Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease
Abstract Background Sleep 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 dimensi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-01019-x |
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| author | Clémence Cavaillès Meredith Wallace Yue Leng Katie L. Stone Sonia Ancoli-Israel Kristine Yaffe |
| author_facet | Clémence Cavaillès Meredith Wallace Yue Leng Katie L. Stone Sonia Ancoli-Israel Kristine Yaffe |
| author_sort | Clémence Cavaillès |
| collection | DOAJ |
| description | Abstract Background Sleep 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. Methods This 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. Results 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). Conclusions These results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes. |
| format | Article |
| id | doaj-art-c61e595cb63a4bcc8f284a4667f2d68f |
| institution | DOAJ |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-c61e595cb63a4bcc8f284a4667f2d68f2025-08-20T03:06:04ZengNature PortfolioCommunications Medicine2730-664X2025-07-015111010.1038/s43856-025-01019-xMultidimensional sleep profiles via machine learning and risk of dementia and cardiovascular diseaseClémence Cavaillès0Meredith Wallace1Yue Leng2Katie L. Stone3Sonia Ancoli-Israel4Kristine Yaffe5Department of Psychiatry and Behavioral Sciences, University of California San FranciscoDepartment of Psychiatry, University of PittsburghDepartment of Psychiatry and Behavioral Sciences, University of California San FranciscoResearch Institute, California Pacific Medical CenterDepartment of Psychiatry, University of California San DiegoDepartment of Psychiatry and Behavioral Sciences, University of California San FranciscoAbstract Background Sleep 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. Methods This 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. Results 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). Conclusions These results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes.https://doi.org/10.1038/s43856-025-01019-x |
| spellingShingle | Clémence Cavaillès Meredith Wallace Yue Leng Katie L. Stone Sonia Ancoli-Israel Kristine Yaffe Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease Communications Medicine |
| title | Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease |
| title_full | Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease |
| title_fullStr | Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease |
| title_full_unstemmed | Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease |
| title_short | Multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease |
| title_sort | multidimensional sleep profiles via machine learning and risk of dementia and cardiovascular disease |
| url | https://doi.org/10.1038/s43856-025-01019-x |
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