Next Article in Journal
Comparing Outcomes in Asymptomatic and Symptomatic Atrial Fibrillation: A Systematic Review and Meta-Analysis of 81,462 Patients
Next Article in Special Issue
Therapeutic Advances in Diabetic Nephropathy
Previous Article in Journal
Foot Revascularization Avoids Major Amputation in Persons with Diabetes and Ischaemic Foot Ulcers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 10
Issue 17
10.3390/jcm10173980
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

Genetics Variants in the Epoxygenase Pathway of Arachidonic Metabolism Are Associated with Eicosanoids Levels and the Risk of Diabetic Nephropathy

by
Sonia Mota-Zamorano
1,
Nicolás R. Robles
2,
Luz M. González
1,
José M. Valdivielso
3,
Juan Lopez-Gomez
4,
Bárbara Cancho
2,
Guadalupe García-Pino
5 and
Guillermo Gervasini
1,*
1
Department of Medical and Surgical Therapeutics, Division of Pharmacology, Medical School, University of Extremadura, 06071 Badajoz, Spain
2
Service of Nephrology, Badajoz University Hospital, 06071 Badajoz, Spain
3
Vascular and Renal Translational Research Group, UDETMA, ISCIII REDinREN, IRBLleida, 25198 Lleida, Spain
4
Service of Clinical Analyses, Badajoz University Hospital, 06071 Badajoz, Spain
5
Zafra Hospital, 8105 Zafra, Spain
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(17), 3980; https://doi.org/10.3390/jcm10173980
Submission received: 16 August 2021 / Revised: 30 August 2021 / Accepted: 30 August 2021 / Published: 2 September 2021
(This article belongs to the Special Issue Update on Diabetic Nephropathy: Recent Progress and Future Perspective)
Versions Notes

Abstract

:
Genes in the epoxygenase pathway of arachidonic acid metabolism leading to vasoactive eicosanoids, mainly 20-hydroxyeicosatetraenoic (20-HETE) and epoxyeicosatrienoic (EETs) acids, have been related to glucose-induced renal damage in preclinical reports. We genotyped 1088 diabetic kidney disease (DKD) patients and controls for seven polymorphisms in five genes (CYP2C8, CYP2J2, CYP4F2, CYP4A11, and EPHX2) along this metabolic route and evaluated their effect on DKD risk, clinical outcomes, and the plasma/urine levels of eicosanoids measured by LC/MS/MS and immunoenzymatic assays. The CYP4F2 433M variant allele was associated with lower incidence of DKD (OR = 0.65 (0.48–0.90), p = 0.008), whilst the CYP2C8*3/*3 genotype was related to increased risk (OR = 3.21 (1.05–9.87), p = 0.036). Patients carrying the 433M allele also showed lower eGFR [median and interquartile range vs. wildtype carriers: 30.8 (19.8) and 33.0 (23.2) mL/min/1.73 m2, p = 0.037). Finally, the 433VM/MM variant genotypes were associated with lower urinary levels of 20-HETE compared with 433VV (3.14 (0.86) vs. 8.45 (3.69) ng/mg Creatinine, p = 0.024). Our results indicate that the CYP4F2 V433M polymorphism, by decreasing 20-HETE levels, may play an important role in DKD.

1. Introduction

The incidence of chronic kidney disease (CKD) has greatly increased in recent years to become a global healthcare issue. CKD is suffered by 10% of the world’s population and it will be one of the most frequent causes of death by 2040 [1]. The greatest contributor to CKD is diabetic kidney disease (DKD), which refers to pathologic structural and functional changes seen in the kidneys of patients with diabetes mellitus (DM). At least 40% of patients with type 2 DM will develop DKD [2], which accounts for over 50% of individuals who require a dialysis treatment and/or renal transplant in some parts of the world [3].
Proteinuria has traditionally been considered the hallmark of DKD [4]. Other indicators include decreased glomerular filtration rate (GFR) or elevated arterial blood pressure [5], which altogether can help the nephrologist to establish a diagnosis that can later be histologically confirmed. However, it is now known that there is a significant proportion of subjects with DKD who, in spite of decreased GFR, do not have elevated concentrations of proteins in urine [6,7]. Therefore, the necessity of new, universal, and more specific biomarkers of DKD has been repeatedly pointed out [8,9].
Several in vitro and animal studies indicate that the cytochrome P450 (CYP450)-mediated epoxygenase pathway of arachidonic acid (AA) (Figure S1) plays a major role in the onset of diabetes and its renal complications [10,11,12]. In addition, our group has reported that the levels of eicosanoids generated in this metabolic route, of which EETs and 20-HETE are the most important, are associated with the risk of DKD and modulate parameters of renal function in these patients [13].
Genetics is receiving increasing attention regarding its role in DKD [14]. In this regard, we have previously shown that the onset of post-transplant diabetes mellitus in renal transplant recipients is dependent on the presence of variants in genes expressed in the kidney that participate in the epoxygenase pathway [15]. In addition, these polymorphisms have also been related to the development of renal injury [16,17], and cardiovascular (CV) disease [18], whose risk is greatly elevated in DKD patients. However, in spite of this background, the influence of genetic polymorphisms in the AA epoxygenase route on DKD remains untested.
Our aim was to determine whether seven functional, common polymorphisms in the five genes mediating this metabolic pathway (CYP2J2, CYP2C8, CYP4A11, CYP4F2, and EPHX2) are involved in the incidence of DKD and/or clinical outcomes in these patients. In search of a mechanistic explanation for the putative SNP-disease associations, we also aimed to establish whether these variants could modulate the levels of vasoactive eicosanoids measured in plasma and/or urine.

2. Patients and Methods

2.1. Study Subjects

A total of 1088 subjects were included in this study, 430 patients with DKD (stage 3 or higher) and 658 individuals with normal renal function. Participants were obtained from the Nephrology Service and the Hypertension Unit of the Badajoz University Hospital, where they were recruited over a four-year period (2017–2020), and from the NEFRONA repository, which archives biological samples that were collected in a former multicenter study of CV morbidity and mortality in Spanish CKD patients and healthy subjects [19].
Patients were over 18 years of age and had had type 2 diabetes (fasting glucose > 126 mg/dL or non-fasting glucose > 200 mg/dL) prior to kidney damage (eGFR < 60 mL/min and albuminuria). Overt albuminuria was considered when albumin excreted in urine over 24 h was higher than 300 mg. Microalbuminuria was defined as 30–300 mg albumin in 24-h urine. DKD was diagnosed histologically or attending to clinical criteria, i.e., the presence of both albuminuria and retinopathy after excluding other probable causes. A biopsy was carried out for confirmation of the DKD diagnosis when proteinuria was higher than 1 g/24 h. Prognostic stratification of patients was conducted with the KDIGO classification and the CONSORTIUM-CKD equation (Kidney Risk Failure; www.kidneyriskfailure.org, accessed on 15 April 2021). A modification of the Diet in Renal Disease (MDRD) equation was used to estimate renal function, as it has been shown to give reliable estimates of the GFR slope in patients with CKD [20]. CV risk was defined as the likelihood of experiencing a fatal or non-fatal CV event in the 4-year follow-up (54 months). CV events included acute myocardial infarction, acute coronary syndrome, coronary catheterization requiring angioplasty, coronary bypass, typical angina with positive stress tests, sudden death, cerebrovascular accident, peripheral arterial disease, and aortic aneurisma. Age- and sex-matched controls with eGFR > 60 mL/min/1.73 m2 were recruited from the Badajoz University Hospital or from primary care centers throughout the country (in the case of samples obtained from the NEFRONA cohort). Exclusion criteria for participation in the study included previous history of any CV event, transplantation of any organ, carotid artery surgery, active infection, pregnancy, or life expectancy below one year.
This study was approved by the Ethics Committee of the Badajoz University Hospital, and it was carried out in accordance with the Declaration of Helsinki and its subsequent revisions. All subjects gave written informed consent for their participation.

