Dysregulation of immune response in patients with COVID-19 in Wuhan, China

Abstract

Background:
In December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and rapidly spread throughout China.

Methods:
Demographic and clinical data of all confirmed cases with COVID-19 on admission at Tongji Hospital from January 10 to February 12, 2020, were collected and analyzed. The data of laboratory examinations, including peripheral lymphocyte subsets, were analyzed and compared between severe and non-severe patients.

Results:
Of the 452 patients with COVID-19 recruited, 286 were diagnosed as severe infection. The median age was 58 years and 235 were male. The most common symptoms were fever, shortness of breath, expectoration, fatigue, dry cough and myalgia. Severe cases tend to have lower lymphocytes counts, higher leukocytes counts and neutrophil-lymphocyte-ratio (NLR), as well as lower percentages of monocytes, eosinophils, and basophils. Most of severe cases demonstrated elevated levels of infection-related biomarkers and inflammatory cytokines. The number of T cells significantly decreased, and more hampered in severe cases. Both helper T cells and suppressor T cells in patients with COVID-19 were below normal levels, and lower level of helper T cells in severe group. The percentage of naïve helper T cells increased and memory helper T cells decreased in severe cases. Patients with COVID-19 also have lower level of regulatory T cells, and more obviously damaged in severe cases.

Conclusions:
The novel coronavirus might mainly act on lymphocytes, especially T lymphocytes. Surveillance of NLR and lymphocyte subsets is helpful in the early screening of critical illness, diagnosis and treatment of COVID-19.

Full text

MAJOR ARTICLE
762 • CID 2020:71 (1 August) • Qin etal
Clinical Infectious Diseases
MAJOR ARTICLE
Received 20 February 2020; editorial decision 4 March 2020; accepted 6 March 2020; published
online March 12, 2020.
aC. Q.and L.Z.contributed equally to this work.
Correspondence: D.-S. Tian, Department of Neurology, Tongji Hospital, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, P.R. China (tiands@
tjh.tjmu.edu.cn or tiandaishi@126.com).
Clinical Infectious Diseases® 2020;71(15):762–8
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society
of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.
DOI: 10.1093/cid/ciaa248
Dysregulation of Immune Response in Patients With
Coronavirus 2019 (COVID-19) in Wuhan,China
Chuan Qin,1,a Luoqi Zhou,1,a Ziwei Hu,1 Shuoqi Zhang,2 Sheng Yang,1 YuTao MD,3 Cuihong Xie,4 Ke Ma,5 Ke Shang,1 Wei Wang,1 and Dai-Shi Tian1
1Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 2Department of Radiology, Tongji Hospital, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan, China, 3Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University
of Science and Technology, Wuhan, China, 4Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, and
5Department of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Background. In December 2019, coronavirus 2019 (COVID-19) emerged in Wuhan and rapidly spread throughout China.
Methods. Demographic and clinical data of all conrmed cases with COVID-19 on admission at Tongji Hospital from 10
January to 12 February 2020 were collected and analyzed. e data on laboratory examinations, including peripheral lymphocyte
subsets, were analyzed and compared between patients with severe and nonsevere infection.
Results. Of the 452 patients with COVID-19 recruited, 286 were diagnosed as having severe infection. e median age was
58years and 235 were male. e most common symptoms were fever, shortness of breath, expectoration, fatigue, dry cough, and my-
algia. Severe cases tend to have lower lymphocyte counts, higher leukocyte counts and neutrophil-lymphocyte ratio (NLR), as well
as lower percentages of monocytes, eosinophils, and basophils. Most severe cases demonstrated elevated levels of infection-related
biomarkers and inammatory cytokines. e number of T cells signicantly decreased, and were more impaired in severe cases.
Both helper T () cells and suppressor T cells in patients with COVID-19 were below normal levels, with lower levels of  cells in
the severe group. e percentage of naive  cells increased and memory  cells decreased in severe cases. Patients with COVID-19
also have lower levels of regulatory T cells, which are more obviously decreased in severe cases.
Conclusions. e novel coronavirus might mainly act on lymphocytes, especially T lymphocytes. Surveillance of NLR and lym-
phocyte subsets is helpful in the early screening of critical illness, diagnosis, and treatment of COVID-19.
Keywords. lymphocyte subsets; T lymphocyte; immune response; COVID-19.
