Hypoxic burden - definitions, pathophysiological concepts, methods of evaluation, and clinical relevance.

PURPOSE OF REVIEW: Obstructive sleep apnea (OSA) is a common chronic condition that affects over a billion people worldwide and is associated with adverse cardio- and cerebrovascular consequences. Currently, the go-to clinical measure that determines the presence and severity of OSA is the apnea-hypopnea index (AHI). The AHI captures the frequency of respiratory events due to changes in ventilation that are associated with either oxygen desaturations or arousal from sleep. The AHI is poorly correlated to adverse outcomes in OSA with poor prognostic ability. To overcome the limitations of AHI and perhaps driven by the ease of acquisition, several studies have suggested characterizing nocturnal hypoxia in OSA, termed as "hypoxic burden". The purpose of this review is to focus on the hypoxic burden in OSA, its various definitions, and its utility in moving OSA diagnosis beyond the AHI. RECENT FINDINGS: Several measures and definitions of hypoxic burden have been proposed and studied that show promise in overcoming limitations of AHI and also have a greater prognostic ability than AHI. More recently, area-based measures that attempt to characterize the depth and duration of oxygen desaturations, i.e., nocturnal hypoxia in OSA, have been shown to better relate to incident cardiovascular disease than AHI. In this review, we delve into the evidence for these novel area-based metrics and also delve into the pathophysiological concepts underlying nocturnal hypoxia while cautioning the reader on interpretation of the recent findings relating hypoxic burden to adverse outcomes in OSA. SUMMARY: In this review on hypoxic burden, we focus on the need that has driven the sudden influx of studies assessing hypoxic burden for various outcomes of OSA, its underlying pathophysiology, the various definitions, and clinical relevance. We hope that the reader can appreciate the nuances underlying hypoxic burden in OSA and suggest the need for a cohesive framework for moving beyond the AHI with hypoxic burden.

A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.

Study Objectives: To investigate whether a foundational transformer model using 8-hour, multichannel data from polysomnograms can outperform existing artificial intelligence (AI) methods for sleep stage classification. Methods: We utilized the Sleep Heart Health Study (SHHS) visits 1 and 2 for training and validation and the Multi-Ethnic Study of Atherosclerosis (MESA) for testing of our model. We trained a self-supervised foundational transformer (called PFTSleep) that encodes 8-hour long sleep studies at 125 Hz with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels. These encodings are used as input for training of an additional model to classify sleep stages, without adjusting the weights of the foundational transformer. We compared our results to existing AI methods that did not utilize 8-hour data or the full set of signals but did report evaluation metrics for the SHHS dataset. Results: We trained and validated a model with 8,444 sleep studies with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels and tested on an additional 2,055 studies. In total, we trained and tested 587,944 hours of sleep study signal data. Area under the precision recall curve (AUPRC) scores were 0.82, 0.40, 0.53, 0.75, and 0.82 and area under the receiving operating characteristics curve (AUROC) scores were 0.99, 0.95, 0.96, 0.98, and 0.99 for wake, N1, N2, N3, and REM, respectively, on the SHHS validation set. For MESA, the AUPRC scores were 0.56, 0.16, 0.40, 0.45, and 0.65 and AUROC scores were 0.94, 0.77, 0.87, 0.91, and 0.96, respectively. Our model was compared to the longest context window state-of-the-art model and showed increases in macro evaluation scores, notably sensitivity (3.7% increase) and multi-class REM (3.39% increase) and wake (0.97% increase) F1 scores. Conclusions: Utilizing full night, multi-channel PSG data encodings derived from a foundational transformer improve sleep stage classification over existing methods.

Obstructive Sleep Apnea, Platelet Aggregation, and Cardiovascular Risk.

