Cortical spectral rebalancing underlies pregabalin’s efficacy in restless legs syndrome
Bibliographic record
Abstract
Abstract Restless legs syndrome afflicts nearly half of all patients with end-stage renal disease, imposing profound burdens through relentless insomnia and functional decline. Dopaminergic agents—the established first-line therapy—trigger augmentation, a paradoxical intensification of symptoms that leaves patients worse than when they began treatment. The resultant therapeutic impasse has left a vulnerable population without effective recourse. Here we demonstrate that pregabalin, an α2δ calcium channel ligand with established renal safety, provides robust and sustained symptom relief in a randomized, placebo-controlled trial of dialysis patients with uremic restless legs syndrome. Clinical benefits emerged rapidly and intensified over three months, with pregabalin-treated patients showing more than twofold greater likelihood of categorical improvement in disease severity. In complementary animal electrophysiology experiments, pregabalin distinctively suppressed cortical hyperarousal—a hallmark of restless legs syndrome—by rebalancing low- and high-frequency oscillatory activity patterns, particularly in motor regions. This neural signature closely resembled that of gabapentin, another therapeutically effective α2δ ligand, suggesting convergent mechanisms. Beyond establishing pregabalin as an urgently needed alternative to dopaminergic therapy, our results demonstrate that cortical oscillatory profiling can reveal mechanistic convergence among candidate therapeutics, offering a rational preclinical screening strategy for neurological disorders before exposure of high-risk patient populations to experimental interventions. This framework may accelerate therapeutic discovery while reducing clinical trial risks in vulnerable populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".