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

Tetyana Kendzerska; Ranjeeta Mallick; Wenshan Li; Rébecca Robillard; Vanessa Taler; Colleen Webber; Mouaz Saymeh; Thien Thanh Dang-Vu; Peter Tanuseputro; Jess G Fiedorowicz

doi:10.1001/jamanetworkopen.2025.49137

. 2025 Dec 26;8(12):e2549137. doi: 10.1001/jamanetworkopen.2025.49137

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

Tetyana Kendzerska ^1,^2,^3,^✉, Ranjeeta Mallick ^3,⁴, Wenshan Li ⁵, Rébecca Robillard ^6,⁷, Vanessa Taler ^6,⁸, Colleen Webber ⁸, Mouaz Saymeh ⁵, Thien Thanh Dang-Vu ^9,¹⁰, Peter Tanuseputro ¹¹, Jess G Fiedorowicz ^3,^12,¹³

¹Inflammation and Chronic Disease Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

²Department of Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

³School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada

⁴Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

⁵Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

⁶School of Psychology, University of Ottawa, Ottawa, Ontario, Canada

⁷Sleep Research Unit, Royal Ottawa Institute of Mental Health Research, Ottawa, Ontario, Canada

⁸Bruyere Health Research Institute, Ottawa, Ontario, Canada

⁹Department of Health, Kinesiology, and Applied Physiology, Concordia University, Montréal, Québec, Canada

¹⁰Centre de recherche de l’Institut universitaire de gériatrie de Montréal (CRIUGM), Centre intégré universitaire de soins et services sociaux (CIUSSS) du Centre-Sud-de-l’île-de-Montréal, Montréal, Québec, Canada

¹¹Department of Family Medicine and Primary Care, Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong, SAR of China

¹²Neuroscience Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

¹³Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada

Accepted for Publication: October 13, 2025.

Published: December 26, 2025. doi:10.1001/jamanetworkopen.2025.49137

^✉

Corresponding Author: Tetyana Kendzerska, MD, PhD, Inflammation and Chronic Disease Program, Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada (tkendzerska@toh.ca).

Author Contributions: Drs Kendzerska and Mallick had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Tanuseputro and Fiedorowicz contributed equally as co–senior authors.

Concept and design: Kendzerska, Webber, Dang-Vu, Tanuseputro, Fiedorowicz.

Acquisition, analysis, or interpretation of data: Kendzerska, Mallick, Li, Robillard, Taler, Webber, Saymeh, Dang-Vu, Tanuseputro.

Drafting of the manuscript: Kendzerska, Saymeh.

Critical review of the manuscript for important intellectual content: Kendzerska, Mallick, Li, Robillard, Taler, Webber, Dang-Vu, Tanuseputro, Fiedorowicz.

Statistical analysis: Mallick, Li, Webber.

Obtained funding: Kendzerska, Taler, Webber, Fiedorowicz.

Administrative, technical, or material support: Kendzerska, Robillard, Saymeh, Fiedorowicz.

Supervision: Kendzerska, Tanuseputro.

Conflict of Interest Disclosures: Dr Robillard reported receiving nonfinancial support from Interaxon and grants from Eisai outside the submitted work. Dr Dang-Vu reported receiving personal fees from Eisai, Idorsia, and Takeda; and grants from Paladin Labs and Jazz Pharmaceuticals outside the submitted work. No other disclosures were reported.

Funding/Support: This project is funded by the Canadian Institutes of Health Research (CIHR) Catalyst Grant: Analysis of Canadian Longitudinal Study on Aging (CLSA) Data (2022-09-08). This research was made possible using the data/biospecimens collected by the CLSA (ie, collection). Funding for the CLSA is provided by the Government of Canada through the CIHR under grant reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA datasets (Baseline Comprehensive, version 7.0 and Follow-up 1, version 5.0), under Application Number 23CA001.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The opinions expressed in this manuscript are the authors’ own and do not reflect the views of the CLSA.

Meeting Presentation: The preliminary results of this study were presented as an oral presentation at the Annual Meeting of CHEST; October 8, 2024; Boston, Massachusetts.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank Rostyslav Vyuha, MedSleep, for his assistance with database cleaning and variable mapping. He received compensation for this work from the principal investigator (Dr Kendzerska). The time and commitment of the participants to the CLSA study platform is gratefully acknowledged, without whom this research would not be possible.

Additional Information: We would like to acknowledge the assistance of ChatGPT, GPT-5, (OpenAI), used October 2025, in supporting the refinement of the manuscript through suggestions for improving flow, clarity, and conciseness, as well as formatting assistance for figures. The final content, interpretation, and conclusions are solely the responsibility of the authors. As per the CLSA Data User Responsibility Checklist, this manuscript was submitted to the CLSA for review and approval prior to journal submission, in accordance with their data use policies. This review ensures that appropriate acknowledgments and data usage statements are included but does not influence the study design, analysis, interpretation, or conclusions.

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Corresponding author.

PMCID: PMC12743280 PMID: 41632160

Key Points

Question

Is high risk of obstructive sleep apnea (OSA) associated with increased odds of concurrent and future mental health conditions among middle-aged and older adults?

Findings

In this national cohort study of 30 097 individuals, those at high risk of OSA had approximately 40% higher odds of mental health conditions at both baseline and follow-up. Over time, high risk of OSA remained associated with a 44% increased odds of reporting new mental health conditions.

Meaning

These findings bridge knowledge gaps on the association between OSA and mental health during aging, highlighting the need for integrated screening and intervention strategies.

Abstract

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.

This cohort study evaluates whether high risk of obstructive sleep apnea is associated with increased odds of concurrent and future mental health conditions among middle-aged and older adults.

