The association between obesity indices and obstructive sleep apnea is modified by age in a sex-specific manner

Effect of bariatric surgery in the treatment of obstructive sleep apnea in obese patients

PURPOSE

To analyze the success rate of bariatric surgery in the treatment of obstructive sleep apnea (OSA) in obese patients and its related factors.

METHODS

Longitudinal, prospective, single cohort study, with consecutive sampling including OSA patients aged 18-65 years intervened of bariatric surgery. An anamnesis regarding OSA, a complete upper airway (UA) exploration and a cardiorespiratory polygraphy (CRP) pre- and post-surgery were performed.

RESULTS

Fifty-seven patients were included in this study. The overall surgical success and cure rates for bariatric surgery as a treatment for OSA were 61.4% and 52.6%, respectively. Factors predicting success were female sex (OR = 12.54; CI95% = 1.75-89.88, p = 0.012), age below 53 years old (OR = 7.24; CI95% = 1.48-35.51, p = 0.015) and pre-surgical weight below 105 kg (OR = 8.1; CI95% = 1.44-45.62, p = 0.018). Surgical success cases had lower weight and body mass index, greater weight loss, smaller postsurgical neck circumference and less palatal webbing, however these were not independent factors in the multivariate analysis.

CONCLUSIONS

Our results show that bariatric surgery is a feasible option for OSA treatment in obese patients, with a higher success rate in female, younger and thinner patients. Moreover, adipose tissue on the UA has been proven to decrease as a result of weight loss, although not correlated with surgery success in the treatment of OSA.

Optimizing Obstructive Sleep Apnea Risk Assessment in Hypertension: Development of a Predictive Nomogram in China

Purpose

Obstructive sleep apnea (OSA) is common in patients with hypertension. Our study aims to construct and validate an objective nomogram that can accurately predict the risk of OSA in patients with hypertension.

Patients and Methods

Retrospective data were collected from patients with hypertension who underwent polysomnography (PSG) at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University, China. All participants were assigned to the training group (used to develop the predictive model). Similarly, data from patients with hypertension who underwent PSG at the Sleep Medicine Center of the Second Affiliated Hospital of Guangdong Medical University, China, were collected, and these participants were assigned to the validation group (used to test the model's performance). Logistic and LASSO regression analyses were used to identify factors and construct the nomogram. C-index, calibration curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to assess the model. Finally, nomogram validation was performed in the validation group.

Results

This study included a training group of 303 patients and a validation group of 217 patients. Based on LASSO and Logistic regression analyses and clinical practicality, we identified gender, age, BMI (body mass index), NC (neck circumference) and ESS (Epworth Sleepiness Scale) as predictors for the nomogram. The C-index is 0.840 in the training group and 0.808 in the validation group. The area under the curve (AUC) of the predictive model and STOP-Bang at the three diagnostic cut-off points of the Apnea-Hypopnea Index (AHI) ≥ 5, AHI ≥ 15 and AHI ≥ 30 were 0.840 vs 0.778, 0.754 vs 0.740, and 0.765 vs 0.751 respectively. The AUC at each intercept point was higher than that of STOP-Bang. DCA and CICA showed that the nomogram is clinically useful.

Conclusion

The nomogram predictive model consisting of the five indicators (gender, age, BMI, NC and ESS) can be useful in determining OSA risk in patients with hypertension.

Analysis of Possible Predictors of Moderate and Severe Obstructive Sleep Apnea in Obese Patients