2.2. Genetic Analysis

Twelve-mL blood samples were extracted from the participants and DNA was subsequently purified by a standard phenol-chloroform extraction method. In the case of the subjects recruited in the former NEFRONA study, DNA was obtained from biological samples stored at the REDinREN biobank [21] with QIAamp DNA Blood kits following the manufacturer’s instructions. Seven SNPs in the five genes of the epoxygenase pathway in AA metabolism were searched for, namely CYP2J2*7 (rs890293), CYP2C8*3 (rs10509681), CYP4F2 V433M (rs2108622), CYP4A11 F434S (rs1126742), EPHX2 3′UTR A>G (rs1042032), EPHX2 R287Q (rs751141), and EPHX2 K55R (rs41507953). Genotyping was carried out by real-time PCR using commercial TaqMan® SNP genotype assays from Thermofisher Scientific (Rockford, Il, USA). These SNPs were selected based on previous reports stating their influence on clinical outcomes [18,22,23,24,25,26].

2.3. Determination of Plasma and Urinary Levels of Eicosanoids

Vasoactive eicosanoids, namely 20-HETE, 14,15-DHET, and 11,12-DHET, could be measured in a group of 334 patients and controls of whom plasma samples were available. Since 20-HETE urinary excretion has been suggested to be a marker of human disease [13,27,28], we also measured concentrations of this metabolite in urine after correcting for renal function (ng 20-HETE/mg Creatinine (Cr)). DHETs, the direct product of EETs biotransformation by EPHX2, are far easier to quantify than their analogous EETs, which disappear rapidly from the biological matrix, and hence they were used as surrogates as described elsewhere [25,29]. For plasma determinations, we processed 0.5-mL aliquots by solid-phase extraction in Hypersep Retain Pep 60 mg 3 mL S columns (Thermofisher Scientific, Waltham, MA, USA). Quantification of the AA metabolites was carried out by mass spectrometry coupled to liquid chromatography (LC/MS/MS) using a UHPLC 1290 system with a 6460 Jet Stream triple quadrupole mass detector (Agilent Technologies, Santa Clara, CA, USA), as previously described [30]. Glucuronidated and free 20-HETE concentrations were also determined in urine by a beta-glucuronidase competitive immunoenzymatic assay kit (Abcam, Cambridge, UK). Inter- and intra-assay variation tests results were below 15 and 10%, respectively. A previous digestion was performed with beta-glucuronidase to allow the identification of conjugated 20-HETE. In brief, samples and standards were diluted and the conjugate was added to the wells as per the manufacturer’s instructions. Following incubation and washing procedures, TMB substrate was added before a final incubation at room temperature. 2N sulphuric acid was used to stop the reaction and the plates were read at 450 nm with a Biotek ELx808 plate reader (Biotek Instruments Inc., Winooski, VT, USA).

2.4. Statistical Analyses

Pearson’s X2 or Fisher’s exact were used to identify differences between categorical variables. Differences between quantitative variables were evaluated with the Student’s t/ANOVA or Mann–Whitney/Kruskal–Wallis tests, as appropriate. Binary logistic regression was carried out to evaluate the impact of the SNPs on the risk of DKD controlling for confounding variables as formerly described [13]. Multivariate linear regression analyses were performed to analyze the influence of genetics on renal parameters. Albumin-to-creatinine ratios (ACR) and eGFR values were log-transformed before their inclusion in the models. Relevant covariates used in the models included sex, age, weight, CKD stage, hyperlipidemia, and hypertension. The association of the different genotypes with CV event-free survival was assessed by Kaplan–Meier curves and the results were compared with the log-rank test. Patients were followed up until the earliest CV event, death, or end of the study. Statistical analyses were performed with the IBM SPSS statistics 22 (Chicago, IL, USA).

3. Results

Table 1 summarizes demographic and clinical characteristics of the study participants. All the SNPs studied were in Hardy–Weinberg equilibrium and showed frequencies that were similar to those reported in 1000 genomes (www.internationalgenome.org, accessed on 15 April 2021) for the Iberian population in Spain (Table 2). Genotyping was successful in 98.6% of the samples.

3.1. Case-Control Study

We compared the distribution of the studied polymorphisms between the 430 patients with stage 3 or higher DKD and 658 individuals with normal renal function (eGFR > 60 mL/min/1.73 m2). After adjusting for confounding variables, carriers of the CYP4F2 433M variant allele were found to be at lower risk of developing DKD (OR = 0.65 (0.48–0.90), p = 0.008, Table 3). The other significant association was observed for CYP2C8*3. Carriers of the homozygous variant genotype showed increased susceptibility to the disease (OR = 3.21 (1.05–9.87), p = 0.036, Table 3). None of the other SNPs showed a relevant relation to DKD risk.
A small subset (n = 65) of the control subjects without renal impairment had diabetes. The comparison of these individuals with the patient group resulted again in the CYP4F2 433 VM/MM genotypes being associated with a lower risk of DKD (OR = 0.42 (0.22–0.80), p = 0.006; Table 4). In addition, for this subgroup, the EPHX2 55KR/RR variant genotypes were also inversely related to DKD risk (OR = 0.39 (0.21–0.76), p = 0.006, Table 4). No significant results were obtained with the recessive model of inheritance (not shown in Table 4).

3.2. Effect of SNPs on Renal Parameters of DKD Patients

First, we examined the effect of the studied SNPs on the renal function of the DKD patients who were not on dialysis at the time of the study. After controlling for meaningful covariates, carriers of the 433M variant allele displayed significantly lower eGFR than wild-type subjects (median (and interquartile range) for VM/MM vs. VV genotypes were, respectively, 30.8 (19.8) vs. 33.0 (23.2) mL/min/1.73 m2, p = 0.037, Table 5). In contrast, the analysis of ACR values did not reveal any relevant associations (Table 5).

