Clinical Anesthesia

Chapter 6

Genomic Basis of Perioperative Medicine

Mihai V. Podgoreanu

Joseph P. Mathew

Key Points

1. Genetic variation can significantly modulate risk of adverse perioperative events.

2. Several methodological approaches are used to study the genetic architecture of perioperative outcomes.

3. Current perioperative risk profiling has limited ability to explain individual variability in adverse outcomes.

4. Genetic variants in inflammatory and coagulation pathways are associated with susceptibility to perioperative myocardial infarction.

5. Biomarkers associated with perioperative atrial fibrillation were identified through genetic association studies and gene expression analysis.

6. Variants in inflammation and platelet activation pathways modify susceptibility to perioperative cerebral injury.

7. A genetic basis for perioperative acute kidney injury has been identified.

8. Pharmacogenomics describes the relationship between inherited variations in genes modulating drug actions and individual variability in drug response.

9. Individual variability in response to anesthetic agents is as high as 24% and has a genetic component.

10.     Individual variability in analgesic responsiveness is attributed to genetic control of peripheral nociceptive pathways and descending central pain modulatory pathways.

11.     Host responses to injury and the clinical trajectory of critically ill injured patients are genetically determined.

12.     Genomic technology applications are beginning to fulfill the “Five Ps” of perioperative medicine and pain management (personalized, preventive, predictive, participatory, and prospective).

Genetic Basis of Disease

Human biological diversity involves interindividual variability in morphology, behavior, physiology, development, susceptibility to disease, and response to stressful stimuli and drug therapy (i.e., phenotypes). This phenotypic variation is determined, at least in part, by differences in the specific genetic makeup (i.e., genotype) of an individual. In 2003, the 50th anniversary of Watson and Crick's description of the DNA double-helix structure also marked the completion of the Human Genome Project.1 This major accomplishment provides the discipline of genomics with basic resources to study the functions and interactions of all genes in a systematic fashion, including their interaction with environmental factors, and translate the findings into clinical and societal benefits. Functional genomics employs large-scale experimental methods and statistical analyses to investigate the regulation of gene expression in response to physiological, pharmacologic, and pathologic changes. It also uses genetic information from clinical studies to examine the impact of genetic variability on disease characterization and outcome.2

Many common diseases like atherosclerosis, coronary artery disease, hypertension, diabetes, cancer, asthma, and our responses to injury, drugs, and nonpharmacologic therapies are genetically complex, characteristically involving an interplay of many genetic variations in molecular and biochemical pathways (i.e., polygenic) and genetic-environmental interactions


(i.e., multifactorial). In other words, complex phenotypes can be viewed as the integrated effect of many susceptibility genes and many environmental exposures. The proportion of phenotypic variance explained by genetic factors is referred to as heritability, and can be estimated by examining the increased similarity of a phenotype in related as compared with unrelated individuals. One of the major challenges and ongoing research efforts facing the postgenomic period are to connect the nearly 25,000 protein-coding genes of mammalian organisms to the genetic basis of complex polygenic diseases and the integrated function of complex biological systems. According to the “common-disease/common-variants hypothesis,”3 individual susceptibility to common complex diseases and the manifestation, severity, and prognosis of the disease process is modulated by multiple common functional polymorphisms, each with only modest effect on disease risk. However, it is likely that rare modest-risk alleles are important as well in polygenic disease, but their detection is more difficult because of sample size and sequencing constraints.

The perioperative period represents a unique and extreme example of such gene-environment interaction. As we appreciate in our daily practice in the operating rooms and intensive care units, one hallmark of perioperative physiology is the striking variability in patient responses to the acute, robust, and generalized perioperative (environmental) perturbations induced by surgical injury, hemodynamic challenges, vascular cannulation, extracorporeal circulation, intra-aortic balloon counterpulsation, mechanical ventilation, partial/total organ resection, transient limb/organ ischemia, transfusions, anesthetic agents, and the pharmacopoeia used in the perioperative period. This translates into substantial interindividual variability in immediate perioperative adverse events (mortality or incidence/severity of organ dysfunction), as well as long-term outcomes (Table 6-1). For decades we have attributed this variability to many complexities such as age, nutritional state, comorbidities—what we colloquially call protoplasm. Now we are beginning to appreciate that genetic variation is also partly responsible for this observed variability in outcomes. Overall, an individual's genetic susceptibility to adverse perioperative events stems not only from genetic contributions to the development of comorbid risk factors (like coronary artery disease [CAD] and reduced preoperative cardiopulmonary reserve) during the patient's lifetime, but also from genetic variability in specified biological pathways participating in pathophysiological events during and after surgery (Fig. 6-1). With increasing evidence suggesting that genetic variation can significantly modulate risk of adverse perioperative events,4,5,6,7 the emerging field of perioperative genomics aims to apply functional genomic approaches to discover underlying biological mechanisms. These approaches will explain why similar patients have such dramatically different outcomes after surgery, and is justified by a unique combination of environmental insults and postoperative phenotypes that characterize surgical and critically ill patient populations.

To integrate this new generation of genetic results into clinical practice, perioperative physicians need to understand the patterns of human genome variation, the methods of population-based genetic investigation, and the principles of gene and protein expression analysis. This chapter reviews general genetic/genomic concepts and highlights current and future applications of genomic technologies for perioperative risk stratification, outcome prediction, mechanistic understanding of surgical stress responses, as well as identification and validation of novel targets for perioperative organ protection.

Overview of Human Genetic Variation

Although the human DNA sequence is 99.9% identical between individuals, the variations may greatly affect a


person's disease susceptibility. In elucidating the genetic basis of disease, much of what has been investigated in the pre-Human Genome Project era focused on identifying rare genetic variants (mutations) responsible for >1,500 monogenic disorders such as hypertrophic cardiomyopathy, long-QT syndrome, sickle cell anemia, cystic fibrosis, or familial hypercholesterolemia, which are highly penetrant (carriers of the mutant gene will likely have the disease) and inherited in mendelian fashion (hence, termed mendelian diseases). However, most of the genetic diversity in the population is attributable to more widespread DNA sequence variations (polymorphisms), typically single nucleotide base substitutions (single nucleotide polymorphisms [SNPs]) or to a broader category of previously overlooked structural genetic variants. These structural variants include short sequence repeats (microsatellites), insertion/deletion of one or more nucleotides (indels), inversions, and the recently discovered copy number variants (CNVs, large segments of DNA that vary in number of copies),8 all of which may or may not be associated with a specific phenotype (Fig. 6-2). To be classified as a polymorphism, the DNA sequence alternatives (i.e., alleles) must exist with a frequency of at least 1% in the population. About 15 million SNPs are estimated to exist in the human genome, approximately once every 300 base pairs, located in genes, as well as in the surrounding regions of the genome. Polymorphisms may directly alter the amino acid sequence and therefore potentially alter protein function, or alter regulatory DNA sequences that modulate protein expression. Sets of nearby SNPs on a chromosome are inherited in blocks, referred to as haplotypes. As will be shown later, haplotype analysis is a useful way of applying genotype information in disease gene discovery. On the other hand, CNVs involve approximately 12% of the human genome, often encompass genes (especially regulating inflammation and brain development), and may influence disease susceptibility through dosage imbalances. The year 2007 was marked by the realization that DNA differs from person to person much more than previously suspected; equipped with faster and cheaper DNA sequencing technologies, researchers have catalogued >3 million SNPs as part of the HapMap Project,9 published the first diploid genome sequence of an individual human,10 launched the 1000 Genomes Project (sequencing the genomes of 1,000 people from around the world), and begun charting CNVs and other structural variants, thus making understanding of human genetic variation the 2007 Sciencemagazine “Breakthrough of the Year.” In the following section we review the common strategies used to incorporate genetic analysis into clinical studies.

Table 6-1 Categories of Perioperative Phenotypes

Immediate perioperative outcomes

·   In-hospital mortality

·   Perioperative myocardial infarction

·   Perioperative low cardiac output syndrome/acute decompensated heart failure

·   Perioperative vasoplegic syndrome

·   Perioperative arrhythmias (atrial fibrillation)

·   Postoperative bleeding

·   Perioperative venous thrombosis

·   Acute postoperative stroke

·   Postoperative delirium

·   Perioperative acute kidney injury

·   Acute perioperative lung injury/prolonged postoperative mechanical ventilation

·   Acute allograft dysfunction/rejection

·   Postoperative sepsis

·   Multiple organ dysfunction syndrome

·   Variability in response to anesthetics, analgesics and other perioperative drugs

·   Intermediate phenotypes (plasma biomarker levels)

Long-term postoperative outcomes

·   Event-free survival/major adverse cardiac events

·   Progression of vein graft disease

·   Chronic allograft dysfunction/rejection

·   Postoperative cognitive dysfunction

·   Postoperative depression

·   Quality of life

Figure 6-1. Perioperative adverse events are complex traits, characteristically involving an interaction between robust operative environmental perturbations (surgical trauma, hemodynamic challenges, exposure to extracorporeal circulation, drug administration) and multiple susceptibility genes. The observed variability in perioperative outcomes can be partly attributed to genetic variability modulating the host response to surgical injury. OR, operating room; CPB, cardiopulmonary bypass.

Methodologic Approaches to Study the Genetic Architecture of Common Complex Diseases

Most ongoing research on complex disorders focuses on identifying genetic polymorphisms that enhance susceptibility to


given conditions. Often the design of such studies is complicated by the presence of multiple risk factors, gene-environment interactions, and a lack of even rough estimates of the number of genes underlying such complex traits. Two broad strategies are being employed to identify complex trait loci. The candidate gene approach is motivated by what is known about the trait biologically and can be characterized as a hypothesis-testing approach, but is intrinsically biased. The second strategy is the genomewide scan, in which thousands of markers uniformly distributed throughout the genome are used to locate regions that may harbor genes influencing the phenotypic variability. This is a hypothesis-free and unbiased approach, in the sense that no prior assumptions are being made about the biological processes involved and no weight is given to known genes, thus allowing the detection of previously unknown trait loci. Both the candidate gene and genome scan approaches can be implemented using one of two fundamental methods of identifying polymorphisms affecting common diseases: linkage analysis or association studies in human populations.

Figure 6-2. Categories of common human genetic variation. A. Single nucleotide polymorphisms (SNP) can be silent or have functional consequences: changes in amino acid sequence or premature termination of protein synthesis (if they occur in the coding regions of the gene) or alterations in the expression of the gene, resulting in more or less protein (if they occur in regulatory regions of the gene such as the promoter region or the intron/exon boundaries). Structural genetic variants include Microsatellites with varying number of dinucleotide (CA)n repeats (B); Insertions/deletions (C); and Copy number variation (CNV; D). A–D are long DNA segments, segment C shows variation in copy number. Glossary: locus, the location of a gene/genetic marker in the genome; alleles, alternative forms of a gene/genetic marker; genotype, the observed alleles for an individual at a genetic locus; heterozygous, two different alleles are present at a locus; homozygous, two identical alleles are present at a locus. A SNP at position 1691 of a gene with alleles G and A would be written as 1691G>A.

Linkage Analysis

Linkage analysis is used to identify the chromosomal location of gene variants related to a given disease by studying the distribution of disease alleles in affected individuals throughout a pedigree and has successfully mapped hundreds of genes for rare, monogenic disorders. However, common complex diseases are characterized by a multitude of genes with rare and/or common alleles, which create an apparently chaotic pattern of heterogeneity within and between families. The overall effect of this heterogeneity, together with the potentially weak influence of many loci, places a heavy burden on the statistical power needed to detect individual contributing genes, and may be the reason why very few genome linkage scans so far have yielded disease loci that meet genomewide significance criteria.11 Furthermore, the nature of most complex diseases (especially for perioperative adverse events) precludes the study of extended multigenerational family pedigrees. Nevertheless, a few positive findings have emerged using this approach: a stroke susceptibility locus was mapped on chromosome 5q12,12 risk of myocardial infarction was mapped to a single region on chromosome 14,13 and a recent meta-analysis of several genomewide scans for pulse pressure variation, an emerging risk factor for perioperative complications, has identified several linkage bins on chromosomes 22 and 10.14

Genetic Association Studies

Association studies examine the frequency of specific genetic polymorphisms in a population-based sample of unrelated diseased individuals and appropriately matched unaffected controls. The increased statistical power to uncover small clinical effects of multiple genes15 and the fact that they do not require family-based sample collections are the main advantages of this approach over linkage analysis. Until very recently, most significant results in dissecting common complex diseases were gathered from candidate gene association studies, with genes selected because of a priori hypotheses about their potential etiologic role in disease based on current understanding of the disease pathophysiology.16 For example, genetic variants within the renin-angiotensin system,17 nitric oxide synthase,18 and β2-adrenergic receptors,19 known to modulate vascular tone, were tested and found to be associated with hypertension. Similarly, the possible effects of polymorphisms on genetic predisposition for CAD20 or restenosis after angioplasty21 have been extensively investigated; more recently, two large-scale association studies have identified gene variants that might affect susceptibility to myocardial infarction.22

As will be presented in more detail later, accumulating evidence from candidate gene association studies also suggests that specific genotypes are associated with a variety of organ-specific perioperative adverse outcomes, including myocardial infarction,23,24 neurocognitive dysfunction,25,26,27 renal compromise,28,29,30 vein graft restenosis,31,32 postoperative thrombosis,33 vascular reactivity,34 severe sepsis,35,36 transplant rejection,37 and death (for reviews, see Podgoreanu and Schwinn4 and Ziegeler et al.7).

One of the main weaknesses of the candidate gene association approach is that, unless the marker of interest “travels” (i.e., is in linkage disequilibrium) with a functional variant, or the marker allele is the actual functional variant, the power to detect and map complex trait loci will be reduced. Other known limitations of genetic association studies include potential false-positive findings resulting from population stratification (i.e., admixture of different ethnic or genetic backgrounds in the case and control groups), and multiple comparison issues when large numbers of candidate genes are being assessed.38 Replication of findings across different populations or related phenotypes remains the most reliable method of validating a true relationship between genetic polymorphisms and disease,16 but poor reproducibility in subsequent studies has been one of the main criticisms of the candidate gene association approach.39 However, a recent meta-analysis suggested that lack of statistical power may be the main contributor to this inconsistent replication, and proposed more stringent statistical criteria to exclude false-positive results and the design of large collaborative association studies.40

At last, after several decades of frustrating limitations in the ability to find genetic variations responsible for common disease risk, with the completion of the second phase of the International HapMap Project (a high-resolution maps of human genetic variation and haplotypes)9 and advances in high-throughput genotyping technologies, the year 2007 marked an explosion of adequately powered and successfully replicated genomewide association studies (GWAS) that identified very significant genetic contributors to risk for common polygenic diseases like CAD,41,42,43 myocardial infarction,44 diabetes (type I and II),45,46 atrial fibrillation,47 obesity,48 asthma, common cancers, rheumatoid arthritis, Crohn disease, and others. GWAS make use of the known linkage disequilibrium pattern between SNPs from the human HapMap and the new high-density SNP chip technology to comprehensively interrogate between 65 and 80% of common variation across the genome, with even higher coverage being possible using statistical imputation techniques. The largest, most comprehensive GWAS to date was conducted by the Wellcome Trust Case-Control Consortium, investigating the association between 500,000 SNPs and seven common diseases in 2,000 cases and 3,000 shared controls. This study identified 25 independent association signals at stringent levels of significance (p < 5 × 10-7).41 Interestingly, variants in or near CDKN2A/B (cyclin-dependent kinase inhibitor 2 A/B) conferred increased risk for both type II diabetes (odds ratio [OR], 1.2; p = 7.8 × 10-15) and myocardial infarction (OR, 1.64; p = 1.2 × 10-20), which may lead to a mechanistic explanation for the link between the two disorders. This finding also highlights the power of GWAS to identify variants outside described genes: while one of the signals occurs in the CDKN2A/B region, the other much stronger association signal occurs >200 kB from these genes, in a gene desert, and thus would not have been picked up by a candidate gene approach. Identifying the mechanism by which this variant may affect CDKN2A/B expression will provide new insights into the regulation of these important genes.


