Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Nov 6;25(22):11921.
doi: 10.3390/ijms252211921.

Dynamic Changes in Lymphocyte Populations and Their Relationship with Disease Severity and Outcome in COVID-19

Ákos Vince Andrejkovits  1   2 , Adina Huțanu  3   4 , Doina Ramona Manu  4 , Minodora Dobreanu  3   4 , Anca Meda Văsieșiu  1   5
Affiliations

Dynamic Changes in Lymphocyte Populations and Their Relationship with Disease Severity and Outcome in COVID-19

Ákos Vince Andrejkovits et al. Int J Mol Sci. .

Abstract

Studies suggest that the dynamic changes in cellular response might correlate with disease severity and outcomes in SARS-CoV-2 patients. The study aimed to investigate the dynamic changes of lymphocyte subsets in patients with COVID-19. In this regard, 53 patients with COVID-19 were prospectively included, classified as mild, moderate, and severe. The peripheral lymphocyte profiles (LyT, LyB, and NK cells), as well as CD4+/CD8+, CD3+/CD19+, CD3+/NK and CD19+/NK ratios, and their dynamic changes during hospitalization and correlation with disease severity and outcome were assessed. We found significant differences in CD3+ lymphocytes between severity groups (p < 0.0001), with significantly decreased CD3+CD4+ and CD3+CD8+ in patients with severe disease (p < 0.0001 and p = 0.048, respectively). Lower CD3+/CD19+ and CD3+/NK ratios among patients with severe disease (p = 0.019 and p = 0.010, respectively) were found. The dynamic changes of lymphocyte subsets showed a significant reduction in NK cells (%) and a significant increase in CD3+CD4+ and CD3+CD8+ cells in patients with moderate and severe disease. The ROC analysis on the relationship between CD3+ cells and fatal outcome yielded an AUC of 0.723 (95% CI 0.583-0.837; p = 0.007), while after addition of age and SpO2, ferritin and NLR, the AUC significantly improved to 0.927 (95%CI 0.811-0.983), p < 0.001 with a sensitivity of 90.9% (95% CI 58.7-99.8%) and specificity of 85.7% (95% CI 69.7-95.2%). The absolute number of CD3+ lymphocytes might independently predict fatal outcomes in COVID-19 patients and T-lymphocyte subset evaluation in high-risk patients might be useful in estimating disease progression.

Keywords: COVID-19; SARS-CoV-2; dynamic changes; lymphocyte profiles; outcome.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Violin-plot showing the distribution of the CD3/CD19 ratio in the three groups on day 1. CD = cluster of differentiation.
Figure 2
Figure 2
Violin-plot showing the distribution of CD3+CD4+ cells in absolute number for day 1 across disease severity, with lower levels in patients with severe disease compared to mild/moderate disease. Number of lymphocytes expressed as absolute value × 103/µL. CD = cluster of differentiation.
Figure 3
Figure 3
Violin-plot showing the distribution of CD3+CD8+ cells as absolute number in severe COVID-19 on day 1 (severe I), day 5 (severe II) and day 10 (severe III) of admission. Number of lymphocytes expressed as absolute value × 103/µL.
Figure 4
Figure 4
Violin-plot showing the distribution of CD19+ cells as absolute number on day 1, day 5 and day 10 in patients with severe disease. Number of lymphocytes expressed as absolute value × 103/µL.
Figure 5
Figure 5
ROC analysis and AUC for CD3+ cells and fatal outcome.
Figure 6
Figure 6
Comparison of ROC curves for three models (model 1 = CD3+) and models with multiple biomarkers (clinical and laboratory) for fatal outcome prediction.
Figure 7
Figure 7
Example of a histogram and gating strategy for lymphocyte subpopulations.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Taj S., Kashif A., Arzinda Fatima S., Imran S., Lone A., Ahmed Q. Role of hematological parameters in the stratification of COVID-19 disease severity. Ann. Med. Surg. 2021;62:68–72. doi: 10.1016/j.amsu.2020.12.035. - DOI - PMC - PubMed
    1. Tsai P.H., Lai W.Y., Lin Y.Y., Luo Y.H., Lin Y.T., Chen H.K., Chen Y.M., Lai Y.C., Kuo L.C., Chen S.D., et al. Clinical manifestation and disease progression in COVID-19 infection. J. Chin. Med. Assoc. JCMA. 2021;84:3–8. doi: 10.1097/JCMA.0000000000000463. - DOI - PubMed
    1. Cherry J.D., Krogstad P. SARS: The first pandemic of the 21st century. Pediatr. Res. 2004;56:1–5. doi: 10.1203/01.PDR.0000129184.87042.FC. - DOI - PMC - PubMed
    1. COVID-19 Deaths|WHO COVID-19 Dashboard. [(accessed on 19 August 2024)]. Available online: https://data.who.int/dashboards/covid19/deaths.
    1. Briciu V., Topan A., Calin M., Dobrota R., Leucuta D.-C., Lupse M. Comparison of COVID-19 Severity in Vaccinated and Unvaccinated Patients during the Delta and Omicron Wave of the Pandemic in a Romanian Tertiary Infectious Diseases Hospital. Healthcare. 2023;11:373. doi: 10.3390/healthcare11030373. - DOI - PMC - PubMed