1. ApoB‐Lipoprotein profile modelling to
derive cardiovascular risk markers
Albert de Graaf

2. Dietary fat
absorption
From: Keith Frayn, “Metabolic Regulation - A Human Perspective”.
Wiley-Blackwell, Third Edition, 2010

3. Exogenous pathway of
lipoprotein metabolism
From: Keith Frayn, “Metabolic Regulation - A Human Perspective”.
Wiley-Blackwell, Third Edition, 2010

4. Endogenous pathway of
lipoprotein metabolism
From: Keith Frayn, “Metabolic Regulation - A Human Perspective”.
Wiley-Blackwell, Third Edition, 2010

5. HDL metabolism
From: Keith Frayn, “Metabolic Regulation - A Human Perspective”.
Wiley-Blackwell, Third Edition, 2010

6. Forward and reverse
cholesterol transport
From: Keith Frayn, “Metabolic Regulation - A Human Perspective”.
Wiley-Blackwell, Third Edition, 2010

7. Lipoprotein structure

8. Different lipoprotein classes
From: Keith Frayn, “Metabolic Regulation - A Human Perspective”.
Wiley-Blackwell, Third Edition, 2010

9. Lipoprotein Distribution (LPD) measurement

10. Cholesterol and heart disease
From: Keith Frayn, “Metabolic Regulation - A Human Perspective”.
Wiley-Blackwell, Third Edition, 2010

11. Stable isotope studies: lipoprotein classes
correspond to kinetically different pools
Multicompartmental model for apoB
metabolism in VLDL1 (Sf 60400), VLDL2 (Sf 20-60), IDL (Sf 1220), and LDL (Sf 0-12).
Tracer data time range 0-250h !!!
Gaw A et al. 1996 Arterioscler Thromb Vasc Biol 16:236-249

12. Our vision …
LPD kinetic model
development
Model-based LPD analysis
Lipoprotein flux ratio
biomarkers

13. Advantage of our concept
From one single plasma measurement to rates of processes
Process rates are closer to functional activity than concentrations!
 better resolution to pick up risk-associated variation in lipoprotein
metabolism ?
 New cardiovascular risk markers?
 New diagnostic?
Otherwise, determination of process rates is only possible with costly
stable-isotope studies

14. Particle Profiler project overview

15. Background of model
Model
development
=> Particle size-dependent rate constants => fluxes of different lipoprotein
processes:
-
Hepatic production
Peripheral lipolysis
Liver attachment (ApoE- and ApoB-mediated)
Hepatic uptake (annihilation of particle)
Hepatic lipolysis
Applied to each e.g. 0.1 nm subclass in the range 100 – 10 nm

16. Lipolysis cascades - 1
Model
development
Best fit overall

17. Lipolysis cascades - 2
The ApoB LPD size range is divided into 11 cascade
fractions
Each cascade has e.g. 1000 particle subfractions
(pools).
Model
development
Each subsequent cascade has smaller size ranges of
itself and of all its subfractions
Each lipolysis step transfers particles from a given
cascade to the next cascade
The other processes can add or remove particles
from each cascade
For every subfraction a particle mass
balance is set up
The computer solves the 11000 mass
balances for steady state simultaneously
The result is a simulated LPD whose
appearance depends on the process
parameter settings
The simulated LPD is compared to a
measured LPD
A parameter optimization algorithm
searches the best parameter settings

18. Lipolysis steps - decreasing particle size
apoB apoE
uptake uptake
HL - lipolysis
LPL - lipolysis
Production
Liver attachment
Model
development

19. Lipoprotein production process model
Production flux (particles / min)
Model
development
VLDL1
VLDL2
IDL
Lipoprotein diameter (nm)
LDL

20. Lipolysis and uptake process models (single particle rates)
0.02
process rate (1 / min)
Model
development
Each subfraction particle P flux component F is modeled as F =
k(r).P with k(r) explicitly depending on particle subfraction
diameter according to a 1- or 2- paramer function
liver attachment
extrahep. lipolysis
uptake
hepatic lipolysis
0.015
0.01
0.005
0
100
80
60
40
lipoprotein diameter (nm)
20

21. Flux Data for model testing
Packard et al. 2000, human study
Model validation
stable isotope fluxes analyzed with multi-compartment model
Three groups of subjects, healthy men
Large LDL peak size (>26 nm)
Intermediate LDL peak size (25 – 26 nm)
Small LDL peak size (<25 nm) – risk group
Data on flux of lipoproteins input to our model.

