contribution of obesity and abdominal fat mass to risk of stroke and transient

Johannes Hebebrand and Tobias BackYaroslav Winter, Sabine Rohrmann, Jakob Linseisen, Oliver Lanczik, Peter A. Ringleb,

Ischemic AttacksContribution of Obesity and Abdominal Fat Mass to Risk of Stroke and Transient

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Contribution of Obesity and Abdominal Fat Mass to Risk ofStroke and Transient Ischemic Attacks

Yaroslav Winter, MD; Sabine Rohrmann, PhD; Jakob Linseisen, PhD; Oliver Lanczik, MD;Peter A. Ringleb, MD; Johannes Hebebrand, MD; Tobias Back, MD

Background and Purpose—Waist circumference has been shown to be a better predictor of cardiovascular risk than bodymass index (BMI). Our case-control study aimed to evaluate the contribution of obesity and abdominal fat mass to therisk 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-statureratio) 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 forphysical inactivity, smoking, hypertension, and diabetes (odds ratio 1.18; 95% CI, 0.77 to 1.79, top tertile versus bottomtertile). 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 waistcircumference 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 versusbottom 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 cerebrovascularevents than BMI. (Stroke. 2008;39:3145-3151.)

Key Words: stroke � transient ischemic attack � obesity � body mass index � waist circumference � risk factors

Obesity has become one of the most prevalent conditionsmaking a significant impact on public health worldwide.

In the United States, 65.7% of adults are either overweight orobese, and 30.4% are obese.1 In Germany, currently 49.6% ofinhabitants are overweight, among those 13.6% are obese.2

The unfavorable association of obesity with coronary heartdisease3 and myocardial infarction4 is well recognized. Large-scale prospective studies have documented that abdominalobesity measured by waist-to-hip ratio (WHR) is morestrongly associated with cardiovascular risk than body massindex (BMI).4,5 However, the relationship between increasedrelative body weight and stroke risk is controversial. Thereare studies showing that increasing BMI is associated with agraded elevated risk of stroke.6,7 In other studies, however, norelation was found between BMI and stroke risk.8–10 Possi-bly, BMI is not an appropriate indicator to assess the risk ofstroke.11 Markers of abdominal obesity have rarely beenstudied 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 wasattenuated after adjustment for cardiovascular risk fac-tors.11,12 Other studies included small numbers of cases13,14 orconcentrated on cardiovascular risk.4 Thus, data on the role ofabdominal obesity for stroke are limited and completelylacking for transient ischemic attacks (TIA), which representimportant cerebrovascular events often preceeding majorstrokes.15 In order to provide evidence for the impact ofbody-fat distribution on the risk of stroke and TIA, weconducted a case-control study in a well-defined populationof central Western Europe.

Patients and MethodsThe study included 1137 participants (379 cases and 758 controls).Consecutive cases of ischemic stroke (n�301, 79%), intracerebralhemorrhage (n�37, 10%) or TIAs (n�41, 11%) were recruited in theDepartments of Neurology of the Klinikum Mannheim and KlinikumHeidelberg between February 1, 2005 and January 31, 2006. Caseswith prior cerebrovascular events were not excluded. Of 401 initiallyrecruited cases, detailed clinical records were unavailable for 22

Received April 15, 2008; final revision received May 6, 2008; accepted May 20, 2008.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.), RheinischeKliniken, University of Duisburg-Essen, Germany; the Department of Neurology (P.A.R.), Klinikum Heidelberg, University of Heidelberg, Germany; theDepartment 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 [email protected]

© 2008 American Heart Association, Inc.

Stroke is available at http://stroke.ahajournals.org DOI: 10.1161/STROKEAHA.108.523001

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patients, who were therefore excluded from analysis. Each indexpatient was matched with 2 controls without a history of cerebro-vascular disease. The study was approved by the local ethicscommittee 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 theWorld Health Organization (WHO).16 The obesity phenotype wascharacterized by anthropometric measures, such as BMI, WHR andwaist circumference. BMI was calculated as weight in kilogramsdivided by height in meters squared.17 WHR was defined as waistdivided by hip circumference.11 Waist circumference was measuredin centimeters at the level of the umbilicus,18 hip circumference atthe level of the bilateral greater trochanters.19 The role of body heightwas also investigated by using the waist-to-stature ratio (WSR),defined as waist circumference divided by body height.20 Theanthropometric measurements were performed in less than 48 hoursafter admission.

