Abstract
Background
People of South Asian background have a high burden of atherosclerotic cardiovascular disease (ASCVD). Few studies have examined if US South Asian individuals develop atherosclerotic cardiovascular disease risk factors at younger ages compared with other racial and ethnic groups.
Methods
Longitudinal data from all eligible participants (ie, those aged between 45 and 55 years at time of the baseline examination) in the MASALA (Mediators of Atherosclerosis in South Asians Living in America) and the MESA (Multi‐Ethnic Study of Atherosclerosis) cohort studies were combined. Data from all available examination visits (2010–2018 in MASALA and 2000–2018 in MESA) were used to estimate prevalence and change in prevalence of clinical and behavioral risk factors at ages 45 and 55 years for each racial and ethnic group and by gender.
Results
At age 45 years, South Asian individuals had the highest prevalence of prediabetes and hypertension compared with White, Chinese, and Hispanic individuals. South Asian men had a higher dyslipidemia prevalence than White, Chinese, and Black men, while South Asian women had a higher prevalence than Chinese and Black women. At age 55 years, South Asian adults had the highest estimated hazard probability of diabetes among all racial and ethnic groups. At an increased age, clinical risk factor prevalence increased in all racial and ethnic groups, diet quality improved, and the prevalence of no leisure‐time exercise decreased (ie, exercise improved).
Conclusions
Significant differences in risk factor prevalence were observed in South Asian adults compared with other US racial and ethnic groups at age 45 years. Understanding trends in cardiovascular risk and protective factors across the life course can help improve prevention and treatment strategies.
Keywords: cohort studies, heart disease risk factors, minority health
Subject Categories: Epidemiology, Lifestyle, Risk Factors, Race and Ethnicity
Nonstandard Abbreviations and Acronyms
- AHEI
Alternative Health Eating Index
- FFQ
food‐frequency questionnaire
- IRAS
Insulin Resistance and Atherosclerosis Study
- MASALA
Mediators of Atherosclerosis in South Asians Living in America
- MESA
Multi‐Ethnic Study of Atherosclerosis
- PA
physical activity
Clinical Perspective.
What Is New?
In a study of 2700 adults, South Asian individuals had higher prediabetes and diabetes prevalence at age 45 years than Black, Chinese, Hispanic, and White individuals, and hypertension was more prevalent among South Asian individuals than in all groups except Black individuals.
Among all racial and ethnic groups, clinical risk factor burden increased between ages 45 and 55 years while behavioral risk factors improved.
What Are the Clinical Implications?
The high and early incidence of atherosclerotic cardiovascular disease in South Asian individuals may be attributable to a higher prevalence of clinical risk factors already present by middle age, highlighting a need for cardiovascular health promotion, screening, and management earlier in life.
South Asian individuals (people who trace their ancestry from Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) have high rates of atherosclerotic cardiovascular disease (ASCVD), with estimates approximating rates 2.5 times higher compared with non‐Hispanic White and East Asian populations. 1 On average, South Asian individuals develop coronary artery disease up to a decade earlier than the average age at which coronary artery disease develops in most people in the United States. 2 , 3 A quarter of heart attacks occur at age <40 years for South Asian individuals and 50% occur at age <50 years. In clinical guidelines, South Asian ancestry is considered an ASCVD “risk‐enhancing factor.” 4
While South Asian individuals in the United States have an overall higher prevalence of ASCVD compared with non‐Hispanic White US populations that manifests at younger ages, there is limited research to know if the prevalence of ASCVD risk factors is higher among South Asian populations compared with other racial and ethnic groups at younger ages. Additionally, evidence suggests that ASCVD risk differs between South Asian men and women, and that risk factor patterns may diverge over time by age and gender. 5 , 6 In one study, South Asian men had an almost 2‐fold higher risk of coronary artery disease compared with White European men, and South Asian women had a coronary artery disease incidence similar to that of European men. 7 ASCVD risk trajectories in South Asian men and women compared with men and women in other racial and ethnic groups has potential implications for tailored prevention strategies across the life course. Therefore, the objective of this study was to combine longitudinal data from the MASALA (Mediators of Atherosclerosis in South Asians Living in America) study and the MESA (Multi‐Ethnic Study of Atherosclerosis) to estimate the prevalence of cardiovascular risk factors during middle age, and to test whether the prevalence of these risk factors and their change over time differ by race and ethnicity. In this analysis, groups were defined by race or ethnicity as a social construct; in alignment with disparities in research guidelines, we submit that genetic differences should not be used to explain differences between groups that are not defined by genetic ancestry.
We also aimed to determine, separately by gender, whether South Asian individuals develop ASCVD risk factors at younger ages than other racial and ethnic groups. Gender, which refers to the “socially constructed roles, behaviors, expressions, and identities of individuals,” is an important determinant of cardiovascular health, and its consideration might help in understanding the role of gender in ASCVD prevalence and trends across the life course. 8 If South Asian men and women develop ASCVD risk factors at younger ages than other racial and ethnic groups, these findings could inform earlier screening, prevention, and treatment strategies for this risk‐enhanced group.
Methods
Study Population
This study was conducted using longitudinal data from all available examinations of the MASALA and MESA studies, both of which are multicenter prospective cohort studies examining the development and determinants of clinical ASCVD risk factors, subclinical ASCVD, and ASCVD outcomes. The MESA data that accompany this study are publicly available upon an approved study proposal from the MESA coordinating center. MASALA data are available upon an approved study proposal from the MASALA coordinating center.
The study design for MASALA has been published elsewhere. 9 Briefly, MASALA included 1164 South Asian (defined as those with Indian, Pakistani, Bangladeshi, Nepali, and Sri Lankan ancestry) participants who were cardiovascular disease free between ages 40 and 84 years recruited through 2 field centers in San Francisco–Bay area (California) and Chicago metropolitan area (Illinois). Participants who could speak and understand English, Hindi, or Urdu were included. Data were collected at baseline (examination 1) from 906 participants in 2010 to 2013, with follow‐up examination 2 data collection in 2016 to 2018 for 748 participants. An additional 258 participants were recruited in 2017 to 2018, and their baseline (examination 1A) data are also included.
