Overall reduced lymphocyte subsets worsening disease severity and prognosis in COVID-19 severe cases with diabetes mellitus in Chengdu, China

Background The outbreak


Introduction
Since the early of December 2019, patients with unexplained pneumonia have appeared in Wuhan, China, and they have been con rmed to be infected with a new coronavirus (2019-nCoV).[1][2][3][4]The World Health Organization (WHO) announced a new name for the epidemic disease caused by 2019-nCoV: coronavirus disease 2019(COVID-19)on 11 February 2020.COVID-19 epidemic has spread rapidly throughout the whole country, even as a world pandemic throughout globally since mid-January 2020, [5][6]as of March 26, 2020, cases were reported in China and 151 other countries, territories, and areas, a total of cumulative con rmed and death cases were 509,164 and 23,335 cases in the whole world, [5] 81394 and 3295 cases in China,[6]respectively.
Most patients with COVID-19had a good prognosis, a few patients were critically illness, it can cause respiratory, digestive, nervous and cardiovascular system damage, multiple organs failure and even death, [7][8][9][10][11] progress is rapid.As of Mar26, the fatality rate in China was 3.5~4.05%.[6,12]The elderly and those with chronic underlying disease have a poor prognosis, diabetes mellitus(DM)is one of the common comorbiditie.[8-9,13] Dysregulation of immune response, especially T lymphocytes, might be highly involved in the pathological process of COVID-19.[14]Increased amounts of proin ammatory cytokines in serum were associated with pulmonary in ammation and extensive lung damage in SARS, [15]MERS-COV[16]andCOVID-19.[8]Patients with DM had cellular immune dysfunction.[17] However the characteristic of lymphocyte and subsets in patients with COVID-19 and DM coexist, the relationship of lymphocyte subsets, DM and disease progression, prognosis is unknown.In this study we aimed to analyze the difference of lymphocyte and subsets between COVID-19severe and non-severe cases with or without DM, and its in uence on disease progression and prognosis.

Methods
Objects 95 patients with COVID-19 was retrospectively recruited from January 16, 2020 to March 16, 2020in hospital isolation ward of the Public and Health Clinic Centre of Chengdu, "the speci c hospital for the treatment of severe patients with COVID-19 in Chengdu" designated by the government.The study was approved by the Public and Health Clinic Centre of Chengdu Ethics Committee (PJ-K2020-26-01).For emerging infectious diseases the Ethics Commission of the designated hospital agreed to waive written informed consent.

