Body size and physical exercise, and the risk of multiple sclerosis
Bibliographic record
Abstract
Background: Whether large body size increases multiple sclerosis (MS) risk in men is not well understood. Concurrently, physical exercise could be an independent protective factor. Objective: To prospectively investigate the association between body mass index (BMI) and aerobic fitness, indicators of body size and exercise, and MS risk in men. Methods: We performed a population-based nested case-control study within the historical cohort of all Norwegian men, born in 1950–1975, undergoing mandatory conscription at the age of 19 years. 1016 cases were identified through linkage to the Norwegian MS registry, while 19,230 controls were randomly selected from the cohort. We estimated the effect of BMI and fitness at conscription on MS risk using Cox regression. Results: Higher BMI (≥25 vs 18.5–<25 kg/m 2 ) was significantly associated with increased MS risk (adjusted relative risk (RR adj ) = 1.36, 95% confidence interval (CI): 1.05–1.76). We also found a significant inverse association between aerobic fitness (high vs low) and MS risk independent of BMI (RR adj = 0.69, 95% CI: 0.55–0.88, p-trend = 0.003), remaining similar when men with MS onset within 10 years from conscription were excluded ( p-trend = 0.03). Conclusion: These findings add weight to evidence linking being overweight to an increased MS risk in men. Furthermore, they suggest that exercise may be an additional modifiable protective factor for 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".