Sage Journals: Discover world-class research

Abstract

Objectives: Association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and kidney injury has been noted in previous studies. However, the mechanisms remain unknown. The present study aimed to explore the potential mechanisms of kidney injury in COVID-19.

Methods: Demographic characteristics, underlying diseases, signs, symptoms, and laboratory data of 100 COVID-19 patients were collected and analyzed in this retrospective study. Patients were divided into three groups: mild, moderate, and severe to critical group. Kidney injury was evaluated by markers including estimated glomerular filtration rate (eGFR), serum creatinine, blood urea nitrogen, and cystatin C.

Results: A total of 100 patients with 12 mild, 63 moderate, and 25 severe to critical COVID-19 were included in this study. The kidney injury markers including eGFR, serum creatinine, blood urea nitrogen, and cystatin C all worsened significantly with an increase in disease severity. The correlation test showed that cytokines IL-2R, IL-6, IL-8, and tumor necrosis factor (TNF)-α were statistically correlated with eGFR and cystatin C. In multivariate analysis, log IL-6 (β = −0.331, p = .001 for eGFR and β = 0.405, p < .001 for cystatin C) and log TNF-α (β = −0.316, p = .001 for eGFR and β = 0.534, p < .001 for cystatin C) were found to be the major independent predictors of kidney injury.

Conclusion: Serum IL-6 and TNF-α levels were the major independent predictors of kidney injury in COVID-19.

Keywords

Coronavirus disease 2019 severe acute respiratory syndrome coronavirus 2 interleukin-6 tumor necrosis factor-α kidney injury

Introduction

Coronavirus disease 2019 (COVID-19) is a highly infective disease caused by a novel coronavirus which was named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Lungs and airways are believed to be the main targeting organs of SARS-CoV-2. However, accumulating evidence supported that SARS-CoV-2 infection not only resulted in damage to lungs but also other critical organs, which significantly increased their mortality.^1,2

Association of SARS-CoV-2 infection and kidney injury has been noted in previous studies.^1,3,4 Kidney injury was reported to be common in COVID-19, especially in critically ill patients.^4,5 Acute kidney injury (AKI), an adverse clinical predictor, was considered to be associated with increased mortality of COVID-19.^1,6 Several pathogenic mechanisms including direct virulence of SARS-CoV-2, cytokine storms, microcirculatory dysfunction, and hypoxia have been proposed to explain the etiology of kidney injury in COVID-19 patients.^7,8 Cytokine storms, resulting from SARS-CoV-2 infection, are believed to be a key event in the development of kidney injury in COVID-19. However, the speculations are mainly based on observations of severe sepsis and other coronavirus infection. To date, limited study evaluated a relationship between dysregulation of cytokines and kidney injury in COVID-19.

The primary goal of the present retrospective study was to evaluate whether dysregulation of cytokines was involved in the etiology of kidney injury in COVID-19 patients.

Methods

Study design and participants

This was a retrospective observational study carried out from 28 January 2020 to 30 March 2020 in Optics Valley Branch of Tongji Hospital. All consecutive patients who tested positive by polymerase chain reaction testing of a nasopharyngeal/oropharyngeal sample for SARS-CoV-2 were enrolled in the study. These patients were treated by Fujian Medical Team aiding Hubei province. Exclusion criteria were as follows: patients younger than 18; patients with incomplete laboratory data of kidney function; patients with a known history of chronic kidney disease; patients with autoimmune diseases or were on drugs affecting serum cytokines. This study was approved by the Ethics Committee of Zhongshan Hospital, Xiamen University (approval no. 2020–012). Written informed consent was obtained from all subjects. All methods are carried out in accordance with relevant guidelines and regulations.

Data collection

We extracted the demographic data, days from illness onset to admission, laboratory data, clinical symptoms, and signs from electronic medical records. The laboratory data consisted of renal function tests, complete blood count, hemostasis parameters, inflammatory markers, and serum cytokines. All data were reviewed by a team of trained physicians.

Definition

Based on the guidelines for diagnosis and management of COVID-19 (seventh edition, in Chinese),⁹ COVID-19 patients were divided into four categories: mild, moderate, severe, and critical cases. Mild cases: the clinical symptoms are mild and no pneumonia manifestation can be found in imaging. Moderate cases: patients have symptoms such as fever and respiratory tract symptoms, etc., and pneumonia manifestation can be seen in imaging. Severe cases: adults who meet any of the following criteria: respiratory rate ≥ 30 breaths/min; SpO₂ ≤ 93% at rest; PaO₂/FiO₂ ≤300. Patients with greater than 50% lesion progression within 24–48 h in pulmonary imaging were also defined as severe cases. Critical cases: patients who meet any of the following criteria: occurrence of respiratory failure requiring mechanical ventilation; presence of shock; other organ failure that requires monitoring and treatment in the ICU. Since only four patients met the criteria of critical cases, severe and critical cases were combined into one group. Therefore, there were three groups finally: mild, moderate, and severe to critical group.

