Systematic Review of Environmental Factors Associated with Late-Onset Multiple Sclerosis: A Synthesis of Epidemiological Evidence
Bibliographic record
Abstract
Background/Objectives: Late-onset multiple sclerosis (LOMS), characterized by an onset of disease at ≥50 years, is a distinct subset of multiple sclerosis (MS) with unique clinical and demographic features. While environmental factors such as smoking, diet, infections, and air pollution are well-studied in regard to early-onset MS, their roles in LOMS are not fully understood. This systematic review evaluates the environmental and clinical factors associated with LOMS risk to provide insights for prevention and management. Methods: A systematic review of MEDLINE, EMBASE, Web of Science, and Cochrane Library was conducted in accordance with PRISMA guidelines. Four studies (one case–control study, two cohort studies, and one cross-sectional study) investigating substance use, diet, disease-modifying therapies (DMTs), and demographic factors were included. Study quality was assessed using the Newcastle–Ottawa Scale (NOS), and findings were synthesized narratively. Results: Substance use, including smoking and the use of alcohol and drugs, was significantly associated with an increased LOMS risk (ORs 1.9–3.2). Diet quality showed no significant association with LOMS risk (HR = 1.02, 95% CI: 0.85–1.22). DMTs reduced disability progression (OR = 0.67, 95% CI: 0.55–0.81) and mortality (HR = 0.78, 95% CI: 0.65–0.94). Regional variations in symptoms were noted, with optic neuritis frequently reported as an initial symptom. Conclusions: This review identifies substance use as a significant modifiable risk factor for LOMS, while DMTs improve outcomes by reducing disability progression and mortality among elderly MS patients. The neutral findings for diet quality suggest a limited role in LOMS prevention. Further research is needed to explore broader environmental exposure and longitudinal outcomes to enhance understanding and management of LOMS.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".