{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:12:20Z","timestamp":1788415940347,"version":"build-2803163510"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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>Background<\/jats:title>\n                    <jats:p>Metabolic disorders are increasingly recognized as key players in pancreatic ductal adenocarcinoma, yet their prognostic value after surgery remains unclear. Simple and reliable markers such as the triglyceride-glucose (TyG) index offer a practical way to capture insulin resistance and related metabolic disturbances.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>We retrospectively enrolled 506 patients who underwent curative resection for PDAC. Based on preoperative clinical and postoperative pathological data, we developed three Cox regression models to predict 1-year and 3-year overall survival: a clinical model, a pathological model, and a combined model that integrated both. We evaluated discrimination, calibration, decision curves, and risk stratification, with particular focus on the TyG index and other metabolic variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>The combined model outperformed the others, achieving a validation C-index of 0.721 and AUCs of 0.821 (1-year). The TyG index consistently emerged as the strongest independent predictor across all models (HR up to 1.50). Subgroup analysis by TNM stage revealed that the prognostic impact of the TyG index, body mass index, and albumin varied substantially with disease stage. Decision curve analysis confirmed the combined model\u2019s clinical utility, especially when more aggressive intervention is considered.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed and validated three prognostic models for resected PDAC based on routine clinical and pathological data. The combined model, which integrates metabolic markers such as the TyG index with traditional pathological features, showed the best discrimination and clinical utility. Our results underscore the important role of metabolic dysregulation in postoperative survival. However, these models should serve as supportive tools to inform clinical decision-making rather than replace comprehensive patient assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1942873","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:13:55Z","timestamp":1788412435000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From metabolic syndrome to survival: a prognostic model for PDAC incorporating insulin resistance markers and pathological variables"],"prefix":"10.3389","volume":"17","author":[{"given":"Tao","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University","place":["Chengdu, 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