Heart Health Study Research Articles

0723 Slow wave activity surrounding K-complexes is associated with long-term all-cause mortality in a large community-dwelling cohort

Abstract Introduction Sleep fragmentation is thought to be associated with adverse long-term consequences including mortality. However, conventional metrics of sleep fragmentation such as time spent in non-REM, REM sleep stages have been inconsistently associated with long-term consequences of sleep fragmentation. Recent studies suggest that morphological features of K-complexes might better describe sleep fragmentation as they are sensitive to effects of sleep disruption and better correlate with short-term outcomes such as daytime sleepiness. We previously demonstrated that the slow wave activity (SWA) surrounding K-complexes (∆SWAK) was a strong predictor of sleepiness in a cohort of sleep apnea subjects, mediated in part by its association with sleep fragmentation, however its association with long-term consequences of sleep fragmentation is not well known. Methods We analyzed nocturnal polysomnography (NPSG) data from Sleep Heart Health Study (1995-1998), a well-characterized cohort examining the cardiovascular consequences of sleep-disordered breathing. The primary quantitative EEG (C3-A2) metrics were: SWA (%relative power in 0.5-4 Hz), K-complex density (number/min of N2), and ∆SWAK. K-complexes were detected automatically using previously published open-source method (DETOKS, Parekh et. al., 2015) during stage N2 of sleep only. All EEG metrics were divided into quartiles to explore potential non-linear associations. Death from any cause up until 2011 (mean follow up of 11 years) was the primary outcome. Cox-regression models were used to assess the relationship between EEG metrics and all-cause mortality. Results After accounting for missing data, 3,909 NPSG’s with at least 6h of usable EEG were available for analysis (age=64±11 yrs., 53.4% female). In multivariate Cox-regression models adjusted for age, gender, race, severity of sleep apnea, total sleep time, time spent in stage N2, and smoking, all-cause mortality was significantly associated with the highest quartile of SWA (HR_q4=0.77 [0.6-0.9], p=0.01), highest two quartiles of ∆SWAK (HR_q3=0.8 [0.6-0.9], p=0.04; HR_q4=0.6 [0.5-0.8], p< 0.001). Conclusion Quantitative EEG measures predict mortality in a large community-dwelling cohort. Specifically, slow-wave activity surrounding K-complexes appear to be strongly associated with all-cause mortality beyond the effect of known covariates. Combined with our previous studies, our data suggest that slow-wave specific EEG measures are predictive of short- and long-term consequences of sleep fragmentation. Support (if any) AASM Foundation BS-233-20, NIH-K25HL151912, R21HL165320

0437 Obstructive Sleep Apnea Symptom Subtypes and Hypoxic Burden Independently Predict Distinct Cardiovascular Outcomes

Abstract Introduction Studies on heterogeneity of obstructive sleep apnea (OSA) have identified clinically relevant symptom-based subtypes and novel OSA-specific nocturnal hypoxemia measures. Evidence suggests that moderate-severe OSA patients with the excessively sleepy subtype have increased risk of incident major adverse cardiovascular events (MACE; composite of coronary heart disease, heart failure, stroke, or cardiovascular mortality) and that OSA-specific hypoxic burden (HB) is associated with increased risk of cardiovascular disease (CVD) mortality. However, no study has comprehensively examined their combined impact. This study provides a systematic assessment of the individual and combined contribution of these factors on incident CVD risk, addressing an important gap in evidence. Methods Participants from the Sleep Heart Health Study with high-quality oxygen saturation, OSA severity quantified by apnea-hypopnea index (AHI), and symptom data were included. Latent class analysis on 14 symptoms was used to classify participants with moderate-severe OSA (AHI≥15) into subtypes. HB was calculated from respiratory event-related hypoxia and sleep time. Cox proportional hazards models were used to assess the relative contributions of OSA symptom subtypes and HB (natural log transformed) on risk for CVD mortality and MACE, adjusted for demographic and CVD risk factors. Analyses were performed in both CVD-free participants and all participants (adjusted for CVD at baseline). Results A total of 5,027 participants were analyzed, with median follow-up of 11.6 years (CVD mortality) and 11.3 years (MACE). HB was independently associated with CVD mortality in all participants (HR[95% CI] = 1.44 [1.06-1.97]; p=0.021) and CVD-free participants (1.62 [1.12-2.35]; p=0.010) controlling for symptom subtype. Conversely, symptom subtypes were independently associated with MACE incidence among CVD-free participants, controlling for HB, with the excessively sleepy participants having nearly two-fold higher risk (HR[95% CI] = 1.98 [1.26-3.10]; p=0.003) compared to those without OSA. Conclusion OSA symptom subtypes and HB are independently associated with distinct CVD-related endpoints. Higher HB was associated with cardiovascular mortality, controlling for symptom subtype. The excessively sleepy subtype was at higher risk of new MACE, controlling for HB. Thus, both factors are important for understanding OSA-related CVD risk. Support (if any) AHA (20CDA35310360), NIH (P01HL094307, U01HL53916, U01HL53931, U01HL53934, U01HL53937, U01HL53938, U01HL53940, U01HL53941, U01HL64360 R24 HL114473, 75N92019R002).

