Search
Close
Search
Advanced Search
Search Menu
AI Discovery Assistant

Abstract

Background

The clinical relevance of arachidonic acid (AA) metabolites in chronic kidney disease (CKD) progression is poorly understood. We aimed to compare the concentrations of 85 enzymatic pathway products of AA metabolism in patients with CKD who progressed to end-stage kidney disease (ESKD) versus patients who did not in a subcohort of Chronic Renal Insufficiency Cohort (CRIC) and to estimate the risk of CKD progression and major cardiovascular events by levels of AA metabolites and their link to enzymatic metabolic pathways.

Methods

A total 123 patients in the CRIC study who progressed to ESKD were frequency matched with 177 nonprogressors and serum eicosanoids were quantified by mass spectrometry. We applied serum collected at patients’ Year 1 visit and outcome of progression to ESKD was ascertained over the next 10 years. We used logistic regression models for risk estimation.

Results

Baseline 15-hydroxyeicosatetraenoate (HETE) and 20-HETE levels were significantly elevated in progressors (false discovery rate Q ≤ 0.026). The median 20-HETE level was 7.6 pmol/mL [interquartile range (IQR) 4.2–14.5] in progressors and 5.4 pmol/mL (IQR 2.8–9.4) in nonprogressors (P < 0.001). In an adjusted model, only 20-HETE independently predicted CKD progression. Each 1 standard deviation increase in 20-HETE was independently associated with 1.45-fold higher odds of progression (95% confidence interval 1.07–1.95; P = 0.017). Principal components of lipoxygenase (LOX) and cytochrome P450 (CYP450) pathways were independently associated with CKD progression.

Conclusions

We found higher odds of CKD progression associated with higher 20-HETE, LOX and CYP450 metabolic pathways. These alterations precede CKD progression and may serve as targets for interventions aimed at halting progression.

cardiovascular, CKD, heart failure, progression, prostaglandins

INTRODUCTION

Eicosanoids are primarily derived from arachidonic acid (AA) stored in the membrane phospholipids [1]. AA is metabolized into numerous classes of eicosanoids via cyclooxygenase (COX), lipoxygenase (LOX), cytochrome P450 (CYP450) and/or nonenzymatic pathways (Figure 1) [1, 2]. When AA is metabolized through the COX pathway, it is first released by cytosolic phospholipase A2 activity followed by COX-mediated incorporation of oxygen at its 11- and 15-carbons and formation of prostaglandin (PG)-G2, a precursor of PG-H2 [1]. PG-H2 is an intermediary product that is used for further production of various molecules and PGs through downstream reactions [1]. When AA is metabolized through the LOX pathway, a series of immunological and nonimmunological stimuli facilitate incorporation of AA through 5-LOX, 12-LOX or 15-LOX, leading to the formation of docosahexanoic acid and a number of downstream products, including leukotrienes, lipoxins and resolvins [1, 3]. Alternatively, AA can be utilized through CYP450 via epoxygenase or ω-hydroxylase enzymatic pathways to produce signaling molecules [2, 3].

Major arachidonic acid metabolic pathways. (A) Eicosanoids measured in this study generated by COX, LOX, CYP450 or nonenzymatic reactions. Omega-3 compounds are shown in red. (B) Conversion of arachidonic acid to 20-HETE is mediated by ω-hydroxylase through CYP450.
FIGURE 1

Major arachidonic acid metabolic pathways. (A) Eicosanoids measured in this study generated by COX, LOX, CYP450 or nonenzymatic reactions. Omega-3 compounds are shown in red. (B) Conversion of arachidonic acid to 20-HETE is mediated by ω-hydroxylase through CYP450.

Open in new tabDownload slide

Eicosanoids take part in several physiological processes [4] and are linked to the pathophysiology of chronic diseases such as cancer and arthritis [5, 6]. In animal models, increased 5-LOX products were responsible for inflammation, tubulointerstitial injury, albuminuria, vascular remodeling, decreased glomerular filtration rate (GFR) and renal blood flow [7, 8]. Similar model systems revealed a role for 12/15-LOX activity in mesangial expansion and albuminuria [9, 10]. Alteration in CYP4A activity and its products such as 20-hydroxyeicosatetraenoic (HETE) acid has also been linked to hypertension, thrombosis and blood vessel injury in animal models [11–14]. Despite convincing evidence of their biological importance, to our knowledge, human studies that systematically identify and quantify eicosanoids in the major AA metabolic pathways (LOX, COX and CYP450) in patients with chronic kidney disease (CKD) have not been performed before. As such, the prognostic value of the differential serum levels of the corresponding AA metabolic products on CKD progression is unknown. In this study we aim to compare the concentrations of products of major enzymatic pathways of AA metabolism, including COX, LOX and CYP, in patients with CKD who eventually progressed to ESKD versus patients who did not in a subcohort of the Chronic Renal Insufficiency Cohort (CRIC) study and to estimate the risk of CKD progression and major cardiovascular events by levels of significant AA metabolites and aggregate representatives of AA metabolic pathways. We hypothesize that significant alterations in the products of the COX, LOX and CYP AA enzymatic pathways precede progression of CKD and that alterations in the aggregated variables representative of the COX, LOX and CYP pathways are independently associated with CKD progression and cardiovascular events.

MATERIALS AND METHODS

Patients

The design of this investigation and details of the CRIC study are presented elsewhere [15–17]. In brief, this is a case–control investigation nested in the parent CRIC study. Baseline estimated GFR (eGFR) ≥30 mL/min and age ≥18 years at the time of enrollment were the inclusion criteria. There were no restrictions on either race or sex. Cases or progressors were defined as patients with CKD who progressed to ESKD needing dialysis or kidney transplant over the next 10 years of follow-up {median 6 years [interquartile range (IQR) 4–7]}. Controls or nonprogressors were defined as patients who had no decline or a <25% decline in eGFR during a comparable follow-up period. Progressors were frequency matched with nonprogressors by age, sex, race and diabetes status. We calculated the eGFR using the Chronic Kidney Disease Epidemiology Collaboration formula [18]. Demographic, laboratory and clinical data including comorbidities and medication were gathered at the time of sample collection. A fasting serum sample collected at each patient’s follow-up visit 1 year after enrollment was obtained for biomarker studies. After the collection of samples and immediate separation of the serum, the samples were stored at −80°C until analysis. All samples were gathered according to a standardized research protocol in place, ensuring high-quality sample collection with minimal expended time prior to storage at −80°C. Fresh unthawed samples were used for analysis and all procedures were performed at 4°C after thawing, ensuring minimal artifactual sample loss during sample preparation.

Outcomes

The primary outcome was progression of CKD to ESKD over the next 10 years after sample collection. Secondary outcomes were defined as the first episode of atrial fibrillation, definitive acute myocardial infarction (AMI), definitive congestive heart failure (CHF), stroke or death after enrollment. The clinical outcomes were adjudicated by at least two reviewers of the adjudication committee.

Biomarker identification

After preparation, the samples were injected into a 6490 Triple Quadrupole mass spectrometer (Agilent, New Castle, DE, USA) for detection and quantification in a targeted multiple reaction monitoring (MRM) mode (see Supplementary data).

Statistical analysis

Mean ± standard deviation (SD) and count (percentage) were the descriptive statistics for the continuous and categorical variables, respectively. Median and IQR were used for the description of skewed continuous variables. A t-test was used to compare the mean of continuous variables in progressors and nonprogressors and the chi-square test was used to test the relationship of categorical variables in the two groups. The identified eicosanoids were normalized by internal standards and then log2 transformed for the downstream analyses. For a compound-by-compound analysis, a t-test adjusted with a false discovery rate (FDR) correction using the Benjamini–Hochberg procedure was applied. Overrepresentation enrichment analysis was performed to test enrichment of the top differentially regulated eicosanoid classes among the compounds that passed the FDR threshold of 0.1 versus members of the same eicosanoid class among the rest of the cohort, using a Fisher’s exact test. All eicosanoids within each metabolic pathway, including COX, 5-LOX, 12-LOX, 15-LOX and CYP, were separately aggregated into one secondary variable representative of the corresponding pathway using principal component (PC) analysis. We used multiple logistic regression models to estimate the risk of progression of CKD by each 1-SD change in the level of the candidate biomarker as a continuous variable presuming a linear association as well as by their quartiles in case of a non-linear association. Similar models were applied for estimating the risk of CKD progression by the PCs. Two sets of models were used: unadjusted and adjusted by the prognostic variables with imbalanced distribution at baseline, including systolic blood pressure, history of hypertension, use of calcium channel blockers, baseline eGFR and urine protein:creatinine ratio (UPCR), as well as sex, the total number of antihypertensive agents (including drugs with known interaction with CYP450 such as statins) and total number of consumed medications.

