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The role of lipids and PCSK9 in long-term outcomes of sepsis Genga, Kelly Roveran 2019

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i  THE ROLE OF LIPIDS AND PCSK9 IN LONG-TERM OUTCOMES OF SEPSIS  by  Kelly Roveran Genga  M.Sc., The Federal University of Ceara, 2014  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  January 2019 © Kelly Roveran Genga, 2019   ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The role of lipids and PCSK9 in long-term outcomes of sepsis  submitted by Kelly Roveran Genga in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Experimental Medicine  Examining Committee: Dr. John H. Boyd Supervisor Dr. Gordon A. Francis Co-supervisor  Dr. Keith R. Walley Supervisory Committee Member Dr. Pascal Bernatchez University Examiner Dr. Pascal Lavoie University Examiner  Additional Supervisory Committee Members:  Supervisory Committee Member     iii  Abstract     Lipids are important players in the host response to sepsis. High-density lipoprotein (HDL) binds avidly to pathogen lipids, such as lipopolysaccharide (LPS) and lipoteichoic acid (LTA), neutralizing their inflammatory effects. Pathogen lipids within HDL are transferred to low-density lipoprotein (LDL) and cleared from the circulation by the liver in a process mediated by the LDL receptor (LDLR).  In recent decades, more patients are surviving sepsis and being discharged from hospital. Studies analyzing long-term outcomes of sepsis have demonstrated a greater risk of late death, re-infection(s), late organ dysfunction(s), and re-hospitalization(s) in sepsis survivors compared to subjects never exposed to sepsis. Inadequate clearance of pathogens at the acute phase of sepsis and persistent immune dysfunction are possible factors associated with increased risk of adverse long-term outcomes in sepsis survivors.  In this work, we hypothesized that plasma HDL-cholesterol levels are positively associated with decreased risk of sepsis-associated acute kidney injury (AKI), late kidney impairment or death, and that genetic variants in genes known to regulate HDL-C would impact the risk of AKI during sepsis. Finally, the role of proprotein convertase subtilisin-kexin type 9 (PCSK9),a major regulator of LDL plasma levels and LDL receptor metabolism in the long-term outcomes of sepsis was evaluated through: i) analysis of the impact of PCSK9 loss-of-function genotype in a composite outcome composed by 1-year death or infection-related readmission (IRR), and ii) analysis of the effects of PCSK9 inhibitors on the long-term inflammation in mouse model of sepsis.  iv  This study demonstrated that low plasma levels of HDL-C measured at sepsis admission increased significantly the risk of AKI, kidney dysfunction and/or long-term death. Moreover, the HDL-related cholesteryl ester transfer protein (CETP) variant rs1800777 (allele A) was strongly associated with low levels of HDL-C and increased risk of AKI during sepsis. Last, we observed that the presence of multiple PCSK9 loss-of-function alleles decreased the risk of the death or IRR in sepsis-survivors.                  v  Lay Summary  Sepsis is one or more organ failures caused by infection(s). Toxins released in blood during infections can be carried inside cholesterol, and both (toxins and cholesterol together) are removed from the blood by the liver. This process is important to decrease the amount of toxins and consequently the risk of organ failure and death during infections.   Using groups of patients with sepsis, this work showed that: 1) HDL-cholesterol  protected against kidney injury and death caused by sepsis; 2) a genetic mutation in a protein that lowers HDL levels – CETP, was associated with increased risk of kidney injury; 3) and mutations associated with low function of PCSK9, a protein that prevents LDL removal from the blood, decreased rates of future hospitalizations or death, possibly through enhanced clearance of toxins during sepsis. Evaluation of HDL and/or mutations in CETP and PCSK9 may help doctors to identify patients at elevated risk of worse outcomes following sepsis.          vi  Preface  Parts of this thesis have been published and accepted in peer-reviewed journals, listed below in chronological order.  1. Section 1.1.1 of the Introduction titled “The Global Burden of Sepsis” includes portions of the manuscript written by: Kelly Roveran Genga, James A Russell. Update of Sepsis in the Intensive Care Unit. J Innate Immun. 2017;9(5):441-455. doi: 10.1159/000477419. Epub 2017 Jul 12.   I performed the literature review, drafted and assisted with editing the article, and generated all figures and tables.  2. Sections 1.1.4 and 1.1.6 of the Introduction titled “Pathophysiology of sepsis” and “Organ Dysfunction induced by sepsis – importance of the endothelium” respectively includes portions of the manuscript written by: Kelly Roveran Genga, Tadanaga Shimada, John H. Boyd, Keith R. Walley, James A Russell. The Understanding and Management of Organism Toxicity in Septic Shock. J Innate Immun. 2018 May 15:1-13. doi: 10.1159/000487818.  I performed the literature review, drafted and assisted with editing the article, and generated 60% of tables and figures.   3. Figure 1.1 of the Introduction titled “Simplified schematic representation of sepsis pathophysiology” I created for the publication: Kelly Roveran Genga, Tadanaga Shimada, John vii  H. Boyd, Keith R. Walley, James A Russell. The Understanding and Management of Organism Toxicity in Septic Shock. J Innate Immun. 2018 May 15:1-13. doi: 10.1159/000487818.   Chapter 2 is a copy of the publication: Kelly Roveran Genga, Cody Lo, Mihai S. Cirstea, Guohai Zhou, Keith R. Walley, James A. Russell, Adeera Levin, John H. Boyd. Two-year follow-up of patients with septic shock presenting with low HDL: the effect upon acute kidney injury, death and estimated glomerular filtration rate. J Intern Med. 2017 May;281(5):518-529. doi: 10.1111/joim.12601. Epub 2017 Mar 19.  I conceived and designed the study, collected part of the data (30%), performed 95% of statistical analyses, drafted and assisted with editing the article, and generated all figures and tables. JHB helped to conceive and design the study and helped to draft and revise the manuscript for important intellectual content; CL helped in the acquisition of the collected data; MSC prepared samples, collected data and helped to draft the manuscript; GZ performed around 5% of statistical analyses; KRW, JAR, and AL revised the manuscript for important intellectual content. All authors provided critical feedback and helped shape the research, analysis and manuscript.  Chapter 3 is a copy of the manuscript: Kelly Roveran Genga, Mark Trinder, HyeJin Julia Kong, Xuan Li, Alex K. K. Leung, Tadanaga Shimada, Keith R. Walley, James A. Russell, Gordon A. Francis, Liam R. Brunham, John H. Boyd. CETP genetic variant rs1800777 (allele A) is associated with abnormally low  HDL-C levels and increased risk of AKI during sepsis. Sci Rep. 2018 Nov 13;8(1):16764.  viii  I conceived and designed the study, collected data (90%), performed 95% of statistical analyses, drafted and assisted with editing the article, and generated all tables and figures, except figure 3.3. JHB helped to conceive and design the study and helped to draft and revise the manuscript for important intellectual content; MT prepared DNA samples, performed DNA sequencing and helped to draft the manuscript; HJK helped with DNA sample preparation (20%) and performed CETP mass analysis; XL performed mendelian randomization analysis; AKKL and TS helped to draft the manuscript; KRW, JAR, GAF and LRB revised the manuscript for important intellectual content. All authors provided critical feedback and helped shape the research, analysis and manuscript.  Chapter 4 is a copy of the manuscript: Kelly Roveran Genga, Cody Lo, Mihai S. Cirstea, Fernando Sergio Leitao Filho, Keith R. Walley, James A. Russell, Adam Linder, Gordon A. Francis, John H. Boyd. Impact of PCSK9 loss-of-function genotype on 1-year mortality and recurrent infection in sepsis survivors. EBioMedicine. 2018 Nov 22. pii: S2352-3964(18)30536-X.   I conceived and designed the study, collected data (50%), performed 95% of statistical analyses, drafted and assisted with editing the article, and generated all tables and figures. JHB helped to conceive and design the study and helped to draft and revise the manuscript for important intellectual content; CL helped with data collection; MSC prepared samples, performed PCSK9 genotyping and helped to draft the manuscript; FSLF performed repeated measures ANOVA analysis; KRW, JAR, AL and GAF revised the manuscript for important intellectual content. All authors provided critical feedback and helped shape the research, analysis and manuscript. ix  The work in this thesis was conducted with approval from University of British Columbia Office of Research (ethics approval number H11-00505) and in accordance with the University of British Columbia Policies and procedures, Biosafety Practices and Public Health Agency of Canada Guidelines.                   x  Table of Contents  Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ...........................................................................................................................x List of Tables ............................................................................................................................ xviii List of Figures ...............................................................................................................................xx List of Abbreviations ................................................................................................................ xxii Acknowledgements ................................................................................................................. xxvii Dedication ................................................................................................................................. xxix Chapter 1: Introduction ................................................................................................................1 1.1 Sepsis ...............................................................................................................................1 1.1.1 The global burning of sepsis ....................................................................................... 2 1.1.2 Old and new definitions of sepsis ............................................................................... 3 1.1.3 Current treatment ........................................................................................................ 4 1.1.4 Pathophysiology of sepsis ........................................................................................... 5 1.1.5 Organ dysfunction induced by sepsis – importance of the endothelium .................... 7 1.1.5.1 Sepsis-induced acute kidney injury .................................................................. 11 1.1.6 Long-term outcomes of sepsis .................................................................................. 14 1.1.6.1 Hospital readmissions ....................................................................................... 15 1.1.6.2 Late mortality after sepsis ................................................................................. 16 1.2 Lipid homeostasis and host response to sepsis ..............................................................16 xi  1.2.1 Pathogen lipids .......................................................................................................... 16 1.2.2 Lipoprotein particles ................................................................................................. 17 1.2.3 Kinetics of pathogen lipids and importanceof plasma lipid transfer proteins .......... 17 1.2.4 Pathogen lipids clearance during sepsis – the role of hepatic LDL receptor ............ 20 1.2.5 Other receptors that may be involved in pathogen lipids uptake .............................. 20 1.3 Lipids in sepsis ...............................................................................................................21 1.3.1 Sepsis affects plasma levels of lipids ........................................................................ 21 1.3.2 Lipid levels and risk of sepsis ................................................................................... 21 1.3.3 Low lipid levels influence sepsis outcomes .............................................................. 22 1.4 HDL and sepsis ..............................................................................................................22 1.4.1 HDL structure and function ...................................................................................... 22 1.4.2 HDL and innate immunity ........................................................................................ 23 1.4.3 HDL modifications during sepsis ............................................................................. 25 1.4.4 HDL-C levels and sepsis outcomes .......................................................................... 26 1.4.5 HDL and kidney injury during sepsis ....................................................................... 26 1.5 Regulation of HDL metabolism .....................................................................................28 1.5.1 Proteins involved in HDL metabolism ..................................................................... 28 1.5.2 CETP ......................................................................................................................... 30 1.5.3 Modulation of HDL by CETP................................................................................... 30 1.5.4 CETP activity affects HDL-C plasma levels ............................................................ 31 1.5.5 CETP activity during sepsis ...................................................................................... 32 1.5.6    CETP variants and sepsis-associated AKI ................................................................ 34     xii  1.6 LDL and sepsis ..............................................................................................................35 1.6.1 LDL structure and function ....................................................................................... 35 1.6.2 LDL and innate immunity ......................................................................................... 35 1.6.3 LDL modifications during sepsis .............................................................................. 35 1.6.4 LDL-C levels and sepsis outcomes ........................................................................... 36 1.7 Regulation of LDL metabolism .....................................................................................36 1.7.1 Proteins involved in LDL metabolism ...................................................................... 36 1.7.2 PCSK9....................................................................................................................... 37 1.7.2.1 PCSK9 function ................................................................................................ 37 1.7.2.2 PCSK9 and sepsis ............................................................................................. 40 1.7.2.3 PCSK9 inhibitors use in sepsis ......................................................................... 41 1.8 Statins .............................................................................................................................42 1.8.1 Use of statins for sepsis treatment ............................................................................ 42 1.8.2 Statins versus PCSK9 inhibitors in sepsis ................................................................ 43 1.9 Hypothesis and aims ......................................................................................................44 1.9.1 Specific aims ............................................................................................................. 44 Chapter 2: 2-year follow-up of septic patients presenting with low HDL: the effect upon acute kidney injury, death, and eGFR .................................................................................................46 2.1 Introduction ....................................................................................................................46 2.2 Material and methods .....................................................................................................47 2.2.1 Patients and study design .......................................................................................... 47 2.2.2 Blood collection and lipid measurements ................................................................. 47 2.2.3 AKI and long-term decreased eGFR......................................................................... 48 xiii  2.2.4 Composite outcome (death or long-term decreased eGFR) ...................................... 48 2.2.5 Renal function over time ........................................................................................... 49 2.2.6 Statistical Analysis .................................................................................................... 49 2.3 Results ............................................................................................................................51 2.3.1 Participants ................................................................................................................ 51 2.3.2 Pre-sepsis HDL and delta HDL ................................................................................ 54 2.3.3 Occurrence of AKI and long-term decreased eGFR ................................................. 54 2.3.4 HDL and risk of sepsis-associated AKI .................................................................... 56 2.3.5 HDL and risk of long-term decreased eGFR ............................................................ 58 2.3.6 2-year follow-up: death or decreased eGFR according to presenting HDL ............. 59 2.4 Discussion ......................................................................................................................61 Chapter 3: CETP genetic variant rs1800777 (allele A) is associated with abnormally low HDL-C levels and increased risk of AKI during sepsis ............................................................68 3.1 Introduction ....................................................................................................................68 3.2 Methods..........................................................................................................................69 3.2.1 Study Design ............................................................................................................. 69 3.2.2 Ethics......................................................................................................................... 69 3.2.3 Patients and Laboratory methods .............................................................................. 70 3.2.4 Defining Sepsis-associated Acute Kidney Injury (AKI) .......................................... 72 3.2.5 Statistical Analysis .................................................................................................... 72 3.3 Results ............................................................................................................................74 3.3.1 Derivation Cohort ..................................................................................................... 74   xiv  3.3.1.1 The candidate CETP variant rs1800777 (allele A) was associated with HDL-C levels at sepsis admission.................................................................................................. 74 3.3.1.2 CETP variant rs1800777 (allele A) was associated with decreased HDL-C levels and increased CETP mass in sepsis .................................................................................. 77 3.3.1.3 CETP variant rs1800777 (allele A) was associated with greater risk of clinically significant sepsis-associated AKI ..................................................................................... 79 3.3.1.4 Causal effect of HDL-C reduction levels on the risk of clinically significant sepsis-associated AKI ....................................................................................................... 80 3.3.2 Validation Cohort...................................................................................................... 82 3.3.2.1 The association between CETP variant rs1800777 (allele A) and increased risk  of clinically significant sepsis-associated AKI was replicated in VASST ....................... 82 3.3.2.2 The CETP variant rs1800777 (allele A) was associated with increased fluid overload and central venous pressure ............................................................................... 83 3.3.2.3 Patients carrying the CETP variant rs1800777 (allele A) presented greater plasma levels of interleukin-8 and peak of creatinine ...................................................... 83 3.4 Discussion ......................................................................................................................84 Chapter 4: Impact of PCSK9 loss-of-function genotype on 1-year mortality and recurrent infection in sepsis survivors.........................................................................................................88 4.1 Introduction ....................................................................................................................88 4.2 Methods..........................................................................................................................89 4.2.1 Ethics......................................................................................................................... 89 4.2.2 Study Design ............................................................................................................. 90 4.2.3 Patients – Inclusion Criteria ...................................................................................... 90 xv  4.2.4 Exclusion Criteria ..................................................................................................... 91 4.2.5 Measurements ........................................................................................................... 91 4.2.6 Blood collection, PCSK9 genotyping, and PCSK9 measurements .......................... 91 4.2.7 PCSK9 measurements, lipids and blood cell counts ................................................. 92 4.2.8 PCSK9 Genotyping and Definitions ......................................................................... 92 4.2.9 Outcomes .................................................................................................................. 93 4.2.10 Statistical Analysis .................................................................................................... 93 4.3 Results ............................................................................................................................95 4.3.1 The risk of 1-year death or infection-related readmission (composite outcome) was decreased in patients carrying multiple PCSK9 LOF alleles ................................................ 95 4.3.2 Characteristics of the study subjects ......................................................................... 96 4.3.3 The presence of multiple PCSK9 LOF alleles was associated with significantly decreased risk of death at 1 year or infection-related readmission (composite outcome) after sepsis ................................................................................................................................... 99 4.3.4 The biological effects of PCSK9 LOF alleles: PCSK9 plasma levels at sepsis  admission and post-sepsis LDL-C levels ............................................................................ 101 4.3.5 The presence of multiple PCSK9 LOF alleles was associated with 90-day mortality .   ................................................................................................................................. 102 4.3.6 The effects of multiple PCSK9 LOF alleles upon white blood cell counts thorough sepsis admission .................................................................................................................. 103 4.4 Discussion ....................................................................................................................104 Chapter 5: Conclusion ...............................................................................................................110 5.1 Summary of findings....................................................................................................111 xvi  5.1.1 Low HDL-C plasma levels are associated with increased risk of sepsis-associated AKI, long-term decreased renal function and long-term mortality .................................... 111 5.1.2 HDL-related variant rs1800777 (allele A) in the CETP gene increases the risk of sepsis-associated AKI ......................................................................................................... 112 5.1.3 Multiple common loss-of-function alleles in the PCSK9 gene decreases the risk of long-term death or readmission in sepsis survivors ............................................................ 113 5.2 The role of HDL in sepsis and kidney injury ...............................................................114 5.3 CETP variant rs1800777 (allele A) and sepsis-associated AKI ..................................116 5.4 PCSK9 loss-of-function – impact on long-term outcomes of sepsis ...........................117 5.5 Final conclusion ...........................................................................................................120 Bibliography ...............................................................................................................................124 Appendices ..................................................................................................................................145 Appendix A    Supplemental Table for Chapter 1 ...................................................................145 A.1 Old sepsis definitions .............................................................................................. 145 A.2 Sequential Organ Failure Assessment Score .......................................................... 147 Appendix B    Supplemental Tables and Figures for Chapter 3 ..............................................148 B.1 Schematic representation of the process of gene(s) and genetic variant(s) selection ...   ................................................................................................................................. 148 B.2 Associations between each gene variant analyzed and HDL-C levels at sepsis admission ............................................................................................................................ 149 B.3 Derivation and Validation Cohorts - CETP rs1800777: Minor Allele Frequency (MAF) and Hardy-Weinberg equilibrium (HWE) .............................................................. 154 xvii  B.4 Patients Baseline Characteristics according to CETP variant rs1800777 (allele A) in the Validation Cohort (VASST) ......................................................................................... 155 Appendix C    Supplemental Table for Chapter 4 ....................................................................156 C.1 Validation Cohort. Baseline characteristics according to PCSK9 genotype .......... 156 C.2 Validation Cohort. PCSK9 genotype - allele frequency and Hardy-Weinberg equilibrium .......................................................................................................................... 157 C.3 Derivation cohort. Frequency of combinations of alleles and HR per allele, per each outcome ............................................................................................................................... 158 C.4 Derivation cohort. Sensitivity analysis including Caucasian ethnicity (phenotype-based) ................................................................................................................................. 159              xviii  List of Tables  Table 1.1 The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).......................................................................................................................................................... 4 Table 1.2 Therapies aiming renal recovery in critically ill patients ............................................. 12 Table 1.3 Distribution and kinetics of LPS and LTA according to lipoprotein fractions (in vitro)....................................................................................................................................................... 18 Table 1.4 Characteristics of lipid transfer proteins in sepsis ........................................................ 19 Table 1.5 HDL modifications during sepsis ................................................................................. 25 Table 1.6 Proteins that regulate composition and HDL-C plasma levels ..................................... 29 Table 2.1 Baseline characteristics according to baseline HDL median (mg/dl) groups ............... 53 Table 2.2 Unadjusted and adjusted ORs for development of sepsis-associated AKI (KDIGO 2 and 3) in multivariate logistic regression in patients who had very early sepsis ................................. 