Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
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 classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.
Details
Original language | English |
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Article number | 235 |
Journal | npj digital medicine |
Volume | 7 |
Issue number | 1 |
Publication status | Published - 6 Sept 2024 |
Peer-reviewed | Yes |
External IDs
unpaywall | 10.1038/s41746-024-01236-z |
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PubMed | 39242660 |
Scopus | 85203273723 |