Obesity and Disease Activity in Pediatric-Onset Multiple Sclerosis (P1.376)
Bibliographic record
Abstract
OBJECTIVE: To determine whether body mass index (BMI) at diagnosis of pediatric-onset multiple sclerosis (MS) predicts disease activity, including annualized relapse rate (ARR) and new MRI lesions. BACKGROUND: Obesity in childhood and adolescence has been shown to be a risk factor for developing MS, particularly amongst females. It is not known whether obesity modifies disease course in established MS. Animal studies suggest an association between obesity and inflammation, with leptin promoting pro-inflammatory T cells and attenuating Treg responses. DESIGN/METHODS: This was a multi-center retrospective observational study, with preliminary analysis of 50 adults with pediatric-onset MS, 74[percnt] female, mean age 22.8+/-3.5 years, mean follow up 8+/-3.9 years. BMI at MS diagnosis was standardized for age and sex based on WHO data. We investigated the relationship between BMI and number of relapses using a quasi-Poisson model corrected for age, sex, and disease modifying therapy use, and an offset log of disease duration. In secondary analyses, the association between BMI and new T2 or T1 gadolinium-enhancing lesions annually was modelled using a repeated measures analysis. RESULTS: Mean BMI z-score adjusted for age and sex at MS diagnosis was 0.93, with 30[percnt] overweight and 22[percnt] obese. Mean ARR was 0.88+/-0.63 and median EDSS at last follow up was 1 (range 0-6.5). 77[percnt] developed new lesions on MRI brain at 1 year. The model constructed to predict number of relapses (p-value 0.18) and the model constructed to predict MRI lesions (p-value 0.78) did not have sufficient data to show evidence of a difference based on BMI at MS diagnosis. CONCLUSIONS: About half of participants were overweight or obese at diagnosis in this pediatric-onset MS cohort of 50, but no association was found between BMI at MS diagnosis and disease activity. Larger studies are needed to confirm this finding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".