Polysomnographic Sleep Research Articles

Quantitative evaluation of the microstructure of rapid eye movement sleep in refractory epilepsy: a preliminary study using electroencephalography and heart rate variability analysis

IntroductionPatients with epilepsy have a disturbed sleep architecture. Polysomnographic studies have shown that patients with refractory epilepsy have decreased rapid eye movement (REM) sleep and longer REM latency than those with medically controlled epilepsy. However, little is known about the differences in the REM sleep microstructure between these patient groups. MethodsWe conducted a retrospective case–control study of 20 patients with refractory epilepsy (refractory group) and 28 patients with medically controlled epilepsy (medically controlled group). All patients completed sleep questionnaires and underwent overnight in-lab polysomnography. Five-minute electroencephalogram recordings at the C3 and C4 electrodes from each REM sleep were selected for spectral analysis, and 5-min electrocardiogram segments recorded during REM sleep were used for heart rate variability analysis. The groups’ scores on the sleep questionnaires, polysomnographic sleep parameters, indices of sleep-related breathing disorders, and REM sleep electroencephalogram spectra were compared. ResultsThe refractory group had decreased REM sleep (p < 0.001) and longer REM latency (p = 0.0357) than those of the medically controlled group. Moreover, electroencephalogram spectral analysis revealed that the refractory group had decreased absolute beta power (p = 0.0039) and relative beta power (p = 0.0035) as well as increased relative delta power (p = 0.0015) compared with the medically controlled group. ConclusionsDifferences in the polysomnographic macrostructure and REM sleep microstructure between the study groups suggest REM sleep dysregulation in patients with refractory epilepsy.

Transfer learning artificial intelligence for automated detection of atrial fibrillation in patients undergoing evaluation for suspected obstructive sleep apnoea: a feasibility study

BackgroundIndividuals with obstructive sleep apnoea (OSA) experience a higher burden of atrial fibrillation (AF) than the general population, and many cases of AF remain undetected. We tested the feasibility of an artificial intelligence (AI) approach to opportunistic detection of AF from single-lead electrocardiograms (ECGs) which are routinely recorded during in-laboratory polysomnographic sleep studies. MethodsUsing transfer learning, an existing ECG AI model was applied to 1839 single-lead ECG traces recorded during in-laboratory sleep studies without any training of the algorithm. Manual review of all traces was performed by two trained clinicians who were blinded to each other's review. Discrepancies between the two investigators were resolved by two cardiologists who were also unaware of each other's scoring. The diagnostic accuracy of the AI algorithm was calculated against the results of the manual ECG review which were considered gold standard. ResultsManual review identified AF in 144 of the 1839 single-lead ECGs (7.8%). The AI detected all cases of manually confirmed AF (sensitivity = 100%, 95% CI: 97.5–100.0). The AI model misclassified many ECGs with artefacts as AF, resulting in a specificity of 76.0 (95% CI: 73.9–78.0), and an overall diagnostic accuracy of 77.9% (95% CI: 75.9%–97.8%). ConclusionTransfer learning AI, without additional training, can be successfully applied to disparate ECG signals, with excellent negative predictive values, and can exclude AF among patients undergoing evaluation for suspected OSA. Further signal-specific training is likely to improve the AI's specificity and decrease the need for manual verification.

O-EG004. Comparing the accuracy of sleep staging data from a new wearable sleep electroencephalography device versus the Fitbit Charge 3 with polysomnography as reference

Introduction . Although sleep disorders are a common social problem, polysomnography (PSG), the gold standard in sleep diagnostics, can be burdensome and may interfere with natural sleep. We have developed a high-precision wearable electroencephalography (EEG) device for objectively measuring sleep at home. Actigraphy devices with the capability of measuring heart rate variability can also be used to evaluate sleep stages at home (e.g., Fitbit Charge 3). However, the accuracy of these inhome devices has not yet been compared. We hypothesized that the accuracy of our wearable sleep EEG device would be higher than that of the Fitbit Charge 3. Methods . We simultaneously recorded PSG, wearable sleep EEG, and data from the Fitbit Charge 3 in 42 (mean ± SD: 23.5 ± 7.8 years, 14 women) healthy participants. A registered polysomnographic technologist scored the PSG and wearable sleep EEG data, and we evaluated the sleep stages captured by the Fitbit Charge 3 using the Fitbit automatic sleep staging algorithm (stage W, N1+N2, N3, and R). We measured and compared the accuracy of the wearable EEG device and Fitbit Charge 3 data with respect to the PSG data. Results . The accuracy of the wearable sleep EEG device and Fitbit Charge 3 was 90.7 ± 2.8% and 69.5 ± 8.3 %, respectively (mean ± SD). The wearable sleep EEG device had significantly higher accuracy than the Fitbit (p < 0.001). Conclusion . Compared with the Fitbit Charge 3 data, data from the wearable sleep EEG device more closely mirrored the PSG data. Thus, the wearable sleep EEG device may be superior to the Fitbit Charge 3 as an alternative to PSG. Our wearable sleep EEG device has potential for use in sleep evaluations for multiple consecutive nights in an in-home setting.

