bluuebunny/crossref_metadata_2025 huggingface dataset:
0652 Abnormal Sleep Slow Wave Morphology in Idiopathic Hypersomnia
Authors: Deshaies-Rugama Anne-Sophie , Mombelli Samantha , Blais Hélène , Sekerovic Zoran , Massicotte Miaclaude , Thompson Cynthia , Nigam Milan , Carrier Julie , Desautels Alex , Moderie Christophe , Montplaisir Jacques , Gosselin Nadia
Published: April 2024

Introduction

Abnormal cortical synchronization during sleep could affect the restorative function of sleep and consequently, increase daytime sleepiness. Slow wave (SW) density and characteristics provide a unique window of how cortical neurons synchronize during non-rapid eye movement (NREM) sleep. Here, we aimed at verifying whether NREM sleep SW density and characteristics differed between patients with idiopathic hypersomnia (IH) and healthy controls.

Methods

56 participants (38.18 ± 11.21 years old; 53% women) with diagnosed IH (full night of in-laboratory polysomnography followed by a multiple sleep latency test; MSLT) were compared to 128 healthy controls (38.16 ± 14.02 years old; 59% women) studied at our center and matched for age and sex. Exclusion criteria were apnea-hypopnea index ≥15, psychiatric conditions, neurological disorders, other sleep disorders, shift work, and use of psychoactive medications before the PSG. SW were automatically detected on C3 and C4 electrodes in N2 and N3 sleep stages. Group X Sleep cycle ANOVAs were used for SW density and characteristics (e.g., duration, amplitude, and slope, averaged for C3 and C4). Age was added as a control variable in all analyses. Statistical significance was set at p < 0.05.

Results

Several group effects were found where, compared to healthy controls, IH patients had significantly smaller slow wave negative amplitudes, peak-to-peak amplitudes, and smoother slopes (p < 0.05). The only significant interaction was for slow wave negative amplitudes, where the sleep cycle effect observed in healthy controls was not found in IH patients (p < 0.05).

Conclusion

Compared to healthy controls, IH patients have slow wave characteristics that suggest a lower cortical synchrony during NREM sleep and could explain the non-restorative effect of sleep often reported by these patients.

Support (if any)

This project was made possible by an award from the American Academy of Sleep Medicine Foundation, a foundation of the American Academy of Sleep Medicine. Nadia Gosselin hold the Canada Research Chair in sleep disorders and brain health.
0654 Longitudinal Symptoms of Idiopathic Hypersomnia in the Hypersomnia Foundation Registry
Authors: Chattaraj Audrika , Saini Prabhjyot , Bliwise Donald , Freeman Amanda , Murray Catherine Friederich , King Rebecca , Nichols Kendall , Ong Jason , Plante David , Trotti Lynn Marie
Published: April 2024

Introduction

Idiopathic hypersomnia (IH) is a chronic neurologic disorder characterized by excessive daytime sleepiness; difficulty awakening, long sleep durations, and cognitive symptoms are variably present. Although spontaneous remission has been reported, little is known about IH’s natural history. We characterized longitudinal changes in a large registry of people with IH.

Methods

Data were collected via the Hypersomnia Foundation's registry at CoRDS from 6/2016 through 8/2023. Participants were considered to have IH if they selected IH from a dropdown diagnosis menu, had a current diagnosis of IH or were told by a doctor that IH explained their symptoms, and reported at least 7 hours sleep/night. Participants who met these criteria and completed the questionnaire at least twice over at least 6 months were included in analyses. Responses from each participant’s earliest and latest entries were compared, via paired t-test for continuous variables and McNemar test for categorical.

Results

Three hundred twenty-three participants with IH (mean age 35.0 +/-SD 12.2; 86.5% women) had an average time between first and last response of 2.4 (+/-1.4) years. Most participants (79.5%) were on IH medications at both timepoints; 5.6% started and 6.5% stopped IH treatment between timepoints. The most used medications at both timepoints were modafinil (21.7%), amphetamine-dextroamphetamine (19.8%), methylphenidate (17.7%), and armodafinil (13.6%). The only medication for which there was a significant usage change over time was modafinil, decreasing from 33.0% to 25.1%, p = 0.0005. Between the two time points, there was no change in proportion endorsing daily excessive sleepiness, long sleep durations, intentional or unintentional naps, requiring multiple alarms, or brain fog, but there was slight improvement in difficulty awakening (78.7% to 73.0%, p=0.01) and slight worsening of automatic behaviors (9.9% to 12.8%, p = 0.05). The most endorsed daily symptoms at final assessment were difficulty awakening (73.0%), excessive sleepiness (61.8%), requiring multiple alarms (55.3%), brain fog (48.4%), and poor memory (43.4%).

Conclusion

Over 2½ years, symptoms of IH remained largely stable. While this may suggest a lack of progression over time, the high proportion of residual symptoms also suggests a failure of current treatments to achieve comprehensive symptom control.

Support (if any)

NS111280
0658 Validity and Reliability of the Pediatric Narcolepsy Patient-Reported Outcomes Scale (PN-PROS)
Authors: Maski Kiran , Mignot Emmanuel , Narang Indra , Marie Morse Anne , Meltzer Lisa
Published: April 2024

Introduction

There are currently no validated, patient-reported outcome measures for pediatric narcolepsy that assess disease burden, determine treatment efficacy, and guide future drug development. To ensure optimal clinical management of pediatric narcolepsy, we developed the Pediatric Narcolepsy Patient-Reported Outcomes Scale (PN-PROS) through literature review, content expert interviews, patient focus groups, and cognitive testing with patients (ages 9-17 years) and their parents. The aim of this study is to provide interim data from our multi-site validation and reliability study of the PN-PROS.

