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Systemic Inflammatory Determinants of Lower Extremity Revascularization Failure

Permanent Link: http://ufdc.ufl.edu/UFE0022785/00001

Material Information

Title: Systemic Inflammatory Determinants of Lower Extremity Revascularization Failure
Physical Description: 1 online resource (60 p.)
Language: english
Creator: Nelson, Peter
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Clinical Investigation (IDP) -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Peripheral arterial disease (PAD) affects 10 million Americans. Many can be managed initially with risk factor modification, but for more advanced disease, lower extremity vein bypass grafting and percutaneous angioplasty/stenting are the mainstays of treatment. Technical success for these procedures is high, but their durability remains a vexing problem. Based on the developing association between inflammation and PAD, we hypothesized that the difference between success and early failure of lower extremity revascularization is dependent upon, and perhaps predicted by, the status of baseline immunity and/or the magnitude of the early post-surgical systemic inflammatory response. Our research team comprises a multidisciplinary collaboration of clinicians, scientists, and statisticians. To study the relationship between systemic inflammation and clinical outcome, we developed a human translational functional genomic/proteomic initiative to study 300 symptomatic patients with PAD undergoing lower extremity revascularization with either angioplasty/stenting or vein graft bypass. This report focuses on the high throughput proteomic analysis of circulating cytokines as the initial step to characterize the perioperative systemic inflammatory profile. We assayed plasma cytokine concentrations taken at 7 time points: preoperatively, at two hours and one day post-procedure, and then at 1 week, and 1, 6, and 12months of follow-up - using the Luminex-100 22-plex bead immunoassay system. We then analyzed normalized proteomic data over time, by type of procedure, and according to success or failure of the intervention. Significant time-dependent differences in cytokine profiles were seen for the entire study group following either treatment. The specific patterns of cytokine expression differed significantly between the two treatment options: angioplasty versus bypass. Finally, cytokine expression patterns were also significantly different between subjects with a successful intervention versus those that went on to failure. Representative examples of cytokine expression (IL-6, IL-8, IFN gamma, TNF alpha, IP-10) are presented. These findings indicate that alterations in both baseline immunity and the systemic inflammatory response to surgery, as evidenced by significant changes in circulating inflammatory cytokine concentrations, occur in PAD patients following lower extremity revascularization. Furthermore, we were able to identify cytokine expression profiles that were distinct between the two types of procedures with an early peak of lesser intensity for angioplasty/stenting, and a higher more persistent elevation for vein bypass. In addition, we were also able to discern distinct patterns of cytokine expression that correlated with the clinical outcome of revascularization. Taken together, these data serve as proof of principle for the development of class prediction models capable of forecasting success or failure of intervention based on the early components of the inflammatory response. Ultimately, the combination of these proteomic data, together with genomic studies and clinical, functional, and quality of life outcome measures, will lead to new knowledge about the mechanisms of failure of vascular interventions and new strategies to improve approaches to lower extremity revascularization.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Peter Nelson.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Limacher, Marian C.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022785:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022785/00001

Material Information

Title: Systemic Inflammatory Determinants of Lower Extremity Revascularization Failure
Physical Description: 1 online resource (60 p.)
Language: english
Creator: Nelson, Peter
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Clinical Investigation (IDP) -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Peripheral arterial disease (PAD) affects 10 million Americans. Many can be managed initially with risk factor modification, but for more advanced disease, lower extremity vein bypass grafting and percutaneous angioplasty/stenting are the mainstays of treatment. Technical success for these procedures is high, but their durability remains a vexing problem. Based on the developing association between inflammation and PAD, we hypothesized that the difference between success and early failure of lower extremity revascularization is dependent upon, and perhaps predicted by, the status of baseline immunity and/or the magnitude of the early post-surgical systemic inflammatory response. Our research team comprises a multidisciplinary collaboration of clinicians, scientists, and statisticians. To study the relationship between systemic inflammation and clinical outcome, we developed a human translational functional genomic/proteomic initiative to study 300 symptomatic patients with PAD undergoing lower extremity revascularization with either angioplasty/stenting or vein graft bypass. This report focuses on the high throughput proteomic analysis of circulating cytokines as the initial step to characterize the perioperative systemic inflammatory profile. We assayed plasma cytokine concentrations taken at 7 time points: preoperatively, at two hours and one day post-procedure, and then at 1 week, and 1, 6, and 12months of follow-up - using the Luminex-100 22-plex bead immunoassay system. We then analyzed normalized proteomic data over time, by type of procedure, and according to success or failure of the intervention. Significant time-dependent differences in cytokine profiles were seen for the entire study group following either treatment. The specific patterns of cytokine expression differed significantly between the two treatment options: angioplasty versus bypass. Finally, cytokine expression patterns were also significantly different between subjects with a successful intervention versus those that went on to failure. Representative examples of cytokine expression (IL-6, IL-8, IFN gamma, TNF alpha, IP-10) are presented. These findings indicate that alterations in both baseline immunity and the systemic inflammatory response to surgery, as evidenced by significant changes in circulating inflammatory cytokine concentrations, occur in PAD patients following lower extremity revascularization. Furthermore, we were able to identify cytokine expression profiles that were distinct between the two types of procedures with an early peak of lesser intensity for angioplasty/stenting, and a higher more persistent elevation for vein bypass. In addition, we were also able to discern distinct patterns of cytokine expression that correlated with the clinical outcome of revascularization. Taken together, these data serve as proof of principle for the development of class prediction models capable of forecasting success or failure of intervention based on the early components of the inflammatory response. Ultimately, the combination of these proteomic data, together with genomic studies and clinical, functional, and quality of life outcome measures, will lead to new knowledge about the mechanisms of failure of vascular interventions and new strategies to improve approaches to lower extremity revascularization.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Peter Nelson.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Limacher, Marian C.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022785:00001


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1 SYSTEMIC INFLAMMATORY DETERMINANTS OF LOWER EXTREMITY REVASCULARIZATION FAILURE By PETER RICHARD NELSON A THESIS PRESENTED TO THE UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2008

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2 2008 Peter Richard Nelson

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3 To My Family – Janice, Max, and PJ – for all their understanding and suppor t during this process.

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4 ACKNOWLEDGMENTS Professionally, I thank Dr. Lyle L. Moldawer and the other members of my advisory and thesis committees, Drs. Marian Limacher, Nabi h Asal, Henry Baker, Ge orge Casella, James Seeger, and Elizabeth Shenkman, for their intere st and availability, thei r willingness to teach, and their committed mentorship during this project and this early part of my academic career. I thank the National Heart, Lung, and Blood Institute of the Nati onal Institutes of Health, the Department of Surgery at the University of Florid a, and the Veterans Health System for financial support. I thank the willing partic ipants in this transl ational research effort without whom this work would not have been possible. I thank th e members of the research team who have been assiduous in finding eligible subj ects for this study and tirele ss in sample processing and analyses. On a personal note, I thank my parents for th eir many years in support of my pursuit of an academic surgical career. I thank my wife and boys for their constant understanding of the long hours, their encouragement to persever e, and their undying love and affection.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES................................................................................................................ .........9 ABSTRACT....................................................................................................................... ............10 CHAPTER 1 INTRODUCTION................................................................................................................. .12 Clinical Relevance............................................................................................................. .....12 Role of Inflammation in Lower Extremity Arterial Disease..................................................13 Overview....................................................................................................................... ..13 C-Reactive Protein as a Potential Biomarker..................................................................14 Conclusions.................................................................................................................... .15 Functional Genomic/Proteomic Applica tion to Human Vascular Disease.............................15 Overview....................................................................................................................... ..15 Current Evidence.............................................................................................................16 Genomics..................................................................................................................16 Proteomics................................................................................................................17 Clinical Trial Application................................................................................................17 Summary........................................................................................................................ .........18 2 MATERIALS AND METHODS...........................................................................................20 Overall Study Design........................................................................................................... ...20 Overview....................................................................................................................... ..20 Patient Selection.............................................................................................................. 20 Clinical Protocol.............................................................................................................. 20 Functional and Quality of Life Assessments...................................................................23 Molecular Analysis Overview.........................................................................................23 Proteomics Pilot Study......................................................................................................... ..24 Statistical Considerations..................................................................................................... ...25 Clinical Data.................................................................................................................. ..25 Molecular Data................................................................................................................2 5 Genomics..................................................................................................................25 Proteomics................................................................................................................26 3 RESULTS...................................................................................................................... .........29 Clinical Results............................................................................................................... ........29 Proteomic Results.............................................................................................................. .....30

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6 4 DISCUSSION................................................................................................................... ......35 Summary and Significance of Results....................................................................................35 Study Limitations.............................................................................................................. ......37 Future Directions.............................................................................................................. ......38 APPENDIX THE SPSS OUTPUT DATA AND STATISTICS........................................................................40 LIST OF REFERENCES............................................................................................................. ..54 BIOGRAPHICAL SKETCH.........................................................................................................60

