In the current study we have investigated the role of HLA high resolution genotypes on age at T1D onset in various populations of European descent. To our knowledge this is the first study to compare the role of HLA on age of onset in different populations.
We further investigated whether such genotyping information could have any predictive value for assessing the risk of very young age at onset in contrast to late onset for type 1 diabetes. Using the largest data set to date to address this question we found that the strongest genetic contribution to age of onset appears to come from the DRB1-DQB1 genotypes, which also have the strongest influence on disease risk [15
]. In addition a few select class I alleles, notably A*24:02, B*18:01, B*39:06, and B*44:03 also influence age at onset. Of these the most consistent effect is that of A*24:02, whereas the other class I alleles either do not influence specific cut-offs of age of onset (early versus late) or show evidence of strong heterogeneity across populations (e.g. B*39:06 for late age of onset).
In both the T1DGC collection and other independent extant collections, we find that genotypes for classical class I and class II HLA can have some modest predictive power for these two outcomes. On the one hand, this confirms the role of HLA polymorphism in influencing age at onset. On the other hand, it highlights that other risk factors not included in our models must also be influencing age at type 1 diabetes onset.
Current approaches for the prediction of type 1 diabetes in screening studies take advantage of the major genetic risk factors, genotyping for HLA-DR and HLA-DQ loci and screening for autoantibodies directed against islet-cell antigens [16
]. For example, children who carry both of the highest-risk HLA haplotypes (DR3/DR4–DQB1*03:02) have a risk of approximately 1 in 20 for a diagnosis of T1D by the age of 15 years [16
]. The results presented here may help improve such models by taking into account also the role of genetic risk factors on age at onset.
We found that gender had little or no predictive value and that because the relationship with age of onset was not consistent across cohorts in most instances it did not improve the AUC. For the DAN and HBDI where the difference in age of onset between genders was strongest, the inclusion of gender did show a slight improvement but not in an additive way. This consistent with what has been reported for the combination of genetic and non genetic factors for other disease areas [17
We note several study limitations. Our analyses have used data derived from affected sib pair cohorts of European descent, selecting for patients with a strong genetic contribution to type 1 diabetes and therefore possibly also to its age at diagnosis. The current data are thus reflective of the prediction of HLA in a group of patients enriched for genetic risk. On the other hand, these data are relevant to clinical research, as studies of first degree relatives (follow-up, prevention trials) involve those who have a family member [16
] already diagnosed with T1D and genetic factors combined with other factors could be applied to the analysis of data from cohorts of relatives. In addition, these results highlight the differences between European descent populations and illustrate the limits and the extent to which HLA may be helpful in predicting age at disease onset.
We have developed and calibrated three risk prediction models for age at early and late onset of type 1 diabetes, based on five independent patient collections. We hope that these models may be used as pilots to lead further research in defining risk prediction for age at onset using other risk factors (e.g., environmental exposures, autoantibodies). The models may be applied at the individual level to predict the most likely category of age at onset (early or late), but also at the population level, with reference to other relative risks from published studies, to estimate the potential population risk reduction that may be gained by primary prevention of any modifiable risk factors that influence type 1 diabetes and the ensuing complications.