Methods

We performed field testing for validity and reliability of the PN-PROs in pediatric narcolepsy patients (9-17 years) with a comparator group of pediatric obstructive sleep apnea patients. We recruited participants from Boston Children’s Hospital, Stanford Medical Center, Toronto Hospital for Sick Children (SickKids), and Geisinger Medical Center, as well as from narcolepsy patient advocacy meetings and websites. Participants completed the PN-PROS, Epworth Sleepiness Scale for Children and Adolescents (ESS-CHAD), Peds QL, and PROMIS Life Satisfaction using the REDCap data capture platform. Participants completed the PN-PROS item bank 1 week later for test-retest reliability.

Results

83 pediatric patients with narcolepsy (mean age=15(2) years, 52.3% female, 26% non-Caucasian) and 60 pediatric patients with OSA (mean age=13.2(2.6), 46.3% female, 22% non-Caucasian) have completed all study measures. Discriminant Validity: Participants with narcolepsy reported a higher PN-PROS mean total score than participants with OSA [narcolepsy=126.9 (28.6), OSA=95 (31.2), p< 0.001]; results retain significance controlling for age, race and gender [group main effect: F=36.6, p< 0.001]. Content Validity: For participants with narcolepsy, the PN-PROS total score was significantly correlated with the ESS-CHAD (r=0.64, p< 0.001), Peds QL (r=-0.84, p< 0.001), and PROMIS Life Satisfaction (r=-0.55, p< 0.001). Reliability: Internal consistent was strong for both participants with narcolepsy (Cronbach alpha=0.94) and OSA (Cronbach alpha=0.93). Test-retest reliability was high for both participants with narcolepsy (interclass coefficient=0.94) and OSA (interclass coefficient=0.93).

Conclusion

Interim results suggest the PN-PROS is a valid and reliable measure for the evaluation of symptom frequency and burden for pediatric patients with narcolepsy. Data collection is ongoing, utilizing additional sites in other regions of the United States to ensure generalizability of our findings.

Support (if any)

American Academy of Sleep Medicine
0659 Establishment of a Hypersomnia Board: A Multidisciplinary Approach to Complex Hypersomnia Cases
Authors: Mallampalli Antara , Sharafkhaneh Amir , Singh Supriya , Dang LeQuan , Moghtader Shahram , Crew Earl , Thomas Tara , Agrawal Ritwick
Published: April 2024

Introduction

Narcolepsy and other central disorders of hypersomnolence are conditions that often present challenging diagnostic and management dilemmas for clinicians. Inspired by the example of the Oncology Tumor Board system, we established a multidisciplinary board to aid in managing these complex sleep disorders with the specific goals of enhancing patient care while also offering additional educational opportunities for trainees. Here we present our initial experience with this Hypersomnia Board.

Methods

Beginning in early 2022, clinical practitioners from various disciplines relevant to the care of hypersomnia patients were recruited for board participation. Participants represent sleep medicine, psychiatry, neurology, clinical psychology, and pharmacy. Board meetings are held virtually once a month. Cases are selected for presentation from the participants’ respective clinics and are presented by the treating physicians. Outcomes of the discussion including any recommended changes to the management plan are documented in the patient’s medical record.

Results

To date, 14 hypersomnia cases have been presented to the board for discussion. Clinical dilemmas prompting board discussion have included refractory daytime sleepiness despite standard therapies, uncertainty related to a previous narcolepsy diagnosis, confounding mental health issues, or medication side effects. Etiology of hypersomnia was thought to be narcolepsy type 1 in 6/14 patients, NT2 in 4/14, idiopathic hypersomnia in 2/14, and obstructive sleep apnea (primary or co-morbid) in 6/14. Comorbid mental illness was common in the cases discussed (PTSD in 5/14, depression in 7/14, both in 2/14), as was concomitant OSA. Board discussion led to recommendations for medication changes in 4/14 and to other interventions (such as earlier referral to mental health specialists) or additional diagnostic testing in the remainder of patients. Clinicians involved including trainees have reported satisfaction with the opportunity to better collaborate on management of these complex cases and the added exposure to challenging hypersomnia cases.

Conclusion

The management of complex and relatively rare sleep disorders such as narcolepsy and other central disorders of hypersomnolence often requires multidisciplinary input, which our Hypersomnia Board has been able to facilitate. Further research is needed to assess the impact on patient-centered outcomes and on improving access to coordinated care for these disorders.

Support (if any)

None
0661 Slow Wave Alterations During Nocturnal Sleep in Hypersomnolence Disorder
Authors: Cook Jesse , Riedner Brady , Tran Michelle , Rumble Meredith , Vascan Ana Maria , Plante David
Published: April 2024

Introduction

Hypersomnolence disorder (HD) is characterized by excessive daytime sleepiness of undetermined etiology. HD is poorly understood with no established biomarkers. Slow waves during non-rapid eye movement (NREM) sleep are associated with the restorative aspects of sleep. Since nonrestorative sleep is a core feature of HD, it is hypothesized that alterations in sleep slow waves may be an HD feature. Thus, this investigation was designed to evaluate slow wave activity (SWA) and characteristics (SWC) in HD, relative to healthy sleeper controls (HSC).

Methods

60 unmedicated clinical patients meeting criteria for HD were compared against 29 HSC. All participants underwent polysomnography (PSG) and multiple sleep latency testing (MSLT) at Wisconsin Sleep, with other subjective and objective measures also collected. All-night, six-channel electroencephalography (EEG) data from ad libitum, nocturnal PSG were processed using a validated automatic protocol followed by manual inspection. For each channel of artifact-free EEG from NREM staged epochs, normalized SWA (1-4.5hz) and SWC (incidence, pre-slope, post-slope, and peak amplitude) were calculated in accordance with previously utilized methodology. Post-hoc analyses evaluated slow wave characteristic differences between low- and high-amplitude (≥40μV) slow waves. Linear regression compared groups across normalized SWA and SWC, with adjusted models accounting for relevant, available covariates.

