PD is highly heterogenous including variable clinical manifestations, copathologies, and over 100 implicated genes and genetic loci. In prior work (Hill Parkinsonism Relat Disord 2021), we showed that quantitative motor performance data can be complementary to standard clinical rating scales for capturing motor heterogeneity in PD.

We evaluated 379 PD patients (mean age 65, 65% male) during routine clinic visits using the motor MDS-UPDRS and a 10-min motor protocol using a body-fixed sensor (DynaPort MT, McRoberts BV). Seventy-five controls underwent the same protocol. Quantitative motor performance data [32-ft walk, Timed Up and Go (TUG), and standing posture with eyes closed] was represented as a continuum on a curve, with smoothing and interpolation to ensure consistent time domains. We applied time warping using the elastic square-root slope framework, which captures both amplitude and phase shift variations, to align performance characteristics across subjects. Multivariate functional principal components analysis (MFPCA) was used to identify shared variation and associations with motor/non-motor features, genetic risk profiles, and blood biomarkers (ptau217, metabolomics) in our study cohort.

The MFPCA projection discriminates PD heterogeneity across quadrants for the first two principal components (PCs), which capture 84% of the variation in motor performance. The PCs readily differentiate cases and controls, and further define promising PD subgroups beyond established motor subtypes (e.g., postural instability gait difficulty), including non-motor manifestations such as cognitive impairment, other neuropsychiatric features, hyposmia, dysautonomia, and REM sleep behavior disorder. Interestingly, MFPCA can stratify genetic subgroups with common polygenic risk versus rare variant disease drivers (GBA1) or modifiers (APOE).

Clinic-based, quantitative mobility assessments using a wearable sensor complement standard clinical scales in capturing motor variability in PD, and may offer enhanced characterization of clinical, pathologic, and genetic heterogeneity, supporting precision medicine applications.