Race- and Sex-Specific Associations of Obesity Measures With Ischemic Stroke Incidence in the Atherosclerosis Risk in Communities (ARIC) Study
Background and Purpose— Studies have suggested differences in the association between obesity and ischemic stroke in black versus white populations. In this study, we explored ischemic stroke risk in relation to a variety of obesity measures by sex and race.
Methods— Using data from the Atherosclerosis Risk in Communities Study, we obtained information on body mass index, waist circumference, and waist-to-hip ratio from 13 549 black and white participants who were aged 45 to 65 years between 1987 and 1989. All were free of cardiovascular disease and cancer at baseline. Incident strokes over a median follow-up of 16.9 years were ascertained from hospital records.
Results— Although crude incidence rates of ischemic stroke varied more than 3-fold by race and sex, the relationship between higher measures of obesity and ischemic stroke risk was positive and linear across all groups. The crude incidence of ischemic stroke was 1.2 per 1000 person-years for white women with the lowest body mass index, ranging up to 8.0 per 1000 person-years for black men with the highest body mass index. Hazard ratios for the highest versus lowest quintile of body mass index, waist circumference, and waist-to-hip ratio ranged from 1.43 to 3.19, indicating increased stroke risk associated with obesity, however it was measured, even after adjustment for potential confounders. Additional adjustment for factors that may mediate the relationship, such as diabetes and hypertension, significantly attenuated the associations, suggesting that these factors may explain much of the stroke risk associated with obesity.
Conclusions— Degree of obesity, defined by body mass index, waist circumference, or waist-to-hip ratio, was a significant risk factor for ischemic stroke regardless of sex or race.
- cerebral infarct
- cerebral infarction
- cohort study
- risk factors
Stroke is the third leading cause of death and the leading cause of serious, long-term disability in the United States. Blacks have approximately twice the incidence of stroke as whites, and this black–white ratio is greater in middle age.1 It has been reported that blacks have poorer rehabilitation outcomes after acute stroke compared with whites.2 Because of these disparities, further studies are warranted addressing possible stroke prevention strategies, especially in blacks.
Well-established risk factors for stroke include hypertension, diabetes mellitus, and current smoking.3,4 Whether being overweight is a risk factor for stroke is less clear. Overweight/obesity as measured by body mass index (BMI) was a risk factor for stroke in US male physicians,5 Swedish men,6 and in Korean men7 and women.8 Nevertheless, there are conflicting results, for example, null9 or no independent association10 in predominantly white women or an inverse association in black women.11 In general, few studies of obesity and stroke risk exist among blacks, as summarized in Table 1.
In addition, there is still a debate about which measure of excess adiposity is most closely associated with disease risk. For example, waist-to-hip ratio (WHR), but not BMI, was related to stroke incidence in a study of US male health professionals aged 40 to 75 years.12 Similar findings were observed in multiethnic13 and German14 case–control studies and a prospective cohort of Swedish middle-aged women.15 In contrast, a study of Finnish men and women suggested that stroke risk was associated with the World Health Organization BMI classification rather than with measures of abdominal obesity like waist circumference.16 BMI, waist circumference, or both are often promoted as the best measures of obesity for defining health risks. However, BMI may be easier to standardize than waist circumference, the location of which is less well defined. Furthermore, these 2 measures are highly correlated.3 It is important to discover whether stroke risk varies on the basis of abdominal obesity and BMI categories within race groups.
In the present study, we describe incidence rates and hazard ratios of ischemic stroke according to race and sex for BMI, waist circumference, and WHR. We tested the hypothesis that there are differences in the association for black versus white men and women. We also hypothesized that any associations of obesity measures with ischemic stroke would be mediated by known stroke predictors (particularly hypertension and diabetes). We also explored whether there is a graded increase in ischemic stroke incidence rates across National Institutes of Health (NIH) BMI–waist circumference categories.17
The Atherosclerosis Risk in Communities (ARIC) Study included a cohort of 15 792 persons between 45 and 64 years of age at recruitment in 1987 through 1989.18 Population samples were selected by probability sampling methods from Forsyth County, NC (n=4035); Jackson, Miss (black only, n=3728); northwest suburbs of Minneapolis, Minn (n=4009); and Washington County, Md (n=4020). Baseline response rates ranged from 46% in Jackson to 65% to 67% in the other 3 communities. Participants were subsequently contacted annually by telephone and 3 additional clinic visits. The retention rate was 93% through 2005, and the rates did not differ appreciably between races.