Results

HD participants were young- to middle-aged (mean age = 28.6 ± 8.6) and predominantly female (percentage female = 78.3%), and HSC were comparable. HD participants displayed significantly reduced SWA across all channels, with effects most pronounced frontally and centrally, and greater in the left relative to right hemispheres. HD participants displayed significant alterations across all SWC, with generally consistent patterns of reductions in incidence, amplitude, and slope across frontal and central EEG derivations. SWC alterations varied significantly by both brain region and low- versus high-amplitude waves.

Conclusion

This study demonstrates and expands on a growing evidence base that alterations in slow waves may be a core feature of HD. Targeted studies that manipulate NREM slow waves in disorders of unexplained hypersomnolence, including modification of incidence and morphology varied by brain region and hemisphere, are required to clarify whether alterations in slow waves are causal to symptoms of hypersomnolence.

Support (if any)

AASM Foundation (138-SR-16) and NINR (R21NR018288)
0662 Accurate Automated Sleep Staging of Narcoleptic Patients Using a Machine Learning Model
Authors: Cakir Ahmet , Josephs David , Kleinschmidt Dave , Pathmanathan Jay , Donoghue Jacob , Chan Alexander
Published: April 2024

Introduction

Accurate sleep staging of EEG data from polysomnography (PSG) is important in the diagnosis of narcolepsy. Human sleep staging is costly and labor intensive, but automated sleep staging algorithms must be rigorously tested in narcoleptic patients to ensure valid performance. PSGs of narcoleptic patients often tend to be more fragmented and variable than in non-narcoleptic populations, making it challenging for both humans and automated algorithms to accurately stage sleep. Here, we evaluate the performance of a deep learning model validated in a general sleep clinic population for staging nocturnal PSGs in patients with narcolepsy.

Methods

SleepStageML™, a deep-learning model for performing sleep staging on EEG signals, was trained on a large database of polysomnography recordings from a heterogenous population within the Beacon Clinico-PSG Database. The algorithm was evaluated on a held-out set of 28 overnight PSGs from patients with narcolepsy or hypersomnolence and 57 overnight PSGs from individuals without narcolepsy or hypersomnolence. Each PSG was manually scored by a human expert, and the performance of the automated algorithm was compared across the two cohorts.

Results

Automated sleep staging performance was high across both cohorts. The average F1-score for the control cohort and the narcolepsy cohort was 0.758 and 0.744 respectively. The positive percent agreements (PPAs) for the control cohort were 87%, 38%, 84%, 91%, and 93% for stages W, N1, N2, N3, and R respectively. For the narcolepsy cohort, the PPAs across the same stages were 91%, 33%, 81%, 86%, and 88% respectively. The algorithm’s median absolute error in estimating REM latency, REM duration, and REM percentage in the control cohort was 1.25 minutes, 8.5 minutes, and 2% points, respectively. The same metrics for the narcolepsy cohort were 2.75 minutes, 11.75 minutes, and 3% points respectively.

Conclusion

A deep-learning model trained on diverse data automatically and accurately staged PSGs from narcoleptic patients and was comparable to performance of a human expert. The algorithm estimated REM parameters accurately in both cohorts. Automated staging algorithms like the one described here have the potential to accelerate diagnosis and monitor therapeutic efficacy for narcolepsy treatments by more efficiently and consistently staging sleep.

Support (if any)
0663 Assessing the Predictive Accuracy of Quantitative Electroencephalography in the Diagnosis of Narcolepsy
Authors: Nguyen Kim , Amico Francesco , Meskill Gerard , Ruan Cheng , Meskill Sarah
Published: April 2024

Introduction

Narcolepsy is a chronic neurological disorder characterized by excessive daytime sleepiness (EDS), disturbed nocturnal sleep, and features of rapid eye movement (REM) sleep dissociation (i.e., cataplexy, hypnagogic hallucinations, sleep paralysis, and shortened REM-onset latency). the diagnosis is typically made by a multiple sleep latency test (MSLT), with reported sensitivity of 90% and specificity of 95%. However, MSLT can be difficult to schedule and can be influenced by other factors, such as anxiety, fear, and prescription medications (e.g., antidepressants). Building upon previous research demonstrating frequency- and region-specific electroencephalographic (EEG) imbalances in patients with narcolepsy, the present study aimed to assess the ability of quantitative electroencephalography (qEEG) to diagnose narcolepsy as compared to the reference standard MSLT.

Methods

Patients seen at an integrative health center in Houston, TX and identified with symptoms of narcolepsy, including chronic fatigue, underwent qEEG. If qEEG was concerning for narcolepsy, patients were referred to a sleep specialist and were included if they subsequently underwent an MSLT.

Results

Seventy-three patients (64 females, 9 males) were recruited with a mean age of 35±12.5 years. Of these, 35 patients had cataplexy. Sixty-nine (94.5%) of the 73 patients with positive qEEG had positive MSLT for narcolepsy: 31 (88.6%) in the cataplexy group and 38 (100%) in the non-cataplexy group. Of the remaining 4 patients, 2 had idiopathic hypersomnia on MSLT.

Conclusion

The findings suggest that qEEG holds promise as a valuable tool for diagnosing narcolepsy, potentially contributing to improved and timely clinical assessments. While potential variations in accuracy within specific symptoms should be considered, more research in the same population sample should evaluate the role of frequency- and region-specific EEG imbalances.

