Next Article in Journal
Increased Risk of Diabetes in Inflammatory Bowel Disease Patients: A Nationwide Population-Based Study in Korea
Next Article in Special Issue
Incidence and Impact of Acute Kidney Injury after Liver Transplantation: A Meta-Analysis
Previous Article in Journal
Clinical and Biomarker Characteristics According to Clinical Spectrum of Alzheimer’s Disease (AD) in the Validation Cohort of Korean Brain Aging Study for the Early Diagnosis and Prediction of AD
Previous Article in Special Issue
Why Have Detection, Understanding and Management of Kidney Hypoxic Injury Lagged behind Those for the Heart?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 8
Issue 3
10.3390/jcm8030342
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Common Inflammation-Related Candidate Gene Variants and Acute Kidney Injury in 2647 Critically Ill Finnish Patients

by
Laura M. Vilander
1,*,
Suvi T. Vaara
1,
Mari A. Kaunisto
2,
Ville Pettilä
1 and
The FINNAKI Study Group
†
1
Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine, University of Helsinki and Helsinki University Hospital, 00014 Helsinki, Finland
2
Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 000014 Helsinki, Finland
*
Author to whom correspondence should be addressed.
†
Membership of the The FINNAKI Study Group is provided in the Acknowledgments.
J. Clin. Med. 2019, 8(3), 342; https://doi.org/10.3390/jcm8030342
Submission received: 31 January 2019 / Revised: 26 February 2019 / Accepted: 4 March 2019 / Published: 11 March 2019
(This article belongs to the Special Issue Diagnostics, Risk Factors, Treatment and Outcomes of Acute Kidney Injury in a New Paradigm)
Browse Figures
Review Reports Versions Notes

Abstract

:
Acute kidney injury (AKI) is a syndrome with high incidence among the critically ill. Because the clinical variables and currently used biomarkers have failed to predict the individual susceptibility to AKI, candidate gene variants for the trait have been studied. Studies about genetic predisposition to AKI have been mainly underpowered and of moderate quality. We report the association study of 27 genetic variants in a cohort of Finnish critically ill patients, focusing on the replication of associations detected with variants in genes related to inflammation, cell survival, or circulation. In this prospective, observational Finnish Acute Kidney Injury (FINNAKI) study, 2647 patients without chronic kidney disease were genotyped. We defined AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria. We compared severe AKI (Stages 2 and 3, n = 625) to controls (Stage 0, n = 1582). For genotyping we used iPLEXTM Assay (Agena Bioscience). We performed the association analyses with PLINK software, using an additive genetic model in logistic regression. Despite the numerous, although contradictory, studies about association between polymorphisms rs1800629 in TNFA and rs1800896 in IL10 and AKI, we found no association (odds ratios 1.06 (95% CI 0.89–1.28, p = 0.51) and 0.92 (95% CI 0.80–1.05, p = 0.20), respectively). Adjusting for confounders did not change the results. To conclude, we could not confirm the associations reported in previous studies in a cohort of critically ill patients.

1. Introduction

Acute kidney injury (AKI) is a syndrome that often complicates critical illness and is associated with significant mortality and morbidity [1,2]. Thus, efforts to distinguish patients at risk for AKI are justifiable, but despite the advances in the understanding of the pathophysiology of AKI, reliable prediction of developing AKI in different clinical scenarios remains a challenge.
In our systematic review in 2014 we found that evidence about genetic predisposition to AKI was heterogeneous, the studies were of inadequate size and the findings were generally not replicated [3]. Based on these findings, we analyzed 27 common genetic variants that situate in genes previously associated with AKI in a Finnish sample of critically ill patients.

2. Materials and Methods

2.1. Patients

We prospectively recruited patients from 17 Finnish intensive care units (ICUs) in the Finnish acute Kidney Injury (FINNAKI) study. The FINNAKI study took place in the years 2011 and 2012, and the study design has been described previously [1]. We included all patients with an emergency ICU admission of any length and elective surgical patients with an expected duration of ICU stay longer than 24 h. We excluded patients that received maintenance dialysis. The complete exclusion criteria are reported in electronic Supplementary Materials (ESM).
Consent for the study was achieved from the patients or next of kin, at the initiation of the study or deferred. A separate consent for genetic analyses was obtained from all patients or their legal representatives. The Ethics Committee of the Department of Surgery in Helsinki University Hospital gave approval for the study (18/13/03/02/2010).

2.2. Definitions

We defined AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria [4]. We performed analyses using both the severe AKI phenotype (KDIGO Stages 2–3 compared to KDIGO 0) and the all-stage (1–3) AKI phenotype. We classified patients into AKI stages according to daily measurements of plasma creatinine and hourly measurements of urine output. Sepsis was defined according to the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) definition [5].

2.3. Data Collection

We collected routine data into Finnish Intensive Care Consortium prospective database (Tieto Ltd., Helsinki, Finland). In addition, we completed a standardized case reporting form (CRF) on admission, as well as daily for 5 days and at discharge from ICU. These study-specific data comprised health status previously and present, medications in use, information about some known AKI risk factors, evaluation of organ dysfunctions such as sepsis, and information about treatments administered.

2.4. DNA Samples and Genotyping

Deoxyribonucleic acid (DNA) was extracted from frozen blood samples collected at enrollment. DNA isolation was performed with Chemagic 360 intrument using Chemagic DNA Blood10k Kit, as instructed by the manufacturer (Perkin Elmer, Baesweiler, Germany). For genotyping, we diluted the sample concentration into 10 ng/μL.
We performed the genotyping in two subsequent assays, in the years 2015 and 2017, at the Genotyping Unit of Institute for Molecular Medicine Finland (FIMM), University of Helsinki. The Agena MassARRAY® system, along with the iPLEXTM Gold Assay (Agena BioscienceTM, San Diego, CA, USA) were used for the genotyping. Here, 20 ng of dried genomic DNA were used for genotyping reactions in 384-well plates using manufacturer’s reagents, and according to their recommendations [6]. For designing primer sequences, MassARRAY Assay Design software (Agena BioscienceTM) was used (see ESM). The MassARRAY Compact System (Agena BioscienceTM) was used for data collection and TyperAnalyzer software (Agena BioscienceTM) for genotype calling. The quality control procedure consisted of checking the success rate, duplicate samples, control wells with water and testing for Hardy–Weinberg Equilibrium (HWE). In addition, all of the genotype calls were manually checked. The genotyping personnel were unaware of the clinical status of the patients.
In the year 2015 assay 49 samples (1.7% of 2968 samples) were rejected because of low success rate in the tested polymorphisms, and in the year 2017 assay the corresponding number was five samples (0.2% of 2968 samples).
For rs699 the success rate reached only 48% due to assay failure in half of the runs; however, the remainder of allele calling was possible with new extension primer and the results are thus reported.