Effects of exogenous melatonin on sleep and circadian rhythms in women with premenstrual dysphoric disorder.

We previously found normal polysomnographic (PSG) sleep efficiency, increased slow-wave sleep (SWS), and a blunted melatonin secretion in women with premenstrual dysphoric disorder (PMDD) compared to controls. Here, we investigated the effects of exogenous melatonin in five patients previously studied. They took 2 mg of slow-release melatonin 1 h before bedtime during their luteal phase (LP) for three menstrual cycles. At baseline, patients spent every third night throughout one menstrual cycle sleeping in the laboratory. Measures included morning urinary 6-sulfatoxymelatonin (aMt6), PSG sleep, nocturnal core body temperature (CBT), visual analog scale for mood (VAS-Mood), Prospective Record of the Impact and Severity of Menstrual Symptoms (PRISM), and ovarian plasma hormones. Participants also underwent two 24-hour intensive physiological monitoring (during the follicular phase and LP) in time-isolation/constant conditions to determine 24-hour plasma melatonin and CBT rhythms. The same measures were repeated during their third menstrual cycle of melatonin administration. In the intervention condition compared to baseline, we found increased urinary aMt6 (p < 0.001), reduced objective sleep onset latency (p = 0.01), reduced SWS (p < 0.001), and increased Stage 2 sleep (p < 0.001). Increased urinary aMt6 was correlated with reduced SWS (r = -0.51, p < 0.001). Circadian parameters derived from 24-hour plasma melatonin and CBT did not differ between conditions, except for an increased melatonin mesor in the intervention condition (p = 0.01). Ovarian hormones were comparable between the conditions (p ≥ 0.28). Symptoms improved in the intervention condition, as measured by the VAS-Mood (p = 0.02) and the PRISM (p < 0.001). These findings support a role for disturbed melatonergic system in PMDD that can be partially corrected by exogenous melatonin.

Obstructive sleep apnea event prediction using recurrence plots and convolutional neural networks (RP-CNNs) from polysomnographic signals

The prediction of Obstructive Sleep Apnea (OSA) through common polysomnographic signals before stop breathing triggers the ventilation-aided machines such as Continuous Positive Airway Pressure (CPAP). In this paper, a novel schema is proposed based on the representation of the dynamical behavior of polysomnographic signals. This procedure is accomplished using a combination of the Recurrence Plots (RPs) and Convolutional Neural Networks (CNNs), called RP-CNNs. In this regard, the OSA events of 30, 60, 90, and 120 s are predicted before the occurrence. The first phase was to create RP images via Electroencephalogram (EEG), Electrocardiogram (ECG), and respiration signals at a single level. Then, the RP images were independently fed into two fast and robust pre-trained CNNs, naming ResNet-18 and ShuffleNet. Thus, the networks were fine-tuned, and the mentioned events were classified. In the second phase, the classification results were fused using the Weighted Majority Voting (WMV) method to make the final decision. Finally, subject-dependent and subject-independent evaluation criteria were utilized for the MIT-BIH polysomnographic and Dublin sleep apnea databases. The RP-ShuffleNet and 10-fold cross-validation were employed to attain the highest average accuracy and Area Under the Curve (AUC) through 30-second intervals before the OSA events at fusion-level in MIT-BIH polysomnographic and Dublin sleep apnea databases. The achieved results were 90.72%, 0.8937, 90.45%, and 0.9010, respectively. Predicting the OSA events using representation of the dynamical behavior of polysomnographic signals and the fusion of results of the fine-tuned CNNs have been led to the enhancement of the results compared to the state-of-the-art studies.

Artificial Neural Networks Analysis of polysomnographic and clinical features in Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS): from sleep alteration to "Brain Fog".