Large-Scale Gene and Protein Expression Profiling: Static versus Dynamic Genomic Markers of Perioperative Outcomes

Genomic approaches are anchored in the “central dogma” of molecular biology, the concept of transcription of messenger RNA (mRNA) from a DNA template, followed by translation of RNA into protein (Fig. 6-3). Since transcription is a key regulatory step that may eventually signal many other cascades of events, the study of RNA levels in a cell or organ (i.e., quantifying gene expression) can improve the understanding of a wide variety of biological systems. Furthermore, while the human genome contains only about 25,000 genes, functional variability at the protein level is far more diverse, resulting from extensive posttranscriptional, translational, and posttranslational modifications. It is believed that there are approximately 200,000 distinct proteins in humans, which are further modified posttranslationally by phosphorylation, glycosylation, oxidation, and disulfide structures. There is increasing evidence that variability in gene expression levels underlies complex disease and is determined by regulatory DNA polymorphisms affecting transcription, splicing, and translation efficiency in a tissue- and stimulus-specific manner.49 Thus, in addition to the assessment of genetic variability at the DNA sequence level using various genotyping techniques as described in previous sections (static genomics), analysis of large-scale variability in the pattern of RNA and protein expression both at baseline and in response to the multidimensional perioperative stimuli (dynamic genomics) using microarray and proteomic approaches provides a much needed complementary understanding of the overall regulatory networks involved in the pathophysiology of adverse postoperative outcomes. Such dynamic genomic markers can be incorporated in genomic classifiers and used clinically to improve perioperative risk stratification or monitor postoperative recovery.50 This emergent concept of molecular classification involves the description of informational features in a training data set using changes in relative RNA and protein abundance in the context of genetic predisposition and applying to a test data set to recognize a defined “fingerprint” characteristic of a particular perioperative phenotype (Table 6-2). For example, Feezor et al.51 used a combined genomic and proteomic approach to identify expression patterns of 138 genes from peripheral blood leukocytes and the concentrations of 7 circulating plasma proteins that discriminated patients who developed multiple organ dysfunction syndrome after thoracoabdominal aortic aneurysm repair from those who did not. More importantly, these patterns of genomewide gene expression and plasma protein concentration were observed before surgical trauma and visceral ischemia-reperfusion injury, suggesting that patients who developed multiple organ dysfunction syndrome differed in either their genetic predisposition or their pre-existing inflammatory state.51

Figure 6-3. Central dogma of molecular biology. Protein expression involves two main processes, RNA synthesis (transcription) and protein synthesis (translation), with many intermediate regulatory steps. A single gene can give rise to multiple protein products (isoforms) via alternative splicing and RNA editing. Thus, functional variability at the protein level, ultimately responsible for biological effects, is the cumulative result of genetic variability as well as extensive posttranscriptional, translational, and posttranslational modifications.

Alternatively, dynamic genomic markers can be used to improve mechanistic understanding of perioperative stress and to evaluate and catalogue organ-specific responses to surgical stress and severe systemic stimuli such as cardiopulmonary bypass (CPB) and endotoxemia, which can be subsequently used to identify and validate novel targets for organ protective strategies.52 Using a similar integrated approach of transcriptomic and proteomic analyses, Tomic et al.53 characterized the molecular response signatures in peripheral blood to cardiac surgery with and without CPB, a robust trigger of systemic inflammation. The authors demonstrated that, rather than being the primary source of serum cytokines, peripheral blood leukocytes only assume a “primed” phenotype on contact with the extracorporeal circuit, which facilitates their trapping and subsequent tissue-associated inflammatory response. Interestingly, many inflammatory mediators achieved similar systemic levels following off-pump surgery but with delayed kinetics, offering novel insights into the concepts of contact activation and compartmentalization of inflammatory responses to major surgery. Several studies have profiled myocardial gene expression in the ischemic heart, demonstrating alterations in the expression of immediate-early genes (c-fosjunB), as well as genes coding for calcium-handling proteins (calsequestrin, phospholamban), extracellular matrix and cytoskeletal proteins.54

Up-regulation of transcripts mechanistically involved in cytoprotection (heat shock proteins), resistance to apoptosis, and cell growth has been found in stunned myocardium.55Moreover, cardiac gene expression profiling after CPB and cardioplegic arrest has identified the up-regulation of inflammatory and transcription activators, apoptotic genes, and stress genes,56 which appear to be age-related.57 Microarray technology has also been used in the quest for novel cardioprotective genes, with the ultimate goal of designing strategies to activate these genes and prevent myocardial injury. Preconditioning is one of such well-studied models of cardioprotection, which can be induced by various triggers including intermittent ischemia, osmotic or redox stress, heat shock, toxins, and inhaled anesthetics. The main functional categories of genes identified as potentially involved in cardioprotective pathways include a host of transcription factors, heat shock proteins, antioxidant genes (heme-oxygenase, glutathione peroxidase), and growth factors, but different gene programs appear to be activated in ischemic versus anesthetic preconditioning, resulting in two distinct cardioprotective phenotypes.58 More recently, a transcriptional response pattern consistent with late preconditioning has been reported in peripheral blood leukocytes following sevoflurane administration in healthy volunteers, characterized by reduced expression of L selectin as well as down-regulation of genes involved in fatty acid oxidation and the PCG1α (peroxisome-activated receptor gamma coactivator 1α) pathway,59 which mirrors changes observed in the myocardium from patients undergoing off-pump coronary artery bypass surgery (CABG; Table 6-2).60 Deregulation of these novel survival pathways


thus appears to generalize across tissues, making them important targets for cardioprotection, but further studies are needed to correlate perioperative gene expression response patterns in end organs such as the myocardium to those in readily available potential surrogate tissues such as peripheral blood leukocytes.

Table 6-2 Summary of Gene Expression Studies with Implications for Perioperative Cardiovascular Outcomes

Tissue (Species)


Genomic Signature: Number/Types of Genes


Myocardium (rat)


14 (wound-healing, Ca-handling)


Myocardium (human)

CPB/circulatory arrest/µA

58 (inflammation, transcription activators, apoptosis, stress response)—adults
50 (cardioprotective, antiproliferative, antihypertrophic)—neonates


Myocardium (rat)


566 differentially regulated/56 jointly regulated (cell defense)


Myocardium (rat)

APC vs ApostC/µA

Opposing genomic profiles, 8 gene clusters, <2% jointly regulated genes


Myocardium (human)

APC, OPCAB, postoperative LV function/µA

319 up-regulated and 281 down-regulated gene sets in response to OPCAB; deregulation of fatty acid oxidation, DNA-damage signaling and G-CSF survival (perioperative) and PGC-1α (constitutive) pathways predict improved LV function in sevoflurane-treated patients


PBMC (human)

APC, sevoflurane/µA

Deregulation of late preconditioning, PGC-1α, fatty acid oxidation, and L selectin pathways


Atrial myocardium (pig)

Pacing-induced AF/µA + P

81 (MCL-2 ventricular/atrial isoform shift)


Atrial myocardium (human)


1,434 (ventricularlike genomic signature)


PBMC (human)

Cardiac surgery, PoAF/µA

1,302 genes uniquely deregulated in PoAF/401 up-regulated (oxidative stress), 902 down-regulated


PBMC (human)

Cardiac surgery, POCD/µA

1,201 genes uniquely deregulated in POCD/531
Up-regulated, 670 down-regulated (inflammation, antigen presentation, cell adhesion, and apoptosis)


PBMC (human)

Heart transplant/µA

30 (profile correlated with biopsy-proven rejection; persistent immune activation in response to treatment)


PBMC (human)

Heart transplant /RT-PCR

20 (AlloMap, AlloMap score)


Myocardium (human)

Heart transplant/P

2 (increased B-crystallin and tropomyosin serum levels)


PBMC, plasma (human)


138 genes and 7 plasma proteins predicted MODS


µA, microarray; CPB, cardiopulmonary bypass; IPC, ischemic preconditioning; APC, anesthetic preconditioning; APostC, anesthetic postconditioning; OPCAB, off-pump coronary artery bypass; LV, left ventricle; G-CSF granulocyte colony-stimulating factor; PGC-1α, peroxisome proliferators-activated receptor γ cofactor-1α; AF, atrial fibrillation; MCL-2, myosin light chain 2; P, proteomics; PBMC, peripheral blood mononuclear cells; PoAF, postoperative atrial fibrillation; POCD, postoperative cognitive decline; RT-PCR, real time polymerase chain reaction; TAAA, thoracoabdominal aortic aneurysm repair; MODS, multiple organ dysfunction syndrome.

The transcriptome (the complete collection of transcribed elements of the genome) is not fully representative of the proteome (the complete complement of proteins encoded by the genome) because many transcripts are not targeted for translation, as evidenced recently with the concept of gene silencing by RNA interference. Alternative splicing, a wide variety of posttranslational modifications, and protein-protein interactions responsible for biological function, therefore would remain undetected by gene expression profiling (Fig. 6-3). This has led to the emergence of a new field, proteomics, studying the sequence, modification, and function of many proteins in a biological system at a given time. Rather than focusing on “static” DNA, proteomic studies examine dynamic protein products with the goal of identifying proteins that undergo changes in abundance, modification, or localization in response to a particular disease state, trauma, stress, or therapeutic intervention (for a review, see Atkins and Johansson61). Thus, proteomics offers a more global and integrated view of biology, complementing other functional genomic approaches. Currently available methods for proteomic analysis include protein extraction, separation by two-dimensional gel electrophoresis or chromatography, followed by identification using mass spectrometry. Although rapidly improving, these methods are currently limited by sensitivity, specificity, and throughput. Several preclinical proteomic studies relevant to perioperative medicine have characterized the temporal changes in brain protein expression in response to various inhaled anesthetics,62,63 or following cardiac surgery with hypothermic circulatory arrest.64 This may focus further studies aimed to identify new anesthetic binding sites, and the development of neuroprotective strategies. Furthermore, detailed knowledge of the plasma proteome has profound implications in perioperative transfusion medicine,65 particularly those related to peptide and protein changes that occur during storage of blood products. The development of protein arrays and real-time proteomic analysis technologies has the potential to allow the use of these versatile and rigorous high-throughput methods for clinical applications, and is the object of intense investigation.


Genomics and Perioperative Risk Profiling

More than 40 million patients undergo surgery annually in the United States at a cost of $450 billion. Each year approximately 1 million patients sustain medical complications after surgery, resulting in costs of $25 billion annually. The proportion of the U.S. population older than 65 is estimated to double in the next two decades, leading to a 25% increase in the number of surgeries, a 50% increase in surgery-related costs, and a 100% increase in complications from surgery. Recognizing the significant increase in surgical burden due to accelerated aging of the population and increased reliance on surgery for treatment of disease, the National Heart, Blood and Lung Institute has recently convened a Working Group on perioperative medicine. The group concluded that perioperative complications are significant, costly, variably reported, and often imprecisely detected, and identified a critical need for accurate comprehensive perioperative outcome databases. Furthermore, presurgical risk profiling is inconsistent and deserves further attention, especially for noncardiac, nonvascular surgery and older patients66 (see Chapter 35).

Although many preoperative predictors have been identified and are constantly being refined, risk stratification based on clinical, procedural, and biological markers explains only a small part of the variability in the incidence of perioperative complications. As previously mentioned, it is becoming increasingly recognized that perioperative morbidity arises as a direct result of the environmental stress of surgery occurring on a landscape of susceptibility that is determined by an individual's clinical and genetic characteristics, and may even occur in otherwise healthy individuals. Such adverse outcomes will develop only in patients whose combined burden of genetic and environmental risk factors exceeds a certain threshold, which may vary with age. Identification of such genetic contributions to not only disease causation and susceptibility, but also influencing the response to disease and drug therapy and incorporation of genetic risk information in clinical decision-making, may lead to improved health outcomes and reduced costs. For instance, understanding the gene-environment interactions involved in atherosclerotic cardiovascular disease and neurologic injury may facilitate preoperative patient optimization and resource utilization. Furthermore, understanding the role of allotypic variation in proinflammatory and prothrombotic pathways, the main pathophysiological mechanisms responsible for perioperative complications, may contribute to the development of target-specific therapies, thereby limiting the incidence of adverse events in high-risk patients. To increase clinical relevance for the practicing perioperative physician, we summarize existing evidence by specific outcome while highlighting candidate genes in relevant mechanistic pathways (Tables 6-3, 6-4and 6-5).

Genetic Susceptibility to Adverse Perioperative Cardiovascular Outcomes

Perioperative Myocardial Infarction

As part of the preoperative evaluation, anesthesiologists are involved in assessing the risks of perioperative complications. It is commonly accepted that patients who have underlying cardiovascular disease are at risk for adverse cardiac events after surgery, and several multifactorial risk indices have been developed and validated for patients undergoing both noncardiac surgical procedures (such as the Goldman or the Lee Cardiac Risk Index), as well as cardiac surgery (such as the Hannan or Sergeant scores). However, identifying patients at the highest risk of perioperative infarction remains difficult. Risk scores, while potentially valuable for population studies, are not an ideal tool for directing care in an individual patient.67 Genomic approaches have been used in the search for a better assessment of the individual coronary risk profile. Numerous reports from animal models, linkage analysis, family, twin, and population association studies have definitely proven the role of genetic influences in the incidence and progression of CAD, with a heritability of death from CAD as high as 0.58. Furthermore, hazardous patterns of angiographic CAD (left main and proximal disease), known major risk factors for perioperative cardiac complications, are also highly heritable. Similarly, genetic susceptibility to myocardial infarction has been established through multiple lines of evidence,13,22 including a recent well-powered and replicated GWAS.44 Although these studies do not directly address the heritability of adverse perioperative myocardial events, they do suggest a strong genetic contribution to the risk of adverse cardiovascular outcomes in general.

