Immunophenotype and genetic risk scores to improve autoantibody negative type 1 diabetes classification: study protocol

Authors

  • Shivani K. Patel Department of Diabetes and Metabolism, UNSW Sydney, Sydney, NSW, Australia St Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
  • Cindy S. Ma Human Immune Disorders, Garvan Institute of Medical Research, Sydney, NSW, Australia St Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
  • Kirstine J. Bell Charles Perkins Centre, Sydney Medical School, NSW, Australia The Children’s Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Australia
  • Richard Oram Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, United Kingdom
  • William A. Hagopian Pacific Northwest Research Institute, Seattle, Washington, USA
  • Spiros Fourlanos Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Melbourne, VIC, Australia
  • Jerry R. Greenfield 1Department of Diabetes and Metabolism, Garvan Institute of Medical Research, Sydney, NSW, Australia St Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia

DOI:

https://doi.org/10.18203/2349-3259.ijct20222690

Keywords:

Autoantibody-negative, Type 1 diabetes, Autoimmune diabetes, Immune phenotype, Characterisation

Abstract

Background: An estimated 10-30% of type 1 diabetes (T1D) individuals do not have detectable autoantibodies at diagnosis, thus are classified as “idiopathic” or “non-immune.” Given the non-pathogenic role of islet autoantibodies, the validity of excluding an immune basis for disease in such individuals needs to be questioned. The pan-autoantibody negative type 1 diabetes in adults (PANDA) study aims to characterise the immune, clinical and metabolic phenotype of autoantibody negative T1D individuals.

Methods: This is a two-part, multi-centre study which is recruiting 100 participants: autoantibody positive T1D (N=25), autoantibody negative T1D (N=25), latent autoimmune diabetes in adults (N=25) and age- and sex-matched normoglycaemic control (N=25) individuals. Study 1 involves baseline pathology collection and high dimensional immune-phenotyping using flow cytometry. DNA will be extracted from saliva samples to calculate type 1 diabetes genetic risk scores (T1DGRS). Autoantibody negative individuals will undergo monogenic diabetes testing. Study 2 is a prospective, longitudinal sub-study of study 1 participants within 5 years of diagnosis. Beta cell function will be assessed using glucagon stimulated C-peptide at 0, 9 and 18 months. The primary outcome of study 1 is to determine the phenotype of immune cells in autoantibody positive and negative T1D compared to healthy controls. Secondary outcomes of study 1 include clinical and metabolic characteristics and the T1DGRS. The primary outcome of study 2 is the rate of decline of stimulated C-peptide over time.

Conclusions: The PANDA study is the first study of its kind which aims to improve diagnosis and characterisation of autoantibody negative T1D.

Author Biography

Shivani K. Patel, Department of Diabetes and Metabolism, UNSW Sydney, Sydney, NSW, Australia St Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia

PhD Candidate

Visiting Endocrinologist

References

Bravis V, Kaur A, Walkey HC, et al. Relationship between islet autoantibody status and the clinical characteristics of children and adults with incident type 1 diabetes in a UK cohort. BMJ Open 2018;8:e020904.

Classification of diabetes mellitus. Available at: https://www.who.int/publications/i/item/classification-of-diabetes-mellitus. Accessed on 20 October 2021.

Vipin VP, Zaidi G, Watson K, et al. High prevalence of idiopathic (islet antibody‐negative) type 1 diabetes among Indian children and adolescents. Pediatr Diab. 2021;22:47-51.

Tiberti C, Buzzetti R, Anastasi E. Autoantibody negative new onset Type 1 diabetic patients lacking high risk HLA alleles in a Caucasian population: are these Type 1b diabetes cases? Diab Metab Res Rev. 2000;16:8-14.

Classification and diagnosis of diabetes: standards of medical care in diabetes-2021. Diab Care. 2021;44:S15-33.

Fourlanos S, Narendran P, Byrnes GB, Colman PG, Harrison LC. Insulin resistance is a risk factor for progression to Type 1 diabetes. Diabetol. 2004;47:1661-7.