2.5. Variant Selection

Variations in or nearby genes related to inflammation, circulation, and cell survival have been suggested in candidate polymorphism studies regarding AKI [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Additionally, the first hypothesis-free studies in AKI genetic predisposition have been published [28,29,30], with some replicated associations [31]. We chose to test polymorphisms in TNFA (rs1800629 [8,19,21,22,23,24,25,26,27]), IL6 (rs1800796 [24,26] and rs1800795 [19,24,26], rs10499563, rs1474347, rs13306435, rs2069842 and rs2069830), CXCL8 (rs4073 [27]), IL10 (rs1800896 [19,21,23,25,26]), NOS3 (rs2070744 [13,24]), NFKB1A (rs1050851 [32]), AGT (rs699 and rs2493133 [24]), VEGFA (rs2010963 and rs3025039 [27]), EPO (rs1617640 [14]), SUFU (rs10748825 [9]), HIF1A (rs11549465 [15]), PNMT (rs876493 [17]), MPO (rs7208693 [16]), COMT (rs4680 [10,11,12]), HSPB1 (rs2868371 [33]), SFTPD (rs2243639 and rs721917 [34]), HAMP (rs10421768 [35]) and BBS9 (rs10262995 [30]) genes (see definitions for abbreviations in ESM).

2.6. Statistical Analyses

We used SPSS Statistics version 22 (IBM Corp., Armonk, NY, USA) for analyzing the clinical and demographic variables. The analyses used were the Fisher’s exact test in cross tabulation for categorical variables and the Mann–Whitney U for continuous variables. The data are presented as medians (with interquartile range), or absolute count (with percentage).
We performed the association test for genetic variants and AKI phenotype with logistic regression in the PLINK software [36]. We used the additive genetic model. For haplotype analysis, we checked for haploblocks with Haploview [37]. In addition, for polymorphisms in TNFA and IL10 we performed an epistasis test. The haplotype analysis and epistasis test were performed with the PLINK software [36]. In the primary analysis we compared patients with KDIGO stage 2 or 3 AKI to patients without AKI, as the primary outcome of the study. In the secondary analysis we compared all stage AKI (KDIGO 1, 2 or 3) to no AKI. In the tertiary analysis we included patients with chronic kidney disease and compared patients with KDIGO stage 2 or 3 AKI to patients without AKI in an adjusted analysis. We used similar covariates to our previously published article [31] (liver failure, body mass index (BMI), use of nonsteroidal anti-inflammatory drugs (NSAID) or warfarin as permanent medication, use of contrast dye prior to ICU admission, use of colloids prior to ICU admission, use of albumin prior to ICU admission, minimum platelet count, and simplified acute physiology score (SAPS) II without renal and age points), omitting the infection focus for irrelevant information, and maximum leucocyte count and operative admission to avoid multicollinearity, while including cardiac surgery status as well as sepsis status. The missing data within covariates (altogether 2.4%) were addressed by imputing (see ESM for details).
For all analyses, we considered a p-value of 0.002 significant after Bonferroni correction for multiple testing (0.05/25 = 0.002).

2.7. Power Calculations

We performed prospective power calculations to determine an appropriate sample size [38]. Assuming an effect size of 1.2 per risk allele (1.4 per homozygote genotype) and a minor allele frequency of 0.2, setting the level of significance to 0.005, there will be 96% (93% for homozygote genotype) power to detect an association in a sample of 1200 cases and 1800 controls.

3. Results

We recruited 2968 patients in the FINNAKI genetic study (Figure 1). We excluded 199 patients (6.7% of 2968 patients) with chronic kidney disease from the primary (green dashed line) and secondary (orange dashed line) analysis. In addition, 122 DNA samples failed at isolation. Of the remaining 2647 patients, 221 (8.3% of 2647) patients had stage 2 AKI, 404 (15.3% of 2647) had stage 3 AKI, and 1582 (59.8% of 2647) patients served as controls without AKI. Overall, 228 (10.9% of 2647) patients received renal replacement therapy (RRT). Moreover, 440 (16.6% of 2647) patients had an ambiguous phenotype (KDIGO stage 1); we excluded them from the primary and the tertiary (blue dashed line) analysis. Table 1 presents patient demographics.
In the primary analysis, none of the previously reported associations replicated in our sample (Figure 2). Of note, the A-allele of rs1800629 in TNFA was not associated with AKI (odds ratio, OR 1.06, 95% confidence interval, CI 0.89–1.28, p = 0.51). In addition, the G-allele of rs1800896 in IL10 was not associated with AKI (OR 0.92, 95% CI 0.80–1.05, p = 0.20) (Table 2). In the epistasis test between A-allele of rs1800629 and G-allele of rs1800896 we detected no evidence of interaction (OR 1.10, p = 0.40). Frequencies of genotype combinations of these two variants are presented in Table 3, grouped according to their reported effect on protein production [25].
We tested IL6 for altogether seven variants and found no association with either endpoint. We found no variation in single nucleotide polymorphisms (SNPs) rs2069842 and rs2069830 in IL6; rs10499563 in IL6 was not in HWE (p = 0.034). The haplotypes of the two tested haploblocks were not associated with AKI (data shown in ESM).
The T-allele of rs3025039 in VEGFA had an odds ratio (OR) of 1.20 (95% CI 1.01–1.44, p = 0.044) for development of stage 2 or 3 AKI. This finding prevailed in the tertiary analysis (adjusted model, OR 1.21, 95% CI 1.00–1.45, p = 0.047; ESM, Table S1).
In the tertiary analysis the G-allele of rs10421768 in HAMP had an OR of 0.81 (95% CI 0.69–0.95, p = 0.0090) for development of stage 2 or 3 AKI; however, none of the variants had a statistically significant association in this adjusted model with CKD patients included (Table S1, ESM).
In the secondary analysis with all stage AKI as the endpoint the results did not change (ESM, Table S2).
The variants we investigated had minor allele frequencies ranging from 0.03 to 0.47 and the retrospectively calculated variant-specific power varied accordingly from 13.0% to 91.2% (ESM, Table S3).

