Genetic-based diagnostics of Parkinson’s disease and other Parkinsonian syndromes
Bibliographic record
Abstract
INTRODUCTION: Parkinson's disease (PD) is a complex disorder with vast clinical heterogeneity. Recent genetic, imaging and clinical evidence suggest that there are multiple subtypes of PD, and perhaps even distinct clinical entities, which are being diagnosed under the umbrella of PD. These might have similar clinical presentation, but potentially different underlying mechanisms, which, in future, will require different treatments. Despite extensive genetic research progress, genetic testing is still not a common practice in clinical patient care. AREAS COVERED: This review examines the numerous genes that have been discovered to affect the risk of, or cause, PD. We also outline genetic variants that affect PD age at onset, its progression, and the presence or severity of motor and non-motor symptoms. We differentiate between PD, other synucleinopathies, and atypical parkinsonism syndromes, and describe genes responsible for familial forms of typical PD and atypical parkinsonism. Lastly, we present current clinical trails that are underway for targeted therapies, particularly for GBA1-PD and LRRK2-PD which are the most significant subtypes. EXPERT OPINION: While genetic studies alone cannot be diagnostic for PD, proper utilization of genetic screening for PD could improve diagnostic accuracy and predictions for prognosis, guide treatment, and identify individuals that qualify for clinical trials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".