Immunosuppression as a Hallmark of Critical COVID-19: Prospective Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Study Design
2.3. Statistical Analyses
3. Results
3.1. Clinical Characteristics of Patients with COVID-19
3.2. Clinical Features and Procedures in Patients with COVID-19
3.3. Serum Cytokine Profiles in COVID-19 Patients
Serum Cytokine Profiles and Outcome in Patients with COVID-19
3.4. Lymphocyte Subpopulations Analysis in Patients with COVID-19
3.4.1. Lymphocyte Subsets in ICU Versus Non-ICU COVID-19 Patients
3.4.2. Lymphocyte Subpopulations and Outcome in Patients with COVID-19
3.5. Changes in the Counts of Lymphocyte Subpopulations during COVID-19
3.6. Predictors of Critical Course of COVID-19
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Bordea, I.R.; Xhajanka, E.; Candrea, S.; Bran, S.; Onișor, F.; Inchingolo, A.D.; Malcangi, G.; Pham, V.H.; Inchingolo, A.M.; Scarano, A.; et al. Coronavirus (SARS-CoV-2) Pandemic: Future Challenges for Dental Practitioners. Microorganisms 2020, 8, 1704. [Google Scholar] [CrossRef]
- Bellocchio, L.; Bordea, I.R.; Ballini, A.; Lorusso, F.; Hazballa, D.; Isacco, C.G.; Malcangi, G.; Inchingolo, A.D.; Dipalma, G.; Inchingolo, F.; et al. Environmental Issues and Neurological Manifestations Associated with COVID-19 Pandemic: New Aspects of the Disease? IJERPH 2020, 17, 8049. [Google Scholar] [CrossRef]
- Inchingolo, A.D.; Inchingolo, A.M.; Bordea, I.R.; Malcangi, G.; Xhajanka, E.; Scarano, A.; Lorusso, F.; Farronato, M.; Tartaglia, G.M.; Isacco, C.G.; et al. SARS-CoV-2 Disease through Viral Genomic and Receptor Implications: An Overview of Diagnostic and Immunology Breakthroughs. Microorganisms 2021, 9, 793. [Google Scholar] [CrossRef]
- Inchingolo, A.D.; Inchingolo, A.M.; Bordea, I.R.; Malcangi, G.; Xhajanka, E.; Scarano, A.; Lorusso, F.; Farronato, M.; Tartaglia, G.M.; Isacco, C.G.; et al. SARS-CoV-2 Disease Adjuvant Therapies and Supplements Breakthrough for the Infection Prevention. Microorganisms 2021, 9, 525. [Google Scholar] [CrossRef] [PubMed]
- Gallais, F.; Velay, A.; Wendling, M.-J.; Nazon, C.; Partisani, M.; Sibilia, J.; Candon, S.; Fafi-Kremer, S. Intrafamilial Exposure to SARS-CoV-2 Induces Cellular Immune Response without Seroconversion. Emerg. Infect. Dis. 2020, 27, 113–121. [Google Scholar] [CrossRef] [PubMed]
- Sekine, T.; Perez-Potti, A.; Rivera-Ballesteros, O.; Strålin, K.; Gorin, J.-B.; Olsson, A.; Llewellyn-Lacey, S.; Kamal, H.; Bogdanovic, G.; Muschiol, S.; et al. Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19. Cell 2020, 183, 158–168. [Google Scholar] [CrossRef] [PubMed]
- Liu, R.; Wang, Y.; Li, J.; Han, H.; Xia, Z.; Liu, F.; Wu, K.; Yang, L.; Liu, X.; Zhu, C. Decreased T Cell Populations Contribute to the Increased Severity of COVID-19. Clin. Chim. Acta 2020, 508, 110–114. [Google Scholar] [CrossRef] [PubMed]