Despite advances in surgical, cardioprotective, and anesthetic techniques, the incidence of perioperative myocardial infarction (PMI) following cardiac and vascular surgery in several large randomized clinical trials has been reported at 7 to 19%68,69 and is consistently associated with reduced short- and long-term survival. In the setting of cardiac surgery, PMI involves three major converging pathophysiological processes, including systemic and local inflammation, “vulnerable” blood, and neuroendocrine stress4 (see Chapter 12). In noncardiac surgery, pathophysiology of PMI is not so clearly understood, but a combination of two mechanisms appears predominant: (1) plaque rupture and coronary thrombosis triggered by perioperative endothelial injury from catecholamine surges, proinflammatory and prothrombotic states; and (2) prolonged stress-induced ischemia and tachycardia in the setting of compromised perfusion. Extensive genetic variability exists in each of these mechanistic pathways, which may combine to modulate the magnitude of myocardial injury. However, only a paucity of studies exists relating genetic risk factors to adverse perioperative myocardial outcomes, mainly following CABG surgery (Table 6-3).31,70,71

Inflammation Variability and Perioperative Myocardial Outcomes. Consistent with the “inflammatory hypothesis” in the pathogenesis of perioperative organ injury, our group has recently identified three inflammatory gene polymorphisms that are independently predictive of PMI following cardiac surgery with CPB (see Chapter 41). These include the proinflammatory cytokine IL6-572G>C (OR 2.47) and two adhesion molecules: intercellular adhesion molecule 1 (ICAM1 Lys469Glu, OR 1.88) and E selectin (SELE 98G>T, OR 0.16).23Importantly, inclusion of genotypic information from these SNPs improves prediction models for postcardiac surgery myocardial infarction based on traditional risk factors alone. Using a similar definition of PMI, Collard et al.24 have reported that a combined haplotype in the mannose-binding lectin gene (MBL2 LYQA secretor haplotype), an important recognition molecule in the lectin complement pathway, is independently associated with PMI in a cohort of white patients undergoing primary CABG with CPB. Furthermore, genetic variants in IL6 and TNFA are associated with increased incidence of postoperative cardiovascular complications (a composite outcome that included PMI) following lung resection for cancer.72 Other genetic variants modulating the magnitude of postoperative inflammatory response have been identified. Polymorphisms in the promoter of the interleukin 6 (IL6) gene (-572G>C and -174G>C) significantly increase the inflammatory response after heart surgery with CPB,73 and have been associated with length of hospitalization after CABG.74Furthermore, apolipoprotein E genotype (the ε4 allele),75



several variants in the tumor necrosis factor genes (TNFA-308G>A, LTA+250G>A),76 and a functional SNP in the macrophage migration inhibitory factor77 have been associated with proinflammatory effects in patients undergoing CPB, and in some instances with postoperative ventricular dysfunction.78 In addition, a genetic variant modulating the release of the anti-inflammatory cytokine interleukin 10 (IL10) in response to CPB has been reported (IL10-1082G>A), with high levels of IL10 being associated with postoperative ventricular dysfunction.79

Table 6-3 Representative Genetic Polymorphisms Associated with Altered Susceptibility to Adverse Perioperative Cardiovascular Events



Type of Surgery



Perioperative Myocardial Infarction/Dysfunction, Early Vein Graft Failure


-572G>C, -174G>C

Cardiac/CPB, thoracic

2.47, 1.8

23, 72












LYQA secretor haplotype





L33P (PlA1/PlA2)

CABG/CPB, major vascular

2.5a, 2.4

83, 85



Major vascular

































Perioperative Vasoplegia, Vascular Reactivity, Coronary Tone










90, 189





34, 91



Tracheal intubation





Response to α-AR agonists





Resting coronary tone



Postoperative Arrhythmias: Atrial Fibrillation, QTc Prolongation



CABG/CPB beta-blocker failure,

3.25 n.r. 1.8

96, 98


190, 72



Beta-blocker failure









-511T>C 5810G>A


1.44, 0.66


Postoperative MACE, Late Vein Graft Failure



Noncardiac with spinal block



















R16G, Q27E

Cardiac surgery/CPB

1.96, 2.82


















Cardiac Allograft Rejection



Cardiac transplant














86-bp VNTR

Thoracic transplant








OR, odds ratio; IL6, interleukin 6; CPB, cardiopulmonary bypass; ICAM-1, intercellular adhesion molecule 1; SELE, E selectin; MBL2, mannose binding lectin 2; CABG, coronary artery bypass graft; ITGB3, glycoprotein IIIa; GP1BA, glycoprotein Ibα; TNFA, tumor necrosis factor-α; TNFB, tumor necrosis factor-β; LTA, lymphotoxin-α; IL10, interleukin 10; n.r., not reported; F5, factor V; FVL, factor V Leiden; CMA1, heart chymase; PAI-1, plasminogen activator inhibitor 1; DDAH II, dimethylarginine dimethyl aminohydrolase II; NOS3, endothelial nitric oxide synthase; ACE, angiotensin-converting enzyme; In/del, insertion/deletion; ADRB2, β2-adrenergic receptor; GNB3, G-protein β3 subunit; α-AR, α-adrenergic receptor; PON1, paraoxonase 1; RANTES, regulated on activation normally T-expressed and secreted; IL1B, interleukin 1β; ADRB1, β1-adrenergic receptor; MTHFR, methylenetetrahydrofolate reductase; PTCA, percutaneous transluminal coronary angioplasty; HP, haptoglobin; CR1, complement component 3b/4b; KDR, kinase inert domain receptor; MICA, MHC I polypeptide; HLA-DPB1, β chain of class II major histocompatibility complex; VTN, vitronectin; LPL, lipoprotein lipase; IL1RN, interleukin 1 receptor antagonist; VNTR, variable number tandem repeat.
aRelative risk.
CHazard ratio.
eIn haplotype with IL1RN VNTR.

Coagulation Variability and Perioperative Myocardial Outcomes. In addition to robust inflammatory activation, the host response to surgery is also characterized by an increase in fibrinogen concentration, platelet adhesiveness, and plasminogen activator inhibitor-1 (PAI-1) production (see Chapter 16). During cardiac surgery, alterations in the hemostatic system are even more complex and multifactorial, including the effects of hypothermia, hemodilution, and CPB-induced activation of coagulation, fibrinolytic, and inflammatory pathways. Perioperative thrombotic outcomes following cardiac surgery (e.g., coronary graft thrombosis, myocardial infarction, stroke, pulmonary embolism) represent one extreme on a continuum of coagulation dysfunction, with coagulopathy at the other end of the spectrum (see Chapter 16). Pathophysiologically, the balance between bleeding, normal hemostasis, and thrombosis is markedly influenced by the rate of thrombin formation and platelet activation. Recent evidence suggests that genetic variability modulates the activation of each of these mechanistic pathways,80 suggesting significant heritability of the prothrombotic state (see Table 6-5 for an overview of genetic variants associated with postoperative bleeding).

Several genotypes have been associated with increased risk of coronary graft thrombosis and myocardial injury following CABG. PAI-1 is an important negative regulator of fibrinolytic activity; a variant in the promoter of the PAI-1 gene, consisting of an insertion (5G)/deletion (4G) polymorphism at position -675, has been consistently associated with changes in the plasma levels of PAI-1. The 4G allele is associated with increased risk of early graft thrombosis after CABG81 and, in a recent meta-analysis, with increased incidence of myocardial infarction.82 Similarly, a polymorphism in the platelet glycoprotein IIIa gene (ITGB3), resulting in increased platelet aggregation (PlA2 polymorphism), is associated with higher postoperative levels of troponin I following CABG83 and increased risk for 1-year thrombotic coronary graft occlusion, myocardial infarction, and death following CABG.84 On the other hand, in patients undergoing major vascular surgery, two SNPs in platelet glycoprotein receptors (ITGB3 and GP1BA) are independent risk predictors of PMI and result in improved discrimination of an ischemia risk assessment tool when added to historic and procedural risk factors.85 One of the most common inherited prothrombotic risk factors is a point mutation in coagulation factor V (1691G>A) resulting in resistance to activated protein C, and referred to as factor V Leiden (FVL). FVL has been associated with various postoperative thrombotic complications following noncardiac surgery (for a review, see Donahue33), but interestingly, also associated with a significant reduction in postoperative blood loss and overall risk of transfusion in cardiac surgery patients.86 In a prospective study of CABG patients with routine 3-month postoperative angiographic follow-up, a higher proportion of FVL carriers had graft occlusion compared to noncarriers.87

Genetic Variability and Perioperative Vascular Reactivity. Perioperative stress responses are also characterized by robust sympathetic nervous system activation, known to play a role in the pathophysiology of PMI, thus patients with CAD and specific adrenergic receptor (AR) genetic polymorphisms may be particularly susceptible to catecholamine toxicity and cardiac complications. Several functionally important SNPs modulating AR pathways have been characterized (for review, see Zaugg et al.88). One such variant, the Arg389Gly polymorphism in β1-AR gene (ADRB1), was recently associated with increased risk of a composite cardiovascular morbidity outcome at 1 year following noncardiac surgery under spinal anesthesia, while perioperative beta-blockade had no significant effect.89 The authors suggest that proper analysis of future perioperative beta-blocker trials should be stratified by AR genotype, which may help identify patients likely to benefit from this therapy. Significantly increased vascular responsiveness to α-adrenergic stimulation (phenylephrine) was found in carriers of the endothelial nitric oxide synthase 894G>T polymorphism,90 and angiotensin-converting enzyme (ACE) insertion/deletion (I/D) polymorphism34,91 undergoing cardiac surgery with CPB. Two studies have reported on the role of β2-AR (ADRB2) genetic variants in perioperative vascular reactivity. Increased blood pressure responses to endotracheal intubation have been associated with a common functional ADRB2 SNP (Glu27).92 The second study, conducted in the obstetric population, showed that incidence and severity of maternal hypotension following spinal anesthesia for cesarean delivery, as well as the response to treatment, was affected by ADRB2 genotype (Gly16 and/or Glu27 led to lower vasopressor use for the treatment of hypotension). In cardiac surgery patients, the development of vasoplegic syndrome is one manifestation of the perioperative systemic inflammatory response, but remains poorly predicted by clinical and procedural risk factors. Vasopressor requirement after surgery is associated with a common polymorphism in the dimethylarginine dimethyl aminohydrolase II (DDAH II) gene, an important regulator of nitric oxide synthase activity.93

Perioperative Atrial Fibrillation

New-onset perioperative atrial fibrillation (PoAF) remains a common complication of cardiac and major noncardiac thoracic surgical procedures (incidence 27 to 40%), and is associated with increased morbidity, hospital length of stay, rehospitalization, health care costs, and reduced survival. Several large prospective multicenter trials have developed and validated comprehensive risk indices for occurrence of PoAF based on demographic, clinical, electrocardiographic, and procedural risk factors, but their predictive accuracy remains at best moderate,94 suggesting an inherent genetic preoperative risk. Heritable forms of AF occur in the ambulatory nonsurgical population, and it appears that both monogenic forms like “lone” AF as well as polygenic predisposition to more common acquired forms like PoAF do exist.95 Recently, a team led by researchers at deCODE genetics (Reykjavik, Iceland) reported the results of a genomewide association study for AF; two polymorphisms on chromosome 4q25 demonstrated a highly significant association (p = 3.3 × 10-41) with AF,47 with findings replicated in other populations from Sweden, the United States, and Hong Kong, although the mechanism of action for these variants remains unknown. On the other hand, candidate susceptibility genes for PoAF include those determining action, potential duration (voltage-gated ion channels, ion transporters), responses to extracellular factors (adrenergic and other hormone receptors, heat shock proteins), remodeling processes, and magnitude of inflammatory and oxidative stress. In particular, a role for inflammation for PoAF is suggested by the fact that baseline C-reactive protein (CRP) levels in male patients and exaggerated postoperative leukocytosis both predict PoAF, whereas postoperative administration of non-steroidal anti-inflammatory drugs shows a protective effect. However, specific evidence for a genetic role in PoAF is sparse. A functional SNP in


the IL6 promoter (-174G>C) is associated with plasma perioperative IL-6 levels and several clinical outcomes after CABG surgery, including PoAF96,97, and independently validated.98Additionally, polymorphisms in two inflammatory genes (IL6 and TNFA) are associated with composite postoperative morbidity (including new-onset arrhythmias) following lung resection procedures.72 There is however a contradictory lack of association between CRP levels (strongly regulated by IL-6) and PoAF in women undergoing cardiac surgery.99 On the other hand, a recent study reported that of 21 serum biomarkers investigated in relationship with PoAF, both pre-and postoperative PAI-1 levels are independently associated with development of PoAF following cardiac surgery.100

Several groups have investigated transcriptional responses to AF in human atrial appendage myocardium obtained at the time of cardiac surgery or in preclinical animal models (Table 6-2), and identified a ventricularlike genomic signature in fibrillating atria, with increased ratios of ventricular to atrial isoforms, suggesting dedifferentiation.101 Although it remains unclear whether this “ventricularization” of atrial gene expression reflects cause or effect of AF, it nevertheless seems to represent an adaptive energy-saving process to the high metabolic demand of fibrillating atrial myocardium, akin to chronic hibernation. A recent study investigating gene expression changes in peripheral blood leukocytes in relationship to PoAF following cardiac surgery has suggested that patients who exhibit PoAF display a differential genomic response to CPB, characterized by up-regulation of oxidative stress genes, which correlated with a significantly larger increase in oxidant stress both systemically (as measured by total peroxide levels) as well as at the myocardial level (as measured in the right atrium).102

Cardiac Allograft Rejection

Identification of peripheral blood gene- and protein-based biomarkers to noninvasively monitor, diagnose, and predict perioperative cardiac allograft rejection is an area of rapid scientific growth (see Chapter 54). While several polymorphisms in genes involved in alloimmune interactions, the renin-angiotensin-aldosterone system and the transforming growth factor-β superfamily have been associated with cardiac transplant outcomes, their relevance as useful clinical monitoring tools remains uncertain. However, peripheral blood mononuclear cell-based molecular assays have shown much promise for monitoring the dynamic responses of the immune system to the transplanted heart, discriminating immunologic allograft quiescence and predicting future rejection.103 A noninvasive molecular test to identify patients at risk for acute cellular rejection is commercially available (AlloMap, XDx Brisbane, CA), in which the expression levels of 20 genes is measured by quantitative real-time polymerase chain reaction (qRT-PCR) and translated using a mathematical algorithm into a clinically actionable AlloMap score that enhances the ability to deliver personalized monitoring and treatment to heart transplant patients. Furthermore, several clinically available protein-based biomarkers of alloimmune activation, microvascular injury (troponins), systemic inflammation (CRP), and wall stress and remodeling (brain natriuretic peptide) correlate well with allograft failure and vasculopathy and have good negative predictive values, but require additional studies to guide their clinical use. Similarly, molecular signatures of functional recovery in end-stage heart failure following left ventricular assist device support using gene expression profiling have been reported,104 and could be used to monitor patients who received a left ventricular assist device as destination therapy or assess the timing of potential device explantation.

Genetic Variability and Postoperative Event-Free Survival

Several large randomized clinical trials examining the benefits of CABG surgery and percutaneous coronary interventions relative to medical therapy and/or to one another have refined our knowledge of early and long-term survival after CABG. While these studies have helped define the subgroups of patients who benefit from surgical revascularization, they also demonstrated a substantial variability in long-term survival after CABG, altered by important demographic and environmental risk factors. Increasing evidence suggests that theACE gene indel polymorphism may influence post-CABG complications, with carriers of the D allele having higher mortality and restenosis rates after CABG surgery compared with theI allele.71 As previously discussed, a prothrombotic amino acid alteration in the β3-integrin chain of the glycoprotein IIb/IIIa platelet receptor (the PlA2 polymorphism) is associated with an increased risk (OR 4.7) for major adverse cardiac events (a composite of myocardial infarction, coronary bypass graft occlusion, or death) following CABG surgery (Table 6-3).84 We found preliminary evidence for association of two functional polymorphisms modulating β2-adrenergic receptor activity (Arg16Gly and Gln27Glu) with incidence of death or major adverse cardiac events following cardiac surgery,105 and recently identified two functional polymorphisms in apolipoprotein E (APOE-219G>T, OR 0.46) and thrombomodulin (THBD Ala455Val, OR 2.64) genes associated with altered 5-year mortality after CABG independent of EuroSCORE.106

Genetic Susceptibility to Adverse Perioperative Neurologic Outcomes

Despite advances in surgical and anesthetic techniques, significant neurologic morbidity continues to occur following cardiac surgery, ranging in severity from coma and focal stroke (incidence 1 to 3%) to more subtle cognitive deficits (incidence up to 69%), with a substantial impact on the risk of perioperative death, quality of life, and resource utilization. Variability in the reported incidence of both early and late neurologic deficits remains poorly explained by procedural risk factors, suggesting that environmental (operative) and genetic factors may interact to determine disease onset, progression, and recovery (see Chapter 41). The pathophysiology of perioperative neurologic injury is thought to involve complex interactions between primary pathways associated with atherosclerosis and thrombosis, and secondary response pathways like inflammation, vascular reactivity, and direct cellular injury. Many functional genetic variants have been reported in each of these mechanistic pathways involved in modulating the magnitude and the response to neurologic injury, which may have implications in chronic as well as acute perioperative neurocognitive outcomes. For example, Grocott at al.107 examined 26 SNPs in relationship to the incidence of acute postoperative ischemic stroke in 1,635 patients undergoing cardiac surgery and found that the interaction of minor alleles of the CRP (1846C>T) and IL-6 promoter SNP-174G>C significantly increases the risk of acute stroke. Similarly, a recent study suggests that P selectin and CRP genes both contribute to modulating the susceptibility to postoperative cognitive decline (POCD) following cardiac surgery.27 Specifically, the loss-of-function minor alleles of CRP 1059G>C and SELP 1087G>A are independently associated with a reduction in the observed incidence of POCD after adjustment for known clinical and demographic covariates (Table 6-4).


