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Human immune phenotyping reveals accelerated aging in type 1 diabetes
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
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
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Research Article Immunology

Human immune phenotyping reveals accelerated aging in type 1 diabetes

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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

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Figure 2

Immunophenotype trajectories in T1D.

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Immunophenotype trajectories in T1D.
(A) Heatmap of smoothed phenotype t...
(A) Heatmap of smoothed phenotype trajectories as a function of age in AAb- individuals with analysis restricted between the ages of 5 and 75 years to avoid predicting from sparse data. The age distribution of the cohort within this age range is shown (top histogram). Immune cell phenotypes were clustered into 4 distinct groups (axis colors, right) using hierarchical clustering (dendrogram, left). (B) Line plots of each smoothed phenotype as a function of age demonstrate distinct dynamic behavior within the 4 clusters. (C) Heatmap of smoothed phenotype trajectories as a function of age in T1D individuals with the rows arranged as in A. (D) Line plots of each smoothed phenotype as in B with the T1D smoothed phenotypes overlaid in red. (See Supplemental Figures 7 and 8.) Shifts in cluster trajectories for T1D versus AAb- were compared using a 2-tailed t test (cluster 1, P < 0.001; cluster 3, P = 0.034).

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