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CLINICAL RESEARCH
Genetic and protein markers of CVD
High-throughput quantification of circulating
metabolites improves prediction of subclinical
atherosclerosis
Peter Wu
¨rtz1, 2,3, Juho R. Raiko4, Costan G. Magnussen4, 5, Pasi Soininen1, 6,
Antti J. Kangas1, Tuulia Tynkkynen1,6, Russell Thomson5, Reino Laatikainen6,
Markku J. Savolainen1,7, Jari Laurikka8, Pekka Kuukasja
¨rvi8, Matti Tarkka8,
Pekka J. Karhunen9, Antti Jula10, Jorma S. Viikari11, Mika Ka
¨ho
¨nen12,
Terho Lehtima
¨ki13, Markus Juonala4,11, Mika Ala-Korpela1, 6, 7*,
and Olli T. Raitakari4,14*
1
Computational Medicine, Institute of Clinical Medicine, University of Oulu, PO Box 5000, 90014 Oulu, Finland;
2
Institute for Molecular Medicine Finland, University of Helsinki,
Helsinki, Finland;
3
Epidemiology and Biostatistics, Imperial College London, London, UK;
4
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku,
PO Box 52, Kiinamyllynkatu 10, 20521 Turku, Finland;
5
Menzies Research Institute Tasmania, University of Tasmania, Hobart, Australia;
6
NMR Metabonomics Laboratory,
Department of Biosciences, University of Eastern Finland, Kuopio, Finland;
7
Department of Internal Medicine and Biocenter Oulu, Clinical Research Center, University of Oulu,
Oulu, Finland;
8
Department of Cardio-Thoracic Surgery, Tampere University Hospital and University of Tampere, Tampere, Finland;
9
Department of Forensic Medicine, Tampere
University Hospital and University of Tampere, Tampere, Finland;
10
Department of Health and Functional Capacity, National Institute for Health and Welfare, Helsinki, Finland;
11
Department of Medicine, University of Turku and Turku University Hospital, Turku, Finland;
12
Department of Clinical Physiology, Tampere University Hospital and University of
Tampere, Tampere, Finland;
13
Department of Clinical Chemistry, Tampere University Hospital and University of Tampere, Tampere, Finland; and
14
Department of Clinical
Physiology, Turku University Hospital, Turku, Finland
Received 14 November 2011; revised 9 January 2012; accepted 22 January 2012; online publish-ahead-of-print 26 March 2012
Aims High-throughput metabolite quantification holds promise for cardiovascular risk assessment. Here, we evaluated
whether metabolite quantification by nuclear magnetic resonance (NMR) improves prediction of subclinical athero-
sclerosis in comparison to conventional lipid testing.
Methods
and results
Circulating lipids, lipoprotein subclasses, and small molecules were assayed by NMR for 1595 individuals aged 24– 39
years from the population-based Cardiovascular Risk in Young Finns Study. Carotid intima – media thickness (IMT), a
marker of subclinical atherosclerosis, was measured in 2001 and 2007. Baseline conventional risk factors and systemic
metabolites were used to predict 6-year incidence of high IMT ( ≥90th percentile) or plaque. The best prediction of
high intima– media thickness was achieved when total and HDL cholesterol were replaced by NMR-determined LDL
cholesterol and medium HDL, docosahexaenoic acid, and tyrosine in prediction models with risk factors from the
Framingham risk score. The extended prediction model improved risk stratification beyond established risk factors
alone; area under the receiver operating characteristic curve 0.764 vs. 0.737, P¼0.02, and net reclassification
index 17.6%, P¼0.0008. Higher docosahexaenoic acid levels were associated with decreased risk for incident
high IMT (odds ratio: 0.74; 95% confidence interval: 0.67–0.98; P¼0.007). Tyrosine (1.33; 1.10– 1.60; P¼0.003)
and glutamine (1.38; 1.13– 1.68; P¼0.001) levels were associated with 6-year incident high IMT independent of
lipid measures. Furthermore, these amino acids were cross-sectionally associated with carotid IMT and the presence
of angiographically ascertained coronary artery disease in independent populations.
Conclusion High-throughput metabolite quantification, with new systemic biomarkers, improved risk stratification for subclinical
atherosclerosis in comparison to conventional lipids and could potentially be useful for early cardiovascular risk
assessment.
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Keywords Intima–media thickness †Risk factors †Lipoproteins †Tyrosine †Metabolomics
*Corresponding author. Tel: +358 40 7682 897 (O.T.R.)/ +358 40 1977 657 (M.A.-K.), Fax: +358 2 3337270 (O.T.R.)/ +358 9 19125737 (M.A.-K.), Email: olli.raitakari@utu.fi
(O.T.R.)/ mika.ala-korpela@computationalmedicine.fi (M.A.-K.)
Published on behalf of the European Society of Cardiology. All rights reserved. &The Author 2012. For permissions please email: journals.permissions@oup.com
European Heart Journal (2012) 33, 2307–2316
doi:10.1093/eurheartj/ehs020
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Introduction
Cardiovascular diseases (CVD) are the leading cause of death
worldwide. Current cardiovascular risk assessment rely on trad-
itional risk factors, including blood pressure (BP) and lipid levels;
however, these characteristics fail to fully explain cardiovascular
risk.
