Evidence map›Paper›PMID 42581394›Full record

ArticleCardiovascular diabetology. Endocrinology reports2026

Metabolic signatures and machine learning identify gut-liver-heart axis dysfunction as a potential link to major adverse cardiovascular events in coronary artery disease.

Min-Qing Lin, Chun-Ka Wong, Ka-Wing Au, Chloe Yu-Yan Cheung, Yee-Man Lau, Song-Yan Liao, Karen Siu-Ling Lam, Aimin Xu, Hung-Fat Tse

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Article in Cardiovascular diabetology. Endocrinology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Min-Qing Lin *Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Chun-Ka Wong *Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Ka-Wing AuDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Chloe Yu-Yan CheungDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Yee-Man LauDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Song-Yan LiaoDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Karen Siu-Ling LamDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Aimin XuDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Hung-Fat TseDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. hftse@hku.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTraditional risk factors do not fully account for the residual cardiometabolic risk of major adverse cardiovascular events (MACE) in coronary artery disease (CAD). We aimed to identify circulating metabolic signatures associated with MACE susceptibility and uncover potential pathobiological mechanisms underlying the gut-liver-heart axis.

methodsIn this retrospective case-control study, untargeted high-performance liquid chromatography-mass spectrometry (HPLC-MS) was performed on fasting serum from 200 patients with CAD and MACE, 200 with CAD without MACE, and 400 matched non-CAD controls. Metabolomics data were processed using univariate analysis, multivariate analysis, and eXtreme Gradient Boosting (XGBoost) machine learning. Pathway enrichment was conducted using metabolite set enrichment analysis. Circulating fibroblast growth factor 19 (FGF19) was quantified via enzyme-linked immunosorbent assay to validate enterohepatic endocrine disruption.

resultsThe MACE cohort exhibited a pronounced cardiometabolic phenotype, characterized by significantly highest rates of diabetes, hypertension, and dyslipidemia (p < 0.01). The XGBoost model robustly discriminated patients with CAD and MACE from non-CAD controls (area under the curve [AUC] = 0.984) and from patients with CAD without MACE (AUC = 0.932). Pathway analysis revealed marked dysregulation of linoleic acid metabolism and peroxisome proliferator-activated receptor (PPAR) signaling (p < 0.05). Specifically, pro-inflammatory oxidized linoleic acid metabolites, including 9- and 13-hydroxyoctadecadienoic acid (HODE)-which drive plaque instability-were significantly elevated in the MACE cohort. Furthermore, atheroprotective primary bile acids were significantly depleted in patients with CAD (p < 0.001). This depletion was accompanied by an elevated serum FGF19 level (p = 0.003), reflecting a potential disruption of the gut-liver-heart endocrine axis.

conclusionsIn conclusion, dysregulated linoleic acid oxidation, altered PPAR signaling, and disturbed primary bile acid-FGF19 metabolism may represent key metabolic pathways associated with MACE susceptibility. Integrating these gut-liver-heart axis signatures into machine learning models holds significant promise for refining cardiovascular risk stratification and guiding targeted preventive interventions.

Indexed as

Cardiometabolic riskCoronary artery diseaseFibroblast growth factor 19Gut-liver-heart axisLinoleic acidMachine learningMajor adverse cardiovascular eventMetabolomicsPrimary bile acid

Identifiers

PMID42581394
PMCPMC13465299

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.