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Original Contribution |

Risk Factors for Early Myocardial Infarction in South Asians Compared With Individuals in Other Countries FREE

Prashant Joshi, MD; Shofiqul Islam, MSc; Prem Pais, MD; Srinath Reddy, MD; Prabhakaran Dorairaj, MD; Khawar Kazmi, MBBS; Mrigendra Raj Pandey, MBBS; Sirajul Haque, MBBS; Shanthi Mendis, MD; Sumathy Rangarajan, MSc; Salim Yusuf, MD, DPhil
[+] Author Affiliations

Author Affiliations: Department of Medicine, Government Medical College, Nagpur, India (Dr Joshi); Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario (Mr Islam, Ms Rangarajan, and Dr Yusuf); Department of Medicine, St John's Medical College, Bangalore, India (Dr Pais); All India Institute of Medical Sciences, New Delhi, India (Drs Reddy and Dorairaj); Department of Cardiology, Aga Khan Univer sity, Karachi, Pakistan (Dr Kazmi); Nepal Hypertension Society, Nepal (Dr Pandey); Department of Cardiology, Bangabandhu Sheikh Medical University, Bangladesh (Dr Haque); and World Health Organization, Geneva, Switzerland (Dr Mendis).

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JAMA. 2007;297(3):286-294. doi:10.1001/jama.297.3.286.
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Published online

Context South Asians have high rates of acute myocardial infarction (AMI) at younger ages compared with individuals from other countries but the reasons for this are unclear.

Objective To evaluate the association of risk factors for AMI in native South Asians, especially at younger ages, compared with individuals from other countries.

Design, Setting, and Participants Standardized case-control study of 1732 cases with first AMI and 2204 controls matched by age and sex from 15 medical centers in 5 South Asian countries and 10 728 cases and 12 431 controls from other countries. Individuals were recruited to the study between February 1999 and March 2003.

Main Outcome Measure Association of risk factors for AMI.

Results The mean (SD) age for first AMI was lower in South Asian countries (53.0 [11.4] years) than in other countries (58.8 [12.2] years; P<.001). Protective factors were lower in South Asian controls than in controls from other countries (moderate- or high-intensity exercise, 6.1% vs 21.6%; daily intake of fruits and vegetables, 26.5% vs 45.2%; alcohol consumption ≥once/wk, 10.7% vs 26.9%). However, some harmful factors were more common in native South Asians than in individuals from other countries (elevated apolipoprotein B100 /apolipoprotein A-I ratio, 43.8% vs 31.8%; history of diabetes, 9.5% vs 7.2%). Similar relative associations were found in South Asians compared with individuals from other countries for the risk factors of current and former smoking, apolipoprotein B100 /apolipoprotein A-I ratio for the top vs lowest tertile, waist-to-hip ratio for the top vs lowest tertile, history of hypertension, history of diabetes, psychosocial factors such as depression and stress at work or home, regular moderate- or high-intensity exercise, and daily intake of fruits and vegetables. Alcohol consumption was not found to be a risk factor for AMI in South Asians. The combined odds ratio for all 9 risk factors was similar in South Asians (123.3; 95% confidence interval [CI], 38.7-400.2] and in individuals from other countries (125.7; 95% CI, 88.5-178.4). The similarities in the odds ratios for the risk factors explained a high and similar degree of population attributable risk in both groups (85.8% [95% CI, 78.0%-93.7%] vs 88.2% [95% CI, 86.3%-89.9%], respectively). When stratified by age, South Asians had more risk factors at ages younger than 60 years. After adjusting for all 9 risk factors, the predictive probability of classifying an AMI case as being younger than 40 years was similar in individuals from South Asian countries and those from other countries.

Conclusion The earlier age of AMI in South Asians can be largely explained by higher risk factor levels at younger ages.

Figures in this Article

The South Asian countries of India, Pakistan, Bangladesh, Sri Lanka, and Nepal account for about a quarter of the world's population and contribute the highest proportion of the burden of cardiovascular diseases compared with any other region globally.13 South Asian migrants living in several countries have higher death rates from coronary heart disease (CHD) at younger ages compared with the local population despite apparently lower levels of conventional risk factors.48 Deaths related to cardiovascular disease also occur 5 to 10 years earlier in South Asian countries than they do in Western countries.9,10 This has raised the possibility that South Asians exhibit a special susceptibility for acute myocardial infarction (AMI) that is not explained by traditional risk factors.

Among individuals living in the United Kingdom, the earlier onset of CHD among South Asian migrants is not an artifact of differences in the population distribution because the higher incidence of CHD is most marked in those younger than age 40 years (about a 3-fold difference), whereas it is less marked in those older than 60 years (about a 1.5-fold difference) based on an analysis of UK mortality data.8 Despite documenting the higher rates of earlier CHD in South Asians, few studies have been able to shed light on its reasons. Most studies do not include information on diet, physical activity, abdominal obesity, psychosocial factors or apolipoprotein levels, and do not have sufficiently large numbers of clinical events to reliably assess the comparative effects of the various risk factors at various ages in South Asians compared with other ethnic groups.1113

The INTERHEART study is uniquely positioned to address the reasons for the higher rates of CHD in native South Asians compared with those from other parts of the world because of its standardized protocol with extensive data collection and inclusion of a large number of cases from South Asian countries and other parts of the world.

Participants

Details of the study methods, including inclusion and exclusion criteria, have been published.14 Briefly, INTERHEART enrolled 15 152 cases of first AMI and 14 820 age-matched (±5 years) and sex-matched controls from 262 centers in 52 countries between February 1999 and March 2003. Of these, 1732 AMI cases and 2204 controls were recruited from 15 centers in 5 South Asian countries (India: 470 cases, 940 controls; Pakistan: 637 cases, 655 controls; Bangladesh: 228 cases, 238 controls; Sri Lanka: 153 cases, 132 controls; Nepal: 244 cases, 239 controls) and 10 728 cases and 12 431 controls were enrolled from other countries. Among individuals from South Asian countries, 252 AMI cases (14.6%) and 307 controls (13.9%) were women. All patients presenting within 24 hours of onset of chest pain were screened and the center attempted to enroll consecutive eligible cases. The majority of participants provided written informed consent. In a few centers, after approval by either the ethics committee or the institution, oral consent was obtained. The study was approved by the ethics committee at each hospital.

Procedures

Information about demographic factors, socioeconomic status, tobacco smoking, physical activity, dietary patterns, personal and family history of cardiovascular disease and risk factors, and measures of stress were obtained through interviewer-administered structured questionnaires. Height, weight, and waist and hip circumferences were measured using standardized protocols.14 Waist-to-hip ratio (WHR) was used to measure abdominal obesity. Staff were trained using standardized manuals, videotapes, and instructions provided at meetings or site visits. Only self-reported history of hypertension was used in the analysis because blood pressure levels may be lowered by AMI or its treatment.

Nonfasting blood samples (20 mL) were obtained from each case and control, which were centrifuged, separated, and frozen immediately at −20°C or −70°C. Samples were shipped in nitrogen vapor tanks to a central blood storage site and stored at −160°C in liquid nitrogen. Blood samples were analyzed for apolipoprotein B100 (ApoB100) and apolipoprotein A-I (ApoA-I) using standardized immunoturbidimetric assays (Roche/Hitachi 917 analyzer with Tina-quant ApoB100 version 2 kits, Roche Diagnostics, Mannheim, Germany). The ratio of ApoB100 to ApoA-I was used as a marker of dyslipidemia because apolipoprotein concentrations are not affected by fasting state, which is unlike the estimation of triglycerides or calculated low-density lipoprotein cholesterol levels, and it has been shown to be superior to other measures in several1519 but not all20 large prospective studies in predicting cardiovascular disease.

Current smokers were defined as individuals who smoked cigarettes or beedis (a form of smoking indigenous to South Asian countries) in the previous 12 months. Individuals who had quit smoking more than a year earlier were classified as former smokers. For the WHR and ApoB/ApoA-I ratio, tertiles were calculated using the overall control data. For WHR, tertiles were calculated separately for men and women. Participants were classified as physically active if they reported moderate (walking, cycling) or strenuous exercise (jogging, football, vigorous swimming) for 4 or more hours per week. Regular alcohol use was defined as consumption at least once per week.

Data were transferred to the Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario. Data on smoking were missing for 5.7% of the participants; diabetes, 1.3%; hypertension, 1.4%; WHR, 2.9%; depression, 13.6%; stress at work or home, 4.2%; physical activity, 1.7%; diet, 2.5%; and alcohol use, 1.9%. Blood samples were available for 1227 AMI cases and 1593 controls from South Asia.

Statistical Analysis

Calculation of age-standardized prevalence of risk factors, odds ratios (ORs), 95% confidence intervals (CIs), and the population attributable risks (PARs) was previously described.14 Means or medians were calculated to summarize variables and compared using the t test or an appropriate nonparametric test. Unconditional logistic regression was used to compute the ORs and 95% CIs; all models were adjusted for age, sex, and smoking status. The results presented herein are derived from models fitted with unconditional logistic regression and adjusted for the matching criteria.

Perfect matching was not possible in all cases and controls. This matching also was not possible when data on a risk factor were missing for a case or a control. Therefore, to include as many individuals as possible for a subgroup analysis, we widened the age-matching criteria and used frequency matching of cases and controls using age and sex strata. The results are comparable between conditional logistic regression, mixed models, and unconditional logistic regression, with adjustment for matching criteria. The estimated ORs and 95% CIs that had been calculated using the different methods were within 5% of each other. There was a slight underestimation of the effects compared with those obtained by the fully matched analysis.

Data on individual risk factors are presented separately for India and Pakistan. The data from Bangladesh, Nepal, and Sri Lanka are combined to provide more stable estimates of ORs and PARs because there were fewer participants from these countries. We also compared the prevalence of risk factors between native South Asians and those from other countries and between controls and cases in each age strata (<40 years, 40-49 years, 50-59 years, and >60 years). Within the cases, we used a logistic model to predict the occurrence of AMI at a younger age (<40 years). Predicted probabilities of first AMI before age 40 years are presented as unadjusted and adjusted for the 9 risk factors. We used SAS statistical software version 9.1 (SAS Institute Inc, Cary, NC) to analyze the data, IRAP software version 2.2 (National Cancer Institute, Division of Cancer Epidemiology and Genetics, Bethesda, Md) to compute all PARs and 95% CIs for various risk factors using a method based on unconditional logistic regression.21

The PARs presented are adjusted for confounders in a similar manner to the corresponding logistic regression models for OR estimates and are stratified by subgroup of interest. For simple dichotomous exposure and disease and no adjustment for confounding, the usual formula for PAR was used. For estimating variance, the methods of Benichou and Gail22 were used to calculate the 95% CIs using a logistic transformation approach. However, when the PAR estimates were negative, conventional Wald-type 95% CIs were used. S-Plus software (Insightful, Seattle, Wash) was used to prepare the figures. The effect of combining all of the exposures was estimated by summation of model coefficients and their antilogs.

The majority (59%) of the participants were from low- or middle-income areas (China, 21%; Southeast Asia, 8%; Africa, 5%; Middle Eastern countries, 13%; South America, 12%), while 26% were from high-income areas (Western Europe, 5%; Eastern Europe, 14%; North America, 2%; Australia, 5%). A total of 702 of 1675 cases (41.9%) and 656 of 2202 controls (29.8%) from South Asia had 8 or fewer years of education compared with 3109 cases (39.5%) and 3169 controls (35.4%) from other countries with the same level of education. Low educational level was strongly associated with increased risk of AMI in native South Asians and in individuals from other countries (P<.001).

The mean (SD) age of first AMI was lower in native South Asians (53.0 [11.4] years) than in participants from other countries (58.8 [12.2]; P<.001). South Asian female cases were 5.6 years older than male cases (mean [SD] age, 58.6 [11.6] years vs 53.0 [11.2] years). Striking variations in the mean age of presentation of cases were observed between countries within South Asia. The youngest patients lived in Bangladesh and had a mean (SD) age of 51.9 (11.0) years and the oldest patients lived in Nepal and had a mean (SD) age 58.9 (11.8) years (Table 1).