RESULTS

Frequency-matched patient groups

We examined the eicosanoid concentration in the serum of 300 adult patients with CKD Stage 3 from the CRIC study [16, 17]. The selected patients consisted of 164 men (54.7%) and 136 women (45.3%). The mean age was 59 ± 10 years. Comparing baseline characteristics of progressors with nonprogressors, the statistically significant differences were a higher proportion with hypertension and use of calcium channel blocker, higher mean systolic blood pressure and a higher median UPCR, but a lower mean eGFR in progressors (P ≤ 0.007, Table 1.).

Table 1
Open in new tab

Comparing baseline characteristics of progressors and nonprogressors

VariableNonprogressorsProgressorsP-value
(n = 177)(n = 123)
Age (years), mean ± SD59 ± 1058 ± 110.737
Sex, n (%)0.515
 Male94 (53.1)70 (56.9)
 Female83 (46.9)53 (43.1)
Race, n (%)0.662
 White88 (49.7)58 (47.2)
 Black89 (50.3)65 (52.8)
Current smoking, n (%)34 (19.2)22 (17.9)0.772
Medications, n (%)
 ACEI91 (51.4)64 (52.5)0.859
 ARB44 (24.9)39 (32.0)0.177
 Beta-blocker81 (45.8)69 (56.6)0.067
 Calcium channel blocker72 (40.7)64 (52.5)0.044
 Diuretics102 (57.6)77 (63.1)0.341
 Statins107 (60.5)75 (61.5)0.859
 Other lipid-lowering agents38 (21.5)20 (16.4)0.275
 Steroids24 (13.6)17 (13.9)0.926
 Aspirin92 (52.0)59 (48.4)0.539
 Antiplatelet94 (53.1)63 (51.6)0.803
Diabetes, n (%)84 (47.5)66 (53.7)0.291
Hypertension, n (%)149 (84.2)116 (94.3)0.007
Height (m), mean ± SD1.7 ± 0.11.7 ± 0.10.850
Weight (kg), mean ± SD94 ± 2297 ± 260.245
BMI (kg/m2), mean ± SD32.4 ± 7.533.4 ± 8.40.309
Waist (m), mean ± SD1.1 ± 0.21.1 ± 0.20.284
Systolic BP (mmHg), mean ± SD125 ± 22133 ± 220.001
Diastolic BP (mmHg), mean ± SD71 ± 1373 ± 140.220
Pulse (beats/min), mean ± SD68 ± 1169 ± 120.809
HbA1c (%), mean ± SDa7.5 ± 1.68.0 ± 1.90.061
Sodium (mmol/L), mean ± SD140 ± 2.5140 ± 3.00.879
CO2 (mmol/L), mean ± SD24.6 ± 2.724.2 ± 3.10.183
Chloride (mmol/L), mean ± SD105 ± 3105 ± 40.224
ALT (IU/L), mean ± SD36 ± 2033 ± 160.103
AST (IU/L), mean ± SD27 ± 1226 ± 160.585
TAG (mg/dL), mean ± SD150 ± 111162 ± 1120.363
Total cholesterol (mg/dL), mean ± SD182 ± 45186 ± 480.427
HDL (mg/dL), mean ± SD50 ± 1748 ± 150.415
LDL (mg/dL), mean ± SD100 ± 36103 ± 350.482
eGFR (mL/min), mean ± SD49 ± 1239 ± 8<0.001
UPCR (mg/g), median (IQR)0.1 [0.1–0.3]1.0 [0.3–2.1]<0.001
VariableNonprogressorsProgressorsP-value
(n = 177)(n = 123)
Age (years), mean ± SD59 ± 1058 ± 110.737
Sex, n (%)0.515
 Male94 (53.1)70 (56.9)
 Female83 (46.9)53 (43.1)
Race, n (%)0.662
 White88 (49.7)58 (47.2)
 Black89 (50.3)65 (52.8)
Current smoking, n (%)34 (19.2)22 (17.9)0.772
Medications, n (%)
 ACEI91 (51.4)64 (52.5)0.859
 ARB44 (24.9)39 (32.0)0.177
 Beta-blocker81 (45.8)69 (56.6)0.067
 Calcium channel blocker72 (40.7)64 (52.5)0.044
 Diuretics102 (57.6)77 (63.1)0.341
 Statins107 (60.5)75 (61.5)0.859
 Other lipid-lowering agents38 (21.5)20 (16.4)0.275
 Steroids24 (13.6)17 (13.9)0.926
 Aspirin92 (52.0)59 (48.4)0.539
 Antiplatelet94 (53.1)63 (51.6)0.803
Diabetes, n (%)84 (47.5)66 (53.7)0.291
Hypertension, n (%)149 (84.2)116 (94.3)0.007
Height (m), mean ± SD1.7 ± 0.11.7 ± 0.10.850
Weight (kg), mean ± SD94 ± 2297 ± 260.245
BMI (kg/m2), mean ± SD32.4 ± 7.533.4 ± 8.40.309
Waist (m), mean ± SD1.1 ± 0.21.1 ± 0.20.284
Systolic BP (mmHg), mean ± SD125 ± 22133 ± 220.001
Diastolic BP (mmHg), mean ± SD71 ± 1373 ± 140.220
Pulse (beats/min), mean ± SD68 ± 1169 ± 120.809
HbA1c (%), mean ± SDa7.5 ± 1.68.0 ± 1.90.061
Sodium (mmol/L), mean ± SD140 ± 2.5140 ± 3.00.879
CO2 (mmol/L), mean ± SD24.6 ± 2.724.2 ± 3.10.183
Chloride (mmol/L), mean ± SD105 ± 3105 ± 40.224
ALT (IU/L), mean ± SD36 ± 2033 ± 160.103
AST (IU/L), mean ± SD27 ± 1226 ± 160.585
TAG (mg/dL), mean ± SD150 ± 111162 ± 1120.363
Total cholesterol (mg/dL), mean ± SD182 ± 45186 ± 480.427
HDL (mg/dL), mean ± SD50 ± 1748 ± 150.415
LDL (mg/dL), mean ± SD100 ± 36103 ± 350.482
eGFR (mL/min), mean ± SD49 ± 1239 ± 8<0.001
UPCR (mg/g), median (IQR)0.1 [0.1–0.3]1.0 [0.3–2.1]<0.001
a

HbA1c was only recorded for patients with diabetes: 80 nonprogressors and 67 progressors. Medications in progressors are recorded for 122 patients.

ACEI, angiotensin-converting-enzyme inhibitor; ALT, alanine aminotransferase; ARB, angiotensin II receptor blockers; AST, aspartate aminotransferase; BP, blood pressure; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TAG, triacylglycerol.