3.3. Analysis of Cardiovascular Risk in the DKD Cohort

The median follow-up time of the DKD patients was 47 months (range 7–54). In this period, a total of 92 patients (21.4%) experienced CV events. Kaplan–Meier analyses did not show any significant associations between the studied SNPs and cumulative CV event-free survival. Figure S2 depicts survival curves and log-rank test results for all the SNPs.

3.4. Association between Eicosanoids Levels and Epoxygenases Polymorphisms

Concentrations of vasoactive eicosanoids were assessed in a subset of 132 DKD patients and 202 controls. In a previous study [13], we had established that these concentrations were different between DKD patients and non-diabetic subjects, and therefore, we analyzed the influence of genetics in both groups separately. No associations were observed in the control group (Table S1). In the patient group, however, carriers of the CYP4F2 433M variant allele displayed significantly lower urinary levels of 20-HETE corrected by creatinine values than wild-type patients did. Mean (and standard error) values of 433M carriers vs. non carriers were, respectively, 3.14 (0.86) vs. 8.45 (3.69) ng/mg Cr, p = 0.024 (Table 6).

4. Discussion

In the last years, we and others have pointed out the importance of vasoactive eicosanoids and their encoding genes in the cardiorenal function of renal patients and kidney transplant recipients [13,18,26,31,32,33,34]. Furthermore, preclinical reports indicate that these compounds are closely related (directly in the case of 20-HETE or inversely in the case of EETs) to the damage induced by hyperglycemia on renal cells [11,12,35,36]. Altogether, these data support the hypothesis tested in the present work, namely that genetic variants in the AA epoxygenase metabolism may be relevant for DKD risk and outcomes.
The results of the risk analysis showed that carriers of a valine (V)-to-methionine (M) substitution in amino acid 433 of CYP4F2 (CYP4F2*3, G1347A) had increased susceptibility to DKD. CYP4F2 is responsible for the synthesis of 20-HETE [37] and there is evidence in cell cultures and animal models indicating that 20-HETE plays a key role in the etiopathology of type 2 DM through the impairment of insulin signaling [38,39]. Moreover, in vitro reports show that 20-HETE is an important mediator of hyperglycemia-mediated kidney injury through several mechanisms [11,12,40], and CYP4 inhibitors and 20-HETE antagonists have been suggested to hold an important therapeutic potential in the treatment of diabetic complications [11]. Moving on to clinical studies, we recently reported that DKD patients had reduced excretion of 20-HETE in urine in comparison with control subjects, suggesting an accumulation of this eicosanoid in tissues where CYP4F2 is highly expressed such as the kidney [13]. Since the 433M allele has been shown to significantly decrease 20-HETE synthesis in vitro [41,42], we hypothesized that the decreased DKD risk observed in carriers of this variant may obey to a deactivation of the aforementioned pathological mechanisms mediated by 20-HETE. Indeed, our analysis of samples obtained from DKD patients confirmed this hypothesis, as carriers of the variant M-allele had lower levels of 20-HETE after correcting for renal function. This is the first time to our knowledge that the consequences of SNPs in the AA epoxygenase pathway have been tested in vivo.
On the other hand, in a previous study on renal transplant recipients, we had observed that V433M SNP was associated with the onset of post-transplant diabetes mellitus (PTDM) [15]. Although it is remarkable that the same SNP was also pinpointed in this cohort, the 433M allele was related to an increased risk of PTDM in this case. Causes for this discrepancy may obey the existence of different mechanisms in the etiology of PTDM and DKD. Thus, 20-HETE has been suggested to be able to exert opposite functions in kidney homeostasis depending on the cell type that produces and/or targets this eicosanoid [40].
Interestingly, the analysis of the patient cohort showed that the same CYP4F2 V433M SNP that decreased the susceptibility to DKD was also related to lower eGFR values. It is tempting to speculate that these two findings might be related. Carriers of the 433M variant (at lower risk) would theoretically produce less 20-HETE resulting in a reduction of the vasoconstrictor activity in renal tissue [43]. In turn, this would alleviate glomerular capillary pressure causing the observed reduction in filtration. This protective mechanism would be similar to that shown by drugs used in CKD, such as RAAS blockers [44] or SGLT2 inhibitors [45]. These medications lower single nephron GFR and reduce the proximal tubular workload, therefore, protecting tubules that may already be compromised by decreased oxygen availability because of the capillary rarefaction characteristics of CKD [46].
The other SNP that was associated with DKD risk was CYP2C8*3, as homozygous carriers displayed higher susceptibility to the disease. CYP2C8 is the main enzyme responsible for EETs synthesis in the kidney and it is known that the *3 variant significantly reduces the biotransformation of AA to these eicosanoids both in vitro [47] and in vivo [48]. EETs are basically renoprotective compounds [49,50] that, amongst other functions, display vasodilator and anti-inflammatory properties [33,51]. Therefore, it is likely that carriers of the *3/*3 genotype have less endogenous resources to counteract hyperglycemia-induced damage in renal cells. We could not confirm a difference in plasma levels of DHETs between CYP2C8 genotypes, although a peripheral measurement of these levels might not reflect the concentrations in renal tissue.
An interesting finding was that CYP4F2 433M and EPHX2 55R allelic variants were inversely related to DKD risk when only a subset of diabetic subjects with normal renal function was considered as the control group. The fact that the V433M SNP, which reduces 20-HETE synthesis, still shows a relevant effect when diabetes is taken out of the equation and only filtration is considered, indicates that 20-HETE must be an important factor for renal function also in mechanisms independent from glucose actions. In this regard, Gangadhariah et al. reported that hypertension is a major contributor to the deleterious actions of 20-HETE in DKD [40]. On the other hand, the protective effect of the EPHX2 55R variant was a surprising result, as this polymorphism has been associated with increased soluble epoxyhydrolase activity (sEH) [48,52], which would presumably lead to a reduction in the concentration of renoprotective EETs. Therefore, we do not have a plausible explanation for the OR value observed for this SNP other than there could be an interaction with other functional EPHX2 SNPs leading to a different net effect on EETs levels, as it has been recently reported [48]. In any case, and in spite of its effect on sEH activity, the impact of this the EPHX2 K55R SNP on EETs levels has been shown to be negligible or small at best [48,53].
These genes mediating AA metabolism have long been claimed to affect cardiovascular function [54]. We have previously reported that both CYP2J2*7 and CYP2C8*3 are related to the occurrence of CV events in renal transplant recipients without a previous CV history [18]. In contrast, in the present DKD cohort, we could not identify any relevant CV associations, which could be due to phenotypic differences between the study populations (renal patients vs. renal recipients). Indeed, even though both populations are groups with a high incidence of CV disease, the occurrence of CV events in the DKD cohort was much more frequent than in the previous transplant cohort (21.4 vs. 11.0 %), in spite of the much longer follow-up of the latter.
A limitation of this study was that the sample size was relatively small, particularly in the subgroup of diabetic individuals with normal renal function. In addition, 20-HETE/Cr determinations could not be carried out in subjects on dialysis (as there was no urine available). Finally, the observed association between 433M and low 20-HETE/Cr ratios in urine was absent in the control group. On this last aspect, we would need a deeper knowledge of the true role of 20-HETE in the cardiorenal function to explain why the 433M variant did not show a significant effect in controls. Other factors present in diabetic patients with renal impairment must contribute to the observed differences. For instance, an overexpression of CYP4F2 in renal tissue producing more 20-HETE in DKD patients [12,55] could make mild differences due to genetic variants being easier to detect than in non-diabetic individuals.
Currently, the development of new treatments for DKD is stalling for a variety of reasons, of which the lack of new predictive and prognostic biomarkers for a more accurate patient stratification is particularly important. In the present work, we have tested the hypothesis that genetic variants in the epoxygenase pathway of AA metabolism may be relevant in this regard. Our results show that the V433M SNP in the CYP4F2 gene, which is responsible for the synthesis of 20-HETE, a major mediator in hyperglycemia-induced renal cell damage, is associated with a decreased risk of DKD and low eGFR values in these patients, which suggests this could be a useful marker in the diagnosis and follow-up of DKD patients. A reduction in the synthesis of 20-HETE could be the mechanism explaining the observed associations. Our results add to the existing body of recent evidence pointing to this metabolic route as a promising therapeutic drug target in DKD.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/jcm10173980/s1, Figure S1: Epoxygenase pathway of arachidonic acid metabolism, Figure S2: Cumulative event-free survival for patients with diabetic kidney disease according to the different genotypes considered, Table S1. Influence of polymorphisms in the epoxygenase pathway on the plasma and urinary levels of vasoactive eicosanoids shown by subjects with normal renal function.

