Levendowski, Daniel J.

Abstract Background Dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) are challenging to differentiate. Mobile health devices that measure motor and sleep domains between clinic visits may improve differential diagnosis. Method Participants with probable AD and probable DLB wore ActiGraph triaxial wGT3X‐BT actigraphy devices on their nondominant wrists for 2 weeks and multichannel Sleep ProfilerX8TM EEG devices on their foreheads for 2 consecutive nights.3 Actigraphy data were processed and scored using GGIR and activity counts in R. The Sleep Profiler characterizes abnormal slow‐wave sleep and non‐REM hypertonia (NRH). Result At baseline, participants with DLB (n = 9) had higher UPDRS scores and a higher likelihood of psychosis than participants with AD (n = 8; see Table 1). Using univariate Wilcoxon Rank‐Sum tests, we found that at baseline participants with DLB had lower daily activity counts, higher % time in NRH, and lower % time in REM sleep than participants with AD (unadjusted p‐values < 0.05; See Figure 1). In a recent 6‐month followup, activity levels were lower on average than baseline activity levels for both DLB (n = 5) and AD (n = 5). at 6‐months, the differences in % sleep in NRH increased between DLB participants (n = 5) and AD participants (n = 3) as average % NRH increase 6% in DLB and decreased 1% in AD (see Table 2). These 6‐month differences are not statistically significant, but data collection are ongoing. An exploration of methods for processing and scoring actigraphy data revealed that processing can result in wide range of findings. As a result, we have focused on actigraphy outcomes which are more suitable to older participants with dementia. Conclusion Participants with DLB demonstrated lower baseline physical activity and higher rates of baseline sleep disturbances than participants with AD; sleep differences between DLB and AD appeared to increase over 6 months. Actigraphy and sleep‐monitoring devices are well tolerated in participants with dementia and they can reliably collect valid, useful activity and sleep data in between clinic visits.

Abstract Introduction In this pilot study, we explored sleep biomarker risk probabilities for different neurodegenerative disorder (NDD) phenotypes across a spectrum of NDD patients, compared with controls. Methods We analyzed a cohort of patients with different NDD phenotypes who underwent in-home recordings with Sleep Profiler, including Lewy body disease (LBD=20), Alzheimer’s disease dementia (AD=29), and isolated REM sleep behavior disorder (iRBD=19). Controls with an MMSE&gt;28 (CG=61) and patients with Parkinson disease (PD=16) and mild-cognitive-impairment (MCI=41) also participated. We developed a machine-learning classifier that assigned NDD probabilities for LBD, AD, iRBD, and CG. The input variables included: time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha. Probabilities &gt;50% were assigned “likely”, and for CG&gt;=50% and a NDD group probability 20-50%, the assignment was normal “plus”. Probability assignments were then made for the NDD and CG groups, then further applied to the PD and MCI patient groups. Results The CG group participants were assigned Normal-Likely=74%, Normal+AD=11%, Normal+iRBD=5%, iRBD-Likely=5%, and AD-Likely=5%. LBD patient distributions were LBD-Likely=70%, iRBD-Likely=5%, AD-Likely=5%, Normal+LBD=5%, Normal+iRBD=5%, Normal+AD=5%, and Normal-Likely=5%. AD group distributions were AD-Likely=71%, LBD-Likely=4%, Normal+AD=14%, Normal+iRBD=4%, Normal-Likely=7%. iRBD patients were characterized with iRBD-Likely=37%, LBD-Likely=5%, AD-Likely=5%, Normal+iRBD=27%, Normal-Likely=26%. PD patients were assigned iRBD-Likely=29%, LBD-likely=14%, AD-Likely=14%, Normal+iRBD=21%, Normal+AD=7%, Normal-Likely=14%. MCI distributions were AD-Likely=45%, LBD-Likely=8%, iRBD-Likely=5%, Normal+AD=21%, Normal-Likely=13%. Conclusions For LBD, AD and CG groups, correct risk assignments were &gt;70% while gross misclassifications were &lt;10%. Classification patterns for PD, MCI and iRBD were disbursed in a manner consistent with the range of severities expected in each group.

