Abstract
Background
Metabolomics helps to identify links between environmental exposures
and intermediate biomarkers of disturbed pathways. We previously
reported variations in phosphatidylcholines in male smokers compared
with non-smokers in a cross-sectional pilot study with a small sample
size, but knowledge of the reversibility of smoking effects on
metabolite profiles is limited. Here, we extend our metabolomics study
with a large prospective study including female smokers and quitters.
Methods
Using targeted metabolomics approach, we quantified 140 metabolite
concentrations for 1,241 fasting serum samples in the population-based
Cooperative Health Research in the Region of Augsburg (KORA) human
cohort at two time points: baseline survey conducted between 1999 and
2001 and follow-up after seven years. Metabolite profiles were compared
among groups of current smokers, former smokers and never smokers, and
were further assessed for their reversibility after smoking cessation.
Changes in metabolite concentrations from baseline to the follow-up
were investigated in a longitudinal analysis comparing current smokers,
never smokers and smoking quitters, who were current smokers at
baseline but former smokers by the time of follow-up. In addition, we
constructed protein-metabolite networks with smoking-related genes and
metabolites.
Results
We identified 21 smoking-related metabolites in the baseline
investigation (18 in men and six in women, with three overlaps)
enriched in amino acid and lipid pathways, which were significantly
different between current smokers and never smokers. Moreover, 19 out
of the 21 metabolites were found to be reversible in former smokers. In
the follow-up study, 13 reversible metabolites in men were measured, of
which 10 were confirmed to be reversible in male quitters.
Protein-metabolite networks are proposed to explain the consistent
reversibility of smoking effects on metabolites.
Conclusions
We showed that smoking-related changes in human serum metabolites are
reversible after smoking cessation, consistent with the known
cardiovascular risk reduction. The metabolites identified may serve as
potential biomarkers to evaluate the status of smoking cessation and
characterize smoking-related diseases.
Keywords: metabolic network, metabolomics, molecular epidemiology,
smoking, smoking cessation
Background
Smoking is responsible for 90% of all lung cancers, accounts for 25% of
cancer deaths worldwide [[62]1-[63]3] and is a significant risk factor
for cardiovascular disease (CVD) [[64]4-[65]7]. The benefits of smoking
cessation are remarkable. Risk of CVD is reduced in former smokers (FS)
compared with current smokers (CS) [[66]8-[67]10]; mortality and future
cardiac events both decline in FS [[68]11,[69]12]. Nevertheless, for
cancers, especially for adenocarcinoma, the risk remains high in FS
compared with never smokers (NS) [[70]13,[71]14]. Studies have made
attempts to find the molecular basis for the influence of smoking and
smoking cessation on cardiovascular risks. For instance, smoking is
associated with the increase of several CVD-related inflammatory
markers, for example, c-reactive protein and fibrinogen
[[72]15-[73]17], and smoking cessation could largely reduce the level
of these markers [[74]18]. However, there is also evidence that other
molecular changes associated with smoking are permanent, for example,
loss of heterozygosity and hypermethylation in the promoter regions of
cancer-related genes [[75]19-[76]23].
The metabolomics approach provides a functional readout of activities
located downstream of the gene expression level that are more closely
related to the physiological status [[77]24] and, thus, may be
particularly useful for the study of environmental influences, namely
the 'exposome' [[78]25]. Studying a strong environmental factor, for
example a lifestyle-related exposure to smoking, may be considered a
very powerful approach for understanding the links between
environmental exposure and the metabolome. In human lung epithelial
cells, it has been shown that metabolite concentration changes in
various pathways, for example, the urea cycle and polyamine metabolism
and lipid metabolism under smoke exposure [[79]26]. In a pilot study
with 283 male participants from the Cooperative Research in the Region
of Augsburg (KORA) F3 in Germany, we have shown that levels of
diacyl-phosphatidylcholines (PCs) were higher in 28 CS compared with
101 NS, except for acyl-alkyl-PCs [[80]1]. The reduced ratios of
acyl-alkyl-to diacyl-PCs in CS may be regulated by the enzyme
alkyl-dihydroxyacetone phosphate in both ether lipid and
glycerophospholipid pathways [[81]1]. However, little has been reported
about the reversibility of the metabolite profile upon smoking
cessation, which is important for comprehensive understanding of
smoking effects. It is also known that metabolite profile is different
between men and women [[82]25], but whether lifestyle factors such as
smoking may induce different metabolite patterns in men and women is
still unknown.
In this study, we analyzed the association between smoking and the
concentration of metabolites in 1,241 serum samples from the KORA
baseline survey 4 (S4) and follow-up (F4) study, aiming to extend the
knowledge of smoking-associated metabolites beyond our pilot study by
including female CS at two time points over seven years, to investigate
whether smoking-associated changes in metabolite profile are reversible
after smoking cessation, and to provide insights into the
pathophysiological consequences of smoking in protein-metabolite
networks.
Methods
Ethics statement
Written informed consent was obtained from KORA S4 and F4 participants.
The KORA study was approved by the ethics committee of the Bavarian
Medical Association in Munich, Germany.
Study population
The KORA surveys are population-based studies conducted in the Region
of Augsburg in Germany [[83]27,[84]28]. Four surveys were conducted
with 18,079 participants recruited from 1984 to 2001. The S4 consists
of 4,261 individuals (25 to 74 years old) examined from 1999 to 2001.
From 2006 to 2008, 3,080 participants (with an age range of 32 to 81
years) took part in the F4 survey. Each participant completed a
lifestyle questionnaire providing information on a number of parameters
including smoking status (current, former, never). Serum samples for
metabolomics analysis were collected in parallel in the KORA S4 and F4
survey as described elsewhere [[85]29-[86]31].
