Effects of cigarette smoking on cardiovascular-related protein profiles in two community-based cohort studies

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Effects of cigarette smoking on cardiovascular-related protein pro ﬁ les in two community-based cohort studies

Biying Huang

^a^,^b

, Per Svensson

^b

, Johan Arnl€ € ov

^c^,^d

, Johan Sundstr€ om

^c

, Lars Lind

^c

, Erik Ingelsson

^a^,^e^,^*

aDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94304, USA

bDepartment of Medicine, Solna, Karolinska Institutet, 17176 Stockholm, Sweden

cDepartment of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, 75185 Uppsala, Sweden

dSchool of Health and Social Studies, Dalarna University, 79188 Falun, Sweden

eDepartment of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, 75185 Uppsala University, Uppsala, Sweden

a r t i c l e i n f o

Article history:

Received 31 May 2016 Received in revised form 25 August 2016

Accepted 14 September 2016 Available online 15 September 2016

Keywords:

Smoking

Cardiovascular disease Proteomics

Epidemiology

a b s t r a c t

Background and aims: Cardiovascular diseases account for the largest fraction of smoking-induced deaths. Studies of smoking in relation to cardiovascular-related protein markers can provide novel in- sights into the biological effects of smoking. We investigated the associations between cigarette smoking and 80 protein markers known to be related to cardiovascular diseases in two community-based cohorts, the Prospective Study of the Vasculature in Uppsala Seniors (PIVUS, n¼969, 50% women, all aged 70 years) and the Uppsala Longitudinal Study of Adult Men (ULSAM, n¼717, all men aged 77 years).

Methods:Smoking status was self-reported and deﬁned as current smoker, former smoker or never- smoker. Levels of the 80 proteins were measured using the proximity extension assay, a novel PCR- based proteomics technique.

Results:We found 30 proteins to be signiﬁcantly associated with current cigarette smoking in PIVUS (FDR<5%); and ten were replicated in ULSAM (p<0.05). Matrix metalloproteinase-12 (MMP-12), growth/

differentiation factor 15 (GDF-15), urokinase plasminogen activator surface receptor (uPAR), TNF-related apoptosis-inducing ligand receptor 2 (TRAIL-R2), lectin-like oxidized LDL receptor 1 (LOX-1), hepatocyte growth factor (HGF), matrix metalloproteinase-10 (MMP-10) and matrix metalloproteinase-1 (MMP-1) were positively associated, while endothelial cell-speciﬁc molecule 1 (ESM-1) and interleukin-27 subunit alpha (IL27-A) showed inverse associations. All of them remained signiﬁcant in a subset of individuals without manifest cardiovascular disease.

Conclusions: Theﬁndings of the present study suggest that cigarette smoking may interfere with several essential parts of the atherosclerosis process, as evidenced by associations with protein markers rep- resenting endothelial dysfunction, inﬂammation, neointimal formation, foam cell formation and plaque instability.

1. Introduction

Smoking is a major threat to public health worldwide. It causes aboutﬁve million deaths every year according to WHO, accounting for 12% of total adult mortality [1]. The number of smoking- attributable deaths is estimated to increase to eight million every year within the next two decades[1]. Cardiovascular diseases (CVD) account for the largest fraction of total smoking-attributable deaths. In year 2000, about 1.6 million (11%) of cardiovascular deaths in the world were estimated to be attributable to smoking [2]. Previous studies indicate that cigarette smoke interferes with processes underlying atherosclerosis and thrombosis[3].

Abbreviations:CVD, cardiovascular disease; COPD, chronic obstructive pulmonary disease; PEA, proximity extension assay; PIVUS, Prospective Study of the Vasculature in Uppsala Seniors; ULSAM, Uppsala Longitudinal Study of Adult Men;

CRP, C-reactive protein; FDR, false discovery rate; MMP-12, matrix metalloproteinase-12; GDF-15, growth/differentiation factor 15; UPAR, urokinase plasminogen activator surface receptor; TRAIL-R2, TNF-related apoptosis-inducing ligand receptor 2; LOX-1, Lectin-like oxidized LDL receptor 1; HGF, hepatocyte growth factor; MMP-10, matrix metalloproteinase-10; MMP-1, matrix metalloproteinase-1; ESM-1, endothelial cell-speciﬁc molecule 1; IL27-A, interleukin-27 subunit alpha; PAD, peripheral artery disease; VSMC, vascular smooth muscle cell;

ECM, extracellular matrix.

*Corresponding author. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94304, USA.

E-mail address:[email protected](E. Ingelsson).

Contents lists available atScienceDirect

Atherosclerosis

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / a t h e r o s c l e r o s i s

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Most prior human proteomic studies investigating effects of smoking have been performed in patients with lung diseases such as chronic obstructive pulmonary disease (COPD), emphysema and lung cancer[4e7]. There is a lack of comprehensive analyses of CVD-associated proteins in relation to smoking patterns in adequately sized cohorts from the general population. In the present study, we used the proximity extension assay (PEA) with high sensitivity and high speciﬁcity [8] to study the association of smoking with 80 circulating cardiovascular-related proteins in two population-based cohorts. Compared to conventional assays such as ELISA, the PEA platform we used is more time-efﬁcient and sample-saving, since it enables simultaneous analysis of up to 92 proteins and only requires 1-ml sample volume per analyte[8].