The study design for MESA has also been published. 10 Briefly, MESA included 6814 self‐identified White, Black, Hispanic, and Chinese men and women free from clinical cardiovascular disease between ages 45 and 84 years who could speak and understand English, Chinese, or Spanish. Baseline examination 1 data were collected between 2000 and 2002, with repeat examinations every 2 years. There have been 6 examinations, the most recent being 2016 to 2018. Participants were recruited through 6 field centers: Forsyth County, North Carolina; Northern Manhattan and the Bronx, New York; Baltimore City and Baltimore County, Maryland; St. Paul, Minnesota; Chicago, Illinois metropolitan area; and Los Angeles County, California.
Ethics and Consent
Study protocols for MESA and MASALA were approved by the institutional review boards at each participating center, and all participants provided written informed consent at each examination.
Analytical Sample
The analysis was restricted to the longitudinal data of participants who were between the ages of 45 and 55 years at the time of the baseline examination. Since the likelihood of clinical ASCVD is higher at older ages, participants aged >55 years at the baseline examination were not included in this analysis because these participants would be less representative of the general population aged ≥55 years. Therefore, we included 554 South Asian adults from the MASALA study and 796 White, 588 Black, 517 Hispanic, and 245 Chinese adults from the MESA study. Harmonized data from MASALA examinations 1/1A and 2 and MESA examinations 1 through 6 were combined. On average, our data set consisted of 3.3 examinations per MESA participant and 1.4 examinations per MASALA participant.
Data Collection and Definitions
Age, gender, race and ethnicity, education, insurance status, and medication use data were obtained by trained personnel using standardized questionnaires during the in‐person examination. Self‐reported fixed categories for race (Black, Chinese, South Asian, or White) and ethnicity (Hispanic) were used in the analysis.
Primary Outcome
The primary outcomes were clinical and behavioral ASCVD risk factors. These risk factors were chosen on the basis of prior research establishing them as risk factors in the development of ASCVD and because they (with the exception of alcohol use) are components of the American Heart Association’s Life’s Essential 8. 11 Risk factor definitions were previously harmonized between the 2 cohorts using a standardized set of definitions. 5 , 12 , 13 All risk factors were available at all examinations, except diet and exercise at MESA examination 4. Diet was assessed for 87% of MESA participants at examination 1, 72% of MESA participants at examination 5, 99% of MASALA participants at examination 1, and 71% of MASALA participants at examination 2.
Clinical risk factors include hypertension, prediabetes or diabetes status, dyslipidemia, and body mass index (BMI). Hypertension was defined as having a systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or use of any antihypertensive medication according to guidelines in use at the time of the baseline examination. 14 Prediabetes was defined as having a fasting plasma glucose level between 100 and 125 mg/dL. Diabetes was defined by self‐reported use of glucose‐lowering medications or fasting plasma glucose ≥126 mg/dL. 15 Dyslipidemia was determined using standard lipid level cut points (total cholesterol ≥200 mg/dL, low‐density lipoprotein ≥160 mg/dL, triglycerides ≥150 mg/dL, or high‐density lipoprotein <40 mg/dL) using blood samples or self‐report of currently taking lipid‐lowering therapy including statin, fibrate, or niacin. 16 BMI was calculated using weight and height measurements at each examination visit and was treated as a continuous variable in the analysis.
Behavioral risk factors included alcohol use, physical activity (PA) level, and diet quality. Alcohol use was defined as currently drinking ≥1 drinks per week. Participants reported exercise using the Typical Week’s Physical Activity Questionnaire, which was quantified as metabolic equivalent of task min/wk. 17 This questionnaire was adapted for MESA from the Cross‐Cultural Activity Participation Study and was designed to capture PA patterns during a typical week in the past month. 17 , 18 To enable cross‐cohort standardization, this questionnaire was also used in MASALA but has not been validated in a US South Asian population. In our main analysis, we dichotomized leisure‐time PA as any versus none, coding 1 for no leisure‐time exercise (0 metabolic equivalent of task min/wk) and 0 for any (>0 metabolic equivalent of task min/wk), so that higher values reflect higher risk.
Diet quality was determined by calculating the Alternative Health Eating Index (AHEI) 2010 score, which has previously been harmonized in MASALA and MESA. 13 This dietary quality index has been used to predict the risk of chronic disease on the basis of nutrient and food intake, with a range between 0 (worst dietary quality) to 110 (best dietary quality) points, using data from food‐frequency questionnaires (FFQs). Dietary data in MASALA were collected using the Study of Health Assessment and Risk in Ethnic Groups FFQ, a 163‐item tool validated to assess dietary intake during the previous year among South Asian adults. 13 , 19 The MESA FFQ was adapted on the basis of an FFQ originally designed for the IRAS (Insulin Resistance and Atherosclerosis Study), the validity of which was previously studied in a sample of non‐Hispanic White, Black, and Hispanic participants. 20 , 21 This FFQ was modified to include foods specific to the Chinese diet for the MESA study. Studies examining associations of dietary measures with cardiometabolic outcomes using the MASALA and MESA FFQ demonstrate concordance of findings with diet patterns examined in other racial and ethnic groups. 22 , 23 , 24 , 25 , 26
Statistical Analysis
Baseline characteristics (age, smoking status, education, insurance status) were calculated for each racial and ethnic group. Comparisons across groups were tested using 1‐way ANOVAs for continuous variables and χ2 tests of independence for categorical variables.
Our estimands of interest are the risk factor prevalences (for binary outcomes) or means (for continuous outcomes) at age 45, their change over 10 years for each racial and ethnic group, and their differences between racial and ethnic groups.
Using longitudinal data from all eligible (ages 45–55 at baseline) MESA and MASALA participants, we modeled clinical and behavioral risk factors as a function of age using mixed‐effects linear regression models for continuous outcomes (BMI, diet quality) and mixed‐effects logistic regression models for binary outcomes (prediabetes, hypertension, dyslipidemia, alcohol use, exercise). Smoking status was not modeled as a longitudinal risk factor because it tended not to vary over time. All models were fit separately by risk factor and gender and controlled for baseline education (less than high school, completed high school, less than college degree, completed college degree, or greater than college degree), insurance status (none, public, private), and income (less than or greater than $75 000/year). These covariates were selected because they are well‐established social determinants of health that can influence both the prevalence and progression of cardiovascular risk factors. 11 Given the disparities in access to care and resources across different racial and ethnic groups, adjusting for these variables allows us to better isolate the differences in risk factor prevalence attributable to race and ethnicity while accounting for these important social factors.
