Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01862-1 |
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| author | Rebecca Ting Jiin Loo Lukas Pavelka Graziella Mangone Fouad Khoury Marie Vidailhet Jean-Christophe Corvol Enrico Glaab On behalf of the NCER-PD Consortium |
| author_facet | Rebecca Ting Jiin Loo Lukas Pavelka Graziella Mangone Fouad Khoury Marie Vidailhet Jean-Christophe Corvol Enrico Glaab On behalf of the NCER-PD Consortium |
| author_sort | Rebecca Ting Jiin Loo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4335af89da084639b99c8a2ca1c22d91 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-4335af89da084639b99c8a2ca1c22d912025-08-20T03:43:36ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111210.1038/s41746-025-01862-1Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s diseaseRebecca Ting Jiin Loo0Lukas Pavelka1Graziella Mangone2Fouad Khoury3Marie Vidailhet4Jean-Christophe Corvol5Enrico Glaab6On behalf of the NCER-PD ConsortiumBiomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of LuxembourgTransversal Translational Medicine, Luxembourg Institute of Health (LIH)Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of NeurologySorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of NeurologySorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of NeurologySorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of NeurologyBiomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of LuxembourgAbstract 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.https://doi.org/10.1038/s41746-025-01862-1 |
| spellingShingle | Rebecca Ting Jiin Loo Lukas Pavelka Graziella Mangone Fouad Khoury Marie Vidailhet Jean-Christophe Corvol Enrico Glaab On behalf of the NCER-PD Consortium Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease npj Digital Medicine |
| title | Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease |
| title_full | Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease |
| title_fullStr | Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease |
| title_full_unstemmed | Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease |
| title_short | Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease |
| title_sort | multi cohort machine learning identifies predictors of cognitive impairment in parkinson s disease |
| url | https://doi.org/10.1038/s41746-025-01862-1 |
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