Objective: This study evaluates if wearable sensors can detect prodromal motor features in non-manifesting GBA1 variant carriers (GBA-NMC). It also investigates if GBA1-PD patients exhibit more motor impairment than idiopathic PD (iPD), with sensors providing finer differentiation. We hypothesize (1) GBA-NMC show subtle motor impairments undetected by clinical scales, and (2) GBA-PD show more severe motor dysfunction than iPD, with wearables revealing finer distinctions.

Background: Variants in GBA1, are the most common genetic risk factor for Parkinson’s disease (PD), found in 10–15% of cases [1]. Penetrance is incomplete, with odds ratios from 2.2 to 30 depending on variant severity [2]. Identifying GBA1 variant carriers at higher PD risk, understanding phenoconversion, and characterizing the GBA1-PD phenotype remain key challenges.

The MDS-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is the gold standard for assessing PD motor symptoms [3] but has limitations, including subjectivity, inter-rater variability, and insensitivity to subtle changes [4]. Wearable sensors may address these limitations, offering objective quantification of early motor dysfunction.

Method: 131 participants were assessed using a single inertial sensor on the index finger (upper limb bradykinesia) and shoe (lower limb bradykinesia, gait). MDS-UPDRS Part III tasks were clinician-rated (0–4), while sensors computed objective scores.

Results: Participants: GBA-NMC (n = 22), Healthy controls (HC, n = 37), GBA-PD (n = 43), iPD (n = 32).

Ordinal regressions showed significant associations between MDS-UPDRS scores and sensor-derived scores for finger tapping (OR = 4.078; p = 3.1e-06), hand movements (OR = 3.19; p = 0.0005), rest tremor (OR = 6.95; p = 8.7e-06), and toe taps (OR = 3.25; p = 0.008).

A two-sample t-test found a significant difference in toe tapping between GBA-NMC and HC (t(45) = 2.09, p = 0.042, 95% CI [0.011, 0.585]). This was not detected on the MDS-UPDRS, which did not significantly vary between groups (W = 293.5, p = 0.090).

Conclusion: Wearable sensor technology enhances early detection of PD-related motor dysfunction in GBA1 variant carriers and refines phenotypic characterization in GBA1-PD. Further analyses will correlate sensor metrics with clinical and imaging biomarkers to advance understanding of GBA1-PD progression.

References: 1. Vieira, S.R.L., et al., Consensus Guidance for Genetic Counseling in GBA1 Variants: A Focus on Parkinson’s Disease. Movement Disorders, 2024. 39(12): p. 2144-2154.
2. Menozzi, E. and A.H.V. Schapira, Exploring the Genotype-Phenotype Correlation in -Parkinson Disease: Clinical Aspects, Biomarkers, and Potential Modifiers. Frontiers in Neurology, 2021. 12.
3. Goetz, C.G., et al., Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results. Movement Disorders, 2008. 23(15): p. 2129-2170.
4. McNeill, A., et al., Hyposmia and cognitive impairment in Gaucher disease patients and carriers. Movement Disorders, 2012. 27(4): p. 526-532.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/investigating-the-use-of-wearable-technologies-as-a-digital-biomarker-for-gba1-parkinsons-disease/