HIGH PERFORMANCE LIPOPROTEIN PROFILING FOR CARDIOVASCULARRISK ASSESSMENT

A Dissertation
by
CRAIG DANIEL LARNER

Submitted to the Office of Graduate Studies ofTexas A&M University
in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY

August 2012

Major Subject: ChemistryHigh Performance Lipoprotein Profiling for Cardiovascular Risk Assessment
Copyright 2012 Craig Daniel LarnerHIGH PERFORMANCE LIPOPROTEIN PROFILING FOR CARDIOVASCULARRISK ASSESSMENT

A Dissertation
by
CRAIG DANIEL LARNER

Submitted to the Office of Graduate Studies ofTexas A&M University
in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY

Approved by:
Chair of Committee,
Committee Members,
Head of Department,

Ronald D. Macfarlane
Gerald L. Coté
Daniel Romo
Karen L. Wooley
David H. Russell

August 2012

Major Subject: Chemistry

iii

ABSTRACTHigh Performance Lipoprotein Profiling for Cardiovascular Risk Assessment(August 2012)
Craig Daniel Larner, B.S., Adrian College
Chair of Committee: Dr. Ronald D. Macfarlane

With the severity of cardiovascular disease (CVD) and the related mortality rate
to this disease, new methods are necessary for risk assessment and treatment prior to theonset of the disease. The current paradigm in CVD risk assessment has shifted towards
the multivariate approach over the individual use of traditional risk factors or lipid
measurements. Through a combination of analytical techniques and multivariate
statistical analysis, a novel method of cardiovascular risk assessment was developed.
The analytical techniques employed include density gradient ultracentrifugation (DGU)
and matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) applied
to human serum. These techniques provided detailed information about thecharacterization of the lipoproteins and their structural components, specifically theapolipoproteins belonging to high density lipoproteins (HDL). This information whencombined with multivariate statistical analysis provided a method that accurately
identified the presence of CVD in clinical studies between cohorts of subjects that had
been previously diagnosed with CVD and cohorts of subjects that had been identified ashealthy controls (CTRL) based on a clear angiography.

iv

The lipoprotein density profiles were divided into subclasses based on theirdensity and measured using a fluorescent probe to tag the lipoprotein particles. Use ofmultiple ethylenediaminetetraacetic acid (EDTA) based solutes allowed for the
manipulation of the density gradient formation in order to separate the lipoproteins byspecific density ranges in order to achieve better baseline separation of the profiles.
Application of the integrated fluorescence intensities for each subclass of lipoprotein to
linear discriminant analysis/sliced inverse regression (LDA/SIR) and quadratic
discriminant analysis (QDA) yielded an advanced and accurate form of risk assessment
for CVD. This method was found to be highly accurate as well as identify potential
atherogenic lipoprotein subclasses through studying the LDA/SIR prediction equation
generated. It was also shown that the LDA/SIR equation could be used to monitor
medical treatment and lifestyle change for their effects on the risk assessment model.
Further study into the atherogenicity of HDL through analysis of theapolipoproteins using MALDI-MS led to identification of potential risk factors that
could be added to the statistical analyses. These risk factors included mass differencesin the Apolipoprotein A-I (Apo A-I) and Apolipoprotein C-I (Apo C-I) between CVD
and CTRL samples as well as the presence of specific mass peaks related to
Apolipoprotein A-II (Apo A-II) that were primarily found in the CVD samples. These
differences, in addition to the lipoprotein density profile data, were found to increase thepotential accuracy of CVD risk assessment. The combination of these methods has
shown great potential in the assessment of CVD risk as well as the ability to increase
researchers’ understanding of the nature of VD and how to treat it.

v

DEDICATION“I knew you could! And you knew it, too –That you’d come out on top after all you’ve been through.
And from here you’ll go farther and see brand-new sights,
You’ll face brand-new hills that rise to new heights.”

To my parents Curtis and Yvonne Larner, my fiancée Katherine Westhoff andher family, my brothers Jayson and Steven Larner, my sister-in-law Sara Larner, mynephews Thomas and Braedon Larner, and the rest of my family and friends for theirlove, support, and constant encouragement. I could never have done this without you
all.

“There’s more about life that you’ll learn as you go,
Because figuring things out on your own helps you grow.
Just trust in yourself, and you’ll climb every hill.
Say, “I think I can!” and you know what?
You will!”
Dorfman, Craig. I Knew You Could!: A Book for all the Stops in Your Life.
New York: Platt & Munk, 2003

vi

ACKNOWLEDGEMENTS

I would like to acknowledge my advisor Dr. Ronald D. Macfarlane for his
support and guidance through the course of my graduate career. I would like to thankhim for emphasizing the importance and relevance of translational research. I also thankmy committee members Dr. Gerald Coté, Dr. Daniel Romo, and Dr. Karen Wooley for
their support in completion of my degree.
I would also like to thank my colleagues Jeffrey Johnson, Ph.D., Ronald
Henriquez, Ph.D., and Kanchan Taori for their collaborative efforts and support over theyears. Jeffrey Johnson and Ronald Henriquez introduced me to the research andprovided valuable advice and input on the application of the analytical methods forstudying lipoproteins as well as personal support in my life. I appreciate the input andaid provided by Kanchan Taori this last year as I finished my research and wrote thisdissertation.
I thank Dr. Catherine J. McNeal at Scott & White Hospital, Temple, TX for theopportunity to apply my research to clinical studies and for her input and collaborative
efforts in advancing the risk assessment for CVD. I would also like to acknowledge Dr.
Simon Sheather and Dr. Samir Sinha for their expertise and guidance in application of
the multivariate statistical analyses.
Finally, thank you to my family and friends for the support and encouragement
they provided over the years and throughout my life.

vii

LIST OF ABBREVIATIONS

%RSD
ACN
ANOVA
Apo
BCA
BSA
bTRL

Percent relative standard deviation
Acetonitrile
Analysis of variance
Apolipoprotein
Bicinchoninic acid
Bovine serum albumin
Buoyant triglyceride-rich lipoprotein

C18
CETP
CM

Carbon tail of eighteen atoms
Choleterylester transfer protein
Chylomicrons

Cs2CdEDTA
CTRL
CVD
DGU

Dicesium cadmium ethylenediaminetetraacetic acidControl
Cardiovascular disease
Density gradient ultracentrifugation

DI H2O
DMSO
DP
DS
dTRL
EDTA
ELISA
FH
FRS
FWHM
HDL
HDL-C
HMG-CoA
HPLDP
hs-CRP
HT
IDL
ITP
L-CAT
LDA

Deionized Water
Dimethyl sulfoxide
Density profile
Dextran Sulfate
Dense triglyceride-rich lipoprotein
Ethylenediaminetetraacetic acid
Enzyme-linked immunosorbent assay
Family History
Framingham Risk Score
Full width half maximum
High density lipoprotein
High density lipoprotein cholesterol
3-hydroxy-3-methylclutaryl-coenzyme A
High performance lipoprotein density profilingHigh sensitivity c-reactive protein
Hypertension
Intermediate density lipoprotein
Isotachophoresis
Lecithin-cholesterol acyltransferase
Linear discriminant analysis

viii

LDL
LDL-C
Lp(a)
MALDI
MS
MWM
NaBiEDTA
NBD
NCEP
NMR
PTM
QDA
RRS
SCORE
sd-LDL
SIR
SPE
TC
TFA
TOF
TRF
UC
VAP
VLDL
X-Val

Low density lipoprotein
Low density lipoprotein cholesterol
Lipoprotein (a)Matrix-assisted laser desorption ionization
Mass spectrometry
Molecular weight modification
Sodium bismuth ethylenediaminetetraacetic acid
7-nitro-2,1,3-benz-oxadiazol-4-yl
National Cholesterol Education Program
Nuclear magnetic resonancePost-translational modification
Quadratic discriminant analysis
Reynolds Risk Score
Systematic Coronary Risk Evaluation
Small, dense low density lipoprotein
Sliced inverse regression
Solid phase extraction
Total cholesterol
Trifluoroacetic acid
Time of flight
Traditional risk factor
Ultracentrifugation
Vertical Auto Profiling
Very low density lipoprotein
Cross validation analysis

ix

TABLE OF CONTENTS

Page
ABSTRACT ..............................................................................................................

iii

DEDICATION ..........................................................................................................

v

ACKNOWLEDGEMENTS ......................................................................................

vi

LIST OF ABBREVIATIONS ...................................................................................

vii

TABLE OF CONTENTS ..........................................................................................

ix

LIST OF FIGURES ...................................................................................................

xiii

LIST OF TABLES .................................................................................................... xviii
1. INTRODUCTION ...............................................................................................

1

1.1 Significance ...................................................................................................
1.2 Cardiovascular Disease Risk Assessment Methods and Therapies ..............
1.2.1 CVD Risk Assessment Methods ..........................................................
1.2.2 CVD Therapies .....................................................................................
1.3 Lipoprotein Composition and Function ........................................................
1.3.1 Lipoprotein Properties ..........................................................................
1.3.2 Lipoprotein Function ............................................................................
1.3.3 Lipoprotein Subclasses and Their Relation to CVD Risk ....................
1.4 Methods for Lipoprotein Analysis ................................................................
1.4.1 Density Gradient Ultracentrifugation (DGU).......................................
1.4.1.1 Sedimentation Theory and Gradient Mapping .........................
1.4.1.2 Methods of DGU ......................................................................
1.4.1.3 Fluorescence Analysis of Lipoproteins ....................................
1.4.2 Mass Spectrometry of Apolipoproteins................................................
1.4.3 Other Methods of Lipoprotein Analysis ...............................................
1.5 Advanced Multivariate Statistical Analysis ..................................................
1.6 Application of Methods .................................................................................

1
3
3
5
7
8
9
11
12
13
14
16
17
19
20
22
24

x

Page
2. MATERIALS AND METHODS ........................................................................

26

2.1 Materials .....................................................................................................
2.1.1 Chemicals and Supplies .......................................................................
2.1.2 Cs2CdEDTA Synthesis .........................................................................
2.1.3 Serum Collection ..................................................................................
2.2 Analytical Methods .......................................................................................
2.2.1 Ultracentrifugation ...............................................................................
2.2.2 Density Gradient Measurement ............................................................
2.2.2.1 Properties of EDTA Solutions..................................................
2.2.2.2 Gradient Formation and Measurement .....................................
2.2.3 Fluorescent Labeling of Serum Samples ..............................................
2.2.4 Fluorescence Image Analysis ...............................................................
2.2.5 Lipoprotein Density Profiling ..............................................................
2.2.6 NaBiEDTA Gradient Optimization Studies .........................................
2.2.6.1 Spatial Separation of Lipoprotein Density Profiles ..................
2.2.6.2 Density Gradient Development over Time ..............................
2.2.6.3 Precision of Density Measurements Using Nano-spheres .......
2.2.6.4 Polar Layering with H2O vs. Non-polar Layering with Hexane
2.2.6.5 Effects of Ultracentrifugation Spin Temperature .....................
2.2.6.6 Tube Orientation for Imaging ..................................................
2.2.6.7 Stability of the Lipoprotein Profile after UC Spin ...................
2.2.6.8 Precision and Normalization of the Density Profiles ...............
2.2.7 High Performance Lipoprotein Density Profiling (HPLDP) ................
2.2.8 Lipoprotein Density Profiling of Commercial HDL/LDL Standards ..
2.2.9 Serum Viability Studies .......................................................................
2.2.10 Preparative Ultracentrifugation and Fraction Collection ...................
2.2.10.1 Dextran Sulfate Precipitation .................................................
2.2.10.2 Preparative Ultracentrifugation ..............................................
2.2.10.3 Freeze/Cut Method for Fraction Collection ...........................
2.2.11 Solid Phase Extraction (SPE) .............................................................
2.2.12 Colorimetric Assays for Measuring Protein Concentration ...............
2.2.12.1 Bicinchoninic Acid Assay (BCA) ..........................................
2.2.12.2 Apo A-I ELISA ......................................................................
2.2.13 MALDI-TOF MS Analysis of HDL Apolipoproteins........................
2.3 Clinical Studies .............................................................................................
2.3.1 Statistical Methods of Analysis ............................................................
2.3.2 Normal Lipidemic Serum Library ........................................................
2.3.3 Comprehensive Serum Library ............................................................
2.3.4 Hypercholesterolemia Serum Library ..................................................
2.3.5 Niacin Treatment Study .......................................................................
2.3.6 Exercise Regime Study ........................................................................

26
26
27
27
28
28
28
28
29
29
30
31
31
31
32
32
33
33
33
34
34
35
36
36
37
37
38
38
40
41
41
42
43
44
44
46
46
47
47
48

xi

Page
3. RESULTS AND DISCUSSION .........................................................................

49

3.1 Analytical Methods .......................................................................................
51
3.1.1 NaBiEDTA Lipoprotein Profile Optimization .....................................
51
3.1.1.1 Initial System Error Analysis ...................................................
51
3.1.1.2 Optimization of the Spatial Separation of the Density Profile .
54
3.1.1.3 Density Gradient Development over Time .................................. 55
3.1.1.4 Precision of Density Measurement Using Nano-spheres .........
60
3.1.1.5 Polar Layering with H2O vs. Non-Polar Layering with Hexane 62
3.1.1.6 Effects of Ultracentrifugation Spin Temperature .....................
65
3.1.1.7 Tube Orientation for Imaging ..................................................
68
3.1.1.8 Stability of the Density Profile after UC Spin ..........................
70
3.1.1.9 Precision of the Density Profiles after Optimization ...............
72
3.1.1.10 Normalization of Density Profile Data ...................................
74
3.1.1.11 Profiling HDL/LDL Commercial Standards ..........................
76
3.1.2 Cs2CdEDTA-Based Gradients for Isolation of LDL and HDL ............
79
3.1.2.1 Cs2CdEDTA Gradient Measurement and Optimization ..........
80
3.1.2.2 Precision of Density Profiles Using Cs2CdEDTA Gradients ...
84
88
3.1.3 Serum Viability Studies .......................................................................
3.1.3.1 Density Profiles Relative to Time Serum Spent at Room
Temperature .............................................................................
88
3.1.3.2 Effect of Multiple Freeze/Thaw Cycles of Serum on Density
Profiles .....................................................................................
91
3.1.4 Optimization of Preparative Techniques for HDL Apo Mass
Spectrometry ............................................................................
92
3.1.4.1 Optimizing Recovery of HDL and HDL Apolipoproteins .......
93
3.1.4.2 Protein Recovery of Delipidated HDL .....................................
96
3.1.4.3 Optimizing Mass Spectrometry Signal for Clinical Applications 99
3.2 Clinical Applications of High Performance Lipoprotein Profiling Methods 101
3.2.1 Lipoprotein Density Profiles and LDA/SIR Analysis for CVD Risk
Assessment ........................................................................................... 101
3.2.1.1 Improving the Predictive Power of the Original Pilot Study
through Application of HPLDP Method ................................. 102
3.2.1.2 Effect of Redefining the Definition of CTRL .......................... 108
3.2.1.3 LDA/SIR Analysis Applied to Normal Lipidemic Patient
Samples for CVD Risk Assessment ......................................... 111
3.2.1.3.1 LDA/SIR Results of Lipoprotein Measurements ...... 113
3.2.1.3.2 LDA/SIR Separation and Coefficients Using 5 and 11
Lipoprotein Subclasses .............................................. 115
3.2.1.4 Advanced Statistical Analyses Applied to the Comprehensive
Patient Library for CVD Risk Assessment .............................. 120

xii

Page
3.2.1.4.1 Relation of HDL/LDL Cholesterol Measurements to
Lipoprotein Density Profile Integrated Intensities ....
3.2.1.4.2 Statistical Comparison of Lipoprotein Measurements
between CVD and CTRL Sample Libraries ..............
3.2.1.4.3 LDA/SIR Analysis on Lipoprotein Measurements ...
3.2.1.4.4 QDA Analysis on Lipoprotein Measurements ..........
3.2.1.5 LDA/SIR Analysis Applied to Hypercholesterolemia .............
3.2.1.5.1 LDA/SIR Analysis for Risk Assessment ofHypercholesterolemia ................................................
3.2.1.5.2 Effects of Therapy/Placebo on Risk Assessment ofHypercholesterolemia ................................................
3.2.2 Further Analytical Methods Applied to the Statistical Analyses to
Improve Risk Assessment of CVD ......................................................
3.2.2.1 Application of Cs2CdEDTA-Based Lipoprotein Density
Profiling for CVD Risk Assessment ........................................
3.2.2.2 Mass Spectrometry of HDL Apolipoproteins for Application
to CVD Risk Assessment .........................................................
3.2.2.2.1 Mass Spectra Differences between CVD and CTRL
Cohorts ......................................................................
3.2.2.2.2 Application of Mass Spectral Data to Statistical
Analyses ....................................................................
3.2.3 Case Studies for Monitoring CVD Therapies ......................................
3.2.3.1 Case Study Monitoring the Effects of Niacin Therapy ............
3.2.3.2 Case Study Monitoring the Effects of Lifestyle Modification .

178
186
186
192

4. CONCLUSIONS .................................................................................................

197

REFERENCES ..........................................................................................................

202

VITA .........................................................................................................................

215

121
125
127
138
141
142
147
156
158
164
171

xiii

LIST OF FIGURESFIGUREPage

1

Basic Lipoprotein Structure .......................................................................

8

2

NBD C6-Ceramide Structure ......................................................................

18

3

Excitation and Emission Spectra of NBD C6-Ceramide ............................

18

4

Overlaid Lipoprotein Profiles for Baseline Error Analysis ........................

53

5

Integrated Intensities and Error Bars for Baseline Error Analysis .............

53

6

Density Gradient Formation over Time (2 and 4 Hours) ...........................

56

7

Density Gradient Formation over Time (6 and 8 Hours) ...........................

57

8

Overlaid Serum Profiles at 2-8 Hours ........................................................

58

9

Replicate Density Gradients for 0.1800M NaBiEDTA Spun for 6 Hours .

59

10

Fluorescent Nanoparticle Profile Based on Spin Time ..............................

60

11

Layering Medium Comparison ..................................................................

64

12

Effect of Spin Temperature on Lipoprotein Profiles ..................................

66

13

Effect of Spin Temperature on Lipoprotein Peak Orientation ...................

67

14

Tube Orientation for Imaging ....................................................................

69

15

Stability of the Lipoprotein Density Profiles over Time ............................

71

16

Repeatability of Lipoprotein Density Profiles for 10 Replicate Samples ..

73

17Application of Mode 2 Analysis for Density Profile Normalization .........

75

18

LDL and HDL Density Profiles Using Commercial Standards .................

77

19

LDL and HDL Calibration Curves Using Commercial Standards .............

78

xiv

FIGURE

Page

20

Lipoprotein Density Profiles for Varying Cs2CdEDTA Concentrations ...

81

21

Cs2CdEDTA Density Gradient Formation .................................................

83

22

Dual Concentration Cs2CdEDTA-Based Gradient Density Profiles ..........

85

23

Dual Concentration Cs2CdEDTA-Based Gradient Profile Error Analysis

86

24

Effects of Exposure at 23°C on Serum over Time .....................................

9025

Effect of Multiple Freeze/Thaw Cycles on Density Profiles .....................

92

26

Mass Spectra of HDL Apos for Different Preparative Methods ................

95

27

Mass Spectra of HDL Apos Based on Sample Concentration ................... 100

28

Influence of Lipoprotein Subclasses Based on Pilot Study Algorithm ...... 104

29

Pilot Study LDA/SIR Coefficients for Lipoprotein Subclasses Using
Original Lipoprotein Profiling Method ...................................................... 104

30

Pilot Study LDA/SIR Separation of CVD/CTRL Using Original Method

31

Pilot Study LDA/SIR Separation of CVD/CTRL Using HPLDP Method . 107

32

LDA/SIR Coefficients for Lipoprotein Subclasses Using HPLDP
Method ....................................................................................................... 107

33

LDA/SIR Separation of CVD/CTRL Using Redefined CTRL Library ..... 109

34

LDA/SIR Coefficients for the Separation of the Pilot Study CVD
Samples and Angiography-Defined CTRL Samples ................................. 110

35

Lipoprotein Density Profile Fractioned into 3, 5, and 11 Subclasses ........ 113

36

LDA/SIR Separation of CVD/CTRL of Normal Lipidemic Library Using 5
Lipoprotein Subclasses ............................................................................... 116

37

Normal Lipidemic LDA/SIR Coefficients for 5 Lipoprotein Subclasses .. 117

38

Normal Lipidemic LDA/SIR Separation of CVD/CTRL Using 11
Lipoprotein Subclasses ............................................................................... 117

105

xv

FIGURE

Page

39

Normal Lipidemic LDA/SIR Coefficients for 11 Lipoprotein Subclasses

118

40

Calibration Curve of Protein Concentration vs. Integrated Intensities forCommercial Lipoprotein Standards............................................................ 122

41

Traditional Lipid Measurements in Relation to Integrated Intensities
Found Using Lipoprotein Density Profiles for CVD Subjects ................... 123

42

Traditional Lipid Measurements in Relation to Integrated Intensities
Found Using Lipoprotein Density Profiles for CTRL Subjects ................. 124

43

LDA/SIR Coefficients for Prediction of CVD for the Comprehensive
Serum Library Using Mode 1 and Mode 2 Data Sets ................................ 130

44

LDA/SIR Separations for the Comprehensive Serum Library for
Lipoprotein Measurement Methods ........................................................... 132

45

LDA/SIR Separations for the Hold Out Sample Set of the Comprehensive
Serum Library Using Mode 1 Data ............................................................ 133

46

LDA/SIR Coefficients for Lipoprotein Subclasses with the Addition of
Tradition Risk Factors ................................................................................ 138

47

LDA/SIR Separation for Hypercholesterolemic Subjects .......................... 142

48

LDA/SIR Coefficients for Prediction of Hypercholesterolemia inChildren ...................................................................................................... 144

49

Classification of Hypercholesterolemia Children's Library Using theComprehensive Serum Library LDA/SIR Algorithm ................................ 146

50

LDA/SIR Values for Hypercholesterolemia Subjects Treated with Statin
Therapy (Subjects I and II) ......................................................................... 148

51

LDA/SIR Values for Hypercholesterolemia Subjects Treated with Statin
Therapy (Subjects III and IV) .................................................................... 149

52

LDA/SIR Values for Hypercholesterolemia Subjects Treated with
Placebos (Subjects V and VI) ..................................................................... 151

53

LDA/SIR Values for Hypercholesterolemia Subjects Treated with
Placebos (Subjects VII and VIII) ............................................................... 152

xvi

FIGURE

Page

54

Comparison of Integrated Fluorescent Intensities and LDA/SIR Values
for Each Lipoprotein Subclass: Subject I (Statin Therapy) ........................ 154

55

Comparison of Integrated Fluorescent Intensities and LDA/SIR Values
for Each Lipoprotein Subclass: Subject V (Placebo Therapy) ................... 155

56

LDA/SIR Coefficients for the 72 Patient Library Using the NaBiEDTABased Gradient System .............................................................................. 157

57

LDA/SIR Coefficients for Lipoprotein Subclasses and Traditional Risk
Factors Using Cs2CdEDTA Density Gradient System on the 72 Subject
Data Set ...................................................................................................... 161

58

Comparison of LDL Lipoprotein Density Profiles for CTRL-Patient 052
between Solute Systems ............................................................................. 162

59

Comparison of LDL Lipoprotein Density Profiles for CVD-Patient 169
between Solute Systems ............................................................................. 163

60

Mass Spectra of HDL Apolipoproteins for CVD Patient 124 .................... 165

61

Mass Spectra of HDL Apolipoproteins for CVD Patient 148 .................... 166

62

Mass Spectra of HDL Apolipoproteins for CVD Patient 171 .................... 167

63

Mass Spectra of HDL Apolipoproteins for CTRL Patient 013 .................. 168

64

Mass Spectra of HDL Apolipoproteins for CTRL Patient 033 .................. 169

65

Mass Spectra of HDL Apolipoproteins for CTRL Patient 043 .................. 170

66

Apo A-I Mass Distribution between CVD and CTRL Cohorts ................. 172

67

Apo C-I Mass Distribution between CVD and CTRL Cohorts ................. 175

68

Apo A-II Mass Distribution between CVD and CTRL Cohorts ................ 177

69

LDA/SIR Coefficients for Lipoprotein Subclasses Using NaBiEDTABased Density Gradients and Mass Spectrometry Data for the HDL
Apolipoproteins when Applied to the 72 Subject Library ......................... 182

xvii

FIGURE

Page

70

LDA/SIR Coefficients for Lipoprotein Subclasses Using Cs2CdEDTABased Density Gradients and Mass Spectrometry Data for the HDL
Apolipoproteins when Applied to the 72 Subject Library ......................... 183

71

LDA/SIR Separation for the 72 Subject Library Using Lipoprotein
Subclasses and Mass Spectrometry Data ................................................... 185

72

Niacin Dosage Effects on Lipoprotein Measurement Methods ................. 188

73

Lipoprotein Density Profiles by Draw Date/Niacin Dosage ...................... 190

74

LDA/SIR Value in Relation to Draw Date/Niacin Dosage ........................ 191

75

Lipoprotein Density Profiles Comparison Based on the Increase of
Physical Exercise – NaBiEDTA-Based Density Gradient ......................... 193

76

Fluorescence Intensities for Major Lipoprotein Fractions Found throughLipoprotein Density Profiling with Respect to Increase in PhysicalExercise ...................................................................................................... 194

77

LDA/SIR Values in Relation to Increased Physical Exercise .................... 195

xviii

LIST OF TABLES

TABLE

Page

1

Lipoprotein Characteristics ........................................................................

92

Lipoprotein Subclass Densities ..................................................................

11

3

Nanoparticle Density Measurements and Resolution ................................

61

4

LDA/SIR Analysis Summary for Normal Lipidemic Serum Library ofDifferent Lipoprotein Measurements ......................................................... 114

5Comparison of Statistical Analysis for Traditional Risk Factors between
CVD/CTRL Groups for the Comprehensive Library ................................. 125

6Comparison of Statistical Analysis for Lipoprotein Density Profiles
between CVD /CTRL Groups .................................................................... 127

7

LDA/SIR Prediction Summary for Traditional Lipid Measurements andLipoprotein Density Profiles ...................................................................... 128

8

Influence of Tradition Risk Factors on the LDA/SIR Analysis of Mode 1
Data of Lipoprotein Density Profiles for the Comprehensive Library....... 135

9Influence of Tradition Risk Factors on the LDA/SIR Analysis of Mode 2
Data of Lipoprotein Density Profiles for the Comprehensive Library....... 135

10Influence of Tradition Risk Factors on the QDA Analysis of Mode 1 Dataof Lipoprotein Density Profiles for the Comprehensive Library ............... 140

11

Influence of Tradition Risk Factors on the QDA Analysis of Mode 2 Dataof Lipoprotein Density Profiles for the Comprehensive Library ............... 140

12LDA/SIR and QDA Statistical Analyses for Cs2CdEDTA-based
Gradients of the 72 Subject Data Set ......................................................... 159

13

Masses of HDL Apolipoproteins between CVD and CTRL Cohorts
Measured Using MALDI-TOF MS ............................................................ 172

xix

TABLE
14

Page
Application of Mass Spectra Data to the LDA/SIR and QDA Analyses for
CVD Risk Assessment Using the 72 Subject Data Set .............................. 180

1

1. INTRODUCTION

1.1 SignificanceWith the significance of cardiovascular disease (CVD) and the related mortality
rate, the current paradigm in risk assessment of CVD has been shifted to the use ofmultiple risk factors in the development of predictive algorithms.1-7 Current risk
assessment methods have only been shown to be 80% accurate, at most, and they arewidely inaccurate for subjects that show limited to no traditional risk factors and thosesubjects that either elderly or youthful. In a recent report, the National Cholesterol
Education Program (NCEP) recommended that lipid screening tests consisting of totalcholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoproteincholesterol (HDL-C), and triglyceride (TG) measurements be performed as a standard
health assessment.8 While these measurements can be beneficial, they do not accountfor all the possible information that can be obtained from the lipoprotein particles.
Lipoproteins are a form of nanoparticle present in human serum that performs amajor role in the metabolism and transportation of lipids, triglycerides, cholesterols, andcholesterol esters in the human body. 9 These nanoparticles can be separated andcharacterized based on their density through density gradient ultracentrifugation (DGU).

________________
This thesis follows the style of Analytical Chemistry.

2

While other techniques such as nuclear magnetic resonance (NMR), chromatographic
separations, and electrophoretic separations have been used to profile lipoproteins, DGU
has long been considered the “gold standard” of lipoprotein characterization.9-11
Through a combination of novel EDTA-based solutes12,13 and NBD C6-Ceramide(NBD), a lipoprotein-specific fluorophore, the self-forming density gradients have
expanded the potential and analytical power of DGU. Previously, application of thismethod has shown potential application for the risk assessment of CVD.14 Throughenhancing the precision of the method, it became possible to increase the accuracy of therisk assessment and apply the methodology to larger clinical studies.
Mass spectrometry is another analytical tool that has shown use in thecharacterization of lipoproteins and their content. Through soft ionization techniquessuch as MALDI-MS15, the apolipoprotein content of the HDL in serum has been studied
and potential risk factors identified.16 Application of the mass/charge data obtained fromthis technique, coupled with the lipoprotein density profile data, has the potential to be
added to the risk assessment methods as possible risk factors.
To develop the risk assessment algorithm, different multivariate statisticalanalyses can be used. Linear discriminant analysis/sliced inverse regression(LDA/SIR)17-20 and quadratic discriminant analysis (QDA)21 are two different types ofmultivariate statistical analyses that can be applied to CVD risk assessment. Thesemethods inter-relate the variables used to generate the prediction equation in order to
optimize the separation of the different sample groups.

3This work utilizes the techniques of DGU and MALDI-MS coupled withLDA/SIR and QDA statistical analyses in order to identify potential risk factors for CVD
and generate a risk assessment algorithm for early detection of CVD.

1.2 Cardiovascular Disease Risk Assessment Methods and Therapies
Cardiovascular disease is a class of diseases that affect the heart and arteries.
Despite the advances in medical treatment, cardiovascular disease remains the leading
cause of death worldwide.22 For this reason, current research in the field ofcardiovascular disease includes identification of novel risk factors, development ofadvanced multivariate risk assessment methods5,23-25, and investigation into therapeutic
methods for risk reduction.

1.2.1 CVD Risk Assessment Methods
Traditional risk factors (TRF) for preclinical screening of CVD include levels of
Total Cholesterol, Triglycerides, High Density Lipoprotein Cholesterol (HDL-C), and
Low Density Lipoprotein Cholesterol (LDL-C). Other factors included in risk
assessment are age, high blood pressure, diagnosis of diabetes, tobacco use, family
history, and hypertension.3,6,26 The old paradigm of cardiovascular risk assessment using
the presence of individual risk factors in an additive risk system has begun to shift to a
multivariate approach of how all risk factors interrelate.2,4,5,23,24,27 Different multivariate
methods that have been developed include the Framingham Risk Score, Reynolds Risk
Score, and Systematic COronary Risk Evaluation (FRS, RRS, and SCORE,

4

respectively).3,6,28 These methods are regularly used to diagnose the risk of disease overa given period of time and are fairly accurate.
FRS is a risk assessment tool developed from the Framingham Heart Study. This
study was established in 1948 and monitored 5,209 subjects between the ages of 28 to 62
over time for risk factors related to CVD. This study was broadened in 1971 to include
the offspring of the original cohort as well as their spouses.3 The risk models developed
from this study have been design to assess 5-year, 10-year, 30-year, and lifetime risk of
CVD.3,7,29 These risk models are based on lipid concentrations, specifically totalcholesterol (TC) and HDL cholesterol (HDL-C). The c-statistics for FRS typically range
from 0.7-0.8 indicating a 70-80% accuracy using this model.2,30 These risk scores,
specifically the 10-year risk score, have weaknesses in diagnosing specific populations.
These populations include young people and women.31 Other populations that have been
inaccurately identified with/without risk include subjects with normal lipid levels3,
subjects of varied ethnicity32, and those subjects with extensive forms of the disease inneed of more aggressive treatments33.
Based on the FRS, multiple different risk score methods have been developed
using additional risk factors or new methods in order to increase the overall accuracy ofrisk assessment. In an attempt to apply novel risk factors for CVD to diagnose women,
the Reynolds Risk Score (RRS) was developed.6 In the RRS, hemoglobin A1c, highsensitivity c-reactive protein (hs-CRP), lipoprotein(a), apolipoproteins A-I and B-100,
and parental history were included to improve the classical risk score. This method was5found to reclassify 50% of the study population into new risk categories that moreaccurately assessed the level or risk for the population.
Due to the difference in the definition of non-fatal end-points used in the FRS
from other clinical studies, the SCORE method was developed to estimate risk inEuropean populations.28,34 This method was based on the TC and the TC/HDL-C ratio.
The results showed that the methods were able to estimate risk at a level suitable forclinical practice by achieving 71-84% accuracy. The over-arching goal of these andother risk assessment methods is to have an accurate and cost efficient method which
could be used for early diagnosis of CVD risk and allow for monitoring of therapeutic
treatments in an effort to reduce risk in the early stages of the disease.30 With theidentification of novel risk factors and enhanced methodologies, current research is
aimed at developing risk assessment algorithms with higher accuracy which can beapplied to clinical applications.

1.2.2 CVD Therapies
Risk assessment methods for CVD are only clinically applicable if methods oftherapeutic treatment can be monitored for risk reduction. Many therapeutic methods
have been studied over the course of CVD research. The primary methods of therapy
currently used include statin therapy, niacin therapy, and lifestyle modifications such as
diet and exercise.26 These therapies are designed to modify lipoproteins in the subject’s
serum in order to promote the “good” forms of lipoproteins and reduce the “bad” forms.

6

Statins have been used in clinical applications in order to lower LDL-C and raise
HDL-C.35-37 Statins work through inhibition of 3-hydroxy-3-methylclutaryl-coenzyme
A (HMG-CoA) reductase which causes increased regulation of LDL-C receptors andresults in enhanced removal of LDL-C from human blood.37 Statin therapy has currently
formulated six different types of statins which all show different efficacies based on
dosage and have different side effects. Intensive statin therapy has been shown to reduce
the rate or progression of atherosclerosis as well as decrease the levels of atherogenic
lipoproteins and CRP.38
Niacin therapies have been coupled with statin therapy to show enhancements in
the reduction of cardiovascular risk.39,40 Niacin has been shown to reduce LDL-C, TG,
and lipoprotein(a) while increasing HDL-C.41 Like statins, there are multiple forms ofniacin therapy, each with their own beneficial properties. The formulations for niacin
are based on their release times: immediate release, extended release, and long acting.41
The different formulations control how the drug is metabolized in the body. Niacin has
been limited in use due to issues with patient tolerability related to dosage andformulation.
Due to the potential adverse effects related to medical therapies, lifestylemodification prior to the onset of CVD shows the most promise in risk reduction. Someof the specific lifestyle characteristics related to the onset of CVD include tobacco
usage, inactivity, and obesity.26 CVD preventative measures for these areas include thecessation of tobacco usage, dietary modification, and exercise. These types ofmodification are considered primordial prevention. Exercise and dietary changes are7targeted to control lipid levels42,43, reduce blood pressure and obesity44, and prevent
onset of conditions that are precursory to CVD, such as diabetes and metabolic
syndrome.45 For dietary changes, it has been recommended that one should increasefiber intake through fruits and vegetables while lowering the intake of saturated fatsfrom meat and dairy products.26 For exercise, it is recommended that one should have atleast 30-60 minutes of moderate to intense physical activity every day if possible.26
Despite the method of therapy chosen, risk reduction associated with lipids andlipoproteins is common to all of them. This is due to the correlations made between
lipid levels and risk of CVD.8 It is for this reason that risk assessment methodologies arefocused around lipoprotein characterization.

