 (51.9%) had two risk behaviours, and 43 (18.1%) had three or more risk behaviours. Out of the total recruited patients, 201 (70%) had multiple risk behaviours (two or more behaviours).

Regarding bivariate linear regression analysis age, sex, residence, ethnicity, marital status, education level and number of actual risk behaviours got p<0.2 ( Table 3 ). These variables were taken in to multivariable linear regression model to identify independent predictors of CV risk factors knowledge.

https://doi.org/10.1371/journal.pone.0234198.t003

In the multivariable linear regression analysis, knowledge of CV risk factors was significantly associated with place of residence, level of education and marital status. There was a statistically significant association between knowledge of CV risk factors and residence (P < 0.001). Urban residents had 12.84 units higher mean knowledge score than rural residents (β = 12.84, 95% CI 6.91 to 18.77; P < 0.001). In addition, level of education is associated with knowledge of CV risk factors (P < 0.001), those who had no formal education had -18.80 units lower mean knowledge score compared to those who completed college or university (β = -18.80, 95% CI -24.76 to -12.85; P < 0.001). Those who attained less than primary school education had -12.02 units less knowledge score compared to those who completed college or university (β = -12.02, 95% CI -17.63 to -6.40; P < 0.001). There was also a statistically significant association between knowledge and marital status (P < 0.001). Those who were never married had -14.01 units lower mean knowledge score than those who were currently married (β = -14.01, 95% CI -20.71 to -7.29; P < 0.001). There was no statistically significant association between knowledge of CV risk factors and actual cumulative risk behaviour (P = 0.076) or age (P = 0.718) or sex (P = 0.259) or ethnicity (P = 0.196) ( Table 4 ).

https://doi.org/10.1371/journal.pone.0234198.t004

This study examined the level of knowledge of cardiovascular risk factors and associated factors among known CV patients who were attending chronic follow up care at two public referral hospitals in eastern Ethiopia. The study demonstrates that almost half of CVD patients have suboptimal knowledge regarding CV risk factors which may impede secondary CV prevention if effective interventions are not implemented. Thus, the findings of this study warrant the need of improved preventive interventions to achieve optimal knowledge in the general population.

Knowledge of CV risk factors among CVD patients was unsatisfactory, and about half of the patients have suboptimal knowledge, which is in line with existing findings reported from India and United Arab Emirates [ 16 , 31 ]. However, the mean CV risk factors knowledge score in the current study (70.5%) is higher compared to finding from Nigeria (48.6%), and this could be due to difference in population characteristics [ 3 ]. Consistent with the finding of this study, a systematic review showed low level knowledge and awareness of CVD and associated risk factors among populations in Sub-Saharan Africa [ 22 ]. The possible reasons for the suboptimal knowledge may be attributed to a lower level of educational attainment of the patients, poor patient counselling during follow up care appointment and absence of intensive lifestyle counselling programs. Moreover, low health literacy may be due to lack of effective patient counselling methodologies that fits the cultural and sociodemographic context and poor health information seeking behaviour of patients. Implementation of innovative health education strategies may help to improve health literacy for CV patients and for the general population as well.

Residence, education level and marital status were associated with knowledge of cardiovascular risk factors, which mirrors that social, cultural and economic factors are major determinants of awareness and health behaviour change [ 32 ]. In line with the finding of the current study, numerous studies [ 25 , 33 , 34 ] have revealed higher education is associated with better health literacy. A review conducted in Sub-Saharan Africa reported that place of residence is an important determinant of knowledge of cardiovascular risk factors, i.e. urban residence is associated with improved knowledge of CV risk factors [ 22 ]. In Ethiopia, rural residents attain lower educational level and have poor access to health information as compared to urban residents who relatively have better health literacy. Thus, low knowledge of CV risk factors in rural residents could be due to their lower education attainment. Moreover, the current study shows that those who were never married have lower levels of knowledge regarding CV risk factors compared to those who were married. Consistent with this, Manfredini et al. reported that being married is associated with, lower risk factors, better knowledge and better CV health status [ 35 ].

Studies from Nigeria, Germany and Luxembourg reported that a higher level of education is associated with healthy lifestyle and appropriate self-care behaviours [ 25 , 36 , 37 ]. In addition, evidence from a review revealed that a lower educational level is associated with lower knowledge of CV risk factors, and this also concurs with the finding of the current study [ 22 ]. Findings from Pakistan also support those of this study where lack of formal education is associated with lower knowledge of cardiovascular disease risk factors [ 38 ]. However, about one-third of the patients in the current study had no formal education, thus, improving literacy in developing countries is vital in tackling the emerging burden of chronic diseases, in particular, CVD and its associated lifestyle behaviours, as demonstrated previously [ 22 ].

The prevalence of alcohol drinking, inadequate fruit and vegetable consumption and physical inactivity in the current study is comparable to findings from Addis Ababa [ 14 ], Kenya [ 39 ] and Nigeria [ 40 ]. However, the rate of smoking in this study is lower compared to findings from Addis Ababa [ 14 ], Ghana [ 41 ], Kenya [ 42 ] and Uganda [ 43 ], and this could be due to differences in sociocultural characteristics of participants.

According to the Health Belief Model, knowledge of health behaviour is an important determinant of adherence to healthy lifestyle behaviours. Though, knowledge alone is not sufficient, and patients’ perceptions and attitudes of health behaviours are also important predictors of health lifestyle behaviours. The current study demonstrated that occurrence of actual cumulative risk behaviours is not associated with knowledge of CV risk factors. Thus, as patients’ perceptions and attitudes of CV risk factors are important determinants of behaviour change, these need to be explored in further research. Consolidating this, Tran et al (2017) states a high level of knowledge of CV risk factors is not sufficient to reduce cardiovascular risk, however, improving the perception of adults regarding CV risk factors plays an important role in reducing long term cardiovascular risk [ 23 ]. Nevertheless, the finding of Alzaman et al. which states awareness of modifiable CV risk factors is positively associated with health behaviour for adult patients [ 44 ] is inconsistent with the finding of the current study. A potential reason may be due to differences in education profile.

Even though the overall actual risk behaviour is not associated with occurrence of actual cumulative risk behaviour, the vast majority of patients had good knowledge and practice healthy behaviour regarding smoking. Available evidence reports that most adults are aware of the fact that cigarette smoking is a risk factor for CV disease [ 22 , 24 , 45 ]. Inadequate consumption of fruit and vegetables was highly and equally (100%) prevalent among those who have good or fair or poor level knowledge of CV risk factors. In addition, the majority of patients knew physical activity lowers the chance of developing heart disease, however, more than half of them failed to achieve this. This shows the existence of other factors that determine patients’ health behaviours, including individual perceptions and beliefs regarding the disease and the risk factors. This issue needs to be explored more through further research.

Findings show intensive lifestyle counselling improves awareness and adherence to healthy lifestyle behaviours [ 25 , 26 , 46 ]. In the current study, about half of CVD patients who had received follow up care with a focus on the management of CV risk factors had sub-optimal knowledge of these and they were indulged in multiple unhealthy behaviours. This is consistent with findings from America which reported African Americans have cluster of CV risk behaviours [ 47 ]. In addition, about one fifth do not know high blood pressure is a risk factor for heart disease, and this indicates a need for implementing targeted education strategies. Overall, the finding of this study show existing follow-up service is not optimal, and the probable reasons for this may be poor patient counselling service and limitation of resources. This signifies there is a need to improve the follow up service to promote healthy lifestyle behaviours for the patients. Implementing intensive lifestyle support programs based on developed guidelines and delivered by trained health professionals may also help to improve patients’ knowledge and health behaviours [ 48 , 49 ]. Absence of CVD prevention policies and strategies at population level could also have contributed to this problem in Ethiopia. Various CVD prevention guidelines have been developed and are in use to promote effective prevention of CVD in developed countries. The European Society of Cardiology guidelines focus on the importance of patient involvement and patient education which may potentially improve knowledge levels and motivation in patients [ 50 ]. The American College of Cardiology (ACC) and the American Heart Association (AHA) guidelines recommend promotion of lifetime risk estimation and which may represent an additional step forward in supporting lifestyle behaviour change counselling programs [ 51 ]. Other than a recently developed National Strategic Action Plan (NSAP) for prevention & control of non-communicable diseases [ 52 ], there are no specific guidelines for prevention of CVD in use in Ethiopia. Therefore, there is a need for the development and implementation of context specific guidelines and innovations to improve knowledge levels and patient motivation towards healthy lifestyle behaviour, particularly for poorly educated and rural residents.

Adoption of healthy lifestyle behaviours promote better health related quality of life [ 53 ], however, the patients in the current study had unhealthy behaviours that may predispose them for further complications and affect their health related quality of life, and this may contribute to the increased CVD related mortality in Ethiopia. Despite the rise in the burden of CV risk factors and lack of awareness among adult population, there is no prevention strategy implemented to reduce the burden of CVD in Ethiopia. The findings of this study have practical implications for health care workers and should inform policy makers that change is required to improve patients’ understanding of cardiovascular disease risk factors and reduce the burden of CV risk behaviours.

Given that the actual risk behaviour is not associated with the required knowledge of risk factors in this population, warrants the design and implementation of innovative interventions, in which patients are educated and empowered to self-manage their risk factors. As an example, structured and systematic nurse-led lifestyle counselling effectively reduce cardiovascular risk behaviour, improve patients’ knowledge of CV risk factors and promote healthy lifestyle behaviours [ 46 ]. Moreover, health care providers should identify patients with limited understanding of risk factors and actual risk behaviours and provide tailored interventions. Indeed, it is essential to explore how patients perceive their own risk of CV disease and the risk factors, since these are key determinants of health behaviour change according to the Health Belief Model. Therefore, the findings of this study warrant attention and are a call for action from policy makers. As such the presented data can be used as baseline data for the development of intervention programs, specifically focussed at Ethiopia that aim to improve patients’ awareness of CV disease risk factors and reduce the burden of CV risk behaviours. Indeed, it is important to design and implement monitoring and evaluation systems to improve the follow up service.

Limitations

This study may be subject to bias. Firstly, the study is subject to the limitations of patient recall and social desirability bias, and the self-reported measurement of risk behaviours may have underestimated the CV risk behaviours. However, this underlines that the real-world problem may be even worse in developing countries, and that a call for action is required. Secondly, the use of cross-sectional study design does not establish causal relationships.

The burden of CV risk behaviours is increasing whilst the patients’ understanding of associated risk factors is limited. Almost half of CVD patients have suboptimal knowledge regarding CV disease risk factors, and they have multiple unhealthy behaviours though they attend chronic follow up care clinics. Lower education, rural residence and single marital status were associated with lower knowledge of cardiovascular risk factors. Therefore, this study is important to demonstrate the need for implementing an effective prevention program. In line with intensive patient counselling and education to improve awareness regarding CV risk factors, implementation of multidisciplinary, innovative interventions and systematic nurse-led lifestyle counselling is indeed important to effectively assist CV patients in adopting positive lifestyle behaviours. Moreover, implementation of CVD prevention programs should be considered for the disease prevention policy agenda in Ethiopia.

Supporting information

S1 checklist. strobe statement—checklist of items that should be included in reports of cross-sectional studies ..

https://doi.org/10.1371/journal.pone.0234198.s001

Acknowledgments

The authors would like to thank the University of Adelaide for supporting this study. We are also grateful to the study participants, data collectors and health care workers who were directly or indirectly involved in this study. We are thankful to Suzzane Edwards for her statistical advice.

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Research article
Open access
Published: 20 June 2018

Cardiovascular disease (CVD) and associated risk factors among older adults in six low-and middle-income countries: results from SAGE Wave 1

Ye Ruan 1 na1 ,
Yanfei Guo 1 na1 ,
Yang Zheng 1 ,
Zhezhou Huang 1 ,
Shuangyuan Sun 1 ,
Paul Kowal 2 , 3 ,
Yan Shi 1 &

BMC Public Health volume 18 , Article number: 778 ( 2018 ) Cite this article

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Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Our study aimed to investigate the prevalence of two conditions, angina and stroke, and relevant risk factors among older adults in six low- and middle- income countries(LMICs).

The data was from World Health Organization (WHO) Study on global AGEing and adult Health (SAGE) Wave 1 in China, Ghana, India, Mexico, Russian Federation and South Africa. Presence of CVD was based on self-report of angina and stroke. Multivariate logistic regression was performed to examine the relationship between CVD and selected variables, including age, sex, urban/rural setting, household wealth, and risk factors such as smoking, alcohol drinking, fruit/vegetable intake, physical activity and BMI.

The age standardized prevalence of angina ranged from 9.5 % (South Africa) to 47.5 % (Russian Federation), and for stoke from 2.0% (India) to 6.1 % (Russia). Hypertension was associated with angina in China, India and Russian Federation after adjustment for age, sex, urban/rural setting, education and marital status (OR ranging from 1.3 [1.1-1.6] in India to 3.8 [2.9-5.0] in Russian Federation), furthermore it was a risk factor of stroke in five countries except Mexico. Low or moderate physical activity were also associated with angina in China, and were also strongly associated with stroke in all countries except Ghana and India. Obesity had a stronger association with angina in Russian Federation and China(ORs were 1.5[1.1-2.0] and 1.2 [1.0-1.5] respectively), and increased the risk of stroke in China. Smoking was associated with angina in India and South Africa(ORs were 1.6[1.0-2.4] and 2.1 [1.2-3.6] respectively ), and was also a risk factor of stroke in South Africa. We observed a stronger association between frequent heavy drinking and stroke in India. Household income was associated with reduced odds of angina in China, India and Russian Federation, however higher household income was a risk factor of angina in South Africa.

While the specific mix of risk factors contribute to disease prevalence in different ways in these six countries – they should all be targeted in multi-sectoral efforts to reduce the high burden of CVD in today’s society.

Peer Review reports

Cardiovascular diseases (CVDs) are by far the leading cause of death in the world. An estimated 17.9 million people died from CVDs in 2015. Ischemic heart disease (IHD) and stroke were the top two leading causes of CVD health lost in each world region [ 1 , 2 ]. By 2030 more than 22.2 million people will die annually from CVDs. Populations in low and middle income countries (LMICs) now contribute 75% of the CVD deaths, which leads to 7% reduction of gross domestic product(GDP) in these countries [ 3 ].

A larger proportion of the global burden of CVDs is now borne by LMICs than in high income countries, this is despite a comparatively lower burden from risk factors in low compared to high income countries [ 4 , 5 , 6 ]. Given the high prevalence of CVD among older adults in LMIC, the projected increases in this population will be a major challenge for the health care system. Twenty-three percent of the total global burden of disease(GBD) was attributed to disorders in people aged 60 years and older. The main contributors to disease burden were CVDs, accounting for 30.3% of the total burden in older people in 2010 [ 7 ]. Reliable and comparable analysis of risks to CVD is especially important for projecting future disease burden and for shaping disease prevention efforts.