- Jiang, M.; Guo, Y.; Luo, Q.; Huang, Z.; Zhao, R.; Liu, S.; Le, A.; Li, J.; Wan, L. T-Cell Subset Counts in Peripheral Blood Can Be Used as Discriminatory Biomarkers for Diagnosis and Severity Prediction of Coronavirus Disease 2019. J. Infect. Dis. 2020, 222, 198–202. [Google Scholar] [CrossRef] [PubMed]
- Qin, C.; Zhou, L.; Hu, Z.; Zhang, S.; Yang, S.; Tao, Y.; Xie, C.; Ma, K.; Shang, K.; Wang, W.; et al. Dysregulation of Immune Response in Patients With Coronavirus 2019 (COVID-19) in Wuhan, China. Clin. Infect. Dis. 2020, 71, 762–768. [Google Scholar] [CrossRef]
- Huang, W.; Berube, J.; McNamara, M.; Saksena, S.; Hartman, M.; Arshad, T.; Bornheimer, S.J.; O’Gorman, M. Lymphocyte Subset Counts in COVID -19 Patients: A Meta-Analysis. Cytometry 2020, 97, 772–776. [Google Scholar] [CrossRef]
- Liu, Z.; Long, W.; Tu, M.; Chen, S.; Huang, Y.; Wang, S.; Zhou, W.; Chen, D.; Zhou, L.; Wang, M.; et al. Lymphocyte Subset (CD4+, CD8+) Counts Reflect the Severity of Infection and Predict the Clinical Outcomes in Patients with COVID-19. J. Infect. 2020, 81, 318–356. [Google Scholar] [CrossRef]
- Chen, G.; Wu, D.; Guo, W.; Cao, Y.; Huang, D.; Wang, H.; Wang, T.; Zhang, X.; Chen, H.; Yu, H.; et al. Clinical and Immunological Features of Severe and Moderate Coronavirus Disease 2019. J. Clin. Investig. 2020, 130, 2620–2629. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- COVID-19 Treatment Guidelines. Available online: https://www.covid19treatmentguidelines.nih.gov/ (accessed on 1 April 2021).
- Mazzoni, A.; Salvati, L.; Maggi, L.; Capone, M.; Vanni, A.; Spinicci, M.; Mencarini, J.; Caporale, R.; Peruzzi, B.; Antonelli, A.; et al. Impaired Immune Cell Cytotoxicity in Severe COVID-19 Is IL-6 Dependent. J. Clin. Investig. 2020, 130, 4694–4703. [Google Scholar] [CrossRef] [PubMed]
- Meckiff, B.J.; Ramírez-Suástegui, C.; Fajardo, V.; Chee, S.J.; Kusnadi, A.; Simon, H.; Eschweiler, S.; Grifoni, A.; Pelosi, E.; Weiskopf, D.; et al. Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4+ T Cells in COVID-19. Cell 2020, 183, 1340–1353. [Google Scholar] [CrossRef] [PubMed]
- Kuri-Cervantes, L.; Pampena, M.B.; Meng, W.; Rosenfeld, A.M.; Ittner, C.A.G.; Weisman, A.R.; Agyekum, R.S.; Mathew, D.; Baxter, A.E.; Vella, L.A.; et al. Comprehensive Mapping of Immune Perturbations Associated with Severe COVID-19. Sci. Immunol. 2020, 5, eabd7114. [Google Scholar] [CrossRef] [PubMed]
- Giamarellos-Bourboulis, E.J.; Netea, M.G.; Rovina, N.; Akinosoglou, K.; Antoniadou, A.; Antonakos, N.; Damoraki, G.; Gkavogianni, T.; Adami, M.-E.; Katsaounou, P.; et al. Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host Microbe 2020, 27, 992–1000. [Google Scholar] [CrossRef]
- Wang, F.; Nie, J.; Wang, H.; Zhao, Q.; Xiong, Y.; Deng, L.; Song, S.; Ma, Z.; Mo, P.; Zhang, Y. Characteristics of Peripheral Lymphocyte Subset Alteration in COVID-19 Pneumonia. J. Infect. Dis. 2020, 221, 1762–1769. [Google Scholar] [CrossRef] [Green Version]