4. Discussion

In this study involving nearly 3000 critically ill adult patients, we were unable to replicate the previously reported associations between selected inflammation-related gene variants and AKI. The 27 tested polymorphisms are within 18 candidate genes, the majority of which relate to inflammation, cell survival, or circulation. Despite the suggestive findings in VEGFA and HAMP, we did not achieve the pre-set statistical significance after correcting for multiple comparisons.
The original studies reporting genetic associations have been generally underpowered, and of moderate quality only [3]. Majority of these studies investigated candidate polymorphisms with unknown biological function. Heterogeneity in reporting has hampered conduction of meta-analyses of reported associations [3,39]. In addition, the few replication attempts have given contradicting results. Moreover, most reports are from cardiac surgery patients, whereas septic and mixed ICU patients have been studied less [3]. In our prospective power calculation we determined that a sample of 1200 cases and 1800 controls would suffice to give a 96% power to detect an association, with realistic assumptions of minor allele frequency and effect size considering complex disease origin. However, the true effects of associations are known to be smaller than the ones reported by first authors [40,41]. In addition, false positive associations are numerous in genetic association studies to identify common variants [41]. This, along with population diversity is a possible explanation as to the failure in replication attempt [42]. Because of multiple variants tested, we used a more stringent level of significance. By using Bonferroni method we determined the acceptable level of type 1 error rate to 0.002.
The first results of hypothesis-free study designs in genetic predisposition to AKI have been published in septic [28] and cardiac surgery-associated AKI [29,30]. In their genome wide association study (GWAS), Stafford-Smith and coworkers [30] reported an association of polymorphism rs10262995 in BBS9 with cardiac surgery-associated AKI. However, in our cohort this association was not found.
One of the most studied polymorphisms is the rs1800629 in TNFA: the low producing genotype (GG) has been associated with more frequent and more severe AKI [7,8]. In addition, low producing genotype AA of variant rs1800896 in IL10 has been associated with AKI [7], along with combined genotype of rs1800629 GG + rs1800896 AA [19]. However, contradicting findings have been presented [22,23,24,25,26]. Additionally, TNFA and IL10 variations have been associated with sepsis development [43,44,45]. Of note, we were unable to detect any significant association between these polymorphisms or their combination in the epistasis test.
Vascular endothelial growth factor (VEGF) is a protein with shown effects in angiogenesis, cell survival and differentiation, as well as vascular permeability [46,47]. In our study, the T-allele in rs3025039 in VEGFA resulted in an OR of 1.20 for AKI (95% CI 1.01–1.44, p = 0.044); however, previously the C-allele has been reported to increase AKI risk [27]. In carriers of T-allele, the plasma VEGF levels are lower [48]. The T-allele has been suggested to relate to susceptibility to ARDS and mortality in ARDS [49,50]. Additionally, variation in VEGFA has been associated with diabetic nephropathy [51].
As the IL-6 cytokine has been associated with AKI development [52,53], we aimed to investigate the IL6 gene variation more broadly. We genotyped seven SNPs, five in addition to the two replication variants. However, two of these SNPs did not have any variation in our sample. The remaining five did not associate with AKI, even when studied as a haplotype. In sepsis, the SNP rs1800795 is not associated with susceptibility or mortality [54]. Consistently, rs1800795 does not correlate with the risk of end-stage renal disease [55]. Nevertheless, in patients with CKD, the SNP rs1800796 is suggested to predispose to sepsis and mortality [56]. However, due to multiple differing etiologies the predisposing genetic variants are generally unique to CKD [57,58,59].
The rs4680 in catechol-O-methyltransferase gene (COMT) causes an amino acid transition (Val158Met), which leads to lower (L) in comparison to higher (H) enzyme activity [60]. The COMT enzyme degrades catecholamines [61], and thus has been thought to contribute in vasodilatory shock and AKI [11]. The LL genotype is associated in cardiac surgery associated AKI (CSA-AKI) in two studies of modest size [11,12], yet in a larger study this association was ruled out [10]. Furthermore, endothelial NO synthase gene (NOS3) variant rs2070744 was studied in association to CSA-AKI with conflicting results [13,24]. The rs2070744 has been investigated in association with diabetic nephropathy, however, results have contradicted [62]. We did not find any association between these variants and AKI.
To our knowledge, this is the first replication of polymorphisms in CXCL8 [27], NFKB1A [32], AGT [24], EPO [14], SUFU [9], HIF1A [15], PNMT [17], MPO [16], and SFTPD [34] in association to AKI phenotype. None of the investigated variants rendered verification for the initial hypotheses.
In addition to the candidate polymorphism replication, we tested two SNPs due to interesting biological hypotheses. Li and colleagues [33] presented in their in vitro septic AKI model that heat shock protein 27 (Hsp27) overexpression caused the renal epithelial cells to outlive. The C-allele of a functional SNP rs2868371 in the HSPB1 gene associates with decreased expression of Hsp27 [63,64]. However, in our sample this SNP was not associated with AKI in any of the analyses. Another intriguing suggestion regarding the pathophysiology of septic AKI was presented by Schaalan and colleagues [35]: the hepcidin levels were elevated in patients with septic AKI. Hepcidin is encoded by HAMP gene, and the promoter SNP rs10421768 is suggested to affect the gene expression [65,66]. We found G-allele to be protective (OR 0.81, 95% CI 0.69–0.95, p = 0.0090) in the adjusted model; however, this was not a replication of an association to a human AKI model, but a pilot study on this association.
We acknowledge that our study has some limitations. First, we were unable to extract the DNA of 122 (4.1%) patients. However, this random selection is unlikely to cause bias in our remaining data.
Second, our cohort of critically ill patients consists of patients with multiple possible etiologies for AKI, rather than tightly defined phenotypes, such as cardiac surgery or sepsis. However, we did adjust for these confounders in our tertiary analysis.
Third, the actual sample size was somewhat smaller than the estimated sample size we prospectively estimated to be needed for an adequate power. However, even with samples of 2207 patients in primary analysis, 2647 patients in the secondary analysis, and 2358 patients in the tertiary analysis, the retrospectively calculated power of the study, holding to the presumptions about allele frequency and effect size, remained adequate (80.6%, 93.6% and 85.5%, respectively). The minor allele frequency was, however, lower for some of the studied SNPs, affecting the power of these specific analyses. Most SNPs had frequencies exceeding the 0.2 we anticipated. A larger sample of patients with a sub-phenotype such as septic AKI is an interesting challenge for the future.

5. Conclusions

In conclusion, we were unable to replicate previous associations between genetic variants and AKI in critically ill patients. Even if short of significance, an interesting previously unpublished variant in the HAMP gene offers possible insight into mechanism of AKI, although future studies are needed to confirm this finding. In the future, the efforts to decipher “the AKI gene” should be targeted on more carefully assigned AKI sub-phenotypes.

Supplementary Materials

The following are available online at https://www.mdpi.com/2077-0383/8/3/342/s1, (1) Exclusion criteria for the study, (2) Primer sequences, (3) Abbreviations for genes, (4) Percentages of imputed values in covariates, (5) Results for analyses 2 and 3, (6) Haplotype analyses results, (7) Retrospective power calculation for each variant, (8) Summary-level data.

Author Contributions

V.P. is the principal investigator for the FINNAKI study. V.P., M.A.K., and L.M.V. designed the study. S.T.V. contributed to the data acquisition and database access. L.M.V. and M.A.K. analyzed the data. L.M.V. made the manuscript draft and the figures. All authors edited the manuscript. All authors revised the article critically for important intellectual content, giving their approval.

