Acute kidney injury (AKI) is a multifactorial syndrome, but knowledge about its pathophysiology and possible genetic background is limited. Recently the first hypothesis-free genetic association studies have been published to explore individual susceptibility to AKI. We aimed to replicate the previously identified associations between five candidate single nucleotide polymorphisms (SNP) in apoptosis-related genes BCL2, SERPINA4, SERPINA5, and SIK3 and the development of AKI, using a prospective cohort of critically ill patients with sepsis/septic shock, in Finland.
Methods
This is a prospective, observational multicenter study. Of 2567 patients without chronic kidney disease and with genetic samples included in the Finnish Acute Kidney Injury (FINNAKI) study, 837 patients had sepsis and 627 patients had septic shock. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria, considering stages 2 and 3 affected (severe AKI), stage 0 unaffected, and stage 1 indecisive. Genotyping was done using iPLEXTM Assay (Agena Bioscience). The genotyped SNPs were rs8094315 and rs12457893 in the intron of the BCL2 gene, rs2093266 in the SERPINA4 gene, rs1955656 in the SERPINA5 gene and rs625145 in the SIK3 gene. Association analyses were performed using logistic regression with PLINK software.
Results
We found no significant associations between the SNPs and severe AKI in patients with sepsis/septic shock, even after adjustment for confounders. Among patients with septic shock (252 with severe AKI and 226 without AKI (149 with KDIGO stage 1 excluded)), the SNPs rs2093266 and rs1955656 were significantly (odds ratio 0.63, p = 0.04276) associated with stage 2–3 AKI after adjusting for clinical and demographic variables.
Conclusions
The SNPs rs2093266 in the SERPINA4 and rs1955656 in the SERPINA5 were associated with the development of severe AKI (KDIGO stage 2–3) in critically ill patients with septic shock. For the other SNPs, we did not confirm the previously reported associations.
American College of Chest Physicians/Society of Critical Care Medicine
ACE
Angiotensin-converting enzyme
AKI
Acute kidney injury
AMP
Adenosine monophosphate
ARB
Angiotensin receptor blocker
BCL2
B-cell CLL/lymphoma 2
BMI
Body mass index
COPD
Chronic obstructive pulmonary disease
CRF
Case report form
DNA
Deoxyribonucleic acid
FIMM
Institute for Molecular Medicine Finland
FINNAKI
Finnish Acute Kidney Injury
HWE
Hardy-Weinberg equilibrium
ICU
Intensive care unit
KDIGO
Kidney Disease: Improving Global Outcomes
LD
Linkage disequilibrium
LPS
Lipopolysaccharide
NSAID
Non-steroidal anti-inflammatory drug
OR
Odds ratio
PCI
Protein C inhibitor
SAPS II
Simplified acute physiology score II
SERPINA4
Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 4
SERPINA5
Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 5
SIK3
Salt-inducible kinase family 3
SNP
Single nucleotide polymorphism(s)
Background
In critically ill patients, the incidence of acute kidney injury (AKI) is high - 39% in the recent prospective, observational, multicenter study, the Finnish Acute Kidney Injury (FINNAKI) study, which was conducted in Finnish intensive care units (ICUs) [1]. In other prospective studies in critically ill patients the incidence of AKI is reported as 24–66% [2, 3]. Although the pathophysiology of AKI has been investigated, there is no single explanation for the condition due to the multifactorial nature of the syndrome. Sepsis is the most common factor predisposing to AKI in the critically ill. In patients with sepsis the incidence of AKI is 31–53% [4‐6] and in patients with septic shock it is even higher, at 47–61% [7, 8]. The previously identified risk factors for septic AKI, such as age, sex, and baseline comorbidities, have failed to reliably predict individual risk of septic AKI [9]. Thus, it is plausible that genetic variability between individuals may explain a significant part of the risk.
Genetic predisposition to AKI has been previously studied in candidate genes, by testing associations between single nucleotide polymorphisms (SNP) from candidate genes and a phenotype. Our recent systematic review confirmed that despite some positive associations there are no conclusive data [10]. A study by Frank et al. [11] provided one of the very first hypothesis-free approaches to septic AKI; it comprised 887 patients in septic shock defined according to American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) criteria [12], who were genotyped using a large-scale genotyping microarray. In this study, five SNPs were associated with AKI after validating the results in an additional sample. Interestingly, the associated SNPs are in the apoptosis pathway genes. The SNP rs8094315 and SNP rs12457893 are located in the intron of B-cell CLL/lymphoma 2 (BCL2) –gene. The SNPs rs2093266 in the serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 4 (SERPINA4) gene and SNP rs1955656 in the serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 5 (SERPINA5) gene are in complete linkage disequilibrium (LD). There was also association between the SNP rs625145 in the salt-inducible kinase family 3 (SIK3) gene and AKI. In this study we aimed to replicate the aforementioned findings [9] in a prospective cohort of critically ill patients with sepsis.