- Xu, B.; Fan, C.; Wang, A.; Zou, Y.; Yu, Y.; He, C.; Xia, W.; Zhang, J.; Miao, Q. Suppressed T Cell-Mediated Immunity in Patients with COVID-19: A Clinical Retrospective Study in Wuhan, China. J. Infect. 2020, 81, e51–e60. [Google Scholar] [CrossRef]
- Liu, J.; Li, S.; Liu, J.; Liang, B.; Wang, X.; Wang, H.; Li, W.; Tong, Q.; Yi, J.; Zhao, L.; et al. Longitudinal Characteristics of Lymphocyte Responses and Cytokine Profiles in the Peripheral Blood of SARS-CoV-2 Infected Patients. EBioMedicine 2020, 55, 102763. [Google Scholar] [CrossRef]
- Sharov, K.S. HIV/SARS-CoV-2 Co-Infection: T Cell Profile, Cytokine Dynamics and Role of Exhausted Lymphocytes. Int. J. Infect. Dis. 2021, 102, 163–169. [Google Scholar] [CrossRef]
- Xu, Z.; Shi, L.; Wang, Y.; Zhang, J.; Huang, L.; Zhang, C.; Liu, S.; Zhao, P.; Liu, H.; Zhu, L.; et al. Pathological Findings of COVID-19 Associated with Acute Respiratory Distress Syndrome. Lancet Respir. Med. 2020, 8, 420–422. [Google Scholar] [CrossRef]
- Zheng, H.-Y.; Zhang, M.; Yang, C.-X.; Zhang, N.; Wang, X.-C.; Yang, X.-P.; Dong, X.-Q.; Zheng, Y.-T. Elevated Exhaustion Levels and Reduced Functional Diversity of T Cells in Peripheral Blood May Predict Severe Progression in COVID-19 Patients. Cell Mol. Immunol. 2020, 17, 541–543. [Google Scholar] [CrossRef] [PubMed]
- Robinson, C.M.; O’Dee, D.; Hamilton, T.; Nau, G.J. Cytokines Involved in Interferon-γ Production by Human Macrophages. J Innate Immun. 2010, 2, 56–65. [Google Scholar] [CrossRef]
- Boehm, U.; Klamp, T.; Groot, M.; Howard, J.C. CELLULAR RESPONSES TO INTERFERON-γ. Annu. Rev. Immunol. 1997, 15, 749–795. [Google Scholar] [CrossRef] [PubMed]
- Levy, D. The Virus Battles: IFN Induction of the Antiviral State and Mechanisms of Viral Evasion. Cytokine Growth Factor Rev. 2001, 12, 143–156. [Google Scholar] [CrossRef]
- De Lang, A.; Osterhaus, A.D.M.E.; Haagmans, B.L. Interferon-γ and Interleukin-4 Downregulate Expression of the SARS Coronavirus Receptor ACE2 in Vero E6 Cells. Virology 2006, 353, 474–481. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hu, Z.-J.; Xu, J.; Yin, J.-M.; Li, L.; Hou, W.; Zhang, L.-L.; Zhou, Z.; Yu, Y.-Z.; Li, H.-J.; Feng, Y.-M.; et al. Lower Circulating Interferon-Gamma Is a Risk Factor for Lung Fibrosis in COVID-19 Patients. Front. Immunol. 2020, 11, 585647. [Google Scholar] [CrossRef] [PubMed]
- Mehta, A.K.; Gracias, D.T.; Croft, M. TNF Activity and T Cells. Cytokine 2018, 101, 14–18. [Google Scholar] [CrossRef]
- Ono, M.; Tanaka, R.J. Controversies Concerning Thymus-derived Regulatory T Cells: Fundamental Issues and a New Perspective. Immunol. Cell Biol. 2016, 94, 3–10. [Google Scholar] [CrossRef] [Green Version]
- Wherry, E.J. T Cell Exhaustion. Nat. Immunol. 2011, 12, 492–499. [Google Scholar] [CrossRef]
- Blank, C.U.; Haining, W.N.; Held, W.; Hogan, P.G.; Kallies, A.; Lugli, E.; Lynn, R.C.; Philip, M.; Rao, A.; Restifo, N.P.; et al. Defining ‘T Cell Exhaustion’. Nat. Rev. Immunol. 2019, 19, 665–674. [Google Scholar] [CrossRef] [PubMed]
