Citations

Abstract Introduction This study investigated the impact of visual editing of sleep recordings for use in predicting neurodegenerative disorder (NDD) risk. Methods All participants underwent in-home Sleep Profiler studies. Forty-four were diagnosed with mild cognitive impartment (MCI = 40) or Alzheimer’s disease (AD = 4)(MCI/AD: 55% women, 72.6+/-8.6 years), 26 with REM sleep behavior disorder (RBD: women = 27%, age = 67+/-9.8), and 29 were controls (NC: women = 69%, age = 63+/-11.5). A 4-class machine learning algorithm used age plus eight sleep biomarkers to assign NDD risk probabilities. Records with CG-assigned probabilities >70% were labeled Probably-Normal, and from 45-70% Likely-Normal. Similar thresholds were used to assign Likely- or Probably-Abnormal. NDD risk characterizations were generated based on auto-scoring (“auto”), and again after the recordings were visually edited for accuracy (“edited”). Results Comparing the auto- vs. edited-NDD classifications in the MCI/AD group, the same NDD risk classifications were observed in 91% (39/43), with 2 cases shifting from Likely-Normal to Probably-Normal, one shifted from Likely-AD to Likely-Normal with AD Indications, and one changed from Probably-AD to Likely-AD. The same NDD risk classifications were observed in 81% (21/26) of the RBD cases, 3 shifted from Probably-Abnormal to Likely-Abnormal, one shifted from Likely-Normal to Probably-Normal, and one changed from Likely-Abnormal to Likely-Normal. The same NDD risk classifications were observed in 86% of the NC group, with 4 cases shifting from Likely-Normal to Probably-Normal. Conclusions These findings suggest that the likelihood of a gross misclassification of NDD risk resulting from auto-detected sleep biomarkers is relatively low with inconsistencies directed toward slightly increased NDD risk classifications.

Abstract Introduction This study investigated differences in opinions about neurodegenerative disorders (NDD) in countries with large populations and expanded access to healthcare. Methods A 5-point Likert scale survey assessed opinions of 30–59-year-olds, pertaining to their parents and NDD conditions. 100 surveys, translated into native languages, were acquired in eight developed countries. Positive (somewhat- or strongly-agree) vs. other responses, sex, and country were submitted to logistic regression and Chi-squared analyses. Results “It is important for me to know if my parent has a risk for developing a NDD condition in the future, even if nothing can be currently done to prevent or cure those diseases” had higher positive responses in Australia (92%) compared to Germany (74%) and the USA (78%). “I believe that knowing about my parent’s risk for developing a NDD condition will help me assist in the planning of their future” had higher number of positive responses in Australia (91%) versus Japan (70%), Germany (76%), Italy (80%) and UK (80%). “If my parent(s) was found to be at risk for a NDD condition, I would want to learn more about medications and adaptive therapies that could delay onset” was responded to more positively in Australia (92%), Canada (78%), France (87%), Germany (78%), Italy (88%), UK (88%), and USA (81%) versus Japan (60%). Conclusions Australians responded more positively to questions related to care of a parent with a NDD disorder. These findings suggest cultural differences that could influence education and support systems requirements for sleep medicine providers and NDD patients.

Abstract Dementia with Lewy bodies (DLB), a progressive neurodegenerative disease with heterogeneous clinical presentations, greatly impacts patients, caregivers, and society. Despite its frequency, diagnosing and treating DLB remains challenging. Advances in in vivo biomarker assays reflecting underlying pathology are improving disease identification, diagnostic accuracy, and therapeutic development for biologically targeted, disease‐modifying agents. Consequently, definitions of Alzheimer's disease and Parkinson's disease (PD) have shifted to focus on pathological changes occurring before clinical features, with proposed frameworks for detecting pathological amyloid and tau, neurodegeneration, and other markers (National Institute on Aging‐Alzheimer's Association) and alpha‐synucleinopathy and dopaminergic degeneration (Neuronal α‐synuclein Disease Integrated Staging System, SynNeurGe). The biological frameworks, particularly those related to alpha‐synuclein (α‐synuclein), have sparked debate about unifying DLB and PD under a single pathobiologic disease. This paper discusses the implications of these biological frameworks for the DLB community, addressing topics regarding multiple pathologies and neurochemical systems, clinical heterogeneity, and functional impairment, and exploring the potential impact on clinical trials and care. Highlights DLB is a progressive neurodegenerative disease with varied clinical presentations. Diagnosing and treating DLB remains challenging despite its frequency. Biological frameworks are reshaping Alzheimer's and Parkinson's definitions. In vivo biomarkers are improving DLB identification and diagnostic accuracy. Debate exists regarding unifying DLB and Parkinson's under one pathobiology.

Abstract 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.