Study ObjectivesPANS (pediatric acute onset neuropsychiatric syndrome) is thought to be the result of several mechanisms and multiple etiologies, ranging from endocrine/metabolic causes to postinfectious autoimmune and neuroinflammatory disorders. Sleep disorders represent one of the most frequent manifestations of PANS, involving around 80% of patients. The present study describes the clinical and polysomnographic features in a group of PANS children identifying the relationships between sleep disorders and other PANS symptoms.MethodsAll participants underwent a clinical evaluation including comprehensive sleep history, polysomnography, cognitive assessment and blood chemistry examination. A data mining approach with fourth-generation artificial neural networks has been used in order to discover subtle trends and associations among variables.ResultsPolysomnography showed abnormality in 17 out of 23 recruited subjects (73.9%). In particular, 8/17 children (47%) had ineffective sleep, 10/17 (58.8%) fragmented sleep, 8/17 (47.1%) periodic limb movement disorder (PLMD) and 11/17 (64.7%) REM-sleep without atonia (RSWA). Most subjects presented more than one sleep disturbances. Notably, among the 19/23 patients diagnosed with Tic/Tourette disorder, 8/19 (42.1%) show PLMD and 10/19 (52.6%) RSWA. Artificial neural network methodology and the auto-contractive map exploited the links among the full spectrum of variables revealing the simultaneous connections among them, facing the complexity of PANS phenotype.ConclusionDisordered sleep represents, for prevalence and impact on quality of life, a cardinal symptom in patients with PANS. Thus, considering the weight of sleep disturbances on diagnosis and prognosis of PANS, we could consider the possibility of including them among the major diagnostic criteria.

Genetic risk for subjective reports of insomnia associates only weakly with polygraphic measures of insomnia in 2,770 adults.

Subjective insomnia complaints and objective sleep changes are mostly studied outside of clinical trial studies. In this study, we tested whether 240 genetic variants associated with subjectively reported insomnia were also associated with objective insomnia parameters extracted from polysomnographic recordings in three studies. The study sample (total n = 2,770) was composed of the Wisconsin Sleep Cohort (n = 1,091) and the Osteoporotic Fractures in Men (n = 1,026) study, two population-based studies, and the Stanford Sleep Cohort, a sleep center patient-based sample (n = 653). Seven objective polysomnographic features related to insomnia defined outcome variables, with each variant allele serving as predictor. Meta-regression was performed, accounting for common confounders as well as variance differences between studies. Additionally, a normalized genetic risk score was generated for each subject to serve as a predictor variable in separate linear mixed models assessing objective insomnia features. After correction for multiple testing, single-nucleotide polymorphisms associated with subjective insomnia were not significantly associated with 6 of 7 objective sleep measures. Only periodic limb movement index was significantly associated with rs113851554 (MEIS1), as found in previous studies. The normalized genetic risk score was only weakly associated with arousal index and duration of wake after sleep onset. Our findings suggest that subjective insomnia does not have a strong genetic signature mapping onto objective (polysomnographic) sleep variables. Foldager J, Peppard PE, Hagen EW, etal. Genetic risk for subjective reports of insomnia associates only weakly with polygraphic measures of insomnia in 2,770 adults. J Clin Sleep Med. 2022;18(1):21-29.

Risk assessment of obstructive sleep apnea syndrome and other sleep disorders in multiple sclerosis patients

BackgroundThe aim of the present study was to determine the possible risk of OSAS in patients with MS through the STOP-BANG questionnaire, and to confirm the pre-diagnosis of OSAS by recording polysomnographic investigation in individuals with high risk. In addition, the relationship between OSAS risk and fatigue, sleepiness, depression, and disability status will be examined. MethodsTotally 97 patients with multiple sclerosis including 36 males and 61 females with an age average of 39.92 ± 9.11 years. All participants completed the following questionnaires: STOP-Bang, Fatigue Severity Scale (FSS), Epworth sleepiness scale (ESS), Beck Depression Inventory (BDI); disability status of the participants was assessed by Expanded Disability Status Scale (EDSS). Polysomnographic sleep record was applied to the patients with high risk of OSAS according to STOP-BANG test scores. ResultsThe STOP_BANG questionnaire revealed that 24.7% of the patients were screened as high risk for OSA. Approximately 11.3% of the patients were detected positive for OSAS based on PSG recording. Comparison of MS patients with high risk of OSA with others suggested a significant difference in terms of the age (p = 0.01). ESS positive scores were significantly correlated with positive STOP BANG outcomes (p < 0.001). ESS positive scores were negatively correlated with positive PSG outcomes. ConclusionThe prevalence of OSAS in MS patients based on questionnaire and PSG was found consistent with literature. Similar to the general population, increasing age was found as a risk factor for OSAS in patients with MS. STOP-BANG test may not be an adequate test to diagnose OSAS, especially in MS patients with high fatigue scores.

REM Sleep and Total Sleep Time Improvement After Routine Repetitive Transcranial Magnetic Stimulation in Active Duty Service Members With Depression.

