Early Age of Migraine Onset is Independently Related to Cognitive Decline and Symptoms of Depression Affect Quality of Life
Bibliographic record
Abstract
BACKGROUND: People with migraine experience cognitive decline more often than healthy controls, resulting in a significant functional impact. Early identifying influencing factors that contribute to cognitive decline in migraineurs is crucial for timely intervention. Although migraine may onset early in childhood and early onset migraine is related to significant disability, there is no research investigating the association between the age of migraine onset and migraineurs' cognitive decline. Therefore we aim to explore possible factors that correlate to the cognitive function of migraineurs, especially focus on age of migraine onset. METHODS: 531 patients with migraine were included. Data on demographics and headache-related characteristics were collected and evaluated using face-to-face interviews and questionnaires. We used the Montreal Cognitive Assessment scale to assess cognitive function. In addition, we analyzed independent correlations between cognitive decline and the age of migraine onset in patients with migraine. And all patients completed the Headache Impact Test-6 to evaluate their quality of life. RESULTS: Migraineurs with cognitive decline showed significant differences from those without in age (OR=1.26, P<0.0001), years of education (OR=0.89, P=0.0182), the intensity of headache (OR=1.03, P=0.0217), age of onset (OR=0.92, P<0.0001) and anxiety scores (OR=1.09, P=0.0235). Furthermore, there was no interaction in the age of onset between subgroups. Multivariate linear regression analyses of HIT-6 scores showed that the intensity of headache (β=0.18, P<0.0001) and depression scores (β=0.26, P=0.0009) had independent effects on decreased quality of life. CONCLUSION: Our findings suggest that younger age of migraine onset is independently related to migraineurs' cognitive decline, and migraine accompanying anxiety symptoms significantly related to decreased quality of life in migraineurs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".