Published online before print August 14, 2008, doi: 10.1161/STROKEAHA.108.523001
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(Stroke. 2008;39:3145.)
© 2008 American Heart Association, Inc.
Original Contributions |
Contribution of Obesity and Abdominal Fat Mass to Risk of Stroke and Transient Ischemic Attacks
From the Department of Neurology (Y.W., O.L., T.B.), Klinikum Mannheim, University of Heidelberg, Germany; the Division of Cancer Epidemiology (S.R., J.L.), German Cancer Research Center, Heidelberg, Germany; the Department of Child and Adolescent Psychiatry (J.H.), Rheinische Kliniken, University of Duisburg-Essen, Germany; the Department of Neurology (P.A.R.), Klinikum Heidelberg, University of Heidelberg, Germany; the Department of Neurology (T.B.), Saxon Hospital Arnsdorf, Arnsdorf/Dresden, Germany; and the Center for Mental Health (Y.W.), Klinikum Stuttgart, Germany.
Correspondence to Prof Tobias Back, MD, Department of Neurology, Saxon Hospital Arnsdorf, Hufelandstr. 15, D-01477 Arnsdorf/Dresden, Germany. E-mail tobias.back{at}skhar.sms.sachsen.de
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Abstract |
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Background and Purpose— Waist circumference has been shown to be a better predictor of cardiovascular risk than body mass index (BMI). Our case-control study aimed to evaluate the contribution of obesity and abdominal fat mass to the risk of stroke and transient ischemic attacks (TIA).
Methods— We recruited 1137 participants: 379 cases with stroke/TIA and 758 regional controls matched for age and sex. Associations between different markers of obesity (BMI, waist-to-hip ratio, waist circumference and waist-to-stature ratio) and risk of stroke/TIA were assessed by using conditional logistic regression adjusted for other risk factors.
Results— BMI showed a positive association with cerebrovascular risk which became nonsignificant after adjustment for physical inactivity, smoking, hypertension, and diabetes (odds ratio 1.18; 95% CI, 0.77 to 1.79, top tertile versus bottom tertile). Markers of abdominal adiposity were strongly associated with the risk of stroke/TIA. For the waist-to-hip ratio, adjusted odds ratios for every successive tertile were greater than that of the previous one (2nd tertile: 2.78, 1.57 to 4.91; 3rd tertile: 7.69, 4.53 to 13.03). Significant associations with the risk of stroke/TIA were also found for waist circumference and waist-to-stature ratio (odds ratio 4.25, 2.65 to 6.84 and odds ratio 4.67, 2.82 to 7.73, top versus bottom tertile after risk adjustment, respectively).
Conclusions— Markers of abdominal adiposity showed a graded and significant association with risk of stroke/TIA, independent of other vascular risk factors. Waist circumference and related ratios can better predict cerebrovascular events than BMI.
Key Words: stroke • transient ischemic attack • obesity • body mass index • waist circumference • risk factors
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Introduction |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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Obesity has become one of the most prevalent conditions making a significant impact on public health worldwide. In the United States, 65.7% of adults are either overweight or obese, and 30.4% are obese.1 In Germany, currently 49.6% of inhabitants are overweight, among those 13.6% are obese.2
The unfavorable association of obesity with coronary heart disease3 and myocardial infarction4 is well recognized. Large-scale prospective studies have documented that abdominal obesity measured by waist-to-hip ratio (WHR) is more strongly associated with cardiovascular risk than body mass index (BMI).4,5 However, the relationship between increased relative body weight and stroke risk is controversial. There are studies showing that increasing BMI is associated with a graded elevated risk of stroke.6,7 In other studies, however, no relation was found between BMI and stroke risk.8–10 Possibly, BMI is not an appropriate indicator to assess the risk of stroke.11 Markers of abdominal obesity have rarely been studied in cerebrovascular disease. In 2 of those studies, WHR was more strongly associated with the risk of ischemic stroke than BMI, but the strength of this association was attenuated after adjustment for cardiovascular risk factors.11,12 Other studies included small numbers of cases13,14 or concentrated on cardiovascular risk.4 Thus, data on the role of abdominal obesity for stroke are limited and completely lacking for transient ischemic attacks (TIA), which represent important cerebrovascular events often preceeding major strokes.15 In order to provide evidence for the impact of body-fat distribution on the risk of stroke and TIA, we conducted a case-control study in a well-defined population of central Western Europe.
