# Patent

## Title
Methods of identifying individuals at risk of perioperative bleeding, renal dysfunction

## Abstract

The present invention relates, in general, to perioperative bleeding and, in particular, to methods of identifying individuals at risk of perioperative bleeding.
The present invention relates, in general, to perioperative renal dysfunction and, in particular, to methods of identifying individuals at risk of perioperative renal dysfunction.
The present invention relates, in general, to perioperative stroke and, in particular, to methods of identifying individuals at risk of perioperative stroke.

## Background
This application claims priority from U.S. Provisional Application No. 60/516,313, filed Nov. 3, 2003, U.S. Provisional Application No. 60/567,793, filed May 5, 2004, and U.S. Provisional Application No. 60/620,666, filed Oct. 22, 2004, the entire contents of these applications being incorporated herein by reference.


TECHNICAL FIELD
The present invention relates, to methods of identifying individuals at risk of perioperative bleeding, perioperative renal dysfunction and/or perioperative stroke.
BACKGROUND
Microvascular bleeding remains a major problem following cardiac surgery with cardiopulmonary bypass (CPB) (Nuttall et al, Anesthesiology 94:773-781, discussion 5A-6A (2001), Hall et al, Cardiovasc. Surg. 10:146-153 (2002)), with up to 5% of patients receiving more than a 10 unit perioperative blood transfusion (Woodman and Harker, Blood 76:1680-1697 (1990)). Approximately 4% of patients require reoperation for hemorrhage (Hall et al, Cardiovasc. Surg. 10:146-153 (2002), Woodman and Harker, Blood 76:1680-1697 (1990)) which is associated with increased mortality and morbidity (Unsworth-White et al, Anannls of Thoracic Surgery 59:664-667 (1995)). Current risk stratification based on clinical, procedural, and biological markers (Wahba et al, Journal of Cardiothoracic & Vascular Anesthesia 11:824-827 (1997), Despotis et al, Anesthesia & Analgesia 82:13-21 (1996)) has been only partially successful, failing to account for much of the postoperative blood loss seen even with “low-risk” primary coronary artery bypass (CABG) surgery (Hardy et al, Canadian Journal of Anaesthesia 38:511-517 (1991)). CPB-induced alterations in the hemostatic system are multifactorial, pertaining to excessive activation of coagulation and fibrinolytic pathways with interplay of cellular and soluble hemostatic and inflammatory systems; hypothermia and hemodilution further complicate the situation (Despotis et al, Annals of Thoracic Surgery 72:S1821-1831 (2001)). Coagulopathy following CPB represents one extreme on a continuum of coagulation function, with perioperative prothrombotic outcomes (e.g. coronary graft thrombosis, myocardial infarction, stroke and pulmonary embolism) at the other end of the spectrum (Spiess and Chandler, Best Practice & Research: Clinical Anaesthesiology 15:195-211 (2001)).
Pathophysiologically, the balance between bleeding, normal hemostasis, and thrombosis is markedly influenced by the rate of thrombin formation and platelet activation (Kunicki and Nugent, Vox Sang. 83 (Suppl 1):85-90 (2002), Slaughter et al, Anesthesiology 80:520-526 (1994)). There is recent evidence that genetic variability modulates the activity in each of these mechanistic pathways (Spiess and Chandler, Best Practice & Research: Clinical Anaesthesiology 15:195-211 (2001)). However, little is known of the role of allotypic coagulation, fibrinolytic and platelet-membrane receptor gene variation in predicting bleeding following CABG surgery; the few studies to date focus only on single-gene variants (Donahue et al, Circulation 107 (7):1003-1008 (2003)). Several prothrombotic genetic polymorphisms are known to exist.
The present invention results, at least in part, from studies designed to investigate the impact of multi-locus genetic influences on the incidence and severity of perioperative bleeding after CABG surgery. The invention provides, in one embodiment, a method of identifying patients with prothrombotic gene polymorphisms, which polymorphisms are associated with increased postoperative bleeding.
Acute renal dysfunction, evidenced by 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 (Hou et al, Am. J. Med. 74(2):243-248 (1983)). Furthermore, acute renal injury remains a common, serious complication of cardiac surgery (Conlon et al, Nephrol. Dial Transplant. 14(5):1158-1162 (1999)); multiple etiologies 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 cardiac surgery patients; an additional 8-15% have moderate renal injury (e.g., >1.0 mg/dl peak creatinine rise) (Conlon et al, Nephrol. Dial Transplant. 14(5):1158-1162 (1999), Abel et al, J. Thorac. Cardiovasc. Surg. 71(3):323-333 (1976), Corwin et al, J. Thorac. Cardiovasc. Surg. 98(6)):1107-1112 (1989), Andersson et al, Thorac. Cardiovasc. Surg. 41(4):237-241 (1993), Mora-Mangano et al, Ann. Intern. Med. 128 (3):194-203 (1998), Mangos et al, Aust. NZ J. Med. 25(4):284-289 (1995), Ostermann et al, Intensive Care Med. 26(5):565-571 (2000)). Lesser renal injuries are even more common (>50% aortocoronary bypass surgery patients have ≧25% postoperative rise in serum creatinine). In many settings, including cardiac surgery, acute renal failure is independently predictive of the in-hospital mortality rate even after adjustment for comorbidities and other complications (Conlon et al, Nephrol. Dial Transplant. 14(5): 1158-1162 (1999), Levy et al, JAMA 275 (19):1489-1494 (1996), Chertow et al, Am. J. Med. 104 (4):343-348 (1998)); all degrees of renal injury are associated with increased mortality and other adverse outcomes (Stafford-Smith, Chapter 5—In: Newman, ed. 2003 Society of Cardiovascular Anesthesiologists Monograph—Perioperative Organ Protection: Lippincott Williams & Wilkins, pgs. 89-124 (2003)). Unfortunately, typical characteristics (e.g., advanced age, history of atherosclerotic vascular disease) of those presenting for cardiac surgery make them generally a group at high “renal risk” (Conlon et al, Nephrol. Dial Transplant. 14(5):1158-1162 (1999), Greenberg et al, Am. J. Kidney Dis. 29(3):334-344 (1997), Porter, Miner Electrolyte Metab. 20(4):232-243 (1994), Novis et al, Anesth. Analg. 78(1):143-149 (1994)).
Paradoxically, although the kidneys receive more blood flow per gram of tissue than any other major organ, they are also the most vulnerable to ischemic injury. Metabolic demands from active tubular reabsorption and the oxygen diffusion shunt characteristic of renal circulation contribute to the precarious physiology of renal perfusion including low medullary pO2 (10-20 mmHg) (Brezis and Rosen, N. Engl. J. Med. 332 (10):647-655 (1995)). Key to regulation of renal blood flow are paracrine systems (e.g., renin-angiotensin system [RAS], nitric oxide[NO]) that modulate microvascular function and oxygen delivery in the renal medulla (Navar et al, Physiol. Rev. 76(2):425-536 (1996)). The inflammatory response to CPB generates cytokines (e.g., tumor necrosis factor alpha [TNFα], interleukin 6 [IL-6]) both systemically and locally in the kidney (Cunningham et al, J. Immunol. 168 (11):5817-5823 (2002), Segerer et al, J. Am. Soc. Nephro. 11(1):152-176 (2000)), that have major effects on the renal microcirculation and may lead to tubular injury (Heyman et al, Exp. Nephrol. 8 (4-5):266-274 (2000)). Recent evidence suggests that heritable differences modulate the activation of these pathways.
Although many preoperative predictors have been identified (these are similar to factors predictive of chronic renal dysfunction), risk stratification based on clinical, intraoperative, and biological markers explains only a small part of the variability in postoperative renal dysfunction ((Conlon et al, Nephrol. Dial Transplant. 14(5):1158-1162 (1999), Abel et al, J. Thorac. Cardiovasc. Surg. 71(3):323-333 (1976), Corwin et al, J. Thorac. Cardiovasc. Surg. 98(6)):1107-1112 (1989), Andersson et al, Thorac. Cardiovasc. Surg. 41(4):237-241 (1993), Mora-Mangano et al, Ann. Intern. Med. 128 (3):194-203 (1998), Mangos et al, Aust. NZ J. Med. 25(4):284-289 (1995), Ostermann et al, Intensive Care Med. 26(5):565-571 (2000), Novis et al, Anesth. Analg. 78(1):143-149 (1994), Zanardo et al, J. Thorac. Cardiovasc. Surg. 107 (6):1489-1495 (1994), Yeh et al, J. Thor. Cardiovasc. Surg. 47:79-95 (1964), Porter et al, J. Thorac Cardiovasc. Surg. 53(1):145-152 (1967), McLeish et al, Surg. Gynecol. Obstet. 145 (1):28-32 (1977), Llopart et al, Ren, Fail. 19(2):319-323 (1997), Hilberman et al, J. Thorac. Cardiovasc. Surg. 77(6):880-888 (1979), Heikkinen et al, Ann. Chir. Cynaecol. 75(5):203-209 (1985), Gailiunas et al, J. Thorac. Cardiovasc. Surg. 79(2):241-243 (1980), Doberneck et al, J. Thor. Cardiovasc. Surg. 43:441-452 (1962), Bhat et al, Ann. Intern. Med. 84(6):677-682 (1976)). However, 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 following aortocoronary bypass graft (CABG) surgery. The few existing studies have focused on only 2 genetic polymorphisms (Apolipoprotein E [ApoE] T448C(ε4), interleukin 6 [IL6] G-174C) (Chew et al, Anesthesiology 93(2):325-331 (2000), Mackensen et al, Ann. Thor. Surg. (in press) (2004), Gaudino et al, J. Thorac. Cardiovasc. Surg. 126 (4):1107-1112 (2003)) and do not take into account other important pathways/proteins or interactions between potentially synergistic insults.
The present invention further results, at least in part, from studies designed to investigate the association between genetic variants of inflammatory and paracrine pathways at multiple loci and susceptibility to perioperative acute renal injury.
Despite advances in the field of cardiac surgery, significant neurologic morbidity continues to occur (Wolman et al, Stroke 30(3):514-522 (1999), Roach et al, N. Engl. J. Med. 335 (25):185701863 (1996), Newman et al, N. Engl. J. Med. 344:395-402 (2001), Bucerius et al, Ann. Thorac. Surg. 75(2):472-478 (2003)). Indeed, over the past several decades, many technologic advancements in surgery, anesthesia, and the conduct of cardiopulmonary bypass (CPB), coupled with an improved understanding of the pathophysiology of neurologic injury, have allowed surgery to be performed on an increasingly elderly and high-risk group of patients (Ferguson et al, Ann. Thorac. Surg. 73(2):480-489, discussion 9-90 (2002)). Stroke, although less frequent than more subtle types of cerebral injury (such as cognitive dysfunction) (Newman et al, N. Engl. J. Med. 344:395-402 (2001)) remains a significant and debilitating complication of cardiac surgery (Roach et al, N. Engl. J. Med. 335 (25):185701863 (1996)). In addition to being a devastating injury to the patient, diminishing quality of life and increasing mortality, stroke following cardiac surgery also incurs a substantial cost in terms of health-care resource utilization (Roach et al, N. Engl. J. Med. 335 (25):185701863 (1996)). Despite many years of study to understand factors associated with postoperative stroke, questions exist regarding its pathophysiology, and as a result, the ability to understand who is at risk is far from complete.
The variable incidence of stroke after cardiac surgery is thought to be influenced both by patient and procedural risk factors (Borger et al, Eur. J. Cardiothorac. Surg. 19(5):627-632 (2001)). While many of the procedural risk factors have been incorporated in stroke risk indices from observational studies (Newman et al, Circulation 94(9 Suppl):II74-II80 (1996)), they provide incomplete information regarding the full risks of stroke. These risk-indices and associated factors do not include information regarding the genetic makeup of the patient, raising the possibility that heterogeneity seen in the clinical presentation of stroke (both in incidence and severity) may partly reflect underlying genotype.
The pathophysiology of stroke in non-surgical settings is thought to involve complex interactions between pathways associated with coagulation, inflammation, lipid metabolism, apoptosis, and direct cellular injury. Within each of these broad etiologic pathways, genetic variants have been identified. As a result, identification of specific genetic variants involved in stroke can be thought of in mechanistic terms. Although not exclusively so, it appears that pro-inflammatory and pro-thrombotic genes may play a significant role in the etiology and outcome after stroke in non-operative settings. Recently, polymorphisms involving cyclooxygenase-2 (Cipollone et al, JAMA 291 (18):2221-2228 (2004)), apolipoprotein (APOE) (McCarron et al, Stroke 29(9):1882-1887 (1998)), myeloperoxidase (Hoy et al, Atherosclerosis 167 (2):223-230 (2003)), interleukin 6 (IL6) (Greisenegger et al, Thromb. Res. 110 (4):181-186 (2003), Pola et al, Stroke 34(4):881-885 (2003)), intercellular adhesion molecule-1 (ICAM1) (Pola et al, Stroke 34(4):881-885 (2003)), vascular cell adhesion molecule-1 (VCAM1) (Adams et al, BMC Med. Genet. 4 (1):6 (2003)), C-reactive protein (CRP) (Rost et al, Stroke 32(11):2575-2579 (2001), Curb et al, Circulation 107(15):2016-2020 (2003), Ford and Giles, Arterioscler. Thromb. Vasc. Biol. 20(4):1052-1056 (2000)), and various prothrombotic genes (Kahn, Sourth Med. J. 96(4):350-353 (2003), Endler and Mannhalter, Clin., Chim. Acta 330(1-2):31-55 (2003)) have all been examined in studies demonstrating variable relationships to stroke.
The present invention additionally results, at least in part, from studies designed to examine a group of genetic polymorphisms for their potential may influence on perioperative stroke risk (McCarron et al, Stroke 29(9):1882-1887 (1998), Greisenegger et al, Thromb. Res. 110 (4):181-186 (2003), Pola et al, Stroke 34(4):881-885 (2003), Rost et al, Stroke 32(11):2575-2579 (2001), Curb et al, Circulation 107(15):2016-2020 (2003), Ford and Giles, Arterioscler. Thromb. Vasc. Biol. 20(4):1052-1056 (2000), Kahn, Sourth Med. J. 96(4):350-353 (2003), Endler and Mannhalter, Clin., Chim. Acta 330(1-2):31-55 (2003), Meschia et al, BMC Neurol. 3 (1):4 (2003)). These studies have resulted in the identification of specific genetic polymorphisms that modulate the risk of stroke following surgery.
SUMMARY OF THE INVENTION
The present invention relates generally to perioperative bleeding, renal dysfunction and stroke. More specifically, the invention relates to methods of identifying individuals at risk of perioperative bleeding, renal dysfunction and stroke, and to kits suitable for use in such methods.
Objects and advantages of the present invention will be clear from the description that follows.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1. CTD denotes the predicted chest tube drainage for all the 128 possible allele combinations of the 7 polymorphisms, for the first 12 postoperative hours. Black bars mark allele combinations actually observed in the study population; the gray bars represent hypothetical allele combinations. The shaded cells in the table mark the prothrombotic allele and illustrate clustering of the prothrombotic alleles in the combinations associated with increased bleeding. The minor prothrombin 20210A allele was rare and did not appear in any of the examples.
FIGS. 2A and 2B: Results of the model predicting peak postoperative serum creatinine rise (% ΔCr) after aortocoronary surgery for all pairwise gene polymorphism combinations identified in multivariable genetic predictive models for Caucasian (FIG. 2A) and African American (FIG. 2B) patients. Six polymorphisms in Caucasians and 4 in African Americans result in sixty-four (26) and sixteen (24) different possible combinations, respectively. Combinations observed in the study population are shown as black bars. The dashed line represents 2-fold (100%) increase in predicted postoperative serum creatinine rise, roughly equivalent to the loss of one kidney.
FIGS. 3A and 3B: Differences in predicted peak postoperative serum creatinine rise after coronary bypass surgery for two gene polymorphism interactions identified in the multivariable genetic predictive models. The dashed line represents 2-fold (100%) increase in predicted renal injury, roughly equivalent to the loss of one kidney. Abbreviations: AGT—angiotensinogen T842C polymorphism, IL6—interleukin 6 G-572C polymorphism, eNOS—endothelial nitric oxide synthase G894T polymorphism, ACE—angiotensin converting enzyme insertion/deletion polymorphism.
FIGS. 4A-4G: Graphic representation of individual two-way gene polymorphism interactions in the multivariable genetic only models for predicted peak postoperative serum creatinine rise (% ΔCr) after aortocoronary surgery. The dashed line represents a 2-fold (100%) increase in predicted renal injury.
FIG. 5. The incidence of postoperative stroke in patients possessing the combination of single nucleotide polymorphisms (SNPs) in both C-reactive protein (CRP) (3′UTR 1846C/T) and interleukin-6 (IL6) (−174G/C).
The combination of these SNPs significantly increased the risk of stroke (odds ratio 3.3, 95% CI 1.4-8.1; p=0.0023).



