Integrated Metabolic and Inflammatory Clustering Reveals Distinct Risk Profiles for Digestive Diseases.

Publisher: WILEY-VCH Country of Publication: Germany NLM ID: 101664569 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2198-3844 (Electronic) Linking ISSN: 21983844 NLM ISO Abbreviation: Adv Sci (Weinh) Subsets: MEDLINE

Original Publication: Weinheim : WILEY-VCH, [2014]-

Digestive System Diseases*/metabolism ; Inflammation*/metabolism; Biomarkers/blood ; Biomarkers/metabolism ; Risk Assessment/methods ; Humans ; Male ; Female ; Middle Aged ; Risk Factors ; Machine Learning ; Cluster Analysis ; Aged

Emerging research highlights the complex relationship between metabolic dysfunction and chronic low-grade inflammation, which disrupts gut homeostasis and drives disease progression. However, most current studies evaluate metabolic and inflammatory markers separately, relying on basic indicators such as body mass index (BMI) or individual biomarkers. In this study, a scalable clustering framework is developed to integrate six clinical parameters in 398 432 participants from the UK Biobank, identifying four distinct metabolic-inflammatory subtypes. Cox proportional hazards models demonstrate significant associations between these subtypes and digestive disease risk. Using 251 plasma metabolites and elastic net regression, cluster-associated metabolite signatures are identified. Mediation analyses indicate that metabolic signatures mediate the association between clusters and digestive disease risk. Machine learning algorithms are applied to construct disease-specific metabolic risk scores, achieving C-indices above 0.70 for ten digestive disease endpoints. Explainable machine learning approaches further identify both shared and disease-specific predictors, with glycoprotein acetyls, valine, tyrosine, and fatty acids emerging as key risk indicators. This integrative approach provides a comprehensive framework for digestive disease risk assessment and offers novel insights into the metabolic mechanisms underlying disease susceptibility.
(© 2025 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

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82170533 National Natural Science Foundation of China; 82370527 National Natural Science Foundation of China; 2023R5239 High-Level Talent Science and Technology Innovation Leading Talent Program of Zhejiang Province; 2025C02136 Science and Technology Program of Zhejiang Province

Keywords: cluster analysis; digestive diseases; machine learning; metabolomics

0 (Biomarkers)

Date Created: 20250929 Date Completed: 20251127 Latest Revision: 20251203

20260130

PMC12667462

10.1002/advs.202511000

41017045