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Purpose: Insomnia symptoms and sleep apnea frequently co-occur and are associated with worse sleep, daytime function, mental health and quality of life, compared to either insomnia or obstructive sleep apnea (OSA) alone. This study aimed to investigate the association of symptoms of co-morbid insomnia and sleep apnea (COMISA) with all-cause mortality. Patients and Methods: Wisconsin Sleep Cohort data were analysed to assess potential associations between COMISA symptoms and all-cause mortality. Nocturnal insomnia symptoms were defined as difficulties initiating sleep, maintaining sleep, and/or early morning awakenings \"often\" or \"almost always\", and/or regular sedative-hypnotic medicine use. OSA was defined as an apneahypopnea index [greater than or equal to]5/hr sleep. Participants were classified as having neither insomnia symptoms nor OSA, insomnia symptoms alone, OSA alone, or COMISA symptoms. Associations between the four groups and all-cause mortality over 20 years of follow-up were examined via multivariable adjusted Cox regression models. Results: Among 1115 adult participants (mean[+ or -] SD age 55 [+ or -] 8 years, 53% males), 19.1% had COMISA symptoms. After controlling for sociodemographic and behavioral factors, COMISA symptoms were associated with an increased risk of all-cause mortality compared to no insomnia symptoms or OSA (HR [95% CI]; 1.71 [1.00-2.93]). OSA alone (0.91 [0.53, 1.57]) and insomnia symptoms alone (1.04 [0.55, 1.97]) were not associated with increased mortality risk. Conclusion: Co-morbid insomnia symptoms and sleep apnea is associated with increased all-cause mortality risk. Future research should investigate mechanisms underpinning COMISA and the effectiveness of different treatment approaches to reduce mortality risk for this common condition. Keywords: sleep disordered breathing, Wisconsin Sleep Cohort, COMISA, obstructive, sleep apnea, insomnia, respiratory

Nowadays, the hectic work life of people has led to sleep deprivation. This may further result in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has become an active research area. Sleep scoring is crucial for detecting sleep-related disorders like sleep apnea, insomnia, narcolepsy, periodic leg movement (PLM), and restless leg syndrome (RLS). Sleep is conventionally monitored in a sleep laboratory using polysomnography (PSG) which is the recording of various physiological signals. The traditional sleep stage scoring (SSG) done by professional sleep scorers is a tedious, strenuous, and time-consuming process as it is manual. Hence, developing a machine-learning model for automatic SSG is essential. In this study, we propose an automated SSG approach based on the biorthogonal wavelet filter bank’s (BWFB) novel least squares (LS) design. We have utilized a huge Wisconsin sleep cohort (WSC) database in this study. The proposed study is a pioneering work on automatic sleep stage classification using the WSC database, which includes good sleepers and patients suffering from various sleep-related disorders, including apnea, insomnia, hypertension, diabetes, and asthma. To investigate the generalization of the proposed system, we evaluated the proposed model with the following publicly available databases: cyclic alternating pattern (CAP), sleep EDF, ISRUC, MIT-BIH, and the sleep apnea database from St. Vincent’s University. This study uses only two unipolar EEG channels, namely O1-M2 and C3-M2, for the scoring. The Hjorth parameters (HP) are extracted from the wavelet subbands (SBS) that are obtained from the optimal BWFB. To classify sleep stages, the HP features are fed to several supervised machine learning classifiers. 12 different datasets have been created to develop a robust model. A total of 12 classification tasks (CT) have been conducted employing various classification algorithms. Our developed model achieved the best accuracy of 83.2% and Cohen’s Kappa of 0.7345 to reliably distinguish five sleep stages, using an ensemble bagged tree classifier with 10-fold cross-validation using WSC data. We also observed that our system is either better or competitive with existing state-of-art systems when we tested with the above-mentioned five databases other than WSC. This method yielded promising results using only two EEG channels using a huge WSC database. Our approach is simple and hence, the developed model can be installed in home-based clinical systems and wearable devices for sleep scoring.

