Association of genetic polymorphisms with risk of renal injury after coronary bypass graft surgery

Association of Genetic Polymorphisms With Risk of Renal Injury After Coronary Bypass Graft Surgery Mark Stafford-Smith, MD, Mihai Podgoreanu, MD, Madhav Swaminathan, MD, Barbara Phillips-Bute, PhD, Joseph P. Mathew, MD, Elizabeth H. Hauser, PhD, Michelle P. Winn, MD, Carmelo Milano, MD, Dahlia M. Nielsen, PhD, Mike Smith, MS, Richard Morris, PhD, Mark F. Newman, MD, and Debra A. Schwinn, MD, for the Perioperative Genetics and Safety Outcomes Study (PEGASUS) Investigative Team ● Background: Post– cardiac surgery renal dysfunction is a common, serious, multifactorial disorder, with interpatient variability predicted poorly by preoperative clinical, procedural, and biological markers. Therefore, we tested the hypothesis that selected gene variants are associated with acute renal injury, reflected by a serum creatinine level increase after cardiac surgery. Methods: One thousand six hundred seventy-one patients undergoing aortocoronary surgery were studied. Clinical covariates were recorded. DNA was isolated from preoperative blood; mass spectrometry was used for genotype analysis. A model was developed relating clinical and genetic factors to postoperative acute renal injury. Results: A race effect was found; therefore, Caucasians and African Americans were analyzed separately. Overall, clinical factors alone account poorly for postoperative renal injury, although more so in African Americans than Caucasians. When 12 candidate polymorphisms were assessed, 2 alleles (interleukin 6 ⴚ572C and angiotensinogen 842C) showed a strong association with renal injury in Caucasians (P < 0.0001; >50% decrease in renal filtration when they present together). Using less stringent criteria for significance (0.01 > P > 0.001), 4 additional polymorphisms are identified (apolipoproteinE 448C [⑀4], angiotensin receptor1 1166C, and endothelial nitric oxide synthase [eNOS] 894T in Caucasians; eNOS 894T and angiotensin-converting enzyme deletion and insertion in African Americans). Adding genetic to clinical factors resulted in the best model, with overall ability to explain renal injury increasing approximately 4-fold in Caucasians and doubling in African Americans (P < 0.0005). Conclusion: In this study, we identify genetic polymorphisms that collectively provide 2- to 4-fold improvement over preoperative clinical factors alone in explaining post– cardiac surgery renal dysfunction. From a mechanistic perspective, most identified genetic variants are associated with increased renal inflammatory and/or vasoconstrictor responses. Am J Kidney Dis 45:519-530. © 2005 by the National Kidney Foundation, Inc. INDEX WORDS: Acute renal failure (ARF); polymorphism; genetic; postoperative; intensive care; cardiopulmonary bypass (CPB); heart surgery; cardiac surgery; human; angiotensin-converting enzyme (ACE); associate study; candidate genes.

A

CUTE RENAL dysfunction, evidenced by a rapid decline in glomerular filtration rate and accumulation of nitrogenous waste products (blood urea nitrogen and creatinine), is a major medical problem occurring in 5% of all patients admitted to the hospital and 30% of those admitted to an intensive care unit.1 Furthermore, acute renal injury remains a common serious complication of cardiac surgery.2 Multiple causes for this observation have been proposed, including nephrotoxins, atheroembolism, ischemia-reperfusion, and cardiopulmonary bypass (CPB)–induced activation of inflammatory pathways. Renal failure requiring dialysis occurs in up to 5% of patients undergoing cardiac surgery; an additional 8% to 15% have moderate renal injury (eg, ⬎1.0mg/dL [88 ␮mol/L] peak creatinine level increase).2-9 Lesser renal injuries are even more common (⬎50% of patients undergoing aortocoronary bypass surgery have a ⱖ25% postoperative increase in serum creatinine level). In many settings, including cardiac surgery, acute renal failure is associated independently with the in-

From the Departments of Anesthesiology, Medicine, Surgery, and Pharmacology/Cancer Biology, Duke University Medical Center, Durham; and the Department of Statistics, North Carolina State University, Raleigh, NC. Received September 30, 2004; accepted in revised form November 24, 2004. Originally published online as doi:10.1053/j.ajkd.2004.11.021 on January 26, 2005. See Appendix for a list of members of the Perioperative Genetics and Safety Outcomes Study (PEGASUS) Investigative Team. M.S.-S. and M.P. are co–first authors. Supported in part by grant no. AG17556 from The National Institutes of Health (D.A.S.); grants no. 0120492U (M.V.P.), 0256342U (J.P.M.), and 9970128N (M.F.N.) from the American Heart Association; and the Duke Clinical Research Centers Program (grant no. M01-RR-30 from The National Institutes of Health). D.A.S. is a senior fellow in the Duke Center for the Study of Aging and Human Development. Address reprint requests to Mark Stafford-Smith, MD, Duke University Medical Center, Department of Anesthesiology, Box 3094, Durham, NC 27710. E-mail: [email protected] © 2005 by the National Kidney Foundation, Inc. 0272-6386/05/4503-0009$30.00/0 doi:10.1053/j.ajkd.2004.11.021

