Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.

Journal: NPJ digital medicine
Published Date: Jul 26, 2025

Abstract

Cognitive impairment is a frequent complication of Parkinson's disease (PD), affecting up to half of newly diagnosed patients. To improve early detection and risk assessment, we developed machine learning models using clinical data from three independent PD cohorts, which are (LuxPARK, PPMI, ICEBERG). Models were trained to predict mild cognitive impairment (PD-MCI) and subjective cognitive decline (SCD) using Explainable Artificial Intelligence (XAI) for classification and time-to-event analysis. Multi-cohort models showed greater performance stability over single-cohort models, while retaining competitive average performance. Age at diagnosis and visuospatial ability were identified as key predictors. Significant sex differences observed highlight the importance of considering sex-specific factors in cognitive assessment. Men were more likely to report SCD. Our findings highlight the potential of multi-cohort machine learning for early identification and personalized management of cognitive decline in PD.

Authors

  • Rebecca Ting Jiin Loo
    Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Lukas Pavelka
    Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg.
  • Graziella Mangone
    Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.
  • Fouad Khoury
    Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Paris, 75013, France.
  • Marie Vidailhet
    Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.
  • Jean-Christophe Corvol
    Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.
  • Enrico Glaab
    Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4362 Esch-sur-Alzette, Luxembourg.

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