<scp>LRRK2</scp> as a Potential Disease‐Modifying Target in Sporadic Parkinson's Disease
Bibliographic record
Abstract
A growing understanding of the role that leucine-rich repeat kinase 2 (LRRK2) plays in Parkinson's disease (PD) supports continued focus on this enzyme as a therapeutic target for PD. Accumulating evidence suggests that there are phenotypic, neuropathologic, and biological similarities between sporadic PD (sPD) and familial forms in which LRRK2 variants are inherited in an autosomal-dominant pattern with variable penetrance (LRRK2-PD). Further, genome-wide association studies have found specific non-coding variants that are risk factors for sPD. In this review, we describe the current state of knowledge as it relates to LRRK2's role in sPD, with a focus on comparing the physiology and pathology of sPD with LRRK2-PD. As in LRRK2-PD, LRRK2 activity may also be increased in sPD, possibly through interactions between genetics and the environment. Increased activity of LRRK2 and associated endolysosomal dysfunction have been observed in sPD patients, including evidence from postmortem brains of patients with sPD and animal models showing increased LRRK2 activity. Additionally, beneficial effects of LRRK2 inhibitors, such as improved lysosomal function, reduced α-synuclein accumulation, and amelioration of neurodegeneration, have been demonstrated in animal models of sPD. Therefore, inhibition of LRRK2 kinase activity may be a promising approach to disease modification for sPD and LRRK2-PD. Ongoing and future clinical studies examining LRRK2 kinase inhibitors will aim to elucidate their clinical efficacy in PD and to assess their potential effects on lysosomal function. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".