A number of population-based studies from lower income countries have suggested that socio-demographic characteristics are associated with CVD, with increasing age, female sex and lower education consistently associated with higher prevalence of CVD. Some epidemiological evidence also suggests that CVD is associated with behavioral risk factors such as smoking, alcohol use, low physical activity levels, and insufficient vegetable and fruit intake, hypertension is also regarded as a very important risk factor for CVD. Independently or in combination, these risk factors present an opportunity for interventions to reduce future CVD burdens in ageing populations in LMIC.A number of large recent studies have compared CVD risks in higher and lower income countries, providing valuable and needed information about CVD and CVD risks [ 4 , 5 , 6 ]. However, the results of these studies may not be representative of the older adult population. For example, the Prospective Urban Rural Epidemiology (PURE) study sampling strategy and distributions provide less reliable estimates at older ages [ 8 ]. The World Health Organization Study on global AGEing and adult health (SAGE) is focused on older adults and use similar methodology across countries to improve comparability of important covariates and disease prevalence. Three of the countries overlap in PURE and SAGE (China, India and South Africa) where SAGE includes three additional middle income countries (Ghana, Mexico and the Russian Federation).

The aim of the present study was to investigate the prevalence of two main CVDs (angina, stroke) and behavioural risk factors and associated social-economic status (SES) factors among older adults using a unique data set with nationally representative samples in six low and middle income countries.

Sample and procedure

The data was from World Health Organization (WHO) Study on Global AGEing and adult health (SAGE) Wave 1, a longitudinal cohort study of ageing and older adults from 2007 to 2010 in six low- and middle-income countries (China, Ghana, India, Mexico, Russian Federation and South Africa) [ 9 ]. SAGE Wave 1 used face-to-face individual interviews to capture data. All six countries implemented multistage cluster sampling strategies which resulted in nationally representative cohorts of older adults ( http://www.who.int/healthinfo/sage/SAGEWorkingPaper5_Wave1Sampling.pdf?ua=1 ). Response rates for SAGE countries were Mexico 51%, India 68%, Ghana 80%, Russian Federation 83%, South Africa 77% and China 93%. Examination of non-respondent data suggested non-significant differences on some covariates (data not shown). Data were obtained following application for access through http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog .

SAGE has been approved by the World Health Organization's Ethical Review Board. Additionally, each partner organization obtained ethical clearance through their respective review bodies. All study participants signed informed consent.

CVDs conditions

Two methods of assessing presence or absence of CVD were used. One was based on self-report of angina or stroke; and the second used an algorithm based on validated symptom-reporting methods to estimate and compare prevalence rates.

Sociodemographic variables

Socio-demographic variables contain age, sex, education, rural/urban residence, and income quintiles. Age was categorized into four groups: 50 to 59 years; 60 to 69 years; 70 to 79 years; and 80 years or older. Education level was classified into seven categories for analysis using an international classification scheme [ 10 ]. The income quintiles were generated using an asset-based approach- possession of assets and dwelling characteristics [ 11 ], with quintile 1(Q1) the quintile of the poorest households and quintile 5(Q5) the quintile of the richest.

Risk factors

Tobacco use.

Tobacco use was assessed by self-report and included different forms (manufactured or hand-rolled cigarettes, cigars, cheroots or whether tobacco is smoked, chewed, sucked or inhaled), and frequency of smoking, snuffing or chewing in each day over the week before interview[ 12 ], classified into four groups: never smoker, not current smokers, smokers(not daily) and current daily smokers.

Alcohol consumption

Alcohol consumption was categorized into four groups: life time abstainer, non-heavy drinkers, infrequent heavy drinkers and frequent heavy drinkers according to the consumption number of standard drinks of beer, wine and or spirit, fermented cider, and other alcoholic drinks during the week before interview.

Physical activity

Physical activity was measured by the Global Physical Activity Questionnaire (GPAQ) and assessed intensity, duration, and frequency of physical activity in three domains: occupational, transport-related, and discretionary or leisure time. Based on a standard classification scheme, three categories were generated: low, moderate and high levels [ 13 ].

Fruit and vegetable consumption

Fruit and vegetable consumption was assessed according to the number of daily servings eaten – with each serving approximating 80 grams. Five or more servings were defined as sufficient daily intake (at least 400 grams per day), fewer than five servings were categorized as insufficient [ 14 ].

Hypertension

The definition of hypertension used was systolic blood pressure ≥140mmHg and/or diastolic blood pressure ≥ 90mmHg and/or self-reported treatment with antihypertensive medication during the two weeks before interview. Blood pressure measurements were conducted three times on the right arm of the seated respondent with an automated recording device (OMRON R6 Wrist Blood Pressure Monitor, HEM-6000-E, Omron Healthcare Europe), and calculated as an average of the latter two measurements.

According to the classification criteria proposed by the WHO [ 15 ], body mass index (BMI) of <18.5 kg/m 2 , 25–29.9 kg/m 2 and ≥30 kg/m 2 are used to define underweight, overweight and obesity, respectively. Modified BMI cutoffs for China and India were used to perform an additional set of analyses that describes overweight (BMI 23.0-27.5) and obesity (BMI >27.5) in Asian populations [ 16 ].

Statistical methods

Statistic analyse were conducted using STATA SE version 11 (Stata Corp, College Station, TX). The prevalence of angina and stroke were calculated by using normalized weights in each country. Weights were based on selection probability, non-response, and post-stratification adjustments. To improve comparability across countries, the prevalence rates were age-standardized using the WHO World Standard Population Distribution based on world average population 2000-2025 [ 17 ]. Multivariate logistic regression was performed to examine the relationship between CVD and selected variables, including the socio-demographics such as age, sex, urban/rural setting, education, household wealth, and health risk factors such as smoking, alcohol drinking, fruit/vegetable intake, physical activity, hypertension and obesity. P < 0.05 from two-sided statistical tests was considered statistically significant.

A total of 34,114 individuals were included in the final analyses. Table 1 shows the sample distribution and demographic, socioeconomic and lifestyle characteristics by countries. The proportions of women are higher than men in four countries, except Ghana and India. The majority of older Indian lived in rural locations, while compared to urban areas in the other countries. The 50-59 age groups had the largest proportions in all countries, but the SAGE sample population distributions match those of the United Nations and US Census Bureau’s International Data Base estimates [ 18 ]. The percentage of respondents with no formal education were higher in Ghana (54.0%) and India (51.2%). In contrast, Russian Federation had the highest educational level with only 0.5% with no formal education and over 20% with a college degree or higher.

The rate of daily smoking ranged from 7.6% (Ghana) to 46.9% (India), frequent heavy drinker was the highest in China (6.4%) and lowest in Mexico (0.1%), and the highest rate of low physical activity was in South Africa (59.5%). Insufficient fruit and vegetable intake was more common in India, the Russian Federation and Mexico (90.6, 81.0 and 81.4%, respectively) compared with China, South Africa and Ghana (35.7, 68.5 and 68.9%, respectively).

The age standardized prevalence of angina ranged from 9.5 % (South Africa) to 47.5 % (Russian Federation). It was higher in women than in men in all six countries. The rates were higher in rural than in urban locations other than in China. Angina rose with age in each country except Mexico, and a slight drop was seen in the highest age group in Ghana, India, Russian Federation and South Africa. The lowest prevalence of angina was found in individuals with the highest household income in China, Ghana, India and Russian Federation, respectively (see Table 2 ).

The prevalence of stroke was 6.1% in Russian Federation, which was higher than the other SAGE countries, while India had the lowest prevalence of 2.0%. In Russian Federation, the prevalence of stroke in men was almost twice that of women. Stroke was higher in urban than in rural locations in all six countries. Stroke prevalence tended to increase with age in all SAGE countries, but a slight drop in 80+ age group in Mexico and Russian Federation. In China, the wealthiest older adults had the lowest stroke prevalence (see Table 3 ).

Table 4 shows the Odds ratios for likelihood of angina by risk factors. Hypertension was associated with angina in China, India and Russian Federation after adjustment for age, sex, urban/rural setting and education (OR ranging from 1.32 [1.13-1.55] in India to 3.80 [2.91-4.96] in Russian Federation). Low and moderate physical activity was also associated with angina in China (ORs were 1.46 [1.22-1.76] and 1.66[1.39-1.99], respectively). Obesity had a stronger association with angina in Russian Federation and China (ORs were 1.48[1.08-2.02] and 1.24[1.01-1.53], respectively). Smoking was associated with angina in India and South Africa (ORs were 1.56[1.02-2.36] and 2.11 [1.23-3.61], respectively). Non-heavy drinking was a protective factor for angina in China (OR was 0.67[0.51-0.87]). The OR (1.56[1.19-2.05]) for insufficient fruits and vegetables intake was highest in Ghana. Household income was associated with reduced odds ratios of angina in China, India and Russian Federation, however higher household income was a risk factor of angina in South Africa (see Table 4 ).

In all six LIMCs except Mexico, hypertension was associated with stroke (OR ranging from 1.98[1.04-3.80] in Ghana to 3.16[1.72-5.83] in Russian Federation). Low, moderate physical activity were also strongly associated with stroke in four LMICs apart from Ghana and India. In China, Obesity increased the risk of stroke (OR was 1.66[1.20-2.28]). Smoking was also a risk factor of stroke in South Africa. We observed a stronger association between frequent heavy drinking and stroke in India (OR 6.64[1.39 – 31.82]). Insufficient fruit and vegetable intake and household income were not significantly associated with stroke in any of the countries (see Table 5 ).

This study reports the prevalence of two common cardiovascular diseases, angina and stroke, and the relevant risk factors among older adults in six LIMCs. Globally, the age-adjusted CVDs mortality continues to be unevenly distributed: where it has decreased in high income countries(HICs) by 43% in recent decades [ 19 ], while LIMCs are drowning in a rising tide of CVD. Although age-standardized rates of death attributable to CVD declined 13% in LMICs from 381 per 100000 in 1990 to 332 per 100000 in 2013, the number of deaths increased 66% from 7.21 million to 12 million in 2013 with ageing and population growth ascribed as the main drivers [ 19 ]. Ischaemic heart disease and cerebrovascular disease (stroke) combined accounted for more than 85.1% of all cardiovascular disease deaths in 2016[ 20 ]. Our study indicated that CVDs(angina, stroke) were prevalent and variable among older adults in six countries. Angina and stroke were both highest in Russian Federation(47.5%, 6.1% respectively). Women were more likely to have angina than men in all six countries. Stroke was more prevalent in urban than in rural. Angina and stroke both tended to increase with age in China.

Prevalence of CVDs generally appeared to be most closely linked to a country’s stage of epidemiological transition [ 21 ], especially when high disease rates in middle age carry through into older ages. Underlying social, environmental, and economic shifts in many countries have led to increasing levels of predominant causes such as tobacco and alcohol use, sedentary lifestyle, unhealthy diets, and suboptimum levels of weight, blood pressure, cholesterol, and plasma glucose. The high and growing prevalence of CVD in LIMCs largely reflects the burden of these key risk factors. Our study revealed that hypertension, high BMI, decreased physical activity, frequent heavy drinking and lower household health were key risk factors of angina and stroke. However, the distribution of risk factors in six counties was unequal, for example, the factor with highest OR of angina in China and Russian Federation was hypertension, whereas it was smoking in India and South Africa.

Hypertension has been shown to be an independent risk factor for acute myocardial infarction and stroke in older people [ 22 , 23 ]. We found that hypertension was associated with angina in China, India and Russian Federation, in addition it was a risk factor of stroke in five of the six countries in this study (not Mexico). Between 1980 and 2008, blood pressure decreased by 2.0mmHg or more (for men) and 3.5 mmHg or more (for women) per decade in western Europe and Australia but increased by up to 2.7 mmHg over this same period in Oceania, East and West Africa and South and Southeast Asia [ 24 ]. Systematic review revealed that blood pressure lowering greatly reduced the major cardiovascular disease events and all-cause mortality, irrespective of starting blood pressure [ 25 ].However among these six LIMCs 66% hypertensives were undiagnosed before the survey, 73% untreated and 90% uncontrolled. Although the proportions of undiagnosed and untreated were lowest in Russia (30% and 35%), the uncontrolled rate was higher (83%) [ 26 ], low level of health care (primary and secondary prevention) and irregular treatment continued to be a major problem [ 27 ]. Hence, further research on early screening strategies, available health care and effective treatment of hypertension may be critical for improving outcomes.

Our study also showed that low physical activity and obesity besides hypertension were both associated with angina and stroke in China, and insufficient fruit and vegetable intake was risk factor of angina in Ghana. Compared with data from 1997, total physical activity in 2009 has decreased by 29% in males and by 38% in females in China [ 28 ], and physical inactivity was estimated the third leading risk factor for coronary heart disease [ 29 ]. As the relation between physical and obesity well recognized, obesity was an important risk factor of CVD. People are becoming more and more obese. Global age-standardised mean BMI increased from 21.7 kg/m 2 in 1975 to 24.2 kg/m 2 in 2014 in men, and from 22.1 kg/m 2 in 1975 to 24.4 kg/m 2 in 2014 in women. Over this period, age-standardised prevalence of obesity increased from 3.2% in 1975 to 10.8% in 2014 in men, and from 6·4 to 14.9% in women [ 30 ]. More than 50% of the obese individuals in the world lived in just 10 countries (listed in order of number of obese individuals): USA, China, India, Russia, Brazil, Mexico, Egypt, Pakistan, Indonesia, and Germany, and China and India jointly accounting for 15% in 2013[ 31 ]. China has moved from 60th place for men and 41st place for women in 1975 to second for both men and women in 2014 in the worldwide ranking of the number of severely obese individuals [ 30 ]. Unfortunately, the prevalence of obesity among children and adolescents are both on the rise. In comparison with obesity rate in 1985, it increased by 8.7 times for children and 38.1 times for adolescents [ 32 ]. In the World Health Survey 2002-2003, prevalence of low fruits and vegetable consumption among individuals aged 18-99 years in Ghana was the lowest among 52 countries [ 33 ]. However, the prevalence was higher (68.9%) among persons aged 50 years and older [ 34 ]. We also found that insufficient fruits and vegetables intake was associated with angina in Ghana. All of these contribute to the increasing burden of CVD.

We observed a relationship between smoking and angina, frequent heavy drinking and stroke in India. The prevalence of angina was 19.6% (95%CI:16.5-23.0) in India, the second highest for these six countries. CVD-related conditions contributed nearly two-thirds of the burden of NCD mortality in India [ 35 ], with ischemic heart disease(IHD) and stroke contributing substantially to CVD mortality in India (83%) [ 36 ].Up to 35% of adults in India consume tobacco [ 37 ], with the rate of daily tobacco use was highest(46.9%) among the six LIMCs in this study, highest in younger individuals (20–35 years) [ 38 ].The relation between alcohol consumption and CVD has been widely studied. Several analyses showed that low-moderate levels of alcohol consumption had cardio protective effects, while heavy drinking is harmful, usually described as “U-shaped” or “J-shaped” relationship [ 39 , 40 ]. Aside from alcohol consumption, drinking pattern (binge-pattern drinking) played an important role in elevating the risk of CVD [ 41 , 42 ]. Another cohort study showed that heterogeneous associations exist between level of alcohol consumption and CVD: compared with moderate drinking, heavy drinking raised risk of coronary death, heart failure, cardiac arrest, ischaemic stroke but a lower risk of myocardial infarction or stable angina [ 43 ]. We found that in China non-heavy drinking was a protective factor for angina and stroke, and frequent heavy drinking showed a dangerous effect for stroke in India.

