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

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

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.

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).

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.