MétaCan
Menu
Back to cohort

Obstructive Sleep Apnea Risk and Mental Health Conditions Among Older Canadian Adults in the Canadian Longitudinal Study on Aging

2025· article· en· W7117304142 on OpenAlexaffabout
Tetyana Kendzerska, Ranjeeta Mallick, Wenshan Li, REBECCA ROBILLARD, Vanessa Taler, Colleen Webber, M Saymeh, Peter Tanuseputro, Jess G. Fiedorowicz

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsConcordia UniversityCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalInstitut Universitaire de Gériatrie de MontréalRoyal Ottawa Mental Health CentreOttawa HospitalBruyèreUniversity of Ottawa
Fundersnot available
KeywordsLongitudinal studyObstructive sleep apneaMental healthCohort studyAssociation (psychology)CohortIntervention (counseling)Bridge (graph theory)Sleep apnea

Abstract

fetched live from OpenAlex

Importance: Despite plausible mechanisms linking obstructive sleep apnea (OSA) and mental health conditions, prospective studies from representative samples are needed to estimate temporal associations between OSA and mental health conditions during aging. Objective: To evaluate whether high risk of OSA is associated with increased odds of concurrent and future mental health conditions among middle-aged and older adults. Design, Setting, and Participants: This cohort study is a secondary analysis of the Canadian Longitudinal Study on Aging (CLSA) and used data from respondents of the CLSA Baseline Comprehensive Cohort (2011-2015) and Follow-up 1 (2015-2018) who were aged 45 to 85 years at baseline. Statistical analysis was performed October 2024. The CLSA is a national community-based prospective cohort study collecting data on the biological, medical, cognitive, psychological, social, lifestyle, and economic aspects of aging. Exposure: Individuals with a score greater or equal to 2 on the STOP (snoring, daytime somnolence, witnessed apnea during sleep, or hypertension) questionnaire were considered at high risk of OSA. Main Outcome and Measures: A composite poor mental health outcome was computed as a binary variable, defined by the presence of any of the following: (1) Center for Epidemiologic Studies Short Depression Scale score of 10 or more, (2) Kessler Psychological Distress Scale score of 20 or more, (3) self-reported physician-diagnosed mental health condition, or (4) self-reported antidepressant use. Multivariate conventional and mixed logistic regressions were used to examine associations. Results: The study included 30 097 individuals at baseline (median age, 62 years [IQR, 54-71 years]; 50.9% women) and 27 765 individuals at follow-up (median age, 65 years [IQR, 57-73 years]; 50.9% women), with a median follow-up of 2.9 years (IQR, 2.8-3.1 years). A total of 7066 of 30 097 individuals (23.5%) at baseline and 7493 of 27 765 individuals (27.0%) at follow-up were at high risk of OSA. The composite mental health outcome was identified in 10 334 of 30 097 individuals (34.3%) at baseline and 8851 of 27 765 individuals (31.9%) at follow-up. In adjusted models, high risk of OSA was associated with an approximately 40% higher odds of the composite outcome concurrently at baseline (odds ratio [OR], 1.39; 95% CI, 1.28-1.50) and at follow-up (OR, 1.40; 95% CI, 1.30-1.50). In a repeated-measures analysis, OSA risk remained associated with a 44% higher odds (OR, 1.44; 95% CI, 1.34-1.53) of the composite outcome. Conclusions and Relevance: In this national longitudinal cohort study, middle-aged and older adults at high risk of OSA had consistently worse mental health outcomes. These findings bridge knowledge gaps on the association between OSA and mental health, highlighting the need for integrated screening and intervention strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.342
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJAMA Network OpenSame topicObstructive Sleep Apnea ResearchFrench-language works237,207