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Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease
Clémence Cavaillès, Meredith L. Wallace, Yue Leng, Katie L. Stone, Sonia Ancoli‐Israel, Kristine S. Yaffe. Communications Medicine, 2025-07-22
Topic: Risk, prevention and care in population studies
We identify three distinct profiles: active healthy sleepers (AHS; 64.0%), fragmented poor sleepers (FPS; 14.1%), and long and frequent nappers (LFN; 21.9%). Over the follow-up, compared to AHS, FPS exhibit increased risks of dementia and CVD events (HR = 1.35, 95% CI = 1.02-1.78 and HR = 1.32, 95% CI = 1.08-1.60, respectively) after multivariable adjustment, whereas LFN show a marginal association with increased CVD events risk (HR = 1.16, 95% CI = 0.98-1.37) but not with dementia (HR = 1.09, 95%CI = 0.86-1.38). These results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes.
Key findings
- We identify three distinct profiles: active healthy sleepers (AHS; 64.0%), fragmented poor sleepers (FPS; 14.1%), and long and frequent nappers (LFN; 21.9%).
- Over the follow-up, compared to AHS, FPS exhibit increased risks of dementia and CVD events (HR = 1.35, 95% CI = 1.02-1.78 and HR = 1.32, 95% CI = 1.08-1.60, respectively) after multivariable adjustment, whereas LFN show a marginal association with increased CVD events risk (HR = 1.16, 95% CI = 0.98-1.37) but not with dementia (HR = 1.09, 95%CI = 0.86-1.38).
- These results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes.
Why it matters
People with a fragmented poor sleep profile had higher risks of dementia and cardiovascular events, while long and frequent nappers did not show a significant dementia association. This identifies possible targets for sleep interventions but tests no treatment, cure, or prevention.
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Study details
- Study designcohort study
- Effect size64.0%; 14.1%; 21.9%
- Evidence strengthModerate
How this study scored 54/100
Published in a peer-reviewed journal, and it is a cohort study following people over time. Scored zero for sample size, 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 (Communications Medicine) |
| Journal's citation record | 0 / 4 | Communications Medicine: journal h-index 55 (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 | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 2 / 7 | Cited 11 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 11.7× 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 11 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Kristine S. Yaffe: h-index 155 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40696147) |
| Free to read | 2 / 2 | Free to read |
| Total | 54 / 100 | — |
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Read the original
- DOI: 10.1038/s43856-025-01019-x
- PubMed: 40696147
- Original source →
- Free full text: Communications 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. Check the citation against the original before you use it.
APA 7th
Cavaillès, C., Wallace, M. L., Leng, Y., Stone, K. L., Ancoli‐Israel, S., & Yaffe, K. S. (2025). Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease. Communications Medicine. https://doi.org/10.1038/s43856-025-01019-x
Vancouver
Cavaillès C, Wallace ML, Leng Y, Stone KL, Ancoli‐Israel S, Yaffe KS. Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease. Communications Medicine. 2025. doi:10.1038/s43856-025-01019-x. PMID: 40696147.
BibTeX
@article{cavailles2025-40696147,
author = {Cavaillès, Clémence and Wallace, Meredith L. and Leng, Yue and Stone, Katie L. and Ancoli‐Israel, Sonia and Yaffe, Kristine S.},
title = {{Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease}},
journal = {Communications Medicine},
year = {2025},
doi = {10.1038/s43856-025-01019-x},
pmid = {40696147},
url = {https://doi.org/10.1038/s43856-025-01019-x}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.