Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts
European Journal of Epidemiology
Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts
Anna Floegel 0 1 2 3
Tilman Ku¨ hn 0 1 2 3
Disorn Sookthai 0 1 2 3
Theron Johnson 0 1 2 3
Cornelia Prehn 0 1 2 3
Ulrike Rolle-Kampczyk 0 1 2 3
Wolfgang Otto 0 1 2 3
Cornelia Weikert 0 1 2 3
Thomas Illig 0 1 2 3
Martin von Bergen 0 1 2 3
Jerzy Adamski 0 1 2 3
Heiner Boeing 0 1 2 3
Rudolf Kaaks 0 1 2 3
Tobias Pischon 0 1 2 3
Stroke 0 1 2 3
0 & Anna Floegel
1 University of Aalborg , Fredrik Bajers Vej 7H, 9220 Aalborg East , Denmark
2 Institute of Human Genetics, Hannover Medical School , Hannover , Germany
3 Institute for Social Medicine, Epidemiology and Health Economics, Charite ́ University Medical Center , Berlin , Germany
Metabolomic approaches in prospective cohorts may offer a unique snapshot into early metabolic perturbations that are associated with a higher risk of cardiovascular diseases (CVD) in healthy people. We investigated the association of 105 serum metabolites, including acylcarnitines, amino acids, phospholipids and hexose, with risk of myocardial infarction (MI) and ischemic stroke in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) and Heidelberg (25,540 adults) cohorts. Using case-cohort designs, we measured metabolites among individuals who were free of CVD and diabetes at blood draw but developed MI (n = 204 and n = 228) or stroke (n = 147 and n = 121) during follow-up (mean, 7.8 and 7.3 years) and among randomly drawn subcohorts (n = 2214 and n = 770). We used Cox regression analysis and combined results using meta-analysis. Independent of classical CVD risk factors, ten metabolites were associated with risk of MI in both cohorts, including sphingomyelins, diacyl-phosphatidylcholines and acyl-alkyl-phosphatidylcholines with pooled relative risks in the range of 1.21-1.40 per one standard deviation increase in metabolite concentrations. The metabolites showed positive correlations with total- and LDL-cholesterol (r ranged from 0.13 to 0.57). When additionally adjusting for total-, LDL- and HDL-cholesterol, triglycerides and C-reactive protein, acylalkyl-phosphatidylcholine C36:3 and diacyl-phosphatidylcholines C38:3 and C40:4 remained associated with risk of MI. When added to classical CVD risk models these metabolites further improved CVD prediction (c-statistics increased from 0.8365 to 0.8384 in EPIC-Potsdam and from 0.8344 to 0.8378 in EPIC-Heidelberg). None of the metabolites was consistently associated with stroke risk. Alterations in sphingomyelin and phosphatidylcholine metabolism, and particularly metabolites of the arachidonic acid pathway are independently associated with risk of MI in healthy adults.
Metabolomics; Myocardial infarction
A better understanding of the pathophysiological
mechanisms preceding the onset of cardiovascular disease (CVD)
events is crucial for development of preventive strategies
Rudolf Kaaks and Tobias Pischon are joint senior authors.
Extended author information available on the last page of the article
and treatment options. Thereby, particularly early
metabolic alterations that already occur in healthy individuals
may be identified as targets for measures to delay or
prevent disease onset. Metabolomic approaches that
simultaneously measure substrates, intermediate- and
endproducts of metabolism offer a unique snapshot of
metabolic perturbations that may be involved in the
development of CVD [
]. In this context, circulating metabolite
concentrations may be altered years before the onset of
Previous prospective metabolomic studies have
identified a number of metabolites linked to risk of CVD
recurrence or death in patient cohorts, as well as CVD risk
in high-risk populations [
]. They reported an altered
metabolism of acylcarnitines, ketone-related metabolites,
fatty acids, choline and its phospholipids in CVD patients
and high-risk individuals. One recent prospective study [
reported phenylalanine and fatty acids, and another study
] found other lipid species to be linked to CVD risk in
population-based cohorts. Ganna et al. [
] recently found
four lipid metabolites that could be useful to predict
coronary heart disease. These previous studies, however,
also had some limitations, e.g. they investigated few
metabolite classes or a composite CVD endpoint. Thus,
there is an urgent need for large metabolomic studies
covering a wide range of metabolites that are conducted in
healthy adult cohorts, which are followed over long for
incidence of a first CVD event. In addition, it is of great
importance to address different CVD endpoints separately,
so as to better understand their individual
The present study aimed to identify metabolites, which
are linked to higher incidence of myocardial infarction
(MI) and ischemic stroke in initially healthy adults.