The prevalence of depression and insomnia in the military are substantial. Several transcranial magnetic stimulation (TMS) studies have used self-report sleep data as secondary research outcomes; however, there are limited studies using the gold standard of polysomnography (PSG) to ascertain actual sleep changes. Here, we provide data from a pilot and feasibility study using PSG to measure sleep changes after repetitive TMS. Thirty-eight active duty service members (ADSM) were consented, of which 20 completed the study. The ADSM who met study criteria where sent for an initial PSG and completed baseline self-report measures. They then completed a standard course of TMS, and self-report measures were completed every fifth session. After TMS completion, ADSM underwent final PSG. Comparison of baseline and postintervention PSG sleep parameters highlight that total rapid eye movement sleep improved after a course of TMS, regardless of improvements in depression. Total sleep time also improved, but only in the TMS responders subgroup. The Public Health Questionnaire-9 showed statistically significant improvement as did the Insomnia Severity Index and some components of the RAND Medical Outcomes Short Form 36. Our small study confirms the feasibility of obtaining pre and post PSG for research purposes. We found similar results to previous studies with regard to depression improvement and self-reported sleep. Interestingly, almost all (including electroconvulsive therapy) somatic depression treatments have been shown to decrease REM, whereas our study found an increase in REM. Overall, this study helps further our understanding of TMS effects on sleep and presents new questions for potential larger follow-on studies.

Evening binge alcohol disrupts cardiovagal tone and baroreflex function during polysomnographic sleep.

Binge alcohol consumption is associated with increased cardiovascular risk. The effects of evening binge alcohol consumption (i.e. 4-5 beverages within 2 h) on the vagal components of HRV and cardiovagal baroreflex sensitivity (cvBRS) during sleep remain largely equivocal. The present study examined the effects of evening binge alcohol consumption on nocturnal cardiac vagal tone and baroreflex sensitivity during stage N2, slow wave (SWS), and rapid eye movement (REM) sleep. We hypothesized that evening binge drinking would reduce HRV and cvBRS in each sleep stage. Following a familiarization night within the laboratory, twenty-three participants were examined following a night of binge alcohol consumption and a fluid control (randomized, crossover design). A quality nocturnal beat-to-beat blood pressure signal was obtained in both conditions in 16 participants (seven men, nine women; 25 ± 1 years). Binge drinking reduced both the high frequency (HF) and time-domain components (i.e. pNN50 and RMSSD) of HRV in stage N2 sleep, SWS, and REM. In addition, cvBRS up-up (vagal activation) was reduced following binge alcohol consumption in stage N2 (21 ± 3 vs. 15 ± 3 ms/mmHg, p = 0.035) and REM (15[11-28] vs. 11[9-18] ms/mmHg, p = 0.009). Binge alcohol consumption reduced cvBRS down-down (vagal withdrawal) in stage N2 (23 ± 2 vs. 14 ± 2 ms/mmHg, p < 0.001), SWS (20[14-30] vs. 14[9-17] ms/mmHg, p = 0.022), and REM (14[11-24] vs. 10[7-15] ms/mmHg, p = 0.006). Evening binge alcohol consumption disrupts cardiac vagal tone and baroreflex function during nearly all sleep stages. These findings provide mechanistic insight into the potential role of binge drinking and alcohol abuse on cardiovascular risk. Alcohol and Neural Cardiovascular Control in Binge Drinkers, www.clinicaltrials.gov/ct2/show/NCT03567434, NCT03567434.

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings.

Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. The Munich Actimetry Sleep Detection Algorithm uses a moving 24-h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep-log validation was performed on two field samples collected over 54 and 34days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures carried out via Bland-Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR]=75%-86%) and specificity of 91% (87%-92%). Mean onset and offset times were highly correlated (r=.86-.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%-100%) but low specificity of 33% (10%-98%), owing to the low frequency of wake episodes in the night-time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21min) and underestimated wake after sleep onset (~26min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep-wake patterns in field studies. With its good performance across daytime and night-time, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.

Alterations of Subcortical Brain Structures in Paradoxical and Psychophysiological Insomnia Disorder

Insomnia disorder (ID) is a common illness associated with mood and cognitive impairments. Subtyping ID is an ongoing debate in sleep medicine, but the underlying mechanisms of each subtype is poorly understood. Growing evidence suggests that subcortical brain structures play the key roles in pathophysiology of ID and its subtypes. Here, we aimed to investigate structural alteration of subcortical regions in patients with two common ID subtypes i.e., paradoxical and psychophysiological insomnia. Fifty-five patients and 49 healthy controls were recruited for this study and T1-weighted images and subjective and objective sleep parameters (i.e., Pittsburgh Sleep Quality Index and polysomnography) were collected from participants. Subcortical structures including the hippocampus, amygdala, caudate, putamen, globus pallidus, nucleus accumbens, and thalamus were automatically segmented in FSL. Volume and shape (using surface vertices) of each structure were compared between the groups, controlled for covariates, and corrected for multiple comparisons. In addition, correlations of sleep parameters and surface vertices or volumes were calculated. The caudate's volume was smaller in patients than controls. Compared with controls, we found regional shrinkage in the caudate, nucleus accumbens, posterior putamen, hippocampus, thalamus, and amygdala in paradoxical insomnia and shrinkage in the amygdala, caudate, hippocampus, and putamen in psychophysiological insomnia. Interestingly, comparing two patients groups, shape alteration in the caudate, putamen, and nucleus accumbens in paradoxical insomnia and shrinkage in the thalamus, amygdala, and hippocampus in psychophysiological insomnia were observed. Both subjective and objective sleep parameters were associated with these regional shape alterations in patients. Our results support the differential role of subcortical brain structures in pathophysiology of paradoxical and psychophysiological insomnia.

