Pain in monogenic Parkinson’s disease: a comprehensive review
Bibliographic record
Abstract
Pain, a challenging symptom experienced by individuals diagnosed with Parkinson’s disease (PD), still lacks a comprehensive understanding of its underlying pathophysiological mechanisms. A systematic investigation of its prevalence and impact on the quality of life in patients affected by monogenic forms of PD has yet to be undertaken. This comprehensive review aims to provide an overview of the association between pain and monogenic forms of PD, specifically focusing on pathogenic variants in SNCA, PRKN, PINK1, PARK7, LRRK2, GBA1, VPS35, ATP13A2, DNAJC6, FBXO7, and SYNJ1. Sixty-three articles discussing pain associated with monogenic PD were identified and analyzed. The included studies exhibited significant heterogeneity in design, sample size, and pain outcome measures. Nonetheless, the findings of this review suggest that patients with monogenic PD may experience specific types of pain depending on the pathogenic variant present, distinguishing them from non-carriers. For instance, individuals with SNCA pathogenic variants have reported painful dystonia, lower extremity pain, dorsal pain, and upper back pain. However, these observations are primarily based on case reports with unclear prevalence. Painful lower limb dystonia and lower back pain are prominent symptoms in PRKN carriers. A continual correlation has been noted between LRRK2 mutations and the emergence of pain, though the conflicting research outcomes pose challenges in reaching definitive conclusions. Individuals with PINK1 mutation carriers also frequently report experiencing pain. Pain has been frequently reported as an initial symptom and the most troublesome one in GBA1-PD patients compared to those with idiopathic PD. The evidence regarding pain in ATP13A2, PARK7, VPS35, DNAJC6, FBXO7, and SYNJ1pathogenic variants is limited and insufficient. The potential linkage between genetic profiles and pain outcomes holds promising clinical implications, allowing for the potential stratification of patients in clinical trials and the development of personalized treatments for pain in monogenic PD. In conclusion, this review underscores the need for further research to unravel the intricate relationship between pain and monogenic forms of PD. Standardized methodologies, larger sample sizes, and longitudinal studies are essential to elucidate the underlying mechanisms and develop targeted therapeutic interventions for pain management in individuals with monogenic PD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".