Introduction

Mental health conditions are among the leading contributors to global disease burden, with anxiety and depressive disorders being the most common.¹ Individuals with mental health conditions face higher risks of cardiometabolic diseases, unemployment, homelessness, disability, and hospitalizations.² Mental disorders cost $1 trillion annually globally in lost productivity.³ Identifying factors associated with mental health outcomes remains an important public health goal.

Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition, characterized by repeated upper airway narrowing during sleep, resulting in sleep fragmentation, sympathetic activation, and intermittent hypoxemia.⁴ OSA affects an estimated 936 million adults aged 30 to 69 globally,⁵ with up to 90% of cases undetected.^6,7 It has been linked with cardiometabolic diseases and greater health care use^{8,9,10,11,12,13,14} and is associated with traffic and occupational accidents and reduced productivity.^15,16 OSA is a treatable condition, with evidence-based cost-effective therapies¹⁷ that can improve symptoms and reduce long-term health risks.⁸

Through hypoxemia and sleep fragmentation, untreated OSA may be associated with the development and progression of mental health conditions.¹⁸ In turn, mental health conditions may be associated with increases in OSA risk via weight gain and altered upper airway muscle tone due to autonomic imbalance, neurotransmitter dysregulation, and neuromuscular impairment.¹⁹ Despite these plausible mechanisms, existing research is limited by small sample sizes, single-center studies, and inadequate adjustment for confounders (eg, no adjustment or adjustment for demographic characteristics only),^11,18,20 limiting conclusions about the cross-sectional and longitudinal associations.²¹ OSA is also underdiagnosed or untreated among individuals with mental health conditions,^22,23 suggesting that the unmet burden of treatable OSA may further worsen mental health outcomes. There is a need for prospective studies in representative samples to estimate temporal associations.

To address this need, we conducted a secondary analysis of the prospective Canadian Longitudinal Study on Aging (CLSA). Our first objective was 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. Our second, exploratory objective was to identify individual characteristics (sociodemographic and lifestyle measures and comorbid sleep and medical conditions) associated with new mental health conditions among individuals at high risk of OSA. We hypothesize that high risk of OSA is independently associated with an increased risk of mental health conditions, both concurrently and longitudinally, and that distinct risk profiles for new mental health conditions can be identified among individuals at high risk of OSA.

Methods

Study Design, Population, and Data Sources

We used data from 45- to 85-year-old (age at baseline) respondents of the CLSA national community-based prospective cohort study that has been collecting data on the biological, medical, cognitive, psychological, social, lifestyle, and economic aspects of middle-aged and older adults without cognitive impairment at baseline.²⁴ The CLSA study design and recruitment process have been published elsewhere.^25,26 Individuals living on federal First Nations reserves, full-time members of the Canadian Armed Forces, residents of institutions, and those unable to respond in English or French or with cognitive impairment were not recruited by CLSA^25,26 (eMethods in Supplement 1). Ethical approval for the CLSA was obtained from research ethics boards at all participating institutions. All participants provided written informed consent. The analyses presented in this article were conducted under a CLSA data application number (23CA001) and were approved by the Hamilton Integrated Research Ethic Board and the Ottawa Hospital Research Institute. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.²⁷

For this study, we used data from the Baseline Comprehensive Cohort (2011-2015; N = 30 097), for which data were collected by face-to-face interviews,²⁵ and from Follow-up 1 (2015-2018; N = 27 765). More details on definitions of variables are presented in eTable 1 in Supplement 1 and at the study website.²⁴

Exposures: Self-Reported High Risk of OSA

The primary exposure, high risk of OSA, was defined using the validated STOP questionnaire,²⁸ which classifies high risk of OSA when at least 2 of the following are reported: snoring, daytime somnolence, witnessed apnea during sleep, and/or hypertension. Meta-analyses and systematic reviews show that the STOP questionnaire (score ≥2) yields a sensitivity of 87% to 90% and a specificity of 29% to 42% for any OSA (apnea-hypopnea index [AHI] ≥5), with a negative predictive value (NPV) typically above 80% for moderate to severe OSA and a positive predictive value (PPV) below 50% in community samples.^{29,30,31,32,33}

Hypertension was defined as systolic blood pressure of at least 140 mm Hg or diastolic blood pressure of at least 90 mm Hg (mean of 4 measures) or self-reported diagnosis of hypertension or antihypertensive medication use.

The secondary exposure was defined by a single question: “Has anyone ever observed you stop breathing in your sleep?” (yes or no). In contrast, witnessed apnea alone has a sensitivity ranging from 20% to 40% and a specificity of 80% to 95%, with a PPV often exceeding 60% and an NPV lower than the STOP questionnaire, due to the lower prevalence of witnessed events in the general population.^30,34

Mental Health–Related Outcomes

The primary mental health–related outcome was a composite of poor mental health as a binary variable, defined by the presence of any of the following: (1) a 10-item Center for Epidemiologic Studies Short Depression Scale (CES-D-10)^35,36 score of 10 or more, (2) a 10-item Kessler Psychological Distress Scale (K10)³⁷ score of 20 or more, (3) self-reported physician-diagnosed mental health condition (“Has a doctor ever told you . . .?”), or (4) self-reported antidepressant use (“Are you currently taking medications for depression?”).