Acknowledgments

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. In addition, we thank the Genotyping Unit of the FIMM technology Center (Institute for molecular Medicine Finland, University of Helsinki, Finland) for the genotyping. We thank DeCode (Reykjavik, Iceland) for extracting the DNA. L.M.V. has received grants from Munuaissäätiö fund and The Finnish Society of Anaesthesiologists. In addition V.P. has received funding by grants TYH 2013343, 2016243, 2017241, and Y102011091 from the Helsinki University Hospital research funding, and a grant from the Sigrid Juselius Foundation. S.T.V. has received funding for Clinical Researchers (317061) from the Academy of Finland. The consent given by the study participants and ethical approval for the study limit the individual-level data availability. Summary-level data are presented in ESM.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

References

  1. Nisula, S.; Kaukonen, K.-M.M.; Vaara, S.T.; Korhonen, A.-M.M.; Poukkanen, M.; Karlsson, S.; Haapio, M.; Inkinen, O.; Parviainen, I.; Suojaranta-Ylinen, R.; et al. Incidence, risk factors and 90-day mortality of patients with acute kidney injury in Finnish intensive care units: The FINNAKI study. Intensive Care Med. 2013, 39, 420–428. [Google Scholar] [CrossRef] [PubMed]
  2. Hoste, E.A.J.; Bagshaw, S.M.; Bellomo, R.; Cely, C.M.; Colman, R.; Cruz, D.N.; Edipidis, K.; Forni, L.G.; Gomersall, C.D.; Govil, D.; et al. Epidemiology of acute kidney injury in critically ill patients: The multinational AKI-EPI study. Intensive Care Med. 2015, 41, 1411–1423. [Google Scholar] [CrossRef] [PubMed]
  3. Vilander, L.M.; Kaunisto, M.A.; Pettilä, V. Genetic predisposition to acute kidney injury—A systematic review. BMC Nephrol. 2015, 16, 197. [Google Scholar] [CrossRef] [PubMed]
  4. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney Int. Suppl. 2012, 2, 1–138. [Google Scholar]
  5. Bone, R.C.; Balk, R.A.; Cerra, F.B.; Dellinger, R.P.; Fein, A.M.; Knaus, W.A.; Schein, R.M.; Sibbald, W.J. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM consensus conference. Chest J. 1992, 101, 1644–1655. [Google Scholar] [CrossRef]
  6. Jurinke, C.; van den Boom, D.; Cantor, C.R.; Koster, H. Automated genotyping using the DNA MassArray technology. Methods Mol. Biol. 2002, 187, 179–192. [Google Scholar] [PubMed]
  7. Hashad, D.I.; Elsayed, E.T.; Helmy, T.A.; Elawady, S.M. Study of the role of tumor necrosis factor-α (–308 G/A) and interleukin-10 (–1082 G/A) polymorphisms as potential risk factors to acute kidney injury in patients with severe sepsis using high-resolution melting curve analysis. Ren. Fail. 2017, 39, 77–82. [Google Scholar] [CrossRef] [PubMed]
  8. Susantitaphong, P.; Perianayagam, M.C.; Tighiouart, H.; Liangos, O.; Bonventre, J.V.; Jaber, B.L. Tumor necrosis factor alpha promoter polymorphism and severity of acute kidney injury. Nephron 2013, 123, 67–73. [Google Scholar] [CrossRef] [PubMed]
  9. Henao-Martinez, A.; Agler, A.H.; LaFlamme, D.; Schwartz, D.A.; Yang, I. V Polymorphisms in the SUFU gene are associated with organ injury protection and sepsis severity in patients with Enterobacteriacea bacteremia. Infect. Genet. Evol. 2013, 16, 386–391. [Google Scholar] [CrossRef] [PubMed]
  10. Kornek, M.; Deutsch, M.A.; Eichhorn, S.; Lahm, H.; Wagenpfeil, S.; Krane, M. COMT-Val158Met-polymorphism is not a risk factor for acute kidney injury after cardiac surgery. Dis. Mark. 2013, 35, 129–134. [Google Scholar] [CrossRef] [PubMed]
  11. Haase-Fielitz, A.; Haase, M.; Bellomo, R.; Lambert, G.; Matalanis, G.; Story, D.; Doolan, L.; Buxton, B.; Gutteridge, G.; Luft, F.C.; et al. Decreased catecholamine degradation associates with shock and kidney injury after cardiac surgery. J. Am. Soc. Nephrol. 2009, 20, 1393–1403. [Google Scholar] [CrossRef] [PubMed]
  12. Albert, C.; Kube, J.; Haase-Fielitz, A.; Dittrich, A.; Schanze, D.; Zenker, M.; Kuppe, H.; Hetzer, R.; Bellomo, R.; Mertens, P.R.; et al. Pilot study of association of catechol-O-methyl transferase rs4680 genotypes with acute kidney injury and tubular stress after open heart surgery. Biomark. Med. 2014, 8, 1227–1238. [Google Scholar] [CrossRef] [PubMed]
  13. Popov, A.F.; Hinz, J.; Schulz, E.G.; Schmitto, J.D.; Wiese, C.H.; Quintel, M.; Seipelt, R.; Schoendube, F.A. The eNOS 786C/T polymorphism in cardiac surgical patients with cardiopulmonary bypass is associated with renal dysfunction. Eur. J. Cardio-Thoracic Surg. 2009, 36, 651–656. [Google Scholar] [CrossRef] [PubMed]
  14. Popov, A.F.; Schulz, E.G.; Schmitto, J.D.; Coskun, K.O.; Tzvetkov, M.V.; Kazmaier, S.; Zimmermann, J.; Schondube, F.A.; Quintel, M.; Hinz, J. Relation between renal dysfunction requiring renal replacement therapy and promoter polymorphism of the erythropoietin gene in cardiac surgery. Artif. Organs 2010, 34, 961–968. [Google Scholar] [CrossRef] [PubMed]
  15. Kolyada, A.Y.; Tighiouart, H.; Perianayagam, M.C.; Liangos, O.; Madias, N.E.; Jaber, B.L. A genetic variant of hypoxia-inducible factor-1alpha is associated with adverse outcomes in acute kidney injury. Kidney Int. 2009, 75, 1322–1329. [Google Scholar] [CrossRef] [PubMed]
  16. Perianayagam, M.; Tighiouart, H.; Liangos, O.; Kouznetsov, D.; Wald, R.; Rao, F.; O’Connor, D.; Jaber, B. Polymorphisms in the myeloperoxidase gene locus are associated with acute kidney injuryrelated outcomes. Kidney Int. 2012, 82, 909–919. [Google Scholar] [CrossRef] [PubMed]