We used threshold categories for obesity measures defined byexpert groups from the WHO.19 In BMI categories, we distinguishedbetween normal weight (BMI �25.0 kg/m2) and overweight (BMI

�25.0 kg/m2), including preobesity (BMI 25.0 to 29.9 kg/m2) andobesity (BMI �30 kg/m2). Obese women had WHR �0.85 andobese men WHR �1.0. Threshold categories for waist circumferencein men were �94.0 cm (normal weight), 94.0 to 101.9 cm (over-weight) 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 wasdefined as systolic blood pressure �140 mm Hg and/or diastolicblood pressure �90 mm Hg or treatment with antihypertensiveagents. Hyperlipidemia was defined as total serum cholesterol level�240 mg/dL or use of antihyperlipidemic agents. Diabetes wasdefined as fastening blood glucose �126 mg/dL or use of insulin ororal hypoglycemic agents.

Regional controls were matched for age and sex from a databasewith 25 540 participants of the population-based cohort study EPIC-Heidelberg. There were no individuals younger than 45 or older than75 years among controls. The age of cases in our study cohort rangedfrom 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 45years (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

Table 1. Descriptive Variables Comparing Cases and Controls Stratified by Gender

Men Women

Controls Cases Controls Cases

Mean �SD Mean �SD P Value* Mean �SD Mean �SD P Value*

All cases and controls

N 476 238 282 141

Age (years) 65.0 �7.6 66.9 �10.9 0.017 65.0 �9.4 68.0 �14.1 0.020

Weight (kg) 83.4 �12.3 84.7 �12.7 0.172 69.0 �11.8 74.8 �18.4 0.001

Height (cm) 175.5 �7.0 173.9 �7.1 0.005 162.8 �5.8 161.7 �6.4 0.097

Waist circ. (cm) 96.9 �10.1 103.6 �11.1 �.001 83.8 �11.6 98.2 �15.5 �.001

Hip circ. (cm) 102.2 �7.0 103.5 �10.6 0.091 102.1 �8.5 105.3 �12.1 0.004

WHR 0.95 �0.06 1.00 �0.08 �.001 0.82 �0.09 0.93 �0.10 �.001

BMI 27.1 �3.7 28.0 �3.7 0.002 26.1 �4.5 28.6 �6.5 0.001

WSR 0.55 �0.06 0.60 �0.07 �.001 0.52 �0.07 0.61 �0.10 �.001

PIN (% yes) 44.3 82.5 �.001 52.1 87.2 �.001

Smoke (% yes) 15.8 30.2 �.001 12.1 24.8 0.001

AHTN (% yes) 53.4 84.3 �.001 50.0 80.4 �.001

Diabetes (% yes) 11.8 30.9 �.001 7.1 30.7 �.001

HLP (% yes) 56.7 50.4 0.111 51.8 53.9 0.680

Only cases aged 45–75 years and 2 controls per case

N 352 176 162 81

Age (years) 64.0 �7.0 64.3 �7.4 0.688 63.6 �8.3 63.9 �8.7 0.853

Weight (kg) 83.1 �12.4 85.5 �12.8 0.041 70.0 �12.2 76.7 �19.0 0.005

Height (cm) 175.9 �6.9 174.1 �6.9 0.006 163.8 �5.8 162.7 �5.8 0.192

Waist circ. (cm) 96.2 �9.8 104.2 �10.9 �.001 83.8 �11.8 100.4 �16.3 �.001

Hip circ. (cm) 102.0 �6.8 103.3 �9.1 0.083 102.6 �8.6 105.9 �13.6 0.052

WHR 0.94 �0.06 1.01 �0.08 �.001 0.81 �0.10 0.95 �0.10 �.001

BMI 26.9 �3.8 28.2 �3.8 0.001 26.1 �4.7 28.9 �6.7 0.001

WSR 0.55 �0.06 0.60 �0.06 �.001 0.51 �0.07 0.62 �0.10 �.001

PIN (% yes) 40.3 80.9 �.001 51.8 86.4 �.001

Smoke (% yes) 15.9 34.7 �.001 14.2 34.6 0.001

AHTN (% yes) 51.1 85.7 �.001 48.8 81.3 �.001

Diabetes (% yes) 11.4 31.2 �.001 5.6 30.9 �.001

HLP (% yes) 56.5 54.0 0.577 48.8 56.8 0.238

*t test for continuous variables; �2 test for categorial variables.Circ indicates circumference; AHTN, arterial hypertension; HLP, hyperlipidemia; PIN, physical inactivity.

3146 Stroke December 2008



group �70 years. The plausibility proof was performed by analyzingonly the exactly matched set of cases and controls aged 45 to 75years.

