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

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Cyril Brzenczek - , University of Luxembourg (Author)
  • Quentin Klopfenstein - , University of Luxembourg (Author)
  • Tom Hähnel - , Department of Neurology, Fraunhofer Institute for Algorithms and Scientific Computing (Author)
  • Holger Fröhlich - , Fraunhofer Institute for Algorithms and Scientific Computing, University of Bonn (Author)
  • Enrico Glaab - , University of Luxembourg (Author)
  • NCER-PD Consortium - (Author)

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 languageEnglish
Article number235
Journal npj digital medicine
Volume7
Issue number1
Publication statusPublished - 6 Sept 2024
Peer-reviewedYes

External IDs

unpaywall 10.1038/s41746-024-01236-z
PubMed 39242660
Scopus 85203273723

Keywords

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