  17. Alam, A.; O’Connor, D.T.; Perianayagam, M.C.; Kolyada, A.Y.; Chen, Y.; Rao, F.; Mahata, M.; Mahata, S.; Liangos, O.; Jaber, B.L. Phenylethanolamine N-methyltransferase gene polymorphisms and adverse outcomes in acute kidney injury. Nephron 2010, 114, 253–259. [Google Scholar] [CrossRef] [PubMed]
  18. Gaudino, M.; Di Castelnuovo, A.; Zamparelli, R.; Andreotti, F.; Burzotta, F.; Iacoviello, L.; Glieca, F.; Alessandrini, F.; Nasso, G.; Donati, M.B.; et al. Genetic control of postoperative systemic inflammatory reaction and pulmonary and renal complications after coronary artery surgery. J. Thorac. Cardiovasc. Surg. 2003, 126, 1107–1112. [Google Scholar] [CrossRef]
  19. Dalboni, M.A.; Quinto, B.M.; Grabulosa, C.C.; Narciso, R.; Monte, J.C.; Durao, M. Tumour necrosis factor-alpha plus interleukin-10 low producer phenotype predicts acute kidney injury and death in intensive care unit patients. Clin. Exp. Immunol. 2013, 173, 242–249. [Google Scholar] [CrossRef] [PubMed]
  20. Wattanathum, A.; Manocha, S.; Groshaus, H.; Russell, J.A.; Walley, K.R. Interleukin-10 Haplotype Associated with Increased Mortality in Critically Ill Patients with Sepsis From Pneumonia But Not in Patients With Extrapulmonary Sepsis. Chest 2005, 128, 1690–1698. [Google Scholar] [CrossRef] [PubMed]
  21. Chang, C.F.; Lu, T.M.; Yang, W.C.; Lin, S.J.; Lin, C.C.; Chung, M.Y. Gene polymorphisms of interleukin-10 and tumor necrosis factor-alpha are associated with contrast-induced nephropathy. Am. J. Nephrol. 2013, 37, 110–117. [Google Scholar] [CrossRef] [PubMed]
  22. Boehm, J.; Eichhorn, S.; Kornek, M.; Hauner, K.; Prinzing, A.; Grammer, J. Apolipoprotein E genotype, TNF-alpha 308G/A and risk for cardiac surgery associated-acute kidney injury in Caucasians. Ren. Fail. 2014, 36, 237–243. [Google Scholar] [CrossRef] [PubMed]
  23. McBride, W.T.; Prasad, P.S.; Armstrong, M.; Patterson, C.; Gilliland, H.; Drain, A. Cytokine phenotype, genotype, and renal outcomes at cardiac surgery. Cytokine 2013, 61, 275–284. [Google Scholar] [CrossRef] [PubMed]
  24. Stafford-Smith, M.; Podgoreanu, M.; Swaminathan, M.; Phillips-Bute, B.; Mathew, J.P.; Hauser, E.H. Association of genetic polymorphisms with risk of renal injury after coronary bypass graft surgery. Am. J. Kidney Dis. 2005, 45, 519–530. [Google Scholar] [CrossRef] [PubMed]
  25. Jaber, B.L.; Rao, M.; Guo, D.; Balakrishnan, V.S.; Perianayagam, M.C.; Freeman, R.B.; Pereira, B.J. Cytokine gene promoter polymorphisms and mortality in acute renal failure. Cytokine 2004, 25, 212–219. [Google Scholar] [CrossRef] [PubMed]
  26. Jouan, J.; Golmard, L.; Benhamouda, N.; Durrleman, N.; Golmard, J.L.; Ceccaldi, R.; Trinquart, L.; Fabiani, J.N.; Tartour, E.; Jeunemaitre, X.; et al. Gene polymorphisms and cytokine plasma levels as predictive factors of complications after cardiopulmonary bypass. J. Thorac. Cardiovasc. Surg. 2012, 144, 467–473.e2. [Google Scholar] [CrossRef] [PubMed]
  27. Cardinal-Fernandez, P.; Ferruelo, A.; El-Assar, M.; Santiago, C.; Gomez-Gallego, F.; Martin-Pellicer, A. Genetic predisposition to acute kidney injury induced by severe sepsis. J. Crit. Care 2013, 28, 365–370. [Google Scholar] [CrossRef] [PubMed]
  28. Frank, A.J.; Sheu, C.C.; Zhao, Y.; Chen, F.; Su, L.; Gong, M.N.; Bajwa, E.; Thompson, B.T.; Christiani, D.C. BCL2 genetic variants are associated with acute kidney injury in septic shock. Crit. Care Med. 2012, 40, 2116–2123. [Google Scholar] [CrossRef] [PubMed]
  29. Zhao, B.; Lu, Q.; Cheng, Y.; Belcher, J.M.; Siew, E.D.; Leaf, D.E.; Body, S.C.; Fox, A.A.; Waikar, S.S.; Collard, C.D.; et al. A Genome-Wide Association Study to Identify Single-Nucleotide Polymorphisms for Acute Kidney Injury. Am. J. Respir. Crit. Care Med. 2017, 195, 482–490. [Google Scholar] [CrossRef] [PubMed]
  30. Stafford-Smith, M.; Li, Y.-J.; Mathew, J.P.; Li, Y.-W.; Ji, Y.; Phillips-Bute, B.G.; Milano, C.A.; Newman, M.F.; Kraus, W.E.; Kertai, M.D.; et al. Genome-wide association study of acute kidney injury after coronary bypass graft surgery identifies susceptibility loci. Kidney Int. 2015, 88, 823–832. [Google Scholar] [CrossRef] [PubMed]
  31. Vilander, L.M.; Kaunisto, M.A.; Vaara, S.T.; Pettilä, V.; FINNAKI Study Group. Genetic variants in SERPINA4 and SERPINA5, but not BCL2 and SIK3 are associated with acute kidney injury in critically ill patients with septic shock. Crit. Care 2017, 21, 47. [Google Scholar] [CrossRef] [PubMed]
  32. Bhatraju, P.; Hsu, C.; Mukherjee, P.; Glavan, B.J.; Burt, A.; Mikacenic, C.; Himmelfarb, J.; Wurfel, M. Associations between single nucleotide polymorphisms in the FAS pathway and acute kidney injury. Crit. Care 2015, 19, 368. [Google Scholar] [CrossRef] [PubMed]
  33. Li, C.; Wu, J.; Li, Y.; Xing, G. Cytoprotective Effect of Heat Shock Protein 27 Against Lipopolysaccharide-Induced Apoptosis of Renal Epithelial HK-2 Cells. Cell. Physiol. Biochem. 2017, 41, 2211–2220. [Google Scholar] [CrossRef] [PubMed]
  34. Liu, J.; Li, G.; Li, L.; Liu, Z.; Zhou, Q.; Wang, G.; Chen, D. Surfactant protein-D (SP-D) gene polymorphisms and serum level as predictors of susceptibility and prognosis of acute kidney injury in the Chinese population. BMC Nephrol. 2017, 18, 67. [Google Scholar] [CrossRef]
  35. Schaalan, M.F.; Mohamed, W.A. Determinants of hepcidin levels in sepsis-associated acute kidney injury: Impact on pAKT/PTEN pathways? J. Immunotoxicol. 2016, 13, 751–757. [Google Scholar] [CrossRef] [PubMed]