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hospital mortality rate, even after adjustment for comorbidities and other complications2,10,11; all degrees of renal injury are associated with increased mortality and other adverse outcomes.12 The grave prognosis associated with this complication may be caused, at least in part, by distant effects of acute renal injury on other organ function.13,14 Unfortunately, typical characteristics of those presenting for cardiac surgery (eg, advanced age and history of atherosclerotic vascular disease) make these patients a group at high “renal risk.”2,15-17 Many of the insults sustained by the kidney as part of cardiac surgery (eg, CPB, atheroembolism) are believed to result in ischemic- and/or inflammation-mediated renal injury. The oxygen diffusion shunt characteristic of renal circulation and metabolic demands from active tubular reabsorption contribute to the precarious physiological process of renal perfusion, including low medullary PO2 (10 to 20 mm Hg).18 Key to the regulation of renal blood flow are paracrine systems (eg, renin-angiotensin system and nitric oxide) that modulate microvascular function and oxygen delivery in the renal medulla.19 The inflammatory response to CPB generates cytokines (eg, tumor necrosis factor ␣ [TNF-␣] and interleukin 6 [IL-6]), both systemically and locally in the kidney,20,21 that have major effects on the renal microcirculation and may lead to tubular injury.22 Recent evidence suggests that heritable differences modulate the activation of these pathways. Although many preoperative and procedural predictors and biological markers have been identified, risk stratification based on these factors explains only a small part of the variability in post-cardiac surgery renal dysfunction.2-9,17,23-32 In addition, little is known regarding the relationship of the several known polymorphisms associated with altered activation of renal paracrine and/or inflammatory pathways with acute renal injury after aortocoronary bypass graft surgery. The few existing studies focused on only 2 genetic polymorphisms (apolipoprotein E [APOE] 448C [⑀4] and IL-6 ⫺174C)33-35 and did not take into account other important pathways and/or proteins or interactions between potentially synergistic insults. Therefore, we tested the hypothesis that genetic variants of inflammatory and paracrine pathways at multiple

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loci are associated with susceptibility to acute renal injury after cardiac surgery. METHODS

Study Population This analysis is a substudy of the Perioperative Genetics and Safety Outcomes Study, an ongoing Institutional Review Board–approved, prospective, longitudinal study at Duke University Medical Center (Durham, NC) in which 3,149 patients have been prospectively enrolled and consented to have clinical and genetic data analyzed in relation to perioperative outcomes. The current substudy targets 2,075 patients undergoing primary elective (ie, scheduled) aortocoronary bypass graft surgery using CPB between April 1995 and May 2002, a prespecified period in which detailed perioperative serum creatinine and dialysis data were systematically and prospectively collected. Three hundred seven patients with missing or misleading serum creatinine data were excluded, including those who died within 2 days of surgery and those requiring perioperative dialysis. Of the final 1,768 patients examined, 1,464 were Caucasian, 207 were African American, and 97 were of another race. Because of small numbers in each of the other race categories, our analysis was limited to Caucasians and African Americans.

Clinical Data Collection Selection of both an appropriate surgical procedure and a relevant marker of renal function is important in the assessment of subtle differences in renal injury that may occur with genetic variation. The impact of procedure type on the incidence and severity of renal injury is important.2 To best study allele effects, patients undergoing the same procedure should be examined; in this regard, nonemergent first-time coronary bypass surgery with CPB is a common, highly monitored, and highly reproducible model. Conversely, an obvious selection for the most appropriate marker of renal function for postoperative studies does not exist. Rigorous study of postoperative acute renal injury has been hampered by a lack of consensus on definitions (1 review evaluated 26 controlled studies, and no 2 reports used the same criteria for acute renal dysfunction or acute renal failure).17 Urine protein markers of tubular cell injury or dysfunction are an alternative, but we previously highlighted serious limitations specific to drugs administered during cardiac surgery that render them essentially without value in this setting.36 New-onset dialysis is a firm end point, but occurs too rarely (⬍2%) to be a practical marker. At our institution, serum creatinine determinations are available daily as part of standard post–cardiac surgery care protocols. We selected peak fractional change in postoperative serum creatinine level (%⌬Cr) as the primary outcome variable for this study, defined as the percentage of difference between preoperative serum creatinine and highest of the daily in-hospital postoperative values; this is a continuous variable generally unaffected by baseline renal function. Serum creatinine level is determined by using a dry slide enzymatic reflectance technique (Vitros 950; Johnson and Johnson, New Brunswick, NJ) with a normal range of 0.5 to