DETAILED DESCRIPTION OF THE INVENTION
In one embodiment, the present invention results from studies designed to prospectively examine specific genetic variants involved in bleeding pathways and how they influence postoperative bleeding. Polymorphisms found to be significantly associated with postoperative bleeding include α2β1 integrin —52C/T and 807C/T dimorphisms, GPIbα 524C/T, tissue factor —603A/G, prothrombin 20210G/A, tissue factor pathway inhibitor —399C/T polymorphisms, and the angiotensin converting enzyme insertion/deletion polymorphism.
Biological effects for each of the single nucleotide polymorphisms (SNPs) referenced above, and described in greater detail in Example 1, have been demonstrated. The present invention provides, in part, definitive association between these genetic variants and clinical postoperative bleeding in the perioperative and intensive care unit setting. This aspect of the invention is exemplified by reference to cardiac surgery patients but includes all perioperative, periprocedure (endoscopy, bronchoscopy, cardiac catheterization, angioplasty, etc.), and intensive care unit settings.
The presence of one or more of the above-referenced polymorphisms present in a sample (e.g., a biological sample such as blood) can be determined using any of a variety of genotyping techniques known in the art. Examples of such techniques include the use of polymerase chain reaction and extension primers (see too Example 1 below). Suitable techniques also include the use of RFLP analysis and mass spectrometry (see also Ye et al, Hum. Mutat. 17(4):305 (2001), Chen et al, Genome Res. 10:549 (2000).) The genetic variants (SNPs) described above and in Example 1 can be used, for example, to predict predict postoperative and ICU bleeding. As indicated above, screening for genetic variants of the invention is also relevant for other invasive procedures including but not limited to endoscopy, bronchoscopy, cardiac catheterization, and angioplasty. Preoperative screening for genetic variants enables clinicians to better stratify a given patient for therapeutic intervention, either with drug therapy or with other modalities to alter homeostatic pathways. Additionally, knowledge of genetic variants allows patients to choose, in a more informed way in consultation with their physician, medical versus procedural therapy. Identifying these genetic variants in patients who are already bleeding or having difficulties with coagulation, can result in the alteration or modification of the therapeutic strategy.
As indicated above, preoperative genotype testing can refine risk stratification and improve patient outcome. Based on the genetic risk factors identified, non-specific therapies to reduce thrombin activation and bleeding, such as aprotinin, prostaglandins (Kozek-Langenecker et al, Anesthesia & Analgesia 87:985-988 (1998)) or more aggressive heparin dosing (Despotis et al, Thrombosis & Haemostasis 76:902-908 (1996)), can be employed for at-risk patients. As certain of the prothrombotic alleles that lead to increased bleeding in the immediate postoperative period are later associated with the return of a prothrombotic tendency (Golanski et al, Platelets 12:241-247 (2001)), a biphasic anti-thrombotic therapeutic approach can have merit, initially to halt sub-clinical thrombosis, then postoperatively to prevent thrombotic complications. Optimum timing and dosing of any such intervention can be established by one skilled in the art.
It will be appreciated from a reading of this disclosure that certain of the particular genetic variants are important independently and others interact to become significant in combination with other genes relative to postoperative bleeding.
The invention also relates to kits suitable for use in testing for the presence of the polymorphisms identified above. Such kits can include, for example, reagents (e.g., probes or primers) necessary to identify the presence of one or more of the above-referenced polymorphisms associated with perioperative bleeding.
In another embodiment, the present invention results from studies designed to examine the association between specific genetic variants and perioperative renal dysfunction. When twelve candidate polymorphisms are assessed, two demonstrate strong association with renal injury (>50% decrease renal filtration); these genes include interleukin 6 G572C and angiotensinogen T842C in Caucasians (when present together p<0.0001). Using less stringent criteria for significance (0.01>p>0.001), 4 additional polymorphisms are identified (apolipoproteine C586T (E2), angiotensin receptor1 A1166C, and eNOS G894T in Caucasians; eNOS G894T and ACE D/I in African Americans). Adding genetic to clinical predictors resulted in the best model, with overall ability to predict renal injury increasing about 4-fold in Caucasians and doubling in African Americans (p<0.0005). Up to 8 genetic polymorphisms collectively provide a 2-4 fold improvement over clinical factors alone in predicting perioperative renal dysfunction. From a mechanistic perspective, most identified genetic variants are associated with increased renal inflammatory and/or vasoconstrictor responses.
Biological effects for each of the single nucleotide polymorphisms (SNPs) referenced above, and described in greater detail in Example 2, have been demonstrated. This aspect of the invention provides definitive association between these genetic variants and clinical postoperative renal dysfunction in the perioperative and intensive care unit setting. The invention is exemplified by reference to cardiac surgery patients but includes all perioperative, periprocedure (endoscopy, bronchoscopy, cardiac catheterization, angioplasty, etc.), and intensive care unit settings.
The presence of one or more of the above-referenced polymorphisms present in a sample (e.g., a biological sample such as blood) can be determined using any of a variety of genotyping techniques known in the art. Examples of such techniques include the use of polymerase chain reaction and extension primers (see too Example 2). Suitable techniques also include the use of RFLP analysis and mass spectrometry (see also Ye et al, Hum. Mutat. 17(4):305 (2001), Chen et al, Genome Res. 10:549 (2000).)
The genetic variants (SNPs) described above and in Example 2 can be used, for example, to predict predict postoperative and ICU renal dysfunction. As indicated above, screening for genetic variants of the invention is also relevant for other invasive procedures including but not limited to endoscopy, bronchoscopy, cardiac catheterization, and angioplasty. Preoperative screening for genetic variants enables clinicians to better stratify a given patient for therapeutic intervention, either with drug therapy or with other modalities. Additionally, knowledge of genetic variants allows patients to choose, in a more informed way in consultation with their physician, medical versus procedural therapy. Identifying these genetic variants in patients who are already experiencing renal injury can result in the alteration or modification of the therapeutic strategy.
As indicated above, preoperative genotype testing can refine risk stratification and improve patient outcome. Based on the genetic risk factors identified, drugs already available and used to delay progression of chronic renal disease (e.g., angiotensin converting enzyme inhibitors and/or angiotensin receptor blockers) may be useful in reducing renal damage in acute settings such as cardiac surgery. Identification of the genetic markers described herein may facilitate individually tailored medical therapy (personalized medicine) designed to reduce acute renal injury and associated morbidity and mortality.
It will be appreciated from a reading of this disclosure that certain of the particular genetic variants are important independently and others interact to become significant in combination with other genes relative to postoperative renal dysfunction.
The invention also relates to kits suitable for use in testing for the presence of the polymorphisms identified above. Such kits can include, for example, reagents (e.g., probes or primers) necessary to identify the presence of one or more of the above-referenced polymorphisms associated with perioperative renal dysfunction.
In yet another embodiment, the present invention results from studies designed to examine the association between specific genetic polymorphisms and stroke risk after surgery (e.g., cardiac surgery). These studies demonstrate that specific genetic variants contribute to the risk of postoperative stroke and suggest that inflammation plays a pivotal role. It will be appreciated from a reading of this disclosure that a SNP pair representing individuals having minor alleles for both of CRP (3′UTR 1846C/T) and IL6 (—174G/C) significantly associated with stroke. Individuals with at least one minor allele at each locus are about three times more likely to have a stroke than individuals without this combination.
Biological effects for the single nucleotide polymorphisms (SNPs) referenced above, and described in greater detail in Example 3, have been demonstrated. The present invention provides definitive association between these genetic variants and clinical postoperative stroke in the perioperative setting. The invention is exemplified by reference to cardiac surgery patients but includes all perioperative, periprocedure (endoscopy, bronchoscopy, cardiac catheterization, angioplasty, etc.), and intensive care unit settings.
The presence of one or more of the above-referenced polymorphisms present in a sample (e.g., a biological sample such as blood) can be determined using any of a variety of genotyping techniques known in the art. Examples of such techniques include the use of polymerase chain reaction and extension primers (see too Example 3). Suitable techniques also include the use of RFLP analysis and mass spectrometry (see also Ye et al, Hum. Mutat. 17(4):305 (2001), Chen et al, Genome Res. 10:549 (2000)).
The genetic variants (SNPs) described above and in Example 3 can be used, for example, to predict postoperative and ICU stroke risk. As indicated above, screening for genetic variants of the invention is also relevant for other invasive procedures including but not limited to endoscopy, bronchoscopy, cardiac catheterization, and angioplasty. Preoperative screening for genetic variants enables clinicians to better stratify a given patient for therapeutic intervention, either with drug therapy or with other modalities. Additionally, knowledge of genetic variants allows patients to choose, in a more informed way in consultation with their physician, medical versus procedural therapy. Identifying these genetic variants in patients who decide to undergo surgery or other invasive procedure enables health care providers to design altered therapeutic strategies aimed at preventing the incidence of stroke in the subset of patients with enhanced risk. In addition, identifying these genetic variants in patients who have already experienced a stroke might also lead to alteration or modification in the therapeutic strategy.
As indicated above, preoperative genotype testing can refine risk stratification and improve patient outcome. Based on the genetic risk factors identified, drugs already available and used to minimize the risk of stroke and/or damage associated therewith (e.g., Apoprotinin) can be useful in reducing stroke risk/damage in acute settings, for example, cardiac surgery. Identification of the genetic markers described herein can facilitate individually tailored medical therapy (personalized medicine) designed to reduce stroke risk and associated morbidity and mortality. Perioperative screening can facilitate alterations in the usual course of the surgical procedure with institution of procedures designed to additionally reduce this risk (e.g., cardiac surgery without aortic cross-clamping).
The invention also relates to kits suitable for use in testing for the presence of the polymorphisms identified above. Such kits can include, for example, reagents (e.g., probes or primers) necessary to identify the presence of one or more of the above-referenced polymorphisms associated with perioperative stroke.
In a further embodiment, the present invention relates to methods of is identifying compounds suitable for use in minimizing the risk of stroke. These methods can comprise screening compounds for their ability to modulate (e.g., inhibit) inflammation (e.g., perioperative inflammation). Such methods are made possible by the presently described interaction of two specific pathways.
Certain aspects of the invention can be described in greater detail in the non-limiting Examples that follow.
EXAMPLE 1
Experimental Details
Study Design
The PeriOperative Genetics and Outcomes (POGO) study is an ongoing prospective and longitudinal study, including over 3300 consenting patients scheduled for cardiac surgery.
Data Collection and Management
Data was prospectively collected as part of a quality assurance database, including demographic, historical, clinical, laboratory, and investigational test information, resource utilization, and adverse outcome. All outcomes are prespecified and defined by protocol.
Study Sample
All patients qualifying within the enrollment period were entered. Of the 3300 patients enrolled, 740 were analyzed for the bleeding clinical end point. During a prospectively identified time period, data pertaining to postoperative bleeding was collected from enrolled patients undergoing primary elective CABG surgery. The following exclusion criteria were used: presence of liver disease, end stage renal disease, preexisting coagulopathy, emergency procedure (including administration of thrombolytic drugs within 48 hours), and reoperation for bleeding.
Patient Data Collection and Definition of Phenotypes:
Blood was collected prior to anesthetic induction for isolation of genomic DNA. Co-variate data included age, sex, race, height, weight, smoking, diabetes, preoperative hemoglobin level and platelet count, preoperative aspirin or intravenous heparin therapy, duration of cardiopulmonary bypass and number of bypass grafts performed, heparin dose, protamine dose, intraoperative blood product usage. Cardiopulmonary bypass was conducted using mild to moderate hypothermia and a target activated clotting time of 480 seconds, with a crystalloid primed circuit and cold blood/crystalloid cardioplegia. All patients received intraoperative ε-aminocaproic acid infusions to inhibit excessive fibrinolysis and protamine sulphate to reverse heparin anticoagulation. The outcome variable was defined as 12 hour post-operative chest tube drainage (CTD).
After collection, genetic samples were linked to covariate and phenotypic variables in a relational database with extensive quality control features; databases were deidentified to ensure patient confidentiality.
Rationale for Candidate Polymorphism Selection:
Overall, 78 candidate genes representing one of nine mechanistic pathways involved in the pathophysiology of organ dysfunction after CPB were selected after a comprehensive review of expression studies, linkage data, functional and positional guesses, and population-based association studies reported in the literature. Broadly, these polymorphisms are grouped into genes involved in inflammatory, thrombosis/coagulation, vascular tone, vascular growth, renin-angiotensin axis, cell-matrix adhesion, reperfusion, lipid and homocysteine metabolism, and cellular homeostasis. Of the 160 candidate polymorphisms identified from public databases, 19 were selected for their high likelihood of association with bleeding outcomes (Table 2). Selection strategies included polymorphisms with previously demonstrated high likelihood (based on location and type) of functionally significant effects on the gene product itself (nonsense or misssense non-synonymous variants or frame shift insertions/deletions in the coding sequence), or in the response of the gene to the product of other genes (i.e., transcription factors) through mutations in the promoter or control sequence of a gene (Tabor et al, Nat. Rev. Genet. 3:391-397 (2002)).
Structure Gene Polymorphism Selection:
58 unlinked marker gene polymorphisms were chosen to assess/control for population admixture (genomic control).
Isolation of Genomic DNA and Genotype Analysis:
DNA extraction was performed using the PuregeneTM system (Gentra Systems, Minneapolis, Minn.), quantitated and stored at 4° C. under chloroform. The genotyping assays were conducted at Agencourt Bioscience Corporation (Beverly, Mass.) by Matrix Assisted Laser Desorption/Ionization, Time-Of-Flight (MALDI-TOF) mass spectrometry, using the SequenomTM MassARRAYTM system (Sequenom, San Diego, Calif.) (Sun et al, Nucleic Acids Research 28:E68 (2000)). Polymerase chain reaction and extension primers for each polymorphism are presented in Tables 5 and 6 along with the details of the polymorphisms. The genotyping accuracy of the SequenomTM MassARRAYTM system was estimated at 99.6% (Gabriel et al, Science 296:2225-2229 (2002)). Polymorphisms with error rates >2%, with genotype call rates <75%, or departing from Hardy-Weinberg equilibrium (p<0.01) were regenotyped. Reproducibility of genotyping was validated by scoring a panel of 6 polymorphisms in 100 randomly selected patients using direct sequencing on an ABI3700 capillary sequencer (Applied Biosystems, Foster City, Calif.).
Angiotensin converting enzyme (ACE) deletion and insertion alleles were identified on the basis of polymerase chain reaction amplification of the respective fragments from the intron 16 of the ACE gene, size fractionation and electrophoretic visualization, as described (Lindpaintner et al, N. Engl. J. Med. 332:706-711 (1995)). The results were scored by two independent investigators, blinded to the clinical phenotype.
Statistical Analysis
Descriptive statistics were calculated for all 19 candidate polymorphisms, including allele frequencies, Hardy-Weinberg equilibrium and linkage disequilibrium, for the overall study population as well as for gender and race subgroups.
Prior to analysis, the three-level genotypes were collapsed into two-level genotypes based on the absence or presence of the putative deleterious allele. This was done primarily for sample size reasons as the frequency of minor alleles did not allow consideration of all genotypes individually.
Individual linear regression models were constructed to test the hypothesis that the absence or presence of an allele, either singly or in combination with other alleles, was associated with postoperative bleeding. Each polymorphism was considered as a main effect, and was also paired with each of the other polymorphisms to investigate all possible two-way interactions.
The next stage of analysis was to develop a multiple linear regression model, predicting 12 hour chest tube drainage, with all 19 selected candidate gene polymorphisms, and all the significant interaction terms as identified above. Terms which became non-significant in this multivariable model were removed from the model until only significant terms remained. Seven different polymorphisms were represented in the final model. The resulting combination of individual allelic effects and interaction terms constitute the final genetic model (Table 3). Predicted CTD values for all 128 (27) possible genotypes were generated by this model (FIG. 1).
A second predictive model was developed using clinical, operative, and demographic variables to predict CTD (the “clinical” model). Finally, all of the predictors from the genetic and clinical models were combined into one model, to address the question of whether genetic information would add predictive ability to the clinical model.
Potential bias from population admixture was investigated in the following ways. First, self-reported race (African American or Caucasian; no other race was sufficiently represented) was tested directly as a predictor of CTD. Secondly, each polymorphism was tested individually in interaction with race as a predictor of CTD. Finally, after the development of the final combined genetic and clinical model, race was included as a covariate in the multivariable model and tested again.
All statistical analysis was performed using SAS/Genetics system version 8.02 (SAS Inc, Cary, N.C.). Continuous variables will be described as mean +/− standard deviation, categorical variables will be described as percentages.
Results
Total enrollment in the perioperative genomic project was 3300. The final size of the sample for this endpoint was 740.
Self-reported black or white race was not associated with bleeding either as an independent predictor or as an interaction term with any of the candidate polymorphisms. A similar analysis using structure genes to assess population admixture also did not correlate with postoperative bleeding. Therefore, the sample was assessed as a single population for all further analyses. Characteristics of the 19 prospectively identified candidate polymorphisms studied are defined for this population in Table 1. Prothrombotic physiology is associated with minor alleles except for α2β1 integrin —52C/T and ACE intron 16 del/ins polymorphisms, where the wild-type major alleles are prothrombotic. Of clinical relevance, many prothrombotic minor alleles are relatively common in the study population (frequency>0.1; see Table 1), however, a significant subgroup are present at lower frequency (0.02-0.09). Study population demographics and clinical co-variates considered related to postoperative bleeding are detailed in Table 2.
Seven polymorphisms demonstrate significant associations with postoperative bleeding, either independently or in interaction with each other (Table 3). These included the α2β1 integrin —52C/T and 807C/T dimorphisms, GPIbα 524C/T, tissue factor —603A/G, prothrombin 20210G/A, tissue factor pathway inhibitor —399C/T and ACE intron 16 del/ins polymorphisms. FIG. 1 models the predicted chest tube drainage for all possible genotype combinations of the seven polymorphisms listed in Table 3, depicting the observed and hypothetical genotype combinations associated with the highest and lowest values of chest tube drainage. This Figure illustrates the major effect that a combination of multi-locus, genetic polymorphisms may have on the variability in bleeding seen after primary CABG surgery.
Genetic factors predict bleeding. The final genetic multivariate linear regression model including these seven polymorphisms had an R2 value of 0.09, predicting 9% of the variability in CTD. Remarkably, this was more predictive than the model encompassing both preoperative co-morbidities and intraoperative risk factors (Table 3). Combining these clinical and genetic predictors produced a powerful model, predicting 14% of CTD variability (R2 value of 0.14). This indicates that genetic effects are primarily independent of, and carry more predictive power than, clinical effects in the study. Combining the genetic and clinical factors improves prediction of postoperative bleeding.
Surprisingly, three of the four two-way genetic interactions identified in the multivariable model identify less bleeding with wild type genotypes than with any combination of the pro-thrombotic alleles. The remaining genetic interaction shows a similar pattern but one prothrombotic allele combination is associated with less bleeding than the wild type group. In other words, in general, prothrombotic polymorphisms appear to be associated with more rather than less bleeding after coronary bypass surgery.
Linkage disequilibrium was present between some of the candidate polymorphisms (Table 4). Confirmation was provided of previously noted within-gene linkage disequilibrium between the α2β1 integrin —52C/T and 807C/T dimorphisms (Jacquelin et al, Blood 97:1721-1726 (2001)) and the two tissue factor polymorphisms (Arnaud et al, Arteriosclerosis, Thrombosis & Vascular Biology 20:892-898 (2000)). In addition linkage disequilibrium between the α2β1 integrin 807C/T and the GPIbα 524C/T polymorphisms was demonstrated.
Conclusions
The findings of the study strongly confirm a genetic basis for bleeding after coronary bypass surgery. In an analysis of nineteen prothrombotic gene polymorphisms, seven alleles were identified that, in interaction with each other, result in a two fold increase in the ability to predict bleeding, compared to models including only clinical factors. Curiously, a pattern emerged with prothrombotic polymorphisms being generally associated with more rather than less bleeding, reflected by 12 hour postoperative chest tube drainage. This study is the first to demonstrate the complex interaction of multiple genetic and clinical factors impacting bleeding after the hemostatic challenge of cardiopulmonary bypass. In addition, a role of several previously known bleeding risk factors was confirmed (Table 3).
While bleeding after cardiac surgery is well described as a major risk factor for adverse outcome, very little is known regarding the genetic basis of this serious problem. In a study of 517 patients undergoing various cardiac surgical procedures (Donahue et al, Circulation 107(7):1003-1008 (2003)), Donahue and colleagues identified an association between reduced bleeding at 12 and 24 hours after surgery and the Factor V Leiden polymorphism; there were 26 heterozygotes for the prothrombotic allele in the study group. However, in the current study it was not possible to confirm such an association even when gene interactions were taken into account. Also conflicting with the general pattern of observations of this study was the reduction of bleeding with a prothrombotic polymorphism noted by Donahue et al. Variability in repeating results has previously been noted for alleles occurring with low frequency (Croft et al, Thrombosis & Haemostasis 81:861-864 (1999)) such as Factor V Leiden. This may be explained by differing study conditions, including selection of antifibrinolytic agent (all patients in the current study received a lysine analogue agents, whereas many in the Donahue study received the serine protease inhibitor aprotinin), and heterogeneity of surgery type in the Donahue study. Alternately, effects such as linkage disequilibrium and population admixture may contribute to differences in findings between studies. The approach used in this study, of choosing to examine multiple genes and their interactions in the context of other clinical factors, is rapidly becoming state of the art, in the context of reports of gene-gene and gene-environment interactions related to outcome (Croft et al, Circulation 104:1459-1463 (2001), Visanji et al, British Journal of Haematology 110:135-138 (2000)), rather than single gene association studies.
The present findings indicate that alleles previously associated with risk of thrombosis are associated with more bleeding after cardiac surgery.
Although seemingly reversed from expected associations, this is consistent with previous findings that platelet activation and thrombin generation during CPB are associated with more postoperative bleeding (De Somer et al, Journal of Thoracic & Cardiovascular Surgery 123:951-958 (2002), Despotis et al, Thrombosis & Haemostasis 76:902-908 (1996)). While collagen and thrombin display a synergistic effect promoting activation and degranulation of platelets, platelet activation during CPB increases both markers of thrombin generation and bleeding after cardiac surgery (Kozek-Langenecker et al, Anesthesia & Analgesia 87:985-988 (1998)). Therefore, increased consumption of activated, thrombotic elements may be modulated by the polymorphisms described, and contribute to more bleeding in this setting.
The functional effects of the alleles evaluated in this study include modulation of platelet activation and thrombin generation. This may alter responses to surgically exposed collagen and tissue factor, which are known to affect postoperative bleeding (De Somer et al, Journal of Thoracic & Cardiovascular Surgery 123:951-958 (2002), Despotis et al, Thrombosis & Haemostasis 76:902-908 (1996), Kozek-Langenecker et al, Anesthesia & Analgesia 87:985-988 (1998), Boisclair et al, Blood 82:3350-3357 (1993), Chung et al, Circulation 93:2014-2018 (1996)). The 807C and —52T minor alleles in the α2 gene of the α2β1 integrin, downregulate the surface expression of this primary collagen receptor (Jacquelin et al, Blood 97:1721-1726 (2001)), Kunicki et al, Blood 89:1939-1943 (1997)), reducing platelet affinity for collagen and prothrombotic outomes (Kunicki, Arteriosclerosis, Thrombosis & Vascular Biology 22:14-20 (2002), Santoso et al, Blood 93:2449-2453 (1999)). Similarly, the 524C allele of GPIbα, the platelet adhesion receptor binding to exposed collagen via von Willebrand factor, is associated with fewer coronary thromboses than the 524T allele, potentially by modifying the structure and function of the GPIbα chain (Kunicki, Arteriosclerosis, Thrombosis & Vascular Biology 22:14-20 (2002)). These alleles were found to be associated with less bleeding after cardiac surgery than the prothrombotic alleles. A similar effect was seen with the —603A tissue factor allele, associated with reduced plasma tissue factor levels (Moatti et al, Thrombosis & Haemostasis 84:244-249 (2000), Arnaud et al, Arteriosclerosis, Thrombosis & Vascular Biology 20:892-898 (2000)), the −33C tissue factor pathway inhibitor allele, associated with higher plasma levels of tissue factor pathway inhibitor, (Moatti et al, Thrombosis & Haemostasis 84:244-249 (2000) and the insertion allele of the ACE polymorphism, known to reduce the risk of venous thrombosis (Philipp et al, Thrombosis & Haemostasis 80:869-873 (1998)). The prevailing pattern is that prothrombotic alleles are associated with increased bleeding, probably by promoting consumptive platelet activation and thrombin generation during cardiopulmonary bypass.
Genetic association studies have limitations. Many prothrombotic polymorphisms that broadly involve the same effect in the same physiologic pathway were studied—while some alleles decrease function and others increase, they all affect thrombosis in a similar way. By the nature of examining numerous multi-locus interactions, multiple comparisons are necessary, and prospective studies, including mechanistic aspects, will be required to confirm the present results. However, although analysis of gene-gene interactions does increase the number of tests performed, these findings are remarkably consistent involving similar directional effects on thrombotic and platelet activation pathways; the consistency of the observations provides confidence that the findings are rooted in biology and not spurious testing. There may be other important alleles, not identified as candidate polymorphisms or in linkage disequilibrium with studied polymorphisms (Jorde, Genome Research 10:1435-1444 (2000)), that have been overlooked in the present study. While population admixture is a potential confounder (Deng, Genetics 159:1319-1323 (2001)), evidence of any population stratification affecting this endpoint was not found, either with self-declared race or a more in depth genetic population stratification evaluation.