Objectives: To determine if there are clinical or anatomical differences between patients with grade II-IV obesity without obstructive sleep apnea (OSA) or mild OSA and patients with moderate or severe OSA and to assess whether any of these factors are predictive of moderate/severe OSA. Methods: Observational case-control study with consecutive sampling including patients between 18 and 65 years of age with grade II-IV obesity who were candidates for bariatric surgery. An anamnesis regarding OSA symptoms, a physical examination of the upper airway and a cardiorespiratory polygraphy were performed. Results: A total of 124 patients were included in the study, of whom 61.3% did not have OSA or had mild OSA and 38.7% had moderate or severe OSA. Age over 48 years was the only independent factor associated with moderate or severe OSA. Other factors showed a relation with moderate/severe OSA after multivariate analysis: male sex, STOP-BANG questionnaire ≥ 3, weight ≥ 105 kg, thick neck, neck circumference ≥ 41.25 cm, flaccid palate, Mallampati III-IV index, Friedman tongue position III-IV and retropalatal narrowing. Conclusions: The only independent predictive factor related to moderate or severe OSA in patients with morbid obesity was age over 48 years, therefore a sleep study remains essential for its diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12070-024-04908-0.

Revealing inconsistencies between Epworth scores and apnea-hypopnea index when evaluating obstructive sleep apnea severity: a clinical retrospective chart review

Introduction

A common practice in clinical settings is the use of the Epworth Sleepiness Scale (ESS) and apnea-hypopnea index (AHI) to demonstrate the severity of obstructive sleep apnea (OSA). However, several instances were noted where there were discrepancies in the reported severity between Epworth scores and AHI in our patient sample, prompting an investigation into whether OSA severity as demonstrated by AHI or predicted by ESS quantification of sleepiness is primarily responsible for inconsistencies.

Methods

Discrepancies were examined between Epworth scores and AHI by categorizing patients into two categories of inconsistency: individuals with either ESS < 10 and AHI ≥ 15 events/h or ESS ≥ 10 and AHI < 15 events/h. The potential influence of sex on these categories was addressed by assessing whether a significant difference was present between mean Epworth scores and AHI values for men and women in the sample. We investigated BMI both by itself as its own respective variable and with respect to the sex of the individuals, along with a consideration into the role of anxiety. Furthermore, we tested anxiety with respect to sex.

Results

In the first category of inconsistency the average ESS of 5.27 ± 0.33 suggests a normal level of daytime sleepiness. However, this contrasts with the average AHI of 32.26 ± 1.82 events/h which is indicative of severe OSA. In the second category the average ESS of 14.29 ± 0.47 suggests severe daytime sleepiness, contradicting the average AHI of 9.16 ± 0.44 events/h which only indicates mild OSA. Sex, BMI (both as a variable by itself and with respect to sex), and anxiety (both as a variable by itself and with respect to sex) contributed to observed inconsistencies.

Conclusion

The findings of our study substantiate our hypothesis that Epworth scores should be de-emphasized in the assessment of OSA and a greater importance should be placed on measures like AHI. While Epworth scores offer insights into patients' daytime sleepiness levels and the perceived severity of their OSA, the inconsistencies highlighted in our results when compared to AHI-based OSA severity underscore their potential inaccuracy. Caution is advised when utilizing Epworth scores for evaluating OSA severity in clinical settings.

Obstructive Sleep Apnea and Pulmonary Hypertension: A Chicken-and-Egg Relationship

Obstructive sleep apnea (OSA) is characterized by repeated episodes of upper airway obstruction during sleep, and it is closely linked to several cardiovascular issues due to intermittent hypoxia, nocturnal hypoxemia, and disrupted sleep patterns. Pulmonary hypertension (PH), identified by elevated pulmonary arterial pressure, shares a complex interplay with OSA, contributing to cardiovascular complications and morbidity. The prevalence of OSA is alarmingly high, with studies indicating rates of 20-30% in males and 10-15% in females, escalating significantly with age and obesity. OSA's impact on cardiovascular health is profound, particularly in exacerbating conditions like systemic hypertension and heart failure. The pivotal role of hypoxemia increases intrathoracic pressure, inflammation, and autonomic nervous system dysregulation in this interplay, which all contribute to PH's pathogenesis. The prevalence of PH among OSA patients varies widely, with studies reporting rates from 15% to 80%, highlighting the variability in diagnostic criteria and methodologies. Conversely, OSA prevalence among PH patients also remains high, often exceeding 25%, stressing the need for careful screening and diagnosis. Treatment strategies like continuous positive airway pressure (CPAP) therapy show promise in mitigating PH progression in OSA patients. However, this review underscores the need for further research into long-term outcomes and the efficacy of these treatments. This review provides comprehensive insights into the epidemiology, pathophysiology, and treatment of the intricate interplay between OSA and PH, calling for integrated, personalized approaches in diagnosis and management. The future landscape of OSA and PH management hinges on continued research, technological advancements, and a holistic approach to improving patient outcomes.