The primary aim was to study associations of current smoking with protein markers, while the secondary aims were to assess if associations were present also in former smokers and if there was evidence of a dose-response effect, whether these associations differed by sex, and if they were similar when restricting analyses to individuals without prior CVD.

2. Materials and methods 2.1. Study samples

The PIVUS study has been described previously[9]and on the Internet (www.medsci.uu.se/pivus/pivus.htm). All 70-year old individuals residing in Uppsala, Sweden, from 2001 to 2005 were eligible for the study. The invitation was sent to 2025 individuals from the general population in a randomized order by mail, within two months of their 70^thbirthday. Among the invited individuals, 1016 (50.2%) participated and 510 (50.2%) of them were women. We excluded 25 individuals due to unsuccessful protein proﬁling (not passing the quality control), 21 individuals due to C-reactive protein (CRP) levels above 20 mg/L (to exclude individuals with possible ongoing infection and other inﬂammatory processes) and one individual due to missing information of smoking status.

Hence, the eligible sample size for the present study was 969 individuals.

The ULSAM study has been described previously[10]and on the Internet (http://www2.pubcare.uu.se/ULSAM/index.htm). The study was initiated in the period 1970e1973 by inviting all men living in Uppsala, Sweden, born between 1920 and 1924. The invitation letter was sent to 2841 men and 2322 (81.7%) of them participated in the initial examination. The participants have been re-examined at multiple time points. The present study was based on the re-examination at age 77, when 748 of the original participants had died, and another 176 men were not eligible for other reasons. From 1997 to 2001, the remaining 1398 individuals were re-invited, and 839 (60.0%) of them participated. We excluded 77 individuals due to unsuccessful protein proﬁling (not passing the quality control), 21 individuals due to CRP levels above 20 mg/L, and 24 individuals due to missing information of smoking status.

Hence, the eligible sample size for the present study was 717 men.

Both studies were approved by the Ethics Committee of Uppsala University in agreement with the Helsinki Declaration, and all subjects gave written informed consent.

2.2. Assessment of clinical covariates

The collection of clinical covariates was performed by similar and standardized methods in PIVUS and ULSAM. Basic information including medical history and regular medication was self-reported by answering a questionnaire. Other information including anthropometrical measurements, blood pressure and fasting blood samples was obtained by the investigators. All participants went

through an overnight fast prior to the investigation, during which neither smoking nor medication was allowed. Traditional lipid variables and fasting blood glucose were measured by standard laboratory techniques.

2.3. Deﬁnition of smoking exposure

The information of smoking status was self-reported through a questionnaire, and defined as current smoker, former smoker or never-smoker, in PIVUS and ULSAM. In both cohorts, an individual was defined as former smoker if he/she reported to ever have smoked regularly in his/her lifetime. The definition of former smoker in ULSAM was based on information from both the age 70 investigations (as the age 77 investigation only included questions about current smoking and smoking in the past five years). In addition, we also assessed smoking as pack years in PIVUS; one pack year was defined as an average consumption of one pack of cigarettes per day during one year.

2.4. Protein proﬁling analysis

Plasma samples (treated with EDTA) were collected in PIVUS, while serum samples were collected in ULSAM. All samples were stored in 80 C without any previous thawing until protein analysis. Candidate protein biomarkers were analyzed at the Clin- ical Biomarkers Facility, Science for Life Laboratory, Uppsala Uni- versity by staff being blinded to all data. We assessed 92 selected CVD-related proteins simultaneously using a high-throughput technique, the OLINK Proseek Multiplex CVD I⁹⁶⁹⁶ kit (www.

olink.com/products/proseek-multiplex/proseek-multiplex-cvd-i).

The kit used a proximity extension assay (PEA) including 92 oligonucleotide-labeled antibody probe pairs allowed to bind to their respective target present in the sample[8,11]. Compared to conventional multiplex immunoassays, the PEA technique has an exceptionally high specificity as only correctly matched antibody pairs are able to generate a signal. PEA is a homogeneous assay where the pairs of antibodies were equipped with DNA reporter molecules. After correct binding, the antibodies form new DNA amplicons each ID-barcoding their respective antigens. Further on, the quantification of the amplicons is performed on a Fluidigm BioMark™HD real-time PCR platform. The Proseek^®Multiplex CVD I⁹⁶⁹⁶has proved high reproducibility and repeatability. The mean intra-assay and inter-assay coefficients of variation are 8% and 12%, respectively, while the mean inter-site variation is 15% [8]. The output was presented as normalized protein expression (NPX) data where a high value corresponds to a high protein concentration, but not an absolute value of the protein concentration.

