The relationship between body size and the risk of multiple sclerosis. The EnvIMS study
Bibliographic record
Abstract
Introduction: Two recent studies from Canada and Sweden have shown that a large body size around 18-20 years may increase the risk of Multiple Sclerosis (MS), suggestive of a possible effect of reduced circulating levels of vitamin D in overweight individuals. We assessed this association in a large multinational case-control study (EnvIMS). \n \nMethods: A population based sample of 959 cases (286 men, 673 women) and 1718 controls (462 men, 1256 women) in Norway and 732 cases (261 men, 471 women) and 1439 controls (471 men, 968 women) in Italy reported their body size using body silhouettes ranging from 1 to 9 where 9 represents the largest. Body sizes at age (in years) 5, 10, 15, 25, 30 and current age (after onset of disease for MS cases) were reported. Self-report of body size was validated against current body mass index. We analyzed men and women separately and compared the cases to controls with independent samples t-test and logistic regression, using body size 3 as a reference group and smoking and education as co-variates. \n \nResults: In Norway cases reported a larger average body size between age 5 and 30, being significant from age 15 to 25 among men and age 10 to 25 among women. In Italy cases reported a slightly larger, non-significant, average body size up to 20 years among men and 25 years among women. Interestingly, at current age cases in general had a lower average body size compared with controls in both countries. \n \nIn Norway we found that a large body size (silhouettes 6-9) at age 25 was associated with an increased risk for MS [men: OR=2.20 (95% CI: 1.14-4.24, p-trend=0.003), women: OR=1.62 (95% CI 1.04-2.53, p-trend=0.0005)]. The corresponding results at age 20 were OR=1.55 (95% CI: 0.71-3.36, p-trend=0.001) for men and OR=1.16 (95% CI: 0.72-1.88, p-trend=0.01) for women. No significant trend was found in Italy. Adjustment for smoking and education did not materially change the results. \n \nIn both countries we found a protective effect for the slimmest body sizes (1-2) compared with body size 3 in all age groups (5-30). \n \nConclusions: Our analyses show that factors related to a large body size, particularly around 20-25 years, seem to be a risk factor for MS in Norway, but less so in Italy. These results are compatible with low circulating vitamin D or a chronic inflammatory state in overweight individuals. The difference between the countries might be related to protection through higher sun exposure in Italy.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".