Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging
- Author(s)
- Jiahang Li, Martin Brenner, Iro Pierides, Barbara Wessner, Bernhard Franzke, Eva Maria Strasser, Steffen Waldherr, Karl Heinz Wagner, Wolfram Weckwerth
- Abstract
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(s)
- Functional and Evolutionary Ecology, Department of Nutritional Sciences, Department of Sport and Human Movement Science
- External organisation(s)
- Nankai University, Research Center Health Sciences, University of Applied Sciences Hochschule Campus Wien
- Journal
- Npj systems biology and applications
- Volume
- 11
- ISSN
- 2056-7189
- DOI
- https://doi.org/10.1038/s41540-025-00580-4
- Publication date
- 12-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 106044 Systems biology, 106057 Metabolomics, 301308 Ageing research
- ASJC Scopus subject areas
- Modelling and Simulation, General Biochemistry,Genetics and Molecular Biology, Drug Discovery, Computer Science Applications, Applied Mathematics
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/8bd0e00e-2fde-401c-bf87-4f98375ae257