Author Contributions

S.M.-Z. recruited patients, carried out genetic analyses, and drafted the manuscript; N.R.R. and J.M.V. helped with study design and the clinical evaluation of the patients; L.M.G. carried out statistical analyses; J.L.-G. participated in sample collection and clinical analyses; B.C. and G.G.-P. collaborated in the recruitment and follow-up of the patients; G.G. designed the study, searched for funding, and wrote the final version of the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by grant PI18/00745 from Instituto de Salud Carlos III, Madrid (Spain), grant IB16014 from Junta de Extremadura, Mérida (Spain), Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa” and grant GR18007 from Consejería de Economía e Infraestructuras, Junta de Extremadura, Mérida (Spain). These sources of funding had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Clinical Research Ethics Committee of Badajoz University Hospital (protocol code 18002900) on 6 July 2016.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

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

Acknowledgments

The authors would like to thank María Dolores López Moreno for her help in the recruitment phase of the study. In the same manner, we thank the NEFRONA team, the REDinREN Biobank, and the members of the Centro Nacional de Genotipado-Instituto de Salud Carlos III (CEGen) for their invaluable technical support.

Conflicts of Interest

The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.

Abbreviations

20-HETE, 20-hydroxyeicosatetraenoic acid; ACR, albumin-to-creatinine ratio; CKD, chronic kidney disease; DHETs, dihydroxyeicosatrienoic acids; DKD, diabetic kidney disease; EETs, epoxyeicosatrienoic acids; eGFR, estimated glomerular filtration rate; IQR, interquartile range; LC/MS/MS, mass spectrometry coupled to liquid chromatography; MDRD, modification of diet in renal disease; SNP, single nucleotide polymorphism.