Statistical AnalysisStatistical analysis was performed with SAS version 9.1 (SASInstitute Inc). In the univariate analysis, categorical variables werecompared by �2 test. Continuous variables expressed as mean�SDwere compared by the t test. Conditional logistic regression modelswere used to calculate the odds ratio (OR) and 95% CI for BMI,WHR, WSR, and waist circumference with stratification by sex andage groups as described above. Adjustment was performed for thefollowing stroke risk factors: arterial hypertension (yes/no), diabetesmellitus (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 theunivariate analysis and was, thus, not included in the conditionallogistic regression model.

Three approaches were chosen to assess the role of obesitymarkers in predicting the risk of stroke/TIA: first, comparisons ofORs across the tertiles of BMI, WHR, WSR and waist circumfer-ence, respectively, using the bottom tertile as a reference category;second, estimation of the OR for 1 SD change in BMI, WHR, WSRor waist circumference; and third, comparisons of the receiver-operator curves (ROC) in relation to stroke or TIA for obesitymeasures. The ROC is a plot of test sensitivity versus its false-positive rate (or 1–specificity). The area under the ROC is a measureof the accuracy of a diagnostic test. A test with an area under theROC 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 0.0is completely inaccurate. ROCs were compared by using the methodof DeLong and coworkers.22

ResultsA total of 379 patients with stroke (n�338) or TIA (n�41)and 758 age- and sex-matched regional controls were evalu-ated. Of these cases, 37.2% (n�141) were female. The meanage 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 noindividuals older than 75 years among the controls. Table 1shows the demographics and distribution of stroke riskfactors in the study population stratified by sex. Patients aged

45 to 75 years were separated within the study cohort for thereasons mentioned above. The prevalence of obesity, definedby 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) hada history of a previous stroke.

The results of conditional logistic regression analysis areshown in Table 2. In a model adjusted for sex and age, BMIshowed a positive association with risk of stroke or TIA (OR2.34; 95% CI, 1.63 to 3.34; P�0.001, top versus bottomtertile). After adjustment for other risk factors (models 2 and3), 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 withincreasing WHR. In a model adjusted for sex and age,patients in the highest tertile had a 12.78-fold (95% CI 7.83to 20.86) elevated risk of cerebrovascular disease (P�0.001)compared with the lowest tertile (Table 2). This associationwas attenuated after adjustment for other risk factors (models2 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 inmen and women, with higher risks in the latter (Table 3). Theplausibility 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 bottomtertile, fully adjusted model).

Increased waist circumference was also related to higherrisk of stroke or TIA. In the highest tertile group this risk was7.13-fold (95% CI 4.65 to 10.94; P�0.001) compared withthe bottom tertile (Table 2). This strong positive associationremained significant after risk adjustment (model 3: OR 4.25;95% CI 2.65 to 6.84; P�0.001). The plausibility proof withpatients aged 45 to 75 years confirmed the strong associationbetween 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

Table 2. Associations Between Anthropometric Variables and Stroke Using All Cases and Controls

Tertile*

All Cases and Controls Only Cases Aged 45 to 75 and 2 Controls Per Case

Cases/Controls

Model 1†OR (95% CI)