The CES-D-10 is a 10-item scale, with higher scores indicating greater depressive symptoms. A cutoff of 10 or more identifies clinically relevant depression symptoms (sensitivity, 97%; specificity, 84%; and PPV, 85% for major depressive disorder).^38,39 The K10 is a 10-item scale assessing symptoms of psychological distress,⁴⁰ with high internal consistency (α = 0.88) and convergent validity (α = 0.84),^37,40 and a recommended cutoff of 20 or more.³⁷ Mental health conditions were based on self-reported physician diagnoses of an anxiety disorder “such as a phobia, obsessive-compulsive disorder, or a panic disorder,” a mood disorder “such as depression (including manic depression), bipolar disorder, mania, or dysthymia,” or “clinical depression.”

The secondary mental health–related outcomes were physician-diagnosed anxiety disorder, mood disorder, and clinical depression, considered separately. Although clinical depression falls under mood disorder, the survey asked about these separately, and participants likely viewed them as distinct. Therefore, we report them as separate outcomes.

Potential Covariates and Risk Factors

Potential covariates were selected based on the literature review^11,41,42 and expert opinion. Self-reported sociodemographic and lifestyle measures⁴³ included age, sex, race and ethnicity (Arab only, Black only, Chinese only, Filipino only, Japanese only, Korean only or West Asian only [combined due to low numbers], Latin American only, South Asian only, Southeast Asian only, White only, other racial or ethnic origin only [not specified], and multiple racial or ethnic origins), urban and marital status, dwelling type, household income, educational level, self-rated general health, satisfaction with life (by Satisfaction with Life Scale),⁴⁴ functional social support, smoking status, alcohol consumption, and physical activity. Body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was based on height and weight measured at the interview. The CLSA collects information on race and ethnicity as part of its core sociodemographic profile to understand diversity in aging experiences across Canada.

Measures of sleep-related symptoms and other sleep disorders aside from OSA risk^43,45 included dissatisfaction with sleep pattern, number of hours of daily sleep, insomnia symptoms, acting out on dreams while asleep, and restless leg syndrome (adapted from validated sleep questionnaires).^46,47 Medical conditions included self-reported number of medications prescribed and a physician-diagnosed (“Has a doctor ever told you . . .?”) chronic condition of interest, such as cardiovascular conditions, diabetes, hypothyroidism, respiratory conditions or problems, cancer, traumatic brain injury, and intensity of pain or discomfort. Self-reported and clinician-diagnosed chronic conditions demonstrated high test-retest reliability in population-based surveys.^25,48

Variable Selection Process for the Final Models

From the set of 28 variables described, we implemented several steps to select a joint core set of variables for the final models, addressing each objective separately for the baseline and follow-up cohorts (eMethods in Supplement 1). First, we removed variables with more than 10% missing values to preserve the sample size. Then, we excluded collinear variables based on a variable clustering algorithm. Finally, we used the step-down procedure described by Ambler et al,⁴⁹ where variables are sequentially deleted to obtain the lowest, stable Akaike information criterion value, selecting the final models, which included 19 variables for baseline and 17 variables for follow-up (Table 1). All statistical models were additionally adjusted for the year of data collection.

Table 1. Population Characteristics of Variables Included in the Statistical Model at Baseline and Follow-Up.

