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- Title
Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease.
- Authors
Brzenczek, Cyril; Klopfenstein, Quentin; Hähnel, Tom; Fröhlich, Holger; Glaab, Enrico; Acharya, Geeta; Aguayo, Gloria; Alexandre, Myriam; Ali, Muhammad; Ammerlann, Wim; Arena, Giuseppe; Bassis, Michele; Batutu, Roxane; Beaumont, Katy; Béchet, Sibylle; Berchem, Guy; Bisdorff, Alexandre; Boussaad, Ibrahim; Bouvier, David; Castillo, Lorieza
- 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.
- Subjects
EUROPE; DIGITAL technology; CROSS-sectional method; RESEARCH funding; PREDICTION models; QUESTIONNAIRES; DIAGNOSIS; GAIT in humans; WEARABLE technology; PARKINSON'S disease; MOVEMENT disorders; GAIT disorders; NEUROLOGICAL disorders; HALLUCINATIONS; METABOLOMICS; MACHINE learning; BIOMARKERS; COMORBIDITY
- Publication
NPJ Digital Medicine, 2024, Vol 7, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-024-01236-z