Table Graphic Jump LocationTable 1. Distribution of Acute Myocardial Infarction Cases
Risk Factors in Native South Asians

The prevalence of protective risk factors (leisure time physical activity, regular alcohol intake, and daily intake of fruits and vegetables) were markedly lower (all P<.001), while that of harmful risk factors such as elevated ApoB100 /ApoA-I ratio was higher in South Asian cases and controls compared with those cases and controls from other countries (P<.001; Figure 1).

Figure 1. Risk Factors Associated With Acute Myocardial Infarction in South Asians Compared With Other Participants in INTERHEART Study
Graphic Jump Location

There are 3936 South Asian participants and 23 159 participants from other countries. Among these participants, data were not available for apolipoprotein B100 /apolipoprotein A-I for approximately 20%. Data markers are in proportion to sample size using a log scale. Data are adjusted for age, sex, and smoking status. CI indicates confidence interval. While calculating PARs, a reference category of “yes” was used for daily intake of fruits and vegetables, exercise, and alcohol consumption once or more per week because these are protective from the odds ratio. The following P values are for interaction: apolipoprotein B100 /apolipoprotein A-I (P = .74); current and former smoking (P = .95); history of hypertension (P = .01); history of diabetes (P = .07); high waist-to-hip ratio (P = .02); psychosocial factors (P = .03); moderate- or high-intensity exercise (P = .92); alcohol consumption once or more per week (P = .02); and daily intake of fruits and vegetables (P = .49).
*Upper one third of global distribution.
†Indicates that the population attributable risk (PAR) related to South Asia vs the rest of the world is significantly different at the .05 level.

Current and former smoking, history of hypertension, history of diabetes mellitus, high WHR, elevated ApoB100 /ApoA-I ratio (top vs lowest tertile), and adverse psychosocial factors were strongly associated with increased risk of AMI (P<.001) in native South Asians. Daily intake of fruits and vegetables (P<.001) and physical activity (P = .03) were protective against AMI. Among individuals from other areas, regular alcohol consumption was protective (OR, 0.79; 95% CI, 0.74-0.85) but it was not protective among native South Asians (OR, 1.06; 95% CI, 0.85-1.30; P for interaction = .02).

Population-Attributable Risks

Compared with individuals from other areas, native South Asians had significantly higher PARs associated with WHR but lower PARs for history of hypertension and psychosocial stress. The risk factors associated with the highest PARs among South Asians were elevated ApoB100/ApoA-I ratio (46.8%), WHR (37.7%), and current and former smoking (37.5%) (Figure 1).

Risk Factors by Age Among AMI Cases

In participants younger than 60 years, the prevalence of the 3 metabolic risk factors of ApoB100 /ApoA-I ratio, diabetes, and WHR was higher in South Asian cases and controls and consumption of fruits and vegetables, physical activity, and alcohol use were lower compared with their counterparts from other areas (Table 2). However, after adjusting for all of the 9 risk factors among the cases, the predicted probability of having an AMI at a younger age (<40 years) was similar among native South Asians and individuals from other areas (Figure 2). This analysis suggests that an AMI at an earlier age among South Asians can be largely explained by higher levels of these metabolic risk factors at younger ages.

Table Graphic Jump LocationTable 2. Risk Factors for Acute Myocardial Infarction Among Individuals in South Asia and Other Countries by Age Group
Figure 2. Predicted Probablility of Acute Myocardial Infarction at a Younger Age in South Asians Compared With Individuals From Other Countries
Graphic Jump Location

This analysis was performed among acute myocardial infarction (AMI) cases only. Before adjustment for the 9 risk factors, there was a higher probability of cases who were younger than 40 years in the South Asian group compared with cases from other countries (P = .001). However, after adjustment for the 9 risk factors, the difference in probabilities of predicted cases of AMI in younger persons was attenuated and not statistically significant (P = .27). Error bars indicate 95% confidence intervals.

Comparison Between Men and Women

Although all risk factors (with the exception of alcohol consumption in both sexes, and physical activity in women) were significantly associated with AMI in both sexes in native South Asians, there was considerable heterogeneity in the prevalence, ORs, and PARs between the sexes (Figure 3). The prevalence of current and former smoking and alcohol consumption were markedly lower in women compared with men. Regular physical activity was extremely low in both cases and controls, particularly in women and this paralleled the higher rates of abdominal obesity in women.

Figure 3. Comparison of Risk Factors Associated With Acute Myocardial Infarction in South Asian Men and Women
Graphic Jump Location

In South Asia there are 3936 participants. Among these participants, 3377 are men and 559 are women. Data were not available for apolipoprotein B100/apolipoprotein A-I for approximately 28%. Data markers are in proportion to sample size using a log scale. CI indicates confidence interval; OR, odds ratio. Data are adjusted for age and smoking status. While calculating population-attributable risks, a reference category of yes was used for daily intake of fruits and vegetables, regular exercise, and alcohol consumption once or more per week because these are protective from the odds ratio. The following P values are for interaction: apolipoprotein B100/apolipoprotein A-I (P = .96); current and former smoking (P = .02); history of hypertension (P = .04); history of diabetes (P = .12); waist-to-hip ratio (P = .63); psychosocial factors (P = .66); moderate- or high-intensity exercise (P = .66); and daily intake of fruits and vegetables (P = .47).
*Upper one third of global distribution.

Among women, the PARs associated with the following risk factors were significantly higher: high WHR (P = .02), lack of exercise (P<.001), lack of daily intake of fruits and vegetables (P = .01), psychosocial factors (P = .005), history of hypertension (P = .001), and history of diabetes (P<.001). Among men, the PARs associated with the following risk factors were significantly higher: current and former smoking (P<.001) and elevated ApoB100 /ApoA-I ratio (P = .006).

Impact of All 9 Risk Factors

The combined OR for all 9 risk factors was similar for native South Asians (123.3; 95% CI, 38.7-400.2) and for individuals from other countries (125.7; 95% CI, 88.5-178.4). This OR explained a high and similar degree of PAR for AMI of 85.8% (95% CI, 78.0%-93.7%) among native South Asians and 88.2% (95% CI, 86.3%-89.9%) among individuals from other areas.