Calculation of glomerular filtration rate

Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation: eGFR = 141×(minimum of standardized serum creatinine [mg/dL]/κ or 1)^α×(maximum of standardized serum creatinine [mg/dL]/κ or 1)^−1.209 × 0.993^age×(1.018 if female) × (1.159 if black), where κ is 0.7 if female and 0.9 if male and α is −0.329 if female and −0.411 if male.¹⁰

Cytokine measurement

Blood samples were collected from the patients on admission or the second day after admission. Chemiluminescent immunoassay by Siemens Immulite 1000 analyzer was used to measure serum cytokines including interleukin (IL)-1β, IL-2R, IL-6, IL-8, IL-10, and tumor necrosis factor (TNF)-α according to the manufacturer’s instructions.

Statistical analysis

All continuous variables were presented as mean ± standard deviation (SD) or median (percentile 25, percentile 75) depending on whether they were normally distributed or not. Categorical variables were presented as number (percentage). Normality was checked with a QQ-plot and Shapiro-Wilk normality test.¹¹ One-way ANOVA was used to compare the baseline data between groups stratified by COVID-19 severity when the data were normally distributed; otherwise, the Kruskal–Wallis H (K) test was used.¹¹ Categorical variables were compared by the Chi-square test or Fisher’s exact test.¹¹ Descriptive data not in normal distribution were log-transformed before analysis. Correlation analyses were evaluated by the spearman rank correlation test. Multiple linear regression was performed to determine the independent predictors of eGFR and cystatin C. p < .05 was considered statistically significant. Statistical analyses were performed using SPSS 22.0 (SPSS Inc, Chicago, IL). GraphPad Prism 8 (GraphPad Software, USA) was used to draw the graphs.

Results

Baseline characteristic of COVID-19 patients according to disease severity

A total of 100 patients (47 women and 53 men) were included in this study. Among them, 12 patients were mild, 63 patients were moderate, 25 patients were severe or critical. The three groups showed comparable data in sex, days from illness onset to admission, heart rate, and presence of symptoms such as fever, cough, fatigue, dyspnea. Patients in severe or critical group were significantly older. As the disease severity increased, the prevalence of any coexisting disorder, temperature, respiratory rate were significantly higher, and the pulse oximeter oxygen saturation was significantly lower. The baseline demographic and clinical data in different groups of COVID-19 were showed in Table 1.

Table 1.

The baseline demographic and clinical data in different groups of COVID-19.

	Mild (n = 12)	Moderate (n = 63)	Severe to critical (n = 25)	p –values
Age, years	43.92 ± 13.73	51.33 ± 15.98	60.32 ± 15.19	0.007
Male sex, n (%)	4 (33.3)	33 (52.4)	16 (64.0)	0.214
Days from illness onset to admission, days	7.50 (2.25–30.00)	19.00 (7.00–30.00)	9.00 (7.00–23.00)	0.125
Coexisting disorder
Any, n (%)	0 (0.0)	14 (22.2)	10 (40.0)	0.025
Hypertension, n (%)	0 (0.0)	9 (14.3)	8 (32.0)	0.034
Diabetes mellitus, n (%)	0 (0.0)	7 (11.1)	5 (20.0)	0.213
CHD, n (%)	0 (0.0)	1 (1.6)	1 (4.0)	0.605
COPD, n (%)	0 (0.0)	1 (1.6)	0 (0.0)	1.000
Symptoms
Fever, n (%)	3 (25.0)	33 (52.4)	16 (64.0)	0.084
Cough, n (%)	5 (41.7)	32 (50.8)	14 (56.0)	0.716
Fatigue, n (%)	0 (0.0)	11 (17.5)	6 (24.0)	0.182
Dyspnea, n (%)	0 (0.0)	3 (4.8)	3 (12.0)	0.433
Vital signs
Temperature (°C)	36.50 (36.33–36.60)	36.50 (36.30–36.60)	36.80 (36.55–37.45)	0.001
Respiratory rate	19.50 (19.00–20.00)	20.00 (20.00–20.00)	20.00 (20.00–22.00)	0.001
Heart rate	93.17 ± 14.48	89.08 ± 14.35	90.64 ± 12.12	0.621
SpO₂ (%)	98.00 (97.25–99.00)	98.00 (97.00–98.00)	96.00 (93.50–98.00)	<0.001

Abbreviation: COVID-19 = coronavirus disease 2019, CHD = coronary artery heart disease, COPD = chronic obstructive pulmonary disease, SpO2 = pulse oximeter oxygen saturation.

