{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:15:55Z","timestamp":1788498955720,"version":"build-2803163510"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"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 investigate the association between the composite Triglyceride-Glucose Index combined with Glycemic Variability (TyG-GVI) and early-death risk in ICU patients with gastrointestinal bleeding, and evaluate whether integrating this biomarker into ensemble-learning models improves prognostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study analyzed adult ICU patients (\u226518 years) with gastrointestinal hemorrhage as the principal diagnosis from the MIMIC-IV v3.1 database. TyG-GVI was calculated as the product of the triglyceride-glucose index and glucose coefficient of variation using first-24-hour laboratory data. Patients were stratified by the median TyG-GVI value. Primary outcomes were ICU and in-hospital mortality. We applied Kaplan-Meier survival analysis, multivariable Cox regression, restricted cubic spline (RCS), and ensemble-learning models (Random Forest, XGBoost, LightGBM) with SHAP interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In total, 998 eligible patients were split into low- (n=499) and high-TyG-GVI (n=499) groups. The high-TyG-GVI group showed more severe physiological disturbance. After adjustment, high TyG-GVI independently predicted ICU mortality (adjusted HR 1.87, 95%CI 1.36\u20132.55,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.001). RCS revealed a non-linear relationship with an inflection threshold at TyG-GVI = 1.535. Subgroup analyses supported result robustness. The XGBoost model incorporating TyG-GVI yielded better discrimination (AUC = 0.827) than the conventional OASIS score (AUC = 0.759).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>TyG-GVI is an independent non-linear prognostic marker for early mortality in ICU patients with gastrointestinal bleeding. Combining TyG-GVI with explainable artificial intelligence enhances risk-stratification for acute-care bedside prognostication.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1897817","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T04:26:16Z","timestamp":1788495976000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Synergistic effect of insulin resistance and glycemic variability on mortality in ICU patients with gastrointestinal bleeding: impact of the TyG-GVI index and development of an explainable web-based clinical calculator"],"prefix":"10.3389","volume":"17","author":[{"given":"Yanqiu","family":"Jiang","sequence":"first","affiliation":[{"name":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u2019s Hospital","place":["Quzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengshan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Jiangmen Central Hospital","place":["Jiangmen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke-Jie","family":"He","sequence":"additional","affiliation":[{"name":"The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u2019s Hospital","place":["Quzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1097\/00003246-198304000-00016","article-title":"APACHE II: a severity of disease classification system","volume":"13","author":"Knaus","year":"1985","journal-title":"Crit Care Med"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1007\/s13167-026-00456-5","article-title":"Blood biomarker trajectories in ICU-directed prediction models \u2013 a scoping review","volume":"17","author":"Von Rekowski","year":"2026","journal-title":"EPMA J"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2010-0288","article-title":"The product of triglycerides and glucose, a simple measure of insulin sensitivity. 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