To estimate risk factor prevalences at age 45 years and their 10‐year change, we used the method of marginal predicted means, 27 the average value of the outcome averaging over all covariates, to estimate the prevalence of each risk factor at ages 45 and 55 for each racial and ethnic group. These marginal predicted means were obtained from our longitudinal mixed‐effects models that use data from all eligible MESA and MASALA participants to model the clinical and behavioral risk factors as a function of age.
To compare risk factor prevalence between South Asian and the other racial and ethnic groups, we first performed, separately by risk factor, an overall test at age 45 years of whether the South Asian prevalence was significantly different from any of the other racial and ethnic groups. If that test was significant, we subsequently performed the 4 pairwise tests comparing South Asian individuals to each of the other racial and ethnic groups.
To test if the change in risk factors between ages 45 and 55 years differed for South Asian individuals compared with the other groups, we calculated the difference (slope) between marginal means at ages 45 and 55 (ie, 55 minus 45) years for all 5 racial and ethnic groups. Then, we again performed an overall test of whether the South Asian slope was significantly different from the slopes of any of the other racial and ethnic groups. If this test was significant, we then performed the 4 pairwise tests comparing the South Asian slope to the slope of each racial and ethnic group. More information on estimation of risk factor prevalences, their change over time, and their comparison across racial and ethnic groups is included in Data S1. Since this is an exploratory analysis of observational data, we did not correct for multiple comparisons. All analyses and postestimation processing were performed using STATA version 18.0 (StataCorp, College Station, TX). 28
To model diabetes incidence, we used a discrete‐time survival analysis model with a complementary log–log link. 29 This approach is appropriate given that diabetes onset is observed at discrete examination times, but the underlying process occurs continuously. Individuals with diabetes at baseline were excluded to ensure the model reflects only those at risk of developing the condition. The model specification is consistent with logistic models for the other risk factors with age and age squared included as time‐varying covariates (see Data S1). Given differences in examination timing across studies, where MESA participants have multiple follow‐up exams while MASALA participants have only 1, we accounted for varying follow‐up durations using continuous variables for the gap between visits, ranging from 0 to 17 years. This model provides estimates of the relative hazard of diabetes, like Cox models, while appropriately handling the discrete nature of the event times. We estimated hazard probabilities and hazard ratios for diabetes incidence at age 55, comparing South Asian individuals to other racial and ethnic groups. The hazard ratio quantifies the relative risk of diabetes in each group compared with South Asian individuals. Pairwise comparisons were conducted, where a hazard ratio >1 indicates a higher risk relative to South Asian individuals, and a value <1 indicates a lower risk.
Results
Baseline Characteristics
The average age of South Asian participants was 48.5 years at the baseline examination, which was similar when compared with the other racial and ethnic groups (Table 1). South Asian participants overall had higher education levels compared with the other racdial and ethnic groups. A majority (60.8%) of South Asian individuals had more than a college‐level education compared with 32.9% of White participants, the next‐highest‐educated group. At baseline, South Asian participants reported the lowest prevalence of ever smoking. A higher proportion of South Asian (86.8%), White (91.7%), and Black (81.0%) participants had private insurance compared with Chinese (68.2%) and Hispanic (67.3%) participants. The South Asian group also had the lowest proportion (20.8%) of individuals with an income ≤$75 000 compared with other racial and ethnic groups, followed by the White (54.5%), Black (71.4%), Chinese (73.1%), and Hispanic (87.0%) groups.
Table 1.
Baseline Characteristics at Examination 1, MASALA and MESA Studies
| South Asian (n =554) | White (n=796) | Chinese (n=245) | Black (n=588) | Hispanic (n=517) | P value | |
|---|---|---|---|---|---|---|
| Age, mean±SD | 48.5±4.4 | 50.4±3.1 | 50.1±3.2 | 50.3±3.0 | 49.9±3.3 | <0.001 |
| Gender | 0.665 | |||||
| Female | 287 (51.8) | 426 (53.5) | 125 (51.0) | 325 (55.3) | 267 (51.6) | |
| Male | 267 (48.2) | 370 (46.5) | 120 (49.0) | 263 (44.7) | 250 (48.4) | |
| Education | <0.001 | |||||
| Less than high school | 15 (2.7) | 11 (1.4) | 40 (16.3) | 34 (5.8) | 170 (32.9) | |
| High school | 22 (4.0) | 75 (9.4) | 35 (14.3) | 90 (15.3) | 101 (19.5) | |
| Less than college | 30 (5.4) | 239 (30.0) | 49 (20.0) | 235 (34.0) | 178 (34.4) | |
| College | 150 (27.1) | 208 (26.1) | 65 (26.5) | 133 (22.6) | 40 (7.7) | |
| Greater than college | 337 (60.8) | 262 (32.9) | 56 (22.9) | 93 (15.8) | 28 (5.4) | |
| Missing | 0 | 1 (0.1) | 0 | 3 (0.5) | 0 | |
| Insurance | <0.001 | |||||
| None | 48 (8.7) | 30 (3.8) | 60 (24.5) | 70 (11.9) | 123 (23.8) | |
| Public | 25 (4.5) | 35 (4.4) | 18 (7.4) | 39 (6.6) | 46 (8.9) | |
| Private | 481 (86.8) | 730 (91.7) | 167 (68.2) | 476 (81.0) | 348 (67.3) | |
| Missing | 0 | 1 (0.1) | 0 | 3 (0.51) | 0 | |
| Income | ||||||
| <$75,000 | 115 (20.8) | 434 (54.5) | 179 (73.1) | 420 (71.4) | 450 (87.0) | |
| ≥$75,000 | 429 (77.4) | 351 (44.1) | 64 (26.1) | 146 (24.8) | 55 (10.6) | |
| Missing | 10 (1.8) | 11 (1.4) | 2 (0.) | 22 (3.7) | 12 (2.3) | |
| Smoking | <0.001 | |||||
| Never | 472 (85.2) | 362 (45.48) | 188 (76.73) | 282 (47.96) | 274 (53.00) | |
| Former | 63 (11.4) | 297 (37.31) | 39 (15.92) | 165 (28.06) | 133 (25.73) | |
| Current | 19 (3.43) | 136 (17.09) | 18 (7.35) | 138 (23.47) | 110 (21.28) | |
| Missing | 0 | 1 (0.13) | 0 | 3 (0.51) | 0 | |
Values are n (%) unless otherwise reported.
MASALA indicates Mediators of Atherosclerosis in South Asians Living in America; and MESA, Multi‐Ethnic Study of Atherosclerosis.
