Online Appendix for the following JACC article
TITLE: Metabolomic Profile of Human Myocardial Ischemia by Nuclear Magnetic
Resonance Spectroscopy of Peripheral Blood Serum: A Translational Study Based on
Transient Coronary Occlusion Models
AUTHORS: Vicente Bodi, MD, Juan Sanchis, MD, Jose M. Morales, BsC, Vannina G.
Marrachelli, PhD, Julio Nunez, MD, Maria J. Forteza, BsC, Fabian Chaustre, BsC,
Cristina Gomez, BsC, Luis Mainar, MD, Gema Minana, MD, Eva Rumiz, MD, Oliver
Husser, MD, Inmaculada Noguera, PhD, Ana Diaz, PhD, David Moratal, PhD, Arturo
Carratala, MD, Xavier Bosch, MD, Angel Llacer, MD, Francisco J. Chorro, MD, Juan R.
Viña, MD, PhD, Daniel Monleon, PhD
APPENDIX
SUPPLEMENTARY MATERIAL
Abbreviations and Acronyms
NMR = nuclear magnetic resonance
PC = principal component
PCA = principal component analysis
PLS-DA = projection to latent structures for discriminant analysis
ppm = parts per million of spectrometer frequency
METHODS
NMR spectroscopy
Total preparation time prior to nuclear magnetic resonance (NMR) detection was less
than 15 min. Two L of D2O were added to an aliquot with 20 L of blood serum and
placed in a 1 mm high resolution NMR tube. All spectra were recorded in a Bruker
Avance DRX 600 spectrometer (Bruker GmbH, Rheinstetten, Germany). Nominal
temperature of the sample was kept at 37º C.
A single-pulse pre-saturation experiment was acquired in all samples. The chemical shift
region including resonances between 0.50 and 4.70 parts per million of spectrometer
frequency (ppm) was investigated. The spectra were normalized to total aliphatic spectral
area to eliminate differences in metabolite total concentration. The spectra were binned
into 0.01 ppm buckets and mean centered for multivariate analysis. Signals belonging to
selected metabolites were integrated and quantified using semi-automated in-house
MATLAB (The MathWorks Inc. 2006) peak-fitting routines.
Multivariate Model Analysis
Chemometrics statistical analyses were performed using in-house MATLAB scripts and
the PLS Toolbox (Eigenvector Research, Inc.). Principal Component Analysis (PCA) and
Projection to Latent Structures for Discriminant Analysis (PLS-DA) were applied to
NMR spectra data sets. PCA is able to find low dimensional embeddings of multivariate
data, in a way that optimally preserves the structure of the data. PCA technique
transforms variables in a data set into a smaller number of new latent variables called
principal components (PCs), which are uncorrelated to each other and account for
decreasing proportions of the total variance of the original variables. Each new PC is a
linear combination of the original variation such that a compact description of the
variation within the data set is generated. Observations are assigned scores according to
the variation measured by the principal component with those having similar scores
clustering together.
Where PCA proved inadequate to define clustering, a supervised approach was used.
PLS-DA is a supervised extension of PCA used to distinguish two or more classes by
searching for variables (X matrix) that are correlated to class membership (Y matrix). In
this approach the axes are calculated to maximize class separation and can be used to
examine separation that would otherwise be across three or more principal components
(1).
Validation of the PLS-DA model
The PLS-DA model discriminating between controls and patients was cross-validated by
the leave-one-out method and externally validated on the new data set obtained on
patients with spontaneous chest pain recruited at the Emergency Department. Cross
validation is a very useful tool that provides two critical functions in chemometrics. First,
cross validation enables an assessment of the optimal complexity of a model. Second, it
allows an estimation of the performance of a model when it is applied to unknown data.
For a given data set, cross validation involves a series of experiments, hereby called sub-
validation experiments, each of which involves the removal of a subset of individuals
from a dataset, construction of a model using the remaining objects in the dataset, and
subsequent application of the resulting model to the removed objects. Therefore, each
sub-validation experiment involves testing a model with objects that were not used to
build the model. Typical cross-validation involves more than one sub-validation
experiment by selection of different subsets of individuals for model building and model
testing. Leave-one-out cross-validation involves the use of a single observation from the
original sample as the validation data, and the remaining observations as the training
data. This is repeated in a manner that each observation in the sample is used once as the
validation data.
Leave-one-out is ideally suited for datasets with limited number of samples. Crossvalidation provided a root mean square of 0.3677 and a cross error of 0.0875. Q residual
and Hotelling T2 for 95% confidence intervals were 0.541 and 12.11 respectively. The
annotated loadings plots of the 3 latent variables of the PLS-DA model are plotted in
Figure S2 of this Supplementary Material.