738 Effects of exogenous melatonin on sleep, circadian rhythms, and mood in women with premenstrual dysphoric disorder

Abstract Introduction Most women with premenstrual dysphoric disorder (PMDD) report sleep disturbances. Our group found normal polysomnographic (PSG) sleep efficiency and increased slow wave sleep (SWS) across the menstrual cycle in women with PMDD and insomnia compared to controls. Reduced melatonin levels were found in PMDD women compared to controls, with reduced secretion during their luteal phase (LP) compared to follicular phase (FP). Here, we investigated the effects of exogenous melatonin in the patients we previously studied. Methods Five patients (age, mean: 33.6, SD: 2.7) diagnosed prospectively with PMDD and insomnia participated in the study. Following a baseline assessment, patients took 2 mg of slow-release melatonin 1h before bedtime during their LP for three consecutive menstrual cycles. At baseline (treatment-free condition), patients spent every third night of their menstrual cycle sleeping in the laboratory. Measures included morning urinary 6-sulfatoxymelatonin (aMt6), PSG sleep, nocturnal core body temperature (CBT), visual analogue scale for mood (VAS-Mood), Prospective Record of the Impact and Severity of Menstrual symptoms (PRISM), subjective sleep and ovarian hormones (estrogen and progesterone). Participants also underwent two 24-hour intensive physiological monitoring (during the FP and LP) in time-isolation/constant conditions to determine 24-hour plasma melatonin and CBT rhythms. The same measures were repeated during their third menstrual cycle of melatonin administration. Results In the intervention condition compared to baseline, we found increased urinary aMt6 (p&amp;lt;0.001), reduced objective SOL (p=0.01), SWS (p&amp;lt;0.001) and increased Stage 2 sleep (p&amp;lt;0.001). Increased urinary aMt6 was associated with reduced SWS (r=-0.51, p&amp;lt;0.001). Circadian parameters derived from 24-hour plasma melatonin and CBT did not differ between conditions, except for an increased melatonin mesor in the intervention condition (p=0.01). Ovarian hormones were comparable between the conditions (p≥0.28). Symptoms improved in the intervention condition, as measured by the VAS-Mood (p=0.02) and the PRISM (p&amp;lt;0.001). Conclusion We have shown normalization of SWS and reduction in self-reported mood and somatic symptoms after administrating exogenous melatonin in women with PMDD. These findings support a role for disturbed melatoninergic system in PMDD that can be partially corrected by exogenous melatonin. Support (if any) This study was supported by the Canadian Institutes of Health Research (CIHR)

648 Associations between objective afternoon and evening physical activity and polysomnographic sleep in fibromyalgia

Abstract Introduction Patients with fibromyalgia (FM) suffer from pain which limits physical activity and disrupts sleep. Research examining the relationship between pre-bedtime physical activity, pain, and objective sleep is limited. This study examined whether objectively measured activity levels (via actigraphy), pain intensity, or their interaction are associated with polysomnographic sleep outcomes and sleep architecture. Methods Adults with FM (n=158, Mage=52, SD=12, 93% female) completed 14 daily pain ratings, 14 days of actigraphy, and a single night of polysomnography. Activity levels (i.e., magnitude of wrist motion captured per 30 second epoch) were recorded, and average afternoon/evening activity for intervals 12:00-15:00, 15:00-18:00, and 18:00-21:00 was computed, removing days in which participants slept during these periods. Sleep architecture was quantified as the percentage of sleep time in rapid eye movement (%rem) and non-rem (i.e. %stage 1, %stage 2, and %stage 3). Multiple regressions examined whether average afternoon/evening activity, average evening pain [0 (no pain)–100 (most intense pain imaginable)], or their interaction predicted polysomnographic sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), %stage 1, %stage 2, %stage 3, and %rem, controlling for age, body mass index, average individual bedtime, time in bed, and sleep or pain medication usage. Results Greater afternoon activity from 12:00-15:00 was independently associated with lower SE (B= -.08, p=.01), greater WASO (B=.45, p&amp;lt;.001), and greater %stage 1 (B=.04, p&amp;lt;.01). Pain intensity interacted with physical activity from 12:00-15:00 such that the association between physical activity and higher WASO (p=.05) and greater %stage 1 (p&amp;lt;.01) was stronger for individuals with higher pain. Pain intensity and activity from 15:00-18:00, and 18:00-21:00 were not associated with sleep outcomes. Conclusion Our results suggest greater afternoon activity is associated with greater polysomnographic sleep fragmentation and greater %stage 1 sleep in FM, and these relationships are stronger for individuals with higher pain. These relationships are consistent with activity pacing recommendations for chronic pain and suggest pacing in the afternoon may be important for good sleep in FM. However, future research examining causal pathways linking physical activity levels and timing, pain, and sleep is needed. Support (if any) Support: NIAMS (R01AR055160/R01AR005160- S1;PI McCrae). Data collected at University of Florida (Clinical Trial-NCT02001077;PI McCrae).