57 Table 2.3 Unadjusted and adjusted ORs for decreased eGFR over 2 years follow-up after sepsis....................................................................................................................................................... 58 Table 2.4 Cox proportional hazards regression models for death or decreased eGFR over 2 years follow-up after sepsis .................................................................................................................... 61 Table 3.1 Patients Baseline Characteristics according to rs1800777 variant (allele A) ............... 76 Table 3.2 Lipids, inflammation and peak of creatinine according to CETP genotype ................. 78 Table 3.3 Adjusted Odds Ratios (aOR)* for development of clinically significant AKI according to CETP genotype (variant rs1800777, allele A) in Derivation and Validation Cohorts ............. 80 Table 4.1 Baseline characteristics according to PCSK9 genotype ............................................... 98 Table 4.2 PCSK9 genotype - allele frequency and Hardy-Weinberg equilibrium ....................... 99 xix  Table 4.3 Adjusted Hazard Ratios (aHR) for 1-year death or IRR (composite outcome), 1-year IRR, and 1-year mortality according to PCSK9 genotype (two or more alleles) ....................... 100 Table 5.1 Possible outcome-biomarker associations for the proposed biomarker studies ......... 122                   xx  List of Figures  Figure 1.1 Simplified schematic representation of sepsis pathophysiology ................................... 9 Figure 1.2 Effects of HDL on immune response during sepsis .................................................... 24 Figure 1.3 Effects of CETP gain-of-function and loss-of-function on HDL-C plasma levels ..... 32 Figure 1.4 CETP effects on HDL-C plasma levels during sepsis ................................................. 33 Figure 1.5 Schematic representation of PCSK9 domains ............................................................. 38 Figure 1.6 PCSK9 effects of LDLR .............................................................................................. 41 Figure 1.7 Potential limitation of statins effects in sepsis due to increases in PCSK9 ................. 43 Figure 2.1 CONSORT diagram of study selection ....................................................................... 52 Figure 2.2 Creatinine and eGFR (mean ± 95% C.I.) over time (A and B) and proportion of patients with decreased eGFR (C and D) according to lower and higher HDL groups during sepsis and after 3 months to 2 years ....................................................................................................................... 55 Figure 2.3 Percentage of sepsis-associated AKI (KDIGO 2 or 3) (A) and long-term decreased estimated glomerular filtration rate (eGFR) (B) according to baseline HDL groups ................... 56 Figure 2.4 Cumulative proportion of death or decreased eGFR over 2 years of follow-up after sepsis and number of patients at risk according to HDL groups .................................................. 60 Figure 3.1 Associations between HDL-C levels at sepsis vs. genetic variations per genes analyzed....................................................................................................................................................... 75 Figure 3.2 Correlation between HDL-C (mg/dL) and CETP mass (ug/mL) ................................ 79 Figure 3.3 Mendelian Randomization Results .............................................................................. 81 Figure 3.4 Cumulative Fluid Balance in VASST and Central Venous Pressure (CVP) according to CETP genotype ............................................................................................................................. 83 xxi  Figure 4.1 Derivation (early sepsis) cohort (N=342). Time to event curves for 1-year death of infection-related readmission according to PCSK9 genotype groups .......................................... 96 Figure 4.2 Derivation (early sepsis) cohort (N=132). PCSK9 levels at sepsis admission according to PCSK9 genotype (WT/single LOF vs. multiple LOF groups) ............................................... 101 Figure 4.3 Meta-analysis of 90-day mortality of Derivation and two Validation cohorts (N= 1481)..................................................................................................................................................... 102 Figure 4.4 Blood counts during sepsis (from day 0 to day 14) according to PCSK9 genotype . 104 Figure 5.1 Potential future studies with the use “new” therapies for sepsis ............................... 123               xxii  List of Abbreviations  Abbreviation Definition ABCA-1 ATP-binding cassette transporter A1 AKI Acute kidney injury aOR Adjusted odds ratio aHR Adjusted hazard ratio AP-1 Activator protein 1 APACHE II Acute physiology and chronic health evaluation II Apo Apolipoprotein  ApoA-I Apolipoprotein A1 ApoA-II Apolipoprotein A2 ApoB Apolipoprotein B APOL1 Apolipoprotein L1 ApoM/S1P Apolipoprotein M/sphingosine 1-phosphate BPI Bactericidal/permeability-increasing protein BSA Bovine serum albumin CARS Compensatory anti-inflammatory response syndrome CCL CC-Chemokine ligand CD Catalytic domain CE Cholesteryl ester CETP Cholesteryl ester transfer protein CHF Congestive heart failure CHRD Cys-His-rich domain CI Confidence interval CKD Chronic kidney disease CLEC C-type lectin-like receptor  CLP Cecal ligation and puncture CNS Central nervous system COPD Chronic obstructive pulmonary disease xxiii  CXCL CXC-chemokine ligand DAMPs Damage-associated molecular patterns EC Endothelial cells ED Emergency department EGF-A Epidermal growth factor-like repeat A eGFR Estimated glomerular filtration rate EL Endothelial lipase ELISA Enzyme-linked immunosorbent assay ER Endoplasmic reticulum FB Fluid balance FDR False discovery rate FiO2 Inspired oxygen fraction FXR Farnesoid X receptor GalNAc-T2  Polypeptide N-acetylgalactosaminyltransferase 2 GALNT2 Polypeptide N-acetylgalactosaminyltransferase 2 HDL High-density lipoprotein HDL-C High-density lipoprotein-cholesterol HGB Hemoglobin HLA-DR Human Leukocyte Antigen – DR isotype HMG-CoA 3-hidroxi-3-methyl-glutaril-CoA reductase HNF1α Hepatocyte nuclear factor 1α HR Heart rate HR Hazard ratio HWE Hardy-Weinberg equilibrium ICAM Intercellular Adhesion Molecule 1 ICU Intensive care unit IFN α Interferon α IL-6 Interleukin 6 IL-8 Interleukin 8 iNOS Inducible nitric oxide synthase INR International normalized ratio xxiv  IQR Interquartile range IRF3 Interferon regulatory transcription factor 3 IRR Infection-related readmission IVW Inverse-variance weighting KDIGO Kidney Disease Improving Global Outcomes KO knockout LBP Lipopolysaccharide binding protein LCAT Lecithin–cholesterol acyltransferase LDL Low-density lipoprotein LDL-C Low-density lipoprotein cholesterol LDLR Low-density lipoprotein receptor LIPG Lipase G endothelial type LOF Loss-of-function LPS Lipopolysaccharide LTA Lipoteichoic acid LXR Liver X receptor MAF Minor allele frequency MAP Mean arterial pressure MCP-1 Monocyte chemoattractant protein-1 MDRD Modification of diet in renal disease MDSC Myeloid-derived suppressor cells MR Mendelian randomization  NET Neutrophil extracellular trap NO Oxide nitric NF-kB Nuclear factor kappa B NIH National Institutes of Health NOD Nucleotide-binding oligomerization domain-like receptors NPC1 Niemann-Pick disease, type C1  NYHA New York heart association OR Odds ratio PAF-AH Platelet-activating factor acetylhydrolase xxv  PAMPs Pathogen-associated molecular pattern  PaO2 Partial pressure of oxygen in arterial blood PCSK9 Proprotein convertase subtilisin/kexin type 9 PD Prodomain PD-1 Program cell death protein-1 PDL Program cell death ligand-1 PL Pathogen lipids PLTP Phospholipid transfer protein PON1 Paraoxonase 1 PRRs Pattern recognition receptors RIG-1 Retinoic acid inducible gene 1 protein RNS Reactive nitrogen species ROC Receiver operator characteristic ROS Reactive oxygen species RR Respiratory rate S1P Sphingosine 1-phosphate SAA Serum amyloid A SCAP SREBP cleavage-activating protein SCARB1 Scavenger receptor class B type 1 sCr Serum creatinine SD Standard deviation SEM Standard error of the mean SNPs Single nucleotide polymorphisms SOFA Sequential Organ Failure Assessment SP Signal peptide sPLA2-IIA Secretory phospholipase A2-IIa SpO2 Peripheral capillary oxygen saturation SR-BI Scavenger receptor class B type 1 SRE Sterol response element SREBP2 Sterol regulatory element binding proteins TF Tissue factor xxvi  TG Triglycerides TLR Toll-like receptor TNFα Tumor necrosis factor α TULIP Tubular lipid-binding VASST Vasopressin and septic shock trial VCAM-1 Vascular cell adhesion protein 1 VLDL Very low-density lipoprotein VLDL-C Very low-density lipoprotein WBC White blood cells WT Wild-type               xxvii  Acknowledgments  First and foremost, I would like to extend infinite thanks to my supervisor Dr. John H. Boyd for his unending support, patience, and valuable life and career guidance. I am extremely grateful to have been his student and have learned so much from him. His confidence has invariably encouraged me to advance in my research.    I would next like to thank my co-supervisor Dr. Gordon A. Francis for his priceless intellectual contribution to my project.   To Dr. Keith R. Walley, for his always supportive enthusiasm and thoughtful criticism regarding my PhD research.   To Dr. James (Jim) A. Russell, for his insightful suggestions for my project, advice for my career, and for the opportunity of co-authoring relevant publications.     To my supervisory committee (Drs. Keith R. Walley, Gordon A. Francis, and Stuart E. Turvey). Their ongoing assistance was invaluable to my achievement.   I am grateful to have received financial support during my doctoral program from the Brazilian funding agency National Council for Scientific and Technological Development – CNPq (Ciencia sem Fronteiras Scholarship Program).   xxviii  I extend special thanks to Dr. Liam R. Brunham and Mark Trinder for their essential contribution to this project.   I offer my enduring gratitude to all members of the Walley/Boyd/Russell Laboratory. I always considered myself enormously fortunate to have the opportunity to work with such an intelligent and friendly “team”.  They have helped and guided in so many ways that it is hard to put into words. Thank you: Julia Kong, Mihai Cirstea, Dr. Alex K. K. Leung, Dr. Mark J. Kearns, Dr. Elena Topchiy, and Dr. Tadanaga Shimada.      I am also thankful to all the faculty, staff, and students at the Centre for Heart Lung Innovation (HLI) at St. Paul’s Hospital. Many people assisted me at different moments of this work. I would like to individually thanks to Jasemine Yang for their support and friendship.   Finally, I would like to express my appreciation to the Experimental Medicine Department. Their members were fundamental at the very early stages of this work.        xxix  Dedication  This thesis is dedicated to my husband and friend Fernando, who has been a constant source of encouragement throughout my “late” graduate studies, and to my parents, who have always supported and guided me, and to anyone who is passionate about research.                 1  Chapter 1: Introduction  1.1 Sepsis Sepsis is a complex and heterogeneous disorder. As such, its pathophysiology has yet to be fully elucidated, and consequently sepsis is a syndrome and not a unique disease with specific signs, symptoms and treatment. This syndrome has intrigued clinicians, scientists and researchers for more than 2,500 years [1] and still presents many unanswered questions related to pathophysiology, etiology, host immune response, progression, resolution, therapy, and complications. Although more than 150,000 scientific articles pertaining to sepsis have been published to date, including approximately 27,000 reviews and 4,000 guidelines since 1946 (https://www.ncbi.nlm.nih.gov/pubmed/), many knowledge gaps remain. To date, the pathophysiological understanding of sepsis remains incomplete and fragmented. Over the past decade clinicians and researchers have chosen to focus upon timely delivery of general supportive care such as fluids, antibiotics and vasopressors, as over 100 trials of host response modifying therapy have failed in demonstrating improvements in sepsis survival [2]. The current therapeutic approach standardizes supportive care and has resulted in substantial improvements in short-term mortality of sepsis. Sepsis survivors are increasingly an area of focus among scientists and clinicians. It is becoming apparent that survivors have a substantial increase in post-discharge mortality, experience continuing organ dysfunction, and have more re-hospitalizations and re-infections than subjects discharged from hospital without sepsis. Given the increasing prevalence of sepsis and the high societal costs associated with morbidity in sepsis survivors,  advances in the field are critical.  2  1.1.1  The global burden of sepsis The incidence and prevalence of sepsis have increased globally over the last years [3-5]. Sepsis is the most frequent cause of admission to an intensive care unit (ICU), the most common cause of death in ICU [6], and a very common cause of hospital readmission in sepsis survivors [7-14] being recently reported as the final common pathway to death from infection [15]. The Global Burden of Disease [16] depicted infection as the cause of death of more than 10 million people per year [17]. The estimated incidence of sepsis is 176–380 cases per 100,000 inhabitants each year, affecting between 3 and 10 per 1,000 people annually in high-income countries [17]. Sepsis-associated mortality rates range from 20% to 50% [18-21].   In 2011, sepsis was associated with one in 18 deaths in Canada [5] and its related costs total approximately $325 million per year [22]. Sepsis is among the 15 leading causes of death in the US [23], and the top 5 in low-income countries [24], representing a major public health problem. Any microorganism infection can progress to sepsis; however, bacterial infections are the leading etiologic factors [25].   The unacceptably high mortality rate associated with severe sepsis and septic shock led to the creation of a global initiative in 2004 in an international effort to increase awareness and improve outcome of severe sepsis – the Surviving Sepsis Campaign guidelines for management of severe sepsis and septic shock [26], and their subsequent updated versions [27-29]. However, despite the existence of these updated sepsis/septic shock guidelines and bundles recommending early diagnosis and prompt institution of therapy to prevent progression to organ dysfunction, this syndrome continues to be a challenge worldwide.  3  1.1.2  Old and new definitions of sepsis Sepsis appears straightforward in its essential element, the host response to an acute infection. However, far from a single process, sepsis is complex and heterogeneous. Although advances in sepsis research have contributed to a better understanding of its pathobiology, they have also served to highlight current limitations related to sepsis and septic shock definitions. One major limitation relates to the definition of disease itself. The former definition (2001) [30] included several criteria for sepsis diagnosis that focused mostly in markers of inflammation, while organ dysfunction(s) indicated the presence of severe sepsis, and hypotension, of septic shock (Appendix A Table A.1). To clarify and standardize different definitions and terminologies that have been used for sepsis, septic shock and their associated organ dysfunction(s), new definitions for sepsis and septic shock were produced in 2016 [31], in which the term “severe sepsis” was excluded. These new sepsis definitions were achieved through an expert consensus process based in part on the evidence from very large, multicenter derivation and validation cohorts, and then derived and validated by Seymour et al. [32] and Shankar-Hari et al. [33], respectively, described in Table 1.1.         4  Table 1.1 The Third International Consensus Definitions for Sepsis and Septic Shock  (Sepsis-3)   Definitions Clinical Criteria Sepsis Life-threatening organ dysfunction caused by a dysregulated host response to infection Signs and symptoms of infection AND presence of organ dysfunction Organ Dysfunction Increase in the Sequential (Sepsis-related) Organ Failure Assessment (SOFA)a score of 2 points or more Impairment of one or more systems of SOFA score (respiration, coagulation, liver,  cardiovascular, central nervous system, renal) (Appendix A Table A.2) Septic Shock A subset of sepsis in which profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone Hypotension: Need for vasopressors to maintain mean arterial pressure (MAP)  ≥ 65 mmHg;  Serum lactate level > 2 mmol/L (18 mg/dL).  a Based on Vincent et al. [34].  1.1.3 Current treatment The current management of sepsis and septic shock is based on a consensus committee of 55 international experts representing 25 international organizations – The Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016 [29]. Recommendations and suggestions according to the strength of evidence at the time of its publication were grouped within 21 treatment topics. Initial resuscitation, screening for sepsis, prompt diagnosis and antimicrobial therapy are among the major components of sepsis management. Interestingly, the majority of these recommendations and suggestions constitute non-5  specific or symptomatic therapy, such as intravenous fluids to treat associated cardiovascular failure [35].   Although over 50 drugs for the treatment of sepsis and septic shock targeting the host immune response to infections have been investigated in phase 2 and 3 clinical trials [2], there is no specific approved drug for this complex and potentially fatal syndrome. Antibiotics, a therapy that aims directly at the pathogens and not the host immune response to sepsis, are the only class of medication that may reduce sepsis induced morbidity and mortality, and even this data is observational in nature. For instance, early antimicrobial administration (within the first hour of documented hypotension) was associated with increased survival [36], while Seymour et al. [37] showed, in a large retrospective study on 50,000 septic patients in the USA, that for every hour antimicrobial administration is delayed, the likelihood of in-hospital death increases by 4%. Delays of greater than 6 hours were associated with hospital mortality rates of more than 25%. In other words, therapies aimed at increasing the clearance or elimination of pathogens seem to be crucial for successful sepsis treatment [38]. Although the improved survival associated with early antibiotic treatment remains unproven in a randomized controlled trial, due to ethical restraints in withholding antibiotics, this data represents the strongest level of proof likely to be available.   1.1.4  Pathophysiology of sepsis The innate immune system acts as the first line of defense against exogenous and endogenous threats to the host, such as infections [39]. Immune activation is a key component of the host response to infection. The host response is triggered via the recognition of pathogen-associated molecular patterns (PAMPs) by pathogen sensors known as pattern recognition receptors (PRRs) 6  on immune cells. PRRs include Toll-like receptors (TLRs), C-type lectin (CLEC)-like receptors, retinoic acid-inducible gene 1 (RIG-I)-like receptors, nucleotide-binding oligomerization domain (NOD)-like receptors, cytosolic DNA sensors, and inflammasomes. A coordinated interplay between these recognition molecules then occurs [40]. The interaction between PRR (e.g. TLR4) on immune cells and PAMPs (e.g. lipopolysaccharide) results in the transcription of genes encoding proinflammatory cytokines such as interleukin (IL)-6, tumor necrosis factor (TNF), interferon (IFN) α, and IL-8 that is followed by a crosstalk with the vascular endothelium and the coagulation system [41].   Disturbances in the fine balance between pro- versus anti-inflammatory, and thrombotic versus anticoagulation responses result in both the widespread activation and the impairment of the innate immune system [42]. The systemic dysregulated host response to infection that is present in sepsis occurs when the host response becomes nonhomeostatic: it is characterized by the presence of an excessive inflammation (initiated by the production and release of proinflammatory cytokines mediated by PRR signaling) that initially overwhelms but in subsequent days ebbs to reveal an immunosuppressed  state [43-46].  These immunological events are crucial to the development of sepsis-associated organ dysfunction, although the inflammatory response to sepsis varies according to the specific organism, organism loads, host genotype, underlying host conditions (especially immunodepression), and use of immunosuppressant agents.   7  1.1.5  Organ dysfunction induced by sepsis – importance of the endothelium In a healthy state, as a result of slow translocation of bacteria across the gut barrier into capillaries and thus the bloodstream, endothelial cells are constantly exposed to low concentrations of pathogens and endotoxins. When pathogen concentrations increase due to a serious infection, it is well described that endothelial injury and dysfunction marks the earliest phases of sepsis. Circulating neutrophils are activated by sepsis, with increases in their rolling, adhesion and diapedesis between endothelial cells. Cytokines, reactive oxygen species (ROS), coagulation factors, proteases and other mediators (e.g. prostaglandins) are released by the activated neutrophils into the tight space between these cells and endothelial cells [47, 48]. These mediators induce a global increase in the production of nitric oxide (NO) mediated by the expression of inducible NO synthase (iNOS) [49]. Consequences of endothelial dysfunction and injury include microvascular blood flow changes (decreased flow and nonlaminar flow), vascular leakage, and coagulation activation. The heterogeneity of iNOS expression by endothelial cells contributes to a variety of alterations in blood flow (decreased in some vascular beds and increased in others) typical of sepsis. There is a concurrent presence of capillaries with increased flow, normal flow, intermittent flow, or even stopped flow [50].  Endothelial barrier disruption causes increased vascular permeability and leakage, the major phenotype of endothelial dysfunction [51]. The cytokine-induced upregulation of adhesion molecules on the endothelium (intracellular adhesion molecule [ICAM] and vascular cell adhesion molecule 1 [VCAM-1]) and neutrophils (selectins) promotes the adhesion and diapedesis of neutrophils and the following release of proteases and ROS [48]. These substances, in association with the effects of iNOS (the loss of inhibitory effects on platelet and neutrophil activation, 8  mediated by endothelial NOS) contribute to denudation of the glycocalyx - a complex of membrane-bound molecules (primarily proteoglycans of  the syndecan family carrying heparan sulphate and receptor-bound hyaluronan) that coats and interacts with endothelial cells. Glycocalyx regulates vascular permeability, adhesion of activated neutrophils, and can modulate inflammatory response [52]. Denudation of glycocalyx results in further exposure of adhesion molecules, and impairment in endothelial barrier function [51].  The intermittent or stopped blood flow induced by both excessive NO and coagulation activation and the interstitial edema and changes in organ architecture caused by vascular leakage lead to difficulties in the diffusion of metabolites and oxygen to parenchymal cells, causing hypoxia and hence organ dysfunction [53]. Hypoxia, NO and ROS are factors that impair intracellular mitochondrial function that can aggravate organ injury during sepsis [54].  Pro-inflammatory cytokines also induce the expression and release of chemokines such as IL-8, CC-chemokine ligand 2 (CCL2), CCL3 and CXC-chemokine ligand 10 (CXCL10) [55]. These molecules generate chemotactic gradients that induce neutrophil migration, initially to the primary source of infection. This is followed (or occur simultaneously) by the migration of activated neutrophils to distant organs, such as heart and lungs, when sepsis-induced endothelial injury is established [56]. Neutrophils accumulate into tissues and intensify organ inflammation, contributing to parenchymal cell injury and organ dysfunction [54].   A schematic representation of sepsis pathophysiology caused by bacteria containing the innate immune trigger lipopolysaccharide (LPS) is represented in Figure 1.1.   9   Figure 1.1  Simplified schematic representation of sepsis pathophysiology.  This figure represents sepsis induced by LPS from gram-negative bacteria. LPS from gram-negative bacteria are recognized by TLR4 on immune cells and activate the transcription of genes encoding the proinflammatory cytokines (IL-6, TNF, IFNα, and IL-8) released in the circulation. LPS may reach the cytosol of immune cells (likely mediated by currently uncharacterized endocytic pathway). LPS is then recognized by inflammatory caspases that activate the 10  inflammasome, resulting in the release of mature IL-1β and IL-18. Proinflammatory cytokines activate neutrophils, platelets, and NET formation that generate DAMPs (histones, HMGB1, and cell-free DNA). DAMPs are recognized by TLR4 that further stimulates a proinflammatory response. Activated platelets and neutrophils lead to increases in TF and ROS that contribute to changes in endothelial cells (coagulation activation with clot formation, decreases in blood flow, and the upregulation of iNOS, ICAM, and VCAM-1 in endothelial cells. There is also upregulation of adhesion molecules in leukocytes (selectins and integrins). DAMPs also contribute to these effects on endothelial cells by the activation of immune cells, and further release of proinflammatory cytokines. Increased vascular permeability, interstitial edema, and capillary leakage cause tissue hypoxia that can progress to organ dysfunction and death. Anti-inflammatory cytokines that are also released into the circulation during sepsis may cause CARS and/or immune suppression. Both contribute to increased rates of secondary and opportunistic hospital-acquired infections and hospital readmissions, factors associated with sepsis-associated late deaths. Major pathways (black arrows); secondary pathways (dotted arrows); activation (+); increases (↑); decreases (↓) [40]. Figure adapted from Genga KR et al. J Innate Immun 2018 May 15:1-13. Abbreviations: TLR, toll-like receptor; LPS, lipopolysaccharide; NF-κB, nuclear factor κB; AP-1, activator protein 1; IRF3, interferon regulatory transcription factor 3; IL, interleukin; NET, neutrophil extracellular trap; DAMPs, damage-associated molecular patterns; HMGB1, high-mobility group box 1; TF, tissue factor; ROS, reactive oxygen species; EC, endothelial cell; iNOS, inducible nitric oxide synthase; CARS, compensatory anti-inflammatory response syndrome; BT, bacterial translocation; SRB1, scavenger receptor class B type 1; LBP, lipopolysaccharide binding protein; PLTP, phospholipid binding protein; CETP, cholesteryl-ester transfer protein; BPI, bactericidal/permeability-increasing protein; HDL, high-density lipoprotein; LDL, low-density lipoprotein; LDLR, LDL receptor; ICAM, intercellular adhesion molecule; VCAM-1, vascular cell adhesion molecule.  11  1.1.5.1 Sepsis-induced acute kidney injury  The most common cause of Acute Kidney Injury (AKI) in critically ill patients is sepsis [57]. Sepsis-induced AKI affects around 40% to 50% of critically ill patients [58]. Importantly, AKI caused by sepsis is a relevant risk factor for hospital mortality [58, 59] and is associated with increased risk for chronic kidney disease in those patients who survive an event of sepsis [60].   Contrary to what was previously thought, recent evidence suggests that the pathophysiology of sepsis-induced AKI is not characterized by hypoperfusion, necrosis or apoptosis of renal cells but involves primarily inflammation [61, 62], alterations in the microcirculatory flow (peritubular and glomerular cells) [63] and metabolic reprogramming [64, 65], resulting in tubular epithelial cell injury and dysfunction.   Even though recent advances have been achieved regarding the pathophysiology of sepsis-induced AKI, the reversible mechanism(s) by which sepsis causes AKI still needs to be elucidated, and no specific therapy for its treatment is currently available other than preventive measures and supportive care. Drugs aimed at the removal of proinflammatory cytokines and endotoxins have shown conflicting results [66, 67]. Detoxifying agents [68], modulation of pro-inflammatory signaling such as TNF-α [69], drugs that act in the microcirculation [70, 71] among others [72, 73] have been explored in pre- and clinical studies. Trials that analyzed these therapies are described in Table 1.2. Further studies analyzing novel drugs or combinations of previously studied therapies are warranted. In addition, studies evaluating how individual characteristics (e.g., genetics) affect the risk for AKI during sepsis or its’ outcomes may help guide targeted therapies.      