title: Usefulness of the metabolic syndrome criteria as predictors of insulin resistance among obese Korean women.
abstract: OBJECTIVE
  To investigate the ability of each metabolic syndrome (MetS) criterion, defined by the International Diabetes Federation, to predict insulin resistance (IR).
  
  
  METHODS
  A cross-sectional study. IR was defined as homeostasis model assessment of IR (HOMA-IR) > or =3.04. The MetS criteria considered were TAG > or = 1.69 mmol/l, HDL cholesterol (HDL-C) <1.29 mmol/l, blood pressure (BP) > or =130/85 mmHg and fasting glucose (FG) > or =5.6 mmol/l.
  
  
  METHODS
  Busan, South Korea.
  
  
  METHODS
  Ninety-six apparently healthy Korean women (mean age 42 (sd 10.6) years) with abdominal obesity (waist circumference (WC) > or =80 cm) were studied.
  
  
  RESULTS
  Of the ninety-six obese women, 11 % were insulin-resistant and 33 % fulfilled the criteria for IDF-defined MetS. Glucose and TAG were more likely to predict IR than BP and HDL-C when assessed using receiver-operating characteristic curves, multiple regression and multiple logistic regression analyses. Of the variation in HOMA-IR, TAG, FG, WC and age explained 42 %. High FG was independently associated with the presence of IR (OR = 8.6, 95 % CI 1.8, 41.8) even after adjusting for other components of MetS. The positive predictive value and positive likelihood ratio to detect IR were the highest for the FG criterion (33 % and 3.9, respectively), followed by TAG (28 %, 3.0), BP (19 %, 1.8) and HDL-C criteria (18 %, 1.7). The IDF definition of MetS exhibited a positive predictive value of 29 % and a positive likelihood ratio of 3.1.
  
  
  CONCLUSIONS
  Of the MetS criteria, high FG and high TAG seem to be more suitable for identifying obese women with IR than high BP and low HDL-C.
authors.affiliation: Department of Family Medicine, Inje University Busan Paik Hospital, 633-165 Kaegum-dong, Pusan Jin-Gu, Busan 614-735, Republic of Korea. kayoung.fmlky@gmail.com
authors.forename: Kayoung
authors.initials: K
authors.lastname: Lee
mesh_terms: D000328:Adult; D000367:Age Factors; D015415:Biomarkers / Q000097:blood*; D001786:Blood Glucose / Q000378:metabolism; D008076:Cholesterol, HDL / Q000097:blood; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D006973:Hypertension / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology; D007333:Insulin Resistance; D007723:Korea / Q000453:epidemiology; D016013:Likelihood Functions; D016015:Logistic Models; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D056128:Obesity, Abdominal / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D016017:Odds Ratio; D011237:Predictive Value of Tests; D015995:Prevalence; D012372:ROC Curve; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood; D055105:Waist Circumference
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D008076:Cholesterol, HDL; D014280:Triglycerides