{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T19:51:36Z","timestamp":1793303496280,"version":"4.5.0"},"reference-count":39,"publisher":"American Medical Association (AMA)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Microstructures of sleep electroencephalography (EEG) are closely related to cognition and undergo age-dependent changes. However, their multidimensional nature makes them challenging to interpret using conventional approaches. The machine learning\u2013based EEG brain age index (BAI) measures the deviation between sleep EEG-based brain age and chronological age.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To determine the association between sleep BAI and incident dementia in community-dwelling populations.<\/jats:p>\n <\/jats:sec>\n \n Data Sources<\/jats:title>\n For this individual participant data (IPD) meta-analysis, sleep study data from 5 community-based longitudinal cohorts were pooled. These cohorts included the Multi-Ethnic Study of Atherosclerosis (MESA; 2010-2013), the Atherosclerosis Risk in Communities (ARIC) study (1987-1989), the Framingham Heart Study\u2013Offspring Study (FHS-OS; 1995-1998), the Osteoporotic Fractures in Men Study (MrOS; 2003-2005), and the Study of Osteoporotic Fractures (SOF; 2002-2004).<\/jats:p>\n <\/jats:sec>\n \n Study Selection<\/jats:title>\n Adults (aged \u226518 years) without dementia at the time of polysomnography were included.<\/jats:p>\n <\/jats:sec>\n \n Data Extraction and Synthesis<\/jats:title>\n The BAI was computed using interpretable machine learning, incorporating sleep EEG features extracted from central channels in overnight, home-based polysomnography. Fine-Gray models were used to assess the association between BAI and incident dementia within each cohort, accounting for death as a competing risk. Cohort-specific estimates were then pooled using random-effects meta-analysis. Analyses were performed between March 2024 and September 2025.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Incident dementia or probable dementia was determined in each cohort, with death as a competing risk.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n This meta-analysis included 7105 participants from the MESA (n\u2009=\u20091802; mean [SD] age, 69.3 [9.0] years; 956 females [53.1%]), ARIC (n\u2009=\u20091796; 62.5 [5.7] years; 918 females [51.1%]), FHS-OS (n\u2009=\u2009617; 59.5 [8.9] years; 318 females [51.5%]), MrOS (n\u2009=\u20092639 males [100%]; 76.0 [5.3] years), and SOF (n\u2009=\u2009251 females [100%]; 82.7 [2.9] years) cohorts. The median (IQR) time to dementia was 4.8 (4.2-5.6) years in the MESA cohort (n\u2009=\u2009119 [6.6%]), 16.9 (14.9-19.8) years in the ARIC cohort (n\u2009=\u2009354 [19.7%]), 13.1 (8.5-16.2) years in the FHS-OS cohort (n\u2009=\u200959 [9.6%]), 3.6 (1.3-7.1) years in the MrOS cohort (n\u2009=\u2009470 [17.8%]), and 4.6 (4.2-5.2) years in the SOF cohort (n\u2009=\u200986 [34.3%]). Across the cohorts, each 10-year increase in BAI was associated with a 39% higher risk of incident dementia (hazard ratio [HR], 1.39 [95% CI, 1.21-1.59];\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001) after adjustment for covariates. These associations remained after additional adjustment for comorbidities and apnea-hypopnea index scores (HR, 1.31 [95% CI, 1.14-1.50];\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001) and apolipoprotein E \u03b54 (HR, 1.22 [95% CI, 1.02-1.45];\n P<\/jats:italic>\n \u2009=\u2009.03), and they were consistent across sex and age groups.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this IPD meta-analysis, a higher sleep EEG-based BAI was associated with a higher risk of incident dementia. These findings highlight the need to evaluate the predictive value of the BAI as a noninvasive digital marker for early detection of dementia in community settings.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2026.1521","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:30:40Z","timestamp":1773934240000},"page":"e261521","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning\u2013Based Sleep Electroencephalographic Brain Age Index and Dementia Risk"],"prefix":"10.1001","volume":"9","author":[{"given":"Haoqi","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sasha","family":"Milton","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, University of California, San Francisco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, University of California, San Francisco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hash Brown","family":"Taha","sequence":"additional","affiliation":[{"name":"Washington University School of Medicine in St Louis, St Louis, Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shreya","family":"Shiju","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, University of California, San Francisco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert J.","family":"Thomas","sequence":"additional","affiliation":[{"name":"Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Ganglberger","sequence":"additional","affiliation":[{"name":"Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew P.","family":"Pase","sequence":"additional","affiliation":[{"name":"School of Psychological Science, Monash University, Melbourne, Victoria, Australia"},{"name":"Harvard T. 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