- Jelinek, D.F.; Splawski, J.B.; Lipsky, P.E. The Roles of Interleukin 2 and Interferon-γ in Human B Cell Activation, Growth and Differentiation. Eur. J. Immunol. 1986, 16, 925–932. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Y.; Fu, B.; Zheng, X.; Wang, D.; Zhao, C.; Qi, Y.; Sun, R.; Tian, Z.; Xu, X.; Wei, H. Pathogenic T-Cells and Inflammatory Monocytes Incite Inflammatory Storms in Severe COVID-19 Patients. Nat. Sci. Rev. 2020, 7, 998–1002. [Google Scholar] [CrossRef] [Green Version]
- De Biasi, S.; Meschiari, M.; Gibellini, L.; Bellinazzi, C.; Borella, R.; Fidanza, L.; Gozzi, L.; Iannone, A.; Lo Tartaro, D.; Mattioli, M.; et al. Marked T Cell Activation, Senescence, Exhaustion and Skewing towards TH17 in Patients with COVID-19 Pneumonia. Nat. Commun. 2020, 11, 3434. [Google Scholar] [CrossRef]
- Yang, X.; Dai, T.; Zhou, X.; Qian, H.; Guo, R.; Lei, L.; Zhang, X.; Zhang, D.; Shi, L.; Cheng, Y.; et al. Analysis of Adaptive Immune Cell Populations and Phenotypes in the Patients Infected by SARS-CoV-2. MedRxiv 2020. [Google Scholar] [CrossRef] [Green Version]
- Wang, F.; Hou, H.; Luo, Y.; Tang, G.; Wu, S.; Huang, M.; Liu, W.; Zhu, Y.; Lin, Q.; Mao, L.; et al. The Laboratory Tests and Host Immunity of COVID-19 Patients with Different Severity of Illness. JCI Insight 2020, 5, e137799. [Google Scholar] [CrossRef]
- Horwitz, D.A.; Zheng, S.G.; Gray, J.D. Natural and TGF-β–Induced Foxp3+CD4+ CD25+ Regulatory T Cells Are Not Mirror Images of Each Other. Trends Immunol. 2008, 29, 429–435. [Google Scholar] [CrossRef]
- Lan, Q.; Fan, H.; Quesniaux, V.; Ryffel, B.; Liu, Z.; Guo Zheng, S. Induced Foxp3+ Regulatory T Cells: A Potential New Weapon to Treat Autoimmune and Inflammatory Diseases? J. Mol. Cell Biol. 2012, 4, 22–28. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.; Qi, G.; Bellanti, J.A.; Moser, R.; Ryffel, B.; Zheng, S.G. Regulatory T Cells: A Potential Weapon to Combat COVID-19? MedComm 2020, 1, 157–164. [Google Scholar] [CrossRef]
- Mohamed Khosroshahi, L.; Rezaei, N. Dysregulation of the Immune Response in Coronavirus Disease 2019. Cell Biol. Int. 2021, 45, 702–707. [Google Scholar] [CrossRef]
- Kalfaoglu, B.; Almeida-Santos, J.; Tye, C.A.; Satou, Y.; Ono, M. T-Cell Dysregulation in COVID-19. Biochem. Biophys. Res. Commun. 2021, 538, 204–210. [Google Scholar] [CrossRef] [PubMed]
- De Candia, P.; Prattichizzo, F.; Garavelli, S.; Matarese, G. T Cells: Warriors of SARS-CoV-2 Infection. Trends Immunol. 2021, 42, 18–30. [Google Scholar] [CrossRef]
- Suvas, S.; Azkur, A.K.; Kim, B.S.; Kumaraguru, U.; Rouse, B.T. CD4 + CD25 + Regulatory T Cells Control the Severity of Viral Immunoinflammatory Lesions. J. Immunol. 2004, 172, 4123–4132. [Google Scholar] [CrossRef] [Green Version]