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Methods
Study population
This study is a predetermined genetic study of the prospective, observational, multicenter FINNAKI study, in which patients were recruited from 17 Finnish ICUs. Details of the FINNAKI study have been published elsewhere [1] and are presented in Additional file 1. The Ethics Committee of the Department of Surgery in Helsinki University Hospital approved the study. An informed separate written consent for genetic samples was obtained from the patient or next of kin at the initiation, with the option of deferred consent. The main study ended on 1 February 2012, and recruitment was extended until 30 April 2012 to achieve an adequate number of patients with sepsis. The study was conducted according to the Declaration of Helsinki.
Data collection
We collected the study-specific data items (approximately 80% of the data) on admission and daily until day 5 or ICU discharge using an electronic case report form (CRF). Data comprised previous and current health status, medication, risk factors for AKI, laboratory values and operations preceding ICU admission, sepsis and related organ dysfunctions, and focus of infection. Presence of AKI and/or sepsis was screened until ICU discharge or day 5 at the latest if the patient was still in the ICU. Routine data were collected with the help of the Finnish Intensive Care Consortium, which consists of 25 ICUs nationwide.
Definitions
For staging of AKI, plasma creatinine was measured daily and urine output hourly. We defined and staged AKI according to the new Kidney Disease: Improving Global Outcomes (KDIGO) AKI criteria [13]. We sought to compare patients with a severe phenotype of AKI (KDIGO stage 2–3) to patients with no AKI. Thus, we excluded patients with KDIGO stage-1 AKI from the current analysis because their phenotype may be seen as indecisive. We defined sepsis and septic shock according to the ACCP/SCCM definitions [12], a definition also used in the previous report [11].
Blood sampling and DNA extraction
Whole blood was collected at enrollment and after separation of plasma, stored at -80 °C for subsequent DNA extraction. DNA was isolated on a Chemagic 360 instrument (Perkin Elmer, Baesweiler, Germany), based on magnetic bead technology, using a Chemagic DNA Blood10k Kit according to the manufacturer’s instructions. DNA concentrations were determined using UV light and PicoGreen methods, and samples were diluted into 10 ng/μl for genotyping.
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Genotype analysis
Genotyping was performed at the Technology Centre of the Institute for Molecular Medicine Finland (FIMM), University of Helsinki. The genotyping was done using Agena MassARRAY® system and the iPLEXTM Gold Assay (Agena BioscienceTM, San Diego, CA, USA). This method has excellent success (>95%) and accuracy (100%) [14]. Genotyping reactions were performed on 20 ng of dried genomic DNA in 384-well plates according to the manufacturer’s recommendations and using their reagents [15]. Both polymerase chain reaction (PCR) and extension primers were designed using MassARRAY Assay Design software (Agena BioscienceTM) (Additional file 2). The data were collected using the MassARRAY Compact System (Agena BioscienceTM) and the genotypes were called using TyperAnalyzer software (Agena BioscienceTM).
We examined genotyping quality by a detailed quality control procedure consisting of success rate check, duplicated samples, water controls, and Hardy-Weinberg equilibrium (HWE) testing. In addition, the genotype calls were checked manually and corrected when necessary. Genotyping personnel were blinded to the clinical status of the patients.
Statistical analysis
We compared the clinical and demographic variables to test for significant differences between groups using the Fisher exact test for categorical variables and the Mann-Whitney U test for continuous variables. We present data as medians and interquartile ranges, or absolute values and percentages. Statistical analyses of the demographic and clinical variables were performed using the SPSS Statistics version 22 (IBM Corp., Armonk, NY, USA).
Associations between AKI and SNP were adjusted for clinical characteristics that differed significantly between patients with and without AKI. In addition to the univariate analysis we performed multivariate analysis using logistic regression by entering variables, selecting characteristics with a p value <0.2 in univariate analysis. The percentage of missing data was 15.9%. The missing data were imputed on the assumption of being unaffected for categorical variables and on the median for continuous variables.
Genetic association tests were performed using logistic regression and the PLINK software [16]. As in the previous study an additive genetic model was assumed. However, confirmatory tests were performed using recessive and dominant genetic models. In the primary analysis patients with sepsis and AKI (KDIGO stage 2–3) were compared to patients with sepsis but no AKI. Secondary analyses were performed including cases and controls from the entire cohort, and was performed separately only in those with septic shock. As this was a replication study, we considered all p values <0.05 significant.