TABLE 1







Table 1. Candidate polymorphisms assessed









Minor allele



frequency














Prothrombotic

White
Black


Gene product
Polymorphism*
allele
Phenotype
(n =)
(n =)















Platelet Membrane Glycoproteins







Platelet glycoprotein IaIIa (α2β1
-52C/T
-52C1,2
↑ surface
0.33
0.4


integrin)


receptor





expression



807C/T
807T2
↑ surface
0.38
0.3





receptor





expression and





collagen binding



-5T/C
-5C2
↑ surface
0.13
0.21





receptor





expression


Platelet glycoprotein Ibα(GPIbα)
524C/T (Thr145Met)
Met145 (Koa)2
unknown
0.09
0.19


Platelet glycoprotein IIIa (β3
1565T/C (Leu33Pro)
Pro33 (PlA2)3
↑ sensitivity to
0.15
0.11


integrin)


activation


Platelet glycoprotein VI (GPVI)
13254T/C
Pro2194
unknown
0.15
0.23



(Ser219Pro)


Coagulation Factors


Tissue factor (TF)
-1208del/ins
-1208ins5
↑ level
0.54
0.46



-603A/G
-603G5
↑ level
0.49
0.57


Tissue factor pathway inhibitor
-399C/T
-399T6
↓ level
0.89
0.11


(TFPI)
1006G/A
Met2647
↓ level
0.02
0.01



(Val264Met)


Prothrombin
20210G/A
20210A8
↑ level
0.02
0.01


Coagulation factor V
1691G/A
Gln506 (FV
APCR
0.02
0.003



(Arg506Gln)
Leiden)9,10



-854G/A
-854A11,12
↑ level
0.14
0.11


Fibrinogen-β chain (FGB)
-455G/A
-455A11,12
↑ level
0.17
0.08



-148C/T
-148T11,12
↑ level
0.17
0.12


Fibrinolytic Factors


Plasminogen activator inhibitor type 1
-675ins/del (5G/4G)
-675del (4G)13
↑ level
0.54
0.38


(PAI-1)


Other Prothrombotic Factors


Thrombomodulin (THBD)
1959C/T
Val45513,14
unknown
0.19
0.1



(Ala455Val)


Methylenetetrahydrofolate reductase
677C/T (Ala222Val)
Val22215
↓ enzyme
0.32
0.19


(MTHFR)


activity,





↑ homocysteine





level


Angiotensin converting enzyme
Intron 16 del/ins
Intron 16 del16,17
↑ enzyme
0.46
0.38





*Minus signs indicate the number of nucleotides upstream to the transcription-initiation site. Major (wild-type) alleles are indicated to the left. For coding region polymorphisms, the resulting nonsynonymous amino acid substitution in shown in parentheses. APCR-activated protein C resistance.


1Jacquelin et al, Blood 97: 1721-1726 (2001)


2Kunicki et al, Arteriosclerosis, Thrombosis & Vascular Biology 22: 14-20 (2002)


3Feng et al, Arteriosclerosis Thrombosis & Vascular Biology 19: 1142-1147 (1999)


4Croft et al, Circulation 104: 1459-1463 (2001)


5Arnaud et al, Arteriosclerosis, Thrombosis & Vascular Biology 20: 892-898 (2000)


6Moatti et al, Thrombosis & Haemostasis 84: 244-249 (2000)


7Moatti et al, Arteriosclerosis, Thrombosis & Vascular Biology 19: 862-869 (1999)


8Girolami et al, Blood Reviews 13: 205-210 (1999)


9Donahue et al, Circulation 107(7): 1003-1008 (2003)


10Rosendaal et al, Blood 85: 1504-1508 (1995)


11Green, Annals of the New York Academy of Sciences 936: 549-559 (2001)


12Cook et al, American Journal of Epidemiology 153: 799-806 (2001)


13Eriksson et al, Proceedings of the National Academy of Sciences of the United States of America 92: 1851-1855 (1995)


14Wu et al, Circulation 103: 1386-1389 (2001)


15Arruda et al, Thrombosis & Haemostasis 77: 818-821 (1997)


16Philipp et al, Thrombosis & Haemostasis 80: 869-873 (1998)


17Hooper et al, American Journal of Hematology 70: 1-8 (2002)













TABLE 2





Table 2. Characteristics of the study population







Demographics










Age (years)
 63 (+/−11)



Race (% Caucasian)
 80*



Height (cm)
171 (+/−14)



Weight (Kg)
 86 (+/−19)







Co-morbidities










Diabetes (%)
35



Hypertension (%)
72



Current smokers (%)
31



Previous myocardial infarction (%)
26



Congestive cardiac failure (%)
16



Previous stroke (%)
 6







Clinical co-variates related to bleeding










Preoperative aspirin use (%)
83



Preoperative heparin infusion (%)
42



Preoperative platelet count (×106/ml)
213 (+/−68)



Preoperative hemoglobin (g/dl)
13.3 (+/−1.7)







Intraoperative factors










Number of bypass grafts
 3.3 (+/−0.8)



Cardiopulmonary bypass duration (minutes)
108 (+/−42)



Total heparin dose (units)
 39000 (+/−16000)



Total protamine dose (mg)
 290 (+/−100)



Packed red blood cells transfused (units)
 1.2 (+/−1.4)



Fresh frozen plasma transfused (ml)
 75 (+/−260)



Platelets transfused (ml)
 47 (+/−134)







*15% of our population were African-American; the remaining 5% were self-reported as Hispanic, Native American or Asian.













TABLE 3







Table 3 Results of three multivariate linear regression models testing


the association of polymorphisms and clinical co-variates with chest


tube drainage. The minor allele is tested as absent or present


(heterozygote or homozygote). The R2 values for the clinical, genetics


and combined models are 0.07, 0.09 and 0.14 respectively.


