{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T05:04:25Z","timestamp":1789967065653,"version":"4.0.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"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. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Type 2 diabetes mellitus (T2DM) complicated by sarcopenia represents a considerable public health challenge, yet early detection remains challenging due to a lack of practical screening tools. The aim of this study was to develop and internally validate a clinically feasible, mechanism-based machine learning model for predicting sarcopenia risk in patients with T2DM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 904 patients with T2DM treated at the First Affiliated Hospital of Xinjiang Medical University from May 2024 to May 2026 were retrospectively enrolled. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2025 consensus. The dataset was randomly partitioned into training (70%) and internal validation (30%) sets. Key predictive features were identified by intersecting the variables selected via least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. Eight machine learning algorithms were subsequently developed and evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The prevalence of sarcopenia among the patients with T2DM was 29.20%. Eight predictors were selected: age, sex, body mass index, serum albumin, alanine aminotransferase, hemoglobin, metabolic score for insulin resistance, and monocyte-to-high-density lipoprotein cholesterol ratio. LightGBM demonstrated the best overall predictive performance in the validation set, yielding an area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.882, a sensitivity of 0.897, a specificity of 0.876, a precision of 0.745, and an F1 score of 0.814. Calibration and decision curve analysis indicated that the model yielded clinical net benefit. A web-based calculator developed using LightGBM enabled three-tier risk stratification: low (&amp;lt;15%), moderate (15%\u201340%), and high (&amp;gt;40%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A machine learning model for predicting sarcopenia risk in patients with T2DM was developed and internally validated, resulting in the implementation of an online risk calculator. This mechanism-based model demonstrated promising internal predictive performance and may provide a practical and cost-effective screening approach for diabetes management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1922889","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T04:29:03Z","timestamp":1789964943000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a predictive model for sarcopenia in patients with type 2 diabetes based on the synergistic mechanism of insulin resistance and inflammation"],"prefix":"10.3389","volume":"17","author":[{"given":"Ajinisha","family":"Akeaji","sequence":"first","affiliation":[{"name":"Department of General Internal Medicine I, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baihetinisha","family":"Akeaji","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Ultrasound, The First People\u2019s Hospital of Kashi","place":["Kashgar, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihe","family":"Han","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine I, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine I, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"101318","DOI":"10.1016\/j.Waojou.2026.101318","article-title":"From reference values to clinical impact: Monocyte-to-HDL ratio (MHR) and its association with inflammation and mortality","volume":"19","author":"Arabi","year":"2026","journal-title":"World Allergy Organ. 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