Open Access

Erythrocyte superoxide dismutase as a biomarker of septic acute kidney injury

  • Nara Aline Costa1,
  • Ana Lúcia Gut1,
  • Paula Schmidt Azevedo1,
  • Suzana Erico Tanni1,
  • Natália Baraldi Cunha1,
  • Eloá Siqueira Magalhães1,
  • Graziela Biude Silva2,
  • Bertha Furlan Polegato1,
  • Leonardo Antonio Mamede Zornoff1,
  • Sergio Alberto Rupp de Paiva1,
  • André Luís Balbi1,
  • Daniela Ponce1 and
  • Marcos Ferreira Minicucci1Email author
Annals of Intensive Care20166:95

https://doi.org/10.1186/s13613-016-0198-5

Received: 11 May 2016

Accepted: 29 September 2016

Published: 6 October 2016

Abstract

Background

Oxidative stress is a key feature of sepsis and could be a common pathophysiological pathway between septic shock and acute kidney injury (AKI) Our objective was to evaluate the erythrocyte superoxide dismutase (SOD1) activity as predictor of AKI in patients with septic shock.

Methods

This is a prospective observational study that evaluated 175 consecutive patients over the age of 18 years with septic shock upon intensive care unit (ICU) admission. However, 43 patients were excluded (27 due to AKI at ICU admission). Thus, 132 patients were enrolled in the study. At the time of the patients’ enrollment, demographic information was recorded. Blood samples were taken within the first 24 h of the patient’s admission to determine the erythrocyte SOD1 activity. All patients were followed throughout the ICU stay, and the development of AKI was evaluated. In addition, we also evaluated 17 control subjects.

Results

The mean age of patients with septic shock was 63.2 ± 15.7 years, 53 % were male and the median ICU stay was 8 days (4–16). Approximately 50.7 % developed AKI during the ICU stay. The median erythrocyte SOD1 activity was 2.92 (2.19–3.92) U/mg Hb. When compared to control subjects, septic shock patients had a higher serum malondialdehyde concentration and lower erythrocyte SOD1 activity. In univariate analysis, erythrocyte SOD1 activity was lower in patients who developed AKI. The ROC curve analysis revealed that lower erythrocyte SOD1 activity was associated with AKI development (AUC 0.686; CI 95 % 0.595–0.777; p < 0.001) at the cutoff of <3.32 U/mg Hb. In the logistic regression models, SOD1 activity higher than 3.32 U/mg Hb was associated with protection of AKI development when adjusted by hemoglobin, phosphorus and APACHE II score (OR 0.309; CI 95 % 0.137–0.695; p = 0.005) and when adjusted by age, gender, chronic kidney disease, admission category (medical or surgery) and APACHE II score (OR 0.129; CI 95 % 0.033–0.508; p = 0.003).

Conclusions

In conclusion, our data suggest that erythrocyte SOD1 activity could play a role as an early marker of septic AKI and could be seen as a new research avenue in the field of biomarker in AKI. However, our study did not show a strong correlation between SOD activity and AKI. Nevertheless, these original data do warrant further research in order to confirm or not this hypothesis.

Keywords

Superoxide dismutaseAcute kidney injurySepsisOxidative stress

Background

Acute kidney injury (AKI) is one of the most serious and frequent complications of sepsis in critically ill patients [1]. It is estimated that AKI develops within the first 24 h in 51–64 % of patients with sepsis with hypotension [2]. Septic shock is the major cause of death in the intensive care unit (ICU), and the presence of AKI in these patients leads to an increase in mortality [3].

The diagnosis of AKI is usually based on either an elevation of serum creatinine or the detection of oliguria [4]. However, these tests have limited sensitivity and specificity for the detection of renal dysfunction [5]. In this way, the development of biomarkers for the early detection of AKI is a research priority [6]. Some AKI markers are currently being intensely studied in several different clinical situations, mainly in critically ill patients [711]. However, some of these markers could be influenced by systemic inflammation and infections, and early AKI predictors in patients with sepsis are still lacking.

