Sleep fragmentation and incidence of congestive heart failure: the Sleep Heart Health Study

Polysomnographic characteristics of patients with heart failure combined with sleep apnea: A systematic review and meta-analysis

AIMS

Objective sleep parameters in patients with HF and their comorbidity with sleep-disordered breathing (SDB) are not fully understood. We aimed to investigate the polysomnography-measured sleep characteristics in HF patients using a systematic review and meta-analysis.

METHODS

PubMed, Embase, and Web of Science databases were searched for studies on PSG in HF patients. Meta-analyses were conducted to compare PSG parameters between HF patients and Non-HF, HF patients with and without SDB, HF patients with different types of SDB, and HF patients before and after SDB treatments.

RESULTS

HF patients showed decreased sleep efficiency (MD = -7.5 %, 95 %CI: [-10.8 %, -4.1 %], P < 0.001) and reduced slow wave sleep (MD = -3.2 %, 95 %CI: [-5.7 %, -0.7 %], P = 0.013) compared with those without HF. With comorbid SDB, HF patients experienced further decreases in sleep efficiency and total sleep time, and disruptions in sleep architecture, including decreased rapid-eye-movement sleep (MD = -3.4 %, 95 %CI: [-6.2 %, -0.5 %], P = 0.020) and SWS (MD = -3.1 %, 95 %CI: [-4.1 %, -2.1 %], P < 0.001).

CONCLUSIONS

HF patients showed poorer sleep continuity and less restorative sleep. Additionally, SDB was associated with poor objective sleep architecture in HF patients, indicated by a decrease in the percentage of deep sleep and an increase in sleep fragmentation. SDB treatments, such as adaptive servo-ventilation (ASV) and continuous positive airway pressure (CPAP), were associated with improved sleep quality and quantity in HF patients with SDB.

Bidirectional predictors between baseline and recovery sleep measures and cardiovascular measures during sleep deprivation and psychological stress

For the first time, we investigated bidirectional predictors between baseline and recovery sleep and cardiovascular (CV) measures during total sleep deprivation (TSD) and psychological stress in a five-day experiment with 32 healthy adults (27-53y; 14 females). CV measures were collected in the morning after two baseline nights (B1, B2) and during TSD morning (TSD AM) and evening following psychological stress (TSD PM). Actigraphy assessed sleep during B2 before TSD and the first recovery night (R1) after TSD. Higher B2 wake after sleep onset (WASO) predicted lower TSD PM stroke volume and higher TSD PM systemic vascular resistance index (SVRI), with greater B2 percent sleep predicting inverse relationships, explaining 12.8%-15.9% of the TSD CV variance. Also, higher B2 WASO predicted higher B2 AM SVRI. Furthermore, longer TSD left ventricular ejection time predicted later R1 sleep offset, longer sleep duration, and higher WASO; by contrast, higher TSD AM and TSD PM heart rate predicted earlier R1 sleep offset. TSD CV indices explained 14.8%-24.9% of the R1 sleep variance. Notably, females showed significant predictive bidirectional relationships. Our novel results demonstrate that baseline sleep predicts CV metrics during TSD and psychological stress, and that these metrics predict recovery sleep, underscoring crucial relationships, mechanisms, and biomarkers between sleep and cardiovascular health.

Global Perspectives on Sleep Health: Definitions, Disparities, and Implications for Public Health

Sleep health is a multidimensional construct encompassing sleep quality, duration, efficiency, regularity, and alignment with circadian rhythms, playing a crucial role in overall well-being. Sleep health remains inconsistently defined across research and clinical settings despite its importance, limiting the ability to standardize assessments and interventions. Recent studies have emphasized the significance of defining sleep health beyond the absence of sleep disorders, integrating subjective and objective measures to assess its impact on physical and mental health outcomes. Disparities in sleep health exist across gender, socioeconomic status, and geographic regions, particularly in low- and middle-income countries where inconsistent work schedules, economic stress, and healthcare access influence sleep patterns. Poor sleep health is associated with increased risks of cardiovascular disease, obesity, metabolic dysfunction, and mental health disorders, reinforcing its role as a modifiable risk factor in public health. Lifestyle factors such as caffeine consumption, physical activity, and irregular eating patterns also contribute to sleep disturbances, highlighting the need for behavioral interventions. This narrative review aims to synthesize the current knowledge on sleep health, focusing on its definitions, measurement tools, global disparities, and associations.

Sex-specific associations between self-reported sleep characteristics and 10-year cardiovascular disease risk in men and women of African descent living in a low socioeconomic status environment

Background

Risk factors for cardiovascular disease (CVD) and sleep health are well-known to be sex- and race-specific. To build on the established relationship between sleep duration and CVD risk, this cross-sectional study aimed to describe sex-specific associations between CVD risk and other sleep characteristics (sleep quality, sleep timing and sleep onset latency) in low-income adults of African descent.