Variable	Baseline, No. (%)			P value	Follow-up, No. (%)			P value
	Total (N = 30 097)	High risk of OSA at baseline^a			Total (N = 27 765)	High risk of OSA at follow-up^b
	Total (N = 30 097)	Yes (n = 7066)	No (n = 20 205)		Total (N = 27 765)	Yes (n = 7493)	No (n = 17 528)
Start date year
2011-2012	3803 (12.6)	842 (11.9)	2572 (12.7)	<.001	NA	NA	NA	.001
2013	11 476 (38.1)	2516 (35.6)	7803 (38.6)		NA	NA	NA
2014	10 363 (34.4)	2457 (34.8)	6993 (34.6)		NA	NA	NA
2015	4455 (14.8)	1251 (17.7)	2837 (14.0)		4328 (15.6)	1159 (15.5)	2719 (15.5)
2016	NA	NA	NA		10 103 (36.4)	2616 (34.9)	6548 (37.4)
2017	NA	NA	NA		10 656 (38.4)	2963 (39.5)	6644 (37.9)
2018	NA	NA	NA		2678 (9.7)	755 (10.1)	1617 (9.2)
Missing^c	NA	2826	2826		NA	2744	2744
Sociodemographic and lifestyle measures
Age groups, y
45-54	7595 (25.2)	1439 (20.4)	5646 (27.9)	<.001	4394 (15.8)	988 (13.2)	3149 (18.0)	<.001
55-64	9856 (32.8)	2486 (35.2)	6595 (32.6)		9173 (33.0)	2522 (33.7)	6014 (34.3)
65-74	7362 (24.5)	1940 (27.5)	4675 (23.1)		8243 (29.7)	2506 (33.4)	4917 (28.1)
≥75	5284 (17.6)	1201 (17.0)	3289 (16.3)		5955 (21.5)	1477 (19.7)	3448 (19.7)
Sex
Female	15 320 (50.9)	2688 (38.0)	11 043 (54.7)	<.001	14 133 (50.9)	2889 (38.6)	9695 (55.3)	<.001
Male	14 777 (49.1)	4378 (62.0)	9162 (45.3)	<.001	13 632 (49.1)	4604 (61.4)	7833 (44.7)	<.001
Dwelling type
House	24 004 (79.8)	5634 (79.8)	16 462 (81.5)	<.001	21 671 (78.1)	5878 (78.5)	14 001 (79.9)	.01
Apartment or condominium	5810 (19.3)	1347 (19.1)	3590 (17.8)		5474 (19.7)	1456 (19.4)	3201 (18.3)
Other	273 (0.9)	Not reported^d	Not reported^d		619 (2.2)	46 (0.6)	89 (0.5)
Household income
<$50 000	7926 (26.3)	2004 (30.2)	4682 (24.7)	<.001	6879 (24.8)	1942 (27.6)	3847 (23.3)	<.001
≥$50 000 to <$100 000	9907 (32.9)	2348 (35.4)	6783 (35.8)		9406 (33.9)	2603 (36.9)	5964 (36.2)
≥$100 000 to <$150 000	5524 (18.4)	1282 (19.3)	3897 (20.6)		5124 (18.5)	1390 (19.7)	3411 (20.7)
≥$150 000	4799 (16.0)	1001 (15.1)	3584 (18.9)		4585 (16.5)	1112 (15.8)	3258 (19.8)
Life satisfaction
Dissatisfied or neutral	4126 (13.7)	1225 (17.5)	2308 (11.6)	<.001	3470 (12.5)	1176 (15.9)	1782 (10.3)	<.001
Slightly satisfied	4471 (14.9)	1205 (17.2)	2803 (14.0)		3441 (12.4)	1047 (14.2)	1986 (11.5)
Satisfied	9221 (30.6)	2175 (31.1)	6220 (31.1)		8222 (29.6)	2241 (30.3)	5163 (29.8)
Extremely satisfied	11 925 (39.6)	2395 (34.2)	8652 (43.3)		12 276 (44.2)	2931 (39.6)	8407 (48.5)
Self-rated general health
Excellent	5995 (19.9)	765 (10.8)	4834 (23.9)	<.001	5071 (18.3)	911 (12.2)	3791 (21.7)	<.001
Very good	12 420 (41.3)	2518 (35.7)	8869 (43.9)		11 460 (41.3)	2704 (36.2)	7798 (44.5)
Good	8877 (29.5)	2612 (37.0)	5260 (26.1)		8199 (29.5)	2661 (35.6)	4631 (26.4)
Fair	2315 (7.7)	949 (13.4)	1046 (5.2)		2450 (8.8)	982 (13.1)	1082 (6.2)
Poor	467 (1.6)	216 (3.1)	182 (0.9)		547 (2.0)	221 (3.0)	212 (1.2)
Current smoker
Yes	2576 (8.6)	642 (12.5)	1565 (11.7)	.16	1944 (7.0)	600 (8.0)	1088 (6.2)	<.001
No	17 918 (59.5)	4502 (87.5)	11 780 (88.3)	.16	25 792 (92.9)	6887 (92.0)	16 421 (93.8)	<.001
Ever alcohol consumption	29 383 (97.6)	6907 (97.8)	19 747 (97.7)	.95	27 033 (97.4)	7310 (97.6)	17 072 (97.4)	.46
Type of drinker (past 12 mo)
Regular drinker (at least once a month)	22 239 (73.9)	5010 (72.6)	15 385 (77.9)	<.001	20 889 (75.2)	5522 (73.7)	13 588 (77.6)	<.001
Occasional drinker	3705 (12.3)	987 (14.3)	2277 (11.5)		3405 (12.3)	970 (13.0)	1945 (11.1)
Did not drink in the last 12 mo	3427 (11.4)	907 (13.1)	2078 (10.5)		3450 (12.4)	996 (13.3)	1984 (11.3)
BMI
Underweight	217 (0.7)	13 (0.2)	180 (0.9)	<.001	210 (0.8)	24 (0.3)	167 (1.0)	<.001
Normal weight	8863 (29.5)	1004 (14.3)	7146 (35.5)		7936 (28.6)	1151 (15.6)	6261 (36.2)
Overweight	12 088 (40.2)	2624 (37.4)	8309 (41.3)		10 738 (38.7)	2743 (37.3)	7184 (41.6)
Obesity class I	5820 (19.3)	1929 (27.5)	3273 (16.3)		5162 (18.6)	2002 (27.2)	2658 (15.4)
Obesity class II	1978 (6.6)	887 (12.7)	904 (4.5)		1777 (6.4)	904 (12.3)	713 (4.1)
Obesity class III	995 (3.3)	557 (7.9)	329 (1.6)		922 (3.3)	538 (7.3)	301 (1.7)
Medical conditions
No. medications taken, median (IQR)	4 (2-7)	5 (2-8)	3 (1-6)	<.001	4 (2-7)	5 (2-8)	3 (1-6)	<.001
TBI	7288 (24.2)	2015 (28.5)	4641 (23.0)	<.001	6475 (23.3)	2135 (29.0)	3898 (22.6)	<.001
Free of pain and discomfort	18 130 (60.2)	3749 (53.7)	13 517 (67.0)	<.001	18 208 (65.6)	4444 (59.4)	12 435 (71.1)	<.001
Usual intensity of pain or discomfort
Mild	4589 (15.3)	1243 (39.0)	3075 (46.8)	<.001	3864 (13.9)	1208 (40.1)	2391 (47.5)	<.001
Moderate	4901 (16.3)	1547 (48.6)	3007 (45.7)		4119 (14.8)	1439 (47.8)	2261 (44.9)
Severe	972 (3.2)	394 (12.4)	496 (7.5)		862 (3.1)	366 (12.2)	385 (7.6)
Diabetes	5310 (17.6)	2025 (28.7)	2674 (13.3)	<.001	5307 (19.1)	2227 (30.1)	2599 (15.0)	<.001
Hypertension	14 127 (46.9)	5777 (82.1)	6601 (32.9)	<.001	13 714 (49.4)	6273 (85.0)	6088 (35.6)	<.001
Respiratory problem	7990 (26.6)	2347 (33.5)	4787 (23.8)	<.001	7337 (26.4)	2548 (34.5)	4175 (24.2)	<.001
CVD or stroke	3301 (11.0)	1182 (16.9)	1667 (8.3)	<.001	3236 (11.7)	1293 (17.5)	1581 (9.3)	.10
Underactive thyroid gland	3962 (13.2)	949 (13.6)	2583 (12.9)	.16	3989 (14.4)	1065 14.5	2557 14.8	.48
Sleep symptoms and other sleep disorders aside from OSA
No. of hours of sleep per night, median (IQR)	7 (6-8)	7 (6-8)	7 (6-8)	<.001	7 (6-8)	7 (6-8)	7 (6-8)	<.001
Insomnia	1299 (4.3)	564 (8.0)	608 (3.0)	<.001	992 (3.6)	458 (6.2)	473 (2.7)	<.001
Restless leg syndrome	7809 (26.0)	2272 (32.3)	4788 (23.8)	<.001	5032 (18.1)	1704 (22.9)	2920 (16.7)	<.001
Acts out dreams while asleep^e	3328 (11.1)	1096 (15.7)	1921 (9.6)	<.001	2859 (10.3)	1121 (15.3)	1563 (91.0)	<.001