Abstract Introduction Atypical N3 sleep (AN3=delta waves with limited theta and sigma) has been associated with ICU delirium and sepsis and averaged 25% of sleep time in Japanese ICU patients. We were interested in exploring whether AN3 might be a marker of cerebral dysfunction in ambulatory patients across a range of neurodegenerative disorders, including those with a dementia diagnosis. Methods After ethics review and with informed consent, patients with Lewy body disease (DLB/PDD: n=20,male=90%,age=70 + 6.2), Alzheimers disease dementia (AD: n=29,male=79%,age=75 + 6.7), Parkinson disease (PD: n=16,male=69%,age=67 + 8.7), mild cognitive impairment (MCI: n=41,male=63%,age=70 + 8.5), isolated REM sleep behavior disorder (iRBD: n=19,male=74%,age=64 + 9.6) and a control group (CG: n=61,male=47%,age=65 + 8.3) were studied with the Sleep Profiler and auto-detected AN3 computed. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The mean percentages of sleep time with AN3 were significantly greater in DLB/PDD (8 + 12.3) vs. PD (4 + 10.8), AD (2 + 3.7), MCI (2 + 2.3), iRBD (1 + 1.6), and CG (1 + 2.4)(all p&lt;0.02). The proportions of records with abnormal AN3 (&gt;5% of sleep time) were significantly greater in those with DLB/PDD=35% vs. MCI=10%, iRBD=5% and CG=5% (all p&lt;0.05), but not AD=17% and PD=13%. Conclusions Further investigations are needed to determine if abnormal AN3 may be a biomarker that distinguishes patients with Lewy body spectrum diseases from alternative pathologies and/or could be helpful in monitoring the side effects of CNS-acting medications

Abstract Introduction Symptoms associated with autonomic nervous system dysfunction are common to patients with neurodegenerative disorders (NDD) resulting from the accumulation of α-synuclein in the neurons and glial cells. In this study we investigate whether a novel biomarker, the autonomic activation index, can discriminate these potentially affected NDD groups. Methods After ethics review and with informed consent, patients with Lewy body disease (DLB/PDD: n=20%), Alzheimer’s disease dementia (AD: n=27), Parkinson disease (PD: n=14), mild cognitive impairment (MCI: n=37), isolated REM sleep behavior disorder (iRBD: n=15) and a control group (CG: n=58) were studied with the Sleep Profiler. AAI was based on a 6 BPM increase in pulse rate compared to the previous/subsequent 10th second. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The AAI values for the DLB/PDD patients were decreased compared to the AD, MCI (both p&lt;0.03), and CG (p&lt;0.0005), while the PD was less than AD and MCI (both &lt; 0.05) and CG (p&lt;0.002). The proportions of cases with abnormal AAI values (&lt;10 events/h) was greatest in DLB/PDD patients, and greater than in ADem (p&lt;0.02), MCI (p&lt;0.02) and CG (p&lt;0.0001), while AAI in PD and iRBD was greater than in CG (p&lt;0.007 and 0.05 respectively). Conclusions These findings suggest that the AAI can be used to objectively confirm subjective complaints of autonomic dysfunction symptoms in patients with synucleinopathies. Longitudinal studies are needed determine factors that influence changes in the AAI.