There were a few limitations in our study. Firstly, although SAGE assembled nationally representative cohorts from six countries, the response rates were different across the countries, ranging from 51% in Mexico to 93% in China. The low response rate in Mexico was for specific reasons related to timing of the survey and inability to engage in repeat visits to households to maintain the sample and we note this introduces the potential for selection bias into the results for Mexico. Secondly, the data for stroke and some risk factors were based on self-reports, which may lead to recall bias. However, validated symptom-reporting methods were also used in these analyses to estimate and compare prevalence rates for angina to improve prevalence estimates. Thirdly, the question on stroke in SAGE did not distinguish between ischemic stroke and hemorrhagic stroke. Last, these results are based on cross-sectional data and as such, cannot be sure of the direction of the associations we identified.

Conclusions

In conclusion, our study provided representative prevalence of angina and stroke and relevant risk factors in elders in six LIMCs. Due to the variation pattern of prevalence and risk factors distribution, policies and health interventions will need to be targeted and tailored for a broad range of local conditions to achieve the health goals set by the United Nations for 2025.

Abbreviations

Body mass index

Cardiovascular disease

Global burden of disease

Gross national income

Global Physical Activity Questionnaire

High income countries

Ischemic heart disease

Low- and middle- income countries

Study on global AGEing and adult Health

Social-economic status

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Acknowledgements

The authors would like to thank the respondents and interviewers from all six SAGE countries for their contributions and hard work.

This work was supported by WHO, the US National Institutes on Aging through Interagency Agreements [OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005-01] and through a research grant (R01-AG034479), and Three-year Action Plan on Public Health, Phase IV, Shanghai, China[15GWZK0801;GWIV-22].

Availability of data and materials

The datasets supporting the conclusions of this article are available upon request in the website of WHO ( http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/sage ).

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Ye Ruan and Yanfei Guo contributed equally to this work.

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Shanghai Municipal Center for Disease Control and Prevention (Shanghai CDC), Shanghai, China

Ye Ruan, Yanfei Guo, Yang Zheng, Zhezhou Huang, Shuangyuan Sun, Yan Shi & Fan Wu

World Health Organization, Geneva, Switzerland

Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand

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Contributions

FW, PK, YFG and YZ designed, implemented the conduct of this study. YR and YFG conceived of the analysis, and drafted the manuscript. YR, YFG, YS, ZZH, YZ and SYS contributed to the statistical analyses. ZZH and SYS contributed to the editing of initial draft. All authors read and approved the final manuscript.

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Correspondence to Yan Shi or Fan Wu .

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WHO’s Ethical Review Committee approved SAGE (RPC146), and each country obtained local ethical approval to conduct the study. Written informed consent was obtained from each respondent.

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Ruan, Y., Guo, Y., Zheng, Y. et al. Cardiovascular disease (CVD) and associated risk factors among older adults in six low-and middle-income countries: results from SAGE Wave 1. BMC Public Health 18 , 778 (2018). https://doi.org/10.1186/s12889-018-5653-9

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DOI : https://doi.org/10.1186/s12889-018-5653-9

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Research Theses

Type:	Thesis
Title:	Cardiovascular Risk Behaviour and Health Literacy among Patients with Cardiovascular Disease in Ethiopia
Author:
Issue Date:	2021
School/Discipline:	School of Nursing
Abstract:	Cardiovascular diseases (CVD) are becoming more prevalent globally. Increased urbanisation and life expectancy accompanied by a lack of CVD prevention policies in developing countries, is predicted to escalate the burden of CVD in the future. Moreover, low- and middle income countries are facing a high burden of CVD in a context of limited resources and lack of evidence-based prevention policies. In addition, research indicates significant gaps in knowledge of CVD and its risk factors among patients with CVD and in the general population. Despite the growing burden of CVD in developing countries, there is limited data available to improve awareness of this area. This study was conducted in two phases, and the overall aim was to assess cardiovascular (CV) risk behaviours and related health literacy among patients with cardiovascular conditions in Ethiopia. In study 1, a hospital-based cross-sectional survey was conducted in two referral hospitals in eastern Ethiopia. Patients with a confirmed diagnosis of CVD such as heart failure, hypertension and myocardial infarction were recruited from the follow-up units of the hospitals, which provide treatment for CVD and counselling of patients to achieve healthy lifestyles. Convenience sampling was used to select the study participants from each hospital. Data were collected through face-to-face interviews with patients using three validated tools: the World Health Organisation STEPs instrument, an international physical activity questionnaire and a heart disease fact questionnaire. The primary aims of the study were assessment of cardiovascular risk behaviours and knowledge of cardiovascular risk factors among patients with cardiovascular conditions. The data were entered on Epidata version 3.0 and was checked for completeness and consistency. Then, it was exported to SPSS version 24 for analysis. Multivariable linear regression was used to examine the relationship between knowledge of CV risk factors and explanatory variables. A total of 287 CVD patients were recruited, of which 56.4% were females and 90.2% were urban residents. Most patients had inadequate consumption of fruit and vegetables, 51.6% were physically inactive, 20% were current khat chewers, 19% were current alcohol drinkers and only 1% were current smokers. Approximately one-third (30%) of the patients had one of these risk behaviours, more than half (51.9%) had two, 15% had three and 3.1% had four risk behaviours. The majority (70%) of the patients had multiple risk behaviours. The prevalence of multiple risk behaviours did not significantly vary with sex, residence and educational level differences, (p > 0.05). More than half of the patients (54%) had a good knowledge of cardiovascular risk factors (scored > 70%), whilst 46% demonstrated suboptimal knowledge levels in this area. Urban residents had 12.84 units higher mean knowledge scores than rural residents (β = 12.84, 95% CI 6.91 to 18.77; P < 0.001). Those who had no formal education had -18.80 units lower mean knowledge score compared to those who completed college or university (β = -18.80, 95% CI -24.76 to -12.85; P < 0.001). In addition, those who attained less than primary school education had -12.02 units less knowledge scores compared to those who completed college or university (β = -12.02, 95% CI -17.63 to -6.40; P < 0.001). Those who were never married had -14.01 units lower mean knowledge scores than those who were currently married (β = -14.01, 95% CI -20.71 to -7.29; P < 0.001). However, there was no statistically significant association between knowledge of cardiovascular risk factors and actual cumulative risk behaviour (p > 0.05). In study 2, qualitative in-depth interviews were employed to collect data. The study participants were patients with hypertension who attended follow-up care. Data were collected through face-to-face in-depth interviews. The study is presented in line with consolidated criteria for reporting qualitative reserach. Audio recorded data were transcribed verbatim, and data analysis was guided by the Braun and Clarke steps of thematic analysis and using Nvivo 12 software. A total of 18 patients with hypertension were interviewed. The findings of this study revealed many patients had a poor understanding of heart disease, were not concerned about developing heart disease in the future and did not know that hypertension predisposes to heart disease. Barriers to fruit and vegetable consumption were poor access, cost and sociocultural factors. Whereas, being busy, poor physical health, and lack of access to an exercise facility were barriers to physical activity. The participants with CVD maintained unhealthy lifestyles even though they attended follow-up care with a specific focus on risk management. The findings of this study demonstrate the high prevalence of physical inactivity, alcohol consumption and inadequate fruit and vegetable consumption in a developing country. The emerging increase of CVD and the continuation of unhealthy lifestyle in patients is somewhat comparable with western countries, indicating this as a global problem. The burden of CV risk behaviours is increasing whilst the patients’ understanding of associated risk factors is limited. Almost half of CVD patients had suboptimal knowledge regarding CVD risk factors, and they had multiple unhealthy behaviours though they attended chronic follow up care clinics. Lower education, rural residence and single marital status were associated with lower knowledge of CVD risk factors. Despite being at high risk for heart disease, patients with hypertension had an inadequate understanding of heart disease and they had deficient understanding that hypertension predisposes to heart disease. However, they were aware that smoking, drinking alcohol, inadequate consumption of fruit and vegetables and physical inactivity causes heart disease. Results indicated that education level influences participants’ understanding of heart disease and the risk factors. This study provides evidence for policy makers that health services reform is required to promote healthy lifestyle behaviours. Implementation of lifestyle support programs should be considered for the disease prevention policy agenda in Ethiopia. In line with intensive patient counselling and education to improve awareness of CVD risk factors, implementation of multidisciplinary, innovative interventions and systematic nurse-led lifestyle counselling is important to assist CVD patients to adopt healthy lifestyles. Healthcare workers need to identify and consider patients’ understanding of health behaviours in planning secondary prevention strategies. Moreover, implementation of CVD prevention programs should be considered for disease prevention policy in Ethiopia.
Advisor:	Magarey, Judy Rasmussen, Philippa Hendriks, Jeroen
Dissertation Note:	Thesis (Ph.D.) -- University of Adelaide, Adelaide Nursing School, 2021
Keywords:	health behaviour cardiovascular disease risk factors knowledge
Provenance:	This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals
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Introduction

Research design and methods, conclusions, article information, cardiovascular risk factor targets and cardiovascular disease event risk in diabetes: a pooling project of the atherosclerosis risk in communities study, multi-ethnic study of atherosclerosis, and jackson heart study.

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Nathan D. Wong , Yanglu Zhao , Rohini Patel , Christopher Patao , Shaista Malik , Alain G. Bertoni , Adolfo Correa , Aaron R. Folsom , Sumesh Kachroo , Jayanti Mukherjee , Herman Taylor , Elizabeth Selvin; Cardiovascular Risk Factor Targets and Cardiovascular Disease Event Risk in Diabetes: A Pooling Project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study. Diabetes Care 1 May 2016; 39 (5): 668–676. https://doi.org/10.2337/dc15-2439

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Controlling cardiovascular disease (CVD) risk factors in diabetes mellitus (DM) reduces the number of CVD events, but the effects of multifactorial risk factor control are not well quantified. We examined whether being at targets for blood pressure (BP), LDL cholesterol (LDL-C), and glycated hemoglobin (HbA 1c ) together are associated with lower risks for CVD events in U.S. adults with DM.

We studied 2,018 adults, 28–86 years of age with DM but without known CVD, from the Atherosclerosis Risk in Communities (ARIC) study, Multi-Ethnic Study of Atherosclerosis (MESA), and Jackson Heart Study (JHS). Cox regression examined coronary heart disease (CHD) and CVD events over a mean 11-year follow-up in those individuals at BP, LDL-C, and HbA 1c target levels, and by the number of controlled risk factors.

Of 2,018 DM subjects (43% male, 55% African American), 41.8%, 32.1%, and 41.9% were at target levels for BP, LDL-C, and HbA 1c , respectively; 41.1%, 26.5%, and 7.2% were at target levels for any one, two, or all three factors, respectively. Being at BP, LDL-C, or HbA 1c target levels related to 17%, 33%, and 37% lower CVD risks and 17%, 41%, and 36% lower CHD risks, respectively ( P < 0.05 to P < 0.0001, except for BP in CHD risk); those subjects with one, two, or all three risk factors at target levels (vs. none) had incrementally lower adjusted risks of CVD events of 36%, 52%, and 62%, respectively, and incrementally lower adjusted risks of CHD events of 41%, 56%, and 60%, respectively ( P < 0.001 to P < 0.0001). Propensity score adjustment showed similar findings.

Optimal levels of BP, LDL-C, and HbA 1c occurring together in individuals with DM are uncommon, but are associated with substantially lower risk of CHD and CVD.

Cardiovascular diseases (CVD), including coronary heart disease (CHD), stroke, and heart failure (HF), are predominant causes of morbidity and mortality among persons with diabetes mellitus (DM) ( 1 ). An important focus of recent guidelines for the management of DM has been the control of modifiable risk factors for the primary prevention of CVD ( 1 , 2 ). Guidelines for over 15 years ( 3 ) have recommended tight control of glycated hemoglobin (HbA 1c ), blood pressure (BP), and LDL cholesterol (LDL-C). Despite evidence of benefit from tighter glycemic control on microvascular complications from the UK Prospective Diabetes Study ( 4 ), its effect on reducing the number of CVD events remains questionable in the light of recent clinical trials ( 5 – 7 ); yet the control of BP ( 8 ) and dyslipidemia ( 9 , 10 ) are well demonstrated to reduce CVD risks. While guidelines over the past 20 years have encouraged tighter control of blood glucose, BP, and LDL-C levels, studies in U.S. cohorts have shown poor CVD risk factor control in persons with DM ( 11 – 13 ); one recent study ( 14 ) showed only 25% to be at target levels for HbA 1c , BP, and LDL-C simultaneously. The Steno-2 Trial ( 15 ) has shown that composite risk factor control in DM can reduce CVD events by >50%. However, data from community-dwelling individuals with DM on the relation of composite risk factor control with CVD risk are limited, especially from ethnically diverse U.S. population-based prospective studies. Understanding the extent to which CVD risk can be reduced from multiple risk factor control can be helpful in providing the evidence-based rationale for composite risk factor control efforts in DM management.

Given the importance of primary prevention of CVD in individuals with DM, in this report we examine, among three major multiethnic U.S. prospective studies of CVD, the association of individual and composite risk factor target attainment for BP, LDL-C, and HbA 1c levels with future risk of CHD and CVD events over an average 11-year follow-up period in adults in whom DM had been diagnosed.