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Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CVD, cardiovascular disease; NA, not applicable; OSA, obstructive sleep apnea; TBI, traumatic brain injury.

^{^a}

Numbers do not total 30 097 because of missing data for OSA status. The final list of variables at baseline (N = 19) included age, sex, dwelling type, total household income, self-rated general health, satisfaction with life, alcohol consumption, BMI, self-reported hypertension, diabetes, respiratory problem, usual intensity of pain or discomfort, positive screen for TBI, underactive thyroid gland, the number of medications taken, self-reported acting out on dream, restless legs, number of sleep hours per night, insomnia with daytime impairment.

^{^b}

Numbers do not total 27 765 because of missing data for OSA status. The final list of variables at follow-up (N = 17) included age, sex, dwelling type, total household income, satisfaction with life, current smoker status, alcohol consumption, self-reported CVD and cerebrovascular conditions, diabetes, respiratory problem, usual intensity of pain or discomfort, positive screen for TBI, underactive thyroid gland, self-reported acting out on dream, restless legs, number of sleep hours per night, insomnia with daytime impairment.

^{^c}

The difference in the total and by OSA status is explained by the missing values by the OSA status. More details on missing values per each variable are presented in eTable 2 in Supplement 1.

^{^d}

Not reported to avoid cells with fewer than 5.

^{^e}

For example, punching, flailing arms in the air, and making running movements.

Statistical Analysis

Statistical analysis was performed October 2024 using SAS, version 9.4 (SAS Institute Inc). Descriptive statistics, such as mean (SD) values, median (IQR) values, or frequency (%), were used to characterize the study populations in total and by the primary exposure (high risk of OSA). All P values were from 2-sided tests and results were deemed statistically significant at P < .05.

Primary Analyses

To address the first objective, our primary analyses involved a series of multivariable logistic regression analyses. A cross-sectional analysis was performed to investigate the association between high risk of OSA and concurrent composite outcome at baseline and follow-up, separately, using multivariate logistic regressions. A longitudinal analysis was performed among individuals without mental health conditions at baseline, to assess the association between high risk of OSA at baseline and new mental health conditions at follow-up, using multivariable logistic regressions. A repeated-measures analysis was performed for participants with data available at both baseline and follow-up; we used mixed-effects multivariable logistic regressions to examine associations between high risk of OSA and new mental health conditions, incorporating repeated observations per individual and accounting for within-individual correlation through random intercepts (eMethods in Supplement 1).

Secondary Analyses

The same analytic approaches described were applied for the secondary exposure (witnessed apnea) and outcomes (physician-diagnosed anxiety disorder, mood disorder, and clinical depression, considered separately). Multivariable mixed-effects linear regressions were used to assess the association between high risk of OSA risk and longitudinal CES-D-10 or K10 scores, considered separately as continuous variables. Finally, we conducted several sensitivity analyses, incorporating all variables considered for selection along with urban residence, racial and ethnic background, and interaction terms between sex, age, and the primary exposure regardless of missing values and collinearity.

Characteristics of Individuals at High Risk of OSA Associated With New Mental Health Conditions (Exploratory)

To address our second objective (to describe risk profiles), we performed 2 subgroup analyses: one for individuals with a high risk of OSA without concurrent mental health conditions at baseline (conventional multivariable logistic regression) and another for individuals with a high risk of OSA regardless of mental health status (mixed multivariable logistic regression). Given the exploratory nature of this objective, we reused the same final models developed for primary analyses.

Results

The study included 30 097 individuals from the baseline cohort (median age, 62 years [IQR, 54-71 years]; 15 320 women [50.9%] and 14 777 men [49.1%]; 89 Arab [0.3%], 221 Black [0.7%], 212 Chinese [0.7%], 47 Filipino [0.2%], 37 Japanese [0.1%], 42 Korean or West Asian [0.1%], 103 Latin American [0.3%], 271 South Asian [0.9%], 52 Southeast Asian [0.2%], 28 372 White [94.3%], 174 other racial or ethnic origin [0.6%], and 447 multiple racial or ethnic origins [1.5%]) and 27 765 individuals from the follow-up cohort (median age, 65 years [IQR, 57-73 years]; 14 133 women [50.9%] and 13 632 men [49.1%]; data on race and ethnicity collected only at baseline), with a median follow-up of 2.9 years (IQR, 2.8-3.1 years) (eTable 2 and eTable 3 in Supplement 1). Most of the participants were from urban areas (25 066 [83.3%]), were high school graduates (26 847 [89.2%]), had at least 1 chronic condition (28 121 [93.4%] at baseline and 25 711 [92.6%] at follow-up), and reported having consumed alcohol in their lifetime (29 383 [97.6%] at baseline and 27 033 [97.4%] at follow-up) (Table 1; eTable 3 in Supplement 1).