Table 1
Open in new tab

Comparing baseline characteristics of progressors and nonprogressors

VariableNonprogressorsProgressorsP-value
(n = 177)(n = 123)
Age (years), mean ± SD59 ± 1058 ± 110.737
Sex, n (%)0.515
 Male94 (53.1)70 (56.9)
 Female83 (46.9)53 (43.1)
Race, n (%)0.662
 White88 (49.7)58 (47.2)
 Black89 (50.3)65 (52.8)
Current smoking, n (%)34 (19.2)22 (17.9)0.772
Medications, n (%)
 ACEI91 (51.4)64 (52.5)0.859
 ARB44 (24.9)39 (32.0)0.177
 Beta-blocker81 (45.8)69 (56.6)0.067
 Calcium channel blocker72 (40.7)64 (52.5)0.044
 Diuretics102 (57.6)77 (63.1)0.341
 Statins107 (60.5)75 (61.5)0.859
 Other lipid-lowering agents38 (21.5)20 (16.4)0.275
 Steroids24 (13.6)17 (13.9)0.926
 Aspirin92 (52.0)59 (48.4)0.539
 Antiplatelet94 (53.1)63 (51.6)0.803
Diabetes, n (%)84 (47.5)66 (53.7)0.291
Hypertension, n (%)149 (84.2)116 (94.3)0.007
Height (m), mean ± SD1.7 ± 0.11.7 ± 0.10.850
Weight (kg), mean ± SD94 ± 2297 ± 260.245
BMI (kg/m2), mean ± SD32.4 ± 7.533.4 ± 8.40.309
Waist (m), mean ± SD1.1 ± 0.21.1 ± 0.20.284
Systolic BP (mmHg), mean ± SD125 ± 22133 ± 220.001
Diastolic BP (mmHg), mean ± SD71 ± 1373 ± 140.220
Pulse (beats/min), mean ± SD68 ± 1169 ± 120.809
HbA1c (%), mean ± SDa7.5 ± 1.68.0 ± 1.90.061
Sodium (mmol/L), mean ± SD140 ± 2.5140 ± 3.00.879
CO2 (mmol/L), mean ± SD24.6 ± 2.724.2 ± 3.10.183
Chloride (mmol/L), mean ± SD105 ± 3105 ± 40.224
ALT (IU/L), mean ± SD36 ± 2033 ± 160.103
AST (IU/L), mean ± SD27 ± 1226 ± 160.585
TAG (mg/dL), mean ± SD150 ± 111162 ± 1120.363
Total cholesterol (mg/dL), mean ± SD182 ± 45186 ± 480.427
HDL (mg/dL), mean ± SD50 ± 1748 ± 150.415
LDL (mg/dL), mean ± SD100 ± 36103 ± 350.482
eGFR (mL/min), mean ± SD49 ± 1239 ± 8<0.001
UPCR (mg/g), median (IQR)0.1 [0.1–0.3]1.0 [0.3–2.1]<0.001
VariableNonprogressorsProgressorsP-value
(n = 177)(n = 123)
Age (years), mean ± SD59 ± 1058 ± 110.737
Sex, n (%)0.515
 Male94 (53.1)70 (56.9)
 Female83 (46.9)53 (43.1)
Race, n (%)0.662
 White88 (49.7)58 (47.2)
 Black89 (50.3)65 (52.8)
Current smoking, n (%)34 (19.2)22 (17.9)0.772
Medications, n (%)
 ACEI91 (51.4)64 (52.5)0.859
 ARB44 (24.9)39 (32.0)0.177
 Beta-blocker81 (45.8)69 (56.6)0.067
 Calcium channel blocker72 (40.7)64 (52.5)0.044
 Diuretics102 (57.6)77 (63.1)0.341
 Statins107 (60.5)75 (61.5)0.859
 Other lipid-lowering agents38 (21.5)20 (16.4)0.275
 Steroids24 (13.6)17 (13.9)0.926
 Aspirin92 (52.0)59 (48.4)0.539
 Antiplatelet94 (53.1)63 (51.6)0.803
Diabetes, n (%)84 (47.5)66 (53.7)0.291
Hypertension, n (%)149 (84.2)116 (94.3)0.007
Height (m), mean ± SD1.7 ± 0.11.7 ± 0.10.850
Weight (kg), mean ± SD94 ± 2297 ± 260.245
BMI (kg/m2), mean ± SD32.4 ± 7.533.4 ± 8.40.309
Waist (m), mean ± SD1.1 ± 0.21.1 ± 0.20.284
Systolic BP (mmHg), mean ± SD125 ± 22133 ± 220.001
Diastolic BP (mmHg), mean ± SD71 ± 1373 ± 140.220
Pulse (beats/min), mean ± SD68 ± 1169 ± 120.809
HbA1c (%), mean ± SDa7.5 ± 1.68.0 ± 1.90.061
Sodium (mmol/L), mean ± SD140 ± 2.5140 ± 3.00.879
CO2 (mmol/L), mean ± SD24.6 ± 2.724.2 ± 3.10.183
Chloride (mmol/L), mean ± SD105 ± 3105 ± 40.224
ALT (IU/L), mean ± SD36 ± 2033 ± 160.103
AST (IU/L), mean ± SD27 ± 1226 ± 160.585
TAG (mg/dL), mean ± SD150 ± 111162 ± 1120.363
Total cholesterol (mg/dL), mean ± SD182 ± 45186 ± 480.427
HDL (mg/dL), mean ± SD50 ± 1748 ± 150.415
LDL (mg/dL), mean ± SD100 ± 36103 ± 350.482
eGFR (mL/min), mean ± SD49 ± 1239 ± 8<0.001
UPCR (mg/g), median (IQR)0.1 [0.1–0.3]1.0 [0.3–2.1]<0.001
a

HbA1c was only recorded for patients with diabetes: 80 nonprogressors and 67 progressors. Medications in progressors are recorded for 122 patients.

ACEI, angiotensin-converting-enzyme inhibitor; ALT, alanine aminotransferase; ARB, angiotensin II receptor blockers; AST, aspartate aminotransferase; BP, blood pressure; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TAG, triacylglycerol.

Compound-by-compound analysis

Of 109 eicosanoid metabolites examined, 85 were detectable and analyzed further (Figure 1 and Supplementary data, Table S1). The top two differentially regulated eicosanoids were 15-HETE and 20-HETE, which displayed the highest fold changes between progressors and nonprogressors (Figure 2). The top 10 differentially regulated eicosanoids passed the nominal threshold with an FDR-adjusted Q-value of <0.1 (Table 2). Accordingly, the median 15-HETE level was 4.5  pmol/mL (IQR 2.8–8.5) in nonprogressors and 6.2 (3.8–13.1) in progressors (P < 0.001). Similarly, the median 20-HETE level was 5.4  pmol/mL (IQR 2.8–9.4) in nonprogressors and 7.6 (4.2–14.5) in progressors (P < 0.001). The median of 20-HETE was 4.6 pmol/mL (IQR 2.7–8.3) in males and 6.2 (3.1–12.0) in females in nonprogressors (P = 0.023). These values were 9.7 pmol/mL (IQR 4.5–15.2) in males and 6.7 (3.5–13.1) in females in progressors (P = 0.232).

Volcano plot demonstrating the fold change on a log2 scale and nominal significance of 85 detectable eicosanoids. The top 2 differentially regulated eicosanoids are labeled.
FIGURE 2

Volcano plot demonstrating the fold change on a log2 scale and nominal significance of 85 detectable eicosanoids. The top 2 differentially regulated eicosanoids are labeled.

Open in new tabDownload slide
Table 2
Open in new tab

The top 10 eicosanoids that passed the nominal significance threshold in progressors versus nonprogressors of CKD and their corresponding FDR-adjusted Q-value

EicosanoidsNonprogressor (pmol/mL), median (IQR)Progressor (pmol/mL), median (IQR)Nominal P-valueFDR Q-value
115-HETE4.5 (2.8–8.5)6.2 (3.8–13.1)0.0001750.014878
220-HETE5.4 (2.8–9.4)7.6 (4.2–14.5)0.0006210.026385
316-HETE23.2 (17.6–31.4)27.8 (21.2–45.0)0.0064170.097499
45-HETE4.9 (3.1–7.6)6.2 (3.9–10.8)0.0075180.097499
517-HDoHE0.7 (0.4–1.8)1.2 (0.5–2.5)0.0088290.097499
612-HEPE0.6 (0.2–1.1)0.8 (0.4–1.2)0.0093560.097499
715-oxoETE3.4 (2.4–5.8)3.9 (2.6–8.1)0.0094140.097499
8Thromboxane-B21.2 (0.4–7.1)2.0 (0.5–12.1)0.010330.097499
9±5(6)-DiHETE3.3 (1.3–6.6)4.6 (2.5–8.9)0.0106960.097499
1012-HETE19.3 (8.4–48.8)29.6 (10.6–81.3)0.011470.097499
EicosanoidsNonprogressor (pmol/mL), median (IQR)Progressor (pmol/mL), median (IQR)Nominal P-valueFDR Q-value
115-HETE4.5 (2.8–8.5)6.2 (3.8–13.1)0.0001750.014878
220-HETE5.4 (2.8–9.4)7.6 (4.2–14.5)0.0006210.026385
316-HETE23.2 (17.6–31.4)27.8 (21.2–45.0)0.0064170.097499
45-HETE4.9 (3.1–7.6)6.2 (3.9–10.8)0.0075180.097499
517-HDoHE0.7 (0.4–1.8)1.2 (0.5–2.5)0.0088290.097499
612-HEPE0.6 (0.2–1.1)0.8 (0.4–1.2)0.0093560.097499
715-oxoETE3.4 (2.4–5.8)3.9 (2.6–8.1)0.0094140.097499
8Thromboxane-B21.2 (0.4–7.1)2.0 (0.5–12.1)0.010330.097499
9±5(6)-DiHETE3.3 (1.3–6.6)4.6 (2.5–8.9)0.0106960.097499
1012-HETE19.3 (8.4–48.8)29.6 (10.6–81.3)0.011470.097499