0417 Pre-sleep wakefulness in patients with insomnia: heart rate variability during sleep onset

Abstract Introduction Pre-sleep stress or hyperarousal is a known key etiological component in insomnia disorder. However, physiological alterations during the sleep onset period are not well-understood. We aimed to characterize heart rate variability (HRV) in patients with insomnia during sleep onset. Methods Using data from the Sleep Heart Health Study at National Sleep Research Resource, we conducted a secondary analysis exploring HRV during the sleep onset period (defined as the time between lights-off and sleep onset) in patients with insomnia (IS group; n = 68) and comorbid obstructive sleep apnea (IS + OSA group; n = 135), compared with normal sleep controls (NSC group; n = 122). Overall group differences were tested using ANOVA. Post-hoc pairwise comparisons were conducted using the least significant difference (LSD) tests. Results During sleep onset, mean heart rates of the IS group (74.4±15.0 beats/min) and IS+OSA group (72.6±11.5 beats/min) were both significantly higher than the NSC group (69.7±9.4 beats/min, p=0.007 and p=0.044 respectively). Both the IS and IS+OSA groups showed lower heart rate variability compared to the NSC group, with significant differences in both time and frequency domains of HRV analysis including SDNN (p=0.002), RMSSD (p=0.035), pNN20 (p< 0.001), pNN50 (p=0.008), LnLF (p< 0.001), LnHF (p< 0.001). For all these measures, IS+OSA group had lower HRV compared to the IS group, although differences were not significant. When controlling for age, gender and BMI, and compared to the NSC group, IS group had significantly higher heart rates (β=4.8, p=0.008), and the IS+OSA group had significantly lower pNN20 (β=-0.039, p=0.002), pNN50 (β=-0.031, p=0.017) and LnHF (β=0.007, p=0.007). Conclusion Patients with insomnia had lower vagal activity during the sleep onset period, reflected in higher heart rates and lower heart rate variability. Such altered heart rate dynamics may serve as a physiological biomarker for insomnia during bedtime wakefulness, or as a potential tool to evaluate the efficacy of behavioral interventions which target bedtime stress. Support (if any) This research project was made possible by a grant from the American Academy of Sleep Medicine Foundation (PI: Yan Ma). We acknowledge the National Sleep Research Resource as the resource through which the Data/Datasets were obtained.

Adolescent BMI trajectory and associations with adult metabolic syndrome and offspring obesity.

This study examinedthe association of adolescent BMI trajectory with adult metabolic syndrome (MetSyn) and with intergenerational obesity. This study used data from the National Heart, Lung, and Blood Institute (NHLBI) Growth and Health Study (1987-1997). Data from the 20-year follow-up (2016-2019) study were included from the original participants (N = 624) and their children (N = 645). Adolescent BMI trajectories were identified using latent trajectory modeling. Mediation analysis using logistic regression models was performed to estimate confounder-adjusted odds ratios (OR) and 95% CI between adolescent BMI trajectory and adult MetSyn. Using similar methods, the association between BMI trajectory and offspring obesity was examined. Latent trajectory modeling identified four patterns: "weight loss then gain" (N = 62); "persistently normal" (N = 374); "persistently high BMI" (N = 127); and "weight gain then loss" (N = 61). Women who had a persistently high BMI trajectoryhad twice the odds of having children who met the definition for obesity compared with the persistently normal group, adjusting for adult BMI (OR: 2.76; 95% CI: 1.39-5.46). None of the trajectory groups was associated with adult MetSyn compared with the persistently normal group. Intermittent adolescent obesity may not confer MetSyn risk during adulthood. However, maternal adolescent BMI trajectories that are persistently highmay increase the odds of intergenerational obesity among offspring.

