The immunological processes that lead to ? -cell destruction are complex and most probably involve, at single or multiple stages, the majority of humoral and cell-based effector and regulatory mechanisms that we currently have knowledge of. Thus, any single aspect of innate and adaptive immune responses has the potential to act as a surrogate for loss of ? -cell function. To date, however, no marker(s) have emerged that are capable of predicting the decline in C-peptide that follows diagnosis of Type 1 diabetes (T1D). In the prevention setting, autoantibodies and HLA genotype can identify non-diabetic subjects at high risk of progression to diabetes (usually 35-50%) over a defined period (usually 5 years): but these biomarkers appear to have limited predictive utility in relation to time of onset or rate of decline of ?-cell function. In the context of both intervention and prevention trials for T1D, this knowledge gap represents a major limitation: better biomarkers or surrogates of ? -cell decline could be used as end-points to make studies shorter (e.g. for prevention they currently last several years) or to stratify for study entry (e.g. rapid or slow progressors). In the context of studying peripheral blood immune cell subsets in patients (treated and placebo) enrolled in the Trial Net Abatacept intervention study in new-onset T1D, we recently made two discoveries that we consider have novelty and importance in the T1D biomarker and therapeutic trial effort: 1) C-peptide loss can be predicted by change in specific T-cell populations. 2) Co-stimulation blockade with Abatacept not only alters CD4 cell population composition but also alters the relationship between change in Central Memory and Naive/Central Memory composition and subsequent metabolic change. We now seek to test these discoveries in a series of replication and validation experiments conducted with alternative specimen sources as well as different clinical populations representing stages of progression to diabetes. In addition, our findings to date highlight the importance of a subset of CD4 T cells defined by possession of CD45R0 and CD62L in T1D progression.
We aim to analyze these cells in greater depth and with greater resolution using polychromatic flow and mass cytometry (""""""""CyTOFTM"""""""") to examine whether there is a particular cell cohort, discernible by activation, migration, lineage or other markers, that emerges as most strongly associated with disease progression. This would not only offer novel biomarker populations, but could also lead by extension to novel targeting strategies using tailored biologics. Success in this research project will likely lead to further investigation of te cell cohorts identified as associated with disease progression, validated biomarkers that are ready for use in clinical trials, and possibly also yield a means for mechanistic- based risk classification in clinical trials.
We recently made two discoveries that we consider have novelty and importance in the T1D biomarker and therapeutic trial effort. We now seek to test these discoveries in a series of replication and validation experiments conducted with alternative specimen sources as well as different clinical populations representing stages of progression to diabetes. In addition, our findings to date highlight the importance of a subset of CD4 T cells defined by possession of CD45R0 and CD62L in T1D progression. We aim to analyze these cells in greater depth and with greater resolution using polychromatic flow and mass cytometry (CyTOFTM), to examine whether there is a particular cell cohort, discernible by activation, migration, lineage or other markers, that emerges as most strongly associated with disease progression. This would not only offer novel biomarker populations, but could also lead by extension to novel targeting strategies using tailored biologics. Success in this research project will likely lead to further investigation of the cell cohorts identified as associated with disease progression, validated biomarkers that are ready for use in clinical trials, and possibly also yiel a means for mechanistic-based risk classification in clinical trials.
