ArticleNPJ Parkinson's disease2019
Parkinson's progression prediction using machine learning and serum cytokines.
Article in NPJ Parkinson's disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
46 citing papers in PubMed, 108 citations in OpenAlex.
- Cell-based immunotherapy for neurodegenerative disease: A promising avenue.Neural regeneration research · 2026Article
- Peripheral immunochemical considerations in Parkinson disease: sources, targets and crosstalk mechanisms.Journal of translational medicine · 2026Review
- Spermidine Suppresses Peripheral Inflammation and Alleviates Non-Motor Symptoms in the 6-OHDA-Induced Rat Model of Parkinson's Disease.Molecules (Basel, Switzerland) · 2026Article
- Optimal doses of mind-body exercise on neuroinflammation in individuals with neuropsychiatric disorders: A systematic review and dose-response meta-analysis.Brain, behavior, & immunity - health · 2026Article
- Machine learning and transcriptomic analysis identify tubular injury biomarkers in patients with chronic kidney disease.International urology and nephrology · 2025Article
- Peripheral Mononuclear Cell Mitochondrial Function Associates with T-Cell Cytokines in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Advancements in Parkinson's Disease Prediction Using Machine Learning: A Neurological Perspective.Healthcare informatics research · 2025Article
- IGF2-Reprogrammed Macrophages Ameliorate the Inflammatory Response and Protect Against the Neuroinflammatory Process in Parkinson's Disease Models.Aging cell · 2025Article
- The Interplay Between Epilepsy and Parkinson's Disease: Gene Expression Profiling and Functional Analysis.Molecular biotechnology · 2025Article
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Article
- The immune system in Parkinson's disease: what we know so far.Brain : a journal of neurology · 2024Review
- Prediction of motor and non-motor Parkinson's disease symptoms using serum lipidomics and machine learning: a 2-year study.NPJ Parkinson's disease · 2024Article
- Neuroinflammatory gene expression profiles of reactive glia in the substantia nigra suggest a multidimensional immune response to alpha synuclein inclusions.Neurobiology of disease · 2024Article
- Associations Between Neuropsychiatric Symptoms and Inflammation in Neurodegenerative Dementia: A Systematic Review.Journal of inflammation research · 2024Review
- Cytokine activity in Parkinson's disease.Neuronal signaling · 2023Review
- Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.Scientific reports · 2023Article
- Brain and Systemic Inflammation in De Novo Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2023Article
- End-to-End Deep Learning Method for Detection of Invasive Parkinson's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- A machine learning approach to predict quality of life changes in patients with Parkinson's Disease.Annals of clinical and translational neurology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The heterogeneous nature of Parkinson's disease (PD) symptoms and variability in their progression complicates patient treatment and interpretation of clinical trials. Consequently, there is much interest in developing models that can predict PD progression. In this study we have used serum samples from a clinically well characterized longitudinally followed Michael J Fox Foundation cohort of PD patients with and without the common leucine-rich repeat kinase 2 (LRRK2) G2019S mutation. We have measured 27 inflammatory cytokines and chemokines in serum at baseline and after 1 year to investigate cytokine stability. We then used the baseline measurements in conjunction with machine learning models to predict longitudinal clinical outcomes after 2 years follow up. Using the normalized root mean square error (NRMSE) as a measure of performance, the best prediction models were for the motor symptom severity scales, with NRMSE of 0.1123 for the Hoehn and Yahr scale and 0.1193 for the unified Parkinson's disease rating scale part three (UPDRS III). For each model, the top variables contributing to prediction were identified, with the chemokines macrophage inflammatory protein one alpha (MIP1α), and monocyte chemoattractant protein one (MCP1) making the biggest peripheral contribution to prediction of Hoehn and Yahr and UPDRS III, respectively. These results provide information on the longitudinal assessment of peripheral inflammatory cytokines in PD and give evidence that peripheral cytokines may have utility for aiding prediction of PD progression using machine learning models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.