12  Table 1.2 Therapies aiming renal recovery in critically ill patients  Author (year) Study Design Study population Intervention Outcomes* Findings Cruz (2009) Prospective, multicenter, randomized controlled trial Patients with severe sepsis/septic shock who underwent emergency surgery for intra-abdominal infection Conventional therapy plus 2 sessions of polymyxin B hemoperfusion Change in organ dysfunction measured using SOFA scores within 72 hours Better delta renal SOFA score at 72 hours was demonstrated in the polymyxin B hemoperfusion group (-0.3 vs. 0.6). Payen (2015) Prospective, multicenter, randomized controlled trial Patients with peritonitis-induced septic shock from abdominal infections Conventional therapy plus 2 sessions of polymyxin B hemoperfusion Change in SOFA score from day 0 to day 3 Day 3 global SOFA score and its organ components were identical in both groups Pickkers (2012) Prospective, double-blind, randomized, placebo-controlled study Critically ill patients with severe sepsis or septic shock with evidence of AKI Alkaline phosphatase (bolus injection plus continuous infusion for 48 hours) Progress in renal function variables after 28 days, changes in circulating inflammatory mediators, urinary excretion of biomarkers of Treatment with alkaline phosphatase improved overall renal function (measured by creatinine clearance) 13  tubular injury, and safety Boerma (2010) Prospective, single-center, randomized, placebo-controlled, double-blind Critically-ill patients with sepsis Intravenous nitroglycerin (bolus injection plus continuous infusion for 24 hours) Maximum ARF RIFLE score, frequency of CVVH use during hospitalization No differences were found between study groups Liakopoulos (2008) Systematic review and meta-analysis Patients undergoing cardiac surgery Preoperative statin therapy (any commercially available statin for any given duration and dose) Risk and incidence of renal failure (increases in serum creatinine or dialysis therapy) after surgery Preoperative statin therapy was not associated with reductions in the odds or incidence or renal failure Song (2009) Prospective, randomized, double-blind, placebo-controlled (pilot study) Patients undergoing coronary artery bypass grafting Administration of 300 U/kg of EPO intravenously before surgery Incidence of AKI, changes in serum creatinine and eGFR over 120 hours post surgery Postoperative increases in sCr were significantly reduced in the EPO group  * renal-related outcomes, including primary and secondary. Abbreviations: SOFA: Sequential Organ Failure Assessment; AKI: acute kidney injury; NAG: N-acetyl-β- D -glucosaminidase; KIM-1: kidney injury molecule-1; SGT: glutathione S-transferase; ARF: acute renal failure; RIFLE: Risk, Injury, Failure, Loss, and Endstage; CVVH: continuous veno-venous hemofiltration; eGFR: estimated glomerular filtration rate; sCR: serum creatinine. 14  It is also important to mention the role of the kidneys in HDL catabolism. Even though HDL particles (TG-rich) are cleared mostly by the liver, renal cells are also involved. Catabolism of Apo-AI and filtered forms of HDL (forms with molecular sizes similar to or smaller than albumin such as pre-β-HDL) occur in the kidney because these forms can be filtered by the glomerular capillaries. Endocytic receptors on tubular cells, such as the cubilin-megalin-amnionless complex, uptakes Apo-AI (and/or pre-β-HDL) that can be reabsorbed or degraded by lysosomes [74]. Animal studies demonstrated that mice carrying a single deletion in the cubilin gene have greater urinary loss of Apo-AI and lower HDL-C plasma than wild-type [75]. Kidney injury in sepsis may impair the cubilin-megalin-amnionless complex causing loss of Apo-AI and hence reductions in HDL-C plasma possibly due to decreases in HDL synthesis.    1.1.6 Long-term outcomes of sepsis Given the progressive increase in the number of sepsis and septic shock survivors, long-term consequences of sepsis are currently relevant topics of discussion and research among critical care researchers and practitioners. Post sepsis consequences include unplanned hospital readmissions [11, 76], late mortality [14, 16], cognitive dysfunction and functional disabilities [77], psychiatric morbidity [78], and decreased health-related quality of life [79]. Immune dysfunction persisting beyond the index sepsis admission is hypothesized to be of paramount importance for the occurrence of long-term outcomes of sepsis [80].   Immune dysfunction of sepsis persists at hospital discharge after clinical recovery, involves both innate immune dysregulation and adaptive immune suppression, and is complicated by the simultaneous waning of the inflammatory response together with the strong persistence of the 15  immunosuppressed state. Features associated with immunosuppression of sepsis involves lymphocyte exhaustion, caused by increased apoptosis of CD4+ and CD8+ T cells induced by expression of programmed cell death 1 (PD1) and PD1 ligand 1 (PDL1) [81, 82], depletion of splenic CD4, CD8, and Human Leukocyte Antigen – DR isotype (HLA-DR) cells [83], decreased antimicrobial function of neutrophils [81], macrophage reprogramming to an M2 (anti-inflammatory) phenotype and decreased cell expression of HL-DR [81, 82], and increases in regulatory T cell populations and myeloid suppression myeloid-derived suppressor cell (MDSC). The innate and adaptive immune systems and the inflammatory and anti-inflammatory responses may fluctuate and conflict. These complex interactions likely play an essential role in the recurrent, secondary, and nosocomial infections, and other long-term outcomes of sepsis, such as hospital readmissions and late mortality [80].   1.1.6.1 Hospital readmissions Studies analyzing large cohorts of septic patients have shown that hospital readmission after sepsis is common and the initial sepsis hospitalization impacts the risk for readmissions [76]. In general, infection is the most frequent cause of readmissions [76], while sepsis is the most common and most expensive reason for early readmissions within 30 days from discharge [11]. Readmissions secondary to sepsis result in longer length of stay in the hospital than admissions due to other health conditions such as acute myocardial infarction, pneumonia, heart failure, and chronic obstructive pulmonary disease [11].     16  1.1.6.2 Late mortality after sepsis Sepsis is associated with increased risk of late mortality after discharge from the index hospitalization [14, 16, 79, 80, 84]. Late mortality rates substantially increase in sepsis survivors and interestingly, these late deaths were not explained by health status before sepsis [84]. Proposed mechanisms for increased risk of late deaths in sepsis survivors include persistent immunosuppression [85], pro-atherosclerotic state [85, 86] and high risk of re-infections [87, 88].   1.2  Lipid homeostasis and host response to sepsis  1.2.1  Pathogen lipids  Lipid moiety-containing molecules are present on microbial cell walls that cause sepsis. These molecules – the pathogen lipids such as lipopolysaccharide (LPS) from Gram-negative bacteria, lipoteichoic acid (LTA) from Gram-positive bacteria, and phospholipomannan from fungi, are released in the circulation when microorganisms are killed by the host immune system and/or by antimicrobial therapy [89]. Pathogen lipids are structurally similar to lipids from their host and share common trafficking and disposal pathways [90].   The lipid A component of LPS is the prototypical bacteria stimulus of the innate immune response [90, 91]. LPS and other pathogen lipids are PAMPs and hence bind to innate immune receptors (PRRs) and initiate the host response to sepsis previously discussed (Section 1.1.6, Figure 1.1).     17  1.2.2 Lipoprotein particles In sepsis, pathogen lipids can be sequestered within lipoprotein particles present in the host plasma (high-density lipoprotein - HDL, low-density lipoprotein - LDL, and very low-density lipoprotein - VLDL) [92, 93]. Within these particles, pathogen lipids cannot bind to PRRs on immune cells (e.g., TLR4) and do not elicit the classic pro-inflammatory response triggered by this pathway [91]. Alternatively, the pathogen lipid-lipoprotein particle complex is conducted to a  disposal (or clearance) pathway – the same pathway used by cholesterol and other host lipids [90, 91].       1.2.3   Kinetics of pathogen lipids and importance of plasma lipid transfer proteins LPS and LTA distribution and kinetics were evaluated by Levels et al. [92, 93]. The authors used fluorescently labeled LPS (Escherichia coli and Salmonella typhimuriun) and LTA (Staphylococcus aureus) to determine the chromatographic profiles of the distribution of both pathogen lipids after whole blood immunostimulatory challenge. Separation of the major lipoprotein fractions was done by high-performance gel chromatography. These studies demonstrated that most of LPS and LTA molecules (around 60% to 70%) get sequestered initially primarily within HDL and then, with the assistance of lipid transfer proteins (described below), are transferred to LDL and VLDL. The results related to both distribution and kinetics of LPS and LTA are described in Table 1.3.      18  Table 1.3 Initial distribution and kinetics of LPS and LTA according to lipoprotein fractions (in vitro)   LPS LTA  Distribution • HDL – 60% • LDL – 25% • VLDL – 12% • HDL – 68% • LDL – 28% • VLDL – 4% Kinetics Redistribution from HDL to LDL and VLDL  Data based on Levels et al. [92, 93]. Abbreviations: LPS: lipopolysaccharide; lipoteichoic acid; HDL: high-density lipoprotein; LDL: low-density lipoprotein; VLDL: very low-density lipoprotein.  Pathogen lipids are amphipathic molecules similar to phospholipids present in host plasma lipoproteins that have analogous binding characteristics. Plasma lipid transfer proteins mediate the exchange of neutral lipids and phospholipids between plasma lipoproteins. They are considered components of the innate immune system and affect pathogen lipid transport during sepsis [94]. Lipopolysaccharide-binding protein (LBP), bactericidal/permeability-increasing protein (BPI), phospholipid transfer protein (PLTP), and to a lesser extent, cholesterol ester transfer protein (CETP) mediate exchange of lipid moieties by forming a tunnel-like structure (tubular fold domain) between the acceptor and receptor molecules, a typical characteristic of the TULIP (tubular lipid-binding) superfamily proteins [95]. Major characteristics of lipid transfer proteins in sepsis are depicted in Table 1.4.      19  Table 1.4 Characteristics of lipid transfer proteins in sepsis Lipid transfer protein Characteristics   LBP   • Acute-phase protein synthesized in the liver [96]  • Delivers LPS to CD14 on macrophages, initiating a proinflammatory signal pathway [97]  • Delivers LPS to HDL [98] • Transfer LPS from HDL to LDL and VLDL [94]  BPI  • Released from azurophilic granules from activated neutrophils [99]  • Binds to LPS at high affinity [100] • Inactivates LPS by directing the complex LPS-BPI to the hepatic clearance pathway [99]   PLTP  • Delivers LPS to HDL [94] • Transfer LPS from HDL to LDL and VLDL [94] • Does not deliver LPS CD14 on macrophages [101] • Modulates HDL size and composition [102]  CETP   • Binds to LPS at low affinity [103] • Modulates HDL size and composition [104] • May have indirect effects on LPS kinetics by altering lipid profile [103] Abbreviations: LBP: lipopolysaccharide-binding protein; LPS: lipopolysaccharide; HDL: high-density lipoprotein; LDL: low-density lipoprotein; VLDL: very low-density lipoprotein; BPI: bactericidal/permeability-increasing protein; PLTP: phospholipid transfer protein; CETP: cholesteryl ester transfer protein;  20  1.2.4  Pathogen lipid clearance during sepsis – the role of hepatic LDL receptor Clearance of pathogen lipids occurs by liver uptake [105, 106]; however, the exact mechanisms involved in pathogen lipid clearance is still not fully understood. Biological plausibility [90, 91], in vitro experiments [107], and animal studies [107, 108] indicate that the LDL receptor (LDLR) is central in this process, particularly in terms of LPS. Topchiy et al. have demonstrated that plasma clearance and uptake of LPS are reduced in LDLR knockout mice compared with wild-type animals [107]. It seems that pathogen lipid clearance is initiated by the interaction of pathogen lipids within LDL particles and hepatic LDLRs, followed by endosomal internalization of the complex ligand/receptor, and eventual clearance into bile [91, 105, 106].   1.2.5  Other receptors that may be involved in pathogen lipids uptake In vitro experiments demonstrated that the VLDL receptor becomes ectopically upregulated in liver cells from LDLR knockout mice [107], suggesting a potential role for the VLDL receptor in LPS clearance. The participation of other receptors besides LDLR was corroborated by the finding that even though LPS liver uptake is greatly reduced in the absence of LDLR, uptake of LPS still happens [107]. Also, preliminary results from our group have shown that VLDL receptors present in fat tissue are involved in LPS uptake [109]. Lastly, LPS carried within HDL may be cleared directly via scavenger receptor B1, which is involved in the selective uptake of cholesteryl esters by the liver [110].      21  1.3  Lipids in sepsis  1.3.1  Sepsis affects plasma levels of lipids  Sepsis is associated with reduction in levels of total cholesterol that is driven mainly by decrements of HDL-C and LDL-C [111, 112], and with increased levels of VLDL-C and triglycerides [113-116]. Lipid levels drop rapidly in sepsis [117], decreasing around 50% at day 3 [112]. Despite the mechanisms that lead to the state of sepsis-associated hypocholesterolemia being unclear, animal and human studies indicate cytokines [118-121] and pathogen lipids (LPS) [122, 123] might be involved. Critical illnesses other than sepsis are also associated with reduced lipid levels including burns [124] and trauma [125]. However, it seems that sepsis has a stronger lipid-lowering effect than trauma, as demonstrated in prospective studies comparing lipid levels in trauma versus septic patients in children [126] and adults [127]. The interaction between HDL particles and pathogen lipids (LPS and LTA) that contributes to decreases in HDL-C levels during sepsis has been speculated as one reason for the distinct lipid profiles found between sepsis and trauma  [128].   1.3.2  Lipid levels and risk of sepsis In a prospective cohort study and case-control analysis, the risk of sepsis was analyzed in hospitalized patients who had no infection or sepsis at admission [129]. Patients who developed sepsis were then paired with control subjects. Total cholesterol, LDL-C, HDL-C, and triglycerides were measured at admission. HDL-C was the only lipid associated with increased risk of sepsis: each 1 mg/dl increase in HDL-C decreased the odds of sepsis by 3% during hospitalization. A similar study evaluated the risk of sepsis in 29,690 subjects (at the community) and demonstrated that the highest risk of a first event of sepsis occurred in the lowest quartiles of both LDL-C and 22  HDL-C [130]. In disagreement with the former study, the latter found that HDL-C levels were no longer associated with risk of sepsis after adjustments for several biomarkers and statin use, while LDL-C was maintained. According to the authors, the high prevalence of chronic inflammatory conditions in the subjects analyzed in this study might have affected HDL function, which was not reflected by HDL-C levels. Low levels of total cholesterol were also associated with increased risk for sepsis in a prospective study of patients admitted for elective cardiac surgery with cardiopulmonary bypass [131]. Despite not being measured, low levels of total cholesterol likely represent reductions in HDL and/or LDL fractions.      1.3.3  Low lipid levels influence sepsis outcomes Lipid levels are influenced by sepsis and the degree of hypocholesterolemia has been associated with worse sepsis outcomes such as mortality and degree of inflammation [117, 130, 132-137]. Most studies have shown that non-survivors of sepsis have persistently lower levels of total cholesterol, HDL-C, and LDL-C than sepsis survivors.  1.4  HDL and sepsis  1.4.1   HDL structure and function Lipoproteins play an important role in pathogen lipid binding and neutralization, and inhibition of the expression of endothelial cell adhesion molecules [138]. HDL binds pathogen lipids with a higher affinity than LDL and VLDL [92, 93] and a growing body of evidence has demonstrated that HDL attenuates inflammation, coagulopathy and endothelial activation in sepsis [139-142].    23  HDL is a small, dense and protein-rich lipoprotein. Its hydrophobic core contains cholesteryl esters and triglycerides that are surrounded by a single surface monolayer of amphipathic phospholipids and cholesterol. HDL’s surface lipid monolayer has proteins embedded on it - the apolipoproteins (Apo), largely represented by ApoA-I and ApoA-II. Other proteins and molecules associated with HDL include lecithin-cholesterol acyltransferase (LCAT), CETP, sphingosine-1-phosphate (S1P) and its carrier apolipoprotein M (ApoM), serum paraoxonase (PON1), and platelet-activating factor acetylhydrolase (PAF-AH) [143].  1.4.2  HDL and innate immunity HDL modulates the availability of cholesterol in plasma membrane lipid rafts and influences innate immunity during infections and sepsis [140]. Protective effects of HDL during sepsis have been associated with its ability to bind and neutralize pathogen lipids, demonstrated with Gram-positive and Gram-negative bacteria [144, 145]; and to mediate clearance of LPS via SR-BI-dependent uptake [110, 146]. In addition, ApoA-I has been consistently demonstrated as the most relevant anti-inflammatory protein responsible for HDL’s protective properties. In vitro, ApoA-I interacts with and inactivates endotoxins [147, 148], inhibits the release of cytokines by monocytes stimulated with LPS [149], and reduces LTA-associated nuclear factor kappa B (NF-kB) activation [138, 150].   Administration of ApoA-I mimetic peptides in animal models reduced sepsis-induced organ injury (lung, endothelium, kidney, and heart) [151, 152], inflammation and mortality [152]. Accordingly, ApoA-I knockout mice and mice overexpressing ApoA-I have reduced LPS neutralization capacity [153], and decreased systemic inflammation and multi-organ damage in sepsis [154], respectively. 24  The inflammatory response is attenuated by the administration of reconstituted [155, 156] and native HDL [157, 158] in both human models of endotoxemia and in vitro experiments. Figure 1.2 summarizes the effects of HDL in the immune system.      Figure 1.2 Effects of HDL on immune response during sepsis. Pathogen lipids are released in the systemic circulation during sepsis: 1) Plasma HDL binds pathogen lipids with high affinity, favoring PL neutralization. 2) PL sequestered by HDL can be transferred to LDL * facilitated by lipid transfer proteins, mainly LBP and PLTP, allowing the LDL-PL complex to bind to LDLR on the surface of hepatocytes. 3) HDL modulates the inflammatory response induced by PL, reducing the release of pro-inflammatory cytokines; 4) HDL modulates neutrophils and endothelial activation, attenuating the inflammatory response, vascular leakage and coagulation activation, decreasing thrombus formation. Figure adapted from Walley KR et al. [91] and Morin EE et al. [159].  Abbreviations: PL, pathogen lipids; LBP: lipopolysaccharide-binding protein; PLTP: phospholipid binding protein; TLR: toll-like receptors; LDLR: LDL receptor; TF: tissue factor. 25  1.4.3  HDL modifications during sepsis Besides the effect of sepsis on lowering plasma levels of HDL-C, sepsis contributes to critical changes in both structure, composition and functioning of HDL particles (Table 1.5).   Table 1.5 HDL modifications during sepsis Structure/composition Function - Displacement of ApoA-I by SAA [160] - Reduced levels of ApoA-I caused by a rapid association of SAA [160, 161] - Reduced number of small and medium-size HDL particles [162] - Reduction of phospholipid content (increased plasma endothelial lipase and sPLA2-IIA) [128] - Modulation of CE content (decreased activity of CETP and LCAT) [128, 163] - Decreased ApoM/S1P [164, 165] - Decreased activity of antioxidant HDL-associated proteins PON1 and PAF-AH [166] - Decreased cholesterol efflux capacity by ABCA-1 and SRBI [162]  Abbreviations: SAA: serum amyloid A protein; ApoA-I: apolipoprotein A-I; sPLA2-IIA: Human group IIA secreted phospholipase A2 (sPLA2-IIA); CE: cholesteryl ester; CETP: cholesteryl ester transfer protein; LCAT: lecithin-cholesteryl acyltransferase; ApoM: apolipoprotein M; S1P: sphingosine-1-phosphate; PON1: paraoxonase-1; ABCA-1: ATP-binding cassette transporter 1; PAF-AH: Platelet-activating factor acetylhydrolase.   The alterations described above lead to dysfunctional HDL particles with both pro-inflammatory  and pro-atherogenic properties.  26  1.4.4  HDL-C levels and sepsis outcomes Low levels of HDL-C are found commonly in patients with sepsis. Prospective and retrospective clinical studies have shown that reduced HDL-C levels are associated with worse outcomes of sepsis. Very low HDL-C levels in sepsis (< 20 to 25 mg/dL) increase the risk of death [133, 167], development of multiple organ dysfunction syndrome [167], hospital-acquired infection, and prolonged ICU stay [133]. HDL-C levels are better predictors of 28-day mortality and organ dysfunction in comparison to lactate levels measured at sepsis admission [167]. Low levels of HDL-C persist for up to one week after sepsis recovery and correlate negatively with pro-inflammatory cytokine levels [137].   1.4.5  HDL properties and kidney during sepsis Sepsis-associated kidney injury involves primarily endothelial impairment (microvascular dysfunction) and inflammation [61, 62, 168]. Anti-inflammatory and endothelial protective properties associated with HDL, therefore, may prevent or attenuate sepsis-associated kidney injury, a prevalent and potentially lethal complication of sepsis.   Also important is the role of the kidneys in HDL catabolism. Even though HDL particles (TG-rich) are cleared mostly by the liver, renal cells are also involved. Catabolism of Apo-AI and filtered forms of HDL (forms with molecular sizes similar to or smaller than albumin such as pre-β-HDL) occur in the kidney because these forms can be filtered by the glomerular capillaries. Endocytic receptors on tubular cells, such as the cubilin-megalin-amnionless complex, uptakes Apo-AI (and/or pre-β-HDL) that can be reabsorbed or degraded by lysosomes [74]. Animal studies 27  demonstrated that mice carrying a single deletion in the cubilin gene have greater urinary loss of Apo-AI and lower HDL-C plasma than wild-type [75].   In sepsis, the use of ApoA-I mimetic peptides improved renal and endothelial function and was associated with decreased plasma cytokine levels in a cecal ligation and puncture (CL) model of sepsis in mice. In this study, endothelial function was evaluated by means of expression of proteins involved in the intercellular junction assembly and barrier Slit2 and its receptor, Robo4. Slit2 bound to Robo4 prevents the dissociation of p120-catetin from vascular endothelial (VE)-cadherin [169]. Stability of VE-cadherin, which is mediated by p120-catenin, is crucial for the maintenance of barrier integrity [170]. In mice, ApoA-I mimetic peptides increase the expression of Robo4 in the kidney and reestablish its pre-sepsis levels, earlier reduced by CLP induction [151].   Based on that, HDL and kidney are indeed interconnected: while HDL-C plasma levels may have an impact on the risk of sepsis-associated AKI, renal function can also alter HDL-C levels. Kidney injury in sepsis may impair the cubilin-megalin-amnionless complex causing loss of Apo-AI and hence reductions in HDL-C plasma possibly due to decreases in HDL synthesis.         28  1.5 Regulation of HDL metabolism  1.5.1  Proteins involved in HDL metabolism HDL composition and HDL-C levels are influenced by several genes and their related proteins.  Table 1.6 describes the role of the major proteins involved in HDL metabolism [171]. Genetic polymorphisms of all of these proteins have been associated with alterations in HDL-C levels in humans.                  29  Table 1.6 Proteins that regulate composition and HDL-C plasma levels  Protein (Gene) Protein function ABCA1 (ABCA1) Rate-limiting transporter of cellular cholesterol and phospholipids to cell surface-bound apolipoproteins to form HDL [172] Apo A-I (ApoA1) Formation, maturation, and metabolism of HDL-C [173]  Apo A-II (ApoA2) Interacts with PLTP and influences PLTP activity [174] CETP (CETP) Transfer of CE in HDL to Apo B-containing lipoproteins in exchange for triglycerides [175] GalNAc-T2 (GALNT2) O-glycosylation of PLTP leading to increased enzymatic activity [176] LCAT (LCAT) Esterification of cholesterol in HDL [177] EL/LIPG HDL catabolism through lipolysis of phospholipids on HDL particles [178] NPC1 Mobilization of intracellular cholesterol from late endosomes/lysosomes to endoplasmic reticulum and plasma membrane [179] PLTP Transfer of phospholipids from triglyceride-rich lipoproteins (VLDL or chylomicrons) to HDL; formation of chylomicron or VLDL remnants; HDL maturation [102] SR-BI (SCARB1) Selective uptake of HDL-derived cholesterol in hepatic and steroidogenic tissues [180] Abbreviations: ABCA1: ATP-binding cassette transporter 1; ApoA-I: apolipoprotein A-I; ApoA-II: apolipoprotein A-2; PLTP: phospholipase transfer protein; CETP: cholesteryl ester transfer protein; CE: cholesteryl ester; GalNAc-T2: polypeptide n-acetylgalactosaminyl transferase 2; GALNT2: polypeptide n-acetylgalactosaminyl transferase 2; 30  LCAT: lecithin cholesterol acyltransferase; EL: endothelial lipase; LIPG: lipase G endothelial type; NPC1: Niemann-Pick disease, type C1; SR-BI: scavenger receptor type BI; SCARBI: scavenger receptor type BI.  1.5.2  CETP  CETP is a key protein in HDL metabolism. CETP is a hydrophobic glycoprotein that contains 476 amino acid residues, synthesized by the liver, small intestine, kidney, heart, spleen, adipose tissues, skeletal muscles, and adrenal gland, regulated by the CETP gene [181]. CETP is present in human plasma at concentrations around 2 μg/mL and is primarily associated with HDL [182].     Dietary cholesterol and endogenous hypercholesterolemia induce CETP gene expression, mediated by the activation of the transcription factors liver X receptor/retinoid X receptor at the CETP gene promoter region [183]. CETP activity is reduced by the use of drugs commonly used to treat dyslipidemias such as statins [184, 185] and fibrates [186].   1.5.3  Modulation of HDL by CETP CETP is crucial for the continuous modulation of plasma HDL particles [110]. As a member of the lipid transfer/lipopolysaccharide binding protein gene family, CETP mediates the transfer of cholesteryl ester within HDL to apo B-containing particles (LDL, IDL, and VLDL) in exchange for triglycerides. While the content of triglycerides raises within HDL, this results in further hydrolysis by hepatic lipase and production of smaller HDL particles. By modulation of HDL, CETP contributes to the lipid balance that exists among all lipoprotein fractions [175].     31  1.5.4  CETP activity affects HDL-C plasma levels Decreased activity of CETP caused by genetic polymorphisms in the CETP gene is associated with distinct increases in plasma HDL-C levels [187, 188]. It is postulated that HDL-C levels rise because of a delayed catabolism of enlarged HDL particles with increased cholesteryl ester content [188]. Inversely, gain of function CETP polymorphisms increase HDL triglyceride content, leading to increased hydrolysis by hepatic lipase and smaller HDL particles (Figure 1.3). Accordingly, drugs that inhibit CETP activity are very effective in their ability to raise HDL-C plasma levels, although cardiovascular benefit has not been proved in large clinical trials [189-193].         32  Figure 1.3 Effects of CETP gain-of-function and loss-of-function on HDL-C plasma levels. CETP mediates the transfer of cholesteryl ester (CE) from HDL to apo-B particles (LDL and VLDL) in exchange for triglycerides. Increased activity of CETP (gain-of-function) leads to the formation of TG-rich HDL particles that are more susceptible to hydrolysis by hepatic lipase, causing more pronounced decreases in HDL-C levels. Decreased activity of CETP (loss-of-function) forms denser HDL particles rich in CE that have a delayed catabolism by hepatic lipase, attenuating its effects on decreasing plasma HDL-C levels.  1.5.5  CETP activity during sepsis Sepsis leads to decreased activity of CETP [194, 195]. The degree of inflammation was negatively associated with CETP activity in patients with sepsis [194]. In transgenic mice expressing CETP, administration of LPS reduced hepatic mRNA CETP expression and both plasma and activity of CETP [195]. Perhaps this is an adaptive mechanism in order to minimize the sepsis-associated decrements in HDL-C plasma levels as reductions on CETP activity result in increased levels of HDL-C. Figure 1.4 is a schematic representation of CETP effects on HDL-C levels during sepsis.  33   Figure 1.4 CETP effects on HDL-C plasma levels during sepsis.  Sepsis is associated with extremely low levels of HDL-C due to mechanisms not fully understood. Studies have demonstrated that sepsis reduces CETP activity, which may be an adaptive mechanism to attenuate the effects of sepsis in lowering HDL-C levels and to keep levels constant.   CETP induces changes in HDL particle and plasma levels of HDL-C and hence may impact sepsis outcomes. In this way, increased activity of CETP represented by CETP gain-of-function genetic variants, by decreasing HDL-C levels, can contribute to adverse outcomes during sepsis. Conversely, CETP loss-of-function genetic variants may benefit septic patients by their effects on increasing HDL-C plasma levels, previously associated with beneficial outcomes in sepsis [195].     