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, with 4245 of 30 097 (14.1%) at baseline and 4673 of 27 765 (16.8%) at follow-up reporting witnessed apnea. The composite outcome of poor mental health was identified in 10 334 of 30 097 individuals (34.3%) at baseline, with mood disorder (5144 [17.1%]) and clinical depression (4919 [16.3%]) being the most prevalent (eTable 4 in Supplement 1); the mean (SD) CES-D-10 score was 5.3 (4.7), and the mean (SD) K10 score was 14.3 (4.6). Similarly, at follow-up, 8851 of 27 765 individuals (31.9%) met criteria for the composite outcome (eTable 4 in Supplement 1). Among individuals who did not meet criteria for the composite outcome at baseline (n = 19 990), 1372 (6.9%) met criteria for poor mental health at follow-up.

Primary Analyses

After adjustment for confounders in cross-sectional analyses, being at high risk of OSA was significantly associated with an approximately 40% increase in the odds of the composite mental health outcome (primary) at both baseline (odds ratio [OR], 1.39; 95% CI, 1.28-1.50) and follow-up (OR, 1.40; 95% CI, 1.30-1.50) (Figure 1; Table 2).

Figure 1. — There were 30 097 participants at baseline and 27 765 participants at follow-up. OR indicates odds ratio.

Table 2. Multivariable Association Between Exposures and the Composite Poor Mental Health Outcome.

Exposure	Cross-sectional associations							Repeated-measures analysis, mixed regression^a
	Baseline		Follow-up					Repeated-measures analysis, mixed regression^a
	OR (95% CI)	Total No.	OR (95% CI)	Total No.	OR (95% CI)	Total No.	OR (95% CI)		Total No.
Composite poor mental health outcome
High risk of OSA	1.39 (1.28-1.50)	23 465	1.40 (1.30-1.50)	21 240	1.20 (1.03-1.40)	14 485	1.44 (1.34-1.53)		32 420
Witnessed apnea during sleep	1.43 (1.31-1.55)	24 156	1.47 (1.35-1.60)	22 217	1.32 (1.13-1.56)	14 878	1.47 (1.37-1.57)		34 730
Separate components
Anxiety disorder
High risk of OSA	1.37 (1.21-1.54)	23 612	1.42 (1.28-1.58)	21 344	1.37 (0.92-2.05)	14 570	1.48 (1.34-1.64)		32 778
Witnessed apnea during sleep	1.29 (1.13-1.46)	24 313	1.37 (1.22-1.54)	22 324	1.32 (0.87-2.02)	14 964	1.36 (1.23-1.51)		35 120
Mood disorder
High risk of OSA	1.32 (1.20-1.45)	23 624	1.48 (1.36-1.60)	21 344	1.65 (1.24-2.20)	14 569	1.46 (1.35-1.58)		32 804
Witnessed apnea during sleep	1.40 (1.27-1.55)	24 321	1.61 (1.46-1.76)	22 323	1.75 (1.32-2.34)	14 963	1.50 (1.39-1.62)		35 134
Clinical depression
High risk of OSA	1.38 (1.25-1.52)	23 598	1.50 (1.37-1.64)	21 272	1.71 (1.17-2.49)	14 562	1.48 (1.36-1.61)		32 638
Witnessed apnea during sleep	1.47 (1.33-1.63)	24 298	1.61 (1.46-1.78)	22 246	1.76 (1.20-2.58)	14 954	1.51 (1.39-1.64)		34 968

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Abbreviations: OR, odds ratio; OSA, obstructive sleep apnea.

^{^a}

The number for mixed regression presents the number of time points used in the analysis.

Among those not meeting criteria for poor mental health at baseline, after adjustment for confounders, high risk of OSA was associated with a 20% increase in the odds (OR, 1.20; 95% CI, 1.03-1.40) of developing the mental health outcome at follow-up (Figure 2; Table 2). In a repeated-measures analysis adjusted for confounders, high risk of OSA remained significantly associated with a 44% increase in the odds (OR, 1.44; 95% CI, 1.34-1.53) of the composite mental health outcome (Figure 3; Table 2).

Figure 2. — There were 19 990 participants. OR indicates odds ratio.

Figure 3. — Analysis was conducted on the merged baseline and follow-up cohorts; available data on 2 time points were used in multivariable mixed regressions. OR indicates odds ratio.

Secondary Analyses

We confirmed the findings using the secondary exposure definition (witnessed apnea) and secondary outcomes (mental health conditions considered separately) (Figures 1-3; Table 2). In a repeated-measures analysis, modest but statistically significant associations were noted between primary and secondary exposures and worsening mental health symptoms over time as measured by the CES-D-10 or K10 (eTable 5 in Supplement 1).

In sensitivity analyses, including all candidate variables and interaction terms, the results remained similar after adjustment, and interaction terms were not statistically significant, although findings suggested a potentially stronger association between high risk of OSA and a composite mental health outcome among women than men (eTable 6 in Supplement 1).

Characteristics of Individuals at High Risk of OSA Associated With New Mental Health Conditions

Among 3213 participants at high risk of OSA without any mental health condition at baseline, 360 (11.2%) had new composite mental health conditions at follow-up. In multivariable analyses (eTable 7 in Supplement 1) female sex, low total household income (<$50 000), being dissatisfied or neutral with life (vs satisfied or extremely satisfied), fair self-rated general health (vs excellent), and other sleep disorders (restless legs, acting out on dreams, and insomnia symptoms) were associated with higher odds of the new composite mental health outcome at follow-up.

