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Usage Information

Cell type–specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes
Matthew J. Dufort, Carla J. Greenbaum, Cate Speake, Peter S. Linsley
Matthew J. Dufort, Carla J. Greenbaum, Cate Speake, Peter S. Linsley
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Research Article

Cell type–specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes

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Abstract

The rate of decline in insulin secretion after diagnosis with type 1 diabetes (T1D) varies substantially among individuals and with age at diagnosis, but the mechanism(s) behind this heterogeneity are not well understood. We investigated the loss of pancreatic β cell function in new-onset T1D subjects using unbiased whole blood RNA-seq and verified key findings by targeted cell count measurements. We found that patients who lost insulin secretion more rapidly had immune phenotypes (“immunotypes”) characterized by higher levels of B cells and lower levels of neutrophils, especially neutrophils expressing primary granule genes. The B cell and neutrophil immunotypes showed strong age dependence, with B cell levels in particular predicting rate of progression in young subjects only. This age relationship suggested that therapy targeting B cells in T1D would be most effective in young subjects with high pretreatment B cell levels, a prediction which was supported by data from a clinical trial of rituximab in new-onset subjects. These findings demonstrate a link between age-related immunotypes and disease outcome in new-onset T1D. Furthermore, our data suggest that greater success could be achieved by targeted use of immunomodulatory therapy in specific T1D populations defined by age and immune characteristics.

Authors

Matthew J. Dufort, Carla J. Greenbaum, Cate Speake, Peter S. Linsley

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Usage data is cumulative from February 2025 through February 2026.

Usage JCI PMC
Text version 657 159
PDF 134 29
Figure 308 6
Table 61 0
Supplemental data 321 7
Citation downloads 81 0
Totals 1,562 201
Total Views 1,763
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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.

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