- Vantourout, P.; Hayday, A. Six-of-the-Best: Unique Contributions of Γδ T Cells to Immunology. Nat. Rev. Immunol. 2013, 13, 88–100. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kalicińska, E.; Szymczak, D.; Andrasiak, I.; Bogucka-Fedorczuk, A.; Zińczuk, A.; Szymański, W.; Biernat, M.; Rymko, M.; Semeńczuk, G.; Jabłonowska, P.; et al. Lymphocyte Subsets in Haematological Patients with COVID-19: Multicentre Prospective Study. Transl. Oncol. 2021, 14, 100943. [Google Scholar] [CrossRef] [PubMed]
Variable | All Patients, n = 82 | Non-ICU COVID-19 Patients, n = 51 | ICU COVID-19 Patients, n = 31 | p |
---|---|---|---|---|
Age (years) | 62 ± 16 | 62 ± 16 | 63 ± 15 | 0.848 |
Male, n (%) | 43 (52%) | 23 (45%) | 20 (65%) | 0.25 |
Comorbidities | ||||
Hypertension, n (%) | 47 (57%) | 26 (51%) | 21 (68%) | 0.14 |
Diabetes mellitus, n (%) | 19 (23%) | 8 (16%) | 11 (35%) | 0.039 |
Heart failure, n (%) | 11 (13%) | 7 (14%) | 4 (13%) | 0.91 |
Coronary artery disease, n (%) | 12 (15%) | 8 (16%) | 4 (13%) | 0.73 |
Renal failure, n (%) | 3 (4%) | 3 (6%) | 0 (0%) | 0.29 |
Stroke/TIA, n (%) | 5 (6%) | 3 (6%) | 2 (6%) | 1 |
Concomitant medications | ||||
ACE-I/ARB, n (%) | 20 (24%) | 11 (22%) | 9 (29%) | 0.18 |
Beta blockers, n (%) | 26 (32%) | 18 (35%) | 8 (26%) | 0.78 |
Calcium blockers, n (%) | 15 (18%) | 11 (22%) | 4 (13%) | 0.57 |
Diuretics, n (%) | 18 (22%) | 15 (29%) | 3 (10%) | 0.09 |
White blood cells (×103/µL)] | 8.2 (5.3–12.7) | 6.0 (4.2–8.4) | 13.8 (9.7–17.0) | <0.001 |
Lymphocytes (×103/µL) | 1.0 (0.6–1.5) | 1.1 (0.9–1.7) | 0.6 (0.4–0.9) | <0.001 |
Neutrophiles (×103/µL) | 5.6 (3.3–10.8) | 3.7 (2.7–5.5) | 11.6 (9.1–15.7) | <0.001 |
Basophiles (×103/µL) | 0.02 (0.01–0.1) | 0.02 (0.01–0.03) | 0.1 (0.04–0.2) | <0.001 |
Eosinophiles (×103/µL) | 0.03 (0–0.1) | 0.02 (0–0.07) | 0.1 (0–0.36) | 0.045 |
Monocytes (×103/µL) | 0.6 (0.5–1.25) | 0.6 (0.5–0.7) | 1.9 (0.5–4.6) | 0.001 |
Hemoglobin (g/dl) | 12.9 (11.4–14.2) | 13.7 (12.4–14.6) | 11.2 (10.7–12.8) | <0.001 |
Platelets (×103/µL) | 228 (171–308) | 220 (154–288) | 238 (188–369) | 0.11 |
Inflammatory markers | ||||
CRP (mg/L) | 46.0 (16.6–118) | 28.6 (7.2–77.1) | 107.5 (28–194) | <0.001 |
IL-6, pg/mL (normal ranges: 0.5–3.9) | 63 (17–127) | 26 (8–91) | 84 (38–209) | 0.001 |
COVID-19 severity | - | - | ||
Moderate, n (%) | 33 (65%) | 0 (0%) | ||
Severe, n (%) | 18 (35%) | 0 (0%) | ||
Critical, n (%) | 0 (0%) | 31 (100%) | ||
Severity of disease in ICU patients (points) | - | - | - | |
APACHE II | 15 (12–22) | |||
SOFA | 9 (5–10) | |||
Time of SARS-CoV-2 infection | 12 (9–19) | 11 (9–16) | 17 (9–20) | 0.16 |
Clinical outcome, death, n (%) | 28 (34%) | 4 (8%) | 24 (77%) | <0.001 |
Treatment | ||||
Oxygen therapy, n (%) | 20 (24%) | 20 (39.2%) | 0 (0%) | 1 |
High-Flow Nasal Oxygen, n (%) | 7 (9%) | 7 (14%) | 0 (0%) | 1 |
Mechanical ventilation, n (%) | 31 (38%) | 0 (0%) | 31 (100%) | 1 |
Remdesivir, n (%) | 20 (24%) | 11 (22%) | 9 (29%) | 0.445 |
Convalescent plasma, n (%) | 30 (37%) | 15 (29%) | 15 (48%) | 0.096 |
Tocilizumab, n (%) | 4 (5%) | 3 (6%) | 1 (3%) | 1 |