Abstract Introduction In 2015, Lee et al. observed sleeping position influenced the efficiency of glymphatic clearance in rats. In 2018, Levendowski et al. reported supine sleep was independently associated with neurodegenerative disorders (NDD)(odds ratio=3.7). This update further evaluates the comparative frequency of the supine sleep position across a spectrum of cognitive impairment, including patients diagnosed with a Parkinsonian spectrum disorder (PSD), Alzheimer’s disease dementia (AD) and mild cognitive impairment (MCI), compared with a control group (CG) of patients without a known cognitive disorder. Methods After ethics review and with informed consent, a control group (CG: n=170), PSD (n=36), AD (n=29) and MCI group (n=41) were studied in-home with the Sleep Profiler; 89% across two-nights. Between-group comparisons were assessed with Mann-Whitney U and Chi-square tests. Results The hours of supine sleep were greater in PSD=3.0 + 1.9, AD=3.2 + 2.0 and MCI=3.0 + 2.0 vs. CG=1.9 + 1.8 (all p&lt;0.002). The proportion of patients with &gt;2 hours of supine sleep were PSD=69%, AD=69% and MCI=59% vs. CG=39%, with odds ratios of PSD=3.6, AD=3.5 and MCI=2.2 (all p&lt;0.001). Conclusions Here we provide further evidence for a relatively strong association between supine sleep and neurodegenerative disease including PSD, AD and MCI cohorts. The nature of this association remains unclear from cross sectional study designs. Future prospective studies should test if there is a causal link between supine sleep and subsequent development of neurodegeneration.

Abstract Introduction Sleep markers can assess prodromal risk for neurodegenerative disorders (NDD). In a recent study conducted by the Mayo Clinic, most isolated REM sleep behavior disorder patients preferred knowing about their risk for parkinsonism or dementia. In their email survey, patients indicated a strong preference for disclosure of prognostic NDD risk in order to spur family discussions and plan for the future. For this pilot study, the Mayo questions were modified in order to investigate the perspectives and preferences of community-based adults about potentially being at-risk of a NDD and how it might impact their lives. Methods A brief survey was administered to Rotary Club attendees prior to a presentation on sleep and neurodegenerative disease. Each question offered five responses: strongly disagree, disagree, neutral, agree, or strongly agree. Personal identifiers were not obtained. Thirty-four surveys were completed from approximately equal numbers of older men and women with estimated ages above 50 years. Personal information were not obtained (e.g., age and sex). Results When asked whether it was important to know their NDD risk given the fact that there are no known methods to prevent or cure the disease, most (83%) strongly agreed or agreed, while only 18% preferred not to know their NDD risk. The majority felt they would be more pessimistic about their future if identified as being at risk of NDD the (62% strongly agreed/agreed). Most believed their priorities and plans for living life would change (68% strongly agreed/agreed). Further, the vast majority felt that knowing NDD risk would help them plan for the future (89% strongly agreed/agreed), including planning for health resources and support (88% strongly agreed/agreed). Conclusion Eighty-three percent of those surveyed reported they would benefit from knowing if they were at risk of NDD, even though the majority would feel more pessimistic about their future. The vast majority (89%) responded that knowing their NDD risk would help them plan for the future. These results suggest that most community adults might be receptive to future sleep screening for NDD with results. Investigations are underway to confirm these findings in a broader community-based study. Support (if any) NIA-R44AG050326

