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<p>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 (<i>PD-MCI</i>) and subjective cognitive decline (<i>SCD</i>) 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 <i>SCD</i>. Our findings highlight the potential of multi-cohort machine learning for early identification and personalized management of cognitive decline in PD.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月26日

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  • Rebecca Ting Jiin Loo,
  • Lukas Pavelka,
  • Graziella Mangone,
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  • Marie Vidailhet,
  • Jean-Christophe Corvol,
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  • Geeta Acharya,
  • Gloria Aguayo,
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  • Nico Diederich,
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  • Ana Festas Lopes,
  • Katrin Frauenknecht,
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  • Carlos Gamio,
  • Manon Gantenbein,
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  • Laura Georges,
  • Soumyabrata Ghosh,
  • Marijus Giraitis,
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  • Tainá M. Marques,
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  • Patricia Martins Conde,
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  • Michel Mittelbronn,
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  • Romain Nati,
  • Ulf Nehrbass,
  • Sarah Nickels,
  • Beatrice Nicolai,
  • Jean-Paul Nicolay,
  • Fozia Noor,
  • Clarissa P. C. Gomes,
  • Sinthuja Pachchek,
  • Claire Pauly,
  • Laure Pauly,
  • Lukas Pavelka,
  • Magali Perquin,
  • Achilleas Pexaras,
  • Armin Rauschenberger,
  • Rajesh Rawal,
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  • Lucie Remark,
  • Ilsé Richard,
  • Olivia Roland,
  • Kirsten Roomp,
  • Eduardo Rosales,
  • Stefano Sapienza,
  • Venkata Satagopam,
  • Sabine Schmitz,
  • Reinhard Schneider,
  • Jens Schwamborn,
  • Raquel Severino,
  • Amir Sharify,
  • Ruxandra Soare,
  • Ekaterina Soboleva,
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  • Rebecca Ting Jiin Loo,
  • Johanna Trouet,
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  • Carlos Vega,
  • Liliana Vilas Boas,
  • Paul Wilmes,
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  • Isabelle Arnulf,
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  • Jonas Ihle,
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  • Suzanne Lesage,
  • Smaranda Leu Semenescu,
  • Richard Levy,
  • Valentine Maheo,
  • Graziella Mangone,
  • Poornima Menon,
  • Fanny Mochel,
  • Vincent Perlbarg,
  • Dijana Petrovska,
  • Fanny Pineau,
  • Nadya Pyatigorskaya,
  • Sophie Rivaud-Péchoux,
  • Sara Sambin,
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摘要

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.