BMI (kg/m2) was calculated from measurements of weight to the nearest pound and height to the nearest centimeter with the participants wearing a scrub suit and no shoes. The ratio of waist (umbilical level) and hip (maximum buttocks) circumference (WHR) was calculated as a measure of fat distribution in addition to waist circumference alone. The intertechnician reliability coefficients for waist and hip circumference and WHR were all r>0.94.19
Questionnaires were used to assess educational level, cigarette smoking, alcohol drinking, leisure time sports index, use of antihypertensive or diabetic medications, and histories of physician-diagnosed diabetes, cancer, coronary heart disease, or stroke. The sports index was derived from questionnaire items on hours per week spent in up to 4 sports and the months per year each sport was done. By assuming a sport intensity level (light, moderate, or heavy), a sport score was calculated ranging from 1 (lowest) to 5 (highest).20 Prevalent coronary heart disease at baseline was defined for exclusion as a reported history of a physician-diagnosed myocardial infarction, prior myocardial infarction detected by electrocardiogram, or prior cardiovascular surgery or coronary angioplasty. Three blood pressure measurements were taken with a random-zero sphygmomanometer; the last 2 measurements were averaged. Fasting blood levels of glucose as well as the levels of high-density lipoprotein cholesterol, albumin, and von Willebrand factor were measured centrally by standard methods. Prevalent diabetes was defined as a history of, or treatment for, diabetes, a fasting glucose level of ≥126 mg/dL, or a casual blood glucose level of ≥200 mg/dL.
Ascertainment of Incident Stroke
Ischemic strokes that occurred by December 31, 2005 (median follow-up, 16.9 years) were included in the present study. During annual telephone contacts, interviewers asked each ARIC participant to list all hospitalizations during the past year; hospital records were obtained. In addition, all local hospitals annually provided lists of stroke discharges (International Classification of Diseases, 9th Revision, Clinical Modification codes 430 to 438), which were scrutinized for ARIC participants’ discharges. Details on quality assurance for ascertainment and classification of stroke are described elsewhere.21 Briefly, the stroke diagnosis was assigned according to criteria adapted from the National Survey of Stroke. A minimum criterion was sudden or rapid onset of neurological symptoms lasting >24 hours or leading to death not secondary to trauma, neoplasm, hematologic abnormality, infection, or vasculitis. A stroke was classified as ischemic when a brain CT or MRI revealed acute infarction or showed no evidence of hemorrhage. Stroke that occurred during hospitalization for another condition or procedures (cardiac catheterization, open heart surgery, cerebral angiography, and carotid endarterectomy) were included (n=28). However, out-of-hospital stroke was not ascertained and validated. Along with a computer-based classification, cases were independently reviewed by a physician who was provided with a detailed report of the information abstracted from the medical record as well as the full discharge summary, the CT and MRI scan reports, reports from any neurological consults, and admission history. The final diagnosis was determined by agreement of computer and reviewer classification. In the rare occasion when there was disagreement between computer and reviewer classifications, the diagnosis was adjudicated by a second physician–reviewer. CT or MRI was available for all the ischemic stroke cases except one cardioembolic stroke classified with carotid artery ultrasound and clinical information. The 92 hemorrhagic stroke cases identified were censored at the time of their occurrence.
Of the 15 744 blacks and whites in ARIC, we excluded 1787 participants (blacks: 365, whites: 1422) who at baseline had a prevalent stroke, coronary heart disease, or cancer because cardiovascular disease treatment and associated behavioral change or cancer-induced weight loss could confound the association between obesity measures and stroke. Participants lacking baseline measurements of BMI, waist circumference, or hip circumference (n=32) were also excluded. Those with missing values of potential confounding variables, including leisure time sport index, smoking status and cigarette-years of smoking, usual ethanol intake, and educational level, were then excluded (n=376) leaving a final sample of 5930 men and 7619 women (n=13 549 in total).
Analyses were done separately for blacks and whites and men and women. Cox proportional hazards regression was used to calculate age- and multivariate-adjusted hazard ratios (HRs) and their 95% CIs of ischemic stroke incidence in relation to quintiles of the obesity measures. Quintile cutoff values of each obesity measure were created by averaging the 4 race- and sex-specific quintile cutoff values. The first model (Model I) adjusted for age, smoking status (current, past, or never), cigarette-years of smoking, usual ethanol intake (grams/week), educational level (high school graduate or not), and leisure time sport index (1.0 to 1.9, 2.0 to 2.4, 2.5 to 2.9, 3.0 to 5.0) score. In a mediation model (Model II, n=288 more excluded), we further adjusted for systolic blood pressure, use of antihypertensive medication, prevalent diabetes, and blood levels of high-density lipoprotein cholesterol, von Willebrand factor, and albumin simultaneously in light of a previous ARIC paper that identified them as predictors of incident stroke.22
The assumption of hazard proportionality was tested by a model including follow-up time by obesity measure quintile interaction. The follow-up time was first treated as a continuous scale and then dichotomized at Year 10 in the model. Interactions for race by obesity measure quintile, sex by obesity measure quintile, and race by sex by obesity measure quintile were tested in Model I using Wald test at a significance level of 0.1.
