Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: results from the prospective TEDDY 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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243-OR: Integrating Genetic Risk Score and Islet Autoantibody Characteristics in the Predictive Model for Type 1 Diabetes in the TrialNet Study

Diabetes ◽

10.2337/db21-243-or ◽

2021 ◽

Vol 70 (Supplement 1) ◽

pp. 243-OR

Author(s):

LAURIC A. FERRAT ◽

ANDREA STECK ◽

HEMANG M. PARIKH ◽

LU YOU ◽

SUNA ONENGUT-GUMUSCU ◽

...

Keyword(s):

Type 1 Diabetes ◽

Predictive Model ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Islet Autoantibody

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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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1535-P: An Insulin Resistance Genetic Risk Score (IR_GRS) Is Associated with Mortality in Type 1 Diabetes (T1D)

Diabetes ◽

10.2337/db19-1535-p ◽

2019 ◽

Vol 68 (Supplement 1) ◽

pp. 1535-P

Author(s):

RACHEL G. MILLER ◽

TINA COSTACOU ◽

SUNA ONENGUT-GUMUSCU ◽

WEI-MIN CHEN ◽

STEPHEN S. RICH ◽

...

Keyword(s):

Insulin Resistance ◽

Type 1 Diabetes ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score

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Prediction of type 1 diabetes at birth: cord blood metabolites versus genetic risk score in the MoBa cohort

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/clinem/dgab400 ◽

2021 ◽

Author(s):

German Tapia ◽

Tommi Suvitaival ◽

Linda Ahonen ◽

Nicolai A Lund-Blix ◽

Pål R Njølstad ◽

...

Keyword(s):

Type 1 Diabetes ◽

Cord Blood ◽

Bile Acids ◽

Risk Score ◽

Genetic Risk ◽

Risk Group ◽

Genetic Risk Score ◽

Blood Concentrations ◽

Polar Metabolites

Abstract Background and aim Genetic markers are established as predictive of type 1 diabetes, but unknown early life environment is believed to be involved. Umbilical cord blood may reflect perinatal metabolism and exposures. We studied whether selected polar metabolites in cord blood contribute to prediction of type 1 diabetes. Methods Using a targeted UHPLC-QQQ-MS platform, we quantified 27 low molecular weight metabolites (including amino acids, small organic acids and bile acids) in 166 children, who later developed type 1 diabetes, and 177 random control children in the Norwegian Mother, Father and Child (MoBa) cohort. We analysed the data using logistic regression (estimating odds ratios per standard deviation [aOR]), area under the receiver operating characteristic curve (AUC) and k-means clustering. Metabolites were compared to a genetic risk score based on 51 established non-HLA SNPs, and a four-category HLA risk group. Results The strongest associations for metabolites were aminoadipic acid (aOR=1.23,95%CI:0.97–1.55), indoxyl sulfate (aOR=1.15,95%CI:0.87–1.51), and tryptophan (aOR=0.84,95%CI:0.65–1.10), with other aORs close to 1.0, and none significantly associated with type 1 diabetes. K-means clustering identified six clusters, none of which were associated with type 1 diabetes. Cross-validated AUC showed no predictive value of metabolites (AUC 0.49), while the non-HLA genetic risk score AUC was 0.56 and the HLA risk group AUC was 0.78. Conclusions In this large study, we found no support of a predictive role of cord blood concentrations of selected bile acids and other small polar metabolites in the development of type 1 diabetes.

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Type 1 diabetes genetic risk score discriminates between monogenic and Type 1 diabetes in children diagnosed at the age of <5 years in the Iranian population

Diabetic Medicine ◽

10.1111/dme.14071 ◽

2019 ◽

Vol 36 (12) ◽

pp. 1694-1702 ◽

Cited By ~ 2

Author(s):

H. Yaghootkar ◽

F. Abbasi ◽

N. Ghaemi ◽

A. Rabbani ◽

M. N. Wakeling ◽

...

Keyword(s):

Type 1 Diabetes ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Iranian Population

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Methods for quick, accurate and cost-effective determination of the type 1 diabetes genetic risk score (T1D-GRS)

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2019-0787 ◽

2020 ◽

Vol 58 (4) ◽

pp. e102-e104 ◽

Cited By ~ 2

Author(s):

Jonathan M. Locke ◽

Mark J. Latten ◽

Renu Y. Datta ◽

Andrew R. Wood ◽

Martin A. Crockard ◽

...

Keyword(s):

Type 1 Diabetes ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Cost Effective

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Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

Journal of Diabetes Research ◽

10.1155/2016/9570424 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 9

Author(s):

Caroline A. Brorsson ◽

Lotte B. Nielsen ◽

Marie Louise Andersen ◽

Simranjeet Kaur ◽

Regine Bergholdt ◽

...

Keyword(s):

Type 1 Diabetes ◽

Glycemic Control ◽

Disease Progression ◽

Candidate Genes ◽

Risk Score ◽

Genetic Risk ◽

Cell Function ◽

Genetic Risk Score ◽

Newly Diagnosed

Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci onβ-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residualβ-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.

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