{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:59:53Z","timestamp":1790312393970,"version":"4.1.0"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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>Pancreatic cancer (PC) is highly aggressive and associated with extremely poor prognosis. Due to the lack of specfic symptoms in the early stage, most patients have already lost the opportunity for curative surgical resection at the time of diagnosis. Therefore, improving the early identification of individuals at elevated risk of PC is clinically important.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using data from the UK Biobank, 157,235 participants were included. Cox proportional hazards models were applied to systematically evaluate the associations of clinical biomarkers, polygenic risk scores (PRS), and 143 metabolites with PC risk, with significant metabolites identified after false discovery rate correction. Elastic net regression was then used to construct a metabolomic risk score (MRS), which was integrated with clinical biomarkers and PRS to develop multivariable prediction models. Model performance was assessed using the C-index and time-dependent area under the curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During the follow-up period, 878 incident PC cases were identified. In total, 15 clinical biomarkers and 55 metabolites were significantly associated with PC risk, with the majority of lipid- and lipoprotein-related metabolites showing inverse associations. Subsequently, 52 metabolites were selected to construct the MRS. The C-index increased from 0.722 in the base model including age and sex to 0.755 with the addition of clinical biomarkers and to 0.768 after further incorporating the PRS; the addition of the MRS yielded a C-index of 0.769, with improved risk reclassification. The integrated model showed good discrimination, with time-dependent AUCs of 0.812, 0.808, 0.768, and 0.783 at 1, 3, 5, and 10 years, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings support the utility of clinical and genetic factors for PC risk assessment, while the additional predictive value of metabolomic profiling warrants further validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1903036","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:26:43Z","timestamp":1790310403000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a predictive model for pancreatic cancer integrating serum metabolomics and polygenic risk 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