Steroids, n (%) | 32 (39%) | 3 (6%) | 29 (94%) | <0.001 |
Azytromycin, n (%) | 36 (44%) | 35 (69%) | 1 (3%) | <0.001 |
Cytokines Levels | Normal Ranges (pg/mL) | All Patients (n = 82) | Non-ICU COVID-19 Patients (n = 51) | ICU COVID-19 Patients (n = 31) | p |
---|---|---|---|---|---|
IL-2, pg/mL | 0–0.4 | 0.3 (0.2–0.5) | 0.2 (0.1–0.3) | 0.4 (0.3–0.8) | 0.001 |
TNFα, pg/mL | 0.8–1.7 | 1.9 (1.3–2.9) | 1.9 (1.4–2.7) | 2.1 (1.2–3.4) | 0.56 |
INFγ, pg/mL | 0–1.0 | 2.7 (0.9–9) | 4.5 (1–11) | 1.9 (0.8–4.7) | 0.032 |
IL-2/INFγ | n/a | 0.09 (0.03–0.3) | 0.03 (0.02–0.1) | 0.25 (0.13–0.7) | <0.011 |
Cytokines Levels | Normal Ranges (pg/mL) | All Patients (n = 82) | Recovered COVID-19 Patients (n = 54) | Died COVID-19 Patients (n = 28) | p |
---|---|---|---|---|---|
IL-2, pg/mL | 0–0.4 | 0.3 (0.16–0.5) | 0.2 (0.1–0.3) | 0.4 (0.3–0.8) | 0.001 |
TNFα, pg/mL | 0.8–1.7 | 1.9 (1.3–2.9) | 1.8 (1.3–2.6) | 2.5 (1.5–3.6) | 0.06 |
INFγ, pg/mL | 0–1.0 | 2.7 (0.9–9) | 3 (1–11) | 1.9 (0.8–5.6) | 0.41 |
IL-2/INFγ | n/a | 0.09 (0.03–0.3) | 0.04 (0.02–0.2) | 0.24 (0.1–0.8) | <0.011 |
IL-6, pg/mL | 0.5–3.9 | 63 (17–127) | 26 (9–77) | 118 (46–261) | <0.001 |
Lymphocyte Subsets | Normal Ranges /µL | All Patients (n = 82) | Non-ICU COVID-19 Patients (n = 51) | ICU COVID-19 Patients (n = 31) | p |
---|---|---|---|---|---|
T cells (CD3+)/µL | 617–2383 | 503 (340–798) | 620 (450–1010) | 340 (250–600) | <0.001 |
Helper T cells (CD3+CD4+)/µL | 424–1513 | 340 (210–536) | 374 (279–588) | 245 (150–395) | 0.002 |
Suppressor T cells (CD3+CD8+)/µL | 101–955 | 152 (95–270) | 191 (116–360) | 100 (68–172) | <0.001 |
Naïve helper T cells (CD3+CD4+CD45RA+)/µL | 84–761 | 160 (98–275) | 163 (111–367) | 125 (64–223) | 0.044 |
Naïve suppressor T cells (CD3+CD8+CD45RA+)/µL | 100–400 | 99 (68–195) | 128 (86–238) | 70 (50–110) | <0.001 |
Activated T cells (CD3+HLA-DR+)/µL | 30–200 | 34 (21–60) | 30 (18–43) | 44 (28–100) | 0.007 |
Activated suppressor T cells (CD3+CD8+HLA-DR+)/µL | 30–180 | 17 (7–33) | 13 (7–27) | 22 (7–55) | 0.18 |
Regulatory T cells (CD3+CD4+CD25+CD127low+)/µL | 30–120 | 21 (16–36) | 26 (18–44) | 17 (12–25) | 0.001 |
Naïve regulatory T cells (CD45RA+CD3+CD4+CD25+CD127low+)/µL | 9–36 | 3 (1–9) | 4 (2–10) | 2 (1–4) | <0.001 |
Induced regulatory T cells (CD45RO+CD3+CD4+CD25+CD127low+)/µL | 21–84 | 18 (12–27) | 22 (14–33) | 14 (10–20) | 0.007 |
TCRα/β/µL | 573–2216 | 488 (320–780) | 597 (412–940) | 330 (250–598) | <0.001 |
TCRγ/δ/µL | 30–119 | 11 (5–44) | 23 (8–60) | 4 (2–13) | <0.001 |
CD25+CD3+/µL | 7–94 | 64 (40–95) | 75 (56–119) | 40 (26–71) | 0.001 |
B cells (CD19+)/µL | 31–527 | 129 (68–198) | 128 (67–220) | 130 (70–185) | 0.85 |
B cells (CD19+CD20+)/µL | 66–528 | 265 (167–375) | 300 (197–423) | 190 (134–310) | 0.001 |
NK cells (CD16+CD56+)/µL | 110–678 | 88 (47–192) | 145 (81–217) | 40 (17–77) | <0.001 |
Lymphocyte Subsets | Normal Ranges /µL | All Patients (n = 82) | Recovered COVID-19 Patients (n = 54) | Died COVID-19 Patients (n = 28) | p |
