Development and validation of a model using routine clinical features to improve diabetes diagnosis

Hemoglobin A1c (A1c) and fasting plasma glucose (FPG) miss diabetes diagnosis in many individuals, but the alternative, oral glucose tolerance testing (OGTT), is difficult to obtain.

This work aimed to develop and validate a model to identify diabetes missed by A1c and FPG but detected by OGTT and associate its outcomes with mortality.

We trained machine-learning models using National Health and Nutrition Examination Survey (NHANES) data, applied them to corresponding National Center for Health Statistics mortality data, and externally validated the models with Diabetes Prevention Program Outcomes Study (DPPOS) data. We tested models including the A1c, vital signs, complete blood count, and comprehensive metabolic panel with and without the FPG. This retrospective analysis included NHANES study participants from 1999 to 2016 and DPPOS study participants from 1996 to 2020. Main outcome measures included the area under the receiver operating curve, sensitivity, and specificity of models to detect diabetes.

The A1c/FPG+ model had better sensitivity for DPPOS individuals with A1c less than 6.5% (48 mmol/mol) and FPG less than 126 mg/dL compared to a logistic regression of A1c and FPG and recalibrated FPG for individuals both on (0.69 vs 0.53 vs 0.57, respectively) and off (0.75 vs 0.65 vs 0.68, respectively) medication. NHANES participants without diabetes but with A1c/FPG+-predicted diabetes had elevated risk of mortality compared to those without A1c/FPG+-predicted diabetes (HR = 1.4; P < 2 × 10^-16).

Machine-learning models can improve the detection of individuals who would need OGTT assessment to diagnose diabetes using standard nonfasting laboratory values and vital signs with and without FPG.