Variations in Risk Factors by Country

Current and former smoking, elevated ApoB100 /Apo-I ratio, history of hypertension, and history of diabetes showed consistently significant associations with AMI in all South Asian countries (Table 3). Bangladesh had the highest prevalence for the most risk factors among the controls: current and former smoking (59.9%), elevated ApoB100 /Apo-I ratio (59.7%), abdominal obesity (43.3%), self-reported history of hypertension (14.3%), and depression (43.0%). However, Bangladesh had the lowest prevalence for regular physical activity (1.3%) and daily intake of fruits and vegetables (8.6%). A history of diabetes was highest among Indians (11.9%). Increasing levels of these risk factors were related to increased AMI risk in all countries.

Table Graphic Jump LocationTable 3. Country-Specific Odds Ratios and Population Attributable Risk for 9 Risk Factors for Acute Myocardial Infarction

Consumption of alcohol was not associated with AMI in South Asians. Among Indians, there was a significant harmful association between alcohol consumption and AMI (OR, 1.64; 95% CI, 1.21-2.27). The prevalence of alcohol consumption was very low in the predominantly Muslim countries of Pakistan (1.5% in controls) and Bangladesh (2.1% controls).

Our study demonstrates that the majority of AMI risk in native South Asians can be explained by 9 potentially modifiable risk factors with similar collective impact as in other countries. Previous studies including analyses of national mortality data in Singapore, the United Kingdom, and Canada indicate that natives of South Asian countries experience fatal CHD at younger ages compared with individuals of European or Chinese ethnicity living in the same country.68 Moreover, there are no major causes of non-CHD deaths that are more common in middle-aged natives of South Asian countries that could distort the age structure. These considerations suggest that the earlier onset of AMI among native South Asians is not an artifact due to differences in the age distribution of the populations under study. In our study, we observed that South Asians had a lower age at presentation of first AMI, which is consistent with previous studies.68 The younger age of first AMI among the South Asian cases in our study appears to be largely explained by the higher prevalence of risk factors in native South Asians.

The 4 main risk factors, which showed consistently significant associations across all South Asian countries in both sexes were current and former smoking, high ApoB100 /Apo-I ratio, history of hypertension, and history of diabetes. Alcohol consumption did not appear to be protective in native South Asians and this may be related to lower prevalence or differences in patterns of drinking (binge drinking in South Asians vs regular drinking in other countries).

The higher PAR due to low daily consumption of fruits and vegetables, lack of regular exercise, and high WHR observed among native South Asians compared with individuals from other countries contributes to the higher rates of CHD observed in South Asians. The 9 risk factors collectively explained 86.0% of the risk in South Asians and suggests that modifying behavior related to known risk factors could lead to a substantial impact. The role of other novel risk factors such as lipoprotein(a) or homocysteine, which are elevated in South Asians, in causing CHD is unclear. Recent randomized trials of homocysteine lowering have not demonstrated a reduction in CHD.2325 Thus, the role of novel risk factors as an important cause for CHD in South Asians is likely to be small.

The rates of consumption of fruits and vegetables were surprisingly lower in South Asian controls compared with controls from other areas despite vegetarianism being common among Indians. Consumption of green leafy vegetables and fruits are associated with lower risk of CHD2629 and a gradient toward lower risk is associated with a greater number of servings consumed (P for trend <.001).27 The consumption of fruits and vegetables was lowest in Bangladesh (8.6% in controls), which is the country that had the youngest age of AMI occurrence. In South Asian households, prolonged cooking of vegetables is a common practice, which may destroy 90% of the folate content.30 A similar inverse association between intake of vegetables and AMI has been reported in a case-control study from India.31 These data collectively provide the basis for public health education aimed at substantially increasing consumption of fruits and vegetables.

Although physical activity was protective in native South Asians, the proportion of South Asians who regularly exercised was low compared with other regions. Leisure time physical activity is culturally unacceptable for most Muslim women. A recent study32 from 2 Indian cities indicates that daily moderate-intensity exercise such as brisk walking for 35 to 40 minutes was associated with more than a 50% reduction in risk for CHD. Physical activity increases insulin sensitivity and high-density lipoprotein cholesterol, lowers blood pressure, improves endothelial function, and reduces the risk of type 2 diabetes mellitus, hypertension, and central adiposity3236; these risk factors are highly prevalent in South Asians. Hence, there is an urgent need to promote moderate-intensity physical activity for South Asians. Adverse psychosocial factors (depression and stress at work or home) were found to be significantly associated with AMI, consistent with the global data37 and data from other studies.3842

Within the 5 South Asian countries studied, Bangladeshis had the highest prevalence of most risk factors. Similar observations also have been made in migrant Bangladeshis living in the United Kingdom.43 Whether this is related to lower income and educational levels in Bangladesh compared with other South Asian countries is unclear and needs to be examined. The striking variation observed in the lower age of presentation of first AMI in South Asians, with Bangladeshis being the youngest and Nepalese the oldest, indicates that the onset of AMI could be delayed by modifying these risk factors.

The main strength of our study is the large size, which enabled a high power level for analysis by sex and comparisons across the South Asian countries. We also have assessed ApoB100 /Apo-I ratio as a more powerful marker of dyslipidemia than the usual lipoproteins, the psychosocial factors of depression and stress at home or work, diet, and exercise; all of which have not been assessed previously in South Asian populations.1113

Case-control studies are potentially susceptible to biases. We minimized these by paying careful attention to the design and analysis. Selection bias was reduced by enrolling incident cases of first AMI, thereby minimizing any biases resulting from changes in lifestyle adopted by individuals with previous coronary artery disease. We also recruited cases and controls from the same source population. We selected both hospital-based and community-based controls and their separate analyses yielded similar results indicating the absence of major selection biases.14 Furthermore, our previous analysis indicated similar results in those who died in the hospital compared with those who were discharged. We minimized measurement biases by using uniform, standardized methods of data collection by trained research assistants for both the cases and the controls. Although our study is the largest study including South Asian women, there were only 252 female cases so sex differences should be cautiously interpreted. We did not study the intake of ghee (a common form of saturated fat consumed in South Asia), which has been reported to be associated with AMI.12 Although we did not directly measure insulin resistance, which may be the underlying factor in the high rates of CHD in native South Asians,44 we assessed factors (high WHR and lack of exercise) that lead to insulin resistance. Similarly, the effect of newer risk factors associated with AMI,45 such as prothrombotic (fibrinogen, plasminogen activator inhihitor-1)4 or proinflammatory factors (C-reactive protein, lipoprotein[a], homocysteine), were not studied. However, given that the 9 risk factors we studied collectively explained 86% of the PAR for AMI, the unmeasured risk factors are unlikely to play a major independent role.

