GP.01 Childhood obesity and multiple sclerosis susceptibility: a Mendelian randomization study
Bibliographic record
Abstract
Background: Observational studies have reported an association between childhood obesity and a higher risk of multiple sclerosis (MS). However, the difficulties to fully account for confounding and long recall periods make causal inference from these studies challenging. The objective of this study was to assess the contribution of childhood obesity to the development of MS through Mendelian randomization, which uses genetic associations to minimize the risk of confounding. Methods: We selected 23 independent genetic variants strongly associated with childhood body mass index (BMI) in a genome-wide association study (GWAS) which included 47,541 children. The corresponding effects of these variants on risk of MS were obtained from a GWAS of 14,802 MS cases and 26,703 controls. Standard two-sample Mendelian randomization methods were performed, with additional sensitivity analyses to assess the likelihood of bias from genetic pleiotropy. Results: The inverse-variance weighted MR analysis revealed that one standard deviation increase in childhood BMI increased odds of MS by 26% (odds ratio=1.26, 95% confidence interval 1.10-1.45, p=0.001). There was no significant heterogeneity across the individual estimates. Sensitivity analyses were consistent with the main findings and provided no evidence of pleiotropy. Conclusions: This study provides genetic support of a role for increased childhood BMI in the development of MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".