For metabolite profiles, serum samples from 1,614 people aged 55 to 74
years old were available [[87]29]. Participants with non-fasting status
(N = 216) or missing values (N = 22) were excluded from the analysis.
We further excluded 145 people in KORA S4 and 116 people in the
longitudinal data of KORA S4 → F4, whose spouses were CS, to rule out
passive smoking effects. Furthermore, metabolite concentrations of
serum samples from 1,036 participants were measured in both KORA S4 and
F4.
Metabolite measurements
Liquid handling of serum samples (10 μl) was performed with Hamilton
star robot (Hamilton Bonaduz AG, Bonaduz, Switzerland) and prepared for
quantification using the AbsoluteIDQ P180 and P150 kits (BIOCRATES Life
Science AG, Innsbruck, Austria) for the KORA S4 and F4 surveys,
respectively. This allowed simultaneous quantification of 188 or 163
metabolites using liquid chromatography and flow injection analysis
mass spectrometry as described previously [[88]32,[89]33]. The complete
analytical process was monitored by quality control steps, reference
samples and the MetIQ software package, which is an integral part of
the Absolute IDQ kit.
Because the two datasets were generated by different platforms,
different quality control processes were introduced. The metabolite
data quality control procedure for the KORA S4 samples was described in
our recently published work [[90]29]. There were 140 metabolites that
passed the two quality controls: one hexose, 21 amino acids, eight
biogenic amines, 21 acylcarnitines, 13 sphingomyelins (SMs), eight
lysoPCs, 33 diacyl-PCs (PC aa Cx:y) and 35 acyl-alkyl-PCs (PC ae Cx:y).
Lipid side chain composition is abbreviated as Cx:y, where × denotes
the number of carbons in the side chain and y the number of double
bonds. The precise position of the double bonds and the distribution of
the carbon atoms in different fatty acid side chains cannot be
determined with this technology. Concentrations of all analyzed
metabolites are reported in μmol/L (μM). The data cleaning procedure
for the KORA F4 samples has previously been described in detail
[[91]24,[92]30]. In total, 121 metabolites were measured in both S4 and
F4, and used for the prospective study.
Statistical analysis
Differences in population characteristics (CS, FS and NS) were tested
by a two-tailed student's t-test. The metabolite concentrations were
log transformed for normalization. We tested cross-sectional
association of each metabolite with smoking using logistic regression
models adjusted for age, body mass index (BMI) and alcohol consumption
(see Figure [93]1). To correct for multiple testing, false discovery
rate (FDR) was calculated using the Benjamini-Hochberg method [[94]34]
and the cut-off for statistical significance was set at FDR <0.05.
Figure 1.
[95]Figure 1
[96]Open in a new tab
Flow diagram illustrating the analysis strategy. CS: current smokers;
FS: former smokers; NS: never smokers.
Linear regression models were used to investigate whether smoking
intensities measured in pack years and cessation time are associated
with metabolite concentrations. In the case of CS, the years of smoking
were calculated as the time period from starting smoking until the
start of the survey. Pack year was calculated as the number of
cigarettes per day multiplied by smoking duration and divided by 20
[[97]35]. Cessation time (in years) was calculated according to the
questionnaire. The models contained the log-transformed metabolite
concentrations as the dependent variable and the smoking intensities as
the explanatory variable, with age, BMI and alcohol consumption as
covariates. Every unit change of one covariate corresponds to a
relative change of the metabolite concentration by Δ (%):
[MATH: Δ=(exp(β<
mrow>i)-1)×100% :MATH]
where β[i ]indicates the estimate of ith covariate in the model.
To assess the role of smoking cessation for the quitters, who were CS
at S4 but FS at F4, we fitted the linear mixed models to the
longitudinal data of KORA S4 → F4. The models contained the fixed
effect of smoking status (CS, FS and NS), age, BMI and alcohol
consumption with a random effect assigned to each participant. All
calculations were performed in R (version 2.14.1).
Network and pathway analysis
We retrieved protein-protein interactions from the databases of the
Search Tool for the Retrieval of Interacting Genes/Proteins [[98]36]
and the relationships between enzymes and metabolites from the Human
Metabolome Database [[99]37] to construct protein-metabolite networks
containing links between metabolites, enzymes and smoking-related
genes. Genes and metabolites were connected allowing for at most one
intermediate enzyme by Dijkstra's algorithm [[100]38], and optimized by
eliminating edges with Search Tool for the Retrieval of Interacting
Genes/Proteins scores less than 0.7. Each edge in the networks was
manually checked. We have implemented this method in our previous
studies [[101]29,[102]39]. The analysis was performed using the R
package igraph [[103]40]. The network was visualized using Cytoscape
[[104]41]. Pathway analysis was performed by MetaboAnalyst [[105]42].
Results
Characteristics of participants of the cross-sectional KORA S4
Participants were divided into three groups according to their
self-reported smoking status. Population characteristics are shown in
Table [106]1. On average, CS were two to three years younger and had a
lower BMI than FS and NS. Male CS showed higher alcohol consumption
than male NS, but there was no significant difference observed in
women. Furthermore, the statistics showed differences in lifestyle
factors between men and women. Alcohol consumption was higher in men
than women (P = 1.5e^-11 (CS); P = 2.2e^-18 (FS); P = 9.5e^-17 (NS)),
and smoking intensity (in pack years) was higher in male than in female
CS (P = 6.0e^-6).
Table 1.
