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Immunological biomarkers for the development and progression of type 1 diabetes

  • Chantal Mathieu
  • Riitta Lahesmaa
  • Ezio Bonifacio
  • Peter Achenbach
  • Timothy Tree
Review

Abstract

Immune biomarkers of type 1 diabetes are many and diverse. Some of these, such as the autoantibodies, are well established but not discriminative enough to deal with the heterogeneity inherent to type 1 diabetes progression. As an alternative, high hopes are placed on T cell assays, which give insight into the cells that actually target the beta cell or play a crucial role in maintaining tolerance. These assays are approaching a level of robustness that may allow for solid conclusions on both disease progression and therapeutic efficacy of immune interventions. In addition, ‘omics’ approaches to biomarker discovery are rapidly progressing. The potential emergence of novel biomarkers creates a need for the introduction of bioinformatics and ‘big data’ analysis systems for the integration of the multitude of biomarker data that will be available, to translate these data into clinical tools. It is worth noting that it is unlikely that the same markers will apply to all individuals. Instead, individualised signatures of biomarkers, combining autoantibodies, T cell profiles and other biomarkers, will need to be used to classify at-risk patients into various categories, thus enabling personalised prediction, prevention and treatment approaches. To achieve this goal, the standardisation of assays for biomarker discovery, the integration of analyses and data from biomarker studies and, most importantly, the careful clinical characterisation of individuals providing samples for these studies are critical. Longitudinal sample-collection initiatives, like INNODIA, should lead to novel biomarker discovery, not only providing a better understanding of type 1 diabetes onset and progression, but also yielding biomarkers of therapeutic efficacy of interventions to prevent or arrest type 1 diabetes.

Keywords

Autoantibodies Bioinformatics Biomarker Immune Review T cell assays Type 1 diabetes 

Abbreviations

FOXP3

Forkhead box P3

GAD

Glutamic acid decarboxylase

IA-2

Islet antigen-2

IDS

Immunology of Diabetes Society

miRNA

MicroRNA

PBMC

Peripheral blood mononuclear cells

Teff

Effector T cells

Treg

Regulatory T cells

ZnT8

Zinc transporter-8

Notes

Acknowledgements

We thank C. Moyson (Department of Endocrinology, UZ Leuven, Leuven, Belgium) for editorial help.

Contribution statement

All authors were responsible for drafting the article and revising it critically for important intellectual content. All authors approved the version to be published.

Funding

Related work in the laboratories of all authors is funded by the Innovative Medicines Initiative 2 Joint Undertaking (IMI2-JU) under grant agreement no. 115797 INNODIA. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA, JDRF International and The Leona M. and Harry B. Helmsley Charitable Trust.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