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7 LIST OF TABLES Table page 2-1 Clinical and biochemical timeline.....................................................................................27 3-1 Study Cohort Demographics.............................................................................................31 3-2 Summary of proteomic results by procedure....................................................................31 3-3 Summary of proteomic results by outcome......................................................................32 A-1 Eotaxin descriptive statistics............................................................................................ .40 A-2 Eotaxin repeated measures ANOVA................................................................................40 A-3 IL-6 descriptive statistics............................................................................................... ...41 A4 IL-6 repeated measures ANOVA......................................................................................41 A-5 IL-8 descriptive statistics............................................................................................... ...42 A-6 IL-8 repeated measures ANOVA......................................................................................42 A-7 TNF alpha descri ptive sta tistics.........................................................................................43 A-8 TNF alpha repeated measures ANOVA............................................................................43 A9 IFN gamma descriptive statistics......................................................................................44 A-10 IFN gamma repeated measures ANOVA.........................................................................44 A11 IP-10 descriptive statistics............................................................................................. ....45 A-12 IP-10 repeated measures ANOVA....................................................................................45 A-13 IL-10 descriptive statistics............................................................................................. ...46 A-14 IL-10 repeated measures ANOVA....................................................................................46 A-15 IL-12 descriptive statistics............................................................................................. ...47 A-16 IL-12 repeated measures ANOVA....................................................................................47 A-17 IL-1alpha desc riptive st atistics......................................................................................... 48 A-18 IL-1alpha repeated measures ANOVA.............................................................................48 A-19 IL-1beta descriptive statistics.......................................................................................... .49

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8 A-20 IL-1beta repeated measures ANOVA...............................................................................49 A-21 MCP-1 descriptive statistics............................................................................................. 50 A-22 MCP-1 repeated measures ANOVA.................................................................................50 A-23 MIP-1alpha desc riptive statistics......................................................................................51 A-24 MIP-1alpha repeated measures ANOVA..........................................................................51 A-25 RANTES descri ptive sta tistics..........................................................................................52 A-26 RANTES repeated measures ANOVA.............................................................................52 A-27 GMCSF descriptive statistics............................................................................................5 3 A-28 GMCSF repeated measures ANOVA...............................................................................53

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9 LIST OF FIGURES Figure page 1-1 Effect of thrombomodulin (TM) on arterial inflammati on following balloon angioplasty.................................................................................................................... .....19 2-1 Overall study algorithm.................................................................................................... .28 2-2 Timing of peripheral blood sampling................................................................................28 3-1 Kaplan-Meier life table analysis of pr imary patency rates of revascularization...............32 3-2 Kaplan-Meier life table analysis of limb salvage..............................................................32 3-3 IL-6 plasma levels......................................................................................................... .....33 3-4 IL-8 plasma levels......................................................................................................... .....33 3-5 TNF plasma levels...........................................................................................................33 3-6 IFN plasma levels.............................................................................................................34 3-7 IP-10 plasma levels........................................................................................................ ....34

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10 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science SYSTEMIC INFLAMMATORY DETERMINANTS OF LOWER EXTREMITY REVASCULARIZATION FAILURE By Peter Richard Nelson December 2008 Chair: Marian C. Limacher Major: Medical Sciences—Clinical Investigation Peripheral arterial disease (PAD) affects 10 million Americans. Many can be managed initially with risk factor modi fication, but for more advanced disease, lower extremity vein bypass grafting and percutaneous an gioplasty/stenting are the mainst ays of treatment. Technical success for these procedures is high, but their durability remains a vexing problem. Based on the developing association between inflammation a nd PAD, we hypothesized that the difference between success and early failure of lower extr emity revascularization is dependent upon, and perhaps predicted by, the status of baseline i mmunity and/or the magnitude of the early postsurgical systemic inflammatory response. Our research team comprises a multidisciplinary collaboration of clinicians, scientists, and statisticians. To study the relationship between systemic inflammation and clinical outcome, we developed a human translational functiona l genomic/proteomic initiative to study 300 symptomatic patients with PAD undergoing lowe r extremity revascularization with either angioplasty/stenting or vein graft bypass. Th is report focuses on the high throughput proteomic analysis of circulating cytokine s as the initial step to charact erize the perioperative systemic inflammatory profile. We assayed plasma cytoki ne concentrations taken at 7 time points: preoperatively, at two hours and one day post-procedure, and then at 1 week, and 1, 6, and 12

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11 months of follow-up using the Luminex-100 22-plex bead immunoassay system. We then analyzed normalized proteomic data over time, by type of procedure, and according to success or failure of the intervention. Signi ficant time-dependent differences in cytokine profiles were seen for the entire study group following either treat ment. The specific patterns of cytokine expression differed significantly between the two tr eatment options – angiopl asty versus bypass. Finally, cytokine expression patter ns were also signifi cantly different between subjects with a successful intervention versus those that went on to failure. Representative examples of cytokine expression (IL-6, IL-8, IFN TNF IP-10) are presented. These findings indicate that alterations in both baseline immunity and the systemic inflammatory response to surgery, as eviden ced by significant cha nges in circulating inflammatory cytokine concentrations, occu r in PAD patients following lower extremity revascularization. Furthermore, we were able to identify cytokine expression profiles that were distinct between the two types of procedures with an early peak of lesser intensity for angioplasty/stenting, and a higher more persistent elevation for vein bypass. In addition, we were also able to discern distinct patterns of cytokine expression that correlated with the clinical outcome of revascularization. Taken together, th ese data serve as proof of principle for the development of class prediction models capable of forecasting success or failure of intervention based on the early components of the inflammatory response. Ultimately, the combination of these proteomi c data, together with genomic studies and clinical, functional, and quality of life outcome measures, will lead to new knowledge about the mechanisms of failure of vascular interventions and new strategies to improve approaches to lower extremity revascularization.

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12 CHAPTER 1 INTRODUCTION Clinical Relevance Cardiovascular disease affects over 40 million Americans and remains the leading cause of death. Peripheral arterial di sease (PAD) affects over 10 million people in the United States and, among this group, over 1 million arterial recons tructions are performed annually. As the population ages, continues to smoke, and suffers from an epidemic of metabolic syndrome and obesity, that number is increasing exponentially.1 Although researchers have made significant progress in the understanding and treatment of cor onary artery atherosclerosis, the same cannot be said for lower extremity PAD. As a result, outcomes following lower extremity revascularization for PAD continue to be disa ppointing. Conventional wisdom suggests 5-year patency rates (length of time the revasculariza tion remains open without reintervention) of 60 to 80% for vein bypass grafting,2-7 but more curent information s uggests a 1-year primary patency rate of only 61%.8 Outcomes are less well defined for angioplasty/stenting, but primary patency rates of 70 to 90% at 3 months that drop to 20 to 50% at 1 to 3 years have been described.9-11 Furthermore, these results are continually being scrutinized in the context of ~80% symptomatic improvement in patients with intermittent claudi cation treated with conservative measures (i.e., smoking cessation, risk factor modifi cation, and structured exercise).12-14 Additionally, studies report poor functional and quality of life outcomes despite suc cessful revascularization following vein bypass surgery.15,16 Unfortunately, many aspects of the disease process of lower extremity PAD and its response to treatment are poorly under stood. Clinicians need a better understanding of the arterial response to angioplasty, the ve in graft response to arterial hemodynamics, and, finally, what metrics constitute the definition of success or failure of such interventions. Consequently, without a defined evidence-based approach to symp tomatic lower extremity PAD,

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13 practitioners frequently make management decisions without clea r guidance of how to individualize the treatment to optimize patient outcomes.17-19 Role of Inflammation in Low er Extremity Arterial Disease Overview Basic research has characterized a role for inflammation in directing local responses to vascular injury at the time of intervention.20 Such studies have esta blished that temporally distinct blood vessel wall inflammatory events predict long term wall architecture,21-24 and have confirmed that physical forces stand as the prim ary regulator of local va scular wall behavior.25,26 For example, animal studies, have defined a spec ific causal links between tumor necrosis factor(TNF) and interleukin-1 (IL-1) and intimal hyperplasia leading to vein graft failure. In addition, treatment with IL-10, an anti-inflammato ry cytokine predicted to have a protective effect, fails to prevent intimal hyperplasia because it is downregulated in th e same vein grafts. A critical link between these local events and sy stemic inflammation come s from the observation that vascular injury, as occurs with angioplasty/ stenting or ve in bypass, results in the acute recruitment and adhesion of monocytes.26 Prior work in our labaoratory provides anot her example of this critical link between systemic inflammation and, in this case, ther apeutic approaches to intimal hyperplasia. Thrombomodulin (TM) is an endogenous endothelial cell surface protein that inhibits the activity of thrombin, the primary mediator of hemostasis a nd clot formation at sites of vascular injury. Our work demonstrated that thrombin was a pot ent stimulus for vascular smooth muscle cell proliferation and migration, two key processes in the development of intimal hyperplasia. We went on to show that TM could inhibit these important processes thr ough direct thrombin inhibition and inhibition of its downstream signaling pathways including G-protein-coupled receptor signaling, mitogen-activated protein kinase pathways, and intracellular calcium

