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Published on: May 6, 2013
Differences in immune cell profiles around the time of islet autoimmunity seroconversion in children with and without
Kirk R Hohsfield1,2, Patrick Carry1,3, Sarah Slack2
1Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA.
Introduction:
Seroconversion (SV) marks islet autoimmunity (IA) onset and preclinical type 1 diabetes (T1D), yet the contributions beyond T and B lymphocytes remain unclear. We evaluated DNA methylation (DNAm)-derived immune cell ratios between T1D cases and controls around SV.
Research Design And Methods:
High-resolution immune cell-type deconvolution of peripheral blood DNAm from nested case-control samples in the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and the Environmental Determinants of Diabetes in the Young (TEDDY; n=166) estimated immune cell proportions at pre-SV (the latest visit before SV) and at SV (the first visit with persistent detected autoantibodies) to construct immune cell ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Linear models compared T1D cases to matched T1D controls (IA negative) at pre-SV, SV, and the change across time points.
Results:
From pre-SV to SV, controls showed expected developmental increases in B-memory/naive, B-CD4T-CD8T memory/naive, and NLR, while cases failed to follow these patterns, with attenuated trajectories of 35%, 38%, and 21%, respectively. Pre-SV, cases had 15% higher NLR and 9% lower CD4T/CD8T. At SV, the combined B-CD4T-CD8T memory/naive ratio was 26% reduced in cases.
Conclusions:
These patterns may reflect increased neutrophil activation or pancreatic infiltration, altered CD4 and CD8 T cell balance, and delayed or disrupted immune maturation with the persistence or expansion of naive B and T cells or impaired transition to memory B and T subsets following antigen exposure. Our findings highlight early shifts in innate and adaptive immune cell dynamics during T1D pathogenesis and support methylation-derived immune cell ratios as potential biomarkers for risk stratification and mechanistic insight.
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