Laboratory results of COVID-19 patients according to disease severity

Table 2 summarized the laboratory data in different groups of COVID-19. There was no statistically significant difference in white-cell count, hemoglobin, platelet, creatine kinase (CK), activated partial thromboplastin time, prothrombin time, IL-1β, IL-8, or IL-10 among three groups. Neutrophil count, CK-MB, lactic dehydrogenase, d-dimer, fibrinogen, high-sensitivity C-reactive protein (hs-CRP), procalcitonin (PCT), IL-2R, IL-6, and TNF-α increased significantly with disease severity. While lymphocyte count decreased with increasing severity of COVID-19. The kidney injury markers blood urea nitrogen (Figure 1(a)), serum creatinine (Figure 1(b)), eGFR (Figure 1(c)), and cystatin C (Figure 1(d)) all worsened with disease severity.

Table 2.

The laboratory data in different groups of COVID-19.

	Mild (n = 12)	Moderate (n = 63)	Severe to critical (n = 25)	p –values
White-cell count, ×10⁹/L	5.80 ± 1.46	6.07 ± 2.23	7.03 ± 2.94	0.177
Neutrophil count,×10⁹/L	3.44 ± 1.16	3.81 ± 2.08	5.25 ± 2.88	0.015
Lymphocyte count, ×10⁹/L	1.78 ± 0.54	1.58 ± 0.69	1.18 ± 0.67	0.015
Hemoglobin, g/L	132.17 ± 13.70	130.27 ± 21.81	121.76 ± 16.39	0.154
PLT, ×10⁹/L	215.08 ± 59.31	237.40 ± 84.16	225.24 ± 119.47	0.686
CK,U/L	84.58 ± 34.67	71.27 ± 36.19	81.68 ± 55.24	0.415
CK-MB, ng/mL	0.67 ± 0.33	1.09 ± 1.85	3.95 ± 7.66	0.010
LDH, U/L	180.00 (157.75–200.25)	190.00 (154.00–238.00)	235.00 (182.00–434.00)	0.012
APTT, s	36.90 (35.33–39.53)	38.80 (35.60–42.00)	36.50 (33.10–39.65)	0.065
PT, s	13.20 (12.90–13.68)	13.50 (12.90–14.00)	13.30 (12.45–14.20)	0.281
d-dimer, μg/mL	0.22 (0.22–0.22)	0.29 (0.22–0.64)	0.70 (0.28–1.50)	<0.001
FIB, g/L	3.05 ± 0.54	3.85 ± 1.51	4.86 ± 1.81	0.002
hsCRP, mg/L (94)	0.90 (0.33–1.83)	2.30 (0.60–11.60)	3.95 (2.43–41.78)	0.001
PCT, ng/ml (96)	0.06 (0.05–0.07)	0.06 (0.05–0.08)	0.09 (0.06–0.30)	0.001
IL-1β, pg/mL	5.00 (5.00–5.00)	5.00 (5.00–5.00)	5.00 (5.00–5.00)	0.888
IL-2R, U/mL	260.00 (229.25–358.50)	436.00 (280.00–658.00)	669.00 (335.50–898.00)	0.002
IL-6, pg/mL	1.85 (1.50–2.74)	3.20 (1.60–9.00)	14.39 (3.18–97.00)	0.001
IL-8, pg/mL	7.05 (6.15–11.18)	9.00 (6.00–12.40)	11.50 (7.90–21.50)	0.059
IL-10, pg/mL	5.00 (5.00–5.53)	5.00 (5.00–5.00)	5.00 (5.00–6.60)	0.129
TNF-α, pg/mL	6.35 (5.43–7.15)	7.70 (6.20–9.70)	8.70 (5.80–11.85)	0.034

Abbreviation: IL = interleukin, TNF = tumor necrosis factor, Hs-CRP = high-sensitivity C-reactive protein, PCT = procalcitonin, COVID-19 = coronavirus disease 2019, PLT = platelet, CK = creatine kinase, LDH = lactic dehydrogenase, APTT = activated partial thromboplastin time, PT = prothrombin time, FIB = fibrinogen, IL = interleukin, TNF = tumor necrosis factor.