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14 trafficking in vitro.27 Next, therapeutic application of TM in vivo, in a rabbit femoral artery injury model, showed significant inhibition of intimal hyperplasia and restenosis when TM was delivered intravenously.28 However, subsequent attempts at more sophisticated local delivery including intrarterial infusi on, adenoviral vector mediated in vivo transfection, in vivo electroporation, and development of a transduc tion recombinant TM protein all failed to reproduce the inhibition of intimal hyperplasia. [unpublished data] Reflection on these findings led us to conclude that systemic administra tion of TM likely produced its effect, not through local influences in the arterial wall, but th rough inhibition of systemic inflammation perhaps through an activated protein C dependent mech anism. Retrospective review of histology specimens supported this theory (Fig ure 1-1). Thus, the focus of th is current investigation is the role of systemic inflammation in local restenosis and intervention failure. C-Reactive Protein as a Potential Biomarker Similar to our experience, other researchers over that last decade have shifted away from a focus on local mediators at sites of vascular inju ry as the cause of rest enosis and intervention failure. Current theory holds that the blood vessel response to injury may be intimately linked to the host's systemic inflammatory response, and th at intervention failure may be driven by these systemic factors.29-34 Therefore, researchers have focuse d efforts on identifying a predictive, clinically useful, circulating biom arker closely associated with vasc ular disease. In patients with coronary atherosclerosis, some such global a ssociations have been established. C-reactive protein (CRP) is a marker of systemic inflammati on which has been associated with a history of myocardial infarction and predicti on of future coronary events.35-37 In PAD, however, results are conflicting as to whether these same biomarke rs predict progression of PAD or have any relationship to the success of failure of its treat ment. Some studies looking at CRP, often in combination with other potential biomarkers including fibrinogen, D-dimer, homocysteine, or

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15 inflammatory cell adhesion molecule (ICAM)-1, have only been able to show a simple association with the presence of PAD. Most of the patient events described in these studies, however, reflect the relationship of CRP with cor onary atherosclerosis. Furthermore, these and other studies have failed to defi ne a consistent relationship betw een CRP and the pathogenesis of PAD, the severity of PAD, the progression of PAD, or the response to treatment for PAD.38-40 One recent study suggested an association be tween pre-operative CRP levels and lower extremity vein bypass failure, however, the pred ominant association was again seen with postoperative coronary events.41 Finally, none of these studies ha s serially studied CRP levels over time, nor have they incorporated CRP in any way into a system-wide approach to proteomic profiling. Conclusions Despite these important findings, decades of focus on local vascular wall events have failed to yield substantial progress toward more durable peripheral interventions,8,19,30,42 and no predictive systemic biomarkers have emerged. Th ese deficiencies likely reflect that, due to the complex and redundant nature of the innate immune response, ch aracterization of a predictive systemic inflammatory profile may best be accomplished using a genome-wide transcriptome and/or system-wide multiplex proteomic approach. Functional Genomic/Proteomic Applic ation to Human Vascular Disease Overview A paradigm shift has occurred recently, away fr om the focused study of local factors in the vessel wall towards the study of the influence of systemic inflammation on these local events that ultimately lead to revascularization failure. This new approach has been fueled in part by a broad based human initiative ta king advantage of advances in the sequencing of the human genome and the development of high throughput genomic and proteomic analyses. This

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16 approach opens further avenues of discover y. Researchers can st art by analyzing gene expression profiles or molecular sign atures in the interactome that are predicitive of successful or unsuccessful clinical outcomes. They can then identify single nucleo tide polymorphisms (SNPs) with association to intervention failure. They can then use this inform ation as reference for pharmacogenomic surveillance of the efficacy of anti-inflammatory treatments. Finally, multidisciplinary groups can apply these comple x genotype-environmental interactions into systems biology approaches to predict outcome.43 In this way, investigators are empowered to translate changes in basic building blocks (i.e., ge ne sequence), to changes in gene function (i.e., functional genomics, protein expression), and fina lly to changes in organ function or clinical phenotype (physiological genomics).44 Current Evidence Genomics Few studies have applied these methods to patients with symptomatic lower extremity PAD.45,46 What is available is a number of obser vational studies, primarily genomic, that have linked a putative single nucleotide polymorphism (SNP) with some aspect of cardiovascular disease – most commonly hypertension or hear t failure, or the response to a particular pharmacologic intervention. Genes associated w ith cardiovascular dise ase in these studies include myocyte enhancer factor-2 (MEF2A),47 connexin 37 gene in men, PAI-1 and stromelysin genes in women,48 5-lipoxygenase activating protein,49 leukotriene A4 hydrolase,50 lymphotoxingene,51 HMG-CoA reductase and ADAMTS-1 meta lloproteinase in statin therapy,52,53 adrenergic receptors with -blockade response,54 and CYP2C9 and vitamin K epoxide reductase1 in warfarin therapy.55,56 The limitations to these studies an d their findings lie in the fact that they often offer little biological or functional linkage from the specific gene to the disease process studied. Furthermore, they are sing le institution observational studies with no

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17 subsequent confirmatory studi es, validation, or intervention.57,58 These underappreciated limitations emphasize the importance of a systemswide approach to define genomic signatures and pattern recognition of genomi c classifiers, with validation coming from the application of such classifiers to other populati ons (e.g., to related patient cohorts or between similar cohorts in a multicenter study design).43 Proteomics Little information is available with respect to functional proteomics in vascular disease. Most of what is available is from in-vitro, ex-v ivo or animal models studying small numbers of select proteins or protein families.59 In one such example, 19 prot eins were identified that were consistently overexpressed in a ra t model of carotid artery stenos is, but these proteins correlated poorly with the parallel gene expression data analyzed.60 High throughput proteomic technology (including 2D gel electrophoresis, high perfor mance liquid chromatography, and tandem mass spectroscopy) is stil l in evolution and lags behind its genomic gene array technology counterpart,61 but holds promise for future appl ication to human clinical disease.62 Complexity in post-translational modification of many proteins poses additional challenges to the reproducibility of the results from these methodologies.45 For now, we are left with studies, similar to those described above, looking for si ngle, predicitive biomar kers that characterize vascular disease.63 Clinical Trial Application The CardioGene Study is an example of an ongoing investigation using comprehensive high throughput genome-wide molecular approaches to study clinical rest enosis in bare metal stents used in the treatment of coronary artery disease.64 The goal is to identify genetic determinants or predictors of inward remode ling and in-stent resten osis to explain the dichotomous outcome of failure following percut aneous coronary intervention. The study is a

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18 collaborative initiative between the NHLBI and two clinical sites in the US, and plans to enroll 350 patients. Blood is sampled pre-interventi on and then again at 2 weeks and 6 months following intervention. Clinical en dpoints include symptomatic rest enosis at 6 months and at 12 months. Genomic studies are performed on circ ulating leukocytes and mononuclear cells using the Affymetrix U133A GeneChip™ platform. Pl asma proteomic studies are performed using multidimensional liquid chromatography and tand em mass spectroscopy. The investigators' initial focus is on gene regulator y regions and transcriptomes asso ciated with modulation of gene expression. They are then pla nning a secondary genome-wide an alysis to identify genes or clusters of genes related to in-stent restenos is and unfavorable outcomes. This will include investigation of candidate SNPs linked to stent failure. The inve stigators then plan a complex bioinformatics approach to define genomic biomarkers that would allow risk-stratification prior to intervention and may lead to development of new techniques to prevent coronary stent restenosis and failure. Results from this trial ar e not yet available, but are eagerly awaited due to the parallel nature of our study design for app lication of these methodolog ies to failure following lower extremity revascularization. Summary Overall, lower extremity PAD is a poorly unde rstood disease lacking predictors for the arterial response to treatment such as angioplas ty or vein bypass and therefore the metrics to define success or failure of such an intervention. Consequently, we currently make management decisions in patients with symp tomatic lower extremity PAD ba sed on soft criteria without a clear understanding of how to fit the treatment to op timize a patient’s outcome.17,18 Through advanced high throughput genomic and proteomi c analyses, we plan to identify molecular evidence that a differential inflammatory response to vascular injury cont ributes to intervention failure and poor clinical outcomes. This will fo rm the basis for what is currently unavailable –

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19 an evidence-based approach to peripheral in tervention and revascular ization for symptomatic lower extremity PAD. Figure 1-1. Effect of thrombomodulin (TM) on arterial inflamma tion following balloon angioplasty. An anti-inflammatory e ffect was only seen with systemic administration (B) compared to either c ontrol (A, dense inflammation indicated by arrows) or with local delivery (not shown).