A statistically significant p value <0.05 is denoted by *.









Combined



model












F value
P value
F value
P value












Individual polymorphisms
Genetic model












α2β1 integrin-52C/T
7.6
0.01*
6.3
0.01*


α2β1 integrin 807C/T
1.4
0.24
1.7
0.19


GPIbα 524C/T
7.5
0.01*
8.8
<0.01*


Tissue Factor-603A/G
0.0
0.94
0.13
0.72


Prothrombin 20210G/A
0.0
0.98
0.11
0.74


TFPI-399C/T
6.5
0.01*
5.9
0.02*


ACE insertion/deletion
6.8
0.01*
6.1
0.01*


2-way interactions


α2β1-52C/T and α2β1 807C/T
12.3
<0.01*
7
<0.01*


α2β1-52C/T
6.6
0.01*
6.4
0.01*


and tissue factor - 603A/G


GPIbα 524C/T and
4.2
0.04*
3.7
0.06


tissue factor - 603A/G


Prothrombin 20210G/A
5.0
0.03*
4.8
0.03*


and TFPI - 399C/T









Clinical Co-variates
Clinical model












Age
5.8
0.02*
1.8
0.18


Weight
7.9
<0.01*
2.1
0.14


Height
11.4
<0.01*
6
0.01*


Diabetes
6.8
<0.01*
1
0.33


Preoperative hemoglobin level
10.9
<0.01*
3.9
0.05


Preoperative platelet count
3.8
0.05*
2.5
0.12


Cardiopulmonary bypass duration
4.5
<0.01*
3.6
0.06
















TABLE 4







Pair-wise linkage disequilibrium (LD) between candidate polymorphisms.


Multiple comparisons were performed, thus possible LD was defined as a p-value


of 0.01-0.05 (grey squares) and likely LD by a disequilibrium p-value of 0-0.01 (black squares).
















α2β1
α2β1
GPIbα
GPIbα
TF

THBD
ACE


polymorphism
−52 C/T
807 C/T
524 C/T
−5 T/C
−603 A/G
TFdel/ins
1959 C/T
del/ins





α2β1 −52 C/T
X










 


α2β1 807 C/T




X






 


GPIbα −524 C/T








X












 


GPIbα −5 T/C






X


 


TF −603 A/G




X











 


TFdel/ins








X


 


THBD 1959 C/T









X


 


ACEdel/ins










X
























TABLE 5







Gene





Previous



Gene
Map
Gene
Poly-
Aminoacid
dbSNP

Association


Gene Name
Symbol1
Locus
Region
morphism
Substitution
ID2
Intermediate Phenotype
Studies















I. Platelet Membrane Glycoproteins















Integrin, alpha 2
ITGA2
5p11.12
5′-UTR
-52C/T
—
rs28095
-52C = ↑surface



(alpha 2 subunit






α2□, receptor


of VLA-2






expression; altered


receptor,






affinity for


platelet 






Sp1/Sp3


GPIaIIa,






transcription factors


CD49B)


exon 7
807C/T
F253F
rs1800198
807T□↑surface
MI, post-









α2b1 receptor
CABG MI,









expression; ↑typeI
stroke









collagen binding


Platelet
GP1BA
17pter-p12
5′-UTR
-5 T/C
—
rs2243093
-5C = ↑surface receptor
MI


glycoprotein






expression (Kozak


Ib, alpha






dimorphism)


polypeptide


exon 2
524C/T
T145M
rs6065
unknown


(GPIbα)




(Kob/Koa)


Integrin, beta 3
ITGB3
17q21.32
exon 3
1565T/C
L33P
rs5918
PlA2 = ↑sensitivity to
early


(platelet GPIIIa,




(PlA1/PlA2)

activation; ↑platelet
CAD/MI


CD61)






aggregability;









□fibrinogen









binding


Platelet
GP6
19q13.4
exon 5
13254T/C
S219P
rs1613662
unknown
MI


glycoprotein


VI (GPVI)







II. Coagulation Factors















Coagulation
F3
1p22-p21
5′-UTR
-1208del/ins
—
—
-1208ins = ↑TF level
early


factor III







CAD/MI,


(Thromboplastin,







early first


Tissue factor)







CABG,










venous










thromboembolic










disease





5′-UTR
-603A/G
—
rs1361600
-603G = ↑TF level


Tissue factor
TFPI
2q31-q32.1
5′-UTR
-399C/T
—
—
-399T = □total and post-


pathway






heparin TFPI level


inhibitor


exon 9
1006G/A
V264M
rs5940
M264 = □total and
acute


(lipoprotein-






free TFPI level
coronary


associated







syndromes


coagulation


inhibitor)


Coagulation factor
F2
11p11-q12
3′-UTR
20210G/A
—
rs1799963
20210A□↑prothrombin


II (Prothrombin)






level


Coagulation factor
F5
1q23
exon 10
1691G/A
R506Q
rs6025
Q506 (FV Leiden) = APCR
deep venous


V (proaccelerin,




(Factor V


thrombosis,


labile factor)




Leiden)


MI, stroke


Fibrinogen, B
FGB
4q28
5′-UTR
-854G/A
—
rs1800791
-854A = ↑FGB level;


beta polypeptide






↑basal transcription









rate





5′-UTR
-455G/A
—
rs1800790
-455A = ↑FGB level;









↑basal transcription









rate





5′-UTR
-148C/T
—
rs1800787
-148T = ↑FGB level







III. Fibrinolytic Factors















Serine (or
SERPINE1
7q21.3-q22
5′-UTR
-675 ins/del
—
rs1799768
-675del (4G) =
early


cysteine)



(5G/4G)


↑PAI-1 level
CAD/MI,


proteinase







atherosclerosis


inhibitor, clade


E (nexin,


plasminogen


activator inhibitor


type I, PAI-l),


member 1







IV. Other Prothrombotic Factors















Thrombomodulin
THBD
20p11.2
exon 1
1959C/T
A455V
rs1042579
unknown
V455 = CAD/MI


5,10-
MTHFR
1p36.3-p36.2
exon 4
677C/T
A222V
rs1801133
V222 = □enzyme
early CAD


Methylenetetra-






activity (thermolabile);


hydrofolate






↑homocysteine level


reductase


Angiotensin I
ACE
17q23
intron
in16del/ins
—
—
in16 del =
early


converting enzyme


16



↑enzyme activity
CAD/MI


(peptidyl-


dipeptidase


A) 1





Candidate genes and polymorphisms studied: intermediate functional phenotypes and associations to thrombotic outcomes


NOTES:


1gene symbols are taken from OMIM (catalog of human genes and genetic disorders)


2from NCBI's dbSNP public database


UTR—untranslated region


















TABLE 6





Gene

dbSNP





Symbol1
Polymorphism
ID2
PCR Primers
Extension Primer







ITGA2
−52 C/T
rs28095





 



807 C/T
rs1800198
ACGTTGGATGTGGCCTATTAGCACCAAAAC
TTACCTTGCATATTGAATTGCTCC





ACGTTGGATGAGACATCCCAATATGGTGGG


 


GP1BA
−5 T/C
rs2243093
ACGTTGGATGATCCACTCAAGGCTCCCTTG
GGCTCCCTTGCCCACAGG





ACGTTGGATGTTGGCAGCAGGAGCAGCAAG


 



524 C/T
rs6065
ACGTTGGATGTGTTGTTAGCCAGACTGAGC
TCCAGCTTGGGTGTGGGC





ACGTTGGATGAAGGCAATGAGCTGAAGACC


 


ITGB3
1565 T/C
rs5918
ACGTTGGATGCCTTCAGCAGATTCTCCTTC
TCACAGCGAGGTGAGCCC





ACGTTGGATGTTGCTGGACTTCTCTTTGGG


 


GP6
13254 T/C
rs1613662
ACGTTGGATGATTTCCCAGGAACCTCTGTG
TACCAACAGAACCACCTTCC





ACGTTGGATGATACGCTGTGCACCAGAATG


 


F3
−1208 del/ins
—
ACGTTGGATGTTTTGCACAGTTTTATTCTG
CTGTTAAAACAAGTGGTTCAGTA





ACGTTGGATGAGTCAGTCTTGCATTTTAAT


 



−603 A/G
rs1361600
ACGTTGGATGTCTTCTACCCCACGAAGGTC
CTACCCCACGAAGGTCAAGAATAC





ACGTTGGATGATTCCCACCGCCTTTCTCCTG


 


TFPI
−399 C/T
—
ACGTTGGATGTAAAAGTCTGAGGGCTACCG
CCGTTGGAGGTCTCTCTTAGTGA





ACGTTGGATGAAGCCTGACACCTGCAATAG


 



1006 G/A
rs5940
ACGTTGGATGAAGGAGGCCTAATTAAAACC
AAAGAAAAAGAAAGAAGCAGAGA





ACGTTGGATGCATTGCTATAACAAATTCAC


 


F2
20210 G/A
rs1799963


 


F5
1691 G/A
rs6025
ACGTTGGATGCTGAAAGGTTACTTCAAGGAC
AGGACAAAATACCTGTATTCCT





ACGTTGGATGTGGGCTAATAGGACTACTTC


 


FGB
−854 G/A
rs1800791
ACGTTGGATGCTCACAGACTAAATGAGGCC
GAGGCCCATTTTCCTTCATTT





ACGTTGGATGCACACAAGTGAACAGACAAG


 



−455 G/A
rs1800790


 



−148 C/T
rs1800787
ACGTTGGATGCATTTAAGCAACATCTTCCC
AAGCAACATCTTCCCAGCAAA





ACGTTGGATGAACTTCCCATCATTTTGTCC


 


SERPINE1
−675 ins/del
rs1799768
ACGTTGGATGCTCCGATGATACACGGCTGA
GATACACGGCTGACTCCCC



(5 G/4 G)

ACGTTGGATGAGGTTGTTGACACAAGAGAG


 


THBD
1959 C/T
rs1042579
ACGTTGGATGAGTCACAGTCGGTGCCAATG
CACAGTCGGTGCCAATGTGGCGG





ACGTTGGATGGGTACCTTCGAGTGCATCTG


 


MTHFR
677 C/T
rs1801133
ACGTTGGATGATGCCTTCACAAAGCGGAAG
GCTGCGTGATGATGAAATCG





ACGTTGGATGCTTGAAGGAGAAGGTGTCTG


 


ACE
in16 del/ins
—





Genotyping assays for the 19 candidate polymorphisms: PCR primers (top-forward, bottom-reverse) and extension primers for MALDI-TOF mass specrometry


NOTE:


the ACE indel polymorphism was genotyped using PCR amplification, size fractionation and electrophoretic visualization as described






Brief description of the gene products and candidate polymorphisms and the current evidence linking them to a prothrombotic phenotype.
Functional phenotypes of candidate genes and polymorphisms and associations with thrombotic outcomes.
Primers for genotyping assays.
Gene Products and Candidate Polymorphisms
Platelet Glycoprotein (GP) IIb/IIIa (ITGB3)
Shear mediated platelet thrombus deposition is significantly dependent upon the PLA1/2 polymorphism of the IIIa chain1, as is responsiveness to epinephrine2 increased thrombin generation in response to microvascular injury3 and a prothrombotic phenotype4.
GP1b_((GP1BA)
The 5T/C substitution (Kozak polymorphism) and the 524 C/T substitution encoding Met145Thr on the GP1b_subunit of the GP1b complex (GP1b_, GP1b_, GP IX and GPV), are associated with increased transcription rate and surface expression of the GP1b complex5 and a prothrombotic phenotype respectively6,7.
GPIaIIa/—2—1 Integrin (ITGA2)
The 807C/T substitution on the —2 gene regulates the surface density of GPIaIIa (integrin—2hd —1) and the T allele has prothrombotic associations8. c-52T also regulates the surface density of GPIaIIa9 but has not clinical associations as yet
GPVI (GP6)
Variation in platelet GPVI content alters procoagulant properties10 and deficiency results in mild bleeding disorder.11 There is a positive association with MI12.
Tissue Factor (F3)
2 common baplotypes exist (1208D/603A, 1208I/603G) and 1208D protects against venous thrombosis.13 
Tissue Factor Pathway Inhibitor (TFPI)
—287C associated with increased TFPI levels and —399T associated with lower post-heparin TFPI levels; therefore —287T and —399T likely prothrombotic during cardiopulmonary bypass14. G1006A also related to post heparin TFPI levels15.
Factor V (F5)
1691 A (Factor V Leiden) is associated with reduced bleeding after CABG [Donahue, 2003 #3614] and there is a strong association with venous and arterial thrombosis via activated Protein C resistance15 
Prothrombin (F2)
20210A of 20210 G/A is associated with venous and arterial thrombosis16 
Fibrinogen_(FGB)
All three polymorphisms are associated with fibrinogen levels but they are in linkage disequilibrium such that —455A is always associated with increased fibrinogen levels (in Caucasians) and the 4 common haplotypes are shown below17. However —148T is associated with higher fibriogen in UK blacks and not 100% in linkage disequilibrium in this group18
















−854
−148
−455










G
C
G
~40%



A
C
G
~20%



G
C
G
~20%



G
T
A
~20%










Common Haplotypes seen with Fibrinogen_polymorphisms

Thrombomodulin (THBD)

Increased MI risk in blacks only19, functional/mechanistic basis for this unclear.
Plasminogen Activator Inhibitor-1 (PAI-1, SERPINE 1)
Increased levels and increased MI associated with 5G/4G polymorphism20 
Cathepsin (CTSG)
Associated with increased fibrinogen levels in patients with CAD21 
Methyltetrahydrofoultereductase (MTHFR)
Homozygotes (freq 0.10) are associated with arterial and venous thrombosis by increasing plasma homocysteine levels22 23.
Angiotensin Converting Enzyme (ACE)
Indel polymorphism increases risk of venous thrombosis24 25 26 27.
REFERENCES