Characteristics of cross-sectional KORA S4.
Current smoker Former smoker Never smoker P^a
__________________________________________________________________
Current versus former smoker Current versus never smoker
Male (N = 646)
N (%) 125 (19.3%) 321 (49.7%) 200 (31.0%)
Age (years) 62.2 ±5.3 65.3 ± 5.3 64.1 ± 5.6 7.9e^-08 3.0e^-03
BMI (kg/m^2) 27.0 ±3.6 28.9 ±3.6 27.8 ±3.4 1.5e^-06 6.5e^-02
Alcohol consumption (g/day) 27.5 ±29.0 24.1 ±24.3 20.5 ±21.3 0.25 0.02
Pack years^b 39.3 ±22.4
Quit time^c (years) 23.6 ±12.6
Female (N = 595)
N (%) 70 (11.8%) 130 (21.8%) 395 (66.4%)
Age (years) 61.3 ±5.2 64.0 ±5.2 64.6 ±5.3 7.5e^-04 5.9e^-06
BMI (kg/m^2) 27.2 ±4.5 28.7 ±5.0 28.5 ±4.6 0.029 0.02
Alcohol consumption (g/day) 6.5 ±10.9 10.0 ±12.8 7.5 ±11.1 0.042 0.48
Pack years^b 25.8 ±15.3
Quit time^c (years) 20.9 ±13.1
[107]Open in a new tab
The study characteristics of KORA S4 are shown separately for current,
former and never smokers. Values are shown as mean ±SD when
appropriate. ^aP-values are calculated by student's t-test;
^bcalculated as the number of cigarettes consumed per day × years of
smoking/20; ^c the time till the survey is conducted since the person
has stopped smoking. BMI: body mass index.
Metabolomic differences between current, former and never smokers
We identified 18 metabolites in men and six in women that were
significantly different (FDR <0.05) between CS and NS. Three
metabolites (PC ae C34:3, PC aa C36:1 and glutamate) were identified in
both men and women showing the same pattern of variation (higher or
lower) (Table [108]2). Compared with FS and NS, in male CS the
concentrations of four unsaturated diacyl-PCs (PC aa C34:1, PC aa
C36:1, PC aa C38:3 and PC aa C40:4) and five amino acids (arginine,
aspartate, glutamate, ornithine and serine) were higher, whereas three
saturated diacyl-PCs, one lysoPC and four acyl-alkyl-PCs, as well as
kynurenine showed lower concentrations. In female CS, we found higher
levels of carnitine and PC aa C32:1, and a lower level of
hydroxysphingomyeline (SM (OH)) C22:2.
Table 2.
Smoking-related metabolites in KORA S4.
Metabolites CS versus NS CS versus FS FS versus NS
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
Odds ratio (95% CI) P Odds ratio (95% CI) P Odds ratio (95% CI) P
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
Men (125 versus 200) (125 versus 321) (321 versus 200)
Arginine 1.7 (1.3, 2.2) 2.6e^-05a 1.3 (1.0, 1.6) 0.03^a 1.2 (1.0, 1.5)
0.03
Aspartate 1.6 (1.2, 2.0) 2.5e^-04a 1.4 (1.1, 1.7) 4.7e^-03a 1.1 (0.9,
1.3) 0.36
Glutamate 1.6 (1.2, 2.0) 6.2e^-04a 1.4 (1.1, 1.9) 0.02^a 1.0 (0.8, 1.3)
0.88
Ornithine 1.4 (1.2, 1.9) 2.2e^-03a 1.3 (1.1, 1.7) 8.3e^-03a 1.0 (0.9,
1.2) 0.78
Serine 1.4 (1.1, 1.8) 3.5e^-03a 1.2 (1.0, 1.5) 0.12 1.1 (0.9, 1.4) 0.25
Kynurenine 0.6 (0.5, 0.9) 3.2e^-03a 0.7 (0.5, 0.9) 2.3e^-03a 1.0 (0.8,
1.2) 0.88
PC aa C32:3 0.7 (0.5, 0.9) 6.4e^-03a 0.8 (0.6, 1.0) 0.07 0.9 (0.7, 1.0)
0.12
PC aa C34:1 1.7 (1.3, 2.2) 2.0e^-04a 1.7 (1.3, 2.2) 2.5e^-05a 0.9 (0.8,
1.1) 0.49
PC aa C36:0 0.6 (0.5, 0.8) 3.5e^-04a 0.6 (0.5, 0.8) 2.7e^-04a 1.0 (0.8,
1.2) 0.72
PC aa C36:1 1.6 (1.2, 2.0) 9.4e^-04a 1.6 (1.3, 2.0) 8.2e^-05a 0.9 (0.8,
1.1) 0.33
PC aa C38:0 0.7 (0.5, 0.9) 2.1e^-03a 0.6 (0.5, 0.8) 1.2e^-04a 1.0 (0.9,
1.3) 0.64
PC aa C38:3 1.5 (1.1, 1.9) 3.4e^-03a 1.3 (1.1, 1.7) 0.01^a 1.0 (0.8,
1.2) 0.85
PC aa C40:4 1.5 (1.2, 2.0) 3.4e^-03a 1.4 (1.1, 1.8) 3.6e^-03a 1.0 (0.8,
1.2) 0.86
PC ae C34:3 0.5 (0.4, 0.7) 3.3e^-06a 0.6 (0.5, 0.8) 6.0e^-05a 0.9 (0.7,
1.1) 0.23
PC ae C38:0 0.7 (0.5, 0.9) 2.1e^-03a 0.6 (0.5, 0.8) 6.7e^-04a 1.0 (0.8,
1.2) 0.94
PC ae C38:6 0.7 (0.5, 0.9) 4.8e^-03a 0.7 (0.5, 0.8) 6.6e^-04a 1.0 (0.8,
1.2) 0.97
PC ae C40:6 0.6 (0.5, 0.8) 8.8e^-04a 0.7 (0.5, 0.8) 8.9e^-04a 0.9 (0.8,
1.1) 0.33
lysoPC a C18:2 0.7 (0.5, 0.9) 3.3e^-03a 0.8 (0.6, 0.9) 0.046^a 0.9
(0.7, 1.1) 0.23
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
Women (70 versus 395) (70 versus 130) (130 versus 395)
carnitine 1.8 (1.4, 2.4) 4.3e^-05a 1.5 (1.1, 2.1) 0.01^a 1.1 (0.9, 1.4)
0.32
Glutamate 1.7 (1.3, 2.2) 1.2e^-04a 1.8 (1.3, 2.5) 1.1e^-03a 0.9 (0.7,
1.1) 0.17
PC aa C32:1 1.5 (1.1, 1.9) 2.1e^-03a 1.4 (1.0, 2.0) 0.03^a 1.1 (0.9,