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Patients and Methods |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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The study included 1137 participants (379 cases and 758 controls). Consecutive cases of ischemic stroke (n=301, 79%), intracerebral hemorrhage (n=37, 10%) or TIAs (n=41, 11%) were recruited in the Departments of Neurology of the Klinikum Mannheim and Klinikum Heidelberg between February 1, 2005 and January 31, 2006. Cases with prior cerebrovascular events were not excluded. Of 401 initially recruited cases, detailed clinical records were unavailable for 22 patients, who were therefore excluded from analysis. Each index patient was matched with 2 controls without a history of cerebrovascular disease. The study was approved by the local ethics committee and all patients gave informed consent.
Each patient received a physical and neurological examination, CT and/or MRI of the head. Stroke was defined according to the World Health Organization (WHO).16 The obesity phenotype was characterized by anthropometric measures, such as BMI, WHR and waist circumference. BMI was calculated as weight in kilograms divided by height in meters squared.17 WHR was defined as waist divided by hip circumference.11 Waist circumference was measured in centimeters at the level of the umbilicus,18 hip circumference at the level of the bilateral greater trochanters.19 The role of body height was also investigated by using the waist-to-stature ratio (WSR), defined as waist circumference divided by body height.20 The anthropometric measurements were performed in less than 48 hours after admission.
We used threshold categories for obesity measures defined by expert groups from the WHO.19 In BMI categories, we distinguished between normal weight (BMI <25.0 kg/m2) and overweight (BMI 
25.0 kg/m2), including preobesity (BMI 25.0 to 29.9 kg/m2) and obesity (BMI 30 kg/m2). Obese women had WHR 0.85 and obese men WHR 1.0. Threshold categories for waist circumference in men were <94.0 cm (normal weight), 94.0 to 101.9 cm (overweight) and 102.0 cm (obesity). In women they were <80.0 cm, 80.0 to 87.9 cm and 88.0 cm, respectively.21 Hypertension was defined as systolic blood pressure 140 mm Hg and/or diastolic blood pressure 90 mm Hg or treatment with antihypertensive agents. Hyperlipidemia was defined as total serum cholesterol level 240 mg/dL or use of antihyperlipidemic agents. Diabetes was defined as fastening blood glucose 126 mg/dL or use of insulin or oral hypoglycemic agents.
Regional controls were matched for age and sex from a database with 25 540 participants of the population-based cohort study EPIC-Heidelberg. There were no individuals younger than 45 or older than 75 years among controls. The age of cases in our study cohort ranged from 25 to 90 years. Age groups <50, 50 to 55, 56 to 60, 61 to 65, 66 to 70 and >70 were used for matching. Cases younger than 45 years (5.3%, n=20) were matched within the age group <50 years; cases older than 75 (24.3%, n=92) were matched within the age group >70 years. The plausibility proof was performed by analyzing only the exactly matched set of cases and controls aged 45 to 75 years.
Statistical Analysis
Statistical analysis was performed with SAS version 9.1 (SAS Institute Inc). In the univariate analysis, categorical variables were compared by 
2 test. Continuous variables expressed as mean±SD were compared by the t test. Conditional logistic regression models were used to calculate the odds ratio (OR) and 95% CI for BMI, WHR, WSR, and waist circumference with stratification by sex and age groups as described above. Adjustment was performed for the following stroke risk factors: arterial hypertension (yes/no), diabetes mellitus (yes/no), smoking (smoker during previous 5 years; yes/no) and physical inactivity (at least 2 hours of physical activity per week; yes/no). Hyperlipidemia was not statistically significant in the univariate analysis and was, thus, not included in the conditional logistic regression model.