References

  1. 1.
    Mathieu C (2018) Are we there yet? finding ways to work together on T1D. Diabetes Care 41:667–669.  https://doi.org/10.2337/dci17-0065 CrossRefPubMedGoogle Scholar
  2. 2.
    Robertson CC, Rich SS (2018) Genetics of type 1 diabetes. Curr Opin Genet Dev 50:7–16.  https://doi.org/10.1016/j.gde.2018.01.006 CrossRefPubMedGoogle Scholar
  3. 3.
    Bonifacio E, Beyerlein A, Hippich M et al (2018) Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: aprospective study in children. PLoS Med 15:e1002548.  https://doi.org/10.1371/journal.pmed.1002548 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Insel RA, Dunne JL, Atkinson MA et al (2015) Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care 38:1964–1974.  https://doi.org/10.2337/dc15-1419 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Bingley PJ, Christie MR, Bonifacio E et al (1994) Combined analysis of autoantibodies improves prediction of IDDM in islet cell antibody-positive relatives. Diabetes 43:1304–1310CrossRefPubMedGoogle Scholar
  6. 6.
    Verge CF, Gianani R, Kawasaki E et al (1996) Prediction of type I diabetes in first-degree relatives using a combination of insulin, GAD, and ICA512bdc/IA-2 autoantibodies. Diabetes 45:926–933CrossRefPubMedGoogle Scholar
  7. 7.
    Ziegler AG, Rewers M, Simell O et al (2013) Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA 309:2473–2479.  https://doi.org/10.1001/jama.2013.6285 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Achenbach P, Hummel M, Thumer L et al (2013) Characteristics of rapid vs slow progression to type 1 diabetes in multiple islet autoantibody-positive children. Diabetologia 56:1615–1622.  https://doi.org/10.1007/s00125-013-2896-y CrossRefPubMedGoogle Scholar
  9. 9.
    Bonifacio E (2015) Predicting type 1 diabetes using biomarkers. Diabetes Care 38:989–996.  https://doi.org/10.2337/dc15-0101 CrossRefPubMedGoogle Scholar
  10. 10.
    Lampasona V, Liberati D (2016) Islet autoantibodies. Curr Diab Rep 16:53.  https://doi.org/10.1007/s11892-016-0738-2
  11. 11.
    Achenbach P, Warncke K, Reiter J et al (2004) Stratification of type 1 diabetes risk on the basis of islet autoantibody characteristics. Diabetes 53:384–392CrossRefPubMedGoogle Scholar
  12. 12.
    Hummel M, Bonifacio E, Schmid S et al (2004) Brief communication: early appearance of islet autoantibodies predicts childhood type 1 diabetes in offspring of diabetic parents. Ann Intern Med 140:882–886CrossRefPubMedGoogle Scholar
  13. 13.
    Ilonen J, Lempainen J, Hammais A et al (2018) Primary islet autoantibody at initial seroconversion and autoantibodies at diagnosis of type 1 diabetes as markers of disease heterogeneity. Pediatr Diabetes 19:284–292.  https://doi.org/10.1111/pedi.12545 CrossRefPubMedGoogle Scholar
  14. 14.
    Bingley PJ, Bonifacio E, Mueller PW (2003) Diabetes Antibody Standardization Program: first assay proficiency evaluation. Diabetes 52:1128–1136CrossRefPubMedGoogle Scholar
  15. 15.
    Crevecoeur I, Vig S, Mathieu C, Overbergh L (2017) Understanding type 1 diabetes through proteomics. Expert Rev Proteomics 14:571–580.  https://doi.org/10.1080/14789450.2017.1345633 CrossRefPubMedGoogle Scholar
  16. 16.
    Gomez-Tourino I, Arif S, Eichmann M, Peakman M (2016) T cells in type 1 diabetes: Instructors, regulators and effectors: a comprehensive review. J Autoimmun 66:7–16.  https://doi.org/10.1016/j.jaut.2015.08.012 CrossRefPubMedGoogle Scholar
  17. 17.
    Mannering SI, Pathiraja V, Kay TW (2016) The case for an autoimmune aetiology of type 1 diabetes. Clin Exp Immunol 183:8–15.  https://doi.org/10.1111/cei.12699 CrossRefPubMedGoogle Scholar
  18. 18.
    Roep BO, Peakman M (2010) Surrogate end points in the design of immunotherapy trials: emerging lessons from type 1 diabetes. Nat Rev Immunol 10:145–152.  https://doi.org/10.1038/nri2705 CrossRefPubMedGoogle Scholar
  19. 19.
    Delong T, Wiles TA, Baker RL et al (2016) Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion. Science 351:711–714.  https://doi.org/10.1126/science.aad2791 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Roep BO, Tree TI (2014) Immune modulation in humans: implications for type 1 diabetes mellitus. Nat Rev Endocrinol 10:229–242.  https://doi.org/10.1038/nrendo.2014.2 CrossRefPubMedGoogle Scholar
  21. 21.