1.4) 0.24
PC aa C36:1 1.6 (1.2, 2.0) 1.1e^-03a 1.5 (1.1, 2.0) 0.02^a 1.0 (0.8,
1.2) 0.87
PC ae C34:3 0.6 (0.4, 0.8) 7.7e^-04a 0.6 (0.4, 0.8) 2.5e^-03a 1.0 (0.8,
1.2) 0.94
SM (OH) C22:2 0.6 (0.5, 0.8) 2.1e^-03a 0.6 (0.4, 0.9) 4.9e^-03a 0.9
(0.7, 1.1) 0.35
[109]Open in a new tab
Results of pair wise comparison by logistic regression of metabolites
on smoking status adjusted for age, body mass index and alcohol
consumption. Men and women were analyzed separately. We present all
results with a false discovery rate (FDR) below 0.05 (in the comparison
between CS and NS, the FDR was calculated by P-value adjusted for all
140 metabolites; for CS versus FS and FS versus NS, the FDR was
calculated by P-value adjusted for the number of metabolites
significantly different between CS and NS). Smoking-related metabolites
found in both men and women are in bold. aa: diacyl-; ae: acyl-alkyl-;
CI: confidence interval; CS: current smokers; FS: former smokers; NS:
never smokers; PC: phosphatidylcholine; lysoPC:
acyl-phosphatidylcholine; SM (OH): hydroxysphingomyeline. ^aFDR <0.05.
Among the 21 smoking-related metabolites (18 in men and six in women),
19 were found to be reversible (that is, significant difference between
FS and CS but without significant difference between FS and NS; FDR
<0.05). No irreversible metabolite was observed (that is, significant
difference between FS and NS). Serine and PC aa C32:3 in men were not
classified because their concentrations were not significantly
different between CS and FS or between FS and NS (Table [110]2). A heat
map representing the concentration profiles of the 21 identified
metabolites in CS, FS and NS is shown in Figure [111]2, demonstrating
the reversibility of metabolites after smoking cessation.
Figure 2.
[112]Figure 2
[113]Open in a new tab
Heat maps of smoking-related metabolites in (A) men and (B) women. The
heat map shows mean residues of smoking-related metabolites in CS, FS
and NS and the reversibility after smoking cessation. The color of each
cell in the heat map represents the relative mean concentration of each
metabolite in NS, FS or CS. The number of samples in each group is
provided. The bar besides the metabolite names indicates the
reversibility of these metabolites after smoking cessation. aa:
diacyl-; ae: acyl-alkyl-; C0: carnitine; CS: current smokers; FS:
former smokers; lysoPC: acyl-phosphatidylcholine; NS: never smokers;
PC: phosphatidylcholine; SM (OH): hydroxysphingomyeline.
In women, SM (OH) C22:2 was significantly associated with cessation
time (FDR <0.05); however, there was no such significant metabolite in
men (Table S1 in Additional file [114]1), indicating a non-linear
relationship between cessation time and the reversion of metabolite
profile. In addition, we grouped the FS by stratified cessation years
(0 to 10, 11 to 20, 21 to 30, 31 to 40, over 40 years). For some
metabolites (for example, PC ae C38:0, PC aa C36:0 and ornithine), the
greatest change of concentration occurred within the first 10 years of
cessation compared with CS (Figure [115]3).
Figure 3.
[116]Figure 3
[117]Open in a new tab
Metabolite concentration variations in relation to smoking cessation
time. Taking NS as baseline, figures show the mean residuals of
metabolites in different groups of CS and FS, giving the trend of
metabolite variation with cessation time. FS were grouped by stratified
cessation time (≤10, 11 to 20, 21 to 30, 31 to 40, 41+). Residuals were
calculated by linear regression model (regression of metabolite
concentration on age, body mass index and alcohol consumption). aa:
diacyl-; ae: acyl-alkyl-; CS: current smokers; FS: former smokers; NS:
never smokers; PC: phosphatidylcholine.
Within CS, we found kynurenine and PC ae C34:3, PC ae C38:0 and PC ae
C38:6 in men, and PC aa C36:1 in women showing significant association
with pack years. In the linear regression model, pack years showed a
negative relation (parameter estimation β <0) to these five metabolites
(Table [118]3) (for example, one pack year increase will lead to a
decrease of the kynurenine level in CS by 0.33%).
Table 3.