Therefore, we conducted targeted metabolomic
measurements, including in total 105 metabolites among amino
acids, acylcarnitines, phosphatidylcholines,
sphingomyelins and hexose, in serum samples from two large
prospective cohorts comprising middle-aged adults from
Germany that were healthy at the time of the blood sample
collection and followed for development of a prime CVD
event. To better understand the biological mechanisms, in
addition, we studied associations between metabolites and
established biomarkers of CVD risk. To evaluate their
usefulness for clinical practice we also calculated measures
of risk prediction.
The present study is based on data from the European
Prospective Investigation into Cancer and Nutrition (EPIC)
Germany study, a prospective cohort study which includes
27,548 adults in Potsdam and 25,540 adults in Heidelberg
aged mainly between 35 and 65 years at time of
recruitment, when also the blood sample was collected [
people are prospectively followed for incidence of chronic
diseases including CVD. More general details about the
cohorts are provided in the Supplementary Methods.
We constructed two case-cohort studies, one in
EPICPotsdam and another in EPIC-Heidelberg, including all
incident cases of MI (n = 274 and n = 290; respectively)
and stroke (n = 260 and n = 220; respectively) that
occurred in the full cohorts until December 2006, after
mean follow-up time of 7.8 years (Potsdam) and 7.3 years
(Heidelberg), and two randomly drawn subcohorts
(Potsdam n = 2500, Heidelberg n = 843) from all participants
who had provided blood samples in the full cohorts. The
selection of the subcohorts has been described in detail
]. For the present analysis, the following
additional exclusion criteria were applied (Fig. 1): history
of CVD or diabetes mellitus at the time of blood donation
(to ensure that initially healthy adults were included),
nonverified incident CVD, non-ischemic incident stroke,
missing biomarker data or missing covariates. Thus, the
final study sample in EPIC-Potsdam consisted of 204
incident cases of MI and 147 incident cases of stroke, and a
random subcohort of n = 2214. From EPIC-Heidelberg,
228 incident cases of MI and 121 incident cases of stroke
were considered, in addition to a random subcohort of
n = 770.
Ascertainment of incident MI and stroke during follow-up
Potential incident cases of MI and stroke were identified
based on self-reports of a new diagnosis of disease, disease
relevant medication or change in diet due to disease, which
were collected every 2–3 years after baseline in follow-up
]. In addition, information from death
certificates or linkage to a hospital information system of
the major hospital in the area was considered. Follow-up
rounds resulted in response rates of about 95% of
]. Self-reported cases were further verified by
actively contacting the treating physician or hospital who
filled in a standard inquiry form that included information
on the exact type and date of diagnosis, the method of
confirmation of the diagnosis and treatment information.
We used the international classification of diseases
(ICD)10 system to classify incident cases: I21 for MI, I60 and
I61 for haemorrhagic stroke, I63 for ischemic stroke and
I64 for undetermined stroke.
Metabolite concentrations were determined in baseline
serum samples of the EPIC-Potsdam and EPIC-Heidelberg
case-cohort studies using two commercial kits
(BIOCRATES Life Sciences AG, Innsbruck, Austria). Blood
samples were stored in liquid nitrogen until analysis.
