Medicine, Faculty of
Medicine, Department of
DSpace
UBCV
Jawi, Motasim Mohammed T
Doctor of Philosophy - PhD
University of British Columbia
Patients with high levels of lipoprotein(a) (Lp[a]) have an increased risk of ischemic heart disease. Persistently elevated resting heart rate (RHR) and arterial wall thickening, two common clinical manifestations in cardiovascular disease (CVD), are known determinants of myocardial ischemia as well as predictors of future cardiovascular events. The association of improved physical activity (PA) with these ischemic heart disease risk factors in patients with high Lp(a) levels is controversial. The overall objectives of this dissertation are to provide a comprehensive investigation of RHR and early structural atherosclerotic vascular changes in patients with high Lp(a) as well as the association of PA with these two parameters. The first objective of this dissertation is to raise awareness about the harmful effects of Lp(a) (Chapter 2). I described the association between RHR and PA in primary and secondary prevention patients enrolled in the Healthy Heart Program (HHP) prevention clinic (Chapter 3). The fourth chapter aimed to evaluate the relative significance of Lp(a) as a predictor of future CVD compared with selected established CVD risk factors (Chapter 4). I also assessed the association of improved PA and RHR in HHP patients with high Lp(a) levels (Chapter 5) and explored the potential beneficial impact of improved PA on early structural atherosclerotic vascular changes in healthy subjects enrolled in the Multicultural Community Health Assessment Trial (M-CHAT) trial (Chapter 6). I showed that participants who increased their PA could reduce their RHR significantly. I also showed that male sex, Lp(a), low-density lipoprotein (LDL-C), and non-high-density lipoprotein (HDL-C) were significant independent risk factors for CVD, in decreasing order of importance. Moreover, RHR, blood pressure, and the Framingham risk score decreased significantly in HHP patients with high Lp(a) levels who improved their PA over three years. Finally, I showed that improving daily PA associated with significantly better carotid artery intima-medial thickness and total plaque area compared to those in less active M-CHAT participants. This dissertation provides new knowledge on RHR and arterial early structural atherosclerotic vascular changes in patients with high Lp(a) and the potential associations between PA with these two ischemic heart disease risk factors in CVD patients.
The Association of Physical Activity with Resting Heart Rate and Arterial Atherosclerotic Markers in Patients with High Lipoprotein(a) by Motasim Mohammed T Jawi MD., King Abdulaziz University, 2010 M.Sc., Ball State University, 2015 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) November 2021 © Motasim Mohammed T Jawi, 2021 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 association of physical activity with resting heart rate and endothelial atherosclerotic markers in patients with high lipoprotein(a). submitted by Motasim Jawi in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Experimental Medicine Examining Committee: Dr. Sammy Chan, Clinical Associate Professor, Department of Medicine, UBC Co-supervisor Dr. Karin Humphries, Associate Professor Emerita, Department of Medicine, UBC Co-supervisor Dr. Haydn Pritchard, Professor Emeritus, Pathology and Laboratory Medicine, UBC Supervisory Committee Member Dr. Jordan Guenette, Associate Professor, Department of Physical Therapy, UBC University Examiner Dr. Liam Brunham, Associate Professor, Department of Medicine, UBC University Examiner Additional Supervisory Committee Members: Dr. Scott Lear, Professor, Faculty of Health Sciences, SFU Supervisory Committee Member iii Abstract Patients with high levels of lipoprotein(a) (Lp[a]) have an increased risk of ischemic heart disease. Persistently elevated resting heart rate (RHR) and arterial wall thickening, two common clinical manifestations in cardiovascular disease (CVD), are known determinants of myocardial ischemia as well as predictors of future cardiovascular events. The association of improved physical activity (PA) with these ischemic heart disease risk factors in patients with high Lp(a) levels is controversial. The overall objectives of this dissertation are to provide a comprehensive investigation of RHR and early structural atherosclerotic vascular changes in patients with high Lp(a) as well as the association of PA with these two parameters. The first objective of this dissertation is to raise awareness about the harmful effects of Lp(a) (Chapter 2). I described the association between RHR and PA in primary and secondary prevention patients enrolled in the Healthy Heart Program (HHP) prevention clinic (Chapter 3). The fourth chapter aimed to evaluate the relative significance of Lp(a) as a predictor of future CVD compared with selected established CVD risk factors (Chapter 4). I also assessed the association of improved PA and RHR in HHP patients with high Lp(a) levels (Chapter 5) and explored the potential beneficial impact of improved PA on early structural atherosclerotic vascular changes in healthy subjects enrolled in the Multicultural Community Health Assessment Trial (M-CHAT) trial (Chapter 6). I showed that participants who increased their PA could reduce their RHR significantly. I also showed that male sex, Lp(a), low-density lipoprotein (LDL-C), and non-high-density lipoprotein (HDL-C) were significant independent risk factors for CVD, in decreasing order of importance. Moreover, RHR, blood pressure, and the Framingham risk score decreased iv significantly in HHP patients with high Lp(a) levels who improved their PA over three years. Finally, I showed that improving daily PA associated with significantly better carotid artery intima-medial thickness and total plaque area compared to those in less active M-CHAT participants. This dissertation provides new knowledge on RHR and arterial early structural atherosclerotic vascular changes in patients with high Lp(a) and the potential associations between PA with these two ischemic heart disease risk factors in CVD patients. v Lay Summary Lipoprotein (a), or Lp(a), is a type of lipoprotein that carries cholesterol in the blood. It consists of a low-density lipoprotein (LDL-C) molecule attached to another protein (apolipoprotein[a]). Genetic and epidemiological studies have identified high Lp(a) levels to be a risk factor for atherosclerosis and coronary heart disease. Unfortunately, there is no approved medication to specifically lower Lp(a) levels yet. Although reducing LDL-C levels through a healthy diet and exercise or even through extensive statin treatment does not significantly influence Lp(a) levels, these interventions should be vigorously implemented in patients with high Lp(a) levels. The working hypothesis in this dissertation is that cardiovascular disease (CVD) patients who increased their physical activity (PA) would show improvement in their CVD risk factors compared to those who did not. The overall goal of this dissertation is to raise awareness about Lp(a) and the importance of improving PA for patients with high Lp(a) levels. vi Preface This statement certifies that the research work presented in my dissertation was conceived, conducted, analyzed, and written by Motasim Mohammed T Jawi. Also, all figures included in this research work was conceived and conducted by Motasim Jawi. Ethical approval to conduct the studies described in this dissertation was obtained at various stages of the research process from the Research Ethics Boards of the Providence Health Research Institute, Vancouver Coastal Health Research Institute, Fraser Health Research Institute, and the University of British Columbia. Dr. Jiri Frohlich and Dr. Sammy Chan are the principal cosupervisors, and Dr. Hayden Pritchard, Dr. Karin Humphries, and Dr. Scott Lear, the supervisory committee members (names arranged in alphabetical order), provided valuable assistance and input throughout the entire research process. I am truly indebted to my supervisory committee members for their generous support in making this thesis possible. Chapter 2 of this thesis has been partly published [Motasim M. Jawi, Jiri Frohlich, Sammy Y. Chan, "Lipoprotein(a) the Insurgent: A New Insight into the Structure, Function, Metabolism, Pathogenicity, and Medications Affecting Lipoprotein(a) Molecule", Journal of Lipids, vol. 2020, Article ID 3491764, 26 pages, 2020. https://doi.org/10.1155/2020/349176.]. I was the lead investigator responsible for all major areas of concept formation, data collection, and manuscript writing. Dr. J. Frohlich and Dr. S. Chan served as expert consultants on the manuscript, Dr. K. Humphries served as an expert consultant on the epidemiology part of the manuscript, and Drs. Frohlich and Chan approved the final academic revision of the manuscript. Chapter 3 is based on work conducted by Motasim Jawi, Dr. Jiri Frohlich, Dr. Sammy Chan, and Dr. Karin Humphries. The study was designed by Motasim Jawi and Dr. Jiri Frohlich, vii with guidance from the cardiologist Dr. Sammy Chan. I was responsible for collecting, analyzing, and interpreting the data and writing and revising the manuscript. Dr. K. Humphries provided valuable statistical support, and Lubomira Cermakova and Ida Priecelova provided technical and logistic support with regard to chart handling and data collection at the Healthy Heart Program Prevention Clinic at Saint Paul's Hospital. This study conformed to the Declaration of Helsinki's guiding principles and was approved by the UBC - Providence Health Care Institute (H18-03099) and St. Paul's Hospital. I was the lead investigator for the projects described in Chapter 4, where I was responsible for all major areas of concept formation, data collection, and analysis. This study conformed to the Declaration of Helsinki's guiding principles and was approved by the UBC - Providence Health Care Institute (H18-03099) and St. Paul's Hospital. Chapter 5 is based on work conducted by Motasim Mohammed T Jawi, Dr. Jiri Frohlich, and Dr. Sammy Chan. The study was designed by Motasim Jawi and Dr. Jiri Frohlich. I was responsible for collecting, analyzing, and interpreting the data and writing and revising the manuscript. Dr. K. Humphries provided valuable statistical support, and Lubomira Cermakova and Ida Priecelova provided technical and logistic support with regard to chart handling and data collection at the Healthy Heart Program Prevention Clinic at Saint Paul's Hospital. Chapter 6: This is a cross-sectional observational study with study participants drawn from the Multicultural Community Health Assessment Trial (M-CHAT), led by Dr. Scott Lear. Members of Dr. Lear's research team recruited and interviewed study participants to assess sociodemographic information and collect blood samples. Blood analysis and body composition, body fat distribution measurements, and carotid artery measurements were performed at St. Paul's Hospital. All participants provided informed consent. This study was approved by the Simon viii Fraser University Research Ethics Board. The study was designed by M. Jawi and Dr. S. Lear, Dr. K. Humphries, and Dr. S. Chan. My involvement with the M-CHAT trial began after data acquisition. I was responsible for all significant areas of concept formation, analyzing and interpreting the data, and writing and revising the manuscript. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ............................................................................................................................ xviii List of Figures ...............................................................................................................................xx List of Abbreviations ................................................................................................................. xxi Acknowledgements ................................................................................................................. xxvii Dedication ................................................................................................................................. xxix Chapter 1: Introduction ................................................................................................................1 1.1 An overview .................................................................................................................... 1 1.2 Key definitions related to physical activity .................................................................... 5 1.3 Physical activity and insufficient physical activity and its relation to cardiovascular health… ....................................................................................................................................... 6 1.3.1 The relationship between PA and cardiovascular health ........................................ 6 1.3.1.1 Primary prevention population ........................................................................... 6 1.3.1.2 Secondary prevention population ....................................................................... 7 1.3.2 The relationship between insufficient physical activity or sedentary behaviour and cardiovascular health .............................................................................................................. 8 1.3.3 How much physical activity is enough? ................................................................. 9 1.4 Physiological benefits of PA on cardiovascular health................................................. 10 x 1.4.1 Physiological benefits of PA on resting heart rate and vascular health ................ 11 1.4.2 The relationship between PA and Carotid intima-media thickness ...................... 13 1.5 The relationship between PA and Lp(a) ....................................................................... 15 1.6 Dissertation overview ................................................................................................... 24 Chapter 2: Lipoprotein(a) the Insurgent: A New Insight into the Structure, Function, Metabolism, Pathogenicity, and Medications Affecting Lipoprotein(a) Molecule ................26 2.1 Overview ....................................................................................................................... 26 2.2 Lp(a) molecule .............................................................................................................. 28 2.2.1 Structure of Lp(a) molecule .................................................................................. 28 2.2.2 The functions of specific kringles ......................................................................... 30 2.2.3 The influence of the LPA gene on size and concentration ................................... 31 2.3 Metabolism of lipoprotein(a) ........................................................................................ 33 2.3.1 Synthesis of lipoprotein(a) .................................................................................... 33 2.3.2 Assembly of lipoprotein(a) ................................................................................... 34 2.4 Catabolism .................................................................................................................... 37 2.4.1 The liver ................................................................................................................ 37 2.4.2 The kidneys ........................................................................................................... 38 2.5 Physiological functions of Lp(a). .................................................................................. 39 2.5.1 The role of Lp(a) in angiogenesis and tumor growth ........................................... 39 2.5.2 Acute-phase reactant ............................................................................................. 39 2.5.3 Binding and carrying of oxidized phospholipids and LP-PLA2 ........................... 40 2.5.4 Wound healing ...................................................................................................... 41 2.5.5 Fibrinolysis ........................................................................................................... 41 xi 2.6 The pathogenicity of Lp(a). .......................................................................................... 41 2.6.1 The entry of Lp(a) into the vascular wall.............................................................. 44 2.6.2 Pro-inflammatory and proatherogenic effects of Lp(a) ........................................ 45 2.6.2.1 Role of the oxidized phospholipids................................................................... 46 2.6.3 The proposed mechanisms of endothelial dysfunction, inflammation and atherosclerosis ....................................................................................................................... 47 2.6.3.1 Endothelial permeability and adhesion molecule expression ........................... 47 2.6.3.2 Cytokine production.......................................................................................... 49 2.6.3.3 Vascular smooth muscles .................................................................................. 51 2.6.3.4 Cell death .......................................................................................................... 53 2.6.4 Pro-thrombotic effect ............................................................................................ 54 2.6.4.1 Platelet responsiveness...................................................................................... 55 2.6.4.2 Inhibition of plasminogen activation and plasmin generation .......................... 56 2.6.4.3 Effect of Lp(a) on tissue factor ......................................................................... 56 2.6.4.4 Role of OxLp(a) & OxPLs ................................................................................ 57 2.6.4.5 Effect of Lp(a) on tissue factor pathway inhibitor ............................................ 57 2.7 Factors that influence Lp(a) levels in the blood............................................................ 58 2.8 Lp(a) measurement. ...................................................................................................... 62 2.8.1 Isoform-dependent vs. isoform-independent ........................................................ 62 2.8.2 Important considerations ....................................................................................... 63 2.8.2.1 Friedewald formula ........................................................................................... 63 2.9 Screening....................................................................................................................... 64 2.10 Interventions. ................................................................................................................ 66 xii 2.10.1 Diet and physical exercise .................................................................................... 66 2.10.2 Medication ............................................................................................................ 66 2.10.2.1 Lipid treatment essentials ............................................................................. 66 2.10.2.1.1 HMG-CoA reductase inhibitors ................................................................ 66 2.10.2.2 Reducing the production of novel Lp(a) ....................................................... 69 2.10.2.2.1 IONIS-APO(a)-LRx (AKCEA-APO(a)-LRx) .......................................... 69 2.10.2.2.2 Mipomersen .............................................................................................. 70 2.10.2.2.3 Lomitapide ................................................................................................ 71 2.10.2.2.4 Niacin ........................................................................................................ 71 2.10.2.2.5 Cholesterylester transfer protein (CETP) inhibitors ................................. 72 2.10.2.2.6 Aspirin....................................................................................................... 73 2.10.2.3 LDL receptor removal or uptake................................................................... 73 2.10.2.3.1 PCSK9 inhibitors ...................................................................................... 73 2.10.2.3.2 Inclisiran ................................................................................................... 74 2.10.2.4 Physical elimination of Lp(a) molecules ...................................................... 75 Chapter 3: Improved physical activity is associated with lower resting heart rate and improved lipid profile among primary and secondary prevention patients. .........................76 3.1 Introduction ................................................................................................................... 76 3.2 Methodology ................................................................................................................. 78 3.2.1 Study design and setting ....................................................................................... 78 3.3 Study procedure ............................................................................................................ 78 3.3.1 Physical activity data (main independent variable) .............................................. 81 3.3.1.1 Study Groups .................................................................................................... 82 xiii 3.3.2 RHR (main dependent variable) ........................................................................... 85 3.3.3 Biochemical cardiac risk factors ........................................................................... 85 3.3.4 Demographics and clinical cardiac risk factors .................................................... 85 3.4 Statistical analysis ......................................................................................................... 86 3.5 Results ........................................................................................................................... 87 3.5.1 Study population and baseline characteristics ...................................................... 87 3.5.2 Participant’s characteristics .................................................................................. 94 3.5.3 Comparison of male and female groups ............................................................... 95 3.5.4 Mixed-effect linear regression analysis ................................................................ 98 3.5.4.1 Sensitivity analyses ......................................................................................... 100 3.6 Discussion ................................................................................................................... 101 3.6.1 Study limitations ................................................................................................. 103 Chapter 4: The influence of lipoprotein(a) on the prevalence of cardiovascular disease risk factors and cardiovascular events among patients in the Healthy Heart Program. ............104 4.1 Introduction ................................................................................................................. 104 4.2 Methods....................................................................................................................... 105 4.2.1 Study design and setting ..................................................................................... 105 4.2.2 Study procedure .................................................................................................. 106 4.2.2.1 Clinical cardiac risk factors at baseline .......................................................... 106 4.2.2.2 Biochemical cardiac risk factors ..................................................................... 107 4.2.2.3 Lp(a) measurements ........................................................................................ 107 4.2.2.4 Cardiovascular disease endpoints before baseline visit .................................. 107 4.2.2.5 Clinical cardiac risk factors before CVD endpoints ....................................... 108 xiv 4.2.3 Statistical analysis ............................................................................................... 108 4.3 Results ......................................................................................................................... 109 4.4 Discussion ................................................................................................................... 118 4.4.1 Strengths and limitations of the study ................................................................. 120 Chapter 5: The association between physical activity and resting heart rate in patients with high Lp(a) ...................................................................................................................................121 5.1 Introduction ................................................................................................................. 121 5.2 Methodology ............................................................................................................... 122 5.2.1 Study design and setting ..................................................................................... 122 5.2.2 Study procedure .................................................................................................. 123 5.2.3 Physical activity data (main independent variable) ............................................ 124 5.2.3.1 Study Groups .................................................................................................. 125 5.2.4 RHR (main dependent variable) ......................................................................... 125 5.2.5 Clinical cardiac risk factors ................................................................................ 125 5.2.6 Biochemical cardiac risk factors ......................................................................... 127 5.2.6.1 Lipid measures ................................................................................................ 127 5.2.6.2 Lp(a) measurements ........................................................................................ 127 5.3 Statistical analysis ....................................................................................................... 127 5.4 Results ......................................................................................................................... 129 5.4.1 Study population ................................................................................................. 129 5.4.2 Participant characteristics ................................................................................... 133 5.4.3 Lp(a) as a predictor for resting heart rate............................................................ 136 5.4.4 The relationship between PA and RHR .............................................................. 137 xv 5.4.5 The association between improved PA and RHR ............................................... 143 5.4.5.1 Sensitivity analyses ......................................................................................... 143 5.5 Discussion ................................................................................................................... 148 5.5.1 Study limitations ................................................................................................. 149 Chapter 6: The association between physical activity and carotid artery intima-medial thickness and total plaque area in healthy subjects with known Lp(a) levels: Results of the Multicultural Community Health Assessment Trial (M-CHAT) ..........................................151 6.1 Introduction ................................................................................................................. 151 6.2 Methodology ............................................................................................................... 152 6.2.1 Study design and setting ..................................................................................... 152 6.2.2 Study population ................................................................................................. 153 6.2.3 Study procedure ................................................................................................. 153 6.2.4 Physical activity (main independent variable) .................................................... 154 6.2.4.1 Study groups ................................................................................................... 154 6.2.5 Carotid artery measurements for cIMT and total plaque area (main dependent variables) ............................................................................................................................ 155 6.2.6 Smoking status .................................................................................................... 156 6.2.7 Biochemical cardiac risk factors ......................................................................... 156 6.2.7.1 Blood pressure ................................................................................................ 156 6.2.7.2 Lipid measures ................................................................................................ 156 6.2.8 Body composition assessment ............................................................................ 157 6.2.8.1 Visceral adipose tissue .................................................................................... 157 6.2.8.2 Total body fat .................................................................................................. 157 xvi 6.3 Statistical analyses ...................................................................................................... 157 6.4 Results ......................................................................................................................... 159 6.4.1 Study population ................................................................................................. 159 6.4.2 Baseline assessments of body composition and metabolic risk factors .............. 161 6.4.3 The association between PA and total plaque area ............................................. 167 6.5 Discussion ................................................................................................................... 170 6.5.1 The limitations of the study ................................................................................ 172 Chapter 7: Conclusion ...............................................................................................................174 7.1 Overview of major findings ........................................................................................ 175 7.1.1 Improved physical activity is associated with lower resting heart rate and improved lipid profile among primary and secondary prevention patients (Chapter 3) ..... 175 7.1.2 The influence of lipoprotein(a) on the prevalence of cardiovascular disease risk factors and cardiovascular events among patients in the Healthy Heart Program (Chapter 4)………. ............................................................................................................................ 176 7.1.3 The association between physical activity and resting heart rate in patients with high Lp(a) (Chapter 5) ........................................................................................................ 178 7.1.4 The association between physical activity and carotid artery intima-medial thickness and total plaque area in healthy subjects with known Lp(a) levels (Chapter 5) . 179 7.2 Strengths and limitations............................................................................................. 181 7.3 Significance and future directions .............................................................................. 183 7.4 Overall conclusion ...................................................................................................... 184 Bibliography ...............................................................................................................................186 Appendices ..................................................................................................................................242 xvii Appendix A ............................................................................................................................. 242 Appendix B ............................................................................................................................. 245 xviii List of Tables Table 1-1. Relative risk reduction observed when comparing active/fit vs inactive/unfit individuals ....................................................................................................................................... 2 Table 2-1. The main factors affecting Lp(a) levels in humans ..................................................... 60 Table 2-2. Whom to screen ........................................................................................................... 65 Table 2-3. Approved and investigational therapeutic drugs to lower LDL & Lp(a) .................... 68 Table 3-1. Inclusion and exclusion criteria. .................................................................................. 80 Table 3-2. METs equivalents of common physical activities classified as light, moderate or vigorous intensity .......................................................................................................................... 83 Table 3-3. Characteristics of the study population variables at baseline ...................................... 90 Table 3-4. Characteristics of the IPA group stratified by sex ....................................................... 93 Table 3-5. Changes in CVD risk factors between the IPA and NIPA groups .............................. 96 Table 3-6. Changes in CVD risk factors by sex for the IPA group .............................................. 96 Table 3-7. Mixed-effect regression analyses for the association between physical activity (IPA vs NIPA) and RHR ............................................................................................................................ 99 Table 4-1. Characteristics of the study population variables ...................................................... 110 Table 4-2. Prevalence of risk factors for CVD ........................................................................... 114 Table 4-3. Hazard ratios from Cox regression analysis of CVD events ..................................... 115 xix Table 5-1. Characteristics of the included and excluded populations ........................................ 131 Table 5-2 . Lp(a) distribution statistics ....................................................................................... 133 Table 5-3. Demographic, diagnostic, biochemical, and hemodynamic comparisons between the high and normal Lp(a) level groups. ........................................................................................... 134 Table 5-4. Multiple regression analysis of RHR ........................................................................ 136 Table 5-5. Demographic data comparison between the improved physical activity (IPA) and nonimproved physical activity (NIPA) groups ........................................................................... 139 Table 5-6. Changes in CVD risk factors between the IPA and NIPA groups in patients with high L(a) ≥ 300mg/L ........................................................................................................................... 142 Table 5-7. Effect of physical activity on RHR healthy heart program participants with known Lp(a) levels ................................................................................................................................. 145 Table 6-1. Baseline characteristics of study participants by physical activity (PA) status (N = 610). ............................................................................................................................................ 159 Table 6-2. Laboratory data and body composition stratified by PA ........................................... 162 Table 6-3. The association between physical activity and intima-medial thickness of the carotid artery in M-CHAT participants with known Lp(a) levels .......................................................... 166 Table 6-4. The association between physical activity and the total plaque area of the carotid artery in M-CHAT participants with known Lp(a) levels .......................................................... 168 xx List of Figures Figure 1-1. Prevalence of traditional risk factors for cardiovascular disease in Canadian society according to sex .............................................................................................................................. 4 Figure 2-1. Lp(a) structure, composition and physicochemical properties .................................. 29 Figure 2-2. Comparison between different Lp(a) isoform sizes ................................................... 32 Figure 2-3. Model for the metabolism of apo(a)........................................................................... 36 Figure 2-4. Different theories of how Lp(a) causes atherosclerosis ............................................. 43 Figure 3-1. Study flow diagram .................................................................................................... 88 Figure 3-2. The change in RHR in HHP patients between the initial and last visits .................... 97 Figure 3-3. The change in systolic blood pressure in HHP patients between the initial and last visits .............................................................................................................................................. 97 Figure 4-1. Cumulative event-free survival based on Lp(a) concentration ................................ 117 Figure 5-1. Study flow chart ....................................................................................................... 130 Figure 5-2. A histogram showing the Lp(a) distribution of the study population ...................... 130 Figure 6-1. Changes in atherosclerotic surrogate markers by physical activity and Lp(a) tertile for all follow-up periods ................................................................................................................... 