1,2
Novel biomarkers for screening and prognosis of CVD
have therefore recently attracted substantial interest.
3,4
Given
the metabolic nature of atherosclerosis, circulating metabolites
represent a source of biomarkers with potential to augment
risk prediction, but the clinical utility remains unknown.
4
Recent-
ly, comprehensive metabolic profiling identified five amino acids
associated with the risk of future diabetes.
5
In addition, technolo-
gies for high-throughput profiling of metabolic status can provide
insight into the pathophysiology of atherosclerosis.
6
Metabolite
quantification using serum nuclear magnetic resonance (NMR)
spectroscopy provides quantitative data on lipoprotein sub-
classes,
7
as well as a variety of small molecules and lipid consti-
tuents.
8
Increased subclinical atherosclerosis, as assessed by
carotid intima– media thickness (IMT), is a strong predictor of
future cardiovascular events.
9,10
The atherosclerotic processes
start early in life and take decades to develop into clinical
disease. Identification of novel biomarkers that could help to
predict the silent subclinical stage would therefore be valuable
for primary prevention.
11
Although the molecular mechanisms
may not be identical for the development of subclinical athero-
sclerosis and cardiovascular endpoints, the identification of bio-
markers associated with subclinical atherosclerosis is of clinical
importance. Here, we evaluate the associations of systemic meta-
bolites with 6-year incidence of high carotid IMT and/or plaque
for risk assessment of accelerated atherosclerosis processes in
a population-based cohort of apparently healthy young adults.
The aim was to assess whether high-throughput quantification
of circulating metabolites by NMR would add to prediction of
subclinical atherosclerosis in comparison to conventional lipid
testing in prediction models with established non-laboratory
risk factors.
Methods
Study sample
The Cardiovascular Risk in Young Finns Study representatively
selected 3596 children and adolescents aged 3 – 18 years in the
first survey in 1980.
11
One thousand seven hundred and seventy-
three participants attending follow-up surveys in both 2001 and
2007 were eligible for inclusion in the present longitudinal study.
An overview of the study design is shown in Figure 1. Participants
in the present study were representative of the original cohort.
12
All participants underwent physical examination, laboratory assess-
ment of risk factors, and ultrasound assessment of subclinical ath-
erosclerosis at both time points. Participants’ smoking status and
family history of CVD were ascertained from questionnaire, and
body mass index (BMI) and BP were measured. Triglycerides, total
cholesterol (total-C), HDL cholesterol (HDL-C), apolipoprotein
A1, apolipoprotein B, glucose, and high-sensitivity C-reactive
protein were assessed from fasting serum samples by standard
assays.
13
Pregnant women (n¼51), individuals on lipid-lowering
medication (n¼7), and individuals missing data (n¼24) at baseline
(2001) were excluded from analyses. The study complies with the
Declaration of Helsinki, all participants gave written informed
consent and the study was approved by the local Ethics Commit-
tees. For a more comprehensive methods section, refer to Supple-
mentary material online.
Ultrasound assessment
Ultrasound studies were performed using Sequoia 512 ultrasound
mainframes (Accustom, CA, USA) with 13.0 MHz linear array transdu-
cers. Carotid IMT was measured on the posterior wall of the left
common carotid artery. The mean of at least four measurements
taken ≈10 mm proximal to the bifurcation was used for carotid
IMT. Identical scanning protocols were used in 2001 and 2007. The
same single reader blinded to subjects’ clinical characteristics and
scan sequence manually analysed the digitally stored scans at both
time points. Atherosclerotic plaque was defined as a distinct area of
the vessel wall protruding into the lumen .50% of the adjacent
intima–media layer. Intra-individual reproducibility of ultrasound mea-
surements 3 months after the initial visit was 6.4%.
11
Figure 1 Study flow chart.
P. Wu¨rtz et al.2308
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Nuclear magnetic resonance spectroscopy
and metabolite quantification
Three NMR spectra were recorded from each serum sample on a
high-throughput NMR platform.
14
Two spectra were measured from
native serum and one from lipid extracts using a standardized protocol
(see Supplementary material online). Standard proton NMR spectra
were used to quantify 7 lipoprotein major lipid fractions and 14 lipo-
protein subclasses in mmoI/L and 18 small molecules by regression
modelling. An NMR spectrum of serum lipid extracts was recorded
to quantify 17 lipid constituent measures.
15
Coefficients of variation
and 6-year tracking of the biomarkers highlighted in this study are
given in Supplementary material online. Inter-correlations of the
assayed metabolites are shown in Supplementary material online,
Figure S1.
Cross-sectional associations of tyrosine
and glutamine with intima– media
thickness and coronary artery disease
in independent populations
Associations of tyrosine and glutamine with carotid IMT were assessed
cross-sectionally in the Health 2000 study, a population-based survey
with 1044 Finns of mean age 58 +8 years (range 46 – 76) who under-
went ultrasound sonography.
16
In addition, cross-sectional associations
of the amino acids were tested in the subset of individuals from the
Cardiovascular Risk in Young Finns Study who only attended the
follow-up surveys in either 2001 or 2007 (n¼830, mean age 34 +6
years). Methods of the Health 2000 study and clinical characteristics
of the individuals studied in the cross-sectional analyses are described
in Supplementary material online.
