Lack of Causal Effects or Genetic Correlation between Restless Legs Syndrome and Parkinson’s Disease
Bibliographic record
Abstract
Abstract Background Epidemiological studies have reported association between Parkinson’s disease (PD) and restless legs syndrome (RLS). Objectives We aimed to use genetic data to study whether these two disorders are causally linked or share genetic architecture. Methods We performed two-sample Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC) using summary statistics from recent genome-wide meta-analyses of PD and RLS. Results We found no evidence for a causal relationship between RLS (as the exposure) and PD (as the outcome, inverse variance-weighted; b=-0.003, se=0.031, p =0.916, F-statistic=217.5). Reverse MR also did not demonstrate any causal effect of PD on RLS (inverse variance-weighted; b=-0.012, se=0.023, p =0.592, F-statistic=191.7). LDSC analysis demonstrated lack of genetic correlation between RLS and PD (rg=-0.028, se=0.042, p =0.507). Conclusions There was no evidence for a causal relationship or genetic correlation between RLS and PD. The associations observed in epidemiological studies could be, in part, attributed to confounding or non-genetic determinants.
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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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".