A potential role for zinc in restless legs syndrome
Bibliographic record
Abstract
STUDY OBJECTIVES: Evaluate serum and brain noniron metals in the pathology and genetics of restless legs syndrome (RLS). METHODS: In two independent studies (cohorts 1 and 2), in which subjects either remained on medications or tapered off medications, we analyzed serum levels of iron, calcium, magnesium, manganese, copper, and zinc both in RLS patients and controls, and assessed the prevalence of the MEIS1 and BTBD9 risk alleles previously established through genome-wide association studies. Human brain sections and a nematode genetic model were also quantified for metal levels using mass spectrometry. RESULTS: We found a significant enrichment for the BTBD9 risk genotype in the RLS affected group compared to control (p = 0.0252), consistent with previous literature. Serum (p = 0.0458 and p = 0.0139 for study cohorts 1 and 2, respectively) and brain (p = 0.0413) zinc levels were significantly elevated in the RLS patients versus control subjects. CONCLUSION: We show for the first time that serum and brain levels of zinc are elevated in RLS. Further, we confirm the BTBD9 genetic risk factor in a new population, although the zinc changes were not significantly associated with risk genotypes. Zinc and iron homeostasis are interrelated, and zinc biology impacts neurotransmitter systems previously linked to RLS. Given the modest albeit statistically significant increase in serum zinc of ~20%, and the lack of association with two known genetic risk factors, zinc may not represent a primary etiology for the syndrome. Further investigation into the pathogenetic role that zinc may play in restless legs syndrome is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".