Power calculations
Power calculations were made using Genetic Power Calculator [17], assuming allele frequencies and odds ratios from the validation cohort from the study by Frank et al. [11], and with the assumption of 299 patients and 354 controls, a prevalence of AKI of 39,% and a type I error rate of 0.005. For detailed power calculations for each SNP see Additional file 3.
Results
Patients
We prospectively enrolled 2968 ICU patients in the FINNAKI genetic study, as presented in the study flowchart (Fig. 1). After excluding ineligible patients, there were 2567 critically ill patients, of whom 837 (32.6%) had sepsis. Of these, 299 (35.7% of the 837) patients developed KDIGO stage 2–3 AKI and 354 (42.3% of 837) patients with no AKI served as controls (Fig. 1, orange dashed line). Thus, the genetic associations were studied among 653 patients with sepsis, of whom 478 (73.2%) had septic shock. Among patients with septic shock, 252 (40.2% of 627) had severe AKI and 226 (36.0% of 627) did not have AKI (Fig. 1, red dashed line). In the entire genetic cohort, there were 601 (23.4% of 2567) patients who developed KDIGO stage 2–3 AKI and 1545 (60.2% of 2567) patients who did not develop AKI (Fig. 1, blue dashed line).
Fig. 1
Study flowchart. AKI acute kidney injury, KDIGO Kidney Disease: Improving Global Outcomes, FINNAKI Finnish Acute Kidney Injury study
×
The baseline characteristics of the patients with sepsis who did and did not have AKI are presented in Table 1. The baseline characteristics of the patients with septic shock who did and did not have AKI are presented in Table 2. The baseline characteristics of the entire cohort are presented in Additional file 4.
Table 1
Demographic and baseline characteristics of the patients with sepsis according to the presence of acute kidney injury
Characteristics
Patients with data available, n
KDIGO 0
(n = 354)
KDIGO 2–3
(n = 299)
All patients
(n = 653)
P value
Age (years)
653
63 (52–73)
65 (54.5–75)
63 (53–74)
0.046
Gender (male)
653
234 (66.1%)
183 (61.2%)
417 (63.9%)
0.195
BMI (kg/m2)
651
26.0 (23.1–29.2)
27.3 (24.5–30.8)
26.5 (23.5–29.6)
0.001
Arterial hypertension
651
164 (46.6%)
159 (53.2%)
323 (49.6%)
0.099
Diabetes
653
68 (19.2%)
82 (27.4%)
150 (23.0%)
0.015
Arteriosclerosis
648
36 (10.3%)
42 (14.1%)
78 (12.0%)
0.147
COPD
649
45 (12.8%)
19 (6.4%)
64 (9.9%)
0.008
Chronic liver disease
647
17 (4.9%)
24 (8.1%)
41 (6.3%)
0.106
Systolic heart failure
649
39 (11.1%)
24 (8.1%)
63 (9.7%)
0.231
Thromboembolus
649
26 (7.4%)
17 (5.7%)
43 (6.6%)
0.432
Rheumatic disease
648
26 (7.4%)
18 (6.1%)
44 (6.8%)
0.534
Serum creatinine
Baseline (μmol/L)
653
80.3 (68.0–94.0)
77.0 (66.5–93.0)
79.0 (67.0–93.2)
0.318
Maximum (μmol/L)
604
72.0 (55.0–89.0)
227.0 (166.0–321.0)
106.5 (68.5–221.5)
<0.001
Pre-ICU daily medication
ACE inhibitor or ARB
640
113 (32.6%)
115 (39.2%)
228 (35.6%)
0.082
NSAID
624
33 (9.7%)
42 (14.7%)
75 (12.0%)
0.064
Aspirin
645
81 (23.1%)
73 (24.7%)
154 (23.9%)
0.644
Diuretic
643
93 (26.8%)
88 (29.7%)
181 (28.1)
0.429
Metformin
647
48 (13.7%)
47 (15.8%)
95 (14.7%)
0.504
Statin
644
86 (24.6%)
79 (26.8%)
165 (25.6%)
0.587
Immunosuppressives