Abstract Introduction In a recent study, most patients diagnosed with isolated REM sleep behavior disorder (iRBD) preferred their physician discussed their risk for developing a future neurodegenerative disorder (NDD) such as parkinsonism or dementia (PD). In the Mayo Clinic email survey, 75% would lose trust in their physician if PD-risk was not discussed following an iRBD diagnosis. For this survey, questions were designed to investigate preferences for how sleep physicians might communicate to community-based adults about risk of a NDD. Methods A survey was administered to Rotary Club attendees prior to a sleep and neurodegenerative disease presentation, with options for responses being either strongly disagree, disagree, neutral, agree, or strongly agree. Thirty-four surveys were obtained from approximately equal numbers of men and women with estimated ages above 50 years. Personal information were not obtained (i.e., sex and age). Results The vast majority of respondents wanted their physician to ask their preference prior to sharing NDD risk (86% strongly agreed or agreed), while virtually all would lose trust in their physician if he/she did not discuss their NDD risk (97% strongly agreed/agreed). If identified with NDD risk, responders would seek out information about medications and adaptive therapies that could delay onset (97% strongly agreed/agreed), while only about half currently had a good understanding of lifestyle patterns that might help delay onset (47% strongly agreed/agreed and 47% disagreed/strongly disagreed). Not knowing when memory problems might begin would cause most responders to feel anxious (85% strongly agreed/agreed). Conclusion Those surveyed wanted their physician to ask their preference prior to sharing NDD risk, but virtually all would lose trust in their health care provider if the information was known but not shared. There appears to be a role for physicians in educating those who may be at NDD risk about interventions that could delay onset of NDD, given approximately half have a limited understanding of lifestyle patterns that could help delay onset. Further, routine follow-up for changes in prodromal NDD biomarker severity might help to reduce anxiety in the 85% of those concerned about when memory problems might begin. Support (if any) NIA-R44AG050326

Abstract Introduction Non-REM hypertonia (NRH) was recently reported to be independently associated with the synucleinopathy-mediated neurodegenerative disorders: dementia with Lewy Bodies/Parkinson Disease Dementia (DLB/PDD), Parkinson Disease (PD), and isolated REM sleep behavior disorder (iRBD). In this NRH investigation, we included progressive supranuclear palsy (PSP), a Parkinsonian-spectrum disorder caused by tau pathology. Methods In this multicenter study, patients broadly characterized as presumed Parkinsonian-spectrum disorders (PSD) included DLB/PDD (n=16), PD (n=16), iRBD (n=19), and PSP (n=13). Presumed non-PSD subjects included Alzheimer’s Disease dementia (AD=22), mild cognitive impairment (MCI=35), and normal cognition (NC=61). Sleep Profiler studies were acquired in all participants. NRH, auto-detected based on persistently elevated electromyographic (EMG) power relative to delta, theta, and sigma bands in the differential Af7-Af8 signal, was measured as a percentage of sleep time and then weight-averaged in the 75% of in-home studies with two-nights of data. A &gt;5% threshold characterized abnormal-NRH. Twenty-nine NC were longitudinally retested after 364- to 563-days. Statistical analyses included inter-class correlations (ICC), Bland-Altman plots, multiple logistic regression, and receiver-operating-characteristic curves (ROC). Results In the PSD=41 and non-PSD=95 records with two-nights of data, NRH-severity demonstrated moderate consistency (ICC=0.78, bias=0.6+/-6.2%, P&lt;0.0001). Across the two-nights, NRH was classified consistently as normal or abnormal in 59.6% and 27.2% of the records, vs. normal/abnormal=4.4% or abnormal/normal=8.8%. The test-retest reliability of NRH-severity was good (ICC=0.84, bias=0.06+/-3.8%, P&lt;0.0001), with all retest comparisons repeating as normal (73%) or abnormal (27%). The frequency of abnormal-NRH in PSP=92% was significantly greater than MCI=26%, AD=14%, and NC=16% (all P&lt;0.0001) and PD=56% (P&lt;0.05), but not DLB/PDD=81% and iRBD=74%. Abnormal-NRH was significantly associated with the PSD group (P&lt;0.0001) and it differentiated PSD versus non-PSD group with an area under the curve of 0.78 (95%CI: 0.72-0.85) based on a sensitivity of 0.75 (95%CI: 0.63-0.84) and a specificity of 0.81 (85%CI: 0.73-0.87). Conclusion NRH independently discriminated PSD patients from age-sex similar non-PSD subjects, suggesting that NRH is a common sleep motor signature across clinical PSD phenotypes. We speculate that NRH could be related to pathological changes within the key non-REM sleep motor modulating center in synucleinopathies and PSP. Support (If Any) NIA grants: R44AG050326, R44AG054256, P30AG62677 and R34AG56639.