1.3 Lipoprotein Composition and Function
Lipoproteins are a distribution of nanoparticles (5-500nm) in serum that play alarge role in the transportation and metabolism of lipids, triglycerides, cholesterols, andcholesterol esters. Lipoproteins are made up of a hydrophilic exterior and a hydrophobic
lipid core similar to the structure of micelles: they have a hydrophilic exterior and ahydrophobic core. The outer shell is made up of phospholipids and cholesterol while theinner core is composed of triglycerides and cholesterol esters. Surrounding the outershell are apolipoproteins which are used in the recognition and transportation of thelipoproteins. The basic structure of lipoproteins is shown in Figure 1.9

8

Phospholipids

Apolipoprotein

Hydrophobic Coreof Triacylglycerols& Cholesterol Esters(Inner Composition)

Un-esterified
Cholesterol

Figure 1: Basic Lipoprotein Structure

1.3.1 Lipoprotein Properties
Lipoproteins are primarily classified by their hydrated densities, but they can also
be identified by their specific composition. Lipoproteins in circulation throughout thehuman body are constantly modified through metabolic processes. As a result of themetabolic changes, there is a distribution of particles with slightly different physical andchemical properties throughout the lipoprotein classes.46,47 The differences in thedensities, composition, and size of the lipoprotein are used in identification of thedifferent classes of lipoproteins: chylomicrons, VLDL, IDL, LDL, HDL, and Lp(a). A
summary of the main lipoprotein characteristics including density, size, lipid content,
and apolipoprotein content is shown in Table 1.9,48

9Lipoprotein
Type

Table 1: Lipoprotein Characteristics
DensitySize
Major Lipids(g/mL)(nm)

Major
Apolipoproteins

Chylomicrons< 0.93100 - 500

B-48, C-II, E

VLDL

0.93 -1.006

30 - 80

IDL
LDL

1.006 -1.019
1.019 -1.063

25 - 50
18 - 28

Dietary TAGs
Endogenous
TAGs
CEs and TAGs
CEs and TAGs

HDL

1.063 -1.210

15-MayPL

Lp(a)1.040 -1.090

25 - 30

CEs

B-100, C-II, E
B-100, E
B-100
A-I, A-II, C-I,
C-II, E
B-100, Apo(a)1.3.2 Lipoprotein Function
The density of lipoproteins is directly related to the biological function of thelipoproteins. As the density of the lipoproteins decrease, the ratio of TAGs to thephospholipid and cholesterol content of the lipoproteins decreases. This change incomposition means that denser lipoproteins have higher protein composition. For thisreason, chylomicrons (CM) and VLDL are commonly paired together and labeled as
triglyceride-rich lipoproteins (TRL). The function of these TRLs is primarily totransport triglycerides throughout the body.49 TRLs exchange triglycerides withcholesteryl esters from nascent HDL using cholesterylester transfer protein (CETP) andinteracts with lipoprotein lipase which causes for loss of lipid content and thetransformation of TRL into IDL and then, eventually, LDL. This change can bemeasured by the progressive change in density to more dense particles.
IDL is a remnant of VLDL as it changes to LDL. It is formed as a result of
lipolysis of VLDL. This particle’s lifetime is normally short lived as it is converted to
LDL or cleared through the liver by receptor-mediated endocytosis.50 IDL is composed10of multiple apolipoproteins Apo E and a singular copy of Apo B-100. Apo E is present
until IDL is transformed into LDL when only Apo B-100 is present.51
LDL is primarily made up of cholesterol esters and the apolipoprotein Apo B100. LDL transports cholesterol and cholesteryl esters from the liver to different tissues
throughout the body using LDL receptors to permeate the membrane. LDL has been
divided even further into five different subclasses labeled as LDL-1 (1.019 – 1.023g/mL), LDL-2 (1.023 – 1.029 g/mL), LDL-3 (1.029 – 1.039 g/mL), LDL-4 (1.039 –1.050 g/mL), and LDL-5 (1.050 – 1.063 g/mL) as identified by equilibrium density
gradient ultracentrifugation.9
HDL reverses the cholesterol transport process by transporting the cholesterol
from the tissues back to the liver. This process is known as reverse cholesterol transport.
HDL has two main subclasses identified as HDL2 and HDL3. Further division of thesesubclasses through sequential centrifugation and DGU identified fractions of thesesubclasses known as HDL2b (1.063-1.091 g/mL), HDL2a (1.091-1.110 g/mL), HDL3a(1.110 –1.133 g/mL), HDL3b (1.133 –1.156 g/mL) and HDL3c (1.156 –1.179 g/mL).9 A
summary of the different lipoprotein subclasses is displayed in Table 2.

11

Table 2: Lipoprotein Subclass Densities
Lipoprotein
Density
Subclass(g/mL)
TRL<1.006IDL
1.006-1.019
LDL-1
1.019-1.023
LDL-2
1.023-1.029
LDL-3
1.029-1.039
LDL-4
1.039-1.050
LDL-5
1.050-1.063
HDL2b
1.063-1.091
HDL2a

1.091-1.110

HDL3a1.110-1.133

HDL3b

1.133-1.156

HDL3c

1.156-1.179

1.3.3 Lipoprotein Subclasses and Their Relation to CVD Risk
The classical view of the role lipoproteins play in the risk for cardiovasculardisease is that LDL is the “bad” form of lipoprotein while HDL is the “good” form oflipoprotein. The idea of LDL as the “bad” form of lipoprotein comes from theestablished direct correlation between LDL and CVD risk. Small, dense LDL (sd-LDL)
particles have been shown to stay in serum for longer periods of time compared to large,
buoyant forms of LDL. sd-LDL also exhibits increased oxidation rates, lower affinity to
the LDL receptor, and increased penetration into the arterial wall.52-55 Oxidized LDL
has been identified to possess pro-atherogenic and pro-inflammatory properties and has
long been a target of medical therapies.56,57
The idea of HDL as the “good” form of lipoprotein comes from the establishedinverse correlation between HDL cholesterol levels (HDL-C) and CVD risk.58 HDL has

12been identified to have many athero-protective properties which reduce the risk of CVD.
Through activation of lecithin-cholesterol acyltransferase (LCAT), HDL promotes a
reduction in oxidized lipids.59,60 This activity has specifically been identified as aproperty of small, dense HDL.61 Apo A-I, the primary apolipoprotein of HDL, has themimetic peptide ETC-642 that has been associated with the anti-inflammatory propertiesof HDL.62 HDL also promotes macrophage cholesterol efflux and reverse cholesteroltransport which are two of the primary methods in which HDL protects against
atherosclerosis.63
This view of HDL as the “good” form of lipoprotein has evolved in recent years
and current research has identified potential atherosclerotic properties of HDL. This
dysfunctional form of HDL has been correlated with inflammatory biomarkers.64 The
dysfunctionality of HDL has also been linked to obesity. In terms of the composition of
HDL, glycation of Apo A-I in diabetes patients has been related to dysfunctional HDL.64
Triglyceride-rich HDL cholesterol has been shown to reduce the efficacy of reverse
cholesterol transport.65 The idea of dysfunctional HDL has promoted research into theimportance of HDL function over the classical view of HDL quantity.66

1.4 Methods for Lipoprotein Analysis
With the varied characteristics and composition of lipoproteins, several methodsof chemical analysis have been developed for commercial use in order to study
lipoproteins and their components. These methods separate lipoproteins based on
properties such as relative density, electrophoretic mobility, molecular weight, and13chemical composition. Despite the continuing development and refinement of thetechniques related to these characteristics, recent review articles have identified
problems in inter-relating the use of the methods for identifying individuals who havetreatable early stage CVD.67,68

1.4.1 Density Gradient Ultracentrifugation (DGU)
Ultracentrifugation has long been the ideal technique for separation,
identification, and quantization of lipoproteins.10,11,69 Ultracentrifugal methods separate
lipoproteins based on their hydrated densities. The different forms of this technique
include rate zonal ultracentrifugation and isopycnic separations70,71. Rate zonal
ultracentrifugation applies a layering method of preformed gradients over the sample.
As the centrifugal force is applied to the sample, particles will move through the gradientrelative to their densities. Separation of the particles is halted and they are characterized
by the zone in which they migrated to during the UC spin. For isopycnic separations,the particles migrate to the specific density of the particles, or isopycnic point, relative tothe density gradient formation during ultracentrifugation. Separation of the particles inthis method is specific to the density properties of the individual particles. Each of thesetechniques has specific advantages and disadvantages including the accuracy of theseparation, its use in fraction preparation, and the extent of skill needed to perform thesetechniques.

14

1.4.1.1 Sedimentation Theory and Gradient Mapping
DGU works through the principles of sedimentation theory. There are two
primary competing forces at work during the experiment: sedimentation and diffusion.
The centrifugal force exerted on a particle is related to the mass of the particle, mp,
angular velocity, ω, and the radius of the rotor, r. The formula for calculating thecentrifugal force is seen in Eq. 172:

Eq. 1

Diffusion forces can also be defined as the buoyancy of the solvent. This force
can be measured by the mass of the volume of solvent displaced. This mass can be
determined by using the mass of the particle, the density (ρ) of the solvent, and thepartial specific volume of the particle (ῡ). The resulting force from buoyancy is given in
Eq. 372:

Eq. 2

A third force which acts on the particles during centrifugation is the frictional
force between the solute particles and the solvent. This force works in opposition toboth the centrifugal and buoyant forces. The frictional force can be calculated using thefrictional coefficient, ƒ, and the velocity of the moving particle, ʋ.

15Using gradient forming solutes beginning from a homogeneous solution, these
density gradients reach a steady state in the form of an exponential curve relating density
to the distance away from the meniscus. In terms of tube coordinate, a lower tube
coordinate is near the meniscus and therefore would have a lower density relative to ahigher tube coordinate which is further away from the meniscus and has a larger density.
The relation of tube coordinate to density can be seen in Eq. 3:

Eq. 3

In this equation, x is the position relative to the meniscus, A and B are coefficients, andy0 is the intercept. The position of the measurement can be related to density throughuse of refractive index and calibration curves relating refractive index to density.73
Research into density gradient formation has shown a relationship between themolecular weight of the gradient-forming solute and the slope of the resulting density
curve.12,73 At equilibrium, the free energy of centrifugation is equal to the free energy ofdiffusion. Through relation of the free energy equations for each of the different acting
forces in ultracentrifugation, a relationship between the molecular weight of the solute(Mw) and the resulting gradient formation is found. The resulting equation relating theslope of the density gradient and molecular weight of the solute is shown in Eq. 4:

Eq. 4

16

In this equation, T represents the temperature of the UC spin, R is the gas constant, andc1/c2 represents the concentration ratio between two points at radii r1 and r2 in the sample
tube, respectively.

1.4.1.2 Methods of DGU
The need for a rapid and straight forward method of lipoprotein density profilingwhich can provide the most precise information possible is necessary if lipoprotein
density profiles are to be used in clinical studies. Currently, the commercialized methodof lipoprotein separation through density gradient ultracentrifugation is Vertical Auto
Profiling (VAP) run by Atherotech. This method involves the use of potassium bromide(KBr) as the salt present in the aqueous gradient.74,75 Use of this high ionic strength
solution is problematic as it has been shown to create multi-component aggregates of the
low-density lipoproteins.76 Fractioning the profile using this technique normally
involves puncturing the sample tube at the bottom and using a continuous-flow analyzer
to monitor and separate each elution. This can allow lipoprotein to adhere to the sample
tube walls causing for error in the measurement. The VAP method measurescomposition of the different elution in terms of UV absorbance. VAP results have been
applied to CVD risk assessment, however, the results can vary and the precision for anindividual patient is questionable.77
Current research into DGU has introduced the viability of using EDTA salts tocontrol the density gradient formation process under ultracentrifugation conditions.12,78,79
In particular, use of the NaBiEDTA complex has been shown to generate a density

17

gradient for profiling the full density distribution of lipoproteins in 6 hours rather than
the 48 hours required for traditional rate zonal ultracentrifugation12. The gradientformation when using EDTA salts also has been shown to be modifiable based on themetal-ion complex used for the salt and the relative concentration of salt used insolution.12 These EDTA salt solutions have a low ionic strength which reduces the riskof aggregation in the low density lipoproteins mentioned previously. However, there issome evidence that apolipoprotein A-1 (apo A-1) loss in the high density lipoproteins
could be affected by the low ionic strength.80

1.4.1.3 Fluorescence Analysis of Lipoproteins
Fluorescent techniques for quantification studies have the advantage of being
highly sensitive with reduced noise. Coupling the lipoprotein density separation with theuse of NBD in order to image the intensity of the lipoprotein subclasses has provided amethod for measurement of a subject’s lipoprotein density profile throughfluorescence.79 NBD is a lipophilic molecule that has been shown useful in labelinglipoproteins.81 This fluorophore consists of a hydrophilic head and a hydrophobic tail(Figure 2). For this reason, NBD has the advantage for lipoprotein studies in that it
fluoresces in hydrophobic environments. The lipid core of lipoprotein particles presents
such an environment. The excitation and emission spectra for NBD is shown in
Figure 3.

18

Figure 2: NBD C6-Ceramide Structure

Figure 3: Excitation and Emission Spectra of NBD C6-Ceramide

Previous research into the association of NBD and lipoprotein for application to
lipoprotein profiling has identified the optimum conditions to use in terms of quantifying
the lipoprotein profile.14,79 By monitoring the fluorescence intensity for all lipoproteins,
it was found that a 30 minute incubation time was necessary for the NBD to completelyinteract with all lipoproteins in serum. It was also found that there was relatively a one-

19

to-one volumetric relationship between the amount of NBD at a concentration of
1mg/mL and serum volume in order to saturate the lipoproteins for all densities. These
conditions were selected to account for the speed and the quantity of fluorophore uptake
in the different types of lipoproteins.

1.4.2 Mass Spectrometry of ApolipoproteinsWith the function of lipoproteins being targets as risk factors for CVD,
characterization of the components of the lipoproteins, specifically the apolipoproteins
which are directly related to the metabolic processes, is necessary. To this end, mass
spectrometry has recently been used to identify the apolipoprotein content of thedifferent lipoproteins. MALDI-MS is one of the forms of biological mass spectrometrythat has been shown useful in the characterization of apolipoproteins.16,82-86 MALDI-MS
is a soft-ionization technique developed in the late 1980’s which minimizes thefractioning of large macromolecules and results in primarily singly, sometimes doubly,
charged ions.87-89 MALDI-MS is commonly paired with a time of flight (TOF) analyzer
in a technique referred to as MALDI-TOF. MALDI-MS is especially useful in theanalysis and characterization of multi-component mixtures. While MALDI-MS has
been used in quantitative studies, it is primarily a qualitative method due to the potential
error in signal intensity related to preparative techniques, ionization efficiencies, and ion
suppression.90,91
MALDI-TOF mass spectrometry has been successful in the characterization of
the protein and apolipoprotein content of HDL. Specific proteomic signatures have been

20

identified in subjects with CVD using tryptic digestion of the HDL.92 Post-translational
modifications (PTM) of apolipoproteins have been identified in HDL relative toCVD.16,82 While the nature of these PTMs and their impact on CVD are not fully
understood, disease specific apolipoprotein signatures using MALDI-MS represent new
potential risk factors in CVD risk assessment.

1.4.3 Other Methods of Lipoprotein Analysis
The classical method for measuring lipoproteins is through measuring thecholesterol content. The most used method of measuring cholesterol is currently theFriedewald method which has been used for over 30 years.93 This method uses
enzymatic techniques to measure TC and TG, rapid precipitation to measure HDL-C,
and then estimates the LDL-C using the following equation (Eq. 5):

Eq. 5

Other methods of measuring lipoproteins include enzymatic assays to quantify
apolipoprotein content94 as well as the bicinchoninic acid assay (BCA) 95 for protein
quantification. The enzyme-linked immunosorbent assays (ELISA) for apolipoprotein
content use monoclonal or polyclonal antibodies to sandwich the apolipoprotein. Then
using a chromatic tag attached to one of the binding antibodies, the concentration of thesolution is calculated based on a calibration curve relating absorbance to concentration.
For BCA, the chemical reaction reduces Cu2+ to Cu1+ through interaction with the21proteins in an alkaline environment. The Cu1+ cations are then chelated withbicinchoninic acid. The absorbance of this BCA/Cu complex at 562nm is directly
related to the protein concentration. These methods just measure total content in asample and give no information about the distribution or potential modification of theapolipoproteins.
Due to the inherent time constraints and technical skill needed for analysis ofdensity based lipoprotein separations, other analytical techniques such as nuclear
magnetic resonance (NMR)96-101 and electrophoresis81,102-108 have been explored in orderto qualify lipoproteins. In these cases, it is not the hydrated densities, but the size of thelipoproteins as well as the electrophoretic mobility in a medium that are used tocharacterize the lipoproteins. NMR, while useful in identifying multiple subclasses oflipoproteins through size differences, has some inherent problems including not being
able to distinguish lipoprotein (a) ( Lp(a) ) from low density lipoproteins ( LDL ) andchylomicrons remnants from the very low density lipoproteins ( VLDL ). There is alsovariability in the conversion of the peak areas into the cholesterol levels due to lipid
composition variability. While NMR profiles do give 15 different subclassmeasurements, only four subclasses of LDL are determined versus the five subclasses
determined using hydrated densities. The NMR profiles do have the advantage ofidentifying five subclasses of the VLDL.
Electrophoretic methods of lipoprotein separation include gel electrophoresis
using agarose and analytical capillary isotachophoresis (ITP).81,83 Both of thesetechniques came about due to the problem of quantification of lipoproteins using

22electrophoretic techniques. Agarose gel electrophoresis has shown applicability inquantifying the VLDL-C, LDL-C, and HDL-C as well as the Lp(a)-C.104 Capillary ITP,
in conjunction with NBD as a dye, has shown the ability to profile multiple subclasses
for HDL and LDL and identify the VLDL and chylomicrons in a patient’s serum or
plasma.81 This technique has a good correlation of peak intensity to cholesterol
measurements and can be done rapidly. However, this technique is limited by the
number of subclasses that can be identified for each subclass.
Even with the advances in alternate techniques, the multiple subclasses and thedifferences in the hydrated densities of the lipoproteins make ultracentrifugation one of
the best possible techniques for lipoprotein studies.

1.5 Advanced Multivariate Statistical Analysis
Based on the paradigm shift in CVD risk assessment to a multivariablerisk model, advanced statistical analysis such as linear discrimination analysis (LDA),
sliced inverse regression (SIR)17,109,110, and quadratic discrimination analysis (QDA)20,21
become available as statistical tools which can be used to develop an algorithm that canbe used to separate defined groups by multiple variables.20,111,112 LDA separates groups
through analysis of the mean values of variables and development of a linear algorithm
using the selected variables. SIR is a modern variation of R.A. Fisher’s original LDA
method published in 1936.19 QDA separates the defined groups by using the mean,variance, and covariance between groups to develop a quadratic algorithm. These

23

algorithms can be used to assess the positive or negative contributions that the risk
factors play in classification.
Application of the LDA/SIR and QDA analyses is tested in three steps: training
set accuracy, cross validation accuracy, and accuracy on a holdout sample set. The
training set uses samples with known definitions to develop the mathematical algorithm
in order to optimally separate the defined groups. Cross validation is a method of data
removal which systematically removes one sample’s data and re-develops a newalgorithm. This new algorithm is then tested on all samples, including the removed
sample, for accuracy of definition. This new accuracy, in comparison to the training set
accuracy, represents the sample dependency of the training set algorithm forclassification. A small difference between the accuracy of the training set and theaccuracy of the cross validation indicates a low sample dependency. A large difference
between the two accuracies indicates that the algorithm generated from the training set ishighly sample-dependent. Cross validation is traditionally used as a test of thesignificance of the separation between groups when only a small number of samples isavailable. For larger sample libraries, the method of a holdout test is applied. The
holdout samples are a set of randomly selected samples from the library which are tested
using the developed algorithm. These samples are not used in the development of thealgorithm and provide a real life sample set with which to test the accuracy of theprediction algorithm developed.
Discriminant analyses have previously been used in the study of CVD diagnosis.
LDA has been used in application to ejection fractions calculated from gated blood pool

24

studies to correctly classify coronary artery disease in 81% of the subjects.18 LDA has
also been applied to cardiovascular risk variables such as HDL-C, LDL-C, TC, TG, Apo
A-I, Apo B, and hs-CRP to show increased potential of risk assessment.113 These factors
were shown to be highly accurate for the training set; however there were not enoughsamples to test a holdout. Application of the LDA/SIR analysis has also been previously
applied to the lipoprotein density profiles for small sample sets to show potential for risk
assessment through just the integrated fluorescence intensities of the lipoprotein
subclasses.14 Application of these discriminant analyses to CVD risk assessment usingnovel risk markers has the potential for improved risk classification as well as
monitoring the effectiveness of therapeutic methods for risk reduction.

1.6 Application of Methods
The goal of the research presented here is to develop an array of high precision
analytical tools that can be used for clinical studies. With traditional risk factors for
CVD being present in roughly only 80% of the population,114 novel risk factors are stillneeded to enhance the accuracy of CVD risk assessment. Using DGU as the primary
tool for lipoprotein analysis, improved definition of the distribution of lipoprotein
particles can be studied. Through fluorescent tagging using NBD, the lipoprotein profile
can be quantified and compared to the classical methods of cholesterol measurement.
The NBD interaction with the lipoprotein particle acts as a probe into the chemical
makeup and morphology of the individual particles. DGU can also be used as apreparative separation technique for further characterization of the lipoproteins through25

examination of their components. MALDI-MS is a strong analytical tool through which
the characterization of the lipoprotein components, specifically the apolipoproteins, canbe performed. The combination of these methods allow for the identification of new
potential risk factors for CVD.
Use of these potential risk factors in application to multivariate statisticalanalyses can provide a novel approach to CVD risk assessment and classification.
Through better medical definition of the presence of disease or lack thereof and throughidentification of significant risk factors, enhanced distinction between groups becomes
possible. Through use of the measured risk factors identified in this work as variables inthe LDA/SIR and QDA analyses, a more accurate risk assessment algorithm can begenerated. The potential for this risk assessment algorithm to monitor the effectivenessof treatment provides a novel method of developing therapies that can be “personalized”
based on the relevant risk factors.
The work presented here establishes high performance methods for lipoproteincharacterization. This work also establishes novel methods of CVD risk assessment
through application of the data obtained from the lipoprotein characterization into
multivariate statistical analyses. These methods have clinical applications in theprescreening of the disease as well as the monitoring of therapeutic treatments aimed at
reducing a patient’s risk.

26

2. MATERIALS AND METHODS

2.1 Materials

2.1.1 Chemicals and Supplies
NBD C6-ceramide (6-((N-(7-nitrobenz-2-oxa-1,3-diazol-4yl)amino)hexanoyl)sphingosine, catalogue # N1154) and Fluorospheres (0.1 µm
carboxylate modified red fluorescent microspheres, catalogue # F-8801) were purchasedfrom Invitrogen, Carlsbad, CA. Acetonitrile (ACN), dimethyl sulfoxide (DMSO) andhexane (>95%) were purchased from EM Science (Darmstadt, Germany).
Trifluoroacetic acid (TFA), ethylenediaminetetraacetic acid (H4EDTA), cesiumhydroxide, cadmium carbonate, sinipinic acid, Dextralip® 50, and magnesium chloride
hexahydrate were purchased from Sigma-Aldrich (St. Louis, MO). Sodium bismuth
EDTA (C10H12N2O8NaBi4H2O) was purchased from TCI America (Portland, Oregon).
Strata C18-E solid phase extraction cartridges and syringe adapter caps were purchasedfrom Phenomenex (Torrance, CA). Deionized water (DI H20) used in all experiments
was from a Milli-Q water purification system (Millipore, Bedford, MA)
HDL and LDL standards standards were purchased from Sigma-Aldrich (St.
Louis, MO). Apo A-I standards were purchased from Academy Biomedical (Houston,
TX).
Polycarbonate thick wall ultracentrifugation tubes (1.5 mL, 34 mm length,
catalogue # 343778) were purchased from Beckman-Coulter (Palo Alto, CA). Strata

27

C18-E solid phase extraction cartridges and syringe adapter caps were purchased from
Phenomenex (Torrance, CA)

2.1.2 Cs2CdEDTA Synthesis
The Cs2CdEDTA complex was synthesized from H4EDTA, cesium hydroxide,
and cadmium carbonate.73 The reagents were combined stoichiometrically in 500 mL of
DI H2O, followed by a two hour reflux, yielding a clear solution. The cesium hydroxide
was added to the clear solution to bring the final pH range to 6-7. The final solution
volume was reconstituted to 500 mL to account for evaporation during reflux to give thedesired final concentration for the solution. The synthesis of the Cs2CdEDTA complex
is shown in Eq. 6.

Eq. 6

2.1.3 Serum Collection
The serum used for these studies was acquired from multiple donors withinformed consent. The serum was collected in a 9.5mL Vacutainer treated with polymer
gel and silica activator (366510, Beckton Dickinson Systems, Franklin Lakes, NJ). The
serum was separated from the red blood cells by centrifugation at 3200 rpm for 30 min
at 5°C and then stored at -80°C prior to use.

28

The work described in this dissertation was carried out in accordance with The
Code of Ethics of the World Medical Association (Declaration of Helsinki) for
experiments involving humans.

2.2 Analytical Methods

2.2.1 Ultracentrifugation
Ultracentrifugation was carried out using an Optima TLX ultracentrifuge and a
TLA 120.2 fixed-angle rotor (Beckman-Coulter, Palo Alto, CA). Samples were spunusing a rotor speed of 120,000 rpm. For the TLA120.2 rotor, these speeds correspond to
average relative centrifugal force of 511000g. Salt concentrations were selected toachieve the desired density gradient profiles.

2.2.2 Density Gradient Measurement

2.2.2.1 Properties of EDTA Solutions
Serial dilutions for each of the EDTA solutions were made in order to constructcalibration curves necessary for relating density, refractive index, and concentration.14,73
Initial concentrations for each stock solution of EDTA salt were 0.4000M, calculated
stoichiometrically. Densities for each serial dilution were determined gravimetricallyusing a calibrated 10-mL glass pipet. The refractive index for each dilution was

29measured at 20°C using an Abbe 60/DR refractometer from Bellingham + Stanley(Lawrenceville, GA).

2.2.2.2 Gradient Formation and Measurement
Density gradient measurements were performed as previously described by
Johnson et al.14,73 Briefly, the density gradients were formed in an Optima TLX
ultracentrifuge, TLA 120.2 fixed-angle rotor. All tubes contained 1150μL of thedesignated EDTA solution and were centrifuged as previously described (Section 2.2.1).
After centrifugation, gradient formations were determined by removing 10μL aliquots
from distinct positions throughout the formed gradient and measuring their respective
refractive indexes. Aliquots were obtained sequentially from the top of the UC tube
down in order to not disturb the density gradient below. Images of the UC tube weretaken using a digital color microscope camera (S99808, Optronics, Goleta, CA) while
the sample was being removed so that the exact location of each aliquot could bedetermined by digital analysis. The density of each aliquot was then mapped in relation
to its orientation inside the tube (tube coordinate).

2.2.3 Fluorescent Labeling of Serum Samples
Serum samples were stained for imaging as follows: 6µL of serum were mixed
with 10µL of NBD C6-ceramide (1mg/mL in DMSO) and diluted to 1300 µL using anaqueous solution of the density-forming solute (NaBiEDTA) followed by incubated for
30 min to achieve saturation.12,79

30

2.2.4 Fluorescence Image Analysis
Fluorescence imaging was used in monitoring the dynamics of the density
gradient formation as well as the measurement of the sedimentation equilibrium density
profile. An image of the tube containing the fluorophore-tagged lipoproteins or
fluorescent nano-spheres was obtained and analyzed using a digital Optronics Microfire
Camera (S99808, Goleta, CA) with a Fiber-Lite MH-100 Illuminator, a metal halidelamp, as a light source (MH100A, Edmund Industrial Optics, Barrington, NJ). A digitalcolor microscope camera (S99808, Optronics, Goleta, CA) was used to record the image.
The camera and light source were placed orthogonally to each other on an optical bench
to illuminate the ultracentrifuge tube mounted in a custom-designed holder. Two filters
matching the excitation and emission characteristics of the fluorophore were chosen.
Specific settings for the Microfire camera software were a gain of 1.000 and a target
intensity of 30% to illuminate the tube. The exposure time was optimized for sensitivity
and to achieve linearity.
The image of the polycarbonate ultracentrifuge tube was then converted to adensity profile following the method described by Johnson et al.12 Briefly, the twodimensional pixel field generated by the camera software was converted to a digitalmatrix of intensity versus tube coordinate (6-33 mm length) using Origin 8.5 software to
generate a graphical representation of the density profile. The relationship between tube
coordinate and density was then used to modify the x-axis in terms of density rather thantube coordinate.

31

2.2.5 Lipoprotein Density ProfilingTo obtain the lipoprotein density profile, the specific instrument set-up andsettings is described here. Following the UC spin, and layering depending on therespective study, an image of the sample tube was obtained and analyzed using themethod for fluorescence imaging previously described. Two filters matching theexcitation and emission characteristics of NBD C6-ceramide from Schott Glass(Elmsford, NY) were chosen. A blue-violet filter (BG-12) with a bandwidth centered at
455 nm and a yellow emission filter (OG-515) with a bandwidth centered at 570 nm
were used as the excitation and emission filters, respectively. Specific settings for theMicrofire camera software were an exposure of 53.3 mS with a gain of 1.000 and atarget intensity of 30% to illuminate the tube prior to image capture.

2.2.6 NaBiEDTA Gradient Optimization Studies

2.2.6.1 Spatial Separation of Lipoprotein Density Profiles
To test for the optimum volume that can be spun inside the UC tube, 1200µL
samples of DI H2O were weighed and then spun inside the ultracentrifuge. After being
spun, the samples were weighed again to measure the sample loss inside the rotor. By
calculating the relationship of weight loss to volume of water, the optimum volume able
to be used inside the tube without sample loss was determined. This volume was thenused as the standard volume in a spin for optimization of the method.

32

2.2.6.2 Density Gradient Development over Time
The density gradient distribution for the 0.1800 M NaBiEDTA was measured forseveral different spin times ranging from 2 – 8 hours. The method used follows aprocedure and calibration method developed by Johnson et al where 10 aliquots (20 µL
volumes) are withdrawn from well-defined positions within the gradient and theirdensities measured by refractive index.12 Gradient curves were then calculated by
mapping the tube coordinate versus density using Origin 8.5.

2.2.6.3 Precision of Density Measurements Using Nano-spheres
Using the fluorospheres and following the method for density gradientmeasurements, the accuracy and precision of the density measurement were studied.
Briefly, 1µL of the fluorosphere solution was mixed with 1299µL of 0.1800M
NaBiEDTA. A volume of 1150µL of this mixture was then spun as described
previously and imaged using a green excitation filter (VG-6) with a bandwidth centeredat 520 nm and a red emission filter (R-60) with a low cutoff at 600 nm (Edmund
Industrial Optics, Barrington, NJ). This was done for a set of 10 replicate samples foreach spin condition. Using Origin 8.5 graphing software, the images were mapped
according to their tube orientation and relative densities. The peak density (g/mL), full
width at half maximum (mm), change in densities, and the density resolution were
calculated for each replicate.

33

2.2.6.4 Polar Layering with H2O vs. Non-polar Layering with Hexane
In preliminary studies, our laboratory used DI H2O as a layering medium forseparation of the different TRL subclasses.14 Initial layering of the samples was donewith 150µL DI H2O layered using gel loading tips (Sigma Aldrich, St. Louis, MO,
Catalogue # CLS4853). Hexane was chosen as the new layering medium in order toremove the meniscus interference from the TRL subclass measurement. Following the
UC spin, 240µL of hexane was slowly added on top of the spun samples without
perturbing the density profile using gel loading tips (Sigma Aldrich, St. Louis, MO).

2.2.6.5 Effects of Ultracentrifugation Spin Temperature
The effect of the temperature in the ultracentrifuge chamber on the lipid profiles
was studied when the samples were run using the pre-described method for the UC spinand varying the temperature at which the samples are run. Specifically, the samples arerun at 5, 15, and 25°C.

2.2.6.6 Tube Orientation for Imaging
Each UC sample tube was marked to indicate its position inside theultracentrifuge rotor. After a standard spin at 5°C, the sample tube was rotated 360°
using 90° increments inside the imaging station sample holder and imaged to test theeffect that the tube orientation has on the lipoprotein profile. Ten replicate samples were
measured in order to assess the repeatability based on orientation in the tube holder.

34

2.2.6.7 Stability of the Lipoprotein Profile after UC Spin
Stability of the lipoprotein profile was studied by taking an initial image of the
spun sample as described previously and then taking consecutive images every 30
minutes for a period of 90 minutes. The images were then analyzed for the density
profiles as described previously (Section 2.2.4).

2.2.6.8 Precision and Normalization of the Density Profiles
Using serum from the single donor, ten replicate measurements were made of thelipoprotein density profile. The ten profiles were overlaid using Origin 8.5 graphing
software to identify any systematic error in the measurement. A more quantitative andinformative approach to measuring the inherent precision of the method was introduced
that involves determining the integrated fluorescence intensities of the eleven subclasses
based on density ranges as described in the literature.9 For each of the subclasses themean value and standard deviation of the intensities of each of the subclasses wasevaluated.
While this analysis gave an overall estimate of the precision of the measurement
related to sample preparation, an additional contribution of error comes from day-to-day
variability in the intensity of the light source. Consequently, two methods for measuring
precision were established. The first method (referred to as Mode 1) determined themean value and standard deviation of the absolute fluorescence intensities of each of the
subclasses. The second method (Mode 2) was a normalization of data where the35

fluorescence intensity of each of the subclasses is given as a percent of the total
integrated intensity. In order to test the efficiency of Mode 2, sample tubes were imaged
using exposure times of 53.3ms and 100ms. The resulting profiles were normalizedusing Mode 2 analysis and the subclass percentages were compared. This approach was
designed to eliminate the day-to-day variability of the light source intensity.

2.2.7 High Performance Lipoprotein Density Profiling (HPLDP)
Based on the previous studies, the optimum method for lipoprotein density
profile using DGU was identified. Serum samples were fluorescently tagged andprepared for the UC spin as follows: 6µL of serum were mixed with 10µL of NBD C6ceramide (1mg/mL in DMSO) and diluted to 1300µL using an aqueous solution of thedesired EDTA salt concentration followed by incubation for 30min to achieve
saturation.12,79
Using 1150µL of the serum/aqueous salt solution, the samples were then spun in
the ultracentrifuge. Ultracentrifugation was carried out as previously described with aspin temperature of 5°C and spin time of six hours. Following the UC spin, samples
were carefully layered with 240µL of hexane and images of the samples were obtained
and analyzed using a digital Optronics Microfire Camera as previously described(Section 2.2.5). Specific settings for the Microfire camera software were a gain of 1.000
and a target intensity of 30% to illuminate the tube. The exposure time was optimized
for sensitivity and to achieve linearity. The image of the polycarbonate ultracentrifuge

36

tube was then converted to a density profile following the method as previously
described (Section 2.2.4).