646
29 (8.3%)
28 (9.5%)
57 (8.8%)
0.581
Corticosteroids
649
41 (11.6%)
32 (10.8%)
73 (11.2%)
0.803
Warfarin
647
39 (11.1%)
41 (13.8%)
80 (12.4%)
0.338
Treatments administered 48 h before admission
Contrast medium
652
83 (23.5%)
51 (17.1%)
134 (20.6%)
0.052
Aminoglycoside antibiotics
653
4 (1.1%)
5 (1.7%)
9 (1.4%)
0.739
Peptidoglycan antibiotics
653
17 (4.8%)
11 (3.7%)
28 (4.3%)
0.563
ACE inhibitor or ARB
641
77 (22.1%)
72 (24.7%)
149 (23.2%)
0.454
NSAID
615
52 (15.6%)
40 (14.2%)
92 (15.0%)
0.651
Amfoterisin B
653
1 (0.3%)
2 (0.7%)
3 (0.5%)
0.596
Diuretics
637
119 (34.6%)
117 (39.9%)
236 (37.0%)
0.188
Colloids (gelatin or starch)
620
102 (30.8%)
117 (40.5%)
219 (35.3%)
0.015
Albumin
647
4 (1.1%)
6 (2.0%)
10 (1.5%)
0.526
Emergency admission
648
343 (98.0%)
292 (98.0%)
635 (98.0%)
1.0
Operative admission
652
88 (24.9%)
66 (22.1%)
154 (23.6%)
0.459
SAPS II score 24 h without renal and age components
649
24.0 (17.0–30.0)
26.0 (20.0–37.0)
25.0 (18.0–33.0)
<0.001
Mechanical ventilation
653
234 (66.1%)
221 (73.9%)
455 (69.7%)
0.033
White blood cell count, maximum (109/L)
583
11.8 (7.9–16.5)
12.2 (7.6–17.8)
11.9 (7.7–17.2)
0.469
Platelet count, minimum (109/L)
632
199.5 (141.0–271.0)
184.0 (112.0–259.0)
191.5 (128.5–265.0)
0.028
Source of infection
653
<0.001
Lung
181 (51.1%)
100 (33.4%)
281 (43.0%)
Abdomen
62 (17.5%)
78 (26.1%)
140 (21.4%)
Urinary tract
10 (2.8%)
26 (8.7%)
36 (5.5%)
Skin
25 (7.1%)
26 (8.7%)
51 (7.8%)
Others
19 (5.4%)
9 (3.0%)
28 (4.3%)
Multiple sources
21 (5.9%)
16 (5.4%)
37 (5.7%)
Unknown source of infection
36 (10.2%)
44 (14.7%)
80 (12.3%)
Results presented as median (interquartile range) for continuous variables and total number (percent of affected in a group) for categorical variables. Continuous variables analyzed by the independent samples Mann-Whitney U test and categorical variables by Fisher’s exact test. KDIGO Kidney Disease: Improving Global Outcomes, BMI body mass index, COPD chronic obstructive pulmonary disease, ARB angiotensin receptor blocker, NSAID non-steroidal anti-inflammatory drug, ACE angiotensin-converting enzyme, SAPS simplified acute physiology score
Table 2
Demographic and baseline characteristics of patients with septic shock according to the presence of acute kidney injury
Characteristics
Patients with data available, n
KDIGO 0
(n = 226)
KDIGO 2–3
(n = 252)
All patients
(n = 478)
P value
Age (years)
478
63.0 (52.0–73.0)
64.5 (54.0–75.0)
63.5 (53.0–74.0)
0.138
Gender (male)
478
155 (68.6%)
156 (61.9%)
311 (65.1%)
0.149
BMI (kg/m2)
476
26.0 (23.0–29.0)
27.3 (24.5–30.5)
26.5 (23.5–29.5)
0.001
Arterial hypertension
476
104 (46.4%)
137 (54.4%)
241 (50.6%)
0.098
Diabetes
478
42 (18.6%)
65 (25.8%)
107 (22.4%)
0.062
Arteriosclerosis
475
21 (9.4%)
36 (14.3%)
57 (12.0%)
0.120
COPD
474
28 (12.5%)
19 (7.6%)
47 (9.9%)
0.090
Chronic liver disease
473
10 (4.5%)
22 (8.8%)
32 (6.8%)
0.069
Systolic heart failure
475
22 (9.8%)
22 (8.8%)
44 (9.3%)
0.752
Thromboembolus
475
14 (6.2%)
14 (5.6%)
28 (5.9%)
0.846
Rheumatic diseases
475
19 (8.5%)
17 (6.8%)
36 (7.6%)
0.493
Serum creatinine
Baseline (μmol/L)
478
82.5 (69.0–93.4)
77.0 (67.0–93.0)
79.7 (67.0–93.0)