Methods

Self-reported sleep (Pittsburgh Sleep Quality Index [PSQI], Epworth Sleepiness Scale [ESS], Insomnia Severity Index [ISI]), demographic and lifestyle data were collected in 412 adults (56 % women, 35.0 ± 7.6y, 40 % employed) living in an informal settlement in South Africa. CVD risk was determined using the BMI-modified Framingham 10-year CVD risk formula.

Results

Logistic regression analyses, adjusted for employment, alcohol use and physical activity, indicated that men reporting poor sleep quality (OR: 1.95[95 %CI: 1.07-3.51], p=0.025) and earlier bedtimes (0.54[0.39-0.74], p<0.001) were more likely to belong to a higher 10-year CVD risk score quintile. Women reporting earlier bedtimes (0.72[0.55-0.95], p=0.020) and wake-up times (0.30[0.13-0.73], p=0.007), longer sleep-onset latency (1.47[1.43-1.88], p=0.003), shorter total sleep times (0.84[0.72-0.98], p=0.029), higher PSQI global scores (1.93[1.29-2.90], p=0.001) and more moderate to severe symptoms of insomnia (ISI≥15)(3.24[1.04-10.04], p=0.016) were more likely to belong to higher 10-year CVD risk score quintile.

Conclusion

In addition to sleep duration, we found that sleep quality, sleep timing and sleep onset latency are additional risk factors for CVD in adults of African descent. Sex-specific differences in the sleep-CVD-risk relationship observed suggests that future studies and recommendations about sleep health in relation to CVD should take sex into account.

Gender-based differences in the association of self-reported sleep duration with cardiovascular disease and diabetes

OBJECTIVE

Insufficient or prolonged sleep each day may contribute to the onset of cardiovascular disease and diabetes, and there may be some variability between genders; however, current research evidence is limited. We aimed to investigate the effects of gender on self-reported sleep duration and the prevalence of cardiovascular disease and diabetes.

RESEARCH DESIGN AND METHODS

This study is a population-based, cross-sectional analysis. Data from a nationally representative sample of US adults obtained from the National Health and Nutrition Examination Survey (NHANES) (2005-2020), and 13,002 participants, including 6,774men and 6,228women, were obtained by excluding the missing values for each variable self-reported sleep duration data obtained by using a habitual baseline questionnaire. Logistic regression models investigated the associations between gender-specific self-reported sleep duration, CVDs, and diabetes events.

RESULT

In all participants, respectively, compared with sleep 7-8 h/day, the multivariable-adjusted odds ratios significantly associated with < 7 h /day and > 8 h /day were (1.43[1.15, 1.78]) and (1.34[1.01, 1.76]) for CHF, (1.62[1.28, 2.06]) for Angina, (1.42[1.17, 1.71]) for heart attack, (1.38[1.13, 1.70]) and (1.54[1.20, 1.97]) for Stroke, (1.21[1.09, 1.35]) and (1.28[1.11, 1.48]) for diabetes. In men, CHF (1.67[1.21, 2.14]), Angina (1.66[1.18, 2.15]), Stroke (1.55[1.13,1.97]), and diabetes (1.15[1.00, 1.32]) were significantly associated with < 7 h /day, and stroke (1.73[1.16, 2.32]) and diabetes (1.32[1.06, 1.52]) were significantly associated with > 8 h /day. In women, angina(1.83[1.16, 2.50]), heart attack(1.63[1.11, 2.15]), and diabetes (1.32[1.11, 1.54]) were significantly associated with < 7 h /day, while diabetes (1.31[1.03, 1.59]) was significantly associated with > 8 h /day.

CONCLUSION

Self-reported long and short sleep duration was independently associated with partial CVDs and diabetes risk. However, sleep duration and gender did not have multiplicative or additive interactions with the onset of diabetes and CVDs.