The outbreak of severe acute respiratory syndrome coro-
navirus 2 (SARS-CoV-2), which first emerged in Wuhan in
December 2019, has rapidly spread throughout China in the
past 2 months [1, 2]. Considering the ongoing outbreak in
China and fast worldwide spread of SARS-Cov-2 caused co-
ronavirus 2019 (COVID-19), it has led to the declaration of
a Public Health Emergency of International Concern by the
World Health Organization (WHO) on 30 January 2020 [3].
As of 16 February 2020, a total of 58 182 laboratory-confirmed
cases have been identified in China (primarily in Wuhan),
with 1696 fatal cases, according to the data from Chinese gov-
ernment official reports [2].
It has been reported that COVID-19 was more likely to occur in
older men with comorbidities [1, 4, 5], who have weaker immune
functions. As a new type of highly contagious disease in human,
the pathophysiology of unusually high pathogenicity for COVID-
19 has not yet been completely understood. Several studies have
shown that increased amounts of proinammatory cytokines in
serum were associated with pulmonary inammation and ex-
tensive lung damage in SARS [6] and middle east respiratory
syndrome coronavirus (MERS-CoV) infection [7], and recently
in COVID-19 [1]. However, little is known about lymphocyte
subsets and the immune response of patients with COVID-19.
is retrospective, single-center study aimed to analyze the
expression of infection-related biomarkers, inammatory cyto-
kines, and lymphocyte subsets by ow cytometry in laboratory-
conrmed cases, and compare the dierence between severe
cases and nonseverecases.
METHODS
Study Design and Participants
We retrospectively recruited a total of 452 patients with
COVID-19 from 10 January to 12 February 2020 at Tongji
Hospital, the largest comprehensive medical treatment center
of central China and “the specific hospital for the treatment of
severe patients with COVID-19 in Wuhan” designated by the
government. The study was performed in accordance with
Tongji Hospital Ethics Committee (Institutional Review Board
ID: TJ-C20200121). Written informed consent was waived by
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Dysregulation of Immunity in COVID-19 • CID 2020:71 (1 August) • 763
the Ethics Commission of the designated hospital for emerging
infectious disease.
e severity of COVID-19 was judged according to the Fih
Revised Trial Version of the Novel Coronavirus Pneumonia
Diagnosis and Treatment Guidance [8]. ose who met the fol-
lowing criteria were dened as having severe-type infection: (1)
respiratory distress with a respiratory rate over 30 breaths per
minute, (2) oxygen saturation ≤93% in the resting state, and (3)
arterial blood oxygen partial pressure (PaO2) /oxygen concen-
tration (FiO2) ≤300mm Hg.
Data Collection
Data including demographic data, medical history, symptoms,
signs, and laboratory findings were collected from patients’ med-
ical records. Laboratory results included blood routine, lym-
phocyte subsets, infection-related biomarkers, inflammatory
cytokines, immunoglobulins, and complement proteins. The total
number of lymphocytes in peripheral blood was counted by he-
mocytometer. Lymphocyte subset percentage were analyzed with
a FA CSCanto flow cytometer(BD, Franklin Lakes, USA) for those
patients with COVID-19 on admission [9]. The absolute numbers
of different lymphocyte subsets were calculated by multiplying the
percentages with total lymphocyte count. Phorbol 12-Myristate
13-Acetate (PMA)/ionomycin-stimulated lymphocyte function
assay was performed as described previously [10]. The percent-
ages of interferon-γ (IFN-γ)–positive cells in different cell subsets
were defined as the active parts of these immune cells. The data
were reviewed by a trained team of physicians in Tongji Hospital.
Real-time Reverse Transcriptase–Polymerase Chain ReactionAssay
A confirmed COVID-19 case was defined as positive for real-time
reverse transcriptase–polymerase chain reaction (RT-PCR) assay
for nasal and pharyngeal swab specimens according to the WHO
guidance. On receipt of the samples, viral RNA extraction was
performed using a magnetic viral RNA/DNA extraction kit on a
PAN9600 Automated Nucleic Acid Extraction System (Tianlong,
Xi’an, China), according to the manufacturer’s instructions, fol-
lowed by PCR screening for the presence of specific 2019-nCoV
with a commercial kit (Tianlong, Xi’an, China) in a volume of
25μL PCR mixture containing 17.5μL reaction solution, 1.5μL
probes, 1.5 μL thermus aquaticus (Taq) DNA polymerase, and
5 μL nucleic acid. Conditions for the amplifications include re-
verse transcription at 50°C for 30 minutes, predenaturation at
95°C for 10 minutes, followed by 5 cycles of 94°C for 15 seconds,
50°C for 30 seconds and 72°C for 30 seconds, and 40 cycles of 94°C
for 10 seconds and 58°C for 30 seconds for fluorescence detection.