A barrier to uncovering new specific biomarkers for sepsis-induced AKI is a lack of understanding of its pathophysiology. It is known that the oxidative stress caused by sepsis is one factor responsible for its development [12, 13]. Oxidative stress is regulated by a balance between the rates of reactive oxygen species (ROS) generation and antioxidant systems, including superoxide dismutase (SOD), catalase, glutathione peroxidase, thioredoxin and vitamins [14]. SOD is considered to be the first line of defense against ROS because it catalyzes the dismutation of superoxide radicals to a less reactive product, hydrogen peroxide. Increased ROS production or an impaired antioxidant system will lead to oxidative damage to proteins, lipids and DNA. There is no ideal marker for oxidative stress; however, with regard to lipid peroxidation, malondialdehyde (MDA) is one of the most commonly used markers for the determination of oxidative stress in humans [15].

Experimental and clinical studies of chronic kidney disease showed that SOD levels were reduced and markers of oxidative damage were increased in this scenario [1618]. In addition, strategies to increase SOD activity with recombinant SOD molecules and gene transfer led to reduced inflammation and oxidative stress in experimental models of ischemia–reperfusion and contrast-induced AKI [1921]. It is also interesting to observe that in patients submitted to kidney transplantation, MDA levels were predictors of graft dysfunction [22, 23]. Despite the importance of oxidative stress, SOD1 activity has not yet been evaluated as predictor of AKI in patients with septic shock.

Therefore, the aim of our study was to evaluate the erythrocyte SOD1 activity as predictor of AKI in patients with septic shock.

Methods

This prospective observational study was conducted from May 2014 to June 2015 with patients admitted to the intensive care unit of our hospital. The protocol was approved by the ethics committee of our institution (30457414.7.0000.5411). Written informed consent was obtained from all patients or relatives prior to their inclusion in the study.

This study was a subanalysis of a larger unpublished study, which evaluated the relationship between oxidative stress and mortality in patients with septic shock. The sample size was calculated using the Fisher and Belle formula, with the following variables: mortality rate of septic shock 40–60 %, 95 % confidence interval and 10 % sample error. The result was a minimum sample size of 96 patients.

Patients were eligible for enrollment if they were 18 years or older and had septic shock on ICU admission. Exclusion criteria were patients with AKI at ICU admission, patients with stage 4 or 5 chronic kidney disease (CKD) (creatinine clearance lower than 30 mL/min/1.73 m2), a delay in septic shock diagnosis (longer than 24 h), pregnant women, patients with confirmed brain death, patients in palliative care, and those who used vasoactive drugs for <24 h.

At the time of the patients’ enrollment, demographic information, the Acute Physiology and Chronic Health Evaluation (APACHE II) score and the Sequential Organ Failure Assessment (SOFA) score were recorded. Blood samples were taken within the first 24 h of the patient’s admission, after hemodynamic stabilization, to determine the erythrocyte SOD1 activity. All patients were followed during their ICU stay, and the development of AKI was evaluated. Mortality rate and the length of the ICU stay were also recorded.

Septic shock was defined according to Survival Sepsis guidelines [24], and AKI was defined according to Kidney Disease Improving Global Outcomes (KDIGO) criteria, using the increase in serum creatinine ≥0.3 mg/dL within 48 h or increase in serum creatinine ≥1.5 times baseline within 7 days [25]. Classification of AKI remains challenging because serum creatinine measurements, before the inciting illness, are often missing. In the absence of a standard method to accommodate missing values, investigators have used a variety of surrogate measures. One of these methods is to use the baseline creatinine as the lowest creatinine value in the last 6 months before AKI or, for those without this measurement, the lowest value achieved during hospitalization in the absence of dialysis [26, 27]. This was the definition we used of baseline creatinine in our study. CKD was defined as glomerular filtration rate lower than 60 mL/min/1.73 m2 using baseline creatinine and CKD Epidemiology Collaboration equation (CKD-EPI) (GFR = 141 × min(Scr/κ, 1)α × max(Scr/κ, 1) − 1.209 × 0.993 Age × 1.018 [if female] _ 1.159 [if black], where Scr is serum creatinine, κ is 0.7 for females and 0.9 for males, α is −0.329 for females and −0.411 for males, min indicates the minimum of Scr/κ or 1, and max indicates the maximum of Scr/κ or 1) [28].

To understand the behavior of erythrocyte SOD1 activity in our patients with septic shock, we also evaluated these variables in 17 control subjects (non-hospitalized individuals without any acute disease).