34  1.5.6  CETP variants and sepsis-associated AKI  Sepsis progression and prognosis are influenced by innumerable factors including those related to the host, to the causative microorganism, and to the external environment. Research about the impact of host genetic variants in the risk of sepsis-related organ dysfunction is appealing as the discovery of new genetic markers may identity patients at high risk for a specific outcome as well as the group of patients who may benefit from the use of certain drugs.   Regarding AKI in sepsis, studies have demonstrated that genetic variants in genes involved in the vasomotor tone [196], inflammatory genes [197-199] and interestingly, apolipoprotein genes [200], have been associated with AKI incidence (TNF, HLA-DRB, CX3CR1, APO E), creatinine clearance (eNOS), and progression of chronic kidney disease (APOL1) to end stage renal disease [201].   It has been observed in animal studies that increased activity of CETP shifts ApoM/S1P (normally associated with HDL) to apoB-containing lipoproteins VLDL and LDL, and that S1P on apoB particles are cleared faster from the circulation [202]. ApoM is the carrier of S1P, a lysophospholipid mediator involved in the protection against endothelial dysfunction of sepsis. Accordingly, CETP gain-of-function variants may reduce HDL-C levels during sepsis, resulting in attenuation of the endothelial-protective property associated with ApoM/S1P that can increase the risk of AKI.     35  1.6  LDL and sepsis  1.6.1  LDL structure and function  LDL particles have similar structural characteristics to HDL: they consist of a hydrophobic core of cholesteryl esters and triglycerides surrounded by a monolayer of amphipathic phospholipids and unesterified cholesterol. Different from HDL, LDL is larger and has lower overall protein content, predominantly represented by apoB-100. LDL carries approximately 60-70% of serum cholesterol and delivers this cholesterol to tissues in a process mediated by the LDLR, which recognizes ApoB-100 [203].     1.6.2  LDL and innate immunity During sepsis, LDL carries pathogen lipids that are primarily transferred to it from HDL via lipid transfer proteins. The primary role of LDL in the host immune response to infection involves the clearance of pathogen lipids mediated by hepatic LDLR, as described in Section 1.2.2. In this process, the LDL-pathogen lipid complex is internalized after binding to LDLR primarily on the surface of liver cells, which is followed by two simultaneous processes: the recycling of LDLR to the cell surface, and the removal of pathogen lipids from the circulation for clearance through the bile duct.   1.6.3  LDL modifications during sepsis Unlike HDL, changes that may occur within LDL particles during sepsis have not been well characterized. In vitro, endotoxins increase LDL oxidative modifications induced by endothelial cells and smooth muscle cells [204].  36  1.6.4  LDL-C levels and sepsis outcomes Sepsis is associated with decreased plasma levels of LDL-C, but the impact of this effect on the risk of sepsis or its related adverse outcomes is not conclusive [117, 130, 135]. Low LDL levels per se are less relevant than LDL clearance [205], a process intrinsically associated with LDL uptake mediated by LDLR mostly in the liver [107, 108, 206].   1.7  Regulation of LDL metabolism  1.7.1  Proteins involved in LDL metabolism Two major proteins involved in LDL uptake and clearance will be discussed here as they are involved in the host immune response to sepsis: LDLR and proprotein convertase subtilisin/kexin type 9 (PCSK9) [107, 108]. The LDLR pathway is the primary route for LDL clearance from the circulation. LDLR expression regulated by the content of intracellular cholesterol. Briefly, high intracellular cholesterol concentration suppresses gene expression of both LDLR and 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase, the rate-limiting enzyme in cholesterol biosynthesis in cells. The opposite happens in situations of low content of intracellular cholesterol: LDLR and HMG-CoA reductase gene expression are upregulated in a process mediated by the sterol regulatory element-binding protein 2 (SREBP2), a transcription factor that undergoes dissociation from SREBP cleavage-activating protein (SCAP) and from the retention protein Insig in the endoplasmic reticulum (ER), and then undergoes proteolytic cleavage and migration to the nucleus to bind to the sterol response element (SRE) in the promoter regions of HMG-CoA reductase and LDLR gene. LDLR encodes LDLR that is mobilized to the cell surface and mediates 37  the uptake of LDL-C in the plasma. HMG-CoA reductase induces intracellular cholesterol synthesis [207].   1.7.2 PCSK9  1.7.2.1 PCSK9 function PCSK9 is a member of the mammalian serine proprotein convertase family. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is intrinsically associated with LDLR synthesis and degradation and therefore is an important regulator of LDL uptake and clearance. It is secreted mostly by the liver and also by the kidneys, intestines, pancreas, and brain [208, 209]. PCSK9 contains an N-terminal signal peptide that is followed by a prodomain, containing the binding site of PCSK9 to LDL; a catalytic domain that contains the LDLR binding site (EGF-A binding site); and a C-terminal domain [210, 211]. It is synthesized as a soluble zymogen (75kDa) and requires autocatalytic cleavage in the ER for its proper secretion [210, 212-214]. The resultant two fragments – prodomain (13kDa) and remaining protein (62kDa), are kept associated with each other by non-covalent bonds. The attachment of the prodomain to the catalytic domain at this phase is crucial for appropriate protein folding, mobilization to the secretory pathway, and regulation of catalytic activity of the enzyme [212]. The mature 62 (+13) kDa PCSK9 is the secreted form found in the circulation that can bind to LDL particles [215-218]. The furin-cleaved form 55 (+13) kDa is also present in the circulation and has decreased ability to degrade LDLR [207, 219-221] (Figure 1.5).   38   Figure 1.5 Schematic representation of PCSK9 domains.  Pro-protein (75kDa) contains a signal peptide (SP), prodomain (PD), catalytic domain (CD) and Cys-His-rich (CHRH) domain. PD contains the binding site of PCSK9 to LDL; the CD is in the N-terminal section (N, yellow box) and contains an epidermal growth factor-like repeat A (EGF-A) binding site, where LDL binds, responsible for the effects on LDLR (dark gray box). Cleavage of the 75 kDa pro-protein is required for protein mobilization out of the endoplasmic reticulum, via the Golgi apparatus and out of the cell: the resulting 13 kDa and 62kDa fragments represent the mature protein secreted as a catalytically inactive protein. This form is active and mediates LDLR degradation and can bind to LDL particles. The PD and CD are held together by non-covalent bonds (*).  The furin-cleaved form (55 + 13 kDa) has decreased activity on LDLR degradation. Figure adapted from Tavori et al. [222].  PCSK9 is a crucial regulator of LDL-C levels; its major function is related to the post-translational regulation of LDLR. Intracellularly, PCSK9 binds to LDLR and direct it for degradation within lysosomes directly from the trans-Golgi network [223]. Extracellularly, PCSK9 binds to the N-terminus of the first epidermal growth factor-like repeat (EGF-A) of the LDLR at the cell surface and targets this receptor that is endocytosed bound to PCSK9 to lysosomal degradation, preventing LDLR recycling to the cell surface [223, 224]. PCSK9 degrades LDL receptor (LDLR) 39  independently of its catalytic activity [225], while the C-terminal domain is required for the protein function [222, 226].   It was demonstrated that a large proportion of PCSK9 is found bound to LDL in human plasma, inhibiting PCSK9 binding to cell surface LDLR [227]. It is proposed that PCSK9 bound to LDL has reduced LDLR binding activity and hence decreased ability to induce LDLR degradation [227]. It appears that PCSK9-LDL binding is specific, where ApoB-100 is likely the LDL binding site to PCSK9 [227].    PCSK9 is codified by the PCSK9 gene mapped at chromosome 1p32. The gene contains 12 exons and is expressed largely in the liver and to a lesser extent in the small intestines, kidney, and brain [208, 228]. At the proximal promoter of the PCSK9 gene and close to SRE motif (crucial for the regulation of intracellular synthesis of cholesterol) resides a highly conserved binding site for hepatocyte nuclear-factor 1α, the transactivator for PCSK9 gene expression [229]. Animal and in vitro studies have demonstrated that statins upregulate PCSK9 [230] and hepatocyte nuclear factor 1  (HNF1α) [231], leading to increases in  PCSK9 levels in humans [232]. Transcriptional upregulation of PCSK9 involves SRE-binding protein (SREBP) 2, SREBP-1a, HNF1α, liver X receptor (LXR) agonist and insulin [233-236]. PCSK9 is downregulated by dietary cholesterol, glucagon, and the bile acid-activated farnesoid X receptor (FXR) [234-236].   Overexpression of PCSK9 in human cell lines and administration of recombinant human PCSK9 in mice were shown to decrease LDLR levels via increases in LDLR degradation [225, 237]. PCSK9 gain-of-function is associated with familial hypercholesterolemia and high risk of coronary 40  artery disease [228] while loss-of-function causes low LDL-C plasma concentrations (up to 30%) and substantial risk reduction of coronary artery disease [238-241].   1.7.2.2  PCSK9 and sepsis  The central role of PCSK9 in LDL metabolism added to the knowledge that pathogen lipids are carried within lipoproteins and ultimately cleared via LDL uptake has led to important studies evaluating the impact of PCSK9 in sepsis. Decreased activity of PCSK9 was associated with increased pathogen lipid clearance via the LDLR, decreased inflammatory response and increased short-term survival in mice [108]. PCSK9 loss-of-function genotype also improved 28-day survival in patients with septic shock [108]. It seems that the survival advantage of PCSK9 loss-of-function genotype is not related directly to reduced plasma LDL-C levels but rather to its effects on increasing LDL clearance [205]. In vitro, PCSK9 reduces the uptake of LPS [108] and LTA [206] by human liver cells, and this effect is mediated by inhibition of the LDLR [107, 108]. PCSK9 knockout mice demonstrated decreased bacterial dissemination to blood and lungs in a cecal-ligation and puncture model of sepsis [242]. Despite these exciting findings, further research is needed as negative results were found regarding the benefits of decreased activity of PCSK9 in an endotoxemia sepsis model (intraperitoneal infection of LPS) in mice [243]. In this study, the use of PCSK9 antibodies did not reduce mortality, regardless of using different approaches for its administration such as injection prior to or shortly after sepsis, increasing doses of antibody, and different modes of administration (intravenously and intraperitoneally) [243].   A schematic representation of the PCSK9 effects on LDLR in sepsis is represented in Figure 1.6. 41  Figure 1.6 PCSK9 effects of LDLR.  LDL particles carrying pathogen lipids bind to LDLR, and pathogen lipids are cleared through bile ducts. PCSK9, when present, binds to LDLR, induces its intracellular lysosomal degradation and prevents LDLR recycling to the cell surface.  Figure adapted from Walley et al. [91].  1.7.2.3  PCSK9 inhibitors use in sepsis The clinical availability of drugs that can inhibit PCSK9 activity to treat patients with hypercholesterolemia [244-247] has raised questions about their potential benefit in the treatment of septic patients. Different models and approaches in animal models of sepsis have led to inconsistent findings [108, 243] regarding their benefits in improving survival. This discrepancy may be explained by dissimilarities between humans and animals concerning lipid metabolism and sepsis induction.   42  1.8  Statins  1.8.1  Use of statins for sepsis treatment HMG-CoA (3-hydroxy-3-methylglutaryl-CoA) reductase, the rate-limiting enzyme for the intracellular cholesterol biosynthesis, is crucial for the regulation of LDL metabolism as this particle carries between 60% to 70% of cholesterol in human plasma [248]. The primary beneficial therapeutic effect of statins is related to their ability to increase LDLR synthesis and expression by the liver (mediated by SREBP-2) [249]. Therefore, statins increase LDL uptake in the liver, enhancing pathogen lipid clearance carried within LDL. Besides these effects, statins have several associated-pleiotropic effects such as anti-inflammatory [250], antioxidant [251] and anti-microbial [252] and have been evaluated in relevant clinical trials in sepsis [253-257]. Despite these properties described, statins may result in increases in PCSK9 levels induced by SREBP-2, in a similar fashion than LDLR (discussed below).   The use of statins in patients with sepsis is still an area of uncertainty, with conflicting results comparing observational studies with randomized clinical trials, where benefits were found in the former [254, 256] but not replicated in the latter [253, 255, 257]. Differences in hydrophilic and lipophilic properties of different statins (e.g., simvastatin and rosuvastatin) may have a role in their efficacy in sepsis, a theory that was substantiated by a recent population-based study that found improved short-term survival in septic patients receiving simvastatin and atorvastatin, but not the ones who received rosuvastatin [258].    43  1.8.2  Statins versus PCSK9 inhibitors in sepsis Statins upregulate LDLR and PCSK9 expression, both mediated by SREBP-2 [230, 259]. Therefore, statins simultaneously decrease LDL-C and raise PCSK9 plasma levels [230, 260],  which might likely limit statins therapeutic effects at higher doses due to PCSK9-induced LDLR degradation. In addition, statin treatment can induce liver expression of HNF1α, which amplifies  its effect on PCSK9 upregulation [231]. These effects may have contributed to the disappointing results of large multicenter clinical trials with the use of statins in sepsis. Compared with statins, PCSK9 inhibitors increase LDLR in the absence of counteracting effects that may reduce pathogen lipid clearance during sepsis (Figure 1.7). Figure 1.7 Potential limitation of statins effects in sepsis due to increases in PCSK9.  Statins upregulate both LDLR and PCSK9 via SREBP-2. Although the net effect results in increases in LDLR, statins effect on PCSK9 upregulation leads to LDLR degradation and may limit their beneficial effects during sepsis. PCSK9 inhibitors, by inhibiting LDLR degradation, can increase the clearance of pathogen lipids via LDLR and therefore contribute to improved outcomes of sepsis. Figure adapted from Zhang et al. [261]. 44  1.9  Hypothesis and aims The information above describes the current knowledge about the existing network between sepsis and lipoprotein metabolism. Despite an area of ongoing and exciting progress, much remains to be unraveled. Specifically, there is compelling evidence pointing towards benefits of HDL and detrimental effects of PCSK9, both components of the sepsis-lipoprotein network in the short-term outcomes of sepsis. Adding to that, the population of sepsis survivors has expanded over the last years and has experienced higher mortality and hospital readmission than subjects who have never had sepsis; however, much is still to be elucidated concerning factors that contribute to these long-term outcomes. Lastly, kidney injury is a common complication of sepsis strongly associated with short- and long-term morbidity and mortality. Its pathophysiology is not fully understood, and it is unknown whether lipid metabolism has a role in the risk of acute kidney injury during sepsis.     The major hypotheses of this study are that low levels of HDL-C have a role in both short- and long-term outcomes of sepsis that is likely influenced by genetic polymorphisms known to regulate HDL-C levels; and that decreased activity of PCSK9, a key player in cholesterol metabolism, is associated with improved long-term outcomes of sepsis.   1.9.1  Specific aims To test these hypotheses, the aims of this study are: 1. To investigate the association between HDL-C levels and the risk of sepsis-associated AKI, decreased estimated glomerular filtration rate (eGFR) from 3 months to 2 years post sepsis discharge and/or all-cause mortality within 2 years from sepsis admission in septic patients; 45  2. To determine whether single nucleotide polymorphisms (SNPs) in gene(s) related to HDL-C levels influence the risk of sepsis-associated AKI;  3. To investigate the relationship between three common missense PCSK9 loss-of-function variants (R46L, A53V, and I474V) and the risk of 1-year infection-related readmission (IRR) and/or all-cause mortality in sepsis survivors.  The work here describes original findings related to the sepsis-lipoprotein network, focusing on long-term outcomes of sepsis, highlights the importance of genetics for the management of sepsis, and discusses the feasibility of new sepsis therapies exploiting clinically available drugs.              46  Chapter 2: 2-year follow-up of septic patients presenting with low HDL: the effect upon acute kidney injury, death, and eGFR  2.1  Introduction  During sepsis, lipopolysaccharide (LPS) and other pathogen lipids are sequestered within lipoprotein fractions (HDL, LDL, and VLDL). HDL can neutralize the inflammatory effects of LPS and potentially increase LPS clearance [153]. Among the lipoprotein fractions, HDL may have the highest affinity for LPS [93] and other pathogen lipids [92] and is associated with anti-inflammatory [140, 262], anti-apoptotic [139], anti-thrombotic [142], and endothelial protective properties [141]. In particular, the administration of Apo A-I mimetic peptides in sepsis was associated with improvement in renal function [151, 152]. In addition, apolipoprotein L1 gene (APOL1) variants have been described as important genetic risk factors for progression of chronic kidney disease among African-Americans [201, 263].    Patients with established septic shock present with low HDL levels [264, 265] and low HDL levels during sepsis are associated with adverse clinical outcomes such as longer ICU length of stay, greater hospital-acquired infection rates, and increased mortality rates [133, 266].   Low levels of HDL were associated with both higher estimated glomerular filtration rate (eGFR) [267] and lower eGFR [268] in two different populations. It is not known if HDL levels at presentation of sepsis have a role in sepsis-associated AKI development or long-term decreased eGFR after sepsis. Therefore, we aimed to investigate the association between HDL levels during 47  sepsis and the frequencies of sepsis-associated AKI, decreased eGFR from 3 months to 2 years post discharge and/or all-cause mortality within 2 years from sepsis admission.  2.2  Materials and methods  2.2.1  Patients and study design In this observational cohort study, a dataset of 200 patients admitted to the Emergency Department at St. Paul’s Hospital, Vancouver, Canada, from January 2011 to July 2013, was analyzed. Patient inclusion criteria was the activation of the Institutional Sepsis Protocol by the attending physician, which requires the presence of a clinically defined infection and at least 2 of the following: (i) Temperature > 38°C or < 36°C; (ii) Heart rate > 90 beats per minute; (iii) White blood cell count > 12,000 per mm3 or < 4,000 per mm3. Study identification numbers were assigned to the secured enrollment forms, and clinical data was stored in an ORACLE-based database on a firewalled, RSS encrypted server at SPH. Patients with previous chronic kidney disease (CKD) at the time of the sepsis protocol activation were excluded from the analysis.   2.2.2  Blood collection and lipid measurements  A 6 mL EDTA tube of blood was collected at the time of the first clinical blood draw at the Emergency Department. Blood was spun at 1800*g for 12 minutes, and plasma was aliquoted and stored at –80ºC until processing. Total cholesterol and HDL were measured in plasma on the SPH clinical laboratory’s ADVIA 1800 Chemistry System (Siemens). Non-HDL was calculated by subtracting HDL from total cholesterol. In order to assess HDL measurements collected previously to the sepsis event, provincial health records of all patients were reviewed, and a convenient sample 48  of patients with pre-sepsis HDL measurements was then used for the determination of the difference between pre-sepsis HDL and sepsis-admission HDL – the delta HDL.   2.2.3  AKI and long-term decreased eGFR  AKI was defined and classified according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines [269] into stages 1 to 3. Patients were also categorized into two groups: no clinically significant AKI (patients with no sepsis-associated AKI and KDIGO stage 1) and clinically significant AKI (patients with sepsis-associated KDIGO 2 and 3).   For the determination of long-term decreased eGFR, patients were followed by reviewing provincial health records for all plasma creatinine concentration measurements during sepsis hospitalization and from 3 months up to 2 years following their initial enrolment. The most recent creatinine level of each patient was used for the estimation of the occurrence of decreased eGFR. Estimated Glomerular Filtration Rate (eGFR) was determined by the Modification of Diet in Renal Disease (MDRD) equation [270] obtained from creatinine, age, sex, and race of patients. Patients were then categorized in two groups: no decreased eGFR (eGFR ≥ 60 ml/min per 1.73 m2) and decreased eGFR (eGFR < 60 ml/min per 1.73 m2).   2.2.4  Composite outcome (death or long-term decreased eGFR)  Death and decreased eGFR are competing events [271] in septic patients (i.e., those with decreased eGFR are more likely to die) both during admission and post-discharge. To account for this statistically, we created a composite endpoint of death or progression to decreased eGFR as we followed patients for 2 years following the index admission. Provincial records were reviewed, 49  and patients who had experienced any one of the events aforementioned were considered to have experienced this composite outcome.  2.2.5  Renal function over time We evaluated kidney function at 5 different time points: at admission, from day 1 (24 hours from admission) to day 28 (in which we considered the worst measurement in our analysis), from 3 to 6 months, from 6 to 12 months, and from 1 to 2 years after admission. In the first 28 days (2 initial time points) we used creatinine measurements for renal function determination and for the remaining ones we used eGFR calculations.   2.2.6  Statistical Analysis Results are expressed as mean ± standard deviation for continuous variables and absolute number (%) for categorical variables. The Kolmogorov-Smirnov test was used to determine if HDL, creatinine and eGFR levels were normally distributed.                   The relationship between sensitivity and specificity was illustrated using a receiver operator characteristic (ROC) curve for sepsis-associated AKI, and the median value for baseline HDL (33.06 mg/dL) was used as the cut-off to dichotomize the patients into lower (baseline HDL < 33.06 mg/dL) and higher (baseline HDL ≥ 33.06 mg/dL) HDL groups. This value (33.06 mg/dl) was associated with sensitivity and specificity of 76% and 60% for AKI KDIGO and was used as the cut-off for all analysis in the present study.   50  We tested for association between baseline characteristics at hospital admission and the development of sepsis-associated AKI, decreased eGFR post discharge or the composite outcome using the Chi-square test for dichotomous variables and Mann-Whitney U tests for continuous variables.   The mean (± 95% CI) values of creatinine were calculated in first 2 time points and mean (± 95% CI) eGFR for the remaining 3 time points. For a simplified analysis, the proportion of patients with decreased renal function was evaluated using eGFR at all time points. Both measurements (the mean creatinine or eGFR and the proportion of decreased eGFR) were compared between the two HDL groups. Specifically for this analysis, we used the Last-Observation-Carried-Forward (LOCF) method for patients with missing data [272].  We performed multivariate logistic regression to identify independent predictors for sepsis-associated AKI and long-term decreased eGFR. Sepsis-associated AKI development was analyzed as a binary variable (clinically significant AKI or no-clinically significant AKI) to identify risk factors using logistic regression. To estimate the probability of occurrence of the composite outcome, a time to event curve was performed. Event time was defined as the date of death or first detection of decreased eGFR (calculated through creatinine measurements). To analyze the effect of several variables on the event of interest, a Cox regression model was performed.  The multivariate models (for both logistic regression and Cox regression analyses) included variables based on univariate association at p<0.1 and/or biologic plausibility for the outcome of 51  interest. To investigate any possible differential effect of HDL levels by statins, we also included an interaction term in our multivariate models between HDL group and statin use.  All analyses were performed using SPSS Statistics version 23.0 for Windows (IBM Corp., Armonk, NY, USA). Statistical significance was claimed when two-sided P values were smaller than 0.05.  2.3  Results  2.3.1  Participants From the initial 200 patients, a total of 180 were analyzed. Pre-sepsis HDL measurements were available in 69 patients. The average period (mean ± SD) between pre-sepsis HDL measurements and sepsis event was 15.5 ± 17.6 months. Patients with prior chronic kidney disease (CKD) according to KDIGO [273] were excluded from the analysis. Plasma HDL and total cholesterol were measured in all patients. Post-discharge creatinine levels were available in 115 patients for determination of eGFR (Figure 2.1). Baseline characteristics of HDL groups (lower and higher) are presented in Table 1. Previous use of statins and vasopressor use were negatively associated with plasma HDL levels. Patients from the lower HDL group had higher APACHE II score, creatinine, and white blood count measurements, and lower hemoglobin levels compared to patients from the higher HDL group (Table 2.1).   52   Figure 2.1 CONSORT diagram of study selection.  *Composite outcome: long-term decreased eGFR or all-cause mortality within 2 years of sepsis.  Abbreviations: CKD: chronic kidney disease; AKI: acute kidney injury; eGFR: estimated glomerular filtration rate     53  Table 2.1 Baseline characteristics according to baseline HDL median (mg/dl) groups Data are presented as means ± standard deviation or absolute number (%). Variable Lower HDL < 33.06 (N=90) Higher HDL ≥ 33.06 (N=90) P value Age (mean ± SD) 55.8±15.2 53.7±18.6 0.421 Male Sex, N (%) 59 (65.6) 53 (58.9) 0.442 Diabetes, N (%) 19 (21.1) 22 (24.4) 0.722 Hypertension, N (%) 31 (34.4) 25 (27.8) 0.421 Statin use, N (%) 29 (32.2) 16 (17.8) 0.039 ICU transfer, N (%) 45 (50.0) 39 (43.3) 0.455 Vasopressor use, N (%) 23 (25.6) 7 (7.8) 0.003 Lactate (mmol/l, mean ± SD) 2.8±2.9  2.2±2.2 0.430 Creatinine (mmol/l, mean ± SD) 141.6±150.6 90.6±78.3 < 0.001 HGB (d/L, mean ± SD)   110.4±24.2 125.4±25.0 < 0.001 WBC (x109/L, mean ± SD)   12.7±7.5 10.1±8.5 0.002 Platelets (x109/L, mean ± SD)   231±138 231±108 0.585 HR (mean ± SD)   89±20 92±21 0.440 Temperature (°C, mean ± SD)   36.9±1.0 36.8±0.6 0.205 MAP (mmHg, mean ± SD)   76±19 89±17 0.209 RR (mean ± SD)   21±6 21±6 0.937 SpO2 (mean ± SD)   96.8±2.3 97.0±3.2 0.293 pH 7.25±0.12 7.23±0.10 0.474 APACHE II score 10.8±5.7 7.1±4.9 <0.001 54  Abbreviations: ICU: intensive care unit; HGB: hemoglobin; WBC: white blood cell; HR: heart rate; MAP: Mean Arterial Pressure: RR: Respiratory Rate; SpO2: Oxygen Saturation; APACHE II: Acute Physiology and Chronic Health Evaluation II.  2.3.2  Pre-sepsis HDL and delta HDL  The overall levels (mean ± SD) of pre-sepsis HDL and delta HDL (N= 69) were 46.78 ± 15.88 mg/dl and – 13.06 ± 19.71 mg/dl respectively. This result suggests that our patients presented HDL levels considered normal, before the sepsis event, and had its levels acutely decreased at the time of sepsis admission.    2.3.3  Occurrence of AKI and long-term decreased eGFR Patients from the lower HDL group had significantly decreased renal function (creatinine and eGFR) at all time points in comparison to patients with higher HDL levels (p<0.05, Figures 2.2A and 2.2B). Additionally, the lower HDL group also presented with a significantly greater frequency of eGFR < 60ml/min per 1.73 m2 at all time points (p<0.05), except the one between 3 to 6 months from sepsis hospitalization (p=0.069) (Figures 2.2C and 2.2D). Fifty-one patients from a total of 180 (28.3%) developed clinically significant sepsis-associated AKI, which was less frequently observed in patients from the higher HDL group in comparison with lower HDL group [12/90 (13.3%) vs. 39/90 (43.3%), p<0.001] (Figure 2.3A). Decreased eGFR within the 3-month to 2-year observation period after sepsis was found in 35/115 patients (30.4%), 101 (87.8%) of whom had prior AKI. Eleven of these patients (19.3%) belonged to the higher baseline HDL group, while 24 (41.4%) to the lower HDL group (p=0.018) (Figure 2.3B).   55   Figure  2.2 Creatinine and eGFR (mean ± 95% C.I.) over time (A and B) and proportion of patients with decreased eGFR (C and D) according to lower and higher HDL groups during sepsis and after 3 months to 2 years.  