166 xxi List of Abbreviations ACTH: Adrenocorticotrophic hormone administration AMPK: Adenosine monophosphate-activated protein kinase Apo-B100: Apolipoprotein-B100 apo: Apolipoprotein Apo(a): Apolipoprotein(a) ASO: Antisense oligonucleotide bFGF: Basic fibroblast growth factor BMI: Body mass index bpm: Beats per min Ca+2: Calcium CAD: Coronary artery disease cAMP: Cyclic AMP CD36: Cluster determinant 36 CE: Cholesteryl esters CETP: Cholesterylester transfer protein CI: Confidence interval cIMT: Carotid intima-media thickness CS-1: Connecting segment 1 CVD: Cardiovascular disease Cx43: Connexin 43 xxii Cys: Cysteine dBP: Diastolic blood pressure DM: Diabetes mellitus EGFR: Epidermal growth factor receptor EGR-1: Early growth response protein 1 Elk-1: ETS-like transcription factor 1 EP2: E-type prostaglandin ER: Endoplasmic reticulum ERK: Extracellular signal-regulated kinase ERK: Extracellular signal-related kinase 1/2 FITT: Frequency, intensity, time, and type GALT2: Galactosyltransferase-2 GBD: Global burden disease GEFs: Guanine nucleotide exchange factors HDACs: Histone deacetylates HDL-C: High-density lipoprotein-cholesterol HepG2: Human hepatoma cells G2 HGH: Human growth hormone HHP: The Healthy Heart Program HMG-CoA: 3-Hydroxy-3- methyl–glutaryl-coenzyme A 9 (statins) HoFH: Homozygous familial hypercholesterolemia HR: Hazard ratio HTN: Hypertension xxiii ICAM-1: Intercellular adhesion molecule-1 IFCC: International Federation of Clinical Chemistry and Laboratory Medicine IGF-I: Insulin-like growth factor I IHD: Ischemic heart disease IL-1β: IL-1 beta IL: Interleukin iNOS: Inducible nitric oxide synthase IPA: Improved physical activity K: Kringle Kcals: kilocalories Klf4: Krüppel-like factor 4 LacCer: Lactosylceramide LBS: Lysine binding sites LDL-C: Low-density lipoprotein cholesterol LDLR: LDL receptor LKB1: Liver kinase B1 Lp-PLA2: Lipoprotein associated-phospholipase A2 Lp(a): Lipoprotein(a) LPO: Lipoxygenase M-CHAT: Multi-Cultural Community Health Assessment Trial MAPK: Mitogen-activated protein kinase MCP-1: Chemoattractant molecule 1 MEK: Metenkephalin xxiv MET: Metabolic equivalent of task MIP-2: Macrophage inhibitor protein-2 MKP1: Mitogen activated protein kinase phosphatase 1 MLC: Myosin light chain MLCK: MLC kinase MMPs: Matrix metalloproteinases MMPs: Matrix metalloproteinases MPO: Myeloperoxidase mTOR: Mammalian target of rapamycin MTTP: Microsomal triglyceride transfers protein NADPH: Nicotinamide adenine dinucleotide phosphate NF-ҡB: Nuclear factor light-chain-enhancer of activated B NFAT: Nuclear factor of activated T cells NIPA: Not improved physical activity NOX2: NADPH oxidase 2 OR: odds ratio OxLDL: Oxidized LDL OxLp(a): Oxidized Lp(a) OxPLs: Oxidized phospholipids PA: Physical activity PAF: Platelet activating factor PAI-1: Plasminogen activator inhibitor-1 PARP-1: Adenosine diphosphate-ribose polymerase-1 xxv PCNA: Proliferating cell nuclear antigen PCSK9: Proprotein convertase subtilisin/kexin type 9 PDGF-BB: Platelet-derived growth factor PI3K: Phosphoinositide 3 kinase PKC: Protein kinase C PLG: Plasminogen PlgRKT: Plasminogen receptor (KT) PPAR α: Peroxisome proliferator-activated receptor α PPR: Pattern-recognition receptors RANTES: Regulated upon activation, normal T-cell expressed, and secreted RHR: Resting heart rate ROS: Reactive oxygen species RR: Relative risk sBP: Systolic blood pressure siRNA: Small interfering RNA molecules SR-B1: scavenger receptor class B type 1 TF: Tissue factor TFPI: Tissue factor pathway inhibitor TG: Triglyceride TGF- β1: Transforming growth factor-β1 TGF-β : Transforming growth factor-β TGs: Triglycerides TLR-4: Toll-like receptors 4 xxvi TNF-α : Tumor necrosis factor-α TNFα: Tumor necrosis factor-alfa tPA: Tissue plasminogen activator u-PA: Urinary-type plasminogen activators uPA: Urokinase plasminogen activator VCAM-1: Vascular cell adhesion molecule-1 VE-cadherin: Vascular endothelial cadherin VEGFR2: Vascular endothelial growth factor receptor 2 VSMC: Vascular smooth muscle VSMC: Vascular smooth muscle cell WHO: World health organization WHO: World Health Organization xxvii Acknowledgements I offer my enduring gratitude to the faculty, staff, and fellow students at the Healthy Heart Program and the Experimental Medicine Program, who have inspired me to continue working in this field. I owe particular thanks to Dr. Sammy Chan, my supervisor, who provided me with every possible support; lessons that I have learned from him will indeed drive my professional career to be successful. I cannot thank Dr. Jiri Frohlich enough for his constructive advice and continuous support over these years. His guidance has helped me increase the breadth and depth of my research skills and realize my research potentials. I thank him for each pat on the shoulder and every smile, and the motivation he gave me throughout my time in the program, especially in the tough times related to the time of uncertainties. Saying thank you just does not seem to be enough now that you have helped me complete this journey. You were an integral part of me being able to make it to the end. I thank Dr. Karin Humphries, my co-supervisor, for her patience and for sharing her valuable knowledge with me. I hope to “pay forward” what you have done for me by helping other students. I will use your selfless support as a model as I move forward. I owe very special thanks to Dr. Hayden Pritchard, who helped me navigate my Ph.D. program. I admire his cleverness, wisdom, and particularly his leadership skills. When he talks, I always listen. I am very proud to be one of his students. I offer my enduring gratitude to Dr. Scott Lear, who expanded my vision of research and provided me with truthful guidance in my last year of the program. I thank him also for sharing his hard work with me and helping me to finish my program. Finally, I want to thank Dr. Lear for accommodating me in his laboratory with his students during my first year here in Canada. xxviii I owe exceptional thanks to Dr. Defen Peng for her statistical support and guidance in Chapter 6. Additionally, I owe very special thanks to Lubomira Cermakova and Ida Priecelova for their support and friendship. I also thank my brother Dr. Bisher Abu-Yassin and his lovely family for the generous help and kindness they gave me during my time here in Canada. I also thank the University of Jeddah and King Abdulaziz University in Jeddah Saudi Arabia and the Saudi Arabian Cultural Bureau in Canada for providing me and my family with all the necessary support to live serenely in Vancouver. Special thanks are owed to my family, who provided all the energy to successfully finish this program. xxix Dedication The most special thanks of all goes to the love of my life, the one and only, my wife, Dr. Dalal Tawati, who has supported me through thick and thin and has kept my sanity in check all these years. Thanks for your unconditional love and support; your smile has kept me going through the toughest days and continues to carry me through every single day of my life. To my father, my mother, and my brothers and sisters, thank you, I love you, and I am coming home soon. To my in-laws, thank you for your support and prayers. To my son Ziyad and my daughter Delilah, I love you to the moon and back. I dedicate this work to all of you, and I hope to make you proud. 1 Chapter 1: Introduction 1.1 An overview The body of evidence demonstrating the critical nature of an active lifestyle for optimal health and well-being has accumulated at an exponential rate. Indeed, the health benefits of PA and exercise are unquestionable; virtually everyone can benefit from increasing their PA or higher fitness level [1]. Regular PA is one of the most simple and cost-effective primary and secondary prevention approach for over 25 chronic medical conditions, including cardiovascular disease (CVD) and premature mortality [1–5]. Inadequate PA on the other hand is the fourth leading risk factor for global mortality, accounting for approximately 3.2 million deaths annually, according to the World Health Organization (WHO) [6]. The prevalence (and population attributable risk) of insufficient PA is typically the same and may even be higher than the total of all other risk factors [7]. The majority of international PA guidelines recommend 150 minutes of moderate-to-vigorous intensity PA per week [1]. Additionally, this messaging has been incorporated into clinical population guidelines (such as those with diabetes) [8]. As a result of PA scientists' combined efforts, this research has contributed to our understanding of the dose-response relationship between PA and various health outcomes, with 20 to 30% risk reductions for premature mortality and chronic disease frequently observed in those who meet or exceed current international recommendations [1,3,6,8]. Even greater risk reduction observed when objective measures of health-related physical fitness taken [1,9] Table 1-1. Thus, the participation in regular PA or exercise is an essential medicine for the primary and secondary prevention of a wide range of chronic medical conditions as mentioned previously. 2 Table 1-1. Relative risk reduction observed when comparing active/fit vs inactive/unfit individuals Active vs inactive (Self-reported activity) Fit vs unfit (When aerobic fitness is assessed) Premature all-cause mortality 31% 45% Cardiovascular disease 33% ≥ 50% Stroke 31% ≥ 60% Hypertension 32% ≥ 50% Colon cancer 30% - Breast cancer 20% - Type two diabetes 40% ≥ 50% Osteoporosis • Bone adaptations to exercise are dose dependent and site specific • Routine PA is associated with improve bone health Data from Warburton et al. [3] Insufficient PA, on the other hand, is a modifiable risk factor for CVD and a number of chronic diseases, including type 2 diabetes, cancer [colon and breast], obesity, hypertension, bone and joint diseases, and depression among others. [4,10–12]. It is estimated that approximately 25% of the world's population suffers from inadequate PA [2]. In Canada, a high-income country where leisure or recreational PA is the predominant form of PA, the prevalence of insufficient PA among adult Canadians is higher than the prevalence of all other modifiable risk factors combined Figure 3 1-1 [13]. Recent research has also clearly shown that the need to reduce the barriers for PA participation for everyone. 4 Figure 1-1. Prevalence of traditional risk factors for cardiovascular disease in Canadian society according to sex. Dyslipidemia was defined as having unhealthy blood concentrations of low-density lipoprotein cholesterol (> 3.5 mmol/L), or a total cholesterol. high-density lipoprotein cholesterol ratio > 5.0, or self-reported use of a lipid modifying medication. Diabetes (in individuals 12 years of age and older) was diagnosed via a health professional. Hypertension was defined as a measured systolic blood pressure > 140 mm Hg, or a measured diastolic blood pressure > 90 mm Hg, or a self-reported diagnosis of high blood pressure, or the self-reported use of antihypertensive medication. Physical activity (for individuals aged 18-79 years) was evaluated via accelerometry. Inactive individuals were considered those who engaged in < 150 minutes of moderate to vigorous physical activity (in 10-minute bouts). Smokers (aged 12 years and older) included those who reported being a current smoker. Obesity was directly measured according to body mass index of > 30. Heavy drinking (aged 12 years and older) included those who reported having 5 or more drinks on 1 occasion, at least once a month in the past 12 months. Data from the Canadian Health 5 Measures Survey (2012-2013) from a nationally representative sample of Canadians. This figure was constructed by MJ and adapted from (http://www.statcan.gc.ca) 1.2 Key definitions related to physical activity PA is a very broad term that refers to any body movement produced by the skeletal muscles that results in an increase in energy expenditure above resting levels, regardless of whether the movement is for recreational or non-recreational purposes [14]. PA can be accomplished in a variety of ways, including walking, cycling, sports, and other active types of recreation (for example, dance, yoga, tai chi). PA can also be used in the workplace or around the home. All forms of PA can be beneficial to our health if done frequently and for a sufficient duration and intensity [14]. The term "exercise" refers to PA that has been organised and arranged with the goal of enhancing one or more aspects of health [14]. It is important to highlight that PA (a behaviour) and exercise (an attained state) are inversely related to chronic disease and all causes mortality [14]. In contrast, exercise or health-related physical fitness has been shown to be associated with better risk reduction [15,16]. This is assumed to be in part because exercise or physical fitness can be tested with more objective precision than regular PA, which is frequently measured subjectively [16]. Physical fitness is a term that refers to a physiological condition of health that enables an individual to meet the demands of daily life or that serves as a foundation for athletic performance, or both. Health-related physical fitness involves the components of physical fitness related to health status, including cardiovascular fitness, musculoskeletal fitness, body composition and metabolism [5]. The terms PA and physical fitness are frequently used interchangeably in large 6 epidemiologic studies, with fitness frequently being treated as a more accurate (albeit indirect) measure of PA than self-report [17]. At the opposite end of the energy expenditure continuum is sedentary behavior, which has been defined as any waking behavior characterized by an energy expenditure < 1.5 metabolic equivalent of tasks (METs), while it’s in a seated, reclined, or laying posture (e.g. watching television, playing computer games, driving a car, or reading) [18]. It is critical to distinguish sedentary behaviour from insufficient PA, which occurs when an individual does not engage in moderate- to vigorous-intensity PA for ≥ 150 min/week [19]. 1.3 Physical activity and insufficient physical activity and its relation to cardiovascular health 1.3.1 The relationship between PA and cardiovascular health 1.3.1.1 Primary prevention population Numerous epidemiological studies have established that PA has a protective (and independent) effect on cardiovascular health and all-cause mortality in adults [3]. Both men and women who reported increased levels of PA and fitness were found to have reductions in relative risk (RR) of death by about 20% to 35% [20,21]. In a study involving 10 224 men and 3120 women who were followed for more than eight years, the lowest quintiles of physical fitness, as measured by the maximal treadmill exercise test, were associated with an increased risk of death from any cause compared to the highest quintile (RR among men 3.4, 95% confidence interval [CI] 2.02 to 5.8, and among women 4.7, 95% CI 2.22 to 9.8 [20,21]. According to two major systematic reviews and meta-analyses, being physically active in adulthood reduces the risk of CVD, all-cause, and cardiovascular death by 30-35 % [3,22]. A 7 nonlinear relationship was observed between PA and these outcomes; a greater reduction in risk was observed between sedentary and moderately active individuals when compared to moderately and highly active individuals. As a result, even small amounts of PA were beneficial in populations with low levels of PA [22]. In an important study [23], participants who had the highest level of physical fitness at the beginning of the study and who maintained or improved their physical fitness over a long period of time had the lowest risk of premature death [12]. Modest improvements in physical fitness and health status have been associated with significant improvements in health status in previously sedentary people [24]. For instance, another study found that people who improved their fitness level over a five-year period had a 44% reduction in their RR of death compared to those who remained unfit [25]. 1.3.1.2 Secondary prevention population PA and fitness benefits also extend to patients with established CVD [26,27]. This is important because previous recommendations have been made for patients with heart disease to rest and not engage in PA. In a large systematic review of 48 trials with total of 8940 patients cardiac rehabilitation was associated with reduce all-cause mortality (odds ratio [OR] = 0.80; 95% CI: 0.68 to 0.93) and cardiac mortality (OR = 0.74; 95% CI: 0.61 to 0.96), and systolic blood pressure (sBP) (weighted mean difference, –3.2 mm Hg; 95% CI: –5.4 to –0.9 mm Hg compared with usual care [28]. Another study examined the effect of various levels of leisure-time PA on cardiorespiratory fitness and the progression of coronary artery disease in patients with coronary artery disease. For 12 months of participation, 62 patients were randomly assigned to an exercise group or to a control group receiving usual care. They conclude that a weekly energy expenditure 8 of approximately 1600 kilocalories [kcals] has been associated to plaque reduction in patients with coronary artery disease [29]. 1.3.2 The relationship between insufficient physical activity or sedentary behaviour and cardiovascular health Since Morris and colleagues’ pioneering work early in the 1950s [30] and the work of Paffenbarger and colleagues in the 1970s era [31,32] there has been a wealth of epidemiological evidence from various follow-up studies. Indeed, in recent years, the area of PA and cardiovascular health has moved and extended to investigate the link between insufficient PA and health. It is critical to emphasise the health risks of excessive sedentary behaviour when discussing insufficient PA [33,34]. Insufficient PA has been more common in recent years, possibly as a result of a higher acceptance of the Western lifestyle, which includes more sedentary time, less active transportation, and more leisure or purposeful PA [35]. Published investigations revealed that various health problems are strongly linked to sedentary time and a wide range of health issues. The relation was linear, independent of PA, and the dose-response relation was detected even among physically active subjects [33,36]. However, even when sedentary time was high, physically active adults had a decreased risk of cardiovascular mortality compared to inactive counterparts [37]. According to the influential report The Impact of Physical Inactivity on the World's Major Non-Communicable Diseases, physical inactivity is estimated to be responsible for 6% of the burden of disease from coronary heart disease; 7% of the burden of type 2 diabetes (3.9 percent to 9.6%), 10% of breast cancer (5.6 percent to 14.1 percent), and 10% of colon cancer (5.7 percent to 13.8 percent) [38]. According to the report, physical inactivity accounts for 9% of premature mortality (5.1–12.5%), or more over 5.3 of the 57 million 9 deaths worldwide in 2008. If inactivity were not eliminated but rather reduced by 10% or 25%, each year, >533,000 and >1.3 million fatalities may be avoided, respectively [38]. Eliminating physical inactivity also is expected to raise the world's population's life expectancy by 0.68 (0.41 to 0.95) years. [19,38] While the benefits of PA are well known, the number of people who have adequate physically active lives continues to be low. This is due to several factors, including personal barriers associated with perceived limitations in self-efficacy, a lack of time, and misconceptions about the amount of exercise required for cardiovascular health benefits [19]. For instance, one could ask, if two people expend the same amount of energy, does it make a difference whether they do so in multiple, shorter bouts of PA or fewer, longer bouts? These overwhelming and confusing ideas may lead to insufficient PA. Despite the evidence demonstrating the cardiovascular benefits of moderate-to-vigorous PA performed even for short periods of time (at least 10 minutes), and the effort made to promote PA, the general public's level of adherence to the guidelines continues to be unacceptable. 1.3.3 How much physical activity is enough? PA is clearly necessary for the prevention of chronic disease and premature death [39]. However, uncertainty persists over the appropriate "volume" (frequency, duration, and intensity of exercise) and the minimal volume required to provide health benefits, particularly regarding the effects of PA intensity on health status. There is evidence that PA intensity is inversely related to mortality [39]. Paffenbarger and colleagues discovered that regular physical exercise (expending > 2000 kcals per week) was associated with an average increase of 1 to 2 years in life expectancy by the age of 80, and that the benefits were linear even at lower energy expenditure levels [32]. 10 Subsequent research has demonstrated an average weekly energy expenditure of approximately 1000 Kcals is related with a 20% to 30% reduction in all-cause death [39,40]. Today, the majority of health and fitness organisations and professionals recommend that people engage in a minimum amount of activity that expense 1000 kcal per week, while still acknowledging the benefits of higher energy expenditures [1]. In a recent prospective cohort study assessing PA levels in 1274 older men over a median follow-up of 5 years, the researchers investigated the effects of bouts of PA lasting at least 10 minutes on mortality [41]. Accelerometers were used to measure moderate-to-vigorous PA that was accumulated in sporadic minutes of PA or in bouts lasting less than 10 minutes. Only 16 percent of older men met the recommended volume of PA over the course of a 7-day PA assessment period when applying the 10 minutes criteria, whereas 66% of older men achieved 150 minutes of recommended activity with minutes of accumulated sporadic PA. Despite the significant differences in the proportion of participants who met PA recommendations, the HR for all-cause mortality when PA was accumulated sporadically hazard ratio (HR= 0.59 (95 %; CI, 0.43–0.81) did not differ from the HR for all-cause mortality when PA was performed in 10-minute bouts (HR=0.58 [95% CI, 0.33–1.00]), indicating that participation in PA is beneficial regardless of how it is accumulated [41]. Yet, because this study only included older men, it clearly necessitates additional validation in younger populations as well as in women, which would finally allow for the development of even more individualised PA recommendations. 1.4 Physiological benefits of PA on cardiovascular health Current recommendations for primary and secondary prevention of cardiovascular disease in general and ischemic heart disease in particular emphasise the importance of lifestyle 11 adjustments such as increased PA. These suggestions are strengthened by evidence that PA and exercise training have beneficial effects on hemodynamic and metabolic variables [42], resulting in a reduction in the risk of CVD [12]. For instance, routine PA has been shown to improve body composition (e.g., by reducing abdominal adiposity and enhancing lipid lipoprotein profiles and weight control) [42] (e.g., by lowering triglyceride levels, increasing high-density lipoprotein cholesterol (HDL-C) levels [43], and decreasing low-density lipoprotein cholesterol [LDL])-to-HDL ratios [42,44], improve glucose homeostasis [45], reduces blood pressure [43] and reduce systemic inflammation [46]. 1.4.1 Physiological benefits of PA on resting heart rate and vascular health A decrease in the resting heart rate (RHR) is one of the first signals of cardiovascular adaptation to aerobic exercise training, which occurs primarily as a result of increased vagal tone[4]. A low intrinsic HR has been linked to atrial enlargement as a result of training-induced increases in blood volume and pressure load. Increased plasma volume, when combined with enhanced venous return to the left ventricle, often results in an increase in diastolic filling and ventricular compliance, which improves cardiac performance [47]. Although PA and exercise have been shown to improve resting heart function, significant disparities in the hemodynamic responses to exercise training have been found between healthy and chronic disease populations. Whereas the majority of research conducted on healthy individuals demonstrated a reduction in RHR of up to 10 beats/min (bpm) following exercise, studies conducted on chronic disease populations demonstrated a moderate reduction in RHR following aerobic activity [48]. PA and Exercise had a similar effect on vascular compliance or arterial stiffness in the general population as it did in hypertensive and diabetic populations 12 [48,49]. There have also been reports of differences in the maintenance of hemodynamic responses to exercise. For instance, among elderly individuals with several cardiovascular risk factors, short-term reductions in RHR and arterial stiffness have been shown to fade after six months, but not in healthy elderly subjects [50]. Taken together, these findings indicate there are still numerous facets of exercise-induced hemodynamic alterations in disease populations that need to be better understood. Adaptations in the neuro-humoral modulation of vascular tone occur also as a result of regular PA and exercise. Improved endothelium-dependent vasodilation associates vascular resistance reduction with sBP and diastolic blood pressure (dBP) [51]. All of these hemodynamic adjustments are accompanied by substantial increases in exercise capacity as a result of endurance training, offering indirect proof for enhanced myocardial oxygenation and cardiovascular health [51]. Thus, a lower RHR and greater vascular compliance are frequently viewed as indicators of cardiorespiratory fitness, and their regulation is critical for CVD risk reduction [51]. PA and exercise increase cardiovascular fitness via a number of pathways, the physiology of which has been well described and reviewed previously [19]. To summarise, the rapid (days or weeks) functional and structural changes in the vascular system associated with ordinary PA appear to be induced by repeated episodes of elevated shear stress [52,53] and oxidative metabolism which is facilitated by an increase in mitochondrial proteins as a result of protein breakdown [54]. This effectively delays the development of metabolic acidosis, boosts free fatty acid oxidation, and lowers glycogen depletion, carbohydrate consumption, and insulin sensitivity during submaximal exercise, hence delaying fatigue [54]. Also, by lowering the expression of the transcription factors responsible for vascular senescence and increasing the expression and activation of nitric oxide synthase, the capillary to fibre ratio increases, resulting in maximal blood 13 flow through the muscle. This provides a larger surface area for metabolite exchange and lengthens blood transit time [53], and maximal blood flow through the muscle. As a result, skeletal muscle oxygen uptake (the difference between arterial and venous blood) is improved. Left ventricular size, contractility, and relaxation all increase, while left ventricular stiffness decreases; these changes result in a larger stroke volume, a slower heart rate during submaximal exercise, and a higher maximum cardiac output [51]. The blood's haemoglobin level rises, allowing it to transport more oxygen. The response to catecholamines is suppressed, which contributes to a reduced heart rate and lactate generation during submaximal activity. 1.4.2 The relationship between PA and Carotid intima-media thickness Carotid intima-media thickness (cIMT) as determined by B-mode ultrasonography is the combined thickness of the carotid artery's intimal and medial layers [55]. Although some arterial wall thickening is mediated by nonatherosclerotic factors (i.e., it is an adaptation process strongly associated with age and blood pressure), arterial thickening can also be caused by the atherosclerotic mechanisms [56]. This type of thickening is pathologically distinct from adaptive thickening and is the precursor to the development of atherosclerosis [55]. Ultrasound for cIMT measures should be performed by a professional sonographer who is equipped with current technology. A proper scan will reveal a "double line" on both the near and far wall of the carotid artery; these are the lumen-intima and media-adventitia interfaces, respectively [57]. After the images are taken, boundary detection tools can be utilised to determine the cIMT by tracing the far wall interfaces of the lumen-intima to the media-adventitia. In addition to imaging for cIMT, a circumferential scan of the common carotid artery’s near and far wall, carotid bifurcation, and internal carotid artery segments can be performed to identify plaques [57]. 14 The thickness of the artery wall is greatly increased by atherosclerotic plaques. Carotid artery plaque was defined by the Mannheim Intima-media Thickness Consensus Panel as a focal structure that encroaches into the arterial lumen of at least 0.5 mm or 50% of the surrounding IMT value or demonstrates a thickness >1.5 mm as measured from the media-adventitia interface to the intima-lumen interface [58]; however, some physicians consider maximal thickness as little as 1 mm indicative of plaque [59]. Because cIMT can identify atherosclerosis with or without stenosis, it is an excellent tool for discovering non-obstructing atherosclerosis [60]. This is important because it has been shown that a large portion of cardiovascular events involve non-stenotic arterial plaques [60]. Thus, what cIMT can detect is an accelerated atherosclerotic disease process and subclinical atherosclerosis[61]. PA is linked to lower cIMT according to the majority of cross-sectional research [62]. Increased cardiorespiratory fitness and changes in a variety of cardiovascular risk variables, such as hyperglycemia, insulin resistance, hyperlipidemia, HTN, and obesity, suggest probable pathways by which exercise training may slow the progression of carotid atherosclerosis [62]. One study comprised 614 healthy men and women who did not have carotid atherosclerosis or an increased risk of coronary heart disease. Their findings indicated that the proportion of minutes spent insufficiently active was directly related to baseline cIMT. Additionally, those who engaged in some bouts of vigorous-intensity PA had a lower 3-year cIMT progression [63]. PA was effective regardless of sedentary time and was mediated by changes in atherosclerotic risk variables. In another study composed of 47 sedentary and 40 physically active young pre-hypertensive or stage 1 hypertensive subjects. This study was able to demonstrate that in the active group, the rate of development of carotid mean IMT and maximum IMT was significantly lower 15 than it is in the sedentary group [64]. In another study, research showed an inverse relationship between cIMT and walking speed in older adults [65]. cIMT testing is an effective, non-invasive, and reasonably inexpensive method for detecting atherosclerotic plaques and carotid artery wall thickening in the early stages of atherosclerosis, as well as an independent predictor of strokes and cardiac events [55]. However, the need to utilize this important tool in to examine the effect of PA on cIMT in special population like patients with high Lipoprotein(a) [Lp(a)], the thesis population of interest, is apparent. 1.5 The relationship between PA and Lp(a) The Lp(a) molecule and its association to CVD are covered in detail in Chapter 2. Lp(a) refers to a group of lipoproteins that are structurally similar to LDL-C but contain a distinct apoprotein called apoprotein (a). Other lipoproteins have been shown to benefit from moderate exercise, and subsequent research has focused on whether Lp(a) levels might be altered by PA. When both LDL-C and Lp(a) are increased, they have a synergistic effect on CVD risk, emphasising the necessity of addressing those risk factors that can be adjusted by exercise and other lifestyle changes. The link between PA and serum Lp(a) concentrations was studied using four different experimental techniques [66]. First, population studies that incorporate PA or level of fitness as a variable [67–69]. In general, these few studies demonstrate a week to non-existent association between serum Lp(a) levels and regular PA, with the exception of a research in children that demonstrated a significant relationship between Lp(a) and PA [69]. Second, cross-sectional comparisons between athletes and non-athletes, as well as athletes from various sports (e.g. weight lifting, distance running) [70–84]. These studies found no association between moderate exercise training and serum Lp(a) levels in adults, however some evidence suggests an inverse link in 16 children [66]. Two other studies raise the idea that long-term, high-intensity exercise training over a long period of time may elevate blood Lp(a) levels, however the potential confounding influence of factors such as race has not been ruled out [66]. Third, studies of exercise intervention ranging from several months to a few years in duration [85–94]. These investigations are generally consistent in concluding that there is no correlation between moderate, realistic PA levels and serum Lp(a) levels. Few papers suggest the possibility that intense exercise training (e.g. distance and interval running, circuit training) may increase serum Lp(a) levels [86,90]. However, when observed, increases in serum Lp(a) levels during long term exercise intervention studies are generally modest, and values still within the clinically normal range [66]. Finally, research on the effects of a single bout of exercise on Lp(a) levels were conducted [95–97]. In response to endurance exercise lasting up to 2 hours, serum Lp(a) levels do not appear to vary in an acute manner, while the potential consequences of longer-term exercise have not been investigated [66]. 17 First author and country Subjects and PA/exercise Effective PA on Lp(a) Mora et al. (USA) [98] 27,055 healthy women. PA was self-reported. Lp(a) level is not related to PA MacAuley et al. (Northern Ireland) [67] 1,600 healthy men and women. PA was self-reported. Lp(a) level is not related to PA Taimela et al. (Finland) [69] 2500 Children and young adults. PA was self-reported. Inverse relationship between LPA and PA history Austin et al. (USA) [70] 59 patients with DM Physical fitness were determined by VO2max during progressive bicycle ergometry to exhaustion. Inverse relationship between physical fitness and lipoprotein(a) Bermúdez et al. (Venezuela) [71] 1340 healthy men and women. PA was self-reported. Lp(a) level is not related to PA Kostka et al. (France) [72] 52 subjects (23 men and 29 women). PA was self-reported. Lp(a) level is not related to PA Lippi et al. (Italy) [73] 60 healthy men vs 142 high endurance athlete control. PA was self-reported. Lp(a) level is not related to PA 18 First author and country Subjects and PA/exercise Effective PA on Lp(a) Suter et al. Canada [74] 97 healthy children. 39 boys and 58 girls. PA was self-reported. Lp(a) level is not related to PA Szymanski et al. (USA) [75] 46 middle-aged men. PA was measured graded treadmill exercise test. Lp(a) level is not related to PA Thomas et al. (USA) [76] 54 healthy men. PA was measured through maximal graded treadmill exercise test. Lp(a) level is not related to PA Cardoso et al. (Mexico) [77] 60 healthy men. 20 sedentary individuals, 20 low-aerobic athletes (bodybuilders), and 20 high-aerobic athletes. PA was measured through maximal graded treadmill exercise test. Inverse correlations between Lp(a) and Vo2max but not with glycaemic control. Oyelola et al. (Nigeria) [78] 28 healthy men. 14 male runners, 14 matched non-athletes. PA was self-reported. Lp(a) level is not related to PA 19 First author and country Subjects and PA/exercise Effective PA on Lp(a) Cohen et al. (South Africa) [79] 18 male body builders; 10 anabolic steroid users and nonusers. PA was measured by high volume resistance training. Lp(a) levels higher than expected (12 of 18 >30 mg/dl); values significantly higher in non-users vs anabolic steroid users. Drowatzky et al. (USA) [80] 160 healthy women. PA was measured by Graded exercise test. Lp(a) level is not related to PA. Duell et al. (USA) [81] 55 mildly hypercholesterolemic healthy adult men. PA was measured by Graded exercise test. Lp(a) levels significantly inversely correlated with PA. Possible confounding variables of hormones or body mass index. Halle et al. (Germany) [82] 105 endurance athletes, 57 power athletes, and 87 sedentary young men. PA was measured by Graded exercise test. Lp(a) level is not related to PA. Hubinger et al. (Australia) [83] 121 healthy men. 57 male middle-aged distance runners and 64 middle aged sedentary Lp(a) level is not related to PA. Young Jae et al. (South Korea) [84] 425 men with type 2 diabetes cardiorespiratory fitness (CRF) was measured using peak oxygen uptake with expired gas analysis during a symptom-limited exercise test. CRF was inversely associated with Lp(a). 20 First author and country Subjects and PA/exercise Effective PA on Lp(a) Byrne et al. (UK) [85] Fit, healthy, army recruits were recruited for an intense 48-hour military exercise. No response in Lp(a) levels with acute exercise Holme et al. (Norway) [86] 219 healthy physically inactive persons with moderately deranged risk factor levels were randomized to four intervention groups: dietary intervention, exercise, diet + exercise and control. Significant increase in median Lp(a) levels in diet + exercise and exercise only groups; no change in controls or diet only group. The increase in Lp(a) within normal levels. Gutin et al. (USA) [87] 12 children completed an aerobic training program Lp(a) increased by 10%. Kadoglou, (Greece) [88] 52 overweight/obese, type 2 diabetic patients, Participants were randomly assigned into resistance exercise group and control group. No change in median Lp(a) levels. Lehmann et al. (Swizerland) [89] 20 diabetic patients enrolled for 3 months of endurance exercise. No change in median Lp(a) levels. 21 First author and country Subjects and PA/exercise Effective PA on Lp(a) Ponjee et al. (Netherland) [90] 36 Sedentary men and women trained 3 to 4 times a week for nine months. Lp(a) increased from 32 mg/l to 39 mg/l in men, and from 65 mg/l to 125.5 mg/l in women. Within recommended range. Rigla et al. (Spain) [91] 27 diabetic patients enrolled for 3 months of endurance exercise. No change in median Lp(a) levels. Ring-Dimitriou et al. (Austria) [92] 30 subjects randomly assigned to exercise and control group for nine months. No change in median Lp(a) levels. Sponder et al. (Austria) [93] 109 patients were allocated and instructed to exercise 75 min/wk. of vigorous intensity and 150 min/wk. of moderate-intensity endurance training for 8 months. No change in median Lp(a) levels. Tokmakidis et al. Greece [94] 27 patients. 14 were randomly allocated to supervised 8-month training program and 13 patients served as a control group. No change in median Lp(a) levels. Durstine et al. (USA) [95] 10 endurance-trained men completed two separate exercise sessions at 70% VO2max. No response in Lp(a) levels with acute exercise. 22 First author and country Subjects and PA/exercise Effective PA on Lp(a) Durstine et al. (USA) [96] 12 physically active men completed two 30-min submaximal treadmill exercise sessions. No response in Lp(a) levels with acute exercise. Hubinger et al. (Australia) [97] 8 men and women recruited for level and downhill treadmill running up to 60-minute duration. No response in Lp(a) levels with acute exercise. 23 Although not conclusive, current research suggests that PA has no effect on Lp(a) levels in the blood. Both cross-sectional and intervention studies in adults also reveal that moderate exercise has no effect on serum Lp(a) levels. These findings add credibility to the hypothesis that serum Lp(a) concentration is regulated independently of body mass, other blood lipids, and lifestyle factors such as PA. The lack of effect of PA on serum Lp(a) concentration points to the importance of attending to other factors that can be influenced by PA and other lifestyle modifications, especially when serum Lp(a) concentration is constantly elevated. It is possible that, by modifying other blood lipids and risk factors for cardiovascular disease, regular moderate PA may lessen the risk of developing atherosclerosis related to high levels of Lp(a). 24 1.6 Dissertation overview The overall objectives of this dissertation are to provide a comprehensive investigation of RHR and early structural atherosclerotic vascular changes in patients with high Lp(a) as well as the effect of PA on these two parameters. The research included in this dissertation was developed as a multi-phase project in which each phase was designed to address a specific research objective. The first objective of this dissertation is to raise awareness about the harmful effects of Lp(a). This project is presented in Chapter 2. In the research work presented in Chapter 3, the association between RHR and PA was investigated in primary and secondary prevention patients enrolled in the HHP prevention clinic. The working hypothesis was participants attending the HHP program prevention clinic who increase their PA between their initial and third year visit would show a significant decrease in their RHR compared to those who did not improve. The work presented in Chapter 4 aimed to address the third objective which evaluate the relative significance of Lp(a) as a predictor of future CVD compared with selected established CVD risk factors. The fourth objective of this research work was to evaluate the correlation between Lp(a) concentrations and RHR. The working hypothesis for this objective was patients with high Lp(a) would show higher RHR at baseline. Also, we explored in our fifth objective the effect of improved PA on RHR in HHP patient’s prevention clinic with high Lp(a) levels. The working hypothesis that patients with high Lp(a) levels who improved their PA will lower their RHR significantly in comparison to non-improved PA patients. The fourth and fifth objectives are presented in Chapter 5. Finally, the sixth objective of this research was to investigate beneficial effects of improved PA on early structural atherosclerotic vascular changes in healthy subjects enrolled in the M-CHAT trial. This project is presented in Chapter 6. The last chapter of this dissertation, Chapter 7, provides a general 25 discussion of the main findings of the dissertation, along with its limitations, future directions, and conclusions. 