Calcitonin Gene-Related Peptide as a Biomarker in Migraine
Bibliographic record
Abstract
Background: Migraine is a prevalent disease with much economic burden and relatively inadequate available treatment. Calcitonin gene-related peptide (CGRP) is the most abundant neuropeptide in the trigeminal nerve and it may be involved in the pathogenesis of migraine. The aim of the study was to determine the plasma level of CGRP in patients with primary headache, and if it could be a potential biomarker for migraine. Methods: The study involved four groups with 20 patients in each one: chronic migraine, episodic migraine, cluster headache and tension type headache subjects, as well as healthy volunteers of matched age and sex as controls. Their venous blood was drawn, plasma was separated, and CGRP was analyzed with commercially available ELISA kit. Results: Plasma CGRP levels were significantly increased in chronic migraine (165.0 ± 17.9 ng/L, range 131.8 - 194.6) as compared with control group (70.5 ± 8.36 ng/L, range 51.7 - 83.65), patients with episodic migraine (94.1 ± 17.83 ng/L, range 69.6 - 121.9), and patients with cluster headache and tension type headache (87.2 ± 13.8 ng/L, range 62.8 - 110.8). Plasma CGRP levels in chronic migraine were significantly higher in patients with aura (191.32 ± 5.09, range 185.45 - 194.60) than without aura (160.30 ± 14.93, range 131.80 - 186.0), and in episodic migraine were significantly higher in patients with aura (109.88 ± 7.54, range 100.70 - 116.30) than without aura (90.16 ± 17.56, range 69.60 - 121.90). Conclusion: The plasma CGRP levels were elevated in patients with chronic migraine and may be considered a potential biomarker for it. This opens the door for a therapeutic role of it for migraine. J Neurol Res. 2017;7(6):103-107 doi: https://doi.org/10.14740/jnr460w
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".