    1. Cadroy Y, Sakariassen K, Grandjean H, Thalamas C, Boneu B, Sie P. The effect of platelet PIA polymorphism on experimental thrombus formation in man depends on blood flow and thrombogenic substrate. Thrombosis & Haemostasis. 2001; 85:1097-103.
    2. Feng D, Lindpaintner K, Larson M G, Rao V S, O'Donnell C J, Lipinska I, Schmitz C, Sutherland P A, Silbershatz H, D'Agostino R B, Muller J E, Myers R H, Levy D, Tofler G H. Increased platelet aggregability associated with platelet GPIIIa PIA2 polymorphism: the Framingham Offspring Study. Arteriosclerosis Thrombosis & Vascular Biology: 1999; 19:1142-7.
    3. Undas A, Brummel K, Musial J. Mann K G, Szczeklik A. PI(A2) polymorphism of beta(3) integrins is associated with enhanced thrombin generation and impaired antithrombotic action of aspirin at the site of microvascular injury. Circulation. 2001; 104:2666-72.
    4. Bray P F. Platelet glycoprotein polymorphisms as risk factors for thrombosis. Current Opinion in Hematology. 2000; 7:284-9.
    5. Afshar-Kharghan V, Li CQ, Khoshnevis-Asl M, Lopez J A. Kozak sequence polymorphism of the glycoprotein (GP) Ibalpha gene is a major determinant of the plasma membrane levels of the platelet GP Ib-IX-V complex. Blood. 1999; 94:186-91.
    6. Gonzalez-Conejero R, Lozano M L, Rivera J, Corral J, Iniesta J A, Moraleda J M, Vicente V. Polymorphisms of platelet membrane glycoprotein Ib associated with arterial thrombotic disease. Blood. 1998; 92:2771-6.
    7. Murata M, Matsubara Y, Kawano K, Zama T, Aoki N, Yoshino H, Watanabe G, Ishikawa K, Ikeda Y. Coronary artery disease and polymorphisms in a receptor mediating shear stress-dependent platelet activation. Circulation. 1997; 96:3281-6.
    8. Santoso S, Kunicki T J, Kroll H. Haberbosch W, Gardermann A. Association of the platelet glycoprotein Ia C807T gene polymorphism with nonfatal myocardial infarction in younger patients. Blood. 1999; 93-2449-53.
    9. Jacquelin B, Tarantino M D, Kritrik M, Rozenshteyn D, Koziol J A, Nurden A T, Kunicki T J. Allele-dependent transcriptional regulation of the human integrin alpha2 gene. Blood. 2001; 97:1721-6.
    10. Furibata K, Clemetson K J, Deguchi H, Kunicki T J. Variation in human platelet glycoprotein VI content modulates glycoprotein VI-specific prothrombinase activity. Arteriosclerosis, Thrombosis & Vascular Biology. 2001; 21:1857-63.
    11. Moroi M. Jung S M, Okuma M, Shinmyozu K. A patient with platelets deficient in glycoprotein VI that lack both collagen-induced aggregation and adhesion. Journal of Clinical Investigation. 1989; 84:1440-5.
    12. dCroft S A, Samani N J, Teare M D, Hampton K K, Steeds R P, Channer K S, Daly M E. Novel platelet membrane glycoprotein VI dimorphism is a risk factor for myocardial infarction.[comment]. Circulation. 2001; 104:1459-63.
    13. Arnaud E, Barbalat V, Nicaud V, Cambien F. Evans A, Morrison C, Arveiler D, Luc G, Ruidavets J B, Emmerich J. Fiessinger J N, Aiach M. Polymorphisms in the 5′ regulatory region of the tissue factor gene and the risk of myocardial infarction and venous thromboembolism: the ECTIM and PATHROS studies. Etude Cos-Temoins de l'Infarctus du Myocarde. Paris Thrombosis case-control Study. Arteriosclerosis, Thrombosis & Vascular Biology. 2000; 20:892-8.
    14. Moatti D, Haidar B, Fumeron F, Gauci L, Boudvillain O, Seknadji P. Olliver V, Aumont M C, de Prost D. A new T-287C polymorphism in the 5′ regulatory region of the tissue factor pathway inhibitor gene. Association study of the T-287C and C-399T polymorphisms with coronary artery disease and plasma TFPI levels. Thrombosis & Haemostasis. 2000; 84:244-9.
    15. Amini-Nekoo A, Futers T S, Moia M, Mannucci P M, Grant P J, Ariens R A. Analysis of the tissue factor pathway inhibitor gene and antigen levels in relation to venous thrombosis. British Journal of Haematology. 2001; 113:537-43.
    16. Doggen C J, Cats V M, Bertina R M, Rosendaal F R. Interaction of coagulation defects and cardiovascular risk factors: increased risk of myocardial infarction associated with factor V Leiden or prothrombin 20210A.[comment]. Circulation. 1998; 97:1037-41.
    17. Green F R. Fibrinogen polymorphisms and atherothrombotic disease. Annals of the New York Academy of Sciences. 2001; 936-549-59.
    18. Cook D G, Cappuccio F P, Atkinson R W, Wicks P D, Chitolie A, Nakandakare E R, Sagnella G A, Humphries S E. Ethnic differences in fibrinogen levels: the role of environmental factors and the beta-fibrinogen gene. American Journal of Epidemiology. 2001; 153:799-806.
    19. Wu K K. Aleksie N, Ahn C, Boerwinkle E, Folsom A R, Juneja H, Atherosclerosis Risk in Communities Study J. Thrombomodulin Ala55Val polymorphism and risk of coronary heart disease. Circulation. 2001; 103:1386-9.
    20. Eriksson P, Kallin B. van't Hooft F M, Bavenholm P, Harnsten A. Allele-specific increase in basal transcription of the plasminogen-activator inhibitor 1 gene is associated with myocardial infarction. Proceedings of the National Academy of Sciences of the United States of America. 1995; 92:1851-5.
    21. Hermann S M, Funke-Kaiser H, Schmidt-Petersen K, Nicaud V, Gautier-Bertrand M, Evans A, Kee F, Arveiler D, Morrison C, Orzechowsiki H D, Elbaz A, Amarenco P, Cambien F, Paul M. Characterization of polymorphic structure of cathepsin G gene: role in cardiovascular and cerebrovascular diseases. Arteriosclerosis, Thrombosis & Vascular Biology. 2001, 21:1538-43.
    22. Arruda V R, von Zuben P M, Chiaparini L C, Annichino-Bizzachi J M, Costa F F. The mutation Ala677->Val in the methylene tetrahydrofolate reductase gene: a risk factor for arterial disease and venous thrombosis. Thrombosis & Haemostasis. 1997; 77:818-21.
    23. Gemmati D, Serino M L, Trivellato C, Fiorini S, Scapoli G L. C677T substitution in the methylenetetrahydrofolate reductase gene as a risk factor for venous thrombosis and arterial disease in selected patients. Haematologica. 1999; 84:824-8.
    24. Philipp C S, Dilley A, Saidi P, Evatt B, Austin H, Zawadsky J, Harwood D, Ellingsen D, Barnhart E, Phillips D J, Hooper W C. Deletion polymorphism in the angiotensin-converting enzyme gene as a thrombophilic risk factor after hip arthroplasty. Thrombosis & Haemostasis. 1998; 80:869-73.
    25. Hooper W C, Dowling N F, Wenger N K, Dilley A, Ellingsen D, Evatt B L. Relationship of venous thromboembolism and myocardial infarction with the renin-angiotensin system in African-Americans. American Journal of Hematology. 2002; 701-8.
    26. Lu Y, Hui R, Zhao Y. Insertion/deletion polymorphsim of the angiotensin I converting enzyme gene and pulmonary thromboembolism in Chinese population. Chung-Hua Chieh Ho Ho Hu Hst Tsa Chih Chinese Journal of Tuberculosis & Respiratory Diseases. 2001; 24:265-8.
    27. Isbir C S, Akgun S, Yilmaz H, Civelek A, Ak K, Tekeli A, Agachan B, Cobanoglu A. Is there a role of angiotensin-converting enzyme gene polymorphism in the failure arteriovenous femoral shunts for hemodialysis? Annals of Vascular Surgery. 2001; 15:443-6.


EXAMPLE 2
Experimental Details
Study Population
This analysis is a sub-study of the Perioperative Genetics and Safety Outcomes Study (PEGASUS), an ongoing IRB approved, prospective, longitudinal study at Duke University Medical Center, where 3,149 patients have been prospectively enrolled and consented to have clinical and genetic data analyzed in relation to perioperative outcomes. The current sub-study targets 2075 patients undergoing primary elective CABG surgery using CPB during a pre-specified period when detailed perioperative serum creatinine and dialysis data was systematically and prospectively collected. Patients were excluded from analysis who died within 2 days of surgery (n=42), were receiving dialysis prior to or after surgery (n=38), or for whom creatinine data at all time points was not available (n=227). Of the final 1,768 patients examined, 1,464 were Caucasian, 207 were African American, and 97 were of another race; due to small numbers in each of the “other” race categories, the analysis was limited to Caucasians and African Americans.
Clinical Data Collection
The primary outcome variable selected was peak fractional change in postoperative serum creatinine (% ΔCr), defined as percent difference between preoperative serum creatinine (CrPre) and highest of the daily in-hospital postoperative values (CrmaxPost); this is a commonly used, validated continuous variable reflecting relative reduction in renal filtration function (Andersson et al, Thorac. Cardiovasc. Surg. 41(4):237-241 (1993)). Serum creatinine was determined using a dry slide enzymatic reflectance technique (Vitros 950, Johnson and Johnson, New Brunswick, N.J.) with a normal range of 44-133 μmol/L (0.5-1.4 mg/dl). Preoperative clinical covariate data includes demographic variables and pre-existing comorbidities (Table 7). Intra- and postoperative variables include duration of CPB, duration of aortic crossclamp, number of aortacoronary bypass grafts, blood product usage, requirement for inotropic drugs and/or intra-aortic balloon pump (IABP) counterpulsation (Table 7). Use of agents with potential renal effects (e.g., intravenous dopamine, furosemide and mannitol) was recorded and taken into account during analysis, but not regulated, since these agents have not demonstrated significant beneficial effects in the setting of cardiac surgery (Conger, Am. J. Kidney Dis. 26(4):565-576 (1995), Vijayan and Miller, Semin. Nephrol. 18(5):523-532 (1998), Galley, Lancet 356(9248):2112-2113 (2000), Bellomo et al, Lancet 356(9248):2139-2143 (2000)). Each CPB circuit was primed with mannitol (250 ml 20% solution).







TABLE 7







Patient, Procedural, and Renal Function Characteristics










Caucasian (n = 1464)
African American





Demographic Variables




Age (years)
64 +/− 11
63 +/− 11


Body mass index (kg/m2)
29.7 +/− 14.1
31.4 +/− 18.5


Female (%)
26
50


Weight (kg)
86.2 +/− 19.1
84.2 +/− 18.2


Preoperative Comorbidities


Carotid bruit (%)
6
5


Chronic Steroid Therapy (%)
1
3


Hannan Mortality Risk Score*
0.025 +/− 0.029
0.028 +/− 0.036


History of congestive heart failure (%)
16
21


History of diabetes (%)
32
42


History of hypertension (%)
65
82


Preoperative inotropic drug infusion(s)∞ (%)
1
1


Preoperative intraaortic balloon counterpulsation (%)
1
2


History of myocardial infarction (%)
27
28


History of obstructive lung disease (%)
12
8


History of peripheral vascular disease (%)
13
18


History of stroke (%)
6
10


Preoperative ejection fraction (%)
52 +/− 14
49 +/− 14


Preoperative hematocrit (%)
39 +/− 7 
37 +/− 8 


Preoperative renal dysfunction (Cr ≧ 1.5 mg/dl) (%)
7
11


Unstable angina prior to surgery (%)
68
74


Procedure


Duration of aortic crossclamping (min)
63 +/− 31
59 +/− 28


Duration of cardiopulmonary bypass (min)
114 +/− 46 
110 +/− 46 


Number of coronary artery bypass grafts
3.2 +/− 0.9
3.1 +/− 0.8


Postoperative Course


Inotropic drug infusion(s)**upon arrival in intensive
21
27


Postoperative intraaortic balloon counterpulsation (%)
1
1


Transfusion{circumflex over ( )} (%)
23
25


Renal Function Variables


Peak postoperative serum creatinine (mg/dL)
1.4 +/− 0.8
1.7 +/− 1.2


Rise in serum creatinine (mg/dL)
0.3 +/− 2.1
0.3 +/− 1.0


Peak postoperative fractional creatinine rise#
30 +/− 52
34 +/− 44


Postoperative estimated creatinine clearance@
73 +/− 31
63 +/− 28


Fall in creatinine clearance@(ml/min)
−15 +/− 21  
−15 +/− 20  


Preoperative estimated creatinine clearance@(ml/min)
88 +/− 34
79 +/− 33


Preoperative serum creatinine (mg/dL)
1.1 +/− 0.5
1.3 +/− 1.0





Abbreviations:


{circumflex over ( )}Transfusion - a marker of perioperative transfusion (>2 units packed red cells and at least one other blood product within 24 hours of surgery)


#Peak percentage change in postoperative creatinine (% ΔCr) defined as the difference between preoperative and peak postoperative values represented as a percentage of the preoperative value.


*The Hannan Score is a risk factor score for in-hospital mortality following CABG surgery, identified by Hannan et al., in the New York State population (Hannan et al, JAMA 264(21): 2768–2774 (1990)).


**Inotropic drug use, defined as postoperative infusion of either dopamine >5 μg/kg/min and/or dobutamine >5 μg/kg/min, or epinephrine >0.03 μg/kg/min.


@Using the Cockroft-Gault equation (Cockcroft and Gault, Nephron 16(1): 31–41 (1976)), preoperative, lowest postoperative, and change in creatinine clearance (CrClPre) were calculated using preoperative and peak postoperative serum creatinine values.






Rationale for Candidate Polymorphism Selection

Twelve polymorphisms in 7 candidate genes were prospectively chosen, 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 anesthesia.duhs.duke.edu/pegasus/renal/1/ (Table 8). Genes selected included angiotensin converting enzyme (ACE), angiotensinogen (AGT), angiotensin receptor 1 (AGTR 1), endothelial constitutive nitric oxide synthase (eNOS, also referred to as NOS 3), IL6 (n=3), tumor necrosis factor alpha (TNFα, n=3), and apoE (n=2). Additionally, 58 unlinked markers were used to assess and control for population admixture, as previously described (Pritchard et al, Genetics 155(2):945-959 (2000)).







TABLE 8







Association of Genetic Polymorphisms with Peak


Postoperative Rise in Serum Creatinine (% ΔCr)











African



Caucasian
American

















% ΔCr

% ΔCr


Gene
Polymorphism
Genotype
Freq (%)
(+/−SD)
Freq (%)
(+/−SD)










Vasomotor regulation genes













Angiotensin Converting Enzyme
Intron 16
DD
32
27 (39)
41
32 (44)



Deletion(D)/
DI
46
27 (60)
46
36 (51)



Insertion(I)
II
22
29 (40)
13
33 (27)


Angiotensinogen
T842C
TT
35
26 (45)
7
23 (46)




TC
47
28 (40)
30
31 (48)




CC
18
36 (82)
63
37 (43)


Angiotensin Receptor 1
A1166C
AA
50
27 (43)
83
31 (43)




CA
40
29 (45)
14
35 (43)




CC
10
36 (96)
3
29 (29)


Endothelial Constitutive
G894T
GG
42
29 (50)
74
34 (46)


Nitric Oxide Synthase

TG
44
28 (55)
23
31 (36)




TT
13
33 (48)
3
52 (68)







Proinflammatory genes













Interleukin 6
G − 174C
GG
26
30 (74)
75
30 (42)




GC
55
30 (46)
23
55 (58)




CC
19
24 (34)
2
12 (11)


Interleukin 6
G − 572C
GG
91
28 (45)
76
35 (44)




GC
8
33 (98)
23
29 (43)




CC
1
27 (—)
1
−39 (—)  


Interleukin 6
G597A
GG
29
31 (72)
74
30 (40)




GA
54
29 (44)
24
48 (54)




AA
17
24 (34)
2
12 (11)


Tumor Necrosis Factor α
G + 488A
GG
87
30 (53)
94
35 (46)




GA
12
21 (30)
6
24 (48)




AA
1
26 (19)
0


Tumor Necrosis Factor α
G − 308A
GG
69
29 (46)
77
33 (46)




GA
28
28 (62)
21
34 (39)




AA
3
35 (45)
2
28 (10)


Tumor Necrosis Factor α
G − 376A
GG
97
28 (49)
99
34 (45)




GA
2
22 (24)
1
14 (20)




AA
1
11 (—)
0







Other gene polymorphisms previously associated with acute renal injury













Apolipoprotein E
T448C
TT
78
29 (53)
63
35 (41)



(APOE ε4)
CT
21
27 (49)
33
37 (52)




CC
2
27 (35)
4
32 (62)


Apolipoprotein E
C586T
CC
84
26 (39)
85
32 (45)



(APOE ε2)
CT
15
25 (41)
14
36 (44)




TT
1
47 (62)
1
29 (—)





Abbreviations:


% ΔCr - Peak percentage change in postoperative creatinine is defined as the difference between the preoperative and peak postoperative values and represented as a percentage of the preoperative value.


SD—standard deviation






Isolation of Genomic DNA and Genotype Analysis

Blood was collected immediately prior to surgery; genomic DNA extraction was performed using the PuregeneTM system (Gentra Systems, Minneapolis, Minn.). Most genotyping assays for single nucleotide polymorphisms were conducted at Agencourt Bioscience Corporation (Beverly, Mass.) by Matrix Assisted Laser Desorption/Ionization, Time-Of-Flight (MALDI-TOF) mass spectrometry, using the SequenomTM MassARRAYTM system (Sequenom, San Diego, Calif.) (Sun et al, Nucleic Acids Res. 28(12):E68 (2000)). Primers used to amplify, and details of, the polymorphisms can be found at anesthesia.duhs.duke.edu/pegasus/renal/1/ (Tables 9-11). Genotyping accuracy of the SequenomTM MassARRAYTM system was estimated at 99.6% (Gabriel et al, Science 296(5576):2225-2229 (2002)); reproducibility of genotyping was validated to be >99% by scoring a panel of 6 polymorphisms in 100 randomly selected patients using direct sequencing on an ABI3700 capillary sequencer (Applied Biosystems, Foster City, Calif.). Angiotensin converting enzyme (ACE) deletion and insertion (D/1) alleles were identified on the basis of polymerase chain reaction amplification of the respective fragments from the intron 16 of the ACE gene, followed by size-fractionation via electrophoresis as previously described (Rigat et al, J. Clin. Invest. 86(4):1343-1346 (1990). Results were scored by 2 independent investigators blinded to the 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.