The criteria of disease diagnosis, clinical typing and cured
The disease diagnosis criteria, the clinical typing criteria, the cured criteria of COVID-19 were judged according to the seventh Trial Version of the Novel Coronavirus Pneumonia Diagnosis and Treatment Guidance.[7] The diagnosis criteria of suspected cases were combined with the following comprehensive analysis of epidemiological history and clinical manifestations: met anyone of the history of epidemiology and any two of the clinical manifestations, and no clear epidemiological history but met three of the clinical manifestations.History of epidemiology was following: travel history or residence history of Wuhan City and surrounding areas, or other communities with case reported within 14 days before onset of illness; had a history of contact with a new coronavirus infection (positive nucleic acid test) within 14 days before onset; had contacted patients with fever or respiratory symptoms from Wuhan and surrounding areas, or from communities with case reported within 14 days before the onset of illness; aggregated disease (2 cases or more of fever and / or respiratory symptoms in small areas such as homes, o ces, and school classes within 2 weeks).Clinical manifestations included following: fever and / or respiratory symptoms; with the imaging features of the above-mentioned new coronavirus pneumonia; the total number of white blood cells in the early stage of the disease was normal or decreased, or the lymphocyte count was normal or decreased.
A cross-sectional study was the research design methods.
The diagnosis criteria of con rmed cases were that suspected cases with one of the following etiological or serological evidence: real-time uorescence RT-PCR detected the positive nucleic acid of the new coronavirus; sequencing of viral genes, highly homologous to known new coronaviruses; serum novel coronavirus-speci c IgM antibodies and IgG antibodies were positive; serum novel coronavirus-speci c IgG antibodies changed from negative to positive or the recovery period was 4 times or more higher than the acute period.
Clinical types included light, common, severe and critically illness four types.And typing criteria was following: The light type criteria: the clinical symptoms were mild, and there were no pneumonia manifestations on imaging; The common type criteria: the clinical symptoms included such as fever and respiratory tract, pneumonia can be seen on imaging; The severe type criteria met any of the following: Respiratory distress, RR≥30 times / min; In the resting state, the oxygen saturation ≤93%;Arterial blood oxygen partial pressure (PaO2) / oxygen concentration (FiO2) ≤300mmHg (1mmHg = 0.133kPa),In areas with high altitude (over 1000 meters above sea level), PaO2 / FiO2 should be corrected according to the following formula: PaO2 / FiO2 * [atmospheric pressure (mmHg) / 760];Pulmonary imaging showed that lesions with signi cant progress over than 50% within 24-48 hours were managed as heavy; The critically illness type criteria included one of the following conditions: Respiratory failure occurs and mechanical ventilation is required; Shock occurs; Combining other organ failure requires ICU monitoring treatment.
The diagnostic criteria of DM were judged according to Guidelines for the Prevention and Treatment of Type 2 Diabetes in China (2017 edition).[18] The grouping and sub-grouping standards The participants were divided in two groups according to with and without DM: the non-DM group (patients without DM) and the DM group (patients with DM).
The participants of each group were further divided in two subgroups according to clinical typing: the non-severe non-DM subgroup (including light and common type and without DM), the severe non-DM subgroup (including severe and critically illness type and without DM), the non-severe DM subgroup (including light and common type and with DM), the severe DM subgroup (including severe and critically illness type and with DM).

De nition of prognosis and the viral negative turnaround time
The prognosis included cured, unhealed and death.
Cured discharge standard was following: the body temperature returned to normal for more than 3 days; respiratory symptoms improved signi cantly; lung imaging showed a signi cant improvement in acute exudative lesions; two consecutive sputum, nasopharyngeal swabs and other respiratory specimens tested negative for nucleic acid (sampling time at least 24 hours apart).
The viral negative conversion time was de ned as the time from onset to the time of the rst collection of nasopharyngeal swabs before qualifying for release of isolation.

Clinical data collection
Data, which included demographic information (age and sex), underline disease history, glucose metabolic parameters [FPG levels and hemoglobin A1c (HbA1c) levels], lymphocyte subsets, clinical data were obtained.

Real-Time Reverse Transcription Polymerase Chain Reaction Assay
Viral RNA extraction was performed using a Magnetic viral RNA/DNA extraction Kit on PAN9600 Automated Nucleic Acid Extraction System (Tianlong, Xi'an, China), according to the manufacturer's instructions, followed by polymerase-chain-reaction (PCR) screening for the presence of speci c 2019-nCoV with a commercial kit (Tianlong, Xi'an, China).Saw literature.[14] Measurement of T-lymphocyte subsets EDTA anticoagulated peripheral blood as ow cytometry samples was drawn from the participants before initial treatment to determine lymphocyte and subsets.All samples were tested within 6 hours of being obtained.Brie y, CD3+ /CD4+ /CD8+ T-cell, CD19+ B-cell, and CD16+ CD56+ NK-cell counts (cells/μL) were measured by multiple-color ow cytometry with human monoclonal anti-CD3-uorescein isothiocyanate (FITC), anti-CD4phycoerythrin (PE), antiCD8-allophycocyanin (APC), anti-CD19-PE, and anti-CD56-PE antibodies (BD Multitest) according to the manufacturer's instructions.The cells were analyzed on a BD FACS Canto II ow cytometry system (BD Biosciences) [19] Databases were established according to the needs of the research.Two researchers simultaneously collected and entered the data.The researchers randomly selected 30% of the data for assessment to ensure data integrity, authenticity, and accuracy.