26 Chapter 2: Lipoprotein(a) the Insurgent: A New Insight into the Structure, Function, Metabolism, Pathogenicity, and Medications Affecting Lipoprotein(a) Molecule 2.1 Overview Atherosclerosis is a chronic inflammatory lipid-fueled disease of the arteries that is initiated very early in childhood and mediated by innate and adaptive immune responses. Atherosclerosis is characterized by progressive accumulation of lipids, necrotic cell debris, and extracellular matrix proteins in the vessel wall and eventually results in partial or total vessel occlusion or thrombosis due to rupture or erosion of the atherosclerotic plaque. Atherosclerosis reveals itself clinically later in life and is influenced by genetic, environmental, behavioural, and dietary risk factors [99–101]. The main risk factors for atherosclerosis include hypercholesterolemia, diabetes mellitus (DM), cigarette smoking and hypertension (HTN). Hypercholesterolemia, for instance, increases the permeability of the vessel walls and initiates the pathogenesis of the disease [102]. The Framingham study showed that LDL-C, triglycerides (TGs) and HDL-C are the major independent predictors of future atherosclerotic events [102]. The need to pinpoint further causal risk factors and thus prospective targets for future interventions is evident [103] by the fact atherosclerosis is still the principal cause of CVD death worldwide despite the decline in mortality rate due to advances in its diagnosis, treatment, prevention, and rehabilitation [104,105]. Currently, lipoprotein(a), aka Lp(a), aka “Lp little a”, is considered a likely candidate and independent strong predictor of atherosclerosis. 27 Lp(a) was first discovered by the Norwegian physician Kåre Berg almost six decades ago [106]. It is an enigmatic class of lipoprotein particles found in plasma and is assumed to be a genetic variant of LDL [107]. The Lp(a) protein moiety comprises two components, a single copy of apolipoprotein (apo) B-100 (apo-B100) tethered to a single copy of a protein denoted as apolipoprotein(a) [apo(a)]. Apo(a) is a polymorphic glycoprotein and carbohydrate-rich moiety whose mRNA is expressed almost entirely in the liver [108]. Lp(a) also has a lipoprotein unit that is essentially identical to LDL both in make-up and in its physical and chemical characteristics [109]. Because Lp(a) and LDL are metabolically distinct due to the presence of apo(a), the special properties of Lp(a), including its mass and density heterogeneity, are virtually totally mediated by apo(a) [109]. The discovery apo(a) has homology with plasminogen (PLG), a substantial enzyme in fibrinolysis, suggested a theoretical association between Lp(a) and thrombosis [110]. The gene controlling the plasma Lp(a) concentration is the LPA gene, which evolved through replication and modification of the kringle (K) domains of the PLG gene. In contrast to LDL-C, which follows a normal Gaussian distribution in the population, Lp(a) levels skew toward lower values in most populations studied to date [66], such that most individuals have low Lp(a) levels and a tail of individuals who display high Lp(a) levels and corresponding prominent CVD risk [111]. Ethnicity powerfully impacts Lp(a) plasma concentrations: Caucasians tend to have the lowest Lp(a) levels, and African Americans have the highest [111]. However, it has become clear that the risk of developing coronary artery disease (CAD) in Caucasians is more than two times higher in individuals with increased Lp(a) levels [112,113]. Several Mendelian randomization studies and meta-analyses have shown undoubted proof that elevated Lp(a) plasma concentrations are correlated with an amplified risk of myocardial infarction, stroke, and aortic valve stenosis 28 [114]. Notwithstanding extensive investigations, the causal mechanisms behind how Lp(a) gives rise to atherosclerotic vascular diseases are still partly understood [115]. 2.2 Lp(a) molecule 2.2.1 Structure of Lp(a) molecule Lp(a) is a has multiple components (see Figure 2-1A) and mainly resembles and consists of an LDL-C molecule. Similar to LDL, Lp(a) has a hydrophilic apo-B100 component located around a lipid core of cholesteryl esters (CEs) and triacylglycerols with many phospholipids and unesterified cholesterol at its surface [116]. According to Siekmeier et al [117], “corresponding to the physical resemblance to LDL, both lipoproteins are very indistinguishable to each other with respect to their structure” [118] (see Figure 2-1-B). However, Lp(a) is distinguished from LDL-C by its hydrophilic highly glycosylated apo(a) portion (the PLG-like pathogenic component of Lp(a)) [114]. In fact, the presence of apo(a) distinguishes Lp(a) from all other lipoprotein classes [119]. Apo(a) is covalently connected to apo-B100 via a single disulfide thioester bond through cysteine residues located in both proteins [115,120], and these residues represent 88% of the total protein mass in Lp(a) [121]. Apo(a) is the key component of Lp(a) and evolved from the PLG gene superfamily a very long time ago through duplication and remodeling. Apo(a) existence imparts distinctive synthetic, catabolic and functional characteristics along with a huge size heterogeneity [114–116,120]. In addition to apo(a) and apo-B100, a recent proteomic study has shown that Lp(a) has 33 more proteins associated with its surface that might be involved in lipid metabolism processes, inflammatory response and coagulation processes [122]. 29 Figure 2-1. Lp(a) structure, composition and physicochemical properties. A- Lp(a) is composed of apo-B100 covalently fastened together with apo(a), which originates from kringle IV (KIV) and KV, and the inactive protease domain of PLG. Apo(a) has important differences compared with PLG. (1) Apo(a) has an unpaired cysteine and forms a disulfide bond with apoB to generate the lipoprotein particle Lp(a). (2) Apo(a) has an inactive protease domain. (3) Apo(a) includes 10 subtypes of KIV repeats, composed of 1 copy each of KIV1, multiple copies of KIV2, and 1 copy of KIV310, KV, and an inactive protease-like domain. (4) Apo(a) lacks kringles 1 to 3 of PLG but has kringles 5 and 10 of KIV, of which KIV2 is present in numerous repeats. OxPLs exist covalently bonded to the apo(a) component and are suspended in the lipid phase of apo-B100. B- Comparison between Lp(a) and LDL with regard to their composition and physicochemical properties. 30 The protease zymogen PLG acts like a proenzyme that is transformed to the fibrinolytic enzyme plasmin by a ternary multicomponent protein that is associated with tissue PLG activator (tPA), PLG, and fibrin either endogenously or iatrogenically [114]. A previous examination of an apo(a) cDNA from a human hepatocyte library revealed apo(a) has outstanding physical similarities with PLG [110,116]. Apo(a) and PLG share high amino acid sequence similarity in several regions, including the protease domain and the Danish pastry-like structure referred to Kringles (K) type 4 (K4) and type 5 (K5) [116,120]. Each kringle contains six conserved cysteine residues that form three disulfide bonds to provide the characteristic triple loop structure of the kringles [119]. These loop-like structures are also present in other coagulation factors, such as PLG, prothrombin, urokinase, and tissue-type PLG activators [106]. In contrast, PLG also has three more Kringles (K1 to K3) (see Figure 1-A). Unlike PLG, apo(a) contains 10 subtypes of K4 (KIV1 to KIV10); KIV1 and KIV3 to KIV10 have a single copy, and KIV2 has repeated copies. Each KIV repeat contains three internal disulfide bonds, one N-linked bond, and six potential O-linked glycosylation sites [123]. It is noteworthy that apo(a) has an inactive serine protease-like domain that cannot be activated by tPA and urokinase PLG activator (uPA) to become an active plasmin even though it has an intact Ser-His-Asp triad [124,125]. This characteristic may suggest that Lp(a) can hamper the physiologic properties of PLG in the fibrinolytic cascade [124]. This characteristic may suggest that Lp(a) can hamper the physiologic properties of PLG in the fibrinolytic cascade [124]. 2.2.2 The functions of specific kringles The kringles on apo(a) serve critical functions (e.g., KIV10 is responsible for the important lysine-binding properties of Lp(a)). Several other kringles play critical pathobiological roles, such 31 as KIV(6-7), which interact with scavenger receptors on foam cells [126]. This interaction results in the secretion of proinflammatory cytokines, such as interleukin (IL)-1, IL-6 and matrix metalloproteinases (MMPs), which can amplify the local inflammatory response and stimulate vascular smooth muscle cell (VSMC) proliferation and initial migration toward the atherosclerotic lesion [66,126,127]. Moreover, KIV(7-8) are essential for Lp(a) formation due to the weak lysine binding sites (LBS) among these Kringles [126]. LBS are critical in the creation of the noncovalent apo(a)-LDL complex by allowing allows the free cysteine in KIV9 to form a covalent disulfide bridge to the apo-B100 component of LDL. The presence of strong LBS within these Kringles, particularly in KIV10, significantly enhances the capacity of Lp(a) to attach to and remain in vessel wall cells and fibrin and may thus be crucial to the atherothrombotic nature of Lp(a) [126]. 2.2.3 The influence of the LPA gene on size and concentration Inheritance of the Lp(a) molecule is dominant and was initially described as a dichotomous and qualitative (Lp+, Lp-) genetic trait [106]. However, it soon became apparent that the genetic variation is related to the levels of Lp(a) in the sera of different individuals (quantitative) rather than to the simple presence or absence of the Lp(a) molecule (qualitative) [128,129]. The gene that retains 90% of the control of apo(a) levels [130] is the LPA gene, which is located at positions 26 and 27 on the long arm of chromosome 6 (6q26-27) [120]. In fact, the LPA gene is one of the most potent monogenetic risk factors for CAD regardless of race [114]. No other quantitative trait is as influenced by sequence differences at a single locus as is Lp(a) [130]. This gene is accountable for the substantial size heterogeneity of apo(a) isoforms [131], which is associated with the variable number of copies (repeats) of kringle KIV2, ranging from 2 to more than 50 repeats with substantial size polymorphism (200 to 800 kilodaltons) [132] (Figure 2-2). 32 Figure 2-2. Comparison between different Lp(a) isoform sizes. In these 2 illustrations, apo(a) molecules of 4 (right) and 18 (left) KIV2 repeats are presented, representing 13 and 27 total KIV repeats. This figure was constructed by MJ. The largest apo(a) isoform described so far has 52–54 KIV repeats [133]. This size variability is a unique phenomenon, as other lipoproteins usually have constant masses [114]. Up to 80% of individuals carry two different size alleles of apo(a), each inherited from one parent [131]. Thus, individuals may have two large, two small or mixed-size apo(a) molecules. Generally, within individuals, the smaller isoform contributes more to net apo(a) production and concentration than does the larger isoform [114,131]. Consequently, apo(a) isoform size is inversely related to Lp(a) density and plasma concentration [116,134,135]. This relationship might 33 occur because small apo(a) size results in easy secretion by the liver, which leads to higher Lp(a) concentration and increases the cardiovascular risk and vice versa [136] 2.3 Metabolism of lipoprotein(a) 2.3.1 Synthesis of lipoprotein(a) Lp(a) synthesis occurs exclusively in the liver, and the LPA gene mainly influences Lp(a) production [115,134,137]. Many studies have confirmed that diet and environmental factors have minimal to no impact on Lp(a) [114]. According to Hobbs and White [130], the rate of apo(a) secretion is determined by different stages inside the hepatocyte: first, the transcription of the apo(a) gene and apo(a) mRNA stability stage; second, apo(a) translation, which is believed to have a dominant influence on the production rate; third, extensive posttranslation modifications, including the formation of three disulfide bonds and the addition of an N-linked glycan, which is essential for folding [123], of apo(a) kringles in the endoplasmic reticulum (ER) [130] and subsequent transport out of the ER [130]; fourth, transport to the Golgi complex, where N-linked and O-linked glycans are further processed [130]; fifth, Golgi-specific addition and modification of apo(a) carbohydrates [138]; and sixth, transport to the cell surface [139]. Many in vivo studies suggest that the third step is the most important step in the production of apo(a) [108,140]. White and colleagues [140] have demonstrated using baboon liver cells that this inverted association could be accounted for by differences in the rate and the extent to which apo(a) isoform chains were processed through the ER. Specifically, these researchers verified that small apo(a) chains more completely and swiftly exit the ER than do large apo(a) chains [108] Figure 2-3. 34 2.3.2 Assembly of lipoprotein(a) Assembly is a two-step procedure. First, to promote, mediate, and reinforce the association between the two apo components of Lp(a), the apo(a)-cysteines (Cys-4057) located at KIV3-7 are positioned close to the only free cysteine (Cys-3734) in apo-B100 via a noncovalent interaction [133,141]. Second, a covalent disulfide bridge is established between KIV9 of apo(a) and apo-B100 of the LDL component [141]. The site of assembly is controversial. The main theory is the assembly occurs either on the surface of the liver cells or in the space of Disse [142,143]. White and Lanford [140] used primary cultures of baboon liver cells to analyze the stages of Lp(a) biogenesis. Their study proved that the Lp(a) association was extracellular because it was inhibited when anti-apo(a) serum was present in the culture medium [140]. In contrast, according to Frank et al [144], mixing recombinants of apo(a) with LDL-C in vitro and incubating the mixture for a few minutes leads to the formation of an intact Lp(a) particle that is entirely similar to the native Lp(a). The latter theory leads to the assumption assembly may occur in either the plasma or the interstitial space [141,144,145]. It is worth noting although apo(a) is predominantly attached to LDL, 2% to 5% apo(a) is free and present in the plasma [146]. The new secreted apo(a) is associated with a recently produced apoB-containing TG-abundant molecule to form Lp(a) with very-low-density lipoprotein (VLDL) properties (see Figure 2-3), which can correspondingly be transformed into a cholesterol-abundant unit with LDL properties [140,143]. Additionally, the linkage could directly occur with a molecule with LDL properties. In the bloodstream, the TG-abundant Lp(a) molecule is swiftly subjected to lipolysis by lipoprotein lipase to form a TG-remnant Lp(a) molecule that is directly catabolized, allowing apo(a) to be recycled [147] to the recently secreted apo(a) pool by the liver [143]. The recycled apo(a) then associates with an additional newly synthesized TG-abundant LDL molecule or is 35 finally eliminated from the plasma by the liver or perhaps by the kidney [143]. TG synthesis may also be critical for the synthesis of apo(a) by hepatoma cells [148]. Understanding whether apo(a) binds to LDL within liver cells before or after secretion to the plasma and which apo-B100 containing lipoprotein is involved in Lp(a) assembly should be the focus of future studies to develop new Lp(a)-lowering therapy. 36 Figure 2-3. Model for the metabolism of apo(a). 1- Lipoprotein (a) production (hepatocyte level). Four stages are likely responsible for apo(a) Lp(a) production in liver cells: (A) transcription of the apo(a) gene and apo(a) mRNA stability in the nucleus; (B) influence of apo(a) translation on the production rate; (c) in the ER, posttranslation modifications and folding of apo(a) kringles; (D) Golgi-specific addition and modification of apo(a) carbohydrates; and (E) transport to the cell surface. 2- Assembly of Lp(a): The site of Lp(a) assembly is controversial. (A) cell surface. (B) The space of Disse. (C) Plasma. 3- Apo(a) associates with a recently made TG-abundant molecule to form Lp(a) with VLDL properties and/or with a cholesterol-abundant molecule with LDL properties. 4- TG-abundant Lp(a) may be transformed into a cholesterol-abundant molecule with LDL properties. 5-Catabolism and clearance: The two Lp(a) components become separated. The generation of apo(a) fragments is most likely from proteolytic cleavage by elastases or metalloproteinases secreted by cells in the arterial wall. (5-A) This permits apo(a) to unite the apo(a) pool recently produced by the hepatocytes. (5-B) Hepatocyte internalization and uptake by megalin, gp330 receptor, macrophage scavenger receptor-BI, lipoprotein receptor, VLDL receptor, PlgRKT receptor, asialoglycoprotein receptor (ASGPR) and LDLR. (5-B) Kidney cellular internalization and uptake. (5-C) Vascular wall deposition. Solid lines represent metabolic pathways; dotted lines represent hypothesized metabolic pathways. This figure was constructed by MJ. 37 2.4 Catabolism Lp(a) clearance is still one of the most fundamental targets of therapies to treat elevated plasma Lp(a). Unfortunately, little is known about the dominant sites and processes accountable for the removal of Lp(a) from circulation: scientists debate between the liver and kidneys as the dominant clearance sites. The spleen and the muscles may also play a modest role in the clearance process [133]. Numerous evidence from in vivo studies suggests that the variations in Lp(a) size and plasma concentration are related to apo(a) production rate and size [149] rather than on its very slow clearance rate [66,123,127,128]. According to Diffenderfer and colleagues [143], apo(a) requires twice the residency time (11 days) required by apo-B100 (4 days), supporting the notion that the apo(a) and apo-B100 components of Lp(a) in circulation are not removed from the bloodstream as a unit in humans. 2.4.1 The liver The unique duality of the Lp(a) molecule’s structure allows Lp(a) to be recognized by multiple LDL and PLG receptors. It was initially thought that LDL receptor (LDLR) in the liver is responsible for the degradation of Lp(a). However, there is evidence that LDLR has minimal or no effect on Lp(a) catabolism [117]. Many kinetic studies have reported that Lp(a) has a longer circulating time than does LDL-C due to the small affinity of Lp(a) for LDLR [114,117]. This low affinity occurs because the apo(a) component interferes with the positioning of the LDLR [114]. Second, many clinical studies have reported that increasing LDLR expression by using statins rather than proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors does not lower Lp(a) levels; however, PCSK9 inhibitor does [114,150]. Other receptors, such as Megalin, gp330 receptor [150], macrophage scavenger receptor-BI [151], lipoprotein receptor (LRP-1) [152], 38 VLDL receptor [143], and galactose-specific asialoglycoprotein receptor (ASGPR) [153], show affinity for Lp(a) and may be involved in Lp(a) internalization and clearance (see Figure 3). The most novel clearance mechanism was presented recently by Sharma and colleagues [147] and involves PLG receptors PlgRKT and proteolytic cleavage and recycling of apo(a) Apparently, PlgRKT is responsible for the uptake and internalization of both circulating Lp(a) and apo(a) to the Rab5+ early endosome. Then, these researchers determined that the apo(a) component of Lp(a) is trafficked to the Golgi network and released via the Rab11+ endosome, which subsequently promotes the re-excretion of apo(a) to circulation and the trafficking of the LDL component to the lysosome for degradation [147]. This mechanism suggests that Lp(a) has a longer plasma residence time in circulation than does LDL [114] and support the theory that the liver is the final clearance organ for apo(a) if this component is not reassembled back into Lp(a) [50]. 2.4.2 The kidneys Several in vivo studies have reported that the kidney play a valuable role in Lp(a) metabolism [57, 58]. An important report has shown that Lp(a) levels are elevated and its clearance rate is lower in patients with end-stage renal disease undergoing hemodialysis. Urinary apo(a) levels significantly decrease once the glomerular filtration rate becomes <70 mL/min [156,157]. Another important study used an in vivo approach by measuring Lp(a) plasma concentrations simultaneously in the ascending aorta and renal vein of one hundred patients undergoing coronary angiography or coronary angioplasty [158]. Lp(a) concentrations changed remarkably between the two vessels even after correcting for hemoconcentration, corresponding to a mean arteriovenous difference of -9% in the arterial concentration [158]. These results suggest that the atherogenic Lp(a) molecules are taken up by the kidney from renal circulation [158]. In familial 39 hypercholesterolemia (FH) patients, LDL apheresis lessened plasma Lp(a) concentrations by up to 75% with an associated abrupt 45% decrease in urinary apo(a) [159]. The kidneys share with the liver some of the important receptors that show affinity for Lp(a) uptake, e.g., PlgRKT, Megalin, gp330 receptor and VLDL receptor [143]. 2.5 Physiological functions of Lp(a). 2.5.1 The role of Lp(a) in angiogenesis and tumor growth Several studies have reported that Lp(a) plays a significant role in angiogenesis and tumor growth [160–162]. The similarity between Lp(a) and PLG may decrease the activation of the proteases, which is mandatory for the activation of MMPs and the subsequent activation of angiogenesis [160]. An animal study conducted by Kim et al [161] reported that Lp(a) plays a significant role in angiogenesis and tumor growth. These researchers demonstrated that recombined kringle fragments derived from apo(a), called rhLK68, significantly inhibit angiogenesis and angiogenesis-dependent tumor growth but interfere with basic fibroblast growth factor (bFGF)-stimulated/mitogen-activated protein kinase (MAPK) signaling pathway in endothelial cells [161]. Furthermore, another study proved that apo(a) and its components present in the urine are favorably efficient inhibitors in tube forming assays, in vitro surrogate tests for angiogenesis [162]. On the other hand, other studies reported centenarians who did not suffer from CVD, suggesting that Lp(a) may also play a protective role against cancers [131,163,164]. 2.5.2 Acute-phase reactant Many studies have reported that Lp(a) levels increase in patients with acute pathologies, such as myocardial infarction, inflammatory bowel disease, and gallbladder fistula [118,165–167]. 40 One study exposed nine subjects with plasma Lp(a) concentrations between 6.4 and 17.7 mg/dL to a single intravenous infusion of bisphosphonates previously liquefied in 250 mL of saline; these subjects showed a substantial increase in Lp(a), ESR and CRP two days after intervention [168]. Moreover, Ramharack et al [169] reported that modulation of Lp(a) by cytokines resulted in some significant changes in Lp(a) concentration in primary monkey hepatocytes. In this study, treatment with IL-6, the primary mediator of acute-phase responses, resulted in a marked 2- to 4-fold increase in Lp(a) concentration and mRNA expression in hepatocyte culture. Therefore, the inflammatory status should always be considered when interpreting Lp(a) assays results [118,168,169]. 2.5.3 Binding and carrying of oxidized phospholipids and LP-PLA2 Oxidized phospholipids (OxPLs) play a fundamental role in the early stages of atherosclerosis; they elicit robust pro-inflammatory responses in murine macrophages and monocytes and are capable of stimulating pro-inflammatory genes, leading to vascular inflammation [170]. Several studies have reported that OxPLs usually form on oxidizing LDL-C and apoptotic cell membranes and are released into circulation afterward [171–173]. However, another important study suggested that Lp(a) and OxPLs would associate at the hepatocyte level and not in circulation [174]. Evidence from several studies has shown that Lp(a) has a unique protective physiological function, which is binding, carrying, and promoting the clearance of OxPLs [171,172]. This occurs through the formation of a covalent bond between the KIV of the apo(a) fragment of Lp(a) and OxPLs [175]. 41 2.5.4 Wound healing Wound healing is achieved by multiple complex processes. Many investigators have reported the positive role of Lp(a) in wound healing [117,118,176]. Yano et al [176] measured the presence of Lp(a) in tissue during healing. They observed markedly positive staining of Lp(a) in healing tissues, especially in the fibrous cap surface, endothelial cells of small vessels, and the extracellular space [176], in the second stage of wound healing. Based on this evidence and given that Lp(a) levels are genetically determined and do not change due to diet or environmental factors, Lp(a) might be a considerable source of cholesterol for use in tissue regeneration and repair. 2.5.5 Fibrinolysis Apo(a) isoforms share substantial structural and functional homology with PLG, the principal component of the fibrinolytic pathway, which is converted to plasmin for fibrinolysis [160]. This homology allows apo(a) to compete with PLG for fibrin affinity sites, as small apo(a) isoforms have a higher affinity for fibrin than do large apo(a) isoforms [177]. Additionally, Lp(a) stimulates the synthesis of PLG activator inhibitor-1 (PAI-1) to inhibit tPA and urinary-type (u-PA) PLG activators and consequently regulate PLG activation to plasmin [178]. These data shed light on the potential physiological role of Lp(a) in fibrinolysis, by which Lp(a) could give the injured tissue enough time to heal and regenerate. 2.6 The pathogenicity of Lp(a). Since the discovery of Lp(a), many basic scientists and clinicians have dedicated their work to explaining the hidden mechanisms that lead Lp(a) to cause atherosclerosis. The duality and uniqueness of Lp(a), which make Lp(a) homologous to both LDL-C and PLG, most likely underlie 42 the different but related atherosclerosis mechanism theories. Most importantly, Lp(a) transmits all of the harmful atherogenic characteristics of LDL units, incorporating their tendency to oxidize before and after entry into the subintimal layer of the vessel walls and creating extremely proinflammatory oxidized Lp(a)[ [OxLp(a)] [114]. In fact, basic medical scientists and clinicians consider Lp(a) far more dangerous than LDL due to the presence of an apo(a) component within Lp(a). In this part of the review, we will discuss the different theories of how Lp(a) causes atherosclerosis. These different theories are summarized in Figure 2-4. 43 Figure 2-4. Different theories of how Lp(a) causes atherosclerosis. Early lesions (1) Lp(a) enters the vascular wall and is oxidized by MPO, 12/15 LO, LPO, NADPH, O2- and H2O2. (2) OxLp(a) and OxPLs constitute a substantial increase in monolayer permeability, resulting in increased Lp(a) and LDL entry into the vascular wall. (3) OxLp(a) and OxPLs bind to the E-type prostaglandin receptor (EP2) receptor, causing deposition of connecting segment 1 (CS-1). Additionally, OxLp(a) stimulates the expression of cell adhesion molecules (ICAM, VCAM E-selectin) that bind to monocytes on the endothelial cell surface. (4) OxLp(a) may also activate specific disintegrin and metalloproteinases (ADAMs) to cause the release of active heparin binding epidermal growth factor (HBEGF) and activation of epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor 2 (VEGFR2), causing IL-8 and monocyte chemotactic protein (MCP)-1 production. (5) These chemokines simplify access of the attracted monocytes to the artery wall. (6) OxPL build-up causes monocytes to differentiate into M1, dendritic cells, Mox cells and foam cells. Advanced lesions (7) Macrophages engulf OxLp(a) through its scavenger receptor CD36 to form the foam cell. (8) Ox-Lp(a) and Lp(a) also induce aberrant proliferation, migration and phenotype switching of smooth muscle cells (SMCs). (9) OxLp(a) stimulates CD36 to activate TLR2/6, which activates ERK and results in ER stress-induced loss of integrity of the mitochondria, which eventually leads to apoptosis. (10) Apoptotic cells provide more OxPLs and stimulate angiogenesis. (11) OxLp(a) may stimulate LKB1/AMPK/ mTOR activity and induce apoptotic cell removal 44 by macrophages. (12) Necrotic core formation and vessel wall rupture. (13) Macrophage and OxLp(a) cause increased platelet aggregation. (14) Apo(a) binding to PLG binding sites blocks the interaction between PLG and tissue PLG activator (tPA). (15) Lp(a) increases the production and activity of tPA inhibitor-1 (PAI-1), which eventually leads to a decrease in fibrinolysis (16). Lp(a) increases the expression of TF and inhibits the potent inhibitory effect of tissue factor pathway inhibitor (TFPI), which leads to thrombosis. Dotted lines: hypothesized pathways. This figure was constructed by MJ. 2.6.1 The entry of Lp(a) into the vascular wall Numerous in vivo kinetic studies have shown that radiolabeled human Lp(a) enters the intima at a similar rate as does LDL-C in normal and atherosclerotic vessels [179], similar to other lipoproteins, through modest molecular filtering without any receptors [180]. However, this entry depends on lipoprotein plasma concentrations, lipoprotein unit size, blood pressure, vessel wall permeability and Lp(a) residence time [179]. LDL-C entry into and accumulation in weak but normal vessels begin when LDL-C reaches a certain threshold as low as 60 mg/dL [181]. Lp(a), on the other hand, is present in dysfunctional atherosclerotic but not normal vessel walls, and these cells exhibit proinflammatory attributes, which suggests that Lp(a) plays a role later in the atherosclerotic process after lesions have developed [130]. For instance, Nielsen et al [182] showed that balloon injury of the thoracic aorta of rabbits leads to accelerated accumulation of radiolabeled Lp(a) in comparison to radiolabeled LDL-C in the balloon-injured intimal wall [182]. Additionally, the loss rate of Lp(a) decreased more than that of LDL-C in atherosclerotic vessels [183]. However, this information does not explain why Lp(a) preferentially traps and accumulates at greater rates than does LDL-C. Recently, many researchers have suggested that this phenomenon occurs due to the long residency time for Lp(a) in comparison to that for LDL-C 45 [180]. This long residency might be due to the enhanced and selective binding capacity of the abundant LBS in the apo(a) fragment of Lp(a) to the matrix intima and small blood clots (fibrin and glycosaminoglycan) in the injured vascular wall [180]. Additionally, this residency might be due to the recycling effect of apo(a) [147]. In fact, the presence of the LBS of Lp(a) was shown to be associated with potent focal deposition of Lp(a) in the vascular endothelial wall [184]. The mutation affecting the LBS of Lp(a) of KIV10 decreases the affinity of Lp(a) to the endothelial wall [185]. Indeed, this information reflects the importance of the role of apo(a) LBS in atherosclerosis pathogenicity. Additionally, under inflammation, leukocytes enhance the persistence and localization of Lp(a) by releasing a polypeptide named defensing [186]. Finally, in a recent study, scavenger receptor class B type 1 (SR-B1) was shown to transports LDL across the endothelial cell monolayer and thereby governed the transcytosis of LDL by the help of DOCK 4 and the buildup of LDL by artery wall macrophages [187]. This study will shed light on the role of SR-B1 on Lp(a) recruitment molecules to the endothelial wall. 2.6.2 Pro-inflammatory and proatherogenic effects of Lp(a) Although there are abundant data confirming that inflammation could elevate plasma Lp(a) concentrations, data have surfaced indicating that the presence of Lp(a), particularly its apo(a) fragment, causes vascular inflammation [115,167,170,171,188]. After Lp(a) enters the vessel’s walls, it undergoes some oxidation and modification processes. This oxidative effect occurs as a repercussion of existing in an aerobic environment. Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase and myeloperoxidase (MPO) play a vital role in the oxidation of Lp(a) by producing reactive oxygen species (ROS). The contact of lipoproteins with lipoxygenase (LPO) or ROS such as superoxide anion and hydrogen peroxide produces miscellaneous OxPL types that 46 commence and augment the inflammatory response [189]. The oxidation and modification processes of Lp(a) influence its atherogenic characteristics by altering their catabolism to alter the catabolic rate, vessel wall retention, uptake by macrophages, and foam cell formation [190]. One of the significant modifications caused by oxidation is the alteration of receptor identification [191]. Thus, receptors no longer have the ability to identify the oxidized lipoproteins [191]. Subsequently, OxLp(a) triggers a sequence of pro-inflammatory events leading to the development and progression of atherosclerosis [170,192]. OxLp(a) is then trapped within the intimal layer of the injured vessel, leading to its degradation by lipoprotein lipase, which liberates free fatty acids and monoacylglycerols, resulting in even more local inflammation [170]. It is worth mentioning that the diacylated and triacylated lipoproteins can be identified by Toll-like receptors (TLR-4) and pattern-recognition receptors (PPRs), which usually respond to attacking microorganisms and activate the inflammatory response [170,193–195]. 2.6.2.1 Role of the oxidized phospholipids One of the key constituents found both in the lipid phase and covalently bound to OxLp(a) in atherosclerotic lesion is OxPLs. However, atherosclerotic lesions are not the only locations where OxPLs are formed. Apoptosis of various cell types has been shown to be associated with OxPLs generated by NADPH oxidase for clearance by macrophages [193]. Thus, apoptotic cells are another source of OxPLs and may contribute to even more vessel inflammation and atherosclerosis [171,172,193]. A wealth of data from papers published in the last decade have documented the regulatory effect of phosphocholine (PC)-containing OxPLs on endothelial cell and macrophage function [115,146,148,150,172,175,196]. Moreover, abundant evidence has suggested that proinflammatory OxPLs are crucial contributors to the early stages of atherogenesis, 47 such as adhesion molecule expression and immune system activation [197]. Additionally, OxPLs might play an important role in the late stages of atherogenesis, such as platelet aggregation and plaque disruption [197]. Furthermore, exposure of endothelial cells to OxPLs likewise reduces their production of nitric oxide, a crucial arbitrator of vascular wall relaxation [197]. Although OxPLs have some protective effects, such as the activation of prostaglandin E2 production and heme oxygenase 1 (HO-1) formation, OxPLs strongly accumulate under high concentrations of atherosclerotic lesions, which led us to conclude that the OxPL molecule is an atherosclerosis promotor [193,196]. In 2008, Bergmark et al [172] showed that among all apo-B100-containing lipoproteins, only Lp(a) preferentially scavenges and carries OxPLs for clearance through a covalent bond with PC-containing OxPLs in humans. This might be because Lp(a) contains the Lp-PLA2 enzyme, which is responsible for the cleavage of OxPL for degradation and platelet activating factor (PAF) catabolism [126]. Unfortunately, this protective effect is inversely related to Lp(a) size [126]. This information led us to believe that the potential protective effect when Lp(a) exceeds its normal concentration may become harmful and promote the pro-inflammatory atherogenic impact of OxPLs by delivering it to the injured vessels. 2.6.3 The proposed mechanisms of endothelial dysfunction, inflammation and atherosclerosis 2.6.3.1 Endothelial permeability and adhesion molecule expression The OxPLs from the apo(a) fragment of OxLp(a) build up in the vascular wall and activate various cell types to express a specific set of proteins that may be involved in the inflammatory reaction through several signaling pathways in endothelial cells and macrophages [193]. The high concentration of OxPLs delivered under high levels of OxLp(a) and apo(a) make the endothelial 48 cell monolayer permeable due to the activation of the Src kinase pathway, which phosphorylates vascular endothelial cadherin (VE-cadherin) (an essential protein for barrier function) [198]. VE-cadherin phosphorylation leads to disassociation of β-catenin and paxillin and thus disrupts the cell-cell junction complexes [198]. Another mechanism by which OxLp(a) with OxPLs disrupts the endothelial monolayer involves vascular endothelial growth factor receptor 2 (VEGFR2) activation. VEGFR2 activation subsequently leads to increased Rho/Rho kinase activation, which triggers the activation of myosin light chain (MLC) phosphorylation by Ca2+/calmodulin-activated MLC kinase (MLCK) [199] and inactivation of MLC phosphatase by direct phosphorylation of its 130-kDa regulatory subunit (MYPT1) [200]. This mechanism leads to the stimulation of actomyosin contractility, eventual endothelial cell retraction and the creation of openings between endothelial cells [201]. Additionally, OxPLs mediate occludin expression and phosphorylation in vascular endothelial cells, which lead to decreased tight junction interactions, increased permeability of the endothelial cells [202] and increased accumulation of additional Lp(a). Adhesion molecules play a major role in this mechanism. The interaction of OxPLs with the E-type prostaglandin receptor (EP2) causes an increase in cyclic AMP (cAMP) [203]. Subsequently, cAMP increases R-Ras activation by inhibiting H-Ras activation [203]. This step leads to the stimulation of phosphoinositide 3 kinase (PI3K), which subsequently leads to α5β1 stimulation on endothelial cells [204]. This causes the buildup of the connecting segment 1 (CS-1) fibronectin, an essential adhesion molecule associated with OxPL, on the apical surface of endothelial cells that binds the attracted monocytes [204]. It is worth mentioning that the association between OxPLs and vascular cell adhesion molecule-1 (VCAM-1) is modest and not detected in large vessels [205]. Indeed, other studies have reported that there is a calcium-dependent interaction of Lp(a) with cultured human coronary artery endothelial cells that does not 49 appear to involve any of the apo(a) LBS that induce efficient surface expression of VCAM-1 and E-selectin adhesion molecules [206]. Moreover, several lines of evidence have shown that OxLp(a) could increase the expression of P-selectin and intercellular adhesion molecule-1 (ICAM-1) in cultured human umbilical vein endothelial cells and suggest its important role in atherogenesis [207,208]. 