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20 CHAPTER 2 MATERIALS AND METHODS Overall Study Design Overview A five-year study is underway in whic h 300 patients undergoing evaluation for symptomatic peripheral arterial disease (PAD) will be enrolled. The study cohort will be comprised of 50 patients treated with medical management alone to serve as a control group, 125 patients undergoing additional lower extremity an gioplasty/stenting, and 125 patients undergoing additional lower extremity vein bypass (Figure 21). Data are collected prospectively with longitudinal evaluation to determine success or failure of the interven tion with corresponding quality of life (QOL) measures. In parallel to the clinical assessment, blood sampling is performed for high throughput genomic and proteo mic analyses (Table 2-1). Bioinformatics tools are then applied to reconcile the molecula r data with clinical outcomes to arrive at molecular profiles that correspond to success or failure of intervention. All study patients sign informed consent under an Institutional Review Board approved protocol. An Access™ database (HIPAA-defined “limited data set”) is currently in use to co llect and store all study data. Patient Selection Eligible patients were identif ied amongst all patients bei ng evaluated for symptomatic PAD in our current vascular surg ical practice. The summary of specific inclusion and exclusion criteria are listed in Table 2-2. Clinical Protocol Evaluation of patients for symptomatic P AD follows current standards of practice.65-67 Generally, patients with severe claudication or critical limb ischemia (Rutherford Grade I

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21 Category 3 level disease or greater)65 are considered candidates for ar terial revascularization. In an effort to optimize patient outcomes68,69 and standardize patients w ith respect to medications that likely influence systemic inflammatory response profiles, all patients are placed on antiplatelet therapy (at least 81 mg ASA daily ) and statin (HMG-CoA reductase inhibitor) therapy (at least atorvastatin 10 mg daily). Statin therapy is adjusted according to the recent National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III revised national guidelines and recommendations.70 Those with an LDL c holesterol level <70mg/dL will be maintained on 10 mg atorvastatin daily ex cept in patients with documented intolerance to statins. Patients requiring intervention then undergo lo wer extremity arteriog raphy with treatment decisions guided by the TransAtlantic In ter-Society Consensus (TASC) I and II recommendations.65,67 For patients undergoing percutaneous intervention, primary angioplasty is the preferred initial approach for superficial femo ral and popliteal artery stenoses with subintimal recanalization and angioplasty for chronic total occlusions.71-73 Primary angioplasty is also performed for infragenicular tibial artery stenoses or occlusions in patients with critical limb ischemia.74-76 Selective stenting is indicated for un acceptable results following angioplasty (e.g., significant lesion recoil with residual stenosis or flow limiting dissection).77 For patients undergoing vein bypass surgery, a non-reversed anatomic bypass using ipsilateral great saphenous vein is the approach of choice with other alternatives c onsidered as the specific case warrants. No patients requiring synthetic bypass ar e eligible for this study. All patients are placed on an antiplatelet regimen consisting of AS A 81 mg daily with the addition of clopidogrel 75 mg daily for 30 days in patients undergoing angioplasty/stent proced ures. Warfarin is

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22 initiated for bypass grafts with compromised outflow,78 and continued in all patients with preexisting indications. The 50 planned control patients will have pre-operative blood sampling only at the time of evaluation for revascularization. If there is no indication for revasc ularization at that time, they will receive best medical therapy as delineated above and be followed clinically for progression of their symptoms/disease. This will constitute a type pf prevalence study to examine baseline immune system profiles with resp ect to progression of PAD. If these patients come to require revascularization, they will be considered for the interventional arm of the study. For the 250 planned intervention subjects, we wi ll collect clinical and laboratory data prospectively to determine preoperative ri sk factors and postoperative response to revascularization. The timing of assessment and da ta collection is summarized in Table 2-2 and is scheduled in accordance with current sta ndard practice for surveillance following lower extremity intervention. As indica ted, clinical evalua tion includes review of symptoms, pulse exam with ABIs, and duplex ultrasound examinati on of the revascularized region. Laboratory evaluation includes standard perioperative hema tology and chemistry panels, as well as a high sensitivity CRP level pre-operatively and 1 w eek and 1 month post-operatively. Intervention failure is defined as any evidence of narrowing (stenosis) of either the site of angioplasty/stenting or any segment of the vein bypass graft that leads to recurrence of ischemic symptoms, a decrease in ABI of 15%, or hemodynamic significance by duplex and CT angiography imaging according to the Society for Vascular Surgery Recommended Standards for Reports Dealing with Lower Extremity Ischemia (revised version).66 Repeat angiography is performed selectively in those patients undergoing evaluatio n for re-intervention or salvag e of their revascularization. Therefore, primary patency is defined as the length of time the revascularization remains open

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23 without signs of clinical failure, without significant stenos is on imaging, and without requiring repeat angiography or reintervention. Functional and Quality of Life Assessments Functional exercise testing and QOL questionn aires are administered at the same time intervals indicated in Table 22. Quality of life instruments used in this study include both generic health and disease-specific QOL questi onnaires. The Medical Outcomes Short Form-36 (SF-36)79 serves as the generic health questionn aire and the Vascular Quality of Life (VascuQol)80 questionnaire to measure spec ific elements of PAD to capture more subtle diseasespecific effects of intervention. Results are compar ed to pre-intervention to determine the impact of intervention on QOL. Molecular Analysis Overview Molecular analyses are performed on periphe ral venous blood and in clude evaluation of the transcriptome from an en riched monocyte population, as we ll as the proteome from the plasma fraction. Initial blood samples are obtained in the pre-operative holding area immediately before the procedure. Subs equent samples are taken 2 hours and 1 day postoperatively and then at 1 week, 1, 6, and 12 months of follo w-up (Figure 2-2). At each time point, 15 mL of blood is sampled to establish genomic and proteomic inflammatory response profiles. All samples are de-identified and a ssigned a study-specific id entification number to assure confidentiality and allow sample tracking. A 7 mL collection of EDTA anti-coagulated whole blood is obtained for flow cytometric analysis of the pe ripheral blood leukocyte phenotype, genomic analyses on th e total leukocyte prep aration, and proteomic analyses of the plasma fraction. Simultaneously, an 8ml whole blood is collected in a Becton-Dickinson CPT™ tube containing sodium citrate to be processed further for the isolation of an enriched blood monocyte fraction. Plasma and leukocyte RNA are al so stored for additional future analysis if

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24 needed. The actual protocols are detailed in two recent publications including a discussion of the advantages and limitations of these analytical approaches.44,81 The protocols are also available through the Large Scale Co llaborative Research Progr am (www.gluegrant.org). Proteomics Pilot Study The focus of our study is limited to the initial analysis of multiplex proteomic data from the first 20 human subjects enrolled in the overa ll study. The same hypotheses were considered, specifically, that differences in the baseline inflammatory state (as determined by baseline plasma cytokine levels), or an exaggerated in flammatory response to intervention (indicated by statistically higher circulating cy tokine levels), can be detected and may differentiate responses to the different procedures. Such differences may then be predictive of clinical failure of lower extremity revascularization. Plasma is collected from the blood samples processed for buffy coat analyses. After separation, each plasma sample is aliquotted into se veral tubes and stored at -70 C until analysis. Freshly thawed plasma samples are then ba tch analyzed using the Luminex 100™ xMAP (Multi-Analyte Profiling) System. This bead -based assay system is essentially a flow cytometric analysis employing novel fluorescent bead s that are covalently linked (in the case of cytokine measurements) to antibodies specifi c for individual analytes. By coupling the specificity of antibody-based capture of specifi c cytokines using chrom ophore-labeled antibodies with flow cytometric analyses of individual r eactions identified by unique fluorescent beads, the analytical system can multiplex the analysis, theoretically, of an unlimited number of cytokines simultaneously from a single sample. Using a two-laser system, the Luminex technology simultaneously identifies the qua ntity of an analyte bound to a specific antibody, as well as its identity, critical for a multiplex approach. Our current working Luminex platform (22-plex) determines simultaneously the concentrations of the following analytes: eotaxin, G-CSF, GM-

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25 CSF, IFN, IL-10, IL-12p70, IL-13, IL-15, IL-17, IL-18, IL-1 IL-1 IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IP-10, MIP1 MCP1 and TNF Data output is presented in an Excel™ spreadsheet that is identified only by s ubject study number and sample number. Protein plasma concentration values are determined for each analyt e in each sample. This spreadsheet is then readily transferable for statistical analyses. Th ere are numerous advantag es to this approach, including parallel analys is of several proteins for class prediction, conservation of precious materials, and a wide spectrum of proteomic analyses available. Statistical Considerations Clinical Data A secure, de-identified Access™ database has been designed to track all aspects of this study from patient specific clinic al information to specimen collec tion and processing. Clinical data is analyzed using standard approaches in cluding Students’ t-test for numerical data, Chisquare analysis for categorical data, and Kaplan-Meier life table analysis with the log rank sum test for time series analysis. Molecular Data Genomics The genomic bioinformatics approaches plan ned for the overall study are beyond the scope of this thesis. Briefly, unsupervised approaches such as multidimensional scaling, cluster analysis and self-organizing maps are used in cl ass discovery exercises to identify relationships among genes. Supervised approaches are then us ed to identify gene expression differences in predefined classes (e.g., angioplasty versus bypass) or subsequent groupings of the data (e.g., patients who develop failure of their revasculariz ation versus those who do not). Unsupervised and supervised analytical approaches are not mu tually exclusive and when used in conjunction with one another represent a ve ry powerful method for identifyi ng relationships among genes.

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26 Finally, principal component analyses (PCA) as part of such a functional data analysis (clustering, pathway analysis) approach offers us the ability to capture subtle profile changes in patient response, and to uncover and describe te mporal differences in the genomic profile of patients who have a successful outcome versus those that go on to failure. Proteomics For the proteomic data specifically analyzed in this study, analysis of variance (ANOVA) was the primary analytic approach. The raw data outcome from the Luminex assay is internally normalized to baseline control concen tration standards within the assay. Then, due to the wide variation in the magnit ude of cytokine levels between different patients, the proteomic dataset is further normalized by conversion to a logarithmic scale which allows for meaningful ANOVA. Because we are primarily interested in proteomic profiling rather than simple association of a single cytoki ne/biomarker, we utilized sp lit-plot ANOVA with repeated measures. In addition to analyzing the baseline pre-operative cytokine levels, this approach analyzes the “shape of the curve” of cytokine expression over the time points post-procedure. These curves, or profiles, can then be compared between groups of intere st such as angioplasty versus bypass, success vs. failure, etc. Subse quently, although not performed here, additional sophisticated bioinformatics approaches descri bed above (i.e., functional analysis, pathway analysis, PCA) can be applied as pa rt of the overall molecular analyses.

