{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:19:46Z","timestamp":1776266386512,"version":"3.50.1"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":104,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Diabetes Research"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      To rank biochemical and clinical features that distinguish normal\u2010weight (BMI 18\u201324\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ) from overweight diabetics already carrying a discharge diagnosis of metabolic dysfunction\u2010associated steatotic liver disease (MASLD), and to build a parsimonious model that can flag lean individuals at highest risk of advanced metabolic complications.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study collected a total of 3524 samples from hospitalized patients with diabetes and nonalcoholic fatty liver disease (NAFLD), with 54 NAFLD features serving as the original dataset. After data preprocessing, 2624 samples and 52 NAFLD features were screened from the original dataset to form the final dataset for model input. Among these, 1848 patients were labeled as Class 0 (BMI\u2009&gt;\u200924\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ), and 776 patients were labeled as Class 1 (BMI between 18 and 24\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ). Data visualization and exploratory data analysis were performed using t\u2010SNE and heat maps. A five\u2010fold cross\u2010validation with 10 repetitions was employed for model optimization. The predictive model was evaluated using a confusion matrix. Three optimal predictive models with the smallest error were established: random forest, logistic regression, and gradient boosting. The top 30 feature variables were ultimately selected. An independent dataset containing 699 cases was used for external validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Uric acid, vitamin D, hemoglobin, and creatine kinase are the most significant features in normal\u2010weight diabetes patients with MASLD. Gradient boosting was considered the best model; the average area under the ROC curve (AUC) was 0.733 (95% CI: 0.7089\u20130.7578).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Gradient boosting is the optimal predictive model, which can assist healthcare professionals in risk assessment and management for diabetic MASLD patients with normal BMI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/jdr\/4207980","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T14:33:51Z","timestamp":1776263631000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2010Based Risk Stratification for Metabolic Dysfunction Severity Among Diabetic Patients With Established MASLD: Distinguishing Normal\u2010Weight From Overweight 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