In a repeated-measures analysis, for individuals at high risk of OSA regardless of mental health status, in addition to the variables listed, younger age, living in an apartment (vs house), no alcohol consumption (vs once a week only), lower BMI, respiratory problems, traumatic brain injury, experiencing pain (with a dose-response association for severity), and a higher number of medications taken were significantly associated with worsening on the composite mental health outcome from baseline to follow-up (eTable 7 in Supplement 1).

Discussion

In this national prospective cohort study, across all mental health outcomes, individuals at high risk of OSA consistently had higher odds of reported poor mental health outcomes, both cross-sectionally and longitudinally, with similar results noted for secondary exposure (ie, witnessed apnea) and outcome definitions (ie, individual mental health indices reflective of psychological distress, anxiety disorders, and mood disorders). Expanding previous knowledge based mostly on cross-sectional data, our findings strengthen the notion of OSA risk recognition for mental health.

The associations between OSA risk and mental health were moderate in strength but consistent across outcomes and analytic approaches. Although significant associations were observed for general psychological distress (K10) and anxiety disorder, the strongest associations appear to be with self-reported mood disorders and clinical depression. This study provides novel longitudinal evidence linking high risk of OSA with evolving anxiety and mood disorders. Although more detailed studies are needed, our findings suggest OSA may influence depressive states in older adults, potentially through its association with cardiovascular health, a known risk factor for depression in this age group.⁵⁰

To our knowledge, this is one of the largest (>30 000) and most comprehensive community-based studies^51,52,53,54 to examine the longitudinal association between OSA risk and mental health, focusing on middle-aged and older adults with comprehensive adjustment for confounders. Previous studies in this age group (n = 350-1021)^55,56 linked OSA with concurrent depression but did not explore its role in the development of new mental health conditions. Similar to most previous studies in the general adult population,^51,52,53,54 we found high risk of OSA to be independently associated with subsequent depression and mood disorders in older adults, although with slightly lower odds, likely due to using high risk of OSA rather than AHI-based diagnoses and adjusting for more confounders. Previous research also suggests lower odds among older adults compared with younger groups,^51,52 which may indicate that while OSA may be associated with poor mental health in this population, other factors also play a role.

Hypoxemia, sleep fragmentation, and inflammation are potential pathways linking untreated OSA with mental health conditions.¹⁸ OSA-related cumulative hypoxemia can disrupt brain systems involved in mood regulation,^57,58,59 while sleep fragmentation may alter neuroendocrine pathways.⁵⁷ OSA is also associated with elevated inflammatory markers,⁶⁰ which may contribute to depression.⁶¹ In addition, OSA-related cardiometabolic comorbidities may elevate mental distress.⁶²

Several factors may interact with OSA to further increase the risk of poor mental health. In our study population, higher odds of new mental health conditions were associated with factors largely beyond an individual’s control, including female sex, younger age (also previously reported in other studies),^51,63 lower income, poorer health and life satisfaction, less spacious living arrangements, and history of traumatic brain injury. We also identified factors, such as respiratory problems, pain, other sleep disorders, and polypharmacy, that may offer potential intervention targets for improving mental health outcomes. We did not confirm previous findings that obesity was associated with increased depression risk in OSA⁵³; instead, lower BMI appeared to be associated with greater risk, possibly reflecting the older population in our study. We also found higher odds of adverse mental health in nondrinkers with OSA, although small numbers and limited data on alcohol intake make this finding difficult to interpret. Pain, mental health, and OSA are intertwined. Depression and anxiety can amplify pain perception, while chronic pain is associated with increased risks of mental health conditions.⁶⁴ OSA has also been associated with pain severity, with OSA therapy potentially improving pain.^65,66 This underscores the need for integrated care approaches to address these overlapping conditions.

These findings highlight the importance of systematic mental health screening for older adults at risk for OSA. Incorporating mental health assessment tools into sleep evaluation may help identify individuals at greatest risk and support early intervention.⁶⁷ Educating older adults about the potential associations of untreated OSA with mood, cognition, and long-term brain health could also improve engagement in diagnostic testing and treatment adherence. As OSA has been associated with increased risk of cognitive impairment and dementia,^68,69 future studies should explore whether integrating screening and prevention strategies⁷⁰ enhances both mental and cognitive health outcomes. Research is also needed to determine whether addressing modifiable characteristics (such as respiratory problems, pain, other sleep disorders, and polypharmacy) observed among individuals at high risk of OSA who develop new mental health conditions is associated with improved mental health in this group, and to further explore possible sex-related differences suggested by our findings. Identifying high-risk subgroups may help target mental health prevention efforts in resource-limited settings. This is critical, as individuals with mental disorders are often underserved in health care,^71,72 and those with OSA and psychiatric conditions are more likely to report unmet mental health needs despite higher service use.⁷³

Strengths and Limitations

Leveraging the CLSA, this study had several strengths, including (1) national-based sampling, with the OSA risk distribution consistent with published population-based studies^5,74,75; (2) prospective assessment of variables of interest; (3) rich details on sleep-related variables beyond OSA risks; (4) mental health conditions assessed using validated scales; and (5) a comprehensive set of covariates, including physical activity, life satisfaction, and social support, that is usually not available in health administrative databases used for population-based and national studies.

