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 to healthy controls, Tregs from children with new-onset T1D have an altered Treg gene signature (TGS), suggesting this could be an immunoregulatory biomarker. METHODS. nanoString was used to assess the TGS in sorted Tregs (CD4+CD25hiCD127lo) or Peripheral Blood Mononuclear Cells (PBMC) 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 to controls, the TGS in isolated Tregs or PBMCs is altered in adult new-onset and cross-sectional T1D cohorts, with sensitivity and specificity of biomarkers increased by including T1D-associated single nucleotide polymorphisms 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 Treg gene signatures could be a new approach to stratify patients and monitor autoimmune activity in T1D.
Anne M. Pesenacker, Virginia Chen, Jana Gillies, Cate Speake, Ashish K. Marwaha, Annika C. Sun, Samuel Chow, Rusung Tan, Thomas Elliott, Jan P. Dutz, Scott J. Tebbutt, Megan K. Levings
Usage data is cumulative from February 2019 through February 2019.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.