GENETICS OF CARDIAC SURGERY RENAL DYSFUNCTION

1.4 mg/dL (44 to 133 ␮mol/L). Creatinine level increase after cardiac surgery typically peaks on postoperative day 2, in the majority of cases returning to baseline by day 4 or 5.12 The average increase in serum creatinine level after nonemergent first-time coronary bypass surgery with CPB is 22%, with wide variation among individuals in both degree and timing. However, this pattern varies little among patient groups with different baseline creatinine values, simply assuming different absolute changes in serum creatinine levels, making fractional creatinine level increase a particularly suitable tool for grouped comparisons of large numbers of patients with differing baseline creatinine values, but similar genetic profiles.12 Patients who undergo new-onset dialysis or die within 48 hours of surgery typically are excluded from these analyses because their peak serum creatinine values are misleading. Several perioperative studies have used fractional creatinine level increase as a marker of renal injury12; %⌬Cr correlates with relative reductions in renal filtration; is independently associated with adverse outcomes after cardiac surgery, including mortality; and consistently has been shown to be highly sensitive to perioperative renal insult.5,12 Preoperative clinical covariate data include demographic variables and preexisting comorbidities (Table 1). Intraoperative and postoperative variables include duration of CPB, duration of aortic cross-clamping, number of aortocoronary bypass grafts, blood product use, requirement for inotropic drugs, and/or intra-aortic balloon pump counterpulsation (Table 1). Use of agents with potential renal effects (eg, intravenous dopamine, furosemide, and mannitol) was not regulated because these agents have not shown significant beneficial effects in the setting of cardiac surgery.37-40 Each bypass circuit was primed with mannitol (250 mL of 20% solution).

Rationale for Candidate Polymorphism Selection Twelve polymorphisms in 7 candidate genes were chosen prospectively based on a priori hypotheses about their probable role in postoperative renal injury; detailed gene and polymorphism information for this study can be found at http://anesthesia.duhs.duke.edu/pegasus/renal/1/ (Web site Table 1). Genes selected included angiotensin-converting enzyme (ACE), angiotensinogen (AGT), angiotensin receptor 1 (AGTR 1), endothelial constitutive NO synthase (eNOS, also referred to as NOS 3), IL-6 (n ⫽ 3), TNF-␣ (n ⫽ 3), and APOE (n ⫽ 2). Additionally, 54 unlinked markers were used to assess and control for population admixture, as previously described.41

Isolation of Genomic DNA and Genotype Analysis Blood was collected immediately before surgery. Genomic DNA extraction was performed using the Puregene system (Gentra Systems, Minneapolis, MN). Most genotyping assays for single-nucleotide polymorphisms were conducted at Agencourt Bioscience Corp (Beverly, MA) by matrixassisted laser desorption/ionization time-of-flight mass spectrometry using the Sequenom MassARRAY system (Sequenom, San Diego, CA).42 Primers used to amplify and details

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of the polymorphisms can be found at http://anesthesia.duhs. duke.edu/pegasus/renal/1/ (Web site Table 2). Genotyping accuracy of the Sequenom MassARRAY system was estimated at 99.6%.43 Reproducibility of genotyping was validated to be greater than 99% by scoring a panel of 6 polymorphisms in 100 randomly selected patients by using direct sequencing on an ABI3700 capillary sequencer (Applied Biosystems, Foster City, CA). ACE deletion and insertion alleles were identified on the basis of polymerase chain reaction amplification of the respective fragments from intron 16 of the ACE gene, followed by size-fractionation through electrophoresis, as previously described.44 Results were scored by 2 independent investigators blinded to clinical phenotype. After completion of genotype analysis, genetic samples were linked to covariate and phenotypic variables in a relational database with extensive quality control features.

