Objective: To systematically review existing literature on gait kinematics in individuals at increased risk for Parkinson’s disease (PD) and evaluate the role of sensor-based kinematic assessments and machine learning approaches as early biomarkers for PD.

Background: Identifying biomarkers in prodromal PD remains critical for early therapeutic interventions. In addition to genetic predisposition, non-motor symptoms, such as REM sleep behavior disorder (RBD) and anosmia, have been associated with an increased risk for developing PD. This systematic review focuses on assessing gait abnormalities through kinematic assessments in individuals at increased PD risk.

Method: A Boolean phrase was applied to search for gait kinematic studies in cohorts with preclinical PD state. Thirty-seven studies were extracted from PubMed: 13 were deemed relevant by abstract screening, 9 met the inclusion criteria while 4 were excluded due to established PD diagnosis in participants.

Results: In individuals with RBD, reduced gait velocity and cadence, increased stride and double-support times, impaired trunk kinematics, and greater gait variability under dual-task conditions were observed. Genetic carriers of PD-related mutations (GCPDM) demonstrated increased arm swing asymmetry and variability, increased stride time variability, and reduced lower-limb excursion. Longitudinal assessments of individuals with idiopathic hyposmia (IH) showed progressive motor deterioration which later turned into overt PD using dopaminergic challenge tests and imaging. When machine learning was applied to sensor assessments, it was capable of distinguishing healthy individuals from those with risk factors for PD or overt PD.

Conclusion: Sensor-based gait analysis is a promising tool for detecting early subclinical gait abnormalities in individuals at increased risk for developing PD. Kinematic assessments not only can assist in identifying at-risk individuals for PD but potentially support disease modifying clinical trials.

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