Sample preparation was done according to standardized
protocols and the metabolomic methods have been
described in detail elsewhere [
]. Details are provided
in the Supplementary Methods.
The p150 metabolomic kit used in EPIC-Potsdam
initially contained 163 metabolites, of them 14 amino acids,
41 acylcarnitines, 1 hexose, 92 glycerophospholipids
Participants with available blood sample (n=26444)
Incident cases MI (n=274) Stroke (n=260)
Random subcohort (n=2500, incl. 57 internal cases)
Participants with available blood sample (n=24236)
(n=843, incl. 8
History of CVD or diabetes
Non-verified cases of CVD
First event if multiple CVD
Missing biomarker data
Missing follow-up data
Missing covariate data
Incident cases MI (n=204) Stroke (n=147)
Random subcohort (n=2214, incl. 40 internal cases)
EPIC-Potsdam CVD case-cohort (n=2525)
Incident cases MI (n=228) Stroke (n=121)
Random subcohort (n=770, incl. 4 internal cases)
EPIC-Heidelberg CVD case-cohort (n=1115)
(phosphatidylcholines and lyso-phosphatidylcholines) and
15 sphingomyelins; a detailed list has been published
earlier by the authors [
]. A new version of the kit including
188 metabolites (similar metabolites but additionally
including biogenic amines) was used for measurement of
EPIC-Heidelberg samples. For the present analysis we
considered only metabolites that were measured in both
studies (n = 161). Data pre-processing was done as
previously described [
]. In brief, we excluded metabolite
species with more than 25% values below limit of
detection, with more than 25% missing values, and with batch
variation of more than 25%. All metabolite values were
log2 transformed and normalized by metabolite wide batch
]. Robust principal component analysis
was used to identify multivariable outliers, which were
]. This led to inclusion of a total of 105
metabolites, of them 13 amino acids, 2 acylcarnitines, 1
hexose, 77 glycerophospholipids and 12 sphingomyelins
into the present analysis.
Baseline characteristics of both study populations were
calculated as age- and sex-adjusted mean and standard
error (continuous variables) or percentages (categorical
variables). Serum metabolite concentrations were
standardized (mean of 0 and SD of 1), to make them directly
comparable, and log2 transformed to better approximate
the normal distribution; and serum metabolite
concentrations according to case status were calculated as geometric
mean and 95% confidence interval (CI).
In both case-cohort studies, we used Cox proportional
hazard regression with weighting as suggested by Prentice
] and robust sandwich covariance estimates to calculate
hazard rate ratios and 95% CI, considering serum
metabolite concentrations as the exposure variable and
diagnosis of MI or stroke as the outcome, with age of each
participant as the underlying time-scale from entry
(baseline) to exit time (diagnosis of MI or stroke or censoring or
death) in the study. We calculated a multivariable adjusted
model considering the following covariates: age; sex;
education (no degree/vocational training; trade/technical
school; university degree); smoking (never, former,
current B 20 cigarettes/day, current heavy [ 20
cigarettes/day); alcohol intake (non-consumers,
women: [ 0–6 g/day, 6–12 g/day, [ 12 g/day;
men: [ 0–12 g/day, 12–24 g/day, [ 24 g/day); physical
activity (Potsdam: cycling and sports in h/week;
Heidelberg: Cambridge physical activity index); fasting status (y/
n); waist circumference (cm); BMI (kg/m2); and prevalent
hypertension (y/n). P values were corrected for multiple
testing by controlling the false discovery rate [
]. We ran
separate analyses for each endpoint and each study
As the selection of metabolites very much depends on
the p value treshold, we used a different approach to
identify metabolites that does not so much rely on the
method used for multiple testing correction. We applied a
meta-analytical approach in the beginning and only
considered those metabolites that were associated with risk of
MI or stroke in both study populations. So reproducibility
of associations was our key selection factor for
identification of metabolites. For the meta-analysis, random effects
model were calculated and heterogeneity was assessed by
measures of I2 [
]. For the selected metabolites, we
calculated further Cox-regression models additionally
adjusting for intake of lipid lowering medication (statin and
fibrate intake) and established CVD biomarkers, including
total cholesterol, HDL-cholesterol, LDL-Cholesterol,
triglycerides and high-sensitivity C-reactive protein
(hsCRP). As LDL-Cholesterol was not measured it was
estimated from the other lipids using the Friedewald formula
]. In addition, we calculated Spearman partial
correlation coefficients between metabolites and established CVD
biomarkers, adjusted for age and sex. In a sensitivity
analysis, we calculated hazard rates across different
followup periods (B 3 years, 3–6 years, [ 6 years) for selected
metabolites. We then tested heterogeneity according to
Hardy and Thompson [
To investigate whether the metabolites are useful to
predict myocardial infarction, we calculated measures of
discrimination (c-statistic [
]) and calibration (Hosmer–
Lemeshow test [
]) for selected metabolites and
established CVD biomarkers with logistic regression models.