Statistical Analysis Descriptive statistics, including genotype frequency, Hardy-Weinberg equilibrium, and linkage disequilibrium,45 were calculated for all 12 candidate polymorphisms (Web site Tables 1 and 3). Because the observed genotype frequencies of the entire population and both racial groups analyzed separately deviated from expectations of the Hardy-Weinberg equilibrium, all subsequent analyses used genotype instead of allele frequencies. Furthermore, genotypes homozygous for the rare allele were combined with heterozygote carriers, and analyses were based on 2 genotypic classes for each candidate polymorphism, reflecting the presence (1 or 2 copies) or absence of the rare allele. Because we previously found self-reported race to be an independent predictor of postoperative acute renal injury,2 and population admixture is a potential confounder of genetic association studies, an a priori decision was made to evaluate Caucasian and African-American groups separately if a race effect was confirmed. For analyses of the effect of specific clinical and genetic variables on postoperative acute renal injury, first clinical models were developed using perioperative and demographic variables previously shown to account for variation in postoperative acute renal injury.2,7 Next, separate analyses were performed for each polymorphism to test the null hypothesis of no association between genotype and postoperative acute renal injury. Polymorphisms also were combined to investigate possible 2-way gene interactions. A multiple linear regression model for %⌬Cr was fit, including 12 candidate gene polymorphisms as main effects and important interaction terms. Stepwise backward elimination was used to obtain a simpler model. To initially protect against multiple comparisons, particularly for gene-gene interactions, ␣ level was set at 0.05 for primary allele associations and 0.001 for secondary pairwise interactions. Permutation testing was performed (discussed next). The resulting combination of polymorphisms constituted the final genetic model. Projected mean %⌬Cr for all possible genotypic combinations was generated by this model. Finally, an overall model, combining variables from the genetic model with those already identified in the clinical model, also was fit to determine the extent to which genetic polymorphisms account for variation in postoperative acute renal injury be-

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STAFFORD-SMITH ET AL Table 1. Patient, Procedural, and Renal Function Characteristics

Demographic variables Age (y) Body mass index (kg/m2) Female (%) Weight (kg) Preoperative comorbidities Carotid bruit (%) Long-term steroid therapy (%) Hannan mortality risk score* History of congestive heart failure (%) History of diabetes (%) History of hypertension (%) Preoperative inotropic drug infusion(s)† (%) Preoperative intra-aortic balloon counterpulsation (%) History of myocardial infarction (%) History of obstructive lung disease (%) History of peripheral vascular disease (%) History of stroke (%) Preoperative ejection fraction (%) Preoperative hematocrit (%) Preoperative renal dysfunction (creatine ⱖ 1.5 mg/dL) (%) Unstable angina before surgery (%) Procedure Duration of aortic cross-clamping (min) Duration of CPB (min) No. of coronary artery bypass grafts Postoperative course Inotropic drug infusion(s)† on arrival in intensive care (%) Postoperative intra-aortic balloon counterpulsation (%) Transfusion‡ (%) In-hospital death (%) Renal function variables Peak postoperative serum creatinine (mg/dL) Increase in serum creatinine (mg/dL) Postoperative serum creatinine increase ⱖ 1.0 mg/dL Postoperative serum creatinine increase ⱖ 2.0 mg/dL Peak %⌬Cr§ (%) Postoperative estimated creatinine clearance储 (mL/min) Decrease in creatinine clearance储 (mL/min) Preoperative estimated creatinine clearance储 (mL/min) Preoperative serum creatinine (mg/dL)

Caucasian (n ⫽ 1,464)

African American (n ⫽ 207)

64 ⫾ 11 29.7 ⫾ 14.1 26 86.2 ⫾ 19.1

63 ⫾ 11 31.4 ⫾ 18.5 50 84.2 ⫾ 18.2

6 1 0.025 ⫾ 0.029 16 32 65 1 1 27 12 13 6 52 ⫾ 14 39 ⫾ 7 7 68

5 3 0.028 ⫾ 0.036 21 42 82 1 2 28 8 18 10 49 ⫾ 14 37 ⫾ 8 11 74

63 ⫾ 31 114 ⫾ 46 3.2 ⫾ 0.9

59 ⫾ 28 110 ⫾ 46 3.1 ⫾ 0.8

21 1 23 0.8

27 1 25 0.4

1.4 ⫾ 0.8 0.3 ⫾ 2.1 3.7 1.0 30 ⫾ 52 73 ⫾ 31 ⫺15 ⫾ 21 88 ⫾ 34 1.1 ⫾ 0.5

1.7 ⫾ 1.2 0.3 ⫾ 1.0 5.3 1.6 34 ⫾ 44 63 ⫾ 28 ⫺15 ⫾ 20 79 ⫾ 33 1.3 ⫾ 1.0

NOTE. To convert serum creatinine in mg/dL to ␮mol/L, multiply by 88.4; creatinine clearance in mL/min to mL/s, multiply by 0.01667. *The Hannan score is a risk factor score for in-hospital mortality following coronary artery bypass grafting surgery, identified by Hannan et al,70 in the New York State population. †Inotropic drug infusion, defined as infusion of either dopamine greater than 5 ␮g/kg/min and/or dobutamine greater than 5 ␮g/kg/min, or epinephrine greater than 0.03 ␮g/kg/min. ‡Transfusion is a marker of perioperative transfusion (⬎2 units packed red cells and at least 1 other blood product within 24 hours of surgery). §Defined as the difference between preoperative and peak postoperative creatinine values, represented as a percentage of the preoperative value. 储Using the Cockcroft-Gault equation,47 preoperative, lowest postoperative, and change in creatinine clearance were calculated using preoperative and peak postoperative serum creatinine values.