Smoking intensity (pack years) related to metabolites
Metabolites β estimate of pack year Δ (%) P
(95% confidence interval)×10^-3
__________________________________________________________________
Men
Arginine -1.1 (-3.6, 1.4) -0.11% 0.38
Aspartate 2.9 (-1.4, 7.1) 0.29% 0.20
Glutamate 2.9 (-1.2, 6.9) 0.29% 0.17
Ornithine -2.4 (-5.2, 0.3) -0.24% 0.09
Serine 1.1 (-1.3, 3.6) 0.11% 0.37
Kynurenine* -3.3 (-6.1, -0.5) -0.33% 0.02
PC aa C32:3 -1.4 (-4.3, 1.4) -0.14% 0.33
PC aa C34:1 -0.9 (-3.5, 1.6) -0.09% 0.48
PC aa C36:0 -2.3 (-4.9, 0.4) -0.23% 0.09
PC aa C36:1 -1.4 (-4.6, 1.8) -0.14% 0.39
PC aa C38:0 -2.1 (-4.9, 0.7) -0.21% 0.15
PC aa C38:3 1.2 (-1.7, 4.1) 0.12% 0.43
PC aa C40:4 1.3 (-2.5, 5.1) 0.13% 0.51
PC ae C34:3* -3.7 (-6.4, -0.9) -0.37% 0.01
PC ae C38:0* -3.6 (-6.6, -0.5) -0.36% 0.02
PC ae C38:6* -2.6 (-5.1, -0.1) -0.26% 0.04
PC ae C40:6 -1.7 (-4.4, 1.0) -0.17% 0.22
lysoPC a C18:2 -3.1 (-6.5, 0.3) -0.31% 0.07
__________________________________________________________________
Women
Carnitine 1.1 (-4.3, 6.5) 0.11% 0.70
PC aa C32:1 0.2 (-10.5, 10.9) 0.02% 0.97
PC aa C36:1* 6.9 (0.6, 13.2) 0.69% 0.04
PC ae C34:3 -2.7 (-7.7, 2.2) -0.27% 0.54
SM (OH) C22:2 -2.8 (-7.7, 2.2) -0.28% 0.28
Glutamate 2.2 (-7.8, 12.2) 0.22% 0.67
[119]Open in a new tab
Results of linear regression of smoking intensity (pack years) on
metabolite concentrations in men and women, adjusted for age, body mass
index and alcohol consumption. All smoking-related metabolites
presented in Table 2 are listed (*P <0.05). aa: diacyl-; ae:
acyl-alkyl-; CS: current smokers; FS: former smokers; lysoPC:
acyl-phosphatidylcholine; NS: never smokers; PC: phosphatidylcholine;
SM (OH): hydroxysphingomyeline.
Prospective change of metabolite profiles (from KORA baseline S4 to follow-up
F4)
The prospective dataset included 40 CS, 432 NS and 49 quitters (people
who were CS in KORA S4 but FS in KORA F4) (Table [120]4). Among the 16
reversible metabolites in men, 13 (except kynurenine, glutamate and
aspartate) were also measured in KORA F4 using a different kit (see
Methods). We employed a linear mixed effect model to investigate the
effects of smoking cessation on metabolite concentrations. Among these
13 metabolites, 10 metabolites showed a significant variation in
quitters, with a period of smoking cessation from one to seven years,
which indicated a reverting process. The arginine level decreased by
11.3% and ornithine by 14.8% in quitters compared with CS, whereas PC
aa C36:0 increased by 18.5%. Figure [121]4 shows the prospective
changes of the significant metabolites. For women, the same analysis
was conducted. Because the number of female quitters was small (N =
10), five metabolites that were measured in both KORA S4 and F4 showed
borderline significance (P <0.05). However, none of these metabolites
was found to be significant considering FDR <0.05 (see Table [122]5).
Table 4.
Characteristics of the prospective dataset (KORA S4 → F4).
Current smoker Former smoker Never smoker
Men (N = 207)
N (%) 31 (15.0%) 30 (14.5%) 146 (70.5%)
Age at S4 (years) 60.2 ±5.3 63.0 ±5.0 63.0 ±5.5
Alcohol consumption (S4/F4)(g/day) 27.7 ±28.2/20.4 ±28.7 29.6
±31.6/19.3 ±21.1 22.2 ±22.8/20.2 ±19.5
BMI (S4/F4) (kg/m^2) 26.8 ±2.9/26.9 ±3.3 28.5 ±3.8/28.9 ±3.9 27.6
±3.3/27.8 ±3.4
__________________________________________________________________
Women (N = 314)
N (%) 18 (5.7%) 10 (3.2%) 286 (91.1%)
Age at S4 61.0 ±5.1 59.5 ±3.1 63.6 ±5.1
Alcohol consumption (S4/F4)(g/day) 7.6 ±11.6/7.4 ±11.8 4.7 ±6.7/10.7
±14.1 7.6 ±11.2/7.3 ±11.4
BMI (S4/F4) (kg/m^2) 27.9 ±5.1/27.7 ±5.3 26.9 ±3.9/27.4 ±5.1 28.6
±4.5/28.9 ±4.7
[123]Open in a new tab
Population characteristics were calculated based on 207 men and 314
women who participated in both the KORA S4 and F4 study. Values are
provided as mean ± SD. BMI: body mass index.
Figure 4.
Figure 4
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Changes of smoking-related metabolites in current, former and never
smokers in KORA S4 → F4. Taking the NS as baseline, the concentration
change of each metabolite is shown as the adjusted mean residue in KORA
S4 and F4 in all three groups (CS, FS and NS). Only metabolites with
significant prospective change in KORA S4 → F4 are shown in the figure.