Model 3†OR (95% CI) Cases/Controls




BMI* 1 86/252 1.00 (reference) 1.00 (reference) 1.00 (reference) 53/188 1.00 (reference) 1.00 (reference) 1.00 (reference)

2 128/254 1.52 (1.05, 2.20) 1.42 (0.95, 2.12) 1.17 (0.77, 1.79) 83/164 1.81 (1.21, 2.71) 1.79 (1.14, 2.79) 1.49 (0.93, 2.40)

3 165/252 2.34 (1.63, 3.34) 1.86 (1.26, 2.74) 1.18 (0.77, 1.79) 121/162 2.67 (1.81, 3.93) 2.11 (1.38, 3.24) 1.37 (0.86, 2.17)

Waist 1 40/248 1.00 (reference) 1.00 (reference) 1.00 (reference) 24/176 1.00 (reference) 1.00 (reference) 1.00 (reference)

Circ* 2 89/266 2.32 (1.47, 3.65) 2.23 (1.38, 3.60) 1.98 (1.21, 3.26) 63/178 2.71 (1.62, 4.55) 2.73 (1.57, 4.73) 2.26 (1.28, 4.01)

3 250/244 7.13 (4.65, 10.94) 5.83 (3.71, 9.16) 4.25 (2.65, 6.84) 170/160 8.34 (5.12, 13.56) 7.04 (4.19, 11.83) 4.84 (2.81, 8.34)

WHR* 1 29/263 1.00 (reference) 1.00 (reference) 1.00 (reference) 16/188 1.00 (reference) 1.00 (reference) 1.00 (reference)

2 78/229 3.66 (2.17, 6.19) 3.34 (1.93, 5.80) 2.78 (1.57, 4.91) 48/158 3.67 (2.01, 6.72) 3.25 (1.73, 6.10) 2.59 (1.35, 4.95)

3 272/266 12.78 (7.83, 20.86) 10.11 (6.07, 16.83) 7.69 (4.53, 13.03) 193/168 14.56 (8.34, 25.45) 10.86 (6.09, 19.34) 7.91 (4.35, 14.36)

WSR* 1 35/246 1.00 (reference) 1.00 (reference) 1.00 (reference) 20/179 1.00 (reference) 1.00 (reference) 1.00 (reference)

2 85/241 2.99 (1.85, 4.84) 2.68 (1.61, 4.45) 2.39 (1.42, 4.04) 59/168 3.42 (1.96, 5.96) 3.05 (1.70, 5.46) 2.56 (1.40, 4.69)

3 259/271 8.66 (5.50, 13.64) 6.67 (4.13, 10.75) 4.67 (2.82, 7.73) 178/167 10.95 (6.48, 18.49) 8.46 (4.88, 14.66) 5.60 (3.14, 10.00)

*Cutpoints for tertiles. Men: waist circumference (cm) 91.8; 100.0; BMI (kg/m2) 25.19; 28.09; WHR 0.92; 0.97; WSR 0.52; 0.57.Women: waist circumference (cm) 76.5; 89.5; BMI (kg/m2) 23.83; 27.45; WHR 0.78; 0.84; WSR 0.475; 0.54.†Model 1: matched for age and sex; Model 2: matched for age and sex and adjusted for physical inactivity, smoking; Model 3: matched for age and sex and adjusted

for physical inactivity, smoking, history of hypertension, history of diabetes.Circ indicates circumference.

Winter et al Abdominal Obesity and Stroke Risk 3147



1), but after adjusting for other covariables, the results of thelogistic regression models were not statistically significant(data not shown). Body height is a component of WSR, whichis another marker of abdominal obesity. In a model adjustedfor sex and age, increased WSR was associated with anelevated cerebrovascular risk (OR 8.66; 95% CI 5.50 to

13.64; P�0.001, top versus bottom tertile; Table 2). Afteradjustment for risk factors (models 2 and 3) this relationremained 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 differentobesity measures (Table 4). The increase in the OR calculated

0.1

1

10

100

Odd

s R

atio

CasesControls

78229

259259

165252

128254

87252

272266

29263

85241

35246

250244

89266

40248

Waist circumference(tertile)

WSR (tertile)

WHR(tertile)

BMI(tertile)

black: matched for age and sex; white: adjusted for stroke risk factors

Figure. 1 Association of specific measures of obesity with risk of stroke and TIA. Vertical bars indicate 95% CI; WSR, waist-to-statureratio; WHR, waist-to-hip ratio; BMI, body-mass index. Filled symbols indicate matched for age and sex; open symbols, adjusted forphysical inactivity, smoking, history of hypertension, and history of diabetes.