0.236
Maximum (μmol/L)
478
69.0 (50.0–89.0)
226.5 (166.0–323.0)
113.5 (67.0–230.0)
<0.0001
Pre-ICU daily medication
ACE inhibitor or ARB
465
78 (35.6%)
96 (39.0%)
174 (37.4%)
0.502
NSAID
453
21 (9.8%)
35 (14.6%)
56 (12.4%)
0.152
Aspirin
471
52 (23.4%)
59 (23.7%)
111 (23.6%)
1.000
Diuretic
468
57 (26.0%)
76 (30.5%)
133 (28.4%)
0.305
Metformin
473
30 (13.5%)
38 (15.2%)
68 (14.4%)
0.602
Statin
469
55 (24.9%)
61 (24.6%)
116 (24.7%)
1.000
Immunosuppressives
471
19 (8.5%)
26 (10.5%)
45 (9.6%)
0.531
Corticosteroids
474
26 (11.6%)
29 (11.6%
55 (11.6%)
1.000
Warfarin
472
26 (11.7%)
34 (13.6%)
60 (12.7%)
0.581
Treatments administered 48 h before admission
Contrast medium
477
58 (25.8%)
46 (18.3%)
104 (21.8%)
0.059
Aminoglycoside antibiotics
478
2 (0.9%)
5 (2.0%
7 (1.5%)
0.455
Peptidoglycan antibiotics
478
12 (5.3%)
10 (4.0%)
22 (4.6%)
0.519
ACE inhibitor or ARB
468
43 (19.3%)
62 (25.3%)
105 (22.4%)
0.122
NSAID
448
28 (13.4%)
36 (15.1%)
64 (14.3%)
0.685
Amfoterisin B
478
0 (0.0%)
2 (0.8%)
2 (0.4%)
0.500
Diuretics
464
70 (32.1%)
97 (39.4%)
167 (36.0%)
0.121
Colloids (gelatin or starch)
452
76 (36.4%)
105 (43.2%)
181 (40.0%)
0.149
Albumin
474
4 (1.8%)
6 (2.4%)
10 (2.1%)
0.756
Emergency admission
474
217 (97.3%)
245 (97.6%)
462 (97.5%)
1.000
Operative admission
477
69 (30.5%)
58 (23.1%)
127 (26.6%)
0.078
SAPS II score 24 h without renal and age components
475
25.0 (19.0–33.0)
26.0 (21.0–37.0)
26.0 (20.0–35.0)
0.054
Mechanical ventilation
478
177 (78.3%)
197 (78.2%)
374 (78.2%)
1.000
White blood cell count, maximum (109/L)
428
12.1 (8.7–16.8)
12.8 (7.6–18.7)
12.3 (8.1–17.4)
0.794
Platelet count, minimum (109/L)
458
191.0 (140.0–260.0)
177.0 (108.5–259.0)
185.0 (121.0–259.0)
0.065
Source of infection
478
0.001
Lung
114 (50.4%)
83 (32.9%)
197 (41.2%)
Abdomen
46 (20.4%)
68 (27.0%)
114 (23.8%)
Urinary tract
7 (3.1%)
20 (7.9%)
27 (5.6%)
Skin
16 (7.1%)
22 (8.7%)
38 (7.9%)
Others
11 (4.9)
8 (3.2%)
19 (4.0%)
Multiple sources
13 (5.8%)
14 (5.6%)
27 (5.6%)
Unknown source of infection
19 (8.4%)
37 (14.7%)
56 (11.7%)
Results presented as median (interquartile range) for continuous variables and total number (percent of affected in a group) for categorical variables. Continuous variables analyzed by the independent samples Mann-Whitney U test and categorical variables by Fisher’s exact test. KDIGO Kidney Disease: Improving Global Outcomes, BMI body mass index, COPD chronic obstructive pulmonary disease, ARB angiotensin receptor blocker, NSAID non-steroidal anti-inflammatory drug, ACE angiotensin-converting enzyme, SAPS simplified acute physiology score
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Genetic associations
All of the polymorphisms tested were in HWE. None of the five SNPs investigated, rs8094315 (odds ratio (OR) 1.10, p = 0.48), rs12457893 (OR 1.02, p = 0.87), rs2093266 (OR 0.77, p = 0.15), rs1955656 (OR 0.77, p = 0.15), and rs625145 (OR 0.88, p = 0.38), was significantly associated with AKI in our analysis in patients with sepsis in the additive genetic model (Table 3). The SNPs rs2093266 and rs1955656 are in complete linkage disequilibrium (LD) and thus present identical results.
