IDF2022-0660 Non-HLA polymorphisms diversely impact progression of preclinical islet autoimmunity in distinct subgroups and stage

Background The HLA region is the major determinant of type 1 diabetes risk. Genome-wide association studies have also identified about 60 non-HLA loci each contributing modestly, albeit significantly, to genetic risk. How they act is incompletely understood, but there are indications that at least some significantly impact progression of asymptomatic disease [1]. Aim We examined whether 8 SNPs in 7 susceptibility loci (INS, SH2B3, PTPN2, PTPN22, CTLA4, CLEC16A and IL2RA) could improve existing predictive models [2,3] for progression from single to multiple autoantibody positivity, and from there to diagnosis. Method The selected SNPs were determined by allelic discrimination quantitative PCR [1] in 460 Belgian, persistently autoantibody-positive siblings and offspring (<40 years of age) of diabetic patients. Allele frequencies were compared with European population data. Disease progression was studied separately in single and multiple autoantibody-positive relatives by conditional forward multivariate Cox regression analysis. Results In our cohort, the prevalence of non-HLA risk alleles significantly exceeded that of the European population only for INS (rs689, p = 0.007; rs1004446, p = 0.001) and PTPN22 (rs6679677, p < 0.001). Only the CTLA4 GA genotype (rs3087243, p = 0.002) and the CLEC16A AA genotype (rs12708716, p = 0.021) were associated with accelerated epitope spreading, but their effects were restricted to presence of HLA-DQ2/DQ8, and IAA as first autoantibody, respectively. The interaction of CTLA4 and DQ2/DQ8 overruled the effect of DQ2/DQ8 alone. The SH2B3 TT genotype (rs3184504) was protective for HLA-DQ8 positive subjects (p = 0.003). At the stage of multiple autoantibodies, only the CTLA4 GA genotype was a minor independent risk factor for progression towards clinical diabetes (p = 0.034). Conclusion Non-HLA polymorphisms can strongly impact progression of islet autoimmunity in a subgroup-, stage- and SNP-specific way, suggesting distinct mechanisms. If confirmed, these findings may help refine risk assessment, follow-up, and prevention trials in risk groups.