期刊
NPJ PARKINSONS DISEASE
卷 8, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41531-022-00409-5
关键词
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资金
- ZonMw Memorabel Grant [733050516]
- Medical Research Council [MR/S011625/1]
- Charles Wolfson Charitable Trust
- BRACE Dementia Research
- Michael J. Fox Foundation for Parkinson's Research
- 4D Pharma
- AbbVie
- AcureX Therapeutics
- Allergan
- Amathus Therapeutics
- Aligning Science Across Parkinson's (ASAP)
- Avid Radiopharmaceuticals
- Bial Biotech
- Biogen
- BioLegend
- Bristol Myers Squibb
- Calico Life Sciences LLC
- Celgene Corporation
- DaCapo Brainscience
- Denali Therapeutics
- Edmond J. Safra Foundation
- Eli Lilly and Company
- GE Healthcare
- GlaxoSmithKline
- Golub Capital
- Handl Therapeutics
- Insitro
- Janssen Pharmaceuticals
- Lundbeck
- Merck Co.
- Meso Scale Diagnostics LLC
- Neurocrine Biosciences
- Pfizer
- Piramal Imaging
- Prevail Therapeutics
- F. Hoffmann-La Roche
- Genentech
- Sanofi Genzyme
- Servier
- Takeda Pharmaceutical Company
- Teva Neuroscience
- UCB
- Vanqua Bio
- Verily Life Sciences
- Voyager Therapeutics
- Yumanity Therapeutics
- University of Exeter
This study aimed to predict cognitive outcomes in Parkinson's disease patients using machine learning models. The research found that clinical variables performed best in predicting cognitive impairment outcomes, and including biofluid and genetic/epigenetic variables slightly improved prediction performance.
Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.
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