---|---|---|---|---|---|
T cells (CD3+)/µL | 617–2383 | 503 (340–798) | 600 (450–990) | 335 (268–535) | <0.001 |
Helper T cells (CD3+CD4+)/µL | 424–1513 | 340 (210–536) | 390 (279–584) | 225 (154–364) | 0.001 |
Suppressor T cells (CD3+CD8+)/µL | 101–955 | 152 (95–270) | 186 (116–352) | 103 (70–170) | 0.001 |
Naïve helper T cells (CD3+CD4+CD45RA+)/µL | 84–761 | 160 (98–275) | 167 (118–280) | 120 (63–204) | 0.016 |
Naïve suppressor T cells (CD3+CD8+CD45RA+)/µL | 100–400 | 99 (68–195) | 126 (78–230) | 79 (53–105) | 0.002 |
Activated T cells (CD3+HLA-DR+)/µL | 30–200 | 34 (21–60) | 33 (20–54) | 35 (23–71) | 0.4 |
Activated suppressor T cells (CD3+CD8+HLA-DR+)/µL | 30–180 | 17 (7–33) | 17 (8–30) | 13 (6–41) | 0.81 |
Regulatory T cells(CD3+CD4+CD25+CD127low+)/µL | 30–120 | 21 (16–36) | 26 (19–43) | 17 (12–21) | <0.001 |
Naïve regulatory T cells (CD45RA+CD3+CD4+CD25+CD127low+)/µL | 9–36 | 3 (1–9) | 4 (2–10) | 2 (1–3) | <0.001 |
Induced regulatory T cells (CD45RO+CD3+CD4+CD25+CD127low+)/µL | 21–84 | 18 (12–27) | 21 (14–32) | 14 (10–20) | 0.003 |
TCRα/β/µL | 573–2216 | 488 (320–780) | 588 (412–940) | 325 (255–532) | <0.001 |
TCRγ/δ/µL | 30–119 | 11 (5–44) | 20 (8–53) | 5 (2–14) | <0.001 |
CD25+CD3+/µL | 7–94 | 64 (40–95) | 75 (60–116) | 40 (27–57) | <0.001 |
B cells (CD19+)/µL | 31–527 | 129 (68–198) | 130 (70–235) | 101 (67–155) | 0.24 |
B cells (CD19+CD20+)/µL | 66–528 | 265 (167–375) | 300 (220–420) | 165 (125–285) | <0.001 |
NK cells (CD16+CD56+)/µL | 110–678 | 88 (47–192) | 135 (78–204) | 42 (18–66) | <0.001 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kalicińska, E.; Szymczak, D.; Zińczuk, A.; Adamik, B.; Smiechowicz, J.; Skalec, T.; Nowicka-Suszko, D.; Biernat, M.; Bogucka-Fedorczuk, A.; Rybka, J.; et al. Immunosuppression as a Hallmark of Critical COVID-19: Prospective Study. Cells 2021, 10, 1293. https://doi.org/10.3390/cells10061293
Kalicińska E, Szymczak D, Zińczuk A, Adamik B, Smiechowicz J, Skalec T, Nowicka-Suszko D, Biernat M, Bogucka-Fedorczuk A, Rybka J, et al. Immunosuppression as a Hallmark of Critical COVID-19: Prospective Study. Cells. 2021; 10(6):1293. https://doi.org/10.3390/cells10061293
Chicago/Turabian StyleKalicińska, Elżbieta, Donata Szymczak, Aleksander Zińczuk, Barbara Adamik, Jakub Smiechowicz, Tomasz Skalec, Danuta Nowicka-Suszko, Monika Biernat, Aleksandra Bogucka-Fedorczuk, Justyna Rybka, and et al. 2021. "Immunosuppression as a Hallmark of Critical COVID-19: Prospective Study" Cells 10, no. 6: 1293. https://doi.org/10.3390/cells10061293
APA StyleKalicińska, E., Szymczak, D., Zińczuk, A., Adamik, B., Smiechowicz, J., Skalec, T., Nowicka-Suszko, D., Biernat, M., Bogucka-Fedorczuk, A., Rybka, J., Martuszewski, A., Gozdzik, W., Simon, K., & Wróbel, T. (2021). Immunosuppression as a Hallmark of Critical COVID-19: Prospective Study. Cells, 10(6), 1293. https://doi.org/10.3390/cells10061293