Residuals were calculated from a linear regression model (regression of
metabolite concentration on age, body mass index and alcohol
consumption). aa: diacyl-; ae: acyl-alkyl-; CS: current smokers; FS:
former smokers; lysoPC: acyl-phosphatidylcholine; NS: never smokers;
PC: phosphatidylcholine.
Table 5.
Association of reversible metabolites with smoking status change in the
prospective dataset (KORA S4 → F4)
β estimate of smoking status
(95% confidence interval) P
Men
Arginine -0.12 (-0.18, -0.06) 1.4e^-04a
Ornithine -0.16 (-0.24, -0.08) 2.1e^-04a
PC aa C34:1 -0.09 (-0.15, -0.03) 3.3e^-03a
PC aa C36:0 0.17 (0.09, 0.25) 6.4e^-05a
PC aa C36:1 -0.12 (-0.18, -0.05) 8.5e^-04a
PC aa C38:0 0.14 (0.06, 0.22) 3.0e^-04a
PC aa C38:3 -0.04 (-0.11, 0.02) 1.7e^-01
PC aa C40:4 -0.11 (-0.18, -0.03) 6.0e^-03
PC ae C34:3 0.14 (0.06, 0.21) 3.5e^-04a
PC ae C38:0 0.13 (0.05, 0.21) 1.8e^-03a
PC ae C38:6 0.11 (0.04, 0.18) 1.5e^-03a
PC ae C40:6 0.08 (0.01, 0.15) 2.1e^-02
lysoPC a C18:2 0.03 (-0.06, 0.11) 5.2e^-01
__________________________________________________________________
Women
Carnitine -0.12 (-0.20, -0.05) 1.4e^-03
PC aa C32:1 -0.18 (-0.32, -0.03) 2.1e^-03
PC aa C36:1 -0.11 (-0.20, -0.02) 2.0e^-02
PC ae C34:3 0.09 (-0.02, 0.19) 0.95
SM (OH) C22.2 0.12 (0.02, 0.22) 1.9e^-02
[125]Open in a new tab
Result of smoking status on metabolite concentrations using linear
mixed model for S4 → F4 longitudinal data, adjusted for age, BMI, and
alcohol consumption. PC: phosphatidylcholine; aa: diacyl-; ae:
acyl-alkyl-; lysoPC: acyl-phosphatidylcholine; SM (OH):
hydroxysphingomyeline. ^a FDR<0.05.
Smoking effects on metabolic network
Enrichment analysis of the 21 identified smoking-related metabolites on
Kyoto Encyclopedia of Genes and Genomes pathways showed enrichment in a
set of amino acid and lipid metabolism pathways (ether lipid,
glycerophospholipid, arginine and proline metabolism). In addition, we
analyzed the impact of the smoking-related metabolites in each pathway
by measuring their structural importance (see Methods). These
metabolites had high betweenness centrality and a strong impact on the
enriched pathways (Figure [126]5 and Table S2 in Additional file
[127]2).
Figure 5.
[128]Figure 5
[129]Open in a new tab
Pathway analyses of smoking-related metabolites. Figure shows
enrichment and impact of smoking-related metabolites in Kyoto
Encyclopedia of Genes and Genomes pathways. The enrichment scores are
shown on y-axis, which was calculated as the negative logarithm of the
P-value from an enrichment test. The x-axis indicates the structural
impact with a score from 0 to 1 of the smoking-related metabolites in
the enriched pathways.
To systematically investigate how the effects of smoking propagate over
the metabolic networks, we evaluated the association between 175
smoking-related genes, previously reported [[130]23], and the 21
smoking-related metabolites we found in this study by analyzing
protein-metabolite networks (see Methods). In men, 15 metabolites
(lysoPC a C18:2, PC aa C32:3,PC aa C34:1, PC aa C36:0, PC aa C36:1, PC
aa C38:0, PC aa C38:3, PC aa C40:4, PC ae C34:3, PC ae C38:0, PC ae
C38:6, PC ae C40:6, arginine, glutamate and serine) were found to be
linked with 11 genes (ADH7, AKR1B1, DHRS3, FTL, GALE, GPC1, KRAS,
S100A10, SLC7A11, SULF1, PLA2G10) by related enzymes. In women, four
metabolites (PC aa C36:1, PC ae C34:3, PC aa C32:1 and glutamate) were
closely linked with nine genes (ADH7, AKR1B1, DHRS3, FTL, GALE, GPC1,
S100A10, SULF1, PLA2G10) (Figure [131]6A and Table S3 in Additional
file [132]3). Similar to enrichment analysis, the network in men and in
women could be generally divided into glycerophospholipids and tightly
associated proteins as well as amino acids and the associated genes and
enzymes. A description of the protein-metabolite and protein-protein
interactions was listed in Table S3 in Additional file [133]3.
Figure 6.
