{"doi":"10.1101/2025.09.21.25336255","title":"Dementia Risk and Machine Learning-Derived Brain Age Index from Sleep Electroencephalography: A Pooled Cohort Analysis of Over 7,000 Individuals Across Five Community Cohorts","abstract":"Importance: Sleep electroencephalographic (EEG) microstructures are closely related to cognition and undergo age-dependent changes. However, their multidimensional nature makes them challenging to interpret using conventional approaches. Machine learning-computed EEG brain age index (BAI) represents the difference between the sleep EEG-based brain age and chronological age, quantifying deviations in sleep EEG microstructures from normative patterns. Objective: To determine the association between sleep BAI and incident dementia in community-dwelling populations. Design: Five individual cohorts and random-effects meta-analysis. Setting: This study pooled data from five community-based, methodologically consistent, longitudinal cohorts: MESA, ARIC, FHS-OS, MrOS, and SOF. We used Fine-Gray models 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-analyses. Participants: 7,071 participants (MESA 54-94 years old, ARIC 52-75, FHS-OS 40-81, MrOS 67-96, SOF 79-93) without dementia at the time of polysomnography were included. Exposure: The sleep EEG-based BAI was computed using interpretable machine learning, incorporating 13 age-dependent features extracted from central EEG channels in overnight, home-based sleep polysomnography. Main Outcomes and Measures: Incident dementia or probable dementia was determined in each cohort, with death as a competing risk. Results: Across the five cohorts, dementia incidence ranged from 6.6% to 34.3% over a median follow-up of 3.5 to 17.0 years. Across cohorts, each 10-year increase in BAI was associated with a 39% increased risk of incident dementia (hazard ratio: 1.39 [95% confidence interval=1.21-1.59], p<0.001) after adjustment for age, sex, race, education, body mass index, current smoking, sleep medications, and physical activity level. The top feature underlying BAI was waveform kurtosis in N2 with a negative association with incident dementia (p<0.001). The associations remained after additional adjustment for multiple comorbidities, APOE e4 status, and apnea-hypopnea index, and were consistent across sex and age groups. Conclusions and Relevance: A higher sleep EEG-based BAI was associated with a higher risk of incident dementia across five community-based longitudinal cohorts. Future studies are warranted to evaluate the predictive value of BAI as a non-invasive digital biomarker for the early detection of dementia in community settings.","journal":"medRxiv","year":2025,"id":559130,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9244,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1460905,"name":"Sasha Milton","orcid":null,"position":1,"is_corresponding":false},{"id":780801,"name":"Yi Fang","orcid":"0000-0002-1199-187X","position":2,"is_corresponding":false},{"id":998632,"name":"Hash Brown Taha","orcid":"0009-0007-3056-8878","position":3,"is_corresponding":false},{"id":1460906,"name":"S Shiju","orcid":null,"position":4,"is_corresponding":false},{"id":501088,"name":"Robert J. Thomas","orcid":"0000-0002-5575-3953","position":5,"is_corresponding":false},{"id":336257,"name":"Wolfgang Ganglberger","orcid":"0000-0002-6029-2450","position":6,"is_corresponding":false},{"id":350988,"name":"Matthew P. Pase","orcid":"0000-0002-4143-8485","position":7,"is_corresponding":false},{"id":325846,"name":"Timothy M. Hughes","orcid":"0000-0002-2919-7199","position":8,"is_corresponding":false},{"id":3821,"name":"Shaun Purcell","orcid":"0000-0002-7402-5812","position":9,"is_corresponding":false},{"id":17008,"name":"Susan Redline","orcid":"0000-0002-6585-1610","position":10,"is_corresponding":false},{"id":245151,"name":"Katie L. Stone","orcid":"0000-0003-2797-3171","position":11,"is_corresponding":false},{"id":55010,"name":"Kristine Yaffe","orcid":"0000-0003-0919-3825","position":12,"is_corresponding":false},{"id":280809,"name":"M. Brandon Westover","orcid":"0000-0003-4803-312X","position":13,"is_corresponding":false},{"id":315532,"name":"Yue Leng","orcid":"0000-0001-5826-4031","position":14,"is_corresponding":false},{"id":305327,"name":"Haoqi Sun","orcid":"0000-0002-5041-8312","position":0,"is_corresponding":true}],"reference_count":1,"raw_metadata":null,"created_at":"2026-07-19T02:55:34.849815Z","pmid":"41040733","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}