{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T06:25:24Z","timestamp":1787034324795,"version":"build-2736575974"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and externally validate an interpretable machine-learning framework for estimating ultrasound-defined carotid plaque vulnerability among patients with type 2 diabetes mellitus (T2DM) and established carotid plaque.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In total, 884 T2DM patients with carotid atherosclerotic plaques from two medical centers were retrospectively recruited and allocated into training, internal validation, and external validation cohorts. Demographic, clinical, biochemical, continuous glucose monitoring (CGM), and inflammatory indicators were collected. CGM was performed for 72 consecutive hours using a retrospective CGM system (Medtronic iPro2; Medtronic, Northridge, CA, USA), and TIR was calculated from valid CGM recordings. Data preprocessing included clinically reviewed missing-data handling, standardization of continuous variables, and Synthetic Minority Over-sampling Technique (SMOTE), which was fitted only within the training data and, during cross-validation, within each training fold to avoid information leakage. Core predictors were identified by integrating feature-importance rankings derived from Random Forest, linear Support Vector Machine, and Logistic Regression. Five predictive models were developed and evaluated using discrimination, calibration, and clinical-utility metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six core predictors were identified: systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), age, time in range (TIR), systemic immune-inflammation index (SII), and smoking status. The XGBoost model showed the best overall validation performance, attaining an AUC of 0.882 (95% CI: 0.795-0.969) in the external validation set. SHAP analysis indicated that higher SBP, higher LDL-C, older age, higher SII, and smoking were associated with a higher predicted probability of vulnerable plaques, whereas higher TIR was associated with a lower predicted probability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An interpretable XGBoost model based on six clinical, laboratory, and CGM-derived indicators showed acceptable discrimination, calibration, and clinical net benefit for estimating ultrasound-defined plaque vulnerability among patients with T2DM and established carotid plaque. The model is not a substitute for carotid ultrasound; pending prospective workflow and economic evaluation, it may support research-stage prioritization for expert plaque characterization when imaging capacity or expertise is constrained.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1911126","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:37:28Z","timestamp":1787031448000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of carotid vulnerable plaques in patients with type 2 diabetes using interpretable machine learning models"],"prefix":"10.3389","volume":"17","author":[{"given":"Qi","family":"Cheng","sequence":"first","affiliation":[{"name":"Jingjiang People's Hospital Affiliated to Yangzhou University","place":["Taizhou, 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