Support (if any)
0665 Does Exercise Have an Effect on Daytime Sleepiness?
Authors: Patel Anshum , Cheung Joseph
Published: April 2024

Introduction

Regular exercise improves sleep quality by reducing sleep onset latency, extending total sleep duration, and enhancing sleep efficiency, which may alleviate daytime sleepiness. However, the specific impact of exercise on daytime sleepiness remains unclear, with conflicting findings in the literature. We analyzed existing interventional studies which used Epworth Sleepiness Scale (ESS) to measure changes in degree of daytime sleepiness before and after an exercise regimen, allowing us to explore the relationship between exercise and daytime sleepiness.

Methods

Following PRISMA guidelines, a systematic search was conducted in electronic database of PubMed and Google Scholar from 1991 to the present. Thorough screening and inclusion criteria were applied to identify relevant studies which included ESS assessment before and after an exercise intervention. Also assessed sleep quality by Pittsburgh Sleep Quality Index (PSQI).

Results

Seven studies met all the inclusion criteria and were included in this study. Exercise exhibited a significantly beneficial impact on sleepiness measured by ESS in four out of seven studies we included in review while two studies showed a marginal reduction in ESS without statistical significance, and one study indicated no change. Additionally, exercise showed an improvement in sleep quality in four out of five studies that included PSQI. Variability in results was noted, influenced by factors such as exercise type, intensity, duration, and timing, as well as individual adherence. Additionally, exercise interventions improved sleep quality, as suggested by reduced scores in PSQI which may indirectly improve daytime sleepiness.

Conclusion

This analysis of existing interventional studies on exercise and daytime sleepiness suggests exercise can be beneficial for reducing daytime sleepiness and improving sleep quality. While the exact mechanism remains elusive, several factors contribute to the impact of both acute and chronic exercise on daytime sleepiness such as improved sleep quality, regulation of circadian rhythm, neurotransmitters release, stress reduction, increased energy levels, and weight reduction ultimately alleviating daytime sleepiness. Future research will be essential for addressing gaps on how exercise regimen and the underlying mechanisms may prove efficacious on improving daytime sleepiness.

Support (if any)

Mayo Clinic Florica Research Accelerator for Clinicians Engaged in Research Program
0667 Narcolepsy & Idiopathic Hypersomnia Patient Journey, Clinical Features and Treatments in a Real-World US Population
Authors: Teltsch Dana Y , Bui Bonnie , Anderson Amy , Crawford Stephen , Duhig Amy , Tetali Srini , Diessner Brandon , Veeranki Phani , Pyatkevich Yelena
Published: April 2024

Introduction

Narcolepsy and idiopathic hypersomnia (IH) are central disorders of hypersomnolence. People with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and IH endure long diagnostic delays (up to 15 years), have many comorbidities, and suffer considerable, poorly recognized disease burden. Administrative health data present an opportunity to characterize these patients, their diagnosis, and treatment journey in real-world clinical practice.

Methods

This retrospective cohort study included patients aged ≥2 years with (non-diagnostic) medical claims for narcolepsy or IH between January 01, 2014 and December 31, 2021 in Optum’s Market Clarity integrated claims and EHR database. Patients with ≥24 months continuous enrollment prior to last narcolepsy/IH diagnosis (index date) were classified into NT1; NT2; IH; NT2/IH cohorts. Patient journeys, including diagnostic evaluations and treatments, were described using all enrolled time, while comorbidities were described during a 12-month baseline period.

Results

Overall, 51,548 patients (median age: 45 years [IQR 32–58]; female: 64%) were included with a median pre-index observation period of 52 months: 7,742 (15%) NT1; 31,132 (60%) NT2; 12,287 (24%) IH; and 387 (1%) NT2/IH. Cardiovascular disease (52%), depression (39%), anxiety disorders (38%), obesity (32%) and sleep apnea (40%) were similarly common across cohorts during the 12 months pre-index. Substance-abuse disorders were reported among 18%–19% of narcolepsy and 13%–14% of IH cohorts. Most patients (81%) had a consistent diagnosis (NT1;NT2;IH) throughout the study period, but 9,608 patients had diagnosis switches, most commonly NT2->NT1 (n=3,355), NT1->NT2 (n=2,656), NT2->IH (n=1,187), IH->NT2 (n=859) or NT1->IH (n=205). Among patients with these most common switches (n=8,262), 68% had no record of sleep tests between the switches and 38% had no sleep test recorded during all observed time. Common treatments included SSRI/SNRI (57%), stimulants (41%), and wake-promoting agents (39%); 15% of NT1 patients received oxybate and 2.6% pitolisant. Among patients without a diagnosis switch (n=41,940), 26% did not receive any NT/IH medication after diagnosis.

Conclusion

In a large real-world US population, sleep diagnostic evaluations were underutilized and many patients were untreated. A small proportion received narcolepsy-specific medications. These results indicate an unmet need for effective diagnostic and treatment strategies for patients with narcolepsy/IH.

Support (if any)

Funded by Takeda
0668 Narcolepsy Disorders Explainability in EEG via Spectral Band Cluster Prevalence
Authors: Fernandez Chris , Rusk Sam , Nygate Yoav , Vanasse Tom , Sprague Matt , Wodnicki Jan , Glattard Nick , Wickwire Emerson , Watson Nathaniel , Van Veen Barry
Published: April 2024

Introduction

AI models have previously demonstrated clinically promising performance for detecting Narcolepsy Type-1 (NT1) versus clinical control patients in overnight polysomnography (PSG), while explainability of AI detection for complex disorders remains an unsolved challenge. Seeking to increase explanatory power of AI results, we introduce a novel analysis method, Spectral-Band Cluster-Prevalence (SBCP), for clustering and categorizing PSG without AI/ML techniques or sleep scoring measures. We demonstrate the method for explainability of EEG comparisons evaluating Narcolepsy versus clinical control groups.