Abstract Rationale Sleep-disordered breathing (SDB) has been associated with total and cardiovascular mortality, but an association with cancer mortality has not been studied. Results from in vitro and animal studies suggest that intermittent hypoxia promotes cancer tumor growth. Objectives The goal of the present study was to examine whether SDB is associated with cancer mortality in a community-based sample. Methods We used 22-year mortality follow-up data from the Wisconsin Sleep Cohort sample (n = 1,522). SDB was assessed at baseline with full polysomnography. SDB was categorized using the apnea-hypopnea index (AHI) and the hypoxemia index (percent sleep time below 90% oxyhemoglobin saturation). The hazards of cancer mortality across levels of SDB severity were compared using crude and multivariate analyses. Measurements and Main Results Adjusting for age, sex, body mass index, and smoking, SDB was associated with total and cancer mortality in a dose–response fashion. Compared with normal subjects, the adjusted relative hazards of cancer mortality were 1.1 (95% confidence interval [CI], 0.5–2.7) for mild SDB (AHI, 5–14.9), 2.0 (95% CI, 0.7–5.5) for moderate SDB (AHI, 15–29.9), and 4.8 (95% CI, 1.7–13.2) for severe SDB (AHI ≥ 30) (P-trend = 0.0052). For categories of increasing severity of the hypoxemia index, the corresponding relative hazards were 1.6 (95% CI, 0.6–4.4), 2.9 (95% CI, 0.9–9.8), and 8.6 (95% CI, 2.6–28.7). Conclusions Our study suggests that baseline SDB is associated with increased cancer mortality in a community-based sample. Future studies that replicate our findings and look at the association between sleep apnea and survival after cancer diagnosis are needed.

Abstract Rationale Obstructive sleep apnea (OSA) is associated with hypertension. Objectives We aimed to quantify the independent association of OSA during REM sleep with prevalent and incident hypertension. Methods We included adults enrolled in the longitudinal community-based Wisconsin Sleep Cohort Study with at least 30 minutes of REM sleep obtained from overnight in-laboratory polysomnography. Studies were repeated at 4-year intervals to quantify OSA. Repeated measures logistic regression models were fitted to explore the association between REM sleep OSA and prevalent hypertension in the entire cohort (n = 4,385 sleep studies on 1,451 individuals) and additionally in a subset with ambulatory blood pressure data (n = 1,085 sleep studies on 742 individuals). Conditional logistic regression models were fitted to longitudinally explore the association between REM OSA and development of hypertension. All models controlled for OSA events during non-REM sleep, either by statistical adjustment or by stratification. Measurements and Main Results Fully adjusted models demonstrated significant dose-relationships between REM apnea–hypopnea index (AHI) and prevalent hypertension. The higher relative odds of prevalent hypertension were most evident with REM AHI greater than or equal to 15. In individuals with non-REM AHI less than or equal to 5, a twofold increase in REM AHI was associated with 24% higher odds of hypertension (odds ratio, 1.24; 95% confidence interval, 1.08–1.41). Longitudinal analysis revealed a significant association between REM AHI categories and the development of hypertension (P trend = 0.017). Non-REM AHI was not a significant predictor of hypertension in any of the models. Conclusions Our findings indicate that REM OSA is cross-sectionally and longitudinally associated with hypertension. This is clinically relevant because treatment of OSA is often limited to the first half of the sleep period leaving most of REM sleep untreated.

Abstract Study Objectives Previous research suggests that reductions in restorative, slow-wave (N3), and rapid eye movement (REM) sleep are associated with weight gain and obesity in mid-to-late life. We extend prior work by examining how within-person (WP) changes and between-person (BP) differences in restorative sleep over several years are associated with body mass trajectories among participants in the Wisconsin Sleep Cohort Study (WSCS). Methods We used data from 4,862 polysomnographic (PSG) sleep studies and physical exams collected from 1,187 WSCS participants over an average duration of 14.9 years. Primary measures of interest included body mass index (BMI = kg/m2) and the percentages of time spent in N3 and REM sleep. We estimated a series of linear mixed regression models to examine how WP changes and BP differences in N3 and REM sleep affected BMI trajectories, controlling for other sleep measures, demographic characteristics, and health behaviors as potential confounders. Results Women in the WSCS experienced more rapid BMI gain than men. With some variation by sex, we found that (1) below-average N3 and REM sleep is associated with above-average BMI, and (2) within-person decreases in N3 and REM sleep over time are associated with gains in BMI. These findings persisted after adjustment for sleep duration and other potential confounders. Conclusions Our findings highlight the importance of PSG indices of restorative sleep in mid-to-late life, suggesting that future clinical treatments and public health policies will benefit from heightened attention to sleep quality.