Table 3. OR of Stroke/TIA Risk Between Threshold Categories of Specific Obesity Measures Using the Entire Study Cohort

Sex WHO Categories Cases/Controls Model 1* OR (95% CI) Model 2† OR (95% CI) Model 3‡ OR (95% CI)

BMI (kg/m2) Men �25.0 45/145 1.00 1.00 1.00

25.0–29.9 133/238 1.95 (1.26 to 3.03) 1.72 (1.07 to 2.76) 1.36 (0.82 to 2.25)

30.0–34.9 60/93 2.77 (1.52 to 4.24) 1.77 (1.02 to 3.08) 0.99 (0.55 to 1.80)

Women �25.0 45/123 1.00 1.00 1.00

25.0–29.9 44/103 1.39 (0.77 to 2.51) 1.43 (0.75 to 2.70) 1.17 (0.60 to 2.28)

30.0–34.9 52/56 2.97 (1.60 to 5.52) 2.35 (1.19 to 4.64) 1.63 (0.78 to 3.37)

Waist circumference (cm) Men �94.0 38/186 1.00 1.00 1.00

94.0–101.9 60/160 2.35 (1.41 to 3.90) 2.09 (1.22 to 3.58) 1.80 (1.03 to 3.15)

102.0� 140/130 6.22 (3.85 to 10.03) 5.07 (3.05 to 8.41) 3.71 (2.18 to 6.32)

Women �80.0 16/120 1.00 1.00 1.00

80.0–87.9 15/56 1.48 (0.59 to 3.70) 1.11 (0.41 to 2.98) 1.29 (0.47 to 3.53)

88.0� 110/106 7.53 (3.85 to 14.71) 5.69 (2.79 to 11.61) 4.49 (2.13 to 9.46)

WHR Men �1.0 103/377 1.00 1.00 1.00

1.0� 135/99 5.81 (3.95 to 8.54) 4.68 (3.11 to 7.02) 4.13 (2.70 to 6.30)

Women �0.85 39/199 1.00 1.00 1.00

0.85� 102/83 9.93 (5.40 to 18.28) 9.56 (4.92 to 18.57) 7.77 (3.87 to 15.61)

*Model 1: matched for age and sex.†Model 2: matched for age and sex and adjusted for physical inactivity, smoking.‡Model 3: matched for age and sex and adjusted for physical inactivity, smoking, history of hypertension, history of diabetes.




for 1 SD increase in WHR was the largest among allanthropometric indices studied. A moderate increase in theOR was found for waist circumference. The associationbetween WSR and risk of stroke or TIA was slightly weakerthan the association with WHR, but stronger than the asso-ciation with waist circumference. The OR associated with 1SD increase in BMI was the weakest one.

The area under the ROC of WHR (0.774) was slightlylarger than that of WSR (0.730) or waist circumference(0.721), showing that the WHR was most accurate in predict-ing the risk of stroke or TIA. The smallest area under theROC was observed for BMI (0.595). All values presentedwere significantly increased (P�0.01). The differences be-tween single values were statistically significant (P�0.01),except for the comparison between WSR and waist circum-ference (P�0.09).

Regarding threshold categories for BMI, ORs were 1.95for preobese men and 2.77 for obese men compared withmales of normal BMI (P�0.01; Table 3). CorrespondingORs in women were 1.39 (preobese females, P�0.28) and2.97 (obese females, P�0.01). After adjustment for cere-brovascular risk factors, OR in BMI categories lost theirsignificance. With respect to threshold categories forWHR, ORs adjusted for risk factors were 4.13 in men and7.77 in women (P�0.01 each). This gender-related differ-ence was not significant in the multivariate model. Forwaist circumference, ORs in the highest category werestrongly attenuated after adjustment for risk factors, butstill remained significant (3.71 in men, 4.49 in women,P�0.01 each).