Statistical analyses
The Statistical Package for the Social Sciences software version 17.0(IBM Inc., Armonk, NY, the USA) and GraphPad Prism 8(GraphPad, CA, the USA) software were used for statistical analysis.The measurement data were expressed as x±SD, and a multi-group comparison was performed using ANOVA when the data had homogeneity of variance and was normally distributed, and further comparison between the two groups was conducted using least signi cant difference (LSD)t test.Independent sample Kruskal-Wallis H(K) test was used for a multi-group comparison when the data had not homogeneity of variance, and further comparison between the two groups was conducted using Mann-Whitney U test.The two groups were compared using an independent-sample t-test.Enumeration data was expressed in terms of percentage or proportion, and a comparison between groups was performed using Chi-square test.Spearman correlation analysis was used for two-factor correlation analysis, and multiple stepwise regression method was used for multi-factor correlation analysis.A p value of <0.05 was considered statistically signi cant.

Results
Similar baseline conditions except glucose metabolic parameters between four subgroups 95 patients with COVID-19 was retrospectively recruited, among them, 76 patients without DM were enrolled in non-DM group, including 57 nonsevere cases and 19 severe cases;19 patients with DM were enrolled in DM group, including 8 non-severe cases and 11 severe cases(of them 8 cases newly diagnosed diabetes mellitus, including 3 non-severe cases and 5 severe cases).The proportion of DM in COVID-19 was 20%(19/95).The severe rate of patients with DM was higher than that of patients without DM, the difference was signi cant (61.11%vs.25.55%,х 2 =2.940,P=0.003), and the proportion of DM in severe cases was higher than that in non-severe cases, the difference was signi cant (36.67%vs.12.31%,х 2 =2.744,P=0.006).
Patients in non-severe non-DM subgroup were signi cantly younger than those in the other three subgroups, but those of other three subgroups had similar age(Table 1).No signi cant difference was observed in terms of gender between four subgroups(Table 1).FPG levels and HbA1c levels in DM group were obviously higher than that in non-DM group(Table 1), and FPG levels and HbA1c levels in severe non-DM subgroup were higher than that in non-severe non-DM subgroup.But there was no signi cant difference of glucose metabolic parameters between non-severe DM subgroup and severe DM subgroup(Table 1).In COVID-19 severe cases without DM, lymphocyte percentage value ,CD3+ count level and percentage value, CD3+CD4+ count level and percentage value, CD3+CD8+ count level and B(CD19+) percentage value were lower than that in non-sever patients without DM, there were obviously statistical difference(Table 2).
In COVID-19 severe cases with DM, lymphocyte count level and percentage value,CD3+ count level and percentage value, CD3+CD4+ count level, CD3+CD8+ count level and B(CD19+)count level were lower than that in non-sever patients with DM, obviously statistical differences were also found (Table 2).

More rate, longer virus negative conversion time and worse prognosis in COVID-19 severe cases with DM
In COVID-19 patients with DM the severe rate was higher and the prognosis was worse, moreover in COVID-19 patients with DM whether severe cases or not, the virus negative conversion time were longer than that in those without DM, patients in severe DM subgroup had the longest in-hospital time compared with that in the other three subgroups, the differences were signi cant (Table 3).4).The important factors in uencing the disease severity by multiple stepwise regression analysis was DM, age, lymphocyte percentage value and CD3+CD4+ percentage value (Table 5).
Only DM and age were positively related to the viral negative conversion time (Table 4) by Spearman correlation analysis.In uencing factors of the virus negative conversion time by multiple stepwise regression analysis was lymphocyte percentage value and B (CD19+) percentage value (Table 5).
DM, age, the disease severity and the coronavirus negative conversion time were positively related to the prognosis, while lymphocyte percentage value was negatively related to the prognosis by Spearman correlation analysis (Table 4).In uencing factors of the prognosis by multiple stepwise regression analysis was age, the disease severity and the coronavirus negative conversion time (Table 5).

