title: A clinically practicable diagnostic score for metabolic syndrome improves its predictivity of diabetes mellitus: the Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto miocardico (GISSI)-Prevenzione scoring.
abstract: BACKGROUND
  Metabolic syndrome (MS) is associated with late-onset diabetes. However, diagnostic criteria for individual components of MS are based on categorical/arbitrary cut points and, therefore, do not exploit the information yield of each factor. We aimed to generate a diagnostic score for MS (MS-Score), aimed at predicting diabetes by giving appropriate weight to the individual components of MS.
  
  
  METHODS
  Of 11,323 patients with prior myocardial infarction and followed up for 3.5 years in the GISSI-Prevenzione study, 3855 subjects with diabetes at baseline or missing information for relevant variables were excluded. A Cox proportional hazards model including age, sex, glycemia, high-density lipoprotein cholesterol, triglycerides, hypertension, and body mass index was fitted to create a diagnostic score. A cutoff point of 28 of the score was the best compromise between sensitivity and specificity for MS diagnosis (MS-Score). The prognostic performance of the MS-Score was compared with that of the diagnostic criteria of MS, as defined by National Cholesterol Education Program Adult Treatment Panel III (MS-ATP).
  
  
  RESULTS
  Of 7468 patients, 940 developed diabetes. The risk of getting diabetes significantly and progressively increased in the quintiles of the score reaching > 6-fold higher risk in the last one. The predictive capability of MS-Score was significantly higher than that of the MS-ATP (AUC = 0.650 vs 0.587, sensitivity 67% vs 52%, specificity 63% vs 66%, P = .0002). The MS-Score, but not the MS-ATP, was significantly associated with mortality.
  
  
  CONCLUSIONS
  MS-Score improves the prediction of diabetes development by using the full informative content of individual components for diagnosis of MS.
authors.affiliation: Consorzio Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Macchia
authors.forename: Giacomo
authors.initials: G
authors.lastname: Levantesi
authors.forename: Giovanna
authors.initials: G
authors.lastname: Borrelli
authors.forename: Maria Grazia
authors.initials: MG
authors.lastname: Franzosi
authors.forename: Aldo Pietro
authors.initials: AP
authors.lastname: Maggioni
authors.forename: RosaMaria
authors.initials: R
authors.lastname: Marfisi
authors.forename: Marco
authors.initials: M
authors.lastname: Scarano
authors.forename: Luigi
authors.initials: L
authors.lastname: Tavazzi
authors.forename: Gianni
authors.initials: G
authors.lastname: Tognoni
authors.forename: Franco
authors.initials: F
authors.lastname: Valagussa
authors.forename: Roberto
authors.initials: R
authors.lastname: Marchioli
mesh_terms: D000368:Aged; D019540:Area Under Curve; D001786:Blood Glucose / Q000032:analysis; D015992:Body Mass Index; D008076:Cholesterol, HDL / Q000097:blood; D003920:Diabetes Mellitus / Q000175:diagnosis*; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis*; D008875:Middle Aged; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012372:ROC Curve; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D014280:Triglycerides / Q000097:blood
chemical_list: D001786:Blood Glucose; D008076:Cholesterol, HDL; D014280:Triglycerides