BACKGROUND: Although related, the precise mechanisms linking obstructive sleep apnea (OSA) and cardiovascular disease (CVD) are unclear. Platelets are mediators of CVD risk and thrombosis and prior studies suggested associations of OSA and platelet activity. The aim of this study is to assess the link between OSA, platelet activity, and CVD-related risk factors. METHODS AND RESULTS: We studied the association of OSA-measures and platelet aggregation in participants dually enrolled in the SHHS (Sleep Heart and Health Study) and FHS (Framingham Heart Study). We applied linear regression models with adjustment for demographic and clinical covariates and explored interactions with OSA and CVD-related factors, including age, sex, body mass index, hypertension, OSA diagnosis (apnea-hypopnea index 4%≥5), and aspirin use. Our final sample was of 482 participants (60 years [14.00], 50.4% female). No associations were observed between apnea-hypopnea index 4% and platelet aggregation in the main sample. Stratified analysis revealed an association in aspirin users (n=65) for our primary exposure (apnea-hypopnea index 4%, ß=0.523; P<0.001; n=65), and secondary exposures: hypoxic burden (ß=0.358; P<0.001), minimum saturation (ß=-0.519; P=0.026), and oxygen desaturation index 3% (ß=74.672; P=0.002). No associations were detected in nonaspirin users (n=417). CONCLUSIONS: No associations were detected between OSA and platelet aggregation in a community sample. Our finding that OSA associates with increased platelet aggregation in the aspirin group, most of whom use it for primary prevention of CVD, suggests that platelet aggregation may mediate the adverse impact of OSA on vascular health in individuals with existing CVD risk, supporting further investigation.

The stability of slow-wave sleep and EEG oscillations across two consecutive nights of laboratory polysomnography in cognitively normal older adults.

Laboratory polysomnography provides gold-standard measures of sleep physiology, but multi-night investigations are resource intensive. We assessed the night-to-night stability via reproducibility metrics for sleep macrostructure and electroencephalography oscillations in a group of cognitively normal adults attending two consecutive polysomnographies. Electroencephalographies were analysed using an automatic algorithm for detection of slow-wave activity, spindle and K-complex densities. Average differences between nights for sleep macrostructure, electroencephalography oscillations and sleep apnea severity were assessed, and test-retest reliability was determined using two-way intraclass correlations. Agreement was calculated using the smallest real differences between nights for all measures. Night 2 polysomnographies showed significantly greater time in bed, total sleep time (6.3 hr versus 6.8 hr, p < 0.001) and percentage of rapid eye movement sleep (17.5 versus 19.7, p < 0.001). Intraclass correlations were low for total sleep time, percentage of rapid eye movement sleep and sleep efficiency, moderate for percentage of slow-wave sleep and percentage of non-rapid eye movement 2 sleep, good for slow-wave activity and K-complex densities, and excellent for spindles and apnea-hypopnea index with hypopneas defined according to 4% oxygen desaturation criteria only. The smallest real difference values were proportionally high for most sleep macrostructure measures, indicating moderate agreement, and proportionally lower for most electroencephalography microstructure variables. Slow waves, K-complexes, spindles and apnea severity indices are highly reproducible across two consecutive nights of polysomnography. In contrast, sleep macrostructure measures all demonstrated poor reproducibility as indicated by low intraclass correlation values and moderate agreement. Although there were average differences in percentage of rapid eye movement sleep and total sleep time, these were numerically small and perhaps functionally or clinically less significant. One night of in-laboratory polysomnography is enough to provide stable, reproducible estimates of an individual's sleep concerning measures of slow-wave activity, spindles, K-complex densities and apnea severity.

Sleep-wake body temperature regulates tau secretion in mice and correlates with CSF and plasma tau in humans.

The sleep-wake cycle regulates interstitial fluid and cerebrospinal fluid (CSF) tau levels in both mouse and human by mechanisms that remain unestablished. Here, we reveal a novel pathway by which wakefulness increases extracellular tau levels in mouse and humans. In mice, higher body temperature (BT) associated with wakefulness and sleep deprivation increased CSF tau. In vitro, wakefulness temperatures upregulated tau secretion via a temperature-dependent increase in activity and expression of unconventional protein secretion pathway-1 components, namely caspase-3-mediated C-terminal cleavage of tau (TauC3), and membrane expression of PIP2 and syndecan-3. In humans, the increase in both CSF and plasma tau levels observed post-wakefulness correlated with BT increase during wakefulness. Our findings suggest sleep-wake variation in BT may contribute to regulating extracellular tau levels, highlighting the importance of thermoregulation in pathways linking sleep disturbance to neurodegeneration, and the potential for thermal intervention to prevent or delay tau-mediated neurodegeneration.

SleepSIM: Conditional GAN-based non-REM sleep EEG Signal Generator.