Cubic spline analyses were performed to qualitatively evaluate any nonlinear relationship between obesity measures and stroke incidence. Spline analyses were carried out using the truncated sample because extreme values would be overinfluential.
Finally, we performed another analysis to estimate stroke incidence according to the NIH classification table of overweight and obesity based jointly on BMI and waist circumference. The reference group was normal weight and normal waist circumference—≤102 cm (40 inches) in men and ≤88 cm (35 inches) in women.
The population-attributable fraction (PAF) was calculated as p multiplied by [(HR for a category being considered−HR for the reference category)/HR for the category being considered], where p is the proportion of cases that are exposed in whichever category is being considered.23
Sensitivity analyses were also performed after truncating the sample at the first and 99th percentiles of each obesity measure, because extreme values had some impact on the association for blacks. All statistical analyses were performed using SAS Version 9.2 (SAS Institute, Cary, NC).
At baseline, mean BMI, waist circumference, and WHR were 30.8 kg/m2, 100.3 cm, and 0.90 in black women; 27.6 kg/m2, 96.7 cm, and 0.94 in black men; 26.6 kg/m2, 93.0 cm, and 0.89 in white women; and 27.4 kg/m2, 99.5 cm, and 0.97 in white men, respectively (Table 2).
During a median of 16.9 years of follow-up (maximum=19.1 years), 598 incident ischemic strokes were identified. Crude incidence rates of ischemic stroke varied >3-fold by race and sex from 1.2 per 1000 person-years in white women to 4.3 in black women in the lowest BMI category and 2.2 in white women to 8.0 in black men in the highest BMI category (Table 3). The absolute difference in incidence rates for being in the highest BMI quintile compared with the lowest quintile ranged from 1.9 per 1000 person-years in white women to 5.6 per 1000 person-years in black women. The HRs of ischemic stroke in relation to BMI quintiles were generally linear in black men and white men and women (all trend P<0.05) with HRs for the highest versus lowest quintile ranging 1.78 to 2.12 (Model I). Although the trend P across BMI quintiles for black women was also significant, the HR estimate for the highest quintile was only 1.43 (95% CI, 0.81 to 2.53). Nevertheless, interaction testing suggested no significant effect modification by race or sex. Continuous BMI showed a significant linear positive association with ischemic stroke only in black men and white men and women (HR1 in Table 3), but exclusion of extreme observations indicated significant positive associations in all race and sex subgroups (HR2 in Table 3; Figure A–D). Adjustment for potential mediating factors (Model II, Table 3), particularly hypertension and diabetes, as expected, significantly attenuated most BMI associations with ischemic stroke.
As shown in Table 4, quintiles of waist circumference were positively and strongly associated with the risk of ischemic stroke in all demographic groups, and there was no interaction by race or sex. In most race–sex groups, continuous waist circumference was also related positively to ischemic stroke incidence (HR1 and HR2 in Table 4; Figure E–H). The association of waist circumference with ischemic stroke incidence was attenuated with adjustment for mediating factors (eg, hypertension and diabetes) but incompletely in white men (trend P=0.021 in Model II).
The analyses using WHR showed similar results as those for waist circumference (Table 5). The HR of ischemic stroke for the highest quintile of WHR ranged from 1.69 to 2.55 across race–sex groups and there was no interaction by race or sex.
The NIH risk classification table performed well predicting stroke risk in both blacks and whites; the risk categories paralleled the observed incidence rates of ischemic stroke (Supplemental Table I; http://stroke.ahajournals.org).
Hypertension did not modify the associations of any obesity measure with ischemic stroke incidence in a race–sex collapsed sample (p for interaction >0.10; Supplemental Table II). BMI, waist circumference, and WHR were positively associated (P<0.05) with the incidence of ischemic stroke in subjects both with and without hypertension.
The PAF represents the proportion that might be prevented by eliminating a risk factor. The PAFs of ischemic stroke for being in the top 40% of BMI, waist circumference, or WHR, compared with the lower 60%, were 17.7%, 21.2%, and 21.7%, respectively.