2.2.8 Lipoprotein Density Profiling of Commercial HDL/LDL Standards
Serial dilutions of the commercial standards for HDL and LDL were used toobtain lipoprotein density profiles using the HPLDP method. The concentrations foreach standard were reported in terms of total protein concentration. For HDL, the serial
dilutions were prepared in concentrations of 520µg/dL, 260µg/dL, 130µg/dL, 65µg/dL,
and 32.5µg/dL. For LDL, the serial dilutions were prepared in concentrations of
160µg/dL, 120µg/dL, 80µg/dL, 40µg/dL, and 20µg/dL. These concentrations for HDL
and LDL represented four times and two times the average concentrations of proteinpresent in a human sample48, respectively. Calibration curves were developed relating
the integrated fluorescence intensities to concentration.

2.2.9 Serum Viability Studies
The stability of the lipoprotein density profiles relative to storage conditions wasstudied in terms of time spent at room temperature and the effect that multiple
freeze/thaw cycles would have on the density profiles. Aliquots of a serum sample
obtained from a volunteer were stored at room temperature for periods of 0.5, 2, 4, and24 hours prior to the UC spin. Further aliquots of this serum were stored in a -80.0°C
freezer. These samples were thawed at room temperature for 30 minutes and then flash

37

frozen using liquid nitrogen for up to 12 cycles. The serum samples were then profiledusing the HPLDP method and compared to study the effects of each condition.

2.2.10 Preparative Ultracentrifugation and Fraction Collection
Preparative ultracentrifugation is a method developed in our laboratories in order
to separate lipoproteins for further analysis using a variety of analytical techniques.73
This method utilizes the density gradient separation applied to larger volumes of serum.
Based on previously identified dysfunction forms of HDL, this work focused on
isolation of HDL for further analysis.

2.2.10.1 Dextran Sulfate Precipitation
In order to eliminate interference from other lipoproteins, serum samples werefirst treated with dextran sulfate (DS) and magnesium chloride hexahydrate. Dextran
sulfate has been previously identified as method or removing all lipoproteins containing
Apo B.115,116 A stock solution with the final concentration of 10.0g/L of DS and 0.500M
magnesium chloride was formulated as follows:
2.500g of the 50,000 molecular weight variant of dextran sulfate (Dextralip® 50)
25.375g of dried magnesium chloride hexahydrate
500mL of DI H2OThis working solution was added to a serum sample at a volume of 10% of the serum
volume. For these studies, 200µL of serum was combined with 20µL of the DS mixture.
The serum/DS solution was mixed briefly through vortexing and incubated at room

38

temperature for 10 minutes. The precipitate was separated using a tabletop centrifuge
spun at 12,000g for 5 minutes. This resulted in the sedimentation of all Apo B
containing lipoprotein particles. The supernatant was then spun in the ultracentrifuge.

2.2.10.2 Preparative Ultracentrifugation
The recovered supernatant from the dextran sulfate precipitation reaction was
spun in the UC. For preparative methods, the 0.2500M concentration of theCs2CdEDTA gradient forming solution was used due to its optimum separation of HDL
over the length of the UC tube. The samples were prepared as follows:~200µL of DS treated serum (supernatant)
1100µL of 0.2500M Cs2CdEDTA solution
NBD was not applied to the samples in order to reduce any effect it might have on thefurther analysis of HDL. 1150µL of this solution was added to the UC tube and spunusing the standard spin conditions as previously described for the HPLDP method(Section 2.2.7).

2.2.10.3 Freeze/Cut Method for Fraction Collection
A freeze/cut method developed through our laboratory was used to fractionate
lipoproteins following preparative UC spin.73 The spun samples were slowly frozen in
their respective UC tubes using liquid nitrogen by placement into a custom 10-slot
holder and lowering the holder into a Dewar of liquid nitrogen. This method caused theliquid in the tubes to freeze from the bottom to the top as it was lowered into the Dewar.

39

The expansion of water as it turns to ice was accounted for by the following equation(Eq. 7):Eq. 7

In the equation, mms corresponds to the relative tube coordinate when in the solid stateand mml corresponds to the tube coordinate when in the liquid state, the state in which
the image of the UC tube was captured. The values for mml corresponded to theendpoints of the specific density range for lipoproteins to be studied. In this case, for
HDL, the densities were 1.063g/mL and 1.179g/mL which corresponded to tube
coordinates of 10.63mm and 25.63mm, respectively.
The constant 1.058 represents the correction factor necessary relating the ratio
water’s density in the liquid state to its corresponding density in the solid state. Water isdenser in its liquid state; therefore the ratio is greater than unity. The subtraction of
10.405mm was necessary to correct for the calibration of the micrometer/tube holder
assembly that was used to dial in the correct cut points. This micrometer/tube holder
assembly contained a micrometer head which functioned to advance the position of the
UC tube relative to the location of the notch for the saw blade. A Dremel® scroll saw(Racine, WI) was fitted with 0.25mm blades for the cutting of the tubes.
.

40

2.2.11 Solid Phase Extraction (SPE)
After preparative ultracentrifugation and fractionation, delipidation and desaltingof the lipoproteins were accomplished using a Strata C18-E reversed phase solid phaseextraction cartridge from Phenomenex (Torrance, CA). Delipidation of samples was
performed based on a published method.117 The method used was as follows:

1) The cartridge was conditioned drop wise with three 1mL aliquots of 0.1%(v/v) TFA in acetonitrile (ACN), allowing no air to enter the cartridge.
2) The cartridge was then conditioned drop wise with three 1mL aliquots of
0.1% (v/v) TFA in DI H2O, allowing no air to enter the cartridge.
3) The sample to be delipidated was first acidified with a volume of 0.1% (v/v)
TFA in DI H2O equal to that of the sample volume. This mixture was thenslowly added to the cartridge, allowing no air to enter.
4) The cartridge was then washed with three 1mL rinses of 0.1% (v/v) TFA in
DI H2O to remove the salts, the non-specifically bound apolipoproteins, andthe water soluble components from the serum sample. After the washes, one
milliliter of air was pushed through the cartridge to remove any remaining
liquid.
5) Elution of the bound apolipoproteins was performed using six 100μL aliquotsof 0.1% (v/v) TFA in acetonitrile and purging with 1mL of air in between
each aliquot. For each aliquot of the TFA solution, an incubation time of at41

least one minute was giving for the sorbent to soak prior to purging with air.
All six elutions were combined as the recovered apolipoproteins.

Following SPE, the recovered samples were evaporated to dryness through theuse of a SVC-100H Speed-Vac concentrator with a refrigerated condensation trap fromSavant Instruments (Farmingdale, NY) which was connected to a 5KC36PN435AX
vacuum pump from General Electric (Fort Wayne, IN). The dried HDL apolipoprotein
were then reconstituted in 0.1% (v/v) TFA in DI H2O for MS experiments. The volumeof the TFA/DI H2O solution used to reconstitute the sample was chosen based on thedesired concentration relative to initial concentration in a subject’s serum. For example,
with 200µL of serum initially used, use of a 100µL volume to reconstitute the driedapolipoproteins would result in a concentration twice that of the initial serum
concentration.

2.2.12 Colorimetric Assays for Measuring Protein Concentration

2.2.12.1 Bicinchoninic Acid Assay (BCA)
In order to determine the protein concentration of the serum samples, fractioned
samples, and recovered apolipoproteins from SPE, a BCA Protein Assay Reagent kit
from Pierce Biotechnology was used (Rockford, IL).95 This kit comes with threedifferent reagents which need to be mixed in the proper ratios in order to make theworking reagent (WR) for the assay and bovine serum albumin (BSA) to be used as a42standard to generate a calibration curve relating absorbance to concentration of protein.
The three reagents are labeled A, B, and C and are composed of the following:

A: Sodium carbonate, sodium bicarbonate, and sodium tartarate in 0.2M NaOH
B: 4% (v/v) Bicinchoninic acid in H2O
C: 4% (v/v) Cupric sulfate pentahydrate in H2O

The volumetric ratio of reagents needed to create the WR is as follows (Eq. 8):Eq. 8150µL of the WR, followed by 150uL of the desired sample or standard to be analyzed,
was pipetted into a 96 well microtitre plate. The microtitre plate was then incubated inan over at 37°C for 2 hours, followed by a cool down period of 10 minutes at room
temperature. The plate was then placed on a µQuant spectrophotometer from Bio-Tek
Instruments (Winooski, VT) and the absorbance at 562nm was recorded for all standards
and sample replicates. Each sample and standard was measured in triplicate to reduce
the systematic error in analysis.

2.2.12.2 Apo A-I ELISA
The commercial immunoassay for measuring Apo A-I concentrations in serumand plasma from Assaypro, LLC was applied to measuring the concentrations in serum,

43

recovered HDL, and recovered apolipoproteins (St. Charles, MO, Cat. # EA5201-1).
The procedure provided by Assaypro was followed with few modifications. Briefly, allsamples were first diluted in 1:200 ratio of initial sample volume to final volume with
the MIX Diluent provided in the assay kit. For samples with higher concentrations,dilutions of up to 1:800 were necessary. Incubation times were kept consistent with theprovided procedure. Absorbance for each sample was measured at 450nm and 570nm.
The readings at 570nm were subtracted from the readings at 450nm to correct forbackground noise. Samples and standards were run in triplicate to reduce systematic
error.

2.2.13 MALDI-TOF MS Analysis of HDL Apolipoproteins
Matrix assisted laser desorption ionization mass spectrometry (MALDI-MS)
analysis was used to characterize the recovered apolipoproteins from the HDL fractions.
A commercial Voyager-DE STR, MALDI-TOF mass spectrometer equipped with a 2m
flight tube from Applied Biosystems (Foster City, CA, USA) and a mass range of 300040,000 Da was used for the analyses. The MALDI matrix used for the samples consistedof a 10mg/dL solution of sinapinic acid in a 1:1 mixture of acetonitrile and 0.1% TFA inwater. A thin-layer sample preparation method in which a MALDI plate was firstspotted with the desired MALDI matrix and allowed to dry followed by a mixture of thesample and matrix (1:1 ratio) deposited atop the original spot was used for this analysis.
Calibration of the instrument was performed with a mixture of bovine insulin, bovine
serum albumin, and myoglobin as an external standard. 1μL of the standard was added

44

to 24μL of the matrix. For the MALDI analysis, the acceleration potential was held at
25kV, the grid potential was at 93%, and the delay time was 575ns. Approximately 100
shots per spectrum were collected.

2.3 Clinical Studies
Serum samples for each clinical study were selected from the serum libraries
collected through collaboration of our laboratory with Scott & White Hospital (Temple,
TX). The complete adult serum library consists of CVD patients that were identified
through patient histories of cardiovascular event and arterial blockage measured byangiography and control patients (CTRL) that were identified through arterial blockageof less than 10% measured by angiography. Patient medical histories including
information about ethnicity, family history of disease, diabetes, etc. were collected using
a standard questionnaire for all subjects. Standard lipid panel measurements for TC, TG,
HDL-C, and LDL-C were performed by Scott & White clinicians and reported with thepatient history data. All serum libraries consist of donated serum from Scott & White
patients. Informed consent was obtained from all donors.

2.3.1 Statistical Methods of Analysis
Data for each clinical study was analyzed using multiple statistical methods.
Linear discriminant analysis (LDA), sliced inverse regression (SIR), and quadratic
discriminant analysis (QDA) were used to develop risk assessment algorithms for CVD.
Analysis of Variance (ANOVA) and 2-sample T-tests were used to indentify factors

45with statistical differences between selected cohorts. LDA, QDA, ANOVA analyses,
and T-tests were applied using Minitab 15 (State College, PA). SIR was applied using
the R statistical package (R Foundation for Statistical Computing, Vienna, Austria).
The LDA and QDA analyses output the prediction accuracy of the training set,cross validation analysis (X-Val), and accuracy of a holdout sample. The training set isa set of defined samples which are used for developing the prediction algorithm. For
large sample libraries, the training set is a group of randomly selected samples from thefull library in order to remove any bias based on sample selection. The X-Val is amethod of removing one random sample from the sample set and then rerunning theanalysis. The resulting new equation is tested on the full sample set; including thesample that was removed. The difference between the new prediction accuracy and theaccuracy of the training set represents the level of dependence that the model has on thesample data that was removed. For sample libraries that are large enough to use arandomly selected training set, the remaining samples are used for a holdout sample set.
These are samples which were not used for development of the prediction algorithm.
The accuracy of the holdout sample set using the prediction algorithm represents the realworld application potential and statistical significance of the algorithm.
SIR is a modern variation of R.A. Fisher’s original Linear Discriminant Analysis(LDA) method published in 1936 19. SIR outputs the coefficients of each variable
relative to the prediction algorithm. These coefficients are relative to the coefficientsfrom the LDA analysis with the inclusion of a scalar modification. SIR also allows for46

the calculation of the p-value which is a measurement of the statistical significance ofthe separation algorithm.

2.3.2 Normal Lipidemic Serum Library
Normal lipidemic serum samples were selected based on the following criteria:
TC<200mg/dL, TG<150mg/dL, HDL-C >40mg/dL for men and >50mg/dL for women,
and LDL-C<100mg/dL. Twelve CTRL subjects were identified based on the criteria andthrough angiography (<10% arterial blockage). Sixteen CVD patients were identified
based on the criteria and having documented CVD.

2.3.3 Comprehensive Serum Library
The comprehensive serum library study consisted of all adult serum samples
collected through the Scott & White collaboration. The library was made up of 100
CVD subjects who were identified through patient histories of cardiovascular event andarterial blockage measured by angiography and 72 CTRL subjects who were identified
to have less than 10% arterial blockage through angiography. Of this library, 70 CVD
and 40 CTRL subjects were selected using a random number generator (Minitab 15) to
be used for the training set. The remaining 30 CVD and 32 CTRL were used as aholdout sample set to test the generated algorithm. The CVD serum library consisted ofsubjects from 34-83 years of age with varied racial backgrounds, primarily Caucasian.
The CTRL serum library consisted of subjects from 29-83 years of age with varied racial

47

backgrounds, again primarily Caucasian. The gender ratios for each library wereapproximately half male and half female.

2.3.4 Hypercholesterolemia Serum Library
The hypercholesterolemia serum library was comprised of 30 children’s serum
samples. The subjects for this study ranged from ages 10 to 20. The definition of disease
for these samples included children with a parental history of hypercholesterolemia orfamily history of premature CVD and also those children with two or more classical risk
factors. Eight healthy children samples were also collected. The healthy children were
defined as having normal lipid levels and no familial history of CVD or related risk
factors. These samples were similar in age range to the hypercholesterolemic children.
This study was designed independent of gender and ethnicity.
After dietary monitoring, hypercholesterolemic children with a fasting LDL-C
remaining ≥ 130 mg/dl (e.g., high risk by NCEP guidelines) were randomized forsimvastatin (20 mg) or placebo for 6 months. The Scott & White inpatient pharmacy
provided and dispensed compounded drug and placebo in identical forms. Serum draws
for each patient were taken every 12 weeks.

2.3.5 Niacin Treatment Study
For this study, a single serum donor with documented CVD was treated withincreasing amounts of niacin therapy from 0mg to 2000mg using increments of 500mg
roughly every four weeks. A baseline serum draw was performed prior to treatment and48

then serum draws were performed after every treatment period. A washout period of one
week was performed after the last treatment to remove all drug effects. A serum draw
was performed after this washout period to assess the effect of stopping medical
treatment. The serum samples were analyzed using the HPLDP method of lipoprotein
density profiling and the resulting integrated fluorescence intensities for the lipoprotein
subclasses were applied to the risk assessment algorithm generated for thecomprehensive serum library.

2.3.6 Exercise Regime Study
For this study, a single volunteer underwent lifestyle modification in the form of
increased exercise. A baseline serum draw was performed prior to any change inexercise. The first phase of the exercise program for this volunteer was made up of three
to five 30 minute runs every week for 16 weeks. This phase’s exercise was designed tokeep the heart rate down to an optimal range for burning fat (60-70% of maximum heartrate). After 16 weeks, the second phase was introduced. This phase increased theexercise to include an extra 30 minute run each day at a pace that was set for
cardiovascular training (70-80% of maximum heart rate) in addition to the fat burning
run. Serum draws were performed weekly through Scott & White (College Station, TX)
to monitor the change in the lipoprotein density profiles relative to the exercise. The
serum samples were analyzed using the HPLDP method of lipoprotein density profilingand the resulting integrated fluorescence intensities for the lipoprotein subclasses wereapplied to the risk assessment algorithm generated for the comprehensive serum library.

49

3. RESULTS AND DISCUSSION

The overall objective of this study was to develop and enhance analyticaltechniques and methods which, when combined, could be used to assess the risk anindividual has of developing cardiovascular disease. The data from these techniques
were combined with advanced statistical analysis methods for developing multivariate
algorithms that not only determine classification, but also identify individual
contributions and importance from the factors used in the analysis. The first stage incompleting this goal dealt with optimizing the methods to be used in the lipoprotein
profile analysis in order to reduce error. This was done through a fundamental
understanding of the conditions and factors which have an effect on the reproducibilityof the results. After successful reduction of error in the measurements of lipoprotein
profiles using density gradient ultracentrifugation and NaBiEDTA, the method wasapplied to multiple clinical studies with much success.
With the success of the NaBiEDTA-based density gradient system for lipoprotein
profiles, further research was carried out employing a more versatile gradient based on
the use of Cs2CdEDTA as the solute. The higher solubility of Cs2CdEDTA allows for
greater manipulation of the density gradient in order to isolate specific sections of thelipoprotein profile. For this study, the selected concentrations were focused on the
optimal separation of the main lipoproteins LDL and HDL. HDL separation was a majorinterest due to the statistical analysis results from the previous clinical studies
identifying the HDL subclasses as the largest contributor to risk classification. While

50

the application of the Cs2CdEDTA profiles did not improve risk assessment, it did
confirm the classification method and application of the LDA/SIR analysis for risk
assessment using lipoprotein profiles. Optimal separation of the HDL subclasses also
allowed for application of the Cs2CdEDTA-based density gradient as an improved
preparatory method for further analysis of the HDL profiles through mass spectrometry.
Use of the Cs2CdEDTA gradient to isolate LDL allowed for the observation of a unique
cationic interaction of the Cs+ with the phospholipids on the surface of an LDL particle
which affects the density of LDL in the profiles in comparison to the NaBiEDTA
profiles.
Using the optimized Cs2CdEDTA gradient and dextran sulfate precipitation as amethod for removal of LDL from serum, further studies were performed to test theviability of using mass spectra of HDL apolipoproteins for LDA/SIR analysis and risk
assessment based on the earlier observation that HDL subclasses had highest
contributions to the LDA/SIR classification. This data was applied in addition to thelipoprotein profile data as well as being used as a separate method for risk assessment.
Despite identification of differences in the masses of the major apolipoproteins present
in HDL, the application of this data into the LDA/SIR analysis showed no improved risk
assessment.
Overall, this research has advanced the field of cardiovascular risk assessment
through application of high performance lipoprotein density profiling and further
analysis through mass spectrometry. Identification of distinct lipoprotein subclasses
which can be used to assess risk and condition specific mass alterations of the

51apolipoproteins specific to HDL have allowed for an 85% accurate risk assessment
model for CVD which applies to the comprehensive serum library available through thecollaboration of Scott & White Hospital and the Laboratory for Cardiovascular
Chemistry at Texas A&M University.

3.1 Analytical Methods

3.1.1 NaBiEDTA Lipoprotein Profile Optimization
The objective of this study was to identify sources of error in the NaBiEDTAbased gradient system in order to focus on those features of the method that could beenhanced and therefore enhance the overall precision of the lipoprotein profile analysis.
To accomplish this, several aspects of the gradient formation and lipoprotein profile
were studied. These factors included studies into the gradient formation, accuracy of thedensity measurement, lipoprotein propagation during the ultracentrifuge spin, spatial
separation of the lipoproteins, layering methods, spin conditions, imaging conditions,and data analysis.

3.1.1.1 Initial System Error Analysis
In order to identify components of the method where enhancements could bemade and error reduced, a baseline analysis of the method was performed. To
accomplish this, a serum sample was spun and imaged in 10 replicate measurements
using the 0.2000M NaBiEDTA-based density gradient with a 1000µL volume spin

52

previously described by Johnson et al. The tubes were layered with 200µL of H2O andthe resulting profiles were integrated based on the density profile and total intensity wascalculated for each of the 12 lipoprotein subclasses. The resulting profiles were overlaid
as shown in Figure 4. The average integrated intensities for each subclass and theirrespective errors, calculated based on the standard deviation, are graphed in Figure 5.
The average error over the 12 subclasses was calculated based on the percent
relative standard deviations (%RSD) and was found to be 23.39%. As can be seen fromthe overlaid profiles and error bar analysis of the subclasses, the largest errors were in
the buoyant TRL, dense TRL, and low density LDL subclasses. There was also a larger
amount of error at the dense region of the HDL subclasses. The error in the TRL and
LDL regions was associated with the H2O layering method. The error in the HDL
region was related to the curvature of the sample tube and the effect it has on theuniform exposure of the NBD fluorophore from the excitation source.

53

2000

dTRL

bTRL

HDL

LDL
1

1800

2

3

4

5

2b

3a
2a

3c
3b

1600

Intensity

1400
1200
1000
800
600
400
200
0
4(<1.000)68

10 1.021
12 14 1.027
16 18 1.040
20 22 1.065
24 26 1.114
28 30 1.210
32

Density (g/mL)

Figure 4: Overlaid Lipoprotein Profiles for Baseline Error Analysis

60000

50000

Intensity

40000

30000

20000

10000

0
bTRL

dTRL

LDL-1

LDL-2

LDL-3

LDL-4

LDL5 HDL-2b HDL-2a HDL-3a HDL-3b HDL-3c

Lipoprotein Subclass
Figure 5: Integrated Intensities and Error Bars for Baseline Error Analysis

54

3.1.1.2 Optimization of the Spatial Separation of the Density Profile
In order to achieve a better overall spatial separation of the lipoprotein particles,
extending the total length of separation was deemed necessary. This meant that the
optimum spin volume needed to be established. A base volume of 1200µL of H2O wasspun under standard conditions similar to that used for lipoprotein profiling. Each tube
was weighed prior to and after the spin. By measuring the weight loss, the total volume
loss was able to be calculated using the density of H2O. It was discovered that an
average of 3.5% by weight or 40µL of H2O was lost per tube during the spin. This loss
was due to overflow from the sample tube into the angled rotor when samples are spunin the ultracentrifuge. To compensate for this loss, the maximum spin volume was set at
1150µL. A second sample loss study was carried out to confirm that no sample loss
would occur using this volume. Results from this study showed that the sample loss
using the 1150µL spin volume was an average of 0.173% or 2µL. This loss was uniform
for all samples and was deemed to have minimal effect on the final analysis. Therefore,
the 1150µL spin volume was confirmed as the optimal volume to allow for best spatial
resolution.
The change in spin volume meant that the initial concentration of the solution
had to be adjusted. Based on density gradient theory, increasing the overall volume spun
inherently increases the total number of moles of the EDTA salt that is present. This
meant that the initial concentration of the NaBiEDTA had to be lowered in order to
optimally spread the lipoprotein profile over the whole tube. At the same time, thesaturation concentration of NaBiEDTA must be avoided and spatial separation on the

55

HDL subclasses must occur away from the curvature at the bottom of the UC tube. The
optimum concentration that allowed for the identification of all lipoprotein subclasses
was found to be 0.1800M NaBiEDTA.

3.1.1.3 Density Gradient Development over Time
Measuring the formation kinetics of the density gradient made possible themapping of a lipoprotein profile based on density as well as a determination of thedensity resolution of the measurements. Figures 6 and 7 shows the formation of thedensity curve over time measured at 2, 4, 6, and 8 hours. (Figures 6A&B, Figures
7A&B). As can be observed from the formation of the density curve, and throughcomparison of the R2 value for curve fit, for full exponential curve formation to occur, ittakes 6 hours. Before 6 hours, the lower density region of the gradient is still not fully
formed and does not fit an exponential curve, i.e the R2 values of 0.955 for a 2 hours
spin. Development of the exponential form of the gradient can be seen as the R2
approaches 1.0 over time. After 6 hours, the curve shows an R2 value of 0.9897 and adensity profile that is close to exponential. After 8 hours, the R2 value increases to0.99095. The 6 hour spin time was chosen as it gives an acceptable exponential gradient
while minimizing the spin time.

56

A
1.14

y = 0.00265*exp(x/9.10952) + 1.03263
R^2= 0.95524

1.12

Density (g/mL)

1.10

1.08

1.06

1.04

1.02
5

10

15

20

25

30

Tube Coordinate (mm)B
y = 0.0012*exp(x/6.45833) + 1.02354
R^2= 0.9895

1.16
1.14

Density (g/mL)

1.12
1.10
1.08
1.06
1.04
1.02
4

6

8

10

12

14

16

18

20

22

24

26

28

30

32

Tube Coordinate (mm)Figure 6: Gradient Formation over Time (2 and 4 Hours). (A) 2 hour spin, (B) 4hour spin

57

A
1.20

y=1.01886 + 0.00087*exp(x/5.93472)
R^2 = 0.98972

1.18
1.16
1.14

Density (g/mL)

1.12
1.10
1.08
1.06
1.04
1.02
1.00
0.98
5

10

15

20

25

30

Tube Coordinate (mm)B
y= 1.01323 + 0.00075*exp(x/5.48516)
R^2 = 0.99095

1.24
1.22
1.20
1.18

Density (g/mL)

1.16
1.14
1.12
1.10
1.08
1.06
1.04
1.02
1.00
0.98
5

10

15

20

25

30

Tube Coordinate (mm)Figure 7: Gradient Formation over Time (6 and 8 Hours). (A) 6 hour spin, (B) 8hour spin

58

As the gradient forms, the progression of the lipoprotein profile, portrayed in
Figure 8, shows that the lipoproteins are resolved at the higher densities first and thenpropagates down the tube as the density gradient becomes steep enough to reach thesame density at the lower portion of the sample tube. As the exponential gradient forms,
the lower density lipoproteins are then separated by their respective densities in a similar
fashion. This shows that the lipoproteins density relatively do not change during
ultracentrifugation. Any change in the lipoproteins would have to happen immediately.

1410

940

470

0

4hour

2hour

6

8

10

12

14

16

18

20

22

24

26

28

30

32

Tube Coordinate (mm)Figure 8: Overlaid Serum Profiles at 2-8 hours

Ti
m

Sp

in

6hour

e

8hour

Intensity

1880

59

For the density profile to be applied to clinical studies, the density gradientformation must be repeatable and precise as well. To test the repeatability of thegradient formation, the density gradient was measured for 3 different ultracentrifuge
spins. It was found that the average error in the density curve was minimal. The 3
density curves are graphed together in Figure 9. When comparing the 3 different
exponential equations, the largest error was in the multiplicative constant applied to the
exponential function. The %RSD was calculated to be 2.44%. The other constants inthe equation had errors no greater than 0.4%. This data showed that the gradientformation was highly repeatable and very precise. This feature of the density profile isan important contribution to the application of this method to clinical studies and the risk
classification. Precision in the measurements will lead to more precise classification.

1.20

Eq 1: y = 9.0768 E-4 * exp(x/5.97782) + 1.02018
Eq 2: y = 8.7348 E-4 * exp(x/5.93462) + 1.01886
Eq 3: y = 9.1429 E-4 * exp(x/5.93743) + 1.01953

1.18
1.16

Density (g/mL)

1.14
1.12
1.10
1.08
1.06
1.04
1.02
1.00
6

8

10

12

14

16

18

20

22

24

26

28

30

32

Tube Coordinate (mm)Figure 9: Replicate Density Gradients for 0.1800M NaBiEDTA Spun for 6 hours

60

3.1.1.4 Precision of Density Measurements Using Nano-spheres
With the density gradient now mapped and spin time identified, the next step was
to measure the inherent resolution of the particle density. To carry out thismeasurement, a mono-dispersed fluorescent tagged nanoparticle was employed. The
objective of this study was to identify how the peak for the mono-dispersed nanoparticle
forms and what is the inherent resolution of the density measurement based on the
exponential density curve. Figure 10 shows the progression of the fluorescent
nanoparticle based on the spin time. By measuring the full width half maximum(FWHM) of the peak intensity, it was observed that the peaks from the nanoparticles
become sharper as spin time increases. Looking at peak resolution in terms of the

500

2hour
4hour
6hour
8hour

Intenstiy

400

300

200

100

0
6

8

10

12

14

16

18

20

22

24

26

28

30

32

Tube Coordinate (mm)Figure 10: Fluorescent Nanoparticle Profile Based on Spin Time

61

percent change in density at the FWHM in relation to the average peak density of thenanoparticle (Table 3), it is seen that this percentage gets larger as the spin timeincreases. This makes sense in terms of the nature of an exponential curve. The change
in density increases rapidly when the peak is portrayed further into the sample tube dueto the rate of density change in relation to the exponential curve. The resolution change
is insignificant as the percent change is no more than 0.20% even at an 8 hour spin.

Table 3: Nanoparticle Density Measurements and Resolution
Spin
Avg
Avg.
Avg. Δ
Avg.
Std.
Time
Density%RSD FWHM
Density
Resolution
Dev.
(Hrs)
(g/mL)(mm)(g/mL)(%)
2
1.05620.0001 0.0118
1.25630.00120.112441.05440.0003 0.0237
0.68450.00140.13046
1.05240.0004 0.0411
0.58610.00190.17828
1.05520.0003 0.0319
0.54620.00210.1979

According to the manufacturer, the reported density of the nanoparticle isbetween 1.040 and 1.060 g/mL. Using the density curve formation for each time spin,
the density of the nanoparticle was shown to be 1.0545 +/- 0.0016 g/mL on average forall spin times and propagates with the density gradient formation similar to the way
serum was shown to in Figure 8. The percent relative standard deviations (%RSD) of
the density measurement ranged from 0.01-0.04% with an average of 0.027% over all
spin times measured. This observation suggests that the nanoparticles propagate from ahomogeneous solution, before it is spun, to a focused band within 2 hours that matches

62

the density of the nanoparticles. For larger spin times, the nanoparticle propagation
follows the formation of the density gradient as it approaches equilibrium. By balancing
the resolution with the gradient curve formation, a 6 hour spin was chosen because it
allows for optimal gradient formation while maximizing the resolution.

3.1.1.5 Polar Layering with H2O vs. Non-Polar Layering with Hexane
The initial method described previously by Johnson et al. used deionized water(DI H2O) as a layering medium for separation of the chylomicrons and TRLs that areless dense than 1.00g/mL from the VLDL and TRLs that have a greater density than
1.00g/mL. This method of layering creates a large amount of experimental error. The
DI H2O can readily mix with the gradient at the meniscus due to turbulence which will
induce mixing of the VLDL/TRL layers with the LDL. There is also an added factor of
creating an error in the fluorescent intensity of the VLDL/TRL layers due to themeniscus scattering light at the wavelength for NBD emission. To counter this effect, amethod of using a non-polar layering medium that is less dense than the lipoproteins wasdeveloped. The use of hexane was chosen as the layering medium. Hexane’s density is
0.68g/mL which means it will trap all of the TRLs, VLDL, and chylomicrons into one
peak while at the same time, removing the error due to meniscus light scattering byseparating the meniscus from the TRL/VLDL layer being measured.
The effects of the two different layering techniques can be seen in Figure 11.
Figure 11A shows the original lipoprotein separation using 0.20M NaBiEDTA and DI
H2O as a layering medium. The fluorescent layer near the top of the sample tube is due

63

to scattered light from the meniscus and a small amount of the low density TRLs. Thisfeature can also be seen in Figure 11B where the lipoprotein separation was carried out
using 0.1800M NaBiEDTA without any layering. The intense band at the top of theimage is mainly from the meniscus and VLDL/TRL fluorescence. This interpretation is
supported by comparing the image to Figure 11C where the sample has been layered
with hexane to remove the meniscus effect and leave the VLDL/TRL band exposed. In
Figure 11C, the hexane volume was chosen in order to shift the meniscus created fromthe hexane from the illumination area, and therefore remove any false fluorescentintensity that would be due to the scattered light from the meniscus. Consequently, theTRL and VLDL form a single band without affecting the measurement since the hexane
will trap all TRL’s based on their densities in relation hexane’s (0.68g/mL).
The use of hexane not only eliminates the meniscus reflection problem, but it
also removes the error that occurs due to the diffusion of the DI H2O into the aqueous
gradient. Figure 11D shows a comparison of the two layering techniques throughoverlaid lipoprotein profiles. As can be distinguished from the graph, when DI H2O isused as the layering medium, the peak at the lower tube coordinates broadens due todiffusion of the DI H2O which then causes error in the lipoprotein profile. Selecting
hexane as the layering medium means that there is no diffusion as the polar properties of
the aqueous gradient and the non-polar properties of hexane prevents the two layers frommixing.

64

A

B

C

6

10

22

28
31

D

2500

1000uL 0.20M NaBiEDTA - DI H2O Layered
1150uL 0.18M NaBiEDTA - Hexane Layered

2250
2000

Intensity

1750
Proteins

Meniscus,
Chylomicrons& bTRL

1500
1250

HDL
HDL

dTRL

1000

LDL

750

LDL

TRL

500
250
0
4

6

8

10

12

14

16

18

20

22

24

26

28

30

32

Tube Coordinate (mm)Figure 11: Layering Medium Comparison. (A) DI H2O as a layering medium, (B)no layering medium, (C) hexane as a layering medium, (D) Lipoprotein profile
comparison of DI H2O layering and hexane layering. Note: (A) does not represent
the same serum used in (B) and (C).

65

3.1.1.6 Effects of Ultracentrifugation Spin TemperatureWith the general ultracentrifugation spin conditions such as EDTA concentrationand spin time established, a more detailed study of other variables was undertaken.
When considering the potential of applying lipoprotein profiles to clinical studies, it isimportant to take into account the viability of the lipoprotein profile over time. With a 6hour spin necessary for the gradient formation, it becomes important to understand theeffect the temperature at which the samples are spun has on the profile.
For this study, the temperatures of 5°C, 15°C, and 25°C were chosen to test a
broad range of possible settings. Figure 12 shows the effect of spin temperature on the
lipoprotein profiles. Figures 12A shows the overlaid lipoprotein profiles for the threeselected temperatures. Figures 12B shows relationship between the average %RSD in
the lipoprotein profiles as it relates to the spin temperature. This %RSD was calculated
based on 10 replicate samples that were spun at the designed spin temperature. The
profile subclasses were integrated and the %RSD was calculated. Figures 13A and 13B
map the peak shifts observed for both LDL and HDL distributions as a function of spintemperature. As temperature increases, the LDL and HDL peaks shift to a lower tube
coordinate. This does not mean that the lipoprotein density has changed, just that thedensity gradient profile has changed due to the spin temperature. Temperature influence
on density gradient formation was previously discussed in Section 1.4.1.1. According todensity gradient theory, temperature is a component of the diffusion forces acting in situ.
Increase in temperature will increase the rate of diffusion and therefore accelerate the

66

2000

A

25C
15C
5C

1800
1600
1400

Intensity

1200
1000
800
600
400
200
0
6 1.0148
10 1.026
12 14 1.038
16 18 1.049
20 22 1.070
24 26 1.117
28 30 1.230
32
Density (g/mL)

Average Error of Serum Samples Vs Spin Temperature

B

11

AVerage %RSD

10

9

8

7

6

5

10

15

20

25

O

Spin Temperature ( C)

Figure 12: Effect of Spin Temperature on Lipoprotein Profiles. (A) Overlaid
lipoprotein profiles for 5, 15, and 25C, (B) Average Repeatability Error vs. Spin
Temperature

67

LDL Peak Orientation Vs Spin TemperatureA
18.8

Tube Coordinate (mm)

18.7

18.6

18.5

18.4

18.3

18.2

18.1
5

10

15

20

25

O

Spin Temperature ( C)HDL Peak Orientation Vs Spin Temperature

B

26.9
26.8

Tube Coordinate (mm)

26.7
26.6
26.5
26.4
26.3
26.2
26.1
26.0
5

10

15

20

25

O

Spin Temperature ( C)

Figure 13: Effect of Spin Temperature on Lipoprotein Peak Orientation. (A) LDL
peak orientation vs. Spin Temperature, (B) HDL peak orientation vs. Spin
Temperature

68change in the resolved density range. It is important to note that repeatability error of
the profiles based on integration of the subclass intensities increases as the spintemperature increases (Figure 12B). This means that to reduce the error in the profilingof lipoproteins, the samples must be spun at low temperatures. Lower spin temperatures
slow the formation of the gradient and therefore minimize the error in profiling while
keeping the serum sample viable.