We drew receiver operating characteristic (ROC) curves
] for comparison of different models when adding
selected metabolites to established risk factors and
The meta-analysis was conducted in R (version 3.2.1)
using the Metagen-package. All other analyses were
conducted with SAS enterprise guide (version 6.1, SAS
Institute Inc., Cary, NC,USA).
Baseline characteristics of the study participants are
presented in Table 1. Mean age of participants from both
subcohorts was about 49 years. In general, participants
who developed CVD were older, less likely to be female
and lifestyle factors and biomarkers were more
unfavourable compared to the subcohorts.
Of the 105 metabolites, three metabolites in
EPICPotsdam and nine metabolites in EPIC-Heidelberg were
associated with risk of stroke at p \ 0.05 (Supplemental
Tables 1 and 2). None of them remained associated after
correction for multiple testing and none of them was
overlapping in both studies. Therefore, the endpoint stroke
was not further investigated.
Of all metabolites, 40 metabolites in EPIC-Potsdam and
15 metabolites in EPIC-Heidelberg were associated with
risk of MI at p \ 0.05 (Supplemental Tables 3 and 4).
After correction for multiple testing, 19 metabolites
remained. In both studies, ten metabolites were
consistently associated with risk of MI, including
diacyl-phosphatidylcholines C38:3 and C40:4;
acyl-alkylphosphatidylcholines C36:3, C38:3, C38:4 and C40:3; as
well as sphingomyelins C16:0, C24:0 and C16:1 and
hydroxy-sphingomyelin C22:1 (Fig. 2). All of these
metabolites were positively associated with risk of MI with
pooled relative risks in the range of 1.21–1.40 per 1 SD
increase in metabolite concentrations; and for all
metabolites there was no heterogeneity between the two studies. In
a sensitivity analysis, we found that the associations
between sphingomyelins and MI risk were stronger for
cases that occurred during the first 6 years of follow-up
(Supplemental Table 5). We did not observe any
We next quantified the correlation of the ten identified
metabolites with traditional CVD biomarkers and
examined to what extent adjustment for these traditional CVD
biomarkers affects the association between serum
metabolites and risk of MI. All metabolites were positively
correlated to total- and LDL-cholesterol with Spearman
correlation coefficients in the range of 0.13–0.57 (Fig. 3).
Diacyl-phosphatidylcholines C38:3 and C40:4 were
positively correlated with triglycerides (Heidelberg r = 53 and
r = 0.45; Potsdam: r = 0.44 and r = 0.30; respectively)
and diacyl-phosphatidylcholine C38:3 with hs-CRP
(Potsdam: r = 0.23; Heidelberg r = 015).