In this ARIC analysis, blacks had approximately 2 to 3 times higher incidence rates of ischemic stroke compared with whites in each obesity quintile. However, obesity, regardless of the measure, was a risk factor for ischemic stroke without statistical evidence for differences by race or sex.
For whites, ARIC findings were consistent with most previous studies. Prior studies in blacks have shown less association of obesity with stroke, except for the Northern Manhattan Stroke Study, which found that obesity defined as a high sex-specific WHR was significantly positively associated with ischemic stroke incidence.13 The black Pooling Project, a meta-analysis of cohort studies using individual data, did not find an association between BMI and stroke mortality in blacks (incidence not examined).24 The unadjusted prevalence of obesity defined by sex-specific WHR did not differ among black ischemic stroke cases and control subjects in the South London Stroke Register Study25 although the control subjects were 7 years younger than the cases and included more women (64% versus 52%). Finally, the lowest BMI quartile was associated with a surprisingly higher nonhemorrhagic stroke incidence in the National Health and Nutrition Examination Survey (NHANES) Epidemiological Follow-up Study (NHEFS).11
One of the reasons for the discrepancy between ARIC and NHEFS, a cohort study like ours, may be differences in the average degree of obesity. The median BMI values of black women (29.8 kg/m2) and men (27.1 kg/m2) at baseline (1987 to 1989) in ARIC were 2.5 kg/m2 greater than those at baseline (1971 to 1975) in NHEFS (27.4 kg/m2 in women and 24.5 kg/m2 in men). The rightward shift of the BMI distribution in blacks over the decades, which was greater than in whites (0.2- and 1.3-kg/m2 increase in white women and men, respectively) may account for the significant positive association between BMI and ischemic stroke incidence in ARIC blacks. Furthermore, NHEFS included only 955 blacks compared with 3694 in the present study, which made our analysis more powerful. In addition, approximately half of NHEFS participants were aged 65 to 74 years at baseline in contrast to ours that included only 45 to 64 years old at baseline. The older age distribution, with possible impact of weight loss in the elderly, might have distorted the BMI–stroke association in NHEFS.26
Based on the fact that we consistently found positive associations between obesity measures and ischemic stroke incidence in blacks in the present study, we believe that obesity, however it is measured, significantly increases ischemic stroke risk in blacks as well as in whites. From a public health point of view, the estimated PAF values suggested that 18% to 20% of ischemic stroke occurrence may be accounted for by BMI ≥28.1 kg/m2, waist circumference ≥100 cm, or WHR ≥0.95. We are aware of debates on PAF calculation methods.23,27,28 In the present study, we defined the obese group as the top 40% of each obesity measure and calculated the PAF using a multivariate (Model I)-adjusted HR.23 Because disease risk in relation to obesity measure is expected to be continuous, misclassification due to the selection of certain cutoff points is likely to underestimate PAF.29 Although there might be possible inaccuracies related to the method used to calculate PAF, we considered such errors would not be critical.
In all race–sex groups, significant positive associations of obesity measures with ischemic stroke incidence were largely explained by mediators related to obesity. In fact, either blood pressure or diabetes mellitus alone in Model II could have eliminated significant associations between obesity measure quintiles and ischemic stroke incidence. Yet, obesity did not fully account for the higher blood pressure and stroke risk of blacks compared with whites. For example, the mean systolic blood pressure in the lowest quintile of BMI in blacks was comparable to the systolic blood pressure in the highest quintile in whites (125.2 versus 126.3 mm Hg).
Hypertension did not modify the associations of obesity measures with ischemic stroke incidence. In other words, in subjects both with and without hypertension, there were significant positive associations between obesity measures and ischemic stroke incidence. These associations were, however, significantly attenuated with additional adjustment for systolic blood pressure. Given the strong association between obesity and hypertension and other risk factors, including diabetes mellitus, obesity would be an important target for the prevention of ischemic stroke.
Strengths of the present study included the prospective design; large sample size and number of ischemic strokes, which allowed race- and sex-specific analyses; the long duration of follow-up, systematic surveillance, and confirmation of outcome events; the detailed assessment of potential confounding and mediating variables; and the standardized recording of multiple obesity measures. As a limitation, although our goal was a careful description of ischemic stroke incidence using anthropometric measurements relevant in the field of public health, formal comparison among these measures for the prediction of ischemic stroke should be considered. Second, most of the blacks were from one field center and the whites from 3 other centers, limiting the generalizability of our findings to other cultural or socioeconomic contexts.
In conclusion, the degree of obesity defined either by BMI, waist circumference, or WHR was a significant risk factor for ischemic stroke incidence regardless of sex or race. Prevention and control of obesity has a potential to reduce the incidence of ischemic stroke.
