Association between weight status and migraine in the paediatric population: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction An association between weight status and migraine has been previously reported; however, this relationship has only been studied in adults, not in the paediatric population. Objective To evaluate the association between weight status and migraine in the paediatric population. Methods We searched PubMed/Medline, Scopus, Web of Science, Ovid Medline, and Embase using a cut-off date of May 2023. We included observational studies that evaluated the association between weight status (underweight, overweight, obese, and excess weight) and migraine in the paediatric population (children and adolescents). Normal weight was the comparator. The outcome was migraine (all types, episodic and chronic). We performed meta-analyses using a random-effects model to estimate the pooled effects for each outcome. Sensitivity analysis was performed based on study design and risk of bias (using the Newcastle–Ottawa Scale). Certainty of evidence was assessed using the GRADE approach. Results Eight studies (6 cross-sectional, 1 case-control and 1 cohort) covering 16,556 patients were included. The overall certainty of evidence was very low for the association between overweight, obesity, and excess weight with migraine. In the sensitivity analysis, meta-analyses of studies with a low risk of bias found that the overweight population probably had an increased odds of migraine (OR: 1.70; 95% CI: 1.14 to 2.53; I2 = 32.3%, p = 0.224) and that excess weight may increase the odds of migraine (OR: 1.58; 95% CI: 1.06 to 2.35; I2 = 83.7%, p = 0.002). Additionally, cohort and case-control studies found that obesity probably increases the odds of migraine. No studies analysed the association between underweight and migraine. Conclusion The associations between overweight, obesity, excess weight and migraine were uncertain, but studies with better methodological quality reported increased odds. Future longitudinal studies with proper confounding control are needed to disentangle their causal relationship. Systematic review registration PROSPERO, identifier CRD42021271533.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".