Chronic sleep fragmentation reduces left ventricular contractile function and alters gene expression related to innate immune response and circadian rhythm in the mouse heart

Sleep disorders have emerged as a widespread public health concern, primarily due to their association with an increased risk of developing cardiovascular diseases. Our previous research indicated a potential direct impact of insufficient sleep duration on cardiac remodeling in children and adolescents. Nevertheless, the underlying mechanisms behind the link between sleep fragmentation (SF) and cardiac abnormalities remain unclear. In this study, we aimed to investigate the effects of SF interventions at various life stages on cardiac structure and function, as well as to identify genes associated with SF-induced cardiac dysfunction. To achieve this, we established mouse models of chronic SF and two-week sleep recovery (SR). Our results revealed that chronic SF significantly compromised left ventricular contractile function across different life stages, leading to alterations in cardiac structure and ventricular remodeling, particularly during early life stages. Moreover, microarray analysis of mouse heart tissue identified two significant modules and nine hub genes (Ddx60, Irf9, Oasl2, Rnf213, Cmpk2, Stat2, Parp14, Gbp3, and Herc6) through protein-protein interaction analysis. Notably, the interactome predominantly involved innate immune responses. Importantly, all hub genes lost significance following SR. The second module primarily consisted of circadian clock genes, and real-time PCR validation demonstrated significant upregulation of Arntl, Dbp, and Cry1 after SF, while subsequent SR restored normal Arntl expression. Furthermore, the expression levels of four hub genes (Ddx60, Irf9, Oasl2, and Cmpk2) and three circadian clock genes (Arntl, Dbp, and Cry1) exhibited correlations with structural and functional echocardiographic parameters. Overall, our findings suggest that SF impairs left ventricular contractile function and ventricular remodeling during early life stages, and this may be mediated by modulation of the innate immune response and circadian rhythm. Importantly, our findings suggest that a short period of SR can alleviate the detrimental effects of SF on the cardiac immune response, while the influence of SF on circadian rhythm appears to be more persistent. These findings underscore the importance of good sleep for maintaining cardiac health, particularly during early life stages.

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study

Background and Objectives

Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes.

Methods

This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk.

Results

There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort.

Discussion

The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

Impact of bimonthly repeated total sleep deprivation and recovery sleep on cardiovascular indices

Since short sleep duration adversely affects cardiovascular (CV) health, we investigated the effects of exposures to total sleep deprivation (TSD), and baseline (BL) and recovery (REC) sleep on CV measures. We conducted a 5-day experiment at months 2 and 4 in two separate studies (N = 11 healthy adults; 5 females). During these repeated experiments, CV measures [stroke volume (SV), cardiac index (CI), systemic vascular resistance index (SVRI), left ventricular ejection time, heart rate (HR), systolic and diastolic blood pressure (SBP and DBP) and mean arterial pressure (MAP)] were collected at three assessment time points after: (1) two BL 8 h time-in-bed (TIB) sleep opportunity nights; (2) a TSD night; and (3) two REC 8-10 h TIB nights. CV measures were also collected pre-study. TSD significantly increased SV and CI, and decreased SVRI, with large effect sizes, which importantly were reversed with recovery, indicating these measures are possible novel biomarkers for assessing the adverse consequences of TSD. Pre-study SV, CI, SVRI, HR, SBP, and MAP measures also significantly associated with TSD CV responses at months 2 and 4 [Pearson's r: 0.615-0.862; r2 : 0.378-0.743], indicating they are robust correlates of future TSD CV responses. Our novel findings highlight the critical impact of sleep on CV health across time.

Age estimation from sleep studies using deep learning predicts life expectancy

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.

Increased Rapid Eye Movement Sleep Is Associated With a Reduced Risk of Heart Failure in Middle-Aged and Older Adults

Objectives

Rapid eye movement (REM) sleep is closely related to all-cause mortality. The aim of this study is to explore the role of REM sleep on the incident heart failure (HF).

Methods

We selected 4490 participants (2480 women and 2010 men; mean age, 63.2 ± 11.0 years) from the Sleep Heart Health Study. HF was identified as the first occurrence during a mean follow-up period of 10.9 years. REM sleep including percentage of REM sleep and total REM sleep time were monitored using in-home polysomnography at baseline. Multivariable Cox regression analysis was utilized to explore the relationship between REM sleep and HF.

Results

In total, 436 (9.7%) cases of HF were observed during the entire follow-up period. After adjusting for potential covariates, an increased percentage of REM sleep (per 5%) was independently associated with a reduced incidence of HF [hazard ratio (HR) 0.88, 95% confidence interval (CI) 0.82–0.94, P < 0.001]. A similar result was also found between total REM sleep time (increased per 5 min) and incident HF (HR 0.97, 95% CI 0.95–0.99, P < 0.001). Moreover, the fourth quartile of both percentage of REM sleep (HR 0.65, 95% CI 0.48–0.88, P = 0.005) and total REM sleep time (HR 0.64, 95% CI 0.45–0.90, P = 0.010) had lower risk of incident HF when compared with the first quartile.

Conclusion

An increased percentage of REM sleep and total REM sleep time were associated with a reduced risk of HF. REM sleep may be a predictor of the incident HF.

Clinical Trial Registration

[ClinicalTrials.gov], identifier [NCT00005275].