A type 1 diabetes genetic risk score can discriminate monogenic autoimmunity with diabetes from early-onset clustering of polygenic autoimmunity with diabetes

Monogenic autoimmune disease often presents with very-early-onset diabetes. For example, hemizygous mutations in FOXP3 cause immunodysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome, which presents in the neonatal period with diabetes, protein-losing enteropathy and severe eczema [1]. Similarly, individuals with infantile-onset multisystem autoimmune disease due to dominant gain-of-function STAT3 mutations or common variable immunodeficiency 8 with autoimmunity due to recessively inherited LRBA mutations may present with neonatal diabetes [2, 3].

While some individuals harbour a causative mutation in a single gene, the clustering of very-early-onset diabetes with autoimmune disease is usually due to a strong polygenic risk resulting from shared predisposing genetic loci. It is well established that the HLA-DR3 haplotype is associated with the development of type 1 diabetes mellitus [4] and coeliac disease through its strong linkage with the HLA-DQ2 haplotype [5]. Outside the HLA region the IL2RA polymorphism rs706778 is associated with increased risk of type 1 diabetes, autoimmune thyroid disease (AITD) and coeliac disease, as well as other paediatric-onset autoimmune disorders [6].

The phenotypic overlap between the two groups means that identifying individuals for testing is difficult using clinical features or biomarkers. While islet autoantibodies are highly discriminatory of type 1 diabetes against type 2 diabetes and MODY [7, 8], they are often present in individuals with monogenic autoimmunity. For example, multiple islet autoantibodies are present in more than half of individuals with IPEX syndrome [9]. Moreover, as it is thought that these individuals have autoimmune destruction of the pancreatic beta cells [10], serum C-peptide levels and treatment type or dose is also likely to be similar in the two groups.

The type 1 diabetes genetic risk score (GRS) is calculated by genotyping the top risk alleles and summing their effective weight to assign a numerical score to the individual that can be compared with control samples [11]. It was recently shown to be highly discriminatory of non-autoimmune monogenic diabetes and type 2 diabetes from type 1 diabetes [11, 12]. We sought to determine whether the type 1 diabetes GRS could distinguish between monogenic autoimmunity and polygenic clustering of autoimmune disease.

We have shown that a type 1 diabetes GRS can be used to identify individuals most likely to have a mutation in a monogenic autoimmune gene and could be used to prioritise individuals for gene discovery studies and, in combination with clinical features, genetic testing. Individuals with confirmed monogenic autoimmune disease have a markedly lower type 1 diabetes GRS than those with isolated type 1 diabetes or type 1 diabetes associated with other autoimmune disease, even when both conditions are diagnosed at a very young age.

The type 1 diabetes-associated antibodies have no discriminatory value, being present both in individuals with and without monogenic autoimmunity. While pancreatic autoantibodies have been previously shown to be specific (>57%) and highly sensitive (>99%) for discriminating between type 1 diabetes and non-autoimmune monogenic diabetes [8], we did not observe this in our cohort as monogenic autoimmunity often leads to autoantibody production. When islet autoantibodies were present, the type 1 diabetes GRS was lower in those with confirmed monogenic autoimmunity than in individuals with an unknown aetiology (0.558 vs 0.716). There is evidence that autoantibodies to harmonin and villin are diagnostic markers for individuals with IPEX syndrome [16], although we were unable to test this in our individuals with hemizygous FOXP3 mutations. C-peptide testing is useful for identifying type 2 diabetes and MODY from type 1 diabetes [17]. However, as monogenic autoimmunity results in destruction of the pancreatic beta cells it is unlikely to be useful in our group and we were unable to assay serum C-peptide in our individuals. The type 1 diabetes GRS (ROC AUC 0.80) gave similar discrimination of monogenic autoimmunity from unknown aetiology than clinical features (ROC AUC for age at diagnosis 0.79) and a combination of these two features gave the best discrimination (ROC AUC 0.88).

The overlap in clinical features may preclude their use for identifying individuals with monogenic autoimmunity. Age at diabetes onset was a good discriminator between the two groups of individuals; when split into quartiles based on age at diabetes diagnosis, 84% of those with monogenic autoimmunity were diagnosed in the first and second quartiles while 79% of those with an unknown aetiology were diagnosed in the third and fourth quartiles (p < 0.0001; Fig. 4). The range of age at diabetes diagnosis overlapped, however (monogenic autoimmunity 0–83 weeks; unknown aetiology 1–258 weeks), meaning it is less useful at an individual level. While autoimmune enteropathy and coeliac disease showed different prevalence in those with and without a mutation (Table 1), both groups included individuals with coeliac disease and autoimmune enteropathy. Furthermore, at the onset of symptoms these disorders can be difficult to distinguish clinically, particularly in very young individuals.