A prospective study of the association of weekend catch-up sleep and sleep duration with mortality in middle-aged adults.

The online version contains supplementary material available at 10.1007/s41105-023-00460-6.

Integrating medical rules to assist attention for sleep apnea detection

AbstractSleep apnea is one of the most common sleep disorders. The consequences of undiagnosed sleep apnea can be very serious, increasing the risk of high blood pressure, heart disease, stroke, and Alzheimer’s disease over a long period of time. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is nighttime polysomnography monitoring in a specialized sleep laboratory. However, these diagnoses are expensive and the number of beds is limited, and there is insufficient monitoring in terms of time dimension. Existing methods for automated detection use no more than three physiological signals, but all other signals are also associated with the patient’s sleep. In addition, the limited amount of medical real annotation data, especially abnormal samples, lead to weak model generalization capability. The gap between model generalization capability and medical field needs still exists. In this paper, we propose a method for integrating medical interpretation rules into a long short-term memory neural network based on self-attention with multichannel respiratory signals as input. We obtain attention weights through a token-level attention mechanism and then extract key rules of medical interpretation to assist the weights, improving model generalization and reducing the dependence on data volume. Compared with the best prediction performance of existing methods, the average improvements of our method in accuracy, precision, and f1-score are 3.26%, 7.03%, and 1.78%, respectively. The algorithm tested the performance of our model on the Sleep Heart Health Study data set and found that the model outperformed existing methods and could help physicians make decisions in their practices.

Relevance of cortical arousals for risk stratification in sleep apnea: a 3 cohort analysis.

There is uncertainty on best approaches for defining apnea-hypopnea events. To clarify the contributions of desaturation vs arousal to defining hypopneas, we examined the associations of events with desaturation (≥ 3%) but not arousal (apnea-hypopnea index [AHI]≥3%Only) vs events with arousals but no desaturation (AHIArOnly) with obstructive sleep apnea-related comorbidities and incident cardiovascular disease across multiple cohorts. In the Sleep Heart Health Study (n = 5,473), the Multi-Ethnic Study of Atherosclerosis (n = 1,904), and the Osteoporotic Fractures in Men Study (n = 2,685), we examined the independent associations of AHI≥3%Only and AHIArOnly with hypertension, diabetes, and daytime sleepiness, and incident cardiovascular disease. After adjusting for covariates and AHI based on events with electroencephalogram arousal (regardless of desaturation), AHI≥3%Only was associated with hypertension in Sleep Heart Health Study (odds ratio: 1.12; 95% confidence interval: 1.04,1.21), per 1 standard deviation increase). Similar associations were observed in the Multi-Ethnic Study of Atherosclerosis and Osteoporotic Fractures in Men Study, as well as for associations with diabetes (odds ratio: 1.30; 1.09,1.54, and 1.25; 1.07,1.47, respectively), sleepiness (odds ratio: 1.19; 1.00,1.41; and 1.17; 1.01-1.35), and incident cardiovascular disease (hazard ratio: 1.37; 1.05,1.77 and 1.14; 1.00,1.29). In contrast, after adjusting for events with desaturation (regardless of arousal), AHIArOnly was unassociated with these outcomes. In Sleep Heart Health Study, greater baseline obstructive sleep apnea severity was associated with a reduction in arousal frequency over 5 years (P < .0001). In middle-aged and older individuals, addition of events with arousals does not improve the strength of associations with comorbidities or incident cardiovascular disease. Research is needed to understand generalizability to younger individuals and the mechanistic role of arousals in obstructive sleep apnea. Azarbarzin A, Sands SA, Han S, etal. Relevance of cortical arousals for risk stratification in sleep apnea: a 3 cohort analysis. J Clin Sleep Med. 2023;19(8):1475-1484.

