Shedding light on the link between early life sun exposure and risk of multiple sclerosis: results from the EnvIMS Study
Bibliographic record
Abstract
BACKGROUND: Lower levels of sun exposure in childhood have been suggested to be associated with increased risk of multiple sclerosis (MS). In this paper we extend previous work, using two novel analytical strategies. METHODS: Data collected in the Environmental risk factors In MS (EnvIMS) study, a case-control study with MS cases and population-based controls from Canada, Italy and Norway, were used. Participants reported on sun exposure behaviours for 5-year age intervals from birth; we focused on the first three age intervals (≤15 years). We compared two life course epidemiology conceptual models, the critical period and the accumulation model. We also used latent class analysis to estimate MS risk for different latent sun exposure behaviour groups. RESULTS: The analyses included 2251 cases and 4028 controls. The accumulation model was found to be the best model, which demonstrated a nearly 50% increased risk of MS comparing lowest reported summer sun exposure with highest [risk ratio (RR) = 1.47 (1.24, 1.74)]. The latent sun exposure behaviour group, characterized by low sun exposure during summer and winter and high sun protection use, had the highest risk of MS; a 76% increased risk as compared with the group with high sun exposure and low sun protection use [RR = 1.76 (1.27, 2.46)]. CONCLUSIONS: Our analyses provide novel insights into the link between sun exposure and MS. We demonstrate that more time indoors during childhood and early adolescence is linked with MS risk, and that sun protection behaviours in those who spend most time indoors may play a key role in increasing risk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".