Objective: To investigate whether sensor–derived gait metrics can detect prodromal motor features in at-risk individuals and evaluate their potential as digital biomarkers for risk stratification.

Background: Variants in GBA1 are the most common genetic risk factor for Parkinson’s disease (PD), present in 10–15% of cases [1]. However, penetrance is incomplete, with odds ratios ranging from 2.2 to 30 depending on variant severity [2]. Identifying carriers at highest risk of developing PD and understanding factors influencing phenoconversion remain key challenges.

MDS-UPDRS is the gold standard for assessing motor symptoms in PD [3], but it has several limitations, including subjectivity, inter-rater variability, and limited sensitivity to subtle motor changes [4]. Wearable sensors may help address these limitations by providing objective and continuous quantification of motor function, potentially enabling detection of early motor abnormalities.

Method: Participants were assessed using an inertial sensor on the shoe measuring lower limb bradykinesia and gait. Raw sensor data were processed using a gait analysis pipeline for stride segmentation and feature calculation [5]. Seven spatial and temporal gait metrics were exported and analysed using t-tests.

Results: The total sample included 57 participants (GBA-NMC=23, HC=34). Although no gait metrics remained significant after FDR correction, nominal trends were observed. GBA1-NMC showed lower maximum lateral excursion during gait compared with controls (p = 0.019, padj = 0.134), suggesting a potential change that may reflect subtle alterations in gait stability. Analyses also showed a significant association between probable REM sleep behaviour disorder and gait measures in reduced initial contact angle (p < .001, padj < .001).

Conclusion: While wearable-derived gait metrics did not differ significantly between GBA1-NMC and healthy controls, a significant difference in initial contact angle was observed in participants with probable RBD, a group known to be at increased risk of PD. This finding may indicate early alterations in gait mechanics, supporting further investigation of wearable-derived gait measures as potential digital biomarkers of early motor changes and phenoconversion risk in larger, longitudinal cohorts.

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., et al., Severe GBA1 variants drive the GBA1-PD clinical phenotype: implications for counselling and clinical trials. npj Parkinson’s Disease, 2025. 11(1).
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.
5. Küderle, A., et al., Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking. IEEE Open Journal of Engineering in Medicine and Biology, 2024. 5: p. 163-172.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/wearable-gait-metrics-as-potential-digital-biomarkers-in-gba1-non-manifesting-carriers/