22. Example output (main classes) compared
to stable isotope study
Particle Pools
Uptake Flux
0.2
400
number of particles fl-1 min-1
Simulated
Data
300
200
100
0
LDL
Simulated
Data
0.15
0.1
0.05
0
IDL
VLDL2 VLDL1
lipoprotein class
Lipolysis-Induced Influx
IDL
VLDL2 VLDL1
lipoprotein class
Fitted Lipolysis and Uptake Rates
Simulated
Data
0.1
0.05
0
LDL
0.025
0.15
process rate min-1
N.B. The LPD is
simulated as
particle
concentration
profiles. These
can be converted
to TG and
cholesterol
concentration
profiles
number of particles fl-1 min-1
Model validation
number of particles fl-1
500
LDL
IDL
VLDL2
lipoprotein class
0.02
Lipolysis rate
Uptake rate
0.015
0.01
0.005
0
10
20
30
40
50
lipoprotein particle size
60

23. Flux Data for model testing
4 results that give credibility to our model
Model validation
Model can reproduce particle concentration & flux data
The model qualitatively reproduces a separately measured
LDL peak size shift
The model can simulate genetic deficiencies
The changed processes the model detected
are biologically plausible
Journal of Lipid Research, Vol. 50, 2398-2411,
December 2009

24. concentration
Model validation
Lipoprotein
Pooled
Fluxes
Lipoprotein
Profile
particle diameter
Particle Profiler
Lipoprotein
Particle
Fluxes
VLDL1
VLDL2
IDL
LDL
Lipoprotein
Metabolic Ratios
Derive model-based
markers

25. What are we doing?
Derive
model-based
markers
Derive ratios between processes
For instance:
VLDL lipolysis outside the liver / VLDL production
See whether that helps to improve Risk prediction
Better risk prediction will help to give
the right therapy to the right people

26. Example new diagnostic for dyslipidemia
Derive
model-based
markers

27. Receiver operating characteristic (ROC) curves
Validate markers
Diagnostic
AUC
'VLDL performance'
0,937
'TG (mmol/l)'
0,900
'LDLc (mmol/l)'
0,794

28. Reclassification analysis
You have three risk categories
Validate markers
People are treated based on their category
Low risk – no treatment
Medium risk – some treatment
High risk – intensive treatment
Reclassification analysis compares diagnostics
for their classification ability
For those people whom we know will NOT have an event
How many move to lower risk categories?
For those people whom we know WILL have an event
How many move to higher risk categories?

29. Validation CVD risk prediction
The Framingham Heart Study Offspring cohort (FOS)
Validate markers
Inclusion criteria:
no history of cardiovascular disease
gave written informed consent for general research use
had complete NMR lipoprotein profiles recorded
had a complete record of classical cardiovascular risk factors.
Cardiovascular events 10 years after baseline measurement
Population size
Events (true positives)
No event (true negatives)
1981
145
1836

30. Statistics
Validate markers
First using logistic regression
No improvement possible
Then SVM methodology

31. Best predicting ratios
Validate markers
In the VLDL size range
VLDLH – VLDL Hepatic turnover indicator
Liver
functional status indicator
VLDLE – VLDL Extrahepatic lipolysis indicator
How well do extrahepatic tissues absorb fat?

32. Predictor variables
Conventional
markers
LDLp
LDLp + HDLp
LDLp + HDLp +
VLDLE + VLDLH
Age
Age
Age
Age
Age
Sex
Sex
Sex
Sex
Sex
Cigarettes per
day
Cigarettes per
day
Cigarettes per
day
Cigarettes per
day
Cigarettes per day
Blood pressure
medication
Blood pressure
medication
Blood pressure
medication
Blood pressure
medication
Blood pressure medication
Systolic blood
pressure
(nurse)
Systolic blood
pressure
(nurse)
Systolic blood
pressure
(nurse)
Systolic blood
pressure
(nurse)
Systolic blood pressure (nurse)
Glucose
Glucose
Glucose
Glucose
Glucose
Total
Cholesterol
Validate markers
Conventional
markers
without
cholesterol
LDL particle
number
LDL particle
number
LDL particle number
HDL particle
number
HDL particle number
HDL
cholesterol
VLDL Extrahepatic lipolysis
indicator
32
VLDL Hepatic turnover
indicator

