title: Abdominal and gynoid adiposity and the risk of stroke.
abstract: BACKGROUND
  Previous studies have indicated that fat distribution is important in the development of cardiovascular disease (CVD). We investigated the association between fat distribution, as measured by dual energy X-ray absorptiometry (DXA), and the incidence of stroke.
  
  
  METHODS
  A cohort of 2751 men and women aged ≥40 years was recruited. Baseline levels of abdominal, gynoid and total body fat were measured by DXA. Body mass index (BMI, kg m(-2)) was calculated. Stroke incidence was recorded using the regional stroke registry until subjects reached 75 years of age.
  
  
  RESULTS
  During a mean follow-up time of 8 years and 9 months, 91 strokes occurred. Of the adiposity indices accessed abdominal fat mass was the best predictor of stroke in women (hazard ratio (HR)=1.66, 95% confidence interval (CI)=1.23-2.24 per standard deviation increase), whereas the ratio of gynoid fat to total fat mass was associated with a decreased risk of stroke (HR=0.72, 95% CI=0.54-0.96). Abdominal fat mass was the only of the adiposity indices assessed that was found to be a significant predictor of stroke in men (HR=1.49, 95% CI=1.06-2.09). The associations between abdominal fat mass and stroke remained significant in both women and men after adjustment for BMI (HR=1.80, 95% CI=1.06-3.07; HR=1.71, 95% CI=1.13-2.59, respectively). However, in a subgroup analyses abdominal fat was not a significant predictor after further adjustment for diabetes, smoking and hypertension.
  
  
  CONCLUSIONS
  Abdominal fat mass is a risk factor for stroke independent of BMI, but not independent of diabetes, smoking and hypertension. This indicates that the excess in stroke risk associated with abdominal fat mass is at least partially mediated through traditional stroke risk factors.
authors.affiliation: Department of Surgical and Perioperative Sciences, Sports Medicine, Umeå University, Umeå, Sweden.
authors.forename: F
authors.initials: F
authors.lastname: Toss
authors.forename: P
authors.initials: P
authors.lastname: Wiklund
authors.forename: P W
authors.initials: PW
authors.lastname: Franks
authors.forename: M
authors.initials: M
authors.lastname: Eriksson
authors.forename: Y
authors.initials: Y
authors.lastname: Gustafson
authors.forename: G
authors.initials: G
authors.lastname: Hallmans
authors.forename: P
authors.initials: P
authors.lastname: Nordström
authors.forename: A
authors.initials: A
authors.lastname: Nordström
mesh_terms: D050153:Abdominal Fat / Q000000981:diagnostic imaging / Q000473:pathology*; D015502:Absorptiometry, Photon; D000328:Adult; D017677:Age Distribution; D000368:Aged; D050218:Body Fat Distribution; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000473:pathology*; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D006973:Hypertension / Q000453:epidemiology / Q000473:pathology*; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000150:complications* / Q000000981:diagnostic imaging / Q000453:epidemiology / Q000473:pathology*; D016016:Proportional Hazards Models; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology / Q000209:etiology* / Q000473:pathology; D013548:Sweden / Q000453:epidemiology