Insulin Resistance and High Sensitivity C-Reactive Protein in Migraine
Bibliographic record
Abstract
BACKGROUND: A relationship between migraine and vascular disorders such as hypertension, stroke, and coronary ischemia has been recently reported. Insulin resistance and endothelial dysfunction, which commonly underlies these disorders, have not been widely investigated in migraine patients. In this study, we aimed to investigate the existence of insulin resistance and endothelial dysfunction, and their relationship to vascular risk factors in patients with migraine. METHODS: We evaluated insulin resistance and high-sensitivity C-reactive protein (hs-CRP), a marker of endothelial dysfunction, in 60 migraine patients and 25 healthy control subjects. Multiple analysis of covariance test was used to adjust for known confounding factors that can influence insulin metabolism and endothelial function, such as obesity, blood pressure, and lipid parameters. RESULTS: Insulin resistance, as measured homeostasis model assessment (HOMA)-R levels, was significantly higher in the migraine group (p<0.001). After adjustment for confounding variables, the relationship between migraine and the HOMA-R levels remained significant (p<0.001). The hs-CRP levels did not differ between the migraine and control groups. CONCLUSIONS: Our data show that insulin resistance is present in migraine patients. Endothelial dysfunction is not found during the headache-free period. Further studies are needed to explain the role of insulin resistance 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".