The Association between Dairy Consumption and the Risk of Developing Multiple Sclerosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is a chronic and disabling disease that commonly affects young adults. We aimed to provide a comprehensive review of the current literature on the association between dairy consumption, particularly cow milk, and the risk of developing MS. Methods: This systematic review and meta-analysis was conducted in accordance with PRISMA guidelines. We searched PubMed, Web of Science, Cochrane Library, and Scopus for peer-reviewed papers published until 2024. Eligible studies included randomized controlled trials, quasi-experimental studies, and observational designs (case-control, cohort, cross-sectional). Studies were excluded if they did not report sufficient data to calculate effect sizes. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). A random-effects meta-analysis was conducted, and heterogeneity was assessed using the I² statistic. Results: Twenty studies met the inclusion criteria. The overall pooled odds ratio (OR) for the association between total dairy consumption and MS risk was 0.96 (95% CI: 0.93–0.99), indicating a weak but statistically significant inverse association. Subgroup analysis of cohort studies, however, showed no significant association (relative risk [RR] = 1.18, 95% CI: 0.95–1.47, P = 0.41). Furthermore, cow's milk consumption alone was not significantly associated with MS development (OR = 0.88, 95% CI: 0.46–1.69, P = 0.21). Conclusion: Although pooled results suggest a slight protective effect of overall dairy consumption on MS risk, findings remain inconsistent and inconclusive, especially when considering specific dairy products and study design. Additional longitudinal studies with rigorous methodology are needed to clarify the potential role of dairy in MS pathogenesis.
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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 | 0.079 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| 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) | 0.000 | 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".