Integrated Cardio-Renal-Metabolic Risk Profiling in Patients with Type 2 Diabetes: A Machine Learning-Assisted Cross-Sectional Analysis.

Background/Objectives: Type 2 diabetes mellitus (T2DM) is characterized by overlapping cardiovascular, renal, metabolic, and hepatic-risk abnormalities. We characterized this integrated phenotype, examined SCORE2-Diabetes gradients, and assessed whether routinely available variables could classify established atherosclerotic cardiovascular disease (ASCVD). Methods: This cross-sectional study included 232 consecutive adults with T2DM. SCORE2-Diabetes tertiles in the full cohort were analyzed descriptively, and a sensitivity analysis was restricted to participants aged 40-69 years without established ASCVD or severe target-organ damage. FIB-4 was recalculated from age, aspartate aminotransferase, alanine aminotransferase, and platelet count. Elastic-net logistic regression, random forest, and gradient boosting were evaluated using nested stratified five-fold cross-validation, with all preprocessing and hyperparameter tuning confined to the training folds. Results: Established ASCVD was present in 49 participants (21.1%), corresponding to 4.45 events per candidate predictor. The SCORE2-Diabetes-eligible sensitivity subgroup included 118 participants (50.9%); 72.9% were in the ≥20% 10-year-risk category. FIB-4 was available for 231 participants (median 1.26 [IQR 0.96-1.80]). Nested cross-validated ROC AUCs were 0.675 (95% CI 0.582-0.763) for elastic-net logistic regression, 0.670 (0.581-0.754) for random forest, and 0.674 (0.589-0.752) for gradient boosting; balanced accuracies were 65.4%, 62.8%, and 56.2%, respectively. Conclusions: The cohort had a high and heterogeneous cardio-renal-metabolic burden. SCORE2-Diabetes findings from the full cohort are descriptive because the score is not intended for patients with established ASCVD or severe target-organ damage. The machine-learning models showed only modest, internally validated discrimination and are not suitable for clinical deployment without larger prospective cohorts and external validation.