Cardiovascular diseases (CVDs) are prevalent among obstructive sleep apnea (OSA) patients, presenting significant challenges in predictive modeling due to the complex interplay of these comorbidities. Current methodologies predominantly lack the dynamic and longitudinal perspective necessary to accurately predict CVD progression in the presence of OSA. This study addresses these limitations by proposing a novel multi-level phenotypic model that analyzes the progression and interaction of these comorbidities over time. Our study utilizes a longitudinal cohort from the Wisconsin sleep cohort, consisting of 1,123 participants, tracked over several decades. The methodology consists of three advanced steps to capture the relationships between these comorbid conditions: (1) performing feature importance analysis using tree-based models to highlight the predominant role of variables in predicting CVD outcomes. (2) developing a logistic mixed-effects model (LGMM) to identify longitudinal transitions and their significant factors, enabling detailed tracking of individual trajectories; (3) and utilizing t-distributed stochastic neighbor embedding (t-SNE) combined with Gaussian mixture models (GMM) to classify patient data into distinct phenotypic clusters. In the analysis of feature importance, clinical indicators such as total cholesterol, low-density lipoprotein, and diabetes emerged as the top predictors, highlighting their significant roles in CVD onset and progression. The LGMM predictive models exhibited a high diagnostic accuracy with an aggregate accuracy of 0.9556. The phenotypic analysis yielded two distinct clusters, each corresponding to unique risk profiles and disease progression pathways. One cluster notably carried a higher risk for major adverse cardiovascular events (MACEs), attributed to key factors like nocturnal hypoxia and sympathetic activation. Analysis using t-SNE and GMM confirmed these phenotypes, which marked differences in progression rates between the clusters. In conclusion, our study provides a profound understanding of the dynamic OSA-CVD interactions, offering robust tools for predicting CVD onset and informing personalized treatment strategies.

Abstract Menopause is considered to be a risk factor for sleep-disordered breathing, but this hypothesis has not been adequately tested. The association of premenopause, perimenopause, and postmenopause with sleep-disordered breathing was investigated with a population-based sample of 589 women enrolled in the Wisconsin Sleep Cohort Study. Menopausal status was determined from menstrual history, gynecologic surgery, hormone replacement therapy, follicle-stimulating hormone, and vasomotor symptoms. Sleep-disordered breathing was indicated by the frequency of apnea and hypopnea events per hour of sleep, measured by in-laboratory polysomnography. Multivariable logistic regression was used to estimate odds ratios for having 5 or more and 15 or more apnea and hypopnea events per hour. Odds ratios (95% confidence interval), adjusted for age, body habitus, smoking, and other potential confounding factors, for 5 or more apnea and hypopnea events per hour were 1.2 (0.7, 2.2) with perimenopause and 2.6 (1.4, 4.8) with postmenopause; odds ratios for 15 or more apnea and hypopnea events per hour were 1.1 (0.5, 2.2) with perimenopause and 3.5 (1.4, 8.8) with postmenopause. The menopausal transition is significantly associated with an increased likelihood of having sleep-disordered breathing, independent of known confounding factors. Evaluation for sleep-disordered breathing should be a priority for menopausal women with complaints of snoring, daytime sleepiness, or unsatisfactory sleep.

To determine whether defining two subtypes of sleep-disordered breathing (SDB) events-with or without hypoxia-results in measures that are more strongly associated with hypertension and sleepiness. A total of 1022 participants with 2112 nocturnal polysomnograms from the Wisconsin Sleep Cohort were analyzed with our automated algorithm, developed to detect breathing disturbances and desaturations. Breathing events were time-locked to desaturations, resulting in two indices-desaturating (hypoxia-breathing disturbance index [H-BDI]) and nondesaturating (nonhypoxia-breathing disturbance index [NH-BDI]) events-regardless of arousals. Measures of subjective (Epworth Sleepiness Scale) and objective (2981 multiple sleep latency tests from a subset of 865 participants) sleepiness were analyzed, in addition to clinically relevant clinicodemographic variables. Hypertension was defined as BP ≥ 140/90 or antihypertensive use. H-BDI, but not NH-BDI, correlated strongly with SDB severity indices that included hypoxia (r ≥ 0.89, p ≤ .001 with 3% oxygen-desaturation index [ODI] and apnea hypopnea index with 4% desaturations). A doubling of desaturation-associated events was associated with hypertension prevalence, which was significant for ODI but not H-BDI (3% ODI OR = 1.06, 95% CI = 1.00-1.12, p < .05; H-BDI OR 1.04, 95% CI = 0.98-1.10) and daytime sleepiness (β = 0.20 Epworth Sleepiness Scale [ESS] score, p < .0001; β = -0.20 minutes in MSL on multiple sleep latency test [MSLT], p < .01). Independently, nondesaturating event doubling was associated with more objective sleepiness (β = -0.52 minutes in MSL on MSLT, p < .001), but had less association with subjective sleepiness (β = 0.12 ESS score, p = .10). In longitudinal analyses, baseline nondesaturating events were associated with worsening of H-BDI over a 4-year follow-up, suggesting evolution in severity. In SDB, nondesaturating events are independently associated with objective daytime sleepiness, beyond the effect of desaturating events.

