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Bazireh, H. ; Efiong, E.E. ; Adam, J. ; Wang-Sattler, R. ; Suhre, K.* ; Adamski, J. ; Lintelmann, J. ; Schlesinger, S.* ; Cecil, A. ; Witting, M. ; Köenig, W.* ; Scheidt, M.v.* ; Schunkert, H.* ; Peters, A. ; Grallert, H. ; Sharma, S.

Integrative multi-omics reveals distinct molecular signatures of metabolic syndrome from KORA FF4 study.

Metabolites 16:821 (2026)
Publ. Version/Full Text DOI
Open Access Gold
Creative Commons Lizenzvertrag
Background/Objectives: Metabolic syndrome (MetS) is a heterogeneous cluster of car-diometabolic risk factors whose underlying molecular heterogeneity remains incompletelyunderstood. Methods: Targeted metabolomics (388 metabolites) and untargeted pro-teomics (842 proteins) were analyzed in the KORA FF4 study (n = 1921). Multivariableregression, LASSO, and Multi-Omics Factor Analysis (MOFA) were applied to identify MetS-associated signatures. As a secondary analysis, soft-clustering archetypal analysis based on22 clinical variables was performed within the MetS subgroup (n = 582) to identify extremeclinical subphenotypes. Results: A total of 117 metabolites and 70 proteins were associatedwith MetS, including five metabolites and eight proteins shared across all MetS traits. Keymetabolomic features comprised diacylglycerols, triglyceride score, hexoses, and branched-chain amino acids. Prominent proteins included APOC4, APOE, APOB, APOD, APOF,adiponectin, LBP, and SHBG, reflecting alterations in lipid metabolism, inflammation, andmetabolic regulation. MOFA identified a latent factor strongly linked to MetS, integratingsignatures of lipid metabolism, insulin resistance, and inflammation. Archetypal analysisidentified extreme MetS profiles and associated with metabolites and proteins shared acrossall Mets. Conclusions: Integrated multi-omics analysis revealed MetS-associated molecularsignatures characterized primarily by dysregulated lipid metabolism, insulin resistance,and chronic low-grade inflammation. Secondary archetypal analysis highlighted clinicalheterogeneity at the extreme boundaries of MetS while supporting a shared molecularfoundation. These findings highlight the value of combining clinical, metabolomics andproteomics to improve characterization of MetS heterogeneity and inform future precisionrisk stratification.
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Publication type Article: Journal article
Document type Scientific Article
ISSN (print) / ISBN 2218-1989
e-ISSN 2218-1989
Journal Metabolites
Quellenangaben Volume: 16, Issue: 11, Pages: , Article Number: 821 Supplement: ,
Publisher MDPI
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
Institute of Translational Genetics (ITG)
Institute of Experimental Genetics (IEG)
Core Facility Metabolomics and Proteomics (CF-MPC)