3.1.1.7 Tube Orientation for Imaging
With the optimal UC spin conditions now established, the next step was toreduce the error contributed by imaging and analysis of the sample. To investigate thesefactors, the influence of the sample tube’s orientation was studied by marking eachsample as it related to the center of the UC rotor and imaging the sample by rotations of
90o from the initial orientation point. The orientation of the tube was found to have aneffect on the resulting profiles due to a circular particulate deposit on the inside surfaceof the sample tube relating to its initial orientation in the UC rotor. This particle deposit
and the resulting profiles can be viewed in Figure 14. Without the rotation, this stain can
result in false intensity peaks in the lipoprotein profile (Figure 14E, 0° rotation). These
peak errors appear in the range of the LDL region of the density profile (12mm-15mm).
Rotating the tube away from the camera still portrayed the deposit in the image
background. Rotating the tube’s orientation towards the light source resulted in lightscattering and uneven intensity of the excitation light source. Rotation of the sample
tube by 90°, so that the particulates faced away from the light source and were not in the

69

B

A

D

C

E

0 Degree Rotation
90 Degree Rotation
180 Degree Rotation
270 Degree Rotation

2500

Intensity

2000

1500

1000

500

0
3

6

9

12

15

18

21

24

27

30

33

Tube Coordinate (mm)Figure 14: Tube Orientation for Imaging. (A) 0° rotation, (B) 90° rotation, (C) 180°
rotation, (D) 270° rotation, (E) Overlaid Lipoprotein Profiles. Images are of 6uL of
Serum in 0.20M NaBiEDTA based density gradient after being spun in the
Ultracentrifuge. Sample was spun for 6 h at 120,000rpm and 5 °C. Initial image isoriented so that the part of the tube facing the camera is the same part that was
facing the center of the UC rotor. The red rectangles signify the area of the image
used to create the lipoprotein density profiles.

70

center of the image, was found to remove this effect on the lipoprotein profile. For thisstudy, layering methods were not implemented.

3.1.1.8 Stability of the Density Profile after UC Spin
The next factor studied in order to limit the error in the imaging process of thelipoprotein profiles was stability of the profile over time. The stability of the lipoprotein
profile over time will have a direct effect on the use of the profile for clinical studies.
With use of the EDTA salt gradients, there will be a slow diffusion of the gradient backto a homogeneous concentration. The profiles must be imaged prior to this diffusion.
For this study, profiles were imaged every 30 minutes after the initial image in order tomap the change in the lipoprotein profile due to time.
Figure 15 shows how the profile changes over time after the sample was spunand layered. Figure 15A shows the resulting profiles overlaid on the same graph while
Figure 15B shows the profiles staggered based on the amount of time after the UC spinthat the sample was imaged. The figures showed that it was the high density range ofthe profile that was more affected by time. The protein peak separated at the highdensity section of the lipoprotein profile began to merge with the HDL3 density range
after just 30 minutes and continued over time. The rest of the lipoprotein profiles stayrelatively stable during the tested time periods. This was most likely due to the gradientstarting to diffuse back to the homogeneous state of the aqueous solution before the UC
spin. For repeatability and quality of the lipoprotein profile, the results show that the

71

A
Time0
Time30Time60Time90

1500
1400

TRL

1300
1200

1

1100

HDL

LDL
2

4

3

2b

5

2a

3a

3c
3b

Intensity

1000
900
800
700
600
500
400
300
200
100
0
6 1.0148 10 1.026
12 14 1.038
16 18 1.049
20 22 1.070
24 26 1.117
28 30 1.230
32
Density (g/mL)

B

1500

900
600

Intensity

1200

300
0
Time0

Time90
6

8

1.014

10 12

1.026

14

16

1.038

18

20 22 24

1.049

1.070

26

28

1.117

30

Tim
e af

Time60

ter S
pin (
min)

Time3032

1.230

Density (g/mL)

Figure 15: Stability of the Lipoprotein Density Profile over Time. (A) Overlaid
lipoprotein density profiles, (B) Staggered lipoprotein density profiles

72

images must be taken within the first 30 minutes after the UC spin to minimize theinfluence of gradient diffusion.

3.1.1.9 Precision of the Density Profiles after Optimization
After incorporating these improvements in the measurement of the lipoprotein
density profiles, an error analysis of the new lipoprotein profiling method wascompleted. By comparing the average %RSD of the lipoprotein subclasses for eachmethod, and the overlaid lipoprotein density profiles, a significant reduction in error wasobserved. For the initial method described by Johnson et al, the average %RSD wasfound to be 23.39%. Applying the enhancements that were identified to create the High
Performance Lipoprotein Density Profiling method (HPLDP), the average %RSD was
reduced to 5.28% +/- 0.88. All subclasses of the lipoprotein profile showed reduction inerror. The %RSD of the TRL and low density LDL regions of the profile were the mostsignificantly reduced. This feature can be linked to the change in layering methods. The
polar/non-polar relationship of the aqueous gradient to the hexane prevents diffusion of
the layering medium and therefore reduced the %RSD layering had on the lipoprotein
profiles. Figure 16A shows the overlaid sample profiles of 10 replicate measurements
using the original method. Figure 16B is the overlaid sample profiles of 10 replicate
measurements using the HPLDP method. The HPLDP method shows a much morecongruent overlay of lipoprotein profiles compared to the original method. Thisobservation corresponds to the reduced error that was calculated from the average%RSD of the subclasses.

73

A

2000

bTRL

dTRL

HDL

LDL
1

1800

2

4

3

5

2b

3a
2a

3c
3b

1600

Intensity

1400
1200
1000
800
600
400
200
0
4(<1.000)68

10 1.021
12 14 1.027
16 18 1.040
20 22 1.065
24 26 1.114
28 30 1.210
32

Density (g/mL)

B
2500

TRL

HDL

LDL

Intensity

2000

1500

1

2

3

4

5

2b

2a

3a

3c
3b

1000

500

0
1.117
1.230
1.014
1.038
1.038
1.070
1.070
1.026
1.026
1.049
6 1.0148
10
12 14
16Coordinate
181.049
20 22
24 26 1.117
28 30 1.230
32
Tube
(mm)
Density (g/mL)

Figure 16: Repeatability of Lipoprotein Density Profiles for 10 Replicate Samples.
(A) Overlaid profiles for original method of density profiling, (B) Overlaid profiles
Using HPLDP method of density profiling74

3.1.1.10 Normalization of Density Profile Data
An analysis of the lipoprotein profiles for light source related errors was
performed by calculating the percent intensity of the lipoprotein subclass in relation to
the total intensity of the lipoprotein density profile. The rationale behind this study was
to reduce any error present due to the pipetting of serum or NBD fluorophore as well as
due to possible fluctuations in the light source over time. Since the classification
analysis is based on the fluorescence intensities of the lipoprotein subclasses, factors that
influence the fluorescence intensity are sources of systematic error.
The initial method of measuring the total intensity of each lipoprotein subclass(Mode 1) showed the average %RSD to be 5.28% +/- 0.88 as previously reported. Using
the percent intensity of each subclass in relation to the total intensity of the profile(Mode 2), the average error of the lipoprotein profiling method was reduced to 3.69%+/- 1.41. Figure 17 illustrates this difference in error between Mode 1 and Mode 2
analysis when applied to profiles that were taken using different exposure settings on thecamera (53.3ms versus 100ms exposure) to simulate possibly fluctuations in lightintensity. As can be observed from Figure 17A, the total intensities are near double forthe higher exposure. However, when applying the Mode 2 analysis, the two different
exposure times show similar values for each lipoprotein subclass; effectively reducing
any error due to the excitation source or preparative variation.

75

A

Mode 1 Data Analysis (Total Intensities)
10000053.3ms exp
100ms exp

80000

Intensity

60000

40000

20000

0
TRL

LDL-1

LDL-2

LDL-3

LDL-4

LDL-5 HDL-2b HDL-2a HDL-3a HDL-3b HDL-3c

Lipoprotein SubclassMode 2 Data Analysis (% Intensities)
B

20

53.3ms exp
100ms exp

% Intensity

15

10

5

0
TRL

LDL-1

LDL-2

LDL-3

LDL-4

LDL-5 HDL-2b HDL-2a HDL-3a HDL-3b HDL-3c

Lipoprotein Subclass

Figure 17: Application of Mode 2 Analysis for Density Profile Normalization. (A)Total Intensity (Mode 1), (B) Percent Intensity (Mode 2). Black Bars Represent
Data Measured at 53.3ms Exposure. Red Bars Represent Data Measured at 100ms
Exposure.

76

Not only was the RSD improved by the normalization of the lipoprotein profiles,
but it also offered the possibility of considering that the population of lipoprotein
subclasses is a coupled system instead of just individual measurements. This realization
has the potential for a new systematic approach to the use of the lipoprotein density
profile in clinical applications. This application will be discussed later in ClinicalApplications Section 3.2.

3.1.1.11 Profiling HDL/LDL Commercial Standards
The final phase of the optimization involved the measurement of HDL and LDL
subclass intensities through serial dilutions of commercial standards. This study wasnecessary so that the relationship between HDL and LDL integrated fluorescenceintensities could be related to the concentration of the lipoproteins present in the spin.
For this study, commercial standards of HDL and LDL were obtained through SigmaAldrich. The concentration of these standards was reported by the manufacturer in termsof amount of total protein (µg/dL). These standards were diluted in situ and the resulting
fluorescent profiles were obtained.
The resulting profiles have been overlaid and are shown in Figures 18A and 18B.
The HDL peak was prominent for the largest concentration showing a peak intensity ofnear 2000. The LDL peak however showed very little intensity with a peak of only 125
for the highest concentration. HDL’s concentration was 3.25 times higher than that of
LDL, but this relationship did not translate to the total intensities. This data leads to

77A

160 ug/dL
120 ug/dL
80 ug/dL
40 ug/dL
20 ug/dL

500
450

dTRL

HDL

LDL

400

Intensity

350
300

1

2

3

4

5

2b

12

15

18

21

24

2c

3a

3b

3c

250
200
150
100
50
0
3

6

9

27

30

33

Tube Coordinate (mm)B

520 ug/dL
260 ug/dL
130 ug/dL
65 ug/dL
32.5 ug/dL

2000
1800

dTRL

HDL

LDL

1600

Intensity

1400
1200

1

2

3

4

5

2b

12

15

18

21

24

2c

3a

3b

3c

1000
800
600
400
200
0
3

6

9

27

30

33

Tube Coordinate (mm)Figure 18: LDL and HDL Density Profiles Using Commercial Standards. (A) LDL
Density Profiles of Serial Dilutions. (B) HDL Density Profiles of Serial Dilutions.

78

A
25000

y= 123.29 * x
R2 = 0.97

Integrated Intensity

20000

15000

10000

5000

0
0

20

40

60

80

100

120

140

160

180

LDL Concentration (ug/dL)

B
200000180000Integrated Intensity

160000y= 322.87 * x
R2 = 0.98

140000120000100000
80000
60000
40000
20000
0
0

100

200

300

400

500

600

HDL Concentration (ug/dL)

Figure 19: LDL and HDL Calibration Curves Using Commercial Standards. (A)LDL Calibration Curve Relating Concentration to Integrated Intensities. (B) HDL
Calibration Curve Relating Concentration to Integrated Intensities.

79

the realization that the LDL and HDL particles interact with the NBD fluorophore basedon their individual composition rather than in a uniform relationship.
By integrating the total fluorescence intensity for each lipoprotein density class,calibrations curves were generated comparing the concentration to the corresponding
intensity. These calibration curves are shown in Figures 19A and 19B. This study
showed that for standard samples, there is a linear relationship between the lipoprotein
concentration and the resulting fluorescent intensities. These findings will be used tocompare the fluorescent intensities for each lipoprotein density class for the clinicalsubjects to determine whether similar relationships exist.

3.1.2 Cs2CdEDTA-Based Gradients for Isolation of LDL and HDLWith the successful error reduction and implementation of the NaBiEDTA-based
density gradient system, the results led to the possibility that enhancing the baseline
separation of the lipoproteins, especially the HDL region, would allow for a moredetailed characterization of lipoprotein profiles that could result in a more accurate risk
assessment when applied to the clinical studies. There was also the added benefit that
this enhanced separation would aid in the preparatory techniques previously applied inorder to isolate specific areas of the lipoprotein profile for further analytical analysis.
Because of NaBiEDTA’s relatively low solubility, its use in forming the density
gradient was limited to profiling the entire density range of serum lipoproteins in asingular spin. The use of Cs2CdEDTA as the solute has previously shown to have ahigher solubility which in turn would allow for greater control over the density gradient.

80This meant that through use of the Cs2CdEDTA solute, initial concentrations could bemanipulated in order to isolate the LDL density range and the HDL density range
through individual spins designed for this purpose. The objective of this study was to
identify the solute concentrations necessary for implementation of a dual spin system toisolate LDL and HDL separately, measure the repeatability and error in these solutesystems, and to compare the resulting profiles to the profiles found when using
NaBiEDTA as the solute in the density gradient.

3.1.2.1 Cs2CdEDTA Gradient Measurement and Optimization
With the optimum conditions for gradient development during the UC spin andfor profile imaging having been previously studied and identified, the first step towardapplication of Cs2CdEDTA as a solute system was the identification of the ideal
concentrations of solute needed to form gradients that would separate the LDL and HDL
subclasses respectively. To carry this out, the first step was to use different
concentrations of solute ranging from 0.05M-0.30M Cs2CdEDTA with serum tomeasure the resulting lipoprotein profiles. The resulting lipoprotein profiles are
displayed in Figure 20. It was anticipated that lower concentrations of the solute would
result in a narrow range of densities that can be resolved once the density gradient isformed. Taking this effect into account when observing the different peak movementcompared to the concentration of solute used, the results clearly show the LDL peakmoving to the lower tube coordinates with increasing initial concentration of the soluteas was expected. The concentrations of 0.0500M and 0.1000M Cs2CdEDTA provided

81

the best conditions for resolving the LDL over the full length of the tube while theconcentration of 0.2500M Cs2CdEDTA was shown to spread out the HDL region of the
profile.

0.3M

Cs2
TA
ED
Cd

0.25M

HDL

tra
en
nc
Co

0.2M

0.15M

in

to
)

(M

0.1M

LDL

0.05M

1650

1100

Intensity

2200

550

0
6

9

12

15

18

21

Tube Coordinate (mm)

24

27

30

Figure 20: Lipoprotein Density Profiles for Varying Cs2CdEDTA Concentrations

These solute concentrations were then mapped for density gradient formation inorder to assess the resolution of the density regions. The 0.2500M Cs2CdEDTA wasfound to resolve the LDL5 subclass and all HDL subclasses over the optimum length of
the UC tube. For the LDL region, it was found that the 0.0500M Cs2CdEDTA resolved
all LDL subclasses over the optimum length of the UC tube and also resolved the density
range of IDL as well. The resulting density gradient curves are portrayed in Figure 21A

82

and 21B. The average error between the constants from the exponential equation for the
0.2500M Cs2CdEDTA-based density gradient was calculated to be 15.73%. The
average error between the constants from the exponential equation for the 0.0500M
Cs2CdEDTA-based density gradient was calculated to be 7.55%. This increased error
was associated with the fact that the Cs2CdEDTA gradients are synthesized versus
simply using a commercially available salt like NaBiEDTA. The error associated withthe synthesis of the Cs2CdEDTA compounds the overall %RSD on the gradientformation.
To assess the extent this factor influences the clinical samples, the %RSD wasthen calculated for the resolution of the lipoprotein subclasses based on the tube
coordinates for each density cut-point between subclasses using the corresponding
exponential equation. The %RSD or for the density cut-points for HDL subclasses wasfound to be 3.87%. The lower density cut-points were associated with the highest%RSD at 15.58%. The %RSD for the density cut-points for LDL subclasses was foundto be 6.24%. The lower density cut-points were again associated with the highest %RSD
at 15.98%. This %RSD at the lower density subclass of each spin is most likely due tothe smaller slope at the beginning of an exponential curve. The %RSD is reduced in thehigher density regions due to the increased slope. While the %RSD was higher for theCs2CdEDTA based gradients when compared to the NaBiEDTA based gradients, theaverage %RSD in subclass definition was still small enough to qualify the gradientsystem for further evaluation of the systematic error when applied to the clinical serum

83

A

0.25M Cs2CdEDTA Gradient Formationy = 0.0091*exp(x/9.2597) + 1.0401
y = 0.0080*exp(x/8.9636) + 1.0394
y = 0.0152*exp(x/10.7605) + 1.0182

Density (g/mL)

1.3

1.2

1.1

1.0
6

8

10

12

14

16

18

20

22

24

26

28

30

32

30

32

Tube Coordinate

B

0.05M Cs2CdEDTA Gradient Formation
1.12

y = 0.00077*exp(x/6.78589) + 1.0163
y = 0.00074*exp(x/6.70602) + 1.0144
y = 0.00103*exp(x/7.17688) + 1.0176

1.10

Density (g/mL)

1.08

1.06

1.04

1.02

1.00
6

8

10

12

14

16

18

20

22

24

26

28

Tube Coordinate (mm)Figure 21: Cs2CdEDTA Density Gradient Formation. (A) 0.2500M Cs2CdEDTA(B) 0.0500M Cs2CdEDTA

84samples based on the definition of error required for clinical testing previously identified
in Section 1.2.1.

3.1.2.2 Precision of Density Profiles Using Cs2CdEDTA Gradients
Before applying the dual Cs2CdEDTA-based gradients to clinical samples, thesystematic error must be low enough to ensure repeatability of the analysis similar tothat of the NaBiEDTA-based gradients. This error was again measured by running astandard serum sample in 10 replicates and calculating the error between lipoprotein
density profiles. The resulting lipoprotein density profiles and error analysis are
displayed in Figure 22 and Figure 23, respectively. As can be observed from the figure,the lipoprotein density profiles again have low error overall. The average %RSD for the
LDL profile was calculated to be 11.00%. The average %RSD for the HDL profile wascalculated to be 7.06%. While this error is higher than the error found for the
NaBiEDTA-based density profiles, it is still within an acceptable range for application toclinical studies.
When comparing the lipoprotein density profiles of the dual Cs2CdEDTA-based
spins to the NaBiEDTA-based spin, some interesting observations can be made. First, in
the NaBiEDTA gradient, LDL’s most prominent peak is portrayed between LDL4 andLDL5 (1.049g/mL). The most prominent peak in the Cs2CdEDTA gradient for LDL was
portrayed at a higher density inside the density range of LDL5 (1.061g/mL). The
difference in density, based on the measured density gradients, is 0.012g/mL. As for the
HDL, in the NaBiEDTA based gradient, the density peak was found at 1.098g/mL. For

85

A

0.0500M Cs2CdEDTA-based LDL Density Profile
1000

LDL
TRL

3

2

1

IDL

4

HDL
&
Proteins

5

Intensity

800

600

400

200

0
8 1.0177
10 12 1.0203
14 16 1.0253
18 20 1.0341
22 24 1.0503
26 28 1.0796
30 32

6

Density (g/mL)
0.2500M Cs2CdEDTA-based HDL Density Profile

B
2000

TRL
&
LDL

1800
1600

HDL

ProteinIntensity

1400
2b

1200

2c

3a

3b 3c

1000
800
600
400
200
0
6

8 1.060
10 12 1.081
14 16 1.104
18 20 1.138
22 24 1.191
26 28

30

32

Density (g/mL)

Figure 22: Dual Concentration Cs2CdEDTA-Based Gradient Density Profiles. (A)
0.0500M Cs2CdEDTA LDL Profiles Overlaid, (B) 0.2500M Cs2CdEDTA HDLProfiles Overlaid

86A

0.0500M Cs2CdEDTA-based LDL Subclass Integrated Intensities
40000
35000

Integrated Intensity

30000
25000
20000
15000
10000
5000
0
TRL

IDL

LDL1

LDL2

LDL3

LDL4

LDL5

Lipoprotein SubclassB

0.2500M Cs2CdEDTA-based HDL Subclass Integrated Intensities
50000

Integrated Intensities

40000

30000

20000

10000

0
LDL5

HDL2b

HDL2a

HDL3a

HDL3b

HDL3c

Lipoprotein Subclass

Figure 23: Dual Concentration Cs2CdEDTA-Based Gradient Profile Error
Analysis. (A) 0.0500M Cs2CdEDTA LDL Subclass Error, (B) 0.2500M
Cs2CdEDTA HDL Subclass Error

87

the Cs2CdEDTA-based gradient, the density of the HDL peak was found to be
1.100g/mL; a difference of only 0.002g/mL. Similar differences in the lipoprotein
density for LDL have been reported by Johnson et al12 who first studied the applicationof different EDTA salt-based gradients. However, a larger difference in HDL density
was reported between the two gradient solutes by Johnson et al12 where only a small
density change was recorded in this study.
The reason for this density shift in the different EDTA solutes could be due to acation-interaction between the phospholipids on the outer shells of the lipoprotein
particles and the different metal cations. Through reviewing sedimentation theory, it
was documented that the use of Cs+, which has a high affinity to bind to phosphategroups, can alter the density of DNA when studied.72 Na+ has the same effect but it isless due to the mass difference between the Cs+ and Na+. Similarly, this would affect themonolayer of lipoproteins since phospholipids can be found there. When referencing
Krilov et al for FT-IR spectra on lipoproteins, it is reported that an LDL particle has 724
phospholipids where HDL2 and HDL3 particles have 137 and 51, respectively.118 This
increase in active sites could explain why the LDL is affected more than the HDL. Withthe use of Cs2CdEDTA, there is the potential for more metal cations to bind with thephospholipids on the monolayer of the LDL due to the fact that the CdEDTA-complex
has a -2 charge and therefore there are twice as many Cs+ cations in the solution as there
are Na+ cations in the NaBiEDTA solution. The potential for unassociatedphospholipids would be decreased with the enhanced number of Cs+ cations present insolution. Despite this density changing effect, the dual concentration Cs2CdEDTA-

88

based gradients were shown to have high repeatability as well as the ability to spread out
the density profiles for the LDL and HDL density ranges. The effect that the metal
cation has on the density profile will be further studied in clinical samples to test on
multiple subjects. It is possible that this effect could be subject dependant and therefore
could contribute as a risk marker for CVD classification.

3.1.3 Serum Viability Studies
With the analytical methods for lipoprotein density profiling optimized, the only
other source of possible error was the viability and stability of serum samples.
Understanding the way that serum is affected by being thawed multiple times and how
serum is affected through the length of exposure it has to room temperature will serve asa guide in how clinical samples should be treated. These studies were designed tounderstand these effects so that the optimum method of serum treatment could be
employed when studying clinical samples in order to minimize sample degradation. The
NaBiEDTA-based density gradient was used for all studies in order to compare results.

3.1.3.1 Density Profiles Relative to Time Serum Spent at Room Temperature
The goal of this study was to examine the effect that serum being stored at room
temperature has on the lipoprotein profile over time. To carry out this study, the same
serum sample was aliquotted into several identical fractions and stored at room
temperature for 0.5hr, 2hr, 4hr, and 24hr. The fractions were then profiled in 5 replicate
measurements. The resulting lipoprotein density profiles are depicted in Figure 24A. As

89

can be observed from the overlaid profiles, there is relatively no change in the TRL,
HDL, or Protein peaks in the serum profiles. There is, however, a distinct change in the
LDL peak of the lipoprotein density profile. This peak shift has been mapped bystudying the peak density as a function of exposure time at room temperature as shownin Figure 24B. This graph shows a direct relationship between the peak density and theexposure time in the form of an exponential decay. This relationship indicates that thedensity change is a process which slows and reaches an endpoint over time. The change
in LDL density can possibly be attributed to increased enzymatic activity as time elapses
or loss of lipids from the lipoproteins. Regardless of the reason for the change, in orderto evaluate lipoprotein density profiles for clinical applications, the exposure toincreased temperature must be minimal. For this reason, a 30 minute thaw time forserum was selected to reduce the temperature effect.

90

30 min2 hour4 hour
24 hour

A
3000

2500

TRL

HDL

LDL

Intensity

2000

1 2

1500

4

3

2b

5

3a 3c
2a 3b

1000

500

0
6

8 10 12 14 16 18 20 22 24 26 28 30 32
1.026 1.038 1.049 1.070 1.117 1.230
1.014

Density (g/mL)
B
Data: Average Density vs Thaw Time
Model: ExpDec1
1.0420

Average Density (g/mL)

y0+A1e^(-x/t1)
Chi^2 = 9.74476E-10
R^2= 0.99903

1.0415y0
A1
t1

1.04101.039120.0028112.9992±1.24213E-4±1.03221E-4±1.54595

1.04051.04001.0395
0

5

10

15

20

25

Thaw Time (Hrs)

Figure 24: Effects of Exposure at 23°C on Serum over Time. (A) An Overlay of
Lipoprotein Density Profiles Obtained from Serum Exposed at Different Times, (B)Density Change in LDL based on Exposure Time at 23°C

91

3.1.3.2 Effect of Multiple Freeze/Thaw Cycles of Serum on Density Profiles
With the thaw time identified for viability of the lipoprotein density profile, thenext factor studied was the stability of the profile over freezer storage time and multiple
freeze/thaw cycles. The goal of this study was to identify any effect that these multiple
freeze/thaw cycles could have on the lipoprotein density profile. To study this factor, aserum sample was thawed for 30 minutes and then flash-frozen using liquid nitrogenseveral times. After each thaw, an aliquot of the serum was withdrawn for profiling.
The %RSD between freeze/thaw cycles was calculated and compared to thebaseline %RSD for ten replicates previously reported as the systematic error of theenhanced lipoprotein profiling method. This error was previously identified at 5.28%+/- 0.88 for Mode 1 and 3.69% +/- 1.41 for Mode 2. For the 12 different thaw cycles,
the %RSD was calculated to be 7.05% +/- 1.59 for Mode 1 and 5.77% +/- 1.10 for Mode
2. The profiles are shown in Figure 25. Only small differences in the profiles wereobserved showing that the relative error due to thaw cycles was insignificant for 12
cycles. This number of cycles is more than a standard clinical sample would undergo.

92

2800
2600
2400
2200
2000

Intensity

1800
1600

TRL

HDL

LDL
1

2

3

4

5

2b

2a

3a

3c
3b

1400

Thaw 1Thaw 2Thaw 3Thaw 4Thaw 5Thaw 6
Thaw 7
Thaw 8Thaw 9Thaw 10
Thaw 11
Thaw 12

1200
1000
800
600
400
200
0
6 1.0148 10 1.026
12 14 1.038
16 Tube18 1.049
20Coordinate
22 1.070
24 26
28 30
(mm)
1.117

32
1.230

Density (g/mL)
Figure 25: Effect of Multiple Freeze/Thaw Cycles on Density Profiles

3.1.4 Optimization of Preparative Techniques for HDL Apolipoprotein Mass
Spectrometry
Differences between control and CVD samples in the mass spectra of HDL
apolipoproteins have been identified through previous work in the Macfarlane researchgroup. These differences have been identified as a possible biomarker for the presenceof CVD. Specifically, a difference in the mass of Apo C-I was found in a small cohortof CVD samples that was not present in a small cohort of control samples. Applicationof mass spectrometry to HDL was previously performed on the HDL2 and HDL3
subclasses. This separate analysis of the major subclasses of HDL identified that the

93

modified Apo C-1 was present in both HDL subclasses. For this reason, it was decided
that mass spectra analysis of the full HDL fraction would be studied.
Before performing this analysis on a large scale cohort of clinical samples, it
was necessary to identify the proper methods for sample preparation that allow for the
optimum recovery of the apolipoproteins off of HDL. Multiple methods of sample
purification have been previously studied and applied to serum to isolate HDL including
dextran sulfate precipitation of the Apo B containing lipoproteins and UC density
separation for isolation of HDL.115 To delipidate the HDL fraction, reverse phase solid
phase extraction has been shown through previous studies as a simple and efficientmethod for removing the lipids.117 In order to verify the efficiency of these methods,
commercially available ELISA kits were used to measure the recovered Apo A-Icontaining lipoproteins taking advantage of the one-to-one relationship between Apo A-I
and the HDL particles. The BCA protein analysis was also used to quantify the total
protein recovery. The objective of these studies was to determine the optimal method
for recovery of the proteins associated with HDL with the highest percent recovery of
the full spectrum of HDL-related protein, realizing this feature of the protocol
maximizes the probability of discovering new CVD-related biomarkers.

3.1.4.1 Optimizing Recovery of HDL and HDL Apolipoproteins
In order to identify the optimum method of sample preparation for mass
spectra analysis, a comparison of the preparative protocols was made using the resulting
mass spectra as the criteria for optimization. The three protocols studied were 1) UC94density preparation with the 0.25M Cs2CdEDTA-based density gradient to recover the
HDL fraction, 2) dextran sulfate precipitation to isolate HDL, and 3) the combination of
dextran sulfate precipitation followed by density gradient separation to isolate HDL.
Each recovered HDL fraction was then desalted and delipidated by SPE. The resulting
mass spectra for the three protocols are shown in Figure 26. When comparing the total
intensities of the peaks for each of the preparative methods, it was found that the highest
Apo C-I peak intensities were observed in the dextran sulfate treated serum. However, amore complete coverage of the mass spectrum was achieved with a combination of theuse of preparative ultracentrifugation and dextran sulfate precipitation (Figure 26C). In
order to establish the most information-rich recovery method, further investigation wasneeded to determine the reason why there were such noticeable differences.

95

A 10000

UC Serum

9000

Intensity

8000
7000
6000
5000
4000
3000
2000
1000
0
5000

10000

15000

20000

25000

30000

Mass/Charge (m/z)

DS Serum

B 10000
9000

Intensity

8000
7000
6000
5000
4000
3000
2000
1000
0
5000

10000

15000

20000

25000

30000

Mass/Charge (m/z)

C 10000

DS/UC Serum

9000
8000

Intensity

7000
6000
5000
4000
3000
2000
1000
0
5000

10000

15000

20000

25000

30000

Mass/Charge (m/z)

Figure 26: Mass Spectra of HDL Apos for Different Preparative Methods. (A)HDL Isolated through UC Preparation, (B) HDL Isolated through DS Preparation,(C) HDL Isolated through Combining UC and DS preparation

96

3.1.4.2 Protein Recovery of Delipidated HDL
In order to understand the differences in the mass spectra between preparative
methods, additional analytical methods were implemented to obtain quantitative datausing Apo A-I specific ELISA and the BCA assay for total protein measurement. The
products of the dextran sulfate precipitation were first tested to measure the total
recovery both before and after solid phase extraction was performed. Initialconcentrations of multiple serum samples were measured and then compared to theconcentration recovered after dextran sulfate precipitation and after solid phaseextraction. The average recovery after dextran sulfate precipitation was 95.20 +/3.24%. This result showed that use of dextran sulfate precipitation had little influence
over the resulting mass spectra due to the high recovery rate of the dextran sulfate
precipitation method. On the other hand, after the sample was run through solid phaseextraction to collect the free apolipoproteins, the recovery was calculated to be only
11.43 +/- 2.84%. When testing the wash sample used to remove the salts from thesample in the SPE cartridge, the total concentration of Apo A-I was calculated to be
90.59 +/- 4.59%. This high recovery yield indicated that the signal loss previously foundin the mass spectrometry analysis of HDL apolipoproteins was due to the solid phaseextraction technique.
To understand this loss, previous research into the method of reverse phasesolid phase extraction for delipidation of lipoproteins was reviewed. The previous
research had shown recoveries of up to 90% on pure protein samples.117 However, whenlipids were present, there was a reduced functionality that limited the loading capacity of

97

the SPE cartridge to no more than 150µg of total protein. When limiting the sample load
to 100µg of protein with the presence of lipids, the total recovery found was 80%. In
reference to the methods performed in this study, 200µL of serum were used. With theaverage concentration of protein in human serum ranging from 60-83mg/mL, this range
translates into a total range of 14,000-16,600ug of total protein present in the initialserum sample. Since dextran sulfate precipitation only removes the proteins that are
related to LDL, the HDL proteins and the free proteins, such as globulin and albumin,
are present in the sample when loaded onto the SPE cartridge. This overload of protein,combined with the presence of lipids, explains the sample loss that was found in thewash step of the SPE method.
With total protein content being the controlling factor in the amount ofapolipoproteins recovered from HDL, it was now possible to explain why the
combination of dextran sulfate precipitation and preparative ultracentrifugation resulted
in the best mass spectra total signal. The preparative ultracentrifugation technique adds
a secondary purification process in isolating HDL that removes abundant globulins andalbumin. The removal of these proteins minimized the total amount of protein that wasloaded onto the SPE cartridge and in turn improved the total recovery of the HDL
apolipoproteins.
The total amount of protein present in HDL is primarily due to the Apo A-I
concentration since it is present on every HDL particle. Other apolipoproteins arepresent, but only in small concentrations relative to the concentration of Apo A-I. The
average concentration of Apo A-I in a normal human’s HDL is 130 mg/dL.48 Coupled

98

with the other apolipoproteins present, the average concentration of the HDL-related
proteins is approximately 200mg/dL. For an average human serum samples, thisconcentration translates into a total protein content of 400µg for HDL in 200µL of
human serum. With the maximum loading capacity of the SPE cartridge being reported
at 150ug of protein when lipids are present, this means that the SPE extraction cartridge
would have been overloaded. In order to test this possibility, multiple recovered protein
samples were measured for their total protein content. While total protein content varied
between subjects, all samples tested at an average recovery of 387 +/- 119µg. These
recovered protein values show an increased recovery from that previously reported whenusing the SPE cartridge and confirm that the majority of the apolipoproteins present in
HDL are being recovered.
With the large spread in HDL-protein concentrations, it became necessary to
identify the reproducibility of the full preparative method. To measure thereproducibility of the protein recovery when combining the dextran sulfate precipitation
method and the ultracentrifugation preparative method, the use of the BCA assay tomeasure the total protein recovered from HDL was implemented in order to measure allprotein content instead of just the Apo A-I. To test the reproducibility, a volunteer
serum sample was run through the method in triplicate and the %RSD was calculated.
%RSD was found to be 15.54%. This low value suggests that the variation in protein
recovery previously found in patient samples was due to patient variability and not error
in the preparative methods. These results show that by combining dextran sulfate
precipitation with preparative ultracentrifugation, a viable technique has been developed

99

for HDL apolipoprotein isolation and recovery that can be combined with mass
spectrometry techniques in order to assess the application of the mass spectra in terms of
the risk assessment for CVD.