Abstract Study Objectives To determine whether sleep apnea, defined by polysomnography, accelerates kidney function decline in generally healthy adults not selected for sleep apnea or kidney disease. Methods We performed a retrospective cohort study in 855 participants from the Wisconsin Sleep Cohort Study, a large 20-year population-based study of sleep apnea, who had at least one polysomnogram and serial measurements of serum creatinine over time. Sleep apnea was defined as an apnea–hypopnea index ≥ 15 or positive airway pressure (PAP) use at baseline. We compared the slope of estimated glomerular filtration rate (eGFR) change and odds of rapid eGFR decline (>2.2 mL/minute/1.73 m2/year) for those with and without sleep apnea. Results The mean follow-up was 13.9 ± 3.4 years. The cohort was 50.4 ± 7.6 years, 55% male, and 97% white. The mean eGFR was 89.3 ± 13.8 mL/minute/1.73 m2 and 11% had sleep apnea. Overall, the mean eGFR change was −0.88 ± 1.12 mL/minute/1.73 m2/year. Compared with those without sleep apnea, participants with sleep apnea had a 0.2 mL/minute/1.73 m2/year slower eGFR decline though this was not statistically significant (95% CI [−0.06–0.45], p = .134). When we excluded those on PAP therapy (n = 17), eGFR decline was even slower among those with sleep apnea (0.36 mL/minute/1.73 m2/year slower, 95% CI [0.08–063], p = .012). Those with sleep apnea had lower odds of rapid eGFR decline but this was not statistically significant, even after excluding PAP users. Conclusion Among healthy middle-aged adults, the presence of sleep apnea at baseline did not accelerate kidney function decline compared with those without sleep apnea over time.

Study Objectives: To examine associations of depression with habitual sleep duration, daytime sleepiness, and objective sleep propensity in a nonclinical population. Methods: Data from adults participating in the Wisconsin Sleep Cohort Study were utilized in analyses. There were 1,287 adults (3,324 observations) who were used in the analysis of subjective hypersomnolence measures; 1,155 adults (2,981 observations) were used in the analysis of objective sleep propensity assessed by the multiple sleep latency test (MSLT). Repeated-measures logistic regression estimated associations between presence of depression (defined as modified Zung Self-Rating Depression Scale ≥ 50 or use of antidepressant medications) and three primary hypersomnolence measures: subjective excessive daytime sleepiness (Epworth Sleepiness Scale [ESS] ≥ 11), self-reported sleep duration ≥ 9 h/d, and objective sleep propensity (MSLT mean sleep latency < 8 min). Results: After adjusting for age, sex, body mass index, chronic medical conditions, sedative hypnotic medication use, caffeine, tobacco, and alcohol use, sleep disordered breathing, as well as insomnia and sleep duration when appropriate, estimated odd ratios (95% confidence interval) for depression were: 1.56 (1.31,1.86) for ESS ≥ 11; 2.01 (1.49, 2.72) for habitual sleep time ≥ 9 h; and 0.76 (0.63–0.92) for MSLT mean sleep latency < 8 min. Conclusions: Our results demonstrate divergent associations between subjective and objective symptoms of hypersomnolence and depression, with subjective sleepiness and excessive sleep duration associated with increased odds of depression, but objective sleep propensity as measured by the MSLT associated with decreased odds of depression. Further research is indicated to explain this paradox and the impact of different hypersomnolence measures on the course of mood disorders. Commentary: A commentary on this article appears in this issue on page 467. Citation: Plante DT, Finn LA, Hagen EW, Mignot E, Peppard PE. Subjective and objective measures of hypersomnolence demonstrate divergent associations with depression among participants in the Wisconsin Sleep Cohort study. J Clin Sleep Med 2016;12(4):571–578.