Abstract Introduction In this pilot study, sleep biomarker-based neurodegenerative disorder (NDD) risk was assessed in a clinical cohort of patients diagnosed with varying severity of obstructive sleep apnea (OSA). Methods Consecutive in-home multi-night Sleep Profiler records from a community sleep clinic were screened for age >54 years old. Cases with comorbid restless leg syndrome, hypersomnia, missing apnea-hypopnea indexes (AHI), AHI< 5 or single-night recordings were excluded. The 67 remaining records were 40% female, ages 63 + 7.4 years and up to 84 years old. NDD risk was assigned using a machine-learning algorithm combining age and nine sleep biomarkers to assign group probabilities: for Alzheimer’s disease, Lewy Body disease, prodromal synucleinopathy and controls (CG). Sleep biomarkers included: time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha. Those with CG probability score >70% were classified as “Probably-normal”. Those with CG probability between 45 - 70% were classified as “Likely-normal”. Insomnia severity >14 and a PHQ 9 > 5 were used as cutoffs for clinical insomnia and depression. Mann-Whitney U-tests were used to evaluate group differences. Results This cohort had 37% mild, 34% moderate, and 29% severe OSA. Self-reported comorbid insomnia and depression were present in 43%. Seventy-three percent of patients were classified as “Probably-normal”, 16% “Likely-normal”, 11% “Likely-NDD” or “Probable-NDD”. The “Probably-normal” group was younger than the rest of the cohort (62 + 6.2 years vs. 68 + 8.6 years, p< 0.01). Of those classified as “Probably-normal” (n=49), OSA was mild in 16, moderate in 17 and severe in 16. Mean AHI trended higher in the “Probably-normal” group than in those with elevated NDD risk (29 + 23.9 vs. 19 + 13.6 events/h, p=0.085). Conclusion Approximately 89% of the clinical cohort was classified as “Probably-normal” or “Likely-normal”. NDD risk was age-dependent. Interactions between OSA severity and NDD risk were not apparent in these findings, possibly related to group-wide effects. NDD risk, as measured by this model, did not appear to be adversely biased by untreated OSA. These results suggest a need for longitudinal studies to compare NDD risk in untreated OSA versus controls without sleep disordered breathing. Support (if any) NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, and R34AG056639.

AbstractBackgroundNo specific clinical rating scale currently exists for Lewy body dementia (LBD). Clinical rating scales developed for other diseases are therefore used instead for LBD clinical trials. However, these scales are not optimized for LBD and may not sufficiently reflect the complex array of LBD‐related phenomenology, making it more difficult to interpret outcome data from LBD clinical trials.MethodA working group comprised of 14 members bridging various international LBD‐focused research groups was convened to develop the first iteration of the LBD‐DRS with a focus on inclusion of key LBD related symptom domains, ensuring that any scale is mapped to regulatory expectations, and an emphasis on being patient‐centered. This draft was then subsequently presented to a broader stakeholder group (»50 participants) at the International Lewy body Dementia Conference in June 2022 for further feedback, leading to a further iteration of the scale.ResultThe LBD‐DRS has been designed to capture ratings of the frequency and severity of symptoms across the key domains relevant to LBD – cognitive, neuropsychiatric, motor, autonomic and sleep – as well as the functional impact and perceived burden of symptoms for patients and their care partners. An international Delphi exercise is now planned with many LBD‐relevant stakeholders, which will be coupled with workgroup engagement (for example, with patient‐caregiver groups) to further develop the scale with the resulting final draft anticipated for pilot use in the near future.ConclusionsIn this presentation we will elaborate on the development of the scale, key areas that it will cover as well as challenge points and next steps. Upon completion of the development and refinement of the LBD‐DRS, we propose that this scale will have utility as valid and LBD specific outcome measure in LBD clinical trials.

AbstractIntroductionOperationalized research criteria for mild cognitive impairment with Lewy bodies (MCI‐LB) were published in 2020. The aim of this systematic review and meta‐analysis was to review the evidence for the diagnostic clinical features and biomarkers in MCI‐LB set out in the criteria.MethodsMEDLINE, PubMed, and Embase were searched on 9/28/22 for relevant articles. Articles were included if they presented original data reporting the rates of diagnostic features in MCI‐LB.ResultsFifty‐seven articles were included. The meta‐analysis supported the inclusion of the current clinical features in the diagnostic criteria. Evidence for striatal dopaminergic imaging and meta‐iodobenzylguanidine cardiac scintigraphy, though limited, supports their inclusion. Quantitative electroencephalogram (EEG) and fluorodeoxyglucose positron emission tomography (PET) show promise as diagnostic biomarkers.DiscussionThe available evidence largely supports the current diagnostic criteria for MCI‐LB. Further evidence will help refine the diagnostic criteria and understand how best to apply them in clinical practice and research.Highlights A meta‐analysis of the diagnostic features of MCI‐LB was carried out. The four core clinical features were more common in MCI‐LB than MCI‐AD/stable MCI. Neuropsychiatric and autonomic features were also more common in MCI‐LB. More evidence is needed for the proposed biomarkers. FDG‐PET and quantitative EEG show promise as diagnostic biomarkers in MCI‐LB.

Abstract Dementia with Lewy bodies (DLB) is clinically defined by the presence of visual hallucinations, fluctuations, rapid eye movement (REM) sleep behavioral disorder, and parkinsonism. Neuropathologically, it is characterized by the presence of Lewy pathology. However, neuropathological studies have demonstrated the high prevalence of coexistent Alzheimer's disease, TAR DNA‐binding protein 43 (TDP‐43), and cerebrovascular pathologic cases. Due to their high prevalence and clinical impact on DLB individuals, clinical trials should account for these co‐pathologies in their design and selection and the interpretation of biomarkers values and outcomes. Here we discuss the frequency of the different co‐pathologies in DLB and their cross‐sectional and longitudinal clinical impact. We then evaluate the utility and possible applications of disease‐specific and disease‐nonspecific biomarkers and how co‐pathologies can impact these biomarkers. We propose a framework for integrating multi‐modal biomarker fingerprints and step‐wise selection and assessment of DLB individuals for clinical trials, monitoring target engagement, and interpreting outcomes in the setting of co‐pathologies.