3.1.4.3 Optimizing Mass Spectra Signal for Clinical Applications
With the apolipoprotein recovery methods now established, a standard serum
sample was prepped using dextran sulfate precipitation and preparative
ultracentrifugation. The sample was then reconstituted to different levels ofconcentration based on the initial serum volume used. The goal of this study was to findan optimum concentration for which the mass spectra would show strong peaks in all
mass regions. For application of the mass spectrum data to the LDA/SIR analysis, there
must be enough signal strength to distinguish peaks in the mass ranges for the lower
abundant apolipoproteins such as Apo A-II, Apo C-II, Apo C-III, etc.
The apolipoprotein concentrations chosen for this study were 1x, 2x, and 4x
more concentrated than what is in the serum sample. For the 200µL of serum used foreach concentration, the recovered apolipoproteins were evaporated to dryness andreconstituted to 200µL, 100µL, and 50µL to provide samples with increasing protein
concentrations. The resulting mass spectra are shown in Figure 27A-27C. The signal
intensities for all mass ranges show close to double the intensity when comparing the 2x
and 1x concentrated samples. However, the 4x concentrated sample shows a large signal
drop for all mass ranges except between 7000Da and 11000Da. For this mass range, the100

A HDL Apolipoproteins - 1x Serum Concentration
10000

Intensity

8000
6000
4000
2000
0

5000 10000 15000 20000 25000 30000 35000

Mass/Charge

B HDL Apolipoproteins - 2x Serum Concentration
10000
8000

Intensity

6000
4000
2000
0

5000 10000 15000 20000 25000 30000 35000

Mass/Charge

C

HDL Apolipoproteins - 4x Serum Concentration
10000

Intensity

8000
6000
4000
2000
0

5000 10000 15000 20000 25000 30000 35000

Mass/Charge

Figure 27: Mass Spectra of HDL Apos Based on Sample Concentration. (A) 1x
Serum Concentration, (B) 2x Serum Concentration, (C) 4x Serum Concentration

101

signal intensity is roughly twice as large as the intensities seen for the 2x concentrated
sample. All other mass ranges show even less peak intensity than the 1x concentrated
sample. This difference in peak intensity is larger than any error that might be present in
MALDI-TOF experiments due to the preparation techniques. The decrease in signal forthe most concentrated HDL apolipoprotein sample could indicate that there is some formof sample aggregation that is suppressing the signal. Based on these results, the 2x
concentrated sample was shown to allow for clean mass spectra with sufficient peakintensities for application of the results to the LDA analysis. For this reason, the 2x
concentration was selected as the method for measuring the mass spectra of all clinicalsamples in future studies.

3.2 Clinical Applications of High Performance Lipoprotein Profiling Methods

3.2.1 Lipoprotein Density Profiles and LDA/SIR Analysis for CVD Risk Assessment
The objective of these studies was to assess the influence of the improved
lipoprotein profiling method for use in CVD risk analysis. Previous research had shown
the potential for the use of advanced statistical analysis of the lipoprotein profiles in theassessment of CVD risk.14 The previous study research was limited by the small sample
library and the precision of the methods used. With the more precise method of thelipoprotein density profiling developed in this thesis work, the application of this method
to a broader library with better clinical definitions of control (CTRL) and CVD was
expected to yield a more accurate CVD risk assessment. The studies described here

102

include the original results of the lipoprotein profiles and LDA/SIR analysis found by
R.Henriquez14 in comparison to the more precise lipoprotein profiles and LDA/SIR
analysis to show improvement in the classification, as well as the implementation of themethods to a normal lipidemic cohort of samples, a cohort of hypercholesterolemicchildren, and, finally, to the comprehensive serum library to test for a universal risk
assessment.

3.2.1.1 Improving the Predictive Power of the Original Pilot Study through Applicationof HPLDP Method
The initial pilot study, prior to improving the precision of the method, wasperformed on a small sample cohort of subjects to assess the use of LDA/SIR inapplication to the lipoprotein density profiles. LDA/SIR is a multivariate analysis that
produces an equation that classifies CVD and CTRL groups. The subjects for this study
were selected based on having normal to elevated levels of HDL cholesterol, normal
levels of LDL cholesterol, and their individual CVD status.
The importance of each lipoprotein subclass in the LDA/SIR multivariate
analysis equation, relative to CVD prediction, was reflected in the coefficients of theequation. The equation generated for the original method of lipoprotein profiling isshown in Equation 9. In the equation, the natural log of the integrated intensities foreach subclass is used to normalize the data. Positive subclass coefficients (red) indicatedthat the subclass was weighted towards risk of CVD while a negative coefficient (green)
was weighted towards being a CTRL. For the pilot study, the HDL-3c, LDL-4, and103

LDL-2 subclasses were found to be the most important factors for a prediction of CTRL.
The HDL-3b, dTRL, and LDL-5 subclasses were found to be the most important factors
for prediction of CVD. Figure 28 highlights each lipoprotein subclass relative to its
coefficient polarity (CVD or CNTL). Seven of the twelve subclasses were weighted
towards CVD classification. To better visualize the relative importance of eachsubclass, the coefficients from the equation are graphed in Figure 29. LDL-1 was foundto not be as important relative to the other lipoprotein subclasses.

(Eq. 9)

104

2000
1800

bTRL

dTRL

LDL
1

2

HDL
3

4

5

2b

2a 3a 3b 3c

1600

Intensity

1400
1200
1000
800
600
400
200
0

4<1.006

8~1.0010 1.02312
14 1.02916
18 1.044
20 22 1.077
24
26 1.150
28
30 >1.30
32

Density (g/mL)

Figure 28: Influence of Lipoprotein Subclasses Based on Pilot Study Algorithm:
Red = CVD factor, Green =CTRL Factor

0.6(+) CVD

0.5

LDA/SIR Subclass Coefficient

0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5

(-) CVD

-0.6
bTRL

dTRL

LDL-1

LDL-2

LDL-3

LDL-4

LDL-5 HDL-2b HDL-2a HDL-3a HDL-3b HDL-3c

Lipoprotein Subclass

Figure 29: Pilot Study LDA/SIR Coefficients for Lipoprotein Subclasses Using
Original Lipoprotein Profiling Method

105

The separation found using the LDA/SIR calculated values for the initial
method, prior to improvement, is shown in Figure 30. Of the 16 CVD subjects, 3
subjects were found to have negative LDA/SIR values which indicated that they weremisclassified as controls based on the LDA/SIR equation. Of the 14 CTRL subjects, 3
subjects were found to have positive LDA/SIR values which indicated that they weremisclassified as CVDs based on the statistical model. Based on the total number ofcorrect classifications, the baseline prediction potential was found to be 80% accurate.

CVD Subjects
CTRL Subjects

CVD Status

CVD

CTRL

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

LDA Value

Figure 30: Pilot Study LDA/SIR Separation of CVD/CTRL Using Original Method

The standard method of evaluating the significance of a statistical model would
be to test the predictive power of the LDA/SIR equation on a set of subjects, for which
the classifications of CVD and CTRL are known. These samples would not have been

106

used to develop the statistical model in order to assess the model’s viability. This set ofsamples is called a hold out set. Because the sample size was limited, a hold out sample
set was not available and therefore the method of cross validation (X-Val) was used as a
substitute evaluation of the method. Cross validation is a method of testing the bias ofthe model in terms of the subject dependency (Section 1.5). X-val is a method of
removing one random sample from the sample set and then rerunning the analysis. The
resulting equation is tested on the full sample set; including the sample that wasremoved. The difference between the new prediction accuracy and the original accuracy
represents the level of dependence that the model has on the sample data that wasremoved. The X-Val score for the pilot study was found to be 60%. This low X-Val
score indicated that the LDA/SIR classification accuracy is sensitive to the sample set
size based on the difference in prediction power being 20% relative to the prediction
accuracy using the full sample set. While the prediction accuracy showed the potentialof combining lipoprotein density profiles with LDA/SIR analysis, the X-Val prediction
indicated that the prediction accuracy was not statistically significant.
To study the effect that the more precise lipoprotein density profiling method
has on CVD risk assessment, the initial pilot study sample library was profiled using thehigh precision method. The LDA/SIR analysis of the enhanced method lipoprotein
profiles produced a prediction equation that was 92.7% accurate with an X-Val accuracyof 63%. Through use of the high precision method of lipoprotein density profiling, only
one subject each were misclassified for the CVD and CTRL groups. The resulting

107

CVD Status

CVD

CTRL

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

LDA/SIR Value

Figure 31: Pilot Study LDA/SIR Separation of CVD/CTRL Using HPLDP Method(+) CVDLDA/SIR Subclass Coefficient

0.4
0.2
0.0
-0.2
-0.4
-0.6

(-) CVD
-0.8
TRL

LDL1

LDL2

LDL3

LDL4

LDL5

HDL2b HDL2a HDL3a HDL3b HDL3c

Lipoprotein Subclass

Figure 32: LDA/SIR Coefficients for Lipoprotein Subclasses Using HPLDP Method

108

separation between CVD and CTRL subjects is shown in Figure 31. The coefficients for
the LDA/SIR equation are graphed in Figure 32.
These coefficients differed from the original analysis in many ways. Five of
the 11 subclasses changed in their polarity from being a factor towards CVD to being afactor towards being a CTRL and vice versa. Most notably, the majority of the LDL
subclasses were now factors for CVD and the majority of the HDL factors were now
factors for CTRL classification. The changes in these coefficients changed theclassification of the previously misclassified samples from an incorrect classification to acorrect classification. The change also changed one subject from each of the CVD and
CTRL cohorts to being misclassified. The increase in prediction accuracy between theoriginal pilot study data and the data obtained from application of the enhanced method
can be directly related to the change in the subclass coefficients. The improved
classification confirmed that the enhanced method increased the risk assessment of CVD
and therefore was an improvement over the original method. However, the X-Val
accuracy still showed a high subject bias.

3.2.1.2 Effect of Redefining the Definition of CTRL
To investigate the reason for the subject bias, the definitions of CTRL werequestioned. The initial method of CTRL definition was that the subject had no risk
factors for CVD and no history of disease. This definition did not account for subjects
that might be in the pre-stages of atherosclerosis. For this reason, a new cohort of CTRL
subjects was acquired for the study. These CTRL samples were identified through109

angiography based on the level of arterial blockage. Any subject who showed less than
10% blockage through angiography was identified as a CTRL and a serum sample wascollected.

CVD Status

yes

no

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

LDA Value

Figure 33: LDA/SIR Separation of CVD/CTRL Using Redefined CTRL Library

Of the new CTRL samples collected to build the redefined library, a subset of
this new library fit the design profile for inclusion to the original pilot study CVD
samples. In this study, LDA/SIR analysis was performed the same CVD samples as
used in the pilot study but using the more accurately defined CTRL samples from the
new library. The LDA/SIR separation of this new library is depicted in Figure 33. The
prediction accuracy for this library was found to be 100%. The X-Val for this library

110

was found to be 84%. This X-Val score showed that the new LDA/SIR separation was
statistically significant.

0.6

LDA/SIR Subclass Coefficient

0.4

0.2

0.0

-0.2

-0.4

-0.6
TRL

LDL1

LDL2

LDL3

LDL4

LDL5

HDL2b HDL2a HDL3a HDL3b HDL3c

Lipoprotein Subclass

Figure 34: LDA/SIR Coefficients for the Separation of the Pilot Study CVD
Samples and Angiography-Defined CTRL Samples

The LDA/SIR constants for the prediction equation are depicted in Figure 34.
When comparing the constants for the newly defined library to the constants from theoriginal pilot study found in Figure 29, there were some significant differences that wereobserved. The relative value of each subclass coefficient between the two libraries wascompared in order to identify the relative contributions of each subclass when comparedto the other subclasses. The LDL-3 coefficient changed in polarity, i.e. the LDL-3
weighted subjects towards a CVD classification in the original pilot study and a CTRL
classification in the new library study. Another difference found when the CTRL
subjects were better defined was observed in the relative importance of the subclasses.

111

Several subclasses, most notably LDL-2 and HDL-2b, changed their relative
contribution to the LDA/SIR value. The overall improvement in classification can be
attributed to having a more precise protocol of lipoprotein density profiling as well as
having a better defined CTRL population.

The enhancement of both the prediction

accuracy and X-Val accuracy proved that the newly defined CTRL library better
identified what is a true control by using a medically defined test rather than thesubjective appearance of no disease. While the X-Val score did improve in comparison
to the original study, there still was a large subject bias with a difference of 16%
between the prediction equation accuracy and the X-Val accuracy. For this reason, alarger serum library was deemed to be necessary to test the overall validity of thismethodology of CVD risk assessment. With a larger serum library to test the accuracy ofthe prediction equation, the hold out sample set protocol can be utilized as the ultimate
test of the utility of the method in a clinical setting.

3.2.1.3 LDA/SIR Analysis Applied to Normal Lipidemic Patient Samples for CVD Risk
Assessment
With the improvements in CVD risk assessment for the original pilot study,
application of the enhanced lipoprotein density profiles and LDA/SIR analysis wasapplied to specific cardiovascular risk studies to better assess the methods potential in
the clinical setting. The first study was to explore whether this methodology could be
used to identify risk for those subjects that showed none of the traditional risk factors,
i.e. normal lipidemic subjects. While other CVD risk assessment methods have been

112

shown to be applicable to patients with identified risk factors7, these methods are still
inaccurate for the normal lipidemic subject population. The results presented heresummarize the application of LDA/SIR to lipoprotein density profiles in order todistinguish between normal lipidemic subjects with documented CVD and those without
CVD. The sample population size for this study was 26 subjects and was composed of
14 CVD subjects and 12 CTRL subjects.
In order to assess the level of improvement that lipoprotein density profiles hasover tradition methods, traditional lipid cholesterol and triglyceride measurements
acquired through a standard lipid panel were compared to the lipoprotein profiles as theaccepted standard for lipoprotein measurement. The effect of sample size versus
number of measurements was also explored by separating the lipoprotein profiles bydensity in three ways: 3 lipoprotein subclasses, 5 lipoprotein subclasses, and 11
lipoprotein subclasses. The 3 lipoprotein subclasses are the integrated intensities basedon the lipoprotein profile identified as TRLs (ρ<1.019g/mL), LDL(1.019g/mL<ρ<1.063g/mL), and HDL (1.063g/mL<ρ<1.179g/mL). These subclasses
are further broken down by density into the 5 lipoprotein subclasses defined as TRLs
(ρ<1.019g/mL), low-density LDL (1.019g/mL<ρ<1.039g/mL, LDL-1 through LDL-3),
high-density LDL (1.039g/mL<ρ<1.063g/mL, LDL-4 through LDL-5), low-density
HDL (1.063g/mL<ρ<1.110g/mL, HDL2), and high-density HDL(1.110g/mL<ρ<1.179g/mL, HDL3). Finally, the lipoprotein subclasses are broken down
into the 11 density subclasses as defined in Section 1.3.3.9,68 A sample of the lipoprotein
density profile and the different methods of fractioning the profile is shown in Figure 35.

113

Though examining the effects that increasingly more distinct lipoprotein subclasses haveon the LDA/SIR analysis, the benefits of lipoprotein density profiles over the traditional
lipid panel measurements were established. The results of the LDA/SIR analysis forthese different data sets are summarized in the following sections.

2500

TRL

HDL

LDL
HD-LDL

LD-LDLLD-HDL HD-HDLIntensity

2000

1500

1

2

3

4

5

2b

3a 3c
2a 3b

1000

500

0
6
8
1.014

16 181.049
20 221.070
24 26 1.117
28 30 1.230
32
1.02612 14
1.026 1.038
1.038
1.117
1.230
1.014101.049 1.070

Density (g/mL)
Figure 35: Lipoprotein Density Profile Fractioned into 3, 5, and 11 Subclasses

3.2.1.3.1 LDA/SIR Results of Lipoprotein Measurements
Table 4 summarizes the accuracy of LDA/SIR analysis on each of the four data
sets: traditional lipid panel measurements, 3 lipoprotein subclasses, 5 lipoprotein
subclasses, and 11 lipoprotein subclasses. Because of the small number of samples
available for this study, X-val was selected to test the subject dependency of theanalysis. As shown from the reported accuracy values for each lipoprotein measurement

114

Table 4: LDA/SIR Analysis Summary for Normal Lipidemic Serum Library ofDifferent Lipoprotein Measurements
LDA/SIR Accuracy
X-validation
Lipoprotein Measurement
p-value
(%)
(%)
73.1
53.8
NA
Traditional Lipid Panel
69.2
69.2
NA
3 Lipoprotein Subclasses
88.5
73.1
0.0186
5 Lipoprotein Subclasses
92.3
69.2
0.095211 Lipoprotein Subclasses

method in Table 4, the accuracy of the LDA/SIR prediction increased with respect to thenumber of subclasses that were included in the lipoprotein profile. The traditional lipid
panel measurements used in standard medical dianostics showed a low accuracy rate of
73.1% when using LDA/SIR analysis. This approach was further influenced by thepatient dependency represented by the X-Val value of 53.8%. For the small number ofsubjects included in the study, the use of the 5 lipoprotein subclasses gave the best
relationship of prediction accuracy and X-Val indicating a low subject bias with valuesof 88.5% and 73.1%, respectively. Use of all 11 possible subclasses increased theaccuracy of the prediction to 92.3%. However, the increase in the number of subclasses
also increased the subject dependency seen through the lower X-Val score. With morevariables included in the LDA/SIR analysis, a larger subject population is required tostrengthen the statistical significance of the classification. When using the p-value todistinguish the statistical significance the LDA/SIR separation, it was found that the 5subclass method of analysis gave a statistically significant separation while the othermethods did not. This statistical significance is represented by a p-value of 0.0186
which is lower than the required value of 0.05 to distinguish a significant difference
between groups. The p-value is larger when the 11 lipoprotein subclass data method is115

used due the calculation of the p-value being related to the number of sample variables
and sample population size. The results found in this study emphasized the need for alarger sample population relative to the number of variables applied to the LDA/SIR
analysis and the benefits that can be obtained through use of lipoprotein density profile
data in the LDA/SIR analysis in comparison to tradition lipid measurements used incurrent medical practices.

3.2.1.3.2 LDA/SIR Separation and Coefficients Using 5 and 11 Lipoprotein Subclasses
With the optimum risk assessment for CVD in normal lipidemic subjects foundusing the 5 and 11 lipoprotein subclass methods, further analysis was performed in order
to assess the differences between the LDA/SIR analysis when 5 and 11 subclasses wereused. Figure 36 illustrates the separation of CVD and Control subjects through
LDA/SIR analysis of 5 lipoprotein subclasses. LDA/SIR separates the CVD and CTRL
samples based on the mathematical equation developed in the statistical analysis. The
two CVD samples with negative LDA/SIR values are considered misclassified, i.e they
were incorrectly identified by this method as not having CVD. The CTRL sample withthe positive LDA/SIR values was also considered misclassified.

116

CVD Classification

CVD

CTRL

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

LDA/SIR Value

Figure 36: LDA/SIR Separation of CVD/CTRL of Normal Lipidemic Library
Using 5 Lipoprotein Subclasses

Figure 37 shows the graphical representation of the coefficient strength foundthrough the LDA/SIR analysis of the 5 subclasses included in the analysis. The
coefficients with negative values identify subclasses that increase the chance of beingclassified as a healthy control, and the coefficients with positive values identify
subclasses that increase the chance of being classified as CVD. As can be seen from thecoefficients, the low density LDL (LDL1-LDL3) and TRL subclass values are identified
as control factors, and the high dense LDL (LDL4-LDL5) and HDL3 subclasses are
identified as CVD factors. The HDL3 subclass has a very small positive coefficient,which could be an indication that there are some properties of low dense HDL that are
atherogenic.

117

0.6

LDA/SIR Coefficient

0.4
0.2
0.0
-0.2
-0.4
-0.6
TRL

LD-LDL

HD-LDL

HDL2

HDL3

Lipoprotein Subclass

Figure 37: Normal Lipidemic LDA/SIR Coefficients for 5 Lipoprotein Subclasses

CVD Classification

CVD

CTRL

-0.2

-0.1

0.0

0.1

0.2

0.3

LDA/SIR Value

Figure 38: Normal Lipidemic LDA/SIR Separation of CVD/CTRL Using 11
Lipoprotein Subclasses

118

Figure 38 represents the LDA/SIR separation of the CVD and CTRL subjects
using the 11 subclasses. The classification is similar to Figure 36 in that the negativevalues represent the classification as a CTRL and the positive values represent
classification as a CVD. The difference between Figure 36 and Figure 38 is that one less
CVD subject was misclassified.

0.6

LDA/SIR Constant

0.4

0.2

0.0

-0.2

-0.4

-0.6
TRL

LDL1

LDL2

LDL3

LDL4

LDL5 HDL2b HDL2a HDL3a HDL3b HDL3c

Lipoprotein Subclass

Figure 39: Normal Lipidemic LDA/SIR Coefficients for 11 Lipoprotein Subclasses

Figure 39 shows the coefficients found using LDA/SIR analysis on the 11
lipoprotein subclasses. When compared to the coefficients found for the 5 subclassseparation, it can be seen that the smaller, more defined subclasses alter the coefficient’s
strength and sign. LDL-1 is part of the low density LDL subclass in the 5 subclassseparation, which had a sign indicating it to be a CTRL factor. However, when using thesmall subclasses, LDL-1 was assigned a positive coefficient indicating it as a factor

119

towards CVD identification. It has also been observed that the HDL subclasses change
sign as well. HDL-2b, the lowest density HDL subclass, and HDL-3c, the highest
density HDL subclass, are identified as a CTRL factors while HDL-2a, 3a, and 3b are
identified as factors toward CVD classification.
This analysis shows the advantage of using the 11, more detailed subclasses oflipoproteins coupled with the advanced statistical methods of LDA/SIR in order todistinguish risk for CVD in normal lipidemic subjects. As the lipoprotein subclasses
became more defined, the LDA/SIR analysis increased the accuracy of its prediction.
When compared to the standard lipid panel values used by practicing physicians, thelipoprotein density profile was better able to distinguish between those normal lipidemic
subjects with CVD and those that are without CVD as defined through angiography.
The only limitation of this method was in the requirement of having a large enoughsample size in order to reduce the patient dependency of the analysis identified through
X-Val.
LDA/SIR analysis of the lipoprotein subclasses goes further in prediction of
CVD by identifying the subclasses that lead to high risk. For the most part, this analysis
fits into the current medical literature that low dense LDL and HDL are healthy and thehigh dense LDL and HDL are not. This analysis, along with the previously describedpilot study, indicates that there could be specific subclasses of the HDL that are
atherogenic. This is a new feature of HDL that is gaining support in recent studies.58,119126

120

3.2.1.4 Advanced Statistical Analyses Applied to the Comprehensive Patient Library for
CVD Risk Assessment
As a result of the success in application of the LDA/SIR analysis to small
libraries, it was determined that the lipoprotein density profiles and statistical analysis
protocols would be more rigorously tested using a larger library that included a morecomprehensive spectrum of subjects. This study was designed to answer questions that
had previously been unanswered, such as the accuracy of the method when used on ahold out sample, what effect patient demographics have on the analysis, how thefluorescent intensity for lipoproteins is affected by presence of CVD, and what methodof analysis is best for the risk assessment of CVD. For this study, the CVD patientswere defined as having been diagnosed with CVD through angiography or cardiac event.
The CTRL patients were defined as having a clear angiogram with less than 10% arterial
blockage. Traditional lipid panels for measurement of triglycerides and cholesterol foreach subject were performed at Scott & White Hospital. To assess the best method ofanalysis, Modes 1 and 2 data sets were analyzed using the LDA/SIR and QDA statisticalanalyses. The comprehensive library consisted of 100 CVD subjects and 58 CTRL
subjects. Of these subjects, 70 CVD and 40 CTRL were selected as the training set for
generating the LDA/SIR equation. The remaining subjects were used as a holdout
sample set to test the accuracy of the method on samples not used to create the LDA/SIR
prediction equation.

121

3.2.1.4.1 Relation of HDL/LDL Cholesterol Measurements to Lipoprotein Density
Profile Integrated Intensities
Before application of the statistical analyses to the lipoprotein density profiles,
the relationship between the profiles and the traditional lipid measurements used for
current risk assessment was explored. Using commercial standards of HDL and LDL, alinear relationship between protein concentration and integrated fluorescence intensities
was found. These relationships are graphed in Figure 40. However, when comparing
cholesterol measurements with integrated fluorescence intensities for clincal subjects, avaried relationship was observed. For the CVD subjects, the relationship was a scatterplot that showed little linearity. When fitting the data to a linear equation, the R2 value
was found to be 0.30 for LDL cholesterol measurements and 0.42 for HDL cholesterol
measurements for the CVD subjects, indicating a poor fit. These relationships aregraphed in Figures 41 A and B. By comparison, for the CTRL subjects, the relationshipbetween the cholesterol measurements and the integrated fluorescence intensities
showed a much more improved fit to the linear equation. The R2 values for the CTRL
measurements were found to be 0.69 for the LDL measurements and 0.67 for the HDL
measurements. This improved relationship between cholesterol measurements andfluorescence intensities for CTRL subjects is graphed in Figures 42 A and B. A possible
explanation for the difference between the CVD and CTRL libraries is that the NBD
fluorophore interacts differently with the lipoprotein particles from a CVD subject whencompared to the lipoprotein particles that are associated with the healthy controls. This

122

A
25000

y= 123.29 * x
R2 = 0.97

Integrated Intensity

20000

15000

10000

5000

0
0

20

40

60

80

100

120

140

160

180

LDL Concentration (ug/dL)

B
200000180000Integrated Intensity

160000y= 322.87 * x
R2 = 0.98

140000120000100000
80000
60000
40000
20000
0
0

100

200

300

400

500

600

HDL Concentration (ug/dL)

Figure 40: Calibration Curve of Protein Concentration vs. Integrated Intensities ofCommercial Lipoprotein Standards. (A) LDL Calibration Curve (B) HDL
Calibration Curve

123

A

CVD LDL Cholesterol Vs. Integrated Intensity
250000LDL Integrated Intensity

200000y = 37707.09 + 529.68 * x
2R Value = 0.29741

150000100000

50000

0
20

40

60

80

100

120

140

160

180

LDL CholesterolB

CVD HDL Cholesterol Vs. Integrated Intensity
220000y = 75295.15 + 1004.89 * x
2R Value = 0.42018

200000HDL Integrated Intensity

180000160000140000120000100000
80000
60000
40000
20000
0
20

30

40

50

60

70

80

90

100

110

HDL Cholesterol

Figure 41: Traditional Lipid Measurements in Relation to Integrated Intensities
Found Using Lipoprotein Density Profiles for CVD Subjects. (A) LDL
Measurements for CVD Library, (B) HDL Measurements for CVD Library

124

A

Control LDL Cholesterol Vs. Integrated Intensity
160000y = 29664.24 + 725.12 * x
2R Value = 0.68605

LDL Integrated Intensity

140000120000100000
80000
60000
40000
20000
0

20

40

60

80

100

120

140

160

180

LDL Cholesterol
Control HDL Cholesterol Vs. Integrated Intensity

B
220000HDL Integrated Intensity

200000y = 45770.76 + 1654.10 * x
2R Value = 0.67078

180000160000140000120000100000
80000
30

40

50

60

70

80

90

HDL Cholesterol

Figure 42: Traditional Lipid Measurements in Relation to Integrated Intensities
Found Using Lipoprotein Density Profiles for CTRL Subjects. (A) LDL
Measurements for CTRL Library, (B) HDL Measurements for CTRL Library125

inherent difference may be contributing to the reason why the LDA/SIR analysis has
been able to distinguish classification better than the tradition lipid measurements. Theinteraction of the NBD-ceramide with the lipoprotein particles may be a sensitive probeof the morphology of these particles.

3.2.1.4.2 Statistical Comparison of Lipoprotein Measurements between CVD and CTRL
Sample Libraries

Table 5: Comparison of Statistical Analysis for Traditional Risk Factors between
CVD/CTRL Groups for the Comprehensive Library

Patient Data

Standard Lipid
Measurements

FactorP-value

Age
Weight
TC
TG
HDL
LDL
Lp(a)

0.564
0.368
0.595
0.352
0.628
0.692
0.519

In order to assess the differences in the traditional lipid measures and patient
background information between CVD and CTRL libraries, a 2-sample T-test wasapplied to compare the two groups using the comprehensive library. Table 5 shows theresults of the 2-sample T-tests applied to the patient’s background data and comparing
the CVD group to the CTRL group. The p-values from the T-tests were used to assess
whether or not there were differences between the two groups. A p-value of greater than
0.05 meant that at a 95% confidence interval, the two groups were not statistically126

different. As can be seen from Table 5, there was no statistical difference between the
CVD and CTRL groups for background data selected or the standard lipid
measurements. This leads to two conclusions: (1) the age distribution between thesamples is similar and therefore differences are not due to age and (2) no difference canbe detected using standard lipid measurements. The findings presented here suggest that
any improvements found in the risk assessment of CVD when applying the lipoprotein
density profiles developed in this thesis to the comprehensive library of samples
represent a significant improvement over the standard lipid measurements. Current
studies have indicated that there are significant relationships between cholesterol
measurements for lipoproteins and CVD mortality rates.42,57,122 For example, elevatedHDL cholesterol was correlated with a reduced level of CVD risk and mortality. Thesestudies were not able to identify a relationship between lipoprotein levels and themortality rate for healthy subjects. When compared to the data presented in this thesis,
this relationship was not seen as no statistical difference could be found when using thecholesterol measurements for the subject population.
Table 6 summarizes the results when the T-test analysis of the lipoprotein
subclasses was applied to the lipoprotein density profiles. The analysis was carried out
on the total intensities of the subclasses (Mode 1) and the percent intensities of the
subclasses (Mode 2). For those lipoprotein subclasses with a p-value of <0.05, a
statistically significant difference was found between the CVD and CTRL values for therespective subclass. By comparing the p-values for each subclass with the standard lipid
measurements, some conclusions were reached. First, the lipoprotein subclasses found127

by density profiling showed statistical differences where the standard lipid
measurements did not. Second, there are differences in the statistical significance of thelipoprotein subclass that were detected when using the Mode 1 data analysis incomparison to Mode 2 data analysis. These differences indicated that the method of data
analysis could have an effect on the outcome of the LDA/SIR predictive outcome due tothe quantitative measurement of the lipoprotein subclass calculated in Mode 1 analysis
versus the measurement of relative contribution to the total lipoprotein density profile
calculated in Mode 2 analysis.

Table 6: Comparison of Statistical Analysis for Lipoprotein Density Profiles
between CVD/CTRL Groups
P-value from 2-Sample t-test
Subclass
Mode 1Mode 2

Lipoprotein Subclass

TRL

0.000

0.000

LDL1
LDL2
LDL3
LDL4
LDL5
HDL2b
HDL2a
HDL3a
HDL3b
HDL3c

0.000
0.002
0.000
0.019
0.309
0.499
0.008
0.043
0.031
0.245

0.024
0.070
0.005
0.490
0.000
0.210
0.877
0.313
0.000
0.347

3.2.1.4.3 LDA/SIR Analysis on Lipoprotein Measurements
With the differences between CVD and CTRL groups that were found throughlipoprotein density profiling, application of the LDA/SIR analysis allowed for enhancing

128

the individual differences into a prediction model using the full lipoprotein density
profile. In order to identify the improvement in risk assessment that the lipoprotein
density profiles had over the traditional lipid measurements, the traditional
measurements were also analyzed using LDA/SIR. Mode 1 and Mode 2 data from thelipoprotein density profiles were also compared to assess the best method for CVD risk
assessment.

Table 7: LDA/SIR Prediction Summary for Traditional Lipid Measurements andLipoprotein Density Profiles
Traditional
Statistical Output
Lipid
Mode 1Mode 2
Measurements
58.5
81.8
81.8
Prediction Accuracy (%)
50.0
76.4
77.3
X-Val Accuracy (%)
50.0
72.9
72.9
Holdout Accuracy (%)
0.613
2.72 E-07 3.62 E-07
p-value

For each data set, the total prediction accuracy, X-Val value, and efficiency ofthe model on a hold out sample were tested. The results of these analyses are
summarized in Table 7. The p-values were calculated for the separations between CVD
and CTRL groups that showed the most accurate prediction. As can be seen from Table
7, the traditional lipid measurements were only able to obtain 58.5% accuracy and an XVal and hold out sample score of 50.0%, indicating that the measurements did not allow
for significant risk assessment. However, the lipoprotein density profiles were able toobtain 81.8% accuracy with significant X-Val and hold-out sample scores. The p-values
were calculated for the LDA/SIR separations using just the Mode 1 and Mode 2 data and129

showed that both data sets gave statistically significant separations between the CVD
and CTRL groups. Therefore, it was not evident which method of data analysis waspreferred based on the similar predictive power between the methods of analysis.
Further investigation into the nature of the LDA/SIR prediction equation was necessary
to study the similar outcome found between Mode 1 and Mode 2 data analysis versus thedifferent outcomes found when using the T-test.
To further investigate the LDA/SIR predictive similarities between Mode 1 andMode 2 data analysis, the coefficients for these analyses were studied for comparison
between Modes 1 and 2 and between the previous studies reported. The coefficients aregraphed in histogram form in Figure 43. The coefficients show similar polarities
between the Mode 1 and Mode 2 data sets with slightly varied importance measured by
the relative size of the coefficients. The strength of each coefficient does not directly
represent the differences found between subclasses in the 2-samples T-test. For
example, the HDL-2b subclass was found to be statistically similar between CVD and
CTRL groups using the T-test analysis, but was given a larger coefficient in the
LDA/SIR prediction equation when compared to other subclasses that were shown to bestatistically different. The reason for this lack of congruence between the LDA/SIR
prediction equation and the T-test results is most likely due to the multivariate nature of
the LDA/SIR analysis and how it includes the relationship of variables inside the CVD
and CTRL groups and not just between the CVD and CTRL groups. When comparing
these coefficients of the comprehensive library study to the coefficients found from thenormal lipidemic library study, the most significant observation is that the alternating

130

polarities of the HDL subclasses found from the normal lipidemic library study arepresent in the comprehensive library study with the exception of the HDL-3a subclass.
The alternating HDL subclass polarities again gave evidence to support the existence ofatherogenic forms of HDL as suggested in recent studies.

0.6

Mode 1Mode 2

LDA/SIR Coefficients

0.4

0.2

0.0

-0.2

-0.4

TRL

LDL1

LDL2

LDL3

LDL4

LDL5

HDL2b HDL2a HDL3a HDL3b HDL3c

Lipoprotein Subclass

Figure 43: LDA/SIR Coefficients for Prediction of CVD for the Comprehensive
Serum Library Using Mode 1 and Mode 2 Data Sets.

131

The graphical separation of CVD and CTRL for the traditional lipid
measurements and each Mode of the training set data are depicted in Figure 44. From
Figure 44, it can be seen that the traditional lipid measurements were not able to separate
the CVD and CTRL groups through a generated LDA/SIR equation. Mode 1 and Mode2 data for lipoprotein subclasses were able to separate the two groups by using therespective LDA/SIR equations generated for each data set. Slightly different
distributions can be seen between Modes 1 and 2, but there was no difference inprediction accuracy between Modes 1 and 2. There was a small increase in the X-Val
score for Mode 2. This increase in X-Val in Mode 2 represented the correctclassification of one additional subject when compared to the X-Val of Mode 1. The
correct classification of one subject did not represent a significant change between
Modes 1 and 2 and therefore it was unclear which method of data analysis was best forrisk assessment. It was clear from the analyses that lipoprotein density profiles did allow
for better risk assessment of CVD in comparison to the traditionalcholesterol/triglyceride measurements.

