A High-Throughput Population Screening System for the Estimation of Genetic Risk for Type 1 Diabetes: An Application for the TEDDY (The Environmental Determinants of Diabetes in the Young) Study

2007 ◽  
Vol 9 (5) ◽  
pp. 460-472 ◽  
Author(s):  
Minna Kiviniemi ◽  
Robert Hermann ◽  
Jussi Nurmi ◽  
Anette G. Ziegler ◽  
Mikael Knip ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
High Throughput ◽  
Genetic Risk ◽  
Population Screening ◽  
Screening System ◽  
Environmental Determinants

A Screening System for Detecting Genetic Risk Markers of Type 1 Diabetes in Dried Blood Spots

2006 ◽  
Vol 8 (4) ◽  
pp. 433-443 ◽  
Author(s):  
Paul Dantonio ◽  
Nancy Meredith ◽  
Marie Earley ◽  
Suzanne Cordovado ◽  
William J. Callan ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Genetic Risk ◽  
Dried Blood Spots ◽  
Screening System ◽  
Risk Markers ◽  
Blood Spots

scholarly journals Is It Time to Screen the General Population for Type 1 Diabetes?

2015 ◽  
Vol 11 (01) ◽  
pp. 10 ◽  
Author(s):  
Kimber M Simmons ◽  
Aaron W Michels ◽  
◽  
Keyword(s):  
Type 1 Diabetes ◽  
General Population ◽  
Genetic Risk ◽  
Population Screening ◽  
Β Cells ◽  
Β Cell ◽  
Targeted Screening ◽  
History Of ◽  
Chronic Autoimmune Disease

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by destruction of insulin-producing β cells in the pancreas. The incidence of T1D is increasing dramatically, and the prevalence has doubled in the last 2 decades, further increasing the morbidity and mortality associated with the disease. T1D is now predictable with the measurement of antibodies directed against β cell proteins. Islet autoantibodies (IAs) are detectable from the peripheral blood months to years before clinical diagnosis. With the presence of two or more antibodies, the risk for developing T1D is nearly 100 % given enough time. Targeted screening for T1D risk has been carried out in first-degree relatives and those with a significant genetic risk. However, more than 85 % of individuals who are diagnosed with T1D do not have a family history. In light of the predictability of T1D and recent advances in IA measurement, general population screening is on the horizon. We provide an overview of the history of general population screening and discuss the rationale for and arguments against screening the general population for T1D risk.

Genetic Risk for Type 1 Diabetes Profoundly Influences the Core Gut Microbiome in Children

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 209-LB ◽  
Author(s):  
JORDAN RUSSELL ◽  
LUIZ ROESCH ◽  
MARK A. ATKINSON ◽  
DESMOND SCHATZ ◽  
ERIC W. TRIPLETT ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Gut Microbiome ◽  
Genetic Risk ◽  
The Core

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

Diabetes ◽  
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

scholarly journals De‐ coding genetic risk variants in type 1 diabetes

2021 ◽  
Author(s):  
Melanie R Shapiro ◽  
Puchong Thirawatananond ◽  
Leeana Peters ◽  
Robert C Sharp ◽  
Similoluwa Ogundare ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Genetic Risk ◽  
Risk Variants ◽  
Genetic Risk Variants

scholarly journals Luminex and Other Multiplex High Throughput Technologies for the Identification of, and Host Response to, Environmental Triggers of Type 1 Diabetes

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Sharad Purohit ◽  
Ashok Sharma ◽  
Jin-Xiong She
Keyword(s):  
Type 1 Diabetes ◽  
High Throughput ◽  
Host Response ◽  
Environmental Pollutants ◽  
Silent Period ◽  
Dietary Factors ◽  
Systematic Evaluation ◽  
Complex Nature ◽  
Diabetes Research

Complex interactions between a series of environmental factors and genes result in progression to clinical type 1 diabetes in genetically susceptible individuals. Despite several decades of research in the area, these interactions remain poorly understood. Several studies have yielded associations of certain foods, infections, and immunizations with the onset and progression of diabetes autoimmunity, but most findings are still inconclusive. Environmental triggers are difficult to identify mainly due to (i) large number and complex nature of environmental exposures, including bacteria, viruses, dietary factors, and environmental pollutants, (ii) reliance on low throughput technology, (iii) less efforts in quantifying host response, (iv) long silent period between the exposure and clinical onset of T1D which may lead to loss of the exposure fingerprints, and (v) limited sample sets. Recent development in multiplex technologies has enabled systematic evaluation of different classes of molecules or macroparticles in a high throughput manner. However, the use of multiplex assays in type 1 diabetes research is limited to cytokine assays. In this review, we will discuss the potential use of multiplex high throughput technologies in identification of environmental triggers and host response in type 1 diabetes.

High-throughput Screening for CD8+ T Cell Epitopes Targeted in Type 1 Diabetes

2010 ◽  
Vol 135 ◽  
pp. S19
Author(s):  
Brian Hondowicz ◽  
Katharine Schwedhelm ◽  
Arnold Kas ◽  
Michael Tasch ◽  
Nirasha Ramchurren ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
T Cell ◽  
High Throughput ◽  
High Throughput Screening ◽  
Cd8 T Cell ◽  
T Cell Epitopes

243-OR: Integrating Genetic Risk Score and Islet Autoantibody Characteristics in the Predictive Model for Type 1 Diabetes in the TrialNet Study

Diabetes ◽  
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

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

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.

scholarly journals Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: results from the prospective TEDDY study

2018 ◽  
Vol 56 (9) ◽  
pp. 602-605 ◽  
Author(s):  
Andreas Beyerlein ◽  
Ezio Bonifacio ◽  
Kendra Vehik ◽  
Markus Hippich ◽  
Christiane Winkler ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Factors ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Islet Autoimmunity ◽  
Islet Autoantibody ◽  
Clinical Type

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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