Effect of Weight Loss on the Apnea Hypopnea Index is Related to Waist Circumference in Chinese Adults with Overweight and Obesity

Purpose

The present study aimed to evaluate the efficiency of traditional anthropometric and body composition parameters in predicting apnea hypopnea index (AHI) change after weight loss.

Patients and Methods

Chinese adults with overweight and obesity were included into this study containing two parts. A cross-sectional study was conducted in 137 individuals using the baseline data from two weight loss intervention trials. The second part was the weight-loss intervention study conducted in 60 overweight and obese patients with obstructive sleep apnea (OSA). All participants underwent physical examination, bioelectrical impedance analysis and overnight polysomnography. Multivariate linear regression models were used to identify the most accurate parameters to predict AHI and the mediation analysis to evaluate the mediators between weight loss and AHI reduction.

Results

Waist circumference (WC), body mass index and fat mass were positively associated with AHI after adjusting multiple collinearities in the cross-sectional study. After weight-loss intervention, body weight decreased from 94.6 ± 15.3 to 88.0 ± 13.9 kg, and AHI decreased from 41.9 (13.0,66.9) to 20.7 (8.7,51.2) events/h. Among these parameters, only percentage changes in WC and AHI across the intervention were positively intercorrelated after controlling for covariates (adjusted r = 0.271, P = 0.041). The mediation analysis supported WC as a mediator between weight loss and AHI reduction (standardized indirect effect [95% CI] = 4.272[0.936,7.999]).

Conclusion

Both general and abdominal obesity are of high prognostic value for OSA. WC as an easily accessible parameter mediates the effects of weight loss in decreasing OSA severity.

Overall Obesity Not Abdominal Obesity Has a Causal Relationship with Obstructive Sleep Apnea in Individual Level Data

Objective

Both obstructive sleep apnea (OSA) and obesity are highly prevalent worldwide, and are intrinsically linked. Previous studies showed that obesity is one of the major risk factors for OSA, but the causality of the relationship is still unclear. The study was to investigate the causal relationships of overall obesity and abdominal obesity with OSA and its quantitative traits.

Methods

In this case-control study, a total of 7134 participants, including 4335 moderate-to-severe OSA diagnosed by standard polysomnography and 2799 community-based controls were enrolled. Anthropometric and biochemical data were collected. Mendelian randomization (MR) analyses were performed using the genetic risk score, based on 29 body mass index (BMI)- and 11 waist-hip-ratio (WHR)-associated single nucleotide polymorphisms as instrumental variables. The causal associations of these genetic scores with OSA and its quantitative phenotypes were analyzed.

Results

Obesity was strongly correlated with OSA in observational analysis (β= 0.055, P = 3.7 × 10-5). In MR analysis, each increase by one standard deviation in BMI was associated with increased OSA risk [odds ratio (OR): 2.21, 95% confidence interval (CI): 1.62-3.02, P = 5.57 × 10-7] and with 2.72-, 4.68-, and 3.25-fold increases in AHI, ODI, and MAI, respectively (all P < 0.05) in men. However, no causal associations were found between WHR and OSA risk or OSA quantitative traits in men and women.

Conclusion

Compared to abdominal obesity, overall obesity showed a causal relationship with OSA and its quantitative traits, especially in men.