132

CVD Classification

A
CVD

CTRL

-0.15

-0.10

-0.05

0.00

0.05

0.10

LDA/SIR ValueB

CVD Classification

CVD

CTRL

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

LDA/SIR Value

CVD Classification

C
CVD

CTRL

-0.4

-0.2

0.0

0.2

0.4

LDA/SIR Value

Figure 44: LDA/SIR Separations for the Comprehensive Serum Library for
Lipoprotein Measurement Methods.
(A) Tradition Lipid Measurement Data, (B) Mode 1 Data, (C) Mode 2 Data

133

The separation of CVD and CTRL groups for the Mode 1 data of the holdout
sample set based on the calculated LDA/SIR values using the generated LDA/SIR
equation is graphed in Figure 45. The subject samples in the hold out set were not used
to develop the LDA/SIR equation. Therefore, the separation/accuracy of the holdout
sample indicated that the LDA/SIR values can be used to assess risk for clinical samples.

CVD Classification

CVD

CTRL

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

LDA/SIR Value

Figure 45: LDA/SIR Separations for the Hold Out Sample Set of theComprehensive Serum Library Using Mode 1 Data

In order to apply this prediction analysis to clinical samples, the error inclassification needed to be studied further. To study the precision of the LDA/SIR
classification, the LDA/SIR analysis was carried out on the 10 replicate measurements
used to assess the lipoprotein density profiling method repeatability. Based on the
LDA/SIR value, this sample was classified as at risk due to the positive value. Using the134

LDA/SIR prediction equation, the average LDA/SIR value for Mode 1 analysis wascalculated to be 0.124 +/- 0.030. The error represented by the standard deviation of themeasurement represented a 23.61% spread in the LDA/SIR value. For Mode 2, theaverage LDA/SIR value was calculated to be 0.152 +/- 0.030. The standard deviation ofthis measurement represented a 19.74% spread in the LDA/SIR value. This reduction
was attributed to the reduced error due to the light source and serum volume when using
the normalized Mode 2 analysis of the data. Further reduction of this error was possible
by using three sets of three replicate measurements and comparing the average data
calculated for each set of three profiles. The average %RSD for this method was
calculated at 11.13% and 9.46% for Modes 1 and 2, respectively, when applying the
LDA/SIR equations to the averages of three replicates. This reduction of the error in the
LDA/SIR values qualified the analysis for clinical assessment of risk as previously
defined.
To further improve in the prediction accuracy of the LDA/SIR analysis, patient
histories and background information were added as additional variables. The
traditional risk factors for CVD that were included in the analysis include tobacco use,family history of disease, presence of hypertension, presence of diabetes, subject age,and body mass index. For the factors that had a yes/no answer as a response, they were
included into the analysis using a 1 for yes and 2 for no. The summaries of the
LDA/SIR analyses on the Mode 1 and Mode 2 data sets are shown in Tables 8 and 9,
respectively. The results for the LDA/SIR analyses are expressed in terms of the135

prediction accuracy, X-Val accuracy, and accuracy on a holdout sample when eachindividual factor was added to the lipoprotein density profile information.

Table 8: Influence of Tradition Risk Factors on the LDA/SIR Analysis of Mode 1
Data of Lipoprotein Density Profiles for the Comprehensive Library
Prediction
X-Val
Holdout
Risk Factor
Accuracy
Accuracy Accuracy
(%)
(%)
(%)
72.9
81.8
76.4
Density Profile Data (DP)
75.0
80.9
76.4
DP + Age
77.1
82.1
75.5
DP + BMI
70.8
80.6
77.8
DP + Diabetes
77.1
82.7
78.2
DP + Tobacco Use (T)
75.0
85.2
75.0
DP + Hypertension (HT)
75.0
83.5
77.1
DP + Family History (FH)
75.0
86.9
77.6
DP + HT/FH
72.9
84.1
79.4
DP + T/HT/FH

Table 9: Influence of Tradition Risk Factors on the LDA/SIR Analysis of Mode 2
Data of Lipoprotein Density Profiles for the Comprehensive Library
Prediction
X-Val
Holdout
Risk Factor
Accuracy
Accuracy
Accuracy
(%)
(%)
(%)
81.8
77.3
72.9
Density Profile (DP)
81.8
74.5
72.9
DP + Age83
76.4
75
DP + BMI
80.6
76.9
75
DP + Diabetes
80.9
77.3
75
DP + Tobacco Use (T)
84.3
78.7
75
DP + Hypertension (HT)
82.6
75.2
75
DP + Family History (FH)
86
80.4
72.9
DP + HT/FH
84.1
78.5
72.9
DP + T/HT/FH

136

Of the six risk factors added to the LDA/SIR analysis, only three showed
improvement in the accuracy of the method. These factors were tobacco use,
hypertension, and family history. The largest improvement was seen when applying thehistory of hypertension in subjects. The prediction accuracy increased from 81.8% to85.2% and 84.3% for Modes 1 and 2, respectively. Limited improvement was realized
in the X-Val scores, but there was an increase of the accuracy of the model when testing
the holdout sample sets. Combinations of the factors which showed the best
improvement were also tested as indicated in Tables 8 and 9. The combination of
hypertension and family history, in addition to the lipoprotein subclasses, was found tohave the best accuracy at 86.9% for Mode 1 and 86.0% for Mode 2. There was also an
increase in the accuracy of the X-Val and holdout sample scores. These results indicatethat the best prediction method was when the lipoprotein density profiles were combinedwith the factors relating to hypertension and family history.
Subject age, BMI, and presence of diabetes were included as factors in theanalysis but there were no significant improvements in the LDA/SIR prediction
accuracy. These results showed that the LDA/SIR analysis on lipoprotein density
profiles for risk assessment of CVD was not improved by the addition of the age, BMI,
or presence of diabetes in the subjects. The hypothesis that the lipoprotein density
profiles may be time dependent fingerprints that represent the status of CVD in a patientat the time that the serum sample was collected would explain the lack of improvementwith the additional risk factors. The effects of age, BMI, and diabetes could be137

inherently represented in the lipoprotein density profiles. The effects these factors haveon the lipoprotein density profiles require further research.
To examine the effect of adding the traditional risk factors to the statisticalanalyses, the coefficients of the LDA/SIR prediction equation formed from using theMode 1 data coupled with the traditional risk factors were compared to the coefficientsfound using just the Mode 1 data set. The coefficients for the addition of traditional risk
factors are depicted in Figure 46. The polarity of the coefficients for family history andpresence of hypertension were both found to be negative, indicating that the risk factors
influence the risk assessment towards being a CTRL. To understand this finding, themethod of applying these values to the LDA/SIR analysis was explored. The presenceof the risk factor was given the value of 1 and the lack of presence of the risk factor was
given the value of 2. With the larger value indicating that the subjects did not have thethe risk factor, the negative value of the LDA/SIR coefficient indicates that the lack ofrisk factors increased the probability of being classified as a CTRL. This finding is
consistent with the established risk factors and gives support to the premise that the
LDA/SIR coefficients have the potential for augmenting the risk assessment panel.

138

0.6

0.4

LDA/SIR Value

0.2

0.0

-0.2

-0.4

-0.6
FH

HT

TRL

LDL1

LDL2

LDL3

LDL4

LDL5 HDL2b HDL2a HDL3a HDL3b HDL3c

FactorFigure 46: LDA/SIR Coefficients for Lipoprotein Subclasses with the Addition of
Tradition Risk Factors. (FH=Family History, HT=Hypertension) for theComprehensive Library

3.2.1.4.4 QDA Analysis of Lipoprotein Measurements
A more advanced statistical analysis that can be applied to the patient libraries is
Quadratic Discrimination Analysis (QDA). QDA is closely related to LDA in that it
creates a classification equation based on the variables for the CVD and CTRL goups.
QDA does not hold to the initial assumption that the covariance between groups is
identical. The model for QDA analysis takes into account the covariance differences
and can be a more comprehensive analysis. For this reason, a larger number of subject
samples are required to achieve statistical significance when using QDA. Consequently,
the comprehensive serum library was utilized for the QDA study. Since the sample size

139

was not increased, an increase in sample bias was expected in the form of a lower X-Val
score.
With the results from the LDA/SIR analysis of the comprehensive serum libraryshowing that application of traditional risk factors can improve the CVD risk
assessment, the same factors were studied using the QDA analysis. Mode 1 and Mode 2
data sets were both used to further explore and identify the best method with which to
represent the data. The summaries of these analyses can be found in Tables 10 and 11
for Modes 1 and 2, respectively.
The best accuracy for CVD risk assessment previously reported using LDA/SIR
was 81.8% for the lipoprotein subclasses and 86.9% with the addition of family history
and hypertension to the Mode 1 data set. Based on the results found when implementing
the QDA analysis, improvements were realized in the prediction accuracy and theaccuracy when testing the holdout sample set. The total prediction accuracy for the
lipoprotein subclasses increased to 84.5% and the addition of the family history andhypertension risk factors further improved the analysis to 89.7% accuracy. The best
result previously seen for the holdout sample set was 75% accuracy. Using thelipoprotein subclasses coupled with the risk factors of family history and hypertension,
the holdout sample set accuracy was improved to 85.4%. There was a reduction in the
X-Val score, but this was anticipated due to the nature of the statistical analysis inrelation to the sample size. A larger sample library would improve the X-Val score.

140

Table 10: Influence of Tradition Risk Factors on the QDA Analysis of Mode 1 Dataof Lipoprotein Density Profiles for the Comprehensive Library
Prediction
X-Val
Holdout
Accuracy
Accuracy
Accuracy
Risk Factor
(%)
(%)
(%)
83.3
84.5
70.9
Density Profile (DP)
81.3
84.5
70.9
DP + Age
77.1
88.7
70.8
DP + BMI
79.2
87.0
67.6
DP + Diabetes
85.4
86.4
68.2
DP + Tobacco Use (T)
77.1
87.0
70.4
DP + Hypertension (HT)
83.3
89.0
70.6
DP + Family History (FH)
85.4
89.7
68.2
DP + HT/FH
72.9
89.7
64.5
DP + T/HT/FH

Table 11: Influence of Tradition Risk Factors on the QDA Analysis of Mode 2 Dataof Lipoprotein Density Profiles for the Comprehensive Library
Prediction
X-Val
Holdout
Accuracy
Accuracy
Accuracy
Risk Factor
(%)
(%)
(%)
79.2
85.5
70.0
Density Profile (DP)
72.9
85.5
68.5
DP + Age
81.3
84.0
69.8
DP + BMI
79.2
88.0
67.6
DP + Diabetes
83.3
87.3
66.4
DP + Tobacco Use (T)
75.0
87.0
70.4
DP + Hypertension (HT)
83.3
89.9
70.6
DP + Family History (FH)
79.2
88.8
70.1
DP + HT/FH
75.0
88.8
67.3
DP + T/HT/FH141

The improvements seen through the application of QDA statistical analysis
proved to show the best classification accuracy for CVD. There were significant
improvements over the LDA/SIR analysis. For both the LDA/SIR and QDA statisticalanalyses, the lipoprotein density profiles have shown to provide a more accurate CVD
risk assessment than traditional lipid measurement or traditional risk factors. Based on
the results in this study, the optimum method of CVD risk assessment for application toclinical studies was found using the QDA analysis of the lipoprotein density profile datawith the addition of the tradition risk factors hypertension and family history of CVD.
Future directions in the improvement of this methodology include increasing the size ofthe library and incorporating the correct epidemiology of the sample library. In addition,
incorporation of novel risk markers will be included to increase the accuracy
determining whether an individual has risk of developing CVD.

3.2.1.5 LDA/SIR Analysis Applied to Hypercholesterolemia
The next sample library in which the lipoprotein density profiles and LDA/SIR
analysis was applied was a library of children’s serum samples for which there were
subjects with and without hypercholesterolemia. Hypercholesterolemia is a disease thatinvolves abnormally high levels of cholesterol in a subject’s serum. This disease canalso be a precursor to the onset of CVD. Early risk assessment of this disease in children
would be a first step in utilizing the potential for using the lipoprotein density profiles asa CVD risk assessment tool. Preventative measures could then be applied to reduce thesubject’s risk of CVD. The hypercholesterolemic subjects from this library were also

142

monitored over a period of time as they were treated with statin therapies or placebos tomeasure the effectiveness of the therapy. The goal of this study was to use the LDA/SIR
analysis to develop a prediction algorithm for hypercholesterolemia and then in casestudies to assess the effectiveness of using the LDA/SIR algorithm to monitor theHypercholesterolemia Presence

influence of statin treatment.

yes

no

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

LDA Value

Figure 47: LDA/SIR Separation for Hypercholesterolemic Subjects

3.2.1.5.1 LDA/SIR Analysis for Risk Assessment of Hypercholesterolemia
The LDA/SIR analysis proved effective in the risk assessment ofhypercholesterolemia. For this study, all 11 lipoprotein subclasses were included. The

143

prediction accuracy of this study was found to be 97.6% with an X-Val value of 92.7%.
The LDA/SIR separation is depicted in Figure 42. Only one subject from the sample
library was misclassified using the LDA/SIR analysis. The difference in the prediction
accuracy and the X-Val score was only 4.9% which indicated that there was very little
sample dependency for this analysis, even though the total sample size was only 41
subjects. This study showed a robust statistical significance based on the X-Val test
despite the small number of subjects in the library.
The low sample dependency for detection of hypercholesterolemia could
indicate that the separation between the two groups in this study was more readily
detected due to the nature of the hypercholesterolemia and the elevated lipoproteincholesterol content related to the disease.127 This elevated cholesterol level affected thefluorescence intensities in the lipoprotein profiles. When comparing the total integrated
fluorescence intensities for the TRL, LDL, and HDL between groups, it was found thatthe intensities for the TRL and LDL of the hypercholesterolemic subjects were almost
double that of the non-hypercholesterolemic subjects. The average HDL intensities
between groups were relatively similar and did not show any difference between groups.
The elevated fluorescence intensities throughout the TRL and LDL subclasses more
readily distinguished between sample groups and reduced the sample dependency of thisstudy.

144

0.7
0.6
0.5

LDA/SIR Constant

0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
TRL

LDL1

LDL2

LDL3

LDL4

LDL5 HDL2b HDL2a HDL3a HDL3b HDL3c

Lipoprotein Subclass
Figure 48: LDA/SIR Coefficients for Prediction of Hypercholesterolemia inChildrenTo access the effect of the lipoprotein subclasses with respect to the prediction
analysis, the coefficients of the prediction algorithm were studied. These coefficientsare depicted in Figure 48. The majority of the coefficients for the hypercholesterolemia
prediction algorithm have the same polarities as the coefficients for the comprehensive
serum library. The coefficients for TRL, LDL-1, and LDL-5 are the only variables thatchange polarity between the studies. The constant that is included in the algorithm alsochanges. The constant for the hypercholesterolemic study was found to be -1.353. The
constant for the comprehensive serum library was found to be 2.054. The polarity
change for the hypercholesterolemic study means that the samples were weighed

145

towards a “healthy” classification and that the classification changed based on theincrease in fluorescent intensities for the variables with positive coefficients. When
compared to the coefficients for the comprehensive serum library, the HDL subclasses
show a similar pattern of polarities for CVD prediction. This again is evidence that
supports the theory of atherogenic forms of HDL.
With the similarities in algorithm coefficients between the comprehensive
serum library and the hypercholesterolemia children’s library, application of thehypercholesteremic subject data from the lipoprotein profiles to the LDA/SIR equation
for the comprehensive serum library was investigated. The resulting LDA/SIR values
for the different subjects have been overlaid onto the separation graph from thecomprehensive library in Figure 49. Of the 31 hypercholesterolemic subjects, six of thesubjects were classified as CTRL and 25 of the subjects were classified as CVD basedon the LDA/SIR calculated values. For the 10 non-hypercholesterolemic subjects, 9 of
the 10 subjects were classified as CVD while only one was classified as CTRL based on
the LDA/SIR calculated values.

146

CVD Classification

CVD

CTRL

HC

Non-HC-0.5 -0.4 -0.3 -0.2 -0.1

0.0

0.1

0.2

0.3

0.4

0.5

LDA/SIR Value

Figure 49: Classification of Hypercholesterolemia Children's Library Using theComprehensive Serum Library LDA/SIR Algorithm. (HC = Hypercholesterolemia
Subject, Non-HC = Non-hypercholesterolemia Subject)

The classification of the hypercholesterolemic subjects as CVD correlated withthe fact that the disease can be a precursor to the onset of CVD. The classification of the“healthy” children as CVD indicated that the algorithm generated for the adult
population of the comprehensive serum library was not applicable to the children
samples since there should be very little, if any, arterial blockage in children. Current
studies into the stability of the lipid panels from childhood to adulthood indicate thepotential changes in lipoprotein profiles during adolescence.128 There are also studies
that have indicated the correlation between lipoprotein levels in childhood and theresulting adult levels.129 The findings from this research coupled with the ambiguity that147

has been reported in the correlation of lipid values between childhood and adulthoodindicate that further investigation is needed into the correlation of the LDA/SIR risk
assessment and the longitudinal development of CVD.

3.2.1.5.2 Effects of Therapy/Placebo on Risk Assessment of Hypercholesterolemia
The next goal of this research was to assess the benefits of implementing the
LDA/SIR risk analysis in identifying the effects that treatment can have on the subject’srisk. The specific treatment studied in this experiment was the use of statin therapy.
Statin therapy has been well documented to reduce cholesterol levels and mortality due
to CVD.37,38,124 To test statin therapy’s effectiveness on the hypercholesterolemicchildren, some were given the treatment while others received a placebo. Treatment wasapplied over a 48 week span with serum samples drawn at the 0, 24, and 48 week point.
There was also a washout period of 12 weeks applied to this study after the 48 week
mark. This washout period halted all therapies and was designed to test the effect thathalting statin therapy had on the lipoprotein profiles. In this study, the effects oftreatment were related to the change in the initial risk assessment for risk ofhypercholesterolemia.
When studying the profiles of subjects treated with statin therapy, results were
subject-dependent. While nearly all subjects treated with statins showed changes in
LDA/SIR values towards the CTRL group at some point during therapy, there weresome patients whose values fluctuated up and down. Four patient’s (I-IV) LDA/SIR
changes over time have been depicted in Figures 50 and 51. Subjects I and II showed a148

Statin Therapy on Subject I

A
0.26
0.24
0.22

LDA/SIR Value

0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0

10

20

30

40

50

60

50

60

Week

Statin Therapy on Subject II

B
0.26
0.24
0.22

LDA/SIR Value

0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0

10

20

30

40

Week

Figure 50: LDA/SIR Values for Hypercholesterolemia Subjects Treated with Statin
Therapy (Subjects I and II)

149

Statin Therapy on Subject III

A
0.20
0.18
0.16
0.14

LDA/SIR Value

0.12
0.10
0.08
0.06
0.04
0.02
0.00
-0.02
-0.04
0

10

20

30

40

50

60

50

60

Week

B

Statin Therapy on Subject IV
0.35
0.30
0.25

LDA/SIR Value

0.20
0.15
0.10
0.05
0.00
-0.05
0

10

20

30

40

Week

Figure 51: LDA/SIR Values for Hypercholesterolemia Subjects Treated with Statin
Therapy (Subjects III and IV)

150

general reduction in the LDA/SIR value which indicated a decrease in the risk ofhypercholesterolemia over the span of statin treatment. This trend was found in six ofthe eight subjects treated with statin therapy. Subjects III and IV showed varied results
including LDA/SIR values that were increased relative to the baseline value at Week 0.
This result could indicate that there is a patient to patient variability in the effectivenessof treatment. In all statin treated subjects, some form of reduced LDA/SIR value wasobserved throughout the study. Three of the eight subjects even showed a change from a
positive LDA/SIR value to a negative LDA/SIR value which would indicate a change in
CVD risk. After the washout period (Week 60), seven of the eight statin-treated subjects
showed an increased LDA/SIR value indicating that when statin treatment was
terminated, the CVD risk increased.
The placebo-treated subject’s LDA/SIR values showed a variety of patterns.
These patterns are depicted in Figures 52 and 53. Of the nine subjects treated withplacebos, three subjects showed varying LDA/SIR values similar to that found with thestatin treated subjects indicating no significant changes over the study period (Subject V,
Figure 52A). Three of the subjects showed increased LDA/SIR value over the study
period indicating stability in the classification identified through the LDA/SIR analysis(Subject VI, Figure 52B). Finally, three subjects displayed a decrease in LDA/SIR
values over the study period which would indicate a reduced risk of disease even though
they were treated with placebos (Subject VII, Figure 53A). Of these three subjects, only
one subject (Subject VIII, Figure 53B) showed a reduction in the LDA/SIR value to a

151

Placebo Therapy on Subject V

A
0.26
0.24

LDA/SIR Value

0.22
0.20
0.18
0.16
0.14
0.12
0.10
0

10

20

30

40

50

60

50

60

Week

Placebo Therapy on Subject VI

B
0.35

0.30

LDA/SIR Value

0.25

0.20

0.15

0.10

0.05
0

10

20

30

40

Week

Figure 52: LDA/SIR Values for Hypercholesterolemia Subjects Treated with
Placebos (Subjects V and VI)

152

Placebo Therapy on Subject VII

A
0.35

LDA/SIR Value

0.30

0.25

0.20

0.15

0.10
0

10

20

30

40

50

60

50

60

Week

Placebo Therapy on Subject VIII

B
0.20

LDA/SIR Value

0.15

0.10

0.05

0.00

-0.05
0

10

20

30

40

Week

Figure 53: LDA/SIR Values for Hypercholesterolemia Subjects Treated with
Placebos (Subjects VII and VIII)

153

negative value indicating a diagnosis change. These studies suggest that the LDA/SIR
value for a subject can fluctuate over a period of time, most like due to lifestyle.
To understand how the changes in the lipoprotein density profiles affected the
LDA/SIR value calculated from the algorithm, a comparison of the integrated
fluorescent intensities and the value of each lipoprotein subclass using the coefficientsfrom the LDA/SIR algorithm was carried out. Figures 54 and 55 compares the
lipoprotein density profiles for Subject I (statin therapy) and Subject V (placebo) and thecalculated LDA/SIR contributions by lipoprotein subclass for the baseline draw (Week0) and final draw (Week 48) for each treatment. For Subject I, analysis of the integrated
fluorescence intensities between week 0 and week 48 showed reduction in LDL-1
through LDL-4 subclasses and a relative increase in the HDL3 subclasses. This patterncorrelated with the reported effects of statin therapy on lipoproteins. The impact of thesechanges was not as prevalent in the LDA/SIR values for each subclass due to the use ofnatural logs to normalize the data. An increase in values for three subclasses withnegative coefficients and decrease in value for one subclass with a positive coefficient
explained the reduction in the total calculated LDA/SIR value for Subject I between
week 0 and week 48. For Subject V, the integrated intensities showed relatively no
difference between week 0 and week 48 except for a reduction in the LDL-3 and LDL-4
subclasses. The calculated LDA/SIR values for each subclass also showed relatively nochange between week 0 and week 48. These findings show that slight changes in the
profile were seen despite using a placebo for treatment.

154

Subject I Integrated Intensities

A
50000

Week 0Week 48

Integrated Intensities

40000

30000

20000

10000

0
TRL

LDL-1

LDL-2

LDL-3

LDL-4

LDL5

HDL-2b HDL-2a HDL-3a HDL-3b HDL-3c

Lipoprotein Subclass

Subject I LDA/SIR Value per SubclassB
6

Week 0Week 48

LDA/SIR Value

4

2

0

-2

TRL

LDL-1

LDL-2

LDL-3

LDL-4

LDL5

HDL-2b HDL-2a HDL-3a HDL-3b HDL-3c

Lipoprotein Subclass

Figure 54: Comparison of Integrated Fluorescent Intensities and LDA/SIR Values
for Each Lipoprotein Subclass: Subject I (Statin Therapy).

155

A

Subject V Integrated Intensities
70000

Week 0Week 48

Integrated Intensities

60000
50000
40000
30000
20000
10000
0

TRL LDL-1 LDL-2 LDL-3 LDL-4 LDL5 HDL-2bHDL-2aHDL-3aHDL-3bHDL-3c

Lipoprotein Subclass

B

Subject V LDA/SIR Value per Subclass
Week 0Week 48

6

LDA/SIR Value

4

2

0

-2

-4
TRL

LDL-1 LDL-2 LDL-3 LDL-4 LDL5 HDL-2bHDL-2aHDL-3aHDL-3bHDL-3c

Lipoprotein Subclass

Figure 55: Comparison of Integrated Fluorescent Intensities and LDA/SIR Values
for Each Lipoprotein Subclass: Subject V (Placebo Therapy)

156

To identify whether or not the changes found in the placebo treated subjects
were insignificant, a study would need to be performed to assess the stability of thelipoprotein density profile and the LDA/SIR value over time. The current study gave
evidence that the effects of statin treatment can be studied using the lipoprotein density
profile and the LDA/SIR analysis and that the treatment did reduce the risk assessmentof hypercholesterolemia. Other forms of medical treatment must still be studied in order
to confirm the use of these methods for the assessment of individual treatment
effectiveness. Similar studies must also be carried out in order to assess the effect of
medical treatments on CVD risk assessment.

3.2.2 Further Analytical Methods Applied to the Statistical Analyses to Improve Risk
Assessment of CVD
In order to assess any improvements that changes in methodology or additionalrisk factors might have on the CVD risk assessment, a subset of serum samples wereselected from the Comprehensive Serum Library to test prior to application to the fullserum library. This subset was made up of 36 CVD and 36 CTRL samples which wereselected based on the LDA/SIR analysis and were a mix of correctly and incorrectlyclassified samples for each condition. The baseline accuracies for the LDA/SIR andQDA analyses on this sample set were found to be 86.1% and 93.1%, respectively. The
X-Val scores for the LDA/SIR and QDA analyses were found to be 79.2% and 70.8%,
respectively. The LDA/SIR prediction equation coefficient histogram for the 72 subject
library is shown in Figure 56.

157

0.6

LDA/SIR Values

0.4
0.2
0.0
-0.2
-0.4
-0.6
TRL

LDL1

LDL2

LDL3

LDL4

LDL5

HDL2b HDL2a HDL3a HDL3b HDL3c

Variables
Figure 56: LDA/SIR Coefficients for the 72 Patient Library Using the NaBiEDTABased Gradient System

The goal of these studies was to measure the improvements in risk assessment
when applying new analytical methods and/or risk factors to the statistical analyses. The
methods tested for improvement were the use of Cs2CdEDTA based gradients to expand
the lipoprotein density profiles and application of mass spectrometry to theapolipoproteins present in each subject’s HDL.

158

3.2.2.1 Application of Cs2CdEDTA-based Lipoprotein Density Profiling for CVD Risk
Assessment
With all of the evidence from the previous CVD risk studies pointing to the HDL
subclasses as the major contributors to the risk assessment, further studies were designed
to specifically look at the HDL subclasses by expanding this region of the lipoprotein
profiles using the Cs2CdEDTA solute based systems. As previously described, theCs2CdEDTA solute-based gradient systems allow for expansion of the HDL or the LDL
subclasses. The goal of this study was to explore the effect that expanding thelipoprotein profiles have on the statistical analyses for risk assessment.
The use of the Cs2CdEDTA-based gradients was analyzed in multiple ways.
First, the total lipoprotein profile was analyzed by combining the results from the LDL
and HDL profiles using Cs2CdEDTA. Next, the use of Cs2CdEDTA for profiling the
HDL subclasses was studied by combining the HDL data with LDL data from the
NaBiEDTA-based profiles. The differences in prediction accuracies and X-Val scores
were used to assess the advantages of the expanded lipoprotein profiles. For thisanalysis, Mode 1 data from the NaBiEDTA-based profiles was used as a baseline due toit previously showing the best accuracy in terms of risk assessment.

159

Table 12: LDA/SIR and QDA Statistical Analyses for Cs2CdEDTA-Based
Gradients of the 72 Subject Data Set
LDA/SIR Analysis
QDA Analysis
Prediction
X-Val
Prediction
X-Val
System
Accuracy Accuracy Accuracy Accuracy
(%)
(%)
(%)
(%)
86.1
79.2
93.1
70.8
NaBiEDTA
88.9
83.3
93.1
76.4
Cs2CdEDTA
86.1
79.2
91.7
77.8
Cs2CdEDTA w/o IDL
NaBiEDTA (TRL & LDL)
87.5
77.8
94.4
77.8
and Cs2CdEDTA (HDL)
88.6
78.6
92.9
74.3
NaBiEDTA + HT & FH
90
82.9
98.6
82.9
Cs2CdEDTA + HT & FH
Cs2CdEDTA w/o IDL + HT
87.1
78.6
95.7
82.9
& FH
NaBiEDTA (TRL & LDL)
87.1
80
94.3
75.7
and Cs2CdEDTA (HDL) +HT & FH

The summary of the LDA/SIR and QDA statistical analyses are displayed inTable 12. The use of Cs2CdEDTA-based lipoprotein density profiles showed minimal
improvements in the prediction accuracy. This improvement was found in the form of
one more subject being correctly classified than when using the NaBiEDTA-based
density profiles. This improvement was not clearly defined as significant due to thesmall number of samples in the study. The application of QDA with the risk factors offamily history and hypertension to the Cs2CdEDTA density profile data, however,showed a very large improvement in the prediction accuracy and the X-Val score. The
prediction accuracy was found to be 98.6% with an X-Val score of 82.9%. The 15.7%
difference between the X-Val score and prediction accuracy indicates some level ofpatient dependency. However, this was just a subset of the data tested to confirm the160potential of expanding the lipoprotein profiles. The increased prediction power
indicated that the Cs2CdEDTA-based gradients for expanding the lipoprotein profiles
have the potential for improving the risk assessment of CVD.
With the best potential for risk assessment of CVD found using the Cs2CdEDTAbased gradients and patient history, further analysis of how these factors impacted therisk assessment by studying the coefficients of the LDA/SIR analysis was performed.
The coefficients are depicted in Figure 57. The addition of the IDL subclass to theanalysis was deemed significant with the large coefficient that was associated with the
lipoprotein subclass. The coefficients for the family history and hypertension, 4.53E-4
and 1.48E-3 respectively, were so small in comparison to the lipoprotein subclasses thatthey are indistinguishable in the figure. The small size of these coefficients explains
why the improvement when adding the traditional risk factors only improves theaccuracy of the analysis by one patient. The largest difference between the coefficientsfrom the Cs2CdEDTA lipoprotein subclasses and the NaBiEDTA subclasses was seen in
the LDL subclasses. The LDL2-LDL5 subclasses all switched polarities and relative
strengths. The LDL1 subclass did not change in polarity, but the size of the coefficient
was largely different. These differences in the LDL subclasses and their application to
the CVD risk analysis required further study.

161

0.6

0.4

LDA/SIR Value

0.2

0.0

-0.2

-0.4

-0.6
FH

HT

TRL

IDL

LDL1 LDL2 LDL3 LDL4 LDL5 HDL2b HDL2a HDL3a HDL3b HDL3c

FactorFigure 57: LDA/SIR Coefficients for Lipoprotein Subclasses and Traditional Risk
Factors Using Cs2CdEDTA Density Gradient System on the 72 Subject Data Set

To further understand the changes in how the LDL subclasses contributed to the
LDA/SIR analysis when applied to the Cs2CdEDTA lipoprotein subclass data, the LDL
profiles of different subjects were compared when using the two different gradientsystems. The change in LDL peak densities between the different solute gradient curves
was seen for both CVD and CTRL subjects. Examples of lipoprotein profiles for both
solute systems are shown for CTRL and CVD subjects in Figures 58 and 59,
respectively. When examining the changes in the densities of the prominent fluorescentpeaks between the CVD and CTRL groups, it was found that the average changes in162

Subject: CTRL - Patient 052
Study: 0.05M Cs2CdEDTA for LDL Profile
1800
TRL

IDL

Intensity

1500
1200

1

2

1800

HDL
&
ProteinLDL

3

4

Study: CTRL - Patient 052
Study: 0.25M Cs2CdEDTA for HDL Profile

B

TRL
&
LDL

1500

Intensity

A

5

900
600

HDL

1200

2b

2a

Protein3a 3b 3c

900
600

300

300

0
6

0

8 1.017
10 12 1.022
14 16 1.027
18 20 1.036
22 24 1.051
26 28 1.080
30 32

6

8

Density (g/mL)

10 12 1.081
14 16 1.104
18 20 1.138
22 24 1.191
26 28
1.060

Density (g/mL)

Subject: CTRL - Patient 052
Study: 0.18M NaBiEDTA for Lipoprotein Profile

C
2500

TRL

LDL

HDL

Intensity

2000
1500

1

2

3

4

5

2b

3a 3c
2a 3b

1000
500
0
6 1.014810

12 14 16 18 20 22 24 261.117
28 30 32
1.026
1.038 1.049 1.070
1.230

Density (g/mL)
Figure 58: Comparison of LDL Lipoprotein Density Profiles for CTRL-Patient 052
between Solute Systems. (A) Cs2CdEDTA for LDL, (B) Cs2CdEDTA for HDL, (C)
NaBiEDTA

163

Subject: CVD - 169
Study: 0.05M Cs2CdEDTA for LDL Profile
1800

TRL

IDL

LDL

Intensity

1200

1

2

4

3

5

900

TRL
&
LDL

1800

HDL
&
Proteins

1500

Subject: CVD - Patient 169
Study: 0.25M Cs2CdEDTA for HDL Profile

B

1500

Intensity

A

2b

1200

600

300

300
0
6

6

8 1.017
10 12 1.022
14 161.027
18 20 1.036
22 241.051
26 28 1.080
30 32

3a

3b

3c

8 1.060
10 12 1.081
14 16 1.104
18 20 1.138
22 24 1.191
26 28

Density (g/mL)

Density (g/mL)C

2a

Protein

900

600

0

HDL

Subject: CVD - 169
Study: 0.18M NaBiEDTA for Lipoprotein Profile
2500

TRL

HDL

LDL

Intensity

2000
1500

1 2

3

4

5

2b

3a 3c
2a 3b

1000
500
0
6 1.0148
10 1.026
12 141.038
16 181.049
20 221.070
24 26 1.117
28 30 1.230
32

Density (g/mL)
Figure 59: Comparison of LDL Lipoprotein Density Profiles for CVD-Patient 169
between Solute Systems. (A) Cs2CdEDTA for LDL, (B) Cs2CdEDTA for HDL, (C)
NaBiEDTA

164

density were 0.0260 +/- 0.0020g/mL and 0.0276 +/- 0.0020g/mL, respectively. Whentesting the difference between these two groups with an ANOVA analysis, the p-value ofthe analysis was found to be 0.131, which means that at a 95% confidence level, the twogroups were not statistically different. This means that the change in densities was
uniform between the disease classifications and that it had nothing to do with the type of
patient tested. The change in the LDA/SIR equation between solute systems wastherefore related to the difference in cationic interactions between the solute and thephospholipids in LDL.