TABLE 9





Pairwise linkage disequilibrium between markers


in IL6 and TNFα genes.




















Allele
+/−D′




IL6 SNP
frequency 
−572G/C
−597G/A
Race





G − 174C

−0.46*
+0.42*
Caucasians


G − 572C


−0.43*


G − 597A


G − 174C

−0.25 NS
+0.5*
African


G − 572C


−0.2 NS
Americans


G − 597A





TNFA SNP

−308G/A
+123(in1)G/A





G − 376A

−0.72*
−0.55*
Caucasians


G − 308A


−0.33*


G + 123A


G − 376A

−1.00*
−1.00*
African


G − 308A


−0.16 NS
Americans


G + 123A
















TABLE 10





Pairwise Linkage Disequilibrium between Markers


in IL 6 and TNFα Genes


















Linkage



Gene
Disequilibrium


Polymorphism
(+/−D′)










IL6
G − 572C
G − 597A
Race





G − 174C
−0.46*
+0.42*
Caucasians


G − 572C

−0.43*


G − 174C
−0.25 NS
+0.5*
African Americans


G − 572C

−0.2 NS





TNFα
G − 308A
G488A





G − 376A
−0.72*
−0.55*
Caucasians


G − 308A

−0.33*


G − 376A
−1.00*
−1.00*
African Americans


G − 308A

−0.16 NS





D′: Lewontin's D′;


*p < 0.01; NS: non-significant













TABLE 11







Genotyping Assays for the 12 Candidate Gene Polymorphisms


PCR primers (top-forward, bottom-reverse) and extension primers for MALDI-TOF


mass spectrometry assays










Gene Symbol
Polymorphism
PCR Primers
Extension Primer














ACE
Intron 16 D/I*
CTGGAGACCACTCCCATCCTTTCT
———





GATGTGGCCATCACATTCGTCAGAT


 


AGT
T842C
ACGTTGGATGTGTGACAGGATGGAAGACTG
AAGACTGGCTGCTCCCTGA




ACGTTGGATGGTGGACGTAGGTGTTGAAAG


 


AGTR1
A1166C
ACGTTGGATGATTCCTCTGCAGCACTTCAC
GCACTTCACTACCAAATGAGC




ACGTTGGATGCGGTTCAGTCCACATAATGC


 


eNOS
G894T
ACGTTGGATGAAACGGTCGCTTCGACGTGC
GCTGCAGGCCCCAGATGA




ACGTTGGATGATCCCTTTGGTGCTCACGTG


 


IL6
G-174C
ACGTTGGATGAGCCTCAATGACGACCTAAG
TTTCCCCCTAGTTGTGTCTTGC




ACGTTGGATGGATTGTGCAATGTGACGTCC


 


IL6
G-572C
ACGTTGGATGACGCCTTGAAGTAACTGCAC
CAGGCAGTCTACAACAGCC




ACGTTGGATGTCTTCTGTGTTCTGGCTCTC


 


IL6
G-597A
ACGTTGGATGACGCCTTGAAGTAACTGCAC
AAGTAACTGCACGAAATTTGAGG




ACGTTGGATGTCTTCTGTGTTCTGGCTCTC


 


TNFα
G488A
ACGTTGGATGGAAAGATGTGCGCTGATAGG
GGGAGGGATGGAGAGAAAAAAAC




ACGTTGGATGCTTGCCACATCTCTTTCTGC


 


TNFα
G-308A
ACGTTGGATGGATTTGTGTGTAGGACCCTG
ACCCTGGAGGCTGAACCCCGTCC




ACGTTGGATGGGTCCCCAAAAGAAATGGAG


 


TNFα
G-376A
ACGTTGGATGCTCCCAGTTCTAGTTCTATC
TTCCTGCATCCTGTCTGGAA




ACGTTGGATGTTGCCTCCATTTCTTTTGGG


 


APOE
T448C
ACGTTGGATGTGTCCAAGGAGCTGCAGGC
GCGGACATGGAGGACGTG




ACGTTGGATGTCGGTGCTCTGGCCGAGCAT


 


APOE
C586T
ACGTTGGATGACATTCCCCTTCCACGCTTG
GAATGGAGGAGGGTGTCTG




ACGTTGGATGTAGAGGTCTTTTGACCACCC





*NOTE:


The ACE indel polymorphism was genotyped using PCR amplification, size fractionation and electrophoretic visualization as described; PCR-polymerase chain reaction






Statistical Analysis
Descriptive statistics, including allele frequency, Hardy-Weinberg equilibrium, and linkage disequilibrium, were calculated for all 12 candidate polymorphisms (Tables 8-1). Due to the presence of some rare alleles, prior to analysis genotypes homozygous for the candidate polymorphisms were combined with heterozygote carriers; consequently analyses were based on 2 genotypic classes for each candidate polymorphism, reflecting the presence (1 or 2 copies) or absence of the candidate allele. Since self-reported race has been found to be an independent predictor of postoperative acute renal injury (Conlon et al, Nephrol. Dial Transplant. 14(5):1158-1162 (1999)), 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 general 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 (Conlon et al, Nephrol. Dial Transplant. 14(5):1158-1162 (1999), Chertow et al, Am. J. Med. 104 (4):343-348 (1998)). Then separate analyses were performed for each polymorphism to test the null hypothesis of no association between genotype and postoperative acute renal injury. Polymorphisms were also combined to investigate possible two-way gene interactions. A multiple linear regression model for % ΔCr 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 protect against multiple comparisons, particularly for gene-gene interactions, the a level was set at 0.05 for primary allele associations, and 0.001 for secondary pair-wise interactions (with 0.001<p<0.01 being considered a trend). The resulting combination of polymorphisms constituted the final genetic model. Projected mean % ΔCr 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, was also fit to determine the extent to which genetic polymorphisms account for variation in postoperative acute renal injury beyond that explained by clinical variables. Initial univariate and two-way interaction queries performed to test the primary hypothesis used all available genetic and/or clinical data.
To further investigate the relationship of race and postoperative acute renal injury, relative population weights (determined by population structure analysis employing 54 unlinked genetic polymorphisms) were used to test for association between race and postoperative acute renal injury, as previously described (Pritchard et al, Genetics 155(2):945-959 (2000)). Statistical analysis was performed using SAS/Genetics system version 8.02 (SAS Inc, Cary, N.C.). Continuous variables are described as mean +/− standard deviation (SD); categorical variables are described as percentages. Throughout this Example, all genetic polymorphisms are described using the following convention: wild type (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 7) (Andersson et al, Thorac. Cardiovasc. Surg. 41(4):237-241 (1993)). Specifically, renal injury was common, demonstrated by more than half of the patients sustaining >30% postoperative creatinine rise; this is equivalent to 25% reduction in creatinine clearance (Cockcroft and Gault, Nephron 16(1):31-41 (1976)). Linkage within genes was identified among IL6 and TNFα polymorphisms (Tables 9-11); while the IL6 linkage has already been reported (Park et al, Exp. Mol. Med. 35(2):76-82 (2003)), the TNFα linkage is novel.
In contrast to some forms of acute renal failure (e.g., rhabdomyolysis-related renal injury where 93% can be explained) (Ward, Arch. Intern. Med. 148 (7):1553-1557 (1988)), clinical predictors of post-cardiac surgery acute renal dysfunction explain very little of the variability in renal injury (overall R2=0.028 in the present study). However, analysis by race (a known risk factor for renal injury) (Conlon et al, Nephrol. Dial Transplant. 14(5):1158-1162 (1999)) did improve overall clinical prediction (R2=0.03 Caucasians; R2=0.13 African Americans), whether ethnic background was determined by self-reporting or via genetic structure analysis (Pritchard et al, Genetics 155 (2):945-959 (2000)); in this study, self-reported race proved the stronger model.
After analyzing clinical predictors, attention was next turned to the predictive value of genetic variables alone. Average postoperative acute renal injury (% ΔCr) associated with each of the 12 candidate polymorphisms is detailed in Table 8. In the multivariable analysis, no primary effects were identified, but 6 polymorphisms (6 for Caucasians; 4 for African Americans) demonstrated significant association with % ΔCr in two-way interactions between alleles (Tables 12 and 13). (See also FIG. 2.) A spectrum of renal injuries are predicted by the model; some allele combinations predict major renal reductions in renal filtration, equivalent to >50% decrease in creatinine clearance (FIG. 4). Raw data shown in FIG. 3 demonstrates association of specific high-risk polymorphism interactions with renal injury, mean % ΔCr values for all allele interactions are presented in FIG. 4. Overall genetic factors predict 7% of the variability in acute renal injury for Caucasians and 10% for African Americans (see Tables 12 and 13) occurring after aortocoronary surgery, a figure which is surprisingly better than clinical predictors alone. Finally, combination of clinical and genetic models are approximately additive, significantly enhancing prediction of postoperative acute renal injury compared to clinical-alone models. In fact 11% of acute renal injury variability in Caucasians and 20% for African Americans (Tables 12 and 13) is predicted with the combined model.







TABLE 12







Genetic, Clinical, and Combined Genetic and Clinical


Multivariable Risk Factor Models Predicting Peak


Postoperative Serum Creatinine Rise in 1464


Caucasians after Aortocoronary Surgery.












F-value
P value










A. Clinical Factors Alone Model (r2 = 0.03)










Clinical factors
age (years)
7.2
0.007



weight (kg)
4.15
<0.0001



history of obstructive pulmonary
6.55
0.01



disease



preoperative creatinine (mg/dL)
7.3
0.007



postoperative inotropic drug infusion
2.75
0.05



duration of cardiopulmonary
4.43
0.04



bypass (min)







B. Genetics Alone Model (r2 = 0.067)










Polymorphism
eNOS
1.89
0.17



TNF α
1.91
0.17



IL 6
20.04
<0.0001



AGTR1
0.04
0.84



AGT
32.19
<0.0001



APOE ε2
6.94
0.009


Interactions
eNOS * AGTR1
7.55
0.006



IL 6 * TNF α
3.58
0.05



AGT * IL 6
23.44
<0.0001



APOE ε2 * AGT
4.49
0.03



AGTR1 * APOE ε2
5.97
0.02







C. Combined Clinical and Genetic Model (r2 = 0.11)










Polymorphism
eNOS
2.05
0.15



TNF α
1.80
0.18



IL 6
22.04
<0.0001



AGTR1
0.03
0.86



AGT
30.05
<0.0001



APOE ε2
5.80
0.02


Interactions
eNOS * AGTR1
6.39
0.01



IL 6 * TNF α
3.84
0.05



AGT * IL 6
23.25
<0.0001



APOE ε2 * AGT
2.76
0.09



AGTR1 * APOE ε2
5.74
0.01


Clinical factors
age (years)
2.91
0.08



weight (kg)
15.94
<0.0001



history of obstructive pulmonary
4.32
0.04



disease



preoperative creatinine (mg/dL)
9.57
0.0002



postoperative inotropic drug infusion
4.91
0.03



duration of cardiopulmonary
5.87
0.02



bypass (min)





Abbreviations:


eNOS—endothelial nitric oxide synthase G894T polymorphism,


TNF α—tumor necrosis factor α G-308A polymorphism,


IL 6—IL 6 G-572C polymorphism,


AGTR1—angiotensin receptor type 1 A1166C polymorphism,


AGT—angiotensinogen T842C polymorphism,


APOE ε2—apolipoprotein E C586T polymorphism













TABLE 13







Genetic, Clinical, and Combined Genetic and Clinical


Multivariable Risk Factor Models Predicting Peak


Postoperative Serum Creatinine Rise in 207 African


Americans after Aortocoronary Surgery.












F-value
P value











A. Clinical Factors Alone Model (r2 = 0.132)











Clinical factors
history of obstructive pulmonary
3.78
0.05



disease



history of peripheral vascular disease
16.33
<0.0001



history of chronic steroid therapy
7.14
0.008



body mass index (kg/m2)
6.85
0.01








B. Genetics Alone Model (r2 = 0.132)











Polymorphism
eNOS
4.29
0.04



AGT
0.00
0.99



ACE D/I
8.40
0.004



APOE ε4
1.02
0.32


Interactions
eNOS * ACE
7.77
0.006



AGT * APOE ε4
4.72
0.03








C. Combined Clinical and Genetic Model (r2 = 0.204)











Polymorphism
eNOS
2.83
0.09



AGT
0.03
0.87



ACE D/I
7.00
0.009



APOE ε4
0.61
0.44


Interactions
eNOS * ACE
7.28
0.008



AGT * APOE ε4
3.45
0.06


Clinical factors
history of obstructive pulmonary
3.17
0.08



disease



history of peripheral vascular disease
9.33
0.003



history of chronic steroid therapy
0.73
0.39



body mass index (kg/m2)
5.31
0.02





Abbreviations:


eNOS—endothelial nitric oxide synthase G894T polymorphism,


AGT—angiotensinogen T842C polymorphism,


ACE D/I—angiotensin converting enzyme deletion/insertion polymorphism,


APOE ε4—apolipoprotein E C448T polymorphism.






