{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:45:42Z","timestamp":1790621142452,"version":"4.1.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"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 construct and validate a clinical model to predict painful diabetic peripheral neuropathy (PDPN) risk in type 2 diabetes mellitus (T2DM) patients for early identification and intervention in primary care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 1,984 patients with T2DM were included in the analysis. After data preprocessing and application of the Synthetic Minority Oversampling Technique (SMOTE) with a 200% oversampling ratio, feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation. Six predictive models: multivariable logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and support vector machine (SVM)\u2014were developed and tuned using repeated 5-fold cross-validation. Model performance was evaluated on the independent test cohort using comprehensive discrimination and calibration metrics. To enhance clinical interpretability, a nomogram and SHapley Additive exPlanations (SHAP) analysis were implemented to visualize predictor contributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Ten variables were selected as predictors. Among 1,984 patients, 81 (4.08%) had PDPN. LR model demonstrated the most favorable trade-off for screening purposes, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.894 (95% CI: 0.814\u20130.964), area under the precision\u2013recall curve (PR-AUC) of 0.470 (95% CI: 0.258\u20130.665), and balanced accuracy of 0.826 (95% CI: 0.667\u20130.932). SHAP analysis identified musculoskeletal disorders and HbA1c as the most influential predictors. A user-friendly dynamic web-based nomogram was constructed to support clinical implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We established and validated a clinically interpretable model for PDPN risk prediction in patients with T2DM. The dynamic nomogram enables individualized risk estimation and may assist timely intervention in routine practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1651493","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:42:46Z","timestamp":1764250966000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and validation of a risk prediction model for painful diabetic peripheral neuropathy in type 2 diabetes mellitus: a multicenter retrospective 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