Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease
Rebecca Ting Jiin Loo, Lukas Pavelka, Graziella Mangone, Fouad Khoury, Marie Vidailhet, Jean‐Christophe Corvol, Enrico Glaab, Geeta Acharya, Gloria Aguayo, Myriam Alexandre, Muhammad Ali, Wim Ammerlann, Giuseppe Arena, Michele Bassis, Roxane Batutu, Katy Beaumont, Sibylle Béchet, Guy Berchem, Alexandre Bisdorff, Ibrahim Boussaad, David Bouvier, Lorieza Castillo, Gessica Contesotto, Nancy De Bremaeker, Brian Dewitt, Nico J. Diederich, Rene Dondelinger, Nancy E. Ramia, M. Uribe, Angelo Ferrari, Ana Festas Lopes, Katrin Frauenknecht, Joëlle V. Fritz, Carlos Gamio, Manon Gantenbein, Piotr Gawron, Laura Georges, Soumyabrata Ghosh, Marijus Giraitis, Enrico Glaab, Martine Goergen, Elisa Gómez de Lope, Jérôme Graas, Mariella Graziano, Valentin Grouès, Anne Grünewald, Gaël Hammot, Anne-Marie Hanff, Linda K. Hansen, Michael T. Heneka, Estelle Henry, Margaux Henry, Sylvia Herbrink, Sascha Herzinger, Alexander Hundt, Nadine Jacoby, Sonja Jónsdóttir, Jochen Klucken, Olga Kofanova, Rejko Krüger, Pauline Lambert, Zied Landoulsi, Roseline Lentz, Victoria Lorentz, Tainá M. Marques, Guilherme Fernandes Marques, Patricia Martins Conde, Patrick May, Deborah McIntyre, Chouaib Mediouni, Françoise Meisch, Alexia Mendibide, Myriam Menster, Maura Minelli, Michel Mittelbronn, Saïda Mtimet, Maeva Munsch, Romain Nati, Ulf Nehrbass, Sarah Nickels, Béatrice Nicolaı̈, 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, Dheeraj Reddy Bobbili, Lucie Remark, I Richard, Olivia Roland, Kirsten Roomp, Eduardo Rosales Jubal, Stefano Sapienza, Venkata Satagopam
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.