Risk Factor Prevalence at Age 45 Years Among Men
South Asian men had the highest prevalence of prediabetes (30.7%; Table 2) when compared with men of all other groups (White, 3.9%; Chinese, 12.6%; Black, 10.4%; Hispanic, 10.5%; P<0.001). They had a significantly greater prevalence of hypertension compared with White, Chinese, and Hispanic men and a significantly greater prevalence of dyslipidemia compared with Black men. They also had a significantly lower BMI than White, Black, and Hispanic men. South Asian men had significantly higher diet quality scores compared with men of all other racial and ethnic groups (Table 3). They also had significantly lower alcohol use than White, Black, and Hispanic men and a higher level of no leisure‐time exercise than Hispanic men.
Table 2.
Clinical Risk Factors Stratified by Age and Racial and Ethnic Group in Men, MASALA Versus MESA Groups
| Age 45 y | Group vs South Asian group at age 45 y (95% CI) | P value* | Age 55 y | Change from age 45 to 55 y (95% CI) | Difference in change (95% CI) | P value† | |
|---|---|---|---|---|---|---|---|
| Prediabetes, % | |||||||
| South Asian | 30.7 | (Reference) | 37.8 | 7.1 (−2.2 to 16.4) | (Ref) | 0.112 | |
| White | 3.9 | −26.8 (−34 to −19.5) | <0.001 | 18.3 | 14.4 (11.1 to 17.7) | 7.3 (−2.6 to 17.2) | |
| Chinese | 12.6 | −18.1 (−28.4 to −7.8) | <0.001 | 25.5 | 13 (4.5 to 21.4) | 5.6 (−6.7 to 18.4) | |
| Black | 10.4 | −20.3 (−29.1 to −11.5) | <0.001 | 16.1 | 5.7 (−0.2 to 11.7) | −1.4 (−12.4 to 9.7) | |
| Hispanic | 10.5 | −20.2 (−28.8 to −11.6) | <0.001 | 21.5 | 11 (5.5 to 16.5) | 3.9 (−6.9 to 14.7) | |
| Hypertension, % | |||||||
| South Asian | 25.5 | (Reference) | 47.2 | 21.7 (12.5 to 30.9) | (Reference) | 0.060 | |
| White | 18.4 | −7.1 (−13.7 to −0.5) | 0.034 | 30.1 | 11.7 (6.4 to 17.0) | −10 (−20.3 to 0.4) | |
| Chinese | 6.6 | −18.8 (−25.5 to −12.1) | < 0.001 | 26.1 | 19.5 (14.1 to 24.9) | −2.2 (−12.8 to 8.5) | |
| Black | 27.3 | 1.8 (−4.2 to 7.7) | 0.556 | 52.1 | 24.8 (18.1 to 31.6) | 3.1 (−8.3 to 14.6) | |
| Hispanic | 10.1 | −15.3 (−22.3 to −8.4) | <0.001 | 28.5 | 18.4 (13 to 23.7) | −3.3 (−13.8 to 7.2) | |
| Dyslipidemia, % | |||||||
| South Asian | 78.2 | (Reference) | 83.2 | 5.1 (−2.3 to 12.5) | (Reference) | 0.388 | |
| White | 73.4 | −4.8 (−12.3 to 2.8) | 0.217 | 76 | 2.6 (−2.7 to 7.8) | −2.5 (−11.6 to 6.5) | |
| Chinese | 67.4 | −10.8 (−22 to 0.5) | 0.061 | 71.1 | 3.7 (−5.8 to 13.3) | −1.4 (−13.4 to 10.7) | |
| Black | 60.6 | −17.6 (−28.1 to −7.1) | 0.001 | 70.2 | 9.7 (1.0 to 18.3) | 4.6 (−6.8 to 16) | |
| Hispanic | 78.9 | 0.8 (−8.0 to 9.5) | 0.865 | 77.9 | −1.0 (−7.5 to 5.4) | −6.1 (−15.9 to 3.6) | |
| BMI, kg/m2 | |||||||
| South Asian | 26.3 | (Reference) | 27 | 0.6 (0.1 to 1.1) | (Reference) | 0.212 | |
| White | 27.7 | 1.4 (0.59 to 2.1) | <0.001 | 28.6 | 0.9 (0.6 to 1.2) | 0.3 (−0.3 to 0.9) | |
| Chinese | 24.5 | −1.8 (−2.8 to −0.76) | <0.001 | 25.1 | 0.5 (0.1 to 1.0) | −0.1 (−0.8 to 0.6) | |
| Black | 28.3 | 2 (1.1 to 2.9) | <0.001 | 29.5 | 1.1 (0.8 to 1.5) | 0.5 (−0.1 to 1.1) | |
| Hispanic | 28.4 | 2.1 (1.1 to 3.0) | <0.001 | 29.2 | 0.8 (0.5 to 1.1) | 0.2 (−0.5 to 0.8) | |
Model adjusted for baseline education, insurance, and income.
BMI indicates body mass index; MASALA, Mediators of Atherosclerosis in South Asians Living in America; and MESA, Multi‐Ethnic Study of Atherosclerosis.
P <0.05 from a pairwise test at age 45 y vs South Asian individuals.
P value from a test of change from ages 45 to 55 y vs South Asian individuals. When the multivariate test was not significant, pairwise tests were not performed.
Table 3.
