Daniel J. Levendowski

Background and Objective: Obstructive sleep apnoea (OSA) pathogenesis is heterogeneous with important contributions from impaired upper airway anatomy and at least three key non‐anatomical endotypes. Detailed physiology assessments in a relatively small number of well‐characterized participants indicate wide interindividual variability in pharyngeal collapsibility and that ~70% have impairment in one or more non‐anatomical endotype. In this study, we applied recently developed models that use standard clinical and polysomnography data to estimate OSA endotypes and oral appliance therapy response in a large clinical population. Methods: OSA endotypes and oral appliance therapy responses were estimated using standard sleep study outputs and clinical data (age and BMI) from > 6500 consecutive home‐based diagnostic studies with an Apnoea/Hypopnea Index (AHI) > 5 events/h (Sleep Profiler PSG‐Advanced Brain Monitoring). The proportion of participants who were estimated to have impairment in one or more OSA endotypes was calculated according to previously published physiological cutoffs. Results: On average, participants (39% female) had moderately severe OSA (AHI = 28 ± 23 events/h [Mean ± SD]), were middle aged (49 ± 16 years) and obese (BMI = 31 ± 7 kg/m ² ). 29% were estimated to have only mild anatomical compromise. 23% had poor dilator muscles, 31% high loop gain and 29% had a low arousal threshold. 62% had severe (“bad”) impairment in one or more non‐anatomical endotypes. ~60% were predicted to respond to oral appliance therapy. Conclusions: These novel data highlight the heterogeneity of OSA in a large clinical OSA patient population and the feasibility for endotype‐informed precision medicine for OSA via application of novel physiology‐based algorithms using home sleep study recordings at scale.

Background and Objectives: Physical activity and sleep are potential modifiable risk factors for the development of Alzheimer’s disease and related disorders (ADRD), but few studies have objectively measured both domains in participants across the cognitive continuum. Research Design and Methods: Standard clinical assessment, accelerometry, and at-home EEG sleep data were obtained from older controls (n=9) and adults who met consensus diagnostic criteria for mild cognitive impairment (MCI; n=7), Alzheimer’s disease (AD; n=10), and dementia with Lewy bodies (DLB; n=11). Given these sample sizes, descriptive statistics are presented rather than formal statistical testing. Results: The MCI group had the most surprising findings—although they were cognitively similar to the control group, they were less physically active than the AD group and had the worst sleep efficiency. The DLB group had the most severe motor and neuropsychiatric symptoms, were the least physically active, spent the least amount of time in rapid eye movement (REM) sleep, and spent the highest amount of time in non-REM sleep with hypotonia (NRH). The AD group had physical activity counts that fell between the DLB and control groups; REM sleep and NRH levels that were similar to the control and MCI groups; and autonomic activation index (AAI) and sleep spindle durations that were higher than the MCI and DLB groups. Discussion and Implications: These findings highlight interesting physical activity and sleep patterns between groups, but larger samples are needed to investigate how objectively measured physical activity and sleep might serve as disease-specific digital biomarkers of neurodegenerative disorders. Translational Significance: This study uses wearable technologies to measure physical activity and sleep in adults with and without cognitive impairment. The study found that adults with mild cognitive impairment had physical activity and sleep patterns that resembled people with dementia despite having cognitive scores that were closer to cognitively healthy controls. Sleep and activity patterns were distinct when comparing participants with Alzheimer’s and dementia with Lewy bodies. Larger studies are needed to validate these findings, but mobile health devices may be an accessible way to detect early cognitive decline and help differentiate dementia subtypes, resulting in earlier, targeted clinical care.

To evaluate the concordance between visual scoring and automated detection of REM sleep without atonia (RSWA) and the validity and reliability of in-home automated-RSWA detection in REM sleep behavior disorder (RBD) patients and a control group (CG). Sleep Profiler signals were acquired during simultaneous in-laboratory polysomnography in 24 isolated RBD patients. Chin and arm RSWA measures visually scored by an expert sleep technologist were compared to algorithms designed to automate RSWA detection. In a second cohort, the accuracy of automated-RSWA detection for discriminating between RBD and CG (n = 21 and 42, respectively) was assessed in multi-night in-home recordings. For the in-laboratory studies, agreement between visual and auto-scored RSWA from the chin and arm were excellent, with intra-class correlations of 0.89 and 0.95, respectively, and substantial, based on Kappa scores of 0.68 and 0.74, respectively. For classification of iRBD patients versus controls, specificities derived from auto-detected RSWA densities obtained from in-home recordings were 0.88 for the chin, 0.93 for the arm, and 0.90 for the chin or arm, while the sensitivities were 0.81, 0.81 and 0.86, respectively. The night-to-night consistencies of the respective auto-detected RSWA densities were good based on intra-class correlations of 0.81, 0.79 and 0.84, however some night-to-night disagreements in abnormal RSWA detection were observed. When compared to expert visual RSWA scoring, automated RSWA detection demonstrates promise for detection of RBD. The night-to-night reliability of chin- and arm-RSWA densities acquired in-home were equivalent.

Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer’s disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test–retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test–retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.

