Usage Information
Citation Information: JCI Insight. 2023;8(17):e170767. https://doi.org/10.1172/jci.insight.170767.
Abstract
The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1–positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.
Authors
Melanie R. Shapiro, Xiaoru Dong, Daniel J. Perry, James M. McNichols, Puchong Thirawatananond, Amanda L. Posgai, Leeana D. Peters, Keshav Motwani, Richard S. Musca, Andrew Muir, Patrick Concannon, Laura M. Jacobsen, Clayton E. Mathews, Clive H. Wasserfall, Michael J. Haller, Desmond A. Schatz, Mark A. Atkinson, Maigan A. Brusko, Rhonda Bacher, Todd M. Brusko
Usage data is cumulative from November 2023 through November 2024.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 1,770 | 626 |
| 209 | 173 | |
| Figure | 341 | 8 |
| Table | 40 | 0 |
| Supplemental data | 100 | 35 |
| Citation downloads | 96 | 0 |
| Totals | 2,556 | 842 |
| Total Views | 3,398 | |
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.
