Simple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus

Background: To develop a simple nomogram which can be used to predict the risk of diabetes mellitus (DM) in asymptomatic non-diabetic general population based on non-laboratory-based and laboratory-based risk algorithms. Methods: Anthropometric data, plasma fasting glucose, full lipid profile, exercise habit and family history of DM were collected from Chinese non-diabetic subjects aged 18-70. Logistic regression analysis was performed on the data of a random sample of 2518 subjects to construct non-laboratory-based and laboratory-based risk assessment algorithms for the detection of undiagnosed DM; both algorithms were validated on the data of the remaining sample (n=839). Hosmer-Lemeshow χ2 statistic and area under the receiver-operating characteristic curve (AUC) were employed to assess the calibration and discrimination of the different DM risk algorithms. Results: Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose≥7.0mmol/L or 2-hour post-load plasma glucose≥11.1mmol/L after oral glucose tolerance test. The non-laboratory-based risk algorithm, with score 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 score 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, respectively) and discrimination (AUC: 0.709 and 0.711, respectively) for the detection of undiagnosed DM. The optimal cutoff point on the receiver-operating characteristic curve was 18 for the detection of undiagnosed DM in both algorithms. Conclusions: Simple-to-use nomogram for detecting undiagnosed DM has been developed using the validated non-laboratory-based and laboratory-based risk algorithms.postprin