Associations of tyrosine and glutamine were further assessed with
angiography-ascertained coronary artery disease (CAD) in the Angiog-
raphy and Genes Study. This study consisted of 967 patients (mean age
62 +10 years) referred to coronary angiography because of chest pain
and clinically suspected CAD.
17
Coronary artery disease diagnosis was
defined by at least 50% stenosis of any major coronary artery and se-
verity of CAD by the number of arteries with .50% stenosis. Methods
of the Angiography and Genes Study and characteristics of the study
population are described in Supplementary material online. Amino
acid quantification was conducted with the same analytical platform
for all studies.
Statistical analyses
Risk factors and circulating metabolites measured in 2001 were used to
predict subclinical atherosclerosis at 6-year follow-up. A dichotomous
score representing increased subclinical atherosclerosis was defined as
incidence of carotid IMT ≥90th percentile (0.750 mm; high IMT) and/
or the presence of carotid plaque in 2007.
18
Individuals with high
carotid IMT or plaque at baseline (n¼86) and missing baseline IMT
data (n¼10) were excluded.
Baseline characteristics were compared using two-tailed t-tests for
normally distributed variables and Kolmogorov–Smirnov tests for vari-
ables with skewed distributions. Associations of individual metabolites
with 6-year incidence of high IMT or plaque were examined using lo-
gistic regression models adjusted for sex, age, systolic BP, BMI, smoking
status, and glucose by including these risk factors as covariates in the
regression models. To assess the prospective associations of non-
lipoprotein metabolite measures beyond established lipid risk factors,
regression models for small molecules and serum extract metabolites
were further adjusted for total-C, HDL-C, and triglycerides. In lack of
additional prospective data on carotid IMT, we tested cross-sectional
associations of the novel amino acid biomarkers, tyrosine and glutam-
ine, with carotid IMT using linear regression models adjusted for sex,
age, BMI, systolic BP, smoking, glucose, total-C, HDL-C, and triglycer-
ides, in an independent population from the Health 2000 study (see
Supplementary material online).
16
Here, variables with skewed distri-
bution were log
e
-transformed prior to analyses. In addition, we
tested associations of tyrosine and glutamine with the presence and se-
verity of CAD as determined by angiography in the Angiography and
Genes Study
17
using regression models adjusted for sex, age, BMI, sys-
tolic BP, smoking, total-C, HDL-C, triglycerides, as well as usage of dia-
betes and lipid-lowering medication.
Evaluation of prediction models
The incremental value of adding circulating metabolite biomarkers to
established risk factors for prediction of high IMT was examined
based on multivariate logistic regression models. The risk factors com-
posing the Framingham risk score
19
were used for the reference
model. Glucose was included as a continuous variable in the models
since less than 1% of the study population were diagnosed with dia-
betes. Because the Framingham risk score has been derived for cardio-
vascular endpoints, the prediction model was here calibrated to the
outcome of 6-year incident high IMT.
Since NMR-based metabolite profiling enables quantification of lipo-
protein measures similar to those obtained from standard lipid
testing,
7,14
the reference model was compared with an extended
model where metabolite measures were allowed to complement or
replace conventional lipid measures. The same non-laboratory risk
factors (sex, age, systolic BP, and smoking status) were included in
both prediction models. Lipid measures and metabolites with signifi-
cant associations (P,0.05) were included in backward stepwise selec-
tion (threshold P,0.05) for model derivation with non-laboratory
risk factors forced into the model. Missing metabolite data (0.4%)
were imputed using the non-linear iterative partial least-squares algo-
rithm for the extended prediction model.
20
The ability to discriminate risk was estimated using area under the
receiver operating characteristic curve (AUC). Comparisons of AUC
between the reference model and the extended model were estimated
using the DeLong algorithm.
21
Global fit of the models was compared
with log-likelihood ratio
x
2
and Akaike Information Criterion. Calibra-
tion of the models within risk deciles was assessed using the Hosmer–
Lemeshow (HL) goodness-of-fit, which compares the observed
number of events with those predicted from the model.
22
Net reclas-
sification index (NRI) and integrated discrimination index (IDI) were
calculated to determine the extent to which risk assessment using
the extended model reassigned individuals to risk categories that
more correctly reflected whether or not the study subjects developed
high IMT during the follow-up.
23,24
Participants were assigned to one of
four categories (,5, 5 – 10, 10 – 20, and .20%) according to their
6-year risk of incident high IMT/plaque based on the reference
model and the extended model. The proportions of participants cor-
rectly reclassified to either higher or lower risk categories were com-
pared. Integrated discrimination index represents a continuous variant
of NRI and is defined as the difference in mean discrimination slopes
between two models.
24
Risk prediction models were evaluated using 10-fold cross-validation
so that prediction of an individual’s risk was not influenced by his or her
own outcome status. The median of discrimination, reclassification,
global fit, and calibration metrics for 100 cross-validation repeats are
presented. Results for derivation of the predicted risk in one random
half of the study population and comparison of the predictive perform-
ance of the models in the other half of the population are presented in
Supplementary material online, Table S7. Because there is no clinical
Metabolomics for prediction of subclinical atherosclerosis 2309
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consensus on what signifies high IMT, we examined the predictive per-
formance of the models using alternate cut-points to define high IMT;
similar results were obtained (see Supplementary material online,
Figure S2).
