Childhood obesity and multiple sclerosis: A Mendelian randomization study
Bibliographic record
Abstract
Background: Higher childhood body mass index (BMI) has been associated with an increased risk of multiple sclerosis (MS). Objective: To evaluate whether childhood BMI has a causal influence on MS, and whether this putative effect is independent from early adult obesity and pubertal timing. Methods: We performed Mendelian randomization (MR) using summary genetic data on 14,802 MS cases and 26,703 controls. Large-scale genome-wide association studies provided estimates for BMI in childhood ( n = 47,541) and adulthood ( n = 322,154). In multivariable MR, we examined the direct effects of each timepoint and further adjusted for age at puberty. Findings were replicated using the UK Biobank ( n = 453,169). Results: Higher genetically predicted childhood BMI was associated with increased odds of MS (odds ratio (OR) = 1.26/SD BMI increase, 95% confidence interval (CI): 1.07–1.50). However, there was little evidence of a direct effect after adjusting for adult BMI (OR = 1.03, 95% CI: 0.70–1.53). Conversely, the effect of adult BMI persisted independent of childhood BMI (OR = 1.43; 95% CI: 1.01–2.03). The addition of age at puberty did not alter the findings. UK Biobank analyses showed consistent results. Sensitivity analyses provided no evidence of pleiotropy. Conclusion: Genetic evidence supports an association between childhood obesity and MS susceptibility, mediated by persistence of obesity into early adulthood but independent of pubertal timing.
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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 | 0.030 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".