HDoHE, hydroxy docosahexaenoic acid; HEPE, hydroxyeicosapentaenoic acid; HETE, hydroxyeicosatetraenoic acid; oxoETE, oxoeicosatetraenoic acid.

Table 2
Open in new tab

The top 10 eicosanoids that passed the nominal significance threshold in progressors versus nonprogressors of CKD and their corresponding FDR-adjusted Q-value

EicosanoidsNonprogressor (pmol/mL), median (IQR)Progressor (pmol/mL), median (IQR)Nominal P-valueFDR Q-value
115-HETE4.5 (2.8–8.5)6.2 (3.8–13.1)0.0001750.014878
220-HETE5.4 (2.8–9.4)7.6 (4.2–14.5)0.0006210.026385
316-HETE23.2 (17.6–31.4)27.8 (21.2–45.0)0.0064170.097499
45-HETE4.9 (3.1–7.6)6.2 (3.9–10.8)0.0075180.097499
517-HDoHE0.7 (0.4–1.8)1.2 (0.5–2.5)0.0088290.097499
612-HEPE0.6 (0.2–1.1)0.8 (0.4–1.2)0.0093560.097499
715-oxoETE3.4 (2.4–5.8)3.9 (2.6–8.1)0.0094140.097499
8Thromboxane-B21.2 (0.4–7.1)2.0 (0.5–12.1)0.010330.097499
9±5(6)-DiHETE3.3 (1.3–6.6)4.6 (2.5–8.9)0.0106960.097499
1012-HETE19.3 (8.4–48.8)29.6 (10.6–81.3)0.011470.097499
EicosanoidsNonprogressor (pmol/mL), median (IQR)Progressor (pmol/mL), median (IQR)Nominal P-valueFDR Q-value
115-HETE4.5 (2.8–8.5)6.2 (3.8–13.1)0.0001750.014878
220-HETE5.4 (2.8–9.4)7.6 (4.2–14.5)0.0006210.026385
316-HETE23.2 (17.6–31.4)27.8 (21.2–45.0)0.0064170.097499
45-HETE4.9 (3.1–7.6)6.2 (3.9–10.8)0.0075180.097499
517-HDoHE0.7 (0.4–1.8)1.2 (0.5–2.5)0.0088290.097499
612-HEPE0.6 (0.2–1.1)0.8 (0.4–1.2)0.0093560.097499
715-oxoETE3.4 (2.4–5.8)3.9 (2.6–8.1)0.0094140.097499
8Thromboxane-B21.2 (0.4–7.1)2.0 (0.5–12.1)0.010330.097499
9±5(6)-DiHETE3.3 (1.3–6.6)4.6 (2.5–8.9)0.0106960.097499
1012-HETE19.3 (8.4–48.8)29.6 (10.6–81.3)0.011470.097499

HDoHE, hydroxy docosahexaenoic acid; HEPE, hydroxyeicosapentaenoic acid; HETE, hydroxyeicosatetraenoic acid; oxoETE, oxoeicosatetraenoic acid.

Class enrichment analysis

Six of the 10 compounds with FDR <0.1 and 6 of the 75 compounds with FDR > 0.1 belonged to the HETE class. An enrichment analysis revealed that HETEs, as a class, were overrepresented among the top 10 that were differentially regulated between progressors and nonprogressors and passed an FDR threshold of <0.1 (Fisher’s exact P-value = 0.0003).

CKD progression by 15-HETE and 20-HETE concentrations

When we divided the patients into quartiles based on serum 20-HETE concentrations, there was a graded increase in the percentage of patients who progressed to ESKD from 25% in the first quartile to 53% in the fourth quartile (P-trend = 0.003). When the patients were split into quartiles based on 15-HETE concentrations, progression to ESKD increased from 29% in the first quartile to 55% in the fourth quartile (P-trend <0.001; Figure 3A).

Alteration of 15- and 20-HETE in CKD. (A) Proportion of progressors of CKD increases by quartiles of the 15-HETE and 20-HETE. Bars are percentages and errors are standard error of estimates. (B) Odds of CKD progression by each 1 SD increase in 15-HETE in an unadjusted model (top model), by each 1 SD increase in 20-HETE in an unadjusted model (middle model) and by each 1 SD increase in 20-HETE after adjusting for 15-HETE, eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker (bottom model) are statistically significant. (C) Odds of CKD progression in the fourth quartile of 20-HETE compared with its first quartile is statistically significant in an unadjusted model and remains significant after adjusting for eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker.
FIGURE 3

Alteration of 15- and 20-HETE in CKD. (A) Proportion of progressors of CKD increases by quartiles of the 15-HETE and 20-HETE. Bars are percentages and errors are standard error of estimates. (B) Odds of CKD progression by each 1 SD increase in 15-HETE in an unadjusted model (top model), by each 1 SD increase in 20-HETE in an unadjusted model (middle model) and by each 1 SD increase in 20-HETE after adjusting for 15-HETE, eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker (bottom model) are statistically significant. (C) Odds of CKD progression in the fourth quartile of 20-HETE compared with its first quartile is statistically significant in an unadjusted model and remains significant after adjusting for eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker.

Open in new tabDownload slide

20-HETE as an independent predictor of CKD progression

Each 1-SD increase in 15-HETE was associated with 1.57-fold higher odds of CKD progression in an unadjusted model (95% CI 1.30–2.00; P < 0.001; Figure 3B). Similarly, each 1-SD increase in 20-HETE was associated with 1.52-fold higher unadjusted odds of CKD progression (95% CI 1.19–1.93; P = 0.001; Figure 3B). However, in an adjusted model, only 20-HETE was associated with progression independent of 15-HETE and other covariates [odds ratio of each 1-SD increase 1.45 (95% CI 1.07–1.95); P = 0.017; Figure 3B). Using 20-HETE concentration levels as a categorical variable, the fourth quartile was significantly associated with higher odds of CKD progression as compared with the first quartile in both unadjusted and adjusted models (Figure 3C).

Lack of association of epoxide: diol ratio with CKD progression

To assess the metabolic function of the enzyme soluble epoxide hydrolase in the CYP epoxygenase pathway and its link with CKD progression, the associations of 14,15-epoxyeicosatrienoic acid:dihydroxyicosatetraenoic acid ratio and 12,13-epoxyoctadecenoic acid:dihydroxyoctadecenoic acid ratio (as markers of the CYP epoxygenase pathway) with CKD progression were tested. We did not find any association between the epoxide:diol ratios and CKD progression (data not shown).