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27 Table 2-1. Clinical and biochemical timeline Pre-op 2 hrs 1 day 1 wk 1 mo 6 mos 12 mos Clinical exam ABIs Exercise test Duplex CT angiogram Quality of life -SF-36 VascuQol Molecular studies Gene array Protein assay *Pre-operative imaging will only be obtained if clinically necessary for decision making Table 2-2. Study inclusion/exclusion criteria Inclusion Exclusion 1. Diagnosis of symptomatic PAD – Rutherford Grade I Category 3 or greater65 confirmed by history and physical and noninvasive studies – planned for lower extremity revascularization 2. Male or female at least 18 years of age 3. Adequate arterial anatomy amenable to revascularization 4. Adequate autogenous vein conduit in patients undergoing bypass surgery 1. < 18 years of age 2. Existing medical condition(s) with resulting life expectancy less than one year 3. Documented intolerance or allergy to aspirin and clopidogrel 4. History of immunosupression on the basis of a preexisting medical condition or immunosuppressant therapy or chronic corticosteroid therapy to treat a preexisting condition 5. Documented active or quiescent autoimmune disorder 6. White blood cell count (WBC) <3.5 x 109/L 7. Platelets < 50 x 109/L 8. Any patient who has received experimental drug(s) (including expe rimental biologic agents) in the previous three months 9. Pregnancy 10. Patients receiving bypass using prosthetic, cryopreserved, or other non-autogenous conduits

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28 Figure 2-1. Overall study algorithm. 50 subjects receiving medical therapy alone will serve as a control prevalence group for comparison to the larger cohorts receiving additional lower extremity (LE) angioplasty or bypass. Figure 2-2. Timing of peripheral blood sampling. At each time point, 15 mL peripheral blood is sampled for the isolation of monocytes fo r flow cytometry and RNA isolation, and for the collection of plasma for cytokine analysis.

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29 CHAPTER 3 RESULTS Clinical Results A total of 51 subjects ente red the overall study by Novemb er 2008. Clinical analysis performed on this entire cohort shows no statistical differences by Student’s t-test or Chi-squared analysis between the three patient cohorts thos e patients receiving medi cal therapy alone, those undergoing additional angioplasty/ste nting, or those having vein bypass surgery with respect to age, gender, race, coronary artery disease, hypertension, hypercholesterole mia, diabetes, renal insufficiency, or smoking. Although there were no statistical differences the angioplasty group was slightly older with a higher proportion of white subjects a nd a higher proportion of hypercholesterolemic subjects. The bypass group had a slightly higher proportion of diabetic subjects. The indication for intervention be tween the angioplasty and vein bypass groups, however, was significantly differe nt with a preponderance of cl audicants in the angioplasty group and patients with critical limb ischemia (CLI) in the vein bypass group (P = .008) (Table 3-1). Kaplan-Meier life table analysis was perf ormed for primary patency for all subjects receiving intervention (Figure 3-1) and limb salv age for the entire cohort (Figure 3-2). Overall primary patency was 73% at 7 months after whic h the standard error exceeded 10% (Figure 3-1, A). There were no early failures in the angi oplasty group and primary patency for the vein bypass procedures was 85% at 4 months after wh ich the standard error exceeded 10% (Figure 31, B). Limb salvage was 93% for the entire cohort. Due to the small numb ers in each treatment group and the overall small number of events, multivariate analysis did not reveal any demographic factors to be predictive of bypass failure.

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30 Proteomic Results Luminex multianalyte cytokine analysis was performed on plasma samples from the first 20 study subjects (5 control, 6 angioplasty/stentin g, 9 vein bypass). Pre-operative samples (P0) for all subjects were evaluated, and then 2 hour (2H), 1 day (1D), 1 week (1W), and 1 month (1M) samples were analyzed for all interventions. Cytokine levels were below the sensitivity of the assay precluding further analysis for the fo llowing cytokines: IL5, IL-12p70, IL-13, and IL15. The remainder of the cytokines were analy zed statistically with results summarized in Tables 3-2 and 3-3. More detailed data and stat istical analyses are provided in Appendix A. Several cytokines demonstrated significant post-procedural changes over time for all subjects undergoing intervention. The analysis of cytokine leve ls separated by procedure type showed that pre-operative valu es did not differ between patien ts who subsequently underwent angioplasty/stenting compared to those who underw ent vein bypass. However, several cytokines were significantly differentially expressed postoperatively discriminating between procedure groups. Two distinct cytokine expression patterns emerged. Fo r the angioplasty/stenting group there appeared to be an early transient peak of moderate leve l increased expression, whereas for the bypass group, the magnitude of expression was larger with a more delayed but prolonged expression (see Figures 3-3 to 3-5, B). These intuitively follow the magnitude of the procedure, but warrant further analysis. The analysis of cytokine levels based on outco me (success vs. failure) was restricted to the vein bypass group since there were no early failures in the angiopl asty/stenting group. Increased expression levels of several inflammatory cyt okines were found to be significantly associated with bypass failure supporting ou r hypothesis that an exaggera ted inflammatory response is associated with poor outcome. Importantly, we found significant differe nces in pre-operative cytokine levels alone between the success and fa ilure groups suggesting th e ability to predict

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31 failure based on pre-operative sampling alone. Furt hermore, we found that these cytokine levels continued to differ over time post-intervention with temporal changes further discriminating between success and failure outcomes. All failu res occurred later than the 1-month time point suggesting that identification of these early changes could allow fo r intervention that still avert failure. Examples of cytokine expression that ar e associated with either procedure or outcome differences, both, or neither are shown in figures 3-3 to 3-7. Table 3-1. Study Cohort Demographics Character Control (N=13) Angioplasty (N=12) Vein Bypass (N=26) Age (mean SD) 62.1 7.4 66.7 6.8 61.4 8.3 Male Gender (%) 100 92 85 White Race (%) 77 92 73 CAD (%) 54 50 54 HTN (%) 100 100 92 Hypercholesterolemia (%) 62 92 69 DM (%) 46 33 58 Smoker (%) 92 100 89 Indication – Claudication (%) 46 83* 15* Indication – CLI (%) 54 17* 85* *Chi square P = .008 for difference in indications between treatment groups Table 3-2. Summary of prot eomic results by procedure Significance over time Significance by pr ocedure No significance by procedure EOTAXIN EOTAXIN IL-12p40 GMCSF IFN IP-10 IFN IL-1 MCP-1 IL-1 IL-6 MIP-1 IL-1 IL-8 RANTES IL-6 TNF GMCSF IL-8 IL-10 IL-10 IL-1 TNF

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32 Table 3-3. Summary of prot eomic results by outcome Significance over time Significance with failure No significance with outcome EOTAXIN EOTAXIN IL-12p40 GMCSF GMCSF IP-10 IFN IFN MCP-1 IL-1 IL-1 MIP-1 IL-1 IL-1 RANTES IL-6 IL-6 IL-8 IL-8 IL-10 IL-10 TNF TNF Figure 3-1. Kaplan-Meier life ta ble analysis of primary patenc y rates of revascularization. Overall patency for all subjects undergoing in tervention (N=38) is depicted in panel A, and patency separated by procedure type (Procedure 1 = angioplasty, N=12; Procedure 2 = bypass, N=26) in panel B. Figure 3-2. Kaplan-Meier life ta ble analysis of limb salvage. Results for the overall cohort

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33 Figure 3-3. IL-6 plasma levels. IL-6 demonstrat ed differences that were significantly different over time and that correlated with both procedural (A, P = .047) and outcome (B, P = .0001) differences. Figure 3-4. IL-8 plasma levels. IL-8 also de monstrated differences that were significantly different over time and that correlated with both procedural (A, P = .035) and outcome (B, P = .0001) differences. Figure 3-5. TNF plasma levels. TNF also demonstrated differen ces that were significantly different over time and that correlated with both procedural (A, P = .010) and outcome (B, P = .019) differences.

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34 Figure 3-6. IFN plasma levels. IFN demonstrated differences that were significantly different over time, but that correlated only with outcome differences (B, P = .019). IFN levels did not differ by procedure (A). Figure 3-7. IP-10 plasma levels. IP-10 expres sion did not demonstrat e significant differences with any of the comparisons [time, proce dure (A), or outcome (B)]. The distinct difference in curve morphologies between outcome groups (A) warrants further evaluation.