Native South Asians consume fewer fruits and vegetables, exercise less, have higher WHRs, elevated ApoB100 /ApoA-I ratios, and consume alcohol less regularly. It is likely that the recent increase in CHD in South Asians is due to lifestyle changes associated with urbanization, perhaps interacting with a genetic predisposition that leads to abdominal obesity, dysglycemia, and dyslipidemia. Because larger hips also appeared to be protective,46 it is possible that there is a loss of lower body muscle mass, which may influence glucose homeostasis and insulin resistance. The higher levels of risk factors in both cases and controls younger than age 60 years may explain why South Asians have earlier onset of CHD.

Eight INTERHEART risk factors account for the majority of AMI cases in South Asian countries and throughout the world. These data suggest that lifestyle changes implemented early in life have the potential to substantially reduce the risk of AMI in South Asians.

Corresponding Author: Salim Yusuf, DPhil, Population Health Research Institute, Second Floor, McMaster Clinic, Hamilton General Hospital, 237 Barton St E, Hamilton, Ontario, Canada L8L 2X2 (yusufs@mcmaster.ca).

Author Contributions: Dr Yusuf had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Yusuf.

Acquisition of data: Joshi, Pais, Reddy, Dorairaj, Kazmi, Pandey, Haque, Mendis, Rangarajan, Yusuf.

Analysis and interpretation of data: Joshi, Islam, Yusuf.

Drafting of the manuscript: Joshi, Islam, Yusuf.

Critical revision of the manuscript for important intellectual content: Joshi, Islam, Pais, Reddy, Dorairaj, Kazmi, Pandey, Haque, Mendis, Rangarajan, Yusuf.

Statistical analysis: Islam.

Obtained funding: Yusuf.

Administrative, technical, or material support: Rangarajan, Yusuf.

Study supervision: Rangarajan, Yusuf.

Financial Disclosures: None reported.

Funding/Support: Dr Yusuf holds an endowed chair of the Heart and Stroke Foundation of Ontario, and held a Senior Scientist Award from the Canadian Institutes of Health Research. The INTERHEART study was funded by the Canadian Institutes of Health Research; the Heart and Stroke Foundation of Ontario; the International Clinical Epidemiology Network; through unrestricted grants from AstraZeneca, Novartis, Hoechst Marion Roussel [now Sanofi-Aventis], Knoll Pharmaceuticals [now Abbott], Bristol-Myers Squibb, Sanofi-Synthelabo, King Pharma; and by funding from the following: Universidad de la Frontera, Sociedad Chilena de Cardiologia Filial Sur (Chile); Colciencias, Ministerio de Salud (Colombia); Croatian Ministry of Science & Technology; Liga Guatemalteca del Corazon (Guatemala); Astra Hassle, National Health Science Council, George Gabor Foundation (Hungary); Iran Ministry of Health; Boehringer-Ingelheim (Italy); Sankyo Pharmaceutical, Banyu Pharmaceutical, and Astra Japan; Endowment Fund for Health Development in Kuwait; ATCO Laboratories (Pakistan); Philippine Council for Health Research and Devevelopment, Pfizer Philippines Foundation, Astra Pharmacetuicals Inc, the Astra Fund for Clinical Research and Continuing Medical Education, and Pharmacia & Upjohn Inc; Foundation PROCLINICA, State Committee for Scientific Research (Poland); Singapore National Heart Association; MRC South Africa, Warner-Parke-Davis Pharmaceuticals, and Aventis (South Africa); grant from the Swedish State under LUA Agreement, Swedish Heart and Lung Foundation; the Heart Association of Thailand, Thailand Research Fund.

Role of the Sponsor: None of the sponsors played a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Acknowledgment: We thank Judy Lindeman, BA, for secretarial assistance, who was compensated for her work as part of her salary from McMaster University. We also thank the World Health Organization and the World Heart Federation for their endorsement and our friends and colleagues for help that led to the successful completion of this global study.

