Unraveling the Age-Dependent Genetic and Early Life Influences on Multiple Sclerosis Onset: Insights from the UK Biobank Study

The study utilized data from the UK Biobank, a longitudinal cohort study that includes a wealth of health-related phenotypes and biological measures from approximately 500,000 individuals aged between 40 and 69. For this analysis, the researchers focused on 345,027 participants of White ethnicity born in England, out of which 1,669 had an MS diagnosis. MS cases were identified using the ICD-10 diagnosis code G35.

Genetic and Early Life Factors
The primary genetic measure was the multiple sclerosis polygenic risk score (MS-PRS), which includes variants across the entire genome, including the HLA region. Early life factors considered in the analysis included sex, year of birth, number of older siblings, maternal smoking around birth, breastfeeding status, being part of a multiple birth, geographical birth location, and body mass index polygenic risk score (BMI-PRS). Additionally, the age at which participants started smoking and received an infectious mononucleosis (IM) diagnosis were included as time-varying covariates.

The study revealed several key findings regarding the influence of genetic and early life factors on MS diagnosis:

Age-Dependent Effects of Sex and Genetic Risk: The analysis showed that the hazard ratios (HRs) for sex (females vs. males) and MS-PRS were age-dependent, with higher HRs observed in younger adults. For instance, the HR for females compared to males decreased from 3.88 at age 20 to 2.15 at age 60. Similarly, the HR for a 2 SD increase in MS-PRS decreased from 6.40 at age 20 to 2.23 at age 60.

Impact of Birth Season: Individuals born in spring, summer, and winter had a significantly higher risk of MS compared to those born in fall, suggesting a potential link between birth season and MS risk.

Smoking and IM Diagnosis: Ever smoking on most or all days and having a previous IM diagnosis were associated with significantly higher MS risk, with HRs of 1.69 and 2.03, respectively.

Non-Linear Effects of Year of Birth and BMI-PRS: The analysis indicated non-linear relationships for both year of birth and BMI-PRS. Being born before 1951 was associated with a lower MS hazard, while higher BMI-PRS values were linked to increased MS risk.

Interactions Between Risk Factors: The study found significant additive and multiplicative interactions between MS-PRS and other risk factors. For example, there was a positive additive interaction between higher MS-PRS and being female, indicating that the combined effect on MS risk was greater than the sum of their individual effects.

This study highlights the importance of considering age-dependent effects in MS risk assessments. The findings suggest that retrospective studies might underestimate the risk roles of sex and genetic variants during younger ages. By employing a time-to-event analysis, the researchers were able to capture the instantaneous risk of MS diagnosis at any given age, providing a more nuanced understanding of how these factors interact over a lifetime.

Conclusion
The research by Nova et al. underscores the value of longitudinal data and time-to-event analysis in studying the complex interplay of genetic and early life factors in MS. This approach not only improves risk prediction but also enhances our understanding of the causal determinants of MS. Future studies should aim to replicate these findings in diverse populations and explore additional early life prognostic variables to further refine risk predictions.

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, 13524585241257205.