title: Association of anthropometric measures with SF-36v2 PCS and MCS in a multi-ethnic Asian population.
abstract: OBJECTIVE
  Obesity adversely affects health-related quality of life (HRQoL). Most studies have used body mass index (BMI) to measure obesity. Other measures of obesity, such as waist circumference (WC) or waist-to-hip ratio (WHR), may be better predictors of cardiovascular disease and mortality. We, therefore, examined the associations between other anthropometric measures and HRQoL in a multi-ethnic Asian population.
  
  
  METHODS
  In this follow-up study from four previous cross-sectional surveys, HRQoL was measured, at follow-up, using the Short-Form 36 version 2 (SF-36v2) questionnaire. Linear regression was used to assess the relationship between anthropometric measures [BMI, WC, waist residuals (WR) (generated by regressing WC on BMI), WHR, waist-to-height ratio (WHtR) and height (Ht)] and HRQoL. We compared the models' R2, Akaike's information criteria (AIC), and Schwarz Bayesian information criteria (BIC) from the different models.
  
  
  RESULTS
  Among 4,981 subjects, 47.6% were men aged 50.6 ± 12.2 and women aged 49.3 ± 11.6 years. All gender-specific anthropometric measures were significantly correlated with BMI, except WR. After adjusting for known determinants of HRQoL, we found significant associations between BMI, WC and WHtR with SF-36v2 Physical Component Summary (PCS) scores in women but not men. In contrast, after adjusting for known determinants of HRQoL, WR and WHR were significantly associated with SF-36v2 Mental component summary (MCS) scores in men, but not women. R², AIC and BIC were similar for all anthropometric measures in the final model.
  
  
  CONCLUSIONS
  The associations between measures of central obesity and HRQoL differed between men and women. In women, associations were seen with SF-36v2 PCS, but measures of central obesity did not have significant associations with HRQoL after controlling for BMI. In men, an association between WC and SF-36v2 MCS was statistically significant independent of BMI. These gender differences require further investigation.
authors.affiliation: Department of Medicine, National University Health System, Singapore, Singapore.
authors.forename: Maudrene L S
authors.initials: ML
authors.lastname: Tan
authors.forename: Hwee-Lin
authors.initials: HL
authors.lastname: Wee
authors.forename: Jeannette
authors.initials: J
authors.lastname: Lee
authors.forename: Stefan
authors.initials: S
authors.lastname: Ma
authors.forename: Derrick
authors.initials: D
authors.lastname: Heng
authors.forename: E-Shyong
authors.initials: ES
authors.lastname: Tai
authors.forename: Julian
authors.initials: J
authors.lastname: Thumboo
mesh_terms: D000328:Adult; D044466:Asian People / Q000208:ethnology / Q000523:psychology*; D001499:Bayes Theorem; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000209:etiology; D003430:Cross-Sectional Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology / Q000523:psychology*; D011237:Predictive Value of Tests; D011788:Quality of Life / Q000523:psychology*; D012044:Regression Analysis; D012307:Risk Factors; D011795:Surveys and Questionnaires; D055105:Waist Circumference; D049629:Waist-Hip Ratio