Genetics of type 1 diabetes Steck, Andrea K; Rewers, Marian J
Clinical chemistry (Baltimore, Md.),
02/2011, Volume:
57, Issue:
2
Journal Article
Peer reviewed
Open access
Type 1 diabetes, a multifactorial disease with a strong genetic component, is caused by the autoimmune destruction of pancreatic β cells. The major susceptibility locus maps to the HLA class II genes ...at 6p21, although more than 40 non-HLA susceptibility gene markers have been confirmed.
Although HLA class II alleles account for up to 30%-50% of genetic type 1 diabetes risk, multiple non-MHC loci contribute to disease risk with smaller effects. These include the insulin, PTPN22, CTLA4, IL2RA, IFIH1, and other recently discovered loci. Genomewide association studies performed with high-density single-nucleotide-polymorphism genotyping platforms have provided evidence for a number of novel loci, although fine mapping and characterization of these new regions remain to be performed. Children born with the high-risk genotype HLADR3/4-DQ8 comprise almost 50% of children who develop antiislet autoimmunity by the age of 5 years. Genetic risk for type 1 diabetes can be further stratified by selection of children with susceptible genotypes at other diabetes genes, by selection of children with a multiple family history of diabetes, and/or by selection of relatives that are HLA identical to the proband.
Children with the HLA-risk genotypes DR3/4-DQ8 or DR4/DR4 who have a family history of type 1 diabetes have more than a 1 in 5 risk for developing islet autoantibodies during childhood, and children with the same HLA-risk genotype but no family history have approximately a 1 in 20 risk. Determining extreme genetic risk is a prerequisite for the implementation of primary prevention trials, which are now underway for relatives of individuals with type 1 diabetes.
Genetics of type 1 diabetes Redondo, Maria J; Steck, Andrea K; Pugliese, Alberto
Pediatric diabetes,
20/May , Volume:
19, Issue:
3
Journal Article
Peer reviewed
Open access
Type 1 diabetes (T1D) results from immune-mediated loss of pancreatic beta cells leading to insulin deficiency. It is the most common form of diabetes in children, and its incidence is on the rise. ...This article reviews the current knowledge on the genetics of T1D. In particular, we discuss the influence of HLA and non-HLA genes on T1D risk and disease progression through the preclinical stages of the disease, and the development of genetic scores that can be applied to disease prediction. Racial/ethnic differences, challenges and future directions in the genetics of T1D are also discussed.
In this phase 2 trial, children and young adults with newly diagnosed overt type 1 diabetes were randomly assigned to receive golimumab, a human monoclonal antibody to tumor necrosis factor
α
, or ...placebo. Golimumab resulted in better endogenous insulin production and less exogenous insulin use than placebo.
Aims:
Our study aims were to determine the frequency of MODY mutations (HNF1A, HNF4A, glucokinase) in a diverse population of youth with diabetes and to assess how well clinical features identify ...youth with maturity-onset diabetes of the young (MODY).
Methods:
The SEARCH for Diabetes in Youth study is a US multicenter, population-based study of youth with diabetes diagnosed at age younger than 20 years. We sequenced genomic DNA for mutations in the HNF1A, HNF4A, and glucokinase genes in 586 participants enrolled in SEARCH between 2001 and 2006. Selection criteria included diabetes autoantibody negativity and fasting C-peptide levels of 0.8 ng/mL or greater.
Results:
We identified a mutation in one of three MODY genes in 47 participants, or 8.0% of the tested sample, for a prevalence of at least 1.2% in the pediatric diabetes population. Of these, only 3 had a clinical diagnosis of MODY, and the majority was treated with insulin. Compared with the MODY-negative group, MODY-positive participants had lower FCP levels (2.2 ± 1.4 vs 3.2 ± 2.1 ng/mL, P < .01) and fewer type 2 diabetes-like metabolic features. Parental history of diabetes did not significantly differ between the 2 groups.
Conclusions/Interpretation:
In this systematic study of MODY in a large pediatric US diabetes cohort, unselected by referral pattern or family history, MODY was usually misdiagnosed and incorrectly treated with insulin. Although many type 2 diabetes-like metabolic features were less common in the mutation-positive group, no single characteristic identified all patients with mutations. Clinicians should be alert to the possibility of MODY diagnosis, particularly in antibody-negative youth with diabetes.
Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require ...early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.
The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%-9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%-54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%-60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.
A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials.
While it is known that there is progression to diabetes in <10 years in 70% of children with two or more islet autoantibodies, predictors of the progression to diabetes are only partially defined.
...The Environmental Determinants of Diabetes in the Young (TEDDY) study has observed 8,503 children who were at increased genetic risk for autoimmune diabetes. Insulin autoantibodies (IAAs), GAD65 autoantibodies (GADAs), and insulinoma-associated protein 2 autoantibodies (IA-2As) were measured every 3 months until 4 years of age and every 6 months thereafter; if results were positive, the autoantibodies were measured every 3 months.