Acycle threshold value (Ct value) ≤37 was defined as a positive
test, which was based on the recommendation by the National
Institute for Viral Disease Control and Prevention (China).
Statistical Analysis
We describe the categorical variables as frequency rates and per-
centages and continuous variables as means and SDs, medians
and interquartile ranges (IQRs). Independent-group t tests were
used for the comparison of means for continuous variables that
were normally distributed; conversely, the Mann-Whitney U
test was used for continuous variables not normally distrib-
uted. Proportions for categorical variables were compared using
the χ
2 test. All statistical analyses were performed using SPSS
(Statistical Package for the Social Sciences) version 20.0 soft-
ware (SPSS, Inc). Two-sided P values of less than .05 were con-
sidered statistically significant.
RESULTS
Demographic and Clinical Characteristics of Patients With COVID-19
By 12 February 2020, 452 consecutive patients with COVID-
19 on admission to hospitalization at Tongji Hospital were
recruited in this study, 286 (63.3%) of whom were clinically
diagnosed as having severe infection. Demographic and clinical
characteristics of the 452 patients with COVID-19 was shown
in Table 1.In total, the median age was 58years (IQR, 47–67;
range, 22–95years) and 235 (52.0%) were men. Compared with
patients with nonsevere infection, patients with severe infection
were significantly older (median age, 61 [IQR, 51–69] years vs
53 [IQR, 41–62] years; P < .001). The proportion of men in
the severe group (54.2% men) were not significantly different
from the nonsevere group. Of the 452 patients with COVID-
19, 201 (44.0%) patients had chronic diseases (ie, hyperten-
sion, diabetes, chronic obstructive pulmonary disease), and a
higher percentage in the severe cases (146 [51.0%]) than in the
mild cases (55 [33.1%]). And those patients with severe infec-
tion were significantly more likely to have concomitant hyper-
tension and cardiovascular diseases (36.7% vs 18.1%; P < .001;
and 8.4% vs 1.8%; P = .004; respectively). The most common
symptoms were fever (92.6%), shortness of breath (50.8%), ex-
pectoration (41.4%), fatigue (46.4%), dry cough (33.3%), and
myalgia (21.4%). Moreover, patients with severe infection were
significantly more likely to have shortness of breath and fatigue
(58.4% vs 39.2%; P < .001; and 51.4% vs 39.2%; P = .014; re-
spectively) than patients with nonsevere infection.
Blood Cell Counts, Infection-Related Biomarkers, Inflammatory Cytokines,
Immunoglobulins, and Complement Proteins in Patients With COVID-19
Table2 presents the laboratory findings in patients with COVID-
19. Among 452 patients who underwent laboratory examinations
on admission, most of them tended to have lymphopenia, higher
infection-related biomarkers (ie, procalcitonin, erythrocyte sedi-
mentation rate, serum ferritin, and C-reactive protein), and sev-
eral elevated inflammatory cytokines (ie, tumor necrosis factor-α
[TNF-α], interleukin [IL]-2R and IL-6), and there were numerous
differences in blood cell counts and infection-related biomarkers
between the severe group and the nonsevere group. Severe cases
had higher leukocyte (5.6 vs 4.9 × 109; P < .001) and neutrophil
(4.3 vs 3.2 × 109; P < .001) counts, lower lymphocytes counts (0.8 vs
1.0 × 109; P < .001), a higher neutrophil-to-lymphocyte ratio (NLR;
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764 • CID 2020:71 (1 August) • Qin etal
5.5 vs 3.2; P < .001), as well as lower percentages of monocytes (6.6
vs 8.4 %; P < .001), eosinophils (0.0 vs 0.2%; P < .001), and basophils
(0.1 vs 0.2%; P = .015). Compared with the nonsevere group, most
of severe cases demonstrated elevated levels of infection-related
biomarkers, including procalcitonin (0.1 vs 0.05ng/mL; P < .001),
serum ferritin (800.4 vs 523.7ng/mL; P < .001), and C-reactive pro-
tein (57.9 vs 33.2mg/L; P < .001). Several inflammatory cytokines
were also elevated in severe cases compared with the nonsevere
cases, including IL-2R (757.0 vs 663.5 U/mL; P = .001), IL-6 (25.2
vs 13.3 pg/mL; P < .001), IL-8 (18.4 vs 13.7 pg/mL; P < .001),
IL-10 (6.6 vs 5.0 pg/mL; P < .001), and TNF-α (8.7 vs 8.4 pg/mL;
P = .037). Immunoglobulins (IgA, IgG, and IgM) and complement
proteins (C3 and C4) in patients with COVID-19 were within the
normal range. There were no significant differences in the levels of
IgA, IgG, and complement proteins C3 or C4 between the mild and
severe groups, while IgM was slightly decreased in severecases.