Discussion
DM is one of the common comorbidities in COVID -19,[8-9,20]the proportion of DM in this COVID-19 cohort was 20%(19/95),consistent with those reports 20% (8/41),[8]higher than those reports 8% (3700/46248) [21]and 13% (13/99), [9] the proportion of DM in severe cases of this COVID-19 cohort was 36.67%(11/30),signi cantly higher than 12.31% (8/65) in non-severe cases.These nds were not consistent with those reports that there is no signi cant difference of the prevalence of DM between non-severe cases and severe cases.[14]The reasons for the inconsistent ndings may be that 8 cases of DM in this COVID-19 cohort were newly diagnosed after admission, of which 3 cases in the non-severe group and 5 cases in the severe group.Therefore, for patients with COVID-19 close monitoring of blood glucose, detection of glycated hemoglobin and performing of glucose tolerance test are necessary to nd potential DM in time.Further using correlation analysis we found that DM was positively related to the disease severity and the viral negative conversion time, by multiple stepwise regression analysis we found that DM was an important in uencing factor of the disease severity, this was consistent with previous research nds that DM was closely related to the disease severity and death of patients with viral pneumonia.For example, in 2003, for acute respiratory distress syndrome (SARS) patients with DM, the rate of mortality, check in the intensive care units, and the need for mechanical ventilation were from 3.0 to 3.3 times that of those without DM, [22]the rate of check in the intensive care units for H1N1 in uenza patients with DM was 4.29 times that of those without DM, [23]DM was also a high-risk factor for the Middle East Respiratory Syndrome Coronavirus infection severe cases in 2014.[23] In this study we found that COVID-19 patients with DM had the highest severe rate, COVID-19 severe cases with DM had the worst prognosis, and overall the lowest lymphocyte and subsets especially CD4+, CD8+ T and B cells, consistent with those reports.[14]But hierarchical analysis was not performed based on COVID-19 patients with or without DM in the literature.[14] Further using correlation analysis we found that lymphocyte and subsets were negatively related to the disease severity, lymphocyte was negatively related to the prognosis.By multiple stepwise regression analysis we found that important in uencing factors of the disease severity included DM, age, CD4+ percentage and lymphocyte percentage, of the prognosis involved the virus negative conversion time, the disease severity and age.
We also found that COVID-19 severe cases with DM had the lowest B (CD19+) cell count levels and the longest virus negative conversion time.By multiple stepwise regression analysis we found that important in uencing factors of the virus negative conversion time consist of lymphocyte percentage value and B (CD19+) percentage value.Those discovery was consistent with those reports [14] that there was no signi cant difference of B(CD19+) count levels or percentage values between COVID-19 non-severe cases and severe cases.
In this study we found that non-severe patients without DM were signi cantly younger than severe patients without DM, consistent with those reports that those COVID-19 patients who is the elderly have a poor prognosis.[8-9,13]Previousstudy found that the landscape of CD4 T cell subsets differs markedly between young and old mice, such that three cell subsets-exhausted, cytotoxic, and activated regulatory T cells (a Tregs)-appear rarely in young mice but gradually accumulate with age.Most unexpected were the extreme pro-and anti-in ammatory phenotypes of cytotoxic CD4 T cells and a Tregs, respectively.[24]Total numbers and relative frequencies of B cells were found to decline upon aging, with reductions in transitional B cells, memory cell types, and plasma blasts in the 70 + y group.[25]Lymphocytes and subsets including T(CD3+) cell, B(CD19+) cell and NK(CD56+) cell are mainly responsible for host immune regulation, T cells play a dominant role in promoting or sustaining in ammatory processes through inducing proin ammatory cytokines production.[23,[26][27][28][29]Activated Th1 cells, one subtype of CD4+ effector T cell, could trigger phagocytedependent in ammation and cell mediated immunity by producing tumor necrosis factor-beta(TNF-β), interleukin-2 (IL-2), interferon-gamma(IFN-γ).
[26]Virus infection through inducing direct cytopathic effects and indirect host immune responses played major roles in disease progression.[14,27]The rst line of defense against viral invisions was a rapid and well-coordinated innate host immune response, but dysregulated immune response will result in an excessive in ammation, even cause death.[14] Severe patients without DM were older than non-severe patients without DM.Furtherusing Spearman correlation analysis we found that age was positively related to the disease severity, the viral negative conversion time and the prognosis, by multiple stepwise regression analysis we found that age was an important in uencing factor of the disease severity and the prognosis, this was consistent with those reports that the elderly have a poor prognosis.[8-9,13]Howevernon-severe patients with DM and severe patients with DM had similar age, this means that age is not a factor for severity in patients with DM, unconsistent with those reports.[8-9,13]Type 2 diabetes mellitus( T2DM) is a systemic chronic low-grade in ammatory disease.An altered function of speci c T lymphocyte populations, including T regulatory (Treg) cells, which led to the hypothesis that part of the in ammatory response mounting in T2D is attributable to an autoimmune phenomenon.[30]T cells play a dominant role in promoting or sustaining in ammatory processes and insulin resistance through inducing pro-in ammatory cytokines in metabolic organs, such as the adipose tissue, liver, muscle, and pancreas.[29,[31][32][33]Macrophages are the major in ammatory cells in the adipose tissue.[29,31,33] [29, [31][32][33] Proin ammatory M1 macrophages contribute to the local and systemic in ammation by releasing proin ammatory cytokines such as TNF-a, IL-6, and IL-1.[33]On the contrary, antiin ammatory M2 macrophages inhibit the activity of most pro-in ammatory cell types including M1 macrophages by secreting IL-10.[33]IL-10 has been shown to suppress TNF-a by interacting with the p38/MAPK pathway.[33]Th1cells promote M1 polarization and enhance its pro-in ammatory functions by producing TNF-a,IL-2,IFN-γ.In contrast, Th2 cells skew the differentiation of macrophage towards M2 by producing anti-in ammatory IL-4,and IL-13.[29,31-33]Therefore,Th1 and Th2 responses, which are closely related to M1/M2 polarization, may also have a critical role in T2DM.