Laboratorial analysis

Total serum levels of sodium, potassium, phosphorus, C-reactive protein (CRP), albumin, creatinine and urea were measured using the dry chemistry method (Ortho-Clinical Diagnostics VITROS 950®, Johnson & Johnson), and lactate was measured using a Roche OMNI® S Blood Gas Analyzer. Hemograms were performed with a Coulter STKS hematologic autoanalyzer (Luton/Bedfordshire, UK).

Serum MDA concentration

Serum MDA levels were analyzed based on the reaction with thiobarbituric acid by high-performance liquid chromatography (HPLC) according to a method developed by Katepe [29]. UV detection was performed at 532 nm.

Erythrocyte SOD1 activity

The SOD enzyme activity in erythrocytes was determined in a Lyasis biochemical analyzer according to methodology recommended by the manufacturer (Ransod kit; Randox Laboratories Ltd., Crumlin, Antrim, UK) [30].

Statistical analysis

Data are expressed as the mean ± SD, the median (including the lower and upper quartiles) or percentage. Comparisons between two groups for continuous variables were made using Student’s t test or the Mann–Whitney U test. Comparisons between two groups for categorical variables were made using the χ 2 test or Fisher’s exact test. Comparisons between continuous variables were made using Spearman correlation test. Receiver operating characteristic (ROC) curve analysis was performed to determine the performance of erythrocyte SOD1 activity to predict AKI development. A logistic regression model was used to predict AKI. We constructed two regression models. Erythrocyte SOD1 activity was tested as a categorical variable (>3.32 U/mg Hb, cutoff obtained by ROC curve). In the first model, erythrocyte SOD1 activity was adjusted with parameters that exhibited significant differences in the univariate analysis. The only exceptions were variables with high collinearity among them (urea, creatinine, potassium, MDA, CKD and SOFA score). In the other, SOD1 was adjusted by age, gender, CKD, admission category (medical or surgery) and APACHE II score. Data analysis was performed using SigmaPlot software for Windows v12.0 (Systat Software Inc., San Jose, CA, USA). p values lower than 0.05 were considered statistically significant.

Results

During the study, 175 consecutive patients were admitted with a diagnosis of septic shock in the ICU; however, 43 patients were excluded (presence of AKI at ICU admission: 27 patients; delay in septic shock diagnosis: 12 patients; presence of advanced chronic kidney disease: 4 patients). Thus, we evaluated 132 patients (Fig. 1). The mean age was 63.2 ± 15.7 years, 53 % were male and the median length of ICU stay was 8 days (4–16). The mortality rate during the ICU stay was 65.9 %. Median erythrocyte SOD1 activity was 2.92 (2.19–3.92) U/mg Hb.
Fig. 1

Flow diagram of studied patients with septic shock

Among those patients with septic shock, 50.7 % developed AKI during the ICU stay. We have baseline serum creatinine levels in 37 patients of 67 patients who developed AKI, and in 30 patients, we used the lowest value achieved during hospitalization in the absence of dialysis. Regarding KDIGO stages, 17.9 % were classified as KDIGO 1, 19.4 % as KDIGO 2 and 62.7 % as KDIGO 3. The median time for AKI diagnosis based on KDIGO criteria was 2.3 days after admission. Among patients who developed AKI, 24.5 % needed dialysis during the ICU stay. The APACHE II and SOFA scores were also higher in patients in the AKI group. The mortality rate increased more than 30 % in patients with AKI. There were no differences between groups in the other demographic and clinical data (Table 1).
Table 1

Demographic and clinical data of 132 patients with septic shock

Variable

Acute kidney injury

p value

Yes (n = 67)

No (n = 65)

Age (years)

67.0 (59.0–75.0)

64.0 (50.5–72.5)

0.20

Male, n (%)

35.0 (52.2)

35 (53.8)

0.99

APACHE II score

18.7 ± 6.0

16.0 ± 6.4

<0.001

SOFA score

10.0 (8.0–12.0)

8.0 (7.0–9.5)

<0.001

RBC transfusions, n (%)

36 (53.7)

29 (44.6)

0.38

Steroids, n (%)

38 (56.7)

30 (46.2)

0.30

Admission category, n (%)

 Medical

18 (26.9)

27 (41.5)

0.11

 Surgery

49 (73.1)

38 (58.5)

 

Sepsis focus, n (%)

  

0.46

 Respiratory

42 (62.7)

34 (52.3)

 

 Abdominal

16 (23.8)

16 (24.6)

 

 Urinary

3 (4.5)