Methods

Our data source was retrospective EEG/EOG recordings from N=78 PSG participants including n=54 Narcolepsy Type-1 diagnosed patients (based on MSLT findings and patient-reported Cataplexy) with n=24 clinical controls. EEG channels were excluded based on artifact, normalized to maximum voltage, EOGs normalized to in-channel voltage, then extracted into 10-second segments. Signal features were extracted for each segment: EEG delta (1-4), theta (4-8), alpha (8-12), beta (12-30) spectral band-powers and EOG broadband-powers. Feature EEG band-powers were projected into 3-dimensional subspace, where optimal parameters for Gaussian Mixture Model (GMM) were identified to allow overlapping EEG states. Cluster quality measures Silhouette, Davies-Bouldin, Akaike-Information-Criterion were evaluated to determine the optimal number of components (i.e. unique EEG states) required by the GMM to maximize explainability based on global optima in cluster quality values. Dwell Fraction was estimated by assigning components to each 10-second EEG segment, and reported for comparison between NT1 and clinical controls.

Results

The global optima GMM identified n=3 unique components as the optimal number of components for describing 10-second segments of EEG/EOG in terms of explainability and predictability of between-group differences for NT1-vs-controls. The n=3 components GMM showed the highest cluster quality scores in Silhouette (0.23), DB (2.30), and AIC (-7,318,399). Components were characterized by differences in spectral and broadband-power distributions. Dwell Fraction, the percent of sleep-time in each component, revealed statistically significant differences associated with Narcolepsy (Component-1: NT1< Normals, Component-2: NT1> Normals) in Mann-Whitney-U and t-test results. ROC-AUCs were calculated for classifying NT1-vs-Normals, based only on percentage of time spent in each component (Component-1: 0.71, Component-2: 0.78).

Conclusion

We demonstrate novel analytic methods for explainability, SBCP, with potential applications to Narcolepsy disorder-specific EEG biomarkers and AI understandability.

Support (if any)
0671 Racial and Ethnic Characteristics in Adults Living with Narcolepsy
Authors: Romero Ronald Gavidia , Kokinda Sussan , Chung Steve , Pader Enrique Tobias , Uy Roma Balbin , Devarajan Anusha , Kramer Joshua , Gallup Jennifer
Published: April 2024

Introduction

Demographics and polysomnographic characteristics (PC) stratified by race and ethnicity in narcolepsy type 1 and 2 (NC1-2) have been occasionally examined. Despite differences in definitions, most studies in NC1-2 have used race and ethnicity terms interchangeably. This study in adults living with narcolepsy (ALWNC) examined demographics and PC by race and ethnicity separately.

Methods

This cross-sectional study at a large academic center identified ALWNC through detailed chart review. Adults aged ≥18 years with NC1-2 diagnosis were included. We stratified participants based on self-reported race (Asian, Black, White, Others) and ethnicity (Hispanic, non-Hispanic, and Others). Descriptive statistics were obtained per racial and ethnic groups. Linear regression models adjusted for age and body mass index (BMI) at diagnosis, gender, NC1-2, and race or ethnicity (accordingly) were utilized to examine associations between race, ethnicity, and mean sleep latency (MSL) and number of sleep onset REM periods (SOREMPs).

Results

We identified 250 participants; 58% had NC2, 70.8 % were females, mean age and BMI at diagnosis were 29.1±12.9 years and 26.0±6.3 Kg/m2, respectively. Asian, Black, White, and Other races were 8, 28, 194, and 20 participants, respectively. Hispanic, non-Hispanic, and Other ethnicities were 14, 225, and 11 participants, respectively. Blacks had significantly shorter MSL and greater number of SOREMPs than Asians, Whites, and Other races (2.7±2.1 vs. 3.8±1.1, 4.8±2.4, 4.7±2.4 minutes, and 3.3±1.3 vs. 2.4±0.9, 2.5±1.3, 2.0±1.3 SOREMPs, respectively). Other ethnicities had shorter MSL and greater number of SOREMPs than Hispanics and non-Hispanics (3.3±2.9 vs. 4.6±2.4, 4.5±2.4 minutes, and 3.1±1.2 vs. 2.2±1.5, 2.5±1.3 SOREMPs, respectively); albeit, statistically insignificant. Adjusted analyses showed associations between Black race and MSL (β=-1.7, 95% CI [-2.9, -0.6] minutes), and between Black, Other races, and number of SOREMPs (β=0.7, 95% CI [0.1,1.3]; β=-0.9, 95% CI [-1.8, -0.1] SOREMPs, respectively). No associations were observed between ethnicity and MSL or number of SOREMPs.

Conclusion

This study highlighted racial differences in PC among ALWNC. Blacks had shorter MSL and greater number of SOREMPs, while Other races had lesser number of SOREMPs. In addition, this report suggested a trend toward ethnic differences in NC PC.

Support (if any)
0676 The Effects of Circadian Acclimation of Performance, Sleep, and 6-sulfatoxymelatonin on QTc and QT Variability
Authors: Saissan Kiyan , Patel Milin , Mak Natalie , Youngstedt Shawn , Patel Salma
Published: April 2024

Introduction

Jet lag is a circadian rhythm disorder whose effects on QTc and QT variability are unknown. QTc and QT variability are markers of ventricular repolarization associated with ventricular arrhythmias including torsade de pointes and overall mortality. The objective of this study is to understand the impact of simulated jet lag on QTc and QT variability.