The Cervical Fat Tissue Volume is a Predictor for Moderate to Severe OSA

Objective  Obstructive sleep apnea (OSA) is a disorder characterized by recurrent pharyngeal obstruction during sleep, in which upper airway anatomy plays a key role in its pathogenesis. The aim of this study was to describe whether the quantification of cervical fat tissue volume (CFTV) obtained by Computed Tomography (CT)cephalometry is related to the severity of OSA. Methods  Retrospective study between 2018 and 2020 in those patients > 18 years old, with diagnosis of OSA who performed a volumetric cephalometric imaging. Three-dimensional reconstruction of the images was performed and CFTV was measured. Results  91 patients were included in this study of which: without OSA (n: 7), mild (n: 19), moderate (n: 39) and severe OSA (n: 26). We observed a progressive increase of CFTV related to OSA severity has been observed (without OSA: 58.9 ml (47.9-87.5), mild: 59.1ml (48.4-78.3), moderate: 71 ml (42.6-127.1) and severe OSA 103.6 ml (81-153); p < 0.01); nevertheless, no differences were found in the airway volume and neck area. It was showed a significant correlation between CFTV and OSA indicators: AHI, ODI and T90 (Sp r: 0.48; 0.38 and 0.36; p < 0.01 respectively). CFTV cut-off value to discriminate AHI >15 ev/h with best sensitivity-specificity relationship was 64.1 ml with an area under the curve of 0.6 ± 0.06. Multivariate analysis showed that CFTV is a predictor for moderate to severe OSA (OR:3.05, IC95%: 1.14-8.17). Conclusion  Cervical fat quantification by CT cephalometry correlates with OSA severity in adults. Fat volume > 64.1 ml increased more than three times the risk of OSA moderate to severe.

A risk-predictive model for obstructive sleep apnea in patients with chronic obstructive pulmonary disease

Background

Obstructive sleep apnea syndrome (OSA) is increasingly reported in patients with chronic obstructive pulmonary disease (COPD). Our research aimed to analyze the clinical characteristics of patients with overlap syndrome (OS) and develop a nomogram for predicting OSA in patients with COPD.

Methods

We retroactively collected data on 330 patients with COPD treated at Wuhan Union Hospital (Wuhan, China) from March 2017 to March 2022. Multivariate logistic regression was used to select predictors applied to develop a simple nomogram. The area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the value of the model.

Results

A total of 330 consecutive patients with COPD were enrolled in this study, with 96 patients (29.1%) confirmed with OSA. Patients were randomly divided into the training group (70%, n = 230) and the validation group (30%, n = 100). Age [odds ratio (OR): 1.062, 1.003–1.124], type 2 diabetes (OR: 3.166, 1.263–7.939), neck circumference (NC) (OR: 1.370, 1.098–1,709), modified Medical Research Council (mMRC) dyspnea scale (OR: 0.503, 0.325–0.777), Sleep Apnea Clinical Score (SACS) (OR: 1.083, 1.004–1.168), and C-reactive protein (CRP) (OR: 0.977, 0.962–0.993) were identified as valuable predictors used for developing a nomogram. The prediction model performed good discrimination [AUC: 0.928, 95% confidence interval (CI): 0.873–0.984] and calibration in the validation group. The DCA showed excellent clinical practicability.

Conclusion

We established a concise and practical nomogram that will benefit the advanced diagnosis of OSA in patients with COPD.

Diagnosis of obstructive sleep apnea in children based on the XGBoost algorithm using nocturnal heart rate and blood oxygen feature

PURPOSE

Obstructive sleep apnea (OSA) is a serious type of obstructive sleep-disordered breathing (SDB) that can cause a series of adverse effects on children's cardiovascular, growth, cognition, etc. The gold standard for diagnosis is polysomnography (PGS), which is used to assess the prevalence of OSA by obtaining the apnea-hypopnea index (AHI), but this diagnosis method is expensive and needs to be performed in a specialized laboratory, making it difficult to be of benefit to children with suspected OSA on a large scale. Our goal was to use a machine learning method to identify children with OSA of varying severity using data on children's nighttime heart rate and blood oxygen data.