Serum uric acid and left ventricular mass index independently predict cardiovascular mortality: The uric acid right for heart health (URRAH) project

A relationship between serum uric acid (SUA) and cardiovascular (CV) events has been documented in the Uric Acid Right for Heart Health (URRAH) study. Aimof this study was to investigate the association between SUA and left ventricular mass index (LVMI) and whether SUA and LVMI or their combination may predict the incidence of CV death. MethodsSubjects with echocardiographic measurement of LVMI included in the URRAH study (n=10733) were part of this analysis. LV hypertrophy (LVH) was defined as LVMI > 95 g/m2 in women and 115 g/m2 in men. ResultsA significant association between SUA and LVMI was observed in multiple regression analysis in men: beta 0,095, F 5.47, P< 0.001 and women: beta 0,069, F 4.36, P<0.001. During follow-up 319 CV deaths occurred. Kaplan–Meier curves showed a significantly poorer survival rate in subjects with higher SUA (> 5.6 mg/dl in men and 5.1 mg/dl in women) and LVH (log-rank chi-square 298.105; P<0.0001). At multivariate Cox regression analysis in women LVH alone and the combination of higher SUA and LVH but not hyperuricemia alone, were associated with a higher risk of CV death, while in men hyperuricemia without LVH, LVH without hyperuricemia and their combination were all associated with a higher incidence of CV death. ConclusionsOur findings demonstrate that SUA is independently associated with LVMI and suggest that the combination of hyperuricemia with LVH is an independent and powerful predictor for CV death both in men and women.

Insomnia with objective, but not subjective, short sleep duration is associated with increased risk of incident hypertension: the Sleep Heart Health Study.

Insomnia with objective short sleep duration has been associated with higher risk of cardiometabolic morbidity. In this study, we examined the association between insomnia with objective short sleep duration, also based on subjective sleep duration, with incident hypertension in the Sleep Heart Health Study. We analyzed data from 1,413 participants free of hypertension or sleep apnea at baseline from the Sleep Heart Health Study, with a median follow-up duration of 5.1 years. Insomnia symptoms were defined based on difficulty falling asleep, difficulty returning to sleep, early morning awakening, or sleeping pill use more than half the days in a month. Objective short sleep duration was defined as polysomnography-measured total sleep time < 6 hours. Incident hypertension was defined based on blood pressure measures and/or use of antihypertensive medications at follow-up. Individuals with insomnia who slept objectively < 6 hours had significantly higher odds of incident hypertension compared to normal sleepers who slept ≥ 6 hours (odds ratio = 2.00, 95% confidence interval = 1.09-3.65) or < 6 hours (odds ratio = 2.00, 95% confidence interval = 1.06-3.79) or individuals with insomnia who slept ≥ 6 hours (odds ratio = 2.79, 95% confidence interval = 1.24-6.30). Individuals with insomnia who slept ≥ 6 hours or normal sleepers who slept < 6 hours were not associated with increased risk of incident hypertension compared to normal sleepers who slept ≥ 6 hours. Finally, individuals with insomnia who self-reported sleeping < 6 hours were not associated with significantly increased odds of incident hypertension. These data further support that the insomnia with objective short sleep duration phenotype based on objective, but not subjective measures, is associated with increased risk of developing hypertension in adults. Dai Y, Chen B, Chen L, etal. Insomnia with objective, but not subjective, short sleep duration is associated with increased risk of incident hypertension: the Sleep Heart Health Study. J Clin Sleep Med. 2023;19(8):1421-1428.

Association of Objective and Self-Reported Sleep Duration With All-Cause and Cardiovascular Disease Mortality: A Community-Based Study.

