Christine von Toerne1, Michael Laimighofer2,3, Peter Achenbach4,5,6, Andreas Beyerlein4,5, Tonia de Las Heras Gala7,8, Jan Krumsiek2,7, Fabian J Theis2,3, Anette G Ziegler9,10,11, Stefanie M Hauck12. 1. Research Unit Protein Science, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764, München, Germany. 2. Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany. 3. Department of Mathematics, Technische Universität München, Garching, Germany. 4. Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764, München, Germany. 5. Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Munich, Germany. 6. Forschergruppe Diabetes e.V., Neuherberg, Germany. 7. German Center for Diabetes Research (DZD), Neuherberg, Germany. 8. Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany. 9. Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764, München, Germany. anette-g.ziegler@helmholtz-muenchen.de. 10. Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Munich, Germany. anette-g.ziegler@helmholtz-muenchen.de. 11. Forschergruppe Diabetes e.V., Neuherberg, Germany. anette-g.ziegler@helmholtz-muenchen.de. 12. Research Unit Protein Science, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764, München, Germany. hauck@helmholtz-muenchen.de.
Abstract
AIMS/HYPOTHESIS: We sought to identify minimal sets of serum peptide signatures as markers for islet autoimmunity and predictors of progression rates to clinical type 1 diabetes in a case-control study. METHODS: A double cross-validation approach was applied to first prioritise peptides from a shotgun proteomic approach in 45 islet autoantibody-positive and -negative children from the BABYDIAB/BABYDIET birth cohorts. Targeted proteomics for 82 discriminating peptides were then applied to samples from another 140 children from these cohorts. RESULTS: A total of 41 peptides (26 proteins) enriched for the functional category lipid metabolism were significantly different between islet autoantibody-positive and autoantibody-negative children. Two peptides (from apolipoprotein M and apolipoprotein C-IV) were sufficient to discriminate autoantibody-positive from autoantibody-negative children. Hepatocyte growth factor activator, complement factor H, ceruloplasmin and age predicted progression time to type 1 diabetes with a significant improvement compared with age alone. CONCLUSION/ INTERPRETATION: Distinct peptide signatures indicate islet autoimmunity prior to the clinical manifestation of type 1 diabetes and enable refined staging of the presymptomatic disease period.
AIMS/HYPOTHESIS: We sought to identify minimal sets of serum peptide signatures as markers for islet autoimmunity and predictors of progression rates to clinical type 1 diabetes in a case-control study. METHODS: A double cross-validation approach was applied to first prioritise peptides from a shotgun proteomic approach in 45 islet autoantibody-positive and -negative children from the BABYDIAB/BABYDIET birth cohorts. Targeted proteomics for 82 discriminating peptides were then applied to samples from another 140 children from these cohorts. RESULTS: A total of 41 peptides (26 proteins) enriched for the functional category lipid metabolism were significantly different between islet autoantibody-positive and autoantibody-negative children. Two peptides (from apolipoprotein M and apolipoprotein C-IV) were sufficient to discriminate autoantibody-positive from autoantibody-negative children. Hepatocyte growth factor activator, complement factor H, ceruloplasmin and age predicted progression time to type 1 diabetes with a significant improvement compared with age alone. CONCLUSION/ INTERPRETATION: Distinct peptide signatures indicate islet autoimmunity prior to the clinical manifestation of type 1 diabetes and enable refined staging of the presymptomatic disease period.
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