Three approaches were chosen to assess the role of obesity markers in predicting the risk of stroke/TIA: first, comparisons of ORs across the tertiles of BMI, WHR, WSR and waist circumference, respectively, using the bottom tertile as a reference category; second, estimation of the OR for 1 SD change in BMI, WHR, WSR or waist circumference; and third, comparisons of the receiver-operator curves (ROC) in relation to stroke or TIA for obesity measures. The ROC is a plot of test sensitivity versus its false-positive rate (or 1–specificity). The area under the ROC is a measure of the accuracy of a diagnostic test. A test with an area under the ROC of 1.0 is perfectly accurate, and a test with the accuracy of 50% (random guessing) has an area of 0.5, and a test with an area of 0.0 is completely inaccurate. ROCs were compared by using the method of DeLong and coworkers.22
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Results |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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A total of 379 patients with stroke (n=338) or TIA (n=41) and 758 age- and sex-matched regional controls were evaluated. Of these cases, 37.2% (n=141) were female. The mean age of controls was slightly lower than the age of cases (65.0±8.3 versus 67.3±12.2, P=0.02) because there were no individuals older than 75 years among the controls. Table 1 shows the demographics and distribution of stroke risk factors in the study population stratified by sex. Patients aged 45 to 75 years were separated within the study cohort for the reasons mentioned above. The prevalence of obesity, defined by BMI, was higher in cases (29.8%, n=113) than in controls (20.1%, n=152; P<0.01). Among cases, 24.5% (n=93) had a history of a previous stroke.
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The results of conditional logistic regression analysis are shown in Table 2. In a model adjusted for sex and age, BMI showed a positive association with risk of stroke or TIA (OR 2.34; 95% CI, 1.63 to 3.34; P<0.001, top versus bottom tertile). After adjustment for other risk factors (models 2 and 3), this association was attenuated (model 3: OR 1.18; 95% CI 0. 0.77 to 1.79) and lost its significance (P=0.45; Figure). The risk of stroke or TIA increased in a graded manner with increasing WHR. In a model adjusted for sex and age, patients in the highest tertile had a 12.78-fold (95% CI 7.83 to 20.86) elevated risk of cerebrovascular disease (P<0.001) compared with the lowest tertile (Table 2). This association was attenuated after adjustment for other risk factors (models 2 and 3), but still remained significant (model 3: OR 7.69; 95% CI, 4.53 to 13.03; P<0.001). It was consistent both in men and women, with higher risks in the latter (Table 3). The plausibility proof with cases/controls aged 45 to 75 years, confirmed the strong association between WHR and stroke/TIA risk (OR 7.91; 95% CI 4.35 to 14.36 top versus bottom tertile, fully adjusted model).
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Increased waist circumference was also related to higher risk of stroke or TIA. In the highest tertile group this risk was 7.13-fold (95% CI 4.65 to 10.94; P<0.001) compared with the bottom tertile (Table 2). This strong positive association remained significant after risk adjustment (model 3: OR 4.25; 95% CI 2.65 to 6.84; P<0.001). The plausibility proof with patients aged 45 to 75 years confirmed the strong association between waist circumference and risk of stroke or TIA (model 3: OR 4.84; 95% CI, 2.81 to 8.34; P<0.001).
Cases were on average 1.4 cm shorter than controls (169.36±9.03 cm versus 170.74±8.94 cm; P<0.05; Table 1), but after adjusting for other covariables, the results of the logistic regression models were not statistically significant (data not shown). Body height is a component of WSR, which is another marker of abdominal obesity. In a model adjusted for sex and age, increased WSR was associated with an elevated cerebrovascular risk (OR 8.66; 95% CI 5.50 to 13.64; P<0.001, top versus bottom tertile; Table 2). After adjustment for risk factors (models 2 and 3) this relation remained significant (model 3: OR 4.67; 95% CI 2.82 to 7.73; P<0.001).