Behavioral Risk Factors Stratified by Age and Race and Ethnic Group in Men, MASALA Versus MESA Groups
| Age 45 y | Group vs South Asian group at age 45 y (95% CI) | P value* | Age 55 y | Change from age 45 to 55 y (95% CI) | Difference in change (95% CI) | P value† | |
|---|---|---|---|---|---|---|---|
| AHEI Diet Score | |||||||
| South Asian | 65.7 | (Reference) | 69.3 | 3.5 (1.7, 5.4) | (Reference) | ||
| White | 48.4 | −17.3 (−19.6 to −15) | <0.001 | 55.5 | 7.1 (5.1 to 9.1) | 3.6 (0.9 to 6.2) | 0.010 |
| Chinese | 56.1 | −9.6 (−13.2 to −6.1) | <0.001 | 60.7 | 4.6 (1.0 to 8.3) | 1.1 (−3 to 5.2) | 0.596 |
| Black | 50.2 | −15.5 (−18.4 to −12.6) | <0.001 | 53.9 | 3.7 (0.8 to 6.5) | 0.1 (−3.3 to 3.6) | 0.939 |
| Hispanic | 50.4 | −15.3 (−18 to −12.6) | <0.001 | 55.9 | 5.5 (3.1 to 7.9) | 2 (−1 to 5) | 0.199 |
| Alcohol use, % | 0.639 | ||||||
| South Asian | 37.6 | (Reference) | 40.3 | 2.7 (−5.6 to 11) | (Reference) | ||
| White | 63.9 | 26.3 (17.3 to 35.3) | <0.001 | 62.2 | −1.8 (−9.1 to 5.6) | −4.5 (−15.6 to 6.6) | |
| Chinese | 35.6 | −2 (−16.8 to 12.8) | 0.791 | 30.1 | −5.5 (−19.7 to 8.7) | −8.2 (−24.7 to 8.2) | |
| Black | 51.6 | 14 (1.6 to 26.5) | 0.028 | 44.1 | −7.5 (−19.5 to 4.5) | −10.2 (−24.8 to 4.4) | |
| Hispanic | 56.9 | 19.3 (8 to 30.5) | 0.001 | 52.2 | −4.6 (−14.7 to 5.4) | −7.3 (−20.3 to 5.7) | |
| No exercise, % | |||||||
| South Asian | 32.7 | (Reference) | 21.1 | −11.6 (−20.9 to −2.3) | (Reference) | ||
| White | 26.3 | −6.4 (−16.8 to 4.0) | 0.225 | 18.1 | −8.2 (−16.3 to −0.1) | 3.4 (−9.0 to 15.7) | 0.593 |
| Chinese | 45.2 | 12.4 (−2.7 to 27.5) | 0.108 | 24.4 | −20.8 (−34.9 to −6.7) | −9.2 (−26.1 to 7.7) | 0.288 |
| Black | 26.5 | −6.2 (−17.6 to 5.1) | 0.282 | 17.6 | −8.9 (−18 to 0.2) | 2.7 (−10.3 to 15.8) | 0.683 |
| Hispanic | 21.9 | −10.8 (−21.2 to −0.5) | 0.040 | 14.7 | −7.2 (−14.3 to −0.1) | 4.4 (−7.4 to 16.1) | 0.464 |
AHEI indicates Alternative Health Eating Index; MASALA, Mediators of Atherosclerosis in South Asians Living in America; and MESA, Multi‐Ethnic Study of Atherosclerosis.
Model adjusted for baseline education, insurance, and income.
P<0.05 from a pairwise test at age 45 y vs South Asian individuals.
P value from a test of change from age 45 to 55 y vs South Asian individuals. When the multivariate test was not significant, pairwise tests were not performed.
Trends Across Age Among Men
There were no significantly different changes in the prevalence of clinical risk factors between ages 45 and 55 years for South Asian men versus men from the other groups. Among the behavioral risk factors, White men had a larger improvement in AHEI diet score (7.1) than South Asian men (3.5; P=0.010) over time.
Risk Factor Prevalence at Age 45 Years Among Women
Similar to South Asian men, South Asian women had the highest prevalence of prediabetes of women of all racial and ethnic groups (Table 4). South Asian women also had a higher hypertension prevalence than White, Chinese, and Hispanic women and a significantly higher prevalence of dyslipidemia compared with Chinese and Black women. BMI among South Asian women was significantly higher than BMI in Chinese women and significantly lower than Black and Hispanic women. South Asian women had the highest AHEI score (67.6) of all groups (White, 51.6, Chinese, 58.9, Black, 48.1, Hispanic, 50.7). They also had a lower percentage of alcohol consumption (15.5%) than White (43.1%), Black (33.1%), and Hispanic (34.6%; all P<0.05) women. The prevalence of no leisure‐time exercise at age 45 years was significantly lower in South Asian women as compared with Chinese women (difference, 23.5%; P=0.002).
Table 4.
Clinical Risk Factors Stratified by Age and Race and Ethnic Group in Women, MASALA Versus MESA Groups
| Age 45 y | Group vs South Asian group at age 45 (95% CI) | P value* | Age 55 y | Change from age 45 to 55 y (95% CI) | Difference in Change (95% CI) | P value† | |
|---|---|---|---|---|---|---|---|
| Prediabetes, % | |||||||
| South Asian | 17.6 | (Reference) | 36.1 | 18.5 (9.4 to 27.7) | (Reference) | ||
| White | 5.7 | −11.9 (−18.9 to −4.9) | 0.001 | 9.8 | 4.1 (0.8 to 7.5) | −14.4 (−24.1 to −4.7) | 0.004 |
| Chinese | 8.2 | −9.3 (−17.8 to −0.8) | 0.031 | 18.5 | 10.2 (3.3 to 17.2) | −8.3 (−19.8 to 3.1) | 0.155 |
| Black | 9 | −8.6 (−16.5 to −0.1) | 0.035 | 9.7 | 0.6 (−4.5 to 5.8) | −17.9 (−28.4 to −7.4) | 0.001 |
| Hispanic | 5.1 | −12.5 (−19.6 to −5.4) | 0.001 | 15.1 | 10 (6.0 to 14) | −8.5 (−18.5 to 1.5) | 0.095 |
| Hypertension, % | |||||||
| South Asian | 27.4 | (Reference) | 33.6 | 6.2 (0.5 to 11.9) | (Reference) | ||
| White | 19.7 | −7.8 (−13.5 to −2) | 0.009 | 29.5 | 10 (5.8 to 13.9) | 3.7 (−3.1 to 10.4) | 0.285 |
| Chinese | 8 | −19.4 (−27.2 to −11.5) | <0.001 | 24.2 | 16.2 (9.7 to 22.7) | 10 (1.2 to 18.7) | 0.025 |
| Black | 29.4 | 1.9 (−3.9 to 7.8) | 0.516 | 59.1 | 29.8 (24.7 to 34.8) | 23.5 (15.8 to 31.3) | <0.001 |
| Hispanic | 15.8 | −11.6 (−18.7 to −4.4) | 0.002 | 30.2 | 14.4 (8.9 to 19.8) | 8.1 (0.4 to 15.8) | 0.038 |
| Dyslipidemia, % | |||||||
| South Asian | 54.1 | (Reference) | 75.4 | 21.3 (11.8 to 30.9) | (Reference) | 0.296 | |
| White | 55.4 | 1.3 (−9.7 to 12.4) | 0.814 | 71.8 | 16.3 (9.3 to 23.3) | −5.0 (−16.8 to 6.8) | |
| Chinese | 38.9 | −15.2 (−30.0 to −0.5) | 0.043 | 68 | 29.2 (17.0 to 41.3) | 7.8 (−7.6 to 23.3) | |
| Black | 40.4 | −13.7 (−25.5 to −1.9) | 0.023 | 62.2 | 21.7 (13.6 to 29.9) | 0.4 (−12.0 to 12.9) | |
| Hispanic | 53.2 | −0.9 (−13.7 to 11.8) | 0.887 | 67.7 | 14.5 (5.9 to 23.1) | −6.8 (−19.6 to 6) | |
| BMI, kg/m2 | |||||||
| South Asian | 27.1 | (Reference) | 29 | 2.0 (1.2 to 2.8) | (Reference) | ||
| White | 27.4 | 0.4 (−0.8 to 1.5) | 0.532 | 28.4 | 1.0 (0.7 to 1.3) | −1.0 (−1.8 to −0.2) | 0.018 |
| Chinese | 23.9 | −3.2 (−4.7 to −1.7) | <0.001 | 23.7 | −0.2 (−0.8 to 0.4) | −2.2 (−3.2 to −1.2) | <0.001 |
| Black | 31.2 | 4.1 (2.9 to 5.4) | <0.001 | 32.5 | 1.3 (0.9 to 1.7) | −0.7 (−1.5 to 0.2) | 0.140 |
| Hispanic | 29.4 | 2.4 (1.1 to 3.7) | <0.001 | 30.6 | 1.2 (0.8 to 1.6) | −0.8 (−1.7 to 0.0) | 0.057 |
Model adjusted for baseline education, insurance, and income.