Table 3
Association between acute kidney injury and the polymorphisms studied in 653 patients with sepsis (additive genetic model)
Univariate
Multivariate
SNP
Chr
Base-pair position
Gene and alleles (major/minor)
Minor allele frequencya
Odds ratio
(95% CI)
P value
Odds ratio
(95% CI)b
P value
rs625145
11
116857220
SIK3 A/T
0.20/0.21
0.88 (0.67–1.17)
0.38
0.93 (0.68–1.25)
0.62
rs1955656
14
94579038
SERPINA5 G/A
0.10/0.12
0.77 (0.53–1.10)
0.15
0.75 (0.51–1.10)
0.14
rs2093266
14
94566450
SERPINA4 G/A
0.10/0.12
0.77 (0.53–1.10)
0.15
0.75 (0.51–1.10)
0.14
rs8094315
18
63268814
BCL2 A/G
0.24/0.22
1.10 (0.85–1.43)
0.48
1.08 (0.81–1.43)
0.61
rs12457893
18
63258928
BCL2 A/C
0.37/0.37
1.02 (0.81–1.29)
0.87
1.06 (0.82–1.36)
0.67
aPatients/controls. bAdjusted for age, body mass index, diabetes mellitus, mechanical ventilation, minimum platelet count, use of non-steroidal anti-inflammatory drugs as daily medication, chronic pulmonary obstructive disease, administration of contrast medium prior to ICU admission, administration of colloids prior to ICU admission, simplified acute physiology score II without age or renal components, operative admission, and source of infection. SNP single nucleotide polymorphism, Chr chromosome
In logistic regression analysis in patients with sepsis, higher body mass index (BMI), not having chronic obstructive pulmonary disease (COPD), use of non-steroidal anti-inflammatory drugs (NSAID) as daily medication, administration of contrast medium prior to ICU admission, simplified acute physiology score II (SAPS II) without renal or age components, and source of infection were significantly associated with KDIGO stage 2–3 AKI (Additional file 5). Adjustment for these clinical and demographic factors in the patients with sepsis did not change the results for genetic association, which were not statistically significant (Table 3).
In patients with septic shock, none of the investigated SNPs was significantly associated with AKI in univariate analysis (Table 4). After adjusting for clinical and demographic variables that remained significant in logistic regression (BMI, use of NSAID as daily medication, arteriosclerosis, COPD, administration of contrast medium prior to ICU admission, administration of colloids prior to ICU admission, SAPS II without age or renal components, operative admission, and source of infection (Additional file 6)) SNPs rs2093266 and rs1955656 were significantly associated with AKI (OR 0.63, 95% CI 0.40 to 0.98, p = 0.043, for each) (Table 4, Fig. 2). The carriers of the minor alleles of these SNPs (A and A, respectively) had a decreased risk of developing AKI. This association was in the same direction as in the previous report. The minor allele frequencies of all investigated SNPs in patients with septic shock are presented in Fig. 2.
Table 4
Association between acute kidney injury and the polymorphisms studied in 478 patients in septic shock (additive genetic model)
Single nucleotide polymorphism
Univariate odds ratio (95% confidence interval)
Univariate
P value
Multivariate odds ratio (95% confidence interval)a
Multivariate
P value
rs625145
0.81 (0.59–1.11)
0.19
0.80 (0.56–1.13)
0.20
rs1955656
0.71 (0.46–1.07)
0.10
0.63 (0.40–0.98)
0.043
rs2093266
0.71 (0.46–1.07)
0.10
0.63 (0.40–0.98)
0.043
rs8094315
1.19 (0.88–1.62)
0.27
1.13 (0.81–1.58)
0.48
rs12457893
1.24 (0.94–1.62)
0.13
1.22 (0.91–1.64)
0.19
aAdjusted for body mass index, use of non-steroidal anti-inflammatory drugs as daily medication, arteriosclerosis, chronic obstructive pulmonary disease, administration of contrast medium prior to ICU admission, administration of colloids prior to ICU admission, simplified acute physiology score II without age or renal components, operative admission, and source of infection
Fig. 2
Minor allele frequencies of all polymorphisms studied in patients with septic shock (n = 478). Patients (red) had severe acute kidney injury (AKI) (Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 AKI, n = 252) and controls (blue) were ICU patients without AKI. The p values shown above the bars are for the multivariate tests of association between the polymorphisms and AKI. *Significant p values
×
When the same analyses were performed in the entire genetic cohort, there was no evidence of association between any of the SNPs and AKI in univariate or multivariate models when adjusting for clinical and demographic variables that remained significant in logistic regression (Additional files 7 and 8).
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Using recessive and dominant genetic models did not change the results: SNPs rs8094315, rs12457893 and rs625145 were not associated with AKI in any of the subgroups studied. The protective association with SNPs rs2093266 and rs1955656 prevailed in patients in septic shock in the dominant adjusted model (OR 0.59, p = 0.034, for each) (Additional file 9). The self-assessed quality score of the study was 9/10 [18] (Additional file 10).
Discussion
In this study we confirmed the previously reported association between AKI and two polymorphisms, rs2093266 and rs1955656 in apoptosis-related genes SERPINA 4 and SERPINA5 in patients with septic shock. Both in our study and in the original study the minor alleles of these SNPs provided protection from AKI. The association signal was seen in both additive and dominant genetic models but not in the recessive model, suggesting that merely one protective allele is sufficient.
Our study was based on a previous report suggesting that polymorphisms within apoptosis-related genes may be associated with the development of AKI. In this study comprising 887 patients with septic shock, AKI was associated with five SNPs [11]. To our knowledge this is the first successful replication of two of these SNPs. There was no evidence in our cohort of significant association between AKI and the other three polymorphisms tested, rs8094315, rs12457893, and rs625145.
