Home › Research › Digital health tools
Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals
Sindhuja Tirumalai Govindarajan, Elizabeth Mamourian, Güray Erus, Ahmed Abdulkadir, Randa Melhem, Jimit J. Doshi, Raymond Pomponio, Duygu Tosun et al.. Nature Communications, 2025-03-19
Topic: Amyloid, tau and blood-test diagnosis
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45-85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45-64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Govindarajan, S. T., Mamourian, E., Erus, G., Abdulkadir, A., Melhem, R., Doshi, J. J., et al. (2025). Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals. Nature Communications. https://doi.org/10.1038/s41467-025-57867-7
Vancouver
Govindarajan ST, Mamourian E, Erus G, Abdulkadir A, Melhem R, Doshi JJ, et al. Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals. Nature Communications. 2025. doi:10.1038/s41467-025-57867-7. PMID: 40108173.
BibTeX
@article{govindarajan2025-40108173,
author = {Govindarajan, Sindhuja Tirumalai and Mamourian, Elizabeth and Erus, Güray and Abdulkadir, Ahmed and Melhem, Randa and Doshi, Jimit J. and Pomponio, Raymond and Tosun, Duygu and others},
title = {{Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals}},
journal = {Nature Communications},
year = {2025},
doi = {10.1038/s41467-025-57867-7},
pmid = {40108173},
url = {https://doi.org/10.1038/s41467-025-57867-7}
}
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.
Key findings
- Using harmonized MRI data from 37,096 participants (45-85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45-64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status.
Why it matters
Machine learning models using MRI data from 37,096 participants identified distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals, with markers most sensitive in mid-life. Integrating these personalized brain signatures could guide early risk detection and stratification, but this does not treat, cure, or prevent Alzheimer's disease.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designcohort study
- Participants37,096
- Evidence strengthModerate
How this study scored 63/100
Published in a leading specialty journal, and it is a cohort study following people over time. Scored zero for effect reported 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 | 7 / 12 | A cohort study following people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” 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 | 4 / 4 | 37,096 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 | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× 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 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Christos A. Davatzikos: h-index 124 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | National Institutes of Health: institution h-index 2,723 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 16 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40108173) |
| Free to read | 2 / 2 | Free to read |
| Total | 63 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41467-025-57867-7
- PubMed: 40108173
- 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).
Related
Studies in the library that share something with this one. The line above each title says what.
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 18.
Related studies in Digital health tools
Also tagged “machine”
834 studies in the library carry the topic tag “machine”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 834 tagged studies.