CONCLUSIONS
In spite of the relatively common occurrence of acute renal failure after surgery, the best clinical models presently available only poorly predict postoperative renal dysfunction. The present study identifies important genetic underpinnings of this disorder. In Caucasians, the combined possession of two polymorphisms (AGT 842C and IL6-572C, a variant pattern that occurs in 6% of Caucasians) predicts major post-operative renal injury, being associated with an average peak serum creatinine rise of 121% (p<0.0001). This is equivalent to a 55% reduction in renal filtration, or the loss (at least temporarily) of more than one kidney, 4 times greater than average for the overall population. These findings emphasize the importance of studying genes in multiple converging biological pathways in understanding predictors of complex disease such as acute renal injury.
Cardiac surgery provokes a vigorous inflammatory response that contributes to renal insult (Laffey et al, Anesthesiology 97:215-252 (2002)). Inflammation is a rapid, highly amplified, humoral and cellular response that can occur both systemically and locally in the kidney. Endotoxin and circulating inflammatory cytokines peak 4-24 hours after cardiopulmonary bypass (Schmartz et al, J. Thorac. Cardiovasc. Surg. 125(1):184-190 (2003), Aydin et al, J. Thorac. Cardiovasc. Surg. 125(4):843-848 (2003)) and have been directly associated with acute renal injury (Cunningham et al, J. Immunol. 168(11):5817-5823 (2002), Meldrum et al, J. Surg. Res. 85(2):185-199 (1999)). Supporting this effect of intermediate markers, in the present study inheriting the AGT 842C and IL6 -572C alleles together is highly related to acute renal injury in Caucasians (p<0.0001); both of these alleles are proinflammatory and have been associated with increased intermediate biological markers (IL6 protein levels) compared with their wild type allele Wang et al, Yi Chuan Xue Bao 30(10):978-982 (2003), Kelberman et al, Biochim. Biophys. Acta 1688(2):160-167 (2004)). In a single allele association study, Gaudino and colleagues noted increased postoperative renal dysfunction in carriers of the 174C polymorphism in 111 coronary bypass patients (Gaudino et al, J. Thorac. Cardiovasc. Surg. 126(4):1107-1112 (2003)). While in the present study, the IL6-572C and not the -174C was related to acute renal injury, the findings of Gaudino and colleagues support the present findings since significantlinkage disequilibrium was noted between these 2 alleles in Caucasians, indicating they are included in a haplotype block (Tables 9-11). Further confirming the present findings, both of these IL6 polymorphisms have been associated with greater rises in IL 6 protein levels after cardiac surgery (Kelberman et al, Biochim. Biophys. Acta 1688(2):160-167 (2004), Burzotta et al, Am. J. Cardiol. 88(10):1125-1128 (2001), Brull et al, Arterioscler. Thromb. Vasc. Biol. 21(9):1458-1463 (2001), Gaudino et al, Circulation 108(Suppl 1):II195-199 (2003), Galley et al, Br. J. Anaesth. 91(3):424-426 (2003)). Other proinflammatory polymorphisms may also contribute to renal injury; the TNFα-308A allele has been associated with increased production of IL6 protein (Heesen et al, Crit. Care Med. 30(3):664-669 (2002)). A weak association (p=0.05) was noted, towards greater renal injury with, 1L6-572C and TNFα-308A alleles in the present study, with patients possessing both enduring 2 times greater creatinine rise compared to other patients. In summary, the main finding of the present study is that a ‘high risk’ proinflammatory polymorphism combination commonly seen in Caucasians presenting for heart surgery predicts a severe postoperative acute renal injury.
In addition to being pro-inflammatory, cardiac surgery also results in atheroembolism and ischemia-reperfusion injury, both potentially important contributors to a cumulative perioperative renal insult. Once renal injury occurs, intense renal vasoconstriction and exaggerated responsiveness to vasoconstrictor agents is observed, effects that takes several weeks to resolve (Myers et al, Kidney Int. 18(4):495-504 (1980)). In the context of the precarious oxygen supply to the renal medulla, subtle polymorphism-related differences in the regulation of renal perfusion may increase the extent of renal injury and delay recovery. In the above study, all 4 pairwise allele-interactions predicting renal injury (i.e., eNOS*AGTR 1, AGT*IL6 and AGTR 1* APOE ε2 in Caucasians, and eNOS*ACE in African Americans; see Tables 12 & 13) involve at least 1 polymorphism associated with increased renal vascular responsiveness; in 2 interactions (eNOS*AGTR 1 and eNOS*ACE), both polymorphisms have these effects (Hopkins et al, J. Hypertens. 14(2):199-207 (1996), Amant et al, Circulation 96(1):56-60 (1997), Henrion et al, J. Vasc. Res. 35(5):356-362 (1998), Philip et al, Circulation 99(24):3096-3098 (1999)). Overall, the greatest magnitude of renal injury is observed in African American subjects, where possession of two vasoconstrictor polymorphisms (the ACE D and eNOS 894T alleles) is associated with mean peak creatinine rise of 162.5% from preoperative levels (p=0.008), equivalent to greater than 60% reduction in glomerular filtration. Should such a renal vulnerability be confirmed in a larger population, this is trend of borderline statistical significance would be a finding of major clinical significance. The renin-angiotensin system (RAS) system and eNOS are central to regulation of renal medullary blood flow (Cowley et al, Am. J. Physiol. Regul. Integr. Comp. Physiol. 284 (6):R1355-1369 (2003), Arendshorst et al, J. Am. Soc. Nephrol. 10(Suppl. 11):S149-161 (1999)); 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 gene to achieve a trend toward significance in the present study is the APOE ε2 allele. In 2 previous single polymorphism association studies (Chew et al, Anesthesiology 93(2):325-331 (2000), Mackensen et al, Ann. Thor. Surg. 78(2):520-526 (2004)), an association was observed between APOE polymorphisms and renal injury in cardiac surgery patients. While the precise role of APOE in acute renal injury remains to be determined, recent evidence suggests a modulating role of apoE on the inflammatory cascade, with different responses among the 3 apoE polymorphisms (Brown et al, Free Radic. Biol. Med. 32(11):1071-1075 2002)).
In summary, in the present study reveals a role for inflammatory and vasoconstrictor genes in predicing acute renal injury after heart 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 robust perioperative model. While strict criteria were used in this study, caution is still appropriate when generalizing from any genetic association study since limitations exist. First, additional prospective studies will be required to confirm the results. In addition, other important alleles, either not considered as primary candidate polymorphisms or in linkage disequilibrium with studied polymorphisms (Jorde, Genome Res. 10(10):1435-1444 (2000)), may not have been tested in the present study. Furthermore, the instant findings do not presume that the causative polymorphism has been identified since the variants could be in linkage disequilibrium with one or more causation polymorphisms; indeed, examination of candidate polymorphisms in Caucasians and African Americans identifies haplotype blocks within both the IL 6 and TNF α genes. However, this new information can be used to predict renal injury nevertheless even if only a genetic marker.
EXAMPLE 3
Experimental Details
Study Population
The patients enrolled in this study were part of the Perioperative Genetics and Safety Outcomes Study (PEGASUS), an ongoing Institutional Review Board approved, prospective, longitudinal study at Duke University Medical Center. Patients undergoing cardiac surgery gave written informed consent to have their clinical and genetic data analyzed in relation to perioperative outcomes. This study of stroke outcomes examined patients undergoing coronary artery bypass graft (CABG), valvular, or combined CABG/valve surgery utilizing CPB, during a specified period, in whom definitive data on the presence or absence of stroke was gathered.
Clinical Data Collection
Ischemic stroke was the primary outcome variable. During the study period, all patients with suspected stroke (defined as any new focal abnormality on neurologic examination occurring within one week of surgery) underwent evaluation by an independent neurologist and confirmatory brain imaging (magnetic resonance [MRI] or computerized tomography [CT]). Patients with cerebral hemorrhage or generalized encephalopathy were specifically excluded. Fulfillment of these criteria were confirmed after independent review of patient hospital records. Preoperative clinical covariate data including age, sex, race, and pre-existing co-morbidities considered important to stroke (Table 14) were also recorded and analyzed.







TABLE 14







Patient Demographics and Intraoperative Characteristics











No Stroke
Stroke



Variable
n = 2066
n = 38
P-value*













Age (Years)
63 ± 12
68 ± 10
0.003+


Male Gender (%)
65.9
60.5
0.493


Caucasian (%)
82.3
78.9
0.667


Ejection Fraction (%)
51 ± 14
48 ± 12
0.212


Unstable Angina (%)
61.6
62.9
0.999


Pre-operative Stroke/TIA (%)
7.1
8.6
0.735


Carotid Bruit (%)
6.5
5.7
0.999


Peripheral Vascular Disease (%)
13.7
14.3
0.999


Diabetes (%)
30.3
37.1
0.458


Congestive Heart Failure (%)
28.1
22.9
0.573


Hypertension (%)
66.5
71.4
0.593


COPD (%)
11.3
17.1
0.279


Obesity (%)
9.1
2.7
0.249


Ever Smoked (%)
48.6
40.0
0.394


Salicylates (%)
75.7
62.9
0.110


Preoperative Beta Blocker (%)
69.2
48.6
0.011+


Redo Cardiac Surgery (%)
7.2
10.5
0.520


Any Valve Surgery (%)
18.0
23.7
0.393


CPB Time (min)
125 ± 57 
146 ± 69 
0.164


Aortic Cross-Clamp Time (min)
71 ± 41
81 ± 45
0.289


Number of Grafts
3.1 ± 0.9
3.2 ± 0.8
0.516





Values represent mean ± standard deviation.


Abbreviations:


TIA = transient ischemic attack;


Beta blocker = beta-adrenergic antagonist therapy;


COPD = chronic obstructive pulmonary disease;


CPB = cardiopulmonary bypass;


Stroke = ischemic stroke.


*P-values are from Wilcoxon Rank-Sum test for continuous measures, and from exact Chi-Squared test for categorical (%) characteristics.


+Note: Preoperative beta blocker became non-significant when added to the genetic model, whereas age remained significant.






Candidate Polymorphism Selection

Based on a literature review regarding stroke in experimental, clinical, and cardiac surgical settings, 26 SNPs were identified as primary candidates for analysis. The 26 SNPs represented 13 genes, which were classified on the basis of biological function into categories of coagulation, inflammation, and lipid metabolism (Table 15). The coagulation and inflammatory categories were chosen based on extensive literature supporting a role of thrombosis and inflammation in stroke whereas the lipid metabolism category (specifically, apolipoprotein E) was chosen because of its previous demonstration of a relationship to other neurologic disorders, including post-cardiac surgery cognitive dysfunction (Tardiff et al, Ann. Thorac. Surg. 64:715-720 (1997)).







TABLE 15







Polymorphisms Investigated in Multivariable Modeling to


Determine Their Relationship to Post-Cardiac Surgery


Ischemic Stroke.










Functional





Category
SNP ID1
Gene Symbol2 (Name)
Alleles (major/minor)





Coagulation
RS1799963
F2 (Prothrombin)
20210G/A



RS1361600
F3 (Tissue factor)
−603A/G



P0213
F3 (Tissue factor)
−1208indel(18bp)



RS6025
F5 (Factor V)
1691G/A



RS1800787
FGB (Fibrinogen beta)
−148C/T



RS1800790
FGB (Fibrinogen beta)
−455G/A



RS1800791
FGB (Fibrinogen beta)
−854G/A



RS1800792
FGG (Fibrinogen gamma)
−649A/G



RS2243093
GP1BA (Glycoprotein Ib alpha)
−5 T/C



RS6065
GP1BA (Glycoprotein Ib alpha)
524C/T



RS1800198
ITGA2 (Glycoprotein IaIIa
807C/T



RS28095
ITGA2 (Glycoprotein IaIIa
−52C/T



RS5918
ITGB3 (Glycoprotein IIIa)
1565T/C



RS1799768
PAI-1 (SERPINE 1)
−675indel(5G/4G)



RS2227631
PAI-1 (SERPINE 1)
−844A/G


Inflammation
RS1205
CRP (C reactive protein)
3′UTR 1846C/T



RS1800947
CRP (C reactive protein)
1059G/C



RS1800795
IL6 (Interleukin 6)
−174G/C



RS1800796
IL6 (Interleukin 6)
−572G/C



RS1800797
IL6 (Interleukin 6)
−597G/A



RS1800610
TNFA (TNF alpha)
+488G/A



RS1800629
TNFA (TNF alpha)
−308G/A



RS1800750
TNFA (TNF alpha)
−376G/A



RS361525
TNFA (TNF alpha)
−238G/A


Lipid
RS429358
APO E (Apolipoprotein E)
448T/C


metabolism



RS7412
APO E (Apolipoprotein E)
586C/T





1From NCBI's dbSNP public database (ncbi.nlm.nih.gov/SNP/)


2From OMIM (catalogue of human genes and genetic disorders; ncbi.nlm.nih.gov/OMIM/)


3Duke polymorphism ID Number; UTR = untranslated region






Isolation of Genomic DNA and Genotype Analysis

Whole blood was collected preoperatively with genomic DNA extraction subsequently performed using the PuregeneTM system (Gentra Systems, Minneapolis, Minn.). Genotyping assays for SNPs were conducted at Agencourt Bioscience Corporation (Beverly, Mass.) by Matrix Assisted Laser Desorption/Ionization Time-Of-Flight (MALDI-TOF) mass spectrometry, using the SequenomTM MassARRAYTM system (Sequenom, San Diego, Calif.) (Sun et al, Nucleic Acids Res. 28(12):E68 (2000)). Primers used and polymorphism details can be found at anesthesia.duhs.duke.edu/pegasus/stroke/1/. (See Table 16.) Genotyping accuracy of the SequenomTM MassARRAYTM system was estimated at 99.6% (Gabriel et al, Science 296(5576):2225-2229 (2002)). Using direct sequencing on an ABI3700 capillary sequencer (Applied Biosystems, Foster City, Calif.), genotyping reproducibility in this study was validated to be >99% by scoring a panel of six polymorphisms in 100 randomly selected patients. After completion of genotype analysis, genetic results were linked to covariate and phenotypic variables in a relational database.







TABLE 16







Genotyping Assays for the Candidate Gene Polymorphisms


http://anesthesia.duhs.duke.edu/pegasus/stroke/1/website table 1.htm












Gene Symbol1


Alleles




(Name)
Chromosome
SNP ID2
(major/minor)
Genotyping Assay Primers3





F2 (Prothrombin)
11p11-q12
RS1799963
20210 G/A
ACGTTGGATGTGGAACCAATCCCGTGAAAG







ACGTTGGATGAGAGAGCTGCCCATGAATAG






CCAATAAAAGTGACTCTCAGC


 


F3 (Tissue factor)
1p22-p21
RS1361600
−603 A/G
ACGTTGGATGTCTTCTACCCCACGAAGGTC






ACGTTGGATGATTCCCACCGCCTTTCTCCTG






CTACCCCACGAAGGTCAAGAATAC


 




P02144
−1208 indel (18 bp)
ACGTTGGATGTTTTGCACAGTTTTATTCTG






ACGTTGGATGAGTCAGTCTTGCATTTTAAT






CTGTTAAAACAAGTGGTTCAGTA


 


F5 (Factor V)
1q23
RS6025
1691 G/A
ACGTTGGATGCTGAAAGGTTACTTCAAGGAC






ACGTTGGATGTGGGCTAATAGGACTACTTC






AGGACAAAATACCTGTATTCCT


 


FGB (Fibrinogen
4q28
RS1800787
−148 C/T
ACGTTGGATGCATTTAAGCAACATCTTCCC


beta



ACGTTGGATGAACTTCCCATCATTTTGTCC






AAGCAACATCTTCCCAGCAAA


 




RS1800790
−455 G/A
ACGTTGGATGGCTTATGTTTTCTGACAATG






ACGTTGGATGTCATAGAATAGGGTATGAAT






CATAATTCTATTTCAAAAGGGGC


 




RS1800791
−854 G/A
ACGTTGGATGCTCACAGACTAAATGAGGCC






ACGTTGGATGCACACAAGTGAACAGACAAG






GAGGCCCATTTTCCTTCATTT


 


FGG (Fibrinogen
4q28
RS1800792
−649 A/G
ACGTTGGATGATGCCCACCTTCAGACAAAG


gamma)



ACGTTGGATGCCTCTGTGTCAACCATGTTC






GAGCTCAAAAGCTCCCTGAG


 


GP1BA
17 pter-p12
RS2243093
−5 T/C
ACGTTGGATGATCCACTCAAGGCTCCCTTG


(Glycoprotein lb



ACGTTGGATGTTGGCAGCAGGAGCAGCAAG


alpha)



GGCTCCCTTGCCCACAGG


 




RS6065
524 C/T
ACGTTGGATGTGTTGTTAGCCAGACTGAGC






ACGTTGGATGAAGGCAATGAGCTGAAGACC






TCCAGCTTGGGTGTGGGC


 


ITGA2 (Glycoprotein
5p11.12
RS1800198
807 C/T
ACGTTGGATGTGGCCTATTAGCACCAAAAC


lalla)



ACGTTGGATGAGACATCCCAATATGGTGGG






TTACCTTGCATATTGAATTGCTCC


 




RS28095
−52 C/T
ACGTTGGATGGATCCGGTGTTTGCGGAATC






ACGTTGGATGAGGGAAAAGTTCTGGGCAG






CGGAATCAGGAGGGGCGGGC


 


ITGB3 (Glycoprotein
17q21.32
RS5918
1565 T/C
ACGTTGGATGCCTTCAGCAGATTCTCCTTC


llla)



ACGTTGGATGTTGCTGGACTTCTCTTTGGG






TCACAGCGAGGTGAGCCC


 


SERPINE1
7q21.3-q22
RS1799768
−675 indel (5 G/4 G)
ACGTTGGATGCTCCGATGATACACGGCTGA


(Plasminogen



ACGTTGGATGAGGTTGTTGACACAAGAGAG


activator inhibitor-1)



GATACACGGCTGACTCCCC


 




RS2227631
−844 A/G
ACGTTGGATGAAGGAAACAGGAGACCAACG






ACGTTGGATGGAGGATAAAGGACAAGCTGC






GACCAACGTGTAAGTTTCACTTC


 


CRP (C reactive
1q21-q23
RS1205
3′UTR 1846 C/T
ACGTTGGATGGCCATCTTGTTTGCCACATG


protein)



ACGTTGGATGGTTTGTCAATCCCTTGGCTC






TTGTTTGCCACATGGAGAGAGACT


 




RS1800947
1059 G/C
ACGTTGGATGGAAATGTGAACATGTGGGAC






ACGTTGGATGAGGACATTAGGACTGAAGGG






TGTGAACATGTGGGACTTTGTGCT


 


