Home › Research › Prevention and risk
Plasma proteomic profiles predict individual future health risk
You J, Guo Y, Zhang Y, Kang JJ, Wang LB, Feng JF, Cheng W, Yu JT.. Nature communications, 2023-11-28
Topic: Brain proteins, microglia and lab models
Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.
Key findings
- Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone.
- We also showed that the good discriminative performance observed could be largely translated into practical clinical utility.
- Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously.
- Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.
Why it matters
Using 1,461 blood proteins measured in 52,006 UK Biobank participants, researchers trained a neural network risk score (ProRS) that separated people by future risk for 45 conditions. Dementia was one of 10 endpoints where accuracy was high (C-index above 0.80), and the score matched or beat established clinical indicators for almost all endpoints. The authors say it was validated only inside UK Biobank and needs independent testing before clinical use; it predicts risk and does not treat or prevent Alzheimer's disease.
New to this subject? Start with our plain-English guide: Prevention →
Study details
- Evidence strengthLow
How this study scored 54/100
Published in a leading specialty 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 | 9 / 10 | Tier 2 — leading specialty journal (Nature communications) |
| Journal's citation record | 4 / 4 | Nature Communications: journal h-index 747 (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 | 2023 — within 6 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 | 5 / 7 | Cited 153 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 30.6 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 11.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 3 / 4 | Cited 152 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Jin‐Tai Yu: h-index 39 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Fudan University: institution h-index 954 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 7 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38016990) |
| Free to read | 2 / 2 | Free to read |
| Total | 54 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41467-023-43575-7
- PubMed: 38016990
- Original source →
- Free full text: Nature Communications →
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 names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
You, J., Guo, Y., Zhang, Y., Kang, J. J., Wang, L. B., Feng, J. F., et al. (2023). Plasma proteomic profiles predict individual future health risk. Nature communications. https://doi.org/10.1038/s41467-023-43575-7
Vancouver
You J, Guo Y, Zhang Y, Kang JJ, Wang LB, Feng JF, et al. Plasma proteomic profiles predict individual future health risk. Nature communications. 2023. doi:10.1038/s41467-023-43575-7. PMID: 38016990.
BibTeX
@article{you2023-38016990,
author = {You, J. and Guo, Y. and Zhang, Y. and Kang, J. J. and Wang, L. B. and Feng, J. F. and Cheng, W. and Yu, J. T.},
title = {{Plasma proteomic profiles predict individual future health risk}},
journal = {Nature communications},
year = {2023},
doi = {10.1038/s41467-023-43575-7},
pmid = {38016990},
url = {https://doi.org/10.1038/s41467-023-43575-7}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
6 studies in the alzhub library list this one in their references.
Related studies in Prevention and risk
Also in Prevention and risk
265 studies in the library are filed under Prevention and risk with no topic tag they share with another; these are this study's neighbours in a fixed tour of them. The Prevention and risk hub.