References

  1. Foreman, K.J.; Marquez, N.; Dolgert, A.; Fukutaki, K.; Fullman, N.; McGaughey, M.; Pletcher, M.A.; Smith, A.E.; Tang, K.; Yuan, C.-W.; et al. Forecasting life expectancy, years of life lost, and all-Cause and cause-Specific mortality for 250 causes of death: Reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet 2018, 392, 2052–2090. [Google Scholar] [CrossRef] [Green Version]
  2. Pugliese, G.; Penno, G.; Natali, A.; Barutta, F.; Di Paolo, S.; Reboldi, G.; Gesualdo, L.; De Nicola, L. Diabetic kidney disease: New clinical and therapeutic issues. Joint position statement of the Italian Diabetes Society and the Italian Society of Nephrology on “The natural history of diabetic kidney disease and treatment of hyperglycemia in patients with type 2 diabetes and impaired renal function”. J. Nephrol. 2020, 33, 9–35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Warren, A.; Knudsen, S.T.; Cooper, M.E. Diabetic nephropathy: An insight into molecular mechanisms and emerging therapies. Expert Opin. Ther. Targets 2019, 23, 579–591. [Google Scholar] [CrossRef] [PubMed]
  4. Furuichi, K.; Shimizu, M.; Hara, A.; Toyama, T.; Wada, T. Diabetic Nephropathy: A Comparison of the Clinical and Pathological Features between the CKD Risk Classification and the Classification of Diabetic Nephropathy 2014 in Japan. Intern. Med. 2018, 57, 3345–3350. [Google Scholar] [CrossRef] [Green Version]
  5. Ahmad, J. Management of diabetic nephropathy: Recent progress and future perspective. Diabetes Metab. Syndr. Clin. Res. Rev. 2015, 9, 343–358. [Google Scholar] [CrossRef]
  6. Zoccali, C.; Mallamaci, F. Nonproteinuric progressive diabetic kidney disease. Curr. Opin. Nephrol. Hypertens. 2019, 28, 227–232. [Google Scholar] [CrossRef]
  7. Yamanouchi, M.; Furuichi, K.; Hoshino, J.; Toyama, T.; Hara, A.; Shimizu, M.; Kinowaki, K.; Fujii, T.; Ohashi, K.; Yuzawa, Y.; et al. Nonproteinuric Versus Proteinuric Phenotypes in Diabetic Kidney Disease: A Propensity Score–Matched Analysis of a Nationwide, Biopsy-Based Cohort Study. Diabetes Care 2019, 42, 891–902. [Google Scholar] [CrossRef] [Green Version]
  8. Currie, G.; McKay, G.; Delles, C. Biomarkers in diabetic nephropathy: Present and future. World J. Diabetes 2014, 5, 763–776. [Google Scholar] [CrossRef]
  9. Robles, N.R.; Villa, J.; Gallego, R.H. Non-Proteinuric Diabetic Nephropathy. J. Clin. Med. 2015, 4, 1761–1773. [Google Scholar] [CrossRef]
  10. Eid, A.A.; Gorin, Y.; Fagg, B.M.; Maalouf, R.; Barnes, J.L.; Block, K.; Abboud, H.E. Mechanisms of Podocyte Injury in Diabetes: Role of Cytochrome P450 and NADPH Oxidases. Diabetes 2009, 58, 1201–1211. [Google Scholar] [CrossRef] [Green Version]
  11. Eid, S.; Abou-Kheir, W.; Sabra, R.; Daoud, G.; Jaffa, A.; Ziyadeh, F.N.; Roman, L.; A Eid, A. Involvement of renal cytochromes P450 and arachidonic acid metabolites in diabetic nephropathy. J. Biol. Regul. Homeost. Agents 2013, 27, 693–703. [Google Scholar]
  12. Eid, S.; Maalouf, R.; Jaffa, A.A.; Nassif, J.; Hamdy, A.; Rashid, A.; Ziyadeh, F.N.; Eid, A.A. 20-HETE and EETs in Diabetic Nephropathy: A Novel Mechanistic Pathway. PLoS ONE 2013, 8, e70029. [Google Scholar] [CrossRef] [Green Version]
  13. Mota-Zamorano, S.; Robles, N.R.; López-Gómez, J.; Cancho, B.; González, L.M.; García-Pino, G.; Navarro-Pérez, M.L.; Gervasini, G. Plasma and urinary concentrations of arachidonic acid-derived eicosanoids are associated with diabetic kidney disease. Excli. J. 2021, 20, 698. [Google Scholar]
  14. Lazaro-Guevara, J.; Fierro-Morales, J.; Wright, A.H.; Gunville, R.; Simeone, C.; Frodsham, S.G.; Pezzolesi, M.H.; Zaffino, C.A.; Al-Rabadi, L.; Ramkumar, N.; et al. Targeted Next-Generation Sequencing Identifies Pathogenic Variants in Diabetic Kidney Disease. Am. J. Nephrol. 2021, 52, 239–249. [Google Scholar] [CrossRef]
  15. Gervasini, G.; Luna, E.; García-Cerrada, M.; García-Pino, G.; Cubero, J.J. Risk factors for post-transplant diabetes mellitus in renal transplant: Role of genetic variability in the CYP450-mediated arachidonic acid metabolism. Mol. Cell. Endocrinol. 2016, 419, 158–164. [Google Scholar] [CrossRef]
  16. Deng, R.; Liao, Y.; Li, Y.; Tang, J. Association of CYP3A5, CYP2C8, and ABCB1 Polymorphisms with Early Renal Injury in Chinese Liver Transplant Recipients Receiving Tacrolimus. Transplant. Proc. 2018, 50, 3258–3265. [Google Scholar] [CrossRef]
  17. Shuey, M.M.; Billings, F.T.T.; Wei, S.; Milne, G.L.; Nian, H.; Yu, C.; Brown, N.J. Association of gain-of-function EPHX2 poly-morphism Lys55Arg with acute kidney injury following cardiac surgery. PLoS ONE 2017, 12, e0175292. [Google Scholar] [CrossRef]
  18. Gervasini, G.; Luna, E.; Garcia-Pino, G.; Azevedo, L.; Mota-Zamorano, S.; Jose Cubero, J. Polymorphisms in genes in-volved in vasoactive eicosanoid synthesis affect cardiovascular risk in renal transplant recipients. Curr. Med. Res. Opin. 2018, 34, 247–253. [Google Scholar] [CrossRef]
  19. Arroyo, D.; Betriu, A.; Martinez-Alonso, M.; Vidal, T.; Valdivielso, J.M.; Fernández, E. Observational multicenter study to evaluate the prevalence and prognosis of subclinical atheromatosis in a Spanish chronic kidney disease cohort: Baseline data from the NEFRONA study. BMC Nephrol. 2014, 15, 1–10. [Google Scholar] [CrossRef] [Green Version]
  20. Rostoker, G.; Andrivet, P.; Pham, I.; Griuncelli, M.; Adnot, S. Accuracy and limitations of equations for predicting the glomerular filtration rate during follow-up of patients with non-diabetic nephropathies. BMC Nephrol. 2009, 10, 16. [Google Scholar] [CrossRef] [Green Version]
  21. Calleros-Basilio, L.; Cortés, M.A.; García-Jerez, A.; Luengo-Rodríguez, A.; Orozco-Agudo, A.; Valdivielso, J.M.; Rodríguez-Puyol, D.; Rodríguez-Puyol, M. Quality Assurance of Samples and Processes in the Spanish Renal Research Network (REDinREN) Biobank. Biopreserv. Biobanking 2016, 14, 499–510. [Google Scholar] [CrossRef] [PubMed]