BMI indicates body mass index; MASALA, Mediators of Atherosclerosis in South Asians Living in America; and MESA, Multi‐Ethnic Study of Atherosclerosis.
P<0.05 from a pairwise test at age 45 y vs South Asian individuals.
P value from a test of change from age 45 to 55 y vs South Asian individuals. When the multivariate test was not significant, pairwise tests were not performed.
Trends Across Age Among Women
South Asian women had a significantly larger increase (18.5%) in prediabetes compared with White (4.1%; P<0.01) and Black (0.6%; P<0.01) women. They had a smaller increase in hypertension prevalence (6.2%) from ages 45 to 55 years compared with Black (29.8%; P<0.001), Chinese (16.2%; P=0.025) and Hispanic (14.4%; P=0.038) women. South Asian women had larger increases in mean BMI (2.0 kg/m2 [95% CI, 1.2–2.8]) compared with White (1.0 kg/m2; P=0.018) and Chinese (−0.2 kg/m2; P<0.001) women. White women had a larger increase in mean AHEI diet score (7.0) compared with South Asian women (3.5, P=0.009; Table 5). In addition, Chinese women showed a significantly larger decline in the prevalence of no leisure‐time exercise from ages 45 to 55 years (−37.3%) compared with South Asian women (−12.7).
Table 5.
Behavioral Risk Factors Stratified by Age and Racial and Ethnic Group in Women, MASALA Versus MESA Groups
| Age 45 | Group vs South Asian group at age 45 y (95% CI) | P value* | Age 55 y | Change from age 45 to 55 y (95% CI) | Difference in change (95% CI) | P value† | |
|---|---|---|---|---|---|---|---|
| AHEI Diet Score | |||||||
| South Asian | 67.6 | (Reference) | 71.1 | 3.5 (1.6 to 5.3) | (Reference) | ||
| White | 51.6 | −16 (−18.3 to −13.7) | <0.001 | 58.6 | 7 (5.1 to 8.8) | 3.5 (0.9 to 6.2) | 0.009 |
| Chinese | 58.9 | −8.7 (−12 to −5.4) | <0.001 | 64.7 | 5.8 (2.6 to 9.1) | 2.4 (−1.4 to 6.2) | 0.216 |
| Black | 48.1 | −19.6 (−22.2 to −16.9) | <0.001 | 53.7 | 5.7 (3.4 to 8) | 2.2 (−0.8 to 5.2) | 0.143 |
| Hispanic | 50.7 | −16.9 (−19.6 to −14.2) | <0.001 | 56.9 | 6.2 (4 to 8.5) | 2.8 (−0.2 to 5.7) | 0.065 |
| Alcohol use, % | |||||||
| South Asian | 15.5 | (Reference) | 14.6 | −0.8 (−6.2 to 4.5) | (Reference) | ||
| White | 43.1 | 27.7 (19.9 to 35.4) | <0.001 | 46.3 | 3.2 (−4 to 10.3) | 4.0 (−4.9 to 13) | 0.379 |
| Chinese | 27.9 | 12.4 (−1.0 to 25.9) | 0.069 | 13.8 | −14.1 (−28.2 to 0.07) | −13.3 (−28.3 to 1.9) | 0.084 |
| Black | 33.1 | 17.6 (9.1 to 26.2) | <0.001 | 22.4 | −10.7 (−18.7 to −2.7) | −9.8 (−19.5 to −0.2) | 0.045 |
| Hispanic | 34.6 | 19.1 (9.7 to 28.6) | <0.001 | 25.0 | −9.6 (−18.2 to −0.1) | −8.8 (−18.9 to 1.3) | 0.089 |
| No exercise, % | |||||||
| South Asian | 31.4 | (Reference) | 18.7 | −12.7 (−22.4 to −3.1) | (Reference) | ||
| White | 22.7 | −8.7 (−18.8 to 1.3) | 0.089 | 16 | −6.7 (−13.7 to 0.3) | 6.0 (−5.9 to 18.0) | 0.323 |
| Chinese | 55.0 | 23.5 (8.4 to 38.7) | 0.002 | 17.7 | −37.3 (−51.1 to −23.5) | −24.6 (−41.5 to −7.6) | 0.004 |
| Black | 26.5 | −4.9 (−16.0 to 6.2) | 0.386 | 18.7 | −7.8 (−16.3 to 0.7) | 4.9 (−8.0 to 17.8) | 0.453 |
| Hispanic | 29.9 | −1.5 (−12.8 to 9.7) | 0.788 | 20 | −9.9 (−18.1 to −1.7) | 2.8 (−10 to 15.5) | 0.663 |
AHEI indicates Alternative Health Eating Index; MASALA, Mediators of Atherosclerosis in South Asians Living in America; MESA, and Multi‐Ethnic Study of Atherosclerosis.