We also compared the effect of 1 SD increase in different obesity measures (Table 4). The increase in the OR calculated for 1 SD increase in WHR was the largest among all anthropometric indices studied. A moderate increase in the OR was found for waist circumference. The association between WSR and risk of stroke or TIA was slightly weaker than the association with WHR, but stronger than the association with waist circumference. The OR associated with 1 SD increase in BMI was the weakest one.
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The area under the ROC of WHR (0.774) was slightly larger than that of WSR (0.730) or waist circumference (0.721), showing that the WHR was most accurate in predicting the risk of stroke or TIA. The smallest area under the ROC was observed for BMI (0.595). All values presented were significantly increased (P<0.01). The differences between single values were statistically significant (P<0.01), except for the comparison between WSR and waist circumference (P=0.09).
Regarding threshold categories for BMI, ORs were 1.95 for preobese men and 2.77 for obese men compared with males of normal BMI (P<0.01; Table 3). Corresponding ORs in women were 1.39 (preobese females, P=0.28) and 2.97 (obese females, P<0.01). After adjustment for cerebrovascular risk factors, OR in BMI categories lost their significance. With respect to threshold categories for WHR, ORs adjusted for risk factors were 4.13 in men and 7.77 in women (P<0.01 each). This gender-related difference was not significant in the multivariate model. For waist circumference, ORs in the highest category were strongly attenuated after adjustment for risk factors, but still remained significant (3.71 in men, 4.49 in women, P<0.01 each).
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Discussion |
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TopAbstractIntroductionPatients and MethodsResultsDiscussionReferences |
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The present case-control study evaluated the predictive value of different markers of adiposity and abdominal body fat for stroke and TIA in a well-defined region of southwestern Germany. Three different statistical approaches uniformly showed that various markers of abdominal adiposity were superior to the BMI in predicting the risk of stroke or TIA. The waist-to-hip ratio served as the best predictor among the obesity markers studied.
The association of abdominal obesity with increased atherosclerotic and cardiovascular risk has been shown in previous studies.23,24 For example, a large international case-control study proved the superiority of abdominal body fat markers compared to the BMI and demonstrated a strong association with the risk of myocardial infarction.4 In our study, comparable results were obtained for the stroke/TIA risk by using a similar statistical approach. Complementary to the findings of other investigators,4,25 the waist-to-hip ratio had the strongest predictive value in our cohort of patients with cerebrovascular disease. By contrast, BMI did not show a consistent graded relation to stroke risk and became nonsignificant after adjustment for other risk factors in both models (with and without inclusion of arterial hypertension and diabetes), which possibly mediate the association between BMI and stroke risk. In the case-control Northern Manhattan Stroke Study, abdominal obesity increased the risk for the ischemic type of stroke by factor 3.0.11 In contrast to our findings, the association of WHR with stroke risk tended to be stronger in men than in women (OR 3.8; 95% CI, 1.8 to 5.0 versus OR 2.5; 95% CI 1.6 to 4.0). However, hemorrhagic stroke and TIA were excluded, as well as patients having a history of stroke.11 In the longitudinal Atherosclerosis Risk in Communities (ARIC) study,25 abdominal adiposity was associated with an increased risk for nonlacunar, but not for lacunar stroke. This finding may result from the adjustment for risk factors that are relevant for microangiopathic brain lesions.
The results of studies investigating obesity as a risk factor of hemorrhagic stroke are inconsistent. There are studies demonstrating no association,26 decreasing risk6 or increasing risk7 of hemorrhagic stroke with increasing BMI. In a longitudinal study including >28 000 male US health professionals, age-adjusted relative risk of total stroke was 2.33 (95% CI 1.25 to 4.37) in a comparison between top and bottom quintiles of WHR.14 A large-scale Scandinavian cohort study found that abdominal obesity (measured by WHR) was weakly associated with total stroke risk only in men (OR 1.55; 95% CI 1.06 to 2.26, top versus bottom tertiles), but not in women.12 TIA was not recorded as an outcome event and participants were free of coronary heart disease at baseline. This may help to explain, besides the different study designs applied and populations studied, the difference to our results. Case-control studies—like the present one—in which obesity markers are measured closely to the time point of the vascular event4,11 frequently show a stronger association between (abdominal) obesity and risk for stroke/TIA compared to longitudinal studies.10,12,14,25 Based on the study design, the predictive value of case-control studies may focus on a short-term perspective, the one of longitudinal studies more on a long-term prediction.
