Simple non-laboratory- and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus

Carlos K.H. Wong, Shing Chung Siu, Eric Y.F. Wan, Fang Fang Jiao, Esther Y.T. Yu, Colman S.C. Fung, Ka Wai Wong, Yee Man Angela Leung, Cindy L.K. Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


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 ≥7.0mmol/L or 2-h post-load plasma glucose ≥11.1mmol/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. Conclusion: A simple-to-use nomogram for detecting undiagnosed DM has been developed using validated non-laboratory-based and laboratory-based risk algorithms.
Original languageEnglish
Pages (from-to)414-421
Number of pages8
JournalJournal of Diabetes
Issue number3
Publication statusPublished - 1 May 2016
Externally publishedYes


  • Nomogram
  • Risk algorithm
  • Undiagnosed diabetes
  • Validation

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism

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