4 (6.2)

 

 Others

6 (9.0)

11 (16.9)

 

Dialysis, n (%)

11 (16.4)

0 (0)

0.002

CKD, n (%)

57 (85.1)

7 (10.8)

<0.001

MV, n (%)

60 (89.6)

55 (84.6)

0.56

Length of ICU stay (days)

7.0 (5.0–15.0)

9.0 (4.0–16.5)

0.63

ICU mortality, n (%)

52.0 (77.6)

35.0 (53.8)

0.007

Data are expressed as the mean ± SD, median (including the lower and upper quartiles) or percentage

APACHE II Acute Physiology and Chronic Health Evaluation, SOFA Sequential Organ Failure Assessment, RBC red blood cells, CKD chronic kidney disease, MV mechanical ventilation, ICU intensive care unit

The laboratory data are presented in Table 2. Patients who developed AKI had higher levels of potassium, phosphorus, MDA, urea and creatinine and lower levels of hemoglobin at baseline than patients without renal injury.
Table 2

Laboratory data of 132 patients with septic shock

Variable

Acute kidney injury

p value

Yes (n = 67)

No (n = 65)

Lactate (mmol/L)

2.2 (1.4–3.5)

2.1 (1.1–3.4)

0.78

Hemoglobin (g/dL)

11.0 (9.1–12.0)

11.6 (10.0–12.7)

0.025

Hematocrit (%)

32.0 ± 6.4

34.1 ± 5.7

0.06

Leukocytes (103/mm3)

16.6 (12.2–21.6)

16.3 (12.2–24.0)

0.89

Sodium (mmol/L)

145 (140–149)

141 (137–148)

0.21

Potassium (mmol/L)

4.5 ± 0.9

4.1 ± 0.7

0.013

Phosphorus (mg/dL)

4.7 (3.4–6.6)

3.9 (2.7–4.9)

0.004

Glycemia (mg/dL)

145 (118–186)

146 (115–184)

0.87

CRP (mg/dL)

36.0 (28.0–44.1)

32.0 (8.5–41.5)

0.07

MDA (µmol/L)

1.65 (1.02–2.52)

1.25 (0.60–2.01)

0.009

Albumin (g/dL)

2.3 (2.0–2.5)

2.1 (1.8–2.8)

0.61

Urea (mg/dL)

95 (67–159)

53 (32–86)

<0.001

Creatinine (mg/dL)

2.1 (1.6–2.6)

0.7 (0.5–1.1)

<0.001

Data are expressed as median (including the lower and upper quartiles)

CRP C-reactive protein

Erythrocyte SOD1 activity was lower in patients who developed AKI [non-AKI: 3.62 (2.59–4.34) vs AKI: 2.62 (2.02–3.63) U/mg Hb; p = 0.001; Fig. 2]. It is also important to observe that, although creatinine levels at ICU admission were higher in patients who developed AKI during ICU stay, erythrocyte SOD1 activity was not correlated with serum creatinine at ICU admission (Fig. 3). Our patients with septic shock had higher levels of serum MDA concentration [septic shock (n:132): 1.43 (0.77–2.19) µmol/L; controls (n:17): 0.86 (0.49–1.07) µmol/L; p = 0.003] and lower erythrocyte SOD1 activity [septic shock (n:132): 2.92 (2.19–3.92) U/mg Hb; controls (n:17): 4.35 (3.32–4.68) U/mg Hb; p = 0.001] when compared to control subjects.
Fig. 2

Erythrocyte SOD1 activity in patients with septic shock

Fig. 3

Correlation of serum creatinine at ICU admission with erythrocyte SOD1 activity

The ROC curve analysis revealed that lower erythrocyte SOD1 activity was associated with AKI development (AUC 0.686; CI 95 % 0.595–0.777; p < 0.001) at the cutoff of <3.32 U/mg Hb [sensibility: 68.7 % (CI 95 % 57.6–79.8 %); specificity: 55.4 % (CI 95 % 43.3–67.5 %); positive predictive value: 61.3 % (CI 95 % 50.3–72.3 %); negative predictive value: 63.2 % (CI 95 % 50.7–75.7 %)] (Table 3; Fig. 4).
Table 3

Cross-tabulation of erythrocyte SOD1 activity and acute kidney injury development

Erythrocyte SOD1 activity

Acute kidney injury

Yes

No

Total

<3.32 U/mg Hb (positive)