  22. Fava, C.; Ricci, M.; Melander, O.; Minuz, P. Hypertension, cardiovascular risk and polymorphisms in genes controlling the cytochrome P450 pathway of arachidonic acid: A sex-specific relation? Prostaglandins Other Lipid Mediat. 2012, 98, 75–85. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Fu, Z.; Ma, Y.; Xie, X.; Huang, D.; Yang, H.; Nakayama, T.; Sato, N. A novel polymorphism of the CYP4A11 gene is associa-ted with coronary artery disease. Clin. Appl. Thromb. Hemost. 2013, 19, 60–65. [Google Scholar] [CrossRef] [PubMed]
  24. Liu, P.-Y.; Li, Y.-H.; Chao, T.-H.; Wu, H.-L.; Lin, L.-J.; Tsai, L.-M.; Chen, J.-H. Synergistic effect of cytochrome P450 epoxygenase CYP2J2*7 polymorphism with smoking on the onset of premature myocardial infarction. Atherosclerosis 2007, 195, 199–206. [Google Scholar] [CrossRef]
  25. Spiecker, M.; Darius, H.; Hankeln, T.; Soufi, M.; Sattler, A.M.; Schaefer, J.R.; Node, K.; Börgel, J.; Mügge, A.; Lindpaintner, K.; et al. Risk of coronary artery disease associated with polymorphism of the cytochrome P450 epoxygenase CYP2J2. Circulation 2004, 110, 2132–2136. [Google Scholar] [CrossRef] [Green Version]
  26. Gervasini, G.; Garcia-Cerrada, M.; Vergara, E.; Garcia-Pino, G.; Alvarado, R.; Fernandez-Cavada, M.J.; Barroso, S.; Doblaré, E.; Cubero, J.J. Polymorp-hisms in CYP-mediated arachidonic acid routes affect the outcome of renal transplantation. Eur. J. Clin. Investig. 2015, 45, 1060–1068. [Google Scholar] [CrossRef]
  27. Minuz, P.; Jiang, H.; Fava, C.; Turolo, L.; Tacconelli, S.; Ricci, M.; Patrignani, P.; Morganti, A.; Lechi, A.; McGiff, J.C. Altered Release of Cytochrome P450 Metabolites of Arachidonic Acid in Renovascular Disease. Hypertension 2008, 51, 1379–1385. [Google Scholar] [CrossRef] [Green Version]
  28. Nithipatikom, K.; Isbell, M.A.; See, W.A.; Campbell, W.B. Elevated 12- and 20-hydroxyeicosatetraenoic acid in urine of patients with prostatic diseases. Cancer Lett. 2006, 233, 219–225. [Google Scholar] [CrossRef]
  29. Yang, T.; Peng, R.; Guo, Y.; Shen, L.; Zhao, S.; Xu, D. The role of 14,15-dihydroxyeicosatrienoic acid levels in inflammation and its relationship to lipoproteins. Lipids Health Dis. 2013, 12, 151. [Google Scholar] [CrossRef] [Green Version]
  30. Orozco, L.D.; Liu, H.; Perkins, E.; Johnson, D.A.; Chen, B.B.; Fan, F.; Baker, R.C.; Roman, R.J. 20-Hydroxyeicosatetraenoic acid inhibition at-tenuates balloon injury-induced neointima formation and vascular remodeling in rat carotid arteries. J. Pharmacol. Exp. Ther. 2013, 346, 67–74. [Google Scholar] [CrossRef] [Green Version]
  31. Gervasini, G.; García-Cerrada, M.; Coto, E.; Vergara, E.; García-Pino, G.; Alvarado, R.; Fernández-Cavada, M.J.; Suárez-Álvarez, B.; Barroso, S.; Doblaré, E.; et al. A 3′-UTR Polymorphism in Soluble Epoxide Hydrolase Gene Is Associated with Acute Rejection in Renal Transplant Recipients. PLoS ONE 2015, 10, e0133563. [Google Scholar] [CrossRef] [Green Version]
  32. Mota-Zamorano, S.; González, L.M.; Luna, E.; Fernández, J.J.; Gómez, Á.; Nieto-Fernández, A.; Robles, N.R.; Gervasini, G. Polymorphisms in vasoactive eicosanoid genes of kidney donors affect biopsy scores and clinical outcomes in renal transplantation. PLoS ONE 2019, 14, e0224129. [Google Scholar] [CrossRef] [Green Version]
  33. Imig, J.D. Epoxide hydrolase and epoxygenase metabolites as therapeutic targets for renal diseases. Am. J. Physiol. Physiol. 2005, 289, F496–F503. [Google Scholar] [CrossRef] [Green Version]
  34. Fang, Q.; Chen, G.Z.; Wang, Y.; Wang, D.W. Role of cytochrome P450 epoxygenase-dependent arachidonic acid metabo-lites in kidney physiology and diseases. Sheng Li Xue Bao 2018, 70, 591–599. [Google Scholar]
  35. Luo, P.; Zhou, Y.; Chang, H.H.; Zhang, J.; Seki, T.; Wang, C.Y.; Inscho, E.W.; Wang, M.H. Glomerular 20-HETE, EETs, and TGF-beta1 in dia-betic nephropathy. Am. J. Physiol. Renal. Physiol. 2009, 296, F556–F563. [Google Scholar] [CrossRef] [Green Version]
  36. Certikova Chabova, V.; Kujal, P.; Skaroupkova, P.; Varnourkova, Z.; Vackova, S.; Huskova, Z.; Kikerlová, S.; Sadowski, J.; Kompanowska-Jezierska, E.; Baranowska, I.; et al. Combined Inhibi-tion of Soluble Epoxide Hydrolase and Renin-Angiotensin System Exhibits Superior Renoprotection to Renin-Angiotensin System Blockade in 5/6 Nephrectomized Ren-2 Transgenic Hypertensive Rats with Established Chronic Kidney Disease. Kidney Blood Press Res. 2018, 43, 329–349. [Google Scholar] [CrossRef]
  37. Lasker, J.M.; Chen, W.B.; Wolf, I.; Bloswick, B.P.; Wilson, P.D.; Powell, P.K. Formation of 20-hydroxyeicosatetraenoic ac-id, a vasoactive and natriuretic eicosanoid, in human kidney. Role of Cyp4F2 and Cyp4A11. J. Biol. Chem. 2000, 275, 4118–4126. [Google Scholar] [CrossRef] [Green Version]
  38. Li, X.; Zhao, G.; Ma, B.; Li, R.; Hong, J.; Liu, S.; Wang, D.W. 20-Hydroxyeicosatetraenoic acid impairs endothelial insulin signal-ing by inducing phosphorylation of the insulin receptor substrate-1 at Ser616. PLoS ONE 2014, 9, e95841. [Google Scholar]
  39. Gilani, A.; Agostinucci, K.; Hossain, S.; Pascale, J.V.; Garcia, V.; Adebesin, A.M.; Falck, J.R.; Schwartzman, M.L. 20-HETE interferes with insulin signaling and contributes to obesity-driven insulin resistance. Prostaglandins Other Lipid Mediat. 2021, 152, 106485. [Google Scholar] [CrossRef]
  40. Gangadhariah, M.H.; Luther, J.M.; Garcia, V.; Paueksakon, P.; Zhang, M.Z.; Hayward, S.W.; Love, H.D.; Falck, J.R.; Manthati, V.L.; Imig, J.D.; et al. Hypertension is a ma-jor contributor to 20-hydroxyeicosatetraenoic acid-mediated kidney injury in diabetic nephropathy. J. Am. Soc. Nephrol. 2015, 26, 597–610. [Google Scholar] [CrossRef] [Green Version]