Table 6-4 Representative Genetic Polymorphisms Associated with Altered Susceptibility to Adverse Perioperative Neurologic Events



Type of Surgery



Perioperative Stroke








Perioperative Cognitive Dysfunction, Neurodevelopmental Dysfunction












L33P (PlA1/PlA2)






CABG/CPB (adults)

n.r. 7,




Cardiac/CPB (children)


115, 116

Postoperative Delirium



Major noncardiac, critically ill

3.64, 7.32

113, 114

OR, odds ratio; IL6, interleukin 6; CPB, cardiopulmonary bypass; CRP, C-reactive protein; SELP, P selectin; ITGB3, platelet glycoprotein IIIa; n.r., not reported; APOE, apolipoprotein E; CABG, coronary artery bypass graft.

Our group has demonstrated a significant association between the apolipoprotein E (APOE) E4 genotype and adverse cerebral outcomes in cardiac surgery patients.25,108 This is consistent with the role of the APOE genotype in recovery from acute brain injury, such as intracranial hemorrhage,109 closed head injury,110 and stroke,111 as well as experimental models of cerebral ischemia-reperfusion injury112; two subsequent studies in CABG patients, however, have not replicated these initial findings. Furthermore, the incidence of postoperative delirium following major noncardiac surgery in the elderly113 and in critically ill patients114 is increased in carriers of the APOE ε4 allele. Unlike adult cardiac surgery patients, infants possessing the APOE ε2 allele are at increased risk for developing adverse neurodevelopmental sequelae following cardiac surgery.115,116 The mechanisms by which the APOE genotypes might influence neurologic outcomes have yet to be determined, but do not seem to be related to alterations in global cerebral blood flow of oxygen metabolism during CPB117; however, genotypic effects in modulating the inflammatory response,75 extent of aortic atheroma burden,118 and risk for premature coronary atherosclerosis119 may play a role.

Recent studies have suggested a role for platelet activation in the pathophysiology of adverse neurologic sequelae. Genetic variants in surface platelet membrane glycoproteins, important mediators of platelet adhesion and platelet-platelet interactions, have been shown to increase the susceptibility to prothrombotic events. Among these, the PlA2polymorphism in glycoprotein IIb/IIIa has been related to various adverse thrombotic outcomes, including acute coronary thrombosis120 and atherothrombotic stroke.121 We found the PlA2 allele to be associated with more severe neurocognitive decline after CPB,26 which could represent exacerbation of platelet-dependent thrombotic processes associated with plaque embolism.

Cardiac surgical patients who develop POCD demonstrate inherently different genetic responses to CPB from those without POCD, as evidenced by acute deregulation in peripheral blood leukocytes of gene expression pathways involving inflammation, antigen presentation, and cellular adhesion.122 These findings corroborate with proteomic changes, in which patients with POCD similarly have significantly higher serologic inflammatory indices compared with those patients without POCD,123,124 and add to the increasing level of evidence that CPB does not cause an indiscriminate variation in gene expression, but rather distinct patterns in specific pathways that are highly associated with the development of postoperative complications such as POCD. The implications for perioperative medicine include identifying populations at risk who might benefit not only from an improved informed consent, stratification, and resource allocation, but also from targeted anti-inflammatory strategies.

Genetic Susceptibility to Adverse Perioperative Renal Outcomes

Acute renal dysfunction is a common, serious complication of cardiac surgery; about 8 to 15% of patients develop moderate renal injury (>1.0 mg/dL peak creatinine rise), and up to 5% of them develop renal failure requiring dialysis.125 Acute renal failure is independently associated with in-hospital mortality rates, exceeding 60% in patients requiring dialysis.125Several studies have demonstrated that inheritance of genetic polymorphisms in the APOE gene (ε4 allele)30 and in the promoter region of the IL6 gene (-174C allele)97 are associated with acute kidney injury following CABG surgery (Table 6-5). Stafford-Smith et al.28 have reported that major differences in peak postoperative serum creatinine rise after CABG are predicted by possession of combinations of polymorphisms that interestingly differ by race: the angiotensinogen (AGT) 842T>C and IL6 -572G>C variants in whites, and the endothelial nitric oxide synthase (NOS3) 894G>T and angiotensin-converting enzyme (ACE) insertion/deletion in African Americans are associated with more than 50% reduction in postoperative glomerular filtration rate. Further identification of genotypes predictive of adverse perioperative renal outcomes may facilitate individually tailored therapy, risk stratify the patients for interventional trials targeting the gene product itself, and aid in medical decision-making (e.g., selecting medical over surgical management; see Chapter 52).

Genetic Variants and Risk for Prolonged Postoperative Mechanical Ventilation

Prolonged mechanical ventilation (inability to extubate patient by 24 hours postoperatively) is a significant complication following cardiac surgery, occurring in 5.6% and 10.5% of


patients undergoing first and repeat CABG surgery, respectively.126 Several pulmonary and nonpulmonary causes have been identified, and scoring systems based on preoperative and procedural risk factors have been proposed and validated. Recently, genetic variants in the renin-angiotensin pathway and in proinflammatory cytokine genes have been associated with respiratory complications post-CPB. The D allele of a common functional insertion/deletion polymorphism in the angiotensin-converting enzyme (ACE) gene, accounting for 47% of variance in circulating ACE levels,127 is associated with prolonged mechanical ventilation following CABG128 and with susceptibility to and prognosis of acute respiratory distress syndrome.129 Furthermore, a hyposecretory haplotype in the neighboring genes tumor necrosis factor-α (TNFA) and lymphotoxin-α (LTA) on chromosome 6 (TNFA-308G/LTA+250Ghaplotype)130 and a functional polymorphism modulating postoperative IL-6 levels (IL6-174G>C)97 are independently associated with higher risk of prolonged mechanical ventilation post-CABG. The association is more dramatic in patients undergoing conventional CABG than in those undergoing off-pump CABG, suggesting that in high-risk patients identified by preoperative genetic screening, off-pump CABG may be the optimal surgical procedure.

Table 6-5 Representative Genetic Polymorphisms Associated with Other Adverse Perioperative Outcomes



Type of Surgery



Perioperative Thrombotic Events



Noncardiac, Cardiac



Perioperative Bleeding












-52C>T, 807C>T






























L33P (PlA1/PlA2)











Brain AVM treatment








Perioperative Acute Kidney Injury




















28, 30

Perioperative Severe Sepsis






OR, odds ratio; F5, factor V; FVL, factor V Leiden; n.r., not reported; CPB, cardiopulmonary bypass; PAI-1, plasminogen activator inhibitor 1; ITGA2, glycoprotein IaIIa; GP1BA, glycoprotein Ibα; TF, tissue factor; TFPI, tissue factor pathway inhibitor; CABG, coronary artery bypass graft; F2, prothrombin; ACE, angiotensin-converting enzyme; In/del, insertion/deletion; ITGB3, glycoprotein IIIa; TNFA, tumor necrosis factor-α; AVM, arteriovenous malformation; APOE, apolipoprotein E; IL6, interleukin 6; AGT, angiotensinogen; NOS3, endothelial nitric oxide synthase.
aβ coefficient.
bOdds ratio.
cHazard ratio.
eRelative risk.

A next crucial step in understanding the complexity of adverse perioperative outcomes is to assess the contribution of variations in many genes simultaneously and their interaction with traditional risk factors to the longitudinal prediction of outcomes in individual patients. The use of such outcome predictive models incorporating genetic information may help stratify mortality and morbidity in surgical patients, improve prognostication, direct medical decision-making both intraoperatively and during postoperative follow-up, and even suggest novel targets for therapeutic intervention in the perioperative period.

Pharmacogenomics and Anesthesia

Interindividual variability in response to drug therapy, both in terms of efficacy and safety, is a rule by which anesthesiologists live. In fact, much of the art of anesthesiology is the astute clinician being prepared to deal with outliers. The term pharmacogenomics is used to describe how inherited variations in genes modulating drug actions are related to interindividual variability in drug response (see Chapter 7). Such variability in drug action may be pharmacokinetic or pharmacodynamic (Fig. 6-4). Pharmacokinetic variability refers to variability in a drug's absorption, distribution, metabolism, and excretion that mediates its efficacy and/or toxicity. The molecules involved in these processes include drug-metabolizing enzymes (such as members of the cytochrome P450, or CYP superfamily), and drug-transport molecules that mediate drug uptake into, and efflux from, intracellular sites. Pharmacodynamic variability refers to variable drug effects despite equivalent drug delivery to molecular sites of action. This may reflect variability in the function of the molecular target of the drug, or in the pathophysiological context in which the drug


interacts with its receptor-target (e.g., affinity, coupling, expression).131 Thus, pharmacogenomics investigates complex, polygenically determined phenotypes of drug efficacy or toxicity, with the goal of identifying novel therapeutic targets and customizing drug therapy.

Figure 6-4. Pharmacogenomic determinants of individual drug response operate by pharmacokinetic and pharmacodynamic mechanisms. A. Genetic variants in drug transporters (e.g., ATP-binding cassette subfamily B member 1 or ABCB1 gene) and drug-metabolizing enzymes (e.g., cytochrome P450 2D6 or CYP2D6 gene, CYP2C9 gene, N-acetyltransferase or NAT2 gene, plasma cholinesterase or BCHE gene) are responsible for pharmacokinetic variability in drug response. B. Polymorphisms in drug targets (e.g., β1- and β2–adrenergic receptor ADRB1ADRB2 genes; angiotensin-I converting enzyme ACE gene), postreceptor signaling molecules (e.g., guanine nucleotide binding protein β3 or GNB3 gene), or molecules indirectly affecting drug response (e.g., various ion channel genes involved in drug-induced arrhythmias) are sources of pharmacodynamicvariability.

Pseudocholinesterase Deficiency

Historically, characterization of the genetic basis for plasma pseudocholinesterase deficiency in 1956 was of fundamental importance to anesthesia and the further development and understanding of genetically determined differences in drug response.132 Individuals with an atypical form of pseudocholinesterase resulting in a markedly reduced rate of drug metabolism are at risk for excessive neuromuscular blockade and prolonged apnea. More than 20 variants have since been identified in the butyrylcholinesterase gene (BCHE), the most common of which are the A-variant (209A>G) and the K-variant (1615G>A), with various and somewhat poorly defined phenotypic consequences on prolonged neuromuscular blockade. Therefore, pharmacogenetic testing is currently not recommended in the population at large, but only as an explanation for an adverse event.133

Genetics of Malignant Hyperthermia

Malignant hyperthermia (MH) is a rare autosomal dominant genetic disease of skeletal muscle calcium metabolism, triggered by administration of general anesthesia with volatile anesthetic agents or succinylcholine in susceptible individuals. The clinical MH syndrome is characterized by skeletal muscle hypermetabolism and manifested as skeletal muscle rigidity, tachycardia, tachypnea, hemodynamic instability, increased oxygen consumption and CO2 production, lactic acidosis and fever, progressing to malignant ventricular arrhythmias, disseminated intravascular coagulation, and myoglobinuric renal failure. MH susceptibility has been initially linked to the ryanodine receptor (RYRI) gene locus on chromosome 19q.134 However, subsequent studies have shown that MH may represent a common severe phenotype that originates not only from point mutations in the RYRI gene (Arg614Cys), but also within its functionally and/or structurally associated proteins regulating excitation-contraction coupling (such as α1DHPR and FKBP12). It is becoming increasingly apparent that MH susceptibility results from a complex interaction between multiple genes and environment (such as environmental toxins), suggested by the heterogeneity observed in the clinical MH syndrome and the variable penetrance of the MH phenotype.135 Current diagnostic methods (the caffeine-halothane contracture test) are invasive and potentially nonspecific. Unfortunately, because of the polygenic determinism and variable penetrance, direct DNA testing in the general population for susceptibility to MH is currently not recommended; in contrast, testing in individuals from families with affected individuals has the potential to greatly reduce mortality and morbidity.133Furthermore, genomic approaches may help elucidate the molecular mechanisms involved in altered RYRI-mediated calcium signaling and identify novel, more specific therapeutic targets.

Genetic Variability and Response to Anesthetic Agents

Anesthetic potency, defined by the minimum alveolar concentration (MAC) of an inhaled anesthetic that abolishes purposeful movement in response to a noxious stimulus, varies among individuals, with a coefficient of variation (the ratio of standard deviation to the mean) of approximately 10%36 (see Chapter 7). This observed variability may be explained by


interindividual differences in multiple genes that underlie responsiveness to anesthetics, by environmental or physiological factors (brain temperature, age), or by measurement errors. With growing public concern over intraoperative awareness, understanding the mechanisms responsible for this variability may facilitate implementation of patient-specific preventive strategies. Evidence of a genetic basis for increased anesthetic requirements is beginning to emerge, suggested for instance by the observation that desflurane requirements are increased in subjects with red hair versus dark hair,137 and by recently reported variability in the immobilizing dose of sevoflurane (as much as 24%) in populations with different ethnic (and thus genetic) backgrounds.138 Several studies evaluating the genetic control of anesthetic responses, coupled with molecular modeling, proteomic, neurophysiology, and pharmacologic approaches, have provided important developments in our understanding of general anesthetic mechanisms.

Triggered by the seminal work of Franks and Lieb,139 research shifted from the membrane lipid bilayer to protein receptors (specifically, ligand- and voltage-gated ion channels) as potential anesthetic targets, ending a few decades of stagnation that were primarily due to an almost universal acceptance of the dogma of nonspecific anesthetic action (the so-called lipid theory). Some of the genes responsible for phenotypic differences in anesthetic effects have been mapped in various animal models and, following genomic manipulation of plausible candidate receptors to investigate their function in vitro, were evaluated in genetically engineered animals for their relationship to various anesthetic end points, such as immobility (i.e., MAC), hypnosis, amnesia, and analgesia (for review, see Sonner et al.140). Several thousand different strains of knockout mice have been created and are used to investigate specific functions of particular genes and mechanisms of drug action, including the sensitivity to general anesthetic in animals lacking the β3 subunit141 or the α6 subunit142 of the GABAA receptor. On the other hand, knockin animals express a site-directed mutation in the targeted gene that remains under the control of endogenous regulatory elements, allowing the mutated gene to be expressed in the same amount, at the same time, and in the same tissues as the normal gene. This method has provided remarkable insight into the mechanisms of action of benzodiazepines143 and intravenous anesthetics. In a seminal study by Jurd et al.,145 a point mutation in the gene encoding the β3 subunit of the GABAA receptor previously known to render the receptor insensitive to etomidate and propofol in vitro,144 was validated in vivo by creating a knockin mouse strain that proved also essentially insensitive to the immobilizing actions of etomidate and propofol. A point mutation in the β2 subunit of the GABAA receptor results in a knockin mouse with reduced sensitivity to the sedative146 and hypothermic effects147 of etomidate. Knockin mice harboring point mutations in the α2A-adrenergic receptor have enabled the elucidation of the role of this receptor in anesthetic-sparing, analgesic, and sedative responses to dexmedetomidine.148

The situation is far more complex for inhaled anesthetics, which appear to mediate their effects by acting on several receptor targets. Based on combined pharmacologic and geneticin vivo studies to date, several receptors are unlikely to be direct mediators of MAC, including the GABAA (despite their compelling role in intravenous anesthetic-induced immobility), 5-HT3, AMPA, kainate, acetylcholine and α2-adrenergic receptors, and potassium channels.149 Glycine, NMDA receptors and sodium channels remain likely candidates.140 These conclusions, however, do not apply to other anesthetic endpoints, such as hypnosis, amnesia and analgesia. Several preclinical proteomic analyses have identified in a more unbiased way a group of potential anesthetic targets for halothane,61 desflurane,62 and sevoflurane,63 which should provide the basis for more focused studies of anesthetic binding sites. Such “omic” approaches have the potential to evolve into preoperative screening profiles useful in guiding individualized therapeutic decisions, such as prevention of anesthetic awareness in patients with a genetic predisposition to increased anesthetic requirements.