Background Previous studies found an association between self-reported sleep duration and mortality. This study aimed to compare the effects of objective and self-reported sleep duration on all-cause and cardiovascular disease (CVD) mortality. Methods and Results A total of 2341 men and 2686 women (aged 63.9±11.1 years) were selected from the SHHS (Sleep Heart Health Study). Objective sleep duration was acquired using in-home polysomnography records, and self-reported sleep duration on weekdays and weekends was based on a sleep habits questionnaire. The sleep duration was categorized as ≤4hours, 4 to 5 hours, 5 to 6 hours, 6 to 7 hours, 7 to 8 hours, and >8 hours. Multivariable Cox regression analysis was used to investigate the association of objective and self-reported sleep duration with all-cause and CVD mortality. During a mean follow-up period of 11 years, 1172 (23.3%) participants died, including 359 (7.1%) deaths from CVD. All-cause and CVD mortality rates decreased gradually with increasing objective sleep duration. In multivariable Cox regression analysis, the greatest association for all-cause and CVD mortality was with an objective sleep duration of 5 hours or shorter. In addition, we found a J-shaped association of self-reported sleep duration on both weekdays and weekends with all-cause and CVD mortality. Self-reported short (≤4 hours) and long (>8 hours) sleep duration on weekdays and weekends were associated with an increased risk of all-cause and CVD mortality compared with 7 to 8 hours sleep duration. Furthermore, a weak correlation was observed between objective and self-reported sleep duration. Conclusions This study showed that both objective and self-reported sleep duration were associated with all-cause and CVD mortality, but with different characteristics. Registration URL: https://clinicaltrials.gov/ct2/show/NCT00005275; Unique identifier: NCT00005275.

Abstract MP45: Nocturnal Hypoxemia, Obstructive Sleep Apnea and the Risk of Cardiovascular Diseases and Mortality: Insights From the Atherosclerosis Risk in Communities Participants of the Sleep Heart Health Study

Background: Nocturnal hypoxemia may better capture disease burden than obstructive sleep apnea (OSA). Using a subset of participants from the Atherosclerosis Risk in Communities (ARIC) study who participated in the Sleep Heart Health Study (SHHS), we examined the association of nocturnal hypoxemia and OSA with cardiovascular diseases (CVD) and mortality in a white, older cohort in the US. Methods: A total of 1,455 participants free of CVD had an in-home, overnight polysomnogram; nocturnal hypoxemia (% of total sleep time with oxygen saturation &lt;90%) and OSA (apnea-hypopnea index - # of apneas/hypopneas per hour of sleep) were derived from these readings. Incident CVD events and mortality were identified through the end of 2019 using annual telephone calls, hospital discharge lists and death certificates and were adjudicated by physician review. Included in this analysis were coronary heart disease (CHD), heart failure (HF), stroke, any CVD event (CHD, HF, or stroke), atrial fibrillation (AF), and all-cause mortality. We used Cox proportional hazards models to assess the association of nocturnal hypoxemia (categories: &lt;1%, 1-&lt;5%, ≥5%) and OSA (categories: &lt;5, 5-&lt;15, 15-&lt;30, ≥30) with each outcome. Results: At the sleep assessment, participants had a mean (SD) age of 62 (6) years, 53% were female, and 14% had a nocturnal hypoxemia ≥5%. Over a mean follow-up of 17.8 years, a total of 408 participants had any CVD event, and 602 died. Nocturnal hypoxemia ≥5% was associated with incident HF and all-cause mortality in the fully adjusted model (Table). Nocturnal hypoxemia ≥5% was also associated with incident CVD and AF in Model 1; however, adjustment for body mass index attenuated these associations (Table, Model 2). OSA was not associated with the outcomes in any model (data not shown). Conclusion: Further research is needed to understand the distinction between nocturnal hypoxemia and OSA and how they relate to the risk of CVD and mortality. It is possible that reducing nocturnal hypoxemia should be targeted over OSA for CVD prevention.