2.6.3.2 Cytokine production IL-8 and chemoattractant molecule 1 (MCP-1) are the chemokines that are responsible for facilitating and guiding the monocyte diapedeses between endothelial cells to infiltrate into the tunica intima or innermost layer of the vascular arterial wall [209]. OxPLs could rapidly induce OxPL MAPK phosphatase 1 (MKP1), thereby stimulating the production of MCP-1 [210]. OxPLs also activate metalloproteinases such as disintegrin and metalloproteinase 10 (ADAM 10) and disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4), which present on the endothelial cell surface [211]. This metalloproteinase activation leads to activation of heparin-binding EGF-like growth factor (HBEGF), which attaches to epidermal growth factor receptor (EGFR) to induce IL-8 synthesis [196,211]. OxPLs also activate the 15-Lox1–15(S)-HETE axis, which leads to the production of ROS and thereby activates EGFR. EGFR then stimulates signal transducer and activator of transcription 3 (STAT3) phosphorylation through the activation of Src kinase, which eventually leads to MCP-1 expression and production [212]. Additionally, OxPLs elevate cytosolic calcium (Ca2+) levels, which are considered an initiator of many signaling pathways. Including the activation of peroxisome proliferator-activated receptor α (PPAR α), which eventually leads to the expression of IL-8 and MCP-1 [213–215]. OxPLs could also activate the VEGFR2 pathway, leading to IL-8 and MCP-1 production. In contrast, Lp(a) and its pathogenic 50 fragment apo(a) may independently induce chemoattraction for monocyte cells through a cGMP-dependent pathway [216] and by binding and carrying MCP-1 from the circulation to the vascular wall and may mediate chemoattraction [217]. Finally, Sotiriou et al [218] reported that apo(a) interaction with β2-integrin Mac-1 promotes the adhesion of monocytes and their transendothelial migration in a Mac-1-dependent manner, especially in the presence of homocysteine. According to Lee et al [196], following the entry of monocytes into the intimal wall, the newly resident monocytes differentiate into several phenotypes of macrophages in the nascent atheroma. This differentiation occurs due to the activation of TLR-2, TLR-4, CD36 and PAF by OxPLs. The first phenotype of the newly differentiated macrophages is chemokine-producing M1 macrophage. It secretes additional MCP-1, macrophage inhibitor protein-2 (MIP-2), IL-1 beta (IL-1β), IL-12, inducible nitric oxide synthase (iNOS), tumor necrosis factor (TNFα) and regulated upon activation, normal T-cell expressed, and secreted (RANTES) [188,196,219]. The Mox phenotype, which is characterized by antioxidant Nrf2-dependent gene expression, may play a role in atherosclerosis [219]. Macrophages could also differentiate into dendritic cells that are not fully functional due to OxPL epigenetic mechanisms [220]. Lastly, the foam cell phenotype is formed by the activation of CD36. CD36 acts as a scavenger receptor and signaling mediator [221]. The foam cell signaling pathway triggers tyrosine protein kinase Lyn/Fyn followed by the stimulation of Vav group proteins, which are cytoplasmic guanine nucleotide exchange factors (GEFs) [196]. Interaction of Vav proteins with dynamin-2/PLC γ generates Ca2+ flux. Increased Ca2+ concentration leads to OxLp(a) internalization and foam cell formation [222] (which are hallmarks of early atherosclerotic lesions) [209]. 51 2.6.3.3 Vascular smooth muscles One of the key features of an advanced atherosclerotic lesion is pathological vascular wall remodeling [223]. Vascular wall remodeling involves VSMC phenotypic switching and endothelial barrier dysfunction. Alexander et al [224] defined phenotypic switching “as a switch between a contractile to a synthetic state (macrophage-like) through repression of the SMC-selective contractile/cytoskeletal proteins that mark differentiated SMCs and concomitant increases in proliferation, migration, and matrix synthesis.” The VSMC differentiation marker includes smooth muscle (SM) actin α, SM myosin heavy chain, myocardin, and other components [224]. The goal of vascular SM remodeling in atherogenesis is to shield the foam cells that assemble under the endothelium and to promote the formation of a stable plaque with a thick fibrous cap, thereby protecting against plaque rupture and thrombosis [209,225]. However, if the atherogenic stimuli persevere over the years, as they often do, the reparative response may become harmful, narrow the vascular lumen, reduce blood flow, and result in eventual ischemia [226]. Numerous previous studies have verified that human Lp(a), apo(a) and OxPLs promote the phenotypic switching, proliferation, and migration of VSMCs in atherosclerotic lesions [227]. OxPLs have been shown to increase MCP-1, IL-1β and TNFα production via macrophage foam cells, which leads to an increase in the inflammatory state of VSMCs by promoting the production of IL-6 and multiple MMPs [206,223]. IL-1β also modulates the VSMC phenotype to a distinct inflammatory phenotype through nuclear factor light-chain-enhancer of activated B cells (NF-ҡB)-dependent mechanisms [224]. Another mechanism by which OxLp(a), native Lp(a) and OxPLs mediate VSMC phenotypic switching involves the phosphorylation of extracellular signal-regulated kinase (ERK) [228], which leads to the activation of ETS-like transcription factor 1 (Elk-1), eventually repressing the SM α-actin gene and SM heavy chain marker. [229] Additionally, 52 OxPLs might suppress the SM α-actin gene through Krüppel-like factor 4 (Klf4), which is involved in most phenotypic switching pathways [230] and eventually binds to histone deacetylases (HDACs), inhibiting the transcription of actin [229–231]. Furthermore, several studies have shown that apo(a) inhibits transforming growth factor-β (TGF-β), which is a cytokine involved in the maintenance of normal endothelial and SMC phenotypes and functions [232]. Numerous pathways increase VSMC replication due to exposure to OxPLs and their constituents. Komai et al[228] showed that Ox-Lp(a) significantly stimulated the growth of human VSMCs in a dose-dependent manner. Moreover, according to Zhao et al [233], the upregulation of platelet-derived growth factor (PDGF-BB) by the native Lp(a) and especially OxLp(a) may be one of the most principal mechanisms accounting for the migration and proliferation of VSMCs and narrowing of the vasculature in atherosclerosis [233]. Another study was able to show that increased atherosclerosis in transgenic rabbits is associated with VSMC proliferation possibly related to impaired fibrinolytic activity by which Lp(a) build-up may inhibit plasmin and stimulate PAI-1 [227]. OxLp(a) and its OxPLs constituent may also promote VSMC proliferation through the phosphorylation of connexin 43 (Cx43) [234] and activation of galactosyltransferase-2 (GALT2) to produce lactosylceramide (LacCer) and eventually increase c-fos and proliferating cell nuclear antigen (PCNA) [196]. Regarding VSMC migration, the inhibition of TGF-β production due to the build-up of Lp(a) molecules reduces the inhibition of VSMC migration from media to the intima and thereby contributes to atherogenesis [235]. Finally, Ox-Lp(a) may promote VSMC migration through the expression of many extracellular matrix membrane proteins. Most importantly, type IIIV collagen causes OxPLs to activate SP-1 to activate the Klf4 pathway, eventually leading to the migration of VSMCs [236]. 53 2.6.3.4 Cell death In advanced atherosclerotic lesions, macrophages and VSMCs die by programmed cell death (apoptosis) or necrosis. This cellular suicide leads to yet another enigmatic feature of atherosclerotic lesions in that the disintegration of these cells leads to the development of a weakening lipid-rich central pool and delicate and rupture-prone fibrous cap [220,226]. OxPLs containing OxLp(a) and apo(a) largely contribute to cell death. They trigger ER-stressed macrophages mainly through the activation of CD36, TLR-2 and TLR-6, which subsequently activate the ERK/MAPK pathway [237]. OxPLs containing Ox-PL(a) are also more potent than oxidized LDL in the generation of ROS and thereby induce apoptosis [238]. ROS generation requires the activation of NADPH oxidase 2 (NOX2) through the activation yet again of the ERK/MAPK pathway [196]. Furthermore, OxLp(a) and its OxPLs may compromise the integrity of the mitochondria to activate the intrinsic apoptotic caspase cascade, thereby inducing macrophage apoptosis [239]. Regarding VSMCs, the results from Loidl and colleagues [225] indicate that activated acid sphingomyelinase is the central mediator in the OxPL-triggered signaling pathway, ultimately leading to apoptosis of VSMCs and causing little but remarkable inflammation [240]. This pathway includes the activation of ceramide to phosphorylate JNK and P38 MAPKs, which have been shown to activate caspase 3 and programmed cell death [196,240]. Afterwards, ROS generated due to the build-up of Ox-Lp(a) molecules also activate macrophage autophagy by two direct and indirect pathways. The indirect pathway is mediated by adenosine diphosphate-ribose polymerase-1 (PARP-1), liver kinase B1 (LKB1), adenosine monophosphate-activated protein kinase (AMPK) and the mammalian target of rapamycin (mTOR) signaling pathway. The direct pathway is mediated by LKB1-AMPK-mTOR signaling[241]. Both signaling pathways eventually lead to decreased mTOR activity via 54 decreased phosphorylation of p70S6K and 4EBP1, which can spark macrophage autophagy [241]. Finally, apoptotic cells release phosphatidylserine-containing OxPLs, which stimulate the macrophage uptake of apoptotic cells and may stimulate angiogenesis. At this point, plaques rupture may occur at their shoulder area and is characterized by decreased VSMCs, a thin fibrous cap, a huge necrotic center, and increased macrophage infiltration into the cap. 2.6.4 Pro-thrombotic effect Following damage to the vessel wall, platelets become triggered and initiate thrombus formation. Fibrin cross-links and stabilizes the clot and then undergoes several fibrinolysis processes [167]. Fibrinolysis is a vastly controlled and restricted process leading to the suspension of fibrin clots and renovation of vascular endothelium [242]. Adsorption of tPA and PLG to the exterior of fibrin permits the creation of plasmin and thus its degradation [124,242]. In fact, PLG binding to fibrin alters the protein from a closed to an open conformation [124]. This binding leads to the development of carboxyl-terminal lysine residues, which promote positive feedback in the fibrinolytic cascade [192]. Additionally, it mediates plasmin-mediated alteration of native Glu1-PLG to Lys77-PLG by cleavage of a 76-amino-acid preactivation peptide [192] and thus becomes an improved substrate for tPA [124]. Thereby, plasmin is responsible for the degradation of the fibrin molecules within clots. The specific cell surface receptors for PLG are articulated by a wide variety of cells with great density in EC and aid in promoting fibrinolysis and native PLG proteolysis [243,244]. In fact, they play a major role in accelerating PLG activation and protecting plasmin from inhibition [243]. Moreover, tPA binds to the PLG receptor (annexin 2) at a separate site close to the PLG binding site, leading to a more efficient generation of plasmin. 55 The structural homology and the abundance of PLG receptors have led to theories about the relationship between Lp(a) and thrombosis. In fact, as mentioned above, Lp(a) may interfere with the PLG activation ternary complex and lead to competitive interference with PLG and the enhancement of tissue factor (TF) pathways. Moreover, OxPLs may play a vital role in thrombosis. It is worth noting that atherosclerosis and its subsequent thrombosis are mechanistically interlinked. Therefore, further studies are needed to determine whether the direct pro-coagulant antifibrinolytic effect of Lp(a) plays a significant role in increasing the risk for atherothrombotic events. Lp(a) is believed to promote thrombosis by a number of separate but related mechanisms. 2.6.4.1 Platelet responsiveness Platelets are activated on the surface of the injured vascular wall once they are exposed to collagen, leading to the production of dense granules to activate additional new platelets in a positive feedback loop [167]. Aggregation of the platelets then occurs when the αIIbβ3 domain of the platelets binds to the attracted fibrinogen molecules, after which clot formation is initiated [245]. Several lines of evidence have associated Lp(a) with enhanced platelet response [246,247]. One study suggests that the enhanced responses of platelets may involve protease-activated receptor-1 thrombin receptor [246]. Another study suggests that Lp(a) blocks PAF-induced platelet activation in a nonspecific manner[247]. The blocking of αIIbβ3 activation and fibrinogen attachment to the activated platelets may denote the major mechanism by which Lp(a) blocks PAF-induced platelet aggregation [247]. Moreover, OxLp(a) and OxPLs may be involved in platelet hypersensitivity via the CD36-dependent pathway in a mechanism similar to foam cell formation [248]. 56 2.6.4.2 Inhibition of plasminogen activation and plasmin generation Many reports have revealed that Lp(a), through its kringles, attaches to fibrin to form the quaternary complex [167] and block new PLG binding and activation at PLG binding sites on fibrin, fibrinogen, and cell surfaces [249]. In fact, Lp(a) may attach to carboxyl-terminal lysine residues of fibrin and consequently interfere with fibrinolysis, as apo(a) in Lp(a) has no catalytic activity [242]. KIV5–9 and kringle V play a critical role in this mechanism [250]. Lp(a) also inhibits PLG activation by the bacterial activator streptokinase [249]. Moreover, other in vitro and in vivo studies have reported that Lp(a) and its apo(a) component inhibit the generation of plasmin on the endothelial cell surface with and without interference and tPA binding attenuation [249,251]. One of the most robust theories behind this phenomenon is that Lp(a) increases the expression of PAI-1, which by definition inhibits the availability of tPA. An important report has shown that PAI-1 inhibits tPA in a protein kinase-C-dependent mechanism [167,252]. Another report showed that Lp(a) also associates with other prothrombotic proteins, including α2-macroglobulin (A2M) (a plasmin inhibitor) and SERPINA1, a tPA inhibitor [122]. Thus, decreased PLG binding and activation on the cell surface may decrease fibrin degradation and create an antifibrinolytic effect. Finally, the antifibrinolytic effect mainly depends on the size of apo(a) polymorphs [192]; the smaller the apo(a) isoforms are, the higher the antifibrinolytic effect is [253]. 2.6.4.3 Effect of Lp(a) on tissue factor TF, which acts as a transmembrane receptor for factor VII/VIIa (FVII/VIIa), is the key cellular motivator of the coagulation protease cascade leading to the triggering of thrombin [254,255]. It is constitutively expressed by VSMCs, pericytes, and adventitial fibroblasts within the vessel wall and cells surrounding blood vessels [254]. The endothelium physically splits this 57 compelling “activator” from its circulating ligand FVII/FVIIa and blocks inappropriate initiation of the clotting cascade [254]. Damage to the endothelial barrier leads to exposure of extravascular TF and swift initiation of the clotting cascade [254]. Several lines of evidence have shown that Lp(a) increases the expression of TF and inhibits the potent inhibitory effect of TF pathway inhibitor (TFPI), which eventually lead to thrombosis [256]. 2.6.4.4 Role of OxLp(a) & OxPLs Microarray studies demonstrated that OxPL exposure of HAEC from 150 donors controlled the quantities of main thrombogenic molecules [196]. OxPLs drastically downregulated thrombomodulin expression by 40% while upregulating TF and Serpin B2 expression by 70% [196]. The postulated mechanism begins with an elevation of OxPLs, which increases cAMP and cytosolic Ca2+ levels. Cytosolic Ca2+ release plays a vital role in many signaling pathways. Increased Ca2+ levels activate the calcineurin and nuclear factor of activated T cells (NFAT) pathway, which leads to a shift in and attachment of NFAT to the TF promoter [257]. Moreover, OxPLs activate protein kinase C (PKC), which activates the early growth response protein 1 (EGR-1) pathway [257]. The latter is a transcription factor that usually associates with genes that mediate inflammation and thrombosis. The induction of EGR-1 is mediated by the metenkephalin/extracellular signal-related kinase 1/2 (MEK/ERK) cascade. EGR-1 and NFAT activation eventually leads to upregulation of TF [257]. 2.6.4.5 Effect of Lp(a) on tissue factor pathway inhibitor TFPI is a protease inhibitor with three tandem Kunitz-type blocking domains (K1, K2 and, K3) that blocks the TF coagulation cascade [258]. Thus, TFPI strongly blocks the initial steps of 58 the extrinsic coagulation pathway [192]. TFPI is present on endothelial cells, activated monocytes, and platelets [192]. Lp(a) and OxPLs inhibit the activity of isolated TFPI, which augments unopposed TF effects. The mechanism underlying its inhibitory activity is direct binding to the active TFPI inhibitor domains with much higher affinity than PLG and inactivation of TFPI activity in the presence or absence of physiologic concentrations of PLG [256]. 2.7 Factors that influence Lp(a) levels in the blood. Primarily, the Lp(a) plasma levels are genetically determined. Nonetheless, several factors may increase or decrease the Lp(a) bloodstream levels, as reviewed [133,160,259]. For example, chronic liver as well as kidney diseases are associated with plasma Lp(a) levels [155,158,177,260–262]. Moreover, as mentioned above, elevated Lp(a) concentrations are an acute phase reactant, for example, following an inflammatory stimulus, pregnancy, myocardial infarction and other situations [259,263]. These augmented Lp(a) levels stabilize subsequently when the trigger signal of the acute phase withdraws [259,263]. Several studies have examined the relationships between Lp(a) concentration and chronic ethanol consumption. Ethanol has a powerful influence and decrease bloodstream Lp(a) levels up to 60% [160] in dose-dependent manner independent of the size distribution of apo(a) isoforms [264]. Tobacco smoking reduces plasma Lp(a) by up to 20% [265,266] although tobacco smoking is one of the major risk factors for CVD, increasing plasma TG and lowering HDL-C [267]. Several underlying diseases and the therapeutic administration of hormones affect Lp(a) plasma levels, partly due to changes occurring in other lipoproteins. For instance, the administration of hormones such as adrenocorticotrophic hormone (ACTH) has a strong effect on Lp(a) levels, decreasing them up to 40% [160]. Moreover, there are divergent effects of growth hormone (HGH) 59 and IGF on plasma Lp(a) levels [160]. Though HGH significantly increases Lp(a) levels by up to 120%, IGF-I decreases Lp(a) concentrations by up to 60% [268]. Insulin inconsistently affects Lp(a) levels [269,270]. Moreover, male and female sexual steroids affect many parameters of fat metabolism [271]. Anabolic sterols considerably decrease Lp(a) levels up to 70% [272]. Lastly, Lp(a) levels increase one- to two-fold or more during the gestational period and normalize after delivery, during the puerperium period [273]. Other factors influencing Lp(a) levels are summarized in Table 2-1. 60 Table 2-1. The main factors affecting Lp(a) levels in humans Decrease Lp(a) levels Cholestatic liver diseases Up to 90%. May be due to impairment of Lp(a) production and/or assembly. Alcohol consumption Chronic consumption may reduce plasma Lp(a) in a dose-dependent manner and up to 60% [264,274]. IGF-1 Was found to decrease Lp(a) levels by 60% [268]. Anabolic steroids Up to 70% reported (N/R) [142,272] Testosterone Only few reported up to 40% reduction[275] ACTH Yield reductions of up to 30–40% [160]. Tamoxifen Anti-estrogen drug that decreases Lp(a) by 35% Ca+2 antagonists Less than 10% reduction [276]. Estrogens Up to 37% reported in postmenopausal women receiving HRT (N/R) [142]. Progesterone Low percentage reported Tibolone Agonist of type I steroid hormone receptor, Leads to 35% reduction Raloxifene Estrogen receptor modulator, leads to 35% reduction. Cigarette smoking Lowers plasma Lp(a) levels by 10–20% [265,266]. Obesity There is an inverse relationship between weight plasma Lp(a) concentration and obesity [160]. 61 Decrease Lp(a) levels Diet Majority of the reports have shown no to minimum reduction[277]; however, a defined plant-based diet [278] and fish oils were found to reduce plasma Lp(a) by 10% [276]. Increase Lp(a) levels Apo(a) gene Up to 90% Acute phase Up to two-fold increase in Lp(a) concentrations. Concentrations normalize after the triggering signal for the acute phase withdraws. The highest upsurge is reported approximately 6–8 days after the acute event [259,263]. Renal disease Renal insufficiency and nephrotic syndrome increase Lp(a) levels up to three-fold [155,158,260] Diabetes mellitus The increase in Lp(a) levels in diabetic patients mainly relates to the stage of kidney disease [269,270]. Hypothyroidism Increase in Lp(a) concentrations was reported [279] Pregnancy Up to 2- to 3-fold increase during pregnancy, with highest Lp(a) levels increase seen at the 20th week [273]. HGH Increases Lp(a) by up to 120% [268]. Cancer Tumors of many origins reported up to cause a two-fold increase [131,163,164] (N/R): not recommended for clinical use. This table was adapted from [133,160,259] 62 2.8 Lp(a) measurement. 2.8.1 Isoform-dependent vs. isoform-independent There are many structural characteristics of Lp(a), which, along with the covalent bond of apo(a) with apo-B100, make it peculiar. Additionally, there are strong structural similarities between apo(a) and PLG. Most importantly, the high particle size heterogeneity is associated with the variability in KIV2 repeats. This peculiar structure results in substantial limitations and challenges to standardizing immunological assays, determining appropriate calibrators and selecting reference material, activities that are crucial for analyzing and comparing the results of different studies[116]. There are two categories of immunological assays used to measure Lp(a) levels. The first category of immunological assays is “isoform-dependent,” representing the entire protein mass of Lp(a), reported in milligrams per (deciliter/liter) [280]. The molecular mass of the apo(a) protein mainly depends on the number of KIV motif repeats, with an extremely wide range of 200-800 kilodaltons [136]. There are many concerns related to measuring Lp(a) mass because most antibodies are polyclonal and cross-react with several KIV2 repeats. These assays would thus overestimate Lp(a) concentrations in patients with large apo(a) isoforms and underestimate Lp(a) concentrations in patients with small apo(a) isoforms [114]. Thus, the impact of the heterogeneity of Lp(a) mass may lead to underestimation of the relationship between Lp(a) concentration and CVS risk assessment. The other category is “isoform-independent,” an assay that recognizes a unique nonrepeating kringle IV (type 9), and it is reported in units of nanomoles per liter (nmol/L) [109]. Use of the isoform-independent immunological assays is considered the gold standard by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and approved by the World Health Organization (WHO) to measure Lp(a) because in these assays, apo(a) size will not affect the final results [281]. It is worth noting that most previous research used the mass- 63 dependent mg/dL instead of nmol/L. Moreover, many scientists have used a mean conversion factor of 2.4 (2.4 nmol/L to 1 mg/dL) to convert mass-based concentrations (mg/dL) of Lp(a) to molar concentrations (nmol/L) [282]. However, the Lp(a) conversion factor, unlike the conversion factors for any analyte with a defined molecular mass, is inaccurate because it ignores the size heterogeneity of apo(a) and should be re-evaluated for accuracy [283]. Moreover, Lp(a) should be preferably measured in freshly isolated plasma, although most laboratories worldwide use frozen samples, which may lead to inaccurate results [109]. As the efficacy of various new Lp(a)-lowering therapies is currently under intense investigation, it is clear that great consideration must be given to the assay that is used to measure plasma Lp(a) levels to have an accurate, reproducible, dependable and standardized quantitation of Lp(a). 2.8.2 Important considerations 2.8.2.1 Friedewald formula We use this formula commonly in clinical practice to calculate LDL-C. LDL-C = TC - (HDL-C + TG/5), where TG/5 represents the cholesterol in VLDL, provided that the plasma TG levels are < 4.5 mmol/L and that type III dyslipoproteinemia is not present [284]. HDL-C is quantified in the plasma supernatant after the apo-B100-containing lipoproteins are precipitated by a polyanion that precisely interacts with the apo-B100 of both Lp(a) and LDL [284]. Thus, the Friedewald formula overestimates the LDL-C value, which is, in fact, the value LDL-C + Lp(a)-C. This is very crucial because elevated Lp(a) levels increase LDL-C thanks to the Friedewald formula and may contribute to the diagnosis of certain diseases, such as FH, which depend clinically on a specific LDL-C threshold. 64 2.9 Screening. Plasma Lp(a) levels increase soon between after birth until the 7th postnatal day [285] and reach a constant concentration within a few months of life [286]. In adults, Lp(a) levels range widely, from <2 up to 2500 mg/L [287]. It used to be thought that there were no differences in Lp(a) levels associated with gender. However, many studies suggest females are more prone to elevated Lp(a) levels than are males [288–290] and that they are especially prone during pregnancy [273]. According to a study of the general population of Copenhagen, the distribution of Lp(a) levels is positively skewed to the left, with a tail toward the highest levels [291] that represents 20% of the general population [292]. Moreover, one of the most distinctive features regarding Lp(a) is that there are significant differences in plasma Lp(a) levels between different populations and ethnic groups [131]. Lp(a) levels are the lowest in Caucasian patients and highest in patients of African ethnicity. Most studies suggest that the Lp(a) cut-off point for CVD should be equal to or above 500 mg/L, which represents the 80th percentile of the general population distribution for Lp(a); this value can be rationally proposed to clinicians as an indicator of augmented risk for CVD [116]. Nevertheless, due to the nonexistence of firmly established race-specific clinical cut-off points for Lp(a) levels in populations other than those of Caucasians, clinicians should exercise their best judgment in the risk assessment of different ethnic groups [116]. Screening for increases in Lp(a) levels in the general population is still not recommended [109]. Because the majority of circulating Lp(a) molecules are genetically determined with little effect from diet and environment [293] and because plasma concentrations do not vary considerably around a preset baseline over a lifespan (<10%) in any individual, it is logical that this measurement is required only once for screening or diagnostic purposes [293]. Moreover, because Lp(a) level screening is a cost-effective test, it could rationally be added to the lipid profile 65 for first-time patients [114]. Nordestgaard and colleagues [291] recommend that Lp(a) be measured in patients with FH, a strong family history of CVD, a personal history of premature CVD, recurrent CVD despite statin treatment, and an inadequate response to statins. Moreover, Lp(a) should be measured in patients with a ≥5% 10-year risk of fatal CVD according to the European guidelines [294] or ≥10% 10-year risk per US guidelines, as well as in patients with a 10 to 19% Framingham risk according to 2012 Canadian Cardiovascular Society recommendations [276]. Finally, consideration of repeat measurement is indicated only in individuals treated for high Lp(a) levels [295] Table 2-2. Table 2-2. Whom to screen Lp(a) should be measured once in all subjects at intermediate or high risk of CVD/CHD who present with one of the following: Premature CVD Recurrent CVD despite statin treatment Familial hypercholesterolemia Strong family history of premature CVD and/or elevated Lp(a) ≥ 50 mg Recurrent CVD despite optimal lipid-lowering treatment ≥ 5% 10-year risk of fatal CVD according to European guidelines ≥ 10% 10-year risk of fatal CVD according to US guidelines 10 to 19% Framingham risk according to the 2012 Canadian Cardiovascular Society recommendations Repeat measurement is only necessary if treatment for high Lp(a) levels is initiated in order to evaluate therapeutic response. This table was adopted from [291] 66 2.10 Interventions. 2.10.1 Diet and physical exercise A healthy diet and regular exercise are recommended for the prevention of coronary artery disease because these practices improve the lipid profile as well as the Framingham risk score [277]. However, their effects on Lp(a) concentration are completely different. The majority of cross-sectional and interventional studies support the empirical evidence that serum Lp(a) concentration is regulated independently of diet, other lipoprotein classes and long-term or acute rigorous physical exercise [66,277], despite these activities producing a favorably improved lipid profile [66,296]. Conversely, a recent study has shown that a plant-based diet substantially decreased inflammatory biomarkers and atherogenic lipoproteins, including Lp(a) [278]. Although lifestyle factors may not directly impact Lp(a) levels themselves, the synergistic multiplier effect of high LDL and Lp(a) concentrations together should be carefully considered. Indeed, studies have shown that in patients with elevated Lp(a) and LDL-C levels, CVD risk is magnified compared to that in patients with only high LDL-C [280,297,298]. Therefore, reducing LDL-C (e.g., via diet) and elevating HDL-C (e.g., via exercise) levels in the bloodstream must be greatly recommended to lessen the synergistic multiplier risk and should always be the core pillar of high Lp(a) CVD preventive care [66,298]. 2.10.2 Medication 2.10.2.1 Lipid treatment essentials 2.10.2.1.1 HMG-CoA reductase inhibitors While 3-hydroxy-3-methyl–glutaryl-coenzyme A (HMG-CoA) reductase inhibitor (aka ‘statin’) intervention is essential in atherosclerosis, the effect of statins on Lp(a) is controversial. 67 It has been assumed that statins slightly reduce or have no effect on Lp(a) concentrations because the LDLR may play either no role or an inconsequential role in Lp(a) clearance. In fact, in patients with FH, statins have been shown to reduce Lp(a) levels by 17% to 22% [276,299]. However, recent data from the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) study showed that HMG-CoA reductase inhibitors tended to increase Lp(a) levels by 10% to 20% [114,300]. This result could be why some subjects do not usually respond to decreased LDL-C levels by statins, as most of their cholesterol is on Lp(a) molecules rather than LDL molecules [301] and Lp(a) can surge with statin treatment [114] Table 2-3. 68 Table 2-3. Approved and investigational therapeutic drugs to lower LDL & Lp(a) Mechanism Agent Status Is it specific for Lp(a)? LDL ∆ % Lp(a) ∆ % Reduce production of new LDL / Lp(a) Statins Approved No 19–49 ↓ 0 –20 ↑ Niacin Approved No Up to 45 ↓ 30–40 ↓ IONIS-APO(a)-LRx (AKCEA-APO(a)-LRx) Investigational Yes - 39–92 ↓ AMG 890 Investigational Yes - 90% ↓ Mipomersen Approved No 21–40 ↓ 20–33 ↓ Lomitapide Approved No 19–51 ↓ 15–17 ↓ CETP inhibitors Stopped No 14–26 ↓ 36–39 ↓ Aspirin Approved No - 10-80 ↓ Increase LDLR expression for LDL/ Lp(a) uptake Evolocumab Approved No 39–75 ↓ 30 ↓ Alirocumab Approved No 29–73 ↓ 30 ↓ Inclisiran Investigational No Up to 60 ↓ 25.6 ↓ Physical elimination of Lp(a) molecules Apheresis Approved No 80 ↓ 68–75 ↓ Lp(a)-apheresis Information not available Yes - 70–80 ↓ 69 2.10.2.2 Reducing the production of novel Lp(a) 2.10.2.2.1 IONIS-APO(a)-LRx (AKCEA-APO(a)-LRx) IONIS-APO(a)-LRx is a second-generation antisense oligonucleotide (ASO) designed to reduce the synthesis of apo(a) in the liver [114]. IONIS-APO(a)-LRx is a chemically modified oligonucleotide (typically 16–20 nucleotides) [302] targeting hepatic mRNA in the nucleus and in the cytoplasm if mRNA is present in this compartment to lower plasma concentrations of apoB-containing lipoproteins, including Lp(a) [114]. ASOs bind to plasma proteins and enter the liver, where they accumulate intracellularly. Then, ASOs selectively bind mRNAs coding for apo(a) proteins and often cause degradation at Watson–Crick hybridization[302]. Once attached, ASOs can act via a number of mechanisms, but the most common mechanism is the recruitment of RNase H1, an enzyme that degrades apo(a) mRNA in a DNA-RNA-like duplex. The activation of RNase H1 eventually reduces plasma concentrations of the apo(a) protein or through translational arrest blocks the ribosome [303–305]. The other mechanisms include the initiation of RNA cleavage through catalytically active ribozymes and RNA interference induced by small interfering RNA (siRNA) molecules [306]. In a recent randomized, double-blind, placebo-controlled, phase I study, 47 healthy individuals aged 18–65 years with Lp(a) levels of 25 nmol/L (100 mg/L) or more were randomized to receive one single dose of IONIS-APO(a) at different concentrations (50 - 400 mg), six consecutive doses at different concentrations or placebo [304]. The multiple-dose treatment produced a substantial dose-dependent reduction in Lp(a) levels from baseline to the end of the fifth week (100 mg: 39.6%, 200 mg: 59.0%, 300 mg: 77.8% vs. placebo) [304]. OxPL-apo[a] and OxPL-apoB levels were also significantly lower at week five [304]. In a phase II trial, 64 participants with high Lp(a) levels were randomly assigned treatments (100 mg, 200 mg, and 300 70 mg once a week for four weeks each) or injections of saline placebo (once a week for 12 weeks). At day 85/99, participants had mean Lp(a) reductions between 66.8% and 71.6% [303]. Furthermore, this drug significantly decreased the inflammatory properties of monocytes, which, as mentioned before, originate and hasten CVD, in addition to plasma LDL-C [303]. IONIS-APO(a)-LRx also contains an N-acetyl-galactosamine (GalNac3)-conjugated that is selectively taken up by hepatocytes, with a mean reduction up to 99% reduction of Lp(a) in some patients [114]. These trials showed that IONIS-APO(a)-LRx is a tolerable, potent, and promising selective Lp(a)-lowering drug. It remains to be seen whether IONIS-APO(a)-LRx will reduce CVD events related to high Lp(a). Also, It remains to be seen whether newer drugs such as AMG 890 [307] have better efficacy and reduce CVD events significantly. 2.10.2.2.2 Mipomersen To date, only mipomersen, a second-generation ASO against the coding region of human apoB mRNA (nucleotides 3249–3269), has been approved by the US FDA as an adjunct to diet and statins for lowering LDL-C, apoB, total cholesterol, and non-HDL-C for the treatment of homozygous FH (HoFH) [308]. Mipomersen also has also significantly decreased Lp(a) concentrations [280,309]. In four phase III trials, 382 participants receiving maximally tolerated lipid-lowering treatment were randomly allocated to weekly administration of 200 mf of the ASO mipomersen or placebo for 26 weeks[308]. The median percent decrease in Lp(a) concentration at 28 weeks was significantly greater in the mipomersen group than in the Placebo group (-26.4% [IQR:-42.8, -5.4] versus 0.0% [IQR:-10.7, 15.3]; P<0.001) [308]. However, mipomersen did not affect the production of apo(a), which continued to be released into the plasma as “free” apo(a) 71 [114]. Similar to lomitapide, hepatotoxicity has been observed with mipomersen, and therefore, mipomersen has only been approved for patients with HoFH. 2.10.2.2.3 Lomitapide Lomitapide is an inhibitor or blocker of microsomal TG transfer protein (MTTP), which is an ER-associated protein [309]. It plays a central role in the biosynthesis of lipoproteins by mediating the allocation of neutral lipids (CE and TG) to the new apo-B100 and apo-B48 polypeptides [309], thus promoting the association of VLDL in the liver and chylomicrons in the intestine [309]. Thus, lomitapide does not depend on the functionality of LDL receptors. Lomitapide combined with a low-fat diet and statins substantially and stably lowers LDL-C by 50% in adult patients with HoFH [310]. In an open-label, phase III study, 29 subjects with HoFH were enrolled to receive lomitapide for 78 weeks [310]. The median dose of lomitapide was 40 mg per day[310]. The drug produced a 15% reduction in Lp(a) concentration at 56 weeks [310]. However, by the end of the study, there was no statistically significant difference in Lp(a) concentration from the baseline [310]. 2.10.2.2.4 Niacin Niacin is considered a broad spectrum hypolipidemic agent [311]. It has an antilipolytic effect, reducing the mobilization of free fatty acids from the adipose tissue to the liver and reducing the trafficking of free fatty acids, which significantly decreases the concentrations of all apo-B–containing lipoproteins from chylomicrons to Lp(a) [312,313]. Niacin also stimulates the degradation of apoB-containing lipoproteins and decreases TG synthesis by inhibiting 72 diacylglycerol acyltransferase-2 [120], an enzyme that catalyzes the final reaction involved in TG production as well as selective inhibition of apoA-I uptake without affecting de novo production [314], which eventually increases HDL-C concentrations [311,314]. A therapeutic dose of niacin is associated with LDL-C and Lp(a) reduction by approximately 45% [314] and 20-30%, respectively, as shown in a meta-analysis of 14 randomized placebo-controlled clinical trials including 9,013 subjects [312], but with detrimental adverse effects. Unfortunately, niacin intervention has not been shown to reduce cardiovascular risk in recent clinical trials [315]. However, in Europe, the use of niacin at a dose of 1–3 g/ day in high-risk patients after suitable LDL-cholesterol reduction is recommended by the European Atherosclerosis Society (EAS) to achieve a Lp(a) concentration <50 mg/dL [280]. Finally, it must be noted that clinical research trials on niacin lack reliability with respect to patient assortment, drug dosage, intervention interval, and techniques used for quantifying Lp(a) [316]. 2.10.2.2.5 Cholesterylester transfer protein (CETP) inhibitors CETP mediates the exchange of cholesterol esters and TGs of LDL-c and HDL-c. Blocking this transfer with CETP inhibitors increases HDL-C levels and reduces t LDL-C levels [317]. In a study that evaluated the effects of the CETP inhibitor anacetrapib on lipid levels and its safety when administered as single therapy or in ad-on therapy with statins in Japanese patients, intervention as single therapy or coadministered with statins significantly decreased Lp(a) and LDL-C levels and increased HDL-C levels [318]. In a randomized, double-blind, placebo-controlled trial to evaluate the efficacy and safety of anacetrapib in 1623 patients with CHD, intervention consistently decreased Lp(a) levels by 38.8% from baseline levels [319]. While CETP 73 inhibitors might significantly improve the lipid profile, no data are available because research activities have been stopped [283]. 2.10.2.2.6 Aspirin Aspirin is mainly used for its antithrombotic effect. In 2002, a study evaluated the effect of aspirin treatment on serum concentrations of Lp(a). Japanese with high Lp(a) patients (n=70) were recruited and received aspirin (81 mg/day). Aspirin decreased serum Lp(a) levels to ~80% of the baseline values in patients with high Lp(a) levels (>300 mg/L) [320]. Aspirin may reduce apo(a) production in human liver cells by suppressing apo(a) gene transcription [321]. 2.10.2.3 LDL receptor removal or uptake 2.10.2.3.1 PCSK9 inhibitors PCSK9 is a protein involved in regulating LDLR recycling, and it was discovered when investigators found gain-of-function genetic alterations in PCSK9 protein in patients with FH [322]. PCSK9 inhibitors such as evolocumab and alirocumab are fully human monoclonal antibodies that attach to the PCSK9 protein and obstruct its contact with the LDLR, resulting in boosted receptor recycling and LDL clearance [323]. These drugs have been shown to reduce LDL-C by up to 60-70% [324]. Trials that have observed lipid profiles in patients treated with PCSK9 inhibitors showed reproducible reductions in Lp(a) levels [111]. However, it is unclear whether the Lp(a)-lowering effect could be expected in the early clinical trials. An analysis of data pooled from 1359 patients enrolled in 4 phase II trials assessed the effects of evolocumab on Lp(a) levels. Significant dose-related decreases in Lp(a) levels compared to those under placebo treatment were reported [325]. Evolocumab therapy for 12 weeks significantly decreased Lp(a) 74 levels by 29.5% (95% CI: 23.3%, 35.7%) and 24.5% (95% CI: 20.4%, 28.7%) when given at 140 mg and 420 mg dosed every two and four weeks, respectively [325]. In a pooled data analysis from 3 double-blind, randomized, placebo-controlled, phase II trials, alirocumab at 150 mg every two weeks significantly decreased Lp(a) levels in patients with hypercholesterolemia [326] from baseline levels compared with those under placebo treatment (30.3% vs 0.3%, P <0.0001) [326]. 