A) Creatinine measurements at admission: 141.69 ± 33.31 vs. 90.67 ± 17.22 mmol/l; from D1 to D28: 153.78 ± 30.66 vs. 103.88 ± 28.59 mmol/l. B) estimated glomerular filtration rate (eGFR) between 3 to 6 months of sepsis hospitalization: 83.01 ± 11.16 vs. 95.92 ± 8.05 ml/min per 1.73m2; between 6 to 12 months of sepsis hospitalization: 83.49 ± 11.74 vs. 95.19 ± 7.68 ml/min per 1.73m2; between 1 to 2 years of sepsis hospitalization: 72.94 ± 8.92 vs. 94.63 ± 7.27 ml/min per 1.73m2. C and D) Proportion of patients with decreased eGFR at different time points: at admission (69.1% vs. 30.9%); from D1 to D28 (70.9% vs. 29.1%); 3m-6m: 64.3% vs. 35.7%; 6m-12m: 68.8% vs. 31.3%; 1y-2y: 73.1% vs. 26.9%. * p< 0.05 between HDL groups; ** p< 0.001 between HDL groups. There were no differences in the proportion of patients with decreased eGFR in the period between 3 to 6 months (p= 0.069). 56   Figure 2.3 Percentage of sepsis-associated AKI (KDIGO 2 or 3) (A) and long-term decreased estimated glomerular filtration rate (eGFR) (B) according to baseline HDL groups.  Lower group: 39/90 (43.3%); Higher group: 12/90 (13.3%), p < 0.001. B) Percentage of patients with decreased eGFR after an episode of sepsis according to baseline HDL groups. Lower group: 24/58 (41.4%); Higher group: 11/57 (19.3%), p =0.018.  Abbreviations: KDIGO: Kidney Disease Improving Global Outcomes   2.3.4  HDL and risk of sepsis-associated AKI  Baseline HDL was significantly associated with the occurrence of sepsis-associated AKI. Each decrement in HDL was directly associated with a higher stage of AKI. HDL levels (mean ± SD) at admission were, respectively: 42.8 ± 15.7 mg/dl; 35.0 ± 20.6 mg/dl; 24.6 ± 11.4 mg/dl; and 23.8 ± 15.7 mg/dl in patients with no AKI, AKI KDIGO 1, AKI KDIGO 2, and AKI KDIGO 3 (p<0.001). HDL lower than the median at admission was an independent predictor for the development of stage 2 or 3 sepsis-associated AKI. The odds ratio (OR) for clinically significant AKI was 2.80 (95% CI 1.08 to 7.25, p=0.033) in the lower baseline HDL group, after adjustment for age, sex, vasopressor use, hyperlactatemia greater than 4 mmol/l, APACHE II score, and statin use. The interaction term between HDL groups and statins use was not statistically significant 57  (p=0.193). Baseline lactate level greater than 4 mmol/l and APACHE II score were also associated with increased risk for AKI (OR 3.62, 95% CI 1.10 – 11.90, p=0.034 and OR  1.19, 95% CI 1.09 – 1.31, p<0.001) (Table 2.2).   Table 2.2 Unadjusted and adjusted ORs for development of sepsis-associated AKI (KDIGO 2 and 3) in multivariate logistic regression in patients who had very early sepsis Abbreviations: OR: odds ratio; APACHE II: Acute Physiology and Chronic Health Evaluation II.        Variable Unadjusted Adjusted OR (95% C.I.) P value OR (95% C.I.) P value Male Sex 0.645 (0.333 - 1.246) 0.192 0.549 (0.222 -1.356) 0.194 Age 1.007 (0.988 - 1.027) 0.458 0.981 (0.948 - 1.015) 0.261 HDL < median 5.034 (2.410 – 10.516) < 0.001 2.809 (1.087 - 7.257) 0.033 APACHE II 1.241 (1.152 – 1.336) < 0.001 1.198 (1.091 - 1.316) < 0.001 Vasopressor use 4.500 (1.988 – 10.185) < 0.001 1.950 (0.686 - 5.541) 0.210 Lactate > 4 mmol/l 4.381 (1.769 – 10.847) 0.001 3.620 (1.101 - 11.900) 0.034 Statins use 1.821 (0.889 – 3.730) 0.101 2.414 (0.805 - 7.240) 0.116 58  2.3.5  HDL and risk of long-term decreased eGFR A low HDL at admission was independently associated with the subsequent increased risk for long-term decreased eGFR following sepsis, after adjustment for age, sex, hypertension, diabetes mellitus, clinically significant sepsis-associated AKI, APACHE II score and statin use (Table 2.3) (p=0.008). The interaction term between HDL groups and statin use was not statistically significant (p=0.150). The OR for long-term decreased eGFR was higher in older patients and lower in men (OR=1.06, 95% CI 1.01 – 1.11, p=0.008; and OR=0.315, 95% CI 0.106 – 0.935, p=0.037, respectively).   Table 2.3 Unadjusted and adjusted ORs for decreased eGFR over 2 years follow-up after sepsis Abbreviations: OR: odds ratio; AKI: acute kidney injury; KDIGO: Kidney Disease Improving Global Outcomes; APACHE II: Acute Physiology and Chronic Health Evaluation II.  Variable Unadjusted Adjusted OR (95% C.I.) P value OR (95% C.I.) P value Male Sex 0.642 (0.284 – 1.453) 0.288 0.315 (0.106-0.935) 0.037 Age 1.053 (1.022 – 1.085) 0.001 1.064 (1.016-1.114) 0.008 HDL < median 2.952 (1.274 – 6.838) 0.012 5.454 (1.570-18.939) 0.008 AKI KDIGO 2 and 3 2.000 (0.859 – 4.654) 0.108 0.793 (0.235-2.674) 0.708 APACHE II score 1.114 (1.027 – 1.207) 0.009 1.002 (0.885 – 1.134)  0.976 Hypertension 2.220 (0.971 – 5.074) 0.059 1.928 (0.579-6.422) 0.285 Diabetes Mellitus  0.483 (0.177 – 1.313) 0.154 0.158 (0.039-0.636) 0.009 Statins use 2.107 (0.915 – 4.854) 0.080 1.157 (0.349-3.830) 0.812 59  2.3.6  2-year follow-up: death or decreased eGFR according to presenting HDL A higher proportion of patients died or progressed to decreased eGFR in the lower HDL group in comparison to patients from the higher HDL group [43/75 (57.3%) vs. 24/66 (36.4%), p=0.020]. Baseline HDL levels below median were independently associated with increased probability of death (as a single outcome) and death or decreased eGRF (as a composite outcome) within 2 years of sepsis. There were 67 events related to the composite outcome (39 deaths and 28 decreased eGFR) during the follow-up period. The probability of death was 27.8% in the lower HDL group and 15.6% in the higher HDL group (p=0.038), and the probability of the composite outcome was 57.3% in the lower HDL group and 36.4 % in the higher HDL group (p=0.013, Figure 2.4). Cox regression analysis showed that lower levels of HDL at admission were independently associated with increased HR for the composite outcome along with female sex, higher APACHE II score and baseline lactate level greater than 4 mmol/l. The HR for the composite outcome in the lower HDL group was 2.20 (95% CI 1.06 – 4.56, p= 0.033) (Table 2.4). The interaction term between HDL groups and statin use was not statistically significant (p=0.798).   60   Figure 2.4 Cumulative proportion of death or decreased eGFR over 2 years of follow-up after sepsis and number of patients at risk according to HDL groups. Solid line: lower HDL groups; dashed line: higher HDL group. Probability of death or decreased eGFR of 57.3% and 36.4% respectively, p= 0.013.        61  Table 2.4 Cox proportional hazards regression models for death or decreased eGFR over 2 years follow-up after sepsis Abbreviations: HR: hazard-ratio; AKI: acute kidney injury; KDIGO: Kidney Disease Improving Global Outcomes; APACHE II: Acute Physiology and Chronic Health Evaluation II.  2.4  Discussion In the present study, we found that low plasma HDL in patients with very early sepsis presenting to the emergency department was highly associated with subsequent sepsis-associated AKI, long-term decreased eGFR and/or all-cause mortality within 2 years from sepsis.   Low levels of HDL have been associated with poor short-term outcomes (i.e., higher mortality rates [135], organ failure development [274], and greater length of ICU stay [133]) in patients with Variable Unadjusted Adjusted HR (95% C.I.) P value HR (95% C.I.) P value Male Sex 0.815 (0.498 – 1.333) 0.416 0.331 (0.152-0.722) 0.005 Age 1.032 (1.015 – 1.048) < 0.001 1.018 (0.996-1.041) 0.116 HDL < median 1.865 (1.131 – 3.075) 0.015 2.289 (1.105-4.742) 0.026 AKI KDIGO 2 and 3 1.773 (1.091 – 2.881) 0.021 0.765 (0.403-1.451) 0.412 APACHE II score 1.116 (1.070 – 1.169) < 0.001 1.079 (1.004 – 1.160)  0.039 Lactate > 4 mmol/l 2.297 (1.210 – 4.359) 0.011 2.417 (1.187 – 4.923) 0.015 Hypertension 1.679 (1.035 – 2.725) 0.036 1.669 (0.900-3.093) 0.104 Diabetes Mellitus  0.704 (0.384 – 1.290) 0.256 0.331 (0.152-0.722) 0.005 Statins use 1.441 (0.878 – 2.365) 0.149 1.318 (0.659 – 2.637) 0.434 62  established septic shock [133, 135] and other acute disorders, such as pancreatitis [274] and burn-injured patients [275]. However, very few studies have investigated the relationship between HDL at presentation and the development of sepsis-associated AKI [151, 276], nor have the factors involved in decreased eGFR after an episode of sepsis been completely elucidated. Abnormally low HDL (normal ≥ 40mg/dl)  as a risk factor for both acute and chronic kidney disease is plausible biologically given HDL’s ability to attenuate inflammation and kidney injury [140, 151, 152, 262]. Here we expand this biological observation to find both worse short and long-term renal outcomes associated with low levels of HDL upon presentation for sepsis.    It appears that the level of HDL represents a continuous risk for renal injury, rather than a threshold effect. This hypothesis is corroborated by our findings, as we observed a significant gap in the mean value for each creatinine or eGFR measurements between the two HDL groups (Figures 2A and 2B), and a greater proportion of patients with decreased eGFR were from the lower HDL group (Figures 2C and 2D). Further, in an additional analysis, baseline HDL and creatinine levels showed an AUC for the occurrence of sepsis-associated AKI KDIGO 2 and 3 of 0.754 (p<0.001) and 0.726 (p<0.001), respectively. Interestingly, after combining HDL to creatinine at sepsis admission, the AUC increased to 0.847, and this increment was statistically significant when compared to single measurements of either HDL or creatinine levels.   The risk of sepsis-associated AKI was increased 2.8-fold in patients with HDL levels < 33.06 mg/dl. Surprisingly, this was a stronger risk factor than shock requiring vasopressors. Indeed, an HDL level < 33.06 mg/dl was comparable to other factors highly associated with organ 63  dysfunction(s) in sepsis, such as hyperlactatemia greater than 4 mmol/L and an elevated APACHE II score (Table 2.2) [28, 277].   The pathophysiology of sepsis-associated AKI involves adhesion of leukocytes by the activation of immune response and the subsequent release of cytokines, chemokines, reactive oxygen species (ROS), and reactive nitrogen species (RNS), cell cycle arrest, cellular oxidative stress, and renal cell apoptosis [278]. HDL has beneficial effects in sepsis including neutralization of endotoxins [153], antioxidant properties (mainly related to paraoxonase 1 - PON1) [279], anti-apoptotic [139] and anti-thrombotic activity [142]. ApoA-I is the major apolipoprotein present in HDL, responsible for many of its anti-inflammatory effects [279]. The use of the synthetic apoA-I mimetic peptide 4F has been studied in experimental animal models of sepsis and has been associated with higher survival rates [152] and decreased organ injury [151]. Some beneficial properties associated with ApoA-I mimetic peptides are downregulation of NF-kB [280], suppression of cell adhesion molecules [280], induction of nitric oxide synthase [281], and attenuation of kidney injury, heart injury, and endothelial dysfunction [151]. Moreira et al. found that the ApoA-I mimetic peptide 4F treatment decreased expression of P-selectin in kidney tissue, decreased renal tubular injury, and reversed sepsis-induced down-regulation of Slit2 and Robo4, both proteins involved in the maintenance of the endothelial barrier integrity [151]. The purported protective mechanism of synthetic apoA-I mimetic peptide 4F could be attenuation of microvascular endothelial injury and hence, decreased fluid accumulation in the renal parenchyma and interstitium, with consequent lower impairment of microvascular renal perfusion and organ dysfunction [151].   64  Taken together, we suggest that patients with low HDL levels suffer an attenuation of the aforementioned beneficial properties related to HDL (and apoA-I) and therefore, are more prone to poor outcomes related to sepsis, such as AKI. Perhaps a higher HDL mitigates oxidative injury (through increased PON1 activity), inflammation (through greater endotoxin neutralization), endothelial damage, apoptosis, and thrombotic events; all factors associated with AKI development in sepsis.   Chronically low HDL levels are associated with progression of CKD in patients with HIV [282] and type 2 diabetic patients [283, 284]. Similar results were demonstrated in two epidemiological studies that evaluated risk factors for the development of kidney disease. Schaeffner et al. prospectively analyzed 4,483 healthy men and found that low HDL levels (<40 mg/dl) were significantly associated with an increase in the risk of developing renal dysfunction [285]. Obermayr et al. showed that a decrease by 10 mg/dl of HDL had an odds ratio of 1.12 (95% C.I 1.07–1.17) for the development of new-onset kidney disease in a prospective analysis of 17,375 healthy volunteers [286]. In this manuscript, we show for the first time that an acute event (sepsis) appears to rapidly lower HDL and that this low HDL is associated with higher risk of decreased eGFR up to 2 years after sepsis (Table 3). Additionally, patients in the lower HDL group were shown to have an approximately 5.5-fold increased risk for long-term decreased eGFR after adjustments for well-known factors associated with decreased renal function, such as hypertension [287] and diabetes mellitus [288]. Surprisingly, low HDL at presentation for sepsis represented a stronger risk factor for decreased eGFR following sepsis than the presence of clinically significant sepsis-associated-AKI. This lack of association between AKI and decreased eGFR can be explained by some factors. First of all, factors that are known to contribute to renal impairment, 65  such as genetic variants [201, 263] and the presence of metabolic syndrome [289, 290] may have overwhelmed the effects of previous AKI. Second, as sepsis is an acute condition, it triggers a timely manner management that can lead to a “treatment bias”, implying that the therapeutic approach used to treat patients with sepsis-associated AKI may result in greater frequency of renal function recovery, particularly in patients with early sepsis. Third, our study excluded patients with previous chronic kidney disease, the most important risk factor for acute kidney injury [290, 291], which maybe resulted in lower severity and duration of sepsis-associated AKI in our patients, also important factors associated with long-term decreased eGFR. Only 3.4% of our patients needed renal replacement therapy (representing 24% of those classified as KDIGO 3), which is lower than previously reported [292]. Moreover and most importantly, studies evaluating the natural history of survivors of sepsis-associated AKI are needed in order to elucidate the mechanisms by which AKI during sepsis influences long-term renal function [293]. This effect was also not related to statin use, which itself was also not associated with higher risk for long-term decreased eGFR in our cohort, in agreement with a recent meta-analysis which did not find an association between statin use and progression of kidney disease [294].  Prior studies postulated that the mechanism through which low HDL levels were associated with decreased renal function was through the co-existent high levels of non-HDL cholesterol [282-285]. Increased non-HDL cholesterol may facilitate intra-renal arteriosclerosis [282], microalbuminuria [283, 284], and lipid-induced glomerular toxicity [295], precipitating renal dysfunction. In contrast, patients with decreased eGFR from our cohort showed lower levels of non-HDL (mean ± SD) when compared to patients with no decreased eGFR, although this difference was not statistically significant (74.7 ± 32.6 mg/dl vs. 86.7 ± 39.7 mg/dl, p=0.111, data 66  not shown). These discrepant findings might be explained by the fact that lipids were measured during sepsis, a condition associated with generalized hypocholesterolemia [117], and not only low HDL levels. Therefore, the concept that decreased eGFR may occur due to increased arteriosclerosis caused by non-HDL cholesterol fractions cannot be applied to our cohort. Moreover, prior studies had longer follow-up, much greater sample size, and lipid measurements were performed during a “non-disease state”.   Low levels of HDL have been shown to be associated with higher short-term mortality rates in sepsis [133, 135, 296]. We extended this observation when we analyzed the association between HDL levels at sepsis presentation with the composite outcome of death or decreased eGFR. We used a composite endpoint both to control for the effects of HDL in patients who died (and thus did not have the “chance” of developing renal injury) and to provide a more comprehensive analysis of the effects of HDL on endpoints related to both morbidity and mortality simultaneously. Lower baseline HDL was associated with increased HR for the composite outcome, having the highest risk for this outcome when compared to variables associated to short-term outcome (e.g. APACHE II and hyperlactatemia), and other variables such as the presence of sepsis-associated AKI (KDIGO 2 and 3), hypertension and older age (Table 2.4).   The strengths of the present study include the analysis of patients with early sepsis in the emergency department who were at risk of progressing to acute kidney injury, the association between HDL levels very early in sepsis with AKI and decreased eGFR risk over 2 years, and the persistent findings even after adjustment for confounding factors that could contribute to AKI and decline in renal function.  67  Our study had several limitations. Firstly, our sample size was relatively small. Secondly, 65 patients had missing data for eGFR calculation, which could modify the incidence of long-term decreased eGFR. However, 40% of these patients (N= 26) were lost due to death (22 within 90 days and 4 from 90 days to 2 years). In addition, all baseline characteristics in the patients with missing data were similar when compared to patients who had follow-up data for eGFR calculation, except for the frequency of previous use of statins, which was higher in the latter group. We believe that the risk of bias associated with this difference is minor since we adjusted all our regression models for this specific variable and included additional interaction terms between statins and HDL levels. Thirdly, we could not use HDL as a continuous variable for all our outcomes because of a non-linear relation between HDL and long-term decreased eGFR. Additionally, we did not use the “normal” HDL as our cut-off (≥ 40mg/dl in healthy conditions), because most of our patients had in fact “abnormal” concentrations of HDL and all of them were not healthy. Finally, this is a single-centre observational cohort study of associations and so cannot prove cause and effect between HDL levels and renal disease in sepsis. More studies are needed to replicate and/or validate our findings, in independent cohorts from other countries or regions. However, it is hypothesis-generating for potential causal mechanisms for HDL and the development of acute or long-term decreased eGFR.       68  Chapter 3: CETP genetic variant rs1800777 (allele A) is associated with abnormally low HDL-C levels and increased risk of AKI during sepsis  3.1  Introduction During sepsis, lipopolysaccharide (LPS) and other pathogen lipids are not free in plasma, but rather carried in particles found in the lipoproteins high-density, low-density and very low-density cholesterol (HDL-C, LDL-C, and VLDL-C, respectively). Amongst the lipoprotein fractions, HDL-C has the highest affinity for LPS [93] and other pathogen lipids [92] and has anti-inflammatory [262], anti-thrombotic [142, 297], and endothelial protective properties [141]. HDL-C levels are low in septic shock [117], and low HDL-C levels are associated with increased hospital mortality [167]. HDL-C can attenuate systemic inflammation [140, 262] and sepsis-induced acute kidney injury (AKI) [152]. Previously, we demonstrated that HDL-C levels drop acutely in sepsis and the magnitude of this drop was a strong predictor of sepsis-associated AKI [298].   HDL-C levels are strongly influenced by genetics in non-septic patients [299]. Single nucleotide polymorphisms (SNPs) in genes involved in HDL-C metabolism such as CETP [171, 300, 301], ABCA1 [171, 301], SCARB1 [302], APOA1 [303], LIPG [301], and LCAT [301] among others, have been associated with changes in HDL-C levels in comparison to WT genotype in the healthy population. Moreover, renal function and plasma HDL-C are tightly linked because kidneys clear and control recycling of senescent HDL-C particles while their filtration function appears to be highly associated with the level and content of HDL-C particles [74].   69  It is still unclear whether host genotype influences HDL-C levels at the onset of and during sepsis. If the decline in HDL-C causes AKI then variants known to alter HDL-C levels should also result in greater risk of sepsis-associated AKI. Therefore, we hypothesized that genetic variation in genes known to modulate HDL-C metabolism is associated with the development of sepsis-associated AKI. Accordingly, the purpose of this study was to investigate associations between SNP(s) in gene(s) related to HDL-C levels and the risk of sepsis-associated AKI.   3.2  Methods  3.2.1  Study Design This was a retrospective observational study of two cohorts. Our Derivation cohort consisted of a 202 patient cohort with lipid measures, sequencing of HDL related genes and AKI status. Details of this cohort have been previously published [304]. Our Validation cohort was a larger 604 patient cohort with DNA polymorphisms and AKI status available. Details are as previously published [305].   3.2.2  Ethics The Institutional Review Board at St. Paul’s Hospital (Providence Health Care Research Ethics Board) and the University of British Columbia Clinical Research Ethics Board approved the present study (Research Ethics Board Number: H11-00505). The Vasopressin And Septic Shock Trial (VASST) was approved by the research ethics boards of all participating institutions (27 centers in Canada, Australia, and the United States [305] and the University of British Columbia Clinical Research Ethics Board (coordinating center) approved the genetic analysis.  70  3.2.3  Patients and Laboratory methods The Derivation Cohort included 202 adult patients admitted to the Emergency Department (ED) at St. Paul’s Hospital, Vancouver, Canada, from January 2011 to June 2014 who had criteria for the activation of the Institutional Sepsis Protocol by the attending physician. The sepsis protocol activation requires the presence of a clinically defined infection and at least 2 of the following: (i) Temperature > 38°C or < 36°C; (ii) Heart rate > 90 beats per minute; (iii) White blood cell count > 12,000 per mm3 or < 4,000 per mm3. All patients from the Derivation Cohort gave written informed consent to the use of both their clinical and analytical data. Study identification numbers were assigned to the secured enrolment forms, and clinical data were stored in an ORACLE-based database on a firewalled, RSS-encrypted server at St. Paul’s Hospital. All experimental methods were carried out in accordance with the approved guidelines.  Lipid and CETP measurements: a 6-mL EDTA tube of blood was collected at the time of the first clinical blood draw at the ED. Blood was spun at 1800 g for 12 min, and plasma was aliquoted and stored at –80 °C until processing. HDL-C was measured in plasma on the hospital clinical laboratory’s ADVIA 1800 Chemistry System (Siemens, Richmond, Canada). Plasma CETP mass was measured using a CETP enzyme-linked immunosorbent assay (ELISA) kit (Cloud-Clone Corp., China). HDL-C measurements collected previously to the sepsis event were assessed by review of provincial health records and were used for the determination of the difference between pre-sepsis HDL-C and sepsis admission HDL-C.    DNA library preparation and sequencing: DNA extraction, DNA concentration determination, and preparation of DNA library were performed as previously described [171]. Sequencing was 71  performed on an Illumina MiSeq instrument in 2 × 151 bp mode using 300 cycle MiSeq Reagent v2 Kits (Illumina) to generate FASTQ files. FASTQ files were analyzed in BaseSpace apps with BWA Aligner v.1.1.4 (BaseSpace Labs) for alignment to the hg19 reference genome, and Enrichment v.3.0.0 (Illumina) for enrichment analyses and variant calls to generate genome.vcf files. We loaded genome.vcf files into VariantStudio 3.0 for variant annotation. Prior to variant annotation variants were filtered for quality scores >30, read depth >15, and minor allele frequency in the literature ≥1%. Gene variants that were previously associated with HDL-C levels in non-acutely ill patients [171] (ABCA1, APOA1, APOA2, CETP, GALNT2, LCAT, LIPG, NPC1, PLTP, and SCARB1) were analyzed in 200 patients as 2 patients had no DNA available for sequencing and were excluded of this analysis.   The Validation Cohort included 604 septic shock patients enrolled into VASST [305] who had DNA available. Approval, enrollment, and consent in the VASST Cohort have been described previously [305]. Briefly, inclusion criteria were age older than 16 years, presence of septic shock defined by the presence of two or more diagnostic criteria for the systemic inflammatory response syndrome, proven or suspected infection, and hypotension despite adequate fluid resuscitation. Additional measures available in this cohort were peak of serum Creatinine (sCR) within 5 days of shock, cumulative fluid balance (FB) during the first 3 days of care (defined as all oral and intravenous intake recorded on nursing flow sheets minus urine output and/or dialysis net output, available in 522 patients), central venous pressure at baseline, 6 hours and 48 hours (available in 428 patients), and a panel of 5 cytokine levels measured at baseline (available in 284 patients). Methods used for fluid balance calculation and cytokines measurements are described elsewhere 72  [108, 306]. Genotyping for the variant(s) identified in Derivation Cohort was performed as previously described [307] in VASST for replication.   3.2.4  Defining Sepsis-associated Acute Kidney Injury (AKI) In both cohorts, AKI was defined and classified according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines [308] into stages 1–3 using methods we have previously published [309]. Patients were categorized into two groups: no clinically significant AKI (patients with no sepsis-associated AKI and KDIGO stage 1) and clinically significant AKI (patients with sepsis-associated KDIGO 2 and 3). The primary outcome was the development of clinically significant AKI over the first 5 days.   3.2.5  Statistical Analyses Results are expressed as median and interquartile range or mean and SEM for continuous variables and absolute number (%) for categorical variables. The Kolmogorov-Smirnov test was used to determine whether continuous variables were normally distributed. Associations of categorical and continuous variables between 2 groups were tested using Chi-square test (or Fisher’s test when appropriate) and unpaired T-test (for variables normally distributed) or Mann-Whitney test (for variables without normal distribution), respectively.   Gene variants of 10 HDL-related genes with minor allele frequency ≥ 1% were chosen for analysis (N=216). We tested associations between each gene variant and baseline HDL-C levels using Mann-Whitney test (or Kruskal-Wallis when appropriate), and assuming an additive model of inheritance. To correct for multiple comparisons, we then selected the candidate(s) variant(s) for 73  testing their associations with clinically significant sepsis-associated AKI based on a corrected P value < 0.05 for the association with HDL-C levels, using the Benjamini-Hochberg method with a false discovery rate (FDR) of 0.05, within each gene. VASST patients were genotyped for the variant(s) that were significantly associated with altered HDL-C levels and tested for association of the variant(s) with clinically significant sepsis-associated AKI (Appendix B Figure B.1) depicts the process of gene(s) and variant(s) selection.   To test our hypothesis that variant(s) in the Derivation Cohort were associated with the risk of clinically significant sepsis-associated AKI, and to identify independent predictors for this outcome, univariate and multiple logistic regression models were performed. The multiple model was adjusted for age, sex, Caucasian ethnicity and the candidate variant. Replication analyses were then performed in the Validation (VASST) cohort.   To better define a clinically relevant mechanism of the association of genetic variant(s) with kidney injury, we also evaluated the association of genetic variant(s) with cumulative FB within 3 days of shock, CVP (baseline, 6-hour and 48-hour), and levels of a panel of 5 cytokines measured at baseline in VASST as an exploratory analysis. All analyses were performed using SPSS Statistics version 23.0 for Windows (IBM Corp., Armonk, NY, USA) and statistical significance was set at an α = 0.05 using two-sided P values.   Variants identified to be associated with HDL-C levels in the derivation cohort were employed in Mendelian randomization (MR) analyses to estimate the causal effect of HDL-C levels on the risk of AKI. We used an inverse-variance weighting (IVW) MR approach, which implements an 74  inverse-variance weighted linear regression of the genetic effects with AKI on the genetic effects with HDL-C levels while constraining the intercept to be zero. Heterogeneity of the SNPs was tested. We also used MR-Egger approach to test pleiotropy. MR analyses were performed using the “MendelianRandomization” package (v0.2.2) for R (v3.4.2).  