Precision medicine in Parkinson’s disease patients with LRRK2 and GBA risk variants – Let’s get even more personal
Bibliographic record
Abstract
Parkinson's disease (PD) is characterized by motor deficits and a wide variety of non-motor symptoms. The age of onset, rate of disease progression and the precise profile of motor and non-motor symptoms display considerable individual variation. Neuropathologically, the loss of substantia nigra dopaminergic neurons is a key feature of PD. The vast majority of PD patients exhibit alpha-synuclein aggregates in several brain regions, but there is also great variability in the neuropathology between individuals. While the dopamine replacement therapies can reduce motor symptoms, current therapies do not modify the disease progression. Numerous clinical trials using a wide variety of approaches have failed to achieve disease modification. It has been suggested that the heterogeneity of PD is a major contributing factor to the failure of disease modification trials, and that it is unlikely that a single treatment will be effective in all patients. Precision medicine, using drugs designed to target the pathophysiology in a manner that is specific to each individual with PD, has been suggested as a way forward. PD patients can be stratified according to whether they carry one of the risk variants associated with elevated PD risk. In this review we assess current clinical trials targeting two enzymes, leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA), which are encoded by two most common PD risk genes. Because the details of the pathogenic processes coupled to the different LRRK2 and GBA risk variants are not fully understood, we ask if these precision medicine-based intervention strategies will prove "precise" or "personalized" enough to modify the disease process in PD patients. We also consider at what phases of the disease that such strategies might be effective, in light of the genes being primarily associated with the risk of developing disease in the first place, and less clearly linked to the rate of disease progression. Finally, we critically evaluate the notion that therapies targeting LRRK2 and GBA might be relevant to a wider segment of PD patients, beyond those that actually carry risk variants of these genes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".