Dysregulation of the peripheral glutamatergic system: A key player in migraine pathogenesis?
Bibliographic record
Abstract
BACKGROUND: Although the role of glutamate in migraine pathogenesis remains uncertain, there has been significant interest in the development of drug candidates that target glutamate receptors. Activation of trigeminovascular afferent fibers is now recognized as a crucial step to the onset of a migraine episode. New evidence suggests a dysfunction in peripheral glutamate regulation may play a role in this process. OBJECTIVE: To provide a narrative review of the role of peripheral glutamate dysfunction in migraine. METHOD: A review of recent literature from neurobiological, pharmacological and genomic studies was conducted to support peripheral glutamate dysfunction as a potential element in migraine pathogenesis. RESULTS: Studies in rats suggest that elevated blood glutamate mechanically sensitizes trigeminal afferent fibers and stimulates the release of calcitonin-gene related peptide and other neuropeptides to promote and maintain neurogenic inflammation. These effects may be driven by upregulation of glutamate receptors, and modifications to reuptake and metabolic pathways of glutamate. Furthermore, genome wide association studies have found polymorphisms in glutamate receptor and transporter genes that are associated with migraine. CONCLUSION: The role of peripheral glutamate signalling in the onset and maintenance of migraine is not completely elucidated and future studies are still needed to confirm its role in migraine pathogenesis.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".