Study Population

We included subjects ≥18 years of age with diagnosed DM who were free of known CVD at baseline from three National Institutes of Health–sponsored prospective studies of persons with follow-up for the following CVD events: 1 ) the Atherosclerosis Risk in Communities (ARIC) study ( 16 ), 2 ) the Multi-Ethnic Study of Atherosclerosis (MESA) ( 17 ), and 3 ) the Jackson Heart Study (JHS) ( 18 ). MESA included Caucasians, African Americans, Hispanics, and Chinese; the ARIC study was composed of Caucasians and African Americans; and the JHS included African Americans exclusively. For our study, we identified those persons with diagnosed DM, as defined by a self-reported physician diagnosis or whether they took medication to lower their blood glucose levels at the time of the identified baseline visit, where HbA 1c and other risk factor information were available (1990–1992 in the ARIC study, 2000–2002 in the JHS, and 2003–2004 for visit 2 in the MESA when baseline HbA 1c measures were available). ARIC study clinical centers included Washington County, MD; Forsyth County, NC; Jackson, MS; and Minneapolis, MN; MESA included clinical sites at the University of Minnesota (Minneapolis, MN), Northwestern University (Chicago, IL), Johns Hopkins University (Baltimore, MD), Columbia University (New York, NY), Wake Forest University (Winston-Salem, NC), and the University of California, Los Angeles (Los Angeles, CA); the JHS has a single site in Jackson, MS. Recruitment was based on probability sampling of four communities from lists of driver licenses, voter registration cards, or identification cards for the ARIC study; lists of residents, dwellings, telephone exchanges, Medicare beneficiaries, and referrals by participants for the MESA; and the Accudata volunteer list and eligible ARIC study participants for the JHS. The mean (maximum) follow-up time, in years, for the subjects included was 14.9 (20.9) for the ARIC study, 7.9 (9.9) for the JHS, and 8.0 (10.5) for the MESA. JHS subjects who were also part of the ARIC study were excluded from our JHS data set and included only in the ARIC study data set used for our analysis. From the original cohort, sample sizes of 15,792 for the ARIC study, 6,814 for the MESA, and 5,302 for the JHS, we identified those individuals with diagnosed DM based on Examination 2 from the ARIC study and the MESA; and, after excluding those with known prior CVD and missing covariate information, 825, 740, and 453 subjects, respectively, were included in the ARIC study, MESA, and JHS data sets, for a total sample size of 2,018 subjects ( Supplementary Fig. 1 ). We excluded prevalent CVD on the basis of information on prior myocardial infarction (MI) and stroke, as well as data on prior bypass surgery, angioplasty, and HF. Information on HF was missing in 8 ARIC study subjects, and in the JHS, 18 subjects did not have information on HF medication. In the ARIC study, 2 participants had missing stroke data and 91 had information missing on bypass surgery and/or coronary angioplasty; in the JHS, 3 subjects had information missing on bypass surgery and/or coronary angioplasty. These subjects ( n = 121) were all assumed not to have prior CVD. There were also 77, 37, and 43 persons with DM in the ARIC study, MESA, and JHS, respectively, who were not included because of missing key risk factor information. These persons tended to be 1.8 years younger than those included in our sample. They had a higher HbA 1c (9.0% vs. 7.8%) and a lower HDL cholesterol (HDL-C) (37.9 vs. 46.6 mg/dL), but other demographic and risk factors were comparable.

Measurements

Goal or recommended levels of risk factors for this study were based on levels recommended by the American Diabetes Association during 2000–2002, which corresponds to the enrollment periods in the JHS and the MESA ( 19 ). Detailed specimen and data collection for the MESA, ARIC study, and JHS have been previously published ( 16 – 18 ). For BP, the average of two sitting BP readings was used. LDL-C was calculated from the Friedwald formula using measurements of total cholesterol, HDL-C, and triglycerides from standardized assays (Roche). HbA 1c level was determined by high-performance liquid chromatography (Tosoh Bioscience). Study subjects were considered to be at their goal level if their specific laboratory value was at or below the following cut points, as recommended by guidelines that were in effect during the conduct of the examinations ( 19 ): LDL-C level <2.6 mmol/L (100 mg/dL), HbA 1c level <53.0 mmol/mol (7%), and BP level <130/80 mmHg. The impact of a more recently defined cut point for BP control of <140/80 mmHg ( 20 ) was examined in a secondary analysis to examine the impact of this more modest, but evidence-based, goal. We also examined the number of factors controlled as none, any one, any two, or a composite goal of all three factors: BP <130/80 mmHg, LDL-C <2.6 mmol/L (100 mg/dL), and HbA 1c <53.0 mmol/mol (7%). A BMI ≥30 kg/m 2 was used to define obesity. Information on medications for the lowering of lipid levels, BP, and DM (insulin or oral DM medication) was obtained from questionnaires and participants bringing in pill containers at the study visit with the medication recorded.

CVD and CHD Event Definitions and Ascertainment

For the ARIC study, MESA, and JHS, incident CVD was defined as a MI, CHD death, cardiac procedure (percutaneous coronary interventions, bypass surgery, or coronary revascularization), stroke, or HF; incident CHD was defined as a MI, CHD death, or cardiac procedure. The adjudication process for events involved a panel to review hospitalization and death data per study protocols previously published ( 16 – 18 ). All events were adjudicated from medical records and death certificates for end-point classification and assignment of incidence dates by the morbidity and mortality classification/review committee in three studies.

Statistical Methods

Using the risk factor and CVD event variables from each study, we created new variables with consistent definitions and then pooled the subjects with DM among cohorts. All continuous variables used in our analyses were normally distributed and, thus, were compared between those at versus those not at target levels for HbA 1c , BP, and LDL-C using the Student t test. The χ 2 test for proportions was used to compare categorical variables, with a test of trend used to examine cumulative CVD event incidence by the number of risk factors under control. CVD/CHD event rates were calculated as per 1,000 person-years. Cox proportional hazards regression models (providing hazard ratios [HRs] and 95% CIs) examined the relation of being at individual and composite risk factor targets with the risk of incident CHD and CVD events, unadjusted and then adjusted for age, sex, ethnicity, smoking status, HDL-C level, BMI, family history of premature CVD, lipid-lowering medication, hypertension medication, and antidiabetes medication (insulin or oral hypoglycemic therapy). To eliminate the potential bias of confounding by indication in those at target levels versus those not at target levels for individual or composite risk factors, we recalculated the HRs in Cox regression adjusted for propensity score ( 21 ). Propensity scores were calculated using logistic regression adjusted by age, sex, ethnicity, systolic and diastolic BP (for LDL-C and HbA 1c propensity score), HbA 1c (for LDL-C and BP propensity score), LDL-C (for BP and HbA 1c propensity score), HDL-C, BMI, smoking status, family history of premature CVD, lipid-lowering medication, hypertension medication, and antidiabetes medication. We included subgroup analyses and interaction terms for sex (male vs. female), ethnicity (African American vs. other races), and cohort (ARIC study, MESA, JHS) with each risk factor variable (and the number of risk factors) to examine the heterogeneity of effects across these strata. We also examined effects stratified by DM duration in a subgroup of participants with this information available. A two-tailed α value of < 0.05 (and P < 0.1 for interaction test) was considered statistically significant. Data analyses used SAS (version 9.4; SAS Institute, Cary, NC).

For the pooled cohort, 2,018 persons with diagnosed DM were included, with 43% male and a mean ± SD age of 60.1 ± 9.7 years (age range 28–86 years); ethnicity included 30% Caucasian, 55% African American, 11% Hispanic, and 4% Asian/Pacific Islander. The JHS had fewer participants who were at the BP target, whereas the ARIC study had fewer participants at LDL-C and HbA 1c targets; the MESA had the highest percentage of subjects at composite control ( Table 1 ). The mean duration of DM was 9.9 years and mean follow-up time in our analysis was 10.8 years (range 0.2–20.9 years). Although 41.8%, 32.1%, and 41.9% of subjects were at target individually for BP, LDL-C, and HbA 1c , respectively, only 7.2% of subjects were at target for all three factors.

Characteristics of all participants with diagnosed DM and no CVD at baseline in the individual parent cohort studies and in the pooled sample

.	ARIC study .	MESA .	JHS .	All 3 studies pooled .
DM	825	740	453	2,018
Age (years)	58.5 ± 5.7	66.2 ± 9.5	53.6 ± 10.7	60.1 ± 9.7
Age range (years)	47–69	46–86	28–82	28–86
Male sex	355 (43.0%)	372 (50.3%)	143 (31.6%)	870 (43.1%)
White	450 (54.6%)	152 (20.5%)	N/A	602 (29.8%)
African American	373 (45.2%)	278 (37.6%)	453 (100%)	1,104 (54.7%)
Hispanic	N/A	228 (30.8%)	N/A	228 (11.3%)
Asian/Pacific Islander	2 (0.2%)	72 (11.6%)	N/A	84 (4.2%)
DM duration (years)	10.0 ± 9.4	10.6 ± 8.4	8.6 ± 8.8	9.9 ± 9.0
BP at target (<130/80 mmHg)	431 (52.2%)	376 (50.8%)	37 (8.2%)	844 (41.8%)
LDL-C at target (<2.6 mmol/L [100 mg/dL])	157 (19.0%)	353 (47.7%)	137 (30.2%)	647 (32.1%)
HbA at target (<53.0 mmol/mol [7%])	266 (32.2%)	378 (50.1%)	201 (44.4%)	845 (41.9%)
None (BP, LDL-C, HbA ) at target	228 (27.6%)	104 (14.1%)	176 (38.9%)	508 (25.2%)
Any one (BP, LDL-C, HbA ) at target	374 (45.3%)	267 (36.1%)	189 (41.7%)	830 (41.1%)
Any two (BP, LDL-C, HbA ) at target	189 (22.9%)	267 (36.1%)	78 (17.2%)	534 (26.5%)
All three (BP, LDL-C, HbA ) at target	34 (4.1%)	102 (13.8%)	10 (2.2%)	146 (7.2%)
Follow-up time (years)	14.9 ± 5.6	7.9 ± 1.4	8.0 ± 1.4	10.8 ± 5.1
Incident CVD ( /1,000 person-years)	444 (45.4)	105 (24.4)	65 (19.1)	614 (35.1)
Incident CHD ( /1,000 person-years)	288 (26.9)	66 (14.9)	24 (6.8)	378 (20.3)

.	ARIC study .	MESA .	JHS .	All 3 studies pooled .
DM	825	740	453	2,018
Age (years)	58.5 ± 5.7	66.2 ± 9.5	53.6 ± 10.7	60.1 ± 9.7
Age range (years)	47–69	46–86	28–82	28–86
Male sex	355 (43.0%)	372 (50.3%)	143 (31.6%)	870 (43.1%)
White	450 (54.6%)	152 (20.5%)	N/A	602 (29.8%)
African American	373 (45.2%)	278 (37.6%)	453 (100%)	1,104 (54.7%)
Hispanic	N/A	228 (30.8%)	N/A	228 (11.3%)
Asian/Pacific Islander	2 (0.2%)	72 (11.6%)	N/A	84 (4.2%)
DM duration (years)	10.0 ± 9.4	10.6 ± 8.4	8.6 ± 8.8	9.9 ± 9.0
BP at target (<130/80 mmHg)	431 (52.2%)	376 (50.8%)	37 (8.2%)	844 (41.8%)
LDL-C at target (<2.6 mmol/L [100 mg/dL])	157 (19.0%)	353 (47.7%)	137 (30.2%)	647 (32.1%)
HbA at target (<53.0 mmol/mol [7%])	266 (32.2%)	378 (50.1%)	201 (44.4%)	845 (41.9%)
None (BP, LDL-C, HbA ) at target	228 (27.6%)	104 (14.1%)	176 (38.9%)	508 (25.2%)
Any one (BP, LDL-C, HbA ) at target	374 (45.3%)	267 (36.1%)	189 (41.7%)	830 (41.1%)
Any two (BP, LDL-C, HbA ) at target	189 (22.9%)	267 (36.1%)	78 (17.2%)	534 (26.5%)
All three (BP, LDL-C, HbA ) at target	34 (4.1%)	102 (13.8%)	10 (2.2%)	146 (7.2%)
Follow-up time (years)	14.9 ± 5.6	7.9 ± 1.4	8.0 ± 1.4	10.8 ± 5.1
Incident CVD ( /1,000 person-years)	444 (45.4)	105 (24.4)	65 (19.1)	614 (35.1)
Incident CHD ( /1,000 person-years)	288 (26.9)	66 (14.9)	24 (6.8)	378 (20.3)

Continuous variables are presented as the mean ± SD; categorical variables are presented as frequencies (%); incident events were presented as the number of events (event rates). The JHS ( n = 3,675) excludes those counted in the ARIC study cohort. Asian/Pacific Islanders are all Chinese American in the MESA and except for two unspecified Asian/Pacific Islanders from the ARIC study. DM duration data were available in 602 ARIC study participants, 457 MESA participants, and 348 JHS participants.

Table 2 shows LDL-C, HDL-C, BMI, DM duration, percentage African American, current smoker, and receiving hypertension and lipid-lowering medication to be significantly different between subjects at versus those not at BP targets. Age, systolic BP, diastolic BP, HbA 1c , BMI, percentage male, percentage African American, and percentage receiving hypertension, DM, and lipid-lowering medication were significantly different between subjects at versus those not at LDL-C target level. Age, LDL-C level, HDL-C level, BMI, DM duration, percentage African American, current smokers, and proportion of subjects receiving antidiabetes medication were significantly different between those at versus not at the HbA 1c target. Age, BMI, percentage male, African American, current smokers, and lipid-lowering medication use were significantly different between subjects at versus those not at composite risk factor targets.

Baseline characteristics by BP, LDL-C, HbA 1c , and composite (BP, LDL-C, HbA 1c ) targets among subjects with DM from the pooled cohort

