Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease.

ABSTRACT: Parkinson's disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data's utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83-92% for PD vs. control and up to 75% for motor severity cla

SUBMITTER: Brzenczek C 

PROVIDER: S-EPMC11379877 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature