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      Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

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          Abstract

          Background

          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.

          Methods and findings

          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.

          Conclusions

          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.

          Abstract

          Anette-Gabriele Ziegler and colleagues report their novel genetic risk score for identifying infants with a high risk of developing type 1 diabetes.

          Author summary

          Why was this study done?
          • Prevention of childhood diseases such as type 1 diabetes is of medical importance.

          • Prevention of type 1 diabetes might be best achieved by intervention prior to the development of islet autoantibodies, which define a pre-symptomatic disease stage.

          • Early intervention requires tools such as measures of genetic risk that identify future cases.

          • Risk for type 1 diabetes in the absence of a family history is currently identified by HLA genotyping, with maximum identified risk reaching around 5%.

          • Genetic scores derived from multiple risk loci may improve risk stratification for pre-symptomatic type 1 diabetes.

          What did the researchers do and find?
          • Two previously proposed genetic scores for type 1 diabetes risk were calculated for over 3,000 children without a family history of type 1 diabetes but with 1 of the 2 highest-risk HLA genotypes (heterozygous DR3 and DR4-DQ8 or homozygous DR4-DQ8) participating in the TEDDY cohort study, which prospectively follows children from birth for the development of islet autoantibodies and diabetes.

          • We found that both of the genetic scores, and a merged genetic score that combined the features of both, stratified the risk for islet autoantibodies and diabetes in the children.

          • The upper quartile of the merged genetic score was associated with a >10% risk for the pre-symptomatic stage of multiple islet autoantibodies, and almost half the children who developed pre-symptomatic or symptomatic diabetes were identified by this score.

          What do these findings mean?
          • Combining genetic information from multiple risk loci can improve the prediction of diseases such as type 1 diabetes.

          • A genetic risk score model is proposed that could be used to recruit infants into early type 1 diabetes primary prevention trials.

          • The model provides a new paradigm for genetic screening and selection of at-risk infants that, together with family history and HLA genotyping, could identify up to 25% of future childhood cases of type 1 diabetes from less than 1% of newborns.

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          Most cited references19

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          Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks.

          The area under the time-dependent ROC curve (AUC) may be used to quantify the ability of a marker to predict the onset of a clinical outcome in the future. For survival analysis with competing risks, two alternative definitions of the specificity may be proposed depending of the way to deal with subjects who undergo the competing events. In this work, we propose nonparametric inverse probability of censoring weighting estimators of the AUC corresponding to these two definitions, and we study their asymptotic properties. We derive confidence intervals and test statistics for the equality of the AUCs obtained with two markers measured on the same subjects. A simulation study is performed to investigate the finite sample behaviour of the test and the confidence intervals. The method is applied to the French cohort PAQUID to compare the abilities of two psychometric tests to predict dementia onset in the elderly accounting for death without dementia competing risk. The 'timeROC' R package is provided to make the methodology easily usable. Copyright © 2013 John Wiley & Sons, Ltd.
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            Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A.

            The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods-recursive partitioning and regression-to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; P(combined) = 2.01 x 10(-19) and 2.35 x 10(-13), respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes.
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              A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults.

              With rising obesity, it is becoming increasingly difficult to distinguish between type 1 diabetes (T1D) and type 2 diabetes (T2D) in young adults. There has been substantial recent progress in identifying the contribution of common genetic variants to T1D and T2D. We aimed to determine whether a score generated from common genetic variants could be used to discriminate between T1D and T2D and also to predict severe insulin deficiency in young adults with diabetes.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Project administrationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Project administrationRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: Project administrationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                3 April 2018
                April 2018
                : 15
                : 4
                : e1002548
                Affiliations
                [1 ] DFG–Center for Regenerative Therapies Dresden, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
                [2 ] Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany
                [3 ] Forschergruppe Diabetes, Technical University of Munich, Klinikum Rechts der Isar, Munich, Germany
                [4 ] Forschergruppe Diabetes e.V. at Helmholtz Zentrum München, Munich, Germany
                [5 ] Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida, United States of America
                [6 ] Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, United Kingdom
                [7 ] Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
                [8 ] Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, Colorado, United States of America
                [9 ] Pacific Northwest Diabetes Research Institute, Seattle, Washington, United States of America
                [10 ] Department of Clinical Sciences, Clinical Research Centre, Skåne University Hospital, Lund University, Malmo, Sweden
                [11 ] Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, Georgia, United States of America
                [12 ] Department of Pediatrics, Turku University Hospital, Turku, Finland
                [13 ] Department of Physiology, University of Turku, Turku, Finland
                [14 ] National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
                [15 ] Clinical Islet Transplant Program, University of Alberta, Edmonton, Alberta, Canada
                [16 ] National Institute for Health Research, Exeter Clinical Research Facility, Exeter, United Kingdom
                [17 ] Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
                Chinese University of Hong Kong, CHINA
                Author notes

