title: Simple non-laboratory- and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus.
abstract: BACKGROUND
  The aim of the present study was to develop a simple nomogram that can be used to predict the risk of diabetes mellitus (DM) in the asymptomatic non-diabetic subjects based on non-laboratory- and laboratory-based risk algorithms.
  
  
  METHODS
  Anthropometric data, plasma fasting glucose, full lipid profile, exercise habits, and family history of DM were collected from Chinese non-diabetic subjects aged 18-70 years. Logistic regression analysis was performed on a random sample of 2518 subjects to construct non-laboratory- and laboratory-based risk assessment algorithms for detection of undiagnosed DM; both algorithms were validated on data of the remaining sample (n = 839). The Hosmer-Lemeshow test and area under the receiver operating characteristic (ROC) curve (AUC) were used to assess the calibration and discrimination of the DM risk algorithms.
  
  
  RESULTS
  Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose ≥7.0 mmol/L or 2-h post-load plasma glucose ≥11.1 mmol/L after an oral glucose tolerance test. The non-laboratory-based risk algorithm, with scores ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise, and uncontrolled blood pressure; the laboratory-based risk algorithm, with scores ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P = 0.229 and P = 0.483) and discrimination (AUC 0.709 and 0.711) for detection of undiagnosed DM.
  
  
  CONCLUSIONS
  A simple-to-use nomogram for detecting undiagnosed DM has been developed using validated non-laboratory-based and laboratory-based risk algorithms.
authors.affiliation: Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong.
authors.forename: Carlos K H
authors.initials: CK
authors.lastname: Wong
authors.affiliation: Department of Medicine and Rehabilitation, Tung Wah Eastern Hospital, Hong Kong.
authors.forename: Shing-Chung
authors.initials: SC
authors.lastname: Siu
authors.affiliation: Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong.
authors.forename: Eric Y F
authors.initials: EY
authors.lastname: Wan
authors.affiliation: Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong.
authors.forename: Fang-Fang
authors.initials: FF
authors.lastname: Jiao
authors.affiliation: Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong.
authors.forename: Esther Y T
authors.initials: EY
authors.lastname: Yu
authors.affiliation: Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong.
authors.forename: Colman S C
authors.initials: CS
authors.lastname: Fung
authors.affiliation: Department of Medicine and Rehabilitation, Tung Wah Eastern Hospital, Hong Kong.
authors.forename: Ka-Wai
authors.initials: KW
authors.lastname: Wong
authors.affiliation: School of Nursing, The University of Hong Kong, Hong Kong.
authors.forename: Angela Y M
authors.initials: AY
authors.lastname: Leung
authors.affiliation: Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong.
authors.forename: Cindy L K
authors.initials: CL
authors.lastname: Lam
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000453:epidemiology*; D005215:Fasting / Q000502:physiology; D005260:Female; D005951:Glucose Tolerance Test; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D008055:Lipids / Q000032:analysis*; D008297:Male; D008875:Middle Aged; D049451:Nomograms; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors
chemical_list: D001786:Blood Glucose; D008055:Lipids
keywords: nomogram; risk algorithm; undiagnosed diabetes; validation; 未诊断的糖尿病; 计算图; 风险评估公式; 验证