3.2.2.2 Mass Spectrometry of HDL Apolipoproteins for Application to CVD Risk
Assessment
Through previous work performed in our laboratory for profiling the mass
spectra of apolipoproteins in the HDL fraction of patient serum, the identification of apost translational modification (PTM) of Apo C-I was identified in a small cohort of
CVD subjects that wasn’t present in the cohort of CTRL subjects.16 This PTM was
identified as a potential risk factor that could aid in the risk assessment of CVD. For thisreason, further analysis of the applications of the mass spectra was performed,
specifically to incorporate mass spectral data into the LDA/SIR and QDA analyses. The
goal of this study was to assess the potential of adding HDL apolipoprotein mass
spectral data to the statistical analyses in order to improve the CVD risk assessment andidentify potential risk factors for CVD based on the mass spectra.

CVD Patient 124
6250Da - 7000Da

16000

Intensity

Apo C-I'
6000

Apo C-I + 209Da

4000

6500

6750

Apo C-III9909 Da

1200

8500

9000

9500

13000

10000

13500

16000Da-18000Da

15000

Apo A-I

2500

Apo A-II + 125Da

Intensity

400

14500

27000Da - 29000Da

3000

Apo A-II - 125Da

14000

Mass/Charge

Mass/Charge

600

0
16000

400

0

0
8000

Apo A-II

800

600

200

200

4000

Apo C-III1

9607 Da

Apo C-II

1000

6000

Apo C-III1'

Apo A-IIM
Apo A-IIM'

7000

Mass/Charge

Intensity

Intensity

0
6250

10000
8000

2+
Apo A-1 (Lys107-Del) 2+ Apo A-I

800

6000

4000

13000Da - 15000Da

1000

Apo C-IIIglyc

2000

2000

12000

Pro Apo C-II

8000

8000

Intensity

14000

8000Da-11000Da

10000

Apo C-I

10000

Intensity

18000

2000
1500

Apo A-I (Lys107-del) Apo A-I + 180Da
Apo A-I + 360Da

1000
500

16500

17000

17500

18000

0
27000

Mass/Charge

27500

28000

28500

29000

Mass/Charge

2000
0
5000

10000

15000

20000

25000

30000

Mass/Charge
165

Figure 60: Mass Spectra of HDL Apolipoproteins for CVD Patient 124

CVD Patient 148
8000Da-10000Da

6250Da - 7000Da
Apo C-I

25000

25000

15000

Intensity

20000

Intensity

30000

Apo C-I'
Apo C-I + 209

10000

5000

Apo A-IIM

4000

Apo A-IIM'

6500

6750

5000

8500

9000

9500

7000

Apo A-IIApo A-1PTM+1802+
Apo A-12+

1000

0
13000

10000

13500

14000

14500

15000

Mass/Charge

27000Da - 29000Da
Apo A-IPTM

6000

Apo A-II - 125Da

5000

Apo A-II + 75Da
Apo A-II - 250Da
Apo A-II + 250Da

2000
1000
0
16000

1500

Apo A-1PTM(Lys107-del)2+

Mass/Charge

4000
3000

2000

500

0
8000

16000Da-18000Da

6000

Intensity

Intensity

20000

10000

9611 Da
9850 Da

Mass/Charge

15000

Apo C-III1'
Apo C-III1

2000

7000

Apo A-1PTM 2+

2500

Apo C-IIIglyc

3000 Apo C-II

13000Da - 15000Da

3000

Pro-Apo C-II

1000

5000
0
6250

Apo C-III

6000

Intensity

30000

Intensity

35000

4000

Apo A-IPTM(Lys107-del)Apo A-IPTM + 180Da
Apo A-IPTM + 360Da

3000

Apo A-I

2000
1000

16500

17000

17500

18000

0
27000

Mass/Charge

27500

28000

28500

29000

Mass/Charge

5000

0
5000

10000

15000

20000

25000

166

Mass/Charge
Figure 61: Mass Spectra of HDL Apolipoproteins for CVD Patient 148

30000

CVD Patient 171
8000Da - 10000Da

6250Da - 7000Da
Apo C-I

14000
12000

8000

Apo C-I'
Apo C-I + 203Da

6000

Intensity

Intensity

Apo C-III1'

4000

Apo A-IIM'
2000

9629DaApo C-II

6500

6750

0
8000

7000

8500

Apo A-12+

400

Mass/Charge

16000Da - 18000Da

Apo A-II - 250Da
Apo A-II + 200Da

3000

14500

15000

27000Da - 29000Da

Apo A-IPTM
2500 (Lys107-del)Apo A-IPTM + 360Da

1500

Apo A-I

1000

1000

500
17000

17500

Apo A-IPTM + 180Da

2000

2000

15000

14000

Apo A-IPTM

18000

0
27000

27500

28000

28500

29000

Mass/Charge

Mass/Charge

10000

13500

Mass/Charge

3000

Apo A-II + 75Da

16500

0
13000

10000

3500

Apo A-II - 125Da

0
16000

5000

9500

4000

Apo A-II

5000
4000

9000

Mass/Charge

Intensity

06250

Intensity

Intensity

Apo A-1PTM(Lys107-del)2+

200

6000

0
5000

600

1000

7000

10000

Apo A-1+3602+

800

Apo C-III1

2000

15000

Apo A-1PTM 2+

1000

Apo C-IIIglyc

3000

13000Da - 15000Da

1200

Pro-Apo C-II

Apo A-IIM

10000

20000

Apo C-III

4000

Intensity

25000

20000

25000

30000

Mass/Charge
167

Figure 62: Mass Spectra of HDL Apolipoproteins for CVD Patient 171

CTRL Patient 013
10000

6250Da - 7000Da

8000

Apo C-I + 205Da

4000
2000

1500

6500

6750

Apo A-IIM

Apo C-IIIglyc

Apo A-IIM'

Apo C-III1'

2000

1000

9635Da

0
8000

7000

8500

9000

16000Da-18000Da
Apo A-II

9500

3000

Apo A-II + 125Da
2500

Apo A-II - 101Da

Apo A-II + 220Da
1000

Apo A-II - 200Da

0
16000

Intensity

1500

Apo A-1PTM(Lys107-del)2+

600
400

0
13000

10000

13500

14000

14500

15000

Mass/Charge

27000Da - 29000Da
Apo A-I
Apo A-1(Lys107-del)Apo A-I + 180Da
Apo A-I + 360Da

1500
1000
500

16500

17000

17500

18000

0
27000

Mass/Charge

10000

Apo A-1 2+

Mass/Charge

500

0
5000

13000Da - 15000Da

200

2000

3000

800

Apo C-III1

Apo C-II

Mass/Charge
2500

6000

1000

Pro-Apo C-II

500

Intensity

Intensity

9000

Intensity

Intensity

6000

0
6250

Apo C-III

2000

Apo C-I'

12000

8000Da-10000Da

2500

Apo C-I

Intensity

15000

15000

28000

29000

Mass/Charge

20000

25000

30000

Mass/Charge
168

Figure 63: Mass Spectra of HDL Apolipoproteins for CTRL Patient 013

120004000

Apo C-I
10000

Intensity

Intensity

6000
4000

Apo C-III1

3000 Apo A-II '
M

8000

12000

Apo C-III Pro-Apo C-II
Apo C-IIIglyc
Apo A-IIM

Apo C-I + 205Da

Apo C-I'

Apo C-III1'

2000

1000

9635DaApo C-II

6750

0
8000

7000

9000

16000Da-18000Da

3500

Apo A-II - 125Da
Apo A-II + 75Da
Apo A-II + 200Da

16500

17000

17500

Mass/Charge

10000

400

0
13000

13500

14000

14500

15000

27000Da - 29000Da
Apo A-IPTM
Apo A-IPTM + 180Da
Apo A-IPTM + 360Da

1500

Apo A-I

1000
500

500
0
16000

Apo A-1 2+

Mass/Charge

2000

Intensity

Apo A-II - 250Da

1000

0
5000

2+

10000

Apo A-1PTM
2500 (Lys107-del)

1500

3000

9500

3000

Apo A-II

2500
2000

Apo A-1PTM
600 (Lys107-del)

Mass/Charge

3000

6000

8500

Mass/Charge

Intensity

Intensity

9000

6500

Apo A-1PTM 2+ Apo A-1PTM+360Da 2+

200

2000
0
6250

13000Da - 15000Da

1000
800

Intensity

15000

CTRL Patient
033
8000Da-10000Da

6250Da - 7000Da

15000

18000

0
27000

27500

28000

28500

29000

Mass/Charge

20000

25000

30000

Mass/Charge
169

Figure 64: Mass Spectra of HDL Apolipoproteins for CTRL Patient 033

12000Intensity

4000

Apo C-IPTM

10000

2000

9000

6500

6750

9489Da4000

2000

16000Da-18000Da

8500

9000

400

3000

0
5000

4000

Apo A-II + 250Da

800

0
16000

0
13000

10000

13500

14000

14500

15000

Mass/Charge

27000Da - 29000Da

Apo A-I

Apo A-I + 180Da
Apo A-1(Lys107-del)Apo A-I + 360Da

3000
2000
1000

16500

17000

17500

18000

0
27000

15000

27500

28000

28500

29000

Mass/Charge

Mass/Charge

10000

Apo A-I+360Da

400

5000

Intensity

6000

9500

6000

1600

Apo A-IIApo A-I+180Da

600

Mass/Charge

Apo A-II + 125Da

1200

Apo A-1(Lys107-del)800

Apo A-I+110Da

200

0
8000

7000

9635DaApo C-II

Mass/Charge

Intensity

Intensity

06250

Apo C-III1

6000

2000

1000

1000

Apo C-III1'

Apo A-IIM

3000

Apo A-I

1200

Apo C-IIIglyc

8000

Apo C-IPTM'

13000Da - 15000Da

1400

Pro-Apo C-II

Apo C-III

Apo C-IPTM + 205Da

Intensity

5000

CTRL Patient
043
8000Da-10000Da

6250Da - 7000Da

Intensity

15000

20000

25000

30000

Mass/Charge
170

Figure 65: Mass Spectra of HDL Apolipoproteins for CTRL Patient 043

171

3.2.2.2.1 Mass Spectra Differences between CVD and CTRL Cohorts
Through examination of the mass spectra between the subsets of CVD and CTRL
samples, differences in the mass spectra for the two groups were identified. Examples
for both CVD and CTRL mass spectra are shown in Figures 60-65. Figures 60-62
represent mass spectra for CVD samples. Figures 63-65 represent mass spectra for
CTRL samples. Each mass spectrum represents a molecular weight modification(MWM) that was observed relative to the CVD or CTRL cohort. The most easily
identifiable MWMs found between the CVD and CTRL groups were in the Apo A-I,
Apo C-I, and Apo A-II mass regions. These features were easily identifiable due to thelarge mass differences between these apolipoproteins in comparison to each other andthe other apolipoproteins present in the HDL particles. A summary of the averageapolipoprotein masses and the MWMs observed are shown in Table 13. The highstandard deviation in masses for the un-modified forms of Apo A-I and Apo A-II could
indicate further MWMs that were not able to be identified through the small number ofsamples selected for this study. In the sections following, the differences between CVD
and CTRL groups for these mass ranges will be further explored.

172

Table 13: Masses of HDL Apolipoproteins between CVD and CTRL Cohorts
Measured Using MALDI-TOF MS
CTRL Cohort
CVD Cohort
Apolipoprotein
Avg. Mass
Std.
Avg. Mass
Std.
(Da)
Dev.
(Da)
Dev.
Apo A-I
28051
118
28110
72
Apo A-I MWM
27558
7
27543
19
Apo A-II
17275
92
17258
124
Apo A-II MWM
16999
2
16988
6
Apo C-I
6631
12
6623
17
Apo C-I MWM
6729
4
NA
NA

Apo A-I Mass
28016 DaCVD Classification

CVD

CTRL

27500

27600

27700

27800

27900

28000

28100

28200

28300

Apo A-I (m/z)

Figure 66: Apo A-I Mass Distribution between CVD and CTRL Cohorts

The difference between the CVD and CTRL groups with respect to theapolipoprotein Apo A-I mass distribution is depicted in Figure 66. The documented
molecular weight for Apo A-I is 28,016Da.48 The masses for Apo A-I and its MWM in
the CVD samples were found to be an average of 28,110 +/- 72 Da and 27543 +/- 19 Da,

173

respectively. The masses for Apo A-I and its MWM in the CTRL samples were found to
be an average of 28,051 +/- 118 Da and 27558 +/- 7 Da, respectively. The difference in
the Apo A-I masses between groups was examined through an ANOVA analysis. The pvalue for this analysis was found to be 0.001 which indicated that there was astatistically significant difference between the CVD and CTRL cohorts. The difference
between groups was attributed to the number of samples in the CVD group with thepresence of the MWM of Apo A-I.
The MWM of Apo A-I was present in a large number of samples in the CVD
cohort in comparison to its presence in the CTRL cohort. 20 of the 36 CVD subjects
were identified to possess the MWM of Apo A-I. Figure 60 (Patient 124) shows a CVD
sample without the Apo A-I MWM. Figures 61 and 62 (Patients 148 and 171,
respectively) show CVD samples with the MWM of Apo A-I. Figure 64 (Patient 033)
shows a CTRL sample with the presense of the MWM of Apo A-I. Figure 63 and 65(Patients 013 and 043, respectively) show CTRL samples without the Apo A-I
modification.
The MWM of Apo A-I identified in this study was attributed to a change in themolecular weight of the apolipoprotein by approximately -450Da to a final molecularweight of ~27,550Da. This molecular weight of Apo A-I has previously been identified
in cows where the Apo A-I is missing two amino acids in comparison to the human formof Apo A-I. It is possible that a loss of two amino acids in the Apo A-I in humans could
result in an increased risk of CVD due to the changes it causes in the metabolism of
HDL. A mass peak for the subjects with the MWM of Apo A-I was present in the mass

174

spectra at the documented molecular weight of Apo A-I; however it was not theprominent mass peak of the region.
Of the 36 CTRL subjects in this study, only four of the CTRL subjects wereidentified as having the MWM of Apo A-I present in their mass spectra. The presenceof this MWM in the CTRL samples indicated that while the MWM could be a risk
factor, having the MWM of Apo A-I does not mean that a subject will have CVD. The
presence of the MWM did show the potential for the application of Apo A-I mass
spectral data to the LDA/SIR and QDA analyses as an added risk factor in themultivariate analyses.
The results of comparing the Apo C-I mass spectra between CVD and CTRL
groups led to an unexpected observation. Previous analysis of the Apo C-I mass spectra
indicated that there was a MWM present for all eight CVD subjects tested while there
was no MWM present in the eight CTRL subjects tested.16 This MWM presented itself
in the form of an additional 90-100Da to the documented molecular weight of 6630Da
for Apo C-I. The results found using the subset of 72 patients, 36 CVDs and 36 CTRLs,
differed from this observation in that the only subjects for whom a MWM was present
were the CTRL subjects. The distribution of the Apo C-I masses for each group is
shown in Figure 67.

175

Apo C-I Mass
6630 DaCVD Classification

CVD

CTRL

6600

6620

6640

6660

6680

6700

6720

6740

Apo C-I (m/z)

Figure 67: Apo C-I Mass Distribution between CVD and CTRL Cohorts

Of the 36 CTRL subjects in this study, eight showed the presence of the Apo C-I
MWM that was previously identified for the CVD subjects in the pilot study. For the
CTRL subjects that did not show the presence of the MWM, the average mass was foundto be 6631 +/- 12 Da. The MWM of Apo C-I was identified with the average mass of6729 +/- 12 Da. Figure 65 (Patient 043) represents the mass spectrum of a subject with
the MWM of Apo A-I. The MWM was not present in any of the 36 new CVD subjects
tested in this study. The average mass for all of the 36 CVD subjects was found to be6623 +/- 17 Da. While the Apo C-I mass between the CVD and CTRL groups wasfound to be statistically significant through ANOVA analysis, the relative error in the
MALDI-TOF instrument for this mass region was reported to be +/-10Da which

176

indicated that the mass difference between the two groups is indistinguishable except for
those subjects with the MWM of Apo C-I.
The presence of the MWMM in the Apo C-I for the CTRL subjects is counter tothe pilot study data that identified the MWM of Apo C-I is a biomarker for CVD. Of the
36 CTRL subjects in this study, seven of the subjects tested were also used for the pilot
study. For all seven subjects, the results of the current mass spectra correlated with theresults found in the pilot study. This indicated that the MWM presence in the CTRL
subjects was not experimental error, but a real observation. The observation that none of
the 36 CVD subjects tested in this study had the reported MWM for Apo C-I was alsocounter to the pilot study. The variation between the pilot study results and the results
found in the current study could be related to the design of each study. The pilot study
was specifically designed to study subjects based on their likelihood to have atherogenic
HDL. The 72 subjects selected for the current study were selected based on theirLDA/SIR classification in the comprehensive library study.
In order to explain the presence of the Apo C-I MWM in CTRL subjects and thelack of the MWM presence in CVD subjects, the subjects’ medical records were put
under further review. When comparing the subject histories between those subjects with
the MWM of Apo C-1 and the subjects without the MWM of Apo C-I, no correlation
could be made to the available patient histories to explain the presence of the MWM.
While the majority of the subjects with the MWM of Apo C-I had the presence ofhypertension in their medical histories, there were many of the subjects without the
MWM of Apo C-I that also had the presence of hypertension. While the MWM of Apo

177

C-I could still be a risk factor for CVD, further exploration into the nature of the MWM
is necessary to understand its influence on CVD risk.

Apo A-II Mass
17414 DaCVD Classification

CVD

CTRL

16900

17000

17100

17200

17300

17400

17500

17600

Apo A-II (m/z)

Figure 68: Apo A-II Mass Distribution between CVD and CTRL Cohorts

The third apolipoprotein that portrayed a difference in the mass spectra between
CVD and CTRL groups was Apo A-II. Apo A-II’s presence in HDL is primarily in theform of a homodimer with the molecular weight of 17,414Da.48 The distribution of Apo
A-II masses is shown in Figure 68. When comparing this region of the mass spectra
between the CVD and CTRL groups, 14 of the 36 CVD subjects portrayed a MWM withprimary peaks in the Apo A-II mass region at 16,988 +/- 6 Da (Figures 61 and 62,
Patients 148 and 171, respectively). The other 22 CVD subjects were found to have theaverage Apo A-II mass of 17,258 +/- 124 Da (Figure 60, Patient 124). For the CTRL

178

subjects, only two of the 36 subjects portrayed a similar MWM for their primary peak inthis mass region with an average mass of 16999 +/- 2 Da (Figures 64, Patient 033). The
average mass of Apo A-II for the remaining 34 CTRL subjects was calculated to be
17,275 +/- 92 Da (Figures 63 and 65, Patient 013 and Patient 043, respectively). The
large deviation in masses for the “unmodified” Apo A-II could indicate the presence offurther MWMs that were not distinguishable due to the sample size of this study.
The mass region of 16,990 Da was identified as a possible MWM due to the
population of CVD samples with this mass as the primary peak in the region. There
were also satellite peaks with masses less than 16,990Da that were primarily present in
the CVD samples. The presence of these peaks could indicate a truncated form of the
Apo A-II dimer or the presence of a hetero dimer. The distinction of this mass peak
between the CVD and CTRL groups showed potential for application of this Apo A-II
PTM to the LDA/SIR and QDA analyses.

3.2.2.2.2 Application of Mass Spectral Data to Statistical Analyses
With the identification of potential mass spectra data that could be used in thestatistical analyses in order to enhance the accuracy of CVD risk assessment, thesefactors were now added systematically to study the individual and combined potentialsof the mass spectrum data as an addition to the lipoprotein density profiles for CVD risk
assessment. These factors were also tested separately to explore the potential of themass spectra as an individual method of risk assessment when applied to the LDA/SIR
and QDA analyses. The mass spectral data for the apolipoproteins was added to the179

statistical analyses in terms of the natural log of the masses for the most prominent peakin the mass regions of Apo A-I, Apo A-II, and Apo C-I.
The summary of the results when applying the mass spectrum data to thestatistical analyses can be found in Table 14. The statistical analyses were carried out
using both the NaBiEDTA-based lipoprotein density profile data and the Cs2CdEDTAbased lipoprotein density profile data. For the NaBiEDTA-based lipoprotein density
profiles, the addition of Apo C-I and Apo A-I mass spectra data to the statistical analyses
was shown to improve the classification. The Apo A-II data only enhanced theprediction analysis when applied to QDA. The best accuracy for the NaBiEDTA-based
profiles was found when combining the data for Apo C-I and Apo A-I for both LDA/SIR
and QDA.
For the Cs2CdEDTA-based lipoprotein density profiles, the addition of Apo C-I
and Apo A-I mass spectra data was shown to best improve the classification similar to
the improvement seen with the NaBiEDTA density profiles. The Apo A-II data showed
limited improvement for both QDA and LDA. Through the combination of
Cs2CdEDTA-based lipoprotein density profile data and Apo A-I and Apo C-I data, theprediction analysis was increased to 93.1% for the LDA/SIR analysis and 100% for the
QDA analysis. The low X-Val score for the QDA analysis indicated that there was alarge subject dependency inherent to the analysis. This subject dependency was to be
expected due to the nature of the QDA analysis in that there is no assumption that thecovariance matrices are equal between groups like in the LDA/SIR analysis. The

180

addition of more risk factors without the addition of more subjects directly affected thesubject dependency.

Table 14: Application of Mass Spectra Data to the LDA/SIR and QDA Analyses for
CVD Risk Assessment Using the 72 Subject Data Set
LDA/SIR Analysis
QDA Analysis
Prediction
X-Val
Prediction
X-Val
System
Accuracy Accuracy Accuracy Accuracy
(%)
(%)
(%)
(%)
86.1
79.2
93.1
70.8
NaBiEDTA DP
84.5
78.9
94.4
72.2
NaBiEDTA + Apo A-I
86.1
77.8
93.1
73.6
NaBiEDTA + Apo C-I
84.7
76.4
95.8
70.4
NaBiEDTA + Apo A-IINaBiEDTA + Apo A-I +
87.5
77.8
93.1
70.8
Apo C-INaBiEDTA + Apo A-I +
84.7
79.2
93.1
73.6
Apo C-I + Apo A-II
88.9
83.3
93.1
76.4
Cs2CdEDTA DP
91.7
86.1
97.2
80.6
Cs2CdEDTA + Apo A-I
93.1
86.1
97.2
76.4
Cs2CdEDTA + Apo C-I
90.3
83.3
94.4
79.2
Cs2CdEDTA + Apo A-II
Cs2CdEDTA + Apo A-I
93.1
86.1
100.0
75.0
+ Apo C-I
Cs2CdEDTA + Apo A-I
93.1
86.1
98.6
77.8
+ Apo C-I + Apo A-II
Apo A-I + Apo C-I +
70.8
68.1
56.9
51.4
Apo A-IIWith the improvements in CVD risk assessment through the application of themass spectrum data to the lipoprotein subclass data, the use of the mass spectrum data asa singular method of CVD risk assessment was further examined. When using only themass spectrum data for the three apolipoproteins identified, the LDA/SIR analysis wasshown to be 70.8% accurate. While the mass spectrum data did not improve the181

prediction accuracy, it did show the potential for use of mass spectrometry of
apolipoproteins for CVD risk assessment. The current preparative methods only isolated
the HDL apolipoproteins for study. Future applications of the mass spectra include thestudy of the lower concentration apolipoproteins in HDL such as Apo C-II and C-III.
The mass region for these apolipoproteins in the MALDI-TOF spectra is 8,000Da10,000Da. Due to the multiple peaks in this mass region and the closely related massesof these apolipoproteins, identification of any potential risk factors for CVD would
require further isolation of the specific apolipoproteins before the analysis of these
apolipoproteins.
The prediction equation coefficients of the LDA/SIR analyses with the additionof the mass spectral data were further studied to identify the influence that the mass
spectrum data had on the CVD risk assessment. The coefficients for each variable usedin the LDA/SIR analysis are graphed for the different density gradient systems inhistogram form in Figures 69 and 70 (NaBiEDTA and Cs2CdEDTA, respectively).

182(0.181)

0.03

0.02

LDA/SIR Value

0.01

0.00

-0.01

-0.02

-0.03
Apo CI Apo AI Apo AII(-0.822) (-0.537)

TRL

LDL1

LDL2

LDL3

LDL4

LDL5

HDL2b HDL2a HDL3a HDL3b HDL3c

Variable

Figure 69: LDA/SIR Coefficients for Lipoprotein Subclasses Using NaBiEDTABased Density Gradients and Mass Spectrometry Data for the HDL
Apolipoproteins when Applied to the 72 Subject Library

When comparing the coefficients found for the NaBiEDTA density profiles of
the 72 subject library (Figure 56) to the coefficients found for the addition of the mass
spectral data, the primary change was in the magnitude of the coefficients. The
coefficients for the apolipoproteins were very large in comparison to the coefficients of
the lipoprotein subclasses. The lipoprotein subclass coefficients were also smaller withthe addition of the mass spectral data when compared to the coefficients found using the183lipoprotein subclasses alone. The majority of the lipoprotein subclasses retained theirpolarities toward CVD or CTRL except for the LDL-5 and HDL-2a.

0.15(0.263)

0.10

LDA/SIR Value

0.05

0.00

-0.05

-0.10

-0.15
Apo CI Apo AI Apo AII TRL(-0.874)(-0.363)

IDL

LDL1

LDL2

LDL3

LDL4

LDL5 HDL2b HDL2a HDL3a HDL3b HDL3c

Variable

Figure 70: LDA/SIR Coefficients for Lipoprotein Subclasses Using Cs2CdEDTABased Density Gradients and Mass Spectrometry Data for the HDL
Apolipoproteins when Applied to the 72 Subject Library

When comparing the coefficients found for the Cs2CdEDTA density profiles of
the 72 subject library (Figure 57) to the coefficients found for the addition of the mass
spectral data, a few differences were observed. First, the coefficient value for each184

lipoprotein subclass was reduced with the addition of the mass spectral data similar towhat was observed with the NaBiEDTA density profiles. Only the LDL-5 subclasschanged in polarity to being a factor towards CVD classification when coupled with themass spectrometry data. Second, the apolipoprotein variables were again associatedwith the largest variables using the Cs2CdEDTa lipoprotein subclass data.
The polarity of the mass spectrometry variables for both density systems
indicated the Apo A-I and Apo C-I as variables for classification as CTRL and Apo A-II
as a variable for CVD. The implication on the mass spectrometry data indicates that thehigher masses of Apo A-I and Apo C-I would weigh the subject towards a CTRL
classification. This observation for the Apo C-I mass is contrary to the Apo C-I datareported in the initial pilot study of 16 subjects. Further exploration into the nature of
the Apo C-I MWM is necessary due to the uncertainty present with respect to the natureof the Apo C-I MWM and how it relates to CVD. The higher masses of Apo A-II would
weigh the subject towards a CVD classification based on the LDA/SIR coefficients.
This coefficient of Apo A-II was contrary to the data for Apo A-II which indicated thatthe lower masses are more prevalent for the CVD subjects. This contradiction could
only be accounted for by the fact that the LDA/SIR analysis inter-relates all the variables
used to generate the equation and does not just compare the differences of single
variables between the defined groups.
The optimum separation of CVD and CTRL subjects through use of the
LDA/SIR equation that was generated with the lipoprotein subclasses and mass
spectrometry data is displayed in Figure 71. The p-value for the LDA/SIR separation of

185

CVD and CTRL was calculated to be 1.14E-05. This p-value indicated that thedistinction found between CVD and CTRL groups using the LDA/SIR equation was
statistically significant. Based on the prediction accuracy of the LDA/SIR and QDA
analyses and the statistical significance of the separation between CVD and CTRL
groups, the coupling of the mass spectrometry data with the lipoprotein subclasses was
decidedly the best method for CVD risk assessment.

CVD Classification

CVD

CTRL

-0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05 0.06

LDA/SIR Value

Figure 71: LDA/SIR Separation for the 72 Subject Library Using Lipoprotein
Subclasses and Mass Spectrometry Data

186

3.2.3 Case Studies for Monitoring CVD Therapies
With the potential for the use of lipoprotein density profiles in the assessment of
CVD risk established and the evidence seen in the hypercholesterolemia study thattherapy effectiveness could be monitored using the LDA/SIR equation, further studies
into the sensitivity of the lipoprotein density profiles and risk analysis were carried out
with respect to the effects of further medical treatment and the increase of exercise. For
the use of the LDA/SIR risk assessment to be valuable in clinical applications, methodsof medical treatment must be able to be monitored using the LDA/SIR equation for CVD
risk when applied to subjects undergoing drug therapy or lifestyle modifications. The
goal of these studies was to show that lipoprotein density profiles, in conjunction with
the LDA/SIR analysis, are sensitive enough to monitor the effectiveness of medical
treatment and lifestyle modification with respect to their impact on CVD risk.

3.2.3.1 Case Study Monitoring the Effects of Niacin Therapy
The effects of statin therapy on lipoprotein density profiles were previously
studied with respect to the hypercholesterolemic children. Another medical treatment
related to the reduction of CVD symptoms is niacin. Niacin has been thought to reverse
atherosclerosis by reducing total cholesterol, triglycerides, very-low-density lipoprotein(VLDL), and low-density lipoprotein (LDL), and increasing high-density lipoprotein(HDL).39-41,130 There are also studies relating niacin therapy with the reduction of
lipoprotein (a) (Lp(a)).131 In this study, the effects of increasing the dosage of niacin
therapy were examined with respect to the effects it had on the lipoprotein profiles.

187

HDL cholesterol and Lp(a) protein concentrations in relation to the niacin dosage werealso examined. The LDA/SIR values for the sequential serum draws were calculatedusing the LDA/SIR equation found for the comprehensive library study using the
NaBiEDTA-based gradient system in order to examine any effects niacin had on the
CVD risk assessment.
The subject in this study was treated with increasing amounts of niacin from 0mg
to 2000mg using increments of 500mg. Serum samples for this subject were drawn overa period of five months with a baseline sample of the subject having no treatment and awashout sample one week after the last treatment was taken. Each serum sample wastested for HDL cholesterol content, Lp(a) protein concentration, and lipoprotein density
profiles. The increase in niacin treatment and the results of the serum tests are graphedin Figure 72. The traditional HDL cholesterol measurements showed a general increase
in relation to the niacin treatment dosage (Figure 72B). With respect to the Lp(a) protein
concentration, an inverse relationship was observed in relation to the increasing niacin
dosage.(Figure 72C) These results concur with the current literature on the effects ofniacin treatment on lipoproteins. Using the lipoprotein density profiles, fluctuation in
the integrated fluorescence intensities for the TRL and total LDL fractions (Figures 72D
and 72E, respectively) of the lipoprotein density profiles led to no specific conclusions
with respect to the effects of the niacin treatment. However, the integrated fluorescenceintensities for the total HDL fraction of the profiles showed a surprising result. There
was an inverse relationship between the total HDL fluorescence intensity and the niacin
dosage. (Figure 72F) This result is contrary to the relationship previously documented

188

Increase in Niacin Dosage Over Time

A
2000

74

1500

HDL (mg/dL)

Niacin Dosage (mg)

HDL Cholesterol Measurement Over Time

B

1000

500

72
70
68
66

0

5/7

5/27

6/16

7/16

9/15

5/7

10/21

5/27

C

Lp(a) Concentration Over Time

65

9/15

10/21

9000
Wash Out Period

8250

55
50

Intensity

Lp(a) (mg/dL)

7/16

Total TRL Flourescence Intensity Over Time

D

60

45
40
35

7500
6750
6000

30

5250

25
5/27

6/16

7/16

9/15

10/21

5/7

5/27

Draw DateE

6/16

Draw Date

Draw Date

6/16

7/16

9/15

10/21 10/29

Draw Date

Total LDL Fluorescence Intensity Over TimeF

Total HDL Fluorescence Intensity Over Time
145000

Wash Out Period

75000

140000

Wash Out Period

67500

Intensity

Intensity

13500060000

13000012500012000011500052500

110000

5/7

5/27

6/16

7/16

9/15

DrawDate

10/21 10/29

5/7

5/27

6/16

7/16

9/15

10/21 10/29

Draw Date

Figure 72: Niacin Dosage Effects on Lipoprotein Measurement Methods. (A) Niacin
Dosage over Time, (B) HDL Cholesterol Concentration over Time, (C) Lp(a)Protein Concentration over Time, (D) TRL Fluorescence Intensity over Time, (E)LDL Fluorescence Intensity over Time, (F) HDL Fluorescence Intensity over Time

189

between the HDL cholesterol concentration and niacin dosage. This discontinuity
between HDL measurement methods further supported the hypothesis that the NBDceramide fluorescence can be influenced by the morphology and chemical content of the
HDL particles.
The effect that the niacin treatment had on the lipoprotein profiles was further
examined in order to understand its effect on the HDL subclasses. The overlaid
lipoprotein density profiles are shown in Figure 73. The HDL peak at the density~1.117g/mL steadily decreased in intensity as the niacin dosage increased. The decreaseof the fluorescence intensity in this density range explained the decrease in the total
HDL fluorescence intensity previously observed. There was also a peak at ~1.049g/mL
in the baseline serum draw on 05/07/2010 that was not apparent in the serum draws
taken after niacin treatment. This peak reappeared in the final serum draw after a one
week washout period of no niacin. The density range for this peak indicates that it ispossibly the result of an increase in Lp(a) concentration.

190

05-07-2010

HDL

05-27-2010
06-16-2010

aw
Dr
te
Da

07-16-2010

09-15-2010
10-21-2010
10-29-2010
1600

800

Intensity

1200

400

0
61.0148
10 1.02612
14 1.038
16
18
1.014
1.026
1.038

1.049
20
1.049

22

1.117 30 1.230
1.230
1.070
24
26 1.117
28
32
1.070

Density (g/mL)

Figure 73: Lipoprotein Density Profiles by Draw Date/Niacin Dosage

With niacin’s effect on the lipoprotein density profile showing significant
changes in the HDL fraction; this effect was now studied in terms of its influence on the
LDA/SIR risk assessment for CVD. The change in the LDA/SIR value over time isgraphed in Figure 74. While niacin treatment is supposed to reduce the risk of CVD, theLDA/SIR value showed a general increase over time with respect to the niacin dosage.
As previously discussed in Section 3.2.1.4, the HDL subclasses were attributed with thelargest coefficients in the LDA/SIR equation. The change in HDL fluorescence intensity
found in relation to the niacin treatment was identified in the HDL-3a subclass. This
subclass was attributed with the largest coefficient for classification of a CTRL.
Reduction of the HDL fluorescence intensity in this region explains why the LDA/SIR

191

value, and therefore the CVD risk, was found to be increased with the increase in niacin
dosage.

0.35

0.30

LDA/SIR Value

0.25

0.20

0.15

0.10

0.05

4/2
2/2
01
0

6/9
/

20
10

7/2
7/2
01
0

9/1
3/2
01
0

Draw Date

Figure 74: LDA/SIR Value in Relation to Draw Date/Niacin Dosage

While the effects of statin therapy were identified to reduce the risk ofhypercholesterolemia in children, this case study indicates that the effects of niacin
therapy have an adverse effect on the CVD risk assessment through the lipoprotein
density profiling and LDA/SIR statistical analysis. Further investigation is required toascertain the true effect of niacin on CVD risk assessment in terms of the methods of thisthesis. This was only one case study and it could have been affected by uncontrolled
factors. A larger study which includes more subjects, a variety of dosages, and a control
arm is recommended.