Methods

Following a baseline circadian rhythm assessment, participants were placed on a 16 hour wake - 8 hour sleep schedule in which the wake-sleep and light-dark schedule was delayed by 8 hours for 3 days (analogous to traveling 8 time zones west). Participants were randomized to one of 3 treatments administered each of the 3 days of the shifted schedule: (A) placebo control, (B) bright light, and (C) bright light + exercise + melatonin. Participants wore an Extended Wear Holter Monitor for the duration of the 6.5 day laboratory stay. Bazette’s correction was used to correct the QT interval for heart rate. The standard deviation of QTc was used as a marker of QT variability.

Results

15 of the 22 patients completed the study with the Holter monitor in place (86.6% men, mean age 28.4 ± 9.56). In the bright light group, the average QTc in milliseconds (± SD) declined in the in the first ultrashort sleep/wake schedule (406 (19), p=0.04), the shifted light dark and sleep wake schedule (404(23), p=0.053) and second ultrashort period (396 (26), p=0.015) when compared with baseline (410(24)). In the bright light + exercise + melatonin group the average QTc (± SD) increased during the shifted schedule when compared to baseline (403 (40) vs. 410 (40), p =0.02). No other significant changes were noted.

Conclusion

There may be effects of jet lag and its treatment on QTc length though larger studies are needed to further explore this relationship.

Support (if any)

Department of Defense, Philips, American Academy of Sleep Medicine Foundation, National Institutes of Health, The University of Arizona.
0679 Sleep Reactivity in Shift Workers: In Association with Sleep Disturbance, Mood Symptoms, and Quality of Life
Authors: Kim Hanseul , Lee Kyung Hwa , Shin Jiyoon , Seo Won Woo , Jeon Jeong Eun , Lee Ha Young , Yoo Jin Hyuk , Kim Sun-Young , Kim Seog Ju , Lee Yu Jin
Published: April 2024

Introduction

Shift work is associated with sleep disturbance, mood problems, and quality of life. Sleep reactivity, a trait-like characteristic reflecting how stress disrupts sleep, is related to insomnia, depression, and shift-work disorder. We aim to investigate the relationship between sleep reactivity and shift work, exploring the associations between sleep reactivity and sleep disturbance, mood symptoms, or quality of life in shift workers.

Methods

Seventy shift workers (SW, 55 females, 30.47±5.90 years old) and 54 healthy controls (HC, 36 females, 31.76±6.59 years old) participated in the current study. All participants completed self-reported measurements, including the Ford Insomnia Response to Stress Test (FIRST), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Morningness-Eveningness Questionnaire (MEQ), and the World Health Organization Quality of Life (WHOQOL). We compared scores on self-reported measurements between SW and HC. Subsequently, we examined the interaction effect between FIRST and shift work. Within SW, we used multiple linear regression to investigate the relationship between FIRST and scores on self-reported questionnaires, adjusting for age, sex, shift work duration, and chronotype. We used SPSS 22 for statistical analysis.

Results

SW showed higher scores on BDI (t=2.97, p=0.004) and ISI (t=3.04, p=0.003) than HC. In SW, the high sleep reactivity (FIRST≥18) subgroup showed higher scores on ESS, PSQI, ISI, BDI, BAI, and lower WHOQOL compared to the low sleep reactivity (FIRST< 18) subgroup. We found a similar pattern, but only PSQI, BDI, and BAI showed significant differences in HC. Two-way ANOVA revealed an interaction effect between FIRST and shift work on WHOQOL (F=5.636, p=0.020). Multiple regression analysis found that high sleep reactivity was associated with high ISI (p=0.002), BDI (p=0.004), BAI (p=0.020), and low WHOQOL (p< 0.001) in SW.

Conclusion

Our findings suggest that sleep reactivity could be key in shift workers’ quality of life. In addition, current results indicate that high sleep reactivity in shift workers can be associated with increased sleep disturbance, depressed mood, and decreased quality of life. This suggests that sleep reactivity may serve as a predictive factor for shift work tolerance.

Support (if any)

National Research Foundation (No. 2016M3C7A1904338 and No. NRF-2022R1A2C1008209)
0680 Body Clock and Aging: Relationships of Circadian Rhythm with Sleep Quality, Mental Health and Geriatric Conditions
Authors: Adirim Zachary , Pan Yulu , Doyle Margaret , Knauert Melissa , Yaggi Henry , Miner Brienne
Published: April 2024

Introduction

Circadian rhythm is associated with sleep quality and mental health. Research to date has been primarily focussed on adolescent and general adult populations, with little inquiry in older adults, whom have relatively earlier circadian phases. We sought to clarify correlates of circadian function with sleep quality and mental health, in addition to geriatric conditions to further explore these relationships in an older adult population.

Methods

We undertook a cross-sectional study of 67 community-dwelling older adults (≥60yrs) with self-reported sleep complaints. Circadian rhythm was assessed with 7-day actigraphy, to derive sleep midpoint and variables of circadian strength (interdaily stability (IS), intradaily variability (IV), amplitude, and MESOR), calculated using non-parametric or cosinor analysis. Assessments evaluated outcome measures of subjective sleep quality (Insomnia Severity Index [ISI], Pittsburgh Sleep Quality Index [PSQI], Epworth Sleepiness Scale [ESS]), objective sleep physiology (nocturnal polysomnography parameters), mental health (prior psychiatric diagnosis [depression, bipolar anxiety, PTSD], Patient Health Questionnaire of depressive symptoms [PHQ8], Generalized Anxiety Disorder Questionnaire [GAD2]), geriatric conditions (age, frailty [Fried phenotype], cognition with Montreal Cognitive Assessment [MOCA], falls (≥1 in past year), multimorbidity [≥3 chronic conditions], and polypharmacy [≥5 medications]). Pearson correlation coefficients were employed to investigate relationships.