Lymphocyte Subset Analysis in Patients With COVID-19
Lymphocyte subsets were analyzed in 44 patients with COVID-
19 on admission (Table3). The total number of B cells, T cells,
and natural killer (NK) cells were significantly decreased in pa-
tients with COVID-19 (852.9/μL), which was more evident in
the severe cases (743.6 vs 1020.1/μL; P = .032) compared with
the nonsevere group. The mean values of the 3 main subsets of
lymphocytes were generally decreased in patients with COVID-
19, as T cells and NK cells were below normal levels and B cells
were within the lower level of normal range. T cells were shown
to be more affected by SARS-CoV-2 as T-cell count was nearly
half the lower reference limit, and tended to be more impaired
in severe cases (461.6 vs 663.8/μL; P = .027) when compared
with the nonseveregroup.
e function of CD4+, CD8+ T cells, and NK cells, as indi-
cated by PMA/ionomycin-stimulated IFN-γ–positive cells in
Table 1. Demographic and Baseline Characteristics of Patients With COVID-19
All Patients (N = 452) Nonsevere (n = 166) Severe (n = 286) P
Characteristics
Age, median (IQR), range, y 58 (47–67), 22–95 53 (41.25–62), 22–92 61 (51–69), 26–95 <.001
Sex .242
Male 235 (52.0) 80 (48.2) 155 (54.2)
Female 217 (48.0) 86 (51.8) 131 (45.8)
Smoking 7 (1.5) 4 (2.4) 3 (1.0) .267
Chronic medical illness
Any 201 (44.0) 55 (33.1) 146 (51.0) <.001
Chronic obstructive pulmonary disease 12 (2.6) 3 (1.8) 9 (3.1) .548
Hypertension 135 (29.5) 30 (18.1) 105 (36.7) <.001
Cardiovascular disease 27 (5.9) 3 (1.8) 24 (8.4) .004
Cerebrovascular disease 11 (2.4) 3 (1.8) 8 (2.8) .753
Chronic liver disease 6 (1.3) 3 (1.8) 3 (1.0) .674
Diabetes 75 (16.4) 22 (13.3) 53 (18.5) .152
Tuberculosis 9 (19.7) 2 (1.2) 7 (2.4) .496
Malignant tumor 14 (3.1) 4 (2.4) 10 (3.5) .587
Chronic kidney disease 10 (2.2) 4 (2.4) 6 (2.1) 1.000
Signs and symptoms
Fever 423 (92.6) 152 (91.6) 271 (94.8) .232
Dry cough 152 (33.3) 56 (33.7) 96 (33.6) 1.000
Expectoration 189 (41.4) 68 (41.0) 121 (42.3) .843
Hemoptysis 12 (2.6) 2 (1.2) 10 (3.5) .225
Shortness of breath 232 (50.8) 65 (39.2) 167 (58.4) <.001
Myalgia 98 (21.4) 32 (19.3) 66 (23.1) .407
Confusion 3 (0.7) 0 (0.0) 3 (1.0) .301
Headache 52 (11.4) 13 (7.8) 39 (13.6) .068
Dizziness 37 (8.1) 9 (5.4) 28 (9.8) . 112
Fatigue 212 (46.4) 65 (39.2) 147 (51.4) . 014
Rhinorrhea 8 (1.8) 2 (1.2) 6 (2.1) .716
Pharyngalgia 22 (4.8) 10 (6.0) 12 (4.2) .376
Anorexia 96 (21.0) 30 (18.1) 66 (23.1) .234
Nausea and vomiting 42 (9.2) 10 (6.0) 32 (11.2) .092
Diarrhea 122 (26.7) 44 (26.5) 78 (27.3) .913
Abdominal pain 23 (5.0) 4 (2.4) 19 (6.6) .073
Data are median (IQR), n (%), in which N is the total number of patients with available data. P values comparing severe and nonsevere cases are derived from χ
2 test, Fisher’ exact test,
or Mann-Whitney U test.