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Murray CJL, Lopez AD. Global Health Statistics, Global Burden of Disease and Injury Series. Boston, Mass: Harvard School of Public Health; 1996
Murray CJL, Lopez AD. Global Comparative Assessments in the Health Sector. Geneva, Switzerland: World Health Organization; 1994
Pais P, Pogue J, Gerstein H.  et al.  Risk factors for acute myocardial infarction in Indians: a case-control study.  Lancet. 1996;348:358-363
PubMed   |  Link to Article
Ismail J, Jafar TH, Jafary FH.  et al.  Risk factors for non-fatal myocardial infarction in young South Asian adults.  Heart. 2004;90:259-263
PubMed   |  Link to Article
Patil SS, Joshi R, Gupta G.  et al.  Risk factors for acute myocardial infarction in a rural population of central India: a hospital-based case-control study.  Natl Med J India. 2004;17:189-194
PubMed
Yusuf S, Hawken S, Ounpuu S.  et al. INTERHEART Study Investigators.  Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART Study): case-control study.  Lancet. 2004;364:937-952
PubMed   |  Link to Article
Sniderman AD, Furberg CD, Keech A.  et al.  Apolipoprotein versus lipids as indices of coronary risk and as targets for statin treatment.  Lancet. 2003;361:777-780
PubMed   |  Link to Article
Walldius G, Jungner I, Holmes I.  et al.  High apolipoprotein B, low apolipoprotein A-1, and improvement in prediction of fatal myocardial infarction (AMORIS study): a prospective study.  Lancet. 2001;358:2026-2033
PubMed   |  Link to Article
Lamarche B, Moorjani S, Lupien PJ.  et al.  Apolipoprotein A-1 and B levels and the risk of ischemic heart disease during a five year follow-up of men in Quebec Cardiovascular Study.  Circulation. 1996;94:273-278
PubMed   |  Link to Article
Talmud PJ, Hawe E, Miller GJ, Humphries SE. Non-fasting apoB and triglycerides levels as a useful predictor of coronary heart disease risk in middle aged UK men.  Arterioscler Thromb Vasc Biol. 2002;22:1918-1923
PubMed   |  Link to Article
Moss AJ, Goldstein RE, Marder VJ.  et al.  Thrombogenic factors and recurrent coronary events.  Circulation. 1999;99:2517-2522
PubMed   |  Link to Article
Ridker PM, Rifai N, Cook NR, Bradwin G, Buring JE. Non-HDL cholesterol, apolipoproteins A-I and B100, standard lipid measures, lipid ratios, and CRP as risk factors for cardiovascular disease in women.  JAMA. 2005;294:326-333
PubMed   |  Link to Article
Walter SD. The distribution of Levin's measure of attributable risk.  Biometrika. 1975;62:371-372
Link to Article
Benichou J, Gail MH. Variance calculations and confidence intervals for estimates of the attributable risk based on logistic models.  Biometrics. 1990;46:991-1003
PubMed   |  Link to Article
Toole JF, Malinow MR, Chambless LE.  et al.  Lowering homocysteine in patients with ischemic stroke to prevent recurrent stroke, myocardial infarction, and death: the Vitamin Intervention for Stroke Prevention (VISP) randomized controlled trial.  JAMA. 2004;291:565-575
PubMed   |  Link to Article
Heart Outcomes Prevention Evaluation (HOPE)-2 Investigators.  Homocysteine lowering with folic acid and B vitamins in vascular disease.  N Engl J Med. 2006;354:1567-1577
PubMed   |  Link to Article
Bønaa KH, Njølstad I, Ueland PM.  et al. NORVIT Trial Investigators.  Homocysteine lowering and cardiovascular events after acute myocardial infarction.  N Engl J Med. 2006;354:1578-1588
PubMed   |  Link to Article
Joshipura KJ, Hu FB, Manson JE.  et al.  The effect of fruit and vegetable intake on risk for coronary heart disease.  Ann Intern Med. 2001;134:1106-1114
PubMed   |  Link to Article
Panagiotakos DB, Pitsavos C, Kokkinos P.  et al.  Consumption of fruits and vegetables in relation to the risk of developing acute coronary syndromes: the CARDIO 2000 case-control study.  Nutrition. 2003;2:2
Link to Article
Van't Veer P, Jansen MC, Klerk M, Kok FJ. Fruits and vegetables in the prevention of cancer and cardiovascular disease.  Public Health Nutr. 2000;3:103-107
PubMed   |  Link to Article
American Heart Association.  Dietary guidelines for healthy American adults.  Circulation. 1996;94:1795-1800
PubMed   |  Link to Article
Matthews JH, Wood JK. Megaloblastic anemia in vegetarian Asians.  Clin Lab Haematol. 1984;6:1-7
PubMed   |  Link to Article
Rastogi T, Reddy KS, Vaz M.  et al.  Diet and risk of ischemic heart disease in India.  Am J Clin Nutr. 2004;79:582-592
PubMed
Rastogi T, Vaz M, Spiegelman D.  et al.  Physical activity and risk of coronary heart disease in India.  Int J Epidemiol. 2004;33:759-767
PubMed   |  Link to Article
Wannamethee SG, Shaper AG, Alberti KG. Physical activity, metabolic factors and the incidence of coronary heart disease and type 2 diabetes.  Arch Intern Med. 2000;160:2108-2116
PubMed   |  Link to Article
NIH Consensus Development Panel on Physical Activity and Cardiovascular Health.  Physical activity and cardiovascular health.  JAMA. 1996;276:241-246
PubMed   |  Link to Article
Hambrecht R, Wolf A, Gielen S.  et al.  Effect of exercise on coronary endothelial function in patients with coronary artery disease.  N Engl J Med. 2000;342:454-460
PubMed   |  Link to Article
Rosengren A, Wilhelmsen L. Physical activity protects against coronary death and deaths from all causes in middle-aged men: evidence from a 20-year follow-up of the primary prevention study in Goteberg.  Ann Epidemiol. 1997;7:69-75
Link to Article
Rosengren A, Hawken S, Ounpuu S.  et al. INTERHEART Investigators.  Association of psychosocial risk factors with risk of acute myocardial infarction in 11,119 cases and 13,648 controls from 52 countries (the INTERHEART Study): case-control study.  Lancet. 2004;364:953-962
PubMed   |  Link to Article
Pratt LA, Ford DE, Crum RM.  et al.  Depression, psychotropic medication, and risk of myocardial infarction: prospective data from the Baltimore ECA follow-up.  Circulation. 1996;94:3123-3129
PubMed   |  Link to Article
Rugulies R. Depression as a predictor of coronary heart disease: a review and meta-analysis.  Am J Prev Med. 2002;23:51-61
PubMed   |  Link to Article
Ghiadoni L, Donald AE, Cropley M.  et al.  Mental stress induces transient endothelial dysfunction in humans.  Circulation. 2000;102:2473-2478
PubMed   |  Link to Article
Brunner E, Davey-Smith G, Marmot M.  et al.  Childhood social circumstances and psychosocial and behavioral factors as determinants of plasma fibrinogen.  Lancet. 1996;347:1008-1013
PubMed   |  Link to Article
von Kanel R, Mills PJ, Fainman C, Dimsdale JE. Effects of psychological stress and psychiatric disorders on blood coagulation and fibrinolysis: a biobehavioral pathway to coronary artery disease?  Psychosom Med. 2001;63:531-544
PubMed
Bhopal R, Unwin N, White M.  et al.  Heterogeneity of coronary heart disease risk factors in Indian, Bangladeshi, and European origin populations: cross sectional study.  BMJ. 1999;319:215-220
PubMed   |  Link to Article
McKeigue PM, Shah B, Marmot MG. Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians.  Lancet. 1991;337:382-386
PubMed   |  Link to Article
Joshi PP. Why is coronary heart disease increasing in India? cardiovascular risk factors in the Indian scenario.  South Asian J Prev Cardiol. 2003;7:195-203
Yusuf S, Hawken S, Ounpuu S.  et al. INTERHEART Study Investigators.  Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study.  Lancet. 2005;366:1640-1649
PubMed   |  Link to Article

Figures

Figure 1. Risk Factors Associated With Acute Myocardial Infarction in South Asians Compared With Other Participants in INTERHEART Study
Graphic Jump Location

There are 3936 South Asian participants and 23 159 participants from other countries. Among these participants, data were not available for apolipoprotein B100 /apolipoprotein A-I for approximately 20%. Data markers are in proportion to sample size using a log scale. Data are adjusted for age, sex, and smoking status. CI indicates confidence interval. While calculating PARs, a reference category of “yes” was used for daily intake of fruits and vegetables, exercise, and alcohol consumption once or more per week because these are protective from the odds ratio. The following P values are for interaction: apolipoprotein B100 /apolipoprotein A-I (P = .74); current and former smoking (P = .95); history of hypertension (P = .01); history of diabetes (P = .07); high waist-to-hip ratio (P = .02); psychosocial factors (P = .03); moderate- or high-intensity exercise (P = .92); alcohol consumption once or more per week (P = .02); and daily intake of fruits and vegetables (P = .49).
*Upper one third of global distribution.
†Indicates that the population attributable risk (PAR) related to South Asia vs the rest of the world is significantly different at the .05 level.