Results

Circadian rhythm markers (lower amplitude, MESOR) were associated with daytime sleepiness (r=0.25-0.29, p values < 0.05). Later sleep midpoint was correlated with reduced total sleep time on PSG (r=0.36, p< 0.05), and lower ISI severity (r=0.29, p< 0.05). Measures of circadian weakness (lower IS, higher IV) were associated with mental health diagnoses (r=0.25-0.30; p values < 0.05), though no significant associations were seen with psychiatric symptoms. Earlier sleep midpoint was correlated with increasing age (r=0.41, p< 0.05). Parameters of circadian weakness (low IS, high IV, low amplitude) were associated with lower cognitive scores (r=0.32-0.44, p values < 0.05) and multimorbidity (r=0.30-0.36, p values < 0.05).

Conclusion

Our data strengthens links between circadian rhythm characteristics with sleep quality and mental health conditions, demonstrating generalizability to older adult populations. Markers of weaker circadian rhythm were associated with cognitive dysfunction and multimorbidity. Future research should examine the association of circadian markers with incident geriatric illness longitudinally.

Support (if any)

NIA (K76AG0749505, P30AG021342)
0682 Circadian Rest/activity Rhythms Are Associated with Daytime Symptoms Among Older Adults with Insomnia
Authors: Jones Ashley , Pfaeffle Claire , Zhao Zhiwei , Verceles Avelino , Johnston Christine , Chen Shuo , Rogers Valerie , Wilkens Kristine , Spira Adam , Buysse Daniel , Wickwire Emerson
Published: April 2024

Introduction

We investigated the association between circadian rest/activity rhythms (RARs) and daytime insomnia symptoms among older adults with insomnia. We hypothesized that: 1) RARs differ between older adults with insomnia and healthy sleepers; and 2) among older adults with insomnia, RARs are associated with daytime insomnia symptoms.

Methods

Older adults with insomnia (n=30) and healthy sleepers (n=33) were recruited from a tertiary academic medical center. Nonparametric RARs were derived from actigraphy data over 14 days: relative amplitude (RA), interdaily stability (IS), intradaily variability (IV), maximum ten-hour activity (M10) and minimum five-hour activity (L5). Secondary parametric RARs included amplitude, acrophase, and midline statistics of rhythm (MESOR). Daytime function was evaluated via the Daytime Insomnia Symptoms Scale (administered via smartphone 4x/day, i.e., 56 assessments per participant) and validated research questionnaires (administered once). Between-groups differences (insomnia vs healthy sleep) were evaluated using t-tests. Among individuals with insomnia, a series of 104 linear regression models examined the association between RARs and daytime symptoms. The Benjamini-Hochberg procedure for correcting false discovery rate (BH-FDR) was used to correct for multiple comparisons.

Results

Relative to healthy sleepers, older adults with insomnia demonstrated significantly lower RA (p=0.028,t=-2.28) and significantly higher L5 (p=0.018,t=2.48). Among older adults with insomnia, IS was positively associated with positive mood (p=0.013, t =2.67) and negatively associated with pre-sleep somatic arousal (p=0.01, t=-3.06). IV was negatively associated with DISS subscales including Alert Cognition (p=0.001, t=-3.64) and Positive Mood (p< 0.001, t=-4.15) and positively associated with Negative Mood (p< 0.001, t=4.74), and Sleepiness/fatigue (p=0.005, t=3.11), as well as validated questionnaires measuring depression (p=0.02, t=2.47), negative mood (p< 0.01, t=3.43), and pre-sleep somatic arousal (p=0.01, t=3.77). M10 was negatively associated with depression (p< 0.0,1 t=-3.29) and anxiety (p=0.01 t=-2.9). Of parametric RARs, amplitude was negatively associated with negative mood (p=0.005, t=-3.08) and depression (p=0.01, t=-2.75), and positively associated with positive mood (p=0.010, t=2.77). MESOR was negatively associated with depression (p=0.02, t=-2.48) and anxiety (p=0.01, t=-2.81).

Conclusion

Among older adults with insomnia, RARs were associated with daytime insomnia symptoms. Future research should examine how to optimize circadian components of therapeutic interventions in insomnia clinical care.

Support (if any)

Merck Investigator Studies Program (#59170).
0683 Rest-activity Pattern in Isolated RBD – from Prodromal Stage to Association with Parkinsonism-first Phenoconversion
Authors: Zhou Li , Feng Hongliang , Huang Bei , Wang Jing , Tang Shi , Zhang Jihui , Chan Joey , Chau Steven WH , Yu Mandy WM , Mok Vincent CT , Liu Yaping , Wing Yun Kwok
Published: April 2024

Introduction

Previous evidence suggested that the alteration of rest-activity pattern occurred in patients with isolated rapid eye movement behavior disorder (iRBD), a prodromal stage of α-synucleinopathy with implication to future phenoconversion. Nonetheless, it remains unclear whether this rest-activity pattern alteration has already emerged in the prodromal stage of RBD, and whether it would be related to conversion of iRBD into parkinsonism-first or dementia-first subtype of α-synucleinopathy.

Methods

We performed 1) a case-control study to compare the rest-activity pattern measured by 7-day actigraphy among prodromal RBD subjects (n = 21) and their age-, sex-, and body mass index-matched control subjects (n = 52) and patients with iRBD (n = 63); and 2) a longitudinally follow-up study to investigate the predictive value of altered rest-activity pattern to parkinsonism-first or dementia-first subtype of α-synucleinopathy in full sample of patients with iRBD (n = 170). Prodromal RBD subjects were defined as subjects who had recurrent dream-enactment behaviors but with subthreshold REM sleep electromyography activity. Generalized linear model was employed to assess rest-activity pattern in prodromal RBD subjects and competing risk regression model was used to determine the predictive value of rest-activity pattern to RBD phenoconversion.