110 Physiological correlates of the Epworth Sleepiness Scale reveal different dimensions of daytime sleepiness

Abstract Introduction The Epworth Sleepiness Scale (ESS) is used as a clinical tool for determining excessive daytime sleepiness. However, the behavior and biology that underlie ESS scores remain to be elucidated. The main objective of this analysis is to determine objective behavioral and physiologic correlates of the ESS. Secondarily, we examine the relationship of the ESS to parallel subjective and objective endpoints that could represent measures of daytime sleepiness. Methods Using two separate machine learning algorithms, Random Forest and Lasso, we determined the association between ESS scores and 55 sleep and medical variables in individuals who participated in the Sleep Heart Health Study (N=2105). These variables include self-reported sleep characteristics (e.g., habitual sleep length and latency, frequency of not getting enough sleep), polysomnographic sleep measures from a single night, medication use, and mental and physical health status. Additional analyses were conducted on data stratified by age and gender. To investigate the relationship between ESS and other measures of daytime sleepiness, cross-correlation analysis was conducted on the ESS and five variables that could analog daytime sleepiness (feeling unrested, nap duration and frequency, sleep latency, frequency of not getting enough sleep). Results Analysis of the main dataset resulted in low explained variance (7.15 - 10.0%), with self-reported frequency of not getting enough sleep as most important predictor (10.3–13.9% of the model variance). Stratification by neither age nor gender significantly improved explained variance. Habitual sleep length was not an important predictor in any model. Cross-correlational analysis revealed low correlation of other daytime sleepiness measures to ESS score. Conclusion Data analyses indicate that ESS scores are not well explained by habitual or polysomnographic sleep values, or a variety of other biomedical characteristics. This suggests that there are different, potentially orthogonal dimensions of the concept of “daytime sleepiness” that may be driven by different aspects of sleep physiology. Caution should be used when considering the ESS as a clinical measure given that the physiologic correlates still remain to be elucidated. Support (if any):

652 Association Between Poor Sleep and Lymphocyte Subsets In Chronic HIV Infection

Abstract Introduction Low CD4+ T lymphocyte counts and CD4+/CD8+ lymphocyte ratios predict mortality and cardiovascular risk among people living with HIV (PLWH). Whether polysomnographic (PSG) sleep measures impact T lymphocyte subset counts among PLWH is unknown. We sought to evaluate the association between lymphocyte subsets and PSG-derived sleep measures in a cohort with HIV seropositive men. Methods We analyzed data from HIV seropositive men who have sex with men participating in the Multicenter AIDS Cohort Study on antiretroviral therapy for &amp;gt;1 year with undetectable (&amp;lt;500 copies/mL) plasma HIV-1 RNA who underwent a sleep evaluation with home polysomnography. The following seven sleep parameters were examined: total sleep time (TST), sleep efficiency, sleep stage (N1, N2, N3, and REM) duration, and apnea-hypopnea index. Multivariable linear regression models adjusted for age and body mass index were used to assess whether sleep measures were associated with CD4+ T cell count, CD8+ T cell count, or CD4+/CD8+ ratio. Results Participants (n= 286) had a mean age of 55.2 ± 11.3 years, 52.8% had sleep apnea and mean CD4+ count was 728 ± 306 cells/mm3. None of the sleep measures were associated with CD4+ counts but longer TST and REM duration were associated with lower CD8+ counts and higher CD4+/CD8+ ratio. In adjusted analyses, every one hour increase in TST was associated with a 35 ± 18 cells/mm3 lower CD8+ count (p=0.049) and 6.3% elevation in CD4+/CD8+ ratio (p=0.006) while every hour increase in REM was associated with 123 ± 50 cells/mm3 lower CD8+ count (p=0.01) and 20% elevation in CD4+/CD8+ ratio (p=0.003). Conclusion In PLWH, longer total sleep time and REM sleep duration are associated with protective CD4+/CD8+ ratios due to lower CD8+ cell count. Further research is needed to assess if longer sleep duration is associated with decreased inflammatory markers. Support (if any) American Thoracic Society Academic Sleep Pulmonary Integrated Research/Clinical (ASPIRE) Fellowship