Those with confirmed monogenic autoimmunity were less likely to have AITD or coeliac disease in addition to type 1 diabetes than those with an unknown aetiology (22% vs 60%, OR 5.33). This is driven by the strong predisposing HLA allele DR3 (through linkage with DQ2) in keeping with previous studies on shared HLA risk for these disorders [18]. The same effect does not appear to modulate disease in monogenic autoimmunity as none of the five individuals carrying the highest risk alleles for concurrent type 1 diabetes and coeliac disease—DR3/DR3 and DR3/DR4 [18]—had coeliac disease and only 3/14 with DR3/X had coeliac disease or AITD. Further study of a larger group of individuals is needed to confirm this effect as they may go on to develop coeliac disease or AITD later in childhood. We selected individuals with an extreme phenotype (diabetes and at least one autoimmune disease diagnosed before the age of 5 years), hence we found the extreme genotypes, both for monogenic and polygenic disease.

Interestingly, one individual in this study who had a type 1 diabetes GRS of 0.73 (65th centile of the type 1 diabetes controls) and was diagnosed with diabetes at the age of 3 weeks, was positive for GAD autoantibodies and had coeliac disease and AITD. Diabetes that presents extremely early suggests monogenic disease as >80% of individuals diagnosed before the age of 6 months have a mutation in a known gene [19]. However, their high genetic risk and positivity for GAD autoantibodies, along with the specific clinical manifestations, suggests that this may be a rare case of polygenic type 1 diabetes presenting in the neonatal period.

This study provides evidence that the polygenic risk of developing autoimmune diabetes does not affect the development of diabetes in individuals with monogenic autoimmunity. Previous reports of individuals with monogenic autoimmunity have shown that many do not develop diabetes (e.g. 70% of those reported to have gain-of-function STAT3 mutations are not diabetic) [2, 20, 21]. The known risk alleles do not modify the phenotype in these individuals as the polygenic risk of developing autoimmune diabetes in our cohort of ‘with diabetes’ is similar to that in healthy controls (p = 0.162, data not shown). Further study of non-diabetic individuals with monogenic autoimmunity is warranted.

We propose that the type 1 diabetes GRS could be used to prioritise individuals for gene discovery studies. Our results suggest that a cut-off based on the 25th centile of type 1 diabetes controls would be suitable to guide selection of individuals for initial discovery as most individuals in this group have a monogenic cause. Furthermore, there was a small enrichment of individuals in the first quartile of the unknown-aetiology group (Fig. 2a), suggesting that some individuals in this group may have monogenic autoimmunity. These novel causes may be mutations in genes not previously associated with disease or deep-intronic/regulatory mutations in known genes. Identifying these novel aetiologies will further our understanding of the adaptive immune system and could provide new therapeutic targets as knowledge of the underlying pathway defect can allow personalised therapies. This is already happening for individuals with recessive LRBA mutations who can be treated with abatacept, which replaces the lost receptor molecule [22], and for individuals with IPEX syndrome who are amenable to haematopoietic stem cell transplantation, which if performed early can prevent the onset of organ-specific autoimmunity. Furthermore, identifying novel aetiologies will assist with research by preventing individuals with monogenic disease from taking part in clinical trials aimed at those with a polygenic aetiology.

The number of individuals with monogenic autoimmune disease available to study in our cohort was low (n = 37). However, to our knowledge, this is the largest series of individuals with monogenic autoimmune diabetes described in the literature to date. Interestingly, we did not identify any individuals with autoimmune polyendocrine syndrome type I due to biallelic AIRE mutations. The onset of autoimmune diabetes in this syndrome is typically later (age 30–50 years) [23, 24] and the specific clinically defining features, namely chronic mucocutaneous candidiasis and hypoparathyroidism, means identification may present less of a challenge.

Seven of the ten genotyped SNPs in this type 1 diabetes GRS cover loci that are associated (positively or negatively) with more than one autoimmune disease (ESM Table 1). However, some variants that predispose to multiple clinically distinct autoimmune disorders were not included in our panel. A recent meta-analysis of associations with childhood-onset autoimmune disease, including diabetes, identified 22 loci that associated with two or more of the disorders seen in our group of individuals [6]. A GRS tailored for regions with pleiotropic effects could offer higher discrimination of polygenic clustering of autoimmune disease and monogenic autoimmunity.

In conclusion, we have demonstrated that the type 1 diabetes GRS is useful for distinguishing clustering of early-onset type 1 diabetes with autoimmunity from monogenic autoimmune disease and could be used to prioritise individuals for gene discovery studies and follow-up genetic testing. Identifying these individuals might enable targeted treatment and would inform families and clinicians of the likely clinical course and increase understanding of the human immune system.