Acyl-alkyl-phosphatidylcholines showed a positive correlation with
HDLcholesterol (r ranged from 0.11–0.36). After adjustment for
LDL-cholesterol as well as total cholesterol the
associations between sphingomyelins and
acyl-alkyl-phosphatidylcholines and risk of MI were attenuated
(Supplemental Table 6), whereas the associations remained
a Presented are age- and sex-adjusted mean (standard error) for continuous variables or percentages for categorical variables
b Unadjusted mean (standard deviation) or percent
c Age-adjusted mean (standard error)
d Average of cycling and sports during summer and winter season
e LDL-cholesterol was estimated using the Friedewald formula [
for the diacyl-phosphatidylcholines. In contrast, additional
adjustment for HDL-cholesterol, triglycerides or hs-CRP
had minor impact on the associations between the
metabolites and risk of MI. Adjusting for all CVD
biomarkers simultaneously had a similar effect as adjusting
for total and LDL-cholesterol; higher concentrations of
diacyl-phosphatidylcholines C38:3 and C40:4, and in
addition acyl-alkyl-phosphatidylcholine C36:3 remained
associated with higher risk of MI in both study cohorts.
We studied these three metabolites in terms of risk
prediction. To better understand their individual
contribution, we first studied unadjusted models including one
biomarker at a time (Table 2). Diacyl-phosphatidylcholine
C38:3 performed best in discrimination with a C-statistic
alone of 0.636 and 0.630 in Potsdam and Heidelberg;
respectively; which was of higher magnitude than
c-statistics alone of total cholesterol, triglycerides and
hsCRP in EPIC-Potsdam. Diacyl-phosphatidylcholine C40:4
had a C-statistic of 0.607 in EPIC-Potsdam and 0.619 in
EPIC-Heidelberg, which was similar to total cholesterol;
and acyl-alkyl-phosphatidylcholine C36:3 had the lowest
c-statistic compared to all other biomarkers
(EPIC-Potsdam: 0.507; EPIC-Heidelberg: 0.544; respectively). All
three metabolites showed a good model calibration with
Hosmer–Lemeshow p values larger than 0.05. When
adding the metabolites to the basic adjustment model including
established CVD risk factors the area under the ROC-curve
could be improved from 0.826 (95% CI 0.798–0.854) to
0.828 (95% CI 0.800–0.857) in EPIC-Potsdam and from
0.824 (95% CI 0.791–0.858) to 0.832 (95% CI
(n = 121)
Fig. 2 Forest plot of
metabolites associated with risk
of myocardial infarction (MI) in
both study cohorts. Presented
are hazard ratios (HR) and 95%
confidence intervals for both
study cohorts and pooled
estimates from meta-analysis.
HR were calculated in
continuous models with
standardized log2 transformed
metabolite concentrations as
exposure and incidence of MI as
outcome. The model was
stratified by age and adjusted for
sex, alcohol intake, smoking,
physical activity, education,
fasting status, prevalent
hypertension, BMI, and waist
circumference. aa, diacyl; ae,
standard error risk estimate;
SM, sphingomyelin; TE, risk
estimate (beta coefficient); W,
0.799–0.865) in EPIC-Heidelberg (Supplemental
Figure 1). In comparison to the model with classical CVD risk
factors and established CVD biomarkers the areas under
the ROC curves where further improved when adding the
three metabolites [EPIC-Potsdam: from 0.836 (95% CI
0.810–0.863) to 0.838 (95% CI 0.811–0.866);
EPIC-Heidelberg: from 0.834 (0.802–0.867) to 0.838 (95% CI
The present study applied a targeted metabolomic approach
to two cohorts of apparently healthy middle-aged adults
who were followed on average over 7.5 years for incident
CVD. Thereby, higher serum concentrations of four
sphingomyelins and six phosphatidylcholines were linked to
higher risk of MI independent of classical CVD risk
factors. Of them diacyl-phosphatidylcholines C38:3 and
C40:4, and acyl-alkyl-phosphatidylcholine C36:3 remained
associated when additionally accounting for traditional
CVD biomarkers in both study populations, and were also
partly useful for CVD prediction. None of the studied
metabolites were consistently associated with stroke risk.