Synthetic data generation has become increasingly popular with the increasing use of generative networks. Recently, Generative Adversarial Network (GAN) architectures have produced exceptional results in synthetic image generation. However, time series generation still needs to be studied. This paper proposes a Conditional GAN-based system to generate unique samples of non-REM sleep electroencephalographic (EEG) signals. The CGAN model had a 1-D Convolution Neural Network based architecture. The model was trained using real EEG from healthy controls. The trained model can generate an artificial 30-second epoch of non-REM sleep whose power spectrum is identical to that of a real sleep EEG.Clinical relevance- Sleep EEG simulation can be used to train and enhance the skillset of fellows and technicians in the sleep medicine field. Variations in EEG signals can be highly complex to model mathematically; however, here, we harness the power of deep learning, using generative models such as CGANs to train, model complex data distributions, and generate diverse and artificial but realistic EEG signals during non-REM sleep.

Ventilatory Burden as a Measure of Obstructive Sleep Apnea Severity Is Predictive of Cardiovascular and All-Cause Mortality.

Rationale: The apnea-hypopnea index (AHI), used for the diagnosis of obstructive sleep apnea, captures only the frequency of respiratory events and has demonstrable limitations. Objectives: We propose a novel automated measure, termed "ventilatory burden" (VB), that represents the proportion of overnight breaths with less than 50% normalized amplitude, and we show its ability to overcome limitations of AHI. Methods: Data from two epidemiological cohorts (EPISONO [Sao Paolo Epidemiological Study] and SHHS [Sleep Heart Health Study]) and two retrospective clinical cohorts (DAYFUN; New York University Center for Brain Health) were used in this study to 1) derive the normative range of VB, 2) assess the relationship between degree of upper airway obstruction and VB, and 3) assess the relationship between VB and all-cause and cardiovascular disease (CVD) mortality with and without hypoxic burden that was derived using an in-house automated algorithm. Measurements and Main Results: The 95th percentiles of VB in asymptomatic healthy subjects across the EPISONO and the DAYFUN cohorts were 25.2% and 26.7%, respectively (median [interquartile range], VBEPISONO, 5.5 [3.5-9.7]%; VBDAYFUN, 9.8 [6.4-15.6]%). VB was associated with the degree of upper airway obstruction in a dose-response manner (VBuntreated, 31.6 [27.1]%; VBtreated, 7.2 [4.7]%; VBsuboptimally treated, 17.6 [18.7]%; VBoff-treatment, 41.6 [18.1]%) and exhibited low night-to-night variability (intraclass correlation coefficient [2,1], 0.89). VB was predictive of all-cause and CVD mortality in the SHHS cohort before and after adjusting for covariates including hypoxic burden. Although AHI was predictive of all-cause mortality, it was not associated with CVD mortality in the SHHS cohort. Conclusions: Automated VB can effectively assess obstructive sleep apnea severity, is predictive of all-cause and CVD mortality, and may be a viable alternative to the AHI.

Detection and Characterization of Walking Bouts Using a Single Wrist-Worn Accelerometer in Free-living Conditions.

Detection and characterization of abnormalities of movement are important to develop a method for detecting early signs of Parkinson's disease (PD). Most of the current research in detection of characteristic reduction of movements due to PD, known as parkinsonism, requires using a set of invasive sensors in a clinical or controlled environment. Actigraphy has been widely used in medical research as a non-invasive data acquisition method in free-living conditions for long periods of time. The proposed algorithm uses triaxial accelerometer data obtained through actigraphy to detect walking bouts at least 10 seconds long and characterize them using cadence and arm swing. Accurate detection of walking periods is the first step toward the characterization of movement based on gait abnormalities. The algorithm was based on a Walking Score (WS) derived using the value of the auto-correlation function (ACF) for the Resultant acceleration vector. The algorithm achieved a precision of 0.90, recall of 0.77, and F1 score of 0.83 compared to the expert scoring for walking bout detection. We additionally described a method to measure arm swing amplitude.

Night-to-night reliability and agreement of obstructive sleep apnea pathophysiologic mechanisms estimated with phenotyping using polysomnography in cognitively normal elderly participants.