568 Sleep Evaluation in Down Syndrome: What is the Adherence to American Academy of Pediatrics DS Guidelines?

Abstract Introduction Specialized health care guidelines for children with Down Syndrome (DS) published by the American Academy of Pediatrics (AAP) provided specific recommendations based on the higher risk needs of individuals with DS. Obstructive sleep apnea (OSA) is reported to be present in 50–79% of individuals with DS. According to the AAP guideline, all individuals with DS should have a polysomnography (PSG) evaluating for OSA by 4 years old and then screened by history and physical exam annually thereafter. An interim analysis of an ongoing Down Syndrome Research study was evaluated to determine rate of adherence to these guidelines. Methods The Dimensional, Sleep, and Genomic Analyses of Down Syndrome to Elucidate Phenotypic Variability study enrolled down syndrome patients 30 months and older, as well as first degree relatives to participate. Patients completed a standardized clinical sleep interview, childhood sleep habits questionnaire and was asked to complete 2 week sleep diary, actigraphy and polysomnography. We aimed to characterize the rate of PSG completion by 4 years of age, number of research PSGs completed and rate of OSA identified on research PSG. Results A total of 31 patients were consented. The median patient age was 10 years old with a slight female predominance (15F:12M). 27 patients completed the sleep interview and 19 successfully completed a scorable polysomnography. Only 7 patients had completed a PSG previously by age of 4 years. 11 of 19 studies demonstrated obstructive sleep apnea ranging from mild to severe severity (1.7–42.5/hr). REM AHI (range 1.2–58.2/hr, mean 19/hr and median 12.3/hr) demonstrated increased severity. Conclusion Despite AAP guidelines recommending universal PSG evaluation by the age of 4 years of age, only 26% of patients interviewed has a PSG successfully completed previously. Additional recommendations by AAP include yearly surveillance of symptoms although there is poor correlation between parent report and polysomnogram results. Of the 19 research completed PSGs, 58% demonstrated OSA with the mean and median results consistent with moderate to severe OSA and worsening during REM sleep. Improved effort to successfully obtain PSG in this population is needed. Further study is ongoing to evaluate the relationship to other health and cognitive outcomes. Support (if any) NIMH

Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth.

The rapid growth of point-of-care polysomnographic alternatives has necessitated standardized evaluation and validation frameworks. The current average across participant validation methods may overestimate the agreement between wearable sleep tracker devices and polysomnography (PSG) systems because of the high base rate of sleep during the night and the interindividual difference across the sampling population. This study proposes an evaluation framework to assess the aggregating differences of the sleep architecture features and the chronologically epoch-by-epoch mismatch of the wearable sleep tracker devices and the PSG ground truth. An AASM-based sleep stage categorizing method was proposed to standardize the sleep stages scored by different types of wearable trackers. Sleep features and sleep stage architecture were extracted from the PSG and the wearable device’s hypnograms. Therefrom, a localized quantifier index was developed to characterize the local mismatch of sleep scoring. We evaluated different commonly used wearable sleep tracking devices with the data collected from 22 different subjects over 30 nights of 8-h sleeping. The proposed localization quantifiers can characterize the chronologically localized mismatches over the sleeping time. The outperformance of the proposed method over existing evaluation methods was reported. The proposed evaluation method can be utilized for the improvement of the sensor design and scoring algorithm.