Relative spectral power quantifying the distribution of intermittent hypoxemia in obstructive sleep apnea is strongly associated with hypertension

Study objectivesTo investigate the association between the periodicity of distribution of intermittent hypoxemia (IH) and hypertension in adults with obstructive sleep apnea (OSA) and search for an index to quantify the association. MethodsSamples were derived from two cross-sectional studies: The Sleep Heart Health Study (SHHS) including 3991 adults with age 64.7 ± 10.9 years; and the Chinese Changgung Sleep Health Study (CSHS) including 906 adults with age 59.5 ± 12.4 years. Spectral analysis of peripheral oxygen saturation (SpO2) was performed and the relative spectral power (PFR) in the frequency band of 0.011–0.037 Hz (PFR0.011–0.037Hz) was extracted to quantify the periodic distribution of IH. Multiple logistic regression models were used to calculate the partially and fully adjusted odd ratios for PFR0.011–0.037Hz. ResultsPFR0.011–0.037Hz was significantly higher in the hypertension group than non-hypertension group (44.4% ± 0.3% vs. 42.1% ± 0.3%, p < 0.001 in SHHS and 57.4% ± 0.7% vs. 50.5% ± 0.8%, p < 0.001 in CSHS). In the fully adjusted model, individuals in the SHHS with PFR0.011–0.037Hz in the highest quintiles had an odd ratio of 1.33 [95% confidence interval (CI) 1.06–1.67]. Similarly, the group in the CSHS with PFR0.011–0.037Hz in the highest quintile had an odd ratio of 3.08 (95% CI 1.80–5.28). ConclusionsWe developed an IH distribution measure which is strongly associated with hypertension independent of multiple confounding variables. The finding suggests that the periodic distribution of sleep related upper airway obstructions is an essential hypertension characterizing feature.

Prediction of Angina Pectoris Events in Middle-Aged and Elderly People Using RR Interval Time Series in the Resting State: A Cohort Study Based on SHHS

Angina pectoris is associated with adverse cardiovascular events. In this study, a Bi-directional Long Short-Term Memory (Bi-LSTM) prediction model with the Attention layer was established to explore the predictive value of the resting-state RR interval time series on the occurrence of angina pectoris. The data of this cohort study were from the Sleep Heart Health Study database, 2,977 people were included with the follow-up of 15 years. We used the RR interval time series of electrocardiogram signals in the resting state. The outcome variables were any angina events during the follow-up. We randomly divided 2,977 participants into training (n = 2680) and testing sets (n = 297) with a partition ratio of 9:1. The prediction model of Bi-LSTM with Attention layer was developed and the predictive performance was assessed. 1,236 had angina pectoris and 1,741 patients did not have angina pectoris during the follow-up period. The predictive performance of the Bi-LSTM model was great with the value of accuracy = 0.913, area under the curve (AUC) = 0.922, precision = 0.913 in the testing set. RR intervals may be the potential predictors of angina events. It is more and more convenient to obtain heart rate with the development of wearable devices; the Bi-LSTM prediction model established in this study is expected to provide support for the intelligent prediction of angina pectoris events.

Serum Uric Acid Predicts All-Cause and Cardiovascular Mortality Independently of Hypertriglyceridemia in Cardiometabolic Patients without Established CV Disease: A Sub-Analysis of the URic acid Right for heArt Health (URRAH) Study.

High serum uric acid (SUA) and triglyceride (TG) levels might promote high-cardiovascular risk phenotypes across the cardiometabolic spectrum. However, SUA predictive power in the presence of normal and high TG levels has never been investigated. We included 8124 patients from the URic acid Right for heArt Health (URRAH) study cohort who were followed for over 20 years and had no established cardiovascular disease or uncontrolled metabolic disease. All-cause mortality (ACM) and cardiovascular mortality (CVM) were explored by the Kaplan-Meier estimator and Cox multivariable regression, adopting recently defined SUA cut-offs for ACM (≥4.7 mg/dL) and CVM (≥5.6 mg/dL). Exploratory analysis across cardiometabolic subgroups and a sensitivity analysis using SUA/serum creatinine were performed as validation. SUA predicted ACM (HR 1.25 [1.12-1.40], p < 0.001) and CVM (1.31 [1.11-1.74], p < 0.001) in the whole study population, and according to TG strata: ACM in normotriglyceridemia (HR 1.26 [1.12-1.43], p < 0.001) and hypertriglyceridemia (1.31 [1.02-1.68], p = 0.033), and CVM in normotriglyceridemia (HR 1.46 [1.23-1.73], p < 0.001) and hypertriglyceridemia (HR 1.31 [0.99-1.64], p = 0.060). Exploratory and sensitivity analyses confirmed our findings, suggesting a substantial role of SUA in normotriglyceridemia and hypertriglyceridemia. In conclusion, we report that SUA can predict ACM and CVM in cardiometabolic patients without established cardiovascular disease, independent of TG levels.