    Viglietta V, Kent SC, Orban T, Hafler DA (2002) GAD65-reactive T cells are activated in patients with autoimmune type 1a diabetes. J Clin Invest 109:895–903.  https://doi.org/10.1172/JCI14114 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Arif S, Tree TI, Astill TP et al (2004) Autoreactive T cell responses show proinflammatory polarization in diabetes but a regulatory phenotype in health. J Clin Invest 113:451–463CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Arif S, Moore F, Marks K et al (2011) Peripheral and islet interleukin-17 pathway activation characterizes human autoimmune diabetes and promotes cytokine-mediated beta-cell death. Diabetes 60:2112–2119.  https://doi.org/10.2337/db10-1643 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Tree TI, Lawson J, Edwards H et al (2010) Naturally arising human CD4 T cells that recognize islet autoantigens and secrete interleukin-10 regulate proinflammatory T cell responses via linked suppression. Diabetes 59:1451–1460.  https://doi.org/10.2337/db09-0503 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Alhadj Ali M, Liu YF, Arif S et al (2017) Metabolic and immune effects of immunotherapy with proinsulin peptide in human new-onset type 1 diabetes. Sci Transl Med 9:eaaf7779.  https://doi.org/10.1126/scitranslmed.aaf7779
  26. 26.
    Roep BO, Solvason N, Gottlieb PA et al (2013) Plasmid-encoded proinsulin preserves C-peptide while specifically reducing proinsulin-specific CD8+ T cells in type 1 diabetes. Sci Transl Med 5:191ra82.  https://doi.org/10.1126/scitranslmed.3006103 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Skowera A, Ladell K, McLaren JE et al (2015) Beta-cell-specific CD8 T cell phenotype in type 1 diabetes reflects chronic autoantigen exposure. Diabetes 64:916–925.  https://doi.org/10.2337/db14-0332 CrossRefPubMedGoogle Scholar
  28. 28.
    Heninger AK, Eugster A, Kuehn D et al (2017) A divergent population of autoantigen-responsive CD4+ T cells in infants prior to beta cell autoimmunity. Sci Transl Med 9:eaaf8848.  https://doi.org/10.1126/scitranslmed.aaf8848
  29. 29.
    Xu X, Shi Y, Cai Y et al (2013) Inhibition of increased circulating Tfh cell by anti-CD20 monoclonal antibody in patients with type 1 diabetes. PLoS One 8:e79858.  https://doi.org/10.1371/journal.pone.0079858 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Viisanen T, Ihantola EL, Nanto-Salonen K et al (2017) Circulating CXCR5+PD-1+ICOS+ follicular t helper cells are increased close to the diagnosis of type 1 diabetes in children with multiple autoantibodies. Diabetes 66:437–447.  https://doi.org/10.2337/db16-0714 CrossRefPubMedGoogle Scholar
  31. 31.
    Long SA, Thorpe J, DeBerg HA et al (2016) Partial exhaustion of CD8 T cells and clinical response to teplizumab in new-onset type 1 diabetes. Sci Immunol 1:eaai7793.  https://doi.org/10.1126/sciimmunol.aai7793 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Orban T, Beam CA, Xu P et al (2014) Reduction in CD4 central memory T cell subset in costimulation modulator abatacept-treated patients with recent-onset type 1 diabetes is associated with slower C-peptide decline. Diabetes 63:3449–3457.  https://doi.org/10.2337/db14-0047 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Hull CM, Peakman M, Tree TIM (2017) Regulatory T cell dysfunction in type 1 diabetes: what’s broken and how can we fix it? Diabetologia 60:1839–1850.  https://doi.org/10.1007/s00125-017-4377-1 CrossRefPubMedGoogle Scholar
  34. 34.
    Long SA, Cerosaletti K, Bollyky PL et al (2010) Defects in IL-2R signaling contribute to diminished maintenance of FOXP3 expression in CD4+CD25+ regulatory T cells of type 1 diabetic subjects. Diabetes 59:407–415.  https://doi.org/10.2337/db09-0694 CrossRefPubMedGoogle Scholar
  35. 35.
    Garg G, Tyler JR, Yang JH et al (2012) Type 1 diabetes-associated IL2RA variation lowers IL-2 signaling and contributes to diminished CD4+CD25+ regulatory T cell function. J Immunol 188:4644–4653.  https://doi.org/10.4049/jimmunol.1100272 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Yang JH, Cutler AJ, Ferreira RC et al (2015) Natural variation in interleukin-2 sensitivity influences regulatory T-cell frequency and function in individuals with long-standing type 1 diabetes. Diabetes 64:3891–3902.  https://doi.org/10.2337/db15-0516 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Hundhausen C, Roth A, Whalen E et al (2016) Enhanced T cell responses to IL-6 in type 1 diabetes are associated with early clinical disease and increased IL-6 receptor expression. Sci Transl Med 8:356ra119.  https://doi.org/10.1126/scitranslmed.aad9943 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Pesenacker AM, Wang AY, Singh A et al (2016) A regulatory T-cell gene signature is a specific and sensitive biomarker to identify children with new-onset type 1 diabetes. Diabetes 65:1031–1039.  https://doi.org/10.2337/db15-0572 CrossRefPubMedGoogle Scholar
