title: Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study.
abstract: Various diabetes risk scores composed of non-laboratory parameters have been developed, but only a few studies performed cross-validation of these scores and a comparison with laboratory parameters. We evaluated the performance of diabetes risk scores composed of non-laboratory parameters, including a recently published Korean risk score (KRS), and compared them with laboratory parameters.
  
  
  
  The data of 26,675 individuals who visited the Seoul National University Hospital Healthcare System Gangnam Center for a health screening program were reviewed for cross-sectional validation. The data of 3,029 individuals with a mean of 6.2 years of follow-up were reviewed for longitudinal validation. The KRS and 16 other risk scores were evaluated and compared with a laboratory prediction model developed by logistic regression analysis.
  
  
  
  For the screening of undiagnosed diabetes, the KRS exhibited a sensitivity of 81%, a specificity of 58%, and an area under the receiver operating characteristic curve (AROC) of 0.754. Other scores showed AROCs that ranged from 0.697 to 0.782. For the prediction of future diabetes, the KRS exhibited a sensitivity of 74%, a specificity of 54%, and an AROC of 0.696. Other scores had AROCs ranging from 0.630 to 0.721. The laboratory prediction model composed of fasting plasma glucose and hemoglobin A1c levels showed a significantly higher AROC (0.838, P < 0.001) than the KRS. The addition of the KRS to the laboratory prediction model increased the AROC (0.849, P = 0.016) without a significant improvement in the risk classification (net reclassification index: 4.6%, P = 0.264).
  
  
  
  The non-laboratory risk scores, including KRS, are useful to estimate the risk of undiagnosed diabetes but are inferior to the laboratory parameters for predicting future diabetes.
authors.affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Chang Ho
authors.initials: CH
authors.lastname: Ahn
authors.affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.|Department of Internal Medicine, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea.
authors.forename: Ji Won
authors.initials: JW
authors.lastname: Yoon
authors.affiliation: Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea.
authors.forename: Seokyung
authors.initials: S
authors.lastname: Hahn
authors.affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.|Department of Internal Medicine, Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Min Kyong
authors.initials: MK
authors.lastname: Moon
authors.affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Kyong Soo
authors.initials: KS
authors.lastname: Park
authors.affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Young Min
authors.initials: YM
authors.lastname: Cho
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000209:etiology; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D008962:Models, Theoretical; D011379:Prognosis; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D066106:Seoul; D054928:Validation Studies as Topic