46

29

75

≥3.32 U/mg Hb (negative)

21

36

57

Total

67

65

132

Fig. 4

ROC curve for the association between erythrocyte SOD1 activity and AKI development

In the logistic regression models, SOD1 activity higher than 3.32 U/mg Hb was associated with protection of AKI development when adjusted by hemoglobin, phosphorus and APACHE II score (OR 0.309; CI 95 % 0.137–0.695; p = 0.005) and when adjusted by age, gender, CKD, admission category (medical or surgery) and APACHE II (OR 0.129; CI 95 % 0.033–0.508; p = 0.003; Table 4).
Table 4

Logistic regression model for the prediction of acute kidney injury in 132 patients with septic shock

Variable

OR

IC 5–95 %

p value

SOD1 >3.32 U/mg Hba

0.326

0.158–0.671

0.002

SOD1 >3.32 U/mg Hbb

0.129

0.033–0.508

0.003

SOD1 >3.32 U/mg Hbc

0.309

0.137–0.695

0.005

aUnadjusted

bAdjusted by gender, age, chronic kidney disease, admission category and APACHE II score

cAdjusted by APACHE II, phosphorus and hemoglobin

Discussion

The objective of our study was to evaluate the erythrocyte SOD1 activity as marker of AKI in patients with septic shock. Although our study is a preliminary data, it showed for the first time in septic shock patients that erythrocyte SOD1 activity plays a role as a biomarker of AKI.

Sepsis is defined as the presence (probable or documented) of infection together with a systemic inflammatory response [24]. Septic shock is defined as sepsis-induced hypotension that persists despite adequate fluid resuscitation [24]. Regardless of the creation of international guidelines for the early diagnosis and treatment of septic shock, its mortality remains high. This mortality rate is even higher when septic shock is associated with AKI. In our study, the mortality rate increased more than 30 % in patients with AKI. Thus, the early detection and treatment of patients who develop AKI are extremely important.

Several studies were performed in different clinical settings to identify biomarkers for AKI prediction, most of them in critically ill patients [58, 10, 11]. Until now, there have been four major categories of biomarkers for AKI in these patients: functional markers, such as serum creatinine; up-regulated proteins, such as NGAL and IL-18; low molecular weight proteins, such as urine cystatin C; and enzymes, such as alpha-glutathione s-transferase and pi-glutathione s-transferase [10]. Recently, the combination of urine tissue inhibitor of metalloproteinase 2 and insulin-like growth factor binding protein 7, known as TIMP-2 × IGFBP7, was allowed for marketing by the US Food and Drug Administration [11]. Up-regulation of these markers in patients with AKI has been proposed to reflect their growth-inhibitory function due to G1 cell-cycle arrest, a known consequence of AKI [11]. Despite all of these new markers, the prediction of AKI in patients with septic shock is still challenging. A barrier to uncovering new biomarkers for this scenario is a lack of understanding of the pathophysiology of sepsis-induced AKI [12, 13]. However, oxidative stress is a key feature of sepsis and could be a common pathophysiological pathway between septic shock and AKI [31].

Oxidative stress is characterized as an imbalance between ROS and antioxidant pathways, leading to oxidative damage to proteins, lipids and DNA. At the nanomolar scale, ROS play an important role in physiological processes by functioning as a second messenger in signal transduction pathways [32]. However, at the micromolar scale, ROS is associated with oxidative injury [32]. SOD is considered the first line of defense against ROS, consisting of three isoenzymes: copper/zinc SOD (SOD1), which is localized in the cytosol, nucleus and intermembrane space of mitochondria; manganese SOD (SOD2), which occurs in the mitochondrial matrix; and extracellular SOD (SOD3) [33]. SOD1 is the most abundant member of the family of antioxidant enzymes, representing approximately 90 % of SODs [34].

Experimental studies showed that SOD concentration and activity are reduced in chronic kidney injury [17, 1921]. Vaziri et al. showed that SOD concentration was reduced and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase expression was increased in the kidneys of rats submitted to nephrectomy [17]. Additionally, in studies with recombinant SOD molecules and SOD gene transfer, the animals submitted to these interventions presented reduced inflammation and oxidative stress in experimental models of ischemia–reperfusion and contrast-induced AKI [1921].