.	BP		LDL-C		HbA		Composite target
.	At target (<130/80 mmHg) ( = 844) .	Not at target (≥130/80 mmHg) ( = 1,174) .	At target (<2.6 mmol/L [100 mg/dL]) ( = 647) .	Not at target (≥2.6 mmol/L [100 mg/dL]) ( = 1,371) .	At target (<53.0 mmol/mol [7%]) ( = 845) .	Not at target (≥53.0 mmol/mol [7%]) ( = 1,173) .	At target ( = 146) .	Not at target ( = 1,872) .
Age, years	60.3 ± 8.4	59.9 ± 10.6	61.8 ± 10.4	59.3 ± 9.3§	61.3 ± 10.4	59.3 ± 9.2§	62.5 ± 9.4	59.9 ± 9.8†
Male	386 (45.7%)	484 (41.2%)	305 (47.1%)	565 (41.2%)	383 (45.3%)	487 (41.5%)	79 (54.1%)	791 (42.3%)†
African American	344 (40.8%)	760 (64.7%)§	330 (51.0%)	774 (56.5%)	434 (51.4%)	670 (57.1%)	49 (33.6%)	1,055 (56.4%)§
Current smoker	157 (18.6%)	147 (12.5%)§	95 (14.7%)	209 (15.2%)	127 (15.0%)	177 (15.11)†	25 (17.1%)	279 (14.9%)
Family history of CVD	429 (50.8%)	619 (52.7%)	337 (52.1%)	711 (51.8%)	434 (51.4%)	614 (52.3%)	76 (52.1%)	972 (51.9%)
DM duration, years	10.6 ± 9.3	9.4 ± 8.7	10.4 ± 9.5	9.6 ± 8.7	8.7 ± 9.3	10.6 ± 8.7‡	10.9 ± 10.8	9.8 ± 8.8
Systolic BP, mmHg	114.0 ± 9.8	145.2 ± 17.6§	130.5 ± 20.3	132.9 ± 21.8	131.4 ± 21.2	132.7 ± 21.5	113.7 ± 9.8	133.6 ± 21.4§
Diastolic BP, mmHg	65.6 ± 7.5	81.9 ± 12.7§	73.6 ± 13.0	75.8 ± 13.6‡	74.9 ± 13.5	75.2 ± 13.5	64.6 ± 8.5	75.9 ± 13.5§
LDL-C, mmol/L (mg/dL)	3.04 ± 0.99 (117.1 ± 37.9)	3.18 ± 1.00† (122.3 ± 38.5)	2.09 ± 0.39 (80.5 ± 14.9)	3.61 ± 0.82§ (138.8 ± 31.1)	2.98 ± 0.94 (114.4 ± 36.5)	3.24 ± 1.03§ (124.2 ± 39.1)	2.05 ± 0.38 (79.2 ± 14.7)	3.22 ± 0.99§ (123.3 ± 37.7)
HDL-C, mmol/L (mg/dL)	1.14 ± 0.36 (45.7 ± 14.4)	1.18 ± 0.34 (47.2 ± 13.6)	1.18 ± 0.38 (47.0 ± 15.1)	1.16 ± 0.34 (46.4 ± 13.4)	1.20 ± 0.37 (47.9 ± 14.6)	1.14.3 ± 0.34§ (45.7 ± 13.5)	1.15 ± 0.39 (45.9 ± 15.7)	1.17 ± 0.35 (46.7 ± 13.8)
HbA , mmol/mol (%)	59.1 ± 15.9 (7.8 ± 2.1)	59.1 ± 15.1 (7.8 ± 2.0)	56.0 ± 12.9 (7.4 ± 1.7)	60.6 ± 15.9§ (8.0 ± 2.1)	45.4 ± 3.8 (6.1 ± 0.5)	68.1 ± 13.6§ (9.0 ± 1.8)	45.4 ± 3.8 (6.1 ± 0.5)	59.8 ± 15.1§ (7.9 ± 2.0)
BMI, kg/m	30.8 ± 6.4	32.5 ± 6.8§	31.5 ± 6.7	31.9 ± 6.7	31.3 ± 6.7	32.2 ± 6.7†	30.5 ± 6.2	31.9 ± 6.7
Hypertension medication	470 (55.7%)	868 (73.9%)§	457 (70.6%)	881 (64.3%)†	559 (66.2%)	779 (66.4%)	87 (59.6%)	1,251 (66.8%)
Antidiabetes medication	686 (81.3%)	934 (79.6%)	545 (84.2%)	1,075 (78.4%)‡	594 (70.3%)	1,026 (87.5%)§	113 (77.4%)	1,507 (80.5%)
Lipid-lowering medication	202 (23.9%)	241 (20.5%)	225 (34.8%)	218 (15.9%)§	197 (23.3%)	246 (21.0%)	54 (37.0%)	389 (20.8%)§
BP <130/80 mmHg	844 (100%)		298 (46.1%)	546 (39.8%)†	356 (42.1%)	488 (41.6%)	146 (100%)
LDL-C <2.6 mmol/L (100 mg/dL)	298 (35.3%)	349 (29.7%)†	647 (100%)		318 (37.6%)	329 (28.1%)§	146 (100%)
HbA <53.0 mmol/mol (7%)	356 (42.2%)	489 (41.7%)	318 (49.2%)	527 (38.4%)§	845 (100%)		146 (100%)
HDL-C <1.0 mmol/L (40 mg/dL) (male) or <1.3 mmol/L (50 mg/dL) (female)	386 (45.7%)	589 (50.2%)	304 (47.0%)	671 (48.9%)	449 (53.1%)	526 (44.8%)‡	63 (43.2%)	912 (48.7%)
Obesity (BMI ≥30 kg/m )	414 (49.1%)	729 (62.1%)§	368 (56.1%)	808 (56.6%)	438 (51.8%)	705 (60.1%)‡	68 (46.6%)	1,075 (57.4%)

.	BP		LDL-C		HbA		Composite target
.	At target (<130/80 mmHg) ( = 844) .	Not at target (≥130/80 mmHg) ( = 1,174) .	At target (<2.6 mmol/L [100 mg/dL]) ( = 647) .	Not at target (≥2.6 mmol/L [100 mg/dL]) ( = 1,371) .	At target (<53.0 mmol/mol [7%]) ( = 845) .	Not at target (≥53.0 mmol/mol [7%]) ( = 1,173) .	At target ( = 146) .	Not at target ( = 1,872) .
Age, years	60.3 ± 8.4	59.9 ± 10.6	61.8 ± 10.4	59.3 ± 9.3§	61.3 ± 10.4	59.3 ± 9.2§	62.5 ± 9.4	59.9 ± 9.8†
Male	386 (45.7%)	484 (41.2%)	305 (47.1%)	565 (41.2%)	383 (45.3%)	487 (41.5%)	79 (54.1%)	791 (42.3%)†
African American	344 (40.8%)	760 (64.7%)§	330 (51.0%)	774 (56.5%)	434 (51.4%)	670 (57.1%)	49 (33.6%)	1,055 (56.4%)§
Current smoker	157 (18.6%)	147 (12.5%)§	95 (14.7%)	209 (15.2%)	127 (15.0%)	177 (15.11)†	25 (17.1%)	279 (14.9%)
Family history of CVD	429 (50.8%)	619 (52.7%)	337 (52.1%)	711 (51.8%)	434 (51.4%)	614 (52.3%)	76 (52.1%)	972 (51.9%)
DM duration, years	10.6 ± 9.3	9.4 ± 8.7	10.4 ± 9.5	9.6 ± 8.7	8.7 ± 9.3	10.6 ± 8.7‡	10.9 ± 10.8	9.8 ± 8.8
Systolic BP, mmHg	114.0 ± 9.8	145.2 ± 17.6§	130.5 ± 20.3	132.9 ± 21.8	131.4 ± 21.2	132.7 ± 21.5	113.7 ± 9.8	133.6 ± 21.4§
Diastolic BP, mmHg	65.6 ± 7.5	81.9 ± 12.7§	73.6 ± 13.0	75.8 ± 13.6‡	74.9 ± 13.5	75.2 ± 13.5	64.6 ± 8.5	75.9 ± 13.5§
LDL-C, mmol/L (mg/dL)	3.04 ± 0.99 (117.1 ± 37.9)	3.18 ± 1.00† (122.3 ± 38.5)	2.09 ± 0.39 (80.5 ± 14.9)	3.61 ± 0.82§ (138.8 ± 31.1)	2.98 ± 0.94 (114.4 ± 36.5)	3.24 ± 1.03§ (124.2 ± 39.1)	2.05 ± 0.38 (79.2 ± 14.7)	3.22 ± 0.99§ (123.3 ± 37.7)
HDL-C, mmol/L (mg/dL)	1.14 ± 0.36 (45.7 ± 14.4)	1.18 ± 0.34 (47.2 ± 13.6)	1.18 ± 0.38 (47.0 ± 15.1)	1.16 ± 0.34 (46.4 ± 13.4)	1.20 ± 0.37 (47.9 ± 14.6)	1.14.3 ± 0.34§ (45.7 ± 13.5)	1.15 ± 0.39 (45.9 ± 15.7)	1.17 ± 0.35 (46.7 ± 13.8)
HbA , mmol/mol (%)	59.1 ± 15.9 (7.8 ± 2.1)	59.1 ± 15.1 (7.8 ± 2.0)	56.0 ± 12.9 (7.4 ± 1.7)	60.6 ± 15.9§ (8.0 ± 2.1)	45.4 ± 3.8 (6.1 ± 0.5)	68.1 ± 13.6§ (9.0 ± 1.8)	45.4 ± 3.8 (6.1 ± 0.5)	59.8 ± 15.1§ (7.9 ± 2.0)
BMI, kg/m	30.8 ± 6.4	32.5 ± 6.8§	31.5 ± 6.7	31.9 ± 6.7	31.3 ± 6.7	32.2 ± 6.7†	30.5 ± 6.2	31.9 ± 6.7
Hypertension medication	470 (55.7%)	868 (73.9%)§	457 (70.6%)	881 (64.3%)†	559 (66.2%)	779 (66.4%)	87 (59.6%)	1,251 (66.8%)
Antidiabetes medication	686 (81.3%)	934 (79.6%)	545 (84.2%)	1,075 (78.4%)‡	594 (70.3%)	1,026 (87.5%)§	113 (77.4%)	1,507 (80.5%)
Lipid-lowering medication	202 (23.9%)	241 (20.5%)	225 (34.8%)	218 (15.9%)§	197 (23.3%)	246 (21.0%)	54 (37.0%)	389 (20.8%)§
BP <130/80 mmHg	844 (100%)		298 (46.1%)	546 (39.8%)†	356 (42.1%)	488 (41.6%)	146 (100%)
LDL-C <2.6 mmol/L (100 mg/dL)	298 (35.3%)	349 (29.7%)†	647 (100%)		318 (37.6%)	329 (28.1%)§	146 (100%)
HbA <53.0 mmol/mol (7%)	356 (42.2%)	489 (41.7%)	318 (49.2%)	527 (38.4%)§	845 (100%)		146 (100%)
HDL-C <1.0 mmol/L (40 mg/dL) (male) or <1.3 mmol/L (50 mg/dL) (female)	386 (45.7%)	589 (50.2%)	304 (47.0%)	671 (48.9%)	449 (53.1%)	526 (44.8%)‡	63 (43.2%)	912 (48.7%)
Obesity (BMI ≥30 kg/m )	414 (49.1%)	729 (62.1%)§	368 (56.1%)	808 (56.6%)	438 (51.8%)	705 (60.1%)‡	68 (46.6%)	1,075 (57.4%)

Continuous variables are expressed as the mean ± SD, and categorical variables are expressed as frequencies (%). BP, LDL-C, HbA 1c , and HDL-C cut points are per previously recommended American Diabetes Association targets. DM duration data were available in 602 ARIC study participants, 457 MESA participants, and 348 JHS participants.

* P < 0.05, † P < 0.01, ‡ P < 0.001, § P < 0.0001 compared with those at target.

Figure 1 shows the CVD and CHD incidence per 1,000 person-years for individuals who were at versus those who were not at target levels for BP, LDL-C, and HbA 1c ( Fig. 1A ), as well as for the number of targets achieved (BP, LDL-C, and/or HbA 1c ) ( Fig. 1B ). For each individual risk factor, individuals at target levels had lower CVD event rates than those who were not at target levels; there were similar findings for CHD events. Incident CVD and CHD risks (per 1,000 person-years) were much greater when no risk factors were at goal compared with when all three targets were achieved (51.1 vs. 20.6 person-years for CVD [ P < 0.0001], and 29.6 vs. 13.7 person-years for CHD [ P = 0.001]). A test of trend showed the proportions with incident CVD and CHD events decreased with increasing number of risk factors controlled ( P < 0.0001).

Figure 1. Unadjusted CVD and CHD event rates per 1,000 person-years for subjects with DM, by status of being at target level for individual risk factors BP, LDL-C, and HbA1c (A) and by the number of risk factors at target levels (B). BP target <130/80 mmHg; LDL-C target <2.6 mmol/L (100 mg/dL); HbA1c target <53.0 mmol/mol (7%).

Unadjusted CVD and CHD event rates per 1,000 person-years for subjects with DM, by status of being at target level for individual risk factors BP, LDL-C, and HbA 1c ( A ) and by the number of risk factors at target levels ( B ). BP target <130/80 mmHg; LDL-C target <2.6 mmol/L (100 mg/dL); HbA 1c target <53.0 mmol/mol (7%).

Table 3 gives the HRs and 95% CIs for CVD events and CHD events, unadjusted, adjusted for covariates (risk factors plus medication), and adjusted for propensity scores. After adjusting for covariates, individuals at versus not at BP targets had 17% lower risks for both CVD events ( P < 0.05) and CHD events ( P = NS). For the more contemporary target of <140/80 mmHg, these HRs were 0.90 (95% CI 0.76–1.07, P = 0.23) for CVD and 0.98 (95% CI 0.79–1.21, P = 0.83) for CHD. Those individuals at the LDL-C target had a 33% lower risk for CVD events and a 41% lower risk for CHD events. Those with HbA 1c at versus not at target levels had a 37% lower risk for CVD events and a 36% lower risk for CHD events. Compared with those individuals with none of the risk factors (BP, LDL-C, HbA 1c ) at target levels, those having any one, any two, and all three factors controlled had 36%, 52%, and 62% lower risks for CVD and 41%, 56%, and 60% lower risks for CHD, respectively. Also, in a sensitivity analysis excluding subjects missing certain prior CVD information ( n = 121, as noted in 2 research design and methods ), results remained virtually identical, indicating the robustness of our findings. Finally, after adjusting by propensity score, very similar HRs were observed for being at target levels for BP and LDL-C and by number of risk factors controlled, but the risk reduction associated with HbA 1c being at target level was lower (30% for CVD and 26% for CHD).

Adjusted HRs (95% CI) for CVD and CHD events among subjects with DM by status of individual and composite risk factor targets

Risk factor comparison .	Incident CVD events			Incident CHD events
Risk factor comparison .	HR (95% CI), unadjusted .	HR (95% CI), adjusted for covariates .	HR (95% CI), adjusted for propensity score .	HR (95% CI), unadjusted .	HR (95% CI), adjusted for covariates .	HR (95% CI), adjusted for propensity score .
Individual risk factor controlled
BP <130/80 mmHg vs. BP ≥130/80 mmHg	0.81 (0.69–0.95)	0.83 (0.70–0.98)	0.81 (0.68–0.96)	0.86 (0.70–1.05)	0.83 (0.67–1.02)	0.78 (0.63–0.97)
LDL-C <2.6 mmol/L (100 mg/dL) vs. LDL-C ≥2.6 mmol/L (100 mg/dL)	0.71 (0.58–0.86)‡	0.67 (0.54–0.82)§	0.71 (0.58–0.87)‡	0.64 (0.49–0.83)‡	0.59 (0.45–0.77)‡	0.62 (0.48–0.81)‡
HbA <53.0 mmol/mol (7%) vs. HbA ≥53.0 mmol/mol (7%)	0.64 (0.54–0.76)§	0.63 (0.53–0.76)§	0.70 (0.58–0.84)§	0.68 (0.54–0.84)‡	0.64 (0.51–0.81)‡	0.74 (0.59–0.93)
Number of risk factors at target
Any one (BP, LDL-C, HbA ) at target vs. none at target	0.67 (0.56–0.80)§	0.64 (0.53–0.77)§	0.65 (0.54–0.78)§	0.65 (0.52–0.82)‡	0.59 (0.47–0.75)§	0.60 (0.47–0.76)§
Any two (BP, LDL-C, HbA ) at target vs. none at target	0.52 (0.41–0.64)§	0.48 (0.38–0.61)§	0.47 (0.38–0.60)§	0.51 (0.38–0.68)§	0.44 (0.33–0.59)§	0.42 (0.32–0.57)§
All three (BP, LDL-C, HbA ) at target vs. none at target	0.46 (0.30–0.69)‡	0.38 (0.25–0.58)§	0.41 (0.27–0.62)§	0.53 (0.32–0.88)	0.40 (0.24–0.67)‡	0.41 (0.25–0.70)‡