STUDY OBJECTIVES: Phenotyping using polysomnography (PUP) is an algorithmic method to quantify physiologic mechanisms underlying obstructive sleep apnea (OSA): loop gain (LG1), arousal threshold (ArTH), and upper airway collapsibility (Vpassive) and muscular compensation (Vcomp). The consecutive-night test-retest reliability and agreement of PUP-derived estimates are unknown. From a cohort of elderly (age ≥55 years), largely non-sleepy, community-dwelling volunteers who underwent in-lab polysomnography (PSG) on 2 consecutive nights, we determined the test-retest reliability and agreement of PUP-estimated physiologic factors. METHODS: Participants who had an apnea-hypopnea index (AHI3A) of at least 15 events per hour on the first night were included. PUP analyses were performed on each of the two PSGs from each participant. Physiologic factor estimates were derived from NREM sleep and compared across nights using intraclass correlation coefficients for reliability and smallest real differences (SRD) for agreement. RESULTS: Two PSGs from each of 43 participants (86 total) were analyzed. A first-night effect was evident with increased sleep time and stability and decreased OSA severity on the second night. LG1, ArTH, and Vpassive demonstrated good reliability (ICC > 0.80). Vcomp had modest reliability (ICC = 0.67). For all physiologic factors, SRD values were approximately 20% or more of the observed ranges, suggesting limited agreement of longitudinal measurements for a given individual. CONCLUSIONS: For NREM sleep in cognitively normal elderly individuals with OSA, PUP-estimated LG1, ArTH, and Vpassive demonstrated consistent relative ranking of individuals (good reliability) on short-term repeat measurement. For all physiologic factors, longitudinal measurements demonstrated substantial intraindividual variability across nights (limited agreement).

The Link between Obstructive Sleep Apnea and Neurocognitive Impairment: An Official American Thoracic Society Workshop Report.

There is emerging evidence that obstructive sleep apnea (OSA) is a risk factor for preclinical Alzheimer's disease (AD). An American Thoracic Society workshop was convened that included clinicians, basic scientists, and epidemiologists with expertise in OSA, cognition, and dementia, with the overall objectives of summarizing the state of knowledge in the field, identifying important research gaps, and identifying potential directions for future research. Although currently available cognitive screening tests may allow for identification of cognitive impairment in patients with OSA, they should be interpreted with caution. Neuroimaging in OSA can provide surrogate measures of disease chronicity, but it has methodological limitations. Most data on the impact of OSA treatment on cognition are for continuous positive airway pressure (CPAP), with limited data for other treatments. The cognitive domains improving with CPAP show considerable heterogeneity across studies. OSA can negatively influence risk, manifestations, and possibly progression of AD and other forms of dementia. Sleep-dependent memory tasks need greater incorporation into OSA testing, with better delineation of sleep fragmentation versus intermittent hypoxia effects. Plasma biomarkers may prove to be sensitive, feasible, and scalable biomarkers for use in clinical trials. There is strong biological plausibility, but insufficient data, to prove bidirectional causality of the associations between OSA and aging pathology. Engaging, recruiting, and retaining diverse populations in health care and research may help to decrease racial and ethnic disparities in OSA and AD. Key recommendations from the workshop include research aimed at underlying mechanisms; longer-term longitudinal studies with objective assessment of OSA, sensitive cognitive markers, and sleep-dependent cognitive tasks; and pragmatic study designs for interventional studies that control for other factors that may impact cognitive outcomes and use novel biomarkers.

Association between lower body temperature and increased tau pathology in cognitively normal older adults.

BACKGROUND: Preclinical studies suggest body temperature (Tb) and consequently brain temperature has the potential to bidirectionally interact with tau pathology in Alzheimer's Disease (AD). Tau phosphorylation is substantially increased by a small (<1 °C) decrease in temperature within the human physiological range, and thermoregulatory nuclei are affected by tau pathology early in the AD continuum. In this study we evaluated whether Tb (as a proxy for brain temperature) is cross-sectionally associated with clinically utilized markers of tau pathology in cognitively normal older adults. METHODS: Tb was continuously measured with ingestible telemetry sensors for 48 h. This period included two nights of nocturnal polysomnography to delineate whether Tb during waking vs sleep is differentially associated with tau pathology. Tau phosphorylation was assessed with plasma and cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (P-tau), sampled the day following Tb measurement. In addition, neurofibrillary tangle (NFT) burden in early Braak stage regions was imaged with PET-MR using the [18F]MK-6240 radiotracer on average one month later. RESULTS: Lower Tb was associated with increased NFT burden, as well as increased plasma and CSF P-tau levels (p < 0.05). NFT burden was associated with lower Tb during waking (p < 0.05) but not during sleep intervals. Plasma and CSF P-tau levels were highly correlated with each other (p < 0.05), and both variables were correlated with tau tangle radiotracer uptake (p < 0.05). CONCLUSIONS: These results, the first available for human, suggest that lower Tb in older adults may be associated with increased tau pathology. Our findings add to the substantial preclinical literature associating lower body and brain temperature with tau hyperphosphorylation. CLINICAL TRIAL NUMBER: NCT03053908.

