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Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging
Jiahang Li, Martin B. Brenner, Iro Pierides, Barbara Wessner, B. Franzke, Eva-Maria Strasser, Steffen Waldherr, Karl‐Heinz Wagner et al.. npj Systems Biology and Applications, 2025-09-24
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- Routine blood tests confirmed significant differences in AST and ALT.
Why it matters
This study combined machine learning and metabolic network analysis to identify metabolite biomarkers linked to fitness in elderly individuals, with aspartate emerging as a dominant marker. It is about understanding active aging and metabolism, not treating, curing or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 36/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (npj Systems Biology and Applications) |
| Journal's citation record | 0 / 4 | Npj Systems Biology and Applications: journal h-index 55 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 4 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 4 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Wolfram Weckwerth: h-index 82 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Vienna: institution h-index 851 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40993122) |
| Free to read | 2 / 2 | Free to read |
| Total | 36 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41540-025-00580-4
- PubMed: 40993122
- Original source →
- Free full text: npj Systems Biology and Applications →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Li, J., Brenner, M. B., Pierides, I., Wessner, B., Franzke, B., Strasser, E.-M., et al. (2025). Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging. npj Systems Biology and Applications. https://doi.org/10.1038/s41540-025-00580-4
Vancouver
Li J, Brenner MB, Pierides I, Wessner B, Franzke B, Strasser E.-M, et al. Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging. npj Systems Biology and Applications. 2025. doi:10.1038/s41540-025-00580-4. PMID: 40993122.
BibTeX
@article{li2025-40993122,
author = {Li, Jiahang and Brenner, Martin B. and Pierides, Iro and Wessner, Barbara and Franzke, B. and Strasser, Eva-Maria and Waldherr, Steffen and Wagner, Karl‐Heinz and others},
title = {{Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging}},
journal = {npj Systems Biology and Applications},
year = {2025},
doi = {10.1038/s41540-025-00580-4},
pmid = {40993122},
url = {https://doi.org/10.1038/s41540-025-00580-4}
}
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