Abbreviations: COVID-19, coronavirus 2019; IQR, interquartile range.
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Dysregulation of Immunity in COVID-19 • CID 2020:71 (1 August) • 765
Table 2. Laboratory Findings of Patients With COVID-19
Laboratory Findings Normal Range All Patients (N = 452) Nonsevere (n = 166) Severe (n = 286) P
Blood routine
Leucocytes, ×109/L 3.5–9.5 5.3 (3.9–7.5) 4.9 (3.7–6.1) 5.6 (4.3–8.4) <.001
Neutrophils, ×109/L 1.8–6.3 3.9 (2.6–5.8) 3.2 (2.1–4.4) 4.3 (2.9–7.0) <.001
Neutrophil percentage, % 40.0–75.0 74.3 (64.3–83.9) 67.5 (57.8–75.8) 77.6 (68.9–86.5) <.001
Lymphocytes, ×109/L 1.1–3.2 0.9 (0.6–1.2) 1.0 (0.7–1.3) 0.8 (0.6–1.1) <.001
Lymphocyte percentage, % 20.0–50.0 17.5 (10.7–25.1) 21.4 (15.3–32.5) 14.1(8.8–21.4) <.001
Neutrophil-to-lymphocyte ratio … 4.2 (2.5–7.7) 3.2 (1.8–4.9) 5.5 (3.3–10.0) <.001
Monocytes, ×109/L 0.1–0.6 0.4 (0.3–0.5) 0.4 (0.3–0.5) 0.4 (0.3–0.5) .395
Monocyte percentage, % 3.0–10.0 7.1 (4.9–9.6) 8.4 (6.5–10.8) 6.6 (4.3–8.8) <.001
Eosinophils, ×109/L 0.02–0.52 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.0) <.001
Eosinophil percentage, % 0.4–8.0 0.0 (0.0–0.4) 0.2 (0.0–0.7) 0.0 (0.0–0.2) <.001
Basophils, ×109/L 0.00–0.10 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.0) .747
Basophil percentage, % 0.0–1.0 0.1 (0.1–0.2) 0.2 (0.0–0.3) 0.1 (0.0–0.2) .015
Infection-related biomarkers
Procalcitonin, ng/mL 0.0–0.05 0.1 (0.0–0.2) 0.05 (0.03–0.09) 0.1 (0.0–0.2) <.001
Erythrocyte sedimentation rate, mm/h 0.0–15.0 31.5 (17.0–58.0) 28.0 (14.0–50.0) 34.0 (19.0–60.0) .123
Serum ferritin, ng/mL 15.0–150.0 662.4 (380.9–1311.9) 523.7 (299.1–840.4) 800.4 (452.9–1451.6) <.001
C-reactive protein, mg/L 0.0–1.0 44.1 (15.5–93.5) 33.2 (8.2–59.7) 57.9 (20.9–103.2) <.001
Inflammatory cytokines
Tumor necrosis factor-α, pg/mL 0.0–8.1 8.6 (6.9–10.9) 8.4 (6.9–10.4) 8.7 (7.1–11.6) .037
Interleukin-1β, pg/mL 0.0–5.0 5.0 (5.0–5.0) 5.0 (5.0–5.0) 5.0 (5.0–5.0) .962
Interleukin-2R, U/mL 223.0–710.0 714.5 (514.5–1040.3) 663.5 (473.3–862.8) 757.0 (528.5–1136.3) .001
Interleukin-6, pg/mL 0.0–7.0 21.0 (6.1–47.2) 13.3 (3.9–41.1) 25.2 (9.5–54.5) <.0 01
Interleukin-8, pg/mL 0.0–62.0 16.7 (10.2–27.0) 13.7 (8.9–21.0) 18.4 (11.3–28.4) <.001
Interleukin-10, pg/mL 0.0–9.1 5.4 (5.0–9.7) 5.0 (5.0–7.0) 6.6 (5.0–11.3) <.001
Immunoglobulins
Immunoglobulin A, g/L 0.82–4.53 2.21 (1.65–2.79) 2.14 (1.66–2.71) 2.26 (1.57–2.89) .285
Immunoglobulin G, g/L 7.51–15.60 11.75 (9.70–13.60) 11.85 (10.13–13.40) 11.7 (9.53–13.8) .551
Immunoglobulin M, g/L 0.46–3.04 0.95 (0.70–1.31) 1.02 (0.77–1.37) 0.90 (0.69–1.28) .033
Complement proteins
C3, g/L 0.65–1.39 0.88 (0.77–1.00) 0.88 (0.77–1.00) 0.89 (0.77–1.00) .942
C4, g/L 0.16–0.38 0.26 (0.20–0.31) 0.26 (0.20–0.31) 0.26 (0.20–0.31) .851
Data are median (IQR). P values comparing severe and nonsevere cases are derived from χ
2 test, Fisher’ exact test, or Mann-Whitney U test.