                I have read the journal’s policy and the authors of this manuscript have the following competing interests: A patent has been applied for (EP17178396/LU100334) with the title "Method the risk to develop type 1 diabetes" by Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH). EB, AGZ, CW and JK are one of the inventors. The patent includes the genetic score that is examined in the manuscript. RAO has a personal funding from Diabetes UK to study the biology of Type 1 diabetes (this includes a research grant to work on genetic risk scores in Type 1 diabetes). RAO has a UK Medical Research Council confidence in concept grant to turn a type 1 diabetes genetic risk score into a diagnostic test for clinical practice. AB, MH, KV, MNW, ML, ATH, BIF, AKS, WAH, JPK, AL, MJR, JXS, JT, BA, SSR have declared that no other competing interests exist.

                ¶ Membership of the TEDDY Study Group is provided in the Acknowledgments.

                Author information
                http://orcid.org/0000-0002-8704-4713
                http://orcid.org/0000-0001-6253-2596
                http://orcid.org/0000-0001-6243-6772
                http://orcid.org/0000-0003-1708-0302
                http://orcid.org/0000-0002-6636-4048
                http://orcid.org/0000-0003-2979-0475
                http://orcid.org/0000-0003-1735-0499
                http://orcid.org/0000-0002-6290-5548
                Article
                PMEDICINE-D-17-04068
                10.1371/journal.pmed.1002548
                5882115
                29614081
                91c8702c-79bb-411b-a481-5a2d20eb8f4a

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 14 November 2017
                : 1 March 2018
                Page count
                Figures: 5, Tables: 0, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, and Contract No. HHSN267200700014C
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Funded by: funder-id http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Funded by: National Institute of Environmental Health Sciences
                Funded by: funder-id http://dx.doi.org/10.13039/100000901, Juvenile Diabetes Research Foundation International;
                Funded by: funder-id http://dx.doi.org/10.13039/100000030, Centers for Disease Control and Prevention;
                Funded by: NIH/NCATS Clinical and Translational Science Awards
                Award ID: UL1 TR000064
                Funded by: funder-id http://dx.doi.org/10.13039/100010174, University of Colorado;
                Award ID: UL1 TR001082
                Funded by: iMed–the Helmholtz Initiative on Personalized Medicine
                Award Recipient :
                Funded by: DFG Research Center and Cluster of Excellence - Center for Regenerative Therapies Dresden
                Award ID: FZ 111
                Award Recipient :
                This work was supported by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Juvenile Diabetes Research Foundation (JDRF), and Centers for Disease Control and Prevention (CDC). This work was supported in part by NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082), and by iMed–the Helmholtz Initiative on Personalized Medicine. EB is supported by the DFG Research Center and Cluster of Excellence - Center for Regenerative Therapies Dresden (FZ 111). BA from the NIDDK was involved in the design and conduct of the study as well as the review of the manuscript, and approval to submit the manuscript. Otherwise, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Endocrinology
                Endocrine Disorders
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                Custom metadata
                The datasets generated and analyzed during the current study will be made available in the NIDDK Central Repository at https://www.niddkrepository.org/studies/teddy. TEDDY Immunochip (SNP) data that support the findings of this study have been deposited in NCBI’s database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001037.v1.p1.

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