33. Area under the ROC-curve statistics for general CVD
Validate markers
Framingham Offspring Study
* Significantly better than conventional, no cholesterol p<0.05
** Significantly better than LDLp, p<0.05
† Significantly better than LDLp+HDLp, p<0.05
Model
AUC
SE
AUC improvement
from random
% incremental AUC
improvement from random
Conventional, no cholesterol
0.759
0.0204
0.259
0.0
Conventional
0.795
0.0193
0.295 (*)
12.2
LDLp
0.791
0.0192
0.291 (*)
11.0
LDLp + HDLp
0.797
0.0192
0.297 (*)
12.8
LDLp + HDLp +
VLDLE + VLDLH
0.812
0.0192
0.312 (*,**, †)
17.0

34. Reclassification analysis
Model 1
Model 2
NRI
Standard
Error
P value
% of
Events
correctly
reclassified
(n=145)
Event Pvalue
% of
Nonevents
correctly
reclassified
(n=1836)
Nonevent
P-value
Conventional
no cholesterol
Conventional
0.1111
0.0418
0.0088
8%
0.0455
3%
0.002
Conventional
no cholesterol
LDLp
0.0830
0.0386
0.0327
6%
0.1441
3%
0.0021
Conventional
LDLp + HDLp
0.0080
0.0339
0.8135
1%
0.8348
0%
0.8802
Conventional
LDLp + HDLp +
VLDLE + VLDLH
0.0902
0.0366
0.0143
5%
0.1779
4%
<.0001
LDLp
LDLp + HDLp
0.0356
0.0307
0.2489
3%
0.2513
0%
0.8751
LDLp
LDLp + HDLp +
VLDLE + VLDLH
0.1178
0.0376
0.0020
8%
0.0411
4%
<.0001
LDLp +
HDLp
LDLp + HDLp +
VLDLE + VLDLH
0.0828
0.0330
0.0127
4%
0.2008
4%
<.0001
Validate markers

35. What is happening? An ‘average’ person.
A - no blood pressure medication
Validate markers
B - no blood pressure medication
0.35
male
female
0.25
10 year risk
10 year risk
0.3
0.4
0.2
0.15
0.1
male, low-med LDLp
female, low-med LDLp
male, med-high LDLp
female, med-high LDLp
0.3
0.2
0.1
0.05
0
50
100
150
200
250
0
300
0
20
40
LDLp
C - with blood pressure medication
100
0.25
male
female
male
female
0.2
10 year risk
0.8
10 year risk
80
D - no blood pressure medication
1
0.6
0.4
0.2
0
-14
60
HDLp
0.15
0.1
0.05
-12
-10
-8
-6
VLDLE
-4
-2
0
0
-6
-5
-4
-3
VLDLH
-2
-1

36. Conclusions
Lipoprotein metabolic ratios derived from Particle Profiler
significantly improve CVD risk prediction
As measured by area-under-the-ROC-curve
4% of the total population is positively reclassified by these markers
In Framingham Offspring Cohort
If we assume Framingham is a good representation of the US
population (it is not, it is generally healthier) then:
Given that approx. 32 million people in the US use statins,
this could help more than 1 million people improve their treatment
regime in the US alone.
“
“

37. But…
Clinicians & customers require an independent validation.
Different cohort e.g. MESA (Multi Ethnic Study of Atherosclerosis)
data would be needed
MESA has slightly different data type (e.g. “Diabetes Y/N” instead of
“plasma glucose”)
Simulating this different data type in Framingham did not result in
better risk prediction using Particle Profiler- derived flux ratio markers
MESA was not analyzed and the project was stopped by TNO
(we might have considered EPIC, but didn’t)

38. Lessons learned
SVM and binary variables – take care!
Introducing new diagnostics in the current clinical setting
is a all-or-nothing business.
Nothing is certain until the last statistical analyses come in,
the final success is highly unpredictable.
Because this is a high-risk enterprise, companies do not easily invest
in early development stages, but are willing to collaborate with data
sharing etc.

39. Towards linking carbohydrate and fat metabolism
Lipoprotein metabolism as a central node for interplay of
glucose, TG and NEFA metabolism
VLDL
Prod
Chylo
microns
-
VLDL
LDL
glucose

40. TNO can contribute datasets

41. Collaborators
TNO
Daan van Schalkwijk
Evgeni Tsivtsivadze
Bianca van der Werff
Ben van Ommen
Albert de Graaf
(Andreas Freidig)
LACDR
Jan van der Greef
TUFTS university Boston
Laurence D. Parnell
José M. Ordovás
Financial support
The official Framingham
investigators were not involved in
the analysis nor commented on
the conclusions drawn