3.3  Results  3.3.1  Derivation Cohort   3.3.1.1            The candidate CETP variant rs1800777 (allele A) was associated with HDL-C levels at sepsis admission We tested for association between common (MAF ≥ 1%) variants in HDL-related genes (ABCA1, APOA1, APOA2, CETP, GALNT2, LCAT, LIPG, NPC1, PLTP, and SCARB1) and HDL-C levels in patients with sepsis in the Derivation Cohort. Only the minor allele of the CETP variant rs1800777 (allele A) showed a significant association with HDL-C levels at sepsis admission after Benjamini-Hochberg correction (P= 0.042) (Figure 3.1). Results of associations between each variant (within each analyzed gene) and HDL-C levels at sepsis admission are shown in Appendix B Table B.1. Accordingly, this CETP variant was selected for subsequent analysis and replication.   CETP variant rs1800777 was in Hardy-Weinberg (HWE) equilibrium (Appendix B Table B.2). Out of 200 patients, 190 (95.0%) were WT, and 10 patients (5.0%) carried one minor allele of the variant rs1800777. No patient was homozygous for the minor allele of this variant. Both CETP 75  genotype groups - WT and rs1800777 (allele A) - had similar baseline demographic, physiological and clinical characteristics (Table 3.1).    Figure 3.1 Associations between HDL-C levels at sepsis vs. genetic variations per genes analyzed.  Plots represent the corrected P value (Benjamini-Hochberg correction with a false discovery rate cutoff of 0.05) per each variant. X-axis: genetic variations analyzed per gene; Y-axis: P values (-log(10)) per each genetic variant; The horizontal dotted line represents the -log(10) for the corrected P value of 0.05. The candidate variant rs1800777 was the only one that showed a statistically significant association with HDL-C levels measured at sepsis admission (P= 0.042). Some plots represent overlapped P values of 2 or more genetic variants.     76  Table 3.1 Patients Baseline Characteristics according to rs1800777 variant (allele A) Variable WT (N=190) rs1800777 (N=10) P value Age – Median (IQR) 57 (44 – 68) 63 (52 – 76) 0.249 Sex (N, % male) 123 (64.7) 7 (70.0) 1.000 Ethnicity – N (% Caucasians) 133 (70.0) 7 (70.0) 0.635 Comorbidities – N (%) • COPD • CKD • Chronic Liver Failure • CHF NYHA Class 4 • Diabetes • Hypertension  41 (21.6) 20 (10.5) 37 (20.2) 19 (10.0) 49 (25.8) 69 (36.3)  2 (20.0) 1 (10.0) 2 (20.0) 1 (10.0) 4 (40.0) 4 (40.0)  1.000 1.000 0.412 1.000 0.461 1.000 Statins Use – N (%)  54 (28.4) 4 (40.0) 0.480 APACHE II score – Median (IQR)* 9 (5 – 14) 15 (8 – 21)   0.043 Lab. Parameters – Median (IQR)* • HGB (g/L) • WBC (x103/L) • Platelets (x103/L) • Lactate (mmol/L) • Creatinine (mmol/L) • INR  116 (96 – 133) 9.7 (6.3 – 14.2) 206 (146 – 289) 1.6 (1.2 – 2.6) 85 (65 – 131) 1.2 (1.0 – 1.4)  109 (94 – 136) 13.5 (5.3 – 22.2) 152 (108 – 221) 2.3 (1.0 – 4.2) 100 (75 – 212) 1.2 (1.1 – 1.7)  0.802 0.380 0.073 0.695 0.342 0.432 77  Abbreviations: WT: wild-type; IQR: interquartile range; COPD: chronic obstructive pulmonary disease; CKD: chronic kidney disease; CHF: congestive heart failure; NYHA: New York Heart Association; HGB: hemoglobin; WBC: white blood cells; INR: International Normalized Ratio.  3.3.1.2             CETP variant rs1800777 (allele A) was associated with decreased HDL-C levels and increased CETP mass in sepsis   Patients who carried the minor allele of CETP variant rs1800777 had significantly lower median HDL-C levels at sepsis admission in comparison to the WT group (17.40 mg/dL vs. 32.87 mg/dL, P=0.002) (Table 3.2). CETP mass was measured in all patients carrying the CETP variant rs1800777 (allele A) (N=10) and 10 WT patients who were matched for age, sex and ethnicity. The presence of the minor allele A was significantly associated with increased median CETP mass in comparison to WT patients (3.43ug/mL vs. 1.32ug/mL respectively, P= 0.034) (Table 3.2). Interestingly, CETP mass showed a significantly negative correlation to HDL levels measured at sepsis admission (Spearman’s correlation= -0.555, R2=0.197, P= 0.011) (Figure 3.2).           78  Table 3.2 Lipids, inflammation and peak of creatinine according to CETP genotype Derivation Cohort Variable, Median (IQR) WT (N=190) rs1800777 – allele A (N=10) P value HDL-C (mg/dL) 32.87 (23.49-46.49) 17.40 (8.99-20.29) 0.002 CETP mass (ug/mL) 1.32 (0.74-2.25)* 3.43 (1.43-5.34) 0.034 Validation Cohort (VASST) Variable (Mean ± SEM) WT (N=501) rs1800777 – allele A (N=31) P value Peak of sCr (mmol/L)** 213 ± 6 296 ± 35 0.008 IL-8 (pg/mL)*** 738 ± 243 1691 ± 1128 0.049 IL-6 (pg/mL)*** 3850 ± 2341 3272 ± 1299 0.133 IL-10 (pg/mL)*** 373 ± 125 401 ± 175 0.081 TNFα (pg/mL)*** 23 ± 2 26 ± 7 0.376 MCP-1 (pg/mL)*** 1576 ± 164 2411 ± 795 0.264 *Measurements of 10 patients matched for age, sex and ethnicity; **within 5 days of shock; ***available in 284 patients (271 WT patients and 13 patients carrying the CETP rs1800777, allele A).  Abbreviations: WT: wild-type; IQR: interquartile range; HDL-C: high-density lipoprotein-cholesterol; CETP: cholesteryl ester transfer protein; SEM: standard error of the mean; sCR: serum Creatinine; IL: interleukin; TNFα: tumor necrosis factor-alpha; MCP-1: Monocyte Chemoattractant protein-1    79            Figure 3.2 Correlation between HDL-C (mg/dL) and CETP mass (ug/mL).  CETP mass and HDL-C measured at sepsis admission showed a statistically significant negative correlation (R2= 0.197, Spearman correlation = -0.555, P = 0.011).  3.3.1.3               CETP variant rs1800777 (allele A) was associated with greater risk of clinically significant sepsis-associated AKI The frequency of clinically significant AKI was 29.7% (60/202). A higher proportion of patients who carried the minor allele (allele A) of the CETP variant rs1800777 variant developed clinically significant sepsis-associated AKI in comparison to the WT group (70.0% vs. 27.9%, P=0.009). In our multiple logistic regression model, the presence of the CETP variant rs1800777 (allele A) variant was independently associated with higher risk of development of sepsis-associated AKI stages 2 and 3 (OR= 8.28, P=0.013). Adjusted and unadjusted logistic regression models are described in (Table 3.3).  80  Table 3.3 Adjusted Odds Ratios (aOR)* for development of clinically significant AKI according to CETP genotype (variant rs1800777, allele A) in Derivation and Validation Cohorts Cohort aOR (95% CI) P value Derivation  8.28 (1.56-43.78) 0.013 Validation (VASST) 2.38 (1.14-4.95) 0.020 *Adjusted for age, sex and ethnicity.   Abbreviations: CETP: cholesteryl ester transfer protein; VASST: vasopressin and septic shock trial.  Interaction analysis among the variants within the CETP gene demonstrated only one strong interaction between rs1800777 and rs5880 (P< 0.001): all patients who carried the variant rs1800777 (N=10) also carried the rs5880 (N=18). Patients who carried the latter variant (rs5880) in the absence of the first (rs1800777) showed no statistically significant differences according to frequency or risk of clinically-significant AKI: P=0.727 (Fisher’s test), and 0.975 (logistic regression), respectively.    3.3.1.4    Causal effect of HDL-C reduction levels on the risk of clinically significant sepsis-associated AKI    In total, 165 SNPs were tested (out of 216 SNPS, 51 were excluded due to limited frequency of minor alleles in our cohort, defined as MAF ≤ 0.5%) for association with HDL-C levels in the derivation cohort using a linear regression. Ten SNPs in 3 genes (ABCA1, CETP and GALNT2) were found to be significant (P < 0.05). These SNPs were then tested for association with AKI in a logistic regression adjusted for age, sex and ethnicity. To ensure the independence of 81  instrumental variants in the MR analyses, only the SNP that was the most significantly associated with AKI in each of the 3 genes were selected (rs4149346 (ABCA1), rs1800777 (CETP), and rs3213497 (GALNT2)). The effect of HDL-C levels on AKI was then evaluated using MR analyses. IVW MR shows that SNPs associated with increased HDL-C levels decreased the risk of AKI (0.89 odds per unit increase in HDL-C, P = 0.00085) (Figure 3.3a). The Heterogeneity test shows no significant heterogeneity between the 3 SNPs (P = 0.64). MR-Egger analysis shows the intercept is not significantly different from 0 (P = 0.61), indicating no unbalanced pleiotropy in the IVW MR analysis. MR-Egger shows a negative but less significant effect of HDL-C on AKI (0.86 odds per unit increase in HDL-C, P = 0.11) (Figure 3.3b).  Figure 3.3 Mendelian Randomization Results.  a) Inverse-variance weighting (IVW) analysis including the selected SNPs rs4149346 (ABCA1), rs1800777 (CETP), and rs3213497 (GALNT2). X-axis: changes in HDL-C (mg/mL) per allele change; Y-axis: changes in natural logarithm (ln) odds per allele change. Estimated effects on acute kidney injury (AKI) risk are plotted against estimated effects on serum HDL-C for 3 SNPs associated with HDL-C and AKI. IVW estimate: red solid lines; 95% CI: red dashed lines. SNPs associated with increased HDL-C levels decreased the risk of AKI by 11% (ln=-0.11, odds=0.89) per 1mg/dL increases in HDL-C, P=0.00085). b) Pleiotropy Analysis: no unbalanced pleiotropy in the IVW MR analysis was found (P = 0.61) as the intercept was not difference from 0. A negative (but less significant) effect of 82  HDL-C on AKI was demonstrated: the risk of AKI decreased by 14% (ln=-0.15, odds=0.86) per 1mg/dL of increases in HDL-C (P=0.11).  3.3.2  Validation Cohort  3.3.2.1  The association between CETP variant rs1800777 (allele A) and increased risk of clinically significant sepsis-associated AKI was replicated in VASST Minor allele of the CETP variant was found in HWE (Appendix B Table B.2). Baseline characteristics according to CETP rs1800777 genotype (allele A) are described in Appendix B Table B.3. According to CETP genotype, 570 patients (94.3%) were classified as WT while 34 (5.6%) carried the minor allele of the variant rs1800777, including one homozygous patient who was analyzed within the heterozygous group.   Out of 532 patients (after the exclusion of 72 patients with pre-existing CKD), 172 (32.3%) developed clinically significant sepsis-associated AKI. Patients carrying the CETP variant rs1800777 (allele A) had significantly increased frequency of sepsis-associated AKI KDIGO stages 2 and 3 compared to WT patients (51.6% vs. 31.1%, P=0.030). CETP variant rs1800777 was validated in VASST as the presence of its minor allele A was associated with independently increased risk for developing AKI in our adjusted logistic regression model (OR 2.38, P= 0.020) (Table 3.3).     83  3.3.2.2  The CETP variant rs1800777 (allele A) was associated with increased fluid overload and central venous pressure  Patients carrying the CETP variant rs1800777 (allele A) had significantly greater cumulative fluid balance at D3 (mean cumulative fluid balance at D3 (12,712mL vs. 10,650mL, P=0.049) (Figure 3.4a), and higher baseline (17.2mmHg vs. 14.4mmHg, P=0.007) and 6-hour (17.4mmHg vs. 14.8mmHg, P=0.014) CVP compared with the WT group (Figure 3.4b).  Figure 3.4 Cumulative Fluid Balance in VASST and Central Venous Pressure (CVP) according to CETP genotype.  Patients carrying the CETP variant rs1800777 (allele A) had greater cumulative fluid balance and CVP measurements compared with the WT group. Black circles represent WT patients; Black squares represent patients carrying the CETP variant rs1800777 (allele A). Panel a: Cumulative Fluid Balance within the first 3 days of shock (D1 to D3); Panel b: CVP measurements (mmHg) at baseline, 6-hour and 48-hour of shock; * P< 0.05.  3.3.2.3  Patients carrying the CETP variant rs1800777 (allele A) presented greater plasma levels of interleukin-8 and peak of creatinine  Increased plasma levels of all 5 cytokines analyzed were found in patients carrying the CETP variant rs1800777 (allele A) in comparison to WT patients. However, only interleukin (IL)-8 84  resulted in statistically significant differences: 1691 pg/mL vs. 738 pg/mL, respectively, P=0.049) (Table 3.2). Similarly, peak of creatinine (mean) during the first 5 days of sepsis was significantly higher in this group of patients when compared to WT (296 mmol/L vs. 213 mmol/L, respectively, P= 0.008) (Table 3.2).  3.4  Discussion  The major new findings in this study are that the CETP variant rs1800777 (allele A) was associated with a much lower plasma HDL-C levels during sepsis, a great increment in CETP mass, and a doubling of the risk for the development of clinically significant sepsis-associated AKI. By using a meticulous approach for the selection of candidate genetic variant(s), we demonstrated that the CETP variant rs1800777 (allele A) was the only one associated with HDL-C levels in sepsis (out of 10 genes known to affect HDL-C in healthy people) [171]. Most importantly, the association of CETP variant rs1800777 (allele A) and increased risk for sepsis-associated AKI was replicated in the Validation Cohort. We showed a plausible clinical explanation for this finding: CETP variant rs1800777 (allele A) was also associated with increases in cumulative fluid balance, CVP and pro-inflammatory cytokine levels at sepsis, all known risk factors for kidney injury [310].   CETP mediates the transfer of cholesteryl esters (CE) from HDL-C to Apo-B containing lipoproteins LDL-C and VLDL-C, in exchange for triglycerides (TG)[104]. In normal situations, as this transfer happens, HDL-C becomes richer in TG and more prone to degradation by hepatic and endothelial lipases, which results in a reduction of plasma levels of HDL-C [311]. In accordance with our findings, the CETP variant rs1800777 (allele A) we identify here was previously associated with low HDL-C levels [299] and high CETP mass [312]. The negative 85  correlation between CETP mass and HDL-C demonstrated by us corroborates the importance of the CETP variant rs1800777 (allele A) for the reduction of HDL levels in patients with sepsis. Therefore, it is biologically plausible that CETP may be a key driver of the acute decline in HDL-C that occurs in sepsis. The first mechanism for the reduction of HDL-C levels in these patients may be mediated by CETP mass elevations that cause greater transfer of CE from HDL to Apo-B lipoproteins, increases in TG-rich HDL amounts, and ultimately increases in HDL-C catabolism by the liver; the second one could be mediated by a prolonged half-life in the mutant form of CETP rs1800777 (allele A) [313].   Although not proven experimentally in this study, we can speculate how CETP variant rs1800777 (allele A) might cause sepsis-associated AKI. Fluid overload [314], high CVP measurements [315] and increased inflammation (IL-8 levels) [316] are involved in kidney injury and were found in patients carrying the minor allele of the CETP variant rs1800777. This suggests that structural alterations in renal cells caused by fluid overload (e.g., interstitial edema), hemodynamic changes induced by high CVP (increased afterload and reduced renal perfusion pressure), and host immune response (increased levels of pro-inflammatory IL-8) are factors clinically relevant for the association between CETP genotype and risk of sepsis-associated AKI. Also, the exaggerated reduction in HDL-C levels and hence the minimization of its protective properties in sepsis might enhance systemic and renal inflammation and contribute for AKI development.   We believe our findings presented here may be highly translatable to clinical practice. There is an ongoing search for biomarker(s) that can identify patients at risk of AKI, as not all patients with clinical risk factors for AKI in sepsis (e.g., older age, diabetes, hypovolemia) eventually develop 86  the disease. Our results suggest that HDL-C levels, CETP genotype (for rs180077) and CETP mass may all have a role in the identification of those at very high risk for AKI. Also, we can argue that in the group of patients defined at high risk of AKI, CETP can be rapidly inhibited with drugs proven effective in phase 3 clinical trials [189, 193, 317, 318]. Although the first CETP inhibitor (Torcetrapib) evaluated in the ILLUMINATE trial was associated with increased mortality rates due to infection and cancer [189], the subsequent CETP inhibitors (Anacetrapib, evacetrapib, and dalcetrapib) seem to be safe [193, 317, 318]. The finding of a novel genetic marker that influences both HDL-C levels and AKI risk in sepsis favors the use of a theranostic approach in critical care as, hypothetically, we could easily monitor HDL-C levels in patients receiving CETP inhibitors, drugs able to raise HDL-C within a relatively short period [189, 193, 317, 318]. Preclinical studies are necessary before future clinical trials of CETP inhibition for the treatment/prevention of sepsis-associated AKI.   Our study has several strengths and limitations. The strengths include the candidate variant selection based on genes that influence HDL-C plasma levels [171], the lipoprotein formerly demonstrated by us as a strong predictor of AKI when present in low concentrations [298] and regulated by CETP [300]. The chances of false-positive results were minimized firstly using a false discovery rate (FDR) cutoff of 0.05 for multiple comparisons and secondly, by the replication in a second cohort. Our findings present a hypothesis for future randomized controlled trials with CETP inhibitors in sepsis-associated AKI.   The retrospective nature of this study is one of its limitations, which although hypothesis-generating, requires further mechanistic studies to determine more clearly association vs. true 87  causality of the CETP variant rs1800777 (allele A) on the development of AKI. We should mention that our Derivation Cohort was relatively small, which when combined with a genetic variant found in low abundance (5%) does present a challenge when  interpreting the biological effect. Moreover, we do not know the reasons for pre-sepsis HDL-C measurements nor for prescription of statins. However, use of statins did not alter HDL-C levels during sepsis in this cohort (HDL-C median value 33.83 mg/dL WT vs. 29.19 mg/dL CETP variant, P= 0.078, data not shown). A further limitation is that our validation (VASST) cohort did not have HDL-C levels measured or data about use of statins. We can reasonably assume that the small impact of statins in HDL-C levels in the Derivation Cohort perhaps would be replicated in VASST.                88  Chapter 4: Impact of PCSK9 loss-of-function genotype on 1-year mortality and recurrent infection in sepsis survivors  4.1  Introduction Sepsis is a major cause of hospitalization, morbidity and mortality worldwide [23, 319]. Although the short-term mortality rate associated with sepsis is falling [320, 321], a substantial number of sepsis survivors experience adverse long-term outcomes, such as hospital readmissions [9], increased late mortality [14, 16, 84], greater incidence of major cardiovascular events [322], and impaired quality of life [323]. Factors that contribute to adverse long-term outcomes are advanced age [80], comorbidity burden [84], organ dysfunction during sepsis [271], persistent inflammation [324] and chronic catabolism [325]. However, to our knowledge, no study has investigated the impact of specific genetic variants on long-term outcomes from sepsis.   Recently, proprotein convertase subtilisin kexin type 9 (PCSK9) loss-of-function (LOF) genotype has been found to be associated with decreased short-term mortality from septic shock and protection against bacterial dissemination in animal models [242]. PCSK9 inhibits the clearance of low-density lipoprotein cholesterol (LDL-C) from the blood by decreasing the density of LDL receptors (LDLR) on hepatic cells [326]. PCSK9 LOF leads to higher hepatic LDLR expression, increased clearance of LDL-C [327], and protection from coronary heart disease [239, 328]. Pathogen lipids, such as endotoxins, are the trigger for the host inflammatory response in sepsis [90]. These pathogen lipids are incorporated into lipoprotein fractions (HDL, LDL, VLDL) and eventually cleared from the blood by the liver; a process mediated by hepatic LDLR [91]. In this way, clearance of pathogen lipids during sepsis is similar to clearance of cholesterol. Consequently, 89  PCSK9 LOF variants are associated with increased clearance of pathogen lipids, decreased systemic inflammatory response, and decreased short-term mortality in sepsis [108].   One factor that may contribute to adverse long-term outcomes associated with sepsis is the increased number of new infection(s) in the weeks and months following the index admission [76]. There are potential beneficial effects of decreased PCSK9 function in decreasing 28-day mortality in patients with severe sepsis/septic shock [108]; however, the literature is still scant regarding the long-term effects of PCSK9 LOF genotype, including hospital readmissions due to an infectious reason.   The primary objectives of this study were to investigate the relationship between three common missense PCSK9 LOF variants rs11591147 (R46L), rs11583680 (A53V), rs562556 (I474V) [329] and the risk of infection-related readmission (IRR) and/or all-cause mortality within one year after an episode of sepsis.   4.2  Methods  4.2.1  Ethics This study was approved by the University of British Columbia Clinical Research Ethics Board (Research Ethics Board Number: H11-00505). Written informed consent was obtained from all patients, their next of kin, or another surrogate decision maker, as appropriate.   90  4.2.2  Study Design This was a retrospective observational study involving 1,481 patients diagnosed with sepsis.   4.2.3  Patients – Inclusion Criteria The Derivation cohort was composed of 402 patients with sepsis who were admitted to St. Paul’s Hospital in Vancouver, Canada between July 2004 and June 2014. Patients presenting a clinically defined infection and at least 2 of the following: (i) Temperature > 38°C or < 36°C; (ii) Heart rate > 90 beats per minute; (iii) Respiratory rate > 20 breaths per minute; (iv) White blood cell count > 12,000 per mm3 or < 4,000 per mm3, and had survived 28 days after admission were included (N=342). The majority of these patients were recruited within the first 3 hours in the Emergency Department, upon activation of the Institutional severe sepsis pathway. Validation Cohort was composed of 1,079 patients with septic shock, including subjects from 2 distinct cohorts merged together: Cohort #1 was composed of 628 septic shock patients enrolled into VASST [305] who had DNA available. Approval, enrollment, and consent in the VASST Cohort have been described previously [305]. Briefly, inclusion criteria were age older than 16 years, presence of septic shock defined by the presence of two or more diagnostic criteria for the systemic inflammatory response syndrome, proven or suspected infection, and hypotension despite adequate fluid resuscitation. Cohort #2 was composed of 451 patients enrolled with septic shock at St Paul’s Hospital in Vancouver Canada between January 2000 and December 2004. Sepsis was classified in accordance with the American College of Chest Physicians and the Society of Critical Care Medicine consensus [30]. Shock was defined as norepinephrine treatment and/or a mean arterial pressure of less than 70 mmHg within the first 5 days after ICU admission.  91  The three cohorts evaluated in this study were composed of a multiethnic population, in which ethnicity was determined phenotypically by the study coordinator or research assistant.  4.2.4  Exclusion Criteria  Patients known to be on chronic use of statins were excluded from the analysis associations between PCSK9 genotype and plasma PCSK9, and post-sepsis LDL-C levels because statins upregulate PCSK9 [230]. Exclusion criteria for VASST (Validation, cohort #1) have been reported [305].  4.2.5  Measurements Clinical and laboratory data were retrospectively obtained by reviewing province of British Columbia electronic records from the index hospitalization for up to one year. For all cohorts, Acute Physiology and Chronic Health Evaluation (APACHE) II score [330] was calculated at the onset of sepsis.  4.2.6  Blood collection, PCSK9 genotyping, and PCSK9 measurements For PCSK9 genotyping, blood was collected from discarded clinical blood samples and spun at 1,400 x g for 12 minutes to separate the plasma and cellular fractions. Total genomic DNA was extracted from the buffy coat fraction using QIAGEN DNeasy Blood & Tissue Kits (QIAGEN 69506). PCSK9 genotyping was performed in all samples for three common PCSK9 missense LOF variants (minor allele frequency (MAF) > 0.5%): R46L, A53V, and I474V, using pre-validated TaqMan SNP Genotyping Assays (ThermoScientific, Probe IDs rs11591147, rs11583680, rs562556 respectively, catalog number 4351379). Assays were run on a ViiA7 platform (software 92  QuantStudio Real-Time PCR software V 1.3) using the system’s single-nucleotide polymorphisms (SNP) genotyping software and protocol. All alleles were called using the software’s clustering algorithm, with a 100% success rate. For quality control, 10% of samples were randomly repeated for each SNP to ensure complete reproducibility.   4.2.7  PCSK9 measurements, lipids and blood cell counts  Plasma PCSK9 was measured via ELISA (R&D Systems DPC900) using the manufacturer's recommended protocol. Absolute white blood cell and neutrophil counts were measured daily from admission to 14 days at the central hospital laboratory on Sysmex XN analyzer. To assess LDL-C measurements collected after the sepsis event, provincial health records of all patients were reviewed and those patients with post-sepsis LDL-C measurements available were analyzed according to PCSK9 genotype.   4.2.8  PCSK9 Genotyping and Definitions Patients were classified according to their PCSK9 genotyping into three groups: i. Wild-type group (WT): patients who did not carry any of the three LOF alleles; ii. Single LOF group: patients who carried exactly one LOF allele; iii. Multiple LOF group: patients who carried two or more LOF alleles, including either one homozygous LOF SNP or multiple different heterozygous LOF SNPs.  These definitions were based on the genotyping technique used by us and on the literature related to LDL-lowering effects (in humans) of the PCSK9 polymorphisms evaluated here [238, 331]. As each SNP only partially diminishes the protein function, we considered two mutations in the same 93  copy as roughly equivalent to one mutation each in two copies, in terms of total protein functional capacity.  4.2.9  Outcomes The primary outcome was a composite outcome defined as death (all-cause mortality after 28 days) or infection-related readmission (IRR) up to 12 months thereafter. We also analyzed all-cause mortality after 28 days or IRR up to 12 months separately.  4.2.10  Statistical Analysis Results are expressed as interquartile range for continuous variables and absolute number (%) for categorical variables. Association among the PCSK9 genotype groups and demographic, physiological and laboratory parameters were tested using Mann-Whitney (or T-test when appropriate), and Chi-square (or Fisher’s test when appropriate) tests for continuous variables and categorical variables, respectively [332].   In order to evaluate the association between PCSK9 genotype and the risk of 1-year all-cause mortality or IRR we applied Kaplan-Meier estimates of time to event function for the composite outcome death or readmission within one year. Log-rank test was used for the comparison between curves. Adjusted Cox regression model (adjusted for age, sex and APACHE II score) was performed to determine the independent relationship of the presence of PCSK9 genotype and the risk for 1-year death of IRR. Additional Kaplan-Meier estimates of time to event function analyses were performed considering each single outcome: 1-year mortality and 1-year IRR. To avoid 94  double counting, patients who died were considered a mortality in the composite analysis and patients who survived 1 year were considered in the IRR analysis.   A meta-analysis for 90-day mortality according to PCSK9 genotype that included the Derivation and Validation cohorts was carried out for validation of our long-term findings. For this, we meta-analyzed using a two-step process and used the results of a first meta-analysis with the two original Validation cohorts for our final analysis. We assessed the derivation for 90-day mortality to make it comparable to the Validation cohorts. This outcome (90-day mortality) was used because that was the longest follow-up time for mortality available in all study cohorts. Data were pooled using the Mantel-Haenszel (M-H) method with a random effects model, and heterogeneity was evaluated using the χ2 and I2 statistics. All 402 patients from the Derivation cohort were included for this analysis.  To investigate relevant clinical factors associated with mortality and hospital readmission we compared white blood cell counts and absolute number of neutrophils according to PCSK9 genotype using repeated measures ANOVA in a subset of patients with laboratory data available during their first 14 days of sepsis. Specifically for this analysis, we used the Last-Observation-Carried-Forward method for patients with missing data [272].   Statistical analyses were performed using SPSS Statistics version 24.0 for Windows (IBM Corp., Armonk, NY, USA) and aov statistical function in R (version 3.4.3, available http://www.R-project.org) was used for the repeated measures ANOVA. Statistical significance was set at α = 0.05 using two-sided P values. 