A previous prospective study reported that alanine as
well as medium and long-chain acylcarnitine levels
predicted CVD events in an elderly high-risk population [
A prospective patient cohort found high concentrations of
acylcarnitines, ketone-related metabolites and fatty acids
and low concentrations of branched chain amino acids to
be associated with higher risk of MI or death [
et al.  reported that dietary choline and gut microbiota
metabolism of phosphatidylcholines promotes CVD events.
These previous studies are not directly comparable to our
study as they have been conducted either in CVD patients
or in high-risk populations. In contrast, in our study we
included originally healthy individuals and followed them
over time until occurrence of a first incident CVD event.
Wu¨rtz et al. [
] recently reported that higher phenylalanine
concentrations were linked to higher CVD risk in
population-based cohorts. This was not observed in our study.
However, we previously observed that higher
phenylalanine levels were linked to higher risk of type 2 diabetes in
our population [
], which is a strong risk factor for CVD.
For the present study we focused on individuals without a
history of diabetes mellitus, which could be a reason for the
discrepancy to the study by Wu¨rtz et al.
Recently, Ganna et al. [
] found 4 lipid metabolites
including lyso-phosphatidylcholines and sphingomyelins
that were linked to risk of coronary heart disease when
investigating three population-based prospective cohorts.
In addition, lipid metabolites, including three
sphingomyelins and two phosphatidylcholines, were associated
with risk of a composite CVD endpoint in the
populationbased Bruneck cohort [
]. In agreement, we found
particularly higher concentrations of phosphatidylcholines and
sphingomyelins linked to higher risk of MI. These
metabolites have been previously suggested to be involved
in the pathophysiologic process of atherosclerosis that
often leads to the onset of CVD events. This process
involves enzyme actions of sphingomyelinase and
secretory phospholipase A2 that release free lipid species, such
Table 2 Measuresa of
discrimination and calibration to
predict risk of myocardial
infarction in EPIC-Potsdam and
EPIC-Heidelberg for individual
metabolites and biomarkers
a Presented are unadjusted models including one biomarker at a time. Better discrimination is mirrored by
larger C-statistics and better calibration is indicated by Homer–Lemeshow smaller v2 values and
p value C 0.05
b Specifically, the c-statistic equals the area under the ROC curve, a measure of discrimination that mirrors
the probability the model assigns a higher risk to future myocardial infarction cases compared to controls. It
may range from 0.5 (no discrimination) to 1.0 (perfect discrimination) [
c As a measure of model calibration, the Hosmer–Lemeshow statistic compares predicted and observed
probabilities of myocardial infarction derived from deciles of predicted risk. Smaller v2 values and larger
p values specify better model fit. P values \ 0.05 indicate difference between expected and observed
as fatty acids, lyso-phosphatidylcholines and ceramides,
which may further rupture vessel walls [
addition, these enzyme actions may cause severe modification
of LDL-particles, and thereby promote inflammatory
processes and ruptures at the vessel wall, which induce
monocyte emigration, differentiation and foam cell
formation, and may eventually result in atherogenic plaques
and thrombosis. It has previously been observed that
LDLparticles in atherogenic plaques were extensively enriched
with sphingomyelins compared to plasma LDL-particles
]. In addition, oral administration of an inhibitor of
sphingomyelin de-novo biosynthesis prevented
atherosclerosis in apo-E knockout mice [
In a randomized controlled trial it was observed that
treatment of CVD patients with statins led to lower plasma
concentrations of sphingomyelins, including C16:0 and
]. This is in line with our observation that
particularly sphingomyelins were linked to total cholesterol,
and that adjustment for cholesterol levels attenuated the
associations between two sphingomyelins and risk of MI.
Previous cross-sectional studies reported that plasma
concentrations of sphingomyelins were associated with
subclinical atherosclerosis and coronary artery disease
]. In addition, in a small cohort of patients with
acute coronary syndromes higher plasma sphingomyelin
levels were linked to a worse prognosis . However, the
Multi Ethnic study did not find an association between total
sphingomyelins and risk of coronary heart disease [
The present study identified several sphingomyelins that
were positively associated with risk of MI in two cohorts,
and it thus provides evidence for a prospective association.
