Abstract
Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.
Highlights
In type 1 diabetes (T1D) the pancreatic β-cells are destroyed by the immune system in a process involving the proinflammatory cytokines interleukin-1-β (IL-1β), interferonγ (IFNγ), and tumor necrosis factor-α (TNFα) released from antigen-presenting cells and T-cells [1, 2]
A genetic risk score was calculated for each individual based on the cumulative number of risk alleles carried for the single nucleotide polymorphisms (SNPs) and was used as a continuous variable to test for association with insulin dose-adjusted HbA1c (IDAA1c) and HbA1c levels at 1, 3, 6, 9, and months after T1D onset in linear regression models
A genetic risk score model was constructed from the Genomewide association scans (GWAS)-identified SNPs linked to the 11 genes identified above to investigate the cumulative effect of T1Dassociated risk alleles on disease progression in new-onset T1D children
Summary
In type 1 diabetes (T1D) the pancreatic β-cells are destroyed by the immune system in a process involving the proinflammatory cytokines interleukin-1-β (IL-1β), interferonγ (IFNγ), and tumor necrosis factor-α (TNFα) released from antigen-presenting cells and T-cells [1, 2]. Many of the GWAS candidate genes have annotated immune-cell functions and most of the genetic risk variants have been suggested to modulate immune-regulatory pathways [4, 5]. Recent studies have highlighted that a significant proportion of the candidate genes are expressed in human islets suggesting functional effects in β-cells [6,7,8] and possibly involvement in inflammation- and immune-mediated β-cell killing mechanisms thereby potentially affecting disease progression after clinical onset [9]. We aimed at investigating whether a combined genetic risk score of T1D risk variants can predict glycemic control and residual β-cell function as assessed by HbA1c and insulin dose-adjusted HbA1c (IDAA1c) during disease progression in children with newly diagnosed T1D. We exclusively included SNPs for candidate genes expressed and transcriptionally regulated by cytokines in the target tissue of T1D, that is, human islets, as we hypothesized that these qualify as the most directly involved predictors
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