Importance 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–based EEG brain age index (BAI) measures the deviation between sleep EEG-based brain age and chronological age. Objective To determine the association between sleep BAI and incident dementia in community-dwelling populations. Data Sources 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–Offspring Study (FHS-OS; 1995-1998), the Osteoporotic Fractures in Men Study (MrOS; 2003-2005), and the Study of Osteoporotic Fractures (SOF; 2002-2004). Study Selection Adults (aged ≥18 years) without dementia at the time of polysomnography were included. Data Extraction and Synthesis 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. Main Outcomes and Measures Incident dementia or probable dementia was determined in each cohort, with death as a competing risk. Results This meta-analysis included 7105 participants from the MESA (n = 1802; mean SD age, 69.3 9.0 years; 956 females 53.1%), ARIC (n = 1796; 62.5 5.7 years; 918 females 51.1%), FHS-OS (n = 617; 59.5 8.9 years; 318 females 51.5%), MrOS (n = 2639 males 100%; 76.0 5.3 years), and SOF (n = 251 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 = 119 6.6%), 16.9 (14.9-19.8) years in the ARIC cohort (n = 354 19.7%), 13.1 (8.5-16.2) years in the FHS-OS cohort (n = 59 9.6%), 3.6 (1.3-7.1) years in the MrOS cohort (n = 470 17.8%), and 4.6 (4.2-5.2) years in the SOF cohort (n = 86 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; P lt; .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; P lt; .001) and apolipoprotein E ε4 (HR, 1.22 95% CI, 1.02-1.45; P = .03), and they were consistent across sex and age groups. Conclusions and Relevance 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.
Sun et al. (Thu,) studied this question.