When Multiple Sclerosis Risk Begins: Genetic and Early-Life Clues from the UK Biobank
Multiple sclerosis is a complex immune-mediated disease of the central nervous system and remains a major cause of permanent neurological disability in young adults. Although its clinical manifestations are well recognized, its etiology is complex and multifactorial, involving genetic susceptibility, immune dysregulation, viral exposure, lifestyle factors, and early developmental conditions. The study by Nova and colleagues advances this field by moving beyond conventional retrospective risk-factor analysis and examining how genetic and early-life factors influence not merely whether multiple sclerosis develops, but when it is diagnosed across the life course.
Why Time-to-Event Analysis Matters
A central strength of this article is its methodological emphasis on time-to-event analysis. Many previous studies of multiple sclerosis risk have relied on case–control designs and logistic regression, which estimate cumulative lifetime associations but do not adequately account for differences in follow-up duration or changes in risk across age. By applying Cox proportional hazards modelling with age as the time scale, the authors were able to estimate instantaneous risk of multiple sclerosis diagnosis at different ages. This approach is particularly important for a disease such as multiple sclerosis, where genetic and environmental exposures may exert stronger effects during specific developmental or young-adult windows.
Study Design and Population
The investigators used data from the UK Biobank, focusing on 345,027 unrelated White participants born in England who had available multiple sclerosis polygenic risk score data. Among these individuals, 1,669 had a recorded diagnosis of multiple sclerosis. The authors considered the observation period from birth until multiple sclerosis diagnosis, death, loss to follow-up, or the end of follow-up in December 2022. Genetic predisposition was assessed using a multiple sclerosis polygenic risk score, while early-life and related factors included sex, birth year, season of birth, older siblings, maternal smoking around birth, breastfeeding, multiple birth status, geographical birth cluster, and genetic predisposition to higher body mass index. Smoking and infectious mononucleosis were incorporated as time-varying exposures, reducing the risk of immortal time bias.
Genetic Risk Is Strongest at Younger Ages
One of the most important findings was that the effect of genetic susceptibility was age dependent. Higher multiple sclerosis polygenic risk scores were associated with substantially greater hazard of diagnosis, but this effect was most pronounced in younger individuals and declined with increasing age. For example, a two-standard-deviation increase in the multiple sclerosis polygenic risk score was associated with a much higher hazard ratio at age 20 than at age 60. This finding suggests that genetic burden may accelerate earlier clinical expression of disease rather than simply increasing lifetime susceptibility in a uniform manner. It also implies that traditional genome-wide association approaches may underestimate the importance of genetic risk during younger ages if they treat risk as constant across the lifespan.
Sex Differences and Early Biological Susceptibility
The study also demonstrated that the excess risk observed in females was not constant across age. Females had a higher hazard of multiple sclerosis diagnosis than males, but the magnitude of this difference was greatest at younger ages and declined later in life. This pattern is biologically plausible, given known sex differences in immune function, hormonal regulation, sex chromosome biology, and inflammatory susceptibility. The authors further observed evidence of additive interaction between female sex and higher genetic risk, suggesting that biological sex and inherited susceptibility may jointly amplify early multiple sclerosis risk beyond their independent effects.
Environmental and Developmental Factors
Several established environmental and developmental risk factors were also supported by the analysis. Smoking was associated with increased multiple sclerosis hazard, as was a prior diagnosis of infectious mononucleosis, a clinical proxy for symptomatic Epstein–Barr virus infection. Higher body mass index polygenic risk was also associated with increased hazard, consistent with evidence linking adiposity-related pathways to multiple sclerosis susceptibility. Season of birth showed a notable pattern: individuals born in spring, summer, or winter had higher hazard than those born in autumn. This observation may reflect prenatal environmental influences, including seasonal variation in sunlight exposure and maternal vitamin D status, although such mechanisms require further validation.
Implications, Limitations, and Future Directions
This study provides an important conceptual shift in multiple sclerosis epidemiology by showing that key risk factors may vary in strength across age. Its findings support the use of longitudinal, survival-based models to refine risk prediction and identify periods of heightened vulnerability. Nevertheless, the authors appropriately note several limitations, including the use of diagnosis date as a proxy for disease onset, reliance on registry and self-reported diagnostic data, limited generalizability beyond White individuals born in England, and possible residual selection bias in UK Biobank participation. Future research should replicate these findings in more diverse cohorts and integrate additional early-life biomarkers, including vitamin D levels, Epstein–Barr virus antibody profiles, pubertal timing, and measured childhood adiposity. Overall, the article demonstrates that understanding multiple sclerosis risk requires attention not only to which factors matter, but also to when they matter most.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Nova, A., Di Caprio, G., Bernardinelli, L., & Fazia, T. (2024). Genetic and early life factors influence on time-to-multiple sclerosis diagnosis: A UK Biobank study. Multiple Sclerosis Journal, 30(8), 994-1003.