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yond that explained by clinical variables. Initial univariate and 2-way interaction queries performed to test the primary hypothesis used all available genetic and/or clinical data.

Permutation Tests to Assess for Multiple Comparisons Because our study involves a large number of hypothesis tests, we used a permutation analysis to formally assess significant P values for multiple comparisons.46 Briefly, permutations are repeated a large number of times to generate a set of P values expected by chance alone for comparison with the study P values. Each permutation involves generating a new data set by reassigning renal injury (%⌬Cr) values randomly to study subjects to break any association between the occurrence of the outcome and patient genotype. For each assessment, using a newly permuted data set, P values are calculated for all possible polymorphism pairs in a similar fashion to the primary analysis; only the smallest P value is retained. For our study, a set of 500 smallest P values thus was generated and compared with the study P values. For example, if 10 of the 500 permuted P values are smaller than the observed P value, the adjusted P value will be 10 of 500, or P ⫽ 0.02. In contrast to the Bonferroni procedure, which adjusts P under the assumption that multiple tests are independent, the permutation procedure is conditional on the data and thereby accommodates correlations among multiple tests. As a consequence, the permutation procedure results in a less conservative P value adjustment than the Bonferroni procedure while maintaining overall control of the excess false-positive rate. Separate permutation procedures were performed on tests for interaction between polymorphism pairs in the Caucasian and AfricanAmerican samples.

Effect of Race In addition to self-reported race, to further investigate the relationship of race and postoperative acute renal injury, stratification determined by relative population weights (from population structure analysis using 54 unlinked genetic polymorphisms) was used to test for association between race and renal injury, as previously described.41 Statistical analysis was performed using SAS/Genetics system, version 8.02 (SAS Inc, Cary, NC). Continuous variables are described as mean ⫾ SD, and categorical variables are described as percentages. Throughout this report, all genetic polymorphisms are described using the following convention: major allele first (left), followed by nucleotide number, then minor allele on the right.

RESULTS

Demographic and intraoperative characteristics of the study population are similar to those reported in other cardiac surgery populations (Table 1).5 Specifically, renal injury was common; more than half the patients sustained a greater than 30% increase in postoperative creatinine levels, equivalent to a 25% reduction in creatinine clearance.47 Linkage disequilibrium

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for polymorphisms within the same gene was identified among IL-6 and TNF-␣ polymorphisms (Web site Table 3); whereas the IL-6 linkage has been reported,48 the TNF-␣ linkage is novel. In contrast to some forms of acute renal failure (eg, rhabdomyolysis-related renal injury in which 93% can be explained),49 preoperative and procedural clinical factors associated with post– cardiac surgery acute renal dysfunction explain very little of the variability in renal injury (overall R2 ⫽ 0.028 in our study). However, analysis by race (a known risk factor for renal injury)2 improved the explanatory power of the overall clinical model (R2 ⫽ 0.030 for Caucasians; R2 ⫽ 0.13 for African Americans), whether ethnic background was determined by self-reporting or genetic structure analysis.41 In this study, selfreported race proved the stronger model. After analyzing clinical factors, we turned to genetic variables alone. Average postoperative acute renal injury (%⌬Cr) associated with each of the 12 candidate polymorphisms is listed in Web site Table 1. In multivariable analysis, no primary effects were identified, but 6 polymorphisms (6 in Caucasians; 4 in African Americans) showed significant association with %⌬Cr in 2-way interactions between alleles (Tables 2 and 3). After adjustment for multiple comparisons by using a permutation analysis, the AGT 842C and IL-6 ⫺572C allele combination in the Caucasian analysis remained significant; adjusted P value for this interaction term is 0.032 (16 of 500 P values were smaller than the unadjusted P ⬍ 0.0001). Possibly because of a smaller number of patients in the African-American analysis, neither interaction term had a significant adjusted P value. Distribution of %⌬Cr values are accounted for by the model; some allele combinations are associated with major reductions in renal filtration, equivalent to a greater than 50% decrease in creatinine clearance (Fig 1). As shown in Fig 2, raw data show the association of specific highrisk polymorphism interactions with renal injury. Mean %⌬Cr values for all allele interactions are presented in Fig 3. Overall genetic factors are associated with 7% of the variability in acute renal injury occurring after aortocoronary surgery for Caucasians and 10% for African Americans (Tables 2 and 3), a figure that significantly