The SNP rs2093266 is located in an intronic region of SERPINA4 gene encoding kallistatin, a serine proteinase inhibitor that has multiple regulatory roles in biological processes [19, 20]. In kallistatin there are two functional domains, an active site and a heparin-binding site, through which it regulates several signaling and biological pathways. Along with its functions in relation to apoptosis, it has anti-inflammatory, antioxidant, vasodilator and angiogenesis inhibiting functions [21]. All these mechanisms are relevant in septic AKI and could thus explain the better outcome.
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In recent mouse model studies, Kallistatin has been associated with attenuated inflammation and organ injury and decreased mortality in established sepsis [22], and with improved survival in sepsis-related acute lung injury [23]. Of note, it may also have a renoprotective role against diabetic nephropathy [24]. rs1955656 in SERPINA5 is strongly correlated with rs2093266 and as suspected before, this association with AKI could be driven by rs2093266. Notably, the product of SERPINA5 is known to inhibit activated protein C. This protein C inhibitor (PCI) plays a role in tumor growth and metastasis through its effect on blood coagulation, but it has also been suggested to inhibit the anti-inflammatory activity of activated protein C [25, 26]. This inflammatory function could possibly explain the protection against septic AKI.
Of the other three SNPs studied, two (rs8094315 and rs12457893) are located in the introns of the apoptosis-related gene BCL2 encoding an integral outer mitochondrial membrane protein BCL2 that blocks the apoptotic death of certain cells [27]. The SNP rs625145 is in SIK3, coding for a member of the AMP-activated protein kinase family that affects the regulation of several genes. This protein is found to suppress inflammatory molecule gene expression in macrophages stimulated with lipopolysaccharides (LPS) [28].
There is no definite consensus on the pathophysiology of AKI, and until recently the most common approach has been to test for association with known genetic variants. These variants have often come up in another phenotype, commonly chronic kidney disease, and, thus, may serve as poor markers of AKI [29, 30]. So far, the most studied polymorphisms are within inflammatory mediator genes [10].
Apoptosis-related genes are good candidates for AKI because there is some evidence that apoptosis is an important mechanism in septic AKI. In a murine model of septic AKI the pathophysiology appears to differ from that of ischemia-reperfusion insult, lacking signs of tubular cell injury by necrosis but showing a substantial number of tubular cells undergoing apoptosis [31]. Lerolle et al. found that in post-mortem kidney biopsies of patients who died of septic shock (n = 19) there was a marked increase in apoptosis and capillary leukocytic infiltration in comparison with patients with trauma and ICU controls [32]. However, there were contradicting results in a separate sample of post-mortem kidney biopsies in patients who died of sepsis [33]. The nature of septic AKI alone appears to be diverse, with temporal and individual variation, and the role of apoptosis is inconclusive [34].
We also tested the association between the five SNPs and development of AKI in all patients with sepsis, but the results were negative. The phenotype of septic shock differs from that of sepsis without shock, reflecting a more severe form of illness and a greater risk of death. Thus, we can speculate that the associations between SNPs and AKI detected in these patients, although not generalizable to the septic cohort, can predict better survival for the carriers of the protective allele in terms of AKI.
The strength of our study is that it was a prospective, relatively large, multicenter study comprising consecutive patients. In addition we used the KDIGO criteria to provide a robust phenotype of AKI and critically ill patients without AKI as controls to increase the power of our study.
There are some important limitations in our study. First, 171 genetic samples (5.8%) could not be analyzed due to failure in the DNA isolation phase or to rejection of samples because of a low genotyping success rate. Second, we did not collect data ont ethnicity. However, 99.9% of Finnish-speaking inhabitants are Caucasian. Third, individual genetic susceptibility factors can be expected to increase the AKI risk with values just exceeding an OR of 1 [35]. In our power calculations we used the ORs reported in the original study, and, thus, our study might have been underpowered to replicate the results for BCL2 and SIK3. Fourth, we did not adjust the p values for multiple testing, in this replication of positive findings.
Finally, in contrast to the previous study [11] we excluded patients with chronic kidney disease. Furthermore, we aimed to strengthen the phenotype by comparing patients without AKI to patients with more severe AKI (KDIGO stage 2–3), thus excluding those with KDIGO stage 1. We reasoned that this group would include patients with only KDIGO stage-1 urine output (not included in [11]) criteria, whose phenotype clearly differs from that of more severe acute kidney.