DiscussionThe present case-control study evaluated the predictive valueof different markers of adiposity and abdominal body fat forstroke and TIA in a well-defined region of southwesternGermany. Three different statistical approaches uniformlyshowed that various markers of abdominal adiposity weresuperior to the BMI in predicting the risk of stroke or TIA.The waist-to-hip ratio served as the best predictor among theobesity markers studied.

The association of abdominal obesity with increased ath-erosclerotic and cardiovascular risk has been shown inprevious studies.23,24 For example, a large international case-control study proved the superiority of abdominal body fatmarkers compared to the BMI and demonstrated a strongassociation with the risk of myocardial infarction.4 In ourstudy, comparable results were obtained for the stroke/TIArisk by using a similar statistical approach. Complementary to

the findings of other investigators,4,25 the waist-to-hip ratiohad the strongest predictive value in our cohort of patientswith cerebrovascular disease. By contrast, BMI did not showa consistent graded relation to stroke risk and becamenonsignificant after adjustment for other risk factors in bothmodels (with and without inclusion of arterial hypertensionand diabetes), which possibly mediate the association be-tween BMI and stroke risk. In the case-control NorthernManhattan Stroke Study, abdominal obesity increased the riskfor the ischemic type of stroke by factor 3.0.11 In contrast toour findings, the association of WHR with stroke risk tendedto be stronger in men than in women (OR 3.8; 95% CI, 1.8 to5.0 versus OR 2.5; 95% CI 1.6 to 4.0). However, hemorrhagicstroke and TIA were excluded, as well as patients having ahistory of stroke.11 In the longitudinal Atherosclerosis Risk inCommunities (ARIC) study,25 abdominal adiposity was asso-ciated with an increased risk for nonlacunar, but not forlacunar stroke. This finding may result from the adjustmentfor risk factors that are relevant for microangiopathic brainlesions.

The results of studies investigating obesity as a risk factorof hemorrhagic stroke are inconsistent. There are studiesdemonstrating no association,26 decreasing risk6 or increasingrisk7 of hemorrhagic stroke with increasing BMI. In alongitudinal study including �28 000 male US health profes-sionals, age-adjusted relative risk of total stroke was 2.33(95% CI 1.25 to 4.37) in a comparison between top andbottom quintiles of WHR.14 A large-scale Scandinaviancohort study found that abdominal obesity (measured byWHR) was weakly associated with total stroke risk only inmen (OR 1.55; 95% CI 1.06 to 2.26, top versus bottomtertiles), but not in women.12 TIA was not recorded as anoutcome event and participants were free of coronary heartdisease at baseline. This may help to explain, besides thedifferent study designs applied and populations studied, thedifference to our results. Case-control studies—like the pres-ent one—in which obesity markers are measured closely tothe time point of the vascular event4,11 frequently show astronger association between (abdominal) obesity and risk forstroke/TIA compared to longitudinal studies.10,12,14,25 Basedon the study design, the predictive value of case-controlstudies may focus on a short-term perspective, the one oflongitudinal studies more on a long-term prediction.

In comparison, our study detected a strong and gradedassociation of abdominal fat markers with the risk of ische-mic and hemorrhagic stroke or TIA for both genders. Therisks presented here tend to be higher than in previous reports,possibly because of the inclusion of TIA as an important

Table 4. Comparative Effect of 1 SD Increase in a Specific Measure of Obesity

Obesity Marker 1 SD Model 1* OR (95% CI) Model 2† OR (95% CI) Model 3‡ OR (95% CI)

BMI (kg/m2) 3.75 (m), 5.33 (f) 1.45 (1.26 to 1.67) 1.30 (1.12 to 1.51) 1.07 (0.91 to 1.26)

Waist circumference (cm) 10.90 (m), 14.68 (f) 2.46 (2.09 to 2.90) 2.21 (1.86 to 2.63) 1.92 (1.60 to 2.30)