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Table 2. Genetic, Clinical, and Combined Genetic and Clinical Multivariable Risk Factor Models Associated With Peak Postoperative Serum Creatinine Increase in 1,464 Caucasians After Aortocoronary Surgery F Value

Clinical factors alone model (R 2 ⫽ 0.03) Clinical factors Age (y) Weight (kg) History of obstructive pulmonary disease Preoperative creatinine (mg/dL) Postoperative inotropic drug infusion Duration of CPB (min) Genetics-alone model (R 2 ⫽ 0.067) Polymorphism eNOS TNF-␣ IL-6 AGTR1 AGT APOE ␧4 Interactions eNOS * AGTR1 IL-6 * TNF-␣ AGT * IL-6 APOE ␧4 * AGT AGTR1 * APOE ␧4 Combined clinical and genetic model (R 2 ⫽ 0.11) Polymorphism eNOS TNF-␣ IL-6 AGTR1 AGT APOE ␧4 Interactions eNOS * AGTR1 IL-6 * TNF-␣ AGT * IL-6 APOE ␧4 * AGT AGTR1 * APOE ␧4 Clinical factors Age (y) Weight (kg) History of obstructive pulmonary disease Preoperative creatinine (mg/dL) Postoperative inotropic drug infusion Duration of CPB (min)

Table 3. Genetic, Clinical, and Combined Genetic and Clinical Multivariable Risk Factor Models Associated With Peak Postoperative Serum Creatinine Level Increase in 207 African Americans After Aortocoronary Surgery

P Value

7.2 4.15 6.55

0.007 ⬍0.0001 0.01

7.3 2.75

0.007 0.05

4.43

0.04

1.89 1.91 20.04 0.04 32.19 6.94

0.17 0.17 ⬍0.0001 0.84 ⬍0.0001 0.009

7.55 3.58 23.44 4.49 5.97

0.006 0.05 ⬍0.0001 0.03 0.02

2.05 1.80 22.04 0.03 30.05 5.80

0.15 0.18 ⬍0.0001 0.86 ⬍0.0001 0.02

6.39 3.84 23.25 2.76 5.74

0.01 0.05 ⬍0.0001 0.09 0.01

2.91 15.94 4.32

0.08 ⬍0.0001 0.04

9.57 4.91

0.0002 0.03

5.87

0.02

Abbreviations: eNOS, eNOS G894T polymorphism; TNF-␣, TNF-␣ G⫺308A polymorphism; IL-6, IL-6 G⫺572C polymorphism; AGTR1, AGTR1 A1166C polymorphism; AGT, AGT T842C polymorphism; APOE ␧4, APOE T448C polymorphism.

F Value

Clinical factors alone model (R 2 ⫽ 0.132) Clinical factors History of obstructive pulmonary disease History of peripheral vascular disease History of long-term steroid therapy Body mass index (kg/m2) Genetics-alone model (R 2 ⫽ 0.132) Polymorphism eNOS AGT ACE D/I APOE ␧2 Interactions eNOS * ACE AGT * APOE ␧2 Combined clinical and genetic model (R 2 ⫽ 0.204) Polymorphism eNOS AGT ACE D/I APOE ␧2 Interactions eNOS * ACE AGT * APOE ␧2 Clinical factors History of obstructive pulmonary disease History of peripheral vascular disease History of long-term steroid therapy Body mass index (kg/m2)

P Value

3.78

0.05

16.33

⬍0.0001

7.14

0.008

6.85

0.01

4.29 0.00 8.40 1.02

0.04 0.99 0.004 0.32

7.77 4.72

0.006 0.03

2.83 0.03 7.00 0.61

0.09 0.87 0.009 0.44

7.28 3.45

0.008 0.06

3.17

0.08

9.33

0.003

0.73

0.39

5.31

0.02

Abbreviations: eNOS, eNOS G894T polymorphism; AGT, AGT T842C polymorphism; ACE D/I, ACE deletion/insertion polymorphism; APOE ␧2, APOE C586T polymorphism.

exceeds that associated with clinical factors alone. Finally, the combination of clinical and genetic models are approximately additive, significantly enhancing the explanatory power of our postoperative acute renal injury model compared with clinical-alone models. Eleven percent of acute renal injury variability in Caucasians and 20% in

GENETICS OF CARDIAC SURGERY RENAL DYSFUNCTION

525

Fig 1. Differences in peak postoperative serum creatinine level increases after coronary bypass surgery for 2 gene polymorphism interactions identified in the multivariable genetic models. The dashed line represents a 2-fold (100%) increase in serum creatinine level from baseline, approximately equivalent to a 50% reduction in glomerular filtration rate (GFR).