The pre-specified hypothesis was improved discrimination and re-
classification of subclinical atherosclerosis by NMR-based lipoprotein
measures and metabolite biomarkers in comparison to conventional
lipids in prediction models with established non-laboratory risk
factors. Thus, although many P-values are reported, statistical signifi-
cance was inferred for two-tailed P,0.05. Statistical analyses were
performed with MATLAB 7.10 R2010a (MathWorks Inc., Natick,
MA, USA).
Results
A total of 1595 individuals had complete ultrasound and lipopro-
tein lipid data available and 150 developed IMT ≥90th percentile
and/or plaque during 6-year follow-up. Baseline characteristics
are provided in Table 1. Median levels of all quantified metabolites
are given in Supplementary material online, Table S1. Odds ratios
(ORs) for incident high IMT are shown in Table 2. Several lipopro-
tein lipid measures determined by NMR had higher ORs than the
conventional lipid measures. Considerable heterogeneity was
observed for HDL subclasses, where large HDL had the lowest
ORs, which was lower than that of HDL-C.
Three systemic amino acids were associated with incident high
IMT. Most prominent associations were observed for tyrosine
and glutamine, with ORs comparable to that of conventional
LDL-C. Since these two amino acids have not previously been
linked with subclinical atherosclerosis, we further tested the asso-
ciations cross-sectionally in an independent population and in a
subset of individuals from the Cardiovascular Risk in Young Finns
Study not eligible for prospective analyses. Both tyrosine and glu-
tamine were found to be associated with carotid IMT in these
cross-sectional analyses as shown in Table 3. To assess whether
tyrosine and glutamine would be linked with clinical manifestations
of atherosclerosis, we tested associations of the amino acids with
angiography-based diagnosis of CAD in an independent cohort.
Here, we found both tyrosine and glutamine to be associated
with the presence of CAD (P¼0.04). Elevated tyrosine levels
were further associated with increased severity of CAD as
defined by the number of major coronary arteries with more
than 50% stenosis (Table 3).
For the serum extract metabolites, esterified cholesterol and
polyunsaturated fatty acid levels were significantly associated
with 6-year incident high IMT. Docosahexaenoic acid, an v-3
fatty acid, was inversely associated with incident high IMT,
whereas linoleic acid, an essential v-6 fatty acid, was directly asso-
ciated with incident high IMT. Results for all assayed metabolites
are shown in Supplementary material online, Table S2.
Evaluation of prediction models
The predictive ability of the reference model (risk factors from the
Framingham risk score) was compared to the extended model
(same non-laboratory risk factors but lipid testing complemented
by NMR-based metabolite quantification). In derivation of the
extended prediction model, the NMR-determined lipid measures
LDL-C and medium HDL replaced conventional total-C and
HDL-C. Notably, no enzymatically measured lipids or apolipopro-
tein measures remained in the prediction models when
NMR-based lipid measures were included in the model selection.
In addition, docosahexaenoic acid and tyrosine were included in
the extended prediction model. Comparison of the prediction
models in terms of discrimination, reclassification, model fit, and
calibration is shown in Table 4. The extended model exhibited
enhanced risk discrimination and the improvement in AUC was sig-
nificant (P¼0.02). Receiver operating characteristic curves for the
prediction models are shown in Figure 2. Notably, the extended
model displayed a reclassification index of 17.6% (P¼0.0008),
...............................................................................................................................................................................
Table 1 Baseline characteristics
IMT <90th percentile (n51445) IMT
≥
90th percentile or plaque (n5150) P-value
Male sex (%) 42 (39–44) 63 (55–71) ,0.0001
Age (years) 31.5 (4.9) 34.3 (4.2) ,0.0001
Body mass index (kg/m
2
) 24.6 (4.1) 26.7 (4.9) ,0.0001
Systolic blood pressure (mmHg) 115 (13) 121 (13) ,0.0001
Current smoker (%) 21 (19–24) 22 (15–29) 0.88
Family history of cardiovascular disease (%) 13 (11–15) 19 (12–25) 0.05
Total-C (mmol/L) 5.1 (0.9) 5.5 (1.0) ,0.0001
LDL-C (mmol/L) 3.2 (0.8) 3.7 (0.9) ,0.0001
HDL-C (mmol/L) 1.3 (0.3) 1.2 (0.3) ,0.0001
Triglycerides (mmol/L) 1.1 (0.8–1.5) 1.2 (0.9–1.8) 0.02
Glucose (mmol/L) 5.0 (4.7–5.2) 5.1 (4.9–5.4) 0.0005
C-reactive protein (mmol/L) 0.7 (0.3–1.7) 0.8 (0.3–1.8) 0.63
Carotid intima– media thickness (mm) 0.56 (0.51–0.62) 0.65 (0.58–0.70) ,0.0001
Baseline characteristics according to incident carotid IMT ≥90th percentile or plaque at 6-year follow-up. Values are percentage (95% confidence interval), mean (SD), and
median (inter-quartile range), for categorical, normally distributed, and skewed variables, respectively. Characteristics were compared using t-tests and Kolmogorov – Smirnov
tests.
