T cell-driven sustained inflammation and immune dysregulation mimicking immunosenescence for up to three years post-COVID-19, 2025, Zheng et al

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T cell-driven sustained inflammation and immune dysregulation mimicking immunosenescence for up to three years post-COVID-19

Zheng, Tian; Gao, Ru; Liu, Yiwei; Wang, Yeming; Wu, Chao; Guo, Li; Chen, Lan; Wang, Xinming; Xiao, Yan; Zhong, Jingchuan; Zhang, Rongling; Wang, Ying; Ren, Xianwen; Cao, Bin; Ren, Lili; Wang, Jianwei

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Abstract
Long COVID has emerged as a major global health concern, yet the long-term trajectory of immune recovery and its contribution to persistent symptoms remain to be elucidated. Here, we conducted a three-year longitudinal follow-up of the 47 COVID-19 patients and applied single-cell RNA sequencing (scRNA-seq) and multiplex cytokine profiling to comprehensively characterize the peripheral immune landscape during convalescence.

We observed persistent immune dysregulation up to three years post-infection, characterized by chronic inflammation and impaired restoration of naïve CD4⁺ T cells, naïve CD8⁺ T cells, and SLC4A10⁺ MAIT cells—features reminiscent of immunosenescence.

Notably, Th17 cells, rather than monocytes, emerged as key drivers of chronic inflammation beyond one year. We identified two distinct Th17 subsets: RORC⁺ Th17 cells and LTB⁺ Th17 cells. While RORC⁺ Th17 cells were negatively correlated with inflammatory cytokine levels, LTB⁺ Th17 cells showed proinflammatory features and were positively associated with long COVID symptoms.

Sustained elevation of S100A8 and IL-16 in follow-up patients may contribute to the persistent presence of LTB⁺ Th17 cells.

Together, our study provides an in-depth longitudinal map of immune remodeling in COVID-19 convalescents, revealing key cellular and molecular drivers of sustained inflammation up to three years post-infection.

Web | DOI | PMC | PDF | Immunity & Inflammation | Open Access
 
Statistical analysis

For continuous variables, mean and standard deviation (standard deviation, SD) or median (interquartile range, IQR) were used to describe the data as appropriate, and comparisons were analysed by using the Wilcoxon rank-sum test or Mann‒Whitney U test. Categorical variables described as counts (proportion, %) were compared with the χ2 test or Fisher’s exact test. Results with P<0.05 were considered statistically significant.
Does this mean that they didn’t correction for multiple comparisons?
 
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