  36. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [PubMed]
  37. Barrett, J.C.; Fry, B.; Maller, J.; Daly, M.J. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics 2005, 21, 263–265. [Google Scholar] [CrossRef] [PubMed]
  38. Purcell, S.; Cherny, S.S.; Sham, P.C. Genetic Power Calculator: Design of linkage and association genetic mapping studies of complex traits. Bioinformatics 2003, 19, 149–150. [Google Scholar] [CrossRef] [PubMed]
  39. Larach, D.B.; Engoren, M.C.; Schmidt, E.M.; Heung, M. Genetic variants and acute kidney injury: A review of the literature. J. Crit. Care 2018, 44, 203–211. [Google Scholar] [CrossRef] [PubMed]
  40. Ioannidis, J.P. Why most discovered true associations are inflated. Epidemiology 2008, 19, 640–648. [Google Scholar] [CrossRef] [PubMed]
  41. Lohmueller, K.E.; Pearce, C.L.; Pike, M.; Lander, E.S.; Hirschhorn, J.N. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat. Genet. 2003, 33, 177–182. [Google Scholar] [CrossRef] [PubMed]
  42. Kraft, P.; Zeggini, E.; Ioannidis, J.P.A. Replication in genome-wide association studies. Stat. Sci. 2009, 24, 561–573. [Google Scholar] [CrossRef] [PubMed]
  43. Teuffel, O.; Ethier, M.C.; Beyene, J.; Sung, L. Association between tumor necrosis factor-α promoter −308 A/g polymorphism and susceptibility to sepsis and sepsis mortality: A systematic review and meta-analysis. Crit. Care Med. 2010, 38, 276–282. [Google Scholar] [CrossRef] [PubMed]
  44. Christaki, E.; Giamarellos-Bourboulis, E.J. The beginning of personalized medicine in sepsis: Small steps to a bright future. Clin. Genet. 2014, 86, 56–61. [Google Scholar] [CrossRef] [PubMed]
  45. Pan, W.; Zhang, A.Q.; Yue, C.L.; Gao, J.W.; Zeng, L.; Gu, W.; Jiang, J.X. Association between interleukin-10 polymorphisms and sepsis: A meta-analysis. Epidemiol. Infect. 2015, 143, 366–375. [Google Scholar] [CrossRef] [PubMed]
  46. Ferrara, N.; Gerber, H.-P.; LeCouter, J. The biology of VEGF and its receptors. Nat. Med. 2003, 9, 669–676. [Google Scholar] [CrossRef] [PubMed]
  47. Bates, D.O. Vascular endothelial growth factors and vascular permeability. Cardiovasc. Res. 2010, 87, 262–271. [Google Scholar] [CrossRef] [PubMed]
  48. Renner, W.; Kotschan, S.; Hoffmann, C.; Obermayer-Pietsch, B.; Pilger, E. A common 936 C/T mutation in the gene for vascular endothelial growth factor is associated with vascular endothelial growth factor plasma levels. J. Vasc. Res. 2000, 37, 443–448. [Google Scholar] [CrossRef] [PubMed]
  49. Medford, A.R.L.; Keen, L.J.; Bidwell, J.L.; Millar, A.B. Vascular endothelial growth factor gene polymorphism and acute respiratory distress syndrome. Thorax 2005, 60, 244–248. [Google Scholar] [CrossRef] [PubMed]
  50. Zhai, R.; Gong, M.N.; Zhou, W.; Thompson, T.B.; Kraft, P.; Su, L.; Christiani, D.C. Genotypes and haplotypes of the VEGF gene are associated with higher mortality and lower VEGF plasma levels in patients with ARDS. Thorax 2007, 62, 718–722. [Google Scholar] [CrossRef] [PubMed]
  51. Nazir, N.; Siddiqui, K.; Al-Qasim, S.; Al-Naqeb, D. Meta-analysis of diabetic nephropathy associated genetic variants in inflammation and angiogenesis involved in different biochemical pathways. BMC Med. Genet. 2014, 15, 103. [Google Scholar] [CrossRef] [PubMed]
  52. Nechemia-Arbely, Y.; Barkan, D.; Pizov, G.; Shriki, A.; Rose-John, S.; Galun, E.; Axelrod, J.H. IL-6/IL-6R axis plays a critical role in acute kidney injury. J. Am. Soc. Nephrol. 2008, 19, 1106–1115. [Google Scholar] [CrossRef] [PubMed]
  53. Simmons, E.M.; Himmelfarb, J.; Sezer, M.T.; Chertow, G.M.; Mehta, R.L.; Paganini, E.P.; Soroko, S.; Freedman, S.; Becker, K.; Spratt, D.; et al. Plasma cytokine levels predict mortality in patients with acute renal failure. Kidney Int. 2004, 65, 1357–1365. [Google Scholar] [CrossRef] [PubMed]
  54. Gao, J.; Zhang, A.; Pan, W.; Yue, C.; Zeng, L.; Gu, W.; Jiang, J. Association between IL-6-174G/C polymorphism and the risk of sepsis and mortality: A systematic review and meta-analysis. PLoS ONE 2015, 10, e0118843. [Google Scholar] [CrossRef] [PubMed]
  55. Feng, Y.; Tang, Y.; Zhou, H.; Xie, K. A meta-analysis on correlation between interleukin-6 -174G/C polymorphism and end-stage renal disease. Ren. Fail. 2017, 39, 350–356. [Google Scholar] [CrossRef] [PubMed]
  56. Panayides, A.; Ioakeimidou, A.; Karamouzos, V.; Antonakos, N.; Koutelidakis, I.; Giannikopoulos, G.; Makaritsis, K.; Voloudakis, N.; Toutouzas, K.; Rovina, N.; et al. -572 G/C single nucleotide polymorphism of interleukin-6 and sepsis predisposition in chronic renal disease. Eur. J. Clin. Microbiol. Infect. Dis. 2015, 34, 2439–2446. [Google Scholar] [CrossRef] [PubMed]
  57. Agrawal, S.; Agarwal, S.; Naik, S. Genetic contribution and associated pathophysiology in end-stage renal disease. Appl. Clin. Genet. 2010, 3, 65–84. [Google Scholar] [CrossRef] [PubMed]
  58. Wuttke, M.; Köttgen, A. Insights into kidney diseases from genome-wide association studies. Nat. Rev. Nephrol. 2016, 12, 549–562. [Google Scholar] [CrossRef] [PubMed]
  59. O’Seaghdha, C.M.; Fox, C.S. Genome-wide association studies of chronic kidney disease: What have we learned? Nat. Rev. Nephrol. 2011, 8, 89–99. [Google Scholar] [CrossRef] [PubMed]