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35 CHAPTER 4 DISCUSSION Summary and Significance of Results This report contains the firs t significant pilot study as part of our larger overall comprehensive functional genomic/proteomic transl ational program in vascular disease. As such, the study provides important early confirma tion that the research design is sound, and it delivers several critical proof-o f-principle findings upon which subsequent study and analysis will be based. Our early clinical results are consistent with previously reported experiences. Our 73% overall short-term primary patency is acceptable, and our 93% limb salvage rate is exceptional. The fact that the angioplasty/stenting procedures have not experienced fail ure is interesting in that angioplasty is typically felt to be less dura ble than bypass. Our succ ess likely represents the small number of cases and our early selection of favorable subjects, and, with both more followup time and more broad inclusion, we will begin to see angioplasty/stenting failures and more representative results. This will then allow us to extend our analyses correlating inflammatory response to outcome to this treatment arm as well. Moving forward, we anticipate making our clinical dataset more robust both by adding more subjects, but also by ad ding analyses of the functional and QOL endpoints. This will ultimatel y allow us to strictly define comprehensive clinical outcomes as mandated in the aims of the study and will lead to far more sophisticated correlative molecular analyses. At the outset, we had concerns that periphera l angioplasty, being a percutaneous procedure with significantly less operative stress than open surgical bypass, may incite only minimal inflammatory response that could fa ll below the sensitivity of the an alyses to detect it. If this were to be the case, these studies may either ha ve provided no meaningful results, or may have

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36 given a false impression that there is no significant system ic inflammatory response to angioplasty. The sensitivity of the Luminex assay however has provided interpretable data for all but four cytokines that fe ll below its minimal detection le vel. These cytokines were consistent between treatment groups, and therefore, it is unclear whether this means that those four cytokines are uninvolved in the inflammato ry reaction to revascularization or that their changes were small and undetectable. What is cl ear, however, is that we can reliably detect and analyze an inflammatory profile following angi oplasty/stenting and that it appears to be distinctly different than that seen with vein bypa ss. This serves as important proof of principle that procedure-specific analyses will be meaningf ul and that potential exists to individualize patient assignment to their optimal procedure based on their immune profile. Finally, clear inflammatory profiles are emergi ng from our early proteomic data that are associated with, and perhaps predictive of inte rvention failure. Enrolle d subjects demonstrated significant alterations in both ba seline immunity and the systemic inflammatory response to intervention that predicted outcome Such evidence is currently present for vein bypass surgery in the data presented here, and we are optimis tic that, as the angioplasty/stenting experience unfolds, similar patterns will hold true for both pro cedure types. This serves as further proof of principle for ongoing studies focused on the de velopment of class pr ediction models to determine whether failure of these surgical in terventions can be pred icted early by selected components of the inflammatory response. The real potential of this woul d rest in the eventual ability to refine patient-specifi c procedure choices based on pre-ope rative molecular testing, or to design post-operative pharmacologic (e.g., anti-inf lammatory) strategies to optimize favorable outcomes. This system-wide, high throughput pl atform would facilitate the conduct of such pharmacoproteomic studies to survey a nd refine such treatment strategies.

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37 Study Limitations The primary limitation to this study is its small sample size. We chose to analyze the first 20 subjects data and only their proteomic data out to one month. This, however, allowed us to analyze a manageable number of samples from a r easonable number of patients to both validate our research protocol as well as to provide initial pr oof of principle that our study design and hypotheses have validity. The ne xt steps will include analyzi ng additional patient samples to establish reproducibility a nd validity of the currents results. In addition, th e analysis of samples out past one month will answer an important ques tion – whether these vascular patients return to their pre-operative baseline imm une status, or whether revascul arization adjusts their overall immune system as reflected in ne w baseline values being established. Another limitation in the overall study design is the lack of randomization of subjects to procedure type. Without randomi zation, the control, angioplasty, and bypass arms of this study were similar demographically. However, because subjects were not randomized to procedure type, the two treatment groups differed signif icantly with respect to the indication for intervention. Since the clinical indications for each procedure di ffers, subjects are not equally eligible for the two procedures. Therefore, rand omization is not readily possible in this setting because procedural decisions are currently made based on angiographic distribution of disease, patient operative risk, availability of bypass c onduit, a prevailing dogma that bypass is superior for advanced critical limb ischemia (i.e., rest pain, ulceration, gangrene), and that bypass is infrequently offered for claudication. One future solution would be to rest rict entry to subjects within one symptom group, or subjects with stri ctly defined angiographi c findings such that randomization to angioplasty/sten ting versus vein bypass was possibl e. This approach, however, would likely hinder enrollment, ma ke subject accrual difficult, a nd make the length of the study

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38 prohibitive for a single clinical center. Th is may require extending this study model to a multicenter design. Finally, one obvious limitation is the lack of pa rallel genomic data to further characterize the inflammatory response to revascularizati on. This will be discussed further in future directions, but reflects a delay in genomic analysis awaiting new cutting edge technology. Future Directions The original research protoc ol delineated genomic studies using the Affymetrix human U133 Plus2 microarray chip which is the standard commercially available chip in use today for human gene expression analysis. Since the incep tion of our study, through collaboration with the Glue Grant [Inflammation and the Host Respons e to Injury, National Institute of General Medical Sciences (NIGMS)] investigators, we now have access to the GG-H2 microarray chip designed by Affymetrix specificall y for and through collaboration w ith the Glue Grant. This chip is an extraordinary advan ce in technology in that it incorporates all th e standard expression analysis probe sets for the human genome, plus it contains exon array probe sets, probe sets for non-coding genomic regions, and single nucleotide polymorphism probe sets all on a single chip. This amounts to upwards of 7 million probe sets a nd produces a data file 750 MB in size. The obvious potential for this chip is the enormity and complexity of data it provides. The down side is that bioinformatics tools are currently lacking to analyze this amount and complexity of data. We anticipate that using this new platform for our genomic analyses moving forward will result in significant important advances and we look forward to being involve d in the process for developing and validating nove l bioinformatics tools. More globally, this approach represents a para digm shift in human investigation into the role of the systemic inflammatory milieu that will yield new knowledge th at will significantly impact patient selection and th e development of novel therap ies for PAD intervention. The

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39 framework established through this project will stand as a vali dated foundation for understanding the mechanistic impact of specific therapeutic inte rventions in peripheral vascular disease. The platform developed here can then be appl ied to other modalities of lower extremity revascularization including ball oon cryoplasty, drug-eluting stents catheter-based atherectomy, and excimer laser. Our approach will provide cr itical insight into patient selection and risk stratification when considering these alternative th erapies. Information obtained will also lead to the development of improved interventional technology and/or pharmacologic adjuncts (e.g., anti-inflammatory, immune modulating therapies) to further impact the durability of lower extremity revascularization. Furt hermore, once established, this pl atform can also be applied to other vascular disease processe s such as the management of elective and ruptured abdominal aortic aneurysms with both open and endovascul ar treatment options, carotid endarterectomy versus carotid stenting, and perhaps treatment of chronic venous insufficiency. Finally, the knowledge and understanding gained through this proj ect will likely be broadly applicable to understanding systemic cardiovascular disease.

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40 APPENDIX THE SPSS OUTPUT DATA AND STATISTICS Table A-1. Eotaxin de scriptive statistics Table A-2. Eotaxin repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 889.982 1 889.982 997.060 .000 Outcome .000 Angioplasty Error 9.819 11 .893 Intercept 981.130 1 981.130 2639.976 .000 Outcome 1.677 1 1.677 4.512 .052 Bypass Error 5.203 14 .372 Procedure Time Outcome Mean Standard deviation N Success 3.7438 .43792 12 lnP0 Total 3.7438 .43792 12 Success 4.1292 .65921 12 lnH2 Total 4.1292 .65921 12 Success 3.6653 .35727 12 lnD1 Total 3.6653 .35727 12 Success 3.7476 .56530 12 lnW1 Total 3.7476 .56530 12 Success 3.9709 .59721 12 Angioplasty lnM1 Total 3.9709 .59721 12 Success 3.8473 .16609 6 Failure 3.8830 .43259 10 lnP0 Total 3.8696 .34899 16 Success 3.5424 .41741 6 Failure 3.6287 .59218 10 lnH2 Total 3.5963 .51995 16 Success 3.3841 .35913 6 Failure 3.7332 .69637 10 lnD1 Total 3.6022 .60367 16 Success 3.1863 .10066 6 Failure 3.4233 .49114 10 lnW1 Total 3.3344 .40267 16 Success 3.3766 .40555 6 Failure 4.1638 .36424 10 Bypass lnM1 Total 3.8686 .53792 16

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41 Table A-3. IL-6 descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 2.6106 1.01568 12 lnP0 Total 2.6106 1.01568 12 Success 3.4992 1.10958 12 ln2H Total 3.4992 1.10958 12 Success 3.3388 1.00879 12 ln1D Total 3.3388 1.00879 12 Success 2.8617 .80438 12 ln1W Total 2.8617 .80438 12 Success 2.8909 1.05519 12 Angioplasty ln1M Total 2.8909 1.05519 12 Success 2.0628 .78437 6 Failure 3.3836 .72500 10 lnP0 Total 2.8883 .97804 16 Success 3.4348 .34473 6 Failure 3.9438 .55433 10 ln2H Total 3.7529 .53735 16 Success 3.7918 .94357 6 Failure 4.3848 .84502 10 ln1D Total 4.1624 .90174 16 Success 1.9830 .35783 6 Failure 3.7280 .65887 10 ln1W Total 3.0736 1.03168 16 Success 1.8539 .48877 6 Failure 3.3958 .71525 10 Bypass ln1M Total 2.8176 .99040 16 Table A4. IL-6 re peated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 554.576 1 554.576 155.082 .000 Outcome .000 Angioplasty Error 39.336 11 3.576 Intercept 766.194 1 766.194 840.458 .000 Outcome 24.449 1 24.449 26.819 .000 Bypass Error 12.763 14 .912