African Americans (Tables 2 and 3) are accounted for by the combined model. Only 13 cases of new-onset dialysis (0.8%) occurred in patients otherwise eligible for our study, too few to permit a meaningful genetic analysis for this group. Genotype profiles for polymorphisms identified in these subjects are listed in Web site Table 4.

DISCUSSION

Acute renal dysfunction is a common contributing factor to hospital admission and intensive care unit stay1 and significantly complicates up to 30% of cardiac, vascular, trauma, and hepatobiliary surgeries.17 Because surgery is a predictable and quantifiable event, it is a good setting in

Fig 2. Graphic representation of individual 2-way gene polymorphism interactions in the multivariable geneticonly models for peak postoperative serum creatinine level increase (%⌬Cr) after aortocoronary surgery. The dashed line represents a 2-fold (100%) increase in serum creatinine level from baseline, approximately equivalent to a 50% reduction in glomerular filtration rate.

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Fig 3. Graphic models for peak postoperative serum creatinine level increase (%⌬Cr) after aortocoronary surgery for Caucasian and African-American patients, including all pairwise gene polymorphism combinations that existed in the study (black bars) and projected values for polymorphism combinations not observed in the population (open bars). Six polymorphisms in Caucasians and 4 polymorphisms in African Americans result in 64 (26) and 16 (24) different possible combinations, respectively. The dashed line represents a 2-fold (100%) increase in serum creatinine level from baseline, approximately equivalent to a 50% reduction in glomerular filtration rate (GFR).

which to examine mechanisms underlying acute renal dysfunction. However, despite the relatively common occurrence of acute renal failure after surgery, the best risk-stratification models available today only poorly predict postoperative renal dysfunction. The present study identifies important genetic underpinnings of this disorder. In Caucasians, the combined possession of 2 polymorphisms (AGT 842C and IL-6 ⫺572C, a variant pattern that occurs in 6% of Caucasians) was associated with major postoperative renal injury, with an average peak serum creatinine level increase of 121% (P ⬍ 0.0001). This is approximately equivalent to a 55% reduction in renal filtration, 4 times greater than average for the overall study population. This finding emphasizes the importance of studying gene polymorphisms in multiple converging biological pathways in understanding factors associated with such complex diseases as acute renal injury. Inflammation may be important in interpreting our findings; both the AGT 842C and IL-6 ⫺572C alleles have been associated with increased circulating IL-6 protein levels compared with their major allele.50,51 Cardiac surgery provokes a vigorous inflammatory response that contributes to renal insult.52 Inflammation is a rapid, highly amplified, humoral and cellular response that can occur both systemically and locally in the kidney. Endotoxin and circulating inflammatory cytokine levels peak 4 to 24 hours after CPB53,54 and have been associated directly with acute renal injury.20,55 In a single-allele association

study, Gaudino et al35 noted increased postoperative renal dysfunction in carriers of the IL-6 ⫺174C polymorphism in 111 patients undergoing coronary bypass surgery. Although in our study, we related the IL-6 ⫺572C and not the ⫺174C polymorphism to acute renal injury, the findings of Gaudino et al35 support our findings because we noted significant linkage disequilibrium between these 2 polymorphisms in Caucasians (Web site Table 3); both have been associated with exaggerated increases in IL-6 protein levels after cardiac surgery.51,56-59 Other proinflammatory polymorphisms also may contribute to renal injury; the TNF-␣ ⫺308A allele has been associated with increased production of IL-6 protein.60 We noted a weak association (P ⫽ 0.05) toward greater renal injury with IL-6 ⫺572C and TNF-␣ ⫺308A alleles in our study; patients who had both had a 2 times greater creatinine level increase compared with other patients. In summary, the main finding of our study is that a high-risk proinflammatory polymorphism combination, commonly seen in Caucasians presenting for heart surgery, is associated with severe postoperative acute renal injury. In addition to being proinflammatory, cardiac surgery also results in atheroembolism and ischemia-reperfusion injury, both potentially important contributors to a cumulative perioperative renal insult. After renal injury occurs, intense renal vasoconstriction and exaggerated responsiveness to vasoconstrictor agents is observed, effects that take several weeks to resolve.61 In

