Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging

Autor(en)

Jiahang Li, Martin Brenner, Iro Pierides, Barbara Wessner, Bernhard Franzke, Eva Maria Strasser, Steffen Waldherr, Karl Heinz Wagner, Wolfram Weckwerth

Abstrakt

Physical inactivity and low fitness have become global health concerns. Metabolomics, as an integrative approach, may link fitness to molecular changes. In this study, we analyzed blood metabolomes from elderly individuals under different treatments. By defining two fitness groups and their corresponding metabolite profiles, we applied several machine learning classifiers to identify key metabolite biomarkers. Aspartate consistently emerged as a dominant fitness marker. We further defined a body activity index (BAI) and analyzed two cohorts with high and low BAI using COVRECON, a novel method for metabolic network interaction analysis. COVRECON identifies causal molecular dynamics in multiomics data. Aspartate-amino-transferase (AST) was among the dominant processes distinguishing the groups. Routine blood tests confirmed significant differences in AST and ALT. Aspartate is also a known biomarker in dementia, related to physical fitness. In summary, we combine machine learning and COVRECON to identify metabolic biomarkers and molecular dynamics supporting active aging.

Organisation(en)

Department für Funktionelle und Evolutionäre Ökologie, Department für Ernährungswissenschaften, Institut für Sport- und Bewegungswissenschaft

Externe Organisation(en)

Nankai University, Research Center Health Sciences, Hochschule Campus Wien

Journal

Npj systems biology and applications

Band

11

ISSN

2056-7189

DOI

https://doi.org/10.1038/s41540-025-00580-4

Publikationsdatum

12-2025

Peer-reviewed

Ja

ÖFOS 2012

106044 Systembiologie, 106057 Metabolomik, 301308 Alternsforschung

ASJC Scopus Sachgebiete

Modelling and Simulation, Allgemeine Biochemie, Genetik und Molekularbiologie, Drug Discovery, Computer Science Applications, Applied Mathematics

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/8bd0e00e-2fde-401c-bf87-4f98375ae257