Risk factor comparison .	Incident CVD events			Incident CHD events
Risk factor comparison .	HR (95% CI), unadjusted .	HR (95% CI), adjusted for covariates .	HR (95% CI), adjusted for propensity score .	HR (95% CI), unadjusted .	HR (95% CI), adjusted for covariates .	HR (95% CI), adjusted for propensity score .
Individual risk factor controlled
BP <130/80 mmHg vs. BP ≥130/80 mmHg	0.81 (0.69–0.95)	0.83 (0.70–0.98)	0.81 (0.68–0.96)	0.86 (0.70–1.05)	0.83 (0.67–1.02)	0.78 (0.63–0.97)
LDL-C <2.6 mmol/L (100 mg/dL) vs. LDL-C ≥2.6 mmol/L (100 mg/dL)	0.71 (0.58–0.86)‡	0.67 (0.54–0.82)§	0.71 (0.58–0.87)‡	0.64 (0.49–0.83)‡	0.59 (0.45–0.77)‡	0.62 (0.48–0.81)‡
HbA <53.0 mmol/mol (7%) vs. HbA ≥53.0 mmol/mol (7%)	0.64 (0.54–0.76)§	0.63 (0.53–0.76)§	0.70 (0.58–0.84)§	0.68 (0.54–0.84)‡	0.64 (0.51–0.81)‡	0.74 (0.59–0.93)
Number of risk factors at target
Any one (BP, LDL-C, HbA ) at target vs. none at target	0.67 (0.56–0.80)§	0.64 (0.53–0.77)§	0.65 (0.54–0.78)§	0.65 (0.52–0.82)‡	0.59 (0.47–0.75)§	0.60 (0.47–0.76)§
Any two (BP, LDL-C, HbA ) at target vs. none at target	0.52 (0.41–0.64)§	0.48 (0.38–0.61)§	0.47 (0.38–0.60)§	0.51 (0.38–0.68)§	0.44 (0.33–0.59)§	0.42 (0.32–0.57)§
All three (BP, LDL-C, HbA ) at target vs. none at target	0.46 (0.30–0.69)‡	0.38 (0.25–0.58)§	0.41 (0.27–0.62)§	0.53 (0.32–0.88)	0.40 (0.24–0.67)‡	0.41 (0.25–0.70)‡

Covariates include age, sex, ethnicity, smoking status, HDL-C, BMI, family history of premature CVD, hypertension medication, antidiabetes and lipid-lowering medication (also include LDL-C and HbA 1c for BP analysis; systolic/diastolic BP and HbA 1c for LDL-C analysis; LDL-C and systolic/diastolic BP for HbA 1c analysis).

* P < 0.05, ‡ P < 0.001, § P < 0.0001.

In analyses stratified by sex and ethnicity ( Supplementary Table 1 ), there was a tendency for lower adjusted risks associated with BP control for females (HR 0.70, P < 0.01 vs. HR 0.99, P = NS, for CVD in males; HR 0.67, P < 0.05 vs. HR 0.97, P = NS, respectively, for CHD) and African Americans (HR 0.69, P < 0.01 vs. HR 0.97, P = NS for CVD in other races; HR 0.61, P < 0.01 vs. HR 0.95, P = NS, respectively, for CHD), whereas LDL-C control was related to lower risk in males (HR 0.55, P < 0.0001 vs. HR 0.85, P = NS for CVD in females; HR 0.53, P < 0.001 vs. HR 0.70, P = NS, respectively, for CHD) and nonblack subjects (HR 0.63, P < 0.001 vs. HR 0.71, P < 0.05 for CVD in African American subjects; HR 0.50, P < 0.0001 vs. HR 0.74, P = NS, respectively, for CHD). However, interaction terms were nonsignificant ( P > 0.10) except for P = 0.03 for LDL-C control by sex for CVD and BP control by race. HbA 1c control risks for CVD and CHD were similar by sex and ethnicity. There was a weak trend toward lower risks from all three risk factors controlled for both CVD and CHD in men (HR 0.34 and 0.39, respectively) versus women (HR 0.47 and 0.49) (sex interaction term P = 0.74 for CVD and P = 0.21 for CHD) as well as for African Americans (HR 0.23 and 0.30, respectively) versus other races (HR 0.49 and 0.45, respectively) (ethnicity interaction term P = 0.04 for CVD and P = 0.64 for CHD).

In addition, when results were stratified by DM duration (available in 602 ARIC study participants, 457 MESA participants, and 348 JHS participants), HRs for CVD events in those participants were at an individual target level for BP, LDL-C, and HbA 1c were 0.71, 0.72, and 0.64 in those below the mean DM duration and 0.92, 0.72, and 0.72 for those above the mean DM duration; similar HRs were also observed for composite risk factor control in these two subgroups (all interaction tests were nonsignificant). Additionally, the interaction terms of the study cohort with BP, lipid, glucose, or composite control were all nonsignificant ( P values of 0.31–0.50 for CHD events and 0.43–0.58 for CVD events), indicating the homogeneity of the effect of risk factor control with outcomes across studies (MESA, JHS, or ARIC study).

In our pooled analysis of subjects with DM in three large-scale U.S. prospective studies, the more factors among HbA 1c , BP, and LDL-C that were at goal levels, the lower are the observed CHD and CVD risks (∼60% lower when all three factors were at goal levels compared with none). However, fewer than one-tenth of our subjects were at goal levels for all three factors. These findings underscore the value of achieving target or lower levels of these modifiable risk factors, especially in combination, among persons with DM for the future prevention of CHD and CVD events.

There is a lack of data from population-based cohorts of adults with DM focusing on the impact of having ideal levels of multiple risk factors on future risk of CHD and CVD events, although some clinical trial and observational data exist. Most noteworthy is the Steno-2 clinical trial involving 160 Danish white patients with type 2 DM who were randomized to intensive therapy or conventional therapy for a mean treatment period of 7.8 years focusing on the following targets: HbA 1c level <48 mmol/mol (6.5%), fasting total cholesterol level <4.5 mmol/L (175 mg/dL), triglyceride level <2.0 mmol/L (150 mg/dL), and BP <130/80 mmHg. Intensive therapy resulted in a 57% reduction in CVD death and a 59% reduction in CVD events ( 15 ), which are nearly identical to our observational study findings. Also, applying UK Prospective Diabetes Study risk engine estimates to combined control of HbA 1c , BP, total cholesterol, HDL-C, and smoking among U.S. adults with DM in the National Health and Nutrition Examination Survey, statistically “controlling” all risk factors to goal was projected to prevent an estimated 36–42% of CHD events and, in the case of aggressive control, was projected to prevent 54–60% of CHD events ( 22 ). Finally, a recently published 5-year follow-up study ( 23 ) of 859,617 adults with DM among 11 U.S. integrated health care organizations showed inadequate risk factor control to be responsible for 11–34% of CVD events. While control of BP, LDL-C, and HbA 1c has improved over recent years in U.S. adults with DM, only about one-fourth of such individuals are at control for all three of these factors, according to recent U.S. data ( 14 ). Control of risk factors (BP, LDL-C, HbA 1c , and smoking cessation) in DM patients with CHD is also suboptimal, with simultaneous control rates varying from 8% to 23% ( 24 ). Risk factor control also varies substantially by ethnicity ( 25 ), suggesting a need for health care systems to develop approaches to ensure better composite control of risk factors. Meta-analyses of randomized trials ( 26 , 27 ) evaluating quality improvement interventions in adults with type 2 DM have shown modest improvements in HbA 1c , BP, and LDL-C with increased use of aspirin and antihypertensive drugs, but not with statin use. In persons with CHD, health care approaches involving physician education, automated reminders, and required performance measures have resulted in improved adherence to recommended therapies and reduced numbers of CHD events and hospitalizations ( 28 , 29 ). Moreover, recently launched is the first real-world global Collaborative Diabetes Registry, an interdisciplinary effort led by the American College of Cardiology in partnership with the American Diabetes Association, the American College of Physicians, the American Association of Clinical Endocrinologists, and the Joslin Diabetes Center ( 30 ). These and other approaches are being implemented to improve the quality of care of persons with DM.

Our large representation of African Americans (55% of our study sample), makes our study particularly unique, demonstrating a possibly greater impact of both BP and composite risk factor target attainment on CHD events in African Americans compared with individuals of other ethnicities. In our study, being at the target level for BP tended to be associated with greater relative risk reductions in women and African Americans, which may be influenced by higher uncontrolled baseline factors (e.g., higher systolic BP among African Americans and females). These findings are consistent with those of prior studies showing a high prevalence of hypertension, particularly in older African American women, with control of BP being poor ( 31 ). The greater benefit of risk factor control, especially of hypertension, that we observe in African Americans, combined with their current status of poor control of risk factors, suggests an unmet opportunity for improved risk factor control in African Americans with DM.

Our study has several strengths and limitations. An important strength of our study derives from the inclusion of subjects with DM from three large-scale, well-characterized U.S. population–based epidemiologic studies (ARIC study, MESA, and JHS), with standardized evaluation of risk factors and ascertainment of CHD and CVD events that were adjudicated by end points committees. We were also able to exclude any significant bias due to confounding by indication, from adjusting for propensity scores. A potential limitation, however, is the pooling of individuals from cohorts of different time periods, where there may be differences both in control rates and the effects of risk factor control on CVD and CHD event risk. Realizing that the baseline examination data we used for the ARIC study cohort was collected ∼10 years earlier than those of the MESA or the JHS, it is not surprising that the ARIC study had the lowest levels of both LDL-C and HbA 1c control, considering that both the guidelines and intensity of treatments available were less stringent. However, interaction terms of this cohort effect with individual and composite risk factor control were all nonsignificant, indicating that the effect of risk factor control on outcomes did not vary by cohort. While our enrolled cohort represents subjects from multiple metropolitan areas around the country, our clinical centers enrolling participants were not entirely representative of the country; it is well recognized that there is significant regional variation in DM care, such as in prescription rates for certain DM medications varying more than twofold between hospital referral regions ( 32 ); thus, results may be different had other regions/communities been studied. Although about half of our cohort was African American, Hispanics and Asians were also included in our sample, but the numbers were too small to examine the impact of risk factor target attainment in these groups. More importantly, our report did not investigate the effect of other targets (e.g., nonsmoking status or ideal BMI levels) that are important in DM control because of sample size limitations to look at more than three targets simultaneously. In addition, our determination of being at target for a given factor was based on a single measure. Without having pretreatment levels, we were unable to examine newer targets, such as those based on the percentage of LDL-C level lowering, as specified by more recent guidelines ( 33 ). Importantly, during follow-up, new risk factors may have developed in participants or the status of participants may have changed with regard to whether or not they were at target for one or more risk factors, which may have influenced our results. The limited and different reexamination periods for the studies we used precluded us from performing such an evaluation.

Also, while our report used more contemporary risk factor goals that were in effect during the beginning of the MESA and the JHS, but which were stricter than those in effect when the ARIC study cohort was recruited, our intention was to test the effect of specific risk factor targets. For BP, we showed that those subjects at a more current, but less aggressive target level of <140/80 mmHg did not have lower CHD or CVD risks, whereas CVD events were 17% lower in those who were at a target level of <130/80 mmHg. With the recent publication of the Systolic Blood Pressure Intervention Trial (SPRINT) trial ( 34 ), subjects randomized to a target systolic BP of <120 mmHg versus <140 mmHg had a 25% lower risk of the development of the primary composite CVD end point. In addition, the recent BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) trial ( 35 ) showed that there was a reduction in CVD secondary to the use of the glucose-lowering agent empagliflozin, but the mechanisms are currently unclear. Finally, although we have chosen specific targets that were based on guidelines for persons with DM free of prior CVD in effect at the time of the conduct of the studies included, an individualized approach for setting targets may be more appropriate. For instance, some higher-risk subjects, such as those with long-standing or more complicated DM, may be suitable for more lenient HbA 1c target levels, as recent guidelines ( 20 ) have suggested, or for different BP or lipid target levels than we have specified.

Our study of three large prospective U.S. cohorts of persons in whom DM has been diagnosed shows those persons who were at target levels for HbA 1c , BP, and LDL-C to have substantially (∼60%) lower risks for CVD and CHD than persons with DM who were not at target levels for such factors. These findings emphasize the importance of composite control of these modifiable risk factors to better address the residual CVD risk seen in persons with DM, the need for the development of health care strategies to better ensure such management, and the need for studies to evaluate and eliminate barriers to risk factor control in persons with DM.

See accompanying articles, pp. 664 , 677 , 686 , 694 , 701 , 709 , 717 , 726 , 735 , and 738 .

Acknowledgments. The authors thank the other investigators, the staff, and the participants of the ARIC study, JHS, and MESA for their valuable contributions.

Funding. This study was funded by a contract from Bristol-Myers Squibb with the University of California, Irvine. The ARIC study was performed as a collaborative study supported by National Heart, Lung, and Blood Institute contracts HHSN-268201100005C, HHSN-268201100006C, HHSN-268201100007C, HHSN-268201100008C, HHSN-268201100009C, HHSN-268201100010C, HHSN-268201100011C, and HHSN-268201100012C. The JHS is supported by contracts HHSN-268201300046C, HHSN-268201300047C, HHSN-268201300048C, HHSN-268201300049C, and HHSN-268201300050C from the National Heart, Lung, and Blood Institute, and the National Institute on Minority Health and Health Disparities. The MESA is supported by contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute and by grants UL1-TR-000040 and UL1-RR-025005 from the National Center for Research Resources.

Duality of Interest. S.K. was an employee of Bristol-Myers Squibb at the time the study was conducted. J.M. is an employee of Bristol-Myers Squibb. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. N.D.W. initiated the study and contributed to the study design, to data analysis and interpretation, and to the drafting, review, and editing of the manuscript, and in doing so led all of the study efforts. Y.Z., R.P., and C.P. contributed to data analysis and interpretation and to the drafting and review of the manuscript. S.M., A.G.B., A.C., A.R.F., S.K., J.M., H.T., and E.S. contributed to the data interpretation and to the editing and review of the manuscript. N.D.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the 74th Scientific Sessions of the American Diabetes Association, San Francisco, CA, 13–17 June 2014.

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Published: 12 June 2023

Incidence and risk factors of cardiovascular disease among population aged 40–70 years: a population-based cohort study in the South of Iran

Abbas Rezaianzadeh 1 ,
Leila Moftakhar 2 ,
Mozhgan Seif 3 ,
Masoumeh Ghoddusi Johari ORCID: orcid.org/0000-0003-3486-7182 4 ,
Seyed Vahid Hosseini 1 &
Seyed Sina Dehghani ORCID: orcid.org/0000-0002-3436-8079 5

Tropical Medicine and Health volume 51 , Article number: 35 ( 2023 ) Cite this article

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Metrics details

Cardiovascular diseases are the main cause of mortality in the world. This study aimed to estimate the incidence and identify the risk factors of these diseases.

This prospective cohort study was performed on 9442 individuals aged 40–70 years in Kharameh, a city in the South of Iran, in 2015–2022. The subjects were followed up for 4 years. The demographic information, behavioral habits, biological parameters, and history of some diseases were examined. The density incidence of cardiovascular disease was calculated. The log-rank test was calculated to assess the cardiovascular incidence difference between men and women. Simple and multiple Cox regression with Firth's bias reduction method were used to identify the predictors of cardiovascular disease.

