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Web End = Diabetologia (2017) 60:287295 DOI 10.1007/s00125-016-4150-x
ARTICLE
Peptide serum markers in islet autoantibody-positive children
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. Ziegler4,5,6 & Stefanie M. Hauck1
Received: 10 August 2016 /Accepted: 5 October 2016 /Published online: 4 November 2016 # Springer-Verlag Berlin Heidelberg 2016
AbstractAims/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 casecontrol 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.
Keywords Autoantibody-positive . Autoimmunity . BABYDIAB/BABYDIET . LC-MS/MS . Progression time . Risk score . Selected reaction monitoring . Targeted proteomic . Type 1 diabetes
Christine von Toerne and Michael Laimighofer contributed equally to this study.
Electronic supplementary material The online version of this article (doi:http://dx.doi.org/10.1007/s00125-016-4150-x
Web End =10.1007/s00125-016-4150- x) contains peer-reviewed but unedited supplementary material, which is available to authorised users.
* Anette G. Ziegler [email protected]
* Stefanie M. Hauck [email protected]
1 Research Unit Protein Science, Helmholtz Zentrum Mnchen, German Research Center for Environmental Health (GmbH), Ingolstdter Landstrae 1, D-85764 Mnchen, Germany
2 Institute of Computational Biology, Helmholtz Zentrum Mnchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
3 Department of Mathematics, Technische Universitt Mnchen, Garching, Germany
4 Institute of Diabetes Research, Helmholtz Zentrum Mnchen, German Research Center for Environmental Health (GmbH), Ingolstdter Landstrae 1,...