Trends in the environmental risks associated with earlier onset in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Several environmental and lifestyle factors relating to sunlight/vitamin D, body mass index (BMI), and smoking are associated with the risk of developing multiple sclerosis (MS). However, their relation to disease progression, particularly age at symptomatic onset, remains inconsistent, which may be the result of significant changes in human-environment interactions over the last century. This study investigates historical trends in the association between common MS environmental risk factors and age at disease onset. METHODS: Using a narrative approach, we evaluated the current literature for published studies assessing the association between vitamin-D, BMI, and tobacco smoking exposures with the risk of early/pediatric-onset MS and direct correlations with age at MS onset using MEDLINE, EMBASE, and Web of Science. Measures were plotted by the average calendar year of disease onset for each cohort to examine trends over time. In total, 25, 9, and 11 articles were identified for vitamin D, BMI, and smoking-related exposures, respectively. RESULTS: Higher sun exposure habits and residential solar radiation were associated with older age at onset. On the contrary, two studies observed a negative correlation between age at onset and serum 25-hydroxyvitamin D (25(OH)D) levels. Higher adolescent BMI was generally associated with younger age at onset, although genetic susceptibility for childhood obesity was not significantly associated. Tobacco smoking was associated with later disease onset, despite being a risk factor for MS. Association with age at onset was inflated for more recent studies relating to smoking, while often weaker for serum vitamin D and BMI. CONCLUSION: Current findings indicate a likely association between age at onset and environmental risk factors, such as sun exposure, adolescent BMI, and tobacco smoking, in certain populations. However, findings are often inconsistent and assessment of the relationships and potential changes over time require further investigation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".