In a clinical study with 134 individuals, Magalhães et al. compared hemodialyzed patients with healthy controls and showed that the activity of SOD is reduced in patients with chronic renal failure undergoing hemodialysis [16]. In our study, the activity of erythrocyte SOD1 was also lower in patients who developed AKI. This association remains statistically significant, even after adjustments for confounding variables in multivariate regression analysis. It is also important to note that erythrocyte SOD1 activity was lower and serum MDA concentration was higher in septic shock patients compared to control subjects. Thus, our results suggest that patients with septic shock had an impaired SOD1 response and increased oxidative stress, and this behavior was more pronounced in patients who developed AKI. In addition, this lower SOD1 activity occurred in the first 24 h of ICU admission.

The AUC of erythrocyte SOD1 activity was only 0.686; however, comparisons of AUCs of several biomarkers for AKI prediction showed also a poor performance with a huge variation in its values [35, 36]. Thus, our data suggest that erythrocyte SOD1 activity could be used as an early predictive marker of AKI.

It is important to remember that patients with chronic kidney disease have an increased risk to develop AKI. This could explain why our patients who developed AKI had higher levels of creatinine at ICU admission. Despite that, there was no correlation between creatinine at ICU admission and erythrocyte SOD1 activity. Thus, our results were not influenced by the initial difference in kidney function.

Considering that the development of AKI in patients with sepsis is related to increased mortality, the identification of early biomarkers of renal injury is extremely important. Erythrocyte SOD1 activity was associated with AKI detection 2.3 days before the KDIGO criteria. Our results have important clinical implications because we were able to identify patients at risk of developing AKI earlier. Thus, patients at risk of AKI could be treated more aggressively, improving septic shock outcomes.

We should consider some limitations of this study. We only included patients from a single medical center. Regarding AKI definition, we only have baseline serum creatinine of 55 % of patients who developed AKI, for the others we used the lowest value achieved during hospitalization in the absence of dialysis. Moreover, AKI was defined based upon the serum creatinine levels and not urine output. In addition, due to the relatively small number of patients, analysis of SOD1 according to the stage of AKI was not performed. Timing of antibiotic dosing was also not recorded, and blood was taken within the first 24 h, which is a relatively large window. Finally, the AUC of erythrocyte SOD1 activity was only 0.686, and our study is a preliminary data with a limited internal and external validity. Despite these limitations, we strongly believe that our data contribute relevant knowledge regarding the roles of SOD as early predictor of AKI in patients with septic shock.

Conclusions

In conclusion, our data suggest that erythrocyte SOD1 activity could play a role as an early marker of septic AKI and could be seen as a new research avenue in the field of biomarker in AKI. However, our study did not show a strong correlation between SOD activity and AKI. Nevertheless, these original data do warrant further research in order to confirm or not this hypothesis.

Abbreviations

AKI: 

acute kidney injury

AKIN criteria: 

acute kidney injury network criteria

APACHE II score: 

Acute Physiology and Chronic Health Evaluation II score

CI 95 %: 

confidence interval 95 %

CKD: 

chronic kidney disease

CRP: 

C-reactive protein

GFR: 

glomerular filtration rate

HPLC: 

high-performance liquid chromatography

ICU: 

intensive care unit

MDA: 

malondialdehyde

NGAL: 

neutrophil gelatinase-associated lipocalin

OR: 

odds ratio

ROS: 

reactive oxygen species

Scr: 

serum creatinine

SOFA score: 

Sequential Organ Failure Assessment score

SOD1: 

superoxide dismutase 1

Declarations

Authors’ contributions

PSA, NBC, ESM and BFB were involved in acquisition of subjects and data, analysis and interpretation of data, revising the article critically; SET, GBS and ALB were involved in laboratorial analysis, statistical analysis, interpretation of data, revising the article critically; NAC, SARP, MFM, LAMZ, ALG and DP were involved in study design, analysis and interpretation of data, and drafting the manuscript. All authors revised the article critically for important intellectual content and approved the final version of the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was funded by the State of São Paulo Research Foundation (FAPESP—2014/17262-0) and by CAPES (“Coordenação de Aperfeiçoamento de Pessoal de Nível Superior”).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Internal Medicine, Botucatu Medical School, UNESP – Univ Estadual Paulista
(2)
Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Science, University of São Paulo

