High Precision Lipoprotein Subclass Analysis (Lipoprotein
TM
Fingerprinting )
Ronald Macfarlane PhD., University Distinguished Professor, Craig Larner, Paul Cammarata, Catherine McNeal PhD.,
MD, Simon Sheather PhD.
Laboratory for Cardiovascular Chemistry, Department of Chemistry, Department of Statistics, Texas A&M University,
Scott & White Hospital
OBJECTIVE: To test the accuracy of a new screening method for CAD on a
cohort of normolipodemic subjects with & without CAD
TM
fingerprint
How the lipoprotein
of an individual is generated:
Equilibrium Density Gradient
Ultracentrifugation
Using a Drop of Blood
TM
Fingerprints
Utilization of Lipoprotein
for Initial Clinical Studies:
Objective:
To determine whether lipoprotein fingerprints can be used to classify
individuals with and without CAD with high accuracy.
Cohorts used in clinical study:
Thirty-five subjects with and without CAD were selected. Their
common feature was that they all had normal lipid levels.
Linear discriminate analysis ( LDA) and Sliced Inverse Regression
(SIR) were selected for the classification.
Statistical Separations
Enhanced Separation Of Normal Lipodemic Profiles
Traditional Lipid Measurement
SIR/LDA Separation
CVD Classifaction
Long-term Objective:
Develop an accurate screening method to identify individuals at high risk for
coronary artery disease (CAD) prior to the development of clinical manifestations
to initiate a prevention strategy.
Hypotheses:
•Circulating lipoprotein particles can serve as an internal probe for identifying high
risk for CAD.
•Effective characterization of the lipoprotein population can be improved
incorporating modern methods from analytical chemistry.
•Selection of lipoprotein density as a critical variable in achieving the long-term
objective.
•Density-gradient ultracentrifugation can be refined to obtain a high precision
lipoprotein density profile compatible for clinical studies. (The profile is referred to as
TM
a lipoprotein fingerprint .
Y
N
Density Gradient Theory
-50
0
50
100
150
200
250
300
SIR/LDA Value
H4EDTA
+ CdCO3
→ H2CdEDTA + H2O + CO2
SIR/LDA of Traditional
Lipid Measurements:
P-value = 0.8852
% Correct = 50%
1.16
1.16
1.14
1.14
1.12
1.12
1.10
1.10
1.08
1.06
+
1.08
1.06
1.04
1.04
1.02
1.02
1.00
1.00
0
2
4
6
8
10
12
14
Tube Coordinate (mm)
16
18
20
0
2
4
6
8
10
12
14
16
18
20
22
Tube Coordinate (mm)
Lipoprotein Fingerprints
Error Reduction: Meniscus Error Removal Through Polar Layering,
Enhanced Volumes Spins, and Quality Control Standards
2500
Non-CAD Sample
Lipoprotein Fingerprint
Prior to Enhancement:
Study: 7770, Patient #142, CVD Patient
0.18M NaBiEDTA, 1150uL Spin
Hexane Layered, Halide Lamp
2500
TRL
2500
TRL
HDL
LDL
2000
HDL
LDL
Mode 1 %RSD – 23.39%
Mode 2 %RSD – 19.75%
Intensity
2000
1500
1
2
3
4
5
2b
2c
3a
3c
3b
1
1500
2
3
4
5
2b
3a 3c
2a 3b
1000
After Enhancement:
4
1000 µL
H2O
Layered
1150 µL
NonLayered
1150 µL
Polar
Layered
6
8
10 12 14 16
18 20 22 24 26 28 30 32
Tube Coordinate (mm)
0
3
6
9
12
15
18
21
24
Tube Coordinate (mm)
27
30
33
Mode 1 %RSD – 4.42%
Mode 2 %RSD – 3.69%
2
3
4
5
2b
3a 3c
2a 3b
1000
0
0
500
1
1500
500
500
1000
Intensity
2000
Intensity
TRL
Non-CAD Contributor – Green
n
Importance of Subclasses
References:
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
SIR/LDA Values
SIR/LDA of Mode 2:
P-value = 0.03290
% Correct = 100%
Conclusion:
HDL
LDL
y
-0.20
CVD Sample Lipoprotein
Fingerprint
Study: 1532C, Patient 001, Angiogram Based Control
0.18M NaBiEDTA, 1150uL Spin
Hexane Layered, Halide Lamp
10 HPLPD Profiles of a Serum Sample - Overlaid
1150uL 0.18M NaBiEDTA, 6uL Serum,
10uL NBD C6-Ceramide, 200+ uL Hexane Layered
Weighted Constants for
SIR/LDA Prediction
Lipid Profiling Measurements - Mode 2
SIR/LDA Separation
CVD Classification
Density (g/mL)
Density (g/mL)
H2CdEDTA + Cs2CO3 → Cs2CdEDTA + H2O + CO2
5
10
15
20
25
30
Tube Coordinate (mm)
CAD Contributor - Red
SIR/LDA Value = 2.218 + 0.014 x Ln(TRL) + 0.042 x Ln(LDL-1) +
-0.124 x Ln(LDL-2) + -0.129 x Ln(LDL-3) + -0.088 x Ln(LDL-4) +
0.336 x Ln(LDL-5) + -0.645 x Ln(HDL-2b) + 0.279 x Ln(HDL-2a) +
-0.448 x Ln(HDL-3a) + 0.280 x Ln(HDL-3b) + -0.269 x Ln(HDL-3c)
•HDL subclasses play a significant role in
classifying CAD/ no CAD individuals with normal
lipid levels with 100 % accuracy.
•TG, HDL-c, LDL-c levels alone cannot classify
CAD/no CAD individuals with normal lipid levels.
•The high precision of the Lipoprotein Profiles is
CRITICAL to classification of samples and how it
works.
•Macfarlane, R. D.; Hosken, B. D.; Farwig, Z. N.;
Espinosa, I. L.; Myers, C. L.; Cockrill, S. L. Lipoprotein
fingerprinting method. U.S. Patent 6,753,185, 2004
•Hosken, B. D.; Cockrill, S. L.; Macfarlane, R. D. Anal.
Chem. 2005, 77, 200-207
•Johnson, J.D.; Bell, N.J.; Donahoe, E.L.; Macfarlane, R.D.
Anal. Chem. 2006, 77, 7054-7061
•Espinosa, I. L.; McNeal, C. J. ; Macfarlane, R. D. Anal.
Chem. 2006, 78 (2): 438-444
•Henriquez, R.; Chandra, R.; Hosken, B. D.; Macfarlane,
R. D. Atheroscler. Suppl. 2006, 7 (3): 587-588
•Larner, C.L.; Henriquez, R.H.; Macfarlane, R.D.;
McNeal, C.J.; Sheather, S. 15th International Symposium
on Atherosclerosis, Boston, MA, June 14-18, 2009;
International Atherosclerosis Society: Milan, Italy, 2009; P790.
This project was funded by NIH/NHLBI,
grant number RO1 HL068794, and Scott &
White Hospital.