These findings provide some interesting questions for future research. The functions of the SNPs rs2093266 in the SERPINA4 and rs1955656 in the SERPINA5 for the protein products are yet to be determined. If further studies can provide independent evidence supporting the role of these SNPs in AKI susceptibility, information about the genotype of either of these SNPs may add to the battery of risk-predicting tools. However, the effect size of the SNPs is too small for the genotype information to work as an independent biomarker in a clinical setting.
Conclusions
In this study we aimed to replicate the previous findings associating polymorphisms within apoptosis-related genes to AKI. We found that SNPs rs2093266 in the SERPINA4 and rs1955656 in the SERPINA5 were associated with KDIGO stage 2–3 AKI in critically ill patients in septic shock.
Acknowledgements
We thank the FINNAKI Study Group: Central Finland Central Hospital: Raili Laru-Sompa, Anni Pulkkinen, Minna Saarelainen, Mikko Reilama, Sinikka Tolmunen, Ulla Rantalainen, Marja Miettinen; East Savo Central Hospital: Markku Suvela, Katrine Pesola, Pekka Saastamoinen, Sirpa Kauppinen; Helsinki University Central Hospital: Ville Pettilä, Kirsi-Maija Kaukonen, Anna-Maija Korhonen, Sara Nisula, Suvi Vaara, Raili Suojaranta-Ylinen, Leena Mildh, Mikko Haapio, Laura Nurminen, Sari Sutinen, Leena Pettilä, Helinä Laitinen, Heidi Syrjä, Kirsi Henttonen, Elina Lappi, Hillevi Boman; Jorvi Central Hospital: Tero Varpula, Päivi Porkka, Mirka Sivula, Mira Rahkonen, Anne Tsurkka, Taina Nieminen, Niina Prittinen; KantaHäme Central Hospital: Ari Alaspää, Ville Salanto, Hanna Juntunen, Teija Sanisalo; Kuopio University Hospital: Ilkka Parviainen, Ari Uusaro, Esko Ruokonen, Stepani Bendel, Niina Rissanen, Maarit Lång, Sari Rahikainen, Saija Rissanen, Merja Ahonen, Elina Halonen, Eija Vaskelainen; Lapland Central Hospital: Meri Poukkanen, Esa Lintula, Sirpa Suominen; Länsi Pohja Central Hospital: Jorma Heikkinen, Timo Lavander, Kirsi Heinonen, Anne-Mari Juopperi; Middle Ostrobothnia Central Hospital: Tadeusz Kaminski, Fiia Gäddnäs, Tuija Kuusela, Jane Roiko; North Karelia Central Hospital: Sari Karlsson, Matti Reinikainen, Tero Surakka, Helena Jyrkönen, Tanja Eiserbeck, Jaana Kallinen; Satakunta Hospital District: Vesa Lund, Päivi Tuominen, Pauliina Perkola, Riikka Tuominen, Marika Hietaranta, Satu Johansson; South Karelia Central Hospital: Seppo Hovilehto, Anne Kirsi, Pekka Tiainen, Tuija Myllärinen, Pirjo Leino, Anne Toropainen; Tampere University Hospital: Anne Kuitunen, Ilona Leppänen, Markus Levoranta, Sanna Hoppu, Jukka Sauranen, Jyrki Tenhunen, Atte Kukkurainen, Samuli Kortelainen, Simo Varila; Turku University Hospital: Outi Inkinen, Niina Koivuviita, Jutta Kotamäki, Anu Laine; Oulu University Hospital: Tero Ala-Kokko, Jouko Laurila, Sinikka Sälkiö; Vaasa Central Hospital: Simo-Pekka Koivisto, Raku Hautamäki, Maria Skinnar. We also thank the Institute for DeCode (Reykjavik, Iceland) for DNA extraction, Molecular Medicine Finland (FIMM, Helsinki, Finland) for genotyping, Tieto Healthcare and Welfare Ltd (Helsinki, Finland) for the FINNAKI study database management, and Martin Sigurdsson MD, PhD for his insightful comments on the manuscript.
Funding
This study was supported by grants TYH 2013343 and 2016243 from the Helsinki University Hospital research funding (VP) and a grant from the Sigrid Juselius Foundation (VP).
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article and its additional files.
Authors’ contributions
LV contributed to the data analysis and interpretation of the data and drafted the manuscript. MK contributed to the data analysis and interpretation of the data and participated in editing and finalizing the manuscript. SV contributed to the data analysis and interpretation of the data, contributed to the data acquisition and database access, and participated in editing and finalizing the manuscript. VP conceived the study, contributed to its design and coordination, and participated in editing and finalizing the manuscript. All authors reviewed and revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
The Ethics Committee of the Department of Surgery in Helsinki University Hospital approved the study (reference number 18/13/03/02/2010). Separate informed written consent for genetic samples was obtained from the patient or next of kin at the initiation, with the option of deferred consent.
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