422 Influence of Sex-Specific Differences in Inpatient Sleep Testing Approach for Diagnosis of Obstructive Sleep Apnea

Abstract Introduction Increased attention has been focused on sex-specific differences in approaches to diagnostic testing for obstructive sleep apnea (OSA) given differences in hypoxia, arousal thresholds and sleep state dependent influences, but with sparse data available for inpatient testing. We postulate that women are more likely to have a lesser degree of sleep apnea on inpatient home sleep apnea testing (HSAT) versus polysomnography (PSG). Methods The Cleveland Clinic Sleep Laboratory registry was queried for inpatient sleep testing (HSAT or PSG conducted over the last 15 years. Demographics, comorbidities, and sleep study (Nihon Kohden®) data were collated. Logistic regression was used to examine sleep study type predictive of OSA at various severity thresholds (apnea hypopnea index (AHI, 3 or 4% hypopnea rule)&amp;gt;5,&amp;gt;15 and &amp;gt;30 and hypoxia (11% (median) time spent with SaO2&amp;lt;90%) adjusted for age, race and body mass index and comorbidities (hypertension, coronary artery disease, arrhythmias, heart failure, diabetes, stroke, chronic obstructive pulmonary disease, mood disorders, respiratory failure and epilepsy with a sex interaction term) (OR, 95%CI presented). Results The analytic sample was comprised of 639 patients: age:55.8±16.3 years, 45% female, 73% Caucasian, BMI:37.5 ± 13.3kg/m2, 74% had OSA and 51% HSAT. Men had higher AHI:16.2 [5.9, 42.3] vs 8.2 [2.9, 20.7]p&amp;lt;0.001, higher arousal index:33.1[18.9,.54] vs 25.3 [15.6, 39.2]p=0.003. Women had higher BMI:40.2 ±14.7, vs 36 ±11.7kg/m2,p&amp;lt;0.001. Unlike AHI&amp;gt;5, at AHI&amp;gt;15, men had lower odds of OSA: OR=0.51:0.32–0.80,p=0.004 for HSAT versus PSG compared to women: OR=1.03:0.61–1.72,p=0.92; interaction p-value=0.046. Men had lower odds of OSA (AHI &amp;gt;30): OR=0.57(0.35,0.92,p=0.022) in HSAT vs PSG; albeit sex-interaction was not statistically significant. Men versus women had 2-versus 3-fold higher hypoxia ie. OR=2.04:1.22–3.41,p=0.006 in men undergoing HSAT versus PSG with strength of association higher in women: OR=3.03:1.68–5.46,p=0.001, interaction p-value=0.32 Conclusion We unexpectedly observe sex-specific differences in inpatient sleep testing such that men had an overall lower odds of detection of moderate to severe and OSA and nocturnal hypoxia relative to women with HSAT versus PSG. Future investigation focused on concurrent inpatient PSG and HSAT should verify these sex-specific findings and clarify potential biophysiologic rationale Support (if any) Transformative Neuroscience Research Development Program: Multimodal Neurocardiorespiratory Physiologic Sleep Signal Repository Transformative Resource Facilitating Transdisciplinary Research Opportunities

010 Association between Objective Sleep Duration and DNA methylation in Adolescents

Abstract Introduction Insufficient sleep and circadian misalignment are highly prevalent in adolescents and have been associated with physical and mental health disorders. Several genome wide association studies (GWAS) in adults have identified genes that may be involved in the regulation of sleep and circadian traits. However, little is known regarding the epigenetic basis and significance of short sleep duration in adolescence, a critical developmental period. Methods To investigate the association between objective sleep duration, as measured by 9-hour in-lab polysomnography (PSG), and DNA methylation in GWAS-informed sleep-related genes, data from 263 adolescents of the Penn State Child Cohort (12-23y, 55.9% male, 23.2% racial/ethnic minorities) were analyzed. Using DNA extracted from peripheral leukocytes, epigenome-wide and GWAS-informed single nucleotide resolution of DNA methylation in cytosine-phosphate-guanine (CpG) sites and surrounding regions were obtained. Multivariable-adjusted linear regression models assessed the association between PSG sleep duration and site-specific methylation levels. Covariates in these models included sex, age, race/ethnicity, body mass index percentile, and psychoactive medication use (i.e., stimulants, anti-depressants, anxiolytics, sedatives, and/or anti-psychotics). P-values were adjusted using the Benjamini &amp; Hochberg method to correct for false discovery rate and, thus, q-values are reported. Results PSG sleep duration was associated with differential methylation at 162 intragenic sites in the epigenome-wide analysis with a q&amp;lt;0.05. In GWAS-informed analysis, five genes were associated with altered DNA methylation, by which shorter PSG sleep duration was associated with hypermethylation in MAD1L1 (q=0.02), MAP2K1 (q=0.03), and RBM19 (q=0.01) and with hypomethylation in Brain Enriched Guanylate Kinase Associated (BEGAIN; q=0.0005) and SLC39A8 (q=0.02). Conclusion Objective sleep duration in adolescents is associated with altered DNA methylation in genes previously identified in adult GWAS of sleep and circadian traits. Importantly, our data also provides evidence for a potential epigenetic link between objective short sleep duration and genes involved in postsynaptic density (BEGAIN), circadian regulation (MAP2K1/RBM19) as well as internalizing (MAD1L1) and psychotic (SLC38A8) disorders. Support (if any) NIH Awards Number R01HL136587, R01MH118308, R01HL97165, R01HL63772, UL1TR000127