ArticleFrontiers in endocrinology2025
Machine learning-optimized metabolic biomarker panel for precision screening of early-stage pancreatic cancer in new-onset diabetes.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Metabolic Syndrome and Pancreatic Cancer: Linking Insulin Resistance, Inflammation, and Carcinogenesis.Diagnostics (Basel, Switzerland) · 2026Review
- Mapping Steroidogenic Perturbations Under Endocrine Disruptor Mixtures Across Demographic Subgroups: Structural and Metabolomic Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Recent Advances in Non-Invasive Blood Markers for Intraductal Papillary Mucinous Neoplasm Grading.Oncology research · 2026Review
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Authors and funding
8 authors.
Funding
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Abstract
Introduction: New-onset diabetes (NOD) represents a high-risk population for pancreatic ductal adenocarcinoma (PDAC), yet effective early detection tools for this specific subgroup remain an unmet clinical need. Methods: We conducted a prospective serum metabolomic analysis using UHPLC-MS/MS in 133 NOD patients aged >65 years, including 60 with PDAC (PDAC+NOD) and 73 without (NOD). Multivariate analysis (OPLS-DA) and machine learning approaches were employed to identify and optimize a diagnostic metabolic biomarker panel. Model performance was evaluated using a hold-out validation set following TRIPOD-ML guidelines. Results: We identified 62 differentially expressed serum metabolites (P<0.05, FDR-corrected), primarily implicating branched-chain amino acid metabolism, bile acid biosynthesis, and sphingolipid signaling pathways. Notably, significant reductions in one-carbon metabolism-related metabolites (serine, glycine, homocysteine) were observed in PDAC+NOD patients. Feature selection yielded an optimized 5-metabolite panel comprising glycine, L-serine, L-methionine, L-homocysteine, and L-homocystine. This panel demonstrated high diagnostic accuracy with an AUC of 0.853 (95% CI: 0.786-0.920) and 75.0% accuracy in distinguishing PDAC+NOD from NOD patients. Discussion: Our study establishes a foundational metabolic biomarker strategy for precision screening of early-stage PDAC in NOD populations. The dysregulated one-carbon metabolites provide novel mechanistic insights into PDAC pathogenesis and offer actionable targets for clinical assay development. Future validation in multi-center cohorts is warranted to confirm clinical utility.
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