The MS Risk You’re Born With: T-Cell Fingerprint Hidden in Our Genes
Multiple sclerosis (MS) is usually diagnosed in adulthood, but many of the processes that shape MS risk—immune development, viral exposures, and genetic “set points”—are established much earlier. The article by de Mol and colleagues asks a deceptively simple question: if common MS risk variants act through the immune system, can we already detect measurable differences in T cell composition in children from the general population, long before any symptoms appear? In other words, does genetic susceptibility leave an early fingerprint in the immune system that might help explain why some people are more vulnerable to MS later in life?
The study population: Generation R and a snapshot at age six
The investigators used the Generation R Study, a large population-based birth cohort in Rotterdam, the Netherlands. At around 6 years of age, the study collected blood for immunophenotyping and linked these immune measurements to genotype data. For the main analyses, they included unrelated children of European ancestry with high-quality genetic data: 1,261 children for analyses of absolute total T cell numbers, and 675 children for detailed T cell subset analyses (as summarized in the participant flowchart and Table 1). This setup is powerful because it avoids the usual “reverse causality” problem in MS research—children in this cohort largely do not have MS—so any immune differences are more plausibly upstream of disease onset rather than a consequence of disease or treatment.
Turning thousands of variants into one number: the MS polygenic risk score
To quantify inherited MS susceptibility, the team built MS polygenic risk scores (PRSs) using a large MS genome-wide association study from the International Multiple Sclerosis Genetics Consortium as the discovery dataset. They computed weighted PRSs across multiple p-value thresholds (to capture both strongly associated and more modest “suggestive” signals), and they explicitly handled the major histocompatibility complex (MHC) region—an immunogenetic hotspot for MS—by creating PRSs that included all variants, excluded the MHC, or used only MHC variants. They then grouped children into PRS quartiles and tested whether higher genetic risk aligned with shifts in T cell counts and frequencies, adjusting for age, sex, and genetic ancestry (principal components).
Measuring the immune “balance sheet”: T cell subsets by flow cytometry
On the immunology side, the study used whole-blood flow cytometry to quantify CD4+ and CD8+ T cell lineages and a set of canonical subsets: naïve (CD45RO−CCR7+), central memory (Tcm; CD45RO+CCR7+), effector memory (CD45RO+CCR7−), TemRA (CD45RO−CCR7−), terminally differentiated (CD57+), and activated (HLA-DR+), excluding CD56+ natural killer T cells. A key derived metric was the CD4+/CD8+ ratio—often treated as a broad indicator of immune homeostasis, and previously reported as elevated in MS patients. Conceptually, you can think of this ratio as a seesaw: if CD8+ numbers fall or CD4+ numbers rise, the ratio tips, potentially reflecting a different baseline “immune architecture.”
The headline result: higher MS genetic risk tracks with fewer CD8+ cells and a higher CD4/CD8 ratio
The striking finding was not a change in overall T cell abundance—absolute total T cell counts were not significantly associated with MS-PRS across thresholds—but rather a compositional shift. Using the best-performing PRS threshold (PT < 0.005), higher MS-PRS was associated with lower CD8+ T cell numbers, particularly naïve CD8+ T cells, and consequently with a higher CD4+/CD8+ ratio. The association with CD4+/CD8+ ratio was especially strong (reported p = 8.27 × 10−9, FDR-adjusted p = 2.81 × 10−7), while CD8+ total and CD8+ naïve associations were also significant after multiple-testing correction (Table 2), and the quartile plots illustrate a stepwise pattern across PRS strata (Figure 2). Notably, they found no evidence that these relationships differed by sex.
Two variants do a lot of the “heavy lifting”: HLA-DRB1*15:01 and an HLA-B signal
When the authors drilled down to individual genome-wide significant MS risk variants available in their dataset, two stood out as major drivers of the CD4+/CD8+ signal: rs3135388 (a tag SNP for HLA-DRB1*15:01, the best-known MS risk allele) and rs9266629 (a tag SNP for an HLA-B risk/protective signal). In their analyses, rs3135388 was associated with higher CD4+ (including naïve CD4+) counts, whereas rs9266629 showed positive associations with multiple CD8+ subsets; together, these effects map cleanly onto the shifted CD4+/CD8+ ratio (Figure 3). The broader MHC region contributed substantially: removing the MHC weakened—but did not fully eliminate—the association, consistent with the central role of antigen presentation genetics in MS. Interestingly, PRSs constructed to be “T-cell-subset-specific” using functional annotation did not show significant associations unless those two key variants were incorporated, suggesting that a small number of highly influential immunogenetic loci may dominate this early-life phenotype.
What this means (and what it doesn’t): early immune set points, not destiny
Biologically, the results support an appealing model: MS risk alleles may tune immune development early, nudging the long-term balance between CD4+ and CD8+ compartments—particularly the naïve CD8+ pool—years before any clinical MS. The authors discuss potential mechanisms such as genetically influenced thymic output or HLA-mediated selection dynamics during T cell development, rather than (for example) disease-driven migration of T cells into the central nervous system, which would be unlikely in healthy 6-year-olds. Importantly, they did not find associations between MS-PRS and several childhood-relevant environmental MS factors measured here (EBV/CMV serostatus and serum 25(OH)D), suggesting these exposures may act independently of inherited polygenic risk at this age (or may require different biomarkers/time windows). The work is cross-sectional and cannot prove causality or forecast who will develop MS; it also does not test functional properties like cytokine profiles or migratory capacity. Still, by showing that genetic MS susceptibility aligns with a measurable shift in T cell composition in the general pediatric population, the study strengthens the idea that part of MS risk is an early, systemic immune “baseline”—one that future longitudinal studies might connect to later immune trajectories and, ultimately, clinical outcomes.
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:
de Mol, C. L., Looman, K. I., van Luijn, M. M., Kreft, K. L., Jansen, P. R., van Zelm, M. C., ... & Neuteboom, R. F. (2021). T cell composition and polygenic multiple sclerosis risk: a population‐based study in children. European Journal of Neurology, 28(11), 3731-3741.