Principal component analysis

Next, we aggregated the eicosanoids of each distinct enzymatic pathway, including COX, 5-LOX, 12-LOX, 15-LOX and CYP450, into PC, each being representative of the corresponding pathway (Figure 4A). Overall, the mean PCs representative of the 5-LOX, 12-LOX, 15-LOX and CYP pathways were significantly higher in progressors as compared with nonprogressors (P ≤ 0.022; Figure 4B). There was a significant increase with a linear trend in the proportion of progressors from the first to the fourth quartiles based on the LOX and CYP representative PCs (P ≤ 0.008; Figure 4C). Accordingly, the proportion of progressors increased from 31% in the first quartile to 55% in the fourth quartile ranked by 5-LOX PC (P-trend = 0.003), from 24% in the first quartile to 49% in the fourth quartile ranked by 12-LOX (P-trend = 0.001), from 29% in the first quartile to 52% in the fourth quartiles ranked by15-LOX (P-trend = 0.004) and from 29% in the first quartile to 53% in the fourth quartile ranked by CYP (P-trend = 0.008; Figure 4C). Using logistic regression models, we showed that the odds of progression were significantly higher in the fourth quartile designated by LOX- and CYP-representative PCs as compared with their first quartiles in unadjusted and adjusted models (Figure 4D and Supplementary data, Table S4).

Alteration of arachidonic acid metabolic pathways preceding CKD progression. (A) Highly statistically significant correlation between constituents of each PC representative of COX, LOX and CYP450 pathways with the corresponding PC is demonstrated. (B) The mean levels of PCs representative of 5-, 12, 15-LOX and CYP are significantly higher in progressors as compared with nonprogressors. (C) Proportion of progressors significantly increases from the first to the last quartiles of 5-, 12-, 15- and CYP PCs (P ≤ 0.008). (D) Odds of CKD progression are significantly higher in the fourth quartile of the PCs representing 5-, 12-, 15-LOX and CYP as compared with their first quartiles in unadjusted models and after adjusting for eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker.
FIGURE 4

Alteration of arachidonic acid metabolic pathways preceding CKD progression. (A) Highly statistically significant correlation between constituents of each PC representative of COX, LOX and CYP450 pathways with the corresponding PC is demonstrated. (B) The mean levels of PCs representative of 5-, 12, 15-LOX and CYP are significantly higher in progressors as compared with nonprogressors. (C) Proportion of progressors significantly increases from the first to the last quartiles of 5-, 12-, 15- and CYP PCs (P ≤ 0.008). (D) Odds of CKD progression are significantly higher in the fourth quartile of the PCs representing 5-, 12-, 15-LOX and CYP as compared with their first quartiles in unadjusted models and after adjusting for eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker.

Open in new tabDownload slide

Secondary cardiovascular outcomes

Finally, we examined the association of the secondary cardiovascular outcomes with 20-HETE concentrations. In this cohort, 46 patients (15.3%) presented with atrial fibrillation, 25 (8.3%) with AMI, 43 (14.3%) with CHF and 7 (2.3%) with stroke and 50 patients (16.7%) died. The secondary outcomes, except for stroke, occurred significantly more often in progressors as compared with nonprogressors (P ≤ 0.007; Table 3). The proportion of patients with CHF increased from 5.3% in the first quartile based on 20-HETE levels to 21.3% in the fourth quartile (P-trend = 0.006; Figure 5A). The proportion of other cardiovascular outcomes was not statistically different by 20-HETE quartiles (Figure 5A). The odds of CHF in the fourth quartile by 20-HETE level was 4.81-fold higher than that of the first quartile in an unadjusted model (95% CI 1.53–15.18; P = 0.007; Figure 5B). Similarly, the odds of CHF were 4.92-fold higher in the fourth quartile when compared with the first quartile in the adjusted model (95% CI 1.47–16.27; P = 0.009; Figure 5B). Although there was a trend toward higher odds of AMI in the second to fourth quartiles based on 20-HETE levels as compared with the first quartile, the odds ratio did not reach statistical significance. There was no association between 20-HETE quartiles and the outcomes of stroke or death (Figure 5 and Supplementary data, Table S5). None of the PCs representative of the COX, LOX or CYP pathways were associated with the secondary outcomes.

The secondary cardiovascular outcomes and 20-HETE. (A) There is a significant increase in the proportion of patients with incident CHF from the first to the last quartile of 20-HETE (P-trend = 0.006). The differences did not reach statistical significance in other cardiovascular outcomes and death. (B) Odds of incident CHF are significantly higher in the fourth quartile of 20-HETE as compared with its first quartile in the unadjusted model (models at the top) as well as after adjusting for eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker (models at the bottom). The odds of other cardiovascular outcome were not significantly different by quartiles of 20-HETE. A-fib, atrial fibrillation.
FIGURE 5

The secondary cardiovascular outcomes and 20-HETE. (A) There is a significant increase in the proportion of patients with incident CHF from the first to the last quartile of 20-HETE (P-trend = 0.006). The differences did not reach statistical significance in other cardiovascular outcomes and death. (B) Odds of incident CHF are significantly higher in the fourth quartile of 20-HETE as compared with its first quartile in the unadjusted model (models at the top) as well as after adjusting for eGFR, urine protein:creatinine ratio, systolic blood pressure, hypertension and use of calcium channel blocker (models at the bottom). The odds of other cardiovascular outcome were not significantly different by quartiles of 20-HETE. A-fib, atrial fibrillation.

Open in new tabDownload slide
Table 3
Open in new tab

Distribution of the secondary outcomes by progression status

OutcomeNonprogressor, n (%)Progressor, n (%)P-value
Death21 (11.9)29 (23.6)0.007
AF10 (5.6)36 (29.3)<0.001
CHF11 (6.2)32 (26.0)<0.001
AMI6 (3.4)19 (15.4)<0.001
Stroke3 (1.7)4 (3.3)0.380
OutcomeNonprogressor, n (%)Progressor, n (%)P-value
Death21 (11.9)29 (23.6)0.007
AF10 (5.6)36 (29.3)<0.001
CHF11 (6.2)32 (26.0)<0.001
AMI6 (3.4)19 (15.4)<0.001
Stroke3 (1.7)4 (3.3)0.380

AF, atrial fibrillation.

Table 3
Open in new tab

Distribution of the secondary outcomes by progression status

OutcomeNonprogressor, n (%)Progressor, n (%)P-value
Death21 (11.9)29 (23.6)0.007
AF10 (5.6)36 (29.3)<0.001
CHF11 (6.2)32 (26.0)<0.001
AMI6 (3.4)19 (15.4)<0.001
Stroke3 (1.7)4 (3.3)0.380
OutcomeNonprogressor, n (%)Progressor, n (%)P-value
Death21 (11.9)29 (23.6)0.007
AF10 (5.6)36 (29.3)<0.001
CHF11 (6.2)32 (26.0)<0.001
AMI6 (3.4)19 (15.4)<0.001
Stroke3 (1.7)4 (3.3)0.380

AF, atrial fibrillation.

DISCUSSION

In this study we showed, at the compound level, that a significantly higher level of 20-HETE, a CYP450 AA product, preceded future progression of CKD and was independently associated with incident ESKD in the CRIC. Similarly, at the pathway level, variables representative of 5-LOX, 12-LOX, 15-LOX and CYP enzymatic pathways were significantly higher in progressors as compared with nonprogressors and were independently associated with CKD progression. 20-HETE was also independently associated with subsequent CHF exacerbation, as evidenced by a graded and significant increase in the rate of CHF as the 20-HETE concentration increased. Although the biology of eicosanoids has been extensively reviewed in model systems [19–23], to our knowledge this is the first comprehensive study in humans with systematic quantification of AA metabolites demonstrating significant alterations in products of LOX and CYP AA enzymatic pathways preceding CKD progression.