  39. 39.
    Kallionpaa H, Elo LL, Laajala E et al (2014) Innate immune activity is detected prior to seroconversion in children with HLA-conferred type 1 diabetes susceptibility. Diabetes 63:2402–2414.  https://doi.org/10.2337/db13-1775 CrossRefPubMedGoogle Scholar
  40. 40.
    Ferreira RC, Guo H, Coulson RMR et al (2014) A type I interferon transcriptional signature precedes autoimmunity in children genetically at risk for type 1 diabetes. Diabetes 63:2538–2550.  https://doi.org/10.2337/db13-1777 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Moulder R, Bhosale SD, Erkkila T et al (2015) Serum proteomes distinguish children developing type 1 diabetes in a cohort with HLA-conferred susceptibility. Diabetes 64:2265–2278.  https://doi.org/10.2337/db14-0983 CrossRefPubMedGoogle Scholar
  42. 42.
    von Toerne C, Laimighofer M, Achenbach P et al (2017) Peptide serum markers in islet autoantibody-positive children. Diabetologia 60:287–295.  https://doi.org/10.1007/s00125-016-4150-x CrossRefGoogle Scholar
  43. 43.
    Liu C-W, Bramer L, Webb-Robertson B-J et al (2018) Temporal expression profiling of plasma proteins reveals oxidative stress in early stages of type 1 diabetes progression. J Proteome 172:100–110.  https://doi.org/10.1016/j.jprot.2017.10.004 CrossRefGoogle Scholar
  44. 44.
    Liu C-W, Bramer L, Webb-Robertson B-J et al (2017) Temporal profiles of plasma proteome during childhood development. J Proteome 152:321–328.  https://doi.org/10.1016/j.jprot.2016.11.016 CrossRefGoogle Scholar
  45. 45.
    Bjelosevic S, Pascovici D, Ping H et al (2017) Quantitative age-specific variability of plasma proteins in healthy neonates, children and adults. Mol Cell Proteomics 16:924–935.  https://doi.org/10.1074/mcp.M116.066720 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Lietzen N, Cheng L, Moulder R et al (2018) Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood. Sci Rep 8:5883.  https://doi.org/10.1038/s41598-018-24019-5 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Sebastiani G, Nigi L, Grieco GE et al (2017) Circulating microRNAs and diabetes mellitus: a novel tool for disease prediction, diagnosis, and staging? J Endocrinol Investig 40:591–610.  https://doi.org/10.1007/s40618-017-0611-4 CrossRefGoogle Scholar
  48. 48.
    Vatanen T, Kostic AD, d’Hennezel E et al (2016) Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165:842–853.  https://doi.org/10.1016/j.cell.2016.04.007 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Kindt ASD, Fuerst RW, Knoop J et al (2018) Allele-specific methylation of type 1 diabetes susceptibility genes. J Autoimmun 89:63–74.  https://doi.org/10.1016/j.jaut.2017.11.008 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Chantal Mathieu
    • 1
  • Riitta Lahesmaa
    • 2
  • Ezio Bonifacio
    • 3
    • 4
    • 5
  • Peter Achenbach
    • 5
    • 6
  • Timothy Tree
    • 7
    • 8
  1. 1.Department of EndocrinologyUniversity Hospital Gasthuisberg, KU LeuvenLeuvenBelgium
  2. 2.Turku Centre for BiotechnologyUniversity of Turku and Åbo Akademi UniversityTurkuFinland
  3. 3.DFG Center for Regenerative Therapies Dresden, Faculty of MedicineTechnische Universität DresdenDresdenGermany
  4. 4.Paul Langerhans Institute Dresden, Helmholtz Zentrum München, University Hospital Carl Gustav Carus, Medical FacultyTechnische Universität DresdenDresdenGermany
  5. 5.German Center for Diabetes Research (DZD)NeuherbergGermany
  6. 6.Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Diabetes ResearchMunich-NeuherbergGermany
  7. 7.Department of Immunobiology, School of Immunology & Microbial Sciences, King’s College LondonBorough Wing Guy’s HospitalLondonUK
  8. 8.NIHR Biomedical Research Centre, Guy’s and St Thomas’ NHS Foundation Trust and King’s College LondonLondonUK

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