title: Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6).
abstract: OBJECTIVE
  The aims of this study were to assess the clinical significance of introducing HbA(1c) into a risk score for diabetes and to develop a scoring system to predict the 5 year incidence of diabetes in Japanese individuals.
  
  
  METHODS
  The study included 7,654 non-diabetic individuals aged 40-75 years. Incident diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l, HbA(1c) ≥6.5% (48 mmol/mol) or self-reported clinician-diagnosed diabetes. We constructed a risk score using non-laboratory assessments (NLA) and evaluated improvements in risk prediction by adding elevated FPG, elevated HbA(1c) or both to NLA.
  
  
  RESULTS
  The discriminative ability of the NLA score (age, sex, family history of diabetes, current smoking and BMI) was 0.708. The difference in discrimination between the NLA + FPG and NLA + HbA(1c) scores was non-significant (0.836 vs 0.837; p = 0.898). A risk score including family history of diabetes, smoking, obesity and both FPG and HbA(1c) had the highest discrimination (0.887, 95% CI 0.871, 0.903). At an optimal cut-off point, sensitivity and specificity were high at 83.7% and 79.0%, respectively. After initial screening using NLA scores, subsequent information on either FPG or HbA(1c) resulted in a net reclassification improvement of 42.7% or 52.3%, respectively (p < 0.0001). When both were available, net reclassification improvement and integrated discrimination improvement were further improved at 56.7% (95% CI 47.3%, 66.1%) and 10.9% (9.7%, 12.1%), respectively.
  
  
  CONCLUSIONS
  Information on HbA(1c) or FPG levels after initial screening by NLA can precisely refine diabetes risk reclassification.
authors.affiliation: Department of Internal Medicine, Niigata University Faculty of Medicine, Niigata, Japan.
authors.forename: Y
authors.initials: Y
authors.lastname: Heianza
authors.forename: Y
authors.initials: Y
authors.lastname: Arase
authors.forename: S D
authors.initials: SD
authors.lastname: Hsieh
authors.forename: K
authors.initials: K
authors.lastname: Saito
authors.forename: H
authors.initials: H
authors.lastname: Tsuji
authors.forename: S
authors.initials: S
authors.lastname: Kodama
authors.forename: S
authors.initials: S
authors.lastname: Tanaka
authors.forename: Y
authors.initials: Y
authors.lastname: Ohashi
authors.forename: H
authors.initials: H
authors.lastname: Shimano
authors.forename: N
authors.initials: N
authors.lastname: Yamada
authors.forename: S
authors.initials: S
authors.lastname: Hara
authors.forename: H
authors.initials: H
authors.lastname: Sone
mesh_terms: D000328:Adult; D000368:Aged; D044466:Asian People / Q000706:statistics & numerical data*; D001786:Blood Glucose / Q000378:metabolism*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D005215:Fasting / Q000097:blood; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D015994:Incidence; D007564:Japan / Q000453:epidemiology; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors; D012907:Smoking / Q000453:epidemiology; D013997:Time Factors
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human