  41. Kim, W.Y.; Lee, S.J.; Min, J.; Oh, K.S.; Kim, D.H.; Kim, H.S.; Shin, J.G. Identification of novel CYP4F2 genetic variants exhibi-ting decreased catalytic activity in the conversion of arachidonic acid to 20-hydroxyeicosatetraenoic acid (20-HETE). Prostaglandins Leukot. Essent. Fatty Acids 2018, 131, 6–13. [Google Scholar] [CrossRef] [PubMed]
  42. Stec, D.E.; Roman, R.J.; Flasch, A.; Rieder, M.J. Functional polymorphism in human CYP4F2 decreases 20-HETE produc-tion. Physiol. Genom. 2007, 30, 74–81. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Hoopes, S.L.; Garcia, V.; Edin, M.L.; Schwartzman, M.L.; Zeldin, D.C. Vascular actions of 20-HETE. Prostaglandins Other Lipid Mediat. 2015, 120, 9–16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Anderson, S.; Rennke, H.G.; Brenner, B.M. Therapeutic advantage of converting enzyme inhibitors in arresting progres-sive renal disease associated with systemic hypertension in the rat. J. Clin. Invest 1986, 77, 1993–2000. [Google Scholar] [CrossRef] [Green Version]
  45. Ferrannini, E.; Baldi, S.; Frascerra, S.; Astiarraga, B.; Heise, T.; Bizzotto, R.; Mari, A.; Pieber, T.R.; Muscelli, E. Shift to Fatty Substrate Utilization in Response to Sodium–Glucose Cotransporter 2 Inhibition in Subjects without Diabetes and Patients with Type 2 Diabetes. Diabetes 2016, 65, 1190–1195. [Google Scholar] [CrossRef] [Green Version]
  46. Satirapoj, B.; Korkiatpitak, P.; Supasyndh, O. Effect of sodium-glucose cotransporter 2 inhibitor on proximal tubular function and injury in patients with type 2 diabetes: A randomized controlled trial. Clin. Kidney J. 2019, 12, 326–332. [Google Scholar] [CrossRef]
  47. Dai, D.; Zeldin, D.; Blaisdell, J.A.; Chanas, B.; Coulter, S.; Ghanayem, B.I.; Goldstein, J.A. Polymorphisms in human CYP2C8 decrease metabolism of the anticancer drug paclitaxel and arachidonic acid. Pharmacogenetics 2001, 11, 597–607. [Google Scholar] [CrossRef]
  48. Duflot, T.; Laurent, C.; Soudey, A.; Fonrose, X.; Hamzaoui, M.; Iacob, M.; Bertrand, D.; Favre, J.; Etienne, I.; Roche, C.; et al. Preservation of epoxyeicosatrienoic acid bioavailability prevents renal allograft dysfunction and cardiovascular alterations in kidney transplant recipients. Sci. Rep. 2021, 11, 1–13. [Google Scholar] [CrossRef]
  49. Lee, C.R.; Imig, J.D.; Edin, M.L.; Foley, J.; DeGraff, L.M.; Bradbury, J.A.; Graves, J.P.; Lih, F.B.; Clark, J.; Myers, P.; et al. Endothelial expression of human cytoch-rome P450 epoxygenases lowers blood pressure and attenuates hypertension-induced renal injury in mice. FASEB J. 2010, 24, 3770–3781. [Google Scholar] [CrossRef] [Green Version]
  50. Sharma, M.; McCarthy, E.T.; Reddy, D.S.; Patel, P.K.; Savin, V.J.; Medhora, M.; Falck, J.R. 8,9-Epoxyeicosatrienoic acid pro-tects the glomerular filtration barrier. Prostaglandins Other Lipid Mediat 2009, 89, 43–51. [Google Scholar] [CrossRef] [Green Version]
  51. Yang, L.; Maki-Petaja, K.; Cheriyan, J.; McEniery, C.; Wilkinson, I.B. The role of epoxyeicosatrienoic acids in the cardio-vascular system. Br. J. Clin. Pharmacol. 2015, 80, 28–44. [Google Scholar] [CrossRef] [Green Version]
  52. Przybyla-Zawislak, B.D.; Srivastava, P.K.; Vázquez-Matías, J.; Mohrenweiser, H.W.; Maxwell, J.E.; Hammock, B.D.; Bradbury, J.A.; EnayetAllah, A.E.; Zeldin, D.C.; Grant, D.F. Polymorphisms in Human Soluble Epoxide Hydrolase. Mol. Pharmacol. 2003, 64, 482–490. [Google Scholar] [CrossRef] [Green Version]
  53. Morisseau, C.; Wecksler, A.T.; Deng, C.; Dong, H.; Yang, J.; Lee, K.S.; Kodani, S.D.; Hammock, B.D. Effect of soluble epoxide hydrolase polymorp-hism on substrate and inhibitor selectivity and dimer formation. J. Lipid. Res. 2014, 55, 1131–1138. [Google Scholar] [CrossRef] [Green Version]
  54. Fava, C.; Bonafini, S. Eicosanoids via CYP450 and cardiovascular disease: Hints from genetic and nutrition studies. Prostaglandins Other Lipid Mediat. 2018, 139, 41–47. [Google Scholar] [CrossRef]
  55. Fava, C.; Montagnana, M.; Melander, O. Overexpression of cytochrome P450 4F2 in mice increases 20-hydroxyeicosatetraenoic acid production and arterial blood pressure. Kidney Int. 2009, 76, 913–914. [Google Scholar] [CrossRef] [Green Version]
Table
Table 1. Descriptive and clinical characteristics of the population of study. Values shown are count (and percentage) or median (and interquartile range).
Table 1. Descriptive and clinical characteristics of the population of study. Values shown are count (and percentage) or median (and interquartile range).
ControlCKD 3CKD 4–5CKD 5DTotalp-Value Control
vs. CKD		p-Value between
CKD Groups
N6581611401291088
Age (yrs)57 (17)65 (13)63.5 (17)65 (19)59 (18)4.51 × 10−210.123
Males (%)359 (54.6)112 (69.6)91 (65.0)88 (68.2)650 (59.7)1.0 × 10−50.691
Weight (kg)76.8 (21)80 (22.6)79 (18.1)72.5 (21.5)77 (20.40)0.0600.001
Hypertension297 (45.1)154 (95.7)136 (97.1)123 (95.3)710 (65.3)5.50 × 10−790.714
Hyperlipidemia199 (30.2)121 (75.2)109 (77.9)83 (64.3)512 (47.1)4.08 × 10−440.031
Creatinine (mg/dL)0.7 (0.5)0.6 (0.4)0.5 (0.1)-0.7 (0.4)0.0170.030
Albumin/Creatinine (mg/g)7.2 (27.8)152.4 (583.3)283.6 (1052.1)-24.3 (173.5)7.24 × 10−310.046
Cardiovascular events9 (1.8)28 (17.4)25 (17.9)39 (30.2)101 (10.8)5.05 × 10−240.014
eGFR (mL/min/1.73 m²) 5.91 × 10−2530.285
<60-159 (98.8)140 (100.0)-299 (31.2)
>60658 (100.0)2 (1.2)--658 (68.8)
CKD, chronic kidney disease; CKD-5D, dialysis; eGFR, estimated glomerular filtration rate.
Table
Table 2. Summary of genotyping results.
Table 2. Summary of genotyping results.
Polymorphismrs NumberAllelesMissing (%)HWEMAF (%)MAF IBS (%)ap-Value
CYP2C8 *1/*3rs10509681A/G1.31.014.815.00.965
CYP2J2 *1/*7rs890293G/T1.00.3875.76.10.729
CYP4F2 V433Mrs2108622C/T1.70.05736.935.50.475
CYP4A11 F433Srs1126742A/G2.10.40014.913.60.806
EPHX2 R287Qrs751141G/A1.00.4246.17.50.806
EPHX2 3′UTRrs1042032A/G1.60.35824.018.70.548
EPHX2 K55Rrs41507953A/G1.00.7309.77.00.420
HWE, Hardy-Weinberg equilibrium; MAF, minor allele frequency; IBS, Iberian populations in Spain. a p-value for the difference between minor allele frequencies.
