title: An innovative prognostic model for predicting diabetes risk in the Thai population.
abstract: OBJECTIVE
  To estimate the prevalence and type 2 diabetes, and to develop a prognostic model for identifying individuals at high risk of undiagnosed type 2 diabetes.
  
  
  METHODS
  The study was designed as a cross-sectional investigation with 4314 participants of Thai background, aged between 15 and 85 years (mean age: 48). Fasting plasma glucose was initially measured, and repeated if the first measurement was more than 126 mg/dl. Type 2 diabetes was diagnosed using the World Health Organization's criteria. Logistic regression model was used to develop prognostic models for men and women separately. The prognostic performance of the model was assessed by the area under the receiver operating characteristic curve (AUC) and a nomogram was constructed from the logistic regression model.
  
  
  RESULTS
  The overall prevalence of type 2 diabetes was 7.4% (n = 125/1693) in men and 3.4% (n = 98/2621) in women. In either gender, the prevalence increased with age and body mass index (BMI). Gender, age, BMI and systolic blood pressure (SBP) were independently associated with type 2 diabetes risk. Based on the estimated parameters of model, a nomogram was constructed for predicting diabetes separated by gender. The AUC for the model with 3 factors was 0.75.
  
  
  CONCLUSIONS
  These data suggest that the combination of age, BMI and systolic blood pressure could help identify Thai individuals at high risk of undiagnosed diabetes.
authors.affiliation: Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
authors.forename: Chatlert
authors.initials: C
authors.lastname: Pongchaiyakul
authors.forename: Praew
authors.initials: P
authors.lastname: Kotruchin
authors.forename: Ekgaluck
authors.initials: E
authors.lastname: Wanothayaroj
authors.forename: Tuan V
authors.initials: TV
authors.lastname: Nguyen
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D044466:Asian People / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D015992:Body Mass Index; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000208:ethnology / Q000503:physiopathology; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D049451:Nomograms; D016017:Odds Ratio; D015995:Prevalence; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D012423:Rural Health / Q000706:statistics & numerical data; D013785:Thailand / Q000453:epidemiology; D055815:Young Adult
chemical_list: D015415:Biomarkers; D001786:Blood Glucose