References

  1. Lafrance JP, Miller DR. Acute kidney injury associates with increased long-term mortality. J Am Soc Nephrol. 2010;21:345–52.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Murugan R, Karajala-Subramanyam V, Lee M, Yende S, Kong L, Carter M, et al. Acute kidney injury in non-severe pneumonia is associated with an increased immune response and lower survival. Kidney Int. 2010;77:527–35.View ArticlePubMedGoogle Scholar
  3. Singbartl K, Kellum JA. AKI in the ICU: definition, epidemiology, risk stratification, and outcomes. Kidney Int. 2012;81:819–25.View ArticlePubMedGoogle Scholar
  4. Mehta RL, Chertow GM. Acute renal failure definitions and classification: time for change? J Am Soc Nephrol. 2003;14:2178–87.View ArticlePubMedGoogle Scholar
  5. Bjornsson TD. Use of serum creatinine concentrations to determine renal function. Clin Pharmacokinet. 1979;4:200–22.View ArticlePubMedGoogle Scholar
  6. American society of nephrology. American society of nephrology renal research report. J Am Soc Nephrol. 2005;16:1886–903.View ArticleGoogle Scholar
  7. Herget-Rosenthal S, Marggraf G, Husing J, Göring F, Pietruck F, Janssen O, et al. Early detection of acute renal failure by serum cystatin C. Kidney Int. 2004;66:1115–22.View ArticlePubMedGoogle Scholar
  8. Mishra J, Dent C, Tarabishi R, Mitsnefes MM, Ma Q, Kelly C, et al. Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery. Lancet. 2005;365:1231–8.View ArticlePubMedGoogle Scholar
  9. Nga HS, Medeiros P, Menezes P, Bridi R, Balbi A, Ponce D. Sepsis and AKI in clinical emergency room patients: the role of urinary NGAL. Biomed Res Int. 2015;2015:413751.PubMedGoogle Scholar
  10. Geus HRH, Betjes MG, Bakker J. Biomarkers for prediction of acute kidney injury: a narrative review on current status and future challenges. Clin Kidney J. 2012;5:102–8.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Vijayan A, Faubel S, Askenazi DJ, Cerda J, Fissell WH, Heung M, et al. Clinical use of the urine biomarker [TIMP-2] × [IGFBP7] for acute kidney injury risk assessment. Am J Kidney Dis. 2016. doi:10.1053/j.ajkd.2015.12.033.Google Scholar
  12. Gomez H, Ince C, De Backer D, Pickkers P, Payen D, Hotchkiss J, et al. A unified theory of sepsis-induced acute kidney injury: inflammation, microcirculatory dysfunction, bioenergetics and the tubular cell adaptation to injury. Shock. 2014;41:3–11.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Himmelfarb J, Mcmonagle E, Freedman S, Klenzak J, McMenamin E, Le P, et al. Oxidative stress is increased in critically ill patients with acute renal failure. J Am Soc Nephrol. 2004;15:2449–56.View ArticlePubMedGoogle Scholar
  14. Noda Y, Ota K, Shirasawa T, Shimizu T. Copper/zinc superoxide dismutase insufficiency impairs progesterone secretion and fertility in female mice. Biol Reprod. 2012;86:1–8.View ArticlePubMedGoogle Scholar
  15. Rodrigo R, Libuy M, Feliú F, Hasson D. Oxidative stress-related biomarkers in essential hypertension and ischemia-reperfusion myocardial damage. Dis Markers. 2013;35:773–90.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Noleto Magalhães RC, Guedes Borges de Araujo C, Batista de Sousa Lima V, Machado Moita Neto J, do Nascimento Nogueira N, do Nascimento Marreiro D. Nutritional status of zinc and activity superoxide dismutase in chronic renal patients undergoing hemodialysis. Nutr Hosp. 2011;26:1456–61.PubMedGoogle Scholar
  17. Vaziri ND, Dicus M, Ho ND, Boroujerdi-Rad L, Sindhu RK. Oxidative stress and dysregulation of superoxide dismutase and NADPH oxidase in renal insufficiency. Kidney Int. 2003;63:179–85.View ArticlePubMedGoogle Scholar
  18. Scholze A, Krueger K, Diedrich M, Räth C, Torges A, Jankowski V, et al. Superoxide dismutase type 1 in monocytes of chronic kidney disease patients. Amino Acids. 2011;41:427–38.View ArticlePubMedGoogle Scholar