Model adjusted for baseline education, insurance, and income.
P<0.05 from a pairwise test at age 45 y vs South Asian individuals.
P value from a test of change from age 45 to 55 y vs South Asian individuals. When the multivariate test was not significant, pairwise tests were not performed.
Incidence of Diabetes in Men and Women
At age 55 years, South Asian individuals had the highest estimated hazard probability of diabetes among all racial and ethnic groups (Table 6). In both men and women, White individuals had a significantly lower risk of developing diabetes compared with South Asian individuals. The hazard ratios at age 55 years for Chinese, Black, and Hispanic/Latino individuals were lower than for South Asian individuals but were not statistically significant, suggesting similar diabetes incidence rates at this age. This elevated risk among South Asian individuals aligns with their higher prevalence of prediabetes at age 45 years (Tables 2 and 4).
Table 6.
Diabetes Incidence Stratified by Racial and Ethnic Group in Men and Women, MASALA Versus MESA Groups at Age 55 Years
| Hazard probabilities | Hazard ratio (95% CI) | P value* | |
|---|---|---|---|
| Men | |||
| South Asian | 0.06 | (Reference) | |
| White | 0.02 | 0.37 (0.16–0.82) | 0.014 |
| Chinese | 0.03 | 0.42 (0.16–1.12) | 0.084 |
| Black | 0.04 | 0.68 (0.30–1.53) | 0.361 |
| Hispanic | 0.03 | 0.54 (0.23–1.27) | 0.158 |
| Women | |||
| South Asian | 0.05 | (Reference) | |
| White | 0.02 | 0.51 (0.28–0.93) | 0.032 |
| Chinese | 0.03 | 0.62 (0.31–1.23) | 0.190 |
| Black | 0.05 | 0.96 (0.52–1.75) | 0.931 |
| Hispanic | 0.04 | 0.81 (0.43–1.51) | 0.538 |
MASALA indicates Mediators of Atherosclerosis in South Asians Living in America; and MESA, Multi‐Ethnic Study of Atherosclerosis.
Model adjusted for baseline education, insurance, and income.
P<0.05 from a pairwise test at age 55 y vs South Asian individuals. When the multivariate test was not significant, pairwise tests were not performed.
Discussion
Principal Findings
In this longitudinal multiracial and ethnic population‐based cohort study of 2700 adults aged 45–55 years at baseline in the United States, we observed significant differences in the prevalence of risk factors for South Asian individuals compared with other US racial and ethnic groups at age 45 years. At age 45 years, South Asian adults had a higher prevalence of prediabetes, diabetes, and hypertension than White adults. Namely, the prevalence of prediabetes among South Asian men and women is almost twice the prevalence of Black and White individuals. Despite observing a higher prevalence of clinical risk factors among South Asian adults at age 45 years, we also found that South Asian adults had the highest (ie, best) diet quality of all racial and ethnic groups, as well as lower alcohol use and a lower prevalence of no leisure‐time exercise than Chinese adults.
Our findings are consistent with previous studies demonstrating that South Asian populations develop diabetes at younger ages compared with non‐Hispanic White populations and even earlier than other Asian subgroups. 30 , 31 Interestingly, our findings on health behaviors differ from past research showing that South Asian individuals are less likely to meet PA recommendations and eat less healthful diets. 32 Previous analyses using MASALA and MESA data have shown that South Asian individuals have, on average, a lower level of exercise measured as metabolic equivalent of task min/wk. 5 , 33 However, prior studies included participants across the adult life span, and the present analysis included only South Asian middle‐aged adults, whom we found to have more cardioprotective behaviors. Our dietary results are similar to Rodriguez et al, who also used the AHEI 2010 score and found that South Asian adults in MASALA have a higher diet quality compared with other groups in MESA. 13
Secondary Findings
Overall, it appears that at an increased age the prevalence of clinical risk factors increased significantly among men and women. Of note, South Asian women had an increase in the prevalence of prediabetes that was almost 2 times higher than that of the next‐highest group in addition to already having the highest prevalence at age 45 years. However, at an increased age, we also observed a favorable increase in diet quality among all groups and exercise in all groups except for Black men and White and Black women. These findings indicate that the increase in ASCVD risk factors among South Asian individuals as they age is in alignment with other groups but that South Asian individuals may develop some risk factors earlier than other groups. In addition, diet quality and exercise improved with age across all groups; however, Chinese women had a significantly larger improvement in exercise (ie, a larger decline in no leisure‐time exercise) than South Asian women, and White men had a larger improvement in diet quality than South Asian men, while other between‐group differences in change were not significant. In a prior study characterizing 10‐year trajectories of cardiovascular health in MESA, PA remained relatively stable 34 ; however, the study did not assess diet due to diet information not being available at examinations 2 and 3 in MESA.
Our paradoxical findings—worse clinical risk factors for South Asian individuals despite a greater prevalence of protective behaviors—may partly stem from lifestyle exposures preceding the study period. The majority of middle‐ and older‐age South Asian adults in the United States are immigrants, and immigration and acculturation have been shown to impact diet and PA. 35 , 36 , 37 , 38 For example, Gadgil et al reported that South Asian individuals in America with stronger traditional cultural beliefs and practices were more likely to have a fried snacks, sweets, and high‐fat dairy pattern compared with those with weaker traditional cultural beliefs and practices. 39 It is possible that as MASALA participants lived in the United States longer and became assimilated, they adopted more PA and healthier eating habits. A prior study in MASALA found that women who used an assimilation or integration acculturation strategy had a more favorable cardiometabolic profile compared with women using a separation strategy. 40 It is plausible that lifestyle or environmental exposures earlier in life may be influencing the development of clinical risk factors in middle‐aged South Asian individuals. Although we adjusted for key variables like education, insurance, and income, other unmeasured factors, such as early‐life exposures, chronic stress, or cultural barriers to understanding and accessing care, may also play a role. Unmeasured confounding could help explain why South Asian individuals show a higher burden of clinical risk factors despite healthier behaviors.
