Concordance and test-retest consistency of sleep biomarker-based neurodegenerative disorder profiling

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.

Keywords: Alzheimer’s disease; Neurodegenerative disease; Non-REM hypertonia; Parkinsonian spectrum disorders; REM sleep behavior disorder; Sleep biomarkers.

Fig. 1

Sample report for a PD patient classified as Probable Mixed with seven abnormal biomarkers. Based on the two-class models, LBD was superior to AD, pSYN was superior to LBD and CG and pSYN were approximately equivalent.

Fig. 2

Distributions of NDD risk classifications that are color coded and tallied based on diagnostic agreement, overt disagreement, and trending toward agreement (i.e., NDD groups classified as Likely Normal with indications consistent with the clinical diagnosis) for: (a) control group, (b) Alzheimer’s disease dementia, (c) Lewy Body dementia, (d) REM sleep behavior disorder, (e) Parkinson’s disease, and (f) mild cognitive impairment. NDD groups classified as Likely Normal with indications consistent with their clinical diagnosis were considered borderline cases and classified as trended-toward-disagreement.