Figure 1.

Comparisons of kidney injury markers among different groups stratified by COVID-19 severity (a). blood urea nitrogen; (b). serum creatinine; (c). eGFR; (d). cystatin C).

Factors associated with kidney injury in COVID-19

In order to explore the factors associated with kidney injury, spearman rank correlation test was performed (Table 3). The correlation test showed that age, any coexisting disorder, PCT, hs-CPR, IL-2R, IL-6,IL-8, and TNF-α were inversely and significantly correlated with eGFR. Lymphocyte count was positively and significantly correlated with eGFR. As regarding cystatin C, age, sex, any coexisting disorder, lymphocyte count, PCT, hs-CPR, IL-2R, IL-6, IL-8, and TNF-α were all significantly correlated with cystatin C. The correlation analyses between IL-1β, IL-2R, IL-6, IL-8, IL-10, TNF-α and eGFR were showed in Figure 2(a) to (f), respectively. The correlation analyses between IL-1β, IL-2R, IL-6, IL-8, IL-10, TNF-α and cystatin C were showed in Figure 3(a) to 3(f), respectively.

Table 3.

Spearman rank correlation coefficients between kidney injury and demographic data, laboratory data, and cytokines.

	eGFR		Cystatin C
	r	p –values	R	p –values
Age	−0.760	<0.001	0.539	<0.001
Sex	0.188	0.060	−0.371	<0.001
Any coexisting disorder	−0.367	<0.001	0.349	<0.001
White-cell count	−0.052	0.606	0.131	0.193
Lymphocyte count	0.353	<0.001	−0.271	0.006
Neutrophil count	−0.118	0.242	0.149	0.140
PCT	−0.461	<0.001	0.434	<0.001
Hs-CRP	−0.411	<0.001	0.448	<0.001
IL-1β	−0.135	0.181	0.073	0.469
IL-2R	−0.500	<0.001	0.485	<0.001
IL-6	−0.472	<0.001	0.456	<0.001
IL-8	−0.242	0.015	0.222	0.027
IL-10	−0.083	0.414	0.153	0.128
TNF-a	−0.378	<0.001	0.414	<0.001

Abbreviation: IL = interleukin, TNF = tumor necrosis factor, Hs-CRP = high-sensitivity C-reactive protein, PCT = procalcitonin, eGFR = estimated glomerular filtration rate.

Figure 2.

Correlations between cytokines and eGFR (a). IL-1β; (b). IL-2 R; (c). IL-6; (d). IL-8; (e). IL-10; (f). TNF-α).

Figure 3.

Correlations between cytokines and cystatin C (a). IL-1β; (b). IL-2 R; (c). IL-6; (d). IL-8; (e). IL-10; (f). TNF-α).

Role of cytokines in kidney injury in COVID-19

Stepwise multiple linear regression was used to determine the independent predictors of kidney injury. The factors significantly associated with kidney injury were included as independent variables. The results showed that age (β = −0.565, p < .001), log TNF-α (β = −0.230, p = .002), log IL-6 (β = −0.212, p = .007) were independently related to elevated eGFR. Age is one of the factors used to calculate eGRF. When age was excluded from independent variable, the results indicated that any coexisting disorder (β = −0.188, p = .041), log TNF-α (β = −0.316, p = .001), log IL-6 (β = −0.331, p = .001) were independently related to elevated eGFR. Using cystatin C as dependent variable, multiple linear regression showed that log TNF-α (β = 0.534, p < .001) and log IL-6 (β = 0.405, p < .001) were independent predictors of elevated cystatin C.

Discussion

Our results showed that kidney injury was significantly correlated with the severity of COVID-19. Cytokine IL-6 and TNF-α were independent predictors of elevated eGFR and cystatin C in COVID-19 patients. Our findings provided a direct evidence to support an important role of cytokines in kidney injury in COVID-19.

Kidney injury in COVID-19 patients has been reported in several previous studies.^1,3,4 It is estimated that AKI occurs in 3%–29% of COVID-19 patients.¹² A study analyzing 701 COVID-19 patients found that the prevalence of proteinuria and hematuria were 43.9% and 26.7%, respectively. 14.4%, 13.1% and 13.1% of COVID-19 patients had elevated serum creatinine, elevated blood urea nitrogen and eGFR under 60 mL/min/1.73 m², respectively. 5.1% patients suffered from AKI during follow-up in this study.¹ Another study focusing on critically ill adult patients showed that 29% of the 52 patients had AKI.³ A postmortem analysis of 26 COVID-19 patients showed evidence of acute proximal tubule injury manifested as the loss of brush border, vacuolar degeneration, dilatation of the tubular lumen with cellular debris.⁸ COVID-19 is a systemic illness affecting multiple organs. The above evidence indicated that kidney injury should not be neglected when treating this disease.