Genetic Variability and Response to Pain

Similar to the observed variability in anesthetic potency, the response to painful stimuli and analgesic manipulations varies among individuals (see Chapter 57). The sources of variability in the report and experience of pain and analgesia (i.e., the “pain threshold”) are multifactorial, including factors extrinsic to the organism (such as cultural factors or circadian rhythms) and intrinsic factors (such as age, gender, hormonal status, or genetic makeup). Increasing evidence suggests that pain behavior in response to noxious stimuli and its modulation by the central nervous system in response to drug administration or environmental stress, as well as the development of persistent pain conditions through pain amplification, are strongly influenced by genetic factors.150,151,152

Results from studies in twins153 and inbred mouse strains154 indicate a moderate heritability for chronic pain syndromes and nociceptive sensitivity, which appears to be mediated by multiple genes (see Chapter 58). Various strains of knockout mice lacking target genes like neurotrophins and their receptors (e.g., nerve growth factor), peripheral mediators of nociception and hyperalgesia (e.g., substance P), opioid and nonopioid transmitters and their receptors, and intracellular signaling molecules have significantly contributed to the understanding of pain-processing mechanisms.155 A locus responsible for 28% of phenotypic variance in magnitude of systemic morphine analgesia in mice has been mapped to chromosome 10, in or near the OPRM (µ-opioid receptor) gene. The µ-opioid receptor is also subject to pharmacodynamic variability; polymorphisms in the promoter region of theOPRM gene modulating interleukin-4–mediated gene expression have been correlated with morphine antinociception. The much quoted OPRM 188A>G polymorphism is associated with decreased responses to morphine-6-glucuronide, resulting in altered analgesic requirements, but also reduced incidence of postoperative nausea and vomiting, and reduced risks of toxicity in patients with renal failure. Conversely, variants of the melanocortin 1 receptor (MC1R) gene, which produce a red hair-fair skin phenotype, are associated with increased analgesic responses to κ-opioid agonists in women but not men, providing evidence for a gene-by-gender interaction in regulating analgesic response (for a review, see Somogyi et al.156). Very recent reports suggest that peripherally located β2-adrenergic receptors (ADRB2) also contribute to basal pain sensitivity, the development of chronic pain states, as well as opioid-induced hyperalgesia.152 Functionally important haplotypes in the ADRB2(151) and catechol-O-methyltransferase (COMT)157 genes are associated with enhanced pain sensitivity in humans.

In addition to the genetic control of peripheral nociceptive pathways, considerable evidence exists for genetic variability in the descending central pain modulatory pathways, further explaining the interindividual variability in analgesic responsiveness. One good example relevant to analgesic efficacy is cytochrome P450D6 (CYP2D6), a member of the superfamily of microsomal enzymes that catalyze phase I drug metabolism, and responsible for the metabolism of a large number of therapeutic compounds. The relationship between the CYP2D6genotype and the enzyme metabolic rate has been extensively characterized, with at least 12 known mutations leading to a


tetramodal distribution CYP2D6 activity: ultrarapid metabolizers (5 to 7% of the population), extensive metabolizers (60%), intermediate metabolizers (25%), and poor metabolizers (10%). Currently, pharmacogenomic screening tests predict CYP2D6 phenotype with >95% reliability. The consequences of inheriting an allele that compromises CYP2D6 function include the inability to metabolize codeine (a prodrug) to morphine by O-demethylation, leading to lack of analgesia but increased side effects from the parent drug (e.g., fatigue) in poor metabolizers.133,150

Genetic Variability in Response to Other Drugs Used Perioperatively

A wide variety of drugs used in the perioperative period display significant pharmacokinetic or pharmacodynamic variability that is genetically modulated (Table 6-6.). Although such genetic variation in drug-metabolizing enzymes or drug targets usually result in unusually variable drug response, genetic markers associated with rare but life-threatening side effects have also been described. Of note, the most commonly cited categories of drugs involved in adverse drug reactions include cardiovascular, antibiotic, psychiatric, and analgesic medications; interestingly, each category has a known genetic basis for increased risk of adverse reactions.

There are more than 30 families of drug-metabolizing enzymes in humans, most with genetic polymorphisms shown to influence enzymatic activity. Of special importance to the anesthesiologist is the CYP2D6, one of the most intensively studied and best understood examples of pharmacogenetic variation, involved in the metabolism of several drugs including analgesics (codeine, dextromethorphan), beta-blockers, antiarrhythmics (flecainide, propafenone, quinidine), and diltiazem. Another important pharmacogenetic variation has been described in cytochrome P450C9 (CYP2C9), involved in metabolizing anticoagulants (warfarin), anticonvulsants (phenytoin), antidiabetic agents (glipizide, tolbutamide), and nonsteroidal anti-inflammatory drugs (celecoxib, ibuprofen), among others. Three known CYP2C9 variant alleles result in different enzyme activities (extensive, intermediate, and slow metabolizer phenotypes), and have clinical implications in the increased risk of life-threatening bleeding complications in slow metabolizers during standard warfarin therapy. This illustrates the concept of “high-risk pharmacokinetics,” which applies to drugs with low therapeutic ratios eliminated by a single pathway (in this case, CYP2C9-mediated oxidation); genetic variation in that pathway may lead to large changes in drug clearance, concentrations, and effects.131 Dose adjustments based on the pharmacogenetic phenotype have been proposed for drugs metabolized via both CYP2D6 and CYP2C9 pathways,133 and a commercially available, Food and Drug Administration (FDA)-approved test (CYP450 AmpliChip, Roche Molecular Diagnostics) allows clinicians for the first time to test patients for a wide spectrum of genetic variation in drug-metabolizing enzymes. Using this technology, Candiotti et al.158 showed that patients carrying either three copies of the CYP2D6 gene, a genotype consistent with ultrarapid metabolism, or both, have an increased risk of ondansetron failure for the prevention of postoperative vomiting but not nausea.158 The strongest evidence to date for use of pharmacogenomic testing is to aid in the determination of warfarin dosage by using genotypes in the CYP2C9 and vitamin K epoxide reductase complex 1 (VKORC1) genes, and at least four FDA-approved tests are now commercially available.

Genetic variation in drug targets (receptors) can have profound effect on drug efficacy, and more than 25 examples have


already been identified. For example, functional polymorphisms in the β2-AR (Arg16Gly, Gln27Glu) influence the bronchodilator and vascular responses to β-agonists, and β1-AR variants (Arg389Gly) modulate responses to beta-blockers and may impact postoperative cardiovascular adverse events.88,89

Finally, clinically important genetic polymorphisms with indirect effects on drug response have been described. These include variants in candidate genes like sodium (SCN5A) and potassium ion channels (KCNH2KCNE2KCNQ1), which alter susceptibility to drug-induced long-QT syndrome and ventricular arrhythmias (torsade de pointes) associated with the use of drugs like erythromycin, terfenadine, disopyramide, sotalol, cisapride, or quinidine. Carriers of such susceptibility alleles have no manifest QT-interval prolongation or family history of sudden death until QT-prolonging drug challenge is superimposed.131 Predisposition to QT-interval prolongation (considered a surrogate for risk of life-threatening ventricular arrhythmias) has been responsible for more drug withdrawals from the market than any other category of adverse event in recent times, so understanding genetic predisposing factors constitutes one of the highest priorities of current pharmacogenomic efforts.

Table 6-6 Examples of Genetic Polymorphisms Involved in Variable Responses to Drugs Used in the Perioperative Period

Drug Class

Gene Name (Gene Symbol)

Effect of Polymorphism

Pharmacokinetic variability


Cytochrome P450 2D6 (CYP2D6)

Enhanced drug effect

Codeine, dextromethorphan


Decreased drug effect

Ca-channel blockers

Cytochrome P450 3A4 (CYP3A4)




Enhanced drug response

Angiotensin-II receptor type 1 blockers

Cytochrome P450 2C9 (CYP2C9)

Enhanced blood pressure response



Enhanced anticoagulant effect, risk of bleeding



Enhanced drug effect


Angiotensin-I converting enzyme (ACE)

Blood pressure response


N-acetyltransferase 2 (NAT2)

Enhanced drug effect


Butyrylcholinesterase (BCHE)

Enhanced drug effect


P-glycoprotein (ABCB1, MDR1)

Increased bioavailability

Pharmacodynamic variability


β1- and β2-adrenergic receptors (ADRB1, ADRB2)

Blood pressure and heart rate response, airway responsiveness to β2-agonists


QT-prolonging drugs (e.g., antiarrhythmics, cisapride, erythromycin)

Sodium and potassium ion channels (SCN5A, KCNH2, KCNE2, KCNQ1)

Long QT-syndrome, risk of torsade de pointes


Aspirin, glycoprotein IIb/IIIa inhibitors

Glycoprotein IIIa subunit of platelet glycoprotein IIb/IIIa (ITGB3)

Variability in antiplatelet effects



Endothelial nitric oxide synthase (NOS3)

Blood pressure response


Pharmacogenomics is emerging as an additional modifying component to anesthesia along with age, gender, comorbidities, and medication usage. Specific testing and treatment guidelines allowing clinicians to appropriately modify drug utilization (e.g., adjust dose or change drug) already exist for a few compounds,133 and will likely be expanded to all relevant therapeutic compounds, together with identification of novel therapeutic targets.

Genomics and Critical Care

Genetic Variability in Response to Injury

Systemic injury (including trauma and surgical stress), shock, or infection trigger physiological responses of fever, tachycardia, tachypnea, and leukocytosis that collectively define the systemic inflammatory response syndrome (see Chapter 12). This can progress to severe sepsis, septic shock, and multiple organ dysfunction syndrome, the pathophysiology of which remains poorly understood. With the genomic revolution, a new paradigm has emerged in critical care medicine: outcomes of critical illness are determined by the interplay between the injury and repair processes triggered by the initial insults.159 Negative outcomes are thus the combined result of direct tissue injury, the side effects of resulting repair processes, and secondary injury mechanisms leading to suboptimal repair. This concept forms the basis of the new PIRO (Predisposition, Infection/Insult, Response, Organ dysfunction) staging system in critical illness.160 Genomic factors play a role along this continuum, from inflammatory gene variants and modulators of pathogen-host interaction, to microbial genomics and rapid detection assays to identify pathogens, to biomarkers differentiating infection from inflammation, to dynamic measures of cellular responses to insult, apoptosis, cytopathic hypoxia, and cell stress. Regulation of these mechanisms is currently being extensively investigated at the genomic, proteomic, and pharmacogenomic levels, aiming to model adaptive and maladaptive responses to injury, aid in development of diagnostic indices predictive of injury, monitor progress of repair, and eventually design novel therapeutic modalities that take into account the individual genetic makeup.

The large interindividual variability in the magnitude of response to injury, including activation of inflammatory and coagulation cascades, apoptosis and fibrosis, suggests the involvement of genetic regulatory factors. Several functional genetic polymorphisms in molecules involved in various components of the inflammatory response have been associated with differences in susceptibility to and mortality from sepsis of different etiologies, including postoperative sepsis. These include polymorphisms in bacterial recognition molecules like lipopolysaccharide binding protein (LBP), bactericidal/permeability increasing protein (BPI), CD14, toll-like receptors (TLR2TLR4), mannose-binding lectin (MBL), and proinflammatory cytokines like tumor necrosis-α (TNFA), lymphotoxin alpha (LTA), interleukin-1 (IL1) and IL-1 receptor antagonist (IL1RN), and interleukin-6 (IL6) (for reviews, see Lin and Albertson161 and De Maio et al.162). Similarly, functional genetic variants in the PAI-1 (PAI-1) and angiotensin I converting enzyme (ACE) genes have been associated with poor outcomes in sepsis, reflecting the complex interaction between inflammation, coagulation, endothelial function, and vascular tone in the pathogenesis of sepsis-induced organ dysfunction.

This continuing effort to identify initial SNP-disease associations is followed by a process of selecting reliable predictive SNPs by validation in independent populations and determining which and how many markers will maximize the power to predict risk for sepsis or mortality following injury.

Functional Genomics of Injury

At a cellular level, injurious stimuli trigger adaptive stress responses determined by quantitative and qualitative changes in interdigitating cascades of biological pathways interacting in complex, often redundant ways. As a result, numerous clinical trials attempting to block single inflammatory mediators, such as TNFα in sepsis, have been largely unsuccessful.163Given these complex interconnections, the standard “single gene” paradigm is insufficient to adequately describe the tissue response to severe systemic stimuli. Instead, organ injury might better be defined by patterns of altered gene and protein expression.164 As previously discussed, DNA microarray technology has become a powerful high-throughput method of analyzing changes induced by various injuries on a genomewide scale, by quantifying mRNA abundance and generating an expression profile for the cell or tissue of interest. Several studies have reported the gene expression profiles in both critically ill patients and in animal models of sepsis,165,166 acute lung injury,167 and burn injury.168 Using gene expression profiling in peripheral blood neutrophils, Tang et al.169 have identified a set of 50 signature genes that correctly identified sepsis with a prediction accuracy of 91%. Importantly, this genomic classifier was a stronger predictor of sepsis than physiologic indices and cytokines, such as procalcitonin. Once gene lists are identified, extracting biological information has proven to be one of the most perplexing challenges. In human subjects administered endotoxin, the number of genes whose expression changed in blood leukocytes was >4,000,170and in severely traumatized patients, the expression of >6,000 genes changed in peripheral blood leukocytes.171 It thus became evident that tools had to be developed that could categorize these genes and responses into “functional modules,” “interactome maps,” and signaling pathways.170 Two large-scale national programs are using gene and protein expression profiles in circulating leukocytes to investigate the biological reasons behind the extreme variability in patient outcomes after similar traumatic insults (the National Institutes of Health-funded Trauma Glue Granta), and to elucidate regulatory mechanisms in response to septic challenge in high-risk patients (the German National Genome Research Networkb).164 Analytical and organizational approaches to a systematic evaluation of the variance associated with genomewide expression analysis in human blood leukocytes in


the “real world” have been reported by these groups, and are very informative in the study of critical illness.172

Since only less than half of the changes at mRNA level are usually translated into changes in protein expression, transcriptional profiling has to be complemented by characterizing the injury proteome, for a more complete understanding of the host response to injury. Integrated analysis of neutrophils transcriptome and proteome in response to lipopolysaccharide stimulation has identified up-regulation of a variety of genes, including transcriptional regulators (NF-κB), cytokines (TNFα, IL-6, IL-1β), and chemokines (MCP-1, MIP-3α), and confirmed the poor concordance between transcriptional and translational responses.173 A recent study has established an extensive reference protein database for trauma patients, providing a foundation for future high-throughput quantitative plasma proteomic investigations of the mechanisms underlying systemic inflammatory responses.174Changes in serum proteome associated with sepsis and septic shock have been reported,175 and may allow rapid subclassification of sepsis syndrome into variants that may better predict responsiveness to fluid resuscitation, intravenous steroids, activated protein C, anti-TNF drugs, or specific antibiotics.61

Modeling disease entities like sepsis and multiple organ dysfunction syndrome, which are complex, nonlinear systems, requires not only the ability to measure many diverse molecular events simultaneously, but also to integrate the data using novel analytical tools based on complex systems theory and nonlinear dynamics.176 Such analysis might help identify the key signaling nodes against which therapeutics can be directed.