IL6 (Interleukin 6)
7p21
RS1800795
−174 G/C
ACGTTGGATGAGCCTCAATGACGACCTAAG






ACGTTGGATGGATTGTGCAATGTGACGTCC






TTTCCCCCTAGTTCTCTCTTGC


 




RS1800796
−572 G/C
ACGTTGGATGACGCCTTGAAGTAACTGCAC






ACGTTGGATGTCTTCTGTGTTCTGGCTCTC






CAGGCAGTCTACAACAGCC


 




RS1800797
−597 G/A
ACGTTGGATGACGCCTTGAGGTAACTGCAC






ACGTTGGATGTCTTCTGTGTTCTGGCTCTC






AAGTAACTGCACGAAATTTGAGG


 


TNFA (TNF alpha)
6p21.3
RS1800610
+488 G/A
ACGTTGGATGGAAAGATGTGCGCTGATAGG






ACGTTGGATGCTTGCCACATCTCTTTCTGC






GGGAGGGATGGAGAGAAAAAAAC


 




RS1800629
−308 G/A
ACGTTGGATGGATTTGTGTGTAGGACCCTG






ACGTTGGATGGGTCCCCAAAAGAAATGGAG






ACCCTGGAGGCTGAACCCCGTCC


 




RS1800750
−376 G/A
ACGTTGGATGCTCCCAGTTCTAGTTCTATC






ACGTTGGATGTTGCCTCCATTTCTTTTGGG






TCCTGCATCCTGTCTGGAA


 




RS361525
−238 G/A
ACGTTGGATGACACAAATCAGTCAGTGGCC






ACGTTGGATGATCAAGGATACCCCTCACAC






GAAGACCCCCCTCGGAATC


 


APOE
19q13.2
RS429358
448 T/C
ACGTTGGATGTGTCCAAGGAGCTGCAGGC


(Apolipoprotein E)



ACGTTGGATGTCGGTGCTCTGGCCGAGCAT






GCGGACATGGAGGACGTG


 




RS7412
586 C/T
ACGTTGGATGACATTCCCCTTCCACGCTTG






ACGTTGGATGTAGAGGTCTTTTGACCACCC






GAATGGAGGAGGGTGTCTG





1From the Online Mendelian Inheritance in Man (OMIM) public database (http://www.ncbi.nlm.nih.gov/OMIM)


2From NCBI's dbSNP public database (http://www.ncbi.nlm.nih.gov/SNP/)


3MALDI-TOF mass spectrometry genotyping assays: from top to bottom-forward PCR primer, reverse PCR primer, extension primer.


4Duke polymorphism ID number.






Statistical Analysis
For purposes of association tests, heterozygotes were combined with homozygotes for the minor frequency allele. Consequently, association analyses for each candidate polymorphism were based on two genotypic classes, distinguished by the presence of at least one copy of the minor allele.
Pearson Chi square tests were used to test for independence between stroke and race or gender. Association between SNP genotype and self-declared race was investigated using Chi square tests. In addition, the presence of cryptic population structure was investigated using 58 unlinked, non-candidate SNPs to fit a hierarchical model of population structure (Humphries and Morgan, Lancet Neurol. 3 (4):227-235 (2004)).
Chi square tests for independence between stroke and SNP genotype were computed for each of the 26 candidate SNPs. Tests for genetic effects of a pair of SNPs were investigated separately within each functional category by distinguishing genotypes bearing at least one minor allele at both SNPs. This particular partition of genotypes yielded a 2×2 table of genotype by stroke thereby allowing a test for independence with individual SNPs. There exist three other partitions of genotypes based on joint presence or absence of the minor allele in pairs of SNPs and tests were carried out for independence on these genotype partitions as well. After these genetic association tests, a logistic regression model was used to test the joint independent effects of significant SNPs and age, a factor repeatedly shown to be of importance to post-cardiac surgery stroke (Roach et al, N. Engl. J. Med. 335 (25):185701863 (1996), Newman et al, Circulation 94(9 Suppl):1174-II80 (1996), Cipollone et al, JAMA 291 (18):2221-2228 (2004), Gaudino et al, J. Thorac. Cardiovasc. Surg. 126 (4):1107-1112 (2003)). In addition, beta blocker therapy (Armory et al, J. Cardiothorac. Vasc. Anesth. 16(3):270-277 (2002)), race, and gender were tested separately in follow-up analyses with the final genetic model that included age.
Because the analysis strategy employed many separate tests of independence, an adjustment of observed p-values was required to account for this multiple testing. Random permutation analysis was used to adjust p-values (Chasman et al, JAMA 291 (23):2821-2827 (2004)). 5000 copies of the data set were generated, randomly reassigning stroke events to study subjects, thereby disassociating genotype from stroke events in the data. For each permutation, p-values were calculated for all four genotype partitions of each pair of SNPs. For each genotype partition, the smallest p-value was retained to estimate the distribution of 5000 “smallest” p-values under the null hypothesis of no association. An adjusted p-value was computed as the fraction of permutation p-values that were smaller than the observed p-value. For example, if among 5000 permutations 50 p-values were smaller than the observed p-value, then the adjusted p-value would be 50/5000 or 0.01.
All statistical analysis was performed using SAS and SAS/Genetics version 8.02 (SAS Inc, Cary, N.C.). Continuous variables were described as mean±standard deviation; categorical variables were described as percentages.
Results
Demographics and intraoperative characteristics of the 2104 study patients are presented in Table 14; with the exception of age and preoperative beta-adrenergic antagonist (beta blocker) therapy, there were no differences between those with or without stroke. Of the 2104 patients, 38 (1.8%) suffered in-hospital stroke. As not all SNPs were available in all patients, a subset of patients with complete clinical and genetic data was used for analysis. The final multivariable genetic model included 1635 patients of whom 28 (1.7%) experienced stroke.
No single SNP alone was significantly associated with stroke. However, in paired tests, a SNP pair representing individuals having minor alleles for both CRP (3′UTR 1846C/T) and IL6 (−174G/C) was significantly associated with stroke. Of the 1635 patients, 583 (36%) had minor alleles for both of these SNPs. The incidence of stroke in patients with this genotype was 3.09% (18/583), compared to 0.95% (10/1052) in patients without this combination (OR 3.3, 95% CI 1.4-8.1; C-index=0.646; p=0.0023; FIG. 5). Thus, individuals with at least one minor allele at each locus were approximately three times more likely to have a stroke than patients without this combination. The two SNPs involved are among the nine in the inflammation category. After adjustment for multiple comparisons (involving 36 pairwise combinations) using permutation testing, the p-value for this combination of minor alleles remained significant (adjusted-p=0.023).
Due to the relatively few events (28 total strokes in 1635 patients), the number of additional terms that could be tested in a multivariable model were limited. However, as the stroke patients were older, a second model was constructed that, in addition to the identified SNPs, included age as a clinical covariable. That model was also highly significant (C-index=0.718, p=0.0001). Coefficients for age and genotype in this model demonstrated an odds ratio of 3.2 (p=0.0020, 95% CI=1.4-7.8) for the minor allele combination and 1.05 for each additional year of age (95% CI=1.02-1.10, p=0.0057). This is not only consistent with prior investigations into risk factors of perioperative stroke (Newman et al, Circulation 94(9 Suppl):H174-1180 (1996)), but further emphasizes the value added by genotype information. When beta blocker therapy as a clinical covariable was tested, it became non-significant (p=0.5015) while the SNP combination remained significant (p=0.0034).
With regard to any gender or population structure effects, there was no association between stroke and gender (p=0.4933) nor between stroke and self-declared race (p=0.8258). Although several genotypes were distributed differentially among races, in the final multivariable genetic model, neither race nor gender were associated with stroke (p=0.2083 and p=0.6437, respectively; Table 17). Furthermore, the ethnic indicator variables from the population structure analysis were similarly non-significant when added to SNPs and age in the final multivariable model (p=0.1910).







TABLE 17







Race and Gender: Effects on stroke outcome


and polymorphism associations











No Stroke
Stroke
P-value*
















Race


0.208



African American
193
 4 (2.0)



Caucasian
1335
22 (1.6)



American Indian
52
0 (0) 



Hispanic
5
0 (0) 



Asian
3
 1 (25.0)



Other/Unknown
19
 1 (5.0)



Gender


0.644



Male
1074
17 (1.6)



Female
533
11 (2.0)







*P-values represent significance level of race or gender with stroke in final multivariable model controlling for both age and genotype;



percentage represented by parentheses ( ).






Summarizing, stroke following cardiac surgery is a relatively uncommon but potentially devastating event; the incidence of stroke in this study (1.8%) was similar to that of other studies (Roach et al, N. Engl. J. Med. 335 (25):185701863 (1996), Bucerius et al, Ann. Thorac. Surg. 75(2):472-478 (2003), Ruel et al, Ann. Thorac. Surg. 78(1):77-83 (2004)). Previous investigations designed to understand risk factors associated with this adverse outcome have typically relied on clinical variables to construct risk indices (Newman et al, Circulation 94(9 Suppl):1174-1180 (1996)). Age, for example, is a robust risk factor for perioperative stroke and was reconfirmed in this present study (Newman et al, Circulation 94(9 Suppl):II74-1180 (1996)). A new significant relationship, independent of age, has now been demonstrated between stroke and an individual's genetic makeup. The present findings reveal that the concurrent presence of at least one minor allele at each of two loci (CRP: 3′UTR 1846C/T; IL6: −174G/C) is a risk factor for stroke, increasing risk more than three-fold.
The observation that the interaction of these two inflammatory SNPs contributes to perioperative stroke suggests that inflammatory pathways may be important mechanistic factors in either initiating or otherwise modulating stroke after cardiac surgery. This interpretation is consistent with current knowledge regarding CPB initiating and IL6 mediating a robust inflammatory response (Levy and Tanaka, Ann. Thorac. Surg. 75(2):S715-720 (2003)). Such a finding is also consistent with the view that inflammation plays an important role in the etiology and propagation of cerebrovascular disease and stroke in the general population (Adams et al, BMC Med. Genet. 4 (1):6 (2003), Humphries and Morgan, Lancet Neurol. 3 (4):227-235 (2004)). Unexpectedly, thrombotic polymorphisms, significantly associated with stroke in non-cardiac surgery settings (Kahn, Sourth Med. J. 96(4):350-353 (2003), Endler and Mannhalter, Clin., Chim. Acta 330(1-2):31-55 (2003)), were not shown to be related to stroke in this analysis, suggesting that inflammatory etiologies may supercede thrombotic causes of stroke in cardiac surgery patients.
The two individual inflammatory polymorphisms identified have been previously characterized outside the setting of cardiac surgery. The polymorphism involving the promoter region of the IL6 gene (−174G/C) has been shown to be an independent risk factor for lacunar stroke (Revilla et al, Neurosci. Lett. 324 (1):29-32 (2002)). In addition to increasing the incidence of stroke, the same SNP has been shown to increase stroke severity worsening three month outcomes after ischemic stroke (Greisenegger et al, Thromb. Res. 110 (4):181-186 (2003)). Interestingly, the IL6 SNP, when combined with a second inflammatory polymorphism (ICAM-1, 469E/K), appears to have an even greater effect on stroke risk. In a study by Pola et al., both IL6 and ICAM-1 SNPs were independently associated with stroke risk (OR 8.6 and 4.0, respectively), but when present together had a synergistic effect (OR, 10.1; 95% CI, 2.1-48.5) (Pola et al, Stroke 34(4):881-885 (2003)). Following cardiac surgery, the IL6 polymorphism is thought to influence plasma levels and functional activity of the IL6 protein and has been associated with other perioperative inflammatory complications (Gaudino et al, J. Thorac. Cardiovasc. Surg. 126 (4):1107-1112 (2003), Gaudino et al, Circulation 108 (Suppl 1):II195-II199 (2003), Burzotta et al, Am. J. Cardiol. 88(10):1125-1128 (2001)). However association between IL6 polymorphism and perioperative neurologic outcome has not been previously described.
C-reactive protein, an acute-phase reactant also indicating underlying inflammation, has repeatedly been shown to influence the risk of cardiovascular disease and stroke (Rost et al, Stroke 32(11):2575-2579 (2001), Curb et al, Circulation 107(15):2016-2020 (2003), Ford and Giles, Arterioscler. Thromb. Vasc. Biol. 20(4):1052-1056 (2000)). Indeed, Rost et I, al., demonstrated that elevated plasma CRP significantly predicts risk of stroke and transient ischemic attack in the elderly (Rost et al, Stroke 32(11):2575-2579 (2001)). The C/T polymorphism in the 3′ UTR of the CRP gene has been associated with increased basal plasma levels of CRP and is part of a haplotype associated with elevated peak CRP levels after CABG (Russell et al, Hum. Mol. Genet. 13(1):137-147 (2004), Brull et al, Arterioscler Thromb. Vasc. Biol. 23(11):2063-2069 (2003)). The mechanism by which this variant might influence CRP expression remains to be established, but it may involve alterations in the stability of the CRP mRNA which depend on the 3′UTR sequences (Murphy et al, J. Biol. Chem. 270 (2):704-708 (1995)). By further increasing CRP levels, this polymorphism may increase the risk of stroke, although the precise mechanism by which CRP may act on stroke in this setting is not well understood (Cao et al, Circulation 108(2):166-170 (2003)).
Of particular interest is the finding that it is the interaction between IL6 and CRP SNPs, and not each individual SNP alone, that appears to be an important determinant of stroke risk after cardiac surgery. Epistasis, or the interaction between genes is a phenomenon of increasing interest in genetic epidemiology (Cordell, Hum. Mol. Genet. 11(20):2463-2468 (2002)). A plausible biological model could be postulated for the epistasis event observed in the current study, as induction of the acute phase CRP response in hepatocytes is promoted by the synergistic action of pro-inflammatory cytokines IL6 and IL1β (Ganter et al, EMBO J. 8 (12):3773-3779 (1989)). Therefore, genetic variants modulating IL6 levels and the expression of CRP may contribute to the perioperative pro-inflammatory phenotype seen in cardiac surgical patients.
There are some potential limitations to this study. Although the sample consisted of 2104 patients, limitations of multiplex genotyping resulted in missing genotypes for some SNPs in some patients. These data are missing at random with respect to patient characteristics and therefore would not be expected to bias tests of association. Population admixture is also a concern in genetic association studies since it can mask, or falsely identify, a phenotype with a genetic trait (Deng et al, J. Clin. Densitom. 4 (4):353-361 (2001)). Importantly, no significant association of race to stroke was identified thereby minimizing this possible confounding effect. Lastly, although it is speculated that this unique gene combination may have mediated its stroke effect via changes in IL6 and CRP protein levels, these were not measured in the patients. However, these SNPs, at least individually, have been previously demonstrated in cardiac surgery patients to influence their respective protein levels (Burzotta et al, Am. J. Cardiol. 88(10):1125-1128 (2001), Brull et al, Arterioscler Thromb. Vasc. Biol. 23(11):2063-2069 (2003)).
The implications of these findings are several-fold. Understanding risk of stroke in cardiac surgery settings can facilitate informing patients of enhanced individual risk. Since this SNP combination occurs in a large percentage of patients, 36% in the study described, such knowledge is important to many surgery patients. In addition, as new neuroprotective strategies and/or related pharmacologic agents become available, a more rational allocation of these likely higher cost therapies to the highest risk patients may be possible. In addition, identification of this unique polymorphism combination emphasizes the potentially significant effect that inflammation plays in cardiac surgery-related stroke. Identifying specific mechanisms whereby this SNP combination mediates its effects will be important in understanding targets whereby modulating perioperative inflammation might result in improvement in patient outcome after cardiac surgery.
All documents and other information sources cited above are hereby incorporated in their entirety by reference.

## Claims

1. A method of identifying a Caucasian patient at risk of perioperative or periprocedure renal dysfunction comprising assaying DNA from said patient for a -572C polymorphism in the interleukin-6 (IL6) gene and an 842C polymorphism in the angiotensinogen (AGT) gene, wherein detection of the IL6-572C and the AGT 842C polymorphisms in the DNA of said patient is indicative that said patient is at risk of perioperative or periprocedure renal dysfunction.