METHODS

This study included 3139 children who received diagnostic PSG with suspected OSA. Age, sex, BMI, 3 % oxygen depletion index (ODI), average nighttime heart rate and fastest heart rate were used as predictive features. Data sets were established with AHI ≥ 1, AHI ≥ 5, and AHI ≥ 10 as the diagnostic criteria for mild, moderate and severe OSA, and the samples of each data set were randomly divided into a training set and a test set at a ratio of 8:2. An OSA diagnostic model was established based on the XGBoost algorithm, and the ability of the machine learning model to diagnose OSA children with different severities was evaluated through different classification ability evaluation indicators. As a comparison, traditional classifier Logistic Regression was used to perform the same diagnostic task. The SHAP algorithm was used to evaluate the role of these features in the classification task.

RESULTS

We established a diagnostic model of OSA in children based on the XGBoost algorithm. On the test set, the AUCs of the model for diagnosing mild, moderate, and severe OSA were 0.95, 0.88, and 0.88, respectively, and the classification accuracy was 90.45 %, 85.67 %, and 89.81 %, respectively, perform better than Logistic Regression classifiers. ODI is the most important feature in all classification tasks, and a higher fastest heart rate and ODI make the model tend to classify samples as positive. A high BMI value caused the model to tend to classify samples as positive in the mild and moderate classification tasks and as negative in the classification task with severe OSA.

CONCLUSION

Using heart rate and blood oxygen data as the main features, a machine learning diagnostic model based on the XGBoost algorithm can accurately identify children with OSA at different severities. This diagnostic modality reduces the number of signals and the complexity of the diagnostic process compared to PSG, which could benefit children with suspected OSA who do not have the opportunity to receive a diagnostic PSG and provide a diagnostic priority reference for children awaiting a diagnostic PSG.

Associations between abdominal obesity and the risk of stroke in Chinese older patients with obstructive sleep apnea: Is there an obesity paradox?

Background and purpose

Abdominal obesity (AO) is a well-known independent risk factor for stroke in the general population although it remains unclear in the case of the elderly, especially in Chinese older patients with obstructive sleep apnea (OSA), considering the obesity paradox. This study aimed to investigate the association between AO and stroke among Chinese older patients with OSA.

Methods

Data were collected from January 2015 to October 2017, and 1,290 older patients (age 60–96 years) with OSA (apnea–hypopnea index ≥ 5 events/h on polysomnography) were consecutively enrolled from sleep centers at six hospitals, evaluated for AO defined as waist circumference (WC) using the standardized criteria for the Chinese population, and followed up prospectively for a median period of 42 months. Logistic regression and Cox regression analyses were used to determine the cross-sectional and longitudinal associations between AO and stroke risk in these participants and different groups of the severity of OSA.

Results

Participants with AO had a higher prevalence of stroke at baseline. A higher incidence of stroke during a median follow-up period of 42 months in participants with AO than in participants without AO (12.4% vs. 6.8% and 8.3% vs. 2.4%, respectively; both P &lt; 0.05) was predicted. Cross-sectional analysis revealed an association between AO and stroke (odds ratio [OR]1.96, 95% confidence interval [CI] 1.31–2.91), which was stronger among participants with moderate OSA only (OR 2.16, 95%CI 1.05–4.43). Cox regression analysis showed that, compared to participants without AO, participants with AO had a higher cumulative incidence of stroke (hazard ratio [HR] 2.16, 95% CI 1.12–4.04) during a median follow-up of 42 months, and this association was observed in patients with severe OSA only (HR 3.67, 95% CI 1.41–9.87) but not for individuals with mild OSA (HR = 1.84, 95% CI 0.43–6.23) and moderate OSA (HR = 1.98, 95% CI 0.73–6.45).

Conclusion

The risk of stroke is associated with AO among Chinese older patients who have OSA, both at baseline and during follow-up, and the strength of the association varied by OSA severity. Active surveillance for early detection of AO could facilitate the implementation of stroke-preventive interventions in the Chinese older OSA population.

