title: Chinese metabolic syndrome risk score.
abstract: BACKGROUND
  The metabolic syndrome (MetS) was first proposed to predict the occurrence of cardiovascular disease and type 2 diabetes. However, it is difficult to identify subjects with MetS early. No previous studies designed to develop a predictive model for MetS in the Chinese population exists; this study was designed to fill that gap.
  
  
  METHODS
  A middle-aged Chinese cohort of 198 men and 154 women were followed for two years. The binary logistic regression and receiver operation characteristic (ROC) curve were used to develop a predictive model for the future development of MetS.
  
  
  RESULTS
  Over two years of follow up, 30 of the 352 subjects (8.52%) without MetS at baseline subsequently developed MetS. Triglycerides (TG) had the highest area under the curve (AUC), while diastolic blood pressure had the lowest. In order to increase the prediction power, MetS components were arranged in the ROC model according to their AUC. After adding waist circumference (WC) to TG (model 1), the AUC was significantly higher than for TG alone. Adding other components into the model did not increase the AUC significantly. A risk score cutoff (0.078) was selected for the best predictive power of model 1 (sensitivity of 76.7%, specificity of 63.4%, with AUC of 76.8%).
  
  
  CONCLUSIONS
  These results imply that WC and TG are related to the pathophysiologies of MetS, and model 1 could also be used clinically for screening subjects at high risks for MetS.
authors.affiliation: Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taiwan, R.O.C.
authors.forename: Fone-Ching
authors.initials: FC
authors.lastname: Hsiao
authors.forename: Chung-Ze
authors.initials: CZ
authors.lastname: Wu
authors.forename: Chang-Hsun
authors.initials: CH
authors.lastname: Hsieh
authors.forename: Chih-Tsueng
authors.initials: CT
authors.lastname: He
authors.forename: Yi-Jen
authors.initials: YJ
authors.lastname: Hung
authors.forename: Dee
authors.initials: D
authors.lastname: Pei
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis* / Q000453:epidemiology*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires; D013624:Taiwan