Association between <i>PRDM16, MEF2D, TRPM8, LRP1</i> gene polymorphisms and migraine susceptibility in the She ethnic population in China
Bibliographic record
Abstract
BACKGROUND: The prevalence of migraines in the She population, a minority in China, is significantly higher than that in Han Chinese and other Asian populations. Two single nucleotide polymorphisms (SNPs) have been found to be associated with migraine susceptibility in the She population. PURPOSE: This study investigated four SNPs, identified in genome-wide association studies, within migraine-susceptible loci in Han Chinese for their association with migraine susceptibility in the She population. METHODS: Two-hundred unrelated migraine patients and 200 healthy controls were recruited. The SNPs examined included rs2651899 (PRDM16 ), rs2274316 (MEF2D ), rs7577262 (TRPM8) and rs11172113 (LRP1). Genotyping of the SNPs was performed by allele-specific polymerase chain reaction and direct sequencing. RESULTS: No significant differences between the participants with migraines and controls (participants without migraines) were demonstrated in genotypes, alleles and allele carriage frequencies for the four SNPs. A subgroup analysis found that migraine with aura had a lower frequency of C allele positivity in rs2651899 than in healthy controls (59.6% vs. 74.5%, respectively; P < 0.034). Univariate analyses indicated that no genotype of the four SNPs had a significant association with migraines. Males had a lower risk of migraines, and advanced age was a significant risk factor for migraines in females. CONCLUSION: The SNPs in four migraine susceptible loci in Han Chinese were not risk factors for migraines in a relatively small sample of the She population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".