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Table 2 Odds ratios for 6-year incident carotid intima – media thickness
≥
90th percentile or plaque
OR 95% CI P-value
Lipoprotein and lipid measures (laboratory)
Total cholesterol 1.28 1.08 – 1.52 0.005
LDL cholesterol (Friedewald) 1.34 1.13 – 1.59 0.0007
LDL cholesterol (direct measure) 1.19 0.83 – 1.71 0.35
HDL cholesterol 0.79 0.64 – 0.97 0.03
Total triglycerides 1.10 0.95 – 1.29 0.21
Total-C/HDL-C 1.29 1.10–1.52 0.002
Apolipoprotein B 1.37 1.14– 1.65 0.0008
Apolipoprotein A-1 0.87 0.71 – 1.05 0.15
Apolipoprotein B/apolipoprotein A-1 1.33 1.11 – 1.59 0.002
Major lipoprotein fractions (NMR)
Total cholesterol 1.35 1.14 – 1.60 0.0005
IDL cholesterol 1.34 1.13 – 1.58 0.0006
LDL cholesterol 1.50 1.26 – 1.77 ,0.0001
HDL cholesterol 0.84 0.67 – 1.06 0.14
Total triglycerides 1.14 0.96 – 1.36 0.14
VLDL triglycerides 1.12 0.94 – 1.33 0.2
IDL triglycerides 1.10 0.93– 1.31 0.27
Lipoprotein subclasses (NMR)
Extremely large VLDL 1.03 0.88 – 1.21 0.69
Very large VLDL 1.08 0.92 – 1.27 0.34
Large VLDL 1.10 0.93 – 1.30 0.26
Medium VLDL 1.15 0.96 – 1.36 0.12
Small VLDL 1.34 1.12– 1.60 0.002
Very small VLDL 1.14 0.96 – 1.36 0.13
IDL 1.28 1.08–1.51 0.004
Large LDL 1.43 1.21 – 1.69 ,0.0001
Medium LDL 1.49 1.26 – 1.77 ,0.0001
Small LDL 1.45 1.21– 1.72 ,0.0001
Very large HDL 0.87 0.70 – 1.09 0.23
Large HDL 0.73 0.57 – 0.94 0.02
Medium HDL 0.80 0.65 – 0.99 0.04
Small HDL 1.08 0.90– 1.29 0.42
Small molecules (NMR)
a
Glutamine 1.38 1.13 – 1.68 0.001
Histidine 1.23 1.02–1.47 0.03
Tyrosine 1.33 1.10– 1.60 0.003
Serum extract metabolites (NMR)
a
Esterified cholesterol 1.38 1.03 – 1.85 0.03
v-6 fatty acids 1.29 1.01– 1.65 0.04
v-3/v-6 fatty acids 0.81 0.67– 0.98 0.03
Linoleic acid 1.32 1.05 – 1.65 0.02
Docosahexaenoic acid 0.74 0.59 – 0.92 0.007
Odds ratios (OR) and 95% confidence intervals (CI) for incidence of carotid IMT ≥90th percentile or plaque at follow-up (2007) according to metabolite measures at baseline
(2001). Odds ratios were adjusted for sex, baseline age, body mass index, systolic blood pressure, smoking status, and fasting glucose. Small molecules and serum extract
metabolite associations were further adjusted for total and HDL cholesterol as well as triglycerides. Values are expressed for 1-SD increase in the predictor variable.
a
Only metabolites with nominally significant associations are shown. Odds ratios for all assayed metabolites are given in Supplementary material online, Table S2.
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indicating that a greater number of individuals were reclassified
towards more appropriate risk categories than inappropriately re-
classified with the prediction model including metabolite biomar-
kers. The reclassification was most substantial among individuals
who did go on to develop high IMT during the 6-year follow-up
(NRI ¼14.0%) as detailed in Table 5. Also improvements in the
IDI were observed (IDI ¼2.9%; P¼0.00003), signifying a signifi-
cant change in the average predicted risk for the study population.
The extended prediction model displayed superior global fit than
the reference model (log-likelihood ratio
x
2
¼30) and lower
Akaike Information Criterion, supporting the conclusion that the
extended model yields improved risk prediction.
25
Calibration
was similar for the two models as evident from the HL statistic,
which indicates that both models are able to accurately predict
the absolute level of risk subsequently observed. Comparable
results were obtained when total-C and HDL-C were included
in derivation of the extended model (see Supplementary material
online, Table S4). The predictive performance was also essentially
similar using alternate percentile cut-points to define high IMT
(see Supplementary material online, Figure S1).
