New Perspectives on Parkinson’s Disease Subtyping: A Narrative Review
Bibliographic record
Abstract
Parkinson's disease (PD) is a complex neurodegenerative disorder that is heterogeneous in both its pathophysiology and clinical presentation. Genetic, imaging and biochemical biomarkers not only provide innovative, objective ways to subtype PD but also offer new insights into the underlying pathophysiology, revealing potential therapeutic targets and improving predictions of clinical phenotype, disease progression and treatment response. In this review, we first summarize the phenotypes linked to key PD genes - such as SNCA, LRRK2, GBA and PRKN - highlighting, for instance, that GBA-PD is often associated with prominent nonmotor features. We then explore studies that have defined new robust subtypes with imaging biomarkers, particularly T1-weighted MRI brain atrophy patterns, and their clinical implications. We also review the role of blood, CSF and urine biomarkers for monitoring disease progression and predicting its presentation in various domains (motor, cognitive, autonomic, psychiatric). These findings could have practical implications by guiding clinicians to individualize symptomatic treatment and helping researchers improve clinical trial design and recruitment, thus bringing us closer to the discovery of effective disease-modifying therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".