Depression with obstructive sleep apnea lead to high cardiovascular disease morbidity/all-cause mortality: Findings from the SHHS cohort.

The aim of this study is to explore the association between depression and obstructive sleep apnea, and cardiovascular disease morbidity/all-cause mortality using Sleep Heart Health Study data. This post hoc analysis of a prospective study used patient data from the Sleep Heart Health Study conducted between 1995 and 1998. The association between depression and obstructive sleep apnea, and cardiovascular disease morbidity/all-cause mortality was explored using multivariable Cox proportional hazard models. A total of 4918 participants, 656 (13.3%) with obstructive sleep apnea (obstructive sleep apnea group), 1614 (32.8%) with depression (depression group), 482 (9.8%) with depression and obstructive sleep apnea (depression and obstructive sleep apnea group), and 2166 (44%) with neither obstructive sleep apnea nor depression (health group), were included. The incidence of cardiovascular disease was 24.5%, 31.0%, 31.6% and 41.7% for healthy, depression, obstructive sleep apnea, and depression and obstructive sleep apnea groups, respectively. The risk of cardiovascular disease in depression and obstructive sleep apnea participants was increased compared with that in healthy participants, which was consistent across various definitions of obstructive sleep apnea (hazard ratio [95% confidence interval]: 1.24 [1.06-1.47]; 1.25 [1.05-1.49]; 1.28 [1.06-1.54]; 1.55 [1.22-1.96] for apnea-hypopnea index ≥ 10 per hr, 15 per hr, 20 per hr, 30 per hr, respectively). The risk of all-cause mortality was increased in the depression and obstructive sleep apnea participants (hazard ratio: 1.46; 95% confidence interval: 1.07-1.99) compared with that in healthy participants when the definition of obstructive sleep apnea was apnea-hypopnea index ≥ 30. Participants with depression and obstructive sleep apnea might be at a greater risk of cardiovascular disease, and those with higher apnea-hypopnea index might be at a greater risk of all-cause mortality.

Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening

Objective Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea–hypopnea index cutoff thresholds: [Formula: see text] 5 and [Formula: see text] 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database ( N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis. Results For cutoff [Formula: see text] 5/hours, logistic regression model achieved the highest Matthew’s correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff [Formula: see text] 30/hours, support vector machine model achieved the highest Matthew’s correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%). Conclusions Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.

Longitudinal trajectories of spectral power during sleep in middle-aged and older adults.

Age-related changes in sleep appear to contribute to cognitive aging and dementia. However, most of the current understanding of sleep across the lifespan is based on cross-sectional evidence. Using data from the Sleep Heart Health Study, we investigated longitudinal changes in sleep micro-architecture, focusing on whether such age-related changes are experienced uniformly across individuals. Participants were 2,202 adults (ageBaseline=62.40±10.38, 55.36% female, 87.92% White) who completed home polysomnography assessment at two study visits, which were 5.23years apart (range: 4-7years). We analyzed NREM and REM spectral power density for each 0.5Hz frequency bin, including slow oscillation (0.5-1Hz), delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz), and beta-1 (15-20Hz) bands. Longitudinal comparisons showed a 5-year decline in NREM delta (p<.001) and NREM sigma power density (p<.001) as well as a 5-year increase in theta power density during NREM (p=.001) and power density for all frequency bands during REM sleep (ps<0.05). In contrast to the notion that sleep declines linearly with advancing age, longitudinal trajectories varied considerably across individuals. Within individuals, the 5-year changes in NREM and REM power density were strongly correlated (slow oscillation: r=0.46; delta: r=0.67; theta r=0.78; alpha r=0.66; sigma: r=0.71; beta-1: r=0.73; ps<0.001). The convergence in the longitudinal trajectories of NREM and REM activity may reflect age-related neural de-differentiation and/or compensation processes. Future research should investigate the neurocognitive implications of longitudinal changes in sleep micro-architecture and test whether interventions for improving key sleep micro-architecture features (such as NREM delta and sigma activity) also benefit cognition over time.