192

3.2.3.2 Case Study Monitoring the Effects of Lifestyle Modification
With the various methods of medical therapy currently being studied for CVD
risk reduction, the most universal and available way for a person to lower risk is throughlifestyle modifications including exercise and diet change. Lifestyle modification canalso be performed prior to any presentation of CVD risk. With the methodology
developed in this thesis, the idea of monitoring the effect of lifestyle modification
through a standard test without the use of more invasive methods became possible. The
case study described here involved a subject that underwent an exercise program thatwas made up of three to five 30 minute runs every week for 16 weeks. The program wasdesigned to keep the heart rate down to an optimal range for burning fat (60-70% of
maximum heart rate). After 16 weeks, the regime was increased to include an extra 30
minute run each day at a pace that was set for cardiovascular training (70-80% of
maximum heart rate) in addition to the fat burning run. Serum draws were performed
each week to monitor the change in the lipoprotein density profiles.

193

Week 28
Week 24dy
Stu

Week 20

Week 16ek
We

Week 12

Week 8

Week 4

800

400

1.049
1.070
61.0148
10 1.02612
14 1.038
16
18 1.04920
22 1.070
24
1.014
1.026
1.038

1.117
1.230
26 1.117
28
30 1.230
32

Intensity

Week 01200

0Density (g/mL)
Figure 75: Lipoprotein Density Profiles Comparison Based on the Increase of
Physical Exercise – NaBiEDTA-Based Density Gradient

A comparison of the lipoprotein density profiles over every four weeks isgraphed in Figure 75. The baseline draw (Red) shows a dual peak in the LDL density
range that is not present for the next three sequential draws. The dual peak was again
present after Week 16 where the cardiovascular run was added to the exercise regime.
There was also an apparent density shift in the primary LDL peak over the exercise
period to a lower density. As for the HDL, the total intensity of the HDL fraction
seemed to increase over the exercise period. Increase in HDL content and the shift of
LDL to a lower density concur with the current theories and trends for healthy
lipoproteins.

194

To further study the changes in the lipoprotein density profiles, the integrated
fluorescence intensities for the TRL, total LDL, and total HDL subclasses were graphed
in relation to the exercise week in Figure 76. The total HDL fluorescence intensity
generally increased over time. The total LDL fluorescence intensity decreased initiallyand then balanced out to a range similar to the baseline value. The TRL fluorescentintensities fluctuate but do not change in a significant manor relative to the exercise
program.

TRL
LDL
HDL

10000090000
80000

Intensity

70000
60000
Change in

50000

Workout

40000
30000
20000
0

4

8

12

16

20

24

28

32

36

Week

Figure 76: Fluorescence Intensities for Major Lipoprotein Fractions Foundthrough Lipoprotein Density Profiling with Respect to Increase in PhysicalExercise

195

0.2

LDA/SIR Value

0.1
0.0
-0.1
-0.2
Change in

-0.3
-0.4

Workout

0

4

8

12

16

20

24

28

32

36

Week
Figure 77: LDA/SIR Values in Relation to Increased Physical Exercise

To assess how the changes in the lipoprotein density profiles affect the LDA/SIR
risk assessment for the subject of this case study, the LDA/SIR equation generated wasused to calculate the LDA/SIR value for each week. The resulting LDA/SIR values aregraphed in Figure 77 with respect to the week of the exercise program. Surprisingly, theLDA/SIR values generally increased over the first 13 weeks of exercise. The increase in
the LDA/SIR values indicated an increase in CVD risk after exercise. After Week 13,
there was a steep drop in LDA/SIR values which would indicate a reduction in CVD risk
for the subject. There was also a brief increase in LDA/SIR values after the change inthe exercise program. This increase only lasted three weeks this time and then LDA/SIR
values decreased to lower than the baseline value and stabilized at this low LDA/SIR
value for the rest of the study. Both increases in the LDA/SIR values occurred directly
after the increase of physical activity. The increase in CVD risk at the beginning of the196

exercise program can be related to the drop in the low density LDL subclasses. It is
possible that the increase in metabolism related to exercise increased the subject’s
catabolism of these low density LDL subclasses and resulted in the increased LDA/SIR
score by reducing the fluorescent intensities of lipoprotein subclasses that wereassociated with the classification of CTRL in the LDA/SIR equation. The reduction in
LDA/SIR values at the end of the exercise program indicated that exercise can be used
for reduction of CVD risk. With the case study subject not having a definitive definitionof CVD or CTRL, further studies involving subjects that have been diagnosed with CVD
and the effects that exercise can have on the lipoprotein density profiles are necessary toestablish this methodology not only as a risk assessment tool but also as a tool formonitoring the effectiveness of risk reduction through lifestyle modification.
This case study and the previous case study involving the niacin therapy show
the sensitivity of the lipoprotein density profiles and how they can fluctuate based on
day-to-day factors. With this sensitivity, the risk assessment for CVD when using thelipoprotein density profiles is a mobile snapshot of a subject’s health at the time theserum sample was extracted. Further investigation into the stability of the lipoprotein
density profile when no outside factors like medical therapy or change in physicalactivity are involved is necessary in order to assess the use of this methodology in alongitudinal CVD risk assessment.

197

4. CONCLUSIONSThe overall objective of this research was to develop and enhance analyticaltechniques and methods which, when combined, could be used to assess the risk anindividual has of developing cardiovascular disease. This objective was achieved
through use of high precision lipoprotein density profiling, mass spectrometry of HDL
apolipoproteins, and the application of advanced multivariate statistical analyses.
Through application of these methods to clinical studies, an equation was developed
using the method variables to accurately identify subjects with and without CVD. For
clinical studies, the definition of a healthy control was redefined from the lack of patient
history and risk factors to having a clean angiogram of less than 10% arterial blockage.
This new definition of CTRL was able to increase the distinction between the CVD and
CTRL groups in the applied clinical studies for both CVD risk assessment andlipoprotein characterization. These clinical studies showed the feasibility of thismethodology as a novel method of risk assessment that has the potential for early
detection of CVD prior to the onset of the disease.
Through studies designed to increase the precision of the lipoprotein density
profiles using heavy metal EDTA complexes for density gradient formation, a better
understanding of the optimal conditions necessary for creating a high precision methodof separating lipoproteins was attained. This high precision method was able to be used
as a profiling method as well as a preparative method for further analysis of thelipoproteins and their content. Future use of these EDTA complexes and high precision

198

lipoprotein density profiles includes the use of larger UC tubes and modifications of thedensity gradient in order to isolate specific lipoprotein subclasses for further studies.
The SPE method of isolation of apolipoproteins was studied using quantitative
ELISAs and protein analysis techniques in order to optimize recovery of theapolipoproteins so that comparative studies between CVD and CTRL cohorts werepossible. The combination of preparative ultracentrifugation techniques and dextran
sulfate precipitation was found to optimally isolate the HDL prior to applying the SPE
method. Through quantitative analysis of the protein content in the recovered fraction,
improved recovery of the apolipoproteins over the previously documented SPE
capabilities was obtained.
Through the use of preparative ultracentrifugation, SPE, and MALDI-TOF MS,
multiple differences in the apolipoprotein content of HDL fractions were identified whencomparing CVD and CTRL groups. The Apo A-I mass spectra for multiple CVD
subjects showed a change in mass of ~450Da when compared to the CTRL subjects.
The Apo A-II mass spectra for CVD subjects showed the presence of peaks at a m/z of<16,900Da that were not present for the majority of CTRL subjects. Finally, the PTM
for Apo C-I that was identified in CVD subject from prior research(ref) was present in
multiple CTRL subjects while not present in the CVD subjects studied in this research.
Future research into the nature of these modifications and their causes can illuminate
methods of medical treatment and the role these modifications play as risk factors for
CVD. Future research into the lower abundant apolipoproteins, such as Apo C-II and
Apo C-III, also showed potential. However, further separation of these compounds

199

would be necessary due to their similar mass characteristics in order to identify any PTM
or truncated form that might be associated with CVD risk.
Application of the high precision lipoprotein density profiling method developedin this thesis, in conjunction with LDA/SIR analysis, was able to generate equations
using the lipoprotein subclasses to accurately assess which subjects were at risk in the
different clinical studies. Compared to the accuracy of the LDA/SIR analysis whenusing traditional lipid measurements performed by medical practitioners, the lipoprotein
density profiles were found to have significant increases in the accuracy of risk
assessment. The method was successfully applied to subjects with normal lipidemic
characteristics with a prediction accuracy of up to 92.7%. When applied to theprediction of hypercholesterolemia in children, the prediction accuracy was found to be
97.6%. These results indicated the potential for lipoprotein density profiles overtraditional methods of lipid measurement.
Further investigation into a sample library with an increased number of samples
was necessary to investigate the subject dependency found in these studies. For the large
comprehensive library of subjects, the prediction accuracy was found to be 81.8%. A
reduction in the subject dependency was observed that was related to the increase insubjects relative to the number of variables used in the LDA/SIR analysis. Throughinclusion of specific traditional risk factors: the presence of hypertension and family
history of CVD, an increased prediction accuracy of 86.9% was attained. Throughexamination of the LDA/SIR equation generated, evidence was found to support the200existence of atherogenic forms of HDL due to multiple subclasses being coupled withcoefficients weighting them towards a CVD classification.
Further research into the LDA/SIR analysis was designed to increase theaccuracy in CVD risk assessment by identifying characteristics that could distinguishbetween CVD and CTRL groups through alternate analytical techniques. This research
included identification of the differences in mass spectrometry between the two groups
and the use of Cs2CdEDTA as the solute for density gradient formation to further
separate the lipoproteins through density profiling. The additional lipoprotein subclassresolved when using the new solute (IDL) and the differences in the mass spectra
between CVD and CTRL groups showed the potential for increasing the prediction
accuracy of the LDA/SIR analysis to 93% in a subset of samples from thecomprehensive library. This result proved the feasibility of adding mass spectra
information to the LDA/SIR analysis for enhanced CVD risk assessment.
In an effort to identify the optimal method of multivariate statistical analysis to
be used for CVD risk assessment, the method of QDA was applied to the comprehensive
library and the 72 patient subset of the comprehensive library. QDA showed an increase
potential of risk assessment through the increased prediction accuracy for every data set
for which it was applied in comparison to the prediction accuracy of the LDA/SIR
analysis. The optimal prediction accuracy was found when using QDA on the data set
that included the lipoprotein density profiles with the addition of HDL apolipoprotein
information from mass spectrometry. With this data the prediction accuracy for the 72
patient subset of the comprehensive library using QDA was found to be 100%. There201

was a large subject dependency that was seen through the X-Val score relative to thisanalysis. Increasing the subject size was hypothesized to remove this dependency.
The results presented in this thesis identified the potential for enhanced CVD risk
assessment through the combination of analytical methods for lipoprotein profiling andcharacterization with advanced multivariate statistical techniques. Use of thesetechniques showed potential for an increased accuracy in risk assessment relative tocurrent methods such as the Framingham and Reynold’s Risk Score. Further applicationof the lipoprotein profiles and the algorithms generated through the statistical analyses
was seen in the sensitivity of the methods for monitoring the effectiveness of medical
therapies designed to reduce a subject’s cardiovascular risk. Through further
characterization of the lipoproteins, potential atherogenic subclasses of HDL and novel
potential risk factors in the HDL apolipoproteins were identified. These novel factors
were shown to improve the CVD risk assessment when added to the integrated
fluorescence intensities from the lipoprotein density profiles. These methods represent
the potential for a more “personalized” view of medical therapy and for monitoring theeffectiveness of such therapies.
In the future, this methodology should be applied to larger libraries of serum
samples for which epidemiological data has been accumulated in order to assess thedifferences in risk assessment when applied as a longitudinal risk study and due tofactors like gender, ethnicity, etc. One such serum library is the Framingham Heart
Study. This would allow for direct comparison between the different methods and forcomparison of the results to the current medical standard.

202

REFERENCES(1)

Grundy, S. M.; Pasternak, R.; Greenland, P.; Smith, S.; Fuster, V.

Circulation 1999, 100, 1481.
(2)

Wilson, P. W. F.; D'Agostino, R. B.; Levy, D.; Belanger, A. M.;

Silbershatz, H.; Kannel, W. B. Circulation 1998, 97, 1837.
(3)

Lloyd-Jones, D. M.; Wilson, P. W.; Larson, M. G.; Beiser, A.; Leip, E.

P.; D'Agostino, R. B.; Levy, D. Am J Cardiol 2004, 94, 20.
(4)Kim, H. C.; Greenland, P.; Rossouw, J. E.; Manson, J. E.; Cochrane, B.

B.; Lasser, N. L.; Limacher, M. C.; Lloyd-Jones, D. M.; Margolis, K. L.; Robinson, J. G.
Journal of the American College of Cardiology 2010, 55, 2080.
(5)

Lakic, D.; Bogavac-Stanojevic, N.; Jelic-Ivanovic, Z.; Kotur-Stevuljevic,

J.; Spasic, S.; Kos, M. Value in Health 2010, 13, 770.
(6)

Ridker, P. M.; Buring, J. E.; Rifai, N.; Cook, N. R. JAMA 2007, 297, 611.

(7)

Wilson, P. W. F.; Castelli, W. P.; Kannel, W. B. The American Journal of

Cardiology 1987, 59, G91.
(8)

Jehle, A. J. Circulation 2002, 106, 3143.

(9)

Rifai, N.; Warnick, G. R.; Dominiczak, M. H. Handbook of Lipoprotein

Testing; AACC Press: Washington D.C., 1997.
(10)

Hoinacki, J. L.; Nicolosi, R. J.; Hoover, G.; Llansa, N.; Ershow, A. G.;

Lozy, M. e.; Hayes, K. C. Analytical Biochemistry 1978, 88, 485.

203

(11)

Burton, R. H.; Van Knippenberd, P. H. In Laboratory Techniques in

Biochemistry and Molecular Biology; Work, T. S., Work, E., Eds.; Elsevier: 1978; Vol.
Volume 6, p 97.
(12)

Johnson, J. D.; Bell, N. J.; Donahoe, E. L.; Macfarlane, R. D. Analytical

Chemistry 2005, 77, 7054.
(13)

Hosken, B. D.; Cockrill, S. L.; Macfarlane, R. D. Analytical Chemistry

2004, 77, 200.
(14)Henriquez, R. R. Dissertation, Texas A&M University, 2007.

(15)

Levander, F.; James, P. Journal of Proteome Research 2004, 4, 71.

(16)

Moore, D.; McNeal, C.; Macfarlane, R. Biochemical and BiophysicalResearch Communications 2011, 404, 1034.
(17)

Li, K. C. J Am Stat Assoc 1991, 86, 316.

(18)

Douglas, K. H.; Links, J. M.; Chen, D. C. P.; Wong, D. F.; Wagner, H. N.

European Journal of Nuclear Medicine and Molecular Imaging 1987, 12, 602.
(19)

Fisher, R. A. Ann Eugenic 1936, 7, 179.

(20)

McLachlan, G. J. In Discriminant Analysis and Statistical Pattern

Recognition; McLachlan, G. J., Ed.; John Wiley & Sons, Inc.: 2005, p i.
(21)

Ryback, R. S.; Eckardt, M. J.; Rawlings, R. R.; Rosenthal, L. S. JAMA:

The Journal of the American Medical Association 1982, 248, 2342.
(22)

Murphy, S. L.; Xu, J.; Kochanek, K. D. National Vital Statistics Report"Death: Preliminary Data for 2010", Center for Disease Control: 2012, 60 (4).

204

(23)

Ikonomidis, I.; Michalakeas, C. A.; Lekakis, J.; Paraskevaidis, I.;

Kremastinos, D. T. Disease Markers 2009, 26, 273.
(24)

Schnabel, R. B.; Schulz, A.; Messow, C. M.; Lubos, E.; Wild, P. S.;

Zeller, T.; Sinning, C. R.; Rupprecht, H. J.; Bickel, C.; Peetz, D.; Cambien, F.; Kempf,
T.; Wollert, K. C.; Benjamin, E. J.; Lackner, K. J.; Munzel, T. F.; Tiret, L.; Vasan, R. S.;
Blankenberg, S. European Heart Journal 2010, 31, 3024.
(25)

Giannessi, D. Pharmacological Research 2011, 64, 11.

(26)

Gupta, R.; Deedwania, P. Cardiology Clinics 2011, 29, 15.

(27)

Lusis, A. J.; Weiss, J. N. Circulation 2010, 121, 157.

(28)

Conroy, R. M.; Pyörälä, K.; Fitzgerald, A. P.; Sans, S.; Menotti, A.; De

Backer, G.; De Bacquer, D.; Ducimetière, P.; Jousilahti, P.; Keil, U.; Njølstad, I.;
Oganov, R. G.; Thomsen, T.; Tunstall-Pedoe, H.; Tverdal, A.; Wedel, H.; Whincup, P.;
Wilhelmsen, L.; Graham, I. M. European Heart Journal 2003, 24, 987.
(29)

Masson, W.; Siniawski, D.; Krauss, J.; Cagide, A. Revista Española deCardiología 2011, 64, 305.
(30)

Enriquez, J. R.; De Lemos, J. A. Preventive Cardiology 2010, 13, 152.

(31)

Marma, A. K.; Lloyd-Jones, D. M. Circulation 2009, 120, 384.

(32)

Lloyd-Jones, D. M. Circulation 2010, 121, 1768.

(33)

Ahmadi, N.; Hajsadeghi, F.; Blumenthal, R. S.; Budoff, M. J.; Stone, G.

W.; Ebrahimi, R. The American Journal of Cardiology 2011, 107, 799.

205

(34)

Gil-Guillén, V. F.; Orozco-Beltrán, D.; Pita-Fernández, S.; Carratalá-

Munuera, C.; Redón, J.; Navarro, J.; Pallarés, V.; Salvador, P. Revista Española deCardiología 2011, 64, 421.
(35)

Davidson, M. H. Cardiology Clinics 2011, 29, 105.

(36)

Hsia, J.; MacFadyen, J. G.; Monyak, J.; Ridker, P. M. J Am Coll Cardiol

2011, 57, 1666.
(37)

Newcomer, J.; Abdel-Rahman, S.; Bollinger, S.; Mangum, S.; Potter, R.

D.; Graham, R.; Garrett, J.; Gronstedt, J.; Lurie, H.; Dwyer, K.; Calloway, S. M.;
Wanner-Barjenbruch, P.; Yasso, J. Statin Therapy, University of Missouri-Kansas City
School of Pharmacy, 2003.
(38)

Nissen, S. E.; Tuzcu, E. M.; Schoenhagen, P.; Crowe, T.; Sasiela, W. J.;

Tsai, J.; Orazem, J.; Magorien, R. D.; O'Shaughnessy, C.; Ganz, P. New England
Journal of Medicine 2005, 352, 29.
(39)

Meyers, C. D.; Kamanna, V. S.; Kashyap, M. L. Curr Opin Lipidol 2004,

15, 659.
(40)

Duggal, J. K.; Singh, M.; Attri, N.; Singh, P. P.; Ahmed, N.; Pahwa, S.;

Molnar, J.; Singh, S.; Khosla, S.; Arora, R. Journal of Cardiovascular Pharmacology
and Therapeutics 2010, 15, 158.
(41)

McKenney, J. Arch Intern Med 2004, 164, 697.

(42)

Yu-Poth, S.; Zhao, G.; Etherton, T.; Naglak, M.; Jonnalagadda, S.; Kris-

Etherton, P. M. The American Journal of Clinical Nutrition 1999, 69, 632.

206

(43)

Chen, Z.-Y.; Ma, K. Y.; Liang, Y.; Peng, C.; Zuo, Y. Journal of

Functional Foods 2011, 3, 61.
(44)

Whelton, S. P.; Chin, A.; Xin, X.; He, J. Annals of Internal Medicine

2002, 136, 493.
(45)

Pollock, M. L.; Franklin, B. A.; Balady, G. J.; Chaitman, B. L.; Fleg, J.

L.; Fletcher, B.; Limacher, M.; Piña, I. L.; Stein, R. A.; Williams, M.; Bazzarre, T.
Circulation 2000, 101, 828.
(46)

Shen, M. M.; Krauss, R. M.; Lindgren, F. T.; Forte, T. M. Journal of

Lipid Research 1981, 22, 236.
(47)

Patsch, W.; Schonfeld, G.; Gotto, A. M.; Patsch, J. R. Journal of

Biological Chemistry 1980, 255, 3178.
(48)

Colowick, S. P.; Kaplan, N. O. Methods in Enzymology; Acedemic Press,

Inc.: New York, 1986; Vol. 128.
(49)

Gurr, M. I.; Harwood, J. L.; Frayn, K. N. Lipid Biochemistry; 5th ed.;Blackwell Science, 2002.
(50)

Havel, R. J.; Eder, H. A.; Bragdon, J. H. The Journal of clinicalinvestigation 1955, 34, 1345.
(51)

Brown, M.; Goldstein, J. Science 1986, 232, 34.

(52)

Anber, V.; Griffin, B. A.; McConnell, M.; Packard, C. J.; Shepherd, J.

Atherosclerosis 1996, 124, 261.
(53)

de Graaf, J.; Hak-Lemmers, H.; Hectors, M.; Demacker, P.; Hendriks, J.;

Stalenhoef, A. Arteriosclerosis, Thrombosis, and Vascular Biology 1991, 11, 298.

207

(54)

Björnheden, T.; Babyi, A.; Bondjers, G.; Wiklund, O. Atherosclerosis

1996, 123, 43.
(55)

Galeano, N. F.; Milne, R.; Marcel, Y. L.; Walsh, M. T.; Levy, E.;

Ngu'yen, T. D.; Gleeson, A.; Arad, Y.; Witte, L.; al-Haideri, M. Journal of Biological
Chemistry 1994, 269, 511.
(56)

Choi, S. H.; Chae, A.; Miller, E.; Messig, M.; Ntanios, F.; DeMaria, A.

N.; Nissen, S. E.; Witztum, J. L.; Tsimikas, S. Journal of the American College of
Cardiology 2008, 52, 24.
(57)

Lankin, V.; Viigimaa, M.; Tikhaze, A.; Kumskova, E.; Konovalova, G.;

Abina, J.; Zemtsovskaya, G.; Kotkina, T.; Yanushevskaya, E.; Vlasik, T. Molecular andCellular Biochemistry 2011, 355, 187.
(58)

Natarajan, P.; Ray, K. K.; Cannon, C. P. J Am Coll Cardiol 2010, 55,(59)

Navab, M.; Ananthramaiah, G. M.; Reddy, S. T.; Van Lenten, B. J.;

1283.

Ansell, B. J.; Fonarow, G. C.; Vahabzadeh, K.; Hama, S.; Hough, G.; Kamranpour, N.;
Berliner, J. A.; Lusis, A. J.; Fogelman, A. M. Journal of Lipid Research 2004, 45, 993.
(60)

Navab, M.; Anantharamaiah, G. M.; Fogelman, A. M. Trends in

Cardiovascular Medicine 2005, 15, 158.
(61)

Kontush, A.; Chantepie, S.; Chapman, M. J. Arteriosclerosis,

Thrombosis, and Vascular Biology 2003, 23, 1881.
(62)

Di Bartolo, B. A.; Nicholls, S. J.; Bao, S.; Rye, K.-A.; Heather, A. K.;

Barter, P. J.; Bursill, C. Atherosclerosis 2011, 217, 395.

208

(63)

deGoma, E. M.; deGoma, R. L.; Rader, D. J. Journal of the American

College of Cardiology 2008, 51, 2199.
(64)

Onat, A.; Hergenç, G. Metabolism 2011, 60, 499.

(65)

Greene, D. J.; Skeggs, J. W.; Morton, R. E. Journal of Biological

Chemistry 2001, 276, 4804.
(66)

Dodani, S.; Grice, D. G.; Joshi, S. Journal of Clinical Lipidology 2009, 3,(67)

Ensign, W.; Hill, N.; Heward, C. B. Clin Chem 2006, 52, 1722.

(68)

Rosenson, R. S.; Brewer, H. B., Jr; Chapman, M. J.; Fazio, S.; Hussain,

70.

M. M.; Kontush, A.; Krauss, R. M.; Otvos, J. D.; Remaley, A. T.; Schaefer, E. J. Clin
Chem 2011, 57, 392.
(69)

Laker, M. F.; Game, F. L. Baillieres Clinical Endocrinology andMetabolism 1990, 4, 693.
(70)

Johnson, C.; Attridge, T.; Smith, H. Biochimica et Biophysica Acta (BBA)- Protein Structure 1973, 317, 219.
(71)

Fourcroy, P. Biochemical and Biophysical Research Communications

1978, 84, 713.
(72)

Freifelder, D. M. Principles of Physical Chemistry With Applications to

the Biological Sciences; 2nd ed.; Jones and Bartlett Publishers, Inc.: Portola Valley, CA,
1984.
(73)

Johnson, J. D. Dissertation, Texas A&M University, 2008.

209

(74)

Cone, J. T.; Segrest, J. P.; Chung, B. H.; Ragland, J. B.; Sabesin, S. M.;

Glasscock, A. Journal of Lipid Research 1982, 23, 923.
(75)

Krishnaji R, K. Clinics in Laboratory Medicine 2006, 26, 787.

(76)

Mauldin, J.; Fisher, W. R. Biochemistry 1970, 9, 2015.

(77)

Superko, H. R. Circulation 2009, 119, 2383.

(78)

Hosken, B. D.; Cockrill, S. L.; Macfarlane, R. D. Analytical Chemistry

2005, 77, 200.
(79)Henriquez, R. R.; Chandra, R.; Hosken, B.; Macfarlane, R. D.

Atherosclerosis Supplements 2006, 7, ThP17428.
(80)

Kunitake, S. T.; Kane, J. P. Journal of Lipid Research 1982, 23, 936.

(81)

Schmitz, G.; Möllers, C.; Richter, V. Electrophoresis 1997, 18, 1807.

(82)

Bondarenko, R.; Farwig, Z. N.; McNeal, C. J.; Macfarlane, R. D.

International Journal of Mass Spectrometry 2002, 219, 671.
(83)

Farwig, Z. N.; Campbell, A. V.; Macfarlane, R. D. Analytical Chemistry

2003, 75, 3823.
(84)

Farwig, Z. N.; McNeal, C. J.; Little, D.; Baisden, C. E.; Macfarlane, R. D.

Biochemical and Biophysical Research Communications 2005, 332, 352.
(85)

Johnson, J. D.; Henriquez, R. R.; Tichy, S. E.; Russell, D. H.; McNeal, C.

J.; Macfarlane, R. D. International Journal of Mass Spectrometry 2007, 268, 227.
(86)

Schiller, J.; Süß, R.; Arnhold, J.; Fuchs, B.; Leßig, J.; Müller, M.;

Petković, M.; Spalteholz, H.; Zschörnig, O.; Arnold, K. Progress in Lipid Research
2004, 43, 449.

210

(87)

Karas, M.; Hillenkamp, F. Analytical Chemistry 1988, 60, 2299.

(88)

Karas, M.; Bachmann, D.; Bahr, U.; Hillenkamp, F. International Journalof Mass Spectrometry and Ion Processes 1987, 78, 53.
(89)

Zenobi, R.; Knochenmuss, R. Mass Spectrometry Reviews 1998, 17, 337.

(90)

Albrethsen, J. Clinical Chemistry 2007, 53, 852.

(91)

El-Aneed, A.; Cohen, A.; Banoub, J. Applied Spectroscopy Reviews 2009,44, 210.
(92)

Vaisar, T.; Mayer, P.; Nilsson, E.; Zhao, X.-Q.; Knopp, R.; Prazen, B. J.

Clinica Chimica Acta 2010, 411, 972.
(93)

Friedewald, W. T.; Levy, R. I.; Fredrickson, D. S. Clinical Chemistry

1972, 18, 499.
(94)

Hogle, D. M.; Smith, R. S.; Curtiss, L. K. Journal of Lipid Research

1988, 29, 1221.
(95)

Smith, P. K.; Krohn, R. I.; Hermanson, G. T.; Mallia, A. K.; Gartner, F.

H.; Provenzano, M. D.; Fujimoto, E. K.; Goeke, N. M.; Olson, B. J.; Klenk, D. C.
Analytical Biochemistry 1985, 150, 76.
(96)

Ala-Korpela, M.; Lankinen, N.; Salminen, A.; Suna, T.; Soininen, P.;

Laatikainen, R.; Ingman, P.; Jauhiainen, M.; Taskinen, M.-R.; Héberger, K.; Kaski, K.
Atherosclerosis 2007, 190, 352.
(97)

Dyrby, M.; Petersen, M.; Whittaker, A. K.; Lambert, L.; Nørgaard, L.;Bro, R.; Engelsen, S. B. Analytica Chimica Acta 2005, 531, 209.

211

(98)

Hodge, A. M.; Jenkins, A. J.; English, D. R.; O'Dea, K.; Giles, G. G.

Diabetes Research and Clinical Practice 2009, 83, 132.
(99)

Mora, S.; Otvos, J. D.; Rifai, N.; Rosenson, R. S.; Buring, J. E.; Ridker,

P. M. Circulation 2009, 119, 931.
(100) Blake, G. J.; Otvos, J. D.; Rifai, N.; Ridker, P. M. Circulation 2002, 106,1930.
(101) Tsai, M. Y.; Georgopoulos, A.; Otvos, J. D.; Ordovas, J. M.; Hanson, N.
Q.; Peacock, J. M.; Arnett, D. K. Clinical Chemistry 2004, 50, 1201.
(102) Cruzado, I. D.; Cockrill, S. L.; McNeal, C. J.; Macfarlane, R. D. Journalof Lipid Research 1998, 39, 205.
(103) Cruzado, I. D.; Song, S. Q.; Crouse, S. F.; Obrien, B. C.; Macfarlane, R.
D. Analytical Biochemistry 1996, 243, 100.
(104) Hu, A. Z.; Cruzado, I. D.; Hill, J. W.; McNeal, C. J.; Macfarlane, R. D.
Journal of Chromatography A 1995, 717, 33.
(105) Hu, A. Z.; Cruzado, I. D.; Macfarlane, R. D. American Laboratory 1996,
28, N18.
(106) Liu, M. Y.; McNeal, C. J.; Macfarlane, R. D. Electrophoresis 2004, 25,2985.
(107) Macfarlane, R. D.; Bondarenko, P. V.; Cockrill, S. L.; Cruzado, I. D.;
Koss, W.; McNeal, C. J.; Spiekerman, A. M.; Watkins, L. K. Electrophoresis 1997, 18,1796.
(108) Stocks, J.; Miller, N. E. Electrophoresis 1999, 20, 2118.

212(109) Cook, R. D.; Weisberg, S. J Am Stat Assoc 1991, 86, 328.
(110) Weisberg, S. Journal of Statistical Software 2002, 7, 1.
(111) Efron, B. J Am Stat Assoc 1975, 70, 892.
(112) Press, S. J.; Wilson, S. J Am Stat Assoc 1978, 73, 699.
(113) Balla, B.; Mocak, J.; Pivovarnikova, H.; Balla, J. Chemometrics andIntelligent Laboratory Systems 2004, 72, 259.
(114) Khot, U. N.; Khot, M. B.; Bajzer, C. T.; Sapp, S. K.; Ohman, E. M.;
Brener, S. J.; Ellis, S. G.; Lincoff, A. M.; Topol, E. J. JAMA: The Journal of theAmerican Medical Association 2003, 290, 898.
(115) Warnick, G. R.; Benderson, J.; Albers, J. J. Clinical Chemistry 1982, 28,1379.
(116) Warnick, G. R.; Nguyen, T.; Albers, A. A. Clinical Chemistry 1985, 31,
217.
(117) Watkins, L. K.; Bondarenko, P. V.; Barbacci, D. C.; Song, S.; Cockrill, S.
L.; Russell, D. H.; Macfarlane, R. D. Journal of Chromatography A 1999, 840, 183.
(118) Krilov, D.; Balarin, M.; Kosović, M.; Gamulin, O.; Brnjas-Kraljević, J.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2009, 73, 701.
(119) Khera, A. V.; Cuchel, M.; de la Llera-Moya, M.; Rodrigues, A.; Burke,
M. F.; Jafri, K.; French, B. C.; Phillips, J. A.; Mucksavage, M. L.; Wilensky, R. L.;
Mohler, E. R.; Rothblat, G. H.; Rader, D. J. New England Journal of Medicine 2011,
364, 127.

213(120) Asztalos, B. F.; Collins, D.; Cupples, L. A.; Demissie, S.; Horvath, K. V.;
Bloomfield, H. E.; Robins, S. J.; Schaefer, E. J. Arterioscler Thromb Vasc Biol 2005, 25,2185.
(121) Lamon-Fava, S.; Herrington, D. M.; Reboussin, D. M.; Sherman, M.;
Horvath, K. V.; Cupples, L. A.; White, C.; Demissie, S.; Schaefer, E. J.; Asztalos, B. F.
Arterioscler Thromb Vasc Biol 2008, 28, 575.
(122) Schaefer, E. J.; Asztalos, B. F. Nat Clin Pract Endocrinol Metab 2006, 2,358.
(123) Schaefer, E. J.; Asztalos, B. F. Curr Opin Cardiol 2007, 22, 373.
(124) Schaefer, E. J.; Asztalos, B. F. Curr Atheroscler Rep 2006, 8, 41.
(125) Medina-Urrutia, A.; Juarez-Rojas, J. G.; Cardoso-Saldaña, G.; JorgeGalarza, E.; Posadas-Sánchez, R.; Martínez-Alvarado, R.; Caracas-Portilla, N.; Mendoza
Pérez, E.; Posadas-Romero, C. Pediatrics 2011, 127, e1521.
(126) Navab, M.; Reddy, S. T.; Van Lenten, B. J.; Fogelman, A. M. Nat Rev
Cardiol 2011, 8, 222.
(127) Kannel, W. B.; Castelli, W. P.; Gordon, T. Annals of Internal Medicine
1979, 90, 85.
(128) Webber, L. S.; Srinivasan, S. R.; Wattigney, W. A.; Berenson, G. S.
American Journal of Epidemiology 1991, 133, 884.
(129) Viikari, J.; Rönnemaa, T.; Seppänen, A.; Mamiemi, J.; Porkka, K.;
Räsänen, L.; Uhari, M.; Salo, M. K.; Kaprio, E. A.; Nuutinen, E. M.; Pesonen, E.;
Pietikäinen, M.; Dahl, M.; Akerblom, H. K. Annals of Medicine 1991, 23, 53.

214(130) Boden, W. E.; Probstfield, J. L.; Anderson, T.; Chaitman, B. R.;
Koprowicz, K.; Koon Teo, M. B. New England Journal of Medicine 2011, 365, 2255.
(131) Rubenfire, M.; Vodnala, D.; Krishnan, S. M.; Bard, R. L.; Jackson, E. A.;
Giacherio, D.; Brook, R. D. Journal of Clinical Lipidology.

215

VITA

Craig Daniel Larner graduated from Adrian College in December of 2005. He
took a position as a Quality Control Lab Technologist in January of 2006. There hedesigned and implemented research into methods for reducing product failure from notmeeting QC standards during testing. In July of 2007, he moved to Texas A&M
University where he joined the Laboratory for Cardiovascular Chemistry under the
direction of Dr. Ronald D. Macfarlane. Craig’s research involved the optimization ofanalytical methods to reduce experimental error in order to apply the methods to clinicalstudies for cardiovascular disease, develop an enhanced risk assessment method for
CVD, and identify novel risk factors for CVD utilizing analytical techniques. The
analytical techniques included density gradient ultracentrifugation, immunoassays, andmass spectrometry. He defended his research in May of 2012 and received his Ph.D. in
August of 2012. The author’s address is Texas A&M University, Department ofChemistry, MS 3255, College Station, TX 77843. His email address isclarnerphd@gmail.com.