[134]Figure 6
[135]Open in a new tab
Protein-metabolite networks and pathways of the smoking-related
metabolites and genes. (A) Network linking metabolites and proteins
encoded by smoking-related genes with maximum one intermediate. Node
color indicates the reversibility after smoking cessation. (B, C)
Effects of smoking on arginine and glutamate as well as on lipid
metabolism. Metabolites are in regular font, protein coding genes are
in italic, gender-specific gene (CPS1) is in bold italic font. aa:
diacyl-; ae: acyl-alkyl-; APOA5: apolipoprotein A-V; BDH:
3-hydroxybutyrate dehydrogenase, type 1; cPLA2: cytosolic phospholipase
A2; CS: current smokers; FS: former smokers; GIIC sPLA2: phospholipase
A2, membrane associated; LRAT: lecithin retinol acyltransferase;
LYPLA1: lysophospholipase I; lysoPC: acyl-phosphatidylcholine; NOS1:
nitric oxide synthase 1; NS: never smokers; PC: phosphatidylcholine;
PLA2G10: group 10 secretory phospholipase A2; SCGB1A1: uteroglobin;
SDH: serine dehydratase; SLC3A2: solute carrier family 3 member 2
The smoking effects on the networks were reversible. With regards to
gene expressions, with the exception of SULF1 and PLA2G10, all changes
in the networks were reversible after smoking cessation [[136]23]. All
changes in metabolites in the network were also reversible, except
serine.
Discussion
In this study, we have used an 'omics' approach to investigate the
association of metabolite concentrations with smoking, delineated the
reversion of metabolite variations after smoking cessation and
demonstrated the results using protein-metabolite networks. We
identified strong associations of various metabolites with smoking, and
confirmed part of the findings of our pilot study [[137]1]. Among the
23 smoking-related metabolites identified in the pilot study, 11
metabolites were measured in this study, five of which (four
unsaturated diacyl-PCs and one acyl-alkyl-PC) were validated in men,
based on about five-fold larger CS samples. Consistent patterns of
smoking effects on metabolite profile were observed in the current
study. Among all the smoking-related metabolites, in CS we found higher
unsaturated diacyl-PCs, but lower acyl-alkyl-PCs and saturated
diacyl-PCs, which may indicate generally increased levels of
unsaturated fatty acids in CS. Unsaturated fatty acids are more
vulnerable to lipid peroxidation and influence the risk of different
diseases [[138]43,[139]44].
Smoking-related metabolites and cardiovascular disease
The study results implied the potential of metabolomics in revealing
the role of an environmental factor, for example a smoking lifestyle,
in the pathogenesis and prognosis of CVD.
One study on the peripheral blood metabolite profile showed an
association of coronary artery disease and urea cycle-related
metabolites, including arginine and glutamate [[140]45], which were
also identified in our study as smoking-related metabolites. By
scrutinizing the smoking-related metabolites in metabolic pathways, we
found further support for the pathophysiological relation between these
metabolites and CVD. Previous findings indicated that the glutamate
transporter in human lung epithelial cells, encoded by the SLC7A11
gene, is activated in CS [[141]23,[142]46], which increases the
transportation of glutamate and subsequently raises the levels of the
downstream metabolites, arginine and ornithine (Figure [143]6B). The
activation of the cysteine-glutamate transporter (encoded by SLC7A11)
and the increased glutamate level as a response to oxidative stress is
also of great importance to endothelial dysfunction involved at all
stages of atherosclerotic plaque evolution, which leads to CVD
[[144]47,[145]48].
Ether lipid and glycerophospholipid metabolisms are associated with
smoking [[146]1,[147]49]. The decreased level of lysoPC a C18:2
reflects the inhibition of upstream synthesis and activation of
downstream hydrolysis. As shown in Figure [148]6C, upregulation of
S100A10 and GPC1 inhibits cytosolic phospholipase A2, which plays a
role in the synthesis of lyso-PCs. The lysophospholipase I isoform,
which hydrolyses lysoPC into glycerophosphocholine, is upregulated in
CS [[149]23]. Interestingly, one recent study showed that a disorder of
phosphatidylcholine metabolism would promote CVD [[150]50], which may
establish a link between smoking-related phosphatidylcholine variation
and cardiovascular events. For example, the phosphatidylcholine
hydroperoxide will promote angiogenesis in endothelial cells that are
associated with atherosclerotic development [[151]51].
The reversibility of metabolite concentrations in a small time window
may reveal a reduced risk of smoking-related diseases after stopping
smoking. Concentrations of arginine and glutamate that are associated
with both smoking and coronary artery diseases quickly returned to
normal levels (within seven years) after smoking cessation, which is in
line with epidemiological findings that the smoking effects on CVD are
quickly and largely reduced after smoking cessation
[[152]8,[153]9,[154]52]. The reversed glutamate level indicates reduced
oxidative stress after smoking cessation, and the reversion of arginine
and ornithine reflects a reversion of functioning in the urea cycle.
Our findings provide metabolic insight into the reduced risk of CVD
after smoking cessation and provide support for the remarkable benefits
people would gain by stopping smoking.
Concordance of reversibility in metabolic network
The protein-metabolite interaction network shows that the reversibility
of metabolite concentrations also coincided with gene expression
(Figure [155]6A). Arginine and glutamate were quickly reversed after
smoking cessation, which was in line with the quick reversibility of
SLC7A11 expression. Expression of enzyme coding genes for the
hydrolysis of diacyl-PCs and acyl-alkyl-PCs, for instance
lysophospholipase, cytosolic phospholipase A2 and S100 calcium binding
protein A2, were quickly reversible and smoking-related diacyl-PCs and
acyl-alkyl PCs shared the same reverse pattern.
Gender-specific effects of smoking
In this study, we found gender-specific effects of smoking on
metabolite profiles (Table S1 in Additional file [156]1). This result
supports the assumption that differences in smoking effects on men and
women are not solely based on smoking intensity but are also
gender-specific. Glutamate was higher in both male and female CS,
however, the levels of arginine and ornithine were only higher in male
CS. According to a previous study of the metabolomic and genetic
biomarkers on sexual dimorphisms [[157]30], the CPS1 gene, which
regulates the formation of arginine, has a gender-specific manner in
certain single nucleotide polymorphisms, with stronger effects in women
than in men. The gender-specific genetic effect might cause a lower
efficiency in women in regard to the transformation of extra glutamate
to citrulline (Figure [158]6C).
