Background It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). Objective To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. Methods Cross‐sectional wearable‐sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I−III) and 100 age‐matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15‐meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual‐task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine‐learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. Results High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%–83%, specificity 69%–80%, and area under the curve (AUC) 0.76–0.90. Measures from upper‐limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid‐stage PD, and stride timing and regularity were discriminative in more advanced stages. Conclusions Applying machine‐learning to multiple, wearable‐derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society

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Protein-coding variants in the GBA gene modulate susceptibility and progression in ~10% of patients with Parkinson’s disease (PD). GBA encodes the β-glucocerebrosidase enzyme that hydrolyzes glucosylceramide. We hypothesized that GBA mutations will lead to glucosylceramide accumulation in cerebrospinal fluid (CSF). Glucosylceramide, ceramide, sphingomyelin, and lactosylceramide levels were measured by liquid chromatography-tandem mass spectrometry in CSF of 411 participants from the Parkinson’s Progression Markers Initiative (PPMI) cohort, including early stage, de novo PD patients with abnormal dopamine transporter neuroimaging and healthy controls. Forty-four PD patients carried protein-coding GBA variants (GBA-PD) and 227 carried wild-type alleles (idiopathic PD). The glucosylceramide fraction was increased (P = 0.0001), and the sphingomyelin fraction (a downstream metabolite) was reduced (P = 0.0001) in CSF of GBA-PD patients compared to healthy controls. The ceramide fraction was unchanged, and lactosylceramide was below detection limits. We then used the ratio of glucosylceramide to sphingomyelin (the GlcCer/SM ratio) to explore whether these two sphingolipid fractions altered in GBA-PD were useful for stratifying idiopathic PD patients. Idiopathic PD patients in the top quartile of GlcCer/SM ratios at baseline showed a more rapid decline in Montreal Cognitive Assessment scores during longitudinal follow-up compared to those in the lowest quartile with a P-value of 0.036. The GlcCer/SM ratio was negatively associated with α-synuclein levels in CSF of PD patients. This study highlights glucosylceramide as a pathway biomarker for GBA-PD patients and the GlcCer/SM ratio as a potential stratification tool for clinical trials of idiopathic PD patients. Our sphingolipids data together with the clinical, imaging, omics, and genetic characterization of PPMI will contribute a useful resource for multi-modal biomarkers development.