  60. Chen, J.; Lipska, B.K.; Halim, N.; Ma, Q.D.; Matsumoto, M.; Melhem, S.; Kolachana, B.S.; Hyde, T.M.; Herman, M.M.; Apud, J.; et al. Functional Analysis of Genetic Variation in Catechol-O-Methyltransferase (COMT): Effects on mRNA, Protein, and Enzyme Activity in Postmortem Human Brain. Am. J. Hum. Genet. 2004, 75, 807–821. [Google Scholar] [CrossRef] [PubMed]
  61. Axelrod, J. O-Methylation of Epinephrine and Other Catechols in vitro and in vivo. Science 1957, 126, 400–401. [Google Scholar] [CrossRef] [PubMed]
  62. Dellamea, B.; Leitão, C.; Friedman, R.; Canani, L. Nitric oxide system and diabetic nephropathy. Diabetol. Metab. Syndr. 2014, 6, 17. [Google Scholar] [CrossRef] [PubMed]
  63. Guo, H.; Bai, Y.; Xu, P.; Hu, Z.; Liu, L.; Wang, F.; Jin, G.; Wang, F.; Deng, Q.; Tu, Y.; et al. Functional promoter -1271G/C variant of HSPB1 predicts lung cancer risk and survival. J. Clin. Oncol. 2010, 28, 1928–1935. [Google Scholar] [CrossRef] [PubMed]
  64. Pang, Q.; Wei, Q.; Xu, T.; Yuan, X.; Lopez Guerra, J.L.; Levy, L.B.; Liu, Z.; Gomez, D.R.; Zhuang, Y.; Wang, L.-E.; et al. Functional Promoter Variant rs2868371 of HSPB1 Is Associated with Risk of Radiation Pneumonitis After Chemoradiation for Non-Small Cell Lung Cancer. Int. J. Radiat. Oncol. 2013, 85, 1332–1339. [Google Scholar] [CrossRef] [PubMed]
  65. Liang, L.; Liu, H.; Yue, J.; Liu, L.; Han, M.; Luo, L.; Zhao, Y.; Xiao, H. Association of Single-Nucleotide Polymorphism in the Hepcidin Promoter Gene with Susceptibility to Extrapulmonary Tuberculosis. Genet. Test. Mol. Biomark. 2017, 21, 351–356. [Google Scholar] [CrossRef] [PubMed]
  66. Parajes, S.; González-Quintela, A.; Campos, J.; Quinteiro, C.; Domínguez, F.; Loidi, L. Genetic study of the hepcidin gene (HAMP) promoter and functional analysis of the c.-582A > G variant. BMC Genet. 2010, 11, 110. [Google Scholar] [CrossRef] [PubMed]
Jcm 08 00342 g001 550
Figure 1. Study flowchart. Abbreviations: FINNAKI; Finnish Acute Kidney Injury; DNA, deoxyribonucleic acid; AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes.
Figure 1. Study flowchart. Abbreviations: FINNAKI; Finnish Acute Kidney Injury; DNA, deoxyribonucleic acid; AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes.
Jcm 08 00342 g001
Jcm 08 00342 g002 550
Figure 2. Odds ratios (OR) and confidence intervals (95% CI) for the minor allele for all the studied polymorphisms. For variants in green, there are several previous studies and both alleles have been reported to associate with AKI, variants in blue have been reported with same risk allele, variants in red have been reported with opposite risk allele, and variants in orange have not been previously reported in association to AKI.
Figure 2. Odds ratios (OR) and confidence intervals (95% CI) for the minor allele for all the studied polymorphisms. For variants in green, there are several previous studies and both alleles have been reported to associate with AKI, variants in blue have been reported with same risk allele, variants in red have been reported with opposite risk allele, and variants in orange have not been previously reported in association to AKI.
Jcm 08 00342 g002
Table
Table 1. Demographics of altogether 2647 patients in the FINNAKI genetic substudy after excluding patients with maintenance dialysis. Data are presented according to presence of severe AKI (KDIGO stage 2 or 3, n = 625), presence of all stage AKI (KDIGO stage 1, 2, 3, n = 1065), or absence of AKI (KDIGO stage 0, n = 1582).
Table 1. Demographics of altogether 2647 patients in the FINNAKI genetic substudy after excluding patients with maintenance dialysis. Data are presented according to presence of severe AKI (KDIGO stage 2 or 3, n = 625), presence of all stage AKI (KDIGO stage 1, 2, 3, n = 1065), or absence of AKI (KDIGO stage 0, n = 1582).
CharacteristicsData AvailableAKINo AKIp *
KDIGO
stage 2 or 3		KDIGO
stage 1, 2, 3
Age (years)264765 (54–74)65 (54–75)62 (48–72)<0.001
Gender (male)2647409 (65.4)700 (65.7)980 (61.9)0.130
BMI (kg/m2)262727.5
(24.5–31.3)		27.5
(24.2–31.2)		25.7
(23.1–28.7)		<0.001
Co-morbidities
Arterial hypertension2633333 (53.3)561 (52.9)641 (40.8)<0.001
Diabetes2643169 (27.0)263 (24.7)280 (17.7)<0.001
Arteriosclerosis262394 (15.1)159 (15.0)160 (10.2)0.002
Chronic obstructive pulmonary disease263043 (6.9)81 (7.7)136 (8.6)0.195
Chronic liver disease261746 (7.4)59 (5.6)51 (3.3)<0.001
Systolic heart failure262879 (12.7)129 (12.2)139 (8.8)0.009
Baseline plasma creatinine (µmol/L)264381.0
(68.9–94.0)		81.0
(69.0–94.0)		79.0
(68.0–94.0)		0.210
Pre ICU daily medication
ACE inhibitor or ARB2585263 (42.8)428 (41.1)475 (30.8)<0.001
NSAID253873 (12.1)112 (10.9)118 (7.8)0.002
Diuretic2596185 (29.8)324 (30.8)323 (20.9)<0.001
Metformin2606109 (17.6)163 (15.5)164 (10.6)<0.001
Statin2603196 (31.6)320 (30.5)397 (25.6)0.005
Corticosteroids261456 (9.0)94 (8.9)105 (6.7)0.070
Warfarin2608107 (17.2)166 (15.8)179 (11.5)0.001
Treatments administered 48 h before admission
Contrast medium2632120 (19.3)223 (21.1)417 (26.5)<0.001
ACE inhibitor or ARB2601167 (27.3)287 (27.5)329 (21.1)0.002
Diuretics2570217 (35.8)353 (34.3)360 (23.4)<0.001
Colloids (gelatin or starch)2479229 (38.3)395 (39.0)394 (26.9)<0.001
Albumin258414 (2.3)18 (1.7)14 (0.9)0.018
Type of admission
Operative2646180 (28.8)343 (32.2)557 (35.2)0.004
Cardiac surgery264735 (5.6)80 (7.5)147 (9.3)0.004
Emergency2621575 (92.6)962 (91.1)1386 (88.6)0.005
SAPS II score 24 h without renal or age components261424.0
(16.0–34.0)		24.0
(16.0–32.0)		20.0