Figure 2. Predicted Probablility of Acute Myocardial Infarction at a Younger Age in South Asians Compared With Individuals From Other Countries
Graphic Jump Location

This analysis was performed among acute myocardial infarction (AMI) cases only. Before adjustment for the 9 risk factors, there was a higher probability of cases who were younger than 40 years in the South Asian group compared with cases from other countries (P = .001). However, after adjustment for the 9 risk factors, the difference in probabilities of predicted cases of AMI in younger persons was attenuated and not statistically significant (P = .27). Error bars indicate 95% confidence intervals.

Figure 3. Comparison of Risk Factors Associated With Acute Myocardial Infarction in South Asian Men and Women
Graphic Jump Location

In South Asia there are 3936 participants. Among these participants, 3377 are men and 559 are women. Data were not available for apolipoprotein B100/apolipoprotein A-I for approximately 28%. Data markers are in proportion to sample size using a log scale. CI indicates confidence interval; OR, odds ratio. Data are adjusted for age and smoking status. While calculating population-attributable risks, a reference category of yes was used for daily intake of fruits and vegetables, regular exercise, and alcohol consumption once or more per week because these are protective from the odds ratio. The following P values are for interaction: apolipoprotein B100/apolipoprotein A-I (P = .96); current and former smoking (P = .02); history of hypertension (P = .04); history of diabetes (P = .12); waist-to-hip ratio (P = .63); psychosocial factors (P = .66); moderate- or high-intensity exercise (P = .66); and daily intake of fruits and vegetables (P = .47).
*Upper one third of global distribution.

Tables

Table Graphic Jump LocationTable 1. Distribution of Acute Myocardial Infarction Cases
Table Graphic Jump LocationTable 2. Risk Factors for Acute Myocardial Infarction Among Individuals in South Asia and Other Countries by Age Group
Table Graphic Jump LocationTable 3. Country-Specific Odds Ratios and Population Attributable Risk for 9 Risk Factors for Acute Myocardial Infarction

References

Reddy KS, Yusuf S. Emerging epidemic of cardiovascular disease in developing countries.  Circulation. 1998;97:596-601
PubMed   |  Link to Article
Yusuf S, Reddy S, Ounpuu S, Anand S. Global burden of diseases, part 1: general considerations, the epidemiologic transition, risk factors and impact of urbanization.  Circulation. 2001;104:2746-2753
PubMed   |  Link to Article
Reddy KS. Cardiovascular diseases in non-Western countries.  N Engl J Med. 2004;350:2438-2440
PubMed   |  Link to Article
Anand SS, Yusuf S, Vuksan V.  et al.  Difference in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE).  Lancet. 2000;356:279-284
PubMed   |  Link to Article
Yusuf S, Reddy S, Ounpuu S, Anand S. Global burden of cardiovascular diseases, part II: variations in cardiovascular diseases by specific ethnic groups and geographic and prevention strategies.  Circulation. 2001;104:2855-2864
PubMed   |  Link to Article
Enas EA, Yusuf S, Mehta J. Prevalence of coronary artery disease in Asian Indians.  Am J Cardiol. 1992;70:945-949
PubMed   |  Link to Article
McKeigue PM, Marmot MG. Mortality from coronary heart disease in Asian communities in London.  BMJ. 1988;297:903
PubMed   |  Link to Article
Balarajan R. Ethnic differences in mortality from ischaemic heart disease and cerebrovascular disease in England and Wales.  BMJ. 1991;302:560-564
PubMed   |  Link to Article
Murray CJL, Lopez AD. Global Health Statistics, Global Burden of Disease and Injury Series. Boston, Mass: Harvard School of Public Health; 1996
Murray CJL, Lopez AD. Global Comparative Assessments in the Health Sector. Geneva, Switzerland: World Health Organization; 1994
Pais P, Pogue J, Gerstein H.  et al.  Risk factors for acute myocardial infarction in Indians: a case-control study.  Lancet. 1996;348:358-363
PubMed   |  Link to Article
Ismail J, Jafar TH, Jafary FH.  et al.  Risk factors for non-fatal myocardial infarction in young South Asian adults.  Heart. 2004;90:259-263
PubMed   |  Link to Article
Patil SS, Joshi R, Gupta G.  et al.  Risk factors for acute myocardial infarction in a rural population of central India: a hospital-based case-control study.  Natl Med J India. 2004;17:189-194
PubMed
Yusuf S, Hawken S, Ounpuu S.  et al. INTERHEART Study Investigators.  Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART Study): case-control study.  Lancet. 2004;364:937-952
PubMed   |  Link to Article
Sniderman AD, Furberg CD, Keech A.  et al.  Apolipoprotein versus lipids as indices of coronary risk and as targets for statin treatment.  Lancet. 2003;361:777-780
PubMed   |  Link to Article
Walldius G, Jungner I, Holmes I.  et al.  High apolipoprotein B, low apolipoprotein A-1, and improvement in prediction of fatal myocardial infarction (AMORIS study): a prospective study.  Lancet. 2001;358:2026-2033
PubMed   |  Link to Article
Lamarche B, Moorjani S, Lupien PJ.  et al.  Apolipoprotein A-1 and B levels and the risk of ischemic heart disease during a five year follow-up of men in Quebec Cardiovascular Study.  Circulation. 1996;94:273-278
PubMed   |  Link to Article
Talmud PJ, Hawe E, Miller GJ, Humphries SE. Non-fasting apoB and triglycerides levels as a useful predictor of coronary heart disease risk in middle aged UK men.  Arterioscler Thromb Vasc Biol. 2002;22:1918-1923
PubMed   |  Link to Article
Moss AJ, Goldstein RE, Marder VJ.  et al.  Thrombogenic factors and recurrent coronary events.  Circulation. 1999;99:2517-2522
PubMed   |  Link to Article
Ridker PM, Rifai N, Cook NR, Bradwin G, Buring JE. Non-HDL cholesterol, apolipoproteins A-I and B100, standard lipid measures, lipid ratios, and CRP as risk factors for cardiovascular disease in women.  JAMA. 2005;294:326-333
PubMed   |  Link to Article
Walter SD. The distribution of Levin's measure of attributable risk.  Biometrika. 1975;62:371-372
Link to Article
Benichou J, Gail MH. Variance calculations and confidence intervals for estimates of the attributable risk based on logistic models.  Biometrics. 1990;46:991-1003
PubMed   |  Link to Article
Toole JF, Malinow MR, Chambless LE.  et al.  Lowering homocysteine in patients with ischemic stroke to prevent recurrent stroke, myocardial infarction, and death: the Vitamin Intervention for Stroke Prevention (VISP) randomized controlled trial.  JAMA. 2004;291:565-575
PubMed   |  Link to Article
Heart Outcomes Prevention Evaluation (HOPE)-2 Investigators.  Homocysteine lowering with folic acid and B vitamins in vascular disease.  N Engl J Med. 2006;354:1567-1577
PubMed   |  Link to Article
Bønaa KH, Njølstad I, Ueland PM.  et al. NORVIT Trial Investigators.  Homocysteine lowering and cardiovascular events after acute myocardial infarction.  N Engl J Med. 2006;354:1578-1588
PubMed   |  Link to Article
Joshipura KJ, Hu FB, Manson JE.  et al.  The effect of fruit and vegetable intake on risk for coronary heart disease.  Ann Intern Med. 2001;134:1106-1114
PubMed   |  Link to Article
Panagiotakos DB, Pitsavos C, Kokkinos P.  et al.  Consumption of fruits and vegetables in relation to the risk of developing acute coronary syndromes: the CARDIO 2000 case-control study.  Nutrition. 2003;2:2
Link to Article
Van't Veer P, Jansen MC, Klerk M, Kok FJ. Fruits and vegetables in the prevention of cancer and cardiovascular disease.  Public Health Nutr. 2000;3:103-107
PubMed   |  Link to Article
American Heart Association.  Dietary guidelines for healthy American adults.  Circulation. 1996;94:1795-1800
PubMed   |  Link to Article
Matthews JH, Wood JK. Megaloblastic anemia in vegetarian Asians.  Clin Lab Haematol. 1984;6:1-7
PubMed   |  Link to Article
Rastogi T, Reddy KS, Vaz M.  et al.  Diet and risk of ischemic heart disease in India.  Am J Clin Nutr. 2004;79:582-592
PubMed
Rastogi T, Vaz M, Spiegelman D.  et al.  Physical activity and risk of coronary heart disease in India.  Int J Epidemiol. 2004;33:759-767
PubMed   |  Link to Article
Wannamethee SG, Shaper AG, Alberti KG. Physical activity, metabolic factors and the incidence of coronary heart disease and type 2 diabetes.  Arch Intern Med. 2000;160:2108-2116
PubMed   |  Link to Article
NIH Consensus Development Panel on Physical Activity and Cardiovascular Health.  Physical activity and cardiovascular health.  JAMA. 1996;276:241-246
PubMed   |  Link to Article
Hambrecht R, Wolf A, Gielen S.  et al.  Effect of exercise on coronary endothelial function in patients with coronary artery disease.  N Engl J Med. 2000;342:454-460
PubMed   |  Link to Article
Rosengren A, Wilhelmsen L. Physical activity protects against coronary death and deaths from all causes in middle-aged men: evidence from a 20-year follow-up of the primary prevention study in Goteberg.  Ann Epidemiol. 1997;7:69-75
Link to Article
Rosengren A, Hawken S, Ounpuu S.  et al. INTERHEART Investigators.  Association of psychosocial risk factors with risk of acute myocardial infarction in 11,119 cases and 13,648 controls from 52 countries (the INTERHEART Study): case-control study.  Lancet. 2004;364:953-962
PubMed   |  Link to Article
Pratt LA, Ford DE, Crum RM.  et al.  Depression, psychotropic medication, and risk of myocardial infarction: prospective data from the Baltimore ECA follow-up.  Circulation. 1996;94:3123-3129
PubMed   |  Link to Article
Rugulies R. Depression as a predictor of coronary heart disease: a review and meta-analysis.  Am J Prev Med. 2002;23:51-61
PubMed   |  Link to Article
Ghiadoni L, Donald AE, Cropley M.  et al.  Mental stress induces transient endothelial dysfunction in humans.  Circulation. 2000;102:2473-2478
PubMed   |  Link to Article
Brunner E, Davey-Smith G, Marmot M.  et al.  Childhood social circumstances and psychosocial and behavioral factors as determinants of plasma fibrinogen.  Lancet. 1996;347:1008-1013
PubMed   |  Link to Article
von Kanel R, Mills PJ, Fainman C, Dimsdale JE. Effects of psychological stress and psychiatric disorders on blood coagulation and fibrinolysis: a biobehavioral pathway to coronary artery disease?  Psychosom Med. 2001;63:531-544
PubMed
Bhopal R, Unwin N, White M.  et al.  Heterogeneity of coronary heart disease risk factors in Indian, Bangladeshi, and European origin populations: cross sectional study.  BMJ. 1999;319:215-220
PubMed   |  Link to Article
McKeigue PM, Shah B, Marmot MG. Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians.  Lancet. 1991;337:382-386
PubMed   |  Link to Article
Joshi PP. Why is coronary heart disease increasing in India? cardiovascular risk factors in the Indian scenario.  South Asian J Prev Cardiol. 2003;7:195-203
Yusuf S, Hawken S, Ounpuu S.  et al. INTERHEART Study Investigators.  Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study.  Lancet. 2005;366:1640-1649
PubMed   |  Link to Article
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