Table
Table 3. Adjusted analysis of the association of polymorphisms in the epoxygenase pathway of arachidonic acid metabolism with the risk of diabetic kidney disease (DKD). Results for the dominant and recessive models are shown.
Table 3. Adjusted analysis of the association of polymorphisms in the epoxygenase pathway of arachidonic acid metabolism with the risk of diabetic kidney disease (DKD). Results for the dominant and recessive models are shown.
Dominant ModelRecessive Model
PolymorphismGenotypeControls%DKD%ORCIpORCIp
CYP2C8 *1/*3*1/*145770.332376.20.81(0.57–1.14)0.2253.21(1.05–9.87)0.036
*1/*3182288921
*3/*3111.7122.8
CYP2J2*7 *1/*7*1/*157989.1380891.01(0.60–1.68)0.975---
*1/*76610.24711
*7/*750.800
CYP4F2 V433MV/V24938.619245.20.65(0.48–0.91)0.0080.7(0.46–1.07)0.097
V/M29946.417040
M/M97156314.8
CYP4A11 F434SF/F46371.931173.90.8(0.57–1.13)0.1981.54(0.61–3.88)0.354
F/S16725.99723
S/S142.2133.1
EPHX2 R287QR/R57087.737788.30.75(0.47–1.18)0.2160.79(0.04–1.19)0.22
R/Q7912.24911.5
Q/Q10.210.2
EPHX2 3′UTR A>GA/A3825924257.10.97(0.71–1.33)0.8451.05(0.57–1.95)0.87
A/G22634.915436.3
G/G396286.6
EPHX2 K55RK/K53281.734681.20.82(0.56–1.21)0.3191.32(0.31–5.69)0.709
K/R11317.47517.6
R/R60.951.2
OR, odds ratio; CI, 95% confidence intervals.
Table
Table 4. Risk analysis considering the 430 diabetic kidney disease (DKD) patients and a subgroup of diabetic individuals with normal renal function.
Table 4. Risk analysis considering the 430 diabetic kidney disease (DKD) patients and a subgroup of diabetic individuals with normal renal function.
PolymorphismGenotypeDiabetics without
DKD (n = 65)		%DKD (n = 430)%ORCIp-Value
CYP2C8 *1/*3*1/*14772.332376.2Ref.
*1/*3-*3/*31827.710123.80.95(0.49–1.85)0.883
CYP2J2 *1/*7*1/*15990.838089Ref.
*1/*7-*7/*769.247111.19(0.45–3.15)0.724
CYP4F2 V433MVV1827.719245.2Ref.
VM-MM4772.323354.80.42(0.22–0.80)0.005
CYP4A11 F433SFF4467.731173.9Ref.
FS-SS2132.311026.10.39(0.39–1.37)0.330
EPHX2 R287QRR5686.237788.3Ref.
RQ-QQ913.85011.70.77(0.33–1.81)0.561
EPHX2 3′UTR (A/G)AA3756.924257.1Ref.
AG-GG2843.118242.90.92(0.51–1.65)0.773
EPHX2 K55RKK4467.734681.2Ref.
KR-RR2132.38018.80.39(0.21–0.76)0.006
OR, odds ratio; CI, 95% confidence intervals; Ref, reference. V, valine; M, methionine; F, phenylalanine; S, serine; R, arginine; Q, glutamine; K, lysine.
Table
Table 5. Adjusted analysis for determining associations of the different genotypes with renal parameters of patients with diabetic kidney disease.
Table 5. Adjusted analysis for determining associations of the different genotypes with renal parameters of patients with diabetic kidney disease.
eGFR (mL/min/1.73 m2)ACR (mg/g)
PolymorphismGenotypeMedianIQRp-ValueMedianIQRp-Value
CYP2C8 *1/*3*1/*132.14220.208195.12853.210.787
*1/*3-*3/*330.6122199.05425.67
CYP2J2 *1/*7*1/*131.31220.247199.56646.660.231
*1/*7-*7/*734.4323131.01261.20
CYP4F2 V433MVV33.0230.037165.28911.650.982
VM-MM30.8020231.95679.33
CYP4A11 F433SFF31.24220.823186.94659.790.387
FS-SS33.022228.60855.30
EPHX2 R287QRR32.47220.286199.56828.480.861
RQ-QQ27.9023131.0489.27
EPHX2 3′UTR (A/G)AA30.41240.284152.43597.330.435
AG-GG33.021299.12835.90
EPHX2 K55RKK30.90220.051167.74653.370.136
KR-RR35.8523386.0846.14
eGFR, estimated glomerular filtration rate; ACR, albumin-to-creatinine ratio; IQR, interquartile range.
Table
Table 6. Influence of polymorphisms in the epoxygenase pathway on the plasma and urinary levels of vasoactive eicosanoids shown by patients with diabetic kidney disease.
Table 6. Influence of polymorphisms in the epoxygenase pathway on the plasma and urinary levels of vasoactive eicosanoids shown by patients with diabetic kidney disease.
14,15-DHET (ng/L)11,12-DHET (ng/L)20-HETE (ng/L)20-HETE ng/mg Cr
PolymorphismGenotypeMeanSEMeanSEMeanSEMeanSE
CYP2C8 *1/*3*1/*1397.3526.68237.6518.79306.9619.896.092.41
*1/*3-3*/*3409.8054.44238.8642.53322.9357.514.761.74
CYP2J2 *1/*7*1/*1409.4025.91239.5619.04315.4122.45.852.03
*1/*7-*7/*7288.7834.69206.7535.48268.2257.324.921.55
CYP4F2 V433MVV393.7336.59226.1626.39309.7535.588.453.69
VM-MM401.4231.91246.1523.67310.6824.43*3.140.86
CYP4A11 F433SFF409.3226.88246.4318.88306.2119.856.02.53
FS-SS370.8550.07212.5839.33319.8253.045.191.63
EPHX2 R287QRR396.0323.93233.6617.15312.0822.325.82.10
RQ-QQ421.75133.57275.29117.82286.7542.625.763.48
EPHX2 3′UTR (A/G)A/A373.7418.8219.2013.14308.321.076.742.77
A/G-G/G446.1661.15269.4744.72314.1647.03.780.95
EPHX2 K55RKK392.6524.35231.0718.47322.4123.586.022.21
KR-RR439.0793.22272.9359.05222.6428.364.411.73
DHET, dihydroxyeicosatrienoic acid; HETE, hydroxyeicosatetraenoic acid; SE, standard error; * p < 0.05.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Mota-Zamorano, S.; Robles, N.R.; González, L.M.; Valdivielso, J.M.; Lopez-Gomez, J.; Cancho, B.; García-Pino, G.; Gervasini, G. Genetics Variants in the Epoxygenase Pathway of Arachidonic Metabolism Are Associated with Eicosanoids Levels and the Risk of Diabetic Nephropathy. J. Clin. Med. 2021, 10, 3980. https://doi.org/10.3390/jcm10173980

AMA Style

Mota-Zamorano S, Robles NR, González LM, Valdivielso JM, Lopez-Gomez J, Cancho B, García-Pino G, Gervasini G. Genetics Variants in the Epoxygenase Pathway of Arachidonic Metabolism Are Associated with Eicosanoids Levels and the Risk of Diabetic Nephropathy. Journal of Clinical Medicine. 2021; 10(17):3980. https://doi.org/10.3390/jcm10173980

Chicago/Turabian Style

Mota-Zamorano, Sonia, Nicolás R. Robles, Luz M. González, José M. Valdivielso, Juan Lopez-Gomez, Bárbara Cancho, Guadalupe García-Pino, and Guillermo Gervasini. 2021. "Genetics Variants in the Epoxygenase Pathway of Arachidonic Metabolism Are Associated with Eicosanoids Levels and the Risk of Diabetic Nephropathy" Journal of Clinical Medicine 10, no. 17: 3980. https://doi.org/10.3390/jcm10173980

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