Figure 6-5. Levels of integration in perioperative systems biology: the “perioptome.” Cellular function is organized as a multilayered set of interdependent processes controlled at the level of the genome (DNA), transcriptome (messenger RNA), proteome (the collection of all proteins encoded within the DNA of a genome), and metabolome (the complete set of small-molecule metabolites to be found in a biological system), which can all be interrogated using high-throughput technologies. Accurate representation of the perioperative phenome (the set of all perioperative phenotypes expressed by an individual patient) requires integration of standardized phenotype definitions (phenotype ontology), state-of-the-art imaging technologies, and comprehensive clinical data warehousing. Relating genome variability to specific perioperative phenotypes through systems biology approaches involves the orthogonal integration of multiple levels of biological organization provided by genomewide data sets with clinical data and literature data, modeling the regulatory networks involved in adverse perioperative outcomes, and identifying critical regulatory nodes for therapeutic manipulation. WG, whole-genome; SNP, single nucleotide polymorphisms; DIGE, differential in-gel electrophoresis; MS, mass spectrometry.

Future Directions

Systems Biology Approach to Perioperative Medicine: The “Perioptome”

Systems biology is a conceptual framework within which scientists attempt to correlate massive amounts of apparently unrelated data into a single unifying explanation of how biological processes occur.177 This evolving discipline that merges experimental and computational approaches to observe, record, and integrate information from the molecular, cellular, tissue, and whole organism levels into testable models of a dynamic biological process can be applied to understand the way patients respond to a multidimensional stimulus such as a surgical procedure and the mechanistic basis of perioperative morbidity (Fig. 6-5). Such an approach involves multiple levels


of data integration. First, delineating the composition of the perioperative phenome (the representation of all perioperative phenotypes expressed by a given patient) requires standardized definitions, controlled vocabularies, and data dictionaries (a perioperative phenotype ontology), new (molecular) imaging technologies, and the availability of comprehensive data warehousing capabilities that will allow cataloguing individual perioperative phenotypes as well as correlations between combinations of phenotypes (organ cross-talk, multiple organ failure). Second, orthogonal integration of whole-genome genotypic, transcriptomic, proteomic, and metabolomic data, augmented by more recent functional genomic and proteomic approaches including protein-protein, protein-DNA, or other “component-component” interaction mapping (interactome), transcript or protein three-dimensional localization mapping (localizome),178 and literature data within individual biological systems involved in perioperative morbidity. This highest level of data integration is the mapping of the integrated high-throughput static and dynamic genomic data into regulatory networks in order to model interactions of the different components of the system, identify modules of highly interconnected genes, and hub points that can be prioritized as therapeutic targets. Ultimately, mathematical models require experimental validation in animal models of disease or tissue culture, in an iterative process that is one of the core characteristics of systems biology.179 Such integrative approaches to study cardiovascular function (the Cardiome Project), but also perioperative morbidity (the perioptome)180 have already been outlined and promise to increase the identification of key drivers of perioperative adverse events beyond what could be achieved by genetic associations alone.

Targeted Therapeutic Applications: The “Five Ps” of Perioperative Medicine and Pain Management

Genomic and proteomic approaches are rapidly becoming platforms for all aspects of drug discovery and development, from target identification and validation to individualization of drug therapy. As previously mentioned, the human genome contains about 25,000 genes encoding for approximately 200,000 proteins that represent potential drug targets. However, only about 120 drug targets are currently being marketed, thus making identification of novel therapeutic targets an area of intense research. Following gene identification, its therapeutic potential needs to be validated by defining the sequence function, its role in disease, and demonstrating that the gene product can be manipulated with beneficial effect and no toxic effects. A developing field, toxicogenomics, studies the influence of toxic or potentially toxic substances on different model organisms by evaluating the gene expression changes induced by novel drugs in a given tissue. Sponsored by the National Institutes of Health, a nationwide collaborative effort called the Pharmacogenetics Research Networkc is aiming to establish a strong pharmacogenomics knowledge based, as well as create a shared computational and experimental infrastructure, required to connect human sequence variation with drug responses and translate information into novel therapeutics.

The epidemiologic framework for assessing the applicability of previously identified biomarkers of perioperative morbidity and the successful implementation of molecular diagnostics in perioperative medicine is contingent on demonstrating their clinical validityanalytical validity, and clinical utility.181 Perioperative genomic investigators are currently conducting replication studies in different surgical patient populations to formally assess the clinical validity of the markers reported so far. For genomic classifiers, the emphasis during external validation is placed on prospectively testing the accuracy of the entire molecular fingerprint in a new patient population rather than corroborating results in individual genes. In perioperative and critical care settings it is vital to have fast turnaround time (several hours) and easy-to-use testing capabilities, so that meaningful therapeutic interventions can take place. In this regard, new molecular diagnostic systems based on the random access technology such as the GeneXpert (Cepheid, Sunnyvale, CA), eSensor (Osmetech, Pasadena, CA), and Liat Analyzer (Iquum, Marlborough, MA) are already becoming available. Clinical utility (targeted interventions to reduce perioperative morbidity among patients with a certain genomic profile) remains to be evaluated in future genomically stratified perioperative trials. Indeed, a landmark study on the effects of a 5-lipoxygenase-activating protein (FLAP) inhibitor on biomarkers associated with the risk of myocardial infarction demonstrates that by defining at-risk patients for two genes in the leukotriene pathway, one can predict who will respond to targeted drug therapy. Specifically, in patients carrying the at-risk variants in the FLAP and in the leukotriene A4 hydrolase genes, use of a FLAP inhibitor in a randomized controlled trial resulted in significant and dose-dependent suppression of biomarkers associated with increased risk of myocardial infarction.182 It is expected that similar principles of targeted therapeutics could be operational in the perioperative period, thus beginning to fulfill the five Ps of modern medicine (Personalized, Preventive, Predictive, Participatory, and Prospective).

Ethical Considerations

Although one of the aims of the Human Genome Project is to improve therapy through genome-based prediction, the birth of personal genomics opens up a Pandora's box of ethical issues, including privacy and the risk for discrimination against individuals who are genetically predisposed for a medical disorder. Such discrimination may include barriers to obtaining health, life, or long-term care insurance, or obtaining employment. Thus, extensive efforts are made to protect patients participating in genetic research from prejudice, discrimination, or uses of genetic information that will adversely affect them. To address the concerns of both biomedical research and health communities, the U.S. Senate has approved in 2003 the Genetic Information and Nondiscrimination Act, which provides the strong safeguards required to protect the public participating in human genome research.

Another ethical concern is the transferability of genetic tests across ethnic groups, particularly in the prediction of adverse drug responses. It is known that most polymorphisms associated with variability in drug response show significant differences in allele frequencies among populations and racial groups. Furthermore, the patterns of linkage disequilibrium are markedly different between ethnic groups, which may lead to spurious findings when markers, instead of causal variants, are used in diagnostic tests extrapolated across populations. In exploring racial disparities in health and disease outcomes, considerable debate has focused on whether race and ethnic identity are primarily social or biological constructs, and the contribution of genetic variability in explaining observed differences in the rates of disease between racial groups. With the goal of personalized medicine being the prediction of risk and treatment of disease on the basis of an individual's genetic profile, some have argued that biological consideration of race will become obsolete. However, in this discovery phase of the


postgenome era, continuing to incorporate racial information in genetic studies should improve our understanding of the architecture of the human genome and its implications for novel strategies aiming at identifying variants protecting against, or conferring susceptibility to, common diseases and modulating drug effects.183


The Human Genome Project has revolutionized all aspects of medicine, allowing us to assess the impact of genetic variability on disease taxonomy, characterization, and outcome, and individual responses to various drugs and injuries. Mechanistically, information gleaned through genomic approaches is already unraveling long-standing mysteries behind general anesthetic action and adverse responses to drugs used perioperatively. However, a strong need remains for prospective, well-powered genetic studies in highly phenotyped surgical populations, which require the development of multidimensional perioperative databases. For the anesthesiologist, this may soon translate into prospective risk assessment incorporating genetic profiling of markers important in thrombotic, inflammatory, vascular, and neurologic responses to perioperative stress, with implications ranging from individualized additional preoperative testing and physiological optimization, to choice of perioperative monitoring strategies and critical care resource utilization. Furthermore, genetic profiling of drug-metabolizing enzymes, carrier proteins, and receptors, using currently available high-throughput molecular technologies, will enable personalized choice of drugs and dosage regimens tailored to suit a patient's pharmacogenetic profile. At that point, perioperative physicians will have far more robust information to use in designing the most appropriate and safest anesthetic plan for given patient.

Future trends and challenges in perioperative genomics are still being defined, but mainly concern interdisciplinary studies designed to combine an analytical system approach, mathematical modeling, and engineering principles with the multiple molecular and genetic factors and stimuli, and the macroscale interactions that determine the pathophysiological response to surgery.


Supported in part by National Institutes of Health grants HL075273 and HL092071 to MVP.


1. Collins FS, Green ED, Guttmacher AE, et al: A vision for the future of genomics research. Nature 2003; 422: 835

2. Schwinn DA, and Booth JV: Genetics infuses new life into human physiology: implications of the human genome project for anesthesiology and perioperative medicine. Anesthesiology 2002; 96: 261

3. Lander ES: The new genomics: Global views of biology. Science 1996; 274: 536

4. Podgoreanu MV, Schwinn DA: New paradigms in cardiovascular medicine: Emerging technologies and practices: perioperative genomics. J Am Coll Cardiol 2005; 46: 1965

5. Fox AA, Shernan SK, Body SC: Predictive genomics of adverse events after cardiac surgery. Semin Cardiothorac Vasc Anesth 2004; 8: 297

6. Stuber F, Hoeft A. The influence of genomics on outcome after cardiovascular surgery. Curr Opin Anaesthesiol 2002; 15: 3

7. Ziegeler S, Tsusaki BE, Collard CD. Influence of genotype on perioperative risk and outcome. Anesthesiology 2003; 99: 212

8. Redon R, Ishikawa S, Fitch KR, et al: Global variation in copy number in the human genome. Nature 2006; 444: 444

9. Frazer KA, Ballinger DG, Cox DR, et al: A second generation human haplotype map of over 3.1 million SNPs. Nature 2007; 449: 851

10. Levy S, Sutton G, Ng PC, et al: The diploid genome sequence of an individual human. PLoS Biol 2007; 5: e254.

11. Podgoreanu MV, Schwinn DA: Genomics and the circulation. Br J Anaesth 2004; 93: 140

12. Gretarsdottir S, Sveinbjornsdottir S, Jonsson HH, et al: Localization of a susceptibility gene for common forms of stroke to 5q12. Am J Hum Genet 2002; 70: 593

13. Broeckel U, Hengstenberg C, Mayer B, et al: A comprehensive linkage analysis for myocardial infarction and its related risk factors. Nat Genet 2002; 30: 210

14. Zintzaras E, Kitsios G, Kent D, et al: Genome-wide scans meta-analysis for pulse pressure. Hypertension 2007; 50: 557

15. Risch N, Merikangas K: The future of genetic studies of complex human diseases. Science 1996; 273: 1516

16. Tabor HK, Risch NJ, Myers RM. Opinion: Candidate-gene approaches for studying complex genetic traits: practical considerations. Nat Rev Genet 2002; 3: 391

17. Zhu X, Chang YP, Yan D, et al: Associations between hypertension and genes in the renin-angiotensin system. Hypertension 2003; 41: 1027

18. Jachymova M, Horky K, Bultas J, et al: Association of the Glu298Asp polymorphism in the endothelial nitric oxide synthase gene with essential hypertension resistant to conventional therapy. Biochem Biophys Res Commun 2001; 284: 426

19. Tomaszewski M, Brain NJ, Charchar FJ, et al: Essential hypertension and beta2-adrenergic receptor gene: linkage and association analysis. Hypertension 2002; 40: 286

20. Winkelmann BR, Hager J: Genetic variation in coronary heart disease and myocardial infarction: methodological overview and clinical evidence. Pharmacogenomics 2000; 1: 73

21. Agema WR, Jukema JW, Pimstone SN, et al: Genetic aspects of restenosis after percutaneous coronary interventions: towards more tailored therapy. Eur Heart J 2001; 22: 2058

22. Ozaki K, Ohnishi Y, Iida A, et al: Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet 2002; 32: 650

23. Podgoreanu MV, White WD, Morris RW, et al: Inflammatory gene polymorphisms and risk of postoperative myocardial infarction after cardiac surgery. Circulation 2006; 114: I275

24. Collard CD, Shernan SK, Fox AA, et al: The MBL2 ‘LYQA secretor’ haplotype is an independent predictor of postoperative myocardial infarction in whites undergoing coronary artery bypass graft surgery. Circulation 2007; 116: I106

25. Tardiff BE, Newman MF, Saunders AM, et al: Preliminary report of a genetic basis for cognitive decline after cardiac operations. The Neurologic Outcome Research Group of the Duke Heart Center. Ann Thorac Surg 1997; 64: 715

26. Mathew JP, Rinder CS, Howe JG, et al: Platelet PlA2 polymorphism enhances risk of neurocognitive decline after cardiopulmonary bypass. Multicenter Study of Perioperative Ischemia (McSPI) Research Group. Ann Thorac Surg 2001; 71: 663

27. Mathew JP, Podgoreanu MV, Grocott HP, et al: Genetic variants in P-selectin and C-reactive protein influence susceptibility to cognitive decline after cardiac surgery. J Am Coll Cardiol 2007; 49: 1934

28. Stafford-Smith M, Podgoreanu M, Swaminathan M, et al: Association of genetic polymorphisms with risk of renal injury after coronary bypass graft surgery. Am J Kidney Dis 2005; 45: 519

29. Chew ST, Newman MF, White WD, et al: Preliminary report on the association of apolipoprotein E polymorphisms, with postoperative peak serum creatinine concentrations in cardiac surgical patients. Anesthesiology 2000; 93: 325

30. MacKensen GB, Swaminathan M, Ti LK, et al: Preliminary report on the interaction of apolipoprotein E polymorphism with aortic atherosclerosis and acute nephropathy after CABG. Ann Thorac Surg 2004; 78: 520

31. Ortlepp JR, Janssens U, Bleckmann F, et al: A chymase gene variant is associated with atherosclerosis in venous coronary artery bypass grafts. Coron Artery Dis 2001; 12: 493

32. Ellis SG, Chen MS, Jia G, et al: Relation of polymorphisms in five genes to long-term aortocoronary saphenous vein graft patency. Am J Cardiol 2007; 99: 1087

33. Donahue BS: Factor V Leiden and perioperative risk. Anesth Analg 2004; 98: 1623

34. Lasocki S, Iglarz M, Seince PF, et al: Involvement of renin-angiotensin system in pressure-flow relationship: role of angiotensin-converting enzyme gene polymorphism. Anesthesiology 2002; 96: 271

35. Stuber F, Petersen M, Bokelmann F, et al: A genomic polymorphism within the tumor necrosis factor locus influences plasma tumor necrosis factor-alpha concentrations and outcome of patients with severe sepsis. Crit Care Med 1996; 24: 381

36. Moretti EW, Morris RW, Podgoreanu M, et al: APOE polymorphism is associated with risk of severe sepsis in surgical patients. Crit Care Med 2005; 33: 2521

37. Slavcheva E, Albanis E, Jiao Q, et al: Cytotoxic T-lymphocyte antigen 4 gene polymorphisms and susceptibility to acute allograft rejection. Transplantation 2001; 72: 935

38. Cardon LR, Bell JI. Association study designs for complex diseases. Nat Rev Genet 2001; 2: 91


39. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K: A comprehensive review of genetic association studies. Genet Med 2002; 4: 45

40. Lohmueller KE, Pearce CL, Pike M, et al: Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet 2003; 33: 177

41. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007; 447: 661

42. Samani NJ, Erdmann J, Hall AS, et al: Genomewide association analysis of coronary artery disease. N Engl J Med 2007; 357: 443

43. McPherson R, Pertsemlidis A, Kavaslar N, et al: A common allele on chromosome 9 associated with coronary heart disease. Science 2007; 316:1488

44. Helgadottir A, Thorleifsson G, Manolescu A, et al: A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007; 316: 1491

45. Todd JA, Walker NM, Cooper JD, et al: Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet 2007; 39: 857

46. Saxena R, Voight BF, Lyssenko V, et al: Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007; 316: 1331

47. Gudbjartsson DF, Arnar DO, Helgadottir A, et al: Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature 2007; 448: 353

48. Scuteri A, Sanna S, Chen WM, et al: Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits. PLoS Genet 2007; 3: e115.

