Migraine Association with Alzheimer’s Disease Risk: Evidence from the UK Biobank Cohort Study and Mendelian Randomization
Bibliographic record
Abstract
ABSTRACT: Background: Epidemiological studies on the association between migraine and Alzheimer’s disease (AD) risk have yielded inconsistent conclusions. We aimed to characterize the phenotypic and genetic relationships between migraine and AD. Methods: To investigate the association between migraine and the risk of AD by analyzing data from a large sample of 404,318 individuals who were initially free from all-cause dementia or cognitive impairment, utilizing the UK Biobank dataset. We employed Cox regression modeling and propensity score matching techniques to examine the relationship between migraine and subsequent occurrences of AD. Additionally, the study utilized Mendelian randomization (MR) analysis to identify the genetic relationship between migraine and the risk of AD. Results: Migraine patients had a significantly increased risk of developing AD, compared to non-migraine patients (adjusted hazard ratio (HR) = 2.34, 95% confidence interval (CI) = 2.01–0.74, P < 0.001). Moreover, the propensity scores matching analyses found that migraine patients had a significantly higher risk of developing AD compared to non-migraine patients (HR = 1.85, 95%CI = 1,68–2.05, P < 0.001). Additionally, the MR suggested that significant causal effects of migraine on AD risks were observed [odds ratio (OR) = 2.315; 95% confidence interval (CI) = 1.029–5.234; P = 0.002]. Moreover, no evidence supported the causal effects of AD on migraine (OR = 1.000; 95%CI = 0.999–1.006; P = 0.971). Conclusion: The present study concludes that migraine patients, compared to a matched control group, exhibit an increased risk of developing AD. Moreover, migraine patients exhibit an increased predisposition of genetic susceptibility to AD. These findings hold significant clinical value for early intervention and treatment of migraines to reduce the risk of AD.
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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 | 0.029 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".