Reference
1. Trygg J, Holmes E, Lundstedt T. Chemometrics in metabonomics. J Proteome Res
2007;6:469-79.
LOGISTICS REPORT
Sample preparation and analysis in an experienced laboratory
The application of the model presented in this manuscript to new data is straightforward
in a properly set up NMR laboratory. The computer controlling the NMR spectrometer
needs to have a valid MATLAB license for samples classification according to our
model. The key aspect in the success of our approach is to have a NMR spectrometer
ready for serum measurements 24 horus a day. If that is the case, the total time since a
serum sample enter the lab until a final classification report is produced may be as short
as 15 minutes.
Sample preparation for NMR measurements is really simple (as mentioned above, it only
includes addition of D2O to the sample). The measurement, including temperature
equilibrium and the pulse sequence takes 8 minutes. Finally, once the free induction
decay is produced, pre-processing of the spectra, transfer to the MATLAB environment
and application of the model may be partially automatized and performed in just 1 to 2
minutes. In our laboratory, the total processing time of an individual sample is around 15
minutes. In addition, large series of samples can be processed by automated robots with
the ability to process hundreds of samples in a few hours.
Financial aspects for setting up a metabolomics laboratory
Although the initial investment required for setting up a NMR laboratory is important, the
low cost of the subsequent measurements combined with the high amount of information
that each spectrum produces can make the laboratory profitable in a few years.
Because of the latest advances in NMR technology, namely, magnet shielding and
compact consoles, a NMR laboratory can be established in almost every normal
laboratory space. Magnetic disturbance and vibrations in the room should be minimized.
A trade off between performance and maintenance costs probably makes a 400 MHz
standard bore magnet the most cost-effective solution. Automatic sample changer is
essential for high throughput although in the case of Emergency Room applications this
may not be required.
Although there are just a few NMR spectrometer manufacturers in the world, the price of
a fully equipped NMR spectrometer varies between countries even for the same
manufacturer and model. A rough estimate of the cost of a standard 400 MHz shielded
magnet with a new compact console and a probe for 1H spectroscopy may be around
350k euros. The magnet requires cryogenic filling of the deward. Liquid nitrogen must be
filled weekly and liquid helium must be filled approximately every three months. The
cost for this cryogenics during 1 year in Spain is 10k euros. Sample solvent-matched
NMR tubes cost 2 euros the unit. For an average of 20 samples per day coming from the
Emergency Department, we estimate that a cost per sample of 15 euros will allow to
cover the costs in just three years.
Figure S1
Title. NMR spectra of the experimental model.
Caption. Comparison of all overlapped spectra collected in blood serum of swine before
(top spectra) and immediately after (bottom spectra) ischemia. Most representative
metabolites have been labeled.
Abbreviations. UFA = unsaturated fatty acids.
Figure S2
Title. Loadings plots of the PLS-DA model for discrimination, based on the NMR spectra
of peripheral blood serum, of patients with angioplasty-induced myocardial ischemia
from angiography control patients.
Caption. The most representative metabolites to discriminate patients with angioplastyinduced myocardial ischemia from angiography control patients have been labeled.
Abbreviations. Aceglyco = acetyl groups of glycoproteins. Ala = alanine. CCC = cholinecontaining compounds. Chol = cholesterol. Cre = total creatine. FA = fatty acids. FA (1)
= CH3 groups in fatty acids. FA (2) = CH3 groups close to a CO group (CH3-CO-) in
fatty acids. Gln = glutamine. Gluc = glucose. Gly = glycine. Glyc = Glycerol. Gsx =
glutamate + glutathione. ILV = isoleucine + leucine + valine. LV = latent variable. PEA
= phosphoethanolamine. PUFA = polyunsaturated fatty acids. Tau = taurine. TG =
triglycerides. UFA = unsaturated fatty acids.
Figure S3
Title. Performance of single metabolites and of the PLS-DA model for discrimination of
spontaneous acute chest pain patients with and without a final diagnosis of myocardial
ischemia.
Caption. ROC curves for the discrimination of spontaneous acute chest pain patients with
and without a final diagnosis of myocardial ischemia based on the PLS-DA model (F)
and on the levels of single metabolites (A to E) showing no spectral overlapping and the
most statistically significant differences in the angioplasty balloon-induced myocardial
ischemia model (column 7 of Table 2 of the manuscript). A) Triglycerides (AUC 0.74);
B) Acetate (AUC 0.65); C) Glutamine (0.67); D) Phosphoethanolamine (AUC 0.73); E)
Lactate (0.69); F) PLS-DA model (AUC 0.90).
