MEIS1 and Restless Legs Syndrome: A Comprehensive Review
Bibliographic record
Abstract
Restless legs syndrome (RLS) is a common sleep-related disorder for which the underlying biological pathways and genetic determinants are not well understood. The genetic factors so far identified explain less than 10% of the disease heritability. The first successful genome-wide association study (GWAS) of RLS was reported in 2007. This study identified multiple RLS associated risk variants including some within the non-coding regions of MEIS1. The MEIS1 GWAS signals are some of the strongest genetic associations reported for any common disease. MEIS1 belongs to the homeobox containing transcriptional regulatory network (HOX). Work in C. elegans showed a link between the MEIS1 orthologue and iron homeostasis, which is in line with the fact that central nervous system (CNS) iron insufficiency is thought to be a cause of RLS. Zebrafish and mice have been used to study the MEIS1 gene identifying an RLS-associated-SNP dependent enhancer activity from the highly conserved non-coding regions (HCNR) of MEIS1. Furthermore, this gene shows a lower expression of mRNA and protein in blood and thalamus of individuals with the MEIS1 RLS risk haplotype. Simulating this reduced MEIS1 expression in mouse models resulted in circadian hyperactivity, a phenotype compatible with RLS. While MEIS1 shows a strong association with RLS, the protein’s function that is directly linked to an RLS biological pathway remains to be discovered. The links to iron and the enhancer activity of the HCNRs of MEIS1 suggest promising links to RLS pathways, however more in-depth studies on this gene’s function are required. One important aspect of MEIS1’s role in RLS is the fact that it encodes a homeobox containing transcription factor, which is essential during development. Future studies with more focus on the transcriptional regulatory role of MEIS1 may open novel venues for RLS research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".