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Metabolomic profiles predict individual multidisease outcomes
Buergel T, Steinfeldt J, Ruyoga G, Pietzner M, Bizzarri D, Vojinovic D, Upmeier Zu Belzen J, Loock L et al.. Nature medicine, 2022-09-22
Topic: Risk, prevention and care in population studies
Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.
Key findings
- We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer.
- Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds.
- Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.
Why it matters
This study found that a metabolomic state derived from blood markers added predictive information beyond standard clinical variables for several diseases, including dementia. It is about predicting risk, not about treating, curing, or preventing Alzheimer's disease.
New to this subject? Start with our plain-English guide: Prevention →
Study details
- Participants117,981
- Evidence strengthLow
How this study scored 70/100
Published in a top-tier journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 10 / 10 | Tier 1 — top-tier general journal (Nature medicine) |
| Journal's citation record | 4 / 4 | Nature Medicine: journal h-index 763 (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 | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 4 / 4 | 117,981 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 6 / 7 | Cited 399 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 66.5 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 5 / 6 | Cited 22.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 5 other studies in this library |
| Still being cited | 4 / 4 | Cited 325 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Ulf Landmesser: h-index 137 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 15 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36138150) |
| Free to read | 2 / 2 | Free to read |
| Total | 70 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41591-022-01980-3
- PubMed: 36138150
- Original source →
- Free full text: Nature Medicine →
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
Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., et al. (2022). Metabolomic profiles predict individual multidisease outcomes. Nature medicine. https://doi.org/10.1038/s41591-022-01980-3
Vancouver
Buergel T, Steinfeldt J, Ruyoga G, Pietzner M, Bizzarri D, Vojinovic D, et al. Metabolomic profiles predict individual multidisease outcomes. Nature medicine. 2022. doi:10.1038/s41591-022-01980-3. PMID: 36138150.
BibTeX
@article{buergel2022-36138150,
author = {Buergel, T. and Steinfeldt, J. and Ruyoga, G. and Pietzner, M. and Bizzarri, D. and Vojinovic, D. and Upmeier Zu Belzen, J. and Loock, L. and others},
title = {{Metabolomic profiles predict individual multidisease outcomes}},
journal = {Nature medicine},
year = {2022},
doi = {10.1038/s41591-022-01980-3},
pmid = {36138150},
url = {https://doi.org/10.1038/s41591-022-01980-3}
}
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