Novel biomarkers derived from the Maintenance of Wakefulness Test as predictors of sleepiness and response to treatment.

The Maintenance of Wakefulness Test (MWT) is a widely accepted objective test used to evaluate daytime somnolence and is commonly used in clinical studies evaluating novel therapeutics for excessive daytime sleepiness. In the latter, sleep onset latency (SOL) is typically the sole MWT endpoint. Here, we explored microsleeps, sleep probability measures derived from automated sleep scoring, and quantitative electroencephalography (qEEG) features as additional MWT biomarkers of daytime sleepiness, using data from a phase 1B trial of the selective orexin receptor 2 agonist danavorexton (TAK-925) in people with narcolepsy type 1 (NT1) or type 2 (NT2). Danavorexton treatment reduced the rate and duration of microsleeps during the MWT in NT1 (days 1 and 7; p ≤ 0.005) and microsleep rate in NT2 (days 1 and 7; p < 0.0001). Use of an EEG-sleep-staging-derived measure to determine the probability of wakefulness for each minute revealed a novel metric to track changes in daytime sleepiness, which were consistent with the θ/α ratio, a known biomarker of drowsiness. The slopes of line-fits to both the log-transformed sleepiness score or log-transformed θ/α ratio correlated well to (inverse) MWT SOL for NT1 (R = 0.93 and R = 0.83, respectively) and NT2 (R = 0.97 and R = 0.84, respectively), suggesting that individuals with narcolepsy have increased sleepiness immediately after lights-off. These analyses demonstrate that novel EEG-based biomarkers can augment SOL as predictors of sleepiness and its response to treatment and provide a novel framework for the analysis of wake EEG in hypersomnia disorders.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months.

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.

Cardiac manifestations of Fabry disease in G3Stg/GlaKO and GlaKO mouse models-Translation to Fabry disease patients.

Fabry disease (FD) is an X-linked disorder of glycosphingolipid metabolism caused by mutations in the GLA gene encoding alpha-galactosidase A (α-Gal). Loss of α-Gal activity leads to progressive lysosomal accumulation of α-Gal substrate, predominately globotriaosylceramide (Gb3) and its deacylated derivative globotriaosylsphingosine (lyso-Gb3). FD manifestations include early onset neuropathic pain, gastrointestinal symptoms, and later onset life-threatening renal, cardiovascular and cerebrovascular disorders. Current treatments can preserve kidney function but are not very effective in preventing progression of cardiovascular pathology which remains the most common cause of premature death in FD patients. There is a significant need for a translational model that could be used for testing cardiac efficacy of new drugs. Two mouse models of FD have been developed. The α-Gal A-knockout (GlaKO) model is characterized by progressive tissue accumulation of Gb3 and lyso-Gb3 but does not develop any Fabry pathology besides mild peripheral neuropathy. Reports of minor cardiac function abnormalities in GlaKO model are inconsistent between different studies. Recently, G3Stg/GlaKO was generated by crossbreeding GlaKO with transgenic mice expressing human Gb3 synthase. G3Stg/GlaKO demonstrate higher tissue substrate accumulation and develop cellular and tissue pathologies. Functional renal pathology analogous to that found in early stages of FD has also been described in this model. The objective of this study is to characterize cardiac phenotype in GlaKO and G3Stg/GlaKO mice using echocardiography. Longitudinal assessments of cardiac wall thickness, mass and function were performed in GlaKO and wild-type (WT) littermate controls from 5-13 months of age. G3Stg/GlaKO and WT mice were assessed between 27-28 weeks of age due to their shortened lifespan. Several cardiomyopathy characteristics of early Fabry pathology were found in GlaKO mice, including mild cardiomegaly [up-to-25% increase in left ventricular (LV mass)] with no significant LV wall thickening. The LV internal diameter was significantly wider (up-to-24% increase at 9-months), when compared to the age-matched WT. In addition, there were significant increases in the end-systolic, end-diastolic volumes and stroke volume, suggesting volume overload. Significant reduction in Global longitudinal strain (GLS) measuring local myofiber contractility of the LV was also detected at 13-months. Similar GLS reduction was also reported in FD patients. Parameters such as ejection fraction, fractional shortening and cardiac output were either only slightly affected or were not different from controls. On the other hand, some of the cardiac findings in G3Stg/GlaKO mice were inconsistent with Fabry cardiomyopathy seen in FD patients. This could be potentially an artifact of the Gb3 synthase overexpression under a strong ubiquitous promoter. In conclusion, GlaKO mouse model presents mild cardiomegaly, mild cardiac dysfunction, but significant cardiac volume overload and functional changes in GLS that can be used as translational biomarkers to determine cardiac efficacy of novel treatment modalities. The level of tissue Gb3 accumulation in G3Stg/GlaKO mouse more closely recapitulates the level of substrate accumulation in FD patients and may provide better translatability of the efficacy of new therapeutics in clearing pathological substrates from cardiac tissues. But interpretation of the effect of treatment on cardiac structure and function in this model should be approached with caution.

Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.

The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understanding of nocturnal polysomnography narcolepsy type 1 features, there remains a need for improving methods to differentiate narcolepsy type 1 nighttime sleep features from those of individuals without narcolepsy type 1. We aimed to develop a machine learning framework for identifying sleep features to discriminate narcolepsy type 1 from clinical controls, narcolepsy type 2 and idiopathic hypersomnia. The population included polysomnography data from 350 drug-free individuals (114 narcolepsy type 1, 90 narcolepsy type 2, 105 idiopathic hypersomnia, and 41 clinical controls) collected at the National Reference Centers for Narcolepsy in Montpelier, France. Several sets of nocturnal sleep features were explored, as well as the value of time-resolving sleep architecture by analysing sleep per quarter-night. Several patterns of nighttime sleep evolution emerged that differed between narcolepsy type 1, clinical controls, narcolepsy type 2 and idiopathic hypersomnia, with increased nighttime instability observed in patients with narcolepsy type 1. Using machine learning models, we identified rapid eye movement sleep onset as the best single polysomnography feature to distinguish narcolepsy type 1 from controls, narcolepsy type 2 and idiopathic hypersomnia. By combining multiple feature sets capturing different aspects of sleep across quarter-night periods, we were able to further improve between-group discrimination and could identify the most discriminative sleep features. Our results highlight salient polysomnography features and the relevance of assessing their time-dependent changes during sleep that could aid diagnosis and measure the impact of novel therapeutics in future clinical trials.

Towards interpretable speech biomarkers: exploring MFCCs.

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.