  19. Yin M, Wheeler MD, Connor HD, Zhong Z, Bunzendahl H, Dikalova A, et al. Cu/Zn-superoxide dismutase gene attenuates ischemia-reperfusion injury in the rat kidney. J Am Soc Nephrol. 2001;12:2691–700.PubMedGoogle Scholar
  20. Mortensen J, Shames B, Johnson CP, Nilakantan V. MnTMPyP, a superoxide dismutase/catalase mimetic, decreases inflammatory indices in ischemic acute kidney injury. Inflamm Res. 2011;60:299–307.View ArticlePubMedGoogle Scholar
  21. Pisani A, Sabbatini M, Riccio E, Rossano R, Andreucci M, Capasso C, et al. Effect of a recombinant manganese superoxide dismutase on prevention of contrast-induced acute kidney injury. Clin Exp Nephrol. 2014;18:424–31.PubMedGoogle Scholar
  22. Fonseca I, Reguengo H, Almeida M, Dias L, Martins LS, Pedroso S, et al. Oxidative stress in kidney transplantation: malondialdehyde is an early predictive marker of graft dysfunction. Transplantation. 2014;97:1058–65.View ArticlePubMedGoogle Scholar
  23. Fonseca I, Reguengo H, Oliveira JC, Martins LS, Malheiro J, Almeida M, et al. A triple-biomarker approach for the detection of delayed graft function after kidney transplantation using serum creatinine, cystatin C, and malondialdehyde. Clin Biochem. 2015;48:1033–8.View ArticlePubMedGoogle Scholar
  24. Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med. 2013;41:580–637.View ArticlePubMedGoogle Scholar
  25. Clinical Practice Guidelines for Acute Kidney Injury; 2012. http://www.kdigo.org/clinical_practice_guidelines/AKI.php.
  26. Gammelager H, Christiansen CF, Johansen MB, Tønnesen E, Jespersen B, Sørensen HT. Five-year risk of end-stage renal disease among intensive care patients surviving dialysis-requiring acute kidney injury: a nationwide cohort study. Crit Care. 2013;17:R145.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Siew ED, Matheny ME, Ikizler TA, Lewis JB, Miller RA, Waitman LR, et al. Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury. Kidney Int. 2010;77:536–42.View ArticlePubMedGoogle Scholar
  28. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Nielsen F, Mikkelsen BB, Nielsen JB, Andersen HR, Grandjean P. Plasma malondialdehyde as biomarker for oxidative stress: reference interval and effects of life-style factors. Clin Chem. 1997;43:1209–14.PubMedGoogle Scholar
  30. Ferro FE, de Sousa Lima VB, Soares NR, de Sousa Almondes KG, Pires LV, Cozzolino SM, et al. Parameters of metabolic syndrome and its relationship with zincemia and activities of superoxide dismutase and glutathione peroxidase in obese women. Biol Trace Elem Res. 2011;143:787–93.View ArticlePubMedGoogle Scholar
  31. Costa NA, Gut AL, Pimentel JAC, Cozzolino SM, Azevedo PS, Fernandes AA, et al. Erythrocyte selenium concentration predicts intensive care unit and hospital mortality in patients with septic shock: a prospective observational study. Crit Care. 2014;18:R92.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Droge W. Free radicals in the physiological control of cell function. Physiol Rev. 2002;82:47–95.View ArticlePubMedGoogle Scholar
  33. Okado-Matsumoto A, Fridovich I. Subcellular distribution of superoxide dismutases (SOD) in rat liver: Cu, Zn-SOD in mitochondria. J Biol Chem. 2001;276:38388–93.View ArticlePubMedGoogle Scholar
  34. Noor R, Mittal S, Iqbal J. Superoxide dismutase—applications and relevance to human diseases. Med Sci Monit. 2002;8:RA210–5.PubMedGoogle Scholar
  35. Lameire NH, Vanholder RC, Van Biesen WA. How to use biomarkers efficiently in acute kidney injury. Kidney Int. 2011;79:1047–50.View ArticlePubMedGoogle Scholar
  36. Metzger J, Kirsch T, Schiffer E, Ulger P, Mentes E, Brand K, et al. Urinary excretion of twenty peptides forms an early and accurate diagnostic pattern of acute kidney injury. Kidney Int. 2010;78:1252–62.View ArticlePubMedGoogle Scholar

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