2.10.2.3.2 Inclisiran Inclisiran is a long-acting siRNA therapeutic agent that reduces the synthesis of PCSK9 protein, a target for the lowering of LDL-C [327]. The inclisiran molecule follows the ordinary pathway of mRNA interference and PCSK9 silencing via RNA interference [328]. In transgenic mice expressing human PCSK9, inclisiran decreased PCSK9 mRNA concentrations up to 70% concomitant with up to a 60% decrease in plasma cholesterol concentrations [328]. In a randomized, single blind, placebo-controlled phase I trial in patients with LDL-C levels > 100 mg/dL and fasting TG levels of < 400 mg/dL, doses of 300 mg or more significantly lowered levels of PCSK9, LDL-C, and Lp(a) by 74.5%, 50%, and 48.1%, respectively, for at least six months [327]. In a phase II, double-blind, placebo-controlled, multiple-ascending-dose trial of inclisiran vs. placebo, 501 patients at high risk for CVD and with LDL-C levels >2.5 mmol/L, or >1.8 mmol/L were enrolled in the study [329]. The experimental group that received 200 mg of inclisiran at baseline and after 90 days experienced a persistent decrease in LDL-C and Lp(a) levels by 52.6% and 25.6%, respectively, at 180 days compared with the values at baseline [329]. 75 2.10.2.4 Physical elimination of Lp(a) molecules Extracorporeal elimination with apheresis is the most effective, well-tolerated and approved treatment for lowering Lp(a) levels [330,331]. This process removes all apoB-containing lipoproteins (specifically LDL-C and Lp(a)) from the blood using antibody-coupled columns, precipitation and complex creation at low pH. Double filtration and direct absorption have been confirmed to lower plasma LDL-c and Lp(a) levels by up to 80% [160]. A longitudinal cohort trial that evaluated the efficiency of lipid apheresis therapy on Lp(a) concentrations and major adverse coronary events reported a median reduction in Lp(a) levels of 73% compared with medical treatment alone [332]. Additionally, the study revealed that a combination of lipid-lowering treatments, such as statins, ezetimibe and nicotinic acid with apheresis, reduced major adverse cardiac events up to 88% over a 10-year follow-up period [280,333]. In a prospective observational multicenter study including 170 participants with Lp(a)-HLP and progressive CVD, comparable results were stated; single apheresis treatment decrease Lp(a) levels by 68.1%, and therapy over a period of 5 years significantly decreased the yearly CVD occurrence rates [334]. Other important benefits of apheresis include lowering the markers of vascular inflammation and improving blood rheology [335]. In addition to removing Lp(a) mass, lipoprotein apheresis reduces the activities of OxPLs and lipoprotein-associated phospholipase A2, which are bound to Lp(a) [336]. One of the significant drawbacks of apheresis is the rapid rebound of Lp(a) levels to those at the baseline within two weeks of intervention, thus requiring repeated, expensive, weekly sessions with limited access to treatment [280]. 76 Chapter 3: Improved physical activity is associated with lower resting heart rate and improved lipid profile among primary and secondary prevention patients. 3.1 Introduction One of the important factors that is associated with shorter life expectancy is elevated RHR [337]. RHR is a well-recognized and accessible clinical variable. More importantly, RHR is considered a significant independent marker of cardiovascular mortality outcomes [338]. Many epidemiological studies have shown a faster RHR magnifies the risk of CVD mortality, especially among older adults [338–340]. This relationship was shown in both sexes and demonstrated in the general population as well as in several important subgroups, including hypertensive, coronary artery disease (CAD) populations, patients with metabolic syndrome, and elderly individuals [341–345]. The risk of dying from CVD rose 18% for every 10 bpm increase in RHR [346]. As a distinct indicator of healthy wellbeing, a faster RHR detected earlier in life may track into late adulthood and act independently toward influencing CVD risk later in life [347]. One of the plausible mechanisms for this relationship is that a faster heart rate will necessarily impose higher shear stresses than a slower heart rate. For instance, in men, RHR > 90 bpm compared to RHR <60 bpm was associated with an almost twofold increase in the risk of CVD mortality in men and a threefold increase in risk in women [348]. This effect was independent and similar in magnitude to the risk associated with current smoking [348]. Additionally, an 77 impaired heart rate increase in response to incremental PA (e.g., chronotropic incompetence) has also been associated with the incidence of CAD and risk of cardiovascular death [349,350]. Regular PA causes a reduction in RHR. Additionally, high PA levels were found to attenuate the positive association of RHR with ischemic events among individuals free from CVD [346]. A reduction in RHR resulting from increased PA means that heart pumps are more powerful and efficient. This effect may contribute to a reduction in all-cause mortality due to regular exercise or sports. In addition, there is a strong consistent link between the amount of PA and the level of improvement in the lipoprotein profile, with more exercise having a much greater beneficial effect on lipids and lipoproteins than a lower amount of exercise [351]. Larson-Meyer et al. showed that blood pressure, total cholesterol, LDL, and insulin resistance improved significantly only with caloric restriction and exercise compared to caloric restriction alone [352]. To promote and maintain health, HHP personnel, at St. Paul’s Hospital in Vancouver, Canada, advise all patients, through its exercise program or clinics, to perform moderate-intensity aerobic PA for a minimum of 30 minutes daily or vigorous-intensity aerobic activity for a minimum of 20 minutes on three days each week. In addition, HHP physicians and nutritionists are helping CVD patients by promoting different types of healthy diets that were proven to tremendously reduce CVD risk. The HHP program also helps patients manage their other modifiable CVD risk factors, i.e., smoking, obesity, hyperlipidemia, DM, and HTN. The purpose of this retrospective chart review was to assess the HHP prevention clinic's efficiency by examining the relationship between improved PA and changes in RHR between first- and third-year visits. We hypothesise that participants who enhance their PA between their initial and third year visits are more likely to have a significantly lower RHR than those who do not. 78 3.2 Methodology 3.2.1 Study design and setting This study conforms to the guiding principles of the Declaration of Helsinki and was approved by the UBC - Providence Health Care Institute (H18-03099) and St. Paul's Hospital. Following study protocol approval by our ethics committee, a retrospective chart review study was undertaken at a single, large prevention clinic at St. Paul's Hospital, a tertiary care university-affiliated hospital in Vancouver, British Columbia, Canada. The follow-up period for this study was three years, and data were extracted from the initial and third-year visits. This prevention clinic focuses on controlling modifiable CVD risk factors, mainly physical inactivity, hyperlipidemia, obesity, smoking, HTN, and DM, thus reducing CVD risk by lifestyle modifications and, when needed, medications in individuals referred for both primary and secondary prevention of CVD. All patients are assessed initially by a clinic team consisting of a nurse, a dietician, and a physician. Then, the HHP prevention clinic team follow-up their patients every 6 to 12 months. All clinical, laboratory, and medications data are dated and recorded for each follow-up visit in the patients' chart. All charts for active patients visiting the clinic between 2016-2019 and still have ongoing appointments are kept in the HHP medical record. 3.3 Study procedure Patients' medical charts were the data source contributing to the retrospective data collection. MJ reviewed these charts under the supervision of a senior physician JF. Demographic data including age, anthropometric measurements, education and employment information, CVD risk factors, laboratory results, and clinical characteristics, including the diagnosis, lipid-lowering medications, other medication history, interventions initiated at the clinic, and study 79 cardiovascular variables of interest were obtained from the HHP prevention clinic enrollment form and first physician report at the initial visit. Similar data were also collected at the third-year visit. A total of 2008 chart were screened. 1088 patients met the inclusion criteria for our study (Table 3-1). To inspect the possibility of selection bias, demographic, laboratory, and clinical information was also collected for the excluded 920 patients. 80 Table 3-1. Inclusion and exclusion criteria. Inclusion criteria • Data should be from the Healthy Heart Program prevention clinics • Active patient at the prevention clinic - chart not archived • Age over 18 • RHR must be recorded at initial and third year visits. • Stable condition (that is, no respiratory infection and/or hospitalization during the HHP) • Normal sinus rhythm Exclusion criteria • Any hospital admission between the initial and third year visit. • Abnormal heart rhythm (i.e., atrial fibrillation, pacemakers) • Left ventricular dysfunction • Uncontrolled thyroid gland abnormality • Medication that may have an effect on RHR: beta-blockers, inhaled corticosteroids, albuterol, calcium-channel blockers, serotonin and norepinephrine reuptake inhibitors (SNRIs) 81 3.3.1 Physical activity data (main independent variable) To promote the patients’ healthy lifestyles and compliance with the exercise regimen in our clinics, we practice the 0-5-30 slogan: zero for no smoking, eating five portions of vegetables and/or fruits daily, and thirty minutes of daily exercise. The PA self-reported data were extracted from the prevention clinic enrollment history form that was completed at patients’ initial visit to the clinic and from the physician report at their third-year visit. For each visit, a detailed description of activity (e.g., typing, cleaning, walking, or jogging for exercise, etc.) and a perceived effort (light, moderate, vigorous) were reported along with the frequency of the patient’s mentioned activity. The frequency, intensity, time, and type (FITT) principle was used to evaluate the patients PA for both visits (initial and 3rd year visits) [353]. The scale of the frequency of PA from lipid clinic enrollment form was applied in our study. Thus, PA was ranked on a scale from 0–2 (0 = no reported activity or activity reported was not enough to qualify, 1 = up to four times a week of PA of at least moderate intensity, and 2 = five or more times of PA a week of at least moderate intensity) [353,354]. PA intensity was evaluated based on METs equivalents of common physical activities classified as light, moderate or vigorous intensity Table 3-2 [355]. For example, if the HHP physician states that a patient's PA consists of running for more than 30 minutes twice a week, the patient's PA score is one at the initial visit. Then, at patient's third-year visit, the patient's self-reported PA was extracted and ranked for the second time. 82 3.3.1.1 Study Groups The patients who increased their PA by one point on our scale (up from the initial visit) were categorized as the improved PA group (IPA) group, while those who remained at the same level or decreased their activity were included in the nonimproved PA (NIPA) group. 83 Table 3-2. METs equivalents of common physical activities classified as light, moderate or vigorous intensity Light <3.0 METs Moderate 3.0 – 6.0 METs Vigorous > 6.0 METs Walking Walking slowly around home, store or office Walking 3.0 mph Walking at very brisk pace (4 mph) Walking, jogging & running Walking at very brisk pace (4.5 mph) Walking/hiking at moderate pace and grade with no or light pack (<10 lb.) Hiking at steep grades and pack 10–42 lb. Jogging at 5 mph Jogging at 6 mph Running at 7 mph Household & occupation Sitting — using computer, working at desk, using light hand tools Standing performing light work such as Cleaning — heavy: washing windows, car, clean garage Sweeping floors or carpet, vacuuming, mopping Carpentry — general Carrying & stacking wood Shoveling sand, coal, etc Carrying heavy loads such as bricks Heavy farming such as baling hay Shoveling, digging ditches 84 Light <3.0 METs Moderate 3.0 – 6.0 METs Vigorous > 6.0 METs making the bed, washing dishes, ironing, preparing food or store clerk Mowing lawn — walk power mower Leisure time & sports Arts & crafts, playing cards Billiards Boating — power Croquet Darts Fishing — sitting Playing most musical instruments Badminton — recreational Basketball— shooting around. Bicycling — on flat: light effort (10–12 mph) Dancing Fishing from riverbank & walking Golf — walking pulling clubs Swimming leisurely Table tennis Volleyball — noncompetitive Basketball game Bicycling — on flat: moderate effort (12–14 mph); fast (14–16 mph) Skiing cross country — slow (2.5 mph = 7.0; fast (5.0–7.9 mph) Soccer — casual or competitive Swimming — moderate/hard = Tennis Volleyball — competitive at gym or beach Haskell, et al. 2007. * On flat, hard surface. Patients reported their PA. We evaluated their PA intensity depends on the exact wording written in the physician report. PA intensity of 3 METs or greater for 30 minutes per day were giving a score of 1 to 2 according to PA intensity level. 85 3.3.2 RHR (main dependent variable) RHR was reported in all included charts at the first and third-year visits. RHR values were obtained from three different sources. The RHR values were primarily collected from electrocardiography (ECG) reports included in patients' charts. Furthermore, RHR and blood pressure were measured using a digital automatic blood pressure monitor (OMRON, HEM-7200) or by palpation of the radial pulse over a 30-second period by an experienced nurse at the HHP clinic after 5 minutes of setting according to HHP clinic protocol. RHR and blood pressure were then reported in the physician report for both visits. 3.3.3 Biochemical cardiac risk factors Plasma lipid levels were measured by standardized enzymatic methods in accredited laboratories in British Columbia. The earliest untreated lipid profile was recorded: total cholesterol (TC), HDL-C, non-HDL-C, TGs, the ratio of TC to HDL-C (TC/HDL-C), and calculated LDL-C (using the Friedewald formula) [356]. 3.3.4 Demographics and clinical cardiac risk factors Age, sex, employment, education, family and/or personal history of CVD, medications and information on smoking, HTN, DM, body mass index (BMI), alcohol use, and PA status were obtained. A positive family history of CAD was defined as the presence of CAD in < 55-year-old first-degree male relatives and < 65-year-old female relatives [357]. Smoking history was obtained from the patient record and categorized dichotomously as ever smoked or never smoked. An electronic sphygmomanometer was used to measure BP. The presence of HTN was defined as a mention of the diagnosis in the chart. DM was diagnosed according to the Canadian Diabetes 86 Association guidelines by two fasting plasma glucose levels greater than 7.0 mmol/L [358]. BMI = (weight [kg]/height2 [m2]) was recorded closest to the time of the initial visit. In addition, alcohol intake was categorized (none, 1–7 drinks/week, 8–14 drinks/week, and 14 drinks/week). The Framingham risk score (FRS) was also calculated at the initial and most recent visits for each patient using the Canadian Cardiovascular Society, which is based on points given for sex, age, HDL-c, total cholesterol, DM and HTN data. The FRS was used to estimate the 10-year risk of CVD for all included HHP prevention clinic patients. FRS also was stratified to three main CVD risk categories. The low-risk category defined as > 10% CVD risk, the intermediate risk category which defined as >10-19 % CVD risk, and the high-risk category with ≥ 20% CVD risk. 3.4 Statistical analysis As descriptive values, continuous variable characteristics are expressed as the means and (+/- SD) or medians (interquartile range), whereas categorical traits are expressed as frequencies. Means of variables of interest were compared by paired t tests to assess differences in the entire cohort between the initial and last visits in the HHP. Student’s t test or the Wilcoxon test was used according to the provision of the normal distribution assumption for independent data analysis. Categorical variables were compared using Chi-square test or Fisher’s exact test, as appropriate. Mixed-effects linear regression with random intercepts was used to examine the associations between PA categories (improved vs. not improved by the end of third year) as the main independent variable and RHR change between initial and third year follow-up as a dependent variable after adjusting for age, sex, DM, HTN, non-HDL-c, education, employment, medications change and follow-up period. The selection of variables for the multivariable models was based on clinical and statistical significance and goodness-of-fit testing. All tests of statistical 87 significance were two-tailed with a 95% CI; P < 0.05 was considered indicative of statistical significance. All statistical analyses were performed with the R-Studio software package (RStudio Inc., Boston, MA, USA). The clinical and laboratory characteristics of the 1088 HHP-included patients were compared with the characteristics of the 920 patients for whom there were missing inclusion data. Different sensitivity analyses were performed to assess the robustness of the association between PA and the RHR in chapter 3, with the following additional adjustments: First, 3.9 percent of the study population, who were more active at baseline and remained so throughout the three-year follow-up period and were initially included in the NIPA group, were transferred to the IPA group for sensitivity analysis (Supplementary table - 1). Second, A sensitivity analysis were performed after excluding patients with comorbid conditions such as diabetes, hypertension, and cardiovascular disease. Third, alcohol consumption was incorporated into the main model. Fourth, age was stratified, and types of prevention (secondary, primary) were added to the model. Fifth, the full model was stratified by sex. Finally, the full model was also stratified by the prevention type (secondary, primary) to examine the effect of clinical and pre-existing illnesses that could influence the initial findings. All of sensitivity analyses were summarized in Supplementary table - 2. 3.5 Results 3.5.1 Study population and baseline characteristics Data were collected from the patients who met the following inclusion criteria summarized in Table 3-1. A total of 920 active charts were excluded. The breakdown of the excluded charts was as follows: 254 charts with no RHR data, 143 charts with missing exercise data, and 127 charts 88 of patients who were on HR-altering medications. Additionally, 116 charts were excluded because the patients were new or their duration of enrollment in our clinic was less than three years, and 113 charts were excluded because patients did not provide consent or declined participation in any study. Finally, of the remaining 1113 patients, 45 were excluded due to other clinical considerations, leaving 1088 patients in the cohort. The review lasted from November 2018 to February 2020. The study flow diagram is presented in Figure 3-1. Figure 3-1. Study flow diagram 89 Of the 2008 active charts in our program, only 1088 patients met the inclusion and did not meet the exclusion criteria. Significant differences in these two groups were limited to age, smoking, and the extent of alcohol use. The mean age of all patients included was 51.7 ± 12.5 years. There were 636 male patients (58.4% mean age of 51.3 ± 12.8 years) and 525 female patients (41.5%, mean age of 53 ± 18 years). Eight hundred forty-six patients (77.7%) were referred to our clinics as primary prevention patients and 242 (22.2%) as secondary prevention patients based on their history of CVD. The baseline characteristics, stratified by PA status are shown in Table 3-3. There were no significant differences in the history of smoking, DM, family history of premature CVD, alcohol consumption, or use of medications between the two groups. Nevertheless, the presence of HTN was higher in patients who did not change their PA habits than in those who improved their PA. Moreover, the majority of the active patients who exercised regularly >4 times a week at baseline were in the unimproved group; however, they remained at this level, or they decreased their activity level over the three years of the follow-up period. Finally, 3.9 % of the study population, who were more active at baseline and continued to be more active till the end of the 3 years follow-up, were included in the NIPA group. 90 Table 3-3. Characteristics of the study population variables at baseline Total (n = 1088) IPA (n = 507) NIPA (n = 581) P value Sex, n (%) Men Women 636 (58.4) 452 (41.5) 290 (57.1) 217 (42.8) 346 (59.6) 235 (40.4) 0.84 Age, years, means ± SD All patients Men Women 51.7 ± 12.5 51.3 ± 12.8 53.0 ± 2.0 51.3 ± 12.8 49.1 ± 11.2 54.4 ± 10.1 52 ± 12.2 51.3 ± 12.3 53.1 ± 4.0 0.37 <0.05 0.25 Hemodynamic parameters, mean ± SD RHR, bpm sBP, mmHg dBP, mmHg 78.9 ± 10.5 128.5 ± 15.0 78.0 ± 10.4 78.3 ± 11.4 128.2 ± 17 77.0 ± 10.9 79.1 ± 9.8 128.8 ± 16.7 78.1 ± 8.6 0.42 0.55 0.18 Type of CVD prevention, n (%) Primary prevention Secondary prevention 846 (77.7) 242 (22.2) 406 (80) 101 (20) 440 (75.7) 141 (24.3) 0.09 History of smoking, n (%) Ever smoked (331) 30.4 (154) 30.3 (177) 30.5 0.99 91 Total (n = 1088) IPA (n = 507) NIPA (n = 581) P value Never smoked (757) 69.5 (353) 69.7 (404) 69.5 HTN, n (%) (306) 28.1 (126) 24.9 (180) 30.9 <0.05 DM, n (%) (152) 13.9 (65) 12.8 (87) 14.9 0.35 Family history of premature CVD, n (%) (512) 47 (244) 48.1 (268) 46.1 0.45 Frequency of physical activity, n (%) <1 times/week 1–4 times/week ≥5times/week (609) 56 (395) 36.3 (84) 7.7 (323) 63.7 (178) 35.1 (6) 1 (286) 49.2 (217) 37.3 (78) 10 <0.001 Alcohol use, n (%) 0 drinks/week 1–6 drinks/week 7–13 drinks/week ≥ 4 drinks/week (340) 31.2 (507) 46.6 (155) 14.2 (86) 7.9 (203) 25 (242) 47.7 (61) 12 (1) 0.2 (137) 23.5 (265) 45.6 (94) 16.7 (85) 14.6 0.6 CVD risk category, n (%) Low risk <10% Intermediate risk 10-19% High risk ≥ 20% (403) 37.0 (223) 20.4 (462) 42.4 (194) 38.2 (104) 20.0 (209) 41.2 (209) 35.6 (119) 20.0 (253) 35.4 0.22 Medications, n (%) 92 Total (n = 1088) IPA (n = 507) NIPA (n = 581) P value No medication Statins Fenofibrates 534 (49) 391 (36) 163 (15) 243 (48) 188 (36) 76 (16) 291 (50) 203 (35) 87 (15) 0.51 Follow-up time, years Means ± SD 3.1 ± 0.2 3.1 ± 0.1 3 ± 0.2 0.9 Abbreviations: IPA: Improved physical activity group; NIPA, Not improved physical activity group; HTN, hypertension; DM, diabetes mellitus; CVD, cardiovascular disease; RHR, resting heart rate; sBP, systolic blood pressure; dBP, diastolic blood pressure. Difference between the groups is significant at P < 0.05. 93 Table 3-4. Characteristics of the IPA group stratified by sex IPA GROUP (N=507) Male (N= 290) Female (N= 217) P- value Age, means ± SD 49.1 ± 11.9 54.4 ± 13.3 <0.001 Type of CVD prevention, n (%) Primary prevention Secondary prevention 231 (79.6) 59 (20.3) 175 (80.6) 42 (19.3) 0.86 History of smoking, n (%) Ever smoked Never smoked 89 (30.6) 201 (69.3) 65 (29.9) 152 (70.1) 0.93 HTN, n (%) 69 (23.8) 57 (26.2) 0.59 DM, n (%) 38 (13.1) 27 (12.4) 0.9 Family history of premature CVD, n (%) 132 (45.5) 112 (51.6) 0.2 Frequency of physical activity, n (%) <1 times/week 1–4 times/week ≥ 5 times/week 171 (59) 114(39.3) 5 (1.7) 152 (70) 64 (29.4) 1(0.4) <0.05 CVD risk category, n (%) Low risk < 10% Intermediate risk 10-19% High risk ≥ 20% 103 (35.5) 65 (22.4) 122 (42) 98 (45.1) 44 (20.2) 75 (34.5) 0.08 94 IPA GROUP (N=507) Male (N= 290) Female (N= 217) P- value Medications, n (%) No medication Statins Fenofibrates 94 (45) 107 (37) 49 (17) 106 (49) 73 (34) 32 (15) 0.9 Follow-up time, years Means ± SD 3.1 ± 0.1 3 ± 0.2 0.9 Abbreviations: M, male; F, female; HTN, hypertension; DM, diabetes mellitus; CVD, cardiovascular disease. Difference between the groups is significant at P < 0.05. 3.5.2 Participant’s characteristics Of the 1088 patients in our study, 507 (46.6%) improved their exercise pattern. In the improved exercise group, RHR decreased significantly by 9.4 bpm (11.7%, P<0.01) between baseline and the third year HHP prevention clinic visit, as illustrated in Figure 3-2. There was also a significant reduction in sBP and dBP by a mean decrease of 6.4 mmHg (4.5%, P<0.01) and 2 mmHg (2.5%, P<0.01), respectively (Figure 3-3). The lipid profile also improved dramatically (a mean TC decrease of 1.5 mmol/L (24.5%, P<0.01), a mean LDL-C decrease of 1.4 mmol/L (34.1%, P<0.01), a mean TG decrease of 0.7 mmol/L (28%, P<0.01), and a 7.1% increase in HDL levels between the first and last HHP clinic visits. Non-HDL-C and TC/HDL-C also significantly declined by 29.2% (P<0.01) and 29.6% (P<0.01), respectively. In comparison with the unimproved group, the improved group had better control over fasting blood glucose (FBG; P<0.05) and BMI (P<0.01). Similarly, sBP and the lipid profile in the NIPA group improved between the initial and 95 last visits. However, RHR significantly increased by a mean of only 1.8 bpm (2.2%, P<0.01) by the end of the follow-up period. Notably, the Framingham risk score (FRS) declined by 15.7% (P<0.01) in the IPA group vs 7.1% (P<0.01) in the NIPA group (Table 3-5). Moreover, the decrease in RHR, sBP, and dBP in IPA groups was significantly greater by 11.7 % vs. 2.2% (p<0.0001), 4.9 % vs. 2.3 % (p<0.001, and 2.6 % vs. 0.3% (p<0.001), respectively in comparison with the NIPA group. The IPA group also had a significantly greater increase in HDL and decrease in BMI by (23% vs. 3.13%, p<0.001) and 2.8 % vs. increase in BMI by 1.4 % compared to the NIPA group P<0.01. 3.5.3 Comparison of male and female groups We further analyzed the IPA cohort according to sex. There were several dissimilarities between the sexes in our study population, as shown in Table 3-4. Both cohorts are representative of middle-aged adult populations. However, the male patients (mean age 49.1 ±11.9) were younger than the female patients (54.4 ±13.3), and they had a preponderance in this analysis (57.1%). At the initial visit, men tended to have a significantly slower RHR, lower total and LDL cholesterol, and lower BMI. While PA had a significant association with lower RHR, sBP and total and LDL cholesterol in both sex groups, men had additional favorable responses to PA in lowering their dBP and FBG and raising HDL cholesterol, as shown in Table 3-6. Notably, the FRS declined by 25.6% (P<0.01) in the female group vs 10% (P<0.01) in the male group between baseline and the last HHP clinic visit. Most importantly, the difference of the change in FRS is significantly greater in the female group in comparison with male group. However, there were no significant difference in the change between groups in all of our important variables. 96 Table 3-5. Changes in CVD risk factors between the IPA and NIPA groups IPA group (n=507) NIPA group (n=581) First Visit 3rd year visit Change Change (%) P-Value Within groups First Visit 3rd year visit Change Change (%) P-Value within groups P-Value for change between groups RHR (bpm) 80.29 ± 11 70.69 ± 5.21 -9.4 ± 9.9 -11.71 <0.01 79.2 ± 11.2 81 ± 10.6 +1.8 ± 9.6 +2.2 <0.01 <0.0001 BP (mmHg) sBP 131.11± 17 124.7 ±14.33 -6.4 ± 17.9 -4.92 < 0.01 129.2 ± 16.6 126.2 ± 15.5 -2.9 ± 16 -2.3 <0.01 <0.001 dBP 78.22 ± 10.7 76.2 ± 9.31 -2 ± 12 -2.612 < 0.01 77.1 ± 10.5 77.5 ± 9.4 +0.4 ± 11.7 +0.5 0.39 <0.001 Lipid profile TC (mmol/L) 6.42 ± 1.7 4.81 ±1.43 -1.57 ±1.73 -24.22 <0.01 6.2±1.93 4.9±1.41 -1.2 ± 1.92 -19.31 0.01 <0.001 LDL-C (mmol/L) 4.12 ± 1.5 2.72 ± 1.22 -1.4 ±1.62 -34.12 <0.01 3.8±1.32 2.8±1.32 -0.9 ± 1.52 -23.61 0.01 <0.0001 HDL-C (mmol/L) 1.33 ± 0.5 1.68 ± 0.63 +0.35 ± 0.66 +23.21 <0.01 1.27±0.62 1.32±0.51 +0.04 ± 0.52 +3.14 0.07 <0.0001 Non-HDL-C (mmol/L) 5.07 ± 1.8 3.11± 1.35 -1.57 ± 1.82 -37.22 <0.01 4.9 ± 1.91 3.6 ± 1.42 -1.2 ± 1.92 -24.42 0.01 <0.01 TC/HDL-C (mmol/L) 5.42± 2.9 3.09 ±1.22 -1.8 ± 2.93 -30.21 <0.01 5.6 ±3.22 4.2±21 -1.4 ± 3.11 -25.22 0.01 <0.01 TG (mmol/L) 1.74 (1.73) 1.34 (1.07) -0.42 -23.45 <0.01 1.9 (1.75) 1.6 (1.75) -0.32 -15.71 <0.01 <0.01 FBG 5.9 1±1.4 5.41±1.12 -0.33± 1.23 -5.61 <0.01 5.6 ±1.22 5.7±1.22 +0.1 ± 1.28 +1.75 <0.05 <0.01 Weight (kg) 81.7 1± 20.3 79.11±17.12 -2.4 ± 12.82 -3.11 <0.01 79.4 ±16.91 80.7 ±19.61 +1.3 ± 9.91 +1.63 <0.01 <0.01 BMI 28.12 ± 6.3 27.12 ± 4.81 -0.8 ± 4.41 -2.81 <0.01 27.8 ±4.81 28.2±5.82 +0.5±2.81 +1.43 <0.01 <0.01 FRS % 15.2 2± 13.6 12.76±12.11 -2.4 ± 9.72 -15.72 <0.01 15.3 ± 15.41 14.2±13.13 -1.1±8.92 -7.18 <0.01 <0.05 Notes: Data shown are presented as the means ± SD for all data except TG, which is reported as the median and IQR P, 0.05, P, 0.01, first vs. last visit; P, 0.01, IPA vs NIPA. The duration of patient follow-up was 3 years. Table 3-6. Changes in CVD risk factors by sex for the IPA group IPA Male (n=290) IPA Female (n=217) First Visit 3rd year visit Change Change (%) P-Value within groups First Visit 3rd year visit Change Change (%) P-Value within groups P-Value for change between groups RHR (bpm) 79.1 ± 10.74 69.4 ± 9 -9.7±10 -12.21 <0.01 81.7 ± 11.4 72.1 ± 9.7 -9.6 ± 9.2 -11.72 <0.01 0.6 BP (mmHg) _ _ _ _ _ _ dBP 131.1 ± 16.74 124.9 ± 13.2 -6.1 ± 17.21 -4.61 < 0.01 131.3 ± 17.6 124.4 ± 16.1 -6.9 ± 19.22 -5.21 <0.01 0.26 dBP 79.54 ± 10.27 76.6 ± 8.8 -2.9 ± 12.23 -3.61 < 0.01 76.6 ± 11.2 72.6± 9.92 -4 ± 11.92 -5.12 <0.01 0.32 Lipid profile _ _ _ _ _ _ TC (mmol/L) 6.15 ± 1.84 4.5±1.26 -1.55 ±1.63 -25.42 <0.01 6.8±1.67 5.2 ±1.52 -1.6±1.76 -23.53 <0.01 0.31 LDL-C (mmol/L) 3.85 ± 1.48 2.58 ± 1 -1.29 ±1.41 -33.91 <0.01 4.4±1.62 2.9 ±1.31 -1.6±1.78 -36.32 <0.01 0.33 HDL-C (mmol/L) 1.16 ± 0.53 1.54 ± 0.73 +0.37 ± 0.72 +31.8 <0.01 1.5±0.54 1.87 ±0.54 +0.32±0.51 +21.98 <0.01 0.91 Non-HDL-C (mmol/L) 4.95 ± 1.84 2.99± 1.11 -1.93 ± 1.72 -38.73 <0.01 5.2 ± 1.72 3.3 ± 1.51 -1.93 ±1.87 -37.12 <0.01 0.38 TC/HDL-C(mmol/L) 5.85 ± 2.66 3.17 ±1.21 -12.68 ± 2.21 -32.76 <0.01 4.94±3.22 2.99 ±1.35 -1.94±3.42 -39.21 <0.01 0.45 TG (mmol/L) 1.88 (1.92) 1.4 (1.13) -0.42 ± 2.31 -22.21 <0.01 1.5 (1.37) 1.2 (0.98) -0.3 ± 2.21 -20.25 <0.01 0.70 FBG 5.97 ±1.1 5.4±0.81 -0.54 ± 1.24 -9.12 <0.01 5.82 ±1.72 5.2 ±1.31 -0.62±1.17 -10.61 <0.01 0.88 Weight (kg) 86.14 ± 18.1 84.3± 16.81 -1.78 ± 7.3 -2.21 <0.01 75.8± 21.52 72.4 ±15.11 -3.3 ± 16.42 -4.35 <0.01 0.08 BMI 27.84 ± 5.6 27.2 ± 5.43 -0.53 ± 3.73 -1.95 <0.01 28.7 ± 8.22 27.5 ±5.21 -1.17±26.22 -4.21 <0.01 0.07 FRS % 17.54 ± 14.5 15.6± 13.52 -1.93 ± 9.13 -10.21 <0.01 12.1 ± 11.73 8.9 ± 8.72 -3.1 ± 8.15 -25.64 <0.01 <0.001 Notes: Data shown are presented as the means ± SD for all data except TG, which is reported as the median and IQR. The duration of patient follow-up was 3 years. 97 Figure 3-2. The change in RHR in HHP patients between the initial and last visits. (A) NIPA (B) IPA Figure 3-3. The change in systolic blood pressure in HHP patients between the initial and last visits. (A) NIPA (B) IPA p = 0.14 p<0.001 p<0.001 p<0.001 98 3.5.4 Mixed-effect linear regression analysis On Mixed-effect linear regression analysis, PA as a main predictor was significantly associated with RHR, with the IPA group exhibiting a mean decrease in RHR of –4.35 bpm (95% CI: –5.43 to –3.27, P<.001; Table 3-7) in comparison with the NIPA group after adjusting for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type (Table 3-7). The model also showed, on average, that for every 1 year increase in age, we expect an increase in RHR by (0.14 bpm, 95% CI: 0.11 to 0.06, P<0.01) adjusted for PA, sex, sBP, dBP, HTN, DM, non-HDL-C, employment, education level, change in medications, and visit type. Regarding BP, statistically significant associations between sBP and dBP and RHR were noted by (0.06 bpm, 95% CI: 0.11 to 0.04, P<0.001) and (0.15 bpm, 95% CI: 0.11 to 0.06, P<0.01) The model also showed that compared with females, males had a decrease in RHR of (–2.35 bpm, 95% CI: –3.49 to –1.21, P<0.001) on average adjusted for PA, Lp(a), sex, ethnicity, smoking status, and visit type. On the other hand, smoking, education status, employment status were not significant independent predictors of RHR. 99 Table 3-7. Mixed-effect regression analyses for the association between physical activity (IPA vs NIPA) and RHR Predictors Estimates 95% CI P-value Physical activity (IPA vs NIPA) –4.35 (–5.43, –3.27) <0.001 Age, years 0.14 (0.11, 0.06) <0.001 Blood pressure sBP, mmHg dBP, mmHg 0.06 0.15 (0.03, 0.09) (0.10, 0.20) <0.001 <0.001 Sex (Men vs Women) –2.35 (–3.49, –1.21) <0.001 Diabetes mellitus (Present vs Absent) 2.90 (1.29, 4.50) <0.001 Hypertension (Present vs Absent) 0.10 (–1.32, 1.52) 0.89 HDL-C, mmol/L –1.25 (–2.05, – 0.45) <0.01 Non-HDL-C, mmol/L 0.85 (0.56, 1.13) <0.001 Smoking status (Former vs Never) (Current vs Never) 0.45 –0.19 (–0.95, 0.1.85) (–1.93, 1.55) 0.53 0.83 Changes in medications –0.15 (–2.12, 1.09) 0.53 Employment (Retired vs Unemployed) (Part time vs Unemployed) (Full time vs Unemployed) 1.97 1.84 0.72 (–0.14, 4.21) (0.03, 3.70) (–2.10, 3.55) 0.10 0.80 0.45 100 Predictors Estimates 95% CI P-value Education (Post-secondary vs High school) (Post grad vs High school) 0.35 –0.24 (–0.95, 0.17) (–1.84, 1.56) 0.42 0.35 Visits type (Initial vs 3rd year visit) –1.30 (–2.12, –0.48) <0.001 Abbreviations: PA, physical activity; IPA, improved physical activity; NIPA; not improved physical activity; RHR, resting heart rate; BP, blood pressure; sBP, systolic blood pressure; dBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; V1, initial visit; V2, 3rd year visit. Beta estimates in the multivariable analyses were adjusted for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type. The reference group is the NIPA group. 3.5.4.1 Sensitivity analyses In general, the main model was robust, beta coefficient –4.35; 95% CI (–5.43, –3.27), to addition of high exercisers to the IPA group 4.70; 95% CI (–5.72, –3.69). It is worth mentioning that the beta coefficient strengthens, but the change is not significant. Additional adjustment for baseline alcohol consumption, age strata, and type of prevention only slightly affected the beta estimates in the model. Moreover, in a stratified analysis regarding sex, the beta estimates were consistent with main model in the study. Also, the effect of IPA demonstrated a trend towards a greater effect in the primary prevention group than the secondary prevention group, but this was not significant. All of sensitivity analyses were summarized in Supplementary table - 2. 101 3.6 Discussion Frequent PA is an essential behaviour for individual and population health [355]. Moreover, it is considered the foundation treatment for all CVDs [359]. Indeed, one of the many identified benefits from exercise, or any increase in PA, was increased cardiac survival rates [360]. Likewise, sBP and dBP reductions have been identified with exercise [361–363]. Studies have shown that weight loss and maintenance of body mass index also result from increasing PA [364]. Additionally, an increase in HDL-C, improvement in insulin sensitivity, slowing in the onset of DM, and improved endothelial function were observed [365]. This study complements the emerging body of literature showing significant association of frequent and moderate to-vigorous intensity PA with improving health (267,268) and highlights that exercise more than four times a week with moderate-to-high intensity is a favourable exercise strategy for improving measures of cardiovascular structure and function for prevention clinic patients. Our study aimed to evaluate HHP by examining the association of increased PA with important hemodynamic parameters, i.e., RHR, blood pressure, and lipid profiles, between the nonimproved and improved PA patients in a retrospective manner (Table 3-5). The data show that almost half of our prevention clinic patients had a lower RHR, sBP and dBP, that was associated with their PA change; they also show that a relatively high amount of regular exercise—even in the absence of clinically significant weight loss in some of the subgroups—can significantly be associated with better overall lipid profile. In particular, the data reveal that changing PA and exercise by one or more points more than four times per week and an intensity equivalent to that of jogging at a moderate pace significantly associated with lower FRS. This amount of exercise also associated with higher HDL concentration and lower TG, non-HDL-C, and TC/HDL-C 102 concentration, which is consistent with other important reports [351]. Of interest, patients who chose to not increase their PA were, on average, more active at the initial visits. This indicates a likelihood that it was easier for those in the IPA group to improve their PA habits. Remarkably, RHR significantly increased by 1.8 bpm (2.2%) in the NIPA group; however, there were no significant differences in other risk factors, which may be related to the efficacy of the drug treatment. Additionally, there was an annual trend of weight loss found in the IPA group compared to the NIPA group. Interestingly, men had significantly lower RHRs at the initial and last visits (P<0.001) than women. Both men and women had almost similar responses regarding RHR and sBP after improving their PA. Remarkably, women had a worse initial lipid profile, which may be related to the fact that women in the IPA group were older than the men. However, women benefited even more by the end of the study. Despite the continual advocacy of the significance of regular exercise, only approximately 50% of HHP patients improved their exercise pattern. Therefore, regular PA needs to be frequently stressed and evaluated when recommending lifestyle changes for current or new patients. According to a study that examined heart rate measurements in clinical trials involving patients with CVD, a single measurement of RHR after rest was found to adequately reflect an individual's resting heart rate in general and was recommended for use in clinical trials. While more extended monitoring of heart rate during exercise is required, bicycle exercise ECGs and 24-hour Holter recordings are more precise at capturing the heart rates recorded during exercise [367]. Additionally, the use of smart wearables to monitor RHR generates a wealth of data via various sensors and software algorithms. Clinicians and scientists can benefit from an understanding of their fundamental engineering concepts and constraints. While evidence supports the use of 103 wearable devices in cardiovascular CVD risk assessment and prevention, diagnosis, and management, large, well-designed trials are needed to show their advantages over traditional approaches being used in clinics such as the HHP program [368]. 3.6.1 Study limitations Several limitations must be addressed while interpreting these data. First, the study was conducted retrospectively, which may have created bias regarding the accuracy and completeness of data extracted from medical records. Second, our criteria for inclusion and exclusion were stringent. As a result, we removed a large number of charts due to the lack of RHR, exercise data, the use of HR-altering medications, patients who were new to our clinic or if their participation lasted less than three years, and patients with cardiac conditions that might influence RHR values. Additionally, we were unable to utilize a validated measure of PA for our main independent variable, and had to extrapolate from the physician notes. This might lead to misclassification (random error) and added bias due to patients' over-reporting their level of PA. Regarding our main dependent variable, RHR is clearly a parameter that might fluctuate under a variety of pathophysiological conditions prior to patients presenting to our clinic. Another limitation of our study could be related to the RHR's measuring nature. Certain resting RHR measurements may have been obtained with patients seated rather than supine, introducing motion artifacts that could affect RHR results. 