ArticleNPJ Parkinson's disease2024
Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis.
Article in NPJ Parkinson's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Cross-species molecular stratification enables T cell receptor diagnosis of Parkinson's disease.Genome medicine · 2026Article
- Long-Duration Response to Levodopa in the PPMI-Cohort.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-Movement disorders clinical practice · 2026Article
- Comparative analysis of progression milestones across Parkinson's disease clinical, pathological, and data-driven subtypes: a 10-year follow-up.NPJ Parkinson's disease · 2026Article
- CSF alpha-Synuclein Seed Amplification Assay results in routine clinically collected samples.Journal of Parkinson's disease · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- Molecular profiling of alpha-synuclein pathology and seeding activity in Parkinson's disease.Acta neuropathologica · 2026Article
- Decomposing heterogeneity in disease progression speeds and pathways.NPJ digital medicine · 2026Article
- Mining the prodrome of neurodegeneration.Nature aging · 2026Article
- The future of clinical trials? Monitoring mobility as an outcome measure in Parkinson's disease: The mobilise-D study.Journal of Parkinson's disease · 2026Article
- Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson's Disease at Risk of Gait Deterioration.Bioengineering (Basel, Switzerland) · 2026Article
- Temporal patterns of motor and nonmotor symptom emergence in Parkinson's disease: a cluster analysis.Clinical parkinsonism & related disorders · 2026Article
- A neuroimmune-enriched multibiofluid proteomic index associated with cognitive-motor progression in early Parkinson's disease: a longitudinal PPMI cohort study.Frontiers in immunology · 2026Article
- Brain age gap as predictor of disease progression in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- MRI structural and functional axial asymmetry in the brain-first versus body-first subtypes of Parkinson's disease.NPJ Parkinson's disease · 2025Article
- PDualNet: a deep learning framework for joint prediction of Parkinson's disease progression subtype and MDS-UPDRS scores.Scientific reports · 2025Article
- Refining the diagnostic accuracy of Parkinsonian disorders using metaphenomic annotation of the clinicopathological literature.NPJ Parkinson's disease · 2025Article
- Factors Associated With the Response to Exercise in Patients With Parkinson's Disease.Journal of movement disorders · 2025Article
- Toward Personalized Medicine in Parkinson's Disease: A Scoping Review of Biomarkers, Genetics, and Treatment Stratification.Journal of clinical practice and research · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.
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Registered trials
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