ArticleCommunications medicine2025
Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Objective prediction of siesta based on machine learning and association with obesity.Sleep health · 2026Article
- SleepJEPA: Learning the latent world of sleep with at-home sleep data to estimate disease risk.medRxiv : the preprint server for health sciences · 2026Article
- Multidimensional self-reported sleep health, cognitive decline, and risk of all-cause dementia: A population-based multi-cohort study.Journal of Alzheimer's disease : JAD · 2026Article
- Control vs. salience: a new axis of circadian brain-body organization.Npj biological timing and sleep · 2026Article
- Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies.Nature communications · 2025Article
- U shaped association between sleep duration and long term cognitive decline trajectories in a national cohort.Scientific reports · 2025Article
- Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary.Sleep · 2025Article
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
backgroundSleep health comprises several dimensions such as sleep duration and fragmentation, circadian activity, and daytime behavior. Yet, most research has focused on individual sleep characteristics. Studies are needed to identify sleep/circadian profiles incorporating multiple dimensions and to assess their associations with adverse health outcomes.
methodsThis multicenter population-based cohort study identified 24 h actigraphy-based sleep/circadian profiles in 2667 men aged ≥65 years using an unsupervised machine learning approach and investigated their associations with dementia and cardiovascular disease (CVD) incidence over 12 years.
resultsWe 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).
conclusionsThese results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.