Automated Sleep Staging via Parallel Frequency-Cut Attention

Stage-based sleep screening is a widely-used tool in both healthcare and neuroscientific research, as it allows for the accurate assessment of sleep patterns and stages. In this paper, we propose a novel framework that is based on authoritative guidance in sleep medicine and is designed to automatically capture the time-frequency characteristics of sleep electroencephalogram (EEG) signals in order to make staging decisions. Our framework consists of two main phases: a feature extraction process that partitions the input EEG spectrograms into a sequence of time-frequency patches, and a staging phase that searches for correlations between the extracted features and the defining characteristics of sleep stages. To model the staging phase, we utilize a Transformer model with an attention-based module, which allows for the extraction of global contextual relevance among time-frequency patches and the use of this relevance for staging decisions. The proposed method is validated on the large-scale Sleep Heart Health Study dataset and achieves new state-of-the-art results for the wake, N2, and N3 stages, with respective F1 scores of 0.93, 0.88, and 0.87 using only EEG signals. Our method also demonstrates high inter-rater reliability, with a kappa score of 0.80. Moreover, we provide visualizations of the correspondence between sleep staging decisions and features extracted by our method, which enhances the interpretability of the proposal. Overall, our work represents a significant contribution to the field of automated sleep staging and has important implications for both healthcare and neuroscience research.

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device.

In clinical conditions, polysomnography (PSG) is regarded as the "golden standard" for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. In practice, the need to simplify PSG to obtain subjective sleep quality by recording a few channels of physiological signals such as single-lead electrocardiogram (ECG) or photoplethysmography (PPG) signal is still very urgent. This study provided a two-step method to differentiate sleep quality into "good sleep" and "poor sleep" based on the single-lead wearable cardiac cycle data, with the comparison of the subjective sleep questionnaire score. First, heart rate variability (HRV) features and ECG-derived respiration features were extracted to construct a sleep staging model (wakefulness (W), rapid eye movement (REM), light sleep (N1&N2) and deep sleep (N3)) using the multi-classifier fusion method. Then, features extracted from the sleep staging results were used to construct a sleep quality evaluation model, i.e., classifying the sleep quality as good and poor. The accuracy of the sleep staging model, tested on the international public database, was 0.661 and 0.659 in Cardiology Challenge 2018 training database and Sleep Heart Health Study Visit 1 database, respectively. The accuracy of the sleep quality evaluation model was 0.786 for our recording subjects, with an average F1-score of 0.771. The proposed sleep staging model and sleep quality evaluation model only requires one channel of wearable cardiac cycle signal. It is very easy to transplant to portable devices, which facilitates daily sleep health monitoring.

Uric acid relationships with lipid profile and adiposity indices: Impact of different hyperuricemic thresholds.

Previous studies focused on the relationships between Serum Uric Acid (SUA) and lipids have found an association mainly with triglycerides. Furthermore, previous studies on adiposity indices have been focused on the evaluation of the Visceral Adiposity Index (VAI). The present study was aimed at providing within the same population a systematic evaluation of lipids and adiposity indices with SUA, employing both the classic cutoff for hyperuricemia and the newly one identified by the Uric Acid Right for Heart Health (URRAH) study. We analyzed data collected in 1892 subjects of the Pressioni Arteriose Monitorate E loro Associazioni (PAMELA) study with available SUA, lipid profile and variables necessary to calculate VAI, Cardio-Metabolic Index (CMI) and Lipid Accumulation Product (LAP). At linear regression model (corrected for confounders) SUA correlated with all the lipids values (with the strongest β for triglycerides) and adiposity indices. When the two different cutoffs were compared, the URRAH one was significantly related to atherogenic lipids profile (OR 1.207 for LDL and 1.33 for non-HDL, P<0.001) while this was not the case for the classic one. Regarding adiposity indices the classic cutoff displays highest OR as compared to the URRAH one. In conclusions, newly reported URRAH cutoff for hyperuricemia better relate to atherogenic lipoprotein (LDL and non-HDL) when compared to the classic one. The opposite has been found for adiposity indexes where the classic cut-off seems to present highest performance. Among adiposity indexes, LAP present the highest OR for the relationship with hyperuricemia.