In the present study, the disease severity was significantly associated with kidney injury. Our findings indicated that it is necessary to have a close monitoring and evaluation of kidney injury in severe to critical COVID-19 patients. Based on the results of our study, we could also conclude that SARS-CoV-2 infection was a risk factor of kidney injury. The underlying mechanisms are multifactorial. Cytokine storms are believed to be one of the most important mechanisms explaining kidney injury in CVOVID-19 patients.¹³ Previous evidence have shown a role of cytokines in the development of AKI after cardiac surgery or during sepsis.^14,15 A clinical study focusing on patients with severe sepsis showed that increased log IL-6 and APACHE II score were significant risk factors of AKI in patients with severe sepsis.¹⁴ Zhang et al.¹⁵ included 960 patients from six academic centers and revealed that elevated first postoperative IL-6 and IL-10 concentration were significantly associated with higher risk of AKI in patients after cardiac surgery. Furthermore, Previous studies have evaluated the relationship between cytokines including IL-8, TNF-α, IL-2R, IL-10, IL-6 and COVID-19 disease severity and confirmed a strong relationship.^16,17 However, limited study focused on association between kidney injury and cytokine storms in COVID-19.

This study sought to identify factors associated with kidney injury in patients with COVID-19. Our study provided a direct evidence of cytokines involvement in the kidney injury in COVID-19. A better understanding of the pathogenesis of kidney injury in COVID-19 will help to promote novel therapies for this condition. It has been reported that tocilizumab therapy, an IL-6 inhibitor, can effectively decrease inflammatory markers, improve radiological imaging, and reduce the demands of ventilatory support.¹⁸ Another study also showed the benefits of tocilizumab treatment in improving symptoms and imaging findings in COVID-19.¹⁹ Our results suggested that log IL-6 was a major independent predictor of kidney injury. Therefore, it will be interesting to explore whether tocilizumab treatment can alleviate the kidney injury in COVID-19 in the future. In addition, TNF-α was also found to be an independent predictor of kidney injury in our study, which highlighted the important role of TNF-α in kidney injury in COVID-19. Feldmann et al.²⁰ proposed that anti-TNF therapy should be evaluated in patients with COVID-19 on hospital admission to prevent progression after summarizing the current evidence. Our results supported this proposal by providing the direct evidence.

The CKD-EPI equation was reported to be as accurate as MDRD in the patients with eGFR < 60 mL/min/1.73 m² and substantially more accurate in the patients with eGFR > 60 mL/min/1.73 m².²¹ So it is a reliable marker assessing kidney function. Cystatin C was considered as a reliable marker of kidney function and could be useful for early diagnosis of kidney injury.²² The result of multivariate analysis of eGFR was similar to that of cystatin C, which confirmed the reliability of these two cytokines in predicting kidney injury.

There were some limitations associated with this study that must be noted. Firstly, the retrospective design allowed us to support a correlation but not a causative relationship. It was also associated with a higher susceptibility to errors caused by bias and confounding. Secondly, no calculation of sample size was carried out prior to this study, and the sample size of the present study might be relatively small, raising the possibility of a type 2 error. A study with a larger sample size could result in more robust conclusions. Thirdly, other data affecting kidney injury such as risky occupation for kidney injury, body weight, and nutritional status were not collected. Finally, the prevalence of AKI could not be evaluated due to lack of longitudinal data of serum creatinine.

Conclusion

Taking together, the present study suggested that cytokines IL-6 and TNF-α were independently related to kidney injury in COVID-19 patients, which suggested that elevated serum cytokines might be involved in the development and progression of this disorder in COVID-19. This result provided a therapeutic target for the treatment of COVID-19 related kidney injury. Animal experiments and clinical studies with a larger sample size are warranted for a better understanding of the mechanisms.

Footnotes

Acknowledgements

We sincerely appreciate all front-line medical staff for their hard work and sacrifice.

Authors’ contributions

LDC, LH, and XBZ conceived and designed the study, analyzed the data and drafted this manuscript. YS, YPH, SJY, and JY contributed to analysis of the data, and revising of the manuscript. All authors have read and approved the final manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Natural Science Foundation of China [82170103], Young people training project from Fujian Province Health Bureau [2020GGB057] and Xiamen Medical and Health Guidance Project [3502Z20214ZD1043].