High sphingomyelin concentrations were particularly
associated with high incidence of MI within the first
6 years of follow-up in the present study. These results
support the hypothesis that elevation of serum
sphingomyelin concentrations is linked to atherosclerosis, which
may trigger the onset of MI.
In the present study, diacyl-phosphatidylcholines C38:3
and C40:4 as well as acyl-alkyl-phosphatidylcholine C36:3
remained associated with risk of MI when accounting for
classical CVD risk factors as well as biomarkers. They
were also partly useful for CVD prediction, particularly
diacyl-phosphatidylcholine C38:3 which showed better
discrimination than total cholesterol, triglycerides and CRP
in EPIC-Potsdam. The identified metabolites have been
previously associated with risk of type 2 diabetes in the
EPIC-Potsdam cohort [
]. The three metabolites contain
fatty acids that are interlinked via desaturase and elongase
reactions (see Fig. 4). They may contain arachidonic acid
as fatty acid residue which is an omega-6 fatty acid and can
be released from the phospholipid by the enzymes
phospholipase A1 and A2. Arachidonic acid is a precursor
essential for eicosanoid biosynthesis such as prostaglandins
and thromboxanes which are inflammatory mediators with
various functions on the vascular system, which could be a
possible mechanism for their positive association with risk
of MI [
The null results that we observed for stroke may suggest
that the serum concentrations of the metabolites measured
in our study do not play a major role in the
pathophysiology of stroke risk. Metabolic changes after an acute stroke
event have previously been related to one-carbon-cycle
metabolism, anaerobic glycolysis and
]. In a recent investigation, low
concentrations of lyso-phosphatidylcholines predicted stroke
recurrence in TIA patients . In our EPIC-Heidelberg
study population, higher concentrations of three
lysophosphatidylcholines were also linked to lower risk of
stroke; however, this result was no longer observed after
multiple testing corrections and was not consistent in
EPIC-Potsdam. Thus, it has to be interpreted with caution.
A number of neuro-protective properties have been
suggested for lyso-phosphatidylcholines in vivo and in vitro,
such as that they can serve as a suppressant for lipoprotein
associated phospholipase A2 and thereby reduce its
neuroinflammative properties [
]. It has also been
reported that lyso-phsophatidylcholine levels increase in the
brain in response to an acute stroke event, to mediate
phagocyte recruitment; this could lead to reduced plasma
levels of lyso-phosphatidylcholines [
]. It is likely that
metabolite concentrations change rapidly in response to an
acute stroke event but this does not necessarily imply that
they were altered years before disease onset. Future
prospective studies are needed for further in depths
investigation of the prospective association between
metabolites and stroke risk.
Strengths and limitations
Strengths of our study include that we conducted
metabolomic measurements covering more than one hundred
metabolites in two large prospective cohort studies that
were well-phenotyped and followed over time for
incidence of CVD. As we used two cohorts, we were able to
directly replicate the results. We measured metabolite
concentrations in blood samples of originally healthy
adults and followed them until occurrence of a first CVD
event, whereas previous studies were focused on high-risk
populations. In addition, we investigated risk of MI and
However, the present study also had some limitations.
We were limited to study only those metabolites that were
included in the kit, and therefore might have missed
associations of other metabolite classes. To address this
limitation, targeted metabolomic studies that focus on other
metabolites and untargeted metabolomic studies with no a
priori assumptions should be conducted in the future.