Abbreviations. AUC = area under the receiver operating characteristics curve. PLS-DA =
projection to latent structures for discriminant analysis. ROC = receiver operating
characteristics.
Table S1. Resonance frequency (in ppm) for metabolites detected and quantified by
NMR spectroscopy in blood serum of angiography controls and in angioplastyinduced myocardial ischemia patients
Metabolite dominant
Chemical shift (ppm)
in the spectral region
Cholesterol
0.70
Triglycerides
0.87
Leucine
0.95
Isoleucine
1.00
Valine
1.02
3-hydroxybutyrate
1.21
Fatty acids (1)
1.27
Alanine (overlapped)
1.48
Fatty acids (2)
1.60
Acetate
1.93
Unsaturated FA
2.00
Glycoproteins (acetyls)
2.04
Fatty acids (3)
2.24
Glutathione
2.32
Glutamate
2.36
Glutamine
2.41
Polyunsaturated FA
2.72
Asparagine
2.95
Albumine lysyl (4)
3.00
Albumine lysyl (5)
3.02
Creatine
3.04
Choline
3.20
Phosphocholine
3.21
Glycerophosphocholine
3.22
Taurine
3.25
Proline
3.37
Glucose
3.48
Glycine
3.54
Glycerol
3.63
Tyrosine+Phenylalanine
3.96
Phosphoethanolamine
4.08
Lactate
4.14
Abbreviations. FA = fatty acids. ppm = parts per million.
Table S2. Clinical characteristics of angiography controls and of angioplastyinduced myocardial ischemia patients
Angioplasty-
Angiography
induced ischemia
controls
(n=20)
(n=10)
Age (mean ± SD. years)
64  10
66  10
0.4
Male sex (%)
15 (75)
4 (40)
0.1
Diabetes (%)
7 (35)
1 (10)
0.3
Current smoker (%)
5 (25)
2 (20)
1.0
Hypertension (%)
13 (65)
6 (60)
1.0
Hypercholesterolemia (%)
15 (75)
5 (50)
0.3
Previous angioplasty (%)
5 (25)
4 (40)
0.6
Previous surgery (%)
0 (0)
0 (0)
1.0
Previous revascularization (%)
5 (25)
4 (40)
0.6
Previous infarction (%)
9 (45)
4 (40)
1.0
ST-segment depression (%)
4 (20)
2 (20)
1.0
T-wave inversion (%)
3 (15)
0 (0)
0.5
Treatment with beta-blockers (%)
11 (55)
5 (50)
1.0
Treatment with nitrates (%)
11 (55)
5 (50)
1.0
Treatment with statins (%)
14 (70)
5 (50)
0.4
p
Table S3. Clinical characteristics of patients with spontaneous chest pain with and
without a final diagnosis of myocardial ischemia
Without
With
myocardial
myocardial
ischemia
ischemia
(n=20)
(n=10)
Age (years)
62  15
64  14
0.5
Male sex (%)
14 (70)
7 (70)
1.0
Diabetes (%)
12 (60)
4 (40)
0.7
Current Smoker (%)
4 (20)
2 (20)
1.0
Hypertension (%)
15 (75)
5 (50)
0.8
Hypercholesterolemia (%)
12 (60)
6 (60)
1.0
Previous angioplasty (%)
2 (10)
2 (20)
0.7
Previous surgery (%)
0 (0)
0 (0)
1.0
Previous revascularization (%)
2 (10)
2 (20)
0.7
Previous infarction (%)
2 (10)
2 (20)
0.7
ST-segment depression (%)
2 (10)
2 (20)
1.0
T-wave inversion (%)
0 (0)
0 (0)
0.5
Treatment with beta-blockers (%)
4 (20)
2 (20)
1.0
Treatment with nitrates (%)
2 (10)
1 (10)
1.0
Treatment with statins (%)
10 (50)
5 (50)
1.0
p
Table S4. Spectral weights/loadings contributions to the PLS-DA model
See Book 1 in MS Excel file.
Table S5. PLS-DA model scores, final diagnosis and predicted diagnosis in the data
set of patients with angioplasty-induced myocardial ischemia and angiography
controls
See Book 2 in MS Excel file.
Table S6. PLS-DA model scores, final diagnosis and predicted diagnosis in the data
set of patients with spontaneous chest pain evaluated in the Emergency Department
See Book 3 in MS Excel file.