The mean ± SD age of the participants was 51.4 ± 8.04 years, and the density incidence was estimated at 1.9 cases per 100,000 person-day. The log-rank test showed that men had a higher risk of cardiovascular disease than women. The Fisher's exact test showed a statistically significant difference between the incidence of cardiovascular diseases in different age groups, education levels, diabetes, and hypertension in men and women. The results of multiple Cox regression revealed that with increasing age, the risk of developing CVDs increased. In addition, the risk of cardiovascular disease is higher in people with kidney disease (HR adj = 3.4, 95% CI 1.3 to 8.7), men (HR adj = 2.3, 95% CI 1.7 to 3.2), individuals with hypertension (HR adj = 1.6, 95% CI 1.3 to 2.1), diabetics (HR adj = 2.3, 95%c CI 1.8 to 2.9), and alcohol consumption (HR adj = 1.5, 95% CI 1.09 to 2.2).

Conclusions

In the present study, diabetes, hypertension, age, male gender, and alcohol consumption were identified as the risk factors for cardiovascular diseases; three variables of diabetes, hypertension and alcohol consumption were among the modifiable risk factors, so if they were removed, the incidence of cardiovascular disease could greatly reduce. Therefore, it is necessary to develop strategies for appropriate interventions to remove these risk factors.

Cardiovascular diseases (CVDs) are among the most common non-communicable diseases [ 1 ]. They are the leading cause of death worldwide, and it is estimated that out of 55 million deaths in 2017, 17.7 million were related to CVDs [ 2 , 3 ]. In addition, it is predicted that this figure will reach 23.6 million in 2030 [ 4 , 5 ]. According to the Global Burden of Diseases (GBD) report in 2019, total cases of CVDs doubled from 1990 to 2019 and increased from 271 to 532 million. In addition, Disability Adjusted Life Years (DALYs) increased from 17.7 million to 34.4 million [ 6 ]. The report on the burden of diseases in 2015 introduced Iran as one of the countries with the highest rates of CVDs in the world due to having more than 9000 cases of CVDs per 100,000 population. In addition, it has been stated that the mortality rate due to these diseases has increased in Iran. Moreover, CVDs have important clinical consequences, cause premature death, and reduce the quality of life [ 5 , 7 ].

There are several risk factors for CVDs that are generally divided into two categories: non-modifiable (age, sex, race, and family history) and modifiable (diabetes, lipid profile, hypertension, alcohol consumption, smoking, inadequate physical activity, inappropriate diet, and obesity) [ 8 , 9 ]. Behavioral, environmental, and social factors are the other risk factors for CVDs [ 6 ]. Global evidence shows that by controlling and managing the modifiable risk factors, up to 90% of cases of CVDs can be reduced [ 5 ]. Therefore, appropriate identification of individuals with these risk factors and those susceptible to CVDs is an important step in controlling these diseases [ 10 ].

Epidemiological studies have played an important role in understanding the risk factors of CVDs [ 6 ]. Nevertheless, health service planning units in Iran do not yet have accurate estimates of the current status of CVDs regionally [ 11 ]. At the same time, we need to accurately estimate the incidence rate and risk factors of CVDs in each region to determine appropriate strategies for preventing and controlling these diseases [ 5 ]. In addition, most studies in this field have been performed on a specific group of patients, and information on the general population has not been extensively reviewed. Therefore, this study was conducted to investigate the incidence rate and identify the risk factors affecting the incidence of CVDs in the population aged 40–70 years in Kharameh in the South of Iran to help health policymakers to develop preventive guidelines.

Kharameh cohort study design

The present prospective cohort study was conducted based on the data of the Kharameh cohort study, which is part of a large Prospective Epidemiological Studies in Iran (PERSIAN) launched in 2014. Other details are explained in the Persian cohort study [ 12 ]. The target population of the Kharameh cohort study includes all individuals aged 40–70 years. At first, all these individuals were invited to participate in the study. Finally, 10,663 subjects (97.3% participation rate) participated in the study after they signed the informed consent forms.

The inclusion criteria for the Kharameh cohort study were age 40–70 years and at least 9 months of residence in Kharameh to allow time to adapt to the environment and culture [ 13 ]. Exclusion criterion was the individuals with mental retardation or mental disorders who could not participate in the study. In addition, the exclusion criteria of this study were having a history of CVDs, heart attacks, and stroke. At first, there were 1221 people; after some of them were excluded, 9442 subjects were followed up in the present study (Fig. 1 ).

Flow chart of the study population

The baseline data of the Kharameh cohort study were collected from March 2015 to March 2017, and the information about the incidence of CVDs in individuals was collected during four stages of the follow-up in 2018, 2019, 2020, and 2021. Trained experts collected the participants' demographic information and behavioral habits through face-to-face interviews; physicians of the Kharameh cohort team recorded their clinical information. Questionnaires related to the PERSIAN cohort study, which had previously been validated, were used to collect the data. The history of chronic diseases in individuals was recorded by their self-declaration and review of medical records by physicians.

Demographic and clinical data of the study patients

In this study, data related to the demographic characteristics of individuals and some of their behavioral habits were used. This information included age, sex, marital status, education, place of residence, having a job, Body Mass Index (BMI), waist circumference, hip circumference, alcohol consumption, smoking, and socioeconomic status (SES). In this study, having a job was defined as working at least 8 h per week at the time of enrollment visit. In addition, alcohol consumption and smoking were considered as drinking approximately 200 ml of beer or 45 ml of liquor, once per week for at least 6 months, and smoking at least 100 cigarettes during lifetime, respectively.

The history of diseases such as diabetes, hypertension, fatty liver disease, and chronic kidney disease was also examined. In addition, fasting blood sugar (FBS), low-density lipoprotein (LDL), triglyceride (TG), and high-density lipoprotein (HDL) were assessed. To assess the SES of the individuals, we completed the PERSIAN cohort questionnaires related to the socioeconomic information of individuals, and the collected variables related to SES were analyzed using Principal Component Analysis (PCA) to identify the components for grouping related individuals into different SES categories. Accordingly, the participants were divided into four classes: low, moderate, high, and very high [ 14 ].

Weight was measured without shoes and with light clothing using a SECA scale (made in Germany), and height was measured using a standard measuring tape. BMI was calculated by dividing the body weight (kilogram) by height squared (meter). Accordingly, the participants were divided into four groups: underweight (less than 18.5 kg/m 2 ), normal (18.5 to 24.9 kg/m 2 ), overweight (25 to 29.9 kg/m 2 ), and obese (over 30 kg/m 2 ) [ 15 ]. For the laboratory experiments, the individuals were requested to fast for 12 defined as a history of diabetes, treatment of diabetes, or fasting blood sugar above 126 mg/dL. Blood pressure was measured from the participants' left arms using a standard calibrated sphygmomanometer (Reister Model, Germany) after a 5-min rest in a sitting position. It was measured twice with an interval of 10 min, and the mean was recorded.

Cardiovascular disease

In this study, individuals were followed for 4 years from 2018 to 2021, and in each follow-up period, we initially recorded the incidence of CVDs according to their self-declaration. Then, their medical records were reviewed by physicians, and if confirmed, they were registered as a new case of CVDs. CVDs in this study included coronary heart disease, cerebrovascular disease, rheumatoid arthritis, myocardial infarction, stroke, and heart valve disease.

Statistical analysis

In the present study, the dependent variable was the time to event of CVDs, from the time of enrollment until the event of CVDs. In addition, individuals were considered right censors if they did not have the event of CVDs until the end of the study. Quantitative and qualitative variables were described with the mean (standard deviation) and number (percentage). The Kolmogorov–Smirnov test determined the normality of quantitative variables. The difference between the mean of quantitative variables and the levels of qualitative variables between the two groups with and without CVDs was assessed using an independent t test, Mann–Whitney, and Fisher's exact test. The density incidence rate was calculated in terms of person-day unit, which is the actual number of days that individuals are at risk of CVDs. We summed the days of observation which started from the participant's enrollment to the date of the event of CVDs or the end of the study. For survival analysis, the Kaplan–Meier curve for CVDs was plotted, and the log-rank test was calculated to compare the risk of CVDs curve between men and women. Finally, simple Cox regression with Firth's bias reduction method was used to identify the risk factors of CVDs. To control the confounders, we entered all variables with a p value less than 0.2 into multiple Cox regression. The association was also reported with Hazard Ratio (HR) with a 95% confidence interval (CI). Firth's method was used in the Cox proportional hazards framework to develop and validate a prediction model for rare event survival data (heavily censored). All analyses were carried out using software R version 4.1.2, the "Coxphf" package, and STATA software version 12.

In the present study, 9442 individuals aged 40–70 with a mean age of 51.47 ± 8.04 were followed up for 19,744,954 person-days. During this period, 386 new cases of CVDs were observed. The density incidence was 1.9 cases per 100,000 persons-days. Women and the illiterate accounted for 55.6% and 50.7% of the participants, respectively. Most participants were married (89.7%), and 59% were overweight and obese (Table 1 ).

Based on the results presented in Table 1 , the incidence of CVDs was higher in the older age groups for both sexes ( p < 0.01). The incidence of CVDs was associated with marital status of women ( p < 0.01) and with the SES level of men ( p = 0.04). In addition, the incidence of CVDs was associated with education level, status of diabetes, and high blood pressure in both men and women. ( p < 0.01).

There was a statistically significant difference between the FBS, LDL, TG, and waist and hip circumference in the two groups of women with and without CVDs. In addition, there was a significant difference between the mean of LDL in men with and without CVDs (Table 2 ).

Figure 2 A shows the Kaplan–Meier survival curve for all individuals, and Fig. 2 B displays the Kaplan–Meier survival curve by gender. It was observed that the risk of developing CVDs was higher in men than in women. The log-rank test ( p < 0.01) also revealed that the difference between women and men was statistically significant.

Kaplan–Meier curve of the time to first diagnosis of cardiovascular diseases in a population of 40–70 years in the Kharameh cohort study ( A ), and by gender ( B )

In addition, we performed simple Cox regression to identify the predictors of CVDs. The results of simple Cox regression showed a statistically significant relationship between the variables of sex, age, marital status, having job, alcohol consumption, smoking, history of diabetes, chronic kidney disease, hypertension, education, location, hip circumference, waist circumference, TG, LDL, and FBS with the risk of developing CVDs. Then, we examined the correlation between smoking and alcohol variables, LDL and TG, LDL and HDL, as well as waist circumference and hip circumference. A high correlation was observed only between waist circumference and hip circumference ( ρ = 0.8. p < 0.0001). For this reason, we did not enter the waist circumference variable into the First Cox multiple regression model. Finally, after performing multiple Cox regression to control the confounders, we found that with increasing age, the risk of developing CVDs increased, so that the risk of developing CVDs in individuals was 2.4 times higher in the age group of 50–60 years and 3.7 times higher in individuals aged 60–70 years than the 40–50-year-old subjects. The risk of developing CVDs in men was 2.3 times higher than in women (HR adj : 2.3, 95% CI 1.7–3.2); in individuals with chronic kidney disease, it was almost 3.4 times higher than in participants without chronic kidney disease. (HR adj : 3.4, 95% CI 1.3–8.7). People with diabetes were 2.3 times more likely to develop CVDs than non-diabetics. The risk of CVDs in the subjects with hypertension was 68% higher than those without hypertension; also, in subjects who consumed alcohol, it was 58% higher. There was also a small but statistically significant positive relationship between LDL and the risk of CVDs (Table 3 ).

This study estimated the incidence and risk factors of CVDs in adults 40–70 years in Kharameh. In this study, 9442 subjects were followed for 4 years. The density incidence in the present study was estimated to be 6.9 cases per 1000 person-year. In the study of Framingham that conducted by Donald et al., the incidence of CVDs was estimated at 15.7 cases per 1000 person-year [ 16 ]. The reason for the difference in incidence density between our study and the study by Donald et al. is probably due to the age difference of the cohort under study; Donald's study included people 50 years and above, and our study was done on 40 to 70-year-old subjects.

In this study, after the age of 60–70 years, chronic kidney disease was identified as the strongest risk factor for CVDs. In another study, in line with the results of the present study, it was stated that kidney dysfunction could double the risk of developing CVDs [ 17 ]. Chen et al. also, in their study on people aged 35–65 years who had kidney disease, stated that the risk of developing CVDs in patients with chronic kidney disease was 3.8 times higher than those without it [ 18 ]. However, there are several specific factors in chronic kidney patients that may increase the risk of developing CVDs, for example, anemia due to kidney disease, albuminuria, hyperparathyroidism, and oxidative stress [ 19 ]. Anemia due to impaired renal function causes left ventricular dysfunction and left ventricular hypertrophy, leading to CVDs and increasing mortality risk fourfold. Other studies have shown that albuminuria plays an important role in the pathogenesis of CVDs and increases the risk of these diseases by 2 to 4 times [ 19 ]. Although the rate of CVD is high in those with kidney disease in our study, there were only 31 subjects (0.33% in Table 1 ), with a total of 4 subjects who eventually developed CVD. However, health policy makers should recognize this to identify and implement appropriate interventions.

Diabetes was also introduced as another risk factor in this study. Dinesh Shah et al. in their cohort study in England on diabetic people over 30 years reported a positive and significant relationship between diabetes and the risk of developing CVDs [ 20 ]. Donald and colleagues in the Framingham Cohort Study of people over 50 years of age also identified diabetes as the strongest risk factor for CVDs [ 16 ]. In addition, Lee et al. in a cohort study of 2879 men in Singapore reported that the risk of developing CVDs in diabetics was 1.77 times higher than non-diabetics [ 21 ]. In Iran, the attributed risk of diabetes for CVDs is reported to be 7.3% [ 22 ]. However, many risk factors for diabetes and CVDs are common, such as the role of obesity, age over 45, unhealthy diet, hypertension, stress, and smoking, the role of which cannot be ignored [ 22 ]. Furthermore, an unhealthy lifestyle is very common in diabetics, especially the non-elderly and those with academic education [ 23 ]. Due to the impact of lifestyle on the incidence of diabetes as a modifiable risk factor for CVDs, health policymakers should develop more up-to-date guidelines to prevent CVDs in individuals with diabetes by modifying their lifestyle [ 23 ]. On the other hand, screening for type-2 diabetes is an important strategy to reduce the incidence of CVDs. A study in Denmark showed that screening middle-aged people significantly reduced the risk of all types of CVDs in individuals with diabetes [ 24 ].