In comparison, our study detected a strong and graded association of abdominal fat markers with the risk of ischemic and hemorrhagic stroke or TIA for both genders. The risks presented here tend to be higher than in previous reports, possibly because of the inclusion of TIA as an important cerebrovascular event.15 The inclusion of patients with prior history of cerebrovascular disease may also account for this difference. However, the consistent trend of higher risks in females compared to males should be regarded with some caution. Female cases were slightly under-represented in the cohort and more than 1 year older on average compared to male patients. In comparison to the corresponding controls, female patients tended to present with a higher degree of obesity than the male counterparts which may have altered their overall vascular risk.
There is growing evidence that the waist-to-stature ratio, a marker of abdominal obesity including body height, may serve as a reliable predictor of cardiovascular risk. Recent data4,27 show that the WSR is a weaker indicator of an increased risk of coronary heart disease than the WHR, but at least as strong as waist circumference and stronger than BMI. Ho et al28 considered WSR to be even the best predictor of cardiovascular risk among other simple anthropometric indices. Our results underline that WSR is an appropriate measure to assess the risk of stroke and TIA comparable to waist circumference, but further studies are needed to clarify which marker is the most robust to predict total or subtype specific cerebrovascular risks.
There are controversies concerning the impact of body height in cardiovascular and cerebrovascular disease. Some authors have described an association between body height and cardiovascular risk.29 Others have reported their inverse relation and observed no association between height and risk of stroke.30 Walker et al14 reported that taller male health professionals tended to carry a lower risk of stroke. There are data showing an increased incidence of fatal stroke in shorter people.31 Interestingly, cases in our patient cohort were significantly shorter than controls, but the results of the logistic regression models were not statistically significant after adjustment for other covariables.
There are several limitations in this study. First, very old patients or patients with very severe strokes or global aphasia were possibly not recruited because of unavailable informed consent or limited capacity of stroke-unit care. Their inclusion may have modified the risk analysis. In the opposite direction, the fact that nearly one quarter of cases reported on former strokes may have led to an overestimation of the stroke risk, because per definition the history of controls was free of previous stroke. Second, controls included only individuals aged 45 to 75 years limiting the range that was available for an exact age match. In order to avoid a systematic error of data, the plausibility proof was conducted by using only the exact age match that confirmed all main results as robust. Both effect modification by age and a biased control selection cannot be fully excluded to explain the trends to higher risk measures in the exact match cohort. Although the source population for cases and controls was identical, participants of the EPIC study potentially were above average concerning their health-oriented lifestyle. Third, the number of cases with intracerebral hemorrhage or TIA was too small for a detailed subgroup analysis. Fourth, we did not record dietary variables in our sample, which could represent potential confounders. Not only normal body weight, but also a healthy diet may prevent stroke.32
Summary
Markers of abdominal adiposity showed a graded and significant association with risk of stroke and TIA, independent of other vascular risk factors. The redefinition of obesity based on the waist-to-hip ratio or waist circumference instead of BMI increases considerably the estimate of cerebrovascular events attributable to obesity. There is a trend for women to be more strongly affected, which needs further investigation. Waist circumference and related ratios, such as waist-to-hip ratio and waist-to-stature ratio, can better predict cerebrovascular events than BMI in a population of central Western Europe.
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Acknowledgments |
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Sources of Funding
This study was funded by the German Ministry of Education and Research (BMBF)/National Genome Research Network (NGFN) research grant 01GS0491 (to project leader: T.B.).
Disclosures
None.
Received April 15, 2008; revision received May 6, 2008; accepted May 20, 2008.
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References |
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