Maternal diabetes as a prenatal risk factor for multiple sclerosis in offspring: a systematic review and meta-analysis.

BACKGROUND: Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system with a complex etiology involving genetic and environmental factors. Maternal diabetes during pregnancy has been hypothesized to influence offspring MS risk through intrauterine metabolic programming, yet the evidence remains inconclusive. OBJECTIVE: To systematically review and meta-analyze the association between maternal diabetes and the risk of MS in offspring. METHODS: A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Embase, identifying 427 records. After removing 188 duplicates, 239 records were screened, 38 full-text reports were assessed for eligibility, and 4 studies met the inclusion criteria. Pooled risk ratios (RR) were calculated using both common-effect and random-effects models. Heterogeneity was assessed using the I² statistic, and publication bias was evaluated using Egger's and Begg's tests. RESULTS: The studies, published between 2009 and 2026, were conducted in Denmark, the USA (two studies), and Norway, utilizing diverse designs including nationwide register-based cohorts and a case-control study. The common-effect model yielded a pooled RR of 1.42 (95% CI: 1.09-1.85), while the random-effects model produced a non-significant pooled RR of 3.13 (95% CI: 0.68-14.30). Substantial heterogeneity was observed across studies (I² = 95.2%, τ² = 2.2357, p < 0.0001). Individual study risk ratios ranged from 0.91 (95% CI: 0.60-1.38) to 28.35 (95% CI: 13.33-60.29). Neither Egger's test (p = 0.4364) nor Begg's test (p = 0.1742) indicated significant publication bias, though these tests have limited power with fewer than ten studies. CONCLUSION: This meta-analysis did not demonstrate a statistically significant association between maternal diabetes and MS risk in offspring, likely owing to substantial between-study heterogeneity and the limited number of included studies. Large, well-characterized prospective cohorts are needed to clarify this relationship, elucidate underlying mechanisms, and inform preventive strategies.