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42 Table A-5. IL-8 descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 1.1641 .78314 12 lnP0 Total 1.1641 .78314 12 Success 1.6914 .89514 12 lnH2 Total 1.6914 .89514 12 Success 1.3496 1.08354 12 lnD1 Total 1.3496 1.08354 12 Success 1.2699 .48094 12 lnW1 Total 1.2699 .48094 12 Success 1.3705 .46846 12 Angioplasty lnM1 Total 1.3705 .46846 12 Success .7072 .69991 6 Failure 2.1678 .57900 10 lnP0 Total 1.6201 .94752 16 Success 1.5141 .73124 6 Failure 2.6136 .75184 10 lnH2 Total 2.2013 .90532 16 Success 1.7139 .80769 6 Failure 2.6007 .90027 10 lnD1 Total 2.2681 .94887 16 Success 1.1765 .70719 6 Failure 2.5606 1.17291 10 lnW1 Total 2.0415 1.21288 16 Success .8920 .73095 6 Failure 2.6368 .65173 10 Bypass lnM1 Total 1.9825 1.09270 16 Table A-6. IL-8 repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 112.466 1 112.466 55.376 .000 Outcome .000 Angioplasty Error 22.340 11 2.031 Intercept 258.996 1 258.996 163.222 .000 Outcome 32.431 1 32.431 20.438 .000 Bypass Error 22.215 14 1.587

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43 Table A-7. TNF alpha de scriptive statistics Procedure Time Outcome Mean Std. deviation N Success 1.9715 .78266 12 lnP0 Total 1.9715 .78266 12 Success 2.4654 .74789 12 lnH2 Total 2.4654 .74789 12 Success 2.0328 .70592 12 lnD1 Total 2.0328 .70592 12 Success 2.2507 .84234 12 lnW1 Total 2.2507 .84234 12 Success 2.2305 1.0397 12 Angioplasty lnM1 Total 2.2305 1.0397 12 Failure 2.4501 1.4100 10 Success .69338 .90851 6 lnP0 Total 1.7913 1.4965 16 Failure 2.8977 1.2840 10 Success 1.3063 .44592 6 lnH2 Total 2.3009 1.2994 16 Failure 2.9576 1.4150 10 Success 1.9618 .86138 6 lnD1 Total 2.5842 1.3025 16 Failure 3.1093 1.5044 10 Success 1.8897 .57604 6 lnW1 Total 2.6520 1.3566 16 Failure 2.8896 1.2710 10 Success 1.7816 .21754 6 Bypass lnM1 Total 2.4741 1.1366 16 Table A-8. TNF alpha repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Angioplasty Intercept 287.814 1 287.814 101.753 .000 Outcome .000 Error 31.114 11 2.829 Bypass Intercept 360.927 1 360.927 76.396 .000 Outcome 33.382 1 33.382 7.066 .019 Error 66.142 14 4.724

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44 Table A9. IFN gamma descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 3.3278 .83455 12 lnP0 Total 3.3278 .83455 12 Success 3.7693 .76727 12 lnH2 Total 3.7693 .76727 12 Success 3.3484 .87515 12 lnD1 Total 3.3484 .87515 12 Success 3.3008 .82089 12 lnW1 Total 3.3008 .82089 12 Success 3.6067 1.0692 12 Angioplasty lnM1 Total 3.6067 1.0692 12 Success 3.1695 .40254 6 Failure 3.8113 .67395 10 lnP0 Total 3.5707 .65538 16 Success 2.3426 .66800 6 Failure 3.5134 .88178 10 lnH2 Total 3.0744 .97874 16 Success 2.4423 .78340 6 Failure 3.6012 .91533 10 lnD1 Total 3.1666 1.0213 16 Success 2.2951 .33028 6 Failure 3.6058 .77113 10 lnW1 Total 3.1143 .90698 16 Success 2.4910 .79138 6 Failure 3.9331 .50529 10 Bypass lnM1 Total 3.3923 .93908 16 Table A-10. IFN gamma repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 722.697 1 722.697 240.784 .000 Outcome .000 Angioplasty Error 33.016 11 3.001 Intercept 730.333 1 730.333 485.382 .000 Outcome 24.576 1 24.576 16.333 .001 Bypass Error 21.065 14 1.505

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45 Table A11. IP-10 descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 4.3198 .44483 12 lnP0 Total 4.3198 .44483 12 Success 4.9684 .50133 12 lnH2 Total 4.9684 .50133 12 Success 4.4301 .65487 12 lnD1 Total 4.4301 .65487 12 Success 4.3053 .43268 12 lnW1 Total 4.3053 .43268 12 Success 4.6940 .69197 12 Angioplasty lnM1 Total 4.6940 .69197 12 Success 4.8071 .49473 6 Failure 4.2850 .55858 10 lnP0 Total 4.4808 .58046 16 Success 4.7805 1.21319 6 Failure 4.0804 .46849 10 lnH2 Total 4.3430 .86303 16 Success 4.7647 1.26787 6 Failure 4.1614 .62228 10 lnD1 Total 4.3876 .92692 16 Success 4.1913 .81478 6 Failure 4.6150 .54991 10 lnW1 Total 4.4561 .66903 16 Success 4.2072 .87781 6 Failure 4.9945 .40820 10 Bypass lnM1 Total 4.6992 .71538 16 Table A-12. IP-10 rep eated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 1238.617 1 1238.617 4123.860 .000 Outcome .000 Angioplasty Error 3.304 11 .300 Intercept 1511.134 1 1511.134 1041.962 .000 Outcome .283 1 .283 .195 .665 Bypass Error 20.304 14 1.450

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46 Table A-13. IL-10 desc riptive statistics Procedure Time Outcome Mean Std. deviation N Success -.0368 .82410 12 lnP0 Total -.0368 .82410 12 Success .4076 .75182 12 lnH2 Total .4076 .75182 12 Success .1589 1.08101 12 lnD1 Total .1589 1.08101 12 Success .0839 .63720 12 lnW1 Total .0839 .63720 12 Success .2024 .94069 12 Angioplasty lnM1 Total .2024 .94069 12 Success -.3314 .70427 6 Failure .6440 1.07767 9 lnP0 Total .2538 1.04184 15 Success .5127 .07188 6 Failure .8742 1.10691 9 lnH2 Total .7296 .85766 15 Success -.0366 1.10474 6 Failure 1.2086 1.01418 9 lnD1 Total .7105 1.19262 15 Success -.6955 .33865 6 Failure 1.4318 1.54251 9 lnW1 Total .5809 1.60134 15 Success -.6531 .30968 6 Failure .8226 1.17978 9 Bypass lnM1 Total .2323 1.17878 15 Table A-14. IL-10 repe ated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 1.598 1 1.598 .522 .485 Outcome .000 Angioplasty Error 33.660 11 3.060 Intercept 10.273 1 10.273 3.931 .069 Outcome 27.543 1 27.543 10.539 .006 Bypass Error 33.976 13 2.614

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47 Table A-15. IL-12 desc riptive statistics Procedure Time Outcome Mean Std. deviation N Success 4.7469 .60598 12 lnP0 Total 4.7469 .60598 12 Success 5.1484 .87530 12 lnH2 Total 5.1484 .87530 12 Success 4.8832 .76977 12 lnD1 Total 4.8832 .76977 12 Success 4.8690 .88359 12 lnW1 Total 4.8690 .88359 12 Success 5.0817 1.12857 12 Angioplasty lnM1 Total 5.0817 1.12857 12 Success 4.5017 .22259 6 Failure 4.9110 .48533 11 lnP0 Total 4.7666 .45094 17 Success 4.3215 .33945 6 Failure 4.8342 .63647 11 lnH2 Total 4.6532 .59411 17 Success 4.4889 .50527 6 Failure 4.9808 .74108 11 lnD1 Total 4.8072 .69408 17 Success 4.3947 .20643 6 Failure 5.0771 .71651 11 lnW1 Total 4.8362 .66871 17 Success 4.2889 .34558 6 Failure 5.1643 .34316 11 Bypass lnM1 Total 4.8553 .54487 17 Table A-16. IL-12 repe ated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 1467.685 1 1467.685 494.649 .000 Outcome .000 Angioplasty Error 32.638 11 2.967 Intercept 1712.526 1 1712.526 3222.871 .000 Outcome 6.857 1 6.857 12.905 .003 Bypass Error 7.970 15 .531

