Evidence map›Paper›PMID 39442064›Full record

ArticleNeurology2024

Association of Self-Reported Sleep Characteristics With Neuroimaging Markers of Brain Aging Years Later in Middle-Aged Adults.

Clémence Cavaillès, Christina Dintica, Mohamad Habes, Yue Leng, Mercedes R Carnethon, Kristine Yaffe

Abstract read
In one paragraph

Article in Neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
  2. Article
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  4. Association of sleep duration with Alzheimer's disease and cognition.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  5. Midlife sleep characteristics and Alzheimer's disease biomarkers 20 years later.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Clémence CavaillèsFrom the Departments of Psychiatry and Behavioral Sciences (C.C., C.D., Y.L., K.Y.), Neurology (K.Y.), and Epidemiology (K.Y.), University of California, San Francisco; Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core (M.H.), Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio; Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics (M.H.), University of Pennsylvania, Philadelphia; and Department of Preventive Medicine (M.R.C.), Northwestern University Feinberg School of Medicine, Chicago, IL.ORCID 0000-0002-8443-2307
Christina DinticaFrom the Departments of Psychiatry and Behavioral Sciences (C.C., C.D., Y.L., K.Y.), Neurology (K.Y.), and Epidemiology (K.Y.), University of California, San Francisco; Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core (M.H.), Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio; Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics (M.H.), University of Pennsylvania, Philadelphia; and Department of Preventive Medicine (M.R.C.), Northwestern University Feinberg School of Medicine, Chicago, IL.ORCID 0000-0001-8186-8140
Mohamad HabesFrom the Departments of Psychiatry and Behavioral Sciences (C.C., C.D., Y.L., K.Y.), Neurology (K.Y.), and Epidemiology (K.Y.), University of California, San Francisco; Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core (M.H.), Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio; Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics (M.H.), University of Pennsylvania, Philadelphia; and Department of Preventive Medicine (M.R.C.), Northwestern University Feinberg School of Medicine, Chicago, IL.ORCID 0000-0001-9447-5805
Yue LengFrom the Departments of Psychiatry and Behavioral Sciences (C.C., C.D., Y.L., K.Y.), Neurology (K.Y.), and Epidemiology (K.Y.), University of California, San Francisco; Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core (M.H.), Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio; Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics (M.H.), University of Pennsylvania, Philadelphia; and Department of Preventive Medicine (M.R.C.), Northwestern University Feinberg School of Medicine, Chicago, IL.ORCID 0000-0001-5826-4031
Mercedes R CarnethonFrom the Departments of Psychiatry and Behavioral Sciences (C.C., C.D., Y.L., K.Y.), Neurology (K.Y.), and Epidemiology (K.Y.), University of California, San Francisco; Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core (M.H.), Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio; Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics (M.H.), University of Pennsylvania, Philadelphia; and Department of Preventive Medicine (M.R.C.), Northwestern University Feinberg School of Medicine, Chicago, IL.ORCID 0000-0001-7035-0848
Kristine YaffeFrom the Departments of Psychiatry and Behavioral Sciences (C.C., C.D., Y.L., K.Y.), Neurology (K.Y.), and Epidemiology (K.Y.), University of California, San Francisco; Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core (M.H.), Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio; Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics (M.H.), University of Pennsylvania, Philadelphia; and Department of Preventive Medicine (M.R.C.), Northwestern University Feinberg School of Medicine, Chicago, IL.ORCID 0000-0003-0919-3825

Funding

VARC CoreP30AG044281 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Alexander Keliimoeanu Smith · 2013 to 2026
$19.9M
Cerebral tau deposition and comorbid cerebrovascular disease across the Alzheimer's disease continuum in Mexican AmericansR01AG085571 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI GONZALES, MITZI MICHELLE, HABES, MOHAMAD · 2024 to 2025
$5.7M
Sleep Quality and Mechanistic Links to Alzheimer Disease and Related Disorders among older Mexican Americans and Non-Hispanic WhitesR01AG066137 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI O'BRYANT, SID E, YAFFE, KRISTINE · 2019 to 2023
$4.3M
Population Based Research for Alzheimer's Innovation (POP BRAIN)R35AG071916 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI YAFFE, KRISTINE · 2021 to 2024
$3.8M
Characterizing Older Mexican (American) Participation in Activity Digitally, Reliably, and Ecologically with the CART Platform (COMPADRE CART): Implications for Cognition and ADRDR01AG077472 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Zachary Beattie, Mitzi Michelle Gonzales · 2022 to 2026
$3.8M
Lifecourse CVD Risk and Midlife Cognitive Trajectories and Brain Aging: Implications for Alzheimer's and Dementia PreventionR01AG063887 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI YAFFE, KRISTINE · 2019 to 2022
$3.0M
Multiethnic machine learning brain signatures of ADRDR01AG080821 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI HABES, MOHAMAD · 2022 to 2025
$2.9M
Vascular Imaging Biomarker Relationships to Alzheimer’s disease (VIBRA)R01AG083865 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Mohamad Habes, Timothy M. Hughes · 2024 to 2026
$2.3M
Lifecourse sleep, cognitive decline and risk of Alzheimer's disease: a pooled cohort study.R01AG083836 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Yue Leng · 2024 to 2026
$2.1M
NIA NIH HHS P30 AG044281NIA NIH HHS R01 AG063887NIA NIH HHS R01 AG066137NIA NIH HHS R01 AG080821NIA NIH HHS R01 AG083836NIA NIH HHS R01 AG083865NIA NIH HHS R01 AG085571NIA NIH HHS R35 AG071916
6 · The paper itself

Abstract

objectivesTo determine the association between early midlife sleep and advanced brain aging patterns in late midlife.

methodsUsing the CARDIA study, we analyzed sleep data at baseline and 5 years later, focusing on short sleep duration, bad sleep quality (SQ), difficulty initiating and maintaining sleep (DIS and DMS), early morning awakening (EMA), and daytime sleepiness. These were categorized into 0-1, 2-3, and >3 poor sleep characteristics (PSC). Brain MRIs obtained 15 years later were used to determine brain age through a machine learning approach based on age-related atrophy.

resultsThis cohort study included 589 participants (mean age 40.4 ± 3.4 years, 53% women). At baseline, around 70% reported 0-1 PSC, 22% reported 2%-3%, and 8% reported >3 PSC. In multivariable linear regression analyses, participants with 2-3 or >3 PSC had 1.6-year (β = 1.61, 95% CI 0.28-2.93) and 2.6-year (β = 2.64, 95% CI 0.59-4.69) older brain age, respectively, compared with those with 0-1 PSC. Of the individual characteristics, bad SQ, DIS, DMS, and EMA were associated with greater brain age, especially when persistent over the 5-year follow-up. DISCUSSION: Poor sleep was associated with advanced brain age in midlife, highlighting the importance of investigating early sleep interventions for preserving brain health.

Indexed as

AgingBrainMagnetic Resonance ImagingSelf ReportAdultCohort StudiesFemaleHumansMaleMiddle AgedNeuroimagingSleepSleep QualitySleep Wake Disorders

Identifiers

PMID39442064
PMCPMC11498938

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