title: Anthropometric measures in cardiovascular disease prediction: comparison of laboratory-based versus non-laboratory-based model.
abstract: OBJECTIVE
  Body mass index (BMI) has been used to simplify cardiovascular risk prediction models by substituting total cholesterol and high-density lipoprotein cholesterol. In the elderly, the ability of BMI as a predictor of cardiovascular disease (CVD) declines. We aimed to find the most predictive anthropometric measure for CVD risk to construct a non-laboratory-based model and to compare it with the model including laboratory measurements.
  
  
  METHODS
  The study included 2675 women and 1902 men aged 55-79 years from the prospective population-based Rotterdam Study. We used Cox proportional hazard regression analysis to evaluate the association of BMI, waist circumference, waist-to-hip ratio and a body shape index (ABSI) with CVD, including coronary heart disease and stroke. The performance of the laboratory-based and non-laboratory-based models was evaluated by studying the discrimination, calibration, correlation and risk agreement.
  
  
  RESULTS
  Among men, ABSI was the most informative measure associated with CVD, therefore ABSI was used to construct the non-laboratory-based model. Discrimination of the non-laboratory-based model was not different than laboratory-based model (c-statistic: 0.680-vs-0.683, p=0.71); both models were well calibrated (15.3% observed CVD risk vs 16.9% and 17.0% predicted CVD risks by the non-laboratory-based and laboratory-based models, respectively) and Spearman rank correlation and the agreement between non-laboratory-based and laboratory-based models were 0.89 and 91.7%, respectively. Among women, none of the anthropometric measures were independently associated with CVD.
  
  
  CONCLUSIONS
  Among middle-aged and elderly where the ability of BMI to predict CVD declines, the non-laboratory-based model, based on ABSI, could predict CVD risk as accurately as the laboratory-based model among men.
authors.affiliation: Department of Epidemiology, Erasmus MC -University Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: Klodian
authors.initials: K
authors.lastname: Dhana
authors.affiliation: Department of Epidemiology, Erasmus MC -University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Neurology, Erasmus MC -University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Radiology, Erasmus MC -University Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: M Arfan
authors.initials: MA
authors.lastname: Ikram
authors.affiliation: Department of Epidemiology, Erasmus MC -University Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: Albert
authors.initials: A
authors.lastname: Hofman
authors.affiliation: Department of Epidemiology, Erasmus MC -University Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: Oscar H
authors.initials: OH
authors.lastname: Franco
authors.affiliation: Department of Epidemiology, Erasmus MC -University Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: Maryam
authors.initials: M
authors.lastname: Kavousi
mesh_terms: D000368:Aged; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D012737:Sex Factors; D013008:Somatotypes; D020521:Stroke / Q000453:epidemiology; D055105:Waist Circumference; D049629:Waist-Hip Ratio