Alternative outcomes of subclinical
atherosclerosis
Results are presented here for 6-year incident high IMT and/or
plaque. Exclusion of individuals at high risk at baseline may poten-
tially cause selection bias; however, all results were similar for
prevalent high IMT and/or plaque at follow-up (see Supplementary
material online, Table S5). In addition, since plaque and high arterial
thickening represent different manifestations of subclinical athero-
sclerosis, these outcomes were also analysed separately (see
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Table 3 Cross-sectional associations of tyrosine and glutamine with carotid intima – media thickness and coronary
artery disease
Carotid IMT
a
Health 2000 Study Subset of the Cardiovascular Risk in Young Finns
Study not included in prospective analyses
Metabolite
b
(SE) [mm] P-value n
b
(SE) [mm] P-value n
Tyrosine 8.9 (4.7) 0.05 1033 6.2 (3.1) 0.02 823
Glutamine 10.1 (4.6) 0.02 1029 7.7 (3.3) 0.009 779
Coronary artery disease
b
Presence of coronary artery disease
(
≥
50% stenosis in major coronary
arteries) in the Angiography and Genes
Study
Number of vessels with angiographically ascertained
severe stenosis in the Angiography and Genes Study
Metabolite OR (95% CI) P-value n
b
(SE) [number of vessels] P-value n
Tyrosine 1.20 (1.01– 1.42) 0.04 944 0.097 (0.042) 0.02 944
Glutamine 1.21 (1.01– 1.44) 0.04 895 0.025 (0.043) 0.56 895
a
Linear regression models were adjusted for sex, age, body mass index, systolic blood pressure, smoking status, glucose, total-C, HDL-C, and triglycerides.
b
-correlation
coefficients (standard error) represent the increase in carotid IMT per 1-SD increase in amino acid concentration.
b
The risk for the presence and severity of coronary artery disease was analysed by logistic and linear regression models adjusted for sex, age, body mass index, systolic blood
pressure, smoking status, total-C, HDL-C, triglycerides, and usage of diabetes and lipid-lowering medication. Odds ratios and
b
-correlations are per 1-SD increase in amino acid
concentration.
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Table 4 Comparison of models for the prediction of 6-year incident carotid intima– media thickness
≥
90th percentile
or plaque based on discrimination, reclassification, model fit, and calibration
Model AUC 95% CI P
AUCa
NRI (%) P
NRIa
IDI (%) P
IDI
a
x
2b
Px2AIC HL P
HL
Reference model: age, sex, systolic BP,
smoking status, glucose, total-C, HDL-C
0.737 0.699– 0.775 — — — — — — — 918 10.5 0.24
Extended model: non-laboratory risk
factors,
c
glucose, LDL-C
NMR
, medium
HDL, docosahexaenoic acid, tyrosine
0.764 0.726– 0.802 0.02 17.6 0.0008 2.9 ,0.0001 30 ,0.0001 892 9.4 0.31
For NRI, participants were assigned to four categories (,5, 5 –10, 10 –20, and ≥20%) that reflected their 6-year risk of incident high IMT based on each model. Median values of
10-fold cross-validation with 100 repeats are shown. AUC, area under the receiver-operating characteristic curve; CI, confidence interval; NRI, net reclassification index; IDI,
integrated discrimination index; AIC, Akaike Information Criterion; HL, Hosmer – Lemeshow statistic.
a
P-values for comparison of the referent model with the extended model.
b
Log-likelihood ratio
x
2
between the two models.
c
Non-laboratory risk factors: age, sex, systolic blood pressure, and smoking status.
P. Wu¨rtz et al.2312
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Supplementary material online, Table S5). The ORs were higher for
plaque than for high IMT, reflecting that this outcome is a more
specific measure of early atherosclerosis. Of note, NMR-based
lipoprotein measures displayed stronger associations than conven-
tional lipids for both outcomes. In addition, tyrosine and glutamine
were associated with increased risk for both plaque and high IMT
at follow-up, despite the limited statistical power for analysis of
carotid plaque. Because accumulation of fatty streaks is a reversible
process, it is of interest to identify metabolites associated with
short-term progression as well as regression of carotid IMT.
26
Results for high progression (DIMT ≥80th percentile), respective-
ly, regression (DIMT ,0mm), in carotid IMT over the 6-year
follow-up period are presented in Supplementary material online,
Table S6. Small very-low-density lipoprotein (VLDL) and LDL mea-
sures were associated with both progression and regression of
IMT, whereas HDL measures were only associated with IMT pro-
gression. Tyrosine and glutamine were suggestively associated in an
adverse direction with both extremes of change in carotid IMT.
Finally, similar results were found in all analyses when excluding
individuals with diabetes and those on anti-hypertensive medica-
tion (data not shown).
Discussion
In this study of healthy young adults, high-throughput metabolite
quantification by NMR improved risk stratification for subclinical
atherosclerosis in comparison to conventional lipid testing as evi-
denced by increased discrimination and improved reclassification.
The extended risk prediction model was composed of a combin-
ation of lipoprotein lipids (LDL-C
NMR
and medium HDL), along
with the novel biomarkers docosahexaenoic acid and tyrosine
in addition to non-laboratory risk factors. While numerous
studies have identified biomarkers for CVD, the incremental
value of single biomarkers has rarely been shown to improve
risk stratification.
4,25
Our data on subclinical atherosclerosis
were consistent with these findings; no single metabolite alone
improved risk discrimination (data not shown). However, metab-
olite quantification from multiple pathways enabled by NMR and
mass spectrometry appears better able to capture the complex
nature of atherosclerosis development,
27
as least in the
Figure 2 Receiver-operating characteristic curves for 6-year
incidence of carotid intima– media thickness ≥90th percentile
or plaque for the reference model with risk factors from the Fra-
mingham risk score and the extended model with identical non-
laboratory measures, but conventional lipid measures replaced by
nuclear magnetic resonance-based lipid measures, docosahexae-
noic acid and tyrosine.