Background: In this pilot study, the diagnostic agreement for sleep biomarker‐based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini‐Mental State Exam scores ≥28.Methods: Sleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4‐class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58. Input variables included age, time‐REM, non‐REM hypertonia, autonomic‐activation index, spindle‐duration, atypical‐N3, time‐supine, sleep‐efficiency, relative‐theta, and theta/alpha. Distributions of NDD risk were then assigned to AD=37, LBD=19, MCI=71, and CG=106 records.Records with CG output probabilities ≥70% were labeled Probably‐Normal, and from 45‐70% Likely‐Normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely‐ or Probably‐ LBD, AD, pSYN, or Mixed based on the group probabilities. Likely‐Normal with indications consistent with the diagnosis trended toward agreement.Results: The proportions of each group with classifications consistent with the diagnosis or trended toward agreement were CG=86%, MCI=75%, LBD=79%, and AD=87%.In the CG group, NDD risk assignments were Probably‐Normal=68% and Likely‐Normal with NDD indications=18%, Likely‐NDD=10%, and Probable‐NDD=4%.The MCI group was assigned Probable‐NDD=38% (AD=26%, Mixed=10%, LBD/pSYN=2%), Likely‐NDD=17% (AD=7%, Mixed=6%, LBD=3%, pSYN=1%), Likely‐Normal with indications=20% (AD=9%, pSYN=7%, Mixed=4%) and Probably‐Normal=25%The LBD group’s assigned risks consistent with the diagnoses included Probable‐NDD=74% and Likely‐Normal with pSYN indications=6% but included inconsistencies from Probable‐AD=5%, Likely‐Normal with AD indications=5%, and Probably‐Normal=11%.AD group’s consistent risk classifications included Probable‐NDD=60%, Likely‐NDD=11%, Likely‐Normal with AD/Mixed indications=13% and countered by inconsistencies from Probable‐LBD/pSYN=8%, Likely‐Normal with pSYN indications=3%, and Probably‐Normal=5%.Conclusions: Consistencies between the diagnoses and assigned NDD risks agreements exceeded 75% in all four groups. Studies are underway to confirm the test‐retest reliability and/or evaluate NDD risk probabilities in other patient populations.Support: NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.

Study objective: This proof-of-concept study aimed to determine whether the combined features of two non-rapid eye movement (NREM) sleep biomarkers acquired predominantly in-home could characterize different neurodegenerative disorders.Methods: Sleep spindle duration and non-REM hypertonia (NRH) were evaluated in seven groups including a control group (CG = 61), and participants with isolated REM sleep behavior disorder (iRBD = 19), mild cognitive impairment (MCI = 41), Parkinson disease (PD = 16), Alzheimer disease dementia (ADem = 29), dementia with Lewy Bodies or Parkinson disease dementia (LBD = 19) and progressive supranuclear palsy (PSP = 13). One-way analysis of variance (ANOVA), Mann–Whitney U, intra-class (ICC) and Spearman ranked correlations, Bland–Altman plots and Kappa scores, Chi-square and Fisher exact probability test, and multiple-logistic regression were focused primarily on spindle duration and NRH and the frequencies assigned to the four normal/abnormal spindle duration/NRH combinations.Results: ANOVA identified group differences in age, sleep efficiency, REM, NRH (p < 0.0001) and sleep time (p = 0.015), Spindle duration and NRH each demonstrated good night-to-night reliabilities (ICC = 0.95 and 0.75, Kappa = 0.93 and 0.66, respectively) and together exhibited an association in the PD and LBD groups only (p < 0.01). Abnormal spindle duration was greater in records of PSP (85%) and LBD (84%) patients compared to CG, MCI, PD and ADem (p < 0.025). Abnormal NRH was greater in PSP = 92%, LBD = 79%, and iRBD = 74% compared to MCI = 32%, ADem = 17%, and CG = 16% (p < 0.005).The combination biomarker normal spindle duration/normal NRH was observed most frequently in CG (56%) and MCI (41%). ADem most frequently demonstrated normal spindle duration/normal NRH (45%) and abnormal spindle duration/normal NRH (38%). Normal spindle duration/abnormal NRH was greatest in iRBD = 47%, while abnormal spindle duration/abnormal NRH was predominant in PSP = 85% and LBD = 74%.Conclusion: The NREM sleep biomarkers spindle duration and NRH may be useful in distinguishing patients with different neurodegenerative disorders. Larger prospective cohort studies are needed to determine whether spindle duration and NRH can be combined for prodromal assessment and/or monitoring disease progression.

Purpose: This study aimed to establish the diagnostic accuracy of a previously validated sleep staging system in patients with probable isolated REM sleep behavior disorder (iRBD), and to compare physicians’ diagnoses of iRBD based on REM sleep without atonia (RSWA) to non-REM hypertonia (NRH), a sleep measure independently associated with Parkinsonian spectrum disorders. Patients and Methods: Twenty-six patients with a history of dream enactment behavior underwent a diagnostic PSG with simultaneous Sleep Profiler (SP) acquisition at two sites. PSG and SP records were sleep staged, and two sleep neurologists independently diagnosed iRBD based on the presence or absence of polysomnographic identified RSWA. Comparisons for PSG vs SP sleep staging and the qualitative presence or absence of PSG-based RSWA vs automated SP-detected NRH was performed using kappa coefficients (k), positive and negative percent agreements (PPA and NPA), and chi-square tests. Results: The kappa scores from Sites-1 and − 2 for PSG vs SP staging were different for Wake (k=0.82 vs 0.65), N2 (k=0.63 vs 0.72) and REM (k=0.83 vs.0.72). The by-site kappa values for stage N3 increased from 0.72 and 0.37 to 0.88 and 0.74 after PSG records were reedited. The kappa values for between-physician agreement in iRBD diagnoses were fair (k = 0.22). The agreement between each physician’s iRBD diagnoses and NRH were also fair (k=0.29 and 0.22). Abnormal NRH agreed with at least one physician’s iRBD diagnosis in 83% of the records. The PPA resulting from between-physician iRBD agreement was stronger and the NPA weaker than the values obtained from comparison of each physician’s iRBD diagnosis and abnormal NRH. Conclusion: The potential utility of RSWA and stage N3 as neurodegenerative disorder biomarkers was influenced by between-site variability in visual scoring. The degree to which NRH was associated with iRBD was similar to the between-physician agreement in their diagnosis of iRBD using RSWA.