Ethics approval

This study was approved by the Ethics Committee of Zhongshan Hospital, Xiamen University (approval no. 2020–012).

Written informed consent

Written informed consent was obtained from all subjects.

Availability of data and material

The data that support the findings of this study are available from the corresponding author upon reasonable request.

ORCID iD

Xiao-bin Zhang

References

Cheng

Luo

Wang

, et al. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int 2020; 97(5): 829–838.

Guan

, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020; 382(18): 1708–1720.

Yang

, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 2020; 8(5): 475–481.

Richardson

Hirsch

Narasimhan

, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized With COVID-19 in the New York City area. JAMA 2020; 323(20): 2052–2059.

Martinez-Rojas

Vega-Vega

Bobadilla

. Is the kidney a target of SARS-CoV-2? Am J Physiol Ren Physiol 2020; 318(6): F1454–F1462.

Ali

Daoud

Mohamed

, et al. Survival rate in acute kidney injury superimposed COVID-19 patients: a systematic review and meta-analysis. Ren Failure 2020; 42(1): 393–397.

Ronco

Reis

Husain-Syed

. Management of acute kidney injury in patients with COVID-19. Lancet Respir Med 2020; 8(7): 738–742.

Yang

Wan

, et al. Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China. Kidney International 2020; 98(1): 219–227.

China TNHCoPsRo . Interpretation of new coronavirus pneumonia diagnosis and treatment plan (trial version 7), 2020. (in Chinese).

10.

Levey

Stevens

Schmid

, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150(9): 604–612.

11.

Bland

. An introduction to medical statistics. 2nd ed. Oxford: Oxford Medical Publications, 1995.

12.

Hassanein

Thomas

Taliercio

. Management of acute kidney injury in COVID-19. Cleve Clin J Med 2020.

13.

Naicker

Yang

Hwang

, et al. The novel coronavirus 2019 epidemic and kidneys. Kidney Int 2020; 97(5): 824–828.

14.

Chawla

Seneff

Nelson

, et al. Elevated plasma concentrations of IL-6 and elevated APACHE II score predict acute kidney injury in patients with severe sepsis. Clin J Am Soc Nephrol 2007; 2(1): 22–30.

15.

Zhang

Garg

Coca

, et al. Plasma IL-6 and IL-10 concentrations predict AKI and long-term mortality in adults after cardiac surgery. J Am Soc Nephrol 2015; 26(12): 3123–3132.

16.

Chen

Guo

, et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest 2020; 130(5): 2620–2629.

17.

Chen

Zhang

Wei

, et al. Association between cytokine profiles and lung injury in COVID-19 pneumonia. Respir Res 2020; 21(1): 201.

18.

Alattar

Ibrahim

TBH

Shaar

, et al. Tocilizumab for the treatment of severe coronavirus disease 2019. J Med Virol 2020; 92(10): 2042–2049.

19.

Han

, et al. Effective treatment of severe COVID-19 patients with tocilizumab. Proc Natl Acad Sci USA 2020; 117(20): 10970–10975.

20.

Feldmann

Maini

Woody

, et al. Trials of anti-tumour necrosis factor therapy for COVID-19 are urgently needed. Lancet 2020; 395(10234): 1407–1409.

21.

Stevens

Schmid

Greene

, et al. Comparative performance of the CKD epidemiology collaboration (CKD-EPI) and the modification of diet in renal disease (MDRD) study equations for estimating GFR levels above 60 mL/min/1.73 m2. Am J Kidney Dis 2010; 56(3): 486–495.

22.

Dharnidharka

Kwon

Stevens

. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis 2002; 40(2): 221–226.

Role of serum IL-6 and TNF-α in coronavirus disease 2019 (COVID-19) associated renal impairment

Abstract

Keywords

Introduction

Methods

Study design and participants

Data collection

Definition

Calculation of glomerular filtration rate

Cytokine measurement

Statistical analysis

Results

Baseline characteristic of COVID-19 patients according to disease severity

Laboratory results of COVID-19 patients according to disease severity

Factors associated with kidney injury in COVID-19

Role of cytokines in kidney injury in COVID-19

Discussion

Conclusion

Footnotes

Acknowledgements

Authors’ contributions

Declaration of conflicting interests

Funding

Ethics approval

Written informed consent

Availability of data and material

ORCID iD

References