The authors thank the staff and participants of the ARIC study for their important contributions. The author (H.Y.) is also grateful to the Uehara Memorial Foundation which supported his research activities at Division of Epidemiology and Community Health, School of Public Health, University of Minnesota.
Sources of Funding
The ARIC Study was funded by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022.
- Received September 4, 2009.
- Revision received November 16, 2009.
- Accepted December 3, 2009.
Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K, Ford E, Furie K, Go A, Greenlund K, Haase N, Hailpern S, Ho M, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott M, Meigs J, Mozaffarian D, Nichol G, O'Donnell C, Roger V, Rosamond W, Sacco R, Sorlie P, Stafford R, Steinberger J, Thom T, Wasserthiel-Smoller S, Wong N, Wylie-Rosett J, Hong Y. Heart disease and stroke statistics—2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009; 119: e21–e181.
Ottenbacher KJ, Campbell J, Kuo Y-F, Deutsch A, Ostir GV, Granger CV. Racial and ethnic differences in postacute rehabilitation outcomes after stroke in the United States. Stroke. 2008; 39: 1514–1519.
Folsom AR, Prineas RJ, Kaye SA, Munger RG. Incidence of hypertension and stroke in relation to body fat distribution and other risk factors in older women. Stroke. 1990; 21: 701–706.
Folsom AR, Rasmussen ML, Chambless LE, Howard G, Cooper LS, Schmidt MI, Heiss G. Prospective associations of fasting insulin, body fat distribution, and diabetes with risk of ischemic stroke. The Atherosclerosis Risk in Communities (ARIC) Study Investigators. Diabetes Care. 1999; 22: 1077–1083.
Jood K, Jern C, Wilhelmsen L, Rosengren A. Body mass index in mid-life is associated with a first stroke in men: a prospective population study over 28 years. Stroke. 2004; 35: 2764–2769.
Song YM, Sung J, Davey Smith G, Ebrahim S. Body mass index and ischemic and hemorrhagic stroke: a prospective study in Korean men. Stroke. 2004; 35: 831–836.
Kurth T, Gaziano JM, Rexrode KM, Kase CS, Cook NR, Manson JE, Buring JE. Prospective study of body mass index and risk of stroke in apparently healthy women. Circulation. 2005; 111: 1992–1998.
Walker SP, Rimm EB, Ascherio A, Kawachi I, Stampfer MJ, Willett WC. Body size and fat distribution as predictors of stroke among US men. Am J Epidemiol. 1996; 144: 1143–1150.
Suk SH, Sacco RL, Boden-Albala B, Cheun JF, Pittman JG, Elkind MS, Paik MC. Abdominal obesity and risk of ischemic stroke: the Northern Manhattan Stroke Study. Stroke. 2003; 34: 1586–1592.
Winter Y, Rohrmann S, Linseisen J, Lanczik O, Ringleb PA, Hebebrand J, Back T. Contribution of obesity and abdominal fat mass to risk of stroke and transient ischemic attacks. Stroke. 2008; 39: 3145–3151.
The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC Investigators. Am J Epidemiol. 1989; 129: 687–702.
Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. 1982; 36: 936–942.
Rosamond WD, Folsom AR, Chambless LE, Wang CH, McGovern PG, Howard G, Copper LS, Shahar E. Stroke incidence and survival among middle-aged adults: 9-year follow-up of the Atherosclerosis Risk in Communities (ARIC) cohort. Stroke. 1999; 30: 736–743.
Chambless LE, Heiss G, Shahar E, Earp MJ, Toole J. Prediction of ischemic stroke risk in the Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2004; 160: 259–269.
Greenland S. Re: Confidence limits made easy: interval estimation using a substitution method. Am J Epidemiol. 1999; 149: 884; author reply 885–886.
Hajat C, Tilling K, Stewart JA, Lemic-Stojcevic N, Wolfe CD. Ethnic differences in risk factors for ischemic stroke: a European case–control study. Stroke. 2004; 35: 1562–1567.
Park HS, Song YM, Cho SI. Obesity has a greater impact on cardiovascular mortality in younger men than in older men among non-smoking Koreans. Int J Epidemiol. 2006; 35: 181–187.
Flegal KM, Graubard BI. Estimates of excess deaths associated with body mass index and other anthropometric variables. Am J Clin Nutr. 2009; 89: 1213–1219.
Wacholder S, Benichou J, Heineman EF, Hartge P, Hoover RN. Attributable risk: advantages of a broad definition of exposure. Am J Epidemiol. 1994; 140: 303–309.