Machine learning-based identification of depression in older patients with type 2 diabetes mellitus: A multicenter retrospective study.

Authors

Xiao Zhang, Juntao Tan, Manni Xiao, Yuxin He, Si Tang

Published in

Science progress. Volume 109. Issue 4. Pages 368504261507162. Epub Nov 06, 2026.

Abstract

ObjectiveType 2 diabetes mellitus (T2DM) and depression frequently co-occur in older adults, creating a bidirectional relationship that complicates disease management and worsens clinical outcomes. Early identification of depression in this vulnerable population remains challenging due to the absence of practical, externally validated screening tools tailored to routine clinical settings in China.MethodsIn this multicenter retrospective study, we developed and validated nine machine learning classifiers to identify clinically documented depression in older patients (aged ≥ 65 years) with T2DM during hospitalization, using electronic health records from 31,980 individuals across six centers. Candidate predictors encompassed demographic characteristics, comorbidities, lifestyle factors, and 15 composite laboratory biomarkers. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection, and model performance was evaluated via discrimination (area under the receiver operating characteristic curve (AUC)), calibration, and decision curve analysis across training, internal validation, and institutionally independent external validation sets.ResultsSeven predictors-age, sex, hypertension, cerebrovascular disease (CeVD), drinking history, surgical history, and neutrophil-to-albumin ratio (NAR)-were retained as selected predictors associated with model-predicted depression risk. All nine algorithms except SVM showed comparable discriminative performance in the external validation set (AUCs 0.769-0.772). XGBoost achieved AUCs of 0.836 (95% CI: 0.829-0.843), 0.784 (95% CI: 0.771-0.797), and 0.772 (95% CI: 0.756-0.787) in the training, internal validation, and external validation sets, respectively, which was comparable to logistic regression (LR) (AUC = 0.772) in the external validation set. XGBoost was therefore selected as a representative model for SHAP-based interpretability analysis.ConclusionsThe machine learning model, integrating routinely available clinical and laboratory variables, may offer an interpretable and institutionally externally validated tool for depression identification in older Chinese patients with T2DM. Its discriminative performance was moderate (external validation AUC = 0.772) and comparable to that of conventional LR, with no clear advantage of more complex algorithms and only minimal differences among most models. Validation in other geographic regions and healthcare systems is warranted.

PMID:
42993899
Bibliographic data and abstract were imported from PubMed on 07 Nov 2026.

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