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At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting β cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, Data Integration Flexible to Account for different Types of data and Outcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D.

Background: Multiple therapeutic strategies to restore immune regulation and slow type 1 diabetes (T1D) progression are in development and testing. A major challenge has been defining biomarkers to prospectively identify subjects likely to benefit from immunotherapy and/or measure intervention effects. We previously found that, compared with healthy controls, Tregs from children with new-onset T1D have an altered Treg gene signature (TGS), suggesting that this could be an immunoregulatory biomarker., Methods: nanoString was used to assess the TGS in sorted Tregs (CD4+CD25hiCD127lo) or peripheral blood mononuclear cells (PBMCs) from individuals with T1D or type 2 diabetes, healthy controls, or T1D recipients of immunotherapy. Biomarker discovery pipelines were developed and applied to various sample group comparisons., Results: Compared with controls, the TGS in isolated Tregs or PBMCs was altered in adult new-onset and cross-sectional T1D cohorts, with sensitivity or specificity of biomarkers increased by including T1D-associated SNPs in algorithms. The TGS was distinct in T1D versus type 2 diabetes, indicating disease-specific alterations. TGS measurement at the time of T1D onset revealed an algorithm that accurately predicted future rapid versus slow C-peptide decline, as determined by longitudinal analysis of placebo arms of START and T1DAL trials. The same algorithm stratified participants in a phase I/II clinical trial of ustekinumab (αIL-12/23p40) for future rapid versus slow C-peptide decline., Conclusion: These data suggest that biomarkers based on measuring TGSs could be a new approach to stratify patients and monitor autoimmune activity in T1D., Funding: JDRF (1-PNF-2015-113-Q-R, 2-PAR-2015-123-Q-R, 3-SRA-2016-209-Q-R, 3-PDF-2014-217-A-N), the JDRF Canadian Clinical Trials Network, the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (UM1AI109565 and FY15ITN168), and BCCHRI.

FOXP3 controls the development and function of T regulatory cells (Tregs). Autoimmunity is linked to changes in FOXP3 activity that can occur at multiple levels and lead to Treg dysfunction. For example, changes in IL-2 signaling, FOXP3 transcription and/or post-translational modifications can all contribute to loss of self-tolerance. As additional pathways of FOXP3 regulation are elucidated, new therapeutic approaches to increase Treg activity either by cell therapy or pharmacological intervention are being tested. Early success from pioneering studies of Treg-based therapy in transplantation has promoted the undertaking of similar studies in autoimmunity, with emerging evidence for the effectiveness of these approaches, particularly in the context of type 1 diabetes., (Copyright © 2016. Published by Elsevier Ltd.)

Type 1 diabetes (T1D) is caused by immune-mediated destruction of insulin-producing β-cells. Insufficient control of autoreactive T cells by regulatory T cells (Tregs) is believed to contribute to disease pathogenesis, but changes in Treg function are difficult to quantify because of the lack of Treg-exclusive markers in humans and the complexity of functional experiments. We established a new way to track Tregs by using a gene signature that discriminates between Tregs and conventional T cells regardless of their activation states. The resulting 31-gene panel was validated with the NanoString nCounter platform and then measured in sorted CD4(+)CD25(hi)CD127(lo) Tregs from children with T1D and age-matched control subjects. By using biomarker discovery analysis, we found that expression of a combination of six genes, including TNFRSF1B (CD120b) and FOXP3, was significantly different between Tregs from subjects with new-onset T1D and control subjects, resulting in a sensitive (mean ± SD 0.86 ± 0.14) and specific (0.78 ± 0.18) biomarker algorithm. Thus, although the proportion of Tregs in peripheral blood is similar between children with T1D and control subjects, significant changes in gene expression can be detected early in disease process. These findings provide new insight into the mechanisms underlying the failure to control autoimmunity in T1D and might lead to a biomarker test to monitor Tregs throughout disease progression., (© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)