In the present study, the risk of CVDs in subjects with hypertension was 64% higher than those without it. Donald et al. also in the Framingham Cohort Study of people over 50 years stated that hypertension was significantly associated with the risk of CVDs [ 16 ]. A cohort study conducted in Singapore also reported hypertension as the strongest risk factor for CVDs [ 21 ]. In another study in Iran, the risk of CVD attributed to hypertension was reported to be 36% [ 5 ]. We must remember that hypertension is an important, independent, and modifiable risk factor for CVDs and causes 50% of heart attacks [ 20 ]. In Iran, the prevalence of hypertension in individuals aged 40–75 years is estimated at 26.9%. This increase in the prevalence of hypertension is related to changes in individuals' lifestyles, increasing urbanization, and increasing life expectancy [ 5 ]. It is, therefore, recommended that the lifestyle should be modified [ 22 ].

In this study, the CVDs incidence rate was twice higher in men than in women. In line with the results of our study, another study in south India reported the prevalence of CVDs in men more than women [ 25 ]. The prevalence of CVDs in the two sexes is generally different due to several factors, including biological factors and sex hormones, especially estrogens and androgens [ 26 ]. The prevalence of CVDs before 50 is higher in men than women and increases in women due to menopause and hormonal changes. In addition, pregnancy-related factors such as diabetes and blood pressure, preeclampsia, and hormonal changes are among the factors that can play a protective role for women [ 20 ].

The present study showed that alcohol consumption increased the CVDs incidence rate by 58%. Lee and colleagues in their cohort study in Singapore reported that alcohol played a protective role against CVDs, and stated that the lack of alcohol consumption increased the risk of CVDs 1.8 times [ 21 ]. The results of this study were not in the same line with those of the present study . Rehm et al. in their modeling study using WHO data also stated that moderate or low alcohol consumption had no beneficial effect on the risk of CVDs [ 27 ]. Another study found that consuming every 30 g of alcohol increased the HDL by 3.66 Mg/dl and Apo Lipoprotein by 8.76 Mg/dl. These factors have a protective role against CVDs [ 27 ]. However, we should note that for some reason, including cultural and religious issues in Iran, individuals may not report their alcohol consumption, and there is a probability of underestimation.

In the present study, a significant relationship was seen between the LDL level and risk of developing CVDs. The results of a cohort study in Iran on 8698 people aged 35 to 65 showed that the risk of developing CVDs was 1.54 times higher with increasing levels of LDL [ 28 ]. Wilson in a cohort study in Europe and Wallece et al. in their cohort study on 30–74-year-old subjects also reported a direct and significant relationship between LDL levels and the risk of developing CVDs [ 29 , 30 ]. Two clinical trial studies have shown that the risk of developing CVDs in patients with dyslipidemia is reduced by treating these individuals with a statin drug that reduces dyslipidemia [ 31 , 32 ].

This study showed a statistically significant relationship between aging and the risk of developing CVDs. In line with the results of our study, Ravi and colleagues stated that the risk of CVD increased with age. They have found that although increasing age has an independent role in the occurrence of CVD, it can be a reflection of the intensity and duration of exposure to other risk factors of CVD [ 33 ].

The present study found no statistically significant relationship between BMI and CVDs. However, contrary to the results of our study, overweight and obesity have been suggested as the risk factors for CVDs in many studies [ 34 , 35 , 36 , 37 ]. On the other hand, in some studies, the obesity paradox is mentioned as an important factor in the relationship between obesity and CVDs [ 38 , 39 ]. It has also been stated that although obesity increases the risk factors of CVDs and has adverse effects on the structure and function of the heart vessels, obese individuals usually have a better prognosis and less mortality due to CVDs. Lavie and his colleagues have stated several factors for the paradox between obesity and CVDs, such as the presence of protective cytokines in obese individuals, poor response to the renin–angiotensin–aldosterone system, and hypertension in these individuals, which leads to the use of cardiac drugs. In addition, other factors are an increase in body muscle mass and muscle strength, presence of genetic factors, and presence of more metabolic reserves [ 38 ].

This study is conducted as the continuation of the cross-sectional study by Baaradeh and his colleagues which investigated the prevalence and risk factors of CVD using the baseline data of the Kharamah cohort [ 34 ]. We must state that cross-sectional studies are unable to correctly estimate the causal relationships due to the lack of time sequence. For this reason, there is a need to conduct this cohort study to carefully examine the risk factors. In addition, for accurate intervention planning, in addition to knowing the prevalence rate, we also need the incidence rate. In the present study and a cross-sectional study by Baradeh et al., CVDs were associated with old age, diabetes and hypertension. This is despite the fact that in our study CVDs were associated with alcohol consumption and male sex, but in Baeradeh’s study this relationship was not seen. In addition, in Baeradeh’s study, CVDs were associated with high TGs and smoking, but it was not observed in our study.

Strengths and limitations

Compared to many other studies, the cohort design and the coverage of a wide range of risk factors are two important strengths of our study. In addition, our study had a large sample size, which increased its generalizability. However, the main limitation of our study was the average duration of the follow-up period (4 years). In addition, we did not have data on the specific types of cardiovascular diseases for each subject. For this reason, we could not calculate the incidence of these diseases separately.

The present study showed that aging was a risk factor for developing CVDs. In addition, except for age, other identified risk factors are modifiable, such as diabetes, hypertension, and alcohol consumption; however, a large share of cardiovascular events can be reduced by modifying these factors. Therefore, determining interventional strategies and planning to implement appropriate interventions to control and eliminate the risk factors affecting the incidence of CVDs are essential in preventing the occurrence of CVDs. In addition, early disease detection in individuals with risk factors and their control can reduce the risk of CVDs and their burden on the society and individuals.

Availability of data and materials

The data sets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Cardiovascular diseases

Disability adjusted life years

Body mass index

Fasting blood sugar

Low-density lipoprotein

Triglyceride

High-density lipoprotein

Principal component analysis

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Acknowledgements

The authors are grateful to the officers and data management staff of Kharameh cohort .

This research received no external funding.

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Gh.M was responsible for the field working including data collection and management. R.A analyzed the data and wrote the method and parts of manuscript. H.SV collected the data. S.M checked all analyses, graphs and tables and managed how to analyze them. M. L, H.SV, and D. SS also wrote a part of manuscript.

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Correspondence to Masoumeh Ghoddusi Johari .

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PERSIAN Cohort Study is being performed in 18 geographical regions of Iran. PERSIAN Cohort Study was approved by the Ethics Committee of the Ministry of Health and Medical Education. This study was done in accordance with the Helsinki Declaration and Iranian national guidelines for ethics in research. This research is extracted from a Ph.D. dissertation under the supervision of Dr. Abbas Rezaianzadeh. It has also been approved by the ethics committee of Shiraz University of Medical Sciences. (IR.SUMS.SCHEANUT.REC.1400.046)

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Rezaianzadeh, A., Moftakhar, L., Seif, M. et al. Incidence and risk factors of cardiovascular disease among population aged 40–70 years: a population-based cohort study in the South of Iran. Trop Med Health 51 , 35 (2023). https://doi.org/10.1186/s41182-023-00527-7

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Received : 12 January 2023

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DOI : https://doi.org/10.1186/s41182-023-00527-7

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Assessment of Knowledge with Regard to Cardiovascular Disease Risk Factors among College Students Using Heart Disease Fact Questionnaire

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Interrelated social factors may affect cardiovascular health in Asian American subgroups

by American Heart Association

Numerous social and structural factors, including immigration status, socioeconomic position and access to health care, contribute to differences in cardiovascular health and heart disease risk for Asian Americans, and these factors affect Asian ethnic subgroups in different ways, according to a new scientific statement published in the journal, Circulation .

This American Heart Association scientific statement, "Social Determinants of Cardiovascular Health in Asian Americans," highlights the evidence for the role of social determinants of health in cardiovascular health among Asian American adults and identifies future directions for research to advance health equity for the Asian American population and reduce health disparities in these communities.

Asian Americans are the fastest growing racial group in the United States, with a population projected to reach up to approximately 46 million by 2060. According to the U.S. Census Bureau, Asian Americans will represent more than 10% of the total U.S. population at that time.

However, Asian Americans remain persistently underrepresented as participants in medical research . Previous studies have found that Asian Americans are less willing to participate in health research compared to other racial/ ethnic groups . Research conducted exclusively in English may also result in underrepresentation of Asian American individuals with lower English proficiency from different Asian ethnic subgroups.

"Due to the small numbers of Asian Americans recruited in research studies, even when Asian American participants are included, they are frequently combined into a single 'Asian' category or grouped with Native Hawaiian and Pacific Islander communities, which results in the masking of clinically relevant differences in health among subgroups of people of Asian descent," said Chair of the statement writing group Nilay S. Shah, M.D., M.P.H., FAHA, an assistant professor of cardiology and preventive medicine at Northwestern University's Feinberg School of Medicine in Chicago.

As of 2021, the six largest Asian-origin ethnic groups in the U.S. were Chinese, Indian, Filipino, Vietnamese, Korean and Japanese Americans. People of other Asian ethnic groups, such as Pakistani, Thai or Cambodian descent, are less often identified in research studies, limiting understanding of health status in these populations.

Shah said, "Asian American ethnic groups should be individually identified, since each sub-group represents a unique population with distinct social, cultural and health characteristics. There are several social factors that uniquely influence health behaviors and disease risk in individual Asian ethnic groups, including reasons for immigration, socioeconomic position and differences in health care access and utilization."

Immigration status and structural racism

Immigration policy, citizenship status and legal documentation are widely recognized as important social determinants of health for people immigrating to the U.S. including people from Asia.

Historically, Asian American immigrants have faced structural racism and anti-Asian prejudice resulting in policies restricting immigration into the U.S. The 1882 Chinese Exclusion Act restricted immigration and citizenship based solely on national origin, and Executive Order 9066 led to the unjust, forced incarceration of Japanese Americans during World War II in 1942.

Differences in histories and reasons for migration and resettlement may contribute to suboptimal heart health. For example, Bhutanese, Burmese, Cambodians, Hmong, Laotians and Vietnamese people have frequently arrived in the U.S. as refugees.

Refugees are more likely to experience chronic stress due to being exposed to war, violence, hunger and trauma, which may worsen heart health. Real and perceived discrimination may also influence cardiovascular health by leading to increased stress, poor sleep habits, and other suboptimal health behaviors.

Asian Americans without documented immigration status often lack employer-based health insurance. Non-U.S. citizens also have limited access to federal and state health insurance programs, which may contribute to disparities in health outcomes. In addition, lack of health insurance and concerns about immigration status can limit access to timely health care and may also deter individuals from seeking preventive care for cardiovascular risk factors.

Socioeconomic and social factors

Due to the socioeconomic diversity of Asian communities, there are substantial differences in the physical and social characteristics of neighborhoods in which Asian Americans live. The complex interplay of social determinants of health, including social support, neighborhood walkability and access to nutritious foods , influence cardiovascular health and contribute to differences across ethnic groups.

While the Asian American population overall is relatively a high-income group, there are significant differences within individual ethnic groups. In 2019, median annual household income ranged from approximately $44,000 per year among Burmese Americans to $119,000 per year among Indian Americans (compared to the average of $85,800 for all Asian Americans).

Employment status in the U.S. is frequently related to health insurance coverage, residence in resource-rich neighborhoods and housing stability. A nationally representative survey of Asian Americans from 10 ethnic backgrounds found that adults who were employed were more likely to report having better health.

In addition, Asian Americans with less than a high school education were 73% less likely to have ideal heart health compared to those with college degrees. A potential explanation is that people employed in low-wage occupations, such as in the service and food industries , may experience greater discrimination and have fewer benefits and employee protections.

Previous research has found that food insecurity, defined as limited or uncertain access to adequate amounts of food, and nutrition security, which refers to the availability, accessibility and affordability of healthy foods, are associated with increased overweight and obesity, type 2 diabetes and cardiovascular mortality in all communities. In the wake of the COVID-19 pandemic, estimates for food insecurity increased by approximately 25% for Vietnamese American adults and 53% for Filipino American adults.

Acculturation, or the process of adapting to a different culture, also affects heart disease risk factors in people who immigrate to the U.S. For example, greater availability and consumption of processed and fast foods and more sedentary lifestyles are known risk factors associated with higher rates of obesity.

Differences in health access and literacy

Asian Americans, especially those not born in the U.S., often experience difficulty in accessing health care services, inadequate health communication between clinicians and patients, cultural differences in health-related beliefs and discrimination in the health care system.

Prior research suggests that gaps in health insurance coverage within some Asian American subgroups, such as Korean and Vietnamese Americans, may be attributable to high rates of employment in occupations that less often provide health insurance coverage , such as jobs in the construction, maintenance or transportation industries, working for a small business or being a small business owner.

English proficiency varies considerably among Asian ethnic groups in the U.S. Limited English proficiency may impact cardiovascular health by preventing patients from adequately reporting symptoms or health concerns. In addition, insufficient use of interpretation/translation services may prevent health care professionals from adequately understanding and addressing health concerns in Asian Americans with limited English proficiency.

Health literacy, or knowledge about health services, also varies across Asian American ethnic groups. Limited health literacy can negatively affect the use of preventive care and/or following medical instructions and taking medications as prescribed. Asian immigrants may also gravitate towards traditional, complementary or alternative medicine practices common in Asian countries, such as acupuncture or herbal therapies.

Shah said, "All of these social determinants of health are likely interrelated, and the cumulative impact of these structural and social risk factors contributes to suboptimal cardiovascular health in Asian Americans.

"There is an urgent need to understand these challenges and address them with effective prevention strategies to help improve their long-term cardiovascular health. Achieving health equity in this rapidly growing population will require multi-level interventions that target the key factors influencing cardiovascular health and account for the unique experiences within individual Asian subgroups."

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Knowledge, awareness, and presence of cardiovascular risk factors among college staff of a Nigerian University

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Ten things to know about ten cardiovascular disease risk factors

Pam R. Taub

Elizabeth Epstein

Richard A. Ferraro

Heval M. Kelli

Keith C. Ferdinand

Melvin R. Echols

Howard Weintraub

John Bostrom

Kara K. Hoppe

Michael D. Shapiro

Charles A. German

Salim S. Virani

Aliza Hussain

Christie M. Ballantyne

Ali M. Agha

1. Introduction

2. Unhealthful nutrition

2.2. Epidemiology

2.3. Diagnosis and treatment

3. Physical inactivity

3.2. Epidemiology

3.3. Diagnosis and treatment

4. Dyslipidemia

4.2. Epidemiology

4.3. Diagnosis and treatment

5. Hyperglycemia

5.2. Epidemiology

5.3. Diagnosis and treatment

6. High blood pressure

6.2. Epidemiology

6.3. Diagnosis and treatment

7. Overweight and obesity

7.2. Epidemiology

7.3. Diagnosis and treatment

8. Considerations of selected populations (older age, race/ethnicity, sex differences)

8.1.2. Race

8.2. Epidemiology

8.3. Diagnosis and treatment

9. Thrombosis and smoking

9.2. Epidemiology

9.3. Diagnosis and treatment

10. Kidney dysfunction

10.2. Epidemiology

10.3. Diagnosis and treatment

11. Genetic abnormalities / familial hypercholesterolemia

11.2. Epidemiology

11.3. Diagnosis and treatment

12. Conclusion

Author contributions

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