Anti-CGRP monoclonal antibodies improve cognitive function in patients affected by chronic migraine complicated with medication overuse-headache
Bibliographic record
Abstract
Background: Migraine represents one of the most disabling neurological diseases in the world. This burden is primarily due to recurrent pain episodes, alongside cognitive function impairments that patients may experience. This paper aims to explore the effect of three anti-calcitonin Gene-Related Peptide (CGRP) monoclonal antibodies (mAbs) – erenumab, fremanezumab, and galcanezumab – on the cognitive performance of a sample of patients suffering from migraine using the Montreal Cognitive Assessment (MoCA) questionnaire.Methods: A total of 215 patients suffering from migraine who visited the Modena Headache Center were enrolled. The MoCA questionnaire was filled in by the patients at the baseline and subsequent assessments were conducted at 6 and 12 months thereafter. Additionally, patients were requested to complete the 6-item Headache Impact Test, Migraine Disability Assessment Score, and Hospital Anxiety and Depression Scale every three months.Results: The sample was composed of 82% of female participants and 87% of the enrolled patients were diagnosed with chronic migraine. Following one year of treatment, there was a significant enhancement observed in MoCA scores compared to baseline measurements. Moreover, higher consumption of analgesics, elevated body mass index (BMI), and prolonged chronic migraine history exhibited an inverse correlation with MoCA score improvements after 12 months.Conclusions: Erenumab, fremanezumab, and galcanezumab have proven to be effective in relieving the cognitive impairment associated with migraine after 1 year of treatment. These findings underscore the reversibility of cognitive impairment among migraine sufferers, even among those suffering from chronic migraine, as delineated by the majority of the patients under study. This study revealed that prolonged chronic migraine history, higher baseline analgesic intake, and elevated BMI were all predictive of diminished cognitive enhancements following treatment.
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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 | 0.000 | 0.000 |
| 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".