A novel model enhances HbA1c-based diabetes screening using simple anthropometric, anamnestic, and demographic information.

Background: The sensitivity of HbA1c is not optimal for the screening of patients with latent diabetes. We hypothesize that simple healthcare information could improve accuracy.

Methods: We retrospectively analyzed data, including HbA1c, from multiple years from the National Health and Nutrition Examination Survey (NHANES) database (2005-2010). The data were used to create a logistic regression classification model for screening purposes.

Results: The study evaluated data for 5381 participants, including 404 with undiagnosed diabetes. The HbA1c screening data were supplemented with information about age, waist circumference, and physical activity in the HbA1c+ model. Alone, HbA1c alone had a receiver operating characteristics (ROC) curve for the area under the curve (AUC) of 0.808 (95% confidence interval [CI] 0.792-0.834). The HbA1c+ model had an ROC AUC of 0.851 (95% CI 0.843-0.872). There was a significant difference in the AUC between our model and using HbA1c without supplementary information (P < 0.05).

Conclusions: We have developed a novel screening model that could help improve screening for type 2 diabetes with HbA1c. It seems beneficial to systematically add additional patient healthcare information in the process of screening with HbA1c.