20-HETE is widely synthesized in the liver, kidneys, brain, lungs, intestine and blood vessels by CYP450 ω-hydroxylase from AA [22]. In humans, the CYP4 ω-hydroxylases include CYP4A11, CYP4F2 and CYP4F3, with the predominant 20-HETE-synthesizing enzymes being CYP4F2, which is the major 20-HETE-producing enzyme in the human kidney, followed by CYP4A11 [22]. In healthy individuals, a 15-HETE serum level as low as 0.32 pmol/mL [24] and a 20-HETE serum level as low as 0.48 pmol/mL has been reported [25]. The levels of 15-HETE and 20-HETE observed in our study are similar to those observed by others in similar disease states. In a study of 262 African Americans, a mean plasma level as high as 1.1 ng/mL was recorded for 20-HETE [26]. In the analysis of the HALT Progression of Polycystic Kidney Disease trial, the mean serum level of 12-HETE and 15-HETE was ∼6 ng/mL in PKD patients with eGFR <60 mL/min [24]. We observed mean serum concentrations of 19.3 pmol/mL (6.2 ng/mL) for 12-HETE, 4.5 pmol/mL (1.44 ng/mL) for 15-HETE and 5.4 pmol/mL (1.7 ng/mL) for 20-HETE in nonprogressors. In nonprogressors, the level of 20-HETE was marginally higher in women as compared with men, which is likely due to differential regulation of ω-hydroxylases by sex hormones [27]. This association was lost in progressors, implying that the sex hormone effect on ω-hydroxylases is diminished in this subgroup. Nevertheless, adjusting the models by sex did not alter the results and the associations with outcome remained independent of sex. In our study we measured the levels of free eicosanoids in the samples. The increased free levels may reflect increased production, altered metabolism, such as decreased glucuronidation in the liver, changes in protein binding or alterations in excretion. As the levels were independent of eGFR and proteinuria, it is unlikely that differences in filtration rate or the severity of proteinuria would have explained the differential levels.

The proposed mechanisms explaining the effects of 20-HETE in model systems include impaired natriuresis, myogenic activity and tubuloglomerular feedback [21, 28, 29] associated with retention of sodium, upregulation of transforming growth factor β (TGF-β) and development of salt-sensitive hypertension [30, 31]; vasoconstriction, endothelial dysfunction [32, 33] and direct activation of thromboxane A2 receptors [11, 20]; intimal hyperplasia and vascular smooth muscle remodeling [13]; accelerated thrombosis formation [14] and impact on glycemic status [34, 35], among others. On the other hand, 15-HETE is an inflammatory mediator that increases in response to inflammation [36] and may act by mitigating respiratory burst capacity [37], abolishing chemotactic response to leukotriene B4 [38] and altering the amount and rate of production of diacylglycerol and inositol triphosphate second messengers [39].

The exact mechanisms underlying the association of elevated eicosanoids with CKD progression in humans need to be determined. In a double-blind, placebo-controlled randomized clinical trial, patients who ingested 4 g of n-3 fatty acids per day for 8 weeks showed a significant decrease in 20-HETE postintervention [40]. Postintervention, 20-HETE levels were also an independent predictor of decreases in systolic and diastolic blood pressure [40], suggesting that hypertension-mediated mechanisms may be the promoters of progression. Such mechanisms may include increased renal or extrarenal vasoconstriction, angiogenesis, endothelial dysfunction, uncoupling of endothelial nitric oxide synthase and increased oxidative stress [13, 32, 33]. CYP450-regulated HETEs as a class may share mechanisms of injury similar to those of 20-HETE, because the PC representative of CYP450-regulated HETEs had a prognostic value similar to 20-HETE.

In our study we did not find any association between the epoxide:diol ratio and CKD outcomes, suggesting that the products of the epoxygenase pathway of CYP450 are unlikely to contribute to CKD progression. As the detected levels of epoxides and diols are significantly above the lower limit of detection for the assay, the lack of association with outcome is a reflection of comparable levels in serum in progressors and nonprogressors rather than technical or sample storage issues.

In animal models, increased 5-LOX products are associated with tubulointerstitial injury and inflammation, proteinuria, decreased renal blood flow and decreased GFR [7, 8]. The postulated mechanisms may involve induction of angiotensin II [41], mitochondrial disruption [42], increased production of reactive oxygen species and activation of p38–mitogen-activated protein kinase (MAPK) [43]. Similarly, renal inflammation and injury, expansion of mesangial cells, albuminuria and decreased GFR and blood flow have been noted with increased activity of 12-LOX and 15-LOX in model systems [10, 44, 45]. The contributing mechanisms may include increased expression of angiotensin II type 1 receptor, insulin resistance [10], increased production of superoxide and the matrix protein fibronectin, hyperactivity of p38- or extracellular signal-regulated kinase1/2 MAPKs, cyclic adenosine monophosphate–responsive element binding protein transcription factor, collagen α5(IV) mRNA [46–49] and overexpression of TGF-β [9].

This study has several strengths. To our knowledge it is the first study in humans to demonstrate the independent prognostic value of AA metabolites in CKD progression. We used a targeted platform in the MRM mode with rigorous quality control. Careful matching of the cases and controls by age, sex, race and diabetes according to the study protocol maximized the power for detection of true signals given the limited sample size. The availability of pertinent covariates allowed proper multivariable adjustments. Longitudinal follow-up to ascertain renal and cardiovascular outcomes in the CRIC provided a unique opportunity to test the prognostic value of the eicosanoids on outcomes.

This study also has limitations. As it is an observational study, we cannot infer causality. It is unclear if the increased level in circulation parallels similar alterations in renal parenchyma. The results may not be extrapolated to other CKD cohorts and results need replication in independent cohorts. The study is underpowered for an AMI secondary outcome. We cannot reject the prognostic value of less abundant compounds with levels closer to the detection limit that did not pass the statistical threshold (type II error). In this study we measured the free circulating levels of the eicosanoids that we believe carry the biological activities of the compounds as opposed to the protein-bound fractions. Further studies are required to assess the prognostic value of protein-bound and total eicosanoids on clinical outcomes. Finally, genetic polymorphisms in genes encoding CYP4F2 might account for the differences in 20-HETE levels [50], which was not explored in this study but should be examined in future studies. We also did not identify the origin or mechanism of increased circulating levels such as increased production, decreased catabolism or alteration in protein binding. Nevertheless, increased levels as downstream metabolic derangements preceding progression of CKD by several years signifies their potential role in renal outcome.

In conclusion, we found higher odds of CKD progression associated with higher 20-HETE, LOX and CYP pathway representatives. These alterations precede CKD progression and may serve as targets for rational interventions aimed at halting CKD progression.

ACKNOWLEDGEMENTS

The CRIC study investigators include Lawrence J. Appel, MD, MPH, Alan S. Go, MD, Jiang He, MD, PhD, John W. Kusek, PhD, James P. Lash, MD, Panduranga S. Rao, MD, Mahboob Rahman, MD and Raymond R. Townsend, MD.

FUNDING

Funding for the CRIC study was obtained under a cooperative agreement from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963 and U01DK060902). In addition, this work is supported in part by grants from the National Institutes of Health [K08DK106523 (to FA), P30DK089503, DK082841, P30DK081943, P30DK020572, DK097153 (to SP), UL1TR000433 (to the University of Michigan), UL1TR000003 (to the University of Pennsylvania), UL1 TR-000424 (to Johns Hopkins University), General Clinical Research Center M01 RR-16500 (to the University of Maryland and the Cleveland Clinical and Translational Science Collaborative), UL1TR000439 (to the National Center for Advancing Translational Sciences), UL1RR029879 (to the University of Illinois), P20 GM109036 (to Tulane University) and UL1 RR-024131 (to Kaiser Permanente)].

AUTHORS’ CONTRIBUTIONS

F.A. designed the study, prepared the samples, analyzed and interpreted the data and wrote the first draft. L.Z. and J.B. conducted the MS runs, and participated in drafting. S.W. performed statistical analysis, interpreted the results, critically evaluated the paper and participated in drafting. R.D., E.R.M. and E.P.R. critically evaluated the paper and participated in drafting. J.C. and L.H. helped with data collection. K.S. contributed intellectually to the scientific aspects. H.I.F. helped with design, data collection and critical evaluation of the project. G.M. helped with the study design, interpretation of the results and drafting. S.P. helped with the design, interpretation of the results and critical evaluation of the paper. All authors have approved the final version.

CONFLICT OF INTEREST STATEMENT

F.A. received a grant from the NIDDK in support of the current study. K.S. received consulting fees from Boerhinger Ingelheim and Sanofi from 2016 to 2018. H.I.F. received grants from the NIDDK, during the conduct of the study and consulted for Kyowa Hakko Kirin outside the submitted work. L.Z., J.B., S.W., R.D., J.C., L.H., E.R.M., E.P.R., M.J.F., G.M. and S.P. declare no conflicts of interest. The results presented in this paper have not been published previously in whole or part, except in abstract format.