Platelet Function Is Associated With Dementia Risk in the Framingham Heart Study.

Background Vascular function is compromised in Alzheimer disease (AD) years before amyloid and tau pathology are detected and a substantial body of work shows abnormal platelet activation states in patients with AD. The aim of our study was to investigate whether platelet function in middle age is independently associated with future risk of AD. Methods and Results We examined associations of baseline platelet function with incident dementia risk in the community-based FHS (Framingham Heart Study) longitudinal cohorts. The association between platelet function and risk of dementia was evaluated using the cumulative incidence function and inverse probability weighted Cox proportional cause-specific hazards regression models, with adjustment for demographic and clinical covariates. Platelet aggregation response was measured by light transmission aggregometry. The final study sample included 1847 FHS participants (average age, 53.0 years; 57.5% women). During follow-up (median, 20.5 years), we observed 154 cases of incident dementia, of which 121 were AD cases. Results from weighted models indicated that platelet aggregation response to adenosine diphosphate 1.0 µmol/L was independently and positively associated with dementia risk, and it was preceded in importance only by age and hypertension. Sensitivity analyses showed associations with the same directionality for participants defined as adenosine diphosphate hyper-responders, as well as the platelet response to 0.1 µmol/L epinephrine. Conclusions Our study shows individuals free of antiplatelet therapy with a higher platelet response are at higher risk of dementia in late life during a 20-year follow-up, reinforcing the role of platelet function in AD risk. This suggests that platelet phenotypes may be associated with the rate of dementia and potentially have prognostic value.

Characterizing sleep-wake patterns in mothers and children in an agrarian community: results from the Ghana Randomized Air Pollution and Health Study.

STUDY OBJECTIVES: Several studies have examined sleep patterns in rural/indigenous communities, however little is known about sleep characteristics in women of reproductive age, and children within these populations. We investigate sleep-wake patterns in mothers and children (ages 3-5 years) leveraging data from the Ghana Randomized Air Pollution and Health Study (GRAPHS). METHODS: The GRAPHS cohort comprises of rural/agrarian communities in Ghana and collected multiday actigraphy in a subset of women and children to assess objective sleep-wake patterns. Data were scored using the Cole-Kripke and Sadeh algorithms for mothers/children. We report descriptive, baseline characteristics and objective sleep measures, compared by access to electricity/poverty status. RESULTS: We analyzed data for 58 mothers (mean age 33 ± 6.6) and 64 children (mean age 4 ± 0.4). For mothers, mean bedtime was 9:40 pm ± 56 min, risetime 5:46 am ± 40 min, and total sleep time (TST) was 6.3 h ± 46 min. For children, median bedtime was 8:07 pm (interquartile range [IQR]: 7:50,8:43), risetime 6:09 am (IQR: 5:50,6:37), and mean 24-h TST 10.44 h ± 78 min. Children with access to electricity had a reduced TST compared to those without electricity (p = 0.02). Mean bedtime was later for both mothers (p = 0.05) and children (p = 0.08) classified as poor. CONCLUSIONS: Mothers in our cohort demonstrated a shorter TST, and earlier bed/risetimes compared to adults in postindustrialized nations. In contrast, children had a higher TST compared to children in postindustrialized nations, also with earlier sleep-onset and offset times. Investigating objective sleep-wake patterns in rural/indigenous communities can highlight important differences in sleep health related to sex, race/ethnicity, and socioeconomic status, and help estimate the impact of industrialization on sleep in developed countries.