104 Chapter 4: The influence of lipoprotein(a) on the prevalence of cardiovascular disease risk factors and cardiovascular events among patients in the Healthy Heart Program. 4.1 Introduction Lp(a) is an enigmatic type of lipoprotein that is exclusively formed in the liver and comprises two main components, a single copy of apolipoprotein (apo) B-100 (apo-B100) tethered to a single copy of a protein denoted as apo(a). Since the discovery of Lp(a) in the 1960s [107], which was described at the time as a qualitative (Lp+, Lp−) genetic trait, many basic researchers and clinicians have dedicated their work to explaining the hidden mechanisms that lead Lp(a) to cause atherosclerosis [356]. The duality and uniqueness of Lp(a), which make Lp(a) homologous to both LDL-C and PLG, most likely underlie the different but related atherosclerosis mechanism theories. Circulating Lp(a) levels are mainly (~90%) hereditarily determined, primarily through distinction in the LPA gene located on chromosome 6 (6q26–27). Differences in plasma Lp(a) levels in humans are mediated by copy number variation in the number of undistinguishable kringle IV type 2 (KIV-2) repeats. In contrast to the effect on other lipoprotein classes, the impact of dietary and conservational factors on Lp(a) levels is minor, and consequently, Lp(a) is a quantitative genetic trait that influences atherogenicity from birth (2). The mechanisms through which Lp(a) may be proatherogenic include the role of the LDL moiety and the proinflammatory effects of the apo(a) component [114]. Most importantly, Lp(a) transmits all of the harmful atherogenic characteristics of LDL units, incorporating their tendency 105 to oxidize before and after entry into the subintimal layer of the vessel walls and creating highly proinflammatory oxidized Lp(a) [OxLp(a)] in comparison with other lipoproteins [114]; induces macrophage programmatic death [369]; and is integrally involved in destabilizing atherosclerotic lesions [118]. The improvement of isoform-independent assays, together with insight from epidemiologic studies, meta-analyses, genome-wide association studies, and Mendelian randomization studies, has established Lp(a) as the single most common independent genetically inherited causal risk factor for CVD [356]. This breakthrough elevated Lp(a) from a biomarker of atherosclerotic risk to a target of therapy. Currently, most studies are trying to find potential targets for therapeutic intervention for high Lp(a) levels. However, few studies have examined the reciprocal relationship between atherosclerotic and hemodynamic parameters, which might provide a clue for underreported CVD events in this unique population. Thus, the primary aim of this retrospective chart review was to phenotypically compare patients who have high levels of Lp(a) with those who have low levels of Lp(a). We also sought as a secondary aim to evaluate the relative significance of Lp(a) as a predictor of CVD events from birth to enrollment in the HHP prevention clinic at St Paul’s Hospital compared with selected established CVD risk factors. 4.2 Methods 4.2.1 Study design and setting This study used a retrospective chart review design and was conducted in the outpatient Healthy Heart Program Prevention Clinic at St. Paul's Hospital, Vancouver, B.C., Canada. This study 106 conforms to the guiding principles of the Declaration of Helsinki and was approved by the Institutional Committee on Human Research at our institution. The study was conducted by reviewing the charts of 694 patients enrolled in the prevention clinic. All patients were active (i.e., they still have ongoing appointments). This retrospective chart review provided data from each patient's primary visit for the study's first aim. For the study's second aim, data regarding cardiac events were extracted from previous history and procedural reports before enrollment to the prevention clinic. 4.2.2 Study procedure 4.2.2.1 Clinical cardiac risk factors at baseline For the primary aim of this study, data regarding each patient’s personal history of CVD, history of HTN, smoking, alcohol consumption, and exercise status were collected at baseline and at the time of the CVD endpoints. A positive family history of CAD was defined as the presence of CAD in < 55-year-old first-degree male relatives and < 65-year-old female relatives [357]. PA was also categorized (none, 1–4x/week and >5x/week). In addition, a body mass index (BMI = weight [kg]/height2 [m2]) was recorded closest to the time of the initial visit. Smoking history was determined from the patient record and categorized dichotomously as ever smoked or never smoked. An electronic sphygmomanometer was used to measure BP in our clinics. The presence of HTN was defined as a mention of the diagnosis in the chart or a prescription for antihypertensive agents such as angiotensin-converting enzyme inhibitors to lower blood pressure. DM was diagnosed according to the Canadian Diabetes Association guidelines by two fasting plasma glucose levels greater than 7.0 mmol/L [358]. In addition, alcohol intake was categorized (none, 107 1–7 drinks/week, 8–14 drinks/week, and 14 drinks/week). RHR was recorded by electrocardiograms. 4.2.2.2 Biochemical cardiac risk factors Plasma lipid levels were measured by standardized enzymatic methods at accredited laboratories in British Columbia. The lipid profile was recorded and comprised total cholesterol (TC), HDL cholesterol (HDL-C), non-HDL-C, TGs, the ratio of TC to HDL-C (TC/HDL-C), and calculated LDL-C (using the Friedewald formula). [370]. 4.2.2.3 Lp(a) measurements All Lp(a) measurements were performed with the Latex microparticle-enhanced immunoturbidimetry assay. As Lp(a) is usually measured after referral to our clinic, HHP patients were rarely off all treatment when it was measured. Nevertheless, as Lp(a) is relatively resistant to lipid-lowering treatments, this is not very likely to considerably affect the quality of the study data. Therefore, the Lp(a) values for each HHP patient was collected under the assumption that this best represented a patient’s exposure to this atherogenic lipoprotein [370]. For the purpose of analysis, Lp(a) results were divided into normal (<300 mg/L) and high (>300 mg/L) [371]. The cut-off point of 300 mg/L represented the 80th percentile of Lp(a) in our cohort. 4.2.2.4 Cardiovascular disease endpoints before baseline visit For the secondary aim of this study, information about the first CVD events was collected. CVD was defined by the presence of at least one of the following: (I) an incident of first myocardial infarction before enrollment in the HHP program, proven by at least two of the 108 following: (a) specific electrocardiographic reports, (b) elevated cardiac enzymes report included in the chart (>2x upper limit of normal); (II) Coronary or peripheral arterial angioplasty; (III) coronary artery bypass graft ;(IV) ischemic stroke (demonstrated by CT- or MRI scan) before the HHP enrollment. The nature and year of the first CVD endpoint were acquired from the physician reports in the charts, and it was confirmed based on the previous physician history, laboratory, and procedural reports included in the same charts and medical record (368). Indications of CVD that were qualitative or susceptible to explanatory bias, such as the manifestation of carotid plaques on ultrasound, history of chest pain, positive exercise stress tests, or history of transient ischemic attack, were not considered endpoints [370]. 4.2.2.5 Clinical cardiac risk factors before CVD endpoints For the secondary aim of this study, data regarding each patient’s personal history of DM, HTN, smoking status, before CVD endpoints were collected. Smoking history was determined from the patient record and categorized dichotomously as ever smoked or never smoked before CVD endpoints. The presence of DM and HTN was defined as a mention of the diagnosis in the chart before the CVD endpoint. 4.2.3 Statistical analysis Continuous variable characteristics are expressed as the means and SD, whereas categorical traits are expressed as frequencies. Means were compared by Student’s t test. Categorical variables were compared using the chi-square test and Fisher’s exact test, as appropriate. The Wilcoxon rank-sum test was performed, as the Lp(a) concentration showed a highly skewed distribution. A multivariable Cox proportional hazard model were used to model 109 the associations between Lp(a) and the occurrence of CVD endpoints after adjusting to variables considered to be of greatest interest and importance like sex, age, and (DM, HTN and, smoking status; before the time of the CVD endpoint). For the preposes of survival analysis, follow-up started at birth as Lp(a) is relatively constant and resistant to lipid-lowering treatments, and therefore it is appropriate to consider date of birth as the start of follow-up. The follow up period ended for each patient at the date of the first occurrence of established CVD, or at the date of initial HHP clinic visit, whichever occurred first. The proportional hazards assumption was tested using the Kaplan Meier method and Schoenfeld residuals. All tests of statistical significance were two-sided with a 95% CI; P < 0.05 was considered statistically significant. Student’s t test was performed on data that were confirmed to have a Gaussian distribution. All statistical analyses were performed using the R-Studio software package (RStudio Inc., Boston, MA, USA). 4.3 Results The high Lp(a) and normal Lp(a) cohorts were dissimilar in several ways; the cohorts' mean biographic information, lipid profiles, and blood glucose are shown in Table 4-1. Both the high and normal Lp(a) cohorts had a significantly higher percentage of men than of women, as expected. The high Lp(a) patients were older and included more men (57.4%), similar to the normal Lp(a) cohort, which had a preponderance of men (58.4%). There was a negligible age gap between the sexes in both cohorts, representative of middle-aged adult populations. Individuals in the high Lp(a) cohort had worse lipid profiles than those in the normal Lp(a) cohort. Although the high Lp(a) group was older, they had a similar frequency of obesity and sedentary lifestyle and a greater frequency of light–to-moderate alcohol consumption, all of which influence the lipid profile. Interestingly, there were dissimilarities regarding the hemodynamic parameters. Notably, RHR 110 was significantly higher (84.95 bpm vs. 76.98 bpm) in the high Lp(a) group than in the normal Lp(a) group. Moreover, the high Lp(a) group tended to have higher sBP (131.8 vs. 130.2) and dBP (79.1 vs. 77.1) than the normal Lp(a) group. Table 4-1. Characteristics of the study population variables Normal Lp(a) <300 mg/L High Lp(a) ≥ 300 mg/L P-value n=344 n=350 Sex, n (%) Men Women 201 (58.42) 143 (41.51) 201 (57.41) 149 (42.52) .81 Age, years, mean ± SD All patients Men Women 49.13 ± 12.42 48.23 ± 11.51 50.41 ± 13.63 51.62 ± 11.81 49.52 ± 11.5 54.32 ± 11.64 <.001 0.22 <.001 Lipid profile, mean ± SD TC, mmol/L HDL-C, mmol/L Non-HDL-C TGs, mmol/L TC/HDL-C, mmol/L LDL-C, mmol/L 6.22 ± 1.73 1.44 ± 0.64 4.72 ± 1.73 1.91 ± 2.00 4.82 ± 2.24 3.91 ± 1.42 6.45 ± 1.99 1.25 ± 0.56 5.25 ± 1.98 3.22 ± 4.59 5.14 ± 3.38 4.12 ± 1.55 .06 <.001 <.01 <.001 <.001 .21 111 Normal Lp(a) <300 mg/L High Lp(a) ≥ 300 mg/L P-value Lp(a), median (IQR), mg/L 29 (9.00 – 99.00) 717 (485.88– 1052.22) Fasting Glucose, mM 5.51 ± 1.42 5.4 ± 2.34 .21 Framingham risk score, n (%) Low, <10% Medium 10-19 % High, >20% 147 (42.74) 74 (21.55) 123 (35.74) 128 (41.12) 76 (21.14) 146 (37.74) 0.19 Hemodynamic parameters, mean ± SD RHR, bpm sBP, mmHg dBP, mmHg 76.91 ± 11.42 130.21 ± 17.84 77.1 ± 10.92 84.94 ± 9.82 131.82 ± 16.72 79.11 ± 7.54 <.001 <.05 <.05 Weight, kg 79.92 ± 16.34 78.32 ± 19.24 .24 Lp(a), Lipoprotein(a); HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglycerides. 112 The prevalence of CVD risk factors in the two cohorts is shown in Table 4-2. The high Lp(a) group had a lower weight by 78.3 ± 19 kg vs. 79.9 ± 16.3 kg, p > 0.5, but exercise and alcohol consumption were similar to those in the normal Lp(a) level group. Interestingly, there was a markedly high prevalence of T2DM in the high Lp(a) cohort. Thirty-five patients (25%) with high Lp(a) had a concurrent diagnosis of T2DM, whereas 42 (12.2%) patients in the normal Lp(a) cohort had T2DM. Finally, the proportion of patients with CVD was significantly higher (29%) in the high Lp(a) cohort than in the normal Lp(a) cohort (17.4 %) p<0.001. We further analyzed our high and normal Lp(a) cohorts according to the prevalence of CVD events. The high Lp(a) with CVD group were older 56.9 ± 10.2 vs. 49.4 ±10, P<0.001 and had a higher prevalence of HTN, DM, different smoking status, and high alcohol use in comparison with the high Lp(a) group without CVD. Regarding the normal Lp(a) cohort stratified by CVD, patients with CVD also tend to be older than patients without CVD 56.9 ± 10.2 and 49.4 ±10 respectively. However, the prevalence of HTN and DM was significantly higher in the normal Lp(a) without reported CVD. For the secondary aim, we stratified the cohort by Lp(a) level, high versus normal, and followed them from birth. In the Cox regression model (Table 4-3) high Lp(a) level was a significant predictor for CVD events even after adjustment for sex, smoking status, and DM, which cause a slight increase of the effect of Lp(a) on CVD events with HR of 1.73 [95% CI, 1.26–2.40; P <0.0001]. Male sex, ever smoking, and the presence of DM were also significant predictors of CVD events with HR of 1.71 (1.23–2.36; P <0.001), 1.57 (1.13–2.21; P < 0.01), and 2.08 (1.42–3.00; P < 0.0001), respectively. History of HTN was not a significant predictor of CVD outcomes (P >0.05) but produced a HR consistent with an increased risk of CVD [HR = 1.28 (0.91–1.80)]. In model six, male sex and the presence of DM were significant predictors of CVD events in the 113 high Lp(a) group with HR of 2.62 [95% CI, 1.71–4.01; P <0.0001], 2.45 (1.85–3.89; P <0.001), respectively. Finally, Kaplan–Meier survival curves were constructed for the main two statistically significant dichotomous variables, Lp(a) levels and sex (Figure 4-1, A and B). Another Kaplan–Meier survival curve was constructed for sex in the high Lp(a) group (Figure 4-1, C). The separation of the survival curves based on these variables was significant in each case (P <0.0001, log-rank test). 114 Table 4-2. Prevalence of risk factors for CVD High Lp(a) Normal Lp(a) High Lp(a) (n=350) Normal Lp(a) (n=344) Positive CVD (n=102) Negative CVD (n=248) Positive CVD (n=60) Negative CVD (n=284) N (%) N (%) P-value N (%) N (%) P-value N (%) N (%) P-value Personal history Age Hypertension Type 2 diabetes mellitus _ 51.59 ± 11.8 89 (25.4) 35 (10) _ 49.1 ± 12.4 83 (24.1) 42 (12.2) <0.01 0.75 0.42 56.9 ± 10.2 41 (40.1) 21 (20.5) 49.4 ±10 48 (19.3) 14 (5.6) <0.001 <0.001 <0.001 54.9 10.4 25 (41.6) 18 (30) 47.9±10 58 (20.4) 24 (8.45) <0.001 <0.001 <0.001 Smoking status Current smoker Ex-smoker Never smoker 34 (9.7) 64 (18.2) 252 (72) 40 (11.8) 58 (16.8) 246 (71.5) 0.66 15 (14.7) 28 (27.4) 59 (57.8) 19 (7.6) 36 (14.5) 193 (77.8) <0.001 12 (20) 16 (26.6) 32 (53.3) 28 (9.8) 42 (14.7) 214 (75.3) <0.01 Alcohol use, % 0 drinks/week 1–6 drinks/week 7–13 drinks/week ≥ 4 drinks/week 99 (28.2) 175 (50) 50 (14.2) 26 (7.4) 95 (27.6) 166 (48.2) 59 (17.15) 24 (6.9) 0.76 23 (22.5) 48 (47) 13 (12.7) 18 (17.6) 76 (30.6) 127 (51.2) 8 (3.2) 37 (14.9) <0.001 10 (16.6) 35 (58.3) 5 (8.3) 10 (16.6) 85 (29.9) 131 (46.1) 5 (1.7) 49 (16.9) 0.15 Physical exercise >4 X/week 1-4 X/week None 26 (7.4) 138 (39.4) 186 (53.1) 33 (9.5) 125 (36.3) 186 (54) 0.49 12 (11.7) 39 (38.2) 51(50) 14 (5.6) 99 (34.8) 135 (47.5) 0.13 4 (4.1) 19 (31.6) 37 (61.6) 29 (10.2) 106 (37.3) 49 (52.4) 0.44 Body mass index > 30 25-30 <25 _ 76 (21.7) 160 (45.7) 114 (32.5) _ 93 (27) 162 (47.1) 89(25.8) 0.09 33 (32.3) 40 (39.2) 29 (28.4) 81 (32.6) 120 (84.3) 47 (18.95) 0.11 21 (35) 29 (48.3) 10 (16.6) 72 (25.3) 133 (46.8) 79 (27.8) 0.12 Ethnic group White Asian Black South Asian Hispanic Others 191 (54.5) 65 (18.5) (2) 18 (5.1) 12 (3.4) 57 (16.2) 201 (58.4) 60 (17.4) 16 (4.6) 14 (4) 10 (2.9) 43 (12.4) 0.35 51 (50) 23 (22.5) 2 (1.9) 7 (6.8) 2 (1.9) 17 (16.6) 140 (56.4) 42 (16.9) 5 (2) 11(4.4) 10 (4) 140 (16) 0.68 31 (51.6) 20 (33.3) 3 (5) 2 (3.3) 1 (1.6) 3 (4.9) 170 (59.8) 40 (14) 13 (4.5) 12 (4.2) 9 (3.1) 40 (14) <0.05 Medical history Personal history of CVD Family history CVD 190 (29.1) 186 (53.1) 60 (17.4) 162 (46.5) <0.001 0.12 102 (29.1) 61 (59.8) 248 (70.8) 125 (50.4) <0.001 0.13 60 (17.4) 27 (45) 384 (82.5) 135 (47.5) <0.001 0.82 CVD, cardiovascular disease; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglycerides., Lp(a); lipoprotein(a) 115 Table 4-3. Hazard ratios from Cox regression analysis of CVD events Risk factor HR 95% CI P-Value All study participants Model 1 (unadjusted): Lp(a) (high vs normal) 1.59 1.16–2.20 <0.01 Model 2: Lp(a) (high vs normal) Sex (men vs women) 1.64 1.90 1.19–2.27 1.38–2.63 <0.001 <0.0001 Model 3: Lp(a) (high vs normal) Sex (men vs women) Smoking status (ever vs never) 1.66 1.75 1.65 1.20–2.28 1.26–2.42 1.21–2.27 <0.01 <0.01 <0.0001 Model 4: Lp(a) (high vs normal) Sex (men vs women) Smoking status (ever vs never) DM (present vs absent) 1.73 1.71 1.59 2.08 1.26–2.40 1.23–2.36 1.17–2.22 1.44–3.01 <0.0001 <0.001 <0.001 <0.0001 Model 4: Lp(a) (high vs normal) Sex (men vs women) Smoking status (ever vs never) DM (present vs absent) 1.73 1.70 1.57 2.00 1.25–2.36 1.20–2.34 1.13–2.21 1.42–3.00 <0.0001 <0.001 <0.001 <0.0001 116 Risk factor HR 95% CI P-Value HTN (present vs absent) 1.28 0.91–1.80 0.14 Patients with Lp(a) levels ≥ 300 mg/L Model 6: Sex (men vs women) DM (present vs absent) 2.62 2.45 1.71–4.01 1.85–3.89 <0.0001 <0.0001 Lp(a), lipoprotein(a)., DM, diabetes mellitus., HTN, hypertension. Lp(a) levels ≥ 300 mg/L considered high. 117 Figure 4-1. Cumulative event-free survival based on Lp(a) concentration (A), sex (B), and sex for patients with Lp(a) levels ≥ 300 mg/L (C). p<0.001 p<0.001 p<0.001 118 4.4 Discussion Various studies have used the FRS, assessments of modifiable and nonmodifiable risk factors, and non-invasive ultrasonography to evaluate the risk of CVD in various patient cohorts [372]. A positive correlation between CVD and Lp(a) concentration has been found in most studies [356]. Our study is the first to compare hemodynamic parameters between groups of patients with extreme Lp(a) levels. The present study also investigated the differences in risk factor presentation between patients with and without positive CVD findings in the HHP population. The first important finding in our study is the difference in RHR between the high and normal Lp(a) level cohorts. RHR in the high Lp(a) group was significantly higher than that in the lower Lp(a) group. This could be an early sign of atherosclerotic progression and CVD mortality [338–340] due to high Lp(a) levels. Although there was no difference in the prevalence of HTN between the high and normal Lp(a) cohorts, RHR, sBP and dBP were remarkably higher in the high Lp(a) group than in the normal Lp(a) group. Our data suggest that an elevated level of Lp(a) may be involved as a cofactor in essential HTN, which is consistent with other reports [373]. Additionally, the high Lp(a) level group had higher fasting blood glucose (6.8 mM) than the normal Lp(a) cohort (5.2 mM). Regarding the calculated Framingham risk score, there was no significant difference between the cohorts, which may suggest that Lp(a) should be considered in calculating the 10-year CVD risk score given that high Lp(a) levels in our study were significantly associated with CVD events compared with normal Lp(a) levels. Another important finding is the difference in the prevalence of CVD events between our main cohorts. The proportion of patients with CVD events was significantly higher in the high Lp(a) cohort than in the normal Lp(a) cohort. Of the 350 individuals with high Lp(a), only 102 (29.1%) had CVD events compared to the 60 (17.4%) patients among the 344 individuals with 119 normal Lp(a). Our data are consistent with other reports [370,374] and suggest that there are continuous independent and modest associations of Lp(a) concentration with the risk of CVD that might exclude vascular outcomes. We conducted further analysis of our main cohorts. Interestingly, there was a difference in the prevalence of HTN between individuals with high Lp(a) and CVD and those with high Lp(a) but no recorded CVD event. The proportion of patients with HTN in the high Lp(a) cohort was markedly higher than that in the normal Lp(a) cohort or that expected in the general population. Of the 102 patients with high Lp(a) with CVD, 41 (40.1%) had HTN, in contrast to 48 patients (19.3%) among the 248 patients in the normal Lp(a) cohort. We would expect to find 26 patients in the high Lp(a) and CVD subgroups with HTN according to the Canadian prevalence of essential HTN (25.4%) in adults in the same age group [375]. Likewise, the prevalence of type 2 DM was significantly different between groups stratified by Lp(a) levels and CVD. The proportion of patients with DM in the high Lp(a) with known CVD subgroup was markedly higher than that in the normal Lp(a) without CVD subgroup and that expected in the general population [376]. There were (22.2%) of the 102 individuals with high Lp(a) and CVD who had DM, which is higher than the prevalence of DM in the general Canadian population (10.3%) in adults of the same age group [376]. In comparison, there are highly conflicting results reported by studies examining the relationship between Lp(a) and type 2 DM, in which high [377] or normal [378,379] Lp(a) levels were observed in patients with type 2 DM compared with healthy controls. Moreover, the present study results confirm the value of Lp(a) as a predictive factor for CVD events in the HHP population. Although the HR associated with high Lp(a) was smaller in our study than in that of Holmes et al [370]. (1.73 vs. 2.59), the difference is almost certainly attributable to the lower cut-off point of 300 mg/L used in our study compared with 560 mg/L used 120 in the other study. However, the HR associated with high Lp(a) was higher than that in other reports [380]. Concerning sex, our study confirmed the findings of other similar studies demonstrating that, apart from age, male sex is one of the most important predictors of CVD risk. Although HTN failed to reach the standard for statistical significance, the point estimate was consistent with an increased risk of CVD. Other studies have indicated that HTN is also not predictive of CVD events for patients with familial hypercholesterolemia [370]. However, another study previously shown that HTN was predictive of CVD events specifically in the high Lp(a) population [381], but this finding is, again, not universal. 4.4.1 Strengths and limitations of the study Regarding the strengths and limitations of this study, the study is strengthened because the HHP population is ethnically heterogeneous. Our results are therefore applicable to the multiethnic populations seen in North American clinics. Furthermore, we chose relatively objective CVD endpoints, electing to reject a history of angina, positive exercise and nuclear medicine studies, or transient ischemic attacks as endpoints. Although this makes statistically significant differences more challenging to demonstrate, it lends credibility to the results. A limitation to our study is that the interpretation of the results cannot be applied to the general population with high Lp(a) and limited to a population who survived adulthood, lived long enough, and enrolled in a specialized clinic at a tertiary clinical center. Additionally, ascertainment bias may have been introduced in this study as a result of assessing individuals referred to a specialized lipid clinic, which might have been avoided by performing a general population cohort. 121 Chapter 5: The association between physical activity and resting heart rate in patients with high Lp(a) 5.1 Introduction Lp(a) was discovered in the 1960s in the laboratory of Norwegian physician Kåre Berg. Since then, we have greatly improved our knowledge of lipids and CVD. Lp(a) is an enigmatic type of lipoprotein that is exclusively formed in the liver and comprises two main components, a single copy of apo-B100 tethered to a single copy of a protein denoted as apo(a). Plasma levels of Lp(a) increase soon after birth to a steady concentration within a few months of life. Years of research and numerous studies have established Lp(a) as the single most common independent genetically inherited causal risk factor for CVD. Lp(a) levels do not appear to be altered by factors known to influence other lipoproteins (e.g., lipid-lowering drugs, dietary modification, and change in body mass). Indeed, the evidence also suggests that PA does not influence the Lp(a) concentration in the blood [382]. CVD is recognized as one of the leading causes of morbidity and mortality worldwide [356]. Emerging evidence indicates that coexisting lipid disorders, particularly high Lp(a) levels, substantially contribute to the overall health burden of CVD [356]. Therefore, identifying specific risk factors and intervention strategies for controlling ischemic heart disease in patients with abnormal Lp(a) levels is currently our primary goal. A persistently elevated RHR has long been recognized as a determinant of myocardial ischemia [383], and numerous epidemiological studies 122 have provided evidence for the predictive power of RHR for cardiovascular mortality in both general [384] and disease populations [385]. In our previous work in chapter 4, we revealed that elevated heart rate and elevated Lp(a) could be closely related. PA, which is the central component of our HHP, is known to reduce RHR [48], with subsequent benefits on cardiovascular health [386]. Since there is no approved medical therapy to reduce Lp(a) concentrations directly, PA should be tested as an essential therapy for controlling ischemic risk in this population. Thus, this study was conducted to examine the association between PA and RHR in individuals with high levels of Lp(a). We hypothesise that participants with high Lp(a) who enhance their PA between their initial and third year visits are more likely to have a significantly lower RHR than those who do not at the third-year follow-up and consequently they are more likely to have lower CVD risk related to high Lp(a), even though Lp(a) concentrations are constant. 5.2 Methodology 5.2.1 Study design and setting This study conforms to the guiding principles of the Declaration of Helsinki and was approved by the UBC - Providence Health Care Institute (H18-03099) and St. Paul's Hospital. Following study protocol approval by our ethics committee, a retrospective chart review study was undertaken at a single, large prevention clinic at St. Paul’s Hospital, a tertiary care university-affiliated hospital in Vancouver, British Columbia, Canada. This prevention clinic focuses on controlling modifiable CVD risk factors, mainly physical inactivity, hyperlipidemia, obesity, smoking, HTN, and DM, thus reducing CV risk by lifestyle modifications and, when needed, medications in individuals referred for both primary and secondary prevention of CVD. All patients are assessed initially by 123 a clinic team consisting of a nurse, a dietician, and a physician. Then, the HHP prevention clinic team follow-up their patients every 6 to 12 months. All clinical, laboratory and medications data are dated and recorded for each follow-up visit in the patients' chart. All charts for active patients visiting the clinic between 2016-2019 and still have ongoing appointments are kept in the HHP medical record. 5.2.2 Study procedure Patients' medical charts were the data source contributing to the retrospective data collection. The active medical charts, between 2016 and December 2019, of CVD patients enrolled in the Healthy Heart Program prevention clinic were screened by MJ under the supervision of JF. To acquire the study population, medical charts was queried manually for patients with known Lp(a) levels. Patient charts were included if the charts were active in between 2016-2019 as mentioned previously, contained patient Lp(a) data, included at least 3 years of follow-up, contained RHR data that were recorded at each visit, belonged to patients who were in a stable condition (that is, no respiratory exacerbation and/or hospitalization during the HHP prevention clinic 3-year follow-up), and reported normal sinus rhythm. Patients’ charts were excluded if they had uninterpretable or irretrievable resting ECG, were missing hemodynamic data at rest, or included conditions that altered RHR such as atrial fibrillation, ventricular paced rhythm, and left ventricular hypertrophy with repolarization abnormalities. Demographic data including age, anthropometric measurements, education and employment information, CVD risk factors, laboratory results, and clinical characteristics, including the diagnosis, lipid-lowering medications, other medication history, interventions initiated at the clinic, and study cardiovascular variables of interest were obtained from the HHP 124 prevention clinic enrollment form and first physician report at the initial visit. Similar data were also collected at the third-year visit. A total of 2008 chart were screened. 694 patients met the inclusion criteria for our study. To inspect the possibility of selection bias, demographic, laboratory, and clinical information was also collected for the excluded 1314 patients. All charts were reviewed by the primary author under the guidance of senior physicians, confidentiality of all patients was maintained by assigning each patient a study number, and all data were securely stored in the HHP research office at St. Paul’s Hospital. 5.2.3 Physical activity data (main independent variable) To promote the patients’ healthy lifestyles and compliance with the exercise regimen in our clinics, we practice the 0-5-30 slogan: zero for no smoking, eating five portions of vegetables and/or fruits daily, and thirty minutes of daily exercise. The PA self-reported data were extracted from the prevention clinic enrollment history form that was completed at patients’ initial visit to the clinic and from the physician report at their third-year visit. For each visit, a detailed description of activity (e.g., typing, cleaning, walking, or jogging for exercise, etc.) and a perceived effort (light, moderate, vigorous) were reported along with the frequency of the patient’s mentioned activity. The frequency, intensity, time, and type (FITT) principle was used to evaluate the patients PA for both visits (initial and 3rd year visits) [353]. The scale of the frequency of PA from lipid clinic enrollment form were applied in our study. Thus, PA was ranked on a scale from 0–2 (0 = no reported activity or activity reported was not enough to qualify, 1 = up to four times a week of PA of at least moderate intensity, and 2 = five or more times of PA a week of moderate intensity) [353,354]. PA intensity was evaluated based on METs equivalents of common physical activities classified as light, moderate or vigorous 125 intensity Table 3-2 [355]. For example, if the HHP physician states that a patient's PA consists of running for more than 30 minutes twice a week, the patient's PA score is one at the initial visit. Then, at patient's third-year visit, the patient's self-reported PA was extracted and ranked for the second time. 5.2.3.1 Study Groups The patients who increased their PA by one point on our scale (up from the initial visit) were categorized as the improved PA group (IPA) group, while those who remained at the same level or decreased their activity were included in the nonimproved PA (NIPA) group. 5.2.4 RHR (main dependent variable) RHR was reported in all included charts at the first and third-year visits. RHR values were obtained from three different sources. The RHR values were primarily collected from electrocardiography (ECG) reports included in patients' charts. Furthermore, RHR and blood pressure were measured using a digital automatic blood pressure monitor (OMRON, HEM-7200) or by palpation of the radial pulse over a 30-second period by an experienced nurse at the HHP clinic after 5 minutes of setting according to HHP clinic protocol. RHR and blood pressure were then reported in the physician report for both visits. 5.2.5 Clinical cardiac risk factors The modifiable and nonmodifiable risk factor data from the patients’ initial and most recent visits were extracted from the patients’ charts. Age, sex, family and/or personal history of CAD, body mass index (BMI) at the most recent visit, alcohol consumption frequency, and 126 information on smoking and HTN status were also obtained. Exercise status at the first and 3rd year visits to the HHP prevention clinic was also recorded. Patients were considered to have CAD if they had a clinical history of CAD (previous myocardial infarction or angina) or had positive investigations (cardiac stress test or carotid artery ultrasound). Patients were regarded as having a positive family history of CAD when information on CAD in < 55-year first-degree male relatives and < 65-year female relatives was recorded in the chart [357]. Smoking history was determined from the patient initial registration form, and patients were categorized as smoker, ex-smoker, or non-smoker. Blood pressure readings that were performed by a nurse using an electronic sphygmomanometer were also obtained from the patients’ charts. The presence of HTN was defined as a mention of the diagnosis in the chart or a prescription for antihypertensive agents such as angiotensin-converting enzyme inhibitors to lower blood pressure [370]. DM was diagnosed according to the Canadian Diabetes Association guidelines by two fasting plasma glucose levels greater than 7.0 mmol/L [358]. BMI = (weight [kg]/height2 [m2]) was recorded at the time of the initial and last visits. In addition, alcohol intake was categorized (none, 1–7 drinks/week, 8–14 drinks/week, and 14 drinks/week). The Framingham risk score (FRS) was also calculated at the initial and most recent visits for each patient using the Canadian Cardiovascular Society table. The FRS was used to estimate the 10-year risk of CVD for all included HHP prevention clinic patients. FRS also was stratified to three main CVD risk categories. The low-risk category defined as > 10% CVD risk, the intermediate risk category which defined as >10-19 % CVD risk, and the high-risk category with ≥ 20% CVD risk. 127 5.2.6 Biochemical cardiac risk factors 5.2.6.1 Lipid measures Plasma lipid levels were measured by standardized enzymatic methods in accredited laboratories in British Columbia. The earliest untreated lipid profile was recorded: total cholesterol (TC), HDL cholesterol (HDL-C), non-HDL-C, TGs, the ratio of TC to HDL-C (TC/HDL-C), and calculated LDL-C (using the Friedewald formula) [356]. 5.2.6.2 Lp(a) measurements All Lp(a) measurements were executed with the Latex microparticle-enhanced immunoturbidimetry assay. Because Lp(a) was regularly measured after referral to our clinic, few patients were off all medications when it was measured. Nevertheless, because Lp(a) is fairly unaffected by lipid-lowering therapies, it is improbable that this significantly affected the quality of the data. Therefore, the mean of all Lp(a) determinations for each patient was collected under the assumption that this best represented a patient’s exposure to this atherogenic lipoprotein. 5.3 Statistical analysis As descriptive values, continuous variable characteristics are expressed as the means and (+/- SD) or medians (interquartile range), whereas categorical traits are expressed as frequencies. The means of variables of interest were compared by paired t tests to compare the differences in the entire cohort between the initial and last visits in the HHP. Student’s t test or the Wilcoxon test was used according to the provision of the normal distribution assumption for independent data analysis. Categorical variables were compared using the x2 test or Fisher’s exact test, as appropriate. For the purpose of analysis, Lp(a) results were divided into normal (<300 mg/L) and 128 high (>300 mg/L) [371]. The cut-off point of 300 mg/L represented the 80th percentile of Lp(a) in our cohort. A multivariable linear regression model was used to assess the associations between continuous and dichotomous Lp(a) concentration as an independent variable and RHR as a dependent variable at the initial visit. The linear regression model was confined to the variables considered to be of greatest interest and importance: sex, age, smoking status, DM status, HTN status, and PA status. A Mixed-effects linear regression with random intercepts was also used to examine the associations between PA categories (improved vs. not improved by the end of third year) as the main independent variable and RHR change between initial and third year follow-up as a dependent variable after adjusting for age, sex, DM, HTN, non-HDL-c, education, employment, medications change and follow-up period. The selection of variables for the multivariable models was based on clinical and statistical significance and goodness-of-fit testing. All tests of statistical significance were two-tailed with a 95% CI; P < 0.05 was considered to indicate statistical significance. All statistical analyses were performed with the R-Studio software package (RStudio Inc., Boston, MA, USA). To examine the potential for selection bias, demographic, laboratory, and clinical data were also extracted on the 1314 patients for whom no Lp(a) data were available. The clinical and laboratory characteristics of the 694 HHP patients who were included were compared with the characteristics of the patients who were missing inclusion data. A sensitivity analysis was performed to assess the robustness of the association between PA and the RHR in this study with the following additional adjustments: 2.1 % percent of the study population, who were more active at baseline and remained so throughout the three-year 129 follow-up period and were initially included in the NIPA group, were transferred to the IPA group for the sensitivity analysis shown in Supplementary table - 3. 5.4 Results 5.4.1 Study population Of the 2008 potential participants, 119 patients (12%) were excluded because they had missing RHR data, 69 charts had missing consent documents or indicated denial of participation in any of our studies, 56 charts were excluded due to unrecorded PA data, and 30 charts were excluded because patients were on HR-altering medications. Additionally, 20 charts were excluded because they were either new patients or the duration of enrollment in our clinic was less than three years. Finally, of the remaining patients, 4 were excluded due to clinical considerations regarding their heartbeat. The remaining 1016 patients were excluded due to missing Lp(a) values. The clinical history characteristics of the cohort and the 1314 out of 2008 patients who were excluded on the basis of our inclusion and exclusion criteria are presented in Table 5-1. Significant differences between these two groups were limited to age, smoking, and the extent of alcohol use. Therefore, 694 patients with known Lp(a) levels were included in the final analysis, of whom 344 patients had Lp(a)< 300 mg/L levels (normal Lp(a) group) and 350 patients had Lp(a) levels ≥ 300 mg/L (high Lp(a) group) according to their IRMA Lp(a) laboratory test. The study flow chart is presented in Figure 5-1. The Lp(a) distribution of the cohort was positively skewed (Figure 5-2). The median concentration (interquartile range) was 301 (29.2–742.2) mg/L, and there was no significant difference in the median Lp(a) concentration between men and women (P = 0.8, Mann–Whitney test). The Lp(a) distribution statistics are summarized in Table 5-2. 