Cardiac and pulmonary findings in dysferlinopathy: A 3-year, longitudinal study

INTRODUCTION/AIMS

There is debate about whether and to what extent either respiratory or cardiac dysfunction occurs in patients with dysferlinopathy. This study aimed to establish definitively whether dysfunction in either system is part of the dysferlinopathy phenotype.

METHODS

As part of the Jain Foundation's International Clinical Outcome Study (COS) for dysferlinopathy, objective measures of respiratory and cardiac function were collected twice, with a 3-y interval between tests, in 188 genetically confirmed patients aged 11-86 y (53% female). Measures included forced vital capacity (FVC), electrocardiogram (ECG), and echocardiogram (echo).

RESULTS

Mean FVC was 90% predicted at baseline, decreasing to 88% at year 3. FVC was less than 80% predicted in 44 patients (24%) at baseline and 48 patients (30%) by year 3, including ambulant participants. ECGs showed P-wave abnormalities indicative of delayed trans-atrial conduction in 58% of patients at baseline, representing a risk for developing atrial flutter or fibrillation. The prevalence of impaired left ventricular function or hypertrophy was comparable to that in the general population.

DISCUSSION

These results demonstrate clinically significant respiratory impairment and abnormal atrial conduction in some patients with dysferlinopathy. Therefore, we recommend that annual or biannual follow-up should include FVC measurement, enquiry about arrhythmia symptoms and peripheral pulse palpation to assess cardiac rhythm. However, periodic specialist cardiac review is probably not warranted unless prompted by symptoms or abnormal pulse findings.

Development and validation of a simple clinical nomogram for predicting obstructive sleep apnea

Obstructive sleep apnea is the most common type of sleep breathing disorder. Therefore, the purpose of our research is to construct and verify an objective and easy-to-use nomogram that can accurately predict a patient's risk of obstructive sleep apnea. In this study, we retrospectively collected the data of patients undergoing polysomnography at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University. Participants were randomly assigned to a training cohort (50%) and a validation cohort (50%). Logistic regression and Lasso regression models were used to reduce data dimensions, select factors and construct the nomogram. C-index, calibration curve, decision curve analysis and clinical impact curve analysis were used to evaluate the identification, calibration and clinical effectiveness of the nomogram. Nomograph validation was performed in the validation cohort. The study included 1035 people in the training cohort and 1078 people in the validation cohort. Logistic and Lasso regression analysis identified age, gender, diastolic blood pressure, body mass index, neck circumference and Epworth Sleepiness Scale as the predictive factors included in the nomogram. The training cohort (C-index = 0.741) and validation cohort (C-index = 0.745) had better identification and calibration effects. The areas under the curve of the nomogram and STOP-Bang were 0.741 (0.713-0.767) and 0.728 (0.700-0.755), respectively. Decision curve analysis and clinical impact curve analysis showed that the nomogram is clinically useful. We have established a concise and practical nomogram that will help doctors better determine the priority of patients referred to the sleep centre.

Analysis of Blood Pressure and Ventilation Efficiency in Different Types of Obesity Aged 40-60 Years by Cardiopulmonary Exercise Test

PURPOSE

This study investigated blood pressure and ventilation efficiency by cardiopulmonary exercise test (CPX) in different types of obesity aged 40-60 years.

MATERIAL AND METHODS

The inclusion criteria of this cross-sectional study were adults aged 40-60 years underwent health checks. CPX was measured according to the relevant standards. According to different body mass index (BMI), there were 3 groups, BMI<24 (kg/m²), 24≤BMI<28 (kg/m²) and BMI≥28 (kg/m²). There were two groups in male, waist circumference≥90 (cm) and waist circumference<90 (cm). Similarly, there were two groups in female, waist circumference≥85 (cm) and waist circumference<85 (cm).

