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

Autor: 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
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Zdroj: NPJ Digital Medicine; 9/6/2024, Vol. 7 Issue 1, p1-16, 16p
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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