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A strategy to find gene combinations that identify children who progress rapidly to type 1 diabetes after islet autoantibody seroconversion

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Abstract

We recently developed a novel approach capable of identifying gene combinations to obtain maximal disease risk stratification. Type 1 diabetes has a preclinical phase including seroconversion to autoimmunity and subsequent progression to diabetes. Here, we applied our gene combination approach to identify combinations that contribute either to islet autoimmunity or to the progression from islet autoantibodies to diabetes onset. We examined 12 type 1 diabetes susceptibility genes (INS, ERBB3, PTPN2, IFIH1, PTPN22, KIAA0350, CD25, CTLA4, SH2B3, IL2, IL18RAP, IL10) in a cohort of children of parents with type 1 diabetes and prospectively followed from birth. The most predictive combination was subsequently applied to a smaller validation cohort. The combinations of genes only marginally contributed to the risk of developing islet autoimmunity, but could substantially modify risk of progression to diabetes in islet autoantibody-positive children. The greatest discrimination was provided by risk allele scores of five genes, INS, IFIH1, IL18RAP, CD25, and IL2 genes, which could identify 80 % of islet autoantibody-positive children who progressed to diabetes within 6 years of seroconversion and discriminate high risk (63 % within 6 years; 95 % CI 45–81 %) and low risk (11 % within 6 years; 95 % CI 0.1–22 %; p = 4 × 10−5) antibody-positive children. Risk stratification by these five genes was confirmed in a second cohort of islet autoantibody children. These findings highlight genes that may affect the rate of the beta-cell destruction process once autoimmunity has initiated and may help to identify islet autoantibody-positive subjects with rapid progression to diabetes.

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Abbreviations

SNP:

Single-nucleotide polymorphism

IAA:

Insulin autoantibodies

GADA:

GAD autoantibodies

IA-2A:

Islet antigen 2 autoantibodies

ZnT8A:

Zinc transporter 8 autoantibodies

AUC:

Area under the curve

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Acknowledgments

We thank Annette Knopff, Stephanie Krause, Claudia Matzke, Marlon Scholz, Joanna Stock, for data collection and expert technical assistance; Ramona Puff for laboratory management; and Harald Grallert for performing the genotyping. We also thank all paediatricians and family doctors in Germany for participating in the BABYDIAB study. The work was supported by grants from the Kompetenznetz Diabetes mellitus (Competence Network for Diabetes mellitus) funded by the Federal Ministry of Education and Research (FKZ 01GI0805-07), the Juvenile Diabetes Research Fund (No. 17-2012-16), and funding from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.). E.B. is supported by the DFG Research Center and Cluster of Excellence—Center for Regenerative Therapies Dresden (FZ 111). Fabian Theis is funded by the European Research Council (starting grant “LatentCauses”). Jan Krumsiek is funded by the Helmholtz Postdoc Programme.

Conflict of interest

We declare that we have no conflict of interest.

Author information

Authors and Affiliations

  1. Center for Regenerative Therapies Dresden, Technische Universität Dresden, Fetscherstrasse 105, 01307, Dresden, Germany

    Ezio Bonifacio

  2. Paul Langerhans Institute Dresden, German Center for Diabetes Research (DZD), Dresden, Germany

    Ezio Bonifacio

  3. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany

    Jan Krumsiek & Fabian J. Theis

  4. Forschergruppe Diabetes e.V., Neuherberg, Germany

    Christiane Winkler & Anette-Gabriele Ziegler

  5. Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum Rechts der Isar, Technische Universität München, 85764, Neuherberg, Germany

    Christiane Winkler & Anette-Gabriele Ziegler

  6. Department of Mathematics, Technische Universität München, Garching, Germany

    Fabian J. Theis

Authors
  1. Ezio Bonifacio
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  2. Jan Krumsiek
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  3. Christiane Winkler
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  4. Fabian J. Theis
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  5. Anette-Gabriele Ziegler
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Corresponding authors

Correspondence to Ezio Bonifacio or Anette-Gabriele Ziegler.

Additional information

Communicated by Antonio Secchi.

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Bonifacio, E., Krumsiek, J., Winkler, C. et al. A strategy to find gene combinations that identify children who progress rapidly to type 1 diabetes after islet autoantibody seroconversion. Acta Diabetol 51, 403–411 (2014). https://doi.org/10.1007/s00592-013-0526-2

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  • DOI: https://doi.org/10.1007/s00592-013-0526-2

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