............................................................. .............................. .....................
...............................................................................................................................................................................
...............................................................................................................................................................................
Table 5 Reclassification of individuals based on 6-year risk for incidence of carotid intima–media thickness
≥
90th
percentile or plaque
a
Predicted
risk
Extended model with LDL-C
NMR
, medium
HDL, docosahexaenoic acid, and tyrosine
Reclassified Net
reclassification
index
0–5% 5–10% 10 –20%
≥
20% Up Down Value P-value
IMT ≥90th percentile or
plaque
Reference model
0–5% 10 (58.8%) 7 (41.2%) 0 (0%) 0 (0%) 40 (26.7%) 19 (12.7%) 14.0% 0.006
5–10% 6 (15.8%) 16 (42.1%) 16 (42.1%) 0 (0%)
10– 20% 0 (0%) 7 (11.9%) 35 (59.3%) 17 (28.8%)
≥20% 0 (0%) 1 (2.8%) 5 (13.9%) 30 (83.3%)
IMT ,90th percentile Reference model
0–5% 537 (89.5%) 62 (10.3%) 1 (0.2%) 0 (0.0%) 201 (13.9%) 253 (17.5%) 3.6% 0.01
5–10% 127 (30.5%) 204 (49.0%) 81 (19.5%) 4 (0.9%)
10– 20% 11 (3.6%) 81 (26.8%) 157 (52.0%) 53 (17.5%)
≥20% 2 (1.6%) 5 (3.9%) 27 (21.3%) 93 (73.2%)
Total 17.6% 0.0008
a
Comparison of models with conventional total-C and HDL-C vs. NMR-based LDL-C, medium HDL, docosahexaenoic acid, and tyrosine. Both models include sex, age, systolic
blood pressure, smoking, and glucose.
Metabolomics for prediction of subclinical atherosclerosis 2313
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preclinical stage as investigated here. The improved risk stratifica-
tion by NMR-based metabolite quantification over conventional
risk factors alone suggested in this study features a single experi-
mental platform. The cost of the high-throughput quantification
is comparable to that of conventional lipid testing and could po-
tentially represent a cost-effective approach for early cardiovas-
cular risk assessment.
The clinical value of lipoprotein subclass testing remains contro-
versial.
28,29
Although lipoprotein subclass measures by definition
are highly correlated with conventional lipid measures
7,15
(cf. see
Supplementary material online, Figure S1), these more detailed
measures provide a better reflection of the underlying biology
30
and appear useful to augment risk assessment in this study. Lipo-
protein subclasses, most notably small LDL, have been associated
with CVD risk in numerous studies.
31
While small LDL was asso-
ciated with incidence of 6-year high IMT, we found no evidence of
higher atherogenicity for small LDL than for other LDL subclasses
or LDL-C (Table 2). However, small LDL was included in the pre-
diction model if the conventional lipid measures (total-C and
HDL-C) were forced into the extended model (see Supplementary
material online, Table S4). In addition, we found significant associa-
tions for small VLDL and IDL, confirming their suspected role in
atherogenesis.
32
Interestingly, NMR-based lipoprotein measures
supplanted conventional lipid and apolipoprotein measures in
our prediction models, which might suggest that standard lipid
testing is not only complemented but could in effect be replaced
by NMR. The stronger associations observed for some
NMR-based lipoprotein measures, such as total-C and triglycer-
ides, than the corresponding conventional measures can be
ascribed to better accuracy in the NMR-based quantification
protocol.
33
The predictive ability of lipoprotein subclasses in relation to
carotid IMT has previously been evaluated only in a small cross-
sectional study that concluded that subclass testing does not
improve identification of subclinical atherosclerosis.
34
In the
present study with larger sample size, longitudinal data and a
wider metabolite spectrum assayed our results contrast this conclu-
sion. More recently, a large study of healthy females showed that
lipoprotein subclass profiling did not improve prediction of cardio-
vascular endpoints beyond conventional lipids.
7
Although the results
in the present study are based on surrogate markers of CVD, the
discrepancy may also be attributed to the combination of metabo-
lites assessed here rather than lipoprotein measures alone.
Metabolite quantification in a high-throughput manner repre-
sents a new analytical approach in cardiovascular epidemi-
ology.
5,6,27
Using a population-based cohort of young adults, our
results suggest that NMR-based quantification of circulating meta-
bolites is a useful tool for identification of biomarkers for subclin-
ical atherosclerosis. In addition to tyrosine and docosahexaenoic
acid, also glutamine exhibited associations with incident high IMT
in this study (P,0.01). While glutamine did not remain in the
extended prediction model, this amino acid has previously been
linked with CAD
27
and could potentially play a role in atherogen-
esis. Although no prospective validation of the amino acids with
6-year high IMT was available, both tyrosine and glutamine were
also cross-sectionally associated with carotid IMT in an independ-
ent population (Table 3).
