Citations

ObjectiveThis study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD).MethodsPatients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story‐tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow‐up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow‐up period of 5 years.ResultsOf 180 patients free of parkinsonism or dementia, 156 provided follow‐up information. After a mean follow‐up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia‐first (n = 16) and parkinsonism‐first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism‐first with normal cognition converters (n = 17).InterpretationAutomated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530–543

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>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 >50% were assigned “likely”, and for CG>=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 >70% while gross misclassifications were <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<0.02). The proportions of records with abnormal AN3 (>5% of sleep time) were significantly greater in those with DLB/PDD=35% vs. MCI=10%, iRBD=5% and CG=5% (all p<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<0.03), and CG (p<0.0005), while the PD was less than AD and MCI (both < 0.05) and CG (p<0.002). The proportions of cases with abnormal AAI values (<10 events/h) was greatest in DLB/PDD patients, and greater than in ADem (p<0.02), MCI (p<0.02) and CG (p<0.0001), while AAI in PD and iRBD was greater than in CG (p<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.