Abbreviations: COVID-19, coronavirus 2019; IQR, interquartile range.
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766 • CID 2020:71 (1 August) • Qin etal
these 3 subsets, was within the normal range. No signicant dif-
ferences were found between severe cases and nonseverecases.
We further analyzed dierent subsets of T cells. Both
helper T () cells (CD3+, CD4+) and suppressor T cells
(CD3+, CD8+) in patients with COVID-19 were below
normal levels, and the decline in  cells was more pro-
nounced in severe cases (285.1 vs 420.5/μL; P = .027). Asim-
ilar tendency was also shown in the decline in suppressor
T cells, although there was no statistical dierence between
mild and severe cases (P = .197). e  and suppressor T
ratio (/Ts) remained in the normal range, and showed no
dierence between the 2 subgroups. e percentage of naive
 cells (CD3+, CD4+, CD45RA+) increased (44.5 vs 35.0 %;
P = .035) and memory  cells (CD3+, CD4+, CD45RO+)
decreased (55.5 vs 65.0 %; P = .035) in severe cases when
compared with nonsevere cases. CD28-positive cytotoxic sup-
pressor T cells (CD3+, CD8+, CD28+) percentage decreased
in severe cases (54.5 vs 67.0 %; P = .035), while no signicant
dierence was found in activated T cells (CD3+, HLA-DR+)
and activated suppressor T cells (CD3+, CD8+, HLA-DR+).
Patients with COVID-19 presented lower levels of regulatory
T cells (CD3+, CD4+, CD25+, CD127low+), which was par-
ticularly obvious in severe cases (3.7 vs 4.5/μL; P = .040). e
decline in naive (CD45RA+, CD3+, CD4+, CD25+, CD127lo
w+) and induced regulatory T cells (CD45RO+, CD3+, CD4+
, CD25+, CD127low+) had a more obvious trend in the severe
group, although there was no signicant dierence.
DISCUSSION
We report here a dysregulated immune system in a cohort of
452 patients with laboratory-confirmed COVID-19 in Wuhan,
China. Increases in NLR and T lymphopenia—in particular, a
decrease in CD4+ T cells—were common among patients with
COVID-19, and more evident in the severe cases, but there was
no significant change in the number of CD8+ cells and B cells.
Based on these data, we suggest that COVID-19 might damage
lymphocytes, especially T lymphocytes, and the immune system
is impaired during the period of disease.