49. Stranger BE, Nica AC, Forrest MS, et al: Population genomics of human gene expression. Nat Genet 2007; 39: 1217

50. Hopf HW: Molecular diagnostics of injury and repair responses in critical illness: what is the future of “monitoring” in the intensive care unit? Crit Care Med 2003; 31: S518

51. Feezor RJ, Baker HV, Xiao W, et al: Genomic and proteomic determinants of outcome in patients undergoing thoracoabdominal aortic aneurysm repair. J Immunol 2004; 172: 7103

52. Hughes TR, Marton MJ, Jones AR, et al: Functional discovery via a compendium of expression profiles. Cell 2000; 102: 109

53. Tomic V, Russwurm S, Moller E, et al: Transcriptomic and proteomic patterns of systemic inflammation in on-pump and off-pump coronary artery bypass grafting. Circulation 2005; 112: 2912

54. Sehl PD, Tai JT, Hillan KJ, et al: Application of cDNA microarrays in determining molecular phenotype in cardiac growth, development, and response to injury. Circulation 2000; 101: 1990

55. Depre C, Tomlinson JE, Kudej RK, et al: Gene program for cardiac cell survival induced by transient ischemia in conscious pigs. Proc Natl Acad Sci USA 2001; 98: 9336

56. Ruel M, Bianchi C, Khan TA, et al: Gene expression profile after cardiopulmonary bypass and cardioplegic arrest. J Thorac Cardiovasc Surg 2003; 126: 1521

57. Konstantinov IE, Coles JG, Boscarino C, et al: Gene expression profiles in children undergoing cardiac surgery for right heart obstructive lesions. J Thorac Cardiovasc Surg 2004; 127: 746

58. Sergeev P, da Silva R, Lucchinetti E, et al: Trigger-dependent gene expression profiles in cardiac preconditioning: evidence for distinct genetic programs in ischemic and anesthetic preconditioning. Anesthesiology 2004; 100: 474

59. Lucchinetti E, Aguirre J, Feng J, et al: Molecular evidence of late preconditioning after sevoflurane inhalation in healthy volunteers. Anesth Analg 2007; 105: 629

60. Lucchinetti E, Hofer C, Bestmann L, et al: Gene regulatory control of myocardial energy metabolism predicts postoperative cardiac function in patients undergoing off-pump coronary artery bypass graft surgery: inhalational versus intravenous anesthetics. Anesthesiology 2007; 106: 444

61. Atkins JH, Johansson JS: Technologies to shape the future: proteomics applications in anesthesiology and critical care medicine. Anesth Analg 2006; 102: 1207

62. Futterer CD, Maurer MH, Schmitt A, et al: Alterations in rat brain proteins after desflurane anesthesia. Anesthesiology 2004; 100: 302

63. Kalenka A, Hinkelbein J, Feldmann RE, Jr., et al: The effects of sevoflurane anesthesia on rat brain proteins: a proteomic time-course analysis. Anesth Analg 2007; 104: 1129

64. Sheikh AM, Barrett C, Villamizar N, et al: Proteomics of cerebral injury in a neonatal model of cardiopulmonary bypass with deep hypothermic circulatory arrest. J Thorac Cardiovasc Surg 2006; 132: 820

65. Queloz PA, Thadikkaran L, Crettaz D, et al: Proteomics and transfusion medicine: future perspectives. Proteomics 2006; 6: 5605

66. Mangano DT: Perioperative medicine: NHLBI working group deliberations and recommendations. J Cardiothorac Vasc Anesth 2004; 18: 1

67. Howell SJ, Sear JW: Perioperative myocardial injury: individual and population implications. Br J Anaesth 2004; 93: 3

68. Mangano DT: Effects of acadesine on myocardial infarction, stroke, and death following surgery. A meta-analysis of the 5 international randomized trials. The Multicenter Study of Perioperative Ischemia (McSPI) Research Group. Jama 1997; 277: 325

69. Mahaffey KW, Roe MT, Kilaru R, et al: Creatine kinase-MB elevation after coronary artery bypass grafting surgery in patients with non-ST-segment elevation acute coronary syndromes predict worse outcomes: results from four large clinical trials. Eur Heart J 2007; 28: 425

70. Delanghe J, Cambier B, Langlois M, et al: Haptoglobin polymorphism, a genetic risk factor in coronary artery bypass surgery. Atherosclerosis 1997; 132: 215

71. Volzke H, Engel J, Kleine V, et al: Angiotensin I-converting enzyme insertion/deletion polymorphism and cardiac mortality and morbidity after coronary artery bypass graft surgery. Chest 2002; 122: 31

72. Shaw AD, Vaporciyan AA, Wu X, et al: Inflammatory gene polymorphisms influence risk of postoperative morbidity after lung resection. Ann Thorac Surg 2005; 79: 1704

73. Brull DJ, Montgomery HE, Sanders J, et al: Interleukin-6 gene -174g>c and -572g>c promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler Thromb Vasc Biol 2001; 21: 1458

74. Burzotta F, Iacoviello L, Di Castelnuovo A, et al: Relation of the -174 G/C polymorphism of interleukin-6 to interleukin-6 plasma levels and to length of hospitalization after surgical coronary revascularization. Am J Cardiol 2001; 88: 1125

75. Grocott HP, Newman MF, El-Moalem H, et al: Apolipoprotein E genotype differentially influences the proinflammatory and anti-inflammatory response to cardiopulmonary bypass. J Thorac Cardiovasc Surg 2001; 122: 622

76. Roth-Isigkeit A, Hasselbach L, Ocklitz E, et al: Inter-individual differences in cytokine release in patients undergoing cardiac surgery with cardiopulmonary bypass. Clin Exp Immunol 2001; 125: 80

77. Lehmann LE, Schroeder S, Hartmann W, et al: A single nucleotide polymorphism of macrophage migration inhibitory factor is related to inflammatory response in coronary bypass surgery using cardiopulmonary bypass. Eur J Cardiothorac Surg 2006; 30: 59

78. Tomasdottir H, Hjartarson H, Ricksten A, et al: Tumor necrosis factor gene polymorphism is associated with enhanced systemic inflammatory response and increased cardiopulmonary morbidity after cardiac surgery. Anesth Analg 2003; 97: 944

79. Galley HF, Lowe PR, Carmichael RL, et al: Genotype and interleukin-10 responses after cardiopulmonary bypass. Br J Anaesth 2003; 91: 424

80. Voetsch B, Loscalzo J: Genetic determinants of arterial thrombosis. Arterioscler Thromb Vasc Biol 2004; 24: 216

81. Rifon J, Paramo JA, Panizo C, et al: The increase of plasminogen activator inhibitor activity is associated with graft occlusion in patients undergoing aorto-coronary bypass surgery. Br J Haematol 1997; 99: 262

82. Iacoviello L, Burzotta F, Di Castelnuovo A, et al: The 4G/5G polymorphism of PAI-1 promoter gene and the risk of myocardial infarction: a meta-analysis. Thromb Haemost 1998; 80: 1029

83. Rinder CS, Mathew JP, Rinder HM, et al: Platelet PlA2 polymorphism and platelet activation are associated with increased troponin I release after cardiopulmonary bypass. Anesthesiology 2002; 97: 1118

84. Zotz RB, Klein M, Dauben HP, et al: Prospective analysis after coronary-artery bypass grafting: platelet GP IIIa polymorphism (HPA-1b/PIA2) is a risk factor for bypass occlusion, myocardial infarction, and death. Thromb Haemost 2000; 83: 404

85. Faraday N, Martinez EA, Scharpf RB, et al: Platelet gene polymorphisms and cardiac risk assessment in vascular surgical patients. Anesthesiology 2004; 101: 1291

86. Donahue BS, Gailani D, Higgins MS, et al: Factor V Leiden protects against blood loss and transfusion after cardiac surgery. Circulation 2003; 107: 1003

87. Moor E, Silveira A, van't Hooft F, et al: Coagulation factor V (Arg506—>Gln) mutation and early saphenous vein graft occlusion after coronary artery bypass grafting. Thromb Haemost 1998; 80: 220

88. Zaugg M, Schaub MC: Genetic modulation of adrenergic activity in the heart and vasculature: implications for perioperative medicine. Anesthesiology 2005; 102: 429

89. Zaugg M, Bestmann L, Wacker J, et al: Adrenergic receptor genotype but not perioperative bisoprolol therapy may determine cardiovascular outcome in at-risk patients undergoing surgery with spinal block: the Swiss Beta Blocker in Spinal Anesthesia (BBSA) study: a double-blinded, placebo-controlled, multicenter trial with 1-year follow-up. Anesthesiology 2007; 107: 33

90. Philip I, Plantefeve G, Vuillaumier-Barrot S, et al: G894T polymorphism in the endothelial nitric oxide synthase gene is associated with an enhanced vascular responsiveness to phenylephrine. Circulation 1999; 99: 3096

91. Henrion D, Benessiano J, Philip I, et al: The deletion genotype of the angiotensin I-converting enzyme is associated with an increased vascular reactivity in vivo and in vitro. J Am Coll Cardiol 1999; 34: 830

92. Kim NS, Lee IO, Lee MK, et al: The effects of beta2 adrenoceptor gene polymorphisms on pressor response during laryngoscopy and tracheal intubation. Anaesthesia 2002; 57: 227

93. Ryan R, Thornton J, Duggan E, et al: Gene polymorphism and requirement for vasopressor infusion after cardiac surgery. Ann Thorac Surg 2006; 82: 895

94. Mathew JP, Fontes ML, Tudor IC, et al: A multicenter risk index for atrial fibrillation after cardiac surgery. Jama 2004; 291: 1720

95. Brugada R: Is atrial fibrillation a genetic disease? J Cardiovasc Electrophysiol 2005; 16: 553

96. Gaudino M, Andreotti F, Zamparelli R, et al: The -174G/C interleukin-6 polymorphism influences postoperative interleukin-6 levels and postoperative atrial fibrillation. Is atrial fibrillation an inflammatory complication? Circulation 2003; 108 Suppl 1: II195


97. Gaudino M, Di Castelnuovo A, Zamparelli R, et al: Genetic control of postoperative systemic inflammatory reaction and pulmonary and renal complications after coronary artery surgery. J Thorac Cardiovasc Surg 2003; 126: 1107

98. Motsinger AA, Donahue BS, Brown NJ, et al: Risk factor interactions and genetic effects associated with post-operative atrial fibrillation. Pac Symp Biocomput 2006: 584

99. Hogue CW, Jr., Palin CA, Kailasam R, et al: C-reactive protein levels and atrial fibrillation after cardiac surgery in women. Ann Thorac Surg 2006; 82: 97

100. Pretorius M, Donahue BS, Yu C, et al: Plasminogen activator inhibitor-1 as a predictor of postoperative atrial fibrillation after cardiopulmonary bypass. Circulation 2007; 116: I1

101. Barth AS, Merk S, Arnoldi E, et al: Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: expression of a ventricular-like genomic signature. Circ Res 2005; 96: 1022

102. Ramlawi B, Otu H, Mieno S, et al: Oxidative stress and atrial fibrillation after cardiac surgery: a case-control study. Ann Thorac Surg 2007; 84: 1166–1172; discussion 1172

103. Mehra MR, Feller E, Rosenberg S: The promise of protein-based and gene-based clinical markers in heart transplantation: from bench to bedside. Nat Clin Pract Cardiovasc Med 2006; 3: 136

104. Hall JL, Birks EJ, Grindle S, et al: Molecular signature of recovery following combination left ventricular assist device (LVAD) support and pharmacologic therapy. Eur Heart J 2007; 28: 613

105. Podgoreanu MV, Booth JV, White WD, et al: Beta adrenergic receptor polymorphisms and risk of adverse events following cardiac surgery. Circulation 2003; 108: IV-434.

106. Lobato RL, Mathew JP, Schwinn DA, et al: Genomic predictors of long-term mortality following coronary artery bypass graft surgery. Anesthesiology 2007; 107: A1440 (abstract).

107. Grocott HP, White WD, Morris RW, et al: Genetic polymorphisms and the risk of stroke after cardiac surgery. Stroke 2005; 36: 1854

108. Newman MF, Booth JV, Laskowitz DT, et al: Genetic predictors of perioperative neurological and cognitive injury and recovery. Best Practice and Research Clinical Anesthesiology 2001; 15: 247

109. Alberts MJ, Graffagnino C, McClenny C, et al: ApoE genotype and survival from intracerebral haemorrhage. Lancet 1995; 346: 575.