Furthermore, we obtained only a single blood sample at
baseline in our studies, and metabolite concentrations may
change over time. However, in a previous study we found a
relatively high reliability of most of the metabolites
included in our study over 4 month and others reported a
high reliability over a 2-year period [
]. Due to
logistic reasons, participants of the EPIC-study did not
necessarily provide fasting blood samples. We addressed
this issue by adjusting for fasting status. In this study, we
did not look at the inter-correlation of metabolites. This
part has already been investigated in two previous studies
by the authors by applying principal component analysis
 and network analysis [
] to the same study
population. As this was an observational study we cannot prove
causality of the associations. However, we used a
prospective design that addresses the issue of temporality
of associations and we reproduced the results in different
populations. Still, the possibility of reverse causality needs
to be considered. We tried to account for this by stratifying
the analysis by follow-up intervals.
In summary, the present study identified novel candidates
of sphingomyelin and phosphatidylcholine classes that
were positively associated with risk of MI in healthy adults
in two prospective cohorts. Of them three metabolites, that
are involved in the arachidonic acid pathway, namely
diacyl-phosphatidylcholines C38:3, C40:4 and
acyl-alkylphosphatidylcholine C36:3, were associated with MI risk
independent of traditional CVD risk factors and
biomarkers, and were partly useful for CVD prediction. In contrast,
we found no association between serum metabolites and
risk of stroke. Based on their correlations with traditional
CVD biomarkers, the identified metabolites point towards
pathways of atherosclerosis and dyslipidaemia; and we
particularly highlight the arachidonic acid pathway;
however, future studies are needed to better understand these
fatty acid synthesis
PC ae C36:3
PC aa C38:3
PC aa C40:4
LysoPC a C20:4
OO P O-O
H OO P
Fig. 4 Schematic of the possible pathways of the association of
acylalkyl-phosphatidylcholine C36:3 and diacyl-phosphatidylcholines
C38:3 and C40:4 with risk of myocardial infarction. Fatty acid
synthesis involves enzymatic reactions catalyzed by desaturases and
elongases resulting in different chain length (e.g. C18) of different
desaturations (e.g. C18:2) along it. These fatty aids are used in lipid
biosynthesis and the same chains may appear in different molecules.
Some specific lipids like the acyl-alkyl phosphatidylcholines (e.g. PC
ae C36:3), diacyl phosphatidylcholines (PC aa C38:3) or
Acknowledgement We thank Martin Floegel for his support in
Funding This study was partly funded by Grants from the Helmholtz
Association as part of the portfolio topic ‘‘Metabolic Dysfunction’’.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
Ethical approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
Informed consent Informed consent was obtained from all individual
participants included in the study.
prostaglandines, thromboxanes, leukotriens,
COX, LOX, CYP
OH arachidonic acid C20:4
lysophosphatidylcholines (LysoPC a C20:4) are associated with MI.
In the following processes the fatty acids might be released from
lipids by the activities of phospholipases PLA2 or PLA1 (cleavage
sites is depicted by zigzag line). In further steps some of fatty acids
such as arachidonic acid (C20:4) are metabolized to oxilipins
(eicosanoids) by cyclooxygenases, lipooxygenases or cytochrome
P450 monooxygenases (COX, LOX, CYP respectively) to
prostaglandins, thromboxanes, leukotriens, or epoxyeicosatrienoic acids
mediating inflammatory processes
Open Access This article is distributed under the terms of the Creative
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commons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
Department of Epidemiology, German Institute of Human
Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
Leibniz Institute for Prevention Research and Epidemiology
– BIPS, Achterstraße 30, 28359 Bremen, Germany
Division of Cancer Epidemiology, German Cancer Research
Center (DKFZ), Heidelberg, Germany
Institute of Experimental Genetics, Helmholtz Zentrum
Mu¨nchen, German Research Center for Environmental
Health, Neuherberg, Germany
Department of Molecular Systems Biology, Helmholtz
Centre for Environmental Research (UFZ), Leipzig, Germany
Department of Food Safety, Federal Institute for Risk
Assessment, Berlin, Germany
Hannover Unified Biobank, Hannover Medical School,
Molecular Epidemiology Group, Max Delbru¨ck Center for
Molecular Medicine (MDC), Berlin, Germany
German Center for Cardiovascular Research (DZHK),
Partner Site Berlin, Berlin, Germany
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