Irritable Bowel Syndrome and Migraine: Evidence From Mendelian Randomization Analysis in the UK Biobank
Bibliographic record
Abstract
Abstract Background: IBS and Migraine are two diseases featuring high prevalence. Previous studies have suggested a relationship between Irritable Bowel Syndrome (IBS) and migraine, although the causal association remains unclear. We sought to explore the causal association between IBS and migraine, and to prove the importance of migraine prevention in IBS patients.Methods: This study used a two-sample Mendelian-randomization analysis to explore the association of IBS with migraine. Genetic association with migraine were acquired from the UK Biobank (UKB) genetic databases (cases: 1,072; controls: 360,122). We performed estimation using Inverse Variance Weighting (IVW), along with Maximum Likelihood, MR-RAPS, MR-Egger and Weighted Median for sensitivity analysis. Considering possible bias, we also conducted polymorphism, heterogeneity, and directional analysis.Results: The IVW estimation genetically predicted the causal association between IBS and migraine (OR=1.09, 95%CI 1.01 to 1.17, p=0.03). Neither statistical horizontal pleiotropy (MR Egger p=0.42; MR-PRESSO p=0.78) nor possible heterogeneity (IVW Q = 26.15, p=0.80) was found. Reverse causation was also not detected (p steiger<0.01).Conclusion: Mendelian randomization analysis supported a positive-going causal association of IBS with migraine, providing enlightenment for disease prevention and control.
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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 | 0.080 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".