This study also has some limitations. As with any observational study, residual confounding is possible,⁷⁶ despite adjusting for potential confounders, and the associations observed should not be interpreted as evidence of causality. Although longitudinal and repeated-measures designs were used, the possibility of reverse causality (eg, depression contributing to OSA risk via weight gain or medication effects) remains. OSA risk and mental health diagnoses were self-reported without physician assessment or sleep tests. However, we used additional measures of mental health and OSA risk to confirm findings. Our primary exposure, the STOP questionnaire, has high sensitivity but modest specificity compared with the AHI.²⁸ As a complementary secondary exposure, the witnessed apnea question provided greater specificity but lower sensitivity. This balance may have reduced misclassification and likely biased results toward the null, leading to conservative effect estimates. The AHI itself has limitations as a criterion standard, as it does not fully capture symptom burden or adverse health outcomes.⁷⁷ Because treatment data were not available, we cannot address whether OSA therapy modifies the risk of mental health disorders. Bias from loss to follow-up and recall is also possible. Finally, the generalizability and representativeness of our study could be affected by the CLSA study design. CLSA participants were not fully representative of the Canadian population; they were predominantly White, healthier, more educated, and from urban areas, limiting finding generalizability to community-dwelling middle-aged and older adults.²⁶ Complete-case analyses may introduce selection bias due to exclusion of participants with missing data, although baseline comparisons suggest minimal association with results (eTable 8 in Supplement 1).

Conclusions

In this national cohort study, we found that middle-aged and older individuals classified as being at high risk of OSA consistently show higher odds of poor mental health than those at low risk of OSA, both cross-sectionally and longitudinally. Findings from our study address knowledge gaps regarding the association between high risk of OSA and mental health and provide valuable information for future intervention studies to develop and evaluate screening programs to protect the mental health of older adults at high risk of OSA.

Supplement 1.

eMethods. Detailed Inclusion and Exclusion Criteria and Data Quality Control as Per Protocol for the Canadian Longitudinal Study on Aging (CLSA)

eTable 1. Details on Variables Extracted From the Canadian Longitudinal Study on Aging (CLSA) Databases

eTable 2. Flow of Participants and Derivation of Final Analytic Samples Across Study Analyses

eTable 3. Population Characteristics of Variables Considered in the Statistical Model at Baseline and Follow-Up

eTable 4. The Distribution of the Composite Poor Mental Health Outcome and Its Components at Baseline and Follow-Up

eTable 5. The Association Between Exposures and Changes in the Center for Epidemiologic Studies Short Depression Scale (CESD-10) or Kessler Psychological Distress Scale (K10) Over Time, Considered Separately as Continuous Variables

eTable 6. The Association Between the High Obstructive Sleep Apnea (OSA) Risk and the Composite Poor Mental Health Outcome in the Fully Adjusted Statistical Model With and Without the Interaction Terms

eTable 7. Characteristics of Individuals With High Obstructive Sleep Apnea (OSA) Risk Associated With New Mental Health Conditions

eTable 8. Comparison of the Population Characteristics at Baseline of Individuals Excluded at the Follow-Up (ie, Data Available at Baseline Only) and Individuals With Available Data at Both Time Points (Baseline and Follow-Up)

eReferences.

jamanetwopen-e2549137-s001.pdf^{(797.5KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2549137-s002.pdf^{(15.9KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eMethods. Detailed Inclusion and Exclusion Criteria and Data Quality Control as Per Protocol for the Canadian Longitudinal Study on Aging (CLSA)

eTable 1. Details on Variables Extracted From the Canadian Longitudinal Study on Aging (CLSA) Databases

eTable 2. Flow of Participants and Derivation of Final Analytic Samples Across Study Analyses

eTable 3. Population Characteristics of Variables Considered in the Statistical Model at Baseline and Follow-Up

eTable 4. The Distribution of the Composite Poor Mental Health Outcome and Its Components at Baseline and Follow-Up

eTable 7. Characteristics of Individuals With High Obstructive Sleep Apnea (OSA) Risk Associated With New Mental Health Conditions

eReferences.

jamanetwopen-e2549137-s001.pdf^{(797.5KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2549137-s002.pdf^{(15.9KB, pdf)}

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PERMALINK

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

Tetyana Kendzerska, MD, PhD

Ranjeeta Mallick, PhD

Wenshan Li, PhD

Rébecca Robillard, PhD

Vanessa Taler, PhD

Colleen Webber, PhD

Mouaz Saymeh, MD

Thien Thanh Dang-Vu, MD, PhD

Peter Tanuseputro, MD

Jess G Fiedorowicz, MD, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposure

Main Outcome and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design, Population, and Data Sources

Exposures: Self-Reported High Risk of OSA

Mental Health–Related Outcomes

Potential Covariates and Risk Factors

Variable Selection Process for the Final Models

Table 1. Population Characteristics of Variables Included in the Statistical Model at Baseline and Follow-Up.

Statistical Analysis

Primary Analyses

Secondary Analyses

Characteristics of Individuals at High Risk of OSA Associated With New Mental Health Conditions (Exploratory)

Results

Primary Analyses

Figure 1. Cross-Sectional Associations at Baseline and Follow-Up of High Risk of Obstructive Sleep Apnea (OSA) and Witnessed Apnea During Sleep and Mental Health Outcomes.

Table 2. Multivariable Association Between Exposures and the Composite Poor Mental Health Outcome.

Figure 2. Longitudinal Associations of High Risk of Obstructive Sleep Apnea (OSA) and Witnessed Apnea During Sleep and Mental Health Outcomes Among Individuals Without Self-Reported Mental Health Conditions at Baseline.

Figure 3. Repeated-Measures Analysis on the Associations of High Risk of Obstructive Sleep Apnea (OSA) and Witnessed Apnea During Sleep and Mental Health Outcomes.

Secondary Analyses

Characteristics of Individuals at High Risk of OSA Associated With New Mental Health Conditions

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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