Strengths and limitations
We used a systematic targeted metabolomics approach with 140
metabolites in a large population-based cohort. Analyzing the effects
of smoking and smoking cessation in this prospective manner (follow-up
of seven years) provides more power to investigate smoking effects by
ruling out individual differences. However, our study is based on a
limited range and number of metabolites and cannot fully represent the
whole metabolome. Thus, an improved metabolomics technique measuring
more metabolites is urgently needed for a comprehensive understanding
of both reversible and permanent effects of smoking on human
metabolism. It would be interesting for future studies to also include
data on other environmental factors such as diet and lifestyle, which
are known to have effects on the human metabolome [[159]53,[160]54].
Conclusions
Our study shows the power of the metabolomics approach in investigating
the molecular signature of lifestyle-related environmental exposures.
We demonstrated that smoking is associated with concentration
variations in amino acids, ether lipid and glycerophospholipid
metabolism at an 'omics' level. The smoking-related changes in the
human serum metabolite profile are reversible after stopping smoking.
This indicates the remarkable benefits of smoking cessation and
provides a link to CVD benefits. Furthermore, linking metabolomic
knowledge to other 'omics' approaches, for example, transcriptomics,
may have the potential to identify novel biomarkers as well as new risk
assessment tools.
Abbreviations
aa: diacyl-; ae: acyl-alkyl-; BMI: body mass index; CS: current
smokers; CVD: cardiovascular disease; FDR: false discovery rate; FS:
former smokers; lysoPC: acyl-phosphatidylcholine; NS: never smokers;
PC: phosphatidylcholine; SM: sphingomyeline; SM (OH):
hydroxysphingomyeline.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HEW, KS, JA, TI, AP and RWS initiated and designed the study. CP, WRM,
WR, HEW, KHL and JA were involved in and performed the experiment. TX
and ZY performed the data analysis. TX, CH, ZY and RWS wrote the
manuscript, XD, EB, CP, KP, MJ, YL, HW, FT, JA and AP revised the
manuscript. The manuscript has been approved by all authors.
Pre-publication history
The pre-publication history for this paper can be accessed here:
[161]http://www.biomedcentral.com/1741-7015/11/60/prepub
Supplementary Material
Additional file 1
Table S1: Cessation time-related metabolites in FS. FDR was calculated
by P-value adjusted for the number of smoking-related metabolites with
Benjamini-Hochberg method. aa: diacyl-; ae: acyl-alkyl-; C0: carnitine;
FS: former smokers; lysoPC: acyl-phosphatidylcholine; PC:
phosphatidylcholine; SM (OH): hydroxysphingomyeline.
[162]Click here for file^ (42.5KB, DOC)
Additional file 2
Table S2: Enrichment and impact of smoking-related metabolites in Kyoto
Encyclopedia of Genes and Genomes pathways. Table shows the enrichment
and impact scores of smoking-related metabolites in Kyoto encyclopedia
of Genes and Genomes pathways. The pathway analysis consists of
enrichment and a structural impact analysis both based on Kyoto
Encyclopedia of Genes and Genomes database. The -log (P) was considered
as the enrichment score. Impact, scored between 0 and 1, indicated the
pathway topological importance of the metabolites. In particular, the
parameter Total is the total number of compounds in the pathway; the
parameter Hits is the actual number of metabolites with significant
variations in the pathway; the Raw P was the original P-value
calculated from the enrichment analysis; the FDR was calculated as the
P-value adjusted using Benjamini-Hochberg method.
[163]Click here for file^ (56KB, DOC)
Additional file 3
Table S3: Links between smoking-related metabolites, enzymes and genes.
The table describes the links showed in Figure [164]6 of the main text.
The smoking-related metabolites, enzymes and genes are listed in the
first and second columns. The score of interaction is given according
to the definition by the Search Tool for the Retrieval of Interacting
Genes/Proteins [[165]1]. A reference for each link and a short
description is provided. The Column of reaction shows the possible
biochemical reaction of the corresponding link or the type of protein
interaction. The enzymes includes, phospholipase A2, membrane
associated (GIIC sPLA2), cytosolic phospholipase A2 (cPLA2), group 10
secretory phospholipase A2 (PLA2G10), lysophospholipase I (LYPLA1),
apolipoprotein A-V (APOA5), uteroglobin (SCGB1A1), lecithin retinol
acyltransferase (LRAT), nitric oxide synthase 1 (NOS1), solute carrier
family 3 member 2 (SLC3A2), serine dehydratase (SDH), 3-hydroxybutyrate
dehydrogenase, type 1 (BDH). The smoking-related gene/protein includes,
S100 calcium binding protein A10 (S100A10), glypican 1 (GPC1),
sulfatase 1 (SULF1), alcohol dehydrogenase 7 (ADH7), dehydrogenase
member 3 (DHRS3), aldose reductase (AKR1B1), acetoacetyl-CoA synthetase
(AACS), V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS),
solute carrier family 7 (SLC7A11) and three enzyme listed above,
PLA2G10, LYPLA1, SCGB1A1. The links in the network for male and female
CS are combined and listed together. Smoking-related genes are show in
italic. aa: diacyl-; ae: acyl-alkyl-; C0: carnitine; lysoPC:
acyl-phosphatidylcholine; PC: phosphatidylcholine; SM (OH):
hydroxysphingomyeline.
[166]Click here for file^ (130.5KB, DOC)
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Acknowledgements