REFERENCES

1

Wang
Y
,
Armando
AM
,
Quehenberger
O
 et al.
Comprehensive ultra-performance liquid chromatographic separation and mass spectrometric analysis of eicosanoid metabolites in human samples
.
J Chromatogr A
2014
;
1359
:
60
–
69

Google Scholar

Crossref
Search ADS
PubMed

 
2

Marion-Letellier
R
,
Savoye
G
,
Ghosh
S.
Fatty acids, eicosanoids and PPAR gamma
.
Eur J Pharmacol
2016
;
785
:
44
–
49

Google Scholar

Crossref
Search ADS
PubMed

 
3

Buczynski
MW
,
Dumlao
DS
,
Dennis
EA.
Thematic review series: proteomics. An integrated omics analysis of eicosanoid biology
.
J Lipid Res
2009
;
50
:
1015
–
1038

Google Scholar

Crossref
Search ADS
PubMed

 
4

Goetzl
EJ
,
An
S
,
Smith
WL.
Specificity of expression and effects of eicosanoid mediators in normal physiology and human diseases
.
FASEB J
1995
;
9
:
1051
–
1058

Google Scholar

Crossref
Search ADS
PubMed

 
5

Panigrahy
D
,
Kaipainen
A
,
Greene
ER
 et al.
Cytochrome P450-derived eicosanoids: the neglected pathway in cancer
.
Cancer Metastasis Rev
2010
;
29
:
723
–
735

Google Scholar

Crossref
Search ADS
PubMed

 
6

Sperling
RI.
Eicosanoids in rheumatoid arthritis
.
Rheum Dis Clin North Am
1995
;
21
:
741
–
758

Google Scholar

PubMed
OpenURL Placeholder Text

 
7

Patel
NS
,
Cuzzocrea
S
,
Chatterjee
PK
 et al.
Reduction of renal ischemia-reperfusion injury in 5-lipoxygenase knockout mice and by the 5-lipoxygenase inhibitor zileuton
.
Mol Pharmacol
2004
;
66
:
220
–
227

Google Scholar

Crossref
Search ADS
PubMed

 
8

Wu
SH
,
Bresnahan
BA
,
Lianos
EA.
Hemodynamic role of arachidonate 12- and 5-lipoxygenases in nephrotoxic serum nephritis
.
Kidney Int
1993
;
43
:
1280
–
1285

Google Scholar

Crossref
Search ADS
PubMed

 
9

Kim
YS
,
Xu
ZG
,
Reddy
MA
 et al.
Novel interactions between TGF-β1 actions and the 12/15-lipoxygenase pathway in mesangial cells
.
J Am Soc Nephrol
2005
;
16
:
352
–
362

Google Scholar

Crossref
Search ADS
PubMed

 
10

Xu
HZ
,
Cheng
YL
,
Wang
WN
 et al.
12-Lipoxygenase inhibition on microalbuminuria in type-1 and type-2 diabetes is associated with changes of glomerular angiotensin II type 1 receptor related to insulin resistance
.
Int J Mol Sci
2016
;
17
:
684

Google Scholar

Crossref
Search ADS

 
11

Fan
F
,
Sun
CW
,
Maier
KG
 et al.
20-Hydroxyeicosatetraenoic acid contributes to the inhibition of K+ channel activity and vasoconstrictor response to angiotensin II in rat renal microvessels
.
PLoS One
2013
;
8
:
e82482

Google Scholar

Crossref
Search ADS
PubMed

 
12

Gangadhariah
MH
,
Luther
JM
,
Garcia
V
 et al.
Hypertension is a major contributor to 20-hydroxyeicosatetraenoic acid-mediated kidney injury in diabetic nephropathy
.
J Am Soc Nephrol
2015
;
26
:
597
–
610

Google Scholar

Crossref
Search ADS
PubMed

 
13

Orozco
LD
,
Liu
H
,
Perkins
E
 et al.
20-Hydroxyeicosatetraenoic acid inhibition attenuates balloon injury-induced neointima formation and vascular remodeling in rat carotid arteries
.
J Pharmacol Exp Ther
2013
;
346
:
67
–
74

Google Scholar

Crossref
Search ADS
PubMed

 
14

Wang
J
,
Li
H
,
He
J
 et al.
20-Hydroxyeicosatetraenoic acid involved in endothelial activation and thrombosis
.
Am J Physiol Heart Circ Physiol
2015
;
308
:
H1359
–
H1367

Google Scholar

Crossref
Search ADS
PubMed

 
15

Afshinnia
F
,
Rajendiran
TM
,
Karnovsky
A
 et al.
Lipidomic signature of progression of chronic kidney disease in the chronic renal insufficiency cohort
.
Kidney Int Rep
2016
;
1
:
256
–
268

Google Scholar

Crossref
Search ADS
PubMed

 
16

Feldman
HI
,
Appel
LJ
,
Chertow
GM
 et al.
The chronic renal insufficiency cohort (CRIC) study: design and methods
.
J Am Soc Nephrol
2003
;
14(7 Suppl 2)
:
S148
–
S153

Google Scholar

Crossref
Search ADS

 
17

Lash
JP
,
Go
AS
,
Appel
LJ
 et al.
Chronic Renal Insufficiency Cohort (CRIC) study: baseline characteristics and associations with kidney function
.
Clin J Am Soc Nephrol
2009
;
4
:
1302
–
1311

Google Scholar

Crossref
Search ADS
PubMed

 
18

Levey
AS
,
Stevens
LA
,
Schmid
CH
 et al.
A new equation to estimate glomerular filtration rate
.
Ann Intern Med
2009
;
150
:
604
–
612

Google Scholar

Crossref
Search ADS
PubMed

 
19

Dobrian
AD
,
Lieb
DC
,
Cole
BK
 et al.
Functional and pathological roles of the 12- and 15-lipoxygenases
.
Prog Lipid Res
2011
;
50
:
115
–
131

Google Scholar

Crossref
Search ADS
PubMed

 
20

Fan
F
,
Muroya
Y
,
Roman
RJ.
Cytochrome P450 eicosanoids in hypertension and renal disease
.
Curr Opin Nephrol Hypertens
2015
;
24
:
37
–
46

Google Scholar

Crossref
Search ADS
PubMed

 
21

Fan
F
,
Roman
RJ.
Effect of cytochrome P450 metabolites of arachidonic acid in nephrology
.
J Am Soc Nephrol
2017
;
28
:
2845
–
2855

Google Scholar

Crossref
Search ADS
PubMed

 
22

Kroetz
DL
,
Xu
F.
Regulation and inhibition of arachidonic acid omega-hydroxylases and 20-HETE formation
.
Annu Rev Pharmacol Toxicol
2005
;
45
:
413
–
438

Google Scholar

Crossref
Search ADS
PubMed

 
23

Roman
RJ.
P-450 metabolites of arachidonic acid in the control of cardiovascular function
.
Physiol Rev
2002
;
82
:
131
–
185

Google Scholar

Crossref
Search ADS
PubMed

 
24

Klawitter
J
,
Klawitter
J
,
McFann
K
 et al.
Bioactive lipid mediators in polycystic kidney disease
.
J Lipid Res
2014
;
55
:
1139
–
1149

Google Scholar

Crossref
Search ADS
PubMed

 
25

Mazaleuskaya
LL
,
Salamatipour
A
,
Sarantopoulou
D
 et al.
Analysis of HETEs in human whole blood by chiral UHPLC-ECAPCI/HRMS
.
J Lipid Res
2018
;
59
:
564
–
575

Google Scholar

Crossref
Search ADS
PubMed

 
26

Dreisbach
AW
,
Smith
SV
,
Kyle
PB
 et al.
Urinary CYP eicosanoid excretion correlates with glomerular filtration in African-Americans with chronic kidney disease
.
Prostaglandins Other Lipid Mediat
2014
;
113-115
:
45
–
51

Google Scholar

Crossref
Search ADS
PubMed

 
27

Wu
CC
,
Schwartzman
ML.
The role of 20-HETE in androgen-mediated hypertension
.
Prostaglandins Other Lipid Mediat
2011
;
96
:
45
–
53