(13.0–29.3)		<0.001
Mechanical ventilation2647432 (69.1)776 (72.9)1031 (65.2)0.080
Sepsis2647309 (49.4)500 (46.9)362 (22.9)<0.001
White blood cell count at admission, max (109/L)218612.0
(8.3–17.4)		11.7
(8.2–16.8)		10.9
(7.8–15.3)		<0.001
Platelet count at admission, min (109/L)2419190.0
(116.5–263.5)		194.0
(127.0–265.0)		205.0
(153.0–268.0)		<0.001
* Comparison of No AKI to KDIGO stages 2 or 3 AKI. The p-values are calculated with Fisher’s exact test for categorical variables and with Mann–Whitney U test for continuous variables. Data presented as medians and interquartile ranges for continuous variables, and absolute counts and percentages for categorical variables. Abbreviations: FINNAKI; Finnish Acute Kidney Injury; AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes; BMI, body mass index; ICU, intensive care unit; ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; NSAID, nonsteroidal anti-inflammatory drug; SAPS II, simplified acute physiology score II.
Table
Table 2. Association of genetic variants with acute kidney injury (AKI) KDIGO stages 2 and 3 compared to stage 0. Odds ratios (OR) and confidence intervals (95% CI) are reported for each copy of minor allele.
Table 2. Association of genetic variants with acute kidney injury (AKI) KDIGO stages 2 and 3 compared to stage 0. Odds ratios (OR) and confidence intervals (95% CI) are reported for each copy of minor allele.
GeneSNPPatientsMinor AlleleMAF (Cases/Controls)Additive Logistic OR95% CIp
TNFArs18006292174A0.15/0.141.060.89–1.280.51
IL10rs18008962173G0.44/0.460.920.80–1.050.20
IL6rs104995632192C0.15/0.141.070.90–1.280.45
rs18007962197C0.03/0.030.880.60–1.290.51
rs18007952189G0.47/0.471.000.88–1.140.97
rs14743472187A0.47/0.471.000.88–1.141.00
rs133064352199A0.03/0.030.910.62–1.330.62
CXCL8rs40732193A0.42/0.420.930.82–1.070.31
NOS3rs20707442174C0.34/0.360.940.82–1.080.37
NFKB1Ars10508512196A0.16/0.170.950.79–1.130.54
AGTrs6991047C0.43/0.421.030.85–1.240.78
rs24931332196C0.41/0.420.940.82–1.070.36
VEGFArs20109632170C0.22/0.240.910.78–1.060.22
rs30250392195T0.16/0.141.201.01–1.440.044
EPOrs16176402173G0.44/0.450.990.87–1.130.91
SUFUrs107488252174G0.37/0.371.020.88–1.170.83
HIF1Ars115494652173T0.05/0.041.190.87–1.620.28
PNMTrs8764932173C0.36/0.380.940.82–1.080.37
MPOrs72086932174A0.10/0.120.880.71–1.090.23
COMTrs46802173G0.47/0.451.060.93–1.210.39
HSPB1rs28683712194G0.19/0.210.890.76–1.050.18
SFTPDrs22436392199T0.39/0.400.950.83–1.090.47
rs7219172193G0.39/0.391.000.88–1.150.97
HAMPrs104217682199G0.23/0.250.880.75–1.030.11
BBS9rs102629952200T0.04/0.040.980.71–1.380.93
Abbreviations: AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes; SNP, single nucleotide polymorphism; MAF, minor allele frequency; OR, odds ratio; CI, confidence interval; TNFA, tumor necrosis factor alpha; IL10, interleukin 10; IL6, interleukin 6; CXCL8, interleukin 8; NOS3, nitric oxide synthase 3; NFKB1A, nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha; AGT, angiotensinogen; VEGFA, vascular endothelial growth factor; EPO, erythropoietin; SUFU, suppressor of fused homolog; HIF1A, hypoxia-inducible factor 1-alpha; PNMT, phenylethanolamine N-methyltransferase; MPO, myeloperoxidase; COMT, catechol-O-methyltransferase; HSPB1, heat shock protein family B (small) member 1; SFTPD, surfactant protein D; HAMP, hepcidin antimicrobial peptide; BBS9, Bardet–Biedl syndrome 9.
Table
Table 3. Number of patients (percentage) with TNFA rs1800629 and IL10 rs1800896 genotype combinations. Genotypes are grouped according to the reported effect in protein production. Acute kidney injury (AKI) KDIGO stages 2 and 3 (cases) compared to stage 0 (controls).
Table 3. Number of patients (percentage) with TNFA rs1800629 and IL10 rs1800896 genotype combinations. Genotypes are grouped according to the reported effect in protein production. Acute kidney injury (AKI) KDIGO stages 2 and 3 (cases) compared to stage 0 (controls).
Genotype CombinationAKI (n = 615)No AKI (n = 1558)
TNFA GG + IL10 AA138 (22%)340 (22%)
TNFA GG + IL10 GA + GG311 (51%)814 (52%)
TNFA GA + AA + IL10 AA52 (8%)116 (8%)
TNFA GA + AA + IL10 GA + GG114 (19%)288 (18%)
Abbreviations: TNFA, tumor necrosis factor alpha; IL10, interleukin 10; AKI, acute kidney injury; KDIGO, Kidney Disease: Improving Global Outcomes; OR, odds ratio. TNFA GG: TNF-α low producer; IL10 AA: IL-10 low producer; IL10 GA + GG: IL-10 intermediate + high producer; TNFA GA + AA: TNF-α high producer.

Share and Cite

MDPI and ACS Style

Vilander, L.M.; Vaara, S.T.; Kaunisto, M.A.; Pettilä, V.; Study Group, T.F. Common Inflammation-Related Candidate Gene Variants and Acute Kidney Injury in 2647 Critically Ill Finnish Patients. J. Clin. Med. 2019, 8, 342. https://doi.org/10.3390/jcm8030342

AMA Style

Vilander LM, Vaara ST, Kaunisto MA, Pettilä V, Study Group TF. Common Inflammation-Related Candidate Gene Variants and Acute Kidney Injury in 2647 Critically Ill Finnish Patients. Journal of Clinical Medicine. 2019; 8(3):342. https://doi.org/10.3390/jcm8030342

Chicago/Turabian Style

Vilander, Laura M., Suvi T. Vaara, Mari A. Kaunisto, Ville Pettilä, and The FINNAKI Study Group. 2019. "Common Inflammation-Related Candidate Gene Variants and Acute Kidney Injury in 2647 Critically Ill Finnish Patients" Journal of Clinical Medicine 8, no. 3: 342. https://doi.org/10.3390/jcm8030342

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop