GR.2 Obesity and multiple sclerosis severity: a Mendelian randomization study
Bibliographic record
Abstract
Background: Obesity is increasingly implicated in the development of multiple sclerosis (MS), but its effect on disease disability is less well-established. This study aims to investigate the association between obesity and MS severity utilizing Mendelian Randomization (MR). Methods: Employing a two-sample MR setting, we examined the effects of various obesity measures and adiposity distribution metrics on MS severity. Genetic proxies for body mass index (BMI) were selected from a study of 806,834 participants, with MS severity determined from a genetic study of age-related MS severity scores in 12,584 individuals with MS. Results: The main analysis reveals an association between elevated BMI and increased MS severity (P = 0.03). This is supported by a significant effect of whole body fat (P = 0.04), aligning with the hypothesis that obesity exacerbates MS disability. Sensitivity analyses suggest minimal heterogeneity and bias, indicating a potential causal effect. Conclusions: Our findings suggest that obesity adversely influences long-term disability outcomes in MS. The convergence of this genetic evidence with some of the prior observational studies strengthens the argument for a causal relationship between obesity and MS severity. These insights highlight obesity as a potentially modifiable risk factor in managing MS, underscoring the importance of weight management in MS treatment strategies.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".