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48 Table A-17. IL-1alpha descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 5.1398 .59605 12 lnP0 Total 5.1398 .59605 12 Success 5.3448 .60824 12 lnH2 Total 5.3448 .60824 12 Success 4.9656 .62261 12 lnD1 Total 4.9656 .62261 12 Success 5.0264 .49231 12 lnW1 Total 5.0264 .49231 12 Success 5.2151 .62969 12 Angioplasty lnM1 Total 5.2151 .62969 12 Success 5.2152 .44141 6 Failure 5.5154 .65314 10 lnP0 Total 5.4028 .58603 16 Success 4.4663 .35426 6 Failure 4.8938 .64905 10 lnH2 Total 4.7335 .58333 16 Success 4.5276 .62515 6 Failure 5.0790 .50688 10 lnD1 Total 4.8723 .60036 16 Success 4.3380 .08824 6 Failure 5.0948 .46655 10 lnW1 Total 4.8110 .52572 16 Success 4.3502 .10899 6 Failure 5.5267 .52698 10 Bypass lnM1 Total 5.0855 .71877 16 Table A-18. IL-1alpha repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 1584.152 1 1584.152 1278.353 .000 Outcome .000 Angioplasty Error 13.631 11 1.239 Intercept 1801.266 1 1801.266 2034.288 .000 Outcome 7.739 1 7.739 8.741 .010 Bypass Error 12.396 14 .885

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49 Table A-19. IL-1beta de scriptive statistics Procedure Time Outcome Mean Std. deviation N Success 2.6441 1.1706 12 lnP0 Total 2.6441 1.1706 12 Success 3.1567 1.2106 12 lnH2 Total 3.1567 1.2106 12 Success 2.5302 1.1475 12 lnD1 Total 2.5302 1.1475 12 Success 2.7097 .93291 12 lnW1 Total 2.7097 .93291 12 Success 2.9904 .87415 12 Angioplasty lnM1 Total 2.9904 .87415 12 Success 1.6155 1.1996 6 Failure 2.4782 1.0901 10 lnP0 Total 2.1547 1.1742 16 Success 1.4224 1.3449 6 Failure 2.3850 1.1471 10 lnH2 Total 2.0240 1.2744 16 Success 1.6671 1.9690 6 Failure 2.4866 1.2685 10 lnD1 Total 2.1793 1.5574 16 Success 1.3667 .63856 6 Failure 3.0214 1.2869 10 lnW1 Total 2.4009 1.3469 16 Success 1.8338 .90716 6 Failure 2.9803 .58829 10 Bypass lnM1 Total 2.5504 .90032 16 Table A-20. IL-1beta re peated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 472.495 1 472.495 93.954 .000 Outcome .000 Angioplasty Error 55.319 11 5.029 Intercept 338.895 1 338.895 67.063 .000 Outcome 22.244 1 22.244 4.402 .055 Bypass Error 70.747 14 5.053

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50 Table A-21. MCP-1 descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 2.9314 .54276 12 lnP0 Total 2.9314 .54276 12 Success 3.5284 .54968 12 lnH2 Total 3.5284 .54968 12 Success 3.0708 .60240 12 lnD1 Total 3.0708 .60240 12 Success 2.9302 .27768 12 lnW1 Total 2.9302 .27768 12 Success 3.1265 .56930 12 Angioplasty lnM1 Total 3.1265 .56930 12 Failure 3.3827 .49730 10 Success 2.9014 .15019 6 lnP0 Total 3.2022 .46241 16 Failure 3.3661 .63994 10 Success 3.5512 1.28763 6 lnH2 Total 3.4355 .89830 16 Failure 3.3881 .79115 10 Success 3.2051 .42579 6 lnD1 Total 3.3194 .66660 16 Failure 3.5669 .64820 10 Success 2.8120 .23112 6 lnW1 Total 3.2838 .64214 16 Failure 3.6230 .47466 10 Success 2.8188 .33877 6 Bypass lnM1 Total 3.3214 .57888 16 Table A-22. MCP-1 repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Angioplasty Intercept 583.117 1 583.117 654.519 .000 Outcome .000 Error 9.800 11 .891 Bypass Intercept 797.813 1 797.813 729.328 .000 Outcome 3.116 1 3.116 2.848 .114 Error 15.315 14 1.094

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51 Table A-23. MIP-1alpha descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 3.7359 .59364 12 lnP0 Total 3.7359 .59364 12 Success 4.0090 .70237 12 lnH2 Total 4.0090 .70237 12 Success 3.6890 .56477 12 lnD1 Total 3.6890 .56477 12 Success 3.7619 .48043 12 lnW1 Total 3.7619 .48043 12 Success 3.9784 .58446 12 Angioplasty lnM1 Total 3.9784 .58446 12 Failure 4.1037 .30018 10 Success 3.9427 .45337 6 lnP0 Total 4.0433 .35925 16 Failure 3.6339 .68569 10 Success 3.3371 1.23172 6 lnH2 Total 3.5226 .89991 16 Failure 3.9175 .47048 10 Success 3.7584 1.45891 6 lnD1 Total 3.8579 .92120 16 Failure 3.9907 .47582 10 Success 2.9964 .77403 6 lnW1 Total 3.6178 .76335 16 Failure 4.0529 .35802 10 Success 3.1699 .97950 6 Bypass lnM1 Total 3.7218 .76919 16 Table A-24. MIP-1alpha repeated measures ANOVA Source Type III sum of squares df Mean square F Sig. Intercept 2003.455 1 2003.455 1267.998 .000 Procedure 3.104 1 3.104 1.965 .173 Outcome 4.666 1 4.666 2.953 .098 Procedure Outcome .000 Error 39.500 25 1.580

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52 Table A-25. RANTES de scriptive statistics Procedure Time Outcome Mean Std. deviation N Success 4.6419 .43316 12 lnP0 Total 4.6419 .43316 12 Success 5.0041 .69442 12 lnH2 Total 5.0041 .69442 12 Success 4.7095 .53157 12 lnD1 Total 4.7095 .53157 12 Success 4.6406 .52622 12 lnW1 Total 4.6406 .52622 12 Success 4.9280 .73824 12 Angioplasty lnM1 Total 4.9280 .73824 12 Failure 4.9500 .38953 10 Success 5.1620 .24185 6 lnP0 Total 5.0295 .34896 16 Failure 4.6410 .50533 10 Success 4.6485 .43405 6 lnH2 Total 4.6438 .46479 16 Failure 4.9349 .48090 10 Success 4.8782 .48189 6 lnD1 Total 4.9137 .46580 16 Failure 4.9528 .65038 10 Success 4.4568 .45791 6 lnW1 Total 4.7668 .62062 16 Failure 5.3435 .30333 10 Success 4.3593 .47699 6 Bypass lnM1 Total 4.9744 .61092 16 Table A-26. RANTES repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Angioplasty Intercept 1373.664 1 1373.664 2635.477 .000 Outcome .000 Error 5.733 11 .521 Bypass Intercept 1751.628 1 1751.628 5924.245 .000 Outcome 1.302 1 1.302 4.402 .055 Error 4.139 14 .296

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53 Table A-27. GMCSF descriptive statistics Procedure Time Outcome Mean Std. deviation N Success 2.3686 .80764 12 lnP0 Total 2.3686 .80764 12 Success 2.7466 .47656 12 lnH2 Total 2.7466 .47656 12 Success 2.3382 .83185 12 lnD1 Total 2.3382 .83185 12 Success 2.5827 .51127 12 lnW1 Total 2.5827 .51127 12 Success 2.5840 .73907 12 Angioplasty lnM1 Total 2.5840 .73907 12 Success 1.4180 .94333 6 Failure 2.8671 1.19722 10 lnP0 Total 2.3237 1.29677 16 Success 1.3398 .90119 6 Failure 3.0766 1.29444 10 lnH2 Total 2.4253 1.42483 16 Success 2.2542 .81722 6 Failure 3.1292 1.37555 10 lnD1 Total 2.8011 1.24471 16 Success 2.1474 .37197 6 Failure 3.2883 1.39400 10 lnW1 Total 2.8605 1.23996 16 Success 2.0837 .34335 6 Failure 3.0881 1.24768 10 Bypass lnM1 Total 2.7115 1.10705 16 Table A-28. GMCSF repeated measures ANOVA Procedure Source Type III sum of squares df Mean square F Sig. Intercept 382.242 1 382.242 198.731 .000 Outcome .000 Angioplasty Error 21.158 11 1.923 Intercept 457.289 1 457.289 105.485 .000 Outcome 28.888 1 28.888 6.664 .022 Bypass Error 60.692 14 4.335

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60 BIOGRAPHICAL SKETCH Peter R. Nelson was born in Framingham, MA, on September 18, 1966. He attended high school at Framingham North High School and graduated as a member of the National Honor Society. He received his dual BS degree in Biology and Classics from Tufts University graduating summa cum laude and Phi Beta Kapp a. He received the Thomas and Emily Carmichael Award in Physiology for research performed as an undergraduate. Peter then attended Medical School at the University of Massachusetts a nd remained there for general surgery residency. During residency he sought specialty research training in the HarvardLongwood Vascular Research Fellows hip. Following residency, he then completed his clinical Vascular Surgery Fellowship at Dartmouth College in 2001. Peter’s first faculty position was at the University of Massachusetts as Assistant Professor of Surg ery and Cell Biology. He then moved to his current position as Assistant Professor of Surgery at the University of Florida College of Medicine in 2004. His research is supported by a K23 Mentored Patient-Oriented Research Career development Award from the National Heart Lung and Blood Institute of the National Institutes of Health. He currently reside s in Gainesville with hi s wife Janice, and their two sons Maxwell (13) and Peter (“PJ”, 4).