95  4.3  Results  4.3.1                The risk of 1-year death or infection-related readmission (composite outcome) was decreased in patients carrying multiple PCSK9 LOF alleles  Thirty-seven percent (70/189) of WT patients, 41% (41/100) from single LOF group, and 19.2% (10/52) who had multiple LOF died or were readmitted within 1 year (P=0.018, overall Log-rank test, Figure 4.1). Overall, patients carrying multiple PCSK9 LOF alleles had the greatest protection against 1-year death or IRR compared with WT patients (P=0.011, pairwise Log-rank test) and with single LOF patients (P=0.005, pairwise Log-rank test). No differences were found between patients carrying single LOF and WT subjects (P=0.511, pairwise Log-rank test).   Based on these findings demonstrating substantial protection against death or readmission in patients carrying multiple LOF alleles, further analyses were done by comparing 2 PCSK9 genotype groups: WT/single LOF (indicated here as reference (Ref.) group), vs. multiple LOF. 96   Figure 4.1 Derivation (early sepsis) cohort (N=342). Time to event curves for 1-year death of infection-related readmission according to PCSK9 genotype groups.  Patients carrying multiple PCSK9 LOF alleles showed significantly decreased risk for this outcome in comparison to patients carrying none (wild-type) or one PCSK9 LOF allele (P=0.018).  4.3.2  Characteristics of the study subjects  Baseline demographic, physiological and clinical characteristics were similar according to the two PCSK9 genotype groups (Ref. vs. multiple LOF) (Table 4.1). Most patients (290/342, 84.7%) were classified as WT/single LOF, while 52 (15.2%) had multiple LOF. All SNPs were in Hardy-Weinberg equilibrium (HWE) (Table 4.2). Minor allele frequency (MAF) of each PCSK9 variant is described in Table 2. A53V (rs11583680), I474V (rs562556), and R46L (rs11591147) variants 97  were present in 93 (27.1%), 94 (27.4%), and 7 (2.0%) patients, respectively. Appendix C Tables C.1 and C.2 describe baseline characteristics and MAF/HWE, respectively, in the Validation cohort.                      98  Table 4.1 Derivation Cohort: Baseline characteristics according to PCSK9 genotype Variable All patients N= 342 Ref. a N= 290 Multiple LOF N= 52 P value Age, Median (IQR) 59 (44 – 69) 59 (44 – 70) 57 (43 – 65) 0.515 Sex (N, % male) 226 (66.1) 190 (65.5) 36 (69.2) 0.717 Ethnicity (N, % Caucasians)b 209 (61.1) 173 (59.7) 36 (69.2) 0.250 HR, Median (IQR)c  108 (91 – 127) 107 (91 – 126) 115 (90 – 136) 0.316 MAP, Median (IQR)c 58 (53 – 67) 59 (53 – 67) 58 (55 – 70) 0.482 Temperature, Median (IQR)c 37.5 (36.5 – 38.4) 37.5 (36.5 – 38.3) 37.8 (36.4 – 39.0) 0.568 APACHE II score, Median (IQR) 14 (7 – 19) 14 (8 – 19) 12 (7 – 21) 0.989 ICU (N, %) 245 (71.6) 207 (71.4) 38 (73.1) 0.934 HGB g/L, Median (IQR)d 112 (92 – 130) 112 (93 – 130) 106 (90 – 127) 0.385 WBC (x109), Median (IQR)d 10.6 (6.3 – 15.0) 10.7 (6.4 – 15.1) 10.4 (5.9 – 13.3) 0.520 Platelets (x109), Median (IQR)d 186 (131 – 271) 190 (133 – 267) 175 (110 – 284) 0.542 Creatinine (mmol/l), Median (IQR)d 89 (65 – 154) 89 (66 – 152) 90 (55 – 158) 0.651 INR, Median (IQR)d 1.2 (1.1 – 1.5) 1.2 (1.1 – 1.5) 1.2 (1.1 – 1.5) 0.616 Lactate (mmol/l), Median (IQR)d 1.6 (1.1 – 2.6) 1.6 (1.2 – 2.6) 1.3 (1.0 – 2.7) 0.424 COPD (N, %) 60 (17.5) 48 (16.6) 12 (23.1) 0.347 Chronic renal failure (N, %) 15 (4.4) 11 (4.0) 4 (7.7) 0.271 Cirrhosis (N, %) 10 (2.9) 9 (3.1) 1 (1.9) 0.985 CHF NYHA Class 3 or 4 (N, %) 29 (8.5) 25 (8.6) 4 (7.6) 0.982 Hypertension (N, %) 88 (25.7) 78 (26.9) 10 (19.2) 0.321 Diabetes mellitus (N, %) 54 (15.8) 46 (15.8) 9 (15.4) 0.913 Statins use (N, %)e 51 (27.9) 42 (27.8) 9 (28.1) 0.972 99  a Ref. indicates WT/Single PCSK9 LOF alleles group; b Determined by the study coordinator or research assistant;            cParameters measured at admission; d Measurements in the first 24 hours of ED admission; e Available in 183 patients.                                              Abbreviations: LOF: Loss-of-function; IQR: Interquartile Range; HR: Heart Rate; RR: Respiratory Rate; SpO2: arterial Oxygen Saturation; MAP: Mean Arterial Pressure; APACHE: Acute Physiology and Chronic Health Evaluation; ICU: Intensive Care Unit; HGB: Hemoglobin; WBC: White Blood Cell; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure; NYHA: New York Heart Association.  Table 4.2 Derivation Cohort: PCSK9 genotype - allele frequency and Hardy-Weinberg equilibrium * N refers to minor allele counts in relation to the total number of alleles in the Derivation Cohort (N=684). Abbreviations: PCSK9: Proprotein Convertase Subtilisin/kexin type 9; SNPs: single nucleotide polymorphisms; HWE: Hardy-Weinberg Equilibrium.  4.3.3  The presence of multiple PCSK9 LOF alleles was associated with significantly decreased risk of death at 1 year or infection-related readmission (composite outcome) after sepsis  Composite endpoint (death or IRR at 1 year): Considering 2 PCSK9 genotype groups (Ref. vs. multiple LOF), the adjusted HR for death or IRR within one year after sepsis in patients carrying two or more PCSK9 LOF alleles was 0.40 (95% C.I. 0.21-0.77, P=0.006, Table 4.3).  PCSK9 SNPs Major (minor) allele Minor Allele Frequency N (%)* HWE P-value A53V (rs11583680) G (A) 100 (14.61%) 0.99 I474V (rs562556) A (G) 105 (15.35%) 0.47 R46L (rs11591147) C (A) 7 (1.02%) 0.98 100  1-year mortality: In relation to Ref. group, the adjusted HR for this outcome in patients carrying multiple PCSK9 LOF alleles was 0.50 (15.3% vs. 24.8%, 95% CI= 0.24-1.04, P=0.064, Table 4.3). No statistically significant difference between our study groups was found for this outcome.        Infection-related readmission: We found that, among patients who did not die within one year (N= 262), those who carried multiple PCSK9 LOF alleles had a significant risk reduction for 1-year IRR in comparison to patients who carried none (WT) or single PCSK9 LOF allele (Ref. group) (adjusted HR= 0.20, 95% C.I. 0.05-0.86, P=0.031, Table 4.3). In the Ref.  group, 17.8% of patients (39/218) were readmitted due to infection while only 4.5% (2/44) of patients were readmitted in the multiple LOF group (P= 0.023, Chi-square test).   Table 4.3 Derivation Cohort: Adjusted Hazard Ratios (aHR)* for 1-year death or IRR (composite outcome), 1-year IRR, and 1-year mortality according to PCSK9 genotype (two or more alleles) Cohort aHR (95% CI) P value Death or IRRa, b  0.40 (0.21-0.77) 0.006 IRRa, c 0.20 (0.05-0.86) 0.031 Mortalitya, b 0.50 (0.24-1.04) 0.064 *Adjusted for age, sex and APACHE II score; a within one year; b excluding patients who died within 28 days; c excluding patients who died within one year.  Abbreviations: IRR: infection-related readmission.   101  4.3.4                The biological effects of multiple PCSK9 LOF alleles: PCSK9 plasma levels at sepsis admission and post-sepsis LDL-C levels Plasma PCSK9 levels at sepsis admission were lower in patients carrying 2 or more PCSK9 LOF alleles than Ref. group patients. In the subset of patients who had PCSK9 levels measured at sepsis admission and who were not taking statins (N=132) the presence of multiple LOF alleles was associated with lower plasma PCSK9 compared to Ref. group (247 ng/mL vs.  287ng/mL, P= 0.037, Figure 4.2).          Figure 4.2 Derivation (early sepsis) cohort (N=132). PCSK9 levels at sepsis admission according to PCSK9 genotype (Ref. vs. multiple LOF group).  Patients carrying multiple PCSK9 LOF alleles (N= 23) had lower PCSK9 levels than patients from the Ref. group (N= 109)  (P=0.037). Ref. indicates WT/Single PCSK9 LOF alleles group. 102  Patients carrying multiple PCSK9 LOF alleles had a strong trend toward reduced LDL-C levels measured after sepsis discharge (19 ± 16 months) compared to Ref. patients. LDL-C levels were available in 55 patients: 41 in Ref. group and 14 in multiple LOF group. Post sepsis LDL-C levels were 94.8 ± 5.6 mg/dL in Ref. group vs. 77.1 ± 7.29 mg/dL in multiple LOF group (mean ± SEM, P=0.09, data not shown).    4.3.5  The presence of multiple PCSK9 LOF alleles was associated with 90-day mortality  In order to validate our findings related to the association of multiple PCSK9 LOF alleles and long-term mortality, we did a meta-analysis using a two-step approach (describe in the Methods section) of the association of PCSK9 genotype with 90-day mortality. All patients from Derivation and Validation cohorts were included (N=1,481). Validation cohort of septic patients (N=1,079) were genotyped for PCSK9 LOF alleles and 90-day mortality data was analyzed. We confirmed a significant association between the presence of multiple LOF alleles and significantly lower 90-day mortality (Odds Ratio 0.69; 95% CI: 0.50-0.94; p=0.02, Figure 4.3).  Figure 4.3 Meta-analysis of 90-day mortality of Derivation and Validation cohorts (N= 1,481).  The presence of multiple PCSK9 alleles had an overall protective effect for this outcome (OR=0.69, 95% C.I. 0.50-0.94, P=0.02). (M-H: Mantel-Haenszel). Ref. indicates WT/Single PCSK9 LOF alleles group.   103  4.3.6  The effects of multiple PCSK9 LOF alleles upon white blood cell counts throughout sepsis admission  In the Derivation Cohort, white blood cell (WBC) and absolute neutrophil counts within the first 14 days of sepsis were available in 170 patients: Ref. group (N=142), multiple LOF (N=28). Patients carrying multiple PCSK9 LOF alleles demonstrated greatly neutrophil count reductions over time in comparison to Ref. patients (P=0.01, Figure 4.4A). No differences in WBC counts were found between these two groups (P=0.812, Figure 4.4A).  WBC counts from admission for septic shock to day 14 were available in 406 patients who survived 28 days in the Validation Cohort: Ref. group (N=332), multiple LOF  (N=74). We found that patients carrying multiple PCSK9 LOF alleles demonstrated a more rapid and complete normalization of their presenting (elevated) WBC counts than Ref. patients (P=0.007, Figure 4.4B). These findings suggest that PCSK9 genotype might influence the host immune response to sepsis.           104  Figure 4.4 Blood counts during sepsis (from day 0 to day 14) according to PCSK9 genotype. X axis represents time in day (0 to 14); Y axis represents the respective blood cell counts for each panel; black circles represent mean values over time in Ref. patients; red squares represent mean values over time in multiple LOF patients. A) Derivation Cohort. Left panel: White blood cell (WBC) counts. Right panel: Neutrophil counts; B) Validation Cohort: WBC counts. Patients carrying multiple PCSK9 LOF alleles demonstrated greatly decreased absolute neutrophil counts in the Derivation Cohort (P=0.01) and WBC counts in the Validation Cohort (P=0.007) in comparison to Ref. patients.  Ref. indicates WT/Single PCSK9 LOF alleles group.    4.4  Discussion The present study showed that reduced PCSK9 function according to PCSK9 genotype was associated with better long-term outcomes: composite of 1-year death or infection-related readmission in 28-day survivors of sepsis. The presence of multiple PCSK9 LOF alleles was associated with decreased hazard-ratio for the composite outcome by more than 50% and by 80% for 1-year infection-related readmission when compared with patients with carrying none or only a single PCSK9 LOF (Ref. group). Furthermore, the presence of multiple PCSK9 LOF alleles was associated with significantly lower plasma PCSK9 levels at sepsis admission than the wild-type/single LOF group. We can then interpret that the “clinically relevant” PCSK9 LOF group was composed of patients who carry multiple PCSK9 LOF alleles. Prior studies of the role of PCSK9 genotype in infectious disease have only interrogated acute illness [108, 242]. To our knowledge, 105  this is the first study to find that common PCSK9 LOF SNPs are associated with improved long-term outcomes following an episode of sepsis.   The three PCSK9 LOF variants analyzed here are relatively common with reported minor allele frequency of > 0.5% [329]. R46L (rs11591147), A53V (rs11583680), and I474V (rs562556) variants have been associated with decreased levels of LDL-cholesterol that ranges from modest to more pronounced reductions: around 10%, 15% and 20% for I474V, A53V and R46L, respectively [23, 24, 239, 327]. Based on their LDL-cholesterol-lowering effects, they have been described as LOF [331]. However, functional in vitro studies are available only for R46L [333].    Our study suggests that PCSK9 LOF genotype (defined here by the presence of multiple LOF alleles) is a novel protective factor for long-term outcomes in 28-day survivors of sepsis, such as infection-related readmission. Sepsis is associated with increased risk of all-cause late mortality rates from 1 to 10 years after index sepsis hospitalization [14, 84, 322, 334, 335]. Additionally, hospital readmissions after sepsis are frequent [14, 76, 336] and expensive [7]. Recent studies have showed that a new event of sepsis was the most common reason for unplanned readmissions among sepsis survivors [14, 76]. How the index infection influences readmission rates is still unclear. In the present study, the presence of multiple PCSK9 LOF alleles was independently associated with decreased HR for the composite outcome death or infection-related readmission within one year, and most important, this finding was particularly driven by reduction in infection-related readmission rates.   106  We speculate that the presence of multiple PCSK9 LOF alleles plays an important role in the process of resolution of infection and/or bacterial clearance, and therefore decreases the risk of unplanned readmissions due to infection in 28-day sepsis survivors for the following reasons. Relapse or recrudescence of the initial infection in sepsis survivors is one of the most relevant factors associated with infection-related readmissions [12, 337]. It has been suggested that patients who survive an episode of sepsis are more prone to re-infection [336], but the reasons for that are not fully elucidated. Dwived et al., using a cecal ligation and puncture model of sepsis, found that PCSK9 knockout (KO) mice had lower bacterial concentrations in the blood, lungs, and peritoneal cavity fluid than WT animals, suggesting that PCSK9 KO genotype is associated with improved bacterial suppression or clearance [242]. This effect may be directly relevant to our observation that infection-related readmission is decreased in patients carrying multiple PCSK9 LOF alleles. Even though this association was not evaluated in our study, the reduced neutrophil counts (Derivation cohort) and WBC counts (Validation cohort) found in patients carrying multiple PCSK9 LOF alleles suggest that the host response to sepsis in this group of patients has a favorable evolution associated with increased rates of infection resolution, and possibly decreased risk of future infection-related readmission. In addition, it has been hypothesized that PCSK9 LOF modulates the inflammatory response due to the increases in LDLR density, particularly in the liver, with subsequent increments in the clearance of endotoxins carried within LDL particles [91]. Interestingly, among sepsis survivors, patients carrying multiple PCSK9 genotype alleles demonstrated lower PCSK9 levels and concordantly lower post-sepsis LDL-C levels than our Ref. patients. It is plausible that this group of patients (multiple LOF) had greater hepatic LDLR density and hence cleared more efficiently endotoxins carried within LDL particles, represented here by decreased LDL-C levels and most importantly, by a risk reduction for infection-related 107  readmission after sepsis. Based on these findings, we can speculate that the presence of multiple PCSK9 LOF alleles plays an important role in the process of resolution of infection and/or bacterial clearance, and therefore decreases the risk of infection-related readmission after sepsis.  Our inability to detect a protective effect by the presence of a single PCSK9 LOF allele may reflect the underlying biology of an additive genetic model, in which multiple PCSK9 LOF variants have recently been shown to confer greater LDL-C lowering and protection against cardiovascular events than a single PCSK9 LOF allele [338]. Frequency of combinations of alleles and HR per allele, per each outcome are described in Appendix C Table C.3.  The suggested benefits of PCKS9 LOF genotype described here were in disagreement with Berger et al. Even though they found that PCSK9 levels were greatly reduced in subjects carrying PCSK9 LOF alleles, no association was demonstrated between PCSK9 LOF genotype and plasma inflammatory markers [243]. These differences may be related to the fact that these authors analyzed different SNPs than us and/or due to subjects’ characteristics, such as the presence of sepsis by itself in our population.  The strengths of our study include the assessment of PCSK9 genotype vs. long-term outcome, a novel composite of death or infection-related readmission over 1 year in 28-day sepsis survivors, the use of three independent cohorts and the biological plausibility based on plasma PCSK9 and LDL-C levels. The novel composite of death or infection-related readmission over 1 year is compelling because of the expansion of the sepsis survivors’ population over recent years, and the growing evidence demonstrating that these patients experience greater risks of death, re-infection 108  and hospital readmission when compared to patients who have never had a hospitalization for sepsis. Our study reinforces the relevance of genetics, particularly of PCSK9 genotype, for long-term outcomes in sepsis.    Our study has several limitations. First, due to its observational nature we were not able to assess potential mechanisms that explain the link between PCSK9 genotype and long-term outcomes of sepsis. We can only speculate that differences in the host response to sepsis and lipid metabolism may be involved. Second, considering an absolute risk reduction of 15% in our composite outcome in patients carrying multiple PCSK9 LOF alleles compared to the reference group, our statistical power was estimated in 68.1%. Nevertheless, the risk difference observed in our study between our two groups was greater than expected (19.4%), which makes our findings appealing. Third, other PCSK9 SNPs that might be associated with sepsis were not analysed; however, a thorough review of PCSK9 GWAS literature [329] allowed us to conclude that the probability of finding additional known PCSK9 missense mutations in our cohort was extremely low. Fourth, we were not able to use the new definitions of sepsis [31] as our inclusion criteria in the Derivation cohort, given that some clinical variables required to an accurate assessment of qSOFA or SOFA scores were not available, such as altered mental score and bilirubin levels. However, according to the data available, 74% (N=253) of participants from this cohort fulfilled the criteria for sepsis diagnosis using the new definitions, implying that this proportion may be underestimated. All Validation cohort patients had sepsis according to its updated definitions [31]. Fifth, the ethnicity data in our study was determined according to patients’ phenotype and provided by the study coordinator, precluding us from adjusting our findings for genetic ancestry scores. However, a sensitivity analysis including Caucasian ethnic group (phenotype-based) in our Cox regression 109  models did not change our major findings (Appendix C Table C.4). Sixth, the reasons for the post sepsis LDL-C measurements being ordered were not assessed because plasma LDL-C levels were obtained by laboratory chart review. Finally, we could only assess 90-day mortality from sepsis admission in our meta-analysis as this time point was the longest follow-up applicable for all our sepsis cohorts. Data concerning 1-year death or readmission was not available in our Validation cohort.                   110  Chapter 5: Conclusion  The relationship between infection and lipid metabolism has been appreciated since 1926 [339]. Sepsis is associated with low levels of total cholesterol, mainly HDL-C and LDL-C, and the degree of these reductions correlate with the severity of sepsis. Although the link between lipids and sepsis outcomes has been well established observationally, the field is undergoing renewed enthusiasm as an  intriguing topic of research in sepsis. This enthusiasm has been driven by the recent surge in targeted lipid therapies, with a large number of new medication classes either in advanced clinical testing or clinically available to treat dyslipidemias. The possibility to rapidly translate sepsis related lipid findings through the repurposing of these new medications represents one of the few viable paths forward for a biologically informed therapy for sepsis.   The body of this work focused on the analysis of the role of lipids (lipoprotein levels, lipid-related proteins and/or genotype) upon the health of sepsis survivors, including recurrent infection, chronic impairment of renal function and survival. We first explored whether there was a correlation between HDL-C levels at sepsis presentation with kidney injury (acute and chronic) and long-term mortality. We felt these clinical outcomes were of great interest as renal dysfunction affects a large proportion of patients with sepsis, and its development has been shown to increase mortality and the risk of chronic kidney disease after resolution of sepsis [57-60]. Following this observational study we explored the pathophysiology of acutely declining lipids and kidney injury more deeply by examining the influence of rare and common genetic variants upon both HDL-C levels and the risk of kidney injury during sepsis. The aim here was to verify a causal role of specific genetic variant(s) for renal outcomes of sepsis, as well as to investigate possible ways of 111  screening patients at high risk of kidney injury. Our last study in the sepsis-lipid arena expanded upon a finding our laboratory had previously made: that inhibition (genetic or pharmacological) of the key cholesterol regulator PCSK9 improved short-term survival in sepsis. It had been hypothesized that rapid clearance of pathogen lipid containing LDL particles would increase the complete clearance of infectious pathogens. This hypothesis was evaluated by analyzing the influence of common loss-of-function genetic variants within the PCSK9 gene upon infection-related readmissions and survival following discharge from an index sepsis admission.   5.1  Summary of findings  5.1.1                Low HDL plasma levels are associated with increased risk of sepsis-associated AKI, long-term decreased renal function and long-term mortality  In the first study, it was found, in a cohort of septic patients, that a low baseline HDL-C level (<33.06 mg/dl) very early in sepsis was independently associated with increased risk for sepsis-associated AKI, and decreased eGFR post-discharge (3 months to 2 years) or all-cause 2-year mortality after sepsis. Interestingly, HDL’s effect size surpassed well-known risk factors for sepsis-associated AKI, such as vasopressor use, and, surprisingly, had a stronger risk prediction for long-term decline in eGFR than sepsis-related AKI or chronic hypertension.   It was also demonstrated that low HDL-C levels dropped acutely during sepsis and were not a risk factor for sepsis as levels measured before sepsis were mostly at the normal range. Both acute and long-term kidney injury related to sepsis were more often found in patients with HDL-C lower than 33.06 ml/dl and this finding was relatively constant over the first 28 days of sepsis regarding 112  AKI, and from 3 months to 2 years after sepsis in relation to chronic renal dysfunction. The risk of sepsis-associated AKI and long-term decreased renal function was increased approximately 3-fold and 5-fold respectively in patients with low levels HDL-C (< 33.06 mg/dl). To account for both long-term mortality and morbidity, the risk of a novel composite outcome of death or decreased eGFR within 2 years according to HDL-C levels was evaluated. Likewise, a risk increase of about 2-fold was observed in patients who had HDL-C lower than 33.06 mg/dl.    5.1.2  HDL-related variant rs1800777 (allele A) in the CETP gene increases the risk of sepsis-associated AKI   The second important finding of this work was that among several HDL-related genetic variants, CETP variant rs1800777 (allele A) was the only one independently associated with increased risk of development of AKI in two cohorts of septic patients (Derivation and Validation). This variant was associated with very low levels of HDL-C, increased CETP mass and a risk 8 times higher for AKI development in the Derivation cohort. Mendelian randomization analysis supported a causal effect between low HDL-C levels and AKI development.   The replication of the main finding - CETP variant rs1800777 (allele A) and increased risk of AKI, in the Validation cohort was categorical. The risk of AKI was 2.3 times higher in patients carrying this variant than in wild-type ones. These patients also presented increased fluid overload and inflammatory response that potentially contribute to AKI development and progression during sepsis.    113  5.1.3  Multiple common loss-of-function alleles in the PCSK9 gene decreases the risk of long-term death or readmission in sepsis survivors Following the findings primarily related to HDL, the influence of LDL metabolism on long-term outcomes of sepsis was further investigated. This study did not emphasize LDL-C levels but rather the LDL-related gene and its encoded protein PCSK9. This approach was chosen because recent evidence concerning the effects of PCSK9 on LDLR in sepsis has been more compelling than the effects of LDL-C levels in itself.    The study demonstrated that PCSK9 loss-of-function variants were associated with better long-term outcomes in 28-day survivors of sepsis. The presence of multiple PCSK9 loss-of-function alleles decreased by more than 50% the composite risk of death or infection-related readmission, and this finding was driven entirely by reductions in infection-related readmission within one year after the index sepsis admission.   Common PCSK9 variants that decrease PCSK9 function (rs11583680, rs11591147, and rs562556) were previously associated with decreased 28-day survival in patients with septic shock [108]. In this study the authors found that the presence of one or more PCSK9 loss-of-function alleles were enough to protect against mortality. This study, on the other hand, evaluated associations between PCSK9 loss-of-function genotype and the long-term outcome death or infection-related readmission within one year as a composite, only in 28-day sepsis survivors. Interestingly, it was found that wild-type group and patients carrying exactly one PCSK9 loss-of-function allele in fact behave almost equally in terms of the risk for the composite outcome, while patients carrying 114  multiple PCSK9 loss-of-function exhibited a very distinct behavior. In these group of patients the benefits of PCSK9 loss-of-function (multiple alleles) were conspicuous.    5.2  The role of HDL in sepsis and kidney injury HDL has very relevant properties that were first discovered in cardiological research, primarily in atherosclerosis, an inflammatory disorder [340]. These original studies about HDL structure observed that this particle contains several proteins and enzymes with related anti-inflammatory, anti-oxidative, anti-thrombotic and endothelial protective properties [140-142, 149, 297]. The pleiotropic properties of HDL were then translated to clinical studies analyzing the effects of HDL-C plasma level in humans, where researchers realized that high levels of HDL-C are indeed not only beneficial to atherosclerosis-related disorders but also to other chronic and acute inflammatory disorders such as rheumatoid arthritis [341], psoriasis [342], community-acquired pneumonia [343, 344], hepatitis B [345], and sepsis [133, 135].   HDL, during sepsis, contributes to the host immune response by sequestering highly immunogenic molecules (LPS and LTA), a phenomenon that is believed to be more dependent on HDL lipid content rather than its protein content, which directs these particles to an alternative, “non-inflammatory” and disposal pathway. On the other hand, sepsis can generate a less functional HDL (Table 1.4) that sometimes predominate and can lead to very low HDL-C levels. In these cases, HDL does not behave favorably, and worse outcomes may happen. In addition, sepsis upregulates or downregulates the expression of genes and proteins that are instrumental in increasing or decreasing HDL-C levels respectively. The balance between factors that bring HDL-C down or up 115  will ultimately route the host immune response to sepsis, likely influencing the risk of sepsis outcomes such as organ injury(s) and mortality.   Kidney injury as a complication of sepsis is common, costly, and associated with poor short- and long-term outcomes. Increasing evidence has demonstrated that the kidneys are involved in the amount of HDL-C in the plasma and in the content of HDL particles via their filtration function [74]. One hypothesis that has been proposed for the pathophysiology of AKI in sepsis suggest that the degree of AKI is proportional to the degree of ongoing systemic inflammation. Circulating bacterial lipids (LPS and LTA), by their immunogenic properties, lead to microvascular [346], endothelial and immune dysfunction [347, 348]. Reversing this systemic inflammation should therefore be an important factor in preventing AKI and can occur via the avid binding between HDL-C and LPS [92, 93]. The greater the plasma concentration of HDL-C the greater the magnitude of its associated effects in decreasing systemic inflammation. Even though this is a very simplistic explanation, the mechanisms involved in the HDL-induced attenuation of systemic and renal inflammation are likely complex. Additionally, HDL-C levels per se, despite being consistently demonstrated as markers of anti-inflammation, may not reflect HDL particle functionality. This topic has been evaluated in ischemic heart disease, where it was observed that patients with coronary artery disease can display decreased protective properties even in cases of normal levels of HDL-C [349]. This is also the case when considering the lack of clinical benefits concerning atherosclerosis in trials with drugs capable of substantially increase HDL-C such as niacin [350] and CETP inhibitors [351]. To what extent this consideration impacts sepsis is unclear, but it is plausible that HDL-C levels in itself are an indicator of lipid-carrying capacity (i.e. pathogen lipid neutralization) and thus sufficient to impact sepsis outcomes.    