Clinical and Inflammatory Profiles Associated with Diabetes in Adults with Obesity: A Machine Learning Study

Background: Obesity is strongly associated with type 2 diabetes, but clinical and metabolic characteristics vary among adults with obesity. This cross-sectional study evaluated whether routinely available nonglycemic variables could classify biochemically defined diabetes status at the time of evaluation. Methods: This retrospective study included 351 adults with obesity, comprising 185 with diabetes and 166 without diabetes. Diabetes was defined by two fasting plasma glucose measurements ≥ 126 mg/dL obtained on separate occasions or an HbA1c value ≥ 6.5%. Complete numerical fasting glucose and HbA1c values were not retained as continuous variables in the analytical database, and insulin-related measures were unavailable. Multivariable logistic regression and four machine learning classifiers were evaluated. Results: Participants with diabetes were older, more frequently male, and more frequently had hypertension, fatty liver, and prior cardiovascular interventions. They had higher triglyceride and CRP concentrations, lower LDL-C concentrations, and no statistically significant difference in HDL-C. Age, hypertension, fatty liver, and LDL-C were associated with diabetes status in the multivariable model. Gradient Boosting and Random Forest showed similar discrimination (AUC-ROC = 0.807 and 0.803, respectively), with overlapping confidence intervals. Conclusions: The models classified prevalent diabetes status within this cohort using variables other than the glycemic criteria used to define the outcome. They were not designed to predict incident diabetes or progression from obesity to diabetes. The findings are exploratory and require external validation before clinical application.