Table 3. Lymphocyte Subset Analysis in Patients With COVID-19
Normal Range
All Patients
(N = 44)
Nonsevere
(n = 17) Severe (n = 27) P
Lymphocyte subsets
T cells + B cells + NK cells/μL1100.0–3200.0 852.9 (412.0) 1020.1 (396.5) 743.6 (384.4) .032
T cells + B cells + NK cells, % 95.0–105.0 98.9 (1.0) 99.2 (0.6) 98.6 (1.2) .103
B cells (CD3− CD19+)/μL90.0–560.0 179.7 (143.1) 196.1 (144.9) 169.0 (140.9) .559
B cells (CD3− CD19+), % 5.0–18.0 20.5 (10.9) 18.5 (8.1) 21.8 (12.2) .353
T cells (CD3+ CD19−)/μL955.0–2860.0 541.5 (292.7) 663.8 (291.3) 461.6 (264.7) .027
T cells (CD3+ CD19−), % 50.0–84.0 61.3 (10.1) 63.4 (8.5) 60.0 (10.8) .283
NK cells (CD3−/CD16+ CD56+)/μL150.0–1100.0 131.7 (83.1) 160.2 (90.8) 113.0 (71.8) .072
NK cells (CD3−/CD16+ CD56+), % 7.0–40.0 17.0 (10.1) 17.2 (10.1) 16.9 (10.1) .926
Lymphocyte function
IFN-γ+ CD4+ T cells/Th, % 14.54–36.96 21.2 (12.2) 22.6 (10.2) 20.2 (13.3) .557
IFN-γ+ CD8+ T cells/Ts, % 34.93–87.95 48.6 (13.7) 46.9 (11.6) 49.7 (14.8) .541
IFN-γ+ NK cells/NK, % 61.2–92.65 68.0 (14.7) 66.7 (19.3) 68.8 (10.5) .677
T-cell subsets
Th cells (CD3+ CD4+)/μL550.0–1440.0 338.6 (196.3) 420.5 (207.8) 285.1 (168.0) .027
Th cells (CD3+ CD4+), % 27.0–51.0 38.3 (8.1) 39.8 (7.5) 37.2 (8.4) .314
Ts cells (CD3+ CD8+)/μL320.0–1250.0 173.4 (115.2) 201.9 (107.1) 154.7 (116.5) .197
Ts cells (CD3+ CD8+), % 15.0–44.0 19.6 (8.1) 19.5 (6.2) 19.7 (9.2) .930
Th/Ts 0.71–2.78 2.4 (1.2) 2.2 (0.6) 2.5 (1.5) .415
Naive Th cells (CD3+ CD4+ CD45RA+)/Th, % 29.41–55.41 40.7 (13.3) 35.0 (13.0) 44.5 (12.2) .035
Memory Th cells (CD3+ CD4+ CD45RO+)/Th % 44.44–68.94 59.3 (13.3) 65.0 (13.0) 55.5 (12.2) .035
CD28 + Th cells (CD3+ CD4+ CD28+)/Th, % 84.11–100.00 90.0 (14.0) 91.2 (12.7) 90.6 (14.7) .911
CD28 + Ts cells (CD3+ CD8+ CD28+)/Ts, % 48.04–77.14 59.6 (17.7) 67.0 (16.0) 54.5 (16.9) .035
Activated T cells (CD3+ HLA-DR+)/μL9.04–25.62 15.0 (5.8) 14.4 (5.2) 15.4 (6.2) .636
Activated Ts cells (CD3+ CD8+ HLA-DR+)/Ts, % 20.73–60.23 39.8 (10.7) 36.3 (10.7) 42.2 (10.1) .109
Regulatory T cells (CD3+ CD4+ CD25+ CD127low+)/μL5.36–6.30 4.1 (1.2) 4.5 (.9) 3.7 (1.3) .040
Naive regulatory T cells (CD45RA+ CD3+ CD4+ CD25
+ CD127low+)/μL
2.07–4.55 1.0 (0.5) 1.1 (0.5) 0.9 (0.5) .502
Induced regulatory T cells (CD45RO+ CD3+ CD4+ CD2
5+ CD127low+)/μL
1.44–2.76 3.1 (1.1) 3.5 (0.8) 1.8 (1.2) .064
Data are mean (SD). P values comparing severe and nonsevere cases are derived from t test or Mann-Whitney U test.
Abbreviations: COVID-19, coronavirus 2019; IFN-γ, interferon-γ; NK, natural killer; Th, helper T; Ts, suppressor T.
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Dysregulation of Immunity in COVID-19 • CID 2020:71 (1 August) • 767
In the cohort, we observed that 44.0% of patients had at least
1 underlying disorder (ie, hypertension, diabetes, chronic ob-
structive pulmonary disease), and a higher percentage of hy-
pertension and cardiovascular disease in the severe cases than
in the mild cases, which is consistent with reports [1, 11] that
suggested that COVID-19 is more likely to infect elderly men
with chronic comorbidities due to weaker immune functions.