Tyrosine is a non-essential amino acid and precursor of thyroid
hormones as well as catecholamine neurotransmitters. Tyrosine
was recently linked with the risk for the development of type 2 dia-
betes; however, the pathogenic mechanism behind this association
remains elusive.
5
Glutamine is the most abundant amino acid in
blood and an important precursor of glucose during fast. In add-
ition, glutamine is known to interact with arginine leading to inhib-
ition of nitric oxide release already at physiological concentrations,
which in turn impairs endothelial function.
35
Both tyrosine and glu-
tamine levels were only weakly correlated to conventional lipid
measures (see Supplementary material online, Figure S1);
however, these amino acids have been shown to be part of a prin-
cipal component which was a strong discriminator between obese
and lean individuals and linked with insulin resistance.
36
When
adjusting for HOMA-insulin resistance, the association of tyrosine
with 6-year incident high IMT was attenuated but remained signifi-
cant (OR ¼1.26; P¼0.02), whereas the association of glutamine
was essentially unaltered (OR ¼1.38; P¼0.001). In connection
to atherosclerosis, a recent metabolic profiling study on CAD
found circulating tyrosine to be a major part of a principal compo-
nent factor, which was independently associated with CAD.
27
Fur-
thermore, glutamine/glutamate levels were the most significant
metabolite discriminator between CAD and non-CAD patients
in that study.
27
These results are supported by the present study
where both tyrosine and glutamine were associated with diagnosis
of CAD in patients referred to angiography due to suspected CAD
(Table 3). The modest ORs suggest no diagnostic ability of the
amino acids; however, the findings nevertheless support a role of
tyrosine and glutamine in connection with the presence of CAD.
Furthermore, tyrosine was associated with the severity of CAD
in terms of the number of coronary arteries with a high degree
of stenosis. These results indicate that tyrosine and glutamine
levels may not only be markers of preclinical atherosclerosis, but
are also be associated with clinical manifestations.
The importance of the dietary mixture of unsaturated fatty acids
for cardiovascular prevention is well accepted.
37
The inverse asso-
ciations of systemic levels of the v-3 fatty acid docosahexaenoic
acid with high IMT support this relation already at the subclinical
stage of atherosclerosis where evidence has been inconclusive.
38
The mechanisms responsible for the protective effect of docosa-
hexaenoic acid are not well established, but triglyceride-lowering
and anti-inflammatory effects are thought to be implicated.
39
Other mechanisms are, however, likely to be implicated as well
as neither triglycerides nor C-reactive protein was associated
with subclinical atherosclerosis in this study. Our results suggest
that the systemic levels of polyunsaturated fatty acids could have
an important role in risk stratification.
The study population represents individuals who might undergo
lipid screening; however, the young age of the participants pre-
vented us from studying associations with cardiovascular end-
points. Despite the well-established correlation with clinical
outcomes,
9
IMT has a limited ability to capture the complexity
of atherosclerotic plaque development and rupture.
10
Neverthe-
less, biomarkers that predict the silent preclinical stage could
have value for primary prevention. We acknowledge that there is
no established definition of what constitutes clinically significant
high IMT. Using alternative definitions of incident high IMT did
P. Wu¨rtz et al.2314
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not modify the conclusions drawn (see Supplementary material
online, Figure S1). The study was conducted in a homogenous
population of healthy Finnish adults and care must be taken
before generalizing to other populations. The biomarkers identi-
fied in the present study yield novel insights into the molecular
aetiology of atherosclerosis; however, further validation in pro-
spective settings and with cardiovascular endpoints is warranted
before used for risk assessment strategies. NMR spectroscopy
represents a low-cost means for high-throughput profiling of meta-
bolites; however, the inherent low sensitivity of the analytical plat-
form limits quantification to highly abundant metabolites. In this
respect, the wider metabolite coverage achieved by mass spec-
trometry holds further promise both for risk assessment and dis-
covery of pathways linked to CVD processes.
40,41
On the other
hand, NMR compares favourably to mass spectrometry in terms
of ease of sample preparation, automation, reproducibility, and
the possibility for lipoprotein subclass profiling, and the two analyt-
ical methods can therefore be regarded as complementary.
42
In summary, NMR-based quantification of circulating metabolites
improved risk stratification of subclinical atherosclerosis in com-
parison with conventional lipid risk factors. We also identified sys-
temic levels of docosahexaenoic acid, glutamine, and tyrosine as
predictors of carotid IMT. More accurate risk assessment at an
early phase of atherosclerosis development obtainable with these
biomarkers has potential to benefit individualized treatment strat-
egies and prevention of cardiovascular events.
Supplementary material
Supplementary material is available at European Heart Journal
online.
Funding
This work was supported by the Academy of Finland (Grants 135973,
121584, 129269, 129429, 250422, Responding to Public Health Chal-
lenges Research Programme of the Academy of Finland), the Emil Aal-
tonen Foundation, the Finnish Foundation for Cardiovascular Research,
the Finnish Cultural Foundation, the Instrumentarium Science Founda-
tion, the Jenny and Antti Wihuri Foundation, the Paulo Foundation, the
Sigrid Juse
´lius Foundation, the Social Insurance Institution of Finland,
the Tampere and Turku University Hospital Medical Funds, and the
Turku University Foundation.
Conflict of interest: none declared.
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