Wilkin TJ. The accelerator hypothesis: a review of the evidence for insulin resistance as the basis for type I as well as type II diabetes. Int J Obesity. 2009;33:716-26.

Hameed S, Ellard S, Woodhead HJ. Persistently autoantibody negative (PAN) type 1 diabetes mellitus in children. Pediatr Diab. 2011;12:142-9.

Carlsson A, Shepherd M, Ellard S. Absence of Islet autoantibodies and modestly raised glucose values at diabetes diagnosis should lead to testing for MODY: lessons from a 5-year pediatric Swedish national cohort study. Diab Care. 2020;43:82-9.

1Guarnotta V, Vigneri E, Pillitteri G, Ciresi A, Pizzolanti G, Giordano C. Higher cardiometabolic risk in idiopathic versus autoimmune type 1 diabetes: a retrospective analysis. Diab Metab Syndrome. 2018;10.

Aguilera E, Casamitjana R, Ercilla G, Oriola J, Gomis R, Conget I. Adult-onset atypical (type 1) diabetes: additional insights and differences with type 1a diabetes in a european mediterranean population. Diab Care. 2004;27:1108-14.

Catarino D, Silva D, Guiomar J. Non-immune-mediated versus immune-mediated type 1 diabetes: diagnosis and long-term differences-retrospective analysis. Diab Metab Syndrome. 2020;12.

Ahmed S, Cerosaletti K, James E. Standardizing T-Cell biomarkers in type 1 diabetes: challenges and recent advances. Diabetes. 2019;68:1366-79.

Patel KA, Oram RA, Flanagan SE. Type 1 Diabetes genetic risk score: a novel tool to discriminate monogenic and type 1 diabetes. Diabetes. 2016;65:2094-9.

Oram RA, Patel K, Hill A. A Type 1 Diabetes Genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diab Care. 2016;39:337-44.

Richardson CC, Dromey JA, McLaughlin KA. High frequency of autoantibodies in patients with long duration type 1 diabetes. Diabetologia 2013;56:2538-40.

Williams GM, Long AE, Wilson IV. Beta cell function and ongoing autoimmunity in long-standing, childhood onset type 1 diabetes. Diabetologia. 2016;59:2722-6.

Payne K, Li W, Salomon R, Ma CS. OMIP-063: 28-Color Flow Cytometry Panel for Broad Human Immunophenotyping. Cytometry A. 2020;97:777-81.

Faber OK, Binder C. C-peptide Response to Glucagon: A Test for the Residual -cell Function in Diabetes Mellitus. Diabetes. 1977;26:605-10.

Fourlanos S. Latent autoimmune diabetes in adults : new clinical, immunogenetic and metabolic perspectives. J Univ Melbourne. 2006.

Kenefeck R, Wang CJ, Kapadi T. Follicular helper T cell signature in type 1 diabetes. J Clin Invest. 2015;125:292-303.

Xu X, Shi Y, Cai Y. Inhibition of Increased Circulating Tfh Cell by Anti-CD20 monoclonal antibody in patients with type 1 diabetes. PLoS One. 2013;8:e79858.

Oras A, Peet A, Giese T, Tillmann V, Uibo R. A study of 51 subtypes of peripheral blood immune cells in newly diagnosed young type 1 diabetes patients. Clin Exper Immunol. 2019;198:57-70.

Menart-Houtermans B, Rütter R, Nowotny B. Leukocyte profiles differ between type 1 and type 2 diabetes and are associated with metabolic phenotypes: results from the german diabetes study (GDS). Diab Care. 2014;37:2326-33.

So M, O’Rourke C, Bahnson H, Greenbaum C, Speake C. Autoantibody Reversion: changing risk categories in multiple-autoantibody–positive individuals. Diab Care. 2020;43:191731.

Speake C. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157-63.

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Published

2022-10-26