RESULTS

There were 543 individuals (64.6% male and 35.4% female) aged 40-60 years in this study. The resting blood pressure (BP) and peak BP have the significant differences in different BMI groups (p < 0.001) and male or female groups (p < 0.001). However, the resting DBP (77.70±9.45 vs 81.16±8.80, p < 0.001) and peak DBP (85.67±10.21 vs 89.03±9.94, p = 0.002) have the significant differences in different male waist circumference groups, and the resting BP (SBP 113.76±14.29 vs 121.86±15.54, p = 0.001, DBP 71.95±10.83 vs 77.27±11.42, p = 0.005) has the significant differences in different female waist circumference groups. Carbon dioxide Ventilation equivalent (VE/VCO₂) has the significant differences in different male waist circumference groups (26.84±3.10 vs 27.68±2.93, p = 0.009), but it has not the significant differences in different BMI groups and different female waist circumference groups. The oxygen pulse (VO₂/HR) is slightly higher in female group than male group (0.93±0.15 vs 0.89±0.15, p = 0.001). Breathing reserve has the statistical significance in BMI ≥28 group compared with the BMI <24 group (0.52±0.13 vs 0.46±0.17, ηp²=0.021).

CONCLUSION

We found that the blood pressure and ventilation efficiency of CPX were different between the obesity and normal. This will provide a basis for accurate cardiopulmonary assessment of obesity.

Association of Sleep Duration With All- and Major-Cause Mortality Among Adults in Japan, China, Singapore, and Korea

IMPORTANCE

The association between long sleep duration and mortality appears stronger in East Asian populations than in North American or European populations.

OBJECTIVES

To assess the sex-specific association between sleep duration and all-cause and major-cause mortality in a pooled longitudinal cohort and to stratify the association by age and body mass index.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study of individual-level data from 9 cohorts in the Asia Cohort Consortium was performed from January 1, 1984, to December 31, 2002. The final population included participants from Japan, China, Singapore, and Korea. Mean (SD) follow-up time was 14.0 (5.0) years for men and 13.4 (5.3) years for women. Data analysis was performed from August 1, 2018, to May 31, 2021.

EXPOSURES

Self-reported sleep duration, with 7 hours as the reference category.

MAIN OUTCOMES AND MEASURES

Mortality, including deaths from all causes, cardiovascular disease, cancer, and other causes. Sex-specific hazard ratios (HRs) and 95% CIs were estimated using Cox proportional hazards regression with shared frailty models adjusted for age and the key self-reported covariates of marital status, body mass index, smoking status, alcohol consumption, physical activity, history of diabetes and hypertension, and menopausal status (for women).

RESULTS

For 322 721 participants (mean [SD] age, 54.5 [9.2] years; 178 542 [55.3%] female), 19 419 deaths occurred among men (mean [SD] age of men, 53.6 [9.0] years) and 13 768 deaths among women (mean [SD] age of women, 55.3 [9.2] years). A sleep duration of 7 hours was the nadir for associations with all-cause, cardiovascular disease, and other-cause mortality in both men and women, whereas 8 hours was the mode sleep duration among men and the second most common sleep duration among women. The association between sleep duration and all-cause mortality was J-shaped for both men and women. The greatest association for all-cause mortality was with sleep durations of 10 hours or longer for both men (hazard ratio [HR], 1.34; 95% CI, 1.26-1.44) and women (HR, 1.48; 95% CI, 1.36-1.61). Sex was a significant modifier of the association between sleep duration and mortality from cardiovascular disease (χ25 = 13.47, P = .02), cancer (χ25 = 16.04, P = .007), and other causes (χ25 = 12.79, P = .03). Age was a significant modifier of the associations among men only (all-cause mortality: χ25 = 41.49, P < .001; cancer: χ25 = 27.94, P < .001; other-cause mortality: χ25 = 24.51, P < .001).

CONCLUSIONS AND RELEVANCE

The findings of this cohort study suggest that sleep duration is a behavioral risk factor for mortality in both men and women. Age was a modifier of the association between sleep duration in men but not in women. Sleep duration recommendations in these populations may need to be considered in the context of sex and age.