Table 1 .
Comparison of baseline conditions and glucose metabolic parameters between four subgroups n=95 subgroup, ##P<0.01,###P<0.001,####P<0.0001.Comparison of age between two of the latter three subgroups, P>0.05.Comparison of FPG and HbA1c between the former two subgroups, and between the latter two subgroups, all P>0.05.Overall decreased lymphocytes subsets in COVID-19 severe cases with DMIn COVID-19 severe cases with DM lymphocyte count level, lymphocyte percentage value,CD3+ count level and percentage value,CD3+CD4+ count level and percentage value,CD3+CD8+ count level, B(CD19+)count level and percentage value were the lowest compared with that in the other three subgroups, especially non-severe non-DM and non-severe DM subgroups, the difference was signi cant (Table2).

Table 3 .
Comparison of the disease severity, the virus negative conversion time and the prognosis between four subgroups n=95Partial decreased lymphocytes subsets as in uencing factors for disease severity, coronavirus negative conversion and prognosis in COVID-19 patients with DM Spearman correlation analysis showed that DM and age were positively, while lymphocyte count level, lymphocyte percentage value,CD3+ count level, CD3+CD4+ count level, CD3+CD8+ count level, CD3+ percentage value, CD3+CD4+ percentage value, B (CD19+) count level were negatively related to the disease severity (Table Abbreviations: FPG, fasting plasma glucose;HbA1c,glycated hemoglobin A1c;DM,diabetesmellitus;non-DM,without diabetes mellitus.Compared with the nonsevere non-DM subgroup,*P<0.05,**P<0.01,****P<0.0001.Compared with the severe non-DM subgroup, #P<0.05.Compared with the non-severe DM subgroup, $P<0.05.Comparison of the virus negative conversion time and the inhospital time between two other subgroups, all P>0.05.

Table 4 .
Spearman correlation analysis of the disease severity,virus negative conversion time,prognosis,lymphocyte subsets, age and DM n=95

Table 5 .
Multiple stepwise regression analysis of influencing factors of the disease severity, the coronavirus negative conversion time and the prognosis n=95 Abbreviations: DM, diabetes mellitus.LY, lymphocytes count.