Results

Similar to patients with iRBD, prodromal RBD subjects had more daytime probable napping (percentage and duration) and lower average weekly activity level and physical activity level during active period as compared to control subjects. Of 170 patients with iRBD, 166 were successfully followed up with a mean follow-up duration of 4.6 years. Lower average weekly activity level, amplitude, and physical activity level during active period were associated with a higher risk of parkinsonism-first subtype of α-synucleinopathy, instead of dementia-first subtype.

Conclusion

Our study found that altered rest-activity pattern has already emerged in prodromal stage of RBD. As for iRBD patients, lower physical activity level and amplitude might serve as predictive markers of parkinsonism-first conversion.

Support (if any)

No
0685 Opioid Treatment of Patients with Painful versus Painless Restless Legs Syndrome
Authors: Karroum Elias , Zackon Jordana , Winkelman John
Published: April 2024

Introduction

Recent studies characterized a clinically more severe and painful (compared to painless) form of restless legs syndrome (RLS). Data comparing pharmacological treatments in patients with painful and painless RLS are scarce. The current study investigated long-term opioid treatment in patients with painful versus painless RLS.

Methods

Data were extracted from the National RLS Opioid Registry, an ongoing observational longitudinal study of adult patients with a confirmed diagnosis of RLS treated with opioids. Painful RLS classification was based on a “Yes/No” response to the question: “Would you consider your RLS to be painful?”. Baseline collected data included age, gender, ethnicity, body mass index, education, RLS family history, age at RLS onset, augmentation history, and International RLS Severity Scale (IRLS). Both RLS concomitant treatments (alpha-two-delta, α2δ) ligands; dopaminergic agents, DA) and RLS opioid treatments with daily dose in morphine milligram equivalents (MME) were collected at baseline and after two years.

Results

Data for 447/500 initially enrolled RLS patients (146 painful and 301 painless RLS) were available after two years. Painful (versus painless) RLS patients were less frequently White (94.5% versus 98.3%; P=0.0347) and had more severe RLS symptoms at baseline (IRLS scores: 16.6±10.0 versus 11.3±9.1; P< 0.0001). Frequency of patients on concomitant DAs or α2δ ligands did not differ between the two subgroups at baseline or after two years. The mean daily opioid dose (MME) did not differ between the two subgroups at baseline or after two years. The frequency of patients with MME ≥50 was higher in the painful RLS subgroup at baseline (27.4% versus 18.6%; P=0.0338) but not after two years. Painful RLS patients either increased or decreased opioid dose (65.1% versus 53.2%; P=0.0170) and more frequently switched, added, or discontinued opioids (25.3% versus 15.6%; P= 0.0135) after two years.

Conclusion

Whether the higher need for change in opioid dose or medication in patients with painful RLS is related to the severity of RLS and/or lack of efficacy remains to be determined.

Support (if any)

The National RLS Opioid Registry received support from the RLS Foundation, Baszucki Group, and Jerry Blakeley.
0686 Systemic Evaluation of REM Without Atonia Associated with Psychotropic Medications
Authors: Araujo Matheus Lima Diniz , Mouchati Christian , Rodriguez Carlos , Foldvary-Schaefer Nancy
Published: April 2024

Introduction

Selective serotonin reuptake inhibitors (SSRIs), Serotonin and norepinephrine reuptake inhibitors (SNRIs), and Tricyclic antidepressants (TCAs) alter REM sleep motor characteristics and have been associated with REM sleep behavior disorder (RBD). We leveraged a large cohort of polysomnograms (PSGs) to analyze the relationship of SSRI, SNRI, and TCA use on REM without atonia (RWA).

Methods

We analyzed 1075 PSGs from the Cleveland Clinic Starlit Registry with >10% total sleep time in REM sleep systematically scored for RWA using AASM 2.6 criteria. Percent of REM epochs meeting RWA criteria in PSGs without medication use were compared with the following medication groups: SSRI (N=303), SNRI (N=142), TCA (N=49), and combinations SSRI+SNRI (N=8), SSRI+TCA(N=11) and SNRI+TCA (N=7). We performed a linear regression analysis using the Ordinary Least Squares (OLS) method from the SciPy library (v1.11.1) to measure the association of RWA epochs by medication group adjusting for age, body mass index (BMI), and gender.

Results

Our sample included 48.2% females with a mean age of 52.7 (SD:16.7) and a mean BMI of 32.6 (8.6). The overall mean percentage of RWA epochs was 11.7 (SD:17.5), with a median, 25% and 75% of 4.1, 0.5, and 14.8, respectively. SSRI and SNRI use was associated with a 4.7% and 7.0% increase in RWA (p=< 0.001), respectively, while a 13.6% increase was observed in the SSRI+SNRI group (p= 0.02). Although the TCA and TCA+SSRI groups were associated with only 1.8% and 5.4% increases, they were not significant (p=0.48 and 0.29). The TCA + SNRI group was associated with an increase of 18% (p=< 0.001).

Conclusion

Use of SSRIs and SNRIs alone or in combination, and TCAs combined with SNRI increase the percentage of REM epochs meeting criteria for RWA. Although TCAs alone and combined with SNRIs were not associated with significant increases, this may be due to small sample sizes. These results suggest a potential synergistic effect on RWA criteria in patients using psychotropic medications that necessitates further study. This research has important implications for the diagnostic criteria for RWA and the diagnostic accuracy of RBD.

Support (if any)

We acknowledge the support of NIH grant R21HL170206.