Human Enterovirus RNA in Monthly Fecal Samples and Islet Autoimmunity in Norwegian Children With High Genetic Risk for Type 1 Diabetes: The MIDIA study

BackgroundProgression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.MethodsIn 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression.ResultsIslet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93).ConclusionsGenetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes.

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A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

Diabetes Care ◽

10.2337/dc18-0087 ◽

2018 ◽

Vol 41 (9) ◽

pp. 1887-1894 ◽

Cited By ~ 33

Author(s):

Maria J. Redondo ◽

Susan Geyer ◽

Andrea K. Steck ◽

Seth Sharp ◽

John M. Wentworth ◽

...

Keyword(s):

At Risk ◽

Type 1 Diabetes ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Islet Autoimmunity

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Integrative analyses of TEDDY Omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes

Genome Biology ◽

10.1186/s13059-021-02262-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Leandro Balzano-Nogueira ◽

Ricardo Ramirez ◽

Tatyana Zamkovaya ◽

Jordan Dailey ◽

Alexandria N. Ardissone ◽

...

Keyword(s):

Type 1 Diabetes ◽

Lipid Metabolism ◽

Islet Autoimmunity ◽

Matrix Remodeling ◽

Omics Data ◽

Multifactorial Diseases ◽

Beta Cell Destruction ◽

High Genetic Risk ◽

Intracellular Ros

Abstract Background The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and understand progression over time. Results We identify a multi-omics signature that was predictive of islet autoimmunity (IA) as early as 1 year before seroconversion. At this time, abnormalities in lipid metabolism, decreased capacity for nutrient absorption, and intracellular ROS accumulation are detected in children progressing towards IA. Additionally, extracellular matrix remodeling, inflammation, cytotoxicity, angiogenesis, and increased activity of antigen-presenting cells are observed, which may contribute to beta cell destruction. Our results indicate that altered molecular homeostasis is present in IA-developing children months before the actual detection of islet autoantibodies, which opens an interesting window of opportunity for therapeutic intervention. Conclusions The approach employed herein for assessment of the TEDDY cohort showcases the utilization of multi-omics data for the modeling of complex, multifactorial diseases, like T1D.

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Modulating the natural history of type 1 diabetes in children at high genetic risk by mucosal insulin immunization

Current Diabetes Reports ◽

10.1007/s11892-008-0017-y ◽

2008 ◽

Vol 8 (2) ◽

pp. 87-93 ◽

Cited By ~ 54

Author(s):

Peter Achenbach ◽

Jennifer Barker ◽

Ezio Bonifacio

Keyword(s):

Type 1 Diabetes ◽

Natural History ◽

Genetic Risk ◽

High Genetic Risk ◽

History Of

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Novel genetic risk factors influence progression of islet autoimmunity to type 1 diabetes

Scientific Reports ◽

10.1038/s41598-020-75690-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Suna Onengut-Gumuscu ◽

Umadevi Paila ◽

Wei-Min Chen ◽

Aakrosh Ratan ◽

Zhennan Zhu ◽

...

Keyword(s):

Risk Factors ◽

Type 1 Diabetes ◽

Genetic Risk ◽

Viral Infections ◽

Genetic Risk Factors ◽

Islet Autoimmunity ◽

Islet Autoantibody ◽

Critical Pathways ◽

Genome Wide

Abstract Type 1 diabetes arises from the autoimmune destruction of insulin-producing beta-cells of the pancreas, resulting in dependence on exogenously administered insulin to maintain glucose homeostasis. In this study, our aim was to identify genetic risk factors that contribute to progression from islet autoimmunity to clinical type 1 diabetes. We analyzed 6.8 million variants derived from whole genome sequencing of 160 islet autoantibody positive subjects, including 87 who had progressed to type 1 diabetes. The Cox proportional-hazard model for survival analysis was used to identify genetic variants associated with progression. We identified one novel region, 20p12.1 (TASP1; genome-wide P < 5 × 10–8) and three regions, 1q21.3 (MRPS21–PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), with suggestive evidence of association (P < 8.5 × 10–8) with progression from islet autoimmunity to type 1 diabetes. Once islet autoimmunity is initiated, functional mapping identified two critical pathways, response to viral infections and interferon signaling, as contributing to disease progression. These results provide evidence that genetic pathways involved in progression from islet autoimmunity differ from those pathways identified once disease has been established. These results support the need for further investigation of genetic risk factors that modulate initiation and progression of subclinical disease to inform efforts in development of novel strategies for prediction and intervention of type 1 diabetes.

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