Importantly, our study did not use genetically inferred ancestry grouping based on genotype data or assess biological sex at birth, limiting the ability to infer a genetic or biological mechanism underlying the divergence in ASCVD risk factor prevalence and trends across racial and ethnic groups and among men and women. In a recent study of UK Biobank participants across genetic ancestry, each SD increase in systolic blood pressure polygenic risk scores among South Asian participants was associated with cross‐sectional midlife systolic blood pressure to a greater extent than in East Asian participants. 41 There are also well‐established sex differences in traditional risk factors including diabetes, hypertension, obesity, and smoking, as well as female‐specific risk factors that could be influencing our findings. 42 Our study contributes to a limited but growing body of literature showing that ASCVD risk factor trajectories diverge early, highlighting the multifactorial pathogenesis of ASCVD and a critical need for early recognition and management of suboptimal cardiovascular health.
The stagnation in adoption of healthy behaviors in South Asian individuals from ages 45 to 55 years compared with other racial and ethnic groups may be due to language and cultural barriers that contribute to reduced health literacy among South Asian individuals living in the United States. Studies have shown that South Asian individuals are less likely to perceive chronic conditions, such as cardiovascular disease, as modifiable and preventive health messaging may not be reaching South Asian individuals effectively. 43 , 44 Among South Asian individuals, motivation for PA often stems from external sources like social support, whereas public health messaging predominantly emphasizes internal motivation and health benefits. 45 , 46 , 47 , 48 In addition, clinical ASCVD risk factors are chronic health conditions that are associated with disability and functional decline. The increased burden of ASCVD risk factors among middle‐aged South Asian individuals compared with other groups may be impacting their ability to engage in positive health behaviors like increased PA. 49 There remains a need for culturally relevant, effective approaches to improve self‐management strategies among South Asian adults with ASCVD risk factors. 50
Future studies should replicate this study design in younger populations to determine the prevalence of clinical and behavioral cardiovascular risk factors before middle age and how behavioral and clinical risk factors may change over time to influence ASCVD outcomes. It is possible that health behaviors at younger ages are more predictive of health status at older ages compared with behaviors at middle age.
Implications
The high and early incidence of ASCVD in South Asian adults may be attributable to a higher prevalence of clinical risk factors already present by middle age (ages 45 to 55 years). Unlike previous studies showing the early onset of ASCVD events in the South Asian population compared with other populations, our longitudinal approach highlights how cardiovascular risk factors accumulate at an earlier age, before clinical disease onset. Despite having the highest diet quality of all the groups included in this study, South Asian individuals still had a high prevalence of ASCVD risk factors at age 45 years. The 2019 American College of Cardiology/American Heart Association Guideline on the Primary Prevention of Cardiovascular Disease recommends that clinicians routinely assess traditional cardiovascular disease risk factors and calculate the 10‐year risk of ASCVD for adults aged 40 to 75 years, but our findings suggest that by age 45 years, South Asian adults already exhibit a disproportionate burden of risk factors. 51 , 52 Given that these disparities emerge in midlife despite seemingly protective lifestyle behaviors, there is a pressing need to understand why South Asian individuals have this earlier risk factor burden. Expanding prevention efforts to include earlier screening, more frequent risk stratification, and culturally tailored interventions will be critical to mitigating the long‐term burden of ASCVD in this high‐risk population. Additionally, our study provides new insights into gender‐based differences, suggesting that risk factor accumulation between ages 45 and 55 years may differ between men and women, emphasizing the need for tailored prevention efforts.
Limitations
Our results should be interpreted in the context of potential limitations. First, selection bias may be possible because MASALA and MESA participants had to be free of clinical cardiovascular disease when they were enrolled in the study. Longitudinal data also heavily rely on patient follow‐up. In general, participants with higher educational attainment have higher socioeconomic status and higher health motivation, and they may be more likely to follow up. 53 , 54 Second, whereas MASALA and MESA offer longitudinal information about populations that have been traditionally underrepresented in research, the findings may not be generalizable to similar racial and ethnic groups across the United States. For example, MASALA recruited from only 2 study sites, and participants were largely of Indian background, limiting generalizability to South Asian individuals living in different parts of the United States or from other South Asian countries. Third, differences between participants in MESA and MASALA may also be influenced by time period–based confounding, given the decade‐long gap between the baseline examinations of the 2 studies. For instance, South Asian participants in MASALA may exhibit better health behaviors not only due to intrinsic differences but also because they were assessed a decade after MESA participants, potentially benefiting from improved preventive health care and evolving public health initiatives. Fourth, recall and social desirability bias can potentially lead to higher self‐reported PA and diet quality. Validation of self‐reported lifestyle behaviors and objective measures of diet and PA are needed in diverse racial and ethnic groups. Fifth, this analysis used BMI as a measure of adiposity, which does not account for differences in fat distribution and body composition across racial and ethnic groups. 55 , 56 Finally, lack of data on sex as a biological variable, and on gender identities other than men and women, limit understanding of how risk factors develop over time in men and women and among gender‐diverse groups.
Conclusions
South Asian individuals have a higher prevalence of several ASCVD clinical risk factors at age 45 years, while having more favorable cardiovascular health behaviors, compared with other racial and ethnic groups. Further research is needed to identify mechanisms of the higher observed burden of clinical risk factors among US South Asian individuals at younger ages despite more favorable cardiovascular health behaviors. Given that heterogeneity in risk factors already existed at age 45 years, future studies should aim to examine the entire life course and identify critical time periods during which the burden of risk factors begins to increase. Understanding trends and disparities in risk and protective factors across the life course can help equitably improve prevention and treatment strategies for US populations.
Sources of Funding
The MESA study was 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, UL1 TR 001079, and UL1‐RR‐025005 from the National Center for Research Resources. The MASALA study was supported by Grants R01HL093009 and R01HL120725 from the National Heart, Lung, and Blood Institute; the National Center for Research Resources; and the National Center for Advancing Translational Sciences, National Institutes of Health; and through the University of California San Francisco–Clinical and Translational Science Grants UL1RR024131 and UL1TR001872. Z.A. and J.S. were supported by Grant R01HL158963 from the National Heart, Lung, and Blood Institute.
Disclosures
None.
Supporting information
Data S1. Supplemental Methods.
Acknowledgments
The authors thank the other investigators, the staff, and the participants of the MESA and MASALA studies for their valuable contributions.
This manuscript was sent to Manju Jayanna, MD, Assistant Editor, for review by expert referees, editorial decision, and final disposition.
Preprint posted on MedRxiv September 28, 2024. doi: https://doi.org/10.1101/2024.09.27.24314520.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.124.041221
For Sources of Funding and Disclosures, see page 11.
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Supplementary Materials
Data S1. Supplemental Methods.