Evidence map›Paper›PMID 41040733›Full record

ArticlemedRxiv : the preprint server for health sciences2025

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.

Haoqi Sun, Sasha Milton, Yi Fang, Hash Brown Taha, Shreya Shiju, Robert J Thomas, Wolfgang Ganglberger, Matthew P Pase, Timothy Hughes, Shaun Purcell and 5 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Haoqi SunDepartment of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.ORCID 0000-0002-5041-8312
Sasha MiltonDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
Yi FangDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.ORCID 0000-0002-1199-187X
Hash Brown TahaDepartment of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Shreya ShijuDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
Robert J ThomasDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.ORCID 0000-0002-5575-3953
Wolfgang GanglbergerDepartment of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Matthew P PaseSchool of Psychological Science & Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia.
Timothy HughesWake Forest University School of Medicine, Winston-Salem, NC, USA.
Shaun PurcellDepartment of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA.ORCID 0000-0002-7402-5812
Susan RedlineDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Katie L StoneResearch Institute, California Pacific Medical Center, San Francisco, CA; Department of Epidemiology, University of California, San Francisco, CA, USA.
Kristine YaffeDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
M Brandon WestoverDepartment of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Yue LengDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.ORCID 0000-0001-5826-4031

Funding

Oregon Clinical and Translational Research Institute - The National COVID Cohort Collaborative (N3C)UL1TR002369 · NCATS · OREGON HEALTH & SCIENCE UNIVERSITY · PI Cynthia D Morris, Christopher G. Slatore · 2017 to 2026
$78.4M
ARIC Neurocognitive Study (ARIC-NCS) Renewal 2023-2028U01HL096812 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI JOSEF CORESH, THOMAS H MOSLEY · 2010 to 2026
$65.7M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
FRAMINGHAM HEART STUDY - YEAR 5 EXAM75N92019D00031 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · 2019 to 2024
$29.8M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
South Texas Alzheimer's Disease Research CenterP30AG066546 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Mohamad Habes · 2021 to 2026
$24.0M
Study of Osteoporotic FracturesR01AG005407 · NIA · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · PI CUMMINGS, STEVEN RON, YAFFE, KRISTINE · 1986 to 2016
$23.2M
PRECURSORS OF STROKE INCIDENCE AND PROGNOSISR01NS017950 · NINDS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Hugo Javier Aparicio, Jose Rafael Romero · 1985 to 2026
$22.9M
EPIDEMIOLOGY OF DEMENTIA IN THE FRAMINGHAM STUDYR01AG008122 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, SESHADRI, SUDHA · 1989 to 2015
$14.4M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - COORDINATING CENTER - TASK AREA B.2 AND B.375N92022D00001 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI COUPER, DAVID · 2022 to 2025
$13.7M
Long term fracture risk and change in peripheral bone in the oldest old men: The MrOS studyR01AG066671 · NIA · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI BOUXSEIN, MARY L, CAWTHON, PEGGY MANNEN · 2020 to 2024
$13.3M
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1 TR002369NHLBI NIH HHS 75N92019D00031NHLBI NIH HHS 75N92022D00001NHLBI NIH HHS 75N92022D00002NHLBI NIH HHS 75N92022D00003NHLBI NIH HHS 75N92022D00004NHLBI NIH HHS 75N92022D00005NHLBI NIH HHS HHSN268201500001CNHLBI NIH HHS HHSN268201500001INHLBI NIH HHS HHSN268201500003CNHLBI NIH HHS HHSN268201500003INHLBI NIH HHS N01 HC025195NHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095166NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS R01 HL070837NHLBI NIH HHS R01 HL070838NHLBI NIH HHS R01 HL070839NHLBI NIH HHS R01 HL070841NHLBI NIH HHS R01 HL070842NHLBI NIH HHS R01 HL070847NHLBI NIH HHS R01 HL070848NHLBI NIH HHS R01 HL071194NHLBI NIH HHS R01 HL098433NHLBI NIH HHS R24 HL114473NHLBI NIH HHS U01 HL053916NHLBI NIH HHS U01 HL053931NHLBI NIH HHS U01 HL053934NHLBI NIH HHS U01 HL053937NHLBI NIH HHS U01 HL053938NHLBI NIH HHS U01 HL053941NHLBI NIH HHS U01 HL063463NHLBI NIH HHS U01 HL064360NHLBI NIH HHS U01 HL096812NHLBI NIH HHS U01 HL096814NHLBI NIH HHS U01 HL096899NHLBI NIH HHS U01 HL096902NHLBI NIH HHS U01 HL096917NIAMS NIH HHS R01 AR035582NIAMS NIH HHS R01 AR035583NIAMS NIH HHS R01 AR035584NIAMS NIH HHS U01 AR066160NIA NIH HHS P30 AG066546NIA NIH HHS R01 AG005394NIA NIH HHS R01 AG005407NIA NIH HHS R01 AG008122NIA NIH HHS R01 AG008415NIA NIH HHS R01 AG016495NIA NIH HHS R01 AG021918NIA NIH HHS R01 AG026720NIA NIH HHS R01 AG027574NIA NIH HHS R01 AG027576NIA NIH HHS R01 AG049607NIA NIH HHS R01 AG054076NIA NIH HHS R01 AG059421NIA NIH HHS R01 AG062531NIA NIH HHS R01 AG066524NIA NIH HHS R01 AG066671NIA NIH HHS R01 AG070867NIA NIH HHS R01 AG073410NIA NIH HHS R01 AG083836NIA NIH HHS R21 AG085495NIA NIH HHS RF1 AG059421NIA NIH HHS RF1 AG064312NIA NIH HHS T32 AG000212NIA NIH HHS U01 AG027810NIA NIH HHS U01 AG042124NIA NIH HHS U01 AG042139NIA NIH HHS U01 AG042140NIA NIH HHS U01 AG042143NIA NIH HHS U01 AG042145NIA NIH HHS U01 AG042168NINDS NIH HHS R01 NS017950NINDS NIH HHS R01 NS102190NINDS NIH HHS R01 NS102574NINDS NIH HHS R01 NS107291NINDS NIH HHS RF1 NS120947NINDS NIH HHS UF1 NS125513
6 · The paper itself

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.

Indexed as

Brain AgeCognitive ImpairmentDementiaElectroencephalogramSleep

Identifiers

PMID41040733
PMCPMC12486002

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.