Google Scholar

Crossref
Search ADS
PubMed

 
28

Nowicki
S
,
Chen
SL
,
Aizman
O
 et al.
20-Hydroxyeicosa-tetraenoic acid (20 HETE) activates protein kinase C. Role in regulation of rat renal Na+, K+-ATPase
.
J Clin Invest
1997
;
99
:
1224
–
1230

Google Scholar

Crossref
Search ADS
PubMed

 
29

Yu
M
,
Lopez
B
,
Dos Santos
EA
 et al.
Effects of 20-HETE on Na+ transport and Na+-K+-ATPase activity in the thick ascending loop of Henle
.
Am J Physiol Regul Integr Comp Physiol
2007
;
292
:
R2400
–
R2405

Google Scholar

Crossref
Search ADS
PubMed

 
30

Ma
YH
,
Schwartzman
ML
,
Roman
RJ.
Altered renal P-450 metabolism of arachidonic acid in Dahl salt-sensitive rats
.
Am J Physiol
1994
;
267(2 Pt 2)
:
R579
–
R589

Google Scholar

OpenURL Placeholder Text

 
31

Zoccali
C
,
Mallamaci
F
,
Grassi
G.
20-Hydroxyeicosatetraenoic acid, a far-reaching autacoid in chronic kidney disease: hypertension and beyond
.
J Hypertens
2015
;
33
:
1764
–
1766

Google Scholar

Crossref
Search ADS
PubMed

 
32

Cheng
J
,
Ou
JS
,
Singh
H
 et al.
20-Hydroxyeicosatetraenoic acid causes endothelial dysfunction via eNOS uncoupling
.
Am J Physiol Heart Circ Physiol
2008
;
294
:
H1018
–
H1026

Google Scholar

Crossref
Search ADS
PubMed

 
33

Cheng
J
,
Wu
CC
,
Gotlinger
KH
 et al.
20-Hydroxy-5,8,11,14-eicosatetraenoic acid mediates endothelial dysfunction via IκB kinase-dependent endothelial nitric-oxide synthase uncoupling
.
J Pharmacol Exp Ther
2010
;
332
:
57
–
65

Google Scholar

Crossref
Search ADS
PubMed

 
34

Eid
AA
,
Gorin
Y
,
Fagg
BM
 et al.
Mechanisms of podocyte injury in diabetes: role of cytochrome P450 and NADPH oxidases
.
Diabetes
2009
;
58
:
1201
–
1211

Google Scholar

Crossref
Search ADS
PubMed

 
35

Luo
P
,
Zhou
Y
,
Chang
HH
 et al.
Glomerular 20-HETE, EETs, and TGF-β1 in diabetic nephropathy
.
Am J Physiol Renal Physiol
2009
;
296
:
F556
–
F563

Google Scholar

Crossref
Search ADS
PubMed

 
36

Badr
KF.
15-Lipoxygenase products as leukotriene antagonists: therapeutic potential in glomerulonephritis
.
Kidney Int Suppl
1992
;
38
:
S101
–
S108

Google Scholar

PubMed
OpenURL Placeholder Text

 
37

Brezinski
ME
,
Serhan
CN.
Selective incorporation of (15S)-hydroxyeicosatetraenoic acid in phosphatidylinositol of human neutrophils: agonist-induced deacylation and transformation of stored hydroxyeicosanoids
.
Proc Natl Acad Sci USA
1990
;
87
:
6248
–
6252

Google Scholar

Crossref
Search ADS
PubMed

 
38

Fischer
DB
,
Christman
JW
,
Badr
KF.
Fifteen-S-hydroxyeicosatetraenoic acid (15-S-HETE) specifically antagonizes the chemotactic action and glomerular synthesis of leukotriene B4 in the rat
.
Kidney Int
1992
;
41
:
1155
–
1160

Google Scholar

Crossref
Search ADS
PubMed

 
39

Girton
RA
,
Spector
AA
,
Gordon
JA.
15-HETE: selective incorporation into inositol phospholipids of MDCK cells
.
Kidney Int
1994
;
45
:
972
–
980

Google Scholar

Crossref
Search ADS
PubMed

 
40

Barden
AE
,
Burke
V
,
Mas
E
 et al.
n-3 fatty acids reduce plasma 20-hydroxyeicosatetraenoic acid and blood pressure in patients with chronic kidney disease
.
J Hypertens
2015
;
33
:
1947
–
1953

Google Scholar

Crossref
Search ADS
PubMed

 
41

Revermann
M
,
Mieth
A
,
Popescu
L
 et al.
A pirinixic acid derivative (LP105) inhibits murine 5-lipoxygenase activity and attenuates vascular remodelling in a murine model of aortic aneurysm
.
Br J Pharmacol
2011
;
163
:
1721
–
1732

Google Scholar

Crossref
Search ADS
PubMed

 
42

Maccarrone
M
,
Taccone-Gallucci
M
,
Finazzi-Agro
A.
5-Lipoxygenase-mediated mitochondrial damage and apoptosis of mononuclear cells in ESRD patients
.
Kidney Int Suppl
2003
;
63
:
S33
–
S36

Google Scholar

Crossref
Search ADS

 
43

Ha
YJ
,
Seul
HJ
,
Lee
JR.
Ligation of CD40 receptor in human B lymphocytes triggers the 5-lipoxygenase pathway to produce reactive oxygen species and activate p38 MAPK
.
Exp Mol Med
2011
;
43
:
101
–
110

Google Scholar

Crossref
Search ADS
PubMed

 
44

Faulkner
J
,
Pye
C
,
Al-Shabrawey
M
 et al.
Inhibition of 12/15-lipoxygenase reduces renal inflammation and injury in streptozotocin-induced diabetic mice
.
J Diabetes Metab
2015
;
6
: 555

Google Scholar

OpenURL Placeholder Text

 
45

Ma
J
,
Natarajan
R
,
LaPage
J
 et al.
12/15-Lipoxygenase inhibitors in diabetic nephropathy in the rat
.
Prostaglandins Leukot Essent Fatty Acids
2005
;
72
:
13
–
20

Google Scholar

Crossref
Search ADS
PubMed

 
46

Kang
SW
,
Adler
SG
,
Nast
CC
 et al.
12-Lipoxygenase is increased in glucose-stimulated mesangial cells and in experimental diabetic nephropathy
.
Kidney Int
2001
;
59
:
1354
–
1362

Google Scholar

Crossref
Search ADS
PubMed

 
47

Kang
SW
,
Natarajan
R
,
Shahed
A
 et al.
Role of 12-lipoxygenase in the stimulation of p38 mitogen-activated protein kinase and collagen α5(IV) in experimental diabetic nephropathy and in glucose-stimulated podocytes
.
J Am Soc Nephrol
2003
;
14
:
3178
–
3187

Google Scholar

Crossref
Search ADS
PubMed

 
48

Kim
YS
,
Reddy
MA
,
Lanting
L
 et al.
Differential behavior of mesangial cells derived from 12/15-lipoxygenase knockout mice relative to control mice
.
Kidney Int
2003
;
64
:
1702
–
1714

Google Scholar

Crossref
Search ADS
PubMed

 
49

Reddy
MA
,
Adler
SG
,
Kim
YS
 et al.
Interaction of MAPK and 12-lipoxygenase pathways in growth and matrix protein expression in mesangial cells
.
Am J Physiol Renal Physiol
2002
;
283
:
F985
–
F994

Google Scholar

Crossref
Search ADS
PubMed

 
50

Ward
NC
,
Tsai
IJ
,
Barden
A
 et al.
A single nucleotide polymorphism in the CYP4F2 but not CYP4A11 gene is associated with increased 20-HETE excretion and blood pressure
.
Hypertension
2008
;
51
:
1393
–
1398

Google Scholar

Crossref
Search ADS
PubMed

 
© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
Topic:
Issue Section:
Original Articles > Clinical Research
Download all slides
  • Supplementary data

    • Supplementary data

    gfy232_Supplementary_Data - zip file

    Comments

    0 Comments
    Comments (0)
    Submit a comment
    You have entered an invalid code
    Thank you for submitting a comment on this article. Your comment will be reviewed and published at the journal's discretion. Please check for further notifications by email.