116  The fact that in this work a very early decrease in HDL-C levels in sepsis was demonstrated, even before clinical evidence of renal impairment, and the long-term effect of low HDL-C levels on renal function, makes it encouraging to think that the influence of HDL-C levels in the renal function is more pronounced than the opposite (meaning the influence of renal function in HDL-C levels). This needs to be elucidated.    5.3  CETP variant rs1800777 (allele A) and sepsis-associated AKI  This work demonstrated that the CETP variant rs1800777 (allele A) is a genetic factor associated with increased risk of sepsis-associated AKI by its lowering-effect on HDL-C level. This finding can help in the early identification of patients at high risk of AKI and corroborates future studies evaluating the use of CETP inhibitors for the treatment or prevention of kidney dysfunction induced by sepsis.   CETP inhibitors are drugs that effectively increase endogenous HDL-C levels. Unfortunately, their HDL increasing effects have not been translated into clinically important cardiovascular risk reduction [189-193]. Based on the relevance of the CETP gain-of-function variant (rs1800777, allele) in lowering HDL-C levels, and the consequences of low HDL levels in increasing the risk of sepsis-associated AKI and long-term renal impairment and mortality, it is plausible to consider the possibility of conducting studies analyzing the impact of CETP inhibitors in sepsis outcomes, including short- and long-term. Moreover, the levels of HDL-C per se appear to be more relevant for sepsis than for cardiovascular diseases. Perhaps increases in endogenous HDL-C levels induced by CETP inhibitors will be sufficient to improve sepsis outcomes, independent of the HDL protein content. Animal and human studies of sepsis have evaluated the use of reconstituted HDL 117  (composed mostly of phospholipid and ApoA-I) [276] or phospholipid emulsions [156, 352, 353] both in vitro and in vivo. Despite these treatments showing anti-inflammatory activity, no improvements in mortality rates were demonstrated. These findings corroborate the importance of properties observed within endogenous HDL-C, which can have its plasma levels raised by CETP inhibitors but might not be perfectly reconstituted.   The use of CETP inhibitors is also associated with reduced levels of LDL-C, the lipoprotein involved in the clearance of pathogen lipids by the liver. Even though this effect seems to be deleterious in sepsis, this may not represent a real concern for two reasons. First, if we consider  the molar excess of LDL-C compared with LPS, which is approximately of one million-fold (30 to 40 mg/dL vs. 200 to 400 pg/mL) [112, 117, 354], the small to moderate reductions in LDL-C levels induced by CETP inhibitors probably would not affect pathogen lipids clearance. Second, it appears that CETP has a minor role in binding, neutralizing and transfer LPS between lipoproteins in comparison to LBP and PLTP [94], as CETP lacks conserved positively charged residues important for LPS binding, resulting in lower LPS affinity than the former transfer proteins [103]. Thus, considering that CETP inhibitors increase HDL-C levels, with only minor effects on both LDL-C levels and/or LPS transfer from HDL to LDL particles, it is plausible that these drugs may improve sepsis outcomes, with no associated detrimental effects.   5.4  PCSK9 loss-of-function – impact on long-term outcomes of sepsis The work presented here is about lipids in sepsis. Therefore, it would be insufficient if no further studies involving LDL were performed. LDL has been positively associated with increased risk of cardiovascular disease and negatively associated with improved cardiovascular outcomes for many 118  years [355, 356]. In sepsis, LDL-C levels have demonstrated inconsistent associations concerning the risk of sepsis-associated adverse outcomes [117, 127, 135]. On the other hand, PCSK9, a paramount predictor of LDL metabolism, has gained relevance in sepsis research, particularly because of its impact on liver LDLR, the receptor involved in pathogen lipid clearance [91].   PCSK9 levels are positively associated with increased risk of sepsis-induced cardiovascular and pulmonary dysfunction [357]. In sepsis, PCSK9 loss-of-function (LOF) genotype is associated with decreased 28-day mortality in humans [108], and decreased inflammatory response [108, 242] and bacterial dissemination in mice [242]. The latter two factors seem to influence long-term outcomes of sepsis in humans as observed by Yende et al. [324] and DeMerle et al. [337]. Sepsis-related long-term sequelae are frequent, costly, and their risk factors have not been fully characterized. Therefore, studies analyzing the risk of long-term outcomes of sepsis are needed.   In this work it was demonstrated that decreased activity of PCSK9, which was defined as the presence of multiple (two or more) loss-of-function alleles, reduced the risk of late mortality and hospital infection-related readmission within one year after sepsis, in patients who survived 28 days. The fact that this protective effect was not found in patients carrying only one LOF allele is possibly because PCSK9 plasma levels were not substantially decreased in these patients in comparison to wild-type ones. PCSK9 protein, encoded by the PCSK9 gene, mostly reflects the gene activity. The inflammatory response induced by LPS has been associated with PCSK9 upregulation [358]. Accordingly, in wild-type patients from this study, PCSK9 levels increased to a similar degree than those carrying one PCSK9 LOF allele. The presence of only one PCSK9 LOF allele, therefore, was not interpreted as a clinically evident LOF in this specific scenario; on 119  the other hand, the presence of two of more LOF alleles was able to attenuate the sepsis-induced upregulation of PCSK9, reflecting a clinically evident LOF and thus, decreased the risk of long-term mortality or readmissions. Whether this effect is caused by LDLR-mediated pathogen lipid clearance requires further studies; however, according to Topchiy et al. [107], that seems to be true.     A single PCSK9 LOF allele was demonstrated to reduce short-term mortality in patients with septic shock; however, PCSK9 plasma levels were not evaluated [108]. Based on this finding, it appears that in patients more acutely and severely ill, one LOF allele can lead to a more pronounced attenuation in the sepsis-induced PCSK9 upregulation. Given that PCSK9 levels are substantially increased in patients with shock [357], one can argue that a single PCSK9 LOF allele has protective effects in patients with acute and severe sepsis, while in patients with less severe sepsis (e.g., early at the ED) and at the long-term, multiple LOF alleles are required to be translated into clinically evident protective effects.   Clinical trials have demonstrated that decreased activity of PCSK9 by the use of specific inhibitors decrease very effectively LDL-C plasma levels and are associated with reductions in cardiovascular outcomes [244-247]. It is reasonable that LDL-C plasma levels per se are not as crucial as LDLR activity/availability when evaluating the impact of PCSK9 inhibitors. This might be true for both sepsis and cardiovascular disorders.      120  5.5  Final conclusion The objective of this work was to study the role of lipids in the development and outcome of sepsis, with a particular emphasis in long-term outcomes. This study confirmed that lipids (HDL-C plasma levels) and/or their related genes CETP (rs1800777) and PCSK9 (rs11591147, rs11583680, and rs562556) are relevant mediators of the host immune response to sepsis and impact the risk of sepsis-associated organ injury such as AKI, and the risk of late deaths, long-term renal impairment and hospital infection-related readmissions.   The scope of this work was not to elucidate complex genetic pathways involved in sepsis outcomes but rather to evaluate the impact of specific genes and genetic variants related to lipid metabolism on long-term outcomes of sepsis. The specifications of this study were based on the biological plausibility and previous findings in the literature concerning lipids and sepsis network. The results reported here were evaluated in real patients in the “real world”, who had their disorder (sepsis) certainly affected by other factors other than genetics. Even in this “uncontrolled” study (in comparison to large randomized clinical trials, in vitro, in vivo or animal studies), this research has led to novel and translational findings.   HDL-C plasma level can be a useful biomarker in early sepsis for assessing the risk of sepsis-associated AKI and/or long-term renal dysfunction or death after sepsis. CETP gain-of-function rs1800777 (allele A) reduces HDL-C levels in sepsis and thus increases the risk of AKI. Future studies evaluating the safety and efficacy of CETP inhibitors in sepsis-associated AKI are warranted.  121  PCSK9 genotype might be useful in the early recognition of septic patients at high-risk of death or future IRR(s). A novel prognostic biomarker is suggested: genotyping septic patients could stratify 28-day sepsis survivors for risk of infection-related readmission.   This work corroborates and expands the knowledge that lipids are key players during sepsis. The major findings of this research are that: HDL-C impacts the incidence and the risk of acute kidney injury (AKI); specific lipid-related genotypes (CETP rs1800777 and PCSK9 rs11591147, rs11583680, rs562556) may screen patients at increased risk for sepsis-associated AKI, and long-term death or infection-related readmission respectively.   Biomarkers and therapies are needed in sepsis. The findings of this study suggest low HDL-C levels at sepsis admission, CETP gain-of-function genotype (rs1800777, allele A), and multiple PCSK9 LOF genotype as potential new biomarkers. However, studies are required for confirmation. Concerning this research, one proposed biomarker identification study can be performed by using a selection of extreme phenotypes in larger and prospective sepsis cohorts. The patients are prospectively classified into unfavourable (e.g., presence of AKI, long-term death or hospital readmission) and favourable phenotypes (absence of outcomes of interest). Then, these two groups (only the extreme phenotypes) are evaluated in screening studies for host factors underlying the specific phenotype, which are hypothesis driven by the present research such as low HDL-C level at sepsis admission, CETP gain-of-function genotype or PCSK9 LOF genotype (potential biomarkers). The presence of associations between the outcome(s) and the potential biomarker(s) does strongly corroborate that the host factors evaluated are valid biomarkers and 122  advocates further studies [359]. Table 5.1 describes possible outcome-biomarker associations regarding the present research.    Table 5.1 Possible outcome-biomarker associations for the proposed biomarker studies Outcome Biomarker Sepsis-associated AKI Low HDL-C levels at sepsis admission CETP rs1800777 (allele A) Long-term decreased eGFR Low HDL-C levels at sepsis admission Long-term death or decreased eGFR Long-term IRR Multiple PCSK9 LOF genotype  Long-term death or IRR     Additionally, the present research supports future studies evaluating novel sepsis therapies such as CETP or PCSK9 inhibitors, drugs that have already been assessed in Phase I trials [189, 193, 317, 318]. Initially, Phase II clinical trials (blinded, randomized and placebo-controlled) testing the efficacy of these drugs in sepsis added to conventional therapy compared with placebo might confirm the hypothesis that inhibition of CETP and/or PCSK9 result in reduced risk for AKI and/or improved survival. Subsequent Phase III trials would be necessary in case of positive results in Phase II. We should also consider that CETP inhibitors or PCSK9 inhibitors may not benefit all patients with sepsis, but those carrying the CETP variant rs1800777 (allele A), or those who do not carry multiple PCSK9 LOF variants (minor alleles of rs11591147, rs11583680, rs562556), respectively. Accordingly, this work provides strong signals for both precision and personalized medicine: the former by the use of therapies that are optimized for genetics, and the latter when 123  prioritizing specific therapies only for the patients who carry specific genetic variants [360]. Last, a theranostic approach, which uses a biomarker to select a particular therapy and concurrently measure the response to this treatment, may be contemplated for HDL-C levels. However, it is uncertain if HDL-C levels are true markers of response to CETP inhibition in sepsis [360]. Figure 5.1 is a schematic representation of simple concepts brought by the findings of this work that may be used for future research in sepsis therapy.             Figure 5.1 Potential future studies with the use “new” therapies for sepsis.        124  Bibliography  1. Geroulanos S, Douka ET. Historical perspective of the word "sepsis". Intensive Care Med. 2006;32(12):2077. 2. Marshall JC. Why have clinical trials in sepsis failed? Trends Mol Med. 2014;20(4):195-203. 3. Hall MJ, Williams SN, DeFrances CJ, Golosinskiy A. Inpatient care for septicemia or sepsis: a challenge for patients and hospitals. NCHS Data Brief. 2011(62):1-8. 4. Hoyert DL, Xu J. Deaths: preliminary data for 2011. Natl Vital Stat Rep. 2012;61(6):1-51. 5. Navaneelan T, Alam S, Peters PA, Phillips O. "Deaths involving sepsis in Canada". Health at a Glance. Statistics Canada catalogue no. 82-624-X. 2015. 6. Perner A, Gordon AC, De Backer D, et al. Sepsis: frontiers in diagnosis, resuscitation and antibiotic therapy. 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Differential effects of reconstituted high-density lipoprotein on coagulation, fibrinolysis and platelet activation during human endotoxemia. Thromb Haemost. 1997;77(2):303-7. 354. Marshall JC, Walker PM, Foster DM, et al. Measurement of endotoxin activity in critically ill patients using whole blood neutrophil dependent chemiluminescence. Crit Care. 2002;6(4):342-8. 144  355. Baigent C, Keech A, Kearney PM, et al. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet. 2005;366(9493):1267-78. 356. Prospective Studies C, Lewington S, Whitlock G, et al. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths. Lancet. 2007;370(9602):1829-39. 357. Boyd JH, Fjell CD, Russell JA, Sirounis D, Cirstea MS, Walley KR. Increased Plasma PCSK9 Levels Are Associated with Reduced Endotoxin Clearance and the Development of Acute Organ Failures during Sepsis. J Innate Immun. 2016;8(2):211-20. 358. Feingold KR, Moser AH, Shigenaga JK, Patzek SM, Grunfeld C. Inflammation stimulates the expression of PCSK9. Biochem Biophys Res Commun. 2008;374(2):341-4. 359. Perez-Gracia JL, Sanmamed MF, Bosch A, et al. Strategies to design clinical studies to identify predictive biomarkers in cancer research. Cancer Treat Rev. 2017;53:79-97. 360. Rello J, van Engelen TSR, Alp E, et al. Towards precision medicine in sepsis: a position paper from the European Society of Clinical Microbiology and Infectious Diseases. Clin Microbiol Infect. 2018;24(12):1264-72.              145  Appendices  Appendix A Supplemental Table for Chapter 1 Table A.1 Old sepsis definitions Infection (documented or suspected) and some of the following: General variables Fever (> 38.3ºC) or hypothermia (< 36ºC) Heart rate > 90 bpm  Tachypnea Altered mental status Positive fluid balance (> 20mL/kg over 24h) or edema Hyperglycemia (glucose > 120mg/dL or 7.7 mmol/L)a Inflammatory variables Altered WBC count (> 12,000 or < 4,000/µL) Normal WBC count with > 10% immature forms Plasma C-reactive protein > 2 SD above the normal value Plasma procalcitonin > 2 SD above the normal value Hemodynamic variables Arterial hypotensionb SvO2 > 70% Cardiac index > 3.5L/min/m2 Tissue perfusion variables Decreased capillary refill or mottling Hyperlactatemia (> 1 mmol/L) Organ dysfunction variables Hypoxemia (PaO2/FiO2 < 300) Coagulation abnormalities (INR > 1.5 or aPTT > 60 secs) Acute oliguria (urine output < 0.5ml/kg/h)c Thrombocytopenia (< 100.000/µL)  Creatinine increase > 0.5 mg/dL Hyperbilirubinemia (bilirubin > 4 mg/dL or 70 mmol/L) Ileus (absent bowel sounds)  146  aIn the absence of diabetes, bSystolic blood pressure < 90mmHg, mean arterial pressure < 70 mmHg, or systolic blood pressure decrease > 40mmHg; cor < 45 mmol/L for at least 2 hours.  Abbreviations: WBC: white blood cell count; SvO2: mixed venous saturation of oxygen; PaO2/FiO2: ratio of partial pressure arterial oxygen and fraction of inspired oxygen; INR: international normalized ratio; aPTT: activated partial thromboplastin time.   147  Table A.2 Sequential Organ Failure Assessment Score System Score 0 1 2 3 4 Respiratory - PaO2/FiO2 (mmHg)  ≥ 400  < 400  < 300  < 200a   < 100a  Coagulation - Platelets (x103/µL)  ≥ 150  < 150  < 100  < 50  < 20 Liver  - Bilirubin (mg/dL)  < 1.2  1.2 - 1.9  2.0 - 5.9  6.0 - 11.9  > 12.0 Cardiovascular MAP ≥ 70 MAP < 70 Dopamineb  Dobutamine any dose Dopaminec Epinephrined Norepinephrined  Dopaminee Epinephrinef Norepinephrinef CNS - Glasgow coma score  15  13 - 14  10 - 12  6 - 9  < 6 Renal  - Creatinine (mg/dL)  - Urine output (mL/d)  < 1.2  1.2 – 1.9  2.0 – 3.4  3.5 – 4.9 < 500  > 5.0 < 200 a with respiratory support; b <5µg/kg/min; c 5.1 – 15µg/kg/min; d ≤ 0.1µg/kg/min; e>15µg/kg/min; f> 0.1µg/kg/min Abbreviations: PaO2: partial pressure of oxygen; FiO2: fraction of inspired oxygen; MAP: mean arterial pressure; CNS: central nervous system.      148  Appendix B Supplemental Tables and Figures for Chapter 3                Figure B.1 Schematic representation of the process of gene(s) and genetic variant(s) selection. *gene variants were chosen based on Sadananda et al. J. Lipid Res. 2015. 56: 1993–2001; **genetic variants were excluded when corrected P value ≥ 0.05 (Benjamini-Hockberg correction within each gene with a false discovery rate cutoff of 0.05); ***clinically significant AKI (AKI Kidney Disease Improving Global Outcomes (KDIGO) = 2 and 3). Abbreviations: ABCA1: ATP-binding cassette transporter A1; ApoA1: Apolipoprotein A1; ApoA2: Apolipoprotein A2; CETP: Cholesteryl Ester Transfer Protein; GALNT2: Polypeptide N-Acetylgalactosaminyltransferase 2; LCAT: Lecithin-Cholesterol Acyltransferase; LIPG: Lipase G; NPC1: Niemann-Pick disease type 1; PLTP: Phospholipid Transfer Protein; SCARB1: Scavenger Receptor B1; MAF: Minor Allele Frequency; HDL-C: High-Density Lipoprotein Cholesterol; AKI: Acute Kidney Injury. 149  Table B.2 Associations between each gene variant analyzed and HDL-C levels at sepsis admission  Gene Variants P value P value (corrected) Gene Variants P value P value (corrected) ABCA1 rs4149346 rs4743763 rs2067484 rs2297404 rs2853579 rs9282540 rs2066717 rs2066714 rs2297399 rs2020927 rs2230806 rs3818689 rs2066715 rs4149336 rs363717 rs75488496 rs2275545 rs2274873 rs2230808 rs2230805 rs41412244 rs41445345 rs73517878 0.001 0.003 0.017 0.024 0.035 0.036 0.038 0.039 0.047 0.051 0.055 0.065 0.068 0.080 0.093 0.105 0.122 0.13 0.191 0.193 0.197 0.197 0.203 0.067 0.100 0.326 0.326 0.326 0.326 0.326 0.326 0.335 0.335 0.335 0.350 0.350 0.382 0.415 0.439 0.480 0.483 0.533 0.533 0.533 0.533 0.533 GALNT2 rs3213497 rs3748008 rs16851269 rs3213495 rs60775651 rs3811486 rs3811485 rs112499399 rs3811487 rs16851339 rs2273967 rs2273969 rs79130823 rs1043900 rs2273966 rs58148281 rs1043908 rs7022 rs2273968 rs11800118 rs3216809 rs78030300 rs11620 0.025 0.043 0.050 0.063 0.070 0.071 0.085 0.113 0.120 0.129 0.137 0.174 0.174 0.183 0.187 0.208 0.236 0.264 0.268 0.284 0.289 0.337 0.340 0.603 0.603 0.603 0.603 0.603 0.603 0.619 0.635 0.635 0.635 0.635 0.635 0.635 0.635 0.635 0.663 0.671 0.671 0.671 0.671 0.671 0.671 0.671 150  rs10991377 rs116728780 rs41537052 rs7341705 rs34788556 rs112338016 rs2066881 rs12003906 rs41419649 rs557492263 rs4149341 rs1800977 rs9282544 rs9282537 rs33918808 rs34078184 rs41494750 rs2777801 rs4149338 rs1883025 rs3029584 rs111337110 rs2230807 rs41410048 rs75141626 rs60913410 rs73519810 rs1799777 0.215 0.216 0.216 0.216 0.230 0.231 0.242 0.256 0.319 0.334 0.350 0.362 0.404 0.452 0.454 0.465 0.465 0.476 0.477 0.523 0.554 0.570 0.591 0.591 0.591 0.619 0.623 0.638 0.533 0.533 0.533 0.533 0.533 0.533 0.540 0.553 0.667 0.678 0.689 0.692 0.751 0.760 0.760 0.760 0.760 0.760 0.760 0.814 0.822 0.822 0.822 0.822 0.822 0.822 0.822 0.822 rs17710666 rs16851328 rs72647711 rs2273970 rs72647712 rs1043897 rs3811488 rs1043909 rs3811484 rs6698963 rs12091838 rs1923950 rs3213494 rs3748006 rs10495294 rs678050 rs4846914 rs2273965 rs7544606 rs10005 rs1043944 rs13728 rs3088075 rs3213496 rs1043941 rs13490 rs111823613 rs76813899 0.350 0.364 0.364 0.367 0.387 0.388 0.395 0.413 0.440 0.451 0.464 0.471 0.507 0.529 0.559 0.568 0.578 0.600 0.605 0.641 0.678 0.678 0.873 0.873 0.931 0.985 0.995 0.995 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.679 0.686 0.686 0.686 0.686 0.718 0.729 0.734 0.734 0.734 0.734 0.734 0.760 0.768 0.768 0.947 0.947 0.989 0.995 0.995 0.995 151  rs1800978 rs2066716 rs2740485 rs41432545 rs73517870 rs2246841 rs60690601 rs2066718 rs1331924 rs4149339 rs41354653 rs35204915 rs35545593 rs78072322 rs4149340 rs2234885 0.638 0.669 0.701 0.720 0.720 0.727 0.786 0.787 0.790 0.791 0.841 0.842 0.842 0.842 0.878 0.962 0.822 0.845 0.854 0.854 0.854 0.854 0.867 0.867 0.867 0.867 0.867 0.867 0.867 0.867 0.891 0.962 APOA1 rs5069 rs5076 rs2070665 rs5070 0.149 0.194 0.406 0.855 0.388 0.388 0.541 0.855 LCAT rs5923 rs13306496 0.230 0.572 0.460 0.572 CETP rs1800777 rs5880 rs11076176 rs5883 rs12720872 rs5886 rs7192120 rs7196174 0.002 0.009 0.024 0.028 0.105 0.105 0.105 0.105 0.042 0.094 0.147 0.147 0.275 0.275 0.275 0.275 LIPG  rs199879783 rs59866846 rs35816125 rs874565 rs35978968 rs2276269 rs874566  0.088 0.105 0.224 0.291 0.302 0.332 0.335  0.824 0.824 0.824 0.824 0.824 0.824 0.824 152  rs1801706 rs1800774 rs289741 rs9930761 rs5884 rs891144 rs9935228 rs289742 rs891143 rs1532625 rs5882 rs7205804 rs891142 0.147 0.152 0.159 0.215 0.256 0.294 0.300 0.442 0.498 0.521 0.531 0.534 0.618 0.303 0.303 0.303 0.376 0.413 0.420 0.420 0.560 0.560 0.560 0.560 0.560 0.618 rs2000813 rs34474737 rs3786248 rs3826577 rs3744840 rs3744841 rs35968328 rs9958734 rs3744843 rs2000812 rs58075967 rs3786247 0.341 0.371 0.562 0.601 0.613 0.644 0.658 0.756 0.784 0.856 0.876 0.906  0.824 0.824 0.940 0.940 0.940 0.940 0.940 0.953 0.953 0.953 0.953 0.953 NPC1 rs6507717 rs2303880 rs2435307 rs145693774 rs1652377 rs116046557 rs7227375 rs8099071 rs6507720 rs1140458 rs12970899 rs1805082 rs3745024 rs7239575 rs58319130 0.098 0.117 0.140 0.171 0.176 0.216 0.216 0.285 0.29 0.311 0.362 0.373 0.376 0.381 0.628 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.771 SCARB1 rs701103 rs5891 rs10396208 rs59809936 rs77740046 rs838898 rs838915 rs4238001 rs58032386 rs10396210 rs5888 rs5892 rs12580323 rs61932577 rs2070242 0.039 0.043 0.100 0.100 0.100 0.100 0.100 0.165 0.181 0.267 0.293 0.384 0.425 0.434 0.556 0.285 0.285 0.285 0.285 0.285 0.285 0.285 0.402 0.402 0.532 0.532 0.62 0.62 0.62 0.676 153  rs61731962 rs73392120 rs9963518 rs74486453 rs1788799 rs1805081 rs117851153 rs55809701 0.628 0.628 0.628 0.652 0.671 0.823 0.867 0.922 0.771 0.771 0.771 0.771 0.771 0.901 0.906 0.922 rs10396211 rs3825140 rs5889 rs838897 rs2293439 0.572 0.575 0.763 0.785 0.923 0.676 0.676 0.826 0.826 0.923 APOA2 rs6413453 0.111 N/A PLTP rs3092096 rs441346 rs2294213 rs6017711 rs73306280 rs11086986 rs553359 rs200238866 0.025 0.149 0.410 0.421 0.421 0.712 0.717 0.893 0.200 0.596 0.673 0.673 0.673 0.819 0.819 0.893          154  Table B.3 Derivation and Validation Cohorts - CETP rs1800777: Minor Allele Frequency (MAF) and Hardy-Weinberg equilibrium (HWE)   MAF HWE Cohort Rare Heterozygous (%) Rare Homozygous (%) Chi-square P-value Derivation Cohort (N=200) 2.50 0.00 0.13 0.72 Validation Cohort (N=604) 2.73 0.16 0.51 0.47                 155  Table B.4 Patients Baseline Characteristics according to CETP variant rs1800777 (allele A) in the Validation Cohort (VASST) Variable WT (N=570) rs1800777 (allele A) (N=34) P value Age – Median (IQR) 63 (50 – 73) 61 (48 – 71) 0.886 Sex (N, % male) 335 (58.7) 20 (60.6) 1.000 Ethnicity – N (% Caucasians) 481 (84.3) 25 (75.7) 0.153 Comorbidities – N (%) • COPD • CKD • Chronic Liver Failure • CHF NYHA Class 4  101 (17.7) 69 (12.1) 61 (10.7) 45 (7.9)  4 (11.8) 3 (9.0) 7 (20.6) 2 (5.9)  0.488 1.000 0.136 1.000 Lab. Parameters, Median (IQR)* • WBC (x103/L) • Platelets (x103/L) • Lactate (mmol/L) • Creatinine (mmol/L)   13.8 (8.1 – 21.0) 157 (86 – 254) 1.8 (0.9 – 3.6) 148 (90- 250)  10.2 (4.3 – 16.6) 121 (49 – 205) 2.5 (1.5 – 6.4) 223 (140 – 305)  0.029 0.033 0.012 0.005 Abbreviations: WT: Wild-type; IQR: interquartile range; COPD: chronic obstructive pulmonary disease; CKD: chronic kidney disease; CHF: congestive heart failure: NYHA: New York Heart Association; WBC: white blood cells.        156  Appendix C Supplemental Tables and Figures for Chapter 4   Table C.1 Validation Cohort. Baseline characteristics according to PCSK9 genotype  a Ref. indicates WT/Single PCSK9 LOF alleles group; b Determined by the study coordinator or research assistant;                      cParameters measured at admission; d Measurements in the first 24 hours of ED admission.  Abbreviations: LOF: Loss-of-function; IQR: Interquartile Range; HR: Heart Rate; RR: Respiratory Rate; SpO2: arterial Oxygen Saturation; MAP: Mean Arterial Pressure APACHE: Acute Physiology and Chronic Health Evaluation; WBC: White Blood Cell; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure; NYHA: New York Heart Association. Variable All patients (N=1079) Ref.a  (N=925) Multiple LOF (N=154) P value Age, Median (IQR) 63 (50-73) 63 (49-73) 63 (51-73) 0.595 Sex (N, % male) 648 (60.1) 556 (60.1) 92 (59.7) 0.931 Ethnicity (N, % Caucasians)b 881 (81.6) 756 (81.7) 125 (81.2) 0.957 HR, Median (IQR)c 122 (105-135) 122 (105-136) 120 (105-135) 0.295 MAP, Median (IQR)c 55 (50-60) 55 (50-60) 56 (50-61) 0.051 Temperature, Median (IQR)c 38.0 (37.1-39.0) 38.0 (37.2-39.0) 38.0 (37.0-39.0) 0.176 APACHE II score, Median (IQR) 26 (21-32) 26 (21-32) 26 (21-31) 0.365 WBC (x109), Median (IQR)d 14.3 (8.8-20.8) 14.3 (8.7-20.4) 15.0 (8.9-23.0) 0.270 Platelets (x109), Median (IQR)d 161 (87-246) 161 (86-245) 155 (93-257) 0.406 Creatinine (mmol/l), Median (IQR)d 151 (91-264) 154 (92-274) 129 (80-213) 0.004 Lactate (mmol/l), Median (IQR)d 1.9 (0.8-3.8) 1.9 (0.8-3.6) 1.9 (0.5-4.0) 0.868 COPD (N, %) 183 (17.0) 163 (17.6) 20 (13.0) 0.185 Chronic renal failure (N, %) 101 (9.4) 90 (9.7) 11 (7.1) 0.376 Cirrhosis (N, %) 115 (10.7) 97 (10.5) 18 (11.7) 0.776 CHF NYHA Class 3 or 4 (N, %) 76 (7.0) 66 (7.1) 10 (6.5) 0.895 157  Table C.2 Validation Cohort. PCSK9 genotype - allele frequency and Hardy-Weinberg equilibrium * N refers to minor allele counts in relation to the total number of alleles in the Derivation Cohort (N=2,158) Abbreviations: PCSK9: Proprotein Convertase Subtilisin/kexin type 9; SNPs: single nucleotide polymorphisms; HWE: Hardy-Weinberg Equilibrium.                PCSK9 SNPs Major (minor) allele Minor Allele Frequency – N (%) HWE P-value A53V (rs11583680) G (A) 299 (13.85%) 0.79 I474V (rs562556) A (G) 352 (16.31%) 0.48 R46L (rs11591147) C (A) 21 (0.97%) 0.87 158  Table C.3 Derivation cohort. Frequency of combinations of alleles and HR per allele, per  each outcome N: Number of patients; n= number of alleles per patient; * HR Upper limit > 500;  A53V: rs11583680; I474V: rs562556; R46L: rs11591147  Abbreviations: Het: heterozygous; Hom: homozygous; CI: confidence interval; IRR: infection-related readmission; Mort: mortality.    Combinations of SNPs N PCSK9 LOF alleles (n) HR per number of PCSK9 LOF allele (95% CI) Composite 1y-IRR 1y-Mort. All SNPs 190 0 0.81 (0.63-1.03)  0.64 (0.41-1.01)  0.91 (0.69-1.21) 100 1 46 2 5 3 0 4 1 5 0 6 rs562556 + rs11591147 245 0 0.57 (0.38-0.85) 0.37 (0.16-0.83)  0.72 (0.46-1.12)  83 1 13 2 1 3 0 4 rs11583680 + rs562556 192 0 0.81 (0.63-1.03)  0.64 (0.41-1.01)  0.91 (0.69-1.21) 99 1 45 2 4 3 1 4 rs11583680+ rs11591147 244 0 0.98 (0.70-1.38) 0.81 (0.43-1.52)  1.09 (0.73-1.63) 90 1 7 2 1 3 0 4 rs562556 248 0 0.57 (0.38-0.86) 0.38 (0.17-0.86) 0.71 (0.45-1.14) 83 1 11 2 rs11591147 335 0 0.39 (0.05-2.82) 0.04 (0.00-*) 0.65 (0.09-4.68) 7 1 0 2 rs11583680 249 0 1.03 (0.73-1.46) 0.87 (0.46-1.65) 1.12 (0.74-1.71) 86 1 7 2 159  Table C.4 Derivation Cohort. Sensitivity analysis including Caucasian ethnicity (phenotype-based): adjusted Hazard Ratios (aHR)* for 1-year death or IRR (composite outcome), 1-year IRR, and 1-year mortality according to PCSK9 genotype (two or more alleles) *Adjusted for age, sex, APACHE II score and Caucasian ethnicity (phenotype-based); a within one year; b excluding patients who died within 28 days; c excluding patients who died within one year.  Abbreviations: IRR: infection-related readmission.  Cohort aHR (95% CI) P value Death or IRRa, b  0.41 (0.21-0.79) 0.008 IRRa, c 0.20 (0.05-0.86) 0.031 Mortalitya, b 0.55 (0.26-1.15) 0.116 

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