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Peer-reviewedCC BY 4.0Metabolites

Integrative Multi-Omics Reveals Distinct Molecular Signatures of Metabolic Syndrome from KORA FF4 Study

  • Homa Bazireh1,2,3,4
  • Esienanwan Esien Efiong1,2,5
  • Jonathan Adam1,2,4
  • Rui Wang-Sattler4,6
  • Karsten Suhre7,8
  • Jerzy Adamski9,10,11
  • Jutta Lintelmann12
  • Sabrina Schlesinger13,14
  • Alexander Cecil12
  • Michael Witting12,15
  • Wolfgang Koenig16,17,18
  • Moritz Von Scheidt16,17
4 more authors
  • Heribert Schunkert16,17
  • Annette Peters2,3,4
  • Harald Grallert1,2,4
  • Sapna Sharma1,2,4
18 affiliations
  1. 1Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
  2. 2Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
  3. 3Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-University München, Marchioninistraße 15, 81377 München, Germany
  4. 4German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
  5. 5Department of Biochemistry, Faculty of Science, Federal University of Lafia, PMB 146, Lafia 950101, Nigeria
  6. 6Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
  7. 7Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
  8. 8Bioinformatics Core, Weill Cornell Medical College, Qatar Education City, Doha P.O. Box 24144, Qatar
  9. 9Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
  10. 10Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
  11. 11Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
  12. 12Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
  13. 13Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
  14. 14German Center for Diabetes Research (DZD), Partner Düsseldorf, Auf’m Hennekamp 65, 40225 Düsseldorf, Germany
  15. 15Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Maximus-von-Imhof-Forum 2, 85354 Freising, Germany
  16. 16Deutsches Herzzentrum München, Technische Universität München, 80333 Munich, Germany
  17. 17German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, 80336 Munich, Germany
  18. 18Institute of Epidemiology and Medical Biometry, University of Ulm, 89081 Ulm, Germany
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Abstract

Background/Objectives: Metabolic syndrome (MetS) is a heterogeneous cluster of cardiometabolic risk factors whose underlying molecular heterogeneity remains incompletely understood. Methods: Targeted metabolomics (388 metabolites) and untargeted proteomics (842 proteins) were analyzed in the KORA FF4 study (n = 1921). Multivariable regression, LASSO, and Multi-Omics Factor Analysis (MOFA) were applied to identify MetS-associated signatures. As a secondary analysis, soft-clustering archetypal analysis based on 22 clinical variables was performed within the MetS subgroup (n = 582) to identify extreme clinical subphenotypes. Results: A total of 117 metabolites and 70 proteins were associated with MetS, including five metabolites and eight proteins shared across all MetS traits. Key metabolomic 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, and metabolic regulation. MOFA identified a latent factor strongly linked to MetS, integrating signatures of lipid metabolism, insulin resistance, and inflammation. Archetypal analysis identified extreme MetS profiles and associated with metabolites and proteins shared across all Mets. Conclusions: Integrated multi-omics analysis revealed MetS-associated molecular signatures characterized primarily by dysregulated lipid metabolism, insulin resistance, and chronic low-grade inflammation. Secondary archetypal analysis highlighted clinical heterogeneity at the extreme boundaries of MetS while supporting a shared molecular foundation. These findings highlight the value of combining clinical, metabolomics and proteomics to improve characterization of MetS heterogeneity and inform future precision risk stratification.

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Cite

BibTeXRIS
BibTeX
@article{bazireh2026integrative,
  title = {{Integrative Multi-Omics Reveals Distinct Molecular Signatures of Metabolic Syndrome from KORA FF4 Study}},
  author = {Homa Bazireh and Esienanwan Esien Efiong and Jonathan Adam and Rui Wang-Sattler and Karsten Suhre and Jerzy Adamski and Jutta Lintelmann and Sabrina Schlesinger and Alexander Cecil and Michael Witting and Wolfgang Koenig and Moritz Von Scheidt and Heribert Schunkert and Annette Peters and Harald Grallert and Sapna Sharma},
  journal = {Metabolites},
  year = {2026},
  month = oct,
  doi = {10.3390/metabo16110821},
  url = {https://www.mdpi.com/2218-1989/16/11/821},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Integrative Multi-Omics Reveals Distinct Molecular Signatures of Metabolic Syndrome from KORA FF4 Study
AU  - Homa Bazireh
AU  - Esienanwan Esien Efiong
AU  - Jonathan Adam
AU  - Rui Wang-Sattler
AU  - Karsten Suhre
AU  - Jerzy Adamski
AU  - Jutta Lintelmann
AU  - Sabrina Schlesinger
AU  - Alexander Cecil
AU  - Michael Witting
AU  - Wolfgang Koenig
AU  - Moritz Von Scheidt
AU  - Heribert Schunkert
AU  - Annette Peters
AU  - Harald Grallert
AU  - Sapna Sharma
PY  - 2026
DA  - 2026/10/28
JO  - Metabolites
DO  - 10.3390/metabo16110821
UR  - https://www.mdpi.com/2218-1989/16/11/821
AB  - Background/Objectives: Metabolic syndrome (MetS) is a heterogeneous cluster of cardiometabolic risk factors whose underlying molecular heterogeneity remains incompletely understood. Methods: Targeted metabolomics (388 metabolites) and untargeted proteomics (842 proteins) were analyzed in the KORA FF4 study (n = 1921). Multivariable regression, LASSO, and Multi-Omics Factor Analysis (MOFA) were applied to identify MetS-associated signatures. As a secondary analysis, soft-clustering archetypal analysis based on 22 clinical variables was performed within the MetS subgroup (n = 582) to identify extreme clinical subphenotypes. Results: A total of 117 metabolites and 70 proteins were associated with MetS, including five metabolites and eight proteins shared across all MetS traits. Key metabolomic 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, and metabolic regulation. MOFA identified a latent factor strongly linked to MetS, integrating signatures of lipid metabolism, insulin resistance, and inflammation. Archetypal analysis identified extreme MetS profiles and associated with metabolites and proteins shared across all Mets. Conclusions: Integrated multi-omics analysis revealed MetS-associated molecular signatures characterized primarily by dysregulated lipid metabolism, insulin resistance, and chronic low-grade inflammation. Secondary archetypal analysis highlighted clinical heterogeneity at the extreme boundaries of MetS while supporting a shared molecular foundation. These findings highlight the value of combining clinical, metabolomics and proteomics to improve characterization of MetS heterogeneity and inform future precision risk stratification.
SN  - 2218-1989
ER  -