In terms of laboratory tests, we noted that most of the in-
fected patients presented with lymphopenia and elevated levels of
infection-related biomarkers, More interestingly, a higher number
of neutrophils and a lower number of lymphocytes (ie, the in-
crease in NLR) were found in the severe group with COVID-19
compared with the mild group. NLR, a well-known marker of sys-
temic inammation and infection, has been studied as a predictor
of bacterial infection, included pneumonia [12–14]. e increase
in NLR in our study, consistent with the ndings from Wang etal
[11] that several patients with COVID-19 had an increased neu-
trophil count and a decreased lymphocyte count during the severe
phase, indicated the potential critical condition and serious dis-
turbance of internal environment in those severe infectedcases.
Higher serum levels of proinammatory cytokines (TNF-α,
IL-1, and IL-6) and chemokines (IL-8) were found in patients
with severe COVID-19 compared with individuals with mild di-
sease, similar to the results in SARS and MERS [6, 15]. Cytokines
and chemokines have been thought to play an important role in
immunity and immunopathology during viral infections [15,
16]. Although there is no direct evidence for the involvement of
proinammatory cytokines and chemokines in lung pathology
during COVID-19, the changes in laboratory parameters, in-
cluding elevated serum cytokine, chemokine levels, and increased
NLR in infected patients, were correlated with the severity of
the disease and adverse outcome, suggesting a possible role for
hyperinammatory responses in COVID-19 pathogenesis.
Virus-induced direct cytopathic eects and viral evasion of
host immune responses are believed to play major roles in di-
sease severity [15, 16]. Arapid and well-coordinated innate im-
mune response is the rst line of defense against viral infections;
however, when the immune response is dysregulated, it will re-
sult in excessive inammation, and even cause death [17]. In
our study, we demonstrated pronounced lymphopenia and low
counts of CD3+ cells and CD4+ cells in COVID-19 cases. e
dierentiation of naive CD4+ T cells into eector and memory
subsets is one of the most fundamental facets of T-cell–medi-
ated immunity [18]. And the balance between the naive and
memory CD4+ T cells is crucial for maintaining an ecient im-
mune response. Our results of lymphocyte subsets with higher
naive CD4+ T-cell subpopulations and smaller percentages of
memory cells and a higher naive-to-memory CD4+ T-cell ratio
in severe cases indicated that the immune system in the severe
infection subgroup was impaired more severely. In addition, the
decrease in regulatory T cells, especially induced regulatory T
cells, which have a key role in restraining allergic inammation
at mucosal surfaces, was demonstrated in those infected pa-
tients, especially in the severe group. Furthermore, a similar
tendency was also present in naive regulatory T cells, which un-
derlie the control of systemic and tissue-specic autoimmunity.
It has been shown that T cells, especially CD4+ and CD8+ T
cells, play an important role in weakening or dampening over-
active innate immune responses during viral infection [17],
although regulatory T cells, a subset of  cells, play a crucial
role in negatively regulating the activation, proliferation, and
eector functions of a wide range of immune cells for the main-
tenance of self-tolerance and immune homeostasis [19, 20].
Given the higher expression of proinammatory cytokines and
chemokines in patients with COVID-19, especially in the severe
cases, the consumption of CD4+ and CD8+ T cells, and the de-
crease in regulatory T cells, presented in our study might result
in aggravated inammatory responses and the production of a
cytokine storm, and worsen damaged tissue. Although not con-
clusive, correlative evidence from those patients with severe in-
fection with a lower number of lymphocytes suggested a role for
dysregulated immune responses in COVID-19 pathogenesis.
ere were several limitations to our study that might cause
some potential bias. First, it was a retrospective, single-center,
small-sample study of patients admitted to the hospital; stand-
ardized data for a larger cohort would be better to assess the tem-
poral change in immune response aer infection with COVID-19.
Second, co-infection with bacteria or superinfection might af-
fect the results of the immune response in those patients with
COVID-19. Most of them presented with an increase in NLR and
procalcitonin, which was more evident in severe cases, and indi-
cated potential bacterial coinfection due to a dysregulated immune
system. Despite that, our study demonstrated several novel nd-
ings on dysregulated immune response in patients with COVID-
19 that SARS-CoV-2 might mainly act on lymphocytes, especially
T lymphocytes, induce a cytokine storm in the body, and generate
a series of immune responses to damage the corresponding organs;
thus, surveillance of NLR and lymphocyte subsets is helpful in the
early screening of critical illness and diagnosis and treatment of
COVID-19.
Note
Potential conicts of interest. e authors: No reported conicts of
interest. All authors have submitted the ICMJE Form for Disclosure of
Potential Conicts of Interest.
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