Waist-to-hip ratio 0.08 (m), 0.10 (f) 3.50 (2.87 to 4.27) 3.03 (2.47 to 3.71) 2.92 (2.35 to 3.62)

Waist-to-stature ratio 0.07 (m), 0.09 (f) 2.68 (2.11 to 3.02) 2.42 (1.98 to 2.87) 2.01 (1.78 to 2.32)

*Model 1: matched for age and sex; †Model 2: matched for age and sex and adjusted for physical inactivity, smoking; ‡Model 3: matched for age and sex andadjusted for physical inactivity, smoking, history of hypertension, history of diabetes.

m indicates male; f, female.




cerebrovascular event.15 The inclusion of patients with priorhistory of cerebrovascular disease may also account for thisdifference. However, the consistent trend of higher risks infemales compared to males should be regarded with somecaution. Female cases were slightly under-represented in thecohort and more than 1 year older on average compared tomale patients. In comparison to the corresponding controls,female patients tended to present with a higher degree ofobesity than the male counterparts which may have alteredtheir overall vascular risk.

There is growing evidence that the waist-to-stature ratio, amarker of abdominal obesity including body height, mayserve as a reliable predictor of cardiovascular risk. Recentdata4,27 show that the WSR is a weaker indicator of anincreased risk of coronary heart disease than the WHR, but atleast as strong as waist circumference and stronger than BMI.Ho et al28 considered WSR to be even the best predictor ofcardiovascular risk among other simple anthropometric indi-ces. Our results underline that WSR is an appropriate measureto assess the risk of stroke and TIA comparable to waistcircumference, but further studies are needed to clarify whichmarker is the most robust to predict total or subtype specificcerebrovascular risks.

There are controversies concerning the impact of bodyheight in cardiovascular and cerebrovascular disease. Someauthors have described an association between body heightand cardiovascular risk.29 Others have reported their inverserelation and observed no association between height and riskof stroke.30 Walker et al14 reported that taller male healthprofessionals tended to carry a lower risk of stroke. There aredata showing an increased incidence of fatal stroke in shorterpeople.31 Interestingly, cases in our patient cohort weresignificantly shorter than controls, but the results of thelogistic regression models were not statistically significantafter adjustment for other covariables.

There are several limitations in this study. First, very oldpatients or patients with very severe strokes or global aphasiawere possibly not recruited because of unavailable informedconsent or limited capacity of stroke-unit care. Their inclu-sion may have modified the risk analysis. In the oppositedirection, the fact that nearly one quarter of cases reported onformer strokes may have led to an overestimation of thestroke risk, because per definition the history of controls wasfree of previous stroke. Second, controls included onlyindividuals aged 45 to 75 years limiting the range that wasavailable for an exact age match. In order to avoid asystematic error of data, the plausibility proof was conductedby using only the exact age match that confirmed all mainresults as robust. Both effect modification by age and a biasedcontrol selection cannot be fully excluded to explain thetrends to higher risk measures in the exact match cohort.Although the source population for cases and controls wasidentical, participants of the EPIC study potentially wereabove average concerning their health-oriented lifestyle.Third, the number of cases with intracerebral hemorrhage orTIA was too small for a detailed subgroup analysis. Fourth,we did not record dietary variables in our sample, whichcould represent potential confounders. Not only normal bodyweight, but also a healthy diet may prevent stroke.32

SummaryMarkers of abdominal adiposity showed a graded and signif-icant association with risk of stroke and TIA, independent ofother vascular risk factors. The redefinition of obesity basedon the waist-to-hip ratio or waist circumference instead ofBMI increases considerably the estimate of cerebrovascularevents attributable to obesity. There is a trend for women tobe more strongly affected, which needs further investigation.Waist circumference and related ratios, such as waist-to-hipratio and waist-to-stature ratio, can better predict cerebrovas-cular events than BMI in a population of central WesternEurope.

Sources of FundingThis study was funded by the German Ministry of Education andResearch (BMBF)/National Genome Research Network (NGFN)research grant 01GS0491 (to project leader: T.B.).

DisclosuresNone.

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contribution of obesity and abdominal fat mass to risk of stroke and transient

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