A thorough investigation into the correlation between migraines and the gut microbiome: an in-depth analysis using Mendelian randomization studies
Bibliographic record
Abstract
Objective: A growing body of evidence underscores a significant association between neurological disorders, particularly migraines, and the gut microbiota. However, a research gap persists in understanding the cause-and-effect dynamics between these elements. Therefore, we employed robust methodologies aimed at thoroughly exploring the causal relationship between the gut microbiome and migraines. Methods: Employing bidirectional Two Sample Mendelian Randomization (TSMR) analysis, we investigated the causal association between the composition of the gut microbiota and migraines. Data summarizing the relationship between gut microbiota and migraines were extracted from one or more genome-wide association studies. The TSMR analysis employed five methods to assess the correlation between the gut microbiota and migraines, with the inverse variance-weighted method serving as the primary approach for analyzing causal links. Sensitivity analyses were applied to address horizontal pleiotropy and heterogeneity. Simultaneously, a meta-analysis was performed to strengthen the robustness of the findings. Additionally, a reverse TSMR was carried out to explore potential occurrences of reverse causal relationships. Results: The ongoing TSMR analysis identified a collection of 14 bacterial taxa connected to migraines. Among these, 8 taxa exhibited a protective effect, while 5 taxa had a detrimental impact, and 1 taxon maintained a neutral relationship. The reverse Mendelian randomization analysis highlighted stable outcomes for only one bacterial taxonomic group. Conclusion: The study confirms a causal relationship between the gut microbiota and migraines, offering a new perspective for migraine research. Strategically targeting specific bacterial taxa with dysregulation may be effective in both preventing and treating migraines, thus opening new avenues for therapeutic strategies.
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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 | 0.078 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".