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the context of precarious oxygen supply to the renal medulla, it is easy to imagine how subtle polymorphism-related differences in the regulation of renal perfusion could increase the extent of renal injury and delay recovery. In our study, all 4 pairwise allele interactions trending toward significance (after taking into account multiple comparisons) in their associations with renal injury (ie, eNOS*AGTR 1, AGT*IL-6, and AGTR 1*APOE ⑀4 in Caucasians, and eNOS*ACE in African Americans; Tables 2 and 3) involve at least 1 polymorphism linked to increased renal vascular responsiveness. In 2 interactions (eNOS*AGTR 1 and eNOS*ACE), both polymorphisms have these effects.62-65 Possession of 2 vasoconstrictor polymorphisms (the ACE deletion and eNOS 894T alleles) in AfricanAmerican subjects is associated with a mean peak creatinine level increase of 162.5% from preoperative levels in our study, equivalent to a greater than 60% reduction in glomerular filtration. Should such a renal vulnerability be confirmed in a larger population, this would be a finding of clinical significance. The renin-angiotensin system and eNOS are central to regulation of renal medullary blood flow66,67; polymorphisms of these pathways that augment vascular tone may contribute to medullary ischemia during the initial insult, but also throughout the recovery phase of an acute renal injury. The remaining interesting polymorphisms, which show weak trends toward significance in our study, are the APOE ⑀2 and ⑀4 alleles. In 2 previous single-polymorphism association studies,33,34 we observed an association between APOE polymorphisms and renal injury in cardiac surgery patients. Although the precise role of APOE in acute renal injury remains to be determined, in light of the findings of this study, it is interesting to note that recent evidence suggests a modulating role of ApoE on the inflammatory cascade, with different responses among the 3 APOE polymorphisms.68 In summary, in this study, we observe a role for inflammatory and vasoconstrictor gene polymorphisms in accounting for acute renal injury after cardiac surgery. These findings support mechanisms thought to contribute to renal injury in many other acute and chronic renal disorders across medicine, reinforcing the usefulness of this perioperative model. Although strict criteria

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were used in this study, caution is still appropriate when generalizing from any genetic association study because limitations exist. First, additional prospective studies are required to confirm our results. In addition, other important alleles, either not considered as primary candidate polymorphisms or in linkage disequilibrium with studied polymorphisms,69 may not have been tested in this study. Furthermore, our findings do not presume that the causative polymorphism has been identified because the variants could be in linkage disequilibrium with 1 or more causation polymorphisms; examination of our candidate polymorphisms in Caucasians and African Americans identifies haplotype blocks within both the IL-6 and TNF-␣ genes. Finally, serum creatinine has limitations as a gold standard method for reflecting filtration abnormalities, particularly during the perioperative period, and any perioperative finding related to creatinine-based estimates of renal function require validation by other methods. However, our new information may be useful in the future to identify a subset of patients at particular risk for acute renal injury. These data may be useful in counseling patients requiring aortocoronary surgery (and perhaps other interventional medical procedures associated with acute renal injury) and may provide a rationale for choosing medical over surgical therapy in patients with specific diseases and genotype combinations. It is intriguing to speculate further that drugs already available and used to delay the progression of chronic renal disease (eg, ACE inhibitors and/or angiotensin receptor blockers) also may be useful in reducing renal damage in such acute settings as cardiac surgery. However, caution is necessary in interpreting our findings until they have been confirmed in other patient populations. Overall, new genetic information, such as that shown in this study, may facilitate individually tailored medical therapy (personalized medicine) designed to reduce acute renal injury and associated morbidity and mortality. ACKNOWLEDGMENT The authors thank Huntington F. Willard, PhD, for helpful guidance and Zarrin Brooks and Cheryl Stetson for assistance in manuscript preparation.

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APPENDIX

Perioperative Genetics and Safety Outcomes Study (PEGASUS) Investigative Team Andrew Allen, PhD, Ellen Bennett, PhD, John V. Booth, MB, ChB, Chonna Campbell, Fiona Clements, MD, R. Duane Davis, MD, Bonita Funk, RN, Xiaoyi Gao, Donald Glower, MD,

Katherine Grichnik, MD, Hilary P. Grocott, MD, Roger Hall, Elizabeth Hauser, PhD, Steven Hill, MD, Robert Jones, MD, Jerry Kirchner, Daniel Laskowitz, MD, Andrew Lodge, MD, James Lowe, MD, Eden Martin, PhD, Joseph P. Mathew, MD, Robert H. Messier, MD, PhD, Carmelo Milano, MD, Eugene Moretti, MD, Richard W. Morris, PhD, Mark F. Newman, MD, Dahlia M. Nielsen, PhD, Margaret Pericak-Vance, PhD, Barbara Phillips-Bute, PhD, Mihai V. Podgoreanu, MD, Debra A. Schwinn, MD, Michael P. Smith, MS, Peter K. Smith, MD, Mark Stafford-Smith, MD, Sunil Suchandran, Madhav Swaminathan, MD, Jeffrey M. Taekman, MD, Jeffrey M. Vance, MD, PhD, Ian J. Welsby, MD, Huntington F. Willard, PhD, Walter Wolfe, MD, William D. White, MPH.