Life table analysis revealed that the cumulative incidence of diabetes by 5 years since the appearance of the first autoantibody differed significantly by the number of positive autoantibodies (47%, 36%, and 11%, respectively, in those with three autoantibodies, two autoantibodies, and one autoantibody, P < 0.001). In time-varying survival models adjusted for first-degree relative status, number of autoantibodies, age at first persistent confirmed autoantibodies, and HLA genotypes, higher mean IAA and IA-2A levels were associated with an increased risk of type 1 diabetes in children who were persistently autoantibody positive (IAAs: hazard ratio HR 8.1 95% CI 4.6-14.2; IA-2A: HR 7.4 95% CI 4.3-12.6; P < 0.0001). The mean GADA level did not significantly affect the risk of diabetes.
In the TEDDY study, children who have progressed to diabetes usually expressed two or more autoantibodies. Higher IAA and IA-2A levels, but not GADA levels, increased the risk of diabetes in those children who were persistently autoantibody positive.
Advances in molecular methods and the ability to share large population-based datasets are uncovering heterogeneity within diabetes types, and some commonalities between types. Within type 1 ...diabetes, endotypes have been discovered based on demographic (e.g. age at diagnosis, race/ethnicity), genetic, immunological, histopathological, metabolic and/or clinical course characteristics, with implications for disease prediction, prevention, diagnosis and treatment. In type 2 diabetes, the relative contributions of insulin resistance and beta cell dysfunction are heterogeneous and relate to demographics, genetics and clinical characteristics, with substantial interaction from environmental exposures. Investigators have proposed approaches that vary from simple to complex in combining these data to identify type 2 diabetes clusters relevant to prognosis and treatment. Advances in pharmacogenetics and pharmacodynamics are also improving treatment. Monogenic diabetes is a prime example of how understanding heterogeneity within diabetes types can lead to precision medicine, since phenotype and treatment are affected by which gene is mutated. Heterogeneity also blurs the classic distinctions between diabetes types, and has led to the definition of additional categories, such as latent autoimmune diabetes in adults, type 1.5 diabetes and ketosis-prone diabetes. Furthermore, monogenic diabetes shares many features with type 1 and type 2 diabetes, which make diagnosis difficult. These challenges to the current classification framework in adult and paediatric diabetes require new approaches. The ‘palette model’ and the ‘threshold hypothesis’ can be combined to help explain the heterogeneity within and between diabetes types. Leveraging such approaches for therapeutic benefit will be an important next step for precision medicine in diabetes.
Graphical abstract
The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we ...present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.
To assess the costs and project the potential lifetime cost-effectiveness of the ongoing Autoimmunity Screening for Kids (ASK) program, a large-scale, presymptomatic type 1 diabetes screening program ...for children and adolescents in the metropolitan Denver region.
We report the resource utilization, costs, and effectiveness measures from the ongoing ASK program compared with usual care (i.e., no screening). Additionally, we report a practical screening scenario by including utilization and costs relevant to routine screening in clinical practice. Finally, we project the potential cost-effectiveness of ASK and routine screening by identifying clinical benchmarks (i.e., diabetic ketoacidosis DKA events avoided, HbA
improvements vs. no screening) needed to meet value thresholds of $50,000-$150,000 per quality-adjusted life-year (QALY) gained over a lifetime horizon.
Cost per case detected was $4,700 for ASK screening and $14,000 for routine screening. To achieve value thresholds of $50,000-$150,000 per QALY gained, screening costs would need to be offset by cost savings through 20% reductions in DKA events at diagnosis in addition to 0.1% (1.1 mmol/mol) improvements in HbA
over a lifetime compared with no screening for patients who develop type 1 diabetes. Value thresholds were not met from avoiding DKA events alone in either scenario.
Presymptomatic type 1 diabetes screening may be cost-effective in areas with a high prevalence of DKA and an infrastructure facilitating screening and monitoring if the benefits of avoiding DKA events and improved HbA
persist over long-run time horizons. As more data are collected from ASK, the model will be updated with direct evidence on screening effects.
Type 1 diabetes (TID) is characterized by a loss of pancreatic islet beta cell function resulting in loss of insulin production. Genetic and environmental factors may trigger immune responses ...targeting beta cells thus generating islet antibodies (IA). Immune response pathways involve a cascade of events, initiated by cytokines and chemokines, producing inflammation which can result in tissue damage.
A nested case-control study was performed to identify temporal changes in cytokine levels in 75 DAISY subjects: 25 diagnosed T1D, 25 persistent IA, and 25 controls. Serum samples were selected at four time points: (T1) earliest, (T2) just prior to IA, (T3) just after IA, and (T4) prior to T1D diagnosis or most recent. Cytokines (IFN-α2a, IL-6, IL-17, IL-1β, IP-10, MCP-1, IFN-γ, IL-1α, and IL-1ra) were measured using the Meso Scale Discovery system Human Custom Cytokine 9-Plex assay.
Multivariate mixed models adjusting for HLA risk, first-degree relative status, age, and gender, showed MCP-1 and IFN-үto be significantly higher at T3 in T1D compared to IA subjects. At T4, IP-10 was significantly higher in IA subjects than controls.
This repeated measures nested case-control study identified increased inflammatory markers in IA children who developed T1D compared to IA children who had not progressed to clinical disease. It also showed increased inflammation in both T1D and IA children when compared to controls. Results suggest inflammation may be related to both the development of IA and progression to T1D.
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