title: A Risk Score with Additional Four Independent Factors to Predict the Incidence and Recovery from Metabolic Syndrome: Development and Validation in Large Japanese Cohorts.
abstract: BACKGROUND
  Although many risk factors for Metabolic syndrome (MetS) have been reported, there is no clinical score that predicts its incidence. The purposes of this study were to create and validate a risk score for predicting both incidence and recovery from MetS in a large cohort.
  
  
  METHODS
  Subjects without MetS at enrollment (n = 13,634) were randomly divided into 2 groups and followed to record incidence of MetS. We also examined recovery from it in rest 2,743 individuals with prevalent MetS.
  
  
  RESULTS
  During median follow-up of 3.0 years, 878 subjects in the derivation and 757 in validation cohorts developed MetS. Multiple logistic regression analysis identified 12 independent variables from the derivation cohort and initial score for subsequent MetS was created, which showed good discrimination both in the derivation (c-statistics 0.82) and validation cohorts (0.83). The predictability of the initial score for recovery from MetS was tested in the 2,743 MetS population (906 subjects recovered from MetS), where nine variables (including age, sex, γ-glutamyl transpeptidase, uric acid and five MetS diagnostic criteria constituents.) remained significant. Then, the final score was created using the nine variables. This score significantly predicted both the recovery from MetS (c-statistics 0.70, p<0.001, 78% sensitivity and 54% specificity) and incident MetS (c-statistics 0.80) with an incremental discriminative ability over the model derived from five factors used in the diagnosis of MetS (continuous net reclassification improvement: 0.35, p < 0.001 and integrated discrimination improvement: 0.01, p<0.001).
  
  
  CONCLUSIONS
  We identified four additional independent risk factors associated with subsequent MetS, developed and validated a risk score to predict both incident and recovery from MetS.
authors.affiliation: Department of Medicine and Biological Science, Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan.
authors.forename: Masaru
authors.initials: M
authors.lastname: Obokata
authors.affiliation: Department of Medicine and Biological Science, Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
authors.forename: Kazuaki
authors.initials: K
authors.lastname: Negishi
authors.affiliation: Clinical Investigation and Research Unit, Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan.
authors.forename: Yoshiaki
authors.initials: Y
authors.lastname: Ohyama
authors.affiliation: Japan Community Healthcare Organization, Gunma Chuo Hospital, Maebashi, Gunma, Japan.
authors.forename: Haruka
authors.initials: H
authors.lastname: Okada
authors.affiliation: Japan Community Healthcare Organization, Gunma Chuo Hospital, Maebashi, Gunma, Japan.
authors.forename: Kunihiko
authors.initials: K
authors.lastname: Imai
authors.affiliation: Department of Medicine and Biological Science, Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan.
authors.forename: Masahiko
authors.initials: M
authors.lastname: Kurabayashi
mesh_terms: D044466:Asian People; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology* / Q000378:metabolism; D008875:Middle Aged; D018570:Risk Assessment; D012307:Risk Factors; D014527:Uric Acid / Q000378:metabolism; D005723:gamma-Glutamyltransferase / Q000378:metabolism
chemical_list: D014527:Uric Acid; D005723:gamma-Glutamyltransferase