130 Figure 5-1. Study flow chart Figure 5-2. A histogram showing the Lp(a) distribution of the study population. The dashed line represents the cutoff value of 300 mg/L for Lp(a). 131 Table 5-1. Characteristics of the included and excluded populations Variables Study population Excluded population P value n Sex, n (%) Men Women 694 _ 402 (58.88) 292 (42.12) 1314 _ 801 (61.22) 513 (39.21) 0.89 Age, years Means ± SD _ 50.31 ± 12.22 _ 51.32 ± 11.93 .11 Type of CVD prevention n (%) Primary prevention Secondary prevention _ 523 (75.31) 162 (24.72) _ 980 (74.63) 334 (25.42) .12 History of smoking, % Ever smoked Never smoked _ 28.22 71.21 _ 36.00 64.00 <0.05 HTN, % 24.74 26.53 .81 DM, % 11.73 14.00 .11 Family history of premature CVD, % 50.14 49.00 .09 Frequency of PA, % <1 times/week 1–4 times/week >5 times/week _ 53.63 37.94 8.50 _ 54.12 35.83 10.00 .45 132 Variables Study population Excluded population P value Alcohol use, % 0 drinks/week 1–6 drinks/week 7–13 drinks/week ≥ 4 drinks/week _ 27.93 49.12 15.73 7.22 _ 25.22 40.21 20.63 13.98 <0.05 CVS risk category, % Low risk <10% Intermediate risk 10-19% High risk ≥20% _ 40.42 21.32 38.42 _ 36.21 18.82 40.02 0.08 Medications, % No medication Statins Fenofibrates _ 52.00 35.00 12.00 _ 50.00 36.00 13.00 0.42 133 Table 5-2 . Lp(a) distribution statistics 5.4.2 Participant characteristics The study population had a mean age (± SD) of 50.3 ± 12.2 years, and 58.4% were male. The normal Lp(a) and high Lp(a) groups did not differ significantly in sex, TC, and fasting blood glucose (Table 5-3). The individuals in both groups were predominantly men (58% in the high Lp(a) group and 57% in the normal Lp(a) group). However, the women tended to be older than the men, especially in the high Lp(a) group (54.3 ± 11.6; p<0.001). Many dissimilarities were found between the groups, including lipid profile, glucose, and FRS, with the high Lp(a) group tending to be worse than the normal Lp(a) group. For instance, the non-HDL level was significantly higher (5.2 ± 1.98 mmol/L) and the HDL level was significantly lower (1.2 ± 0.56 mmol/L) in the high Lp(a) group. Moreover, RHR, sBP, and dBP tended to be significantly higher than those in the normal Lp(a) group (84.9 ± 9.8 bpm vs. 76.9 ± 11.4 bpm, 131.8 ± 16.7 mmHg vs. 30.2 ± 17 mmHg, and 79.1 ± 77.5 mmHg vs. 79.1 ± 77.5 mmHg, respectively). All Men Women Range 4 to 2967 4 to 2967 4 to 2663 Mean (SD) 451.8 (501) 436.5 (485) 472.9 (522.3) Skewness 1.32 2.12 1.19 Kurtosis 1.63 1.41 1.05 Median (IQR) for all 301 (29.25 – 742.25) 298 (37.25 - 706.75) 306.50 (29.00 - 810.80) All results mg/L. IQR: Interquartile range. 134 Table 5-3. Demographic, diagnostic, biochemical, and hemodynamic comparisons between the high and normal Lp(a) level groups. Variables Normal Lp(a) (n=344) High Lp(a) (n=350) P-value Sex, n (%) Men Women 201 (58.40) 143 (41.50) 201 (57.000) 149 (42.500) 0.8 Age, years, Mean ± SD All patients Men Women 49.0 ± 12.41 48.20 ± 11.51 50.40± 13.63 51.6 ± 11.80 49.5 ± 11.50 54.3 ± 11.60 <.001 0.2 <.001 Hemodynamic parameters, Mean ± SD RHR, bpm sBP, mmHg DBP, mmHg 76.91 ± 11.40 130.20 ± 17.00 77.10 ± 10.12 84.90 ± 9.80 131.80 ± 16.70 79.11 ± 77.51 <.001 <.05 <.05 135 Variables Normal Lp(a) (n=344) High Lp(a) (n=350) P-value Lipid profile, Mean ± SD TC, mmol/L HDL-C, mmol/L Non-HDL-C TGs, mmol/L TC/HDL-C, mmol/L LDL-C, mmol/L Lp(a) median (IQR) mg/L 6.2 ± 1.70 1.41 ± 0.60 4.70 ± 1.70 1.96 ± 20 4.80 ± 2.23 3.95 ± 1.40 29 (9.00 – 99.00) 6.43 ± 1.99 1.21 ± 0.56 5.21 ± 1.98 3.213 ± 4.59 5.21 ± 3.38 4.12± 1.55 717 (485.88– 1052.22) .06 <.001 <.01 <.001 <.001 .2 Fasting Glucose, mM 5.51 ± 1.41 5.43 ± 2.3 .21 Framingham risk score, n (%) Low risk <10% Intermediate risk 10-19% High risk ≥20% 147 (42.71) 74 (21.53) 123 (35.72) 128 (41.12) 76 (21.14) 146 (37.71) 0.19 Weight, kg 79.91 ± 16.32 78.3 ± 19.21 .24 TC, total cholesterol; Lp(a), lipoprotein(a); HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglycerides. RHR, resting heart rate; sBP, systolic blood pressure; dBP, diastolic blood pressure. 136 5.4.3 Lp(a) as a predictor for resting heart rate linear regression analyses were constructed and indicated that Lp(a) continued to be a significant predictor for RHR after adjusting for SBP, dBP, sex, age, DM diagnosis, non-HDL-C, and PA status (log-Lp(a) β = 1.75, p < 0.001). These results were also significant (β = 8.26, p < 0.001) when Lp(a) levels were expressed as a dichotomous variable (Table 5-4). Table 5-4. Multiple regression analysis of RHR Independent variables β estimates 95% CI P value Model 1 (Lp[a] as continuous variable) Log (Lp[a]) 1.75 1.35 to 2.14 <0.001 sBP, mmHg 0.05 0.002 to 0.11 <0.05 dBP, mmHg 0.15 0.064 to 0.23 <0.001 Age, years 0.11 0.18 to 0.04 <0.01 Non-HDL-C (mmol/L) 0.96 0.53 to –1.38 <0.001 Sex: Ref category (women) Men –3.4 –4.97 to –1.78 <0.001 DM: Ref category (No) Yes 4.75 2.24 to 7.25 <0.001 PA: Ref category (<1 times/week) 1–4 times/week >5 times/week –0.96 –4.42 –1.3 to –0.6 –7.2 to –1.5 <0.001 <0.001 Model 2 (Lp[a] as dichotomous variable) Lp(a): Ref category (normal Lp[a]) 137 Independent variables β estimates 95% CI P value High Lp(a) 8.26 6.75 to 9.78 <0.001 sBP, mmHg 0.04 –0.008 to 0.1 NS dBP, mmHg 0.15 0.06 to 0.23 <0.001 Age, years 0.11 0.45 to 0.17 <0.01 Non-HDL-C, mmol/L 1.07 0.65 to 1.48 <0.001 Sex: Ref category (Women) Men –3.14 –4.69 to –1.58 <0.001 DM: Ref category (Absent) Present 3.95 1.51 to 6.38 <0.001 PA: Ref category (<1 times/week) 1–4 times/week >5 times/week –1.37 –4.11 –2.9 to 0.22 –6.89 to –1.32 NS <0.001 Lp(a); lipoprotein(a), RHR, resting heart rate; sBP, systolic blood pressure; dBP, diastolic blood pressure; PA; physical activity. 5.4.4 The relationship between PA and RHR For the second aim of the study, the improved PA (IPA) and unchanged PA (NIPA) groups were balanced in most cardiovascular risk factors Table 5-5. However, men in the NIPA group tended to be significantly older than men in the IPA group (51.41 ± 12.1, p <0.05). Patients in the NIPA group had significantly higher HTN rates than those in the IPA group (28.1% vs. 21%, p<0.01). 138 The frequency of PA was also significantly different between the groups. The IPA group was less active at the beginning of the follow-up period. Of the 694 patients in our study, 328 (47.2%) improved their exercise pattern. Also, 2.1 % of the study population, who were more active at baseline and continued to be more active till the end of the 3 years follow-up, were included in the NIPA group which consist of 366 (52.8 %) patients. In the improved exercise group, RHR decreased significantly by 16.1 bpm (18.9%, P<0.01) between baseline and the third year HHP prevention clinic visit, as shown in Table 5-6. There was also a significant reduction in sBP and dBP by a mean decrease of 8.5 mmHg (6.4%, P<0.01) and 2.4 mmHg (3.1%, P<0.01), respectively. The lipid profile also improved dramatically (a mean TC decrease of 1.7 mmol/L (26.8%, P<0.01), a mean LDL-C decrease of 1.5 mmol/L (36.3%, P<0.01), and a 22.6% increase in HDL levels between the first and last HHP clinic visits. Non-HDL-C and TC/HDL-C also significantly declined by 41.8% (P<0.01) and 43% (P<0.01), respectively. In comparison with the unimproved group, the improved group had better control over fasting blood glucose (FBG; P<0.05) and BMI (P<0.01). Similarly, sBP and the lipid profile in the NIPA group improved between the initial and last visits. However, RHR significantly decreased by a mean of only 4.8 bpm (5.7%, P<0.01) by the end of the follow-up period. Notably, the Framingham risk score (FRS) declined by 27.1% in the IPA group vs 4.2% (P<0.001) in the NIPA group. Moreover, regarding the change between groups, the RHR, sBP, and dBP in the IPA group have decreased significantly by 18.9 % vs. 4.8% (p<0.0001), 6.4 % vs. 3.4 % (p<0.001, and 3.1 % vs. 1.1% (p<0.001), respectively in comparison with the NIPA group. The IPA group also has more significant improvements increase in HDL-c by (22.6% vs. 3.5%, p<0.001) and lowering the BMI by 2.8 % vs. increase in BMI by 2.5 % for NIPA group P<0.01. 139 Table 5-5. Demographic data comparison between the improved physical activity (IPA) and nonimproved physical activity (NIPA) groups Variables IPA (N= 328) NIPA (N= 366) P-value Demographics, n (%) Sex, n (%) Men Women 188 (57.33) 140 (42.62) 214 (58.44) 152 (41.62) 0.88 Age, years, mean ± SD All patients Men Women 49.71 ± 12.63 46.90 ± 12.51 54.11 ± 11.92 50.9 ± 12.24 51.41 ± 12.12 54.24 ± 12.23 <0.05 <0.05 075 Hemodynamic parameters, mean ± SD RHR, bpm sBP, mmHg dBP, mmHg 78.31 ± 11.42 128.42 ± 16.32 78.01 ± 10.73 78.1 ± 9.62 128.3± 16.71 78.5 ± 8.52 0.39 0.48 0.67 Lp(a) median (IQR) mg/L 298 (28.0-684.50) 337 (33-790.8) 0.18 Type of CVD prevention, n (%) Primary prevention Secondary prevention 264 (80.44) 64 (19.62) 268 (73.34) 98 (26.75) 0.01 History of smoking, n (%) Ever smoked 94 (28.65) 102 (27.84) 0.12 140 Variables IPA (N= 328) NIPA (N= 366) P-value Never smoked 234 (71.46) 264 (72.26) HTN, n (%) 69 (21.00) 103 (28.15) <0.01 DM, n (%) 33 (10.00) 44 (12.00) 0.12 Family history of premature CVD, n (%) 165 (50.00) 183 (51.01) 0.43 Frequency of physical activity, n (%) <1 times/week 1–4 times/week ≥ 5 times/week 214 (65.27) 111 (33.86) 3 (0.94) 158 (43.16) 152 (41.55) 56 (15.36) <0.001 Alcohol use, n (%) 0 drinks/week 1–6 drinks/week 7–13 drinks/week ≥ 14 drinks/week 122 (37.21) 160 (48.45) 41 (12.25) 5 (3.00) 124 (34.05) 177 (48.00) 55 (15.00) 10 (3.00) 0.6 CVD risk category, n (%) Low risk <10% Intermediate risk 10-19% High risk ≥20% 139 (42.36) 63 (19.37) 126 (38.48) 136 (37.16) 83 (22.65) 147 (40.38) 0.22 Medications, n (%) No medication 148 (45.12) 176 (48.01) 0.34 141 Variables IPA (N= 328) NIPA (N= 366) P-value Statins Fenofibrates 134 (40.8) 46 (14.08) 146 (39.89) 44 (12.02) Abbreviations: IPA: Improved physical activity group; NIPA, Not improved physical activity group; HTN, hypertension; DM, diabetes mellitus; CVD, cardiovascular disease; RHR, resting heart rate; sBP, systolic blood pressure; dBP, diastolic blood pressure; Lp(a), lipoprotein(a). Difference between the groups is significant at P < 0.05. 142 Table 5-6. Changes in CVD risk factors between the IPA and NIPA groups in patients with high L(a) ≥ 300mg/L IPA Group NIPA Group First Visit 3rd year visit Change Change (%) P-Value Within groups First Visit 3rd year visit Change Change (%) P-Value within groups P-Value for change between groups RHR (bpm) 84.93 ± 9.95 68.75 ± 9.73 -16.1 ± 7.63 -18.93 <0.01 85 ± 9.83 80.1 ± 9.63 -4.8 ± 8.83 -5.72 P<0.01 <0.0001 BP (mmHg) sBP 132.45 ± 16 123.9±15.05 -8.5 ± 17.34 -6.42 < 0.01 131.0±17.12 126.7 ± 16.95 -4.5 ± 17.82 -3.42 P <0.01 <0.001 dBP 79.44 ± 11.5 77.00 ± 9.35 -2.4 ± 12.17 -3.15 < 0.05 78.92±11.23 78 ± 10.11 -0.88±12.33 -1.11 P =0.88 <0.001 Lipid profile TC (mmol/L) 6.42 ± 1.92 4.73 ± 1.35 -1.7 ±1.72 -26.83 <0.01 6.05 ± 1.42 4.92 ± 1.45 -1.12 ± 1.52 -18.52 P<0.01 <0.001 LDL-C (mmol/L) 4.07 ± 1.63 2.65 ± 1.22 -1.5 ± 1.63 -36.32 <0.01 3.83 ± 1.22 2.83 ±1.33 -1.07 ± 1.63 -27.72 P<0.01 <0.0001 HDL-C (mmol/L) 1.42 ± 0.63 1.83 ± 0.71 +0.3 ± 0.64 +22.65 <0.01 1.43 ± 0.62 1.45 ± 0.62 -0.05 ± 0.63 +2.55 P=0.24 <0.0001 Non-HDL-C (mmol/L) 4.92 ± 2.01 2.95 ± 1.21 -2 ± 1.83 -41.82 <0.01 5.65 ± 1.33 3.43 ± 1.43 -1.17 ±1.53 -20.72 P<0.01 <0.001 TC/HDL-C (mmol/L) 4.93 ± 2.17 2.85 ±0.93 -2.1 ± 1.92 -43.11 <0.01 4.93 ± 2.35 3.85 ± 1.85 -1.05 ± 1.82 -21.53 P<0.01 <0.001 TG (mmol/L) 1.44 (1.32) 1.16 (0.83) -0.3 ± 1.64 -21.12 <0.01 2.22± 1.97 1.73 ± 1.23 -0.28 ± 1.92 -14.00 P<0.05 <0.01 FBG 5.14 ± 0.90 5.32 ± 0.71 -0.3 ± 0.94 -0.25 <0.01 6.11 ± 1.125 5.55 ± 0.92 -0.04 ± 1.13 -0.72 P<0.61 <0.01 Weight (kg) 79.36±22.63 76.82±19.63 -2.5 ± 10.44 -9.53 <0.01 77.32±16.73 79.52 ± 21.73 +2.12±12.4 +2.82 P<0.01 <0.01 BMI 26. ± 5.19 25.83 ± 4.71 -0.7 ± 2.43 -2.82 <0.01 27.13± 4.83 27.87 ± 6.63 -0.7 ± 3.51 +2.58 P<0.01 <0.01 FRS % 12.8 ± 13.00 9.32 ± 8.32 -3.5 ± 9.95 -27.13 <0.01 12.7 ± 12.25 12.12 ± 10.92 -0.5 ± 7.12 -4.23 P=0.41 <0.01 Notes: Data shown are presented as the means ± SD for all data except TG, which is reported as the median and IQR. The duration of patient follow-up was 3 years. 143 5.4.5 The association between improved PA and RHR On mixed-effect linear regression analysis, PA as a main predictor was significantly associated with RHR, with the IPA group exhibiting a mean decrease in RHR of –5.39 bpm (95% CI: –5.61, –2.97, P<.001) in comparison with the NIPA group after adjusting for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type (Table 5-7). Also, our model indicated that there is an association between Lp(a) and RHR by (log-Lp(a) β = 1.75, p < 0.001) adjusted for PA, sex, sBP, dBP, HTN, DM, non-HDL-C, employment, education level, change in medications, and visit type. The model also showed, on average, that for every 1 year increase in age, we expect an increase in RHR by 0.10 bpm (95% CI: 0.05 to 0.16, P<0.001) adjusted for PA, sex, sBP, dBP, HTN, DM, non-HDL-C, employment, education level, change in medications, and visit type. Regarding to BP, statistically significant associations between sBP and dBP and RHR were noted by (0.07 bpm, 95% CI: 0.03 to 0.09P<0.001) and (0.18 bpm, 95% CI: 0.10, 0.22, P<0.01). Also, compared with females, males had a decrease in RHR of –3.78 bpm (95% CI: –5.18 to –2.38, P<0.001) on average adjusted for PA, sex, sBP, dBP, HTN, DM, non-HDL-C, employment, education level, change in medications, and visit type. On the other hand, smoking, education status, employment status failed as independent predictors of RHR. 5.4.5.1 Sensitivity analyses A sensitivity analysis was performed to assess the robustness of the association between PA and the RHR. The beta coefficient of the main model after adding patients who were more active at baseline and remained so throughout the three-year follow-up period (–5.55; 95% CI [– 144 5.87, –3.22] were consistent with main model in the study (4.70; 95% CI [–5.72, –3.69]. It is worth mentioning that the beta coefficient strengthens, but the change is not significant Supplementary table - 3. 145 Table 5-7. The association between physical activity and RHR in healthy heart program participants with known Lp(a) levels Predictors β estimates 95% CI P-value Model 1 (Lp[a] as continuous variable) Physical activity (IPA vs NIPA) –5.39 (–5.61, –2.97) <0.001 Log (Lp[a]) 1.36 (0.91, 1.81) <0.001 Age, years 0.11 (0.05, 0.16) <0.001 Blood pressure sBP, mmHg dBP, mmHg 0.07 0.18 (0.03, 0.09) (0.10, 0.22) <0.001 <0.001 Sex (Men vs Women) –3.78 (–5.18, –2.38) <0.001 Diabetes mellitus (Present vs Absent) 4.05 (1.87, 6.24) <0.001 Hypertension (Present vs Absent) 0.20 (0.11, 1.21) <0.01 HDL-C –1.31 (–2.33, – 0.30) 0.011 Non-HDL-C 1.06 (0.71, 1.40) <0.001 Smoking status (Former vs Never) (Current vs Never) 1.82 0.50 (–0.02, 3.35) (–1.73, 2.58) 0.052 0.690 Changes in medications –0.67 (–2.77, 1.42) 0.530 146 Predictors β estimates 95% CI P-value Employment (Retired vs Unemployed) (Part time vs Unemployed) (Full time vs Unemployed) 1.5 1.81 0.80 (–0.14, 4.21) (0.03, 3.70) (–2.10, 3.55) 0.10 0.80 0.45 Education (Post-secondary vs High school) (Post grad vs High school) 0.14 0.29 (–0.95, 0.1.75) (–1.84, 1.56) 0.47 0.39 Visits (V1 vs V2) –2.73 (–3.73, –1.72) <0.001 Model 2 (PA and Lp[a] as dichotomous variables) Physical activity (IPA vs NIPA) –4.37 (–5.69, –3.05) <0.001 Lp(a) (<300mg/L vs ≥300 mg/L) –3.86 (–5.69, –3.05) <0.001 Age, years 0.10 (0.04, 0.15) <0.001 Blood pressure sBP, mmHg dBP, mmHg 0.06 0.15 (0.03, 0.09) (0.10, 0.20) <0.001 <0.001 Sex (Men vs Women) –3.75 (–5.15, –2.35) <0.001 Diabetes mellitus (Present vs Absent) 4.05 (1.87, 6.24) <0.001 147 Predictors β estimates 95% CI P-value Hypertension (Present vs Absent) 0.20 (0.11, 1.21) <0.01 HDL-C, mmol/L –1.31 (–2.33, – 0.30) 0.011 Non-HDL-C, mmol/L 1.05 (0.71, 1.40) <0.001 Smoking status (Former vs Never) (Current vs Never) 1.76 0.43 (–0.02, 3.35) (–1.73, 2.58) 0.052 0.69 Changes in medications –0.15 (–2.12, 1.09) 0.53 Employment (Retired vs Unemployed) (Part time vs Unemployed) (Full time vs Unemployed) 1.97 1.84 0.72 (–0.14, 4.21) (0.03, 3.70) (–2.10, 3.55) 0.10 0.80 0.45 Education (Post-secondary vs High school) (Post grad vs High school) 0.25 -0.14 (–0.95, 0.16) (–1.84, 1.56) 0.50 0.42 Visit type (Initial vs 3rd year visit) –2.73 (–3.73, –1.72) <0.001 Lp(a); lipoprotein(a), RHR, resting heart rate; sBP, systolic blood pressure; dBP, diastolic blood pressure; PA; physical activity adjusting for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type. 148 5.5 Discussion First and foremost, the data reported here showed for the first time that atherogenic Lp(a) levels was significantly associated with RHR after adjusting for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type in the HHP lipid clinic population. The results extend previous studies that showed positive association between other blood cholesterol parameters and RHR [387] or blood pressure [388] by showing that RHR and blood pressure are independently associated to non-HDL-C levels. The retrospective cross-sectional study design is not well suited for making inferences about cause and effect; nevertheless, such data can provide relative support to different hypotheses about causes and/or mechanisms. It seems unlikely that serum lipid levels in itself modulates heart rate because reducing the heart rate with B-adrenergic blocking agents may alter serum lipid levels in the opposite direction to what is shown here [387]. It is more likely that a faster heart rate is a consequence of high blood lipid levels such as Lp(a) or that heart rate and blood lipids are related through common underlying factors or both [389]. One possibility is the relationship between heart rate and Lp(a) is dependent on alterations in pulse wave transmission as a consequence of early atherosclerotic stiffening and intimal thickening of the arterial tree [387,390,391]. Another possibility involved the relationship between Lp(a) and LDL in manipulating catecholamine levels and manipulating RHR [392]. PA has been described as "an agent with lipid-lowering, antihypertensive, positive inotropic, negative chronotropic, vasodilating, diuretic, negative chronotropic, vasodilating, anorexigenic, weight-reducing, cathartic, hypoglycemic, tranquilizing, hypnotic and anti-depressive qualities" [393,394]. However, serum Lp(a) levels are largely genetically determined and appear to be resistant to PA, and most interventions (e.g., diet and lipid-lowering medication) 149 are known to alter other lipoproteins. This highlights the need to focus on other fitting outcomes, such as RHR and blood pressure, and improve lipid profiles for patients with high Lp(a) levels. Indeed, elevation of both LDL-C and Lp(a) levels in the blood acts synergistically to increase CVD risk [356]. Attention should focus on effective means of decreasing RHR and blood pressure, increasing HDL-C (e.g., by improving PA) and lowering LDL-C (e.g., by dietary changes or lipid-lowering drugs) and their associated apolipoproteins in the blood. This study is the first to address the relationship between RHR and PA in patients with high Lp(a) levels. We were able to show that patients with high Lp(a) who choose to improve their PA were more likely to lower their RHR, and sBP significantly, hence decreasing their CVD risk tremendously even though PA does not cause Lp (a) to fluctuate more than 10% from baseline [382]. Consistent with other studies, a linear regression analysis of our data showed a β estimate for RHR of -11.35 bpm, which means that our patients were more likely to lower their RHR by 5.39 bpm on average when they improved their PA after adjusting for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type. 5.5.1 Study limitations The results of this study should be interpreted in light of its limitations. To begin, the study was carried out retrospectively, which could have influenced the accuracy and completeness of the data extracted from medical records. Second, our inclusion and exclusion criteria were stringent. A large number of charts were excluded due to a lack of RHR and exercise data, the use of HR-altering medications, patients who were new to our clinic or whose engagement lasted less than three years, and patients with cardiac problems that could affect RHR values. Furthermore, 150 because we were unable to use a validated PA measure for our PA data, we were forced to extrapolate from the physicians' reports. As a result, patients may exaggerate their reporting of their PA. This probably led to misclassification and additional social desirability bias. RHR also is unquestionably a variable that can fluctuate in a range of different of pathophysiological circumstances prior to patients presenting to our clinic. Another potential limitation of our study is the RHR measurement accuracy, which is due to the various methodologies used. 151 Chapter 6: The association between physical activity and carotid artery intima-medial thickness and total plaque area in healthy subjects with known Lp(a) levels: Results of the Multicultural Community Health Assessment Trial (M-CHAT) 6.1 Introduction Elevated circulating levels of Lp(a), particularly extremely high levels, are considered an independent risk factor for CVD [395]. Since the discovery of Lp(a), many basic researchers and clinicians have dedicated their work to explaining the hidden mechanisms that lead Lp(a) to cause atherosclerosis [356]. The duality and uniqueness of Lp(a), which makes this lipoprotein homologous to both LDL-C and PLG, most likely underlie the different but related atherosclerosis and thrombosis mechanism theories [356]. Most importantly, Lp(a) transmits all of the harmful atherogenic characteristics of LDL units, incorporating their tendency to oxidize before and after entry into the subintimal layer of the vessel walls and creating extremely proinflammatory oxidized Lp(a) [OxLp(a)] [114]. In fact, basic medical researchers and clinicians consider Lp(a) to be far more dangerous than LDL due to the presence of the apo(a) component within Lp(a). The cIMT and total plaque area, which represents the combined value of both cIMT and the plaque area, are among the best accepted surrogate sonographic measurements, reflecting not only local but also generalized atherosclerosis associated with cardio- and cerebrovascular morbidity and mortality, even in healthy and asymptomatic individuals [395,396]. These surrogate 152 indices of the carotid artery are very important because of the different mechanisms by which Lp(a) is involved in diffuse intima-medial thickening and atherosclerotic plaque formation [397]. PA has been shown to lessen the risk of developing numerous diseases, including CVD. This protective effect has been attributed to improvements in the lipid profile, RHR, and blood pressure and is associated with a decrease in the risk of related comorbidities, including type 2 diabetes mellitus and obesity [398,399]. However, the evidence from both cross-sectional and interventional studies in adults suggests that PA does not influence Lp(a) levels in the blood [382]. Since elevated levels of both LDL-C and Lp(a) in the blood exert a synergistic effect on CVD risk and there is no approved medication to lower Lp(a) levels directly, attention should focus on altering lifestyle factors to reduce CVD risk related to Lp(a). The purpose of the present study was to evaluate the impact of PA on cIMT and total plaque area in healthy subjects with elevated Lp(a) levels. We hypothesize that those with the lowest PA levels at baseline and highest Lp(a) concentrations will have the most significant increase in cIMT, and total plaque area compared to subjects with higher PA levels in the Multicultural Community Health Assessment Trial (M-CHAT) multiethnic population. 6.2 Methodology 6.2.1 Study design and setting Participants recruited for the M-CHAT trial were included in the present investigation. The MCHAT trial which was designed to compare body fat distributions in four different ethnic populations [400]. This study used a prospective observational design and was conducted between 2004 and 2005 in a tertiary care university-affiliated hospital in Vancouver, British Columbia, Canada. This study conforms to the guiding principles of the Declaration of Helsinki. All 153 participants provided informed consent. This study was approved by the Simon Fraser University Research Ethics Board. 6.2.2 Study population Healthy men and women (aged between 30 and 65 years) with exclusive ancestry of Aboriginal (reserve and nonreserve residents), Chinese (China, Hong Kong, and Taiwan), European (continental Europe, Ireland, and the United Kingdom), or South Asian (Bangladesh, India, Nepal, Pakistan, and Sri Lanka) origin were potential candidates and were screened for eligibility. Ethnicity was determined by participant self-report. Subjects who had recent weight change (>2.2 kg in 3 months), a previous diagnosis of CVD or significant comorbidity (such as HIV, immunocompromised condition, type 1 diabetes mellitus), or significant prosthetics or amputations were excluded. Those who were currently taking medications for CVD risk factors (i.e., lipid-lowering, antihypertensive, or hypoglycemic treatments) were also excluded to adequately adjust for the physiologic relation between risk factors and atherosclerosis. Because of the high prevalence of mixed ethnic origins in Aboriginal populations, subjects with at least 3 grandparents of exclusive Aboriginal origin were included. 6.2.3 Study procedure Participants were assessed for sociodemographics, medical history, and family history of CVD and type 2 diabetes mellitus (occurrence in parents or siblings at any age) according to a standardized interview. BMI was calculated as weight in kilograms divided by height in meters squared. Waist circumference (WC) was the average of 2 measurements taken against the skin at the point of maximal narrowing of the waist. Hip circumference was the average of 2 154 measurements taken over undergarments at the point of maximal gluteal protuberance. The waist-hip ratio (WHR) was calculated by dividing waist circumference by hip circumference. 6.2.4 Physical activity (main independent variable) Leisure time PA was reported as hours per week and assessed by the Modifiable Activity Questionnaire, which has been used in multiethnic populations [400–402]. Participants were given a list of leisure time PA choices to select from, and they were asked to recall how often and for how long they engaged in each activity over the course of a year. Domestic chores including cleaning, yard work, and shovelling were included in leisure-time PA, as well as recreational and sports activities like jogging, yoga, soccer, and tennis. Non-listed activities were reported in the other category. In the following section of the questionnaire, subjects were asked to estimate the frequency and duration of each of the mentioned activities. Additionally, questions were asked on television and video viewing, time spent confined to a bed or chair due to illness, and level of function ability, such as walking for ten minutes without assistance. Minutes per week for all activities were summed then to determine total leisure minutes per week averaged over the past year. The sum of moderate and vigorous physical activity was computed to represent the total number of minutes of PA per week. 6.2.4.1 Study groups For individuals to be classed as active, they needed to perform an average of at least 150 minutes/week (min/week) of PA based on the WHO PA classification [403]. Thus, the study population was divided into three main tertile groups. The inactive or less active group was sedentary or performed < 150 minutes/week of moderate to vigorous PA. The moderately active 155 group participants were able to be moderately physically active (≥150 minutes/week to < 352 minutes/week). Finally, the more active group was defined by ≥ 352 minutes/week of moderate to vigorous PA. 6.2.5 Carotid artery measurements for cIMT and total plaque area (main dependent variables) Carotid artery ultrasound scans were performed for each participant with a 10-MHz linear-array transducer as previously described [404,405]. The cIMT was assessed by measurement over a uniform length of 10 mm in the far wall of the right and left common carotid arteries within 2 cm proximal to the carotid bulb. The region with the thickest IMT, excluding areas with focal lesions, was measured. The average IMT was calculated from the right and left IMT measurements [397]. All focal plaques within the carotid tree (common, internal, and external carotid arteries and bulb) identified as wall thickness that was increased compared with the surrounding IMT were measured. The area of each plaque was calculated as the average lesion thickness (in mm) multiplied by the lesion length (in mm). In those participants with multiple plaques, plaque area was the sum of the areas of all plaques observed in the carotid tree. Total area, a superior correlate of risk factors and a better prognostic indicator than IMT alone [397,404,405], was calculated as the sum of the IMT area and plaque area. The IMT area (in mm2) was calculated as the length (10 mm) multiplied by the average IMT for the measured length. The areas of both the right and left IMTs were summed. The intraclass and interobserver correlation values for this method were 0.922 to 0.948 and 0.850 to 0.901, respectively [397,404]. For repeated measures in the same subject, the accuracy and precision were -0.001 mm and 0.04 mm, respectively, for an IMT of -0.21 mm² and total area of 3.61 mm² [397]. 156 6.2.6 Smoking status Smoking status was assessed by self-report and recorded as nonsmoker, former smoker, or current smoker. For former and current smokers, the average number of cigarettes per day and the number of years smoked were recorded. 6.2.7 Biochemical cardiac risk factors 6.2.7.1 Blood pressure Blood pressure was assessed in the left arm following 10 minutes of seated rest. Five measurements were taken in a five-minute period, with the average recorded, by using a BpTRU model BPM-200 oscillometric office blood pressure monitor (VSM MedTech Ltd., Coquitlam, British Columbia). 6.2.7.2 Lipid measures Fasting blood samples were collected and immediately processed for total cholesterol, HDL cholesterol, TG, apolipoprotein B, Lp(a), C-reactive protein, glucose, insulin, fibrinogen, and homocysteine. LDL-C was calculated with the Friedewald equation. All measurements were carried out in the same clinical laboratory at St. Paul’s Hospital with standard enzymatic procedures. The inter- and intra-assay precision of this laboratory meets the stringent criteria required by the Canadian Reference Foundation Laboratory. For the purpose of analysis, Lp(a) results were divided into low (<300 mg/L) and high (>300 mg/L) [371]. The cut-off point of 300 mg/L represented the 80th percentile of Lp(a) for our cohort. 157 6.2.8 Body composition assessment 6.2.8.1 Visceral adipose tissue Visceral adipose tissue (VAT) was measured by CT with a CTi Advantage scanner (General Electric, Milwaukee, Wis). A cross-sectional 10-mm slice at the L4/L5 intervertebral disc was obtained, and the attenuation range of –190 to –30 Hounsfield units was used to identify adipose tissue. Computation of surface areas from the CT scans was conducted with the use of SliceOmatic 4.2 medical imaging software (SliceOmatic v.4.2, Tomovision, Montreal, Canada). The total abdominal fat area was calculated as all pixels within the attenuation range, and VAT was defined as the area of adipose tissue within the inside edge of the abdominal wall. Subcutaneous abdominal adipose tissue (SAAT) is the difference between total abdominal adipose tissue and VAT [400]. 6.2.8.2 Total body fat Total body fat was assessed by dual energy X-ray absorptiometry with a Norland XR-36 scanner with Host Software Version 3.9.4 and Scanner Software 2.1.0 (Norland Medical Systems, White Plains, NY). Participants were instructed to remove all jewelry and metallic objects that could affect the scan results. The percent total body fat was calculated as total body fat in kilograms divided by total body mass in kilograms [400]. 6.3 Statistical analyses Descriptive statistics were used to summarize baseline and 3-year assessments of body composition and carotid ultrasound factors. Continuous variable characteristics are expressed as 158 the mean and (+/- SD) or median (min-max), whereas categorical and binary traits are expressed as percentages and frequencies. The following variables were not normally distributed and were (natural log) transformed before analysis: average IMT, plaque area, total area, VAT, SAT, TG, apolipoprotein B, Lp(a), C-reactive protein, glucose, insulin, and weekly PA. The results are presented as geometric means and 95% CIs. Categorical variables of interest were compared using the x2 test or Fisher’s exact test, as appropriate. Differences in our important variables, including IMT, plaque area, and total area, among PA tertiles were analyzed by 1-way ANOVA, to assess differences in the entire cohort between the initial and 3-year visits. Mixed-effects linear regression with random intercepts was used to assess the associations between PA and Lp(a) and each of the 2-dependent variables of interest, cIMT and total plaque area. Models were investigated to determine whether the relations differed by sex by using a sex × PA interaction term. The mixed effects linear regression models were adjusted for age, sex, Lp(a), smoking status, education level, income, and visit type. For independent variables that were not normally distributed, standardized logarithmically transformed variables were used in all regression models. Model adequacy was satisfied as assessed by residual plots. Moreover, our dependent variables were log transformed due to a very skewed distribution. Fully adjusted models were based on the 610 subjects who had complete data. All tests of statistical significance were two-tailed with a 95% CI; P < 0.05 was considered to indicate statistical significance. All statistical analyses were performed with the R-Studio software package (RStudio Inc., Boston, MA, USA). 159 6.4 Results 6.4.1 Study population Of the 802 participants from the M-CHAT trial in the study, 610 (76.2%) had a 3-year follow-up assessment. A comparison between participants included in and those excluded from the analysis showed that those excluded were more likely to be Aboriginal, have a lower income, be a current smoker at baseline, or have increased baseline VAT (p < 0.05). No differences were found with respect to age, sex, baseline BMI, cIMT or total plaque area. The baseline categorical characteristics of the study are displayed in Table 6-1. The study population was grouped based on their PA into less active, moderately active, and more active groups. There was no difference in mean age between groups (47± 8.6, 48 ±8.9, and 48 ±9.2, respectively). Moreover, all groups were also balanced in the female-to-male ratio, family history of CVD, BMI, and income. However, the ethnic representation and smoking status were significantly different between the study groups. Table 6-1. Baseline characteristics of study participants by physical activity (PA) status (N = 610). Less active (n=219) Moderately active (n=198) More active (n=193) P value Female, n (%) 113 (51.51) 105 (53.01) 92 (47.64) 0.54 Mean age, years, (SD) 47 (8.62) 48 (8.92) 48 (9.23) 0.75 Family history of CVD, n (%) 93 (42.41) 95 (47.91) 84 (43.51) 0.49 Ethnicity, n (%) <0.001 160 Less active (n=219) Moderately active (n=198) More active (n=193) P value Aboriginal Chinese European South Asian 31 (14.22) 69 (31.53) 44 (20.12) 75 (43.24) 28 (14.13) 52 (26.24) 53 (26.73) 65 (32.83) 36 (18.62) 45 (23.32) 84 (43.54) 28 (14.54) Current smoker, n (%) 20 (9.15) 13 (6.52) 16 (8.22) <0.001 Body mass index, n (%) <25 kg/m2 25–29.9 kg/m2 >30 kg/m2 61 (27.85) 61 (27.82) 97 (44.25) 70 (35.33) 53 (26.73) 75 (37.83) 74 (38.32) 45 (23.33) 74 (38.35) 0.25 Household income, n (%) <$20,000 >$60,000 $20,000–$30,000 $30,000–$40,000 $40,000–$50,000 $50,000–$60,000 Other 26 (11.83) 73 (33.34) 25 (11.46) 28 (12.74) 34 (15.56) 30 (13.64) 3 (1.30) 20 (10.13) 77 (38.85) 24 (12.13) 31 (15.66) 24 (12.14) 17 (8.56) 5 (2.50) 23 (11.93) 83 (43.00) 22 (11.34) 29 (15.00) 17 (8.84) 16 (8.23) 3 (1.50) 0.44 p-value determined from the Chi-square test or t test, where applicable 161 6.4.2 Baseline assessments of body composition and metabolic risk factors Table 6-2 displays the baseline assessments of body composition and metabolic risk factors. In unadjusted analysis, significant differences were observed among the 3 PA groups with respect to lipids, anthropometric measurements, body fat composition, blood pressure, and carotid artery ultrasound results. In the post hoc analysis, with Bonferroni correction for multiple comparison analysis, we found that the HDL-C levels were significantly higher in the more active group than in the less active group (p<0.001). However, the difference in the HDL-C levels between the moderately active and less active groups failed to achieve significance (P>0.05). Regarding the total-to-HDL C ratio, both the active and moderately active groups had significantly better values than the less active group. In addition, the more active group had a lower body fat percentage, total abdominal adipose tissue (TAAT), and VAT (P<0.001) than the other two groups. Other variables such as the lipid profile, FBG, blood pressure, and anthropometric measurements were not significantly different (P>0.05) from those of the moderately and less active groups. Regarding the baseline results of carotid artery ultrasound, the less active group had the highest percentage of plaques and total plaque area; however, this difference failed to reach our significance level (p>0.05). 162 Table 6-2. Laboratory data and body composition stratified by PA Less active (n=219) (%) Moderately active (n=198) (%) More active (n=193) (%) P for Overall comparison P for Moderately vs Less active P for More vs Less active Lipid profile TC, mmol/L 5.27 (1.02) 5.25 (0.93) 5.28 (1.03) 0.54 – – LDL-C, mmol/L 3.31 (0.91) 3.25 (0.83) 3.24 (0.91) 0.68 – – HDL-C, mmol/L 1.23 (0.32) 1.27 (0.33) 1.36 (0.41) <0.001 0.45 <0.001 TG, mmol/L 1.31 (0.92-1.9) 1.28 (0.92-1.72) 1.31 (1.07, 1.64) 0.27 – – TC/HDL-C 4.35 (1.41) 4.38 (1.39) 4.23 (1.56) <0.05 <0.01 <0.001 Apo B, g/L 1.02 (0.26) 0.99 (0.22) 1.01 (0.38) 0.41 – – Lp(a), mg/L 151 (54-289) 161 (60.2, 307.5) 111 (49, 260) 0.15 – – CRP, mg/L 1.3 (0.7- 3) 1 (0.8-3.2) 0.9 (0.6, 2.55) <0.05 1.00 <0.05 FBG, mmol/L 5.2 (4.9-5.5) 5.1 (4.9-5.22) 5.2 (4.9, 5.5) 0.64 – – Insulin, pmol/L 72 (50-105) 67.5 (47-97) 52 (38, 78) <0.001 <0.001 <0.001 163 Less active (n=219) (%) Moderately active (n=198) (%) More active (n=193) (%) P for Overall comparison P for Moderately vs Less active P for More vs Less active Fibrinogen, g/L 3.29 (0.6) 3.29 (0.66) 3.24 (0.61) <0.01 0.45 <0.001 Blood pressure sBP 118.8 (14.94) 118.7 (15.89) 116.5 (12.72) 0.19 – – dBP 77.68 (9.76) 77.76 (9.09) 76 (9.33) 0.11 – – Anthropometrics WC, cm 89.14 (12) 87.73 (12.58) 87.05 (12.43) 0.21 – – WHR 0.88 (0.08) 0.87 (0.09) 0.88 (0.89) 0.45 – – Body fat composition Body fat, % 34.64 (8.94) 33.9 (9.26) 30.84 (10.03) <0.001 0.53 <0.001 TAAT, cm2 432.7 (167.7) 402.55 (149) 363.73 (176.5) <0.001 <0.01 <0.001 SAT, cm2 118.70 (79.9-167.2) 111.10 (77.8-156.7) 102.66 (63.6, 151) <0.001 <0.01 <0.001 VAT, cm2 110.81 (88.33-145) 109.65 (80.9-146.8) 86.93 (60.3, 122) <0.001 0.61 <0.001 164 Less active (n=219) (%) Moderately active (n=198) (%) More active (n=193) (%) P for Overall comparison P for Moderately vs Less active P for More vs Less active Results of carotid artery ultrasound Plaques, % 45.2% (99) 42.9% (85) 48.7% (94) 0.51 – – cIMT, mm* 0.66 (0.59-0.74) 0.65 (0.58-0.73) 0.66 (0.59-0.73) 0.72 – – Total area, mm2* 16.60 (12.6-22.7) 16.30 (12.6-23.5) 16 (12.5-23.7) 0.91 – – Abbreviations: TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; FBG, fasting blood glucose; sBP, systolic blood pressure; dBP, diastolic blood pressure; WC, weight circumference; WHR, waist-hip ratio; TAAT, total abdominal adipose tissue; SAT, subcutaneous abdominal adipose tissue; VAT, visceral adipose tissue; cIMT, carotid intima-medial thickness; total area, total plaque area (cIMT + plaque area). Between-group differences were analyzed by ANOVA or the Kruskal-Wallis test, where applicable. Pairwise comparisons were analyzed by the Tukey or Dunn test; only data with P < 0.017 are reported, based on the Bonferroni correction for multiple comparisons. Medians; 25th and 75th percentiles in parentheses. 