110. Teasdale GM, Nicoll JA, Murray G, et al: Association of apolipoprotein E polymorphism with outcome after head injury. Lancet 1997; 350: 1069

111. Slooter AJ, Tang MX, van Duijn CM, et al: Apolipoprotein E epsilon4 and the risk of dementia with stroke. A population-based investigation. Jama 1997; 277: 818

112. Sheng H, Laskowitz DT, Bennett E, et al: Apolipoprotein E isoform-specific differences in outcome from focal ischemia in transgenic mice. J Cereb Blood Flow Metab 1998; 18: 361

113. Leung JM, Sands LP, Wang Y, et al: Apolipoprotein E e4 allele increases the risk of early postoperative delirium in older patients undergoing noncardiac surgery. Anesthesiology 2007; 107: 406

114. Ely EW, Girard TD, Shintani AK, et al: Apolipoprotein E4 polymorphism as a genetic predisposition to delirium in critically ill patients. Crit Care Med 2007; 35: 112

115. Gaynor JW, Gerdes M, Zackai EH, et al: Apolipoprotein E genotype and neurodevelopmental sequelae of infant cardiac surgery. J Thorac Cardiovasc Surg 2003; 126: 1736

116. Zeltser I, Jarvik GP, Bernbaum J, et al: Genetic factors are important determinants of neurodevelopmental outcome after repair of tetralogy of Fallot. J Thorac Cardiovasc Surg 2008; 135: 91

117. Ti LK, Mathew JP, Mackensen GB, et al: Effect of apolipoprotein E genotype on cerebral autoregulation during cardiopulmonary bypass. Stroke 2001; 32: 1514

118. Ti LK, Mackensen GB, Grocott HP, et al: Apolipoprotein E4 increases aortic atheroma burden in cardiac surgical patients. J Thorac Cardiovasc Surg 2003; 125: 211

119. Newman MF, Laskowitz DT, White WD, et al: Apolipoprotein E polymorphisms and age at first coronary artery bypass graft. Anesth Analg 2001; 92: 824

120. Weiss EJ, Bray PF, Tayback M, et al: A polymorphism of a platelet glycoprotein receptor as an inherited risk factor for coronary thrombosis. N Engl J Med 1996; 334: 1090

121. Carter AM, Catto AJ, Bamford JM, et al: Platelet GP IIIa PlA and GP Ib variable number tandem repeat polymorphisms and markers of platelet activation in acute stroke. Arterioscler Thromb Vasc Biol 1998; 18: 1124

122. Ramlawi B, Otu H, Rudolph JL, et al: Genomic expression pathways associated with brain injury after cardiopulmonary bypass. J Thorac Cardiovasc Surg 2007; 134: 996

123. Ramlawi B, Rudolph JL, Mieno S, et al: C-Reactive protein and inflammatory response associated to neurocognitive decline following cardiac surgery. Surgery 2006; 140: 221

124. Ramlawi B, Rudolph JL, Mieno S, et al: Serologic markers of brain injury and cognitive function after cardiopulmonary bypass. Ann Surg 2006; 244: 593

125. Mangano CM, Diamondstone LS, Ramsay JG, et al: Renal dysfunction after myocardial revascularization: risk factors, adverse outcomes, and hospital resource utilization. The Multicenter Study of Perioperative Ischemia Research Group. Ann Intern Med 1998; 128: 194

126. Yende S, Wunderink R: Causes of prolonged mechanical ventilation after coronary artery bypass surgery. Chest 2002; 122: 245

127. Rigat B, Hubert C, Alhenc-Gelas F, et al: An insertion/deletion polymorphism in the angiotensin I-converting enzyme gene accounting for half the variance of serum enzyme levels. J Clin Invest 1990; 86: 1343

128. Yende S, Quasney MW, Tolley EA, et al: Clinical relevance of angiotensin-converting enzyme gene polymorphisms to predict risk of mechanical ventilation after coronary artery bypass graft surgery. Crit Care Med 2004; 32: 922

129. Marshall RP, Webb S, Bellingan GJ, et al: Angiotensin converting enzyme insertion/deletion polymorphism is associated with susceptibility and outcome in acute respiratory distress syndrome. Am J Respir Crit Care Med 2002; 166: 646

130. Yende S, Quasney MW, Tolley E, et al: Association of tumor necrosis factor gene polymorphisms and prolonged mechanical ventilation after coronary artery bypass surgery. Crit Care Med 2003; 31: 133

131. Roden DM: Cardiovascular pharmacogenomics. Circulation 2003; 108: 3071

132. Lehmann H, Ryan E: The familial incidence of low pseudocholinesterase level. Lancet 1956; 271: 124.

133. Bukaveckas BL, Valdes R, Jr., Linder MW: Pharmacogenetics as related to the practice of cardiothoracic and vascular anesthesia. J Cardiothorac Vasc Anesth 2004; 18: 353

134. McCarthy TV, Healy JM, Heffron JJ, et al: Localization of the malignant hyperthermia susceptibility locus to human chromosome 19q12-13.2. Nature 1990; 343: 562

135. Pessah IN, Allen PD: Malignant hyperthermia. Best Practice and Research Clinical Anesthesiology 2001; 15: 277

136. Eger EI, 2nd: Anesthetic uptake and action. Baltimore,: Williams and Wilkins, 1974.

137. Liem EB, Lin CM, Suleman MI, et al: Anesthetic requirement is increased in redheads. Anesthesiology 2004; 101: 279

138. Ezri T, Sessler D, Weisenberg M, et al: Association of ethnicity with the minimum alveolar concentration of sevoflurane. Anesthesiology 2007; 107: 9

139. Franks NP, Lieb WR: Molecular and cellular mechanisms of general anaesthesia. Nature 1994; 367: 607

140. Sonner JM, Antognini JF, Dutton RC, et al: Inhaled anesthetics and immobility: mechanisms, mysteries, and minimum alveolar anesthetic concentration. Anesth Analg 2003; 97: 718

141. Wong SM, Cheng G, Homanics GE, Kendig JJ: Enflurane actions on spinal cords from mice that lack the beta3 subunit of the GABA(A) receptor. Anesthesiology 2001; 95: 154

142. Homanics GE, Ferguson C, Quinlan JJ, et al: Gene knockout of the alpha6 subunit of the gammaaminobutyric acid type A receptor: lack of effect on responses to ethanol, pentobarbital, and general anesthetics. Mol Pharmacol 1997; 51: 588

143. Rudolph U, Crestani F, Benke D, et al: Benzodiazepine actions mediated by specific gamma-aminobutyric acid(A) receptor subtypes. Nature 1999; 401: 796

144. Belelli D, Lambert JJ, Peters JA, et al: The interaction of the general anesthetic etomidate with the gamma-aminobutyric acid type A receptor is influenced by a single amino acid. Proc Natl Acad Sci USA 1997; 94: 11031

145. Jurd R, Arras M, Lambert S, et al: General anesthetic actions in vivo strongly attenuated by a point mutation in the GABA(A) receptor beta3 subunit. Faseb J 2003; 17: 250

146. Reynolds DS, Rosahl TW, Cirone J, et al: Sedation and anesthesia mediated by distinct GABA(A) receptor isoforms. J Neurosci 2003; 23: 8608

147. Cirone J, Rosahl TW, Reynolds DS, et al: Gamma-aminobutyric acid type A receptor beta 2 subunit mediates the hypothermic effect of etomidate in mice. Anesthesiology 2004; 100: 1438

148. Lakhlani PP, MacMillan LB, Guo TZ, et al: Substitution of a mutant alpha2a-adrenergic receptor via “hit and run” gene targeting reveals the role of this subtype in sedative, analgesic, and anesthetic-sparing responses in vivo. Proc Natl Acad Sci USA 1997; 94: 9950

149. Gerstin KM, Gong DH, Abdallah M, et al: Mutation of KCNK5 or Kir3.2 potassium channels in mice does not change minimum alveolar anesthetic concentration. Anesth Analg 2003; 96: 1345

150. Sternberg WF, Mogil JF: Genetic and hormonal basis of pain states. Best Practice and Research Clinical Anesthesiology 2001; 15: 229

151. Diatchenko L, Anderson AD, Slade GD, et al: Three major haplotypes of the beta2 adrenergic receptor define psychological profile, blood pressure, and the risk for development of a common musculoskeletal pain disorder. Am J Med Genet B Neuropsychiatr Genet 2006; 141: 449

152. Diatchenko L, Nackley AG, Tchivileva IE, et al: Genetic architecture of human pain perception. Trends Genet 2007; 23: 605

153. Bengtsson B, Thorson J: Back pain: a study of twins. Acta Genet Med Gemellol (Roma) 1991; 40: 83

154. Mogil JS, Wilson SG, Bon K, et al: Heritability of nociception I: responses of 11 inbred mouse strains on 12 measures of nociception. Pain 1999; 80: 67

155. Lacroix-Fralish ML, Ledoux JB, Mogil JS: The Pain Genes Database: An interactive web browser of pain-related transgenic knockout studies. Pain 2007; 131: 3 e1


156. Somogyi AA, Barratt DT, Coller JK: Pharmacogenetics of opioids. Clin Pharmacol Ther 2007; 81: 429

157. Diatchenko L, Nackley AG, Slade GD, et al: Catechol-O-methyltransferase gene polymorphisms are associated with multiple pain-evoking stimuli. Pain 2006; 125: 216

158. Candiotti KA, Birnbach DJ, Lubarsky DA, et al: The impact of pharmacogenomics on postoperative nausea and vomiting: do CYP2D6 allele copy number and polymorphisms affect the success or failure of ondansetron prophylaxis? Anesthesiology 2005; 102: 543

159. Lin LH, Hopf HW: Paradigm of the injury-repair continuum during critical illness. Crit Care Med 2003; 31: S493

160. Angus DC, Burgner D, Wunderink R, et al: The PIRO concept: P is for predisposition. Crit Care 2003; 7: 248

161. Lin MT, Albertson TE: Genomic polymorphisms in sepsis. Crit Care Med 2004; 32: 569

162. De Maio A, Torres MB, Reeves RH: Genetic determinants influencing the response to injury, inflammation, and sepsis. Shock 2005; 23: 11

163. Zeni F, Freeman B, Natanson C: Anti-inflammatory therapies to treat sepsis and septic shock: a reassessment. Crit Care Med 1997; 25: 1095

164. Cobb JP, O'Keefe GE: Injury research in the genomic era. Lancet 2004; 363: 2076

165. Prucha M, Ruryk A, Boriss H, et al: Expression profiling: toward an application in sepsis diagnostics. Shock 2004; 22: 29

166. Cobb JP, Laramie JM, Stormo GD, et al: Sepsis gene expression profiling: murine splenic compared with hepatic responses determined by using complementary DNA microarrays. Crit Care Med 2002; 30: 2711

167. Leikauf GD, McDowell SA, Wesselkamper SC, et al: Acute lung injury: functional genomics and genetic susceptibility. Chest 2002; 121: 70S

168. Dasu MR, Cobb JP, Laramie JM, et al: Gene expression profiles of livers from thermally injured rats. Gene 2004; 327: 51

169. Tang BM, McLean AS, Dawes IW, et al: The use of gene-expression profiling to identify candidate genes in human sepsis. Am J Respir Crit Care Med 2007; 176: 676

170. Calvano SE, Xiao W, Richards DR, et al: A network-based analysis of systemic inflammation in humans. Nature 2005; 437: 1032

171. Laudanski K, Miller-Graziano C, Xiao W, et al: Cell-specific expression and pathway analyses reveal alterations in trauma-related human T cell and monocyte pathways. Proc Natl Acad Sci U S A 2006; 103: 15564

172. Cobb JP, Mindrinos MN, Miller-Graziano C, et al: Application of genome-wide expression analysis to human health and disease. Proc Natl Acad Sci USA 2005; 102: 4801

173. Fessler MB, Malcolm KC, Duncan MW, et al: A genomic and proteomic analysis of activation of the human neutrophil by lipopolysaccharide and its mediation by p38 mitogen-activated protein kinase. J Biol Chem 2002; 277: 31291

174. Liu T, Qian WJ, Gritsenko MA, et al: High dynamic range characterization of the trauma patient plasma proteome. Mol Cell Proteomics 2006; 5: 1899

175. Kalenka A, Feldmann RE, Jr., Otero K, et al: Changes in the serum proteome of patients with sepsis and septic shock. Anesth Analg 2006; 103: 1522

176. Buchman TG, Cobb JP, Lapedes AS, et al: Complex systems analysis: a tool for shock research. Shock 2001; 16: 248

177. Strange K: The end of “naive reductionism”: rise of systems biology or renaissance of physiology? Am J Physiol Cell Physiol 2005; 288: C968

178. Ge H, Walhout AJ, Vidal M: Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends Genet 2003; 19: 551

179. Lusis AJ: A thematic review series: systems biology approaches to metabolic and cardiovascular disorders. J Lipid Res 2006; 47: 1887

180. Shaw A: Exploring the perioptome: the role of genomics in thoracic surgery and anaesthesia. Curr Opin Anaesthesiol 2007; 20: 32

181. Khoury MJ, Yang Q, Gwinn M, et al: An epidemiologic assessment of genomic profiling for measuring susceptibility to common diseases and targeting interventions. Genet Med 2004; 6: 38

182. Hakonarson H, Thorvaldsson S, Helgadottir A, et al: Effects of a 5-lipoxygenase-activating protein inhibitor on biomarkers associated with risk of myocardial infarction: a randomized trial. JAMA 2005; 293: 2245

183. Phimister EG: Medicine and the racial divide. N Engl J Med 2003; 348: 1081

184. Lucchinetti E, da Silva R, Pasch T, et al: Anaesthetic preconditioning but not postconditioning prevents early activation of the deleterious cardiac remodeling programme: evidence of opposing genomic responses in cardioprotection by pre- and postconditioning. Br J Anaesth 2005; 95: 140

185. Lai LP, Lin JL, Lin CS, et al: Functional genomic study on atrial fibrillation using cDNA microarray and two-dimensional protein electrophoresis techniques and identification of the myosin regulatory light chain isoform reprogramming in atrial fibrillation. J Cardiovasc Electrophysiol 2004; 15: 214

186. Horwitz PA, Tsai EJ, Putt ME, et al: Detection of cardiac allograft rejection and response to immunosuppressive therapy with peripheral blood gene expression. Circulation 2004; 110: 3815

187. Mehra MR, Kobashigawa JA, Hunt SA, et al: Molecular testing and prediction of clinical outcome in heart transplantation: a prospective multicenter trial. J Heart Lung Transplant 2004; 23: S106 (abstr).

188. Borozdenkova S, Westbrook JA, Patel V, et al: Use of proteomics to discover novel markers of cardiac allograft rejection. J Proteome Res 2004; 3: 282

189. Heusch G, Erbel R, Siffert W: Genetic determinants of coronary vasomotor tone in humans. Am J Physiol Heart Circ Physiol 2001; 281: H1465

190. Donahue BS, Roden D: Inflammatory cytokine polymorphisms are associated with beta-blocker failure in preventing postoperative atrial fibrillation. Anesth Analg 2005; 100: SCA30 (abstract).

191. Podgoreanu MV, Morris R, Zhang Q, et al: Gene variants in Interleukin 1-beta are associated with early QTc prolongation after cardiac surgery. Anesthesiology 2007; 107: A1287 (abstract).

192. Botto N, Andreassi MG, Rizza A, et al: C677T polymorphism of the methylenetetrahydrofolate reductase gene is a risk factor of adverse events after coronary revascularization. Int J Cardiol 2004; 96: 341

193. Taylor KD, Scheuner MT, Yang H, et al: Lipoprotein lipase locus and progression of atherosclerosis in coronary-artery bypass grafts. Genet Med 2004; 6: 481

194. Holweg CT, Weimar W, Uitterlinden AG, et al: Clinical impact of cytokine gene polymorphisms in heart and lung transplantation. J Heart Lung Transplant 2004; 23: 1017

195. Borozdenkova S, Smith J, Marshall S, et al: Identification of ICAM-1 polymorphism that is associated with protection from transplant associated vasculopathy after cardiac transplantation. Hum Immunol 2001; 62:247

196. Vamvakopoulos JE, Taylor CJ, Green C, et al: Interleukin 1 and chronic rejection: possible genetic links in human heart allografts. Am J Transplant 2002; 2: 76

197. Duggan E, O'Dwyer MJ, Caraher E, et al: Coagulopathy after cardiac surgery may be influenced by a functional plasminogen activator inhibitor polymorphism. Anesth Analg 2007; 104: 1343

198. Welsby IJ, Podgoreanu MV, Phillips-Bute B, et al: Genetic factors contribute to bleeding after cardiac surgery. J Thromb Haemost 2005; 3:1206

199. Morawski W, Sanak M, Cisowski M, et al: Prediction of the excessive perioperative bleeding in patients undergoing coronary artery bypass grafting: role of aspirin and platelet glycoprotein IIIa polymorphism. J Thorac Cardiovasc Surg 2005; 130: 791

200. Achrol AS, Kim H, Pawlikowska L, et al: Association of tumor necrosis factor-alpha-238G>A and apolipoprotein E2 polymorphisms with intracranial hemorrhage after brain arteriovenous malformation treatment. Neurosurgery 2007; 61: 731

Editors: Barash, Paul G.; Cullen, Bruce F.; Stoelting, Robert K.; Cahalan, Michael K.; Stock, M. Christine