165 When stratified by Lp(a) tertiles, the change in total area was significantly higher in those with the highest levels of Lp(a) in the less active tertile Figure 6-1. There was no difference in cIMT across Lp(a) tertiles Figure 6-1. Overall, statistically significant associations between PA as a continuous variable and abnormal thickening of the cIMT were noted (Table 6-3). On average, for each standard-deviation increase in PA, we would expect a significant 1.9% standard deviation (SD) decrease in cIMT of the carotid artery (0.019 SD, 95% CI: –0.03 to –0.001, P<0.001) age, sex, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. The model also showed that compared with females, males had a SD increase in cIMT of 7% (0.07 SD, 95% CI: –0.04 to 0.09, P<0.01) on average age, PA, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. Remarkably, the model showed that South Asian and/or Aboriginal ethnicity had a protective effect in comparison with European ethnicity (–0.06 SD, 95% CI: –0.09 to –0.02, P<0.01 and –0.06 SD, 95% CI: –0.09 to –0.02, P<.001, respectively). Additionally, compared to never smokers, the participants classified as current smokers had a 3% SD increase (0.03 SD, 95% CI: 0.001 to 0.07, P<0.001). Finally, Lp(a) on average was responsible for 0.14% of the cIMT SD increase; however, it failed to reach our significance level (–0.0014 SD, 95% CI: –0.01 to –0.02, P<0.01). 166 Figure 6-1. Changes in atherosclerotic surrogate markers by physical activity and Lp(a) tertile for all follow-up periods. * P<0.05 less active vs. more active in Lp(a) tertile 3, †P<0.001 compared with tertile 1. ‡P<0.005 compared with tertile 2. Table 6-3. The association between physical activity and intima-medial thickness of the carotid artery in M-CHAT participants with known Lp(a) levels Predictors Estimates 95% CI P-value Physical activity –0.019 (–0.03, –0.001) <0.05 Lp(a) 0.0014 (–0.01, 0.02) 0.6 Sex (Men vs Women) 0.07 (0.04, 0.09) <0.001 Ethnicity (Chinese vs European) –0.01 (–0.04, 0.03) 0.7 167 Predictors Estimates 95% CI P-value (South Asian vs European) (Aboriginal vs European) –0.06 –0.06 (–0.09, –0.02) (–0.10, –0.02) <0.01 <0.01 Smoking status (Former vs never) (Current vs never) 0.03 0.03 (–0.03, 0.08) (0.001, 0.07) 0.3 <0.05 Visits (V1 vs V2) 0.05 (0.01, 0.05) <0.001 All dependent variables were log transformed. All independent variables were standardized in standard deviation units before analysis. All models adjusted to age, sex, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. All non-significant association were not reported in the table. 6.4.3 The association between PA and total plaque area We constructed mixed-effect linear regression models with random intercepts to examine the association between PA and total plaque area. Overall, statistically significant associations between continuous PA as a main predictor and total plaque area were noted (–0.04 SD, 95% CI: –0.08 to –0.001, P<0.01;Table 6-4) adjusted for to age, sex, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. The model also showed, on average, that for every 1 SD increase in Lp(a) levels, we expect an increase in total plaque area by 5% (0.05 SD, 95% CI: –0.01 to –0.02, P<0.001) adjusted for to age, sex, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. The model also showed that compared with females, males had a SD increase in total plaque area of 25% (0.25 SD, 95% CI: 0.14 to 0.35, P<0.001) on average adjusted for to age, sex, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. Compared with never smokers, the current smokers had a SD increase in total plaque area of 12% (0.12 SD, 95% CI: –0.02 to 0.10, P<0.01) on average adjusted for to age, sex, Lp(a), smoking status, 168 education level, income, and visit type. Moreover, compared with subjects identified as European or white, the subjects identified as Chinese, South Asian and Aboriginal had total plaque area SDs of – 21%, –32%, and –18%, respectively, to age, sex, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. These results were similar when PA and Lp(a) were expressed as dichotomous predictors. Table 6-4. The association between physical activity and the total plaque area of the carotid artery in M-CHAT participants with known Lp(a) levels Predictors Estimates 95% CI P-value Model 1 (PA and Lp[a] as continuous variables) Physical activity –0.05 (–0.08, –0.001) <0.01 Lp(a) 0.05 (0.01, 0.02) <0.01 Age 0.11 (0.08, 0.24) <0.001 Sex (Male vs Female) 0.25 (0.14, 0.35) <0.001 Ethnicity (Chinese vs European) (South Asian vs European) (Aboriginal vs European) –0.21 –0.32 –0.18 (–0.31, –0.10) (–0.43, –0.22) (–0.31, –0.06) <0.001 <0.001 <0.001 Smoking status (Former vs never) (Current vs never) 0.01 0.12 (–0.14, 0.16) (0.02, 0.21) 0.91 <0.05 Body fat composition Body fat percentage 0.07 (0.01, 0.12) <0.001 169 Predictors Estimates 95% CI P-value Visits (Initial visit vs 3rd year visit) 0.05 (0.04, 0.05) <0.001 Model 2 (PA and Lp[a] as dichotomous variables) Physical activity (Moderately vs less active) (More vs less active) –0.05 –0.14 (–0.14, 0.04) (–0.23, –0.04) 0.30 <0.05 Lp(a) tertiles (Middle vs high tertile) (Low vs high tertile) –0.10 –0.11 (–0.20, –0.01) (–0.21, 0.01) <0.05 <0.05 Age 0.11 (0.08, 0.24) <0.001 Smoking status (Former vs never) (Current vs never) -0.2 0.11 (–0.17, 0.21) (–0.30, 0.21) 0.81 <0.05 Sex (Male vs female) 0.23 (0.15, 0.30) <0.001 Ethnicity (Chinese vs European) (South Asian vs European) (Aboriginal vs European) –0.20 –0.31 –0.17 (–0.30, –0.09) (–0.42, –0.19) (–0.30, –0.05) <0.001 <0.001 <0.01 Visits (Initial visit vs 3rd year visit) 0.13 (0.11, 0.16) <0.001 All dependent variables were log transformed. All independent variables were standardized in standard deviation units before analysis. All models adjusted to age, sex, Lp(a), smoking status, education level, income, and 3rd year follow-up visit. All non-significant association were not reported in the table. 170 6.5 Discussion This study evaluated the longitudinal changes in cIMT and total area with PA and Lp(a) levels over three years in a multiethnic healthy cohort. The data reported here showed for the first time that healthy participants who were physically active had a statistically significantly lower adjusted beta estimate of abnormal carotid artery wall thickening after controlling for Lp(a) levels and other important risk factors. Moreover, we found that the rate of progression of or change in cIMT and total plaque area was lower in the more active group compared with the less active group and that these differences persisted after controlling for dichotomous Lp(a) tertiles and several lifestyle and biologic factors. This relation was graded, showing benefits from the less active to more physically active groups. Consistent with the current study, several studies reported an inverse correlation between cIMT and PA [406,407]. The results from our study support the literature by demonstrating the inverse association between PA and cIMT and total plaque area in multiethnic male and female adults. Given that we included participants with no history of CVD at baseline and adjusted for an extensive number of risk factors, such as Lp(a), ethnicity, and smoking status, our findings indicate that maintaining PA status is favorably associated with a lower burden of carotid atherosclerosis. Accordingly, adopting a physically active lifestyle may reduce CVD-related morbidity and mortality, as well as the cost of health care related to Lp(a). Lp(a) is a well-recognized risk factor for myocardial infarction and CVD, as discussed in a recent review [356]. The results of the present study confirm the value of Lp(a) as a predictive factor for CVD, as discussed in previous reports [408,409]. Our model showed that there is a strong association between Lp(a) levels and carotid atherosclerotic markers as assessed by carotid artery ultrasonography scan. Our mixed-effect linear regression model showed that for every 1 unit of 171 SD increase in Lp(a) levels, cIMT would exhibit a 0.14% SD increase; however, this increase did not reach our significance levels. On the other hand, our models also demonstrated that Lp(a) is a strong predictor for total plaque area, such that for every 1 SD increase in Lp(a) levels, the total plaque area would show a 5% SD increase. Our model showed that compared with subjects of European ethnicity, the participants of Chinese, South Asian and Aboriginal ethnicity had total plaque area SDs of – 21%, –32%, and –18%, respectively, adjusted for PA, Lp(a), sex, smoking status, and body fat composition variables. Our results support other important reports indicating that among healthy subjects, European ethnicity has a significantly stronger association with atherosclerotic surrogate markers than Chinese and South Asian ethnicity [410]. However, among CVD patients, South Asian ethnicity had a higher prevalence of CVD than either European or Chinese ethnicity, and European subjects tended to have a higher prevalence than Chinese individuals [410]. Perhaps the higher number of lipid and glucose abnormalities described in South Asian populations not only might be correlated with the degree of atherosclerosis but also could lead to reduced stability of atherosclerotic plaques. This possibility combined with a greater prothrombogenic environment because of higher concentrations of prothrombotic factors could partly explain why South Asian individuals have more CVD events for any given amount of atherosclerosis than people of other ethnic groups [410–412]. Our study adds more value to the pool of previous work done by others on the association between Lp(a) and atherosclerosis, which has been reviewed in detail previously [114,356]. First, unlike other investigators who worked on identifying whether PA would change Lp(a) levels [74,90,413], we examined the association between PA and atherosclerotic surrogate markers in low, middle and high Lp(a) tertiles. We also analyzed the relation between Lp(a) and measures of 172 atherosclerotic plaque, which have a prognostic ability superior to that of cIMT alone. Second, carotid ultrasonography was used to evaluate CIMT, which is a direct measure of atherosclerosis and is highly sensitive and interconnected with early stages of coronary atherogenesis [397]. Third, whereas other studies reported associations between Lp(a) and atherosclerosis, our investigation considered body fat composition variables that are not included in previous reports. Fourth, the M-CHAT cohort consisted of healthy men and women who were free from CVD and did not use medications for CVD risk factors. Although our recruitment strategy may have resulted in a selection bias, it is unlikely that this bias had an effect on the variables of primary interest, namely, Lp(a) and carotid atherosclerosis. Fifth, our study is strengthened because the included M-CHAT participants are ethnically heterogeneous. Our results are therefore applicable to the multiethnic populations seen in Canada. Other reports indicate that patients should be of the same ethnicity, as different ethnicities have their own cutoff points. The M-CHAT trial cohort was purposely recruited from individuals with a wide range of BMI values and was not a random population sample because the M-CHAT study was designed to investigate differences in body fat distribution. The M-CHAT trial selection was very strict, which makes statistically significant differences more challenging to demonstrate. However, this lends credibility to the results. 6.5.1 The limitations of the study The study's findings should be viewed in light of the study's limitations. Our M-CHAT study participants were purposefully recruited to represent a range of BMI values and ethnicities, limiting generalizability to the general population, and perhaps introducing selection bias. However, the sample allows for generalizability to a diverse range of ethnic groups and body sizes. Additionally, the current study contained a sub-sample of 75% of the original M-CHAT population 173 due to missing data and loss and follow-up. Furthermore, the study's reliance on self-reported one-year recollection of PA measurements is another limitation, since individuals frequently overestimate PA due to social desirability bias. Additionally, recollection of vigorous PA is generally higher than that of moderate PA. Finally, our analysis is limited using Lp(a) dichotomous tertiles. The assumption that risk is comparable within these tertiles is often inappropriate, especially for highly skewed variables such as Lp(a). 174 Chapter 7: Conclusion Elevated Lp(a) levels are inherited and were first described more than 50 years ago as a binary (Lp+, Lp-) genetic trait. However, we now know that an elevated Lp(a) concentration is a quantitative genetic trait manipulated mainly through the LPA gene located on chromosome 6. Epidemiological and clinical studies indicate that there is a strong independent link between elevated Lp(a) concentrations > 300 mg/L and an increased risk of atherosclerosis, but the molecular association between these conditions is far from clear. Reducing LDL-C levels through a healthy diet and exercise or extensive statin treatment does not significantly reduce Lp(a) levels. Moreover, there is no approved drug on the market that directly reduces Lp(a) levels. Thus, the overall goals for this research work were to deliver a broad investigation into the association between Lp(a) and RHR, blood pressure, and arterial wall thickness in patients with high Lp(a), as well as the association between PA, used either in a standard cardiac rehabilitation program and/or as a routine at home, with these parameters. RHR, a clinical parameter regularly examined in a hospital setting, has been shown to be an element of myocardial ischemia and a risk factor for CVD mortality. Despite the recent experimental and epidemiological confirmation of a link between PA, RHR, and other atherosclerotic surrogate markers such as cIMT and plaque area, which are also strong predictors of cardiovascular morbidity and mortality, there has been no investigation into this relationship in patients with high Lp(a). PA is known to reduce RHR, delay atherosclerotic lesion progression and as a result, have a beneficial impact on overall cardiovascular health; thus, understanding whether PA will affect RHR and arterial wall thickening in patients with high Lp(a) can serve as a basis for enhanced cardiac risk stratification and higher efficacy of cardiac rehabilitation 175 programs in this population. The main findings obtained in this research work are summarized below, along with the strengths and limitations of the research methodology. Finally, the importance of this subject and potential future directions are presented along with the overall conclusions. 7.1 Overview of major findings 7.1.1 Improved physical activity is associated with lower resting heart rate and improved lipid profile among primary and secondary prevention patients (Chapter 3) Before proceeding to the main studies related to Lp(a) of this dissertation, a one-step approach was taken to refine the boundaries of our research work. Thus, we started our research work by developing an idea to explore and evaluate the efficiency of our HHP, driven by the fact that frequent PA is a critical behaviour for HHP patients and general population health [355]. The working hypothesis was that participants attending the HHP program prevention clinic who increased their PA between their initial and third year visits would be more likely to have lower RHR compared to those who did not improve. Chapter 3 complements the emerging body of literature showing significant benefits of frequent and moderate [354]-to-vigorous intensity PA for improving health [366] and highlights that exercise more than four times a week with moderate-to-high intensity is a promising PA strategy to improve measures of cardiovascular structure and function for prevention clinic patients. The data in chapter 3 showed that our HHP improved almost half of its patients by lowering their RHR, sBP and dBP; they also showed that a relatively high amount of regular exercise, even in the absence of clinically significant weight loss in some of the subgroups, can significantly be associated with significantly better lipid profile. In particular, the data reveal that changing PA and 176 exercise by one or more points more than four times per week and an intensity equivalent to that of jogging at a moderate pace significantly associated with lower FRS. This amount of PA also was associated with higher HDL concentration and lower TG, non-HDL-C, and TC/HDL-C, which is consistent with other important reports [351]. Remarkably, RHR significantly increased by 1.8 bpm (2.2%) in the NIPA group. However, there were no significant differences in the other risk factors, likely because the drug treatment was working. Additionally, there was a trend of weight loss, when measured annually, in the IPA group compared to the general Canadian population (this trend occurred even for the participants in the NIPA group). Interestingly, after conducting further analysis to compare our important subgroup, men had significantly lower RHRs at the initial and last visits (P<0.05) than women. Men also managed to lower their dBP by 3.6 mmHg (3.6%, P<0.01). However, both men and women had an identical response regarding RHR and sBP after improving their PA. Remarkably, women had a worse lipid profile to begin with, which may be related to the fact that the women in the IPA group were older than the men. 7.1.2 The influence of lipoprotein(a) on the prevalence of cardiovascular disease risk factors and cardiovascular events among patients in the Healthy Heart Program (Chapter 4) We performed a retrospective cohort study to examine the prevalence of CVD risk factors and evaluate Lp(a) as an independent risk factor for hard CVD outcomes in our multiethnic HHP population. Our study is the first to compare hemodynamic parameters among patients with extreme Lp(a) levels. Chapter 4 also investigates the differences in risk factor presentation between patients with and without positive CVD findings in the HHP population. The first important 177 finding in our study is the difference in RHR between the high and normal Lp(a) cohorts. RHR in the high Lp(a) group was significantly higher than that in the lower Lp(a) group. This could be an early sign of progression of atherosclerosis and CVD mortality [338–340] due to high Lp(a) levels. Although there was no difference in the prevalence of HTN between the high and normal Lp(a) groups sBP and dBP were remarkably higher in the high Lp(a) group than in the normal Lp(a) group. Our data suggest that elevated Lp(a) levels may be involved as cofactors in essential HTN, which is consistent with other reports [373]. Additionally, the high Lp(a) group had higher fasting blood glucose (6.8 mM) than the normal Lp(a) cohort (5.2 mM). There was no significant difference in calculated FRS between the cohorts, which may suggest Lp(a) should be considered in the calculation of the 10-year CVD risk score given that high Lp(a) levels were significantly associated with CVD events compared with normal Lp(a) levels in our study. Male sex, Lp(a), LDL-C, and the non-HDL ratio were significant independent risk factors for future CVD, in decreasing order of importance. Associations of smoking and HTN with increased HR were found but failed to reach statistical significance. We conclude Lp(a) is a clinically important independent predictor of CVD in our multiethnic HHP population. However, the possible mechanism is still not straightforward. Additionally, we do not know what physiological events lead to CVD. Thus, we recommend that Lp(a) be assessed at the time of presentation and considered along with other CVD risk factors in the management of CVD prevention patients. Moreover, further research may be warranted to determine whether therapeutic intervention aimed at lowering Lp(a) could alter RHR, HTN, and CVD. 178 7.1.3 The association between physical activity and resting heart rate in patients with high Lp(a) (Chapter 5) The working hypothesis in this chapter was patients with high Lp(a) would show higher RHR at baseline. Also, patients with high Lp(a) levels who improved their PA are more likely to lower their RHR significantly in comparison to non-improved PA patients. First and foremost, the data reported in chapter 5 showed for the first time RHR was significantly associated with atherogenic Lp(a) levels after adjusting for blood pressure, age, sex, non-HDL-C, and PA status in the HHP lipid clinic population. The results extend previous studies that showed positive correlations between other blood cholesterol parameters and RHR [387] or blood pressure [388] by showing RHR and blood pressure are independently related to non-HDL-C levels. The retrospective study design is not well suited for making inferences about cause and effect; nevertheless, such data can provide relative support to different hypotheses about causes and/or mechanisms. It seems unlikely that heart rate modulates serum lipid levels because the reduction in heart rate by B-adrenergic blocking agents may alter serum lipid levels in the opposite direction to what was shown here [387]. It is more likely that a faster heart rate is a consequence of high blood lipid levels such as Lp(a), that heart rate and blood lipids are related through common underlying factors or both [389]. One possibility is that the relationship between heart rate and Lp(a) is dependent on alterations in pulse wave transmission as a consequence of early atherosclerotic stiffening and intimal thickening of the arterial tree [387,390,391]. Another possibility involves the relationship between Lp(a) and LDL-C in manipulating catecholamine levels and thus manipulating RHR [392]. 179 Chapter 5 highlights the need to focus on other fitting outcomes, such as RHR, blood pressure, and improvements in lipid profiles for patients with high Lp(a) levels. Indeed, elevated levels of both LDL-C and Lp(a) in the blood synergistically increase CVD risk [356]. This study is the first to address the relationship between PA and RHR in patients with high Lp(a) levels. We were able to show that improved PA in patients with high Lp(a) is associated with lower RHR, sBP, and dBP significantly, thus lesser CVD risk, even though PA does not cause Lp(a) to fluctuate more than 10% from baseline [382]. Consistent with other studies, our data showed, in a linear regression analysis, a β estimate for RHR of -5.35 bpm, which means that our patients were able to lower their RHR by 5.35 bpm on average when they improved their PA after adjusting for age, sex HTN, DM, education level and employment status. Therefore, the overall conclusions of Chapters 5 were that patient with high Lp(a) had elevated RHR, which could signal an increased risk of myocardial infarction injury. These findings represent a significant contribution because they (i) indicate RHR as a convenient clinical tool for estimating the risk of high levels of Lp(a) in patients attending a CVD prevention and cardiac rehabilitation program and (ii) raise awareness for carefully adjusting exercise instructions and counseling after considering elevated RHR. Moreover, this study justified our interest in further exploring the potential of RHR for improving cardiac risk stratification and evaluating the efficacy of the HHP in controlling CVD in patients with high Lp(a). 7.1.4 The association between physical activity and carotid artery intima-medial thickness and total plaque area in healthy subjects with known Lp(a) levels (Chapter 5) This study evaluated the longitudinal changes in cIMT and total plaque area with PA and Lp(a) levels over three years in a healthy multiethnic cohort. The data reported here showed for 180 the first time that healthy participants who were physically active showed a statistically significant association with adjusted beta estimate of abnormal carotid artery wall thickening after controlling for Lp(a) levels and other important risk factors. Moreover, we found the rate of progression of or change in cIMT, and total area was decreased in the more active group compared with the less active group and that these differences persisted after controlling for dichotomous Lp(a) tertiles and several lifestyle and biologic factors. This relation was graded, showing benefits from the less active to more physically active groups. The results from our study support the literature by demonstrating the inverse link between PA and cIMT and total plaque area in multiethnic male and female adults. Given that we included participants with no history of CVD at baseline and adjusted for an extensive number of risk factors, such as Lp(a), age, sex, ethnicity, and smoking status, education level, income, our findings indicate that maintaining PA status is favorably associated with a lower burden of carotid atherosclerosis. Accordingly, adopting a physically active lifestyle may reduce CVD-related morbidity and mortality, as well as the cost of health care related to Lp(a). Our model showed that compared with subjects of European ethnicity, the subjects of Chinese, South Asian, or Aboriginal ethnicity had a total plaque area SD of – 21%, –32%, and –18%, respectively, adjusted for PA, Lp(a), age, sex, ethnicity, and smoking status, education level, and income. Our results support other important reports indicating that European ethnicity has a significantly stronger association with atherosclerotic surrogate markers in healthy subjects than Chinese or South Asian ethnicity [410]. 181 7.2 Strengths and limitations The strengths of this dissertation lie in its clinical relevance and strong methodological component. RHR is a vital sign routinely checked in clinical and cardiac rehabilitation settings but is often not used to its full clinical potential. Exploring the influence of PA on RHR and arterial wall thickness is a novel approach in CVD prevention and cardiac rehabilitation for patients with high levels of Lp(a). Moreover, the results of the projects included in the present dissertation could be of use to CVD prevention and cardiac rehabilitation specialists in their clinical practice for better prescription and monitoring of PA and exercise training in the CVD population with high Lp(a). Our findings are supported by the reliability and validity of our self-reported PA methods [414]. Additionally, our studies are strengthened by the ethnic diversity of the people involved in this research. Our results are therefore applicable to the multiethnic populations seen in Canada. Additionally, unlike other investigators who worked to identify whether PA would change Lp(a) levels [74,90,413], we examined the association between PA, RHR and, atherosclerotic surrogate markers in different Lp(a) level strata. In chapter 6, carotid ultrasonography was used to evaluate cIMT, a direct measure of atherosclerosis that is highly sensitive and interconnected with early stages of coronary atherogenesis [397]. We also analyzed the relation between Lp(a) and measures of atherosclerotic plaque, which have a prognostic ability that is superior to that of cIMT alone. The M-CHAT trial selection was meticulous, which makes statistically significant differences more challenging to demonstrate. However, this lends credibility to our results. Several limitations and caveats need to be carefully considered in the interpretation of this research work. First and foremost, the studies in chapters 3-5 had a retrospective design, which might have introduced bias related to the accuracy and completeness of data from the medical charts. Second, our inclusion and exclusion criteria were strict. Thus, we excluded a large number 182 of charts because of missing RHR and exercise data or HR-altering medications. Third, charts were also excluded because patients were new or the duration of enrollment in our clinic was less than three years. Fourth, we also excluded patients with cardiac conditions, ventricular paced rhythm, left bundle branch block and left ventricular hypertrophy with repolarization abnormalities known to alter RHR values. Furthermore, RHR is a parameter that can undoubtedly change under various pathophysiological circumstances before patients come to our clinic. For example, stress, pain or seasonal influences on the cardiovascular system can impact these results. These aspects also require further investigation. One of the critical limitations of Chapters 3-5 is referral and ascertainment bias; patients were selected from a specialty lipid clinic. Regarding PA in chapter 3 and 5, we were unable to utilize a validated measure of PA and had to extrapolate from the physician notes. This might result in misclassification (random error) and added bias due to patients' over-reporting their level of PA due to social desirability bias. In large cohort studies like M-CHAT trial, self-report questionnaires are frequently employed to assess PA levels. As a result, compelling evidence demonstrating that PA helps protect one's health is widely accepted. However, validation studies utilizing objective measurements of PA or energy expenditure (diamond-labeled water, accelerometers, pedometers, etc.) reveal that survey methodologies have a lower degree of accuracy and precision. PA questionnaires may underestimate low-intensity physical activity in particular. They place an excessive emphasis on voluntary exercise (i.e., biking, running, and walking), while neglecting to include activities of daily living and low to moderate intensity movements. Also, the self-report of a one-year recall of PA is another limitation, as individuals tend to overestimate PA due to social desirability bias as previously mentioned. Despite the limitations of self-reported PA, PA questionnaires are useful for identifying conditions in which an increase in PA would be advantageous for monitoring 183 population activity trends. Attempts to interpret the data more precisely in terms of exercise dosage and the magnitude of the ensuing health benefits appear premature. Such usage of self-reported PA may become conceivable, however, with the addition of standardised devices capable of recording the low-intensity activities of sedentary societies and ascribing a uniform biological meaning to terminology such as light, moderate, and severe exercise. 7.3 Significance and future directions The major significance of this dissertation work is represented by the novel information brought to the field of lipids and cardiac rehabilitation with regard to its potential to delay, control or prevent CVD risk in patients with high Lp(a). By studying RHR and arterial wall thickness, which are two CVD risk factors that frequently exist in patients with CVD, we amplified our understanding of their methodological strengths and limitations in the high Lp(a) population and their susceptibility to PA. The results of our study will be of assistance to researchers in planning future clinical controlled trials involving similar measures. Our findings will assist them in making appropriate decisions related to sample size, controlling confounding factors, and posing new questions related to hemodynamic physiopathology in patients with high Lp(a). Additionally, these findings raise awareness among clinicians working in lipid and cardiac rehabilitation settings about the beneficial association of PA with decreasing the progression of arterial thickness and lowering RHR. An important future direction for this research work will include evaluating the association among arterial wall thickness, arterial stiffness, and RHR and the modulatory effect of resistance training prescribed in cardiac rehabilitation programs on patients with persistently elevated Lp(a) levels in a randomized controlled setting. Another possible future direction from this research 184 would involve studying the relationship between RHR and arterial wall thickness variables before and after the cardiac rehabilitation program and non-invasive cardiac ischemic tests in a randomized controlled trial. Overall, patients with high Lp(a) levels, clinicians, and even physiotherapists can benefit from the information resulting from this dissertation work, which is aligned with the current trend of optimizing healthy heart-inspired programs through a better understanding of the potential of improving PA to reduce CVD risk. 7.4 Overall conclusion PA remains a pressing public health issue. Technology and economic incentives tend to discourage activity, technology by reducing the energy needed for activities of daily living and economics by paying higher wages for sedentary work than active work. Thus, one of the main goals of the HHP program and its three major clinics and facilities is to promote improving PA and exercise. This is driven by the fact that increasing PA and exercise on a regular basis decreases CVD risk, especially by decreasing the 10-year FRS score [359]. The exercise regimens may differ, but the simplest is to recommend 30 minutes or two 15-minute periods of brisk walking or any type of moderate intensity exercise the patient likes daily. In fact, the HHP promotes the 0-5-30 motto: zero for no smoking, five portions of vegetables and/or fruits daily, and thirty minutes of daily moderate intensity exercise. This recommended amount of PA is in addition to routine activities of daily living of light intensity (e.g., cooking, casual walking or shopping) or those lasting less than 10 minutes in duration (e.g., walking around the building or picking up groceries, walking from or to the parking lot) [355]. 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Reimers AK, Knapp G, Reimers CD. Effects of exercise on the resting heart rate: a systematic review and meta-analysis of interventional studies. Journal of clinical medicine. 2018 Dec;7(12):503. 242 Appendices Appendix A Different sensitivity analyses were performed to assess the robustness of the association between PA and the RHR in chapter 3, with the following additional adjustments: First, 3.9 percent of the study population, who were more active at baseline and remained so throughout the three-year follow-up period and were initially included in the NIPA group, were transferred to the IPA group for sensitivity analysis (Supplementary table - 1). Second, A sensitivity analysis were performed after excluding patients with comorbid conditions such as diabetes, hypertension, and cardiovascular disease. Third, alcohol consumption was incorporated into the main model. Fourth, age groups and types of prevention were added to the model. Fifth, the full model was constructed according to sex subgroups. Finally, the full model was also conducted according to the type of prevention subgroups to examine the effect of clinical and pre-existing illnesses that could influence the initial findings. All of sensitivity analyses were summarized in Supplementary table - 2. 243 Supplementary table - 1 Predictors Estimates 95% CI P-value Physical activity (IPA vs NIPA) –4.70 (–5.72, –3.69) <0.001 Age, years 0.16 (0.11, 0.21) <0.001 Blood pressure sBP, mmHg dBP, mmHg 0.09 0.13 (0.05, 0.12) (0.08, 0.18) <0.001 <0.001 Sex (Men vs Women) –2.30 (–3.45, –1.16) <0.001 Diabetes mellitus (Present vs Absent) 2.90 (1.29, 4.50) <0.001 Hypertension (Present vs Absent) 0.10 (–1.32, 1.52) 0.89 HDL-C, mmol/L –1.28 (–2.07, – 0.48) <0.01 Non-HDL-C, mmol/L 1.10 (0.85, 1.34) <0.001 Smoking status (Former vs Never) (Current vs Never) 0.56 –0.38 (–0.84, 0.1.96) (-2.12, 1.36) 0.53 0.43 Changes in medications –0.50 (–2.11, 1.10) 0.53 Employment (Retired vs Unemployed) (Part time vs Unemployed) (Full time vs Unemployed) 2.01 1.16 0.64 (–0.32, 5.4) (–1.64, 3.96) (–1.79, 3.06) 0.09 0.41 0.45 Education (Post-secondary vs High school) (Post grad vs High school) 0.30 –0.24 (–0.90, 0.75) (–1.84, 1.56) 0.42 0.35 Visits type (Initial vs 3rd year visit) –1.30 (–2.12, –0.48) <0.001 Abbreviations: PA, physical activity; IPA, improved physical activity; NIPA; not improved physical activity; RHR, resting heart rate; BP, blood pressure; sBP, systolic blood pressure; dBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; V1, initial visit; V2, 3rd year visit. Beta estimates in the multivariable analyses were adjusted for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type. The reference group is the NIPA group. 244 Supplementary table - 2 The association between PA and RHR models β estimates 95% CI P-value Full model + Alcohol consumption -4.49 (-5.63, -3.35) <0.01 Full model for (DM, HTN, CVD, BMI > 25 patients were excluded) -4.65 (-5.96, -3.96) <0.01 Full model + Alcohol consumption + age strata < 60 and > 60 years -4.44 (-5.54, -3.29) <0.01 Full model + Alcohol consumption + age strata + type of prevention -4.42 (-5.56, -3.28) <0.01 Full model for (female subgroup) -3.20 (-4.88, -1.58) <0.01 Full model for (male subgroup) -5.10 (-6.53, -3.66) <0.01 Full model for (Primary prevention subgroup) -4.66 (-5.87, -3.44) <0.01 Full model for (Secondary prevention subgroup) -3.31 (-5.74, -0.88) <0.01 245 Appendix B A sensitivity analysis was performed to assess the robustness of the association between PA and the RHR in chapter 5, with the following additional adjustments: 2.1 % percent of the study population, who were more active at baseline and remained so throughout the three-year follow-up period and were initially included in the NIPA group, were transferred to the IPA group for sensitivity analysis shown in Supplementary table - 3. Supplementary table - 3 Predictors β estimates 95% CI P-value Model 1 (Lp[a] as continuous variable) Physical activity (IPA vs NIPA) –5.55 (–5.87, –3.22) <0.001 Log (Lp[a]) 1.40 (0.95, 1.85) <0.001 Age, years 0.15 (0.08, 0.21) <0.001 Blood pressure sBP, mmHg dBP, mmHg 0.07 0.14 (0.03, 0.08) (0.09, 0.20) <0.001 <0.001 Sex (Men vs Women) –3.57 (–4.96, –2.17) <0.001 Diabetes mellitus (Present vs Absent) 3.96 (1.78, 6.14) <0.001 Hypertension (Present vs Absent) –0.04 (–1.90, 1.82) 0.96 HDL-C –1.46 (–2.46, – 0.45) <0.001 Non-HDL-C 1.48 (1.17, 1.78) <0.001 246 Predictors β estimates 95% CI P-value Smoking status (Former vs Never) (Current vs Never) 1.90 0.51 (–0.02, 3.35) (–1.73, 2.58) 0.052 0.69 Changes in medications –0.45 (–-2.55, 164) 0.67 Employment (Retired vs Unemployed) (Part time vs Unemployed) (Full time vs Unemployed) 1.81 1.70 0.89 (–0.34, 5.96) (0.03, 3.70) (–2.15, 3.63) 0.54 0.10 0.45 Education (Post-secondary vs High school) (Post grad vs High school) 0.13 0.25 (–0.90, 0.1.71) (–1.80, 1.56) 0.28 0.39 Visits (V1 vs V2) –3.25 (–4.22, –2.28) <0.001 Model 2 (PA and Lp[a] as dichotomous variables) Physical activity (IPA vs NIPA) –4.58 (–5.90, –3.26) <0.001 Lp(a) (<300mg/L vs ≥300 mg/L) –4.12 (–5.44, –2.80) <0.001 Age, years 0.15 (0.08, 0.21) <0.001 Blood pressure sBP, mmHg dBP, mmHg 0.07 0.15 (0.03, 0.11) (0.09, 0.20) <0.001 <0.001 Sex (Men vs Women) –3.54 (–4.94, –2.14) <0.001 Diabetes mellitus (Present vs Absent) 3.68 (1.50, 5.86) <0.001 Hypertension 247 Predictors β estimates 95% CI P-value (Present vs Absent) 0.02 (0.11, 1.21) 0.15 HDL-C, mmol/L –1.40 (–2.39, – 0.41) 0.011 Non-HDL-C, mmol/L 0.95 (0.61, 1.29) <0.001 Smoking status (Former vs Never) (Current vs Never) 1.97 0.60 (0.20, 3.74) (–1.55, 2.74) 0.029 0.58 Changes in medications –0.57 (–2.66, 1.52) 0.59 Employment (Retired vs Unemployed) (Part time vs Unemployed) (Full time vs Unemployed) 1.80 –1.23 0.84 (–0.19, 4.10) (–4.53, 2.06) (–2.03, 3.71) 0.19 0.46 0.45 Education (Post-secondary vs High school) (Post grad vs High school) 0.28 -0.18 (–0.89, 0.16) (–1.84, 1.40) 0.50 0.42 Visit type (Initial vs 3rd year visit) –3.24 (–4.22, –2.27) <0.001 Lp(a); lipoprotein(a), RHR, resting heart rate; sBP, systolic blood pressure; dBP, diastolic blood pressure; PA; physical activity. adjusting for age, sex, sBP, dBP, HTN, DM, non-HDL-C, smoking status, employment, education level, change in medications, and visit type.
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Experimental Medicine
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Chan, Sammy H. S.
Humphries, Karin H.
The association of physical activity with resting heart rate and arterial atherosclerotic markers in patients with high lipoprotein(a)
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