We removed twelve proteins from further analysis due to a call rate <85% in PIVUS and/or ULSAM. These proteins were IL-4, melusin, BNP, beta-NGF, SIRT2, NEMO, mAmP, PTX3, NT-pro-BNP, MMP-7, cystatin-B and heat shock 27 kDa protein. Thus, 80 proteins remained in our analysis (Supplementary Table 1). Individuals with excess missingness (more than 5% and 3% missing protein values in PIVUS and ULSAM, respectively) based on a histogram were excluded. Each protein was normalized by plate (by setting the mean¼0 and standard deviation¼1 within each plate) and further by storage time (correction based on the observed values and predicted values from a spline model).

2.5. Statistics

We evaluated the distribution of protein values and clinical covariates using the Shapiro-Wilk normality test and histograms.

Non-normality distributed variables were transformed using 1/x²- transformation for fasting glucose in both PIVUS and ULSAM, while

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pack years, alcohol consumption and physical activity were analyzed as quartiles in PIVUS. All protein variables and other covariates were considered to be normally distributed. All covariates considered as confounders that had missing observations were imputed using multiple imputation methods. The imputa- tions were based on all clinical characteristics in respective study (listed inTable 1). The PIVUS study wasa prioridesignated discovery cohort as it was larger and had equal proportions of males and females. For protein markers with a false discovery rate (FDR;

estimated using the Benjamini&Hochberg method[12])<5% in PIVUS, we attempted replication in ULSAM. We considered protein markers showing a nominally signiﬁcant p-value (p < 0.05) in ULSAM to be successfully replicated. We have previously shown that this replication strategy is conservative[13,14], and indeed, the risk of false positiveﬁndings in the replication stage (vFDR) was calculated to 0.4% using these methods (15), that are also available as an online application (http://fafner.meb.ki.se/personal/yudpaw/

rdr/).

Linear regression models were used for all analyses. In our primary analyses, smoking status defined as current smoker or non-smoker (i.e. former or never-smoker) was used as the independent variable, and the 80 proteins were used as dependent variables in separate models (one per protein marker). The analysis of the protein profiling data was performed in an age- and sex- adjusted model (only age in ULSAM as all participants were males), and a multivariable-adjusted model, in PIVUS and ULSAM, respectively. We considered the multivariable-adjusted models as our main models, both when deciding which protein markers to pursue for replication, and which to annotate as significantly associated. For the multivariable-adjusted model, confounders were considered using a directed acyclic graph (DAG;

Supplementary Fig. 1) informed by literature search. The fully adjusted model in PIVUS included age, sex, alcohol consumption, education level, body mass index, total daily energy intake, physical activity, fasting glucose level, history of CVD, COPD and diabetes mellitus as covariates. CVD was deﬁned as myocardial infarction, stroke and/or congestive heart failure. In ULSAM, the same covariates were used, except sex (all were males) and total daily energy intake (unavailable).

We also performed four sets of secondary analyses of the proteins that were signiﬁcantly replicated. First, we compared current smokers, former smokers and never-smokers (in three discrete

groups) to investigate whether smoking-induced effects remained after smoking cessation. Second, we investigated the dose- response relationships of smoking with protein levels, in current and former smokers within PIVUS, using pack years in quartiles as the independent variable and protein levels as dependent variables in separate models. Third, we performed analyses stratiﬁed on sex within PIVUS to look at gender-speciﬁc effects. Finally, we performed a sensitivity analysis where all individuals with previous CVD were excluded to indicate whether the associations were driven by existing atherosclerotic disease. All analyses were performed using STATA 14.1 (College Station, TX, USA).

3. Results

The clinical characteristics of the study samples are presented in Table 1. In PIVUS, 11% of the participants were current smokers (12%

of women and 10% of men) and 41% were former smokers (40% of women and 54% of men). ULSAM participants had similar proportions of current smokers (8%) and former smokers (41%).

3.1. Primary analyses

We found 30 protein markers to be significantly associated with current cigarette smoking at an FDR<5% in PIVUS (corrected overall criticalp<0.0183; Supplementary Table 1). When attempting to replicate thesefindings in ULSAM, ten protein markers remained significantly associated at a nominalp<0.05. These were matrix metalloproteinase-12 (MMP-12), growth/differentiation factor 15 (GDF-15), urokinase plasminogen activator surface receptor (uPAR), TNF-related apoptosis-inducing ligand receptor 2 (TRAIL-R2), lectin-like oxidized LDL receptor 1 (LOX-1), hepatocyte growth factor (HGF), matrix metalloproteinase-10 (MMP-10) and matrix metalloproteinase-1 (MMP-1) (positively associated), and endothelial cell-specific molecule 1 (ESM-1) and interleukin-27 subunit alpha (IL27-A) (inversely associated;Table 2).

3.2. Secondary analyses

The ten proteins associated with smoking in our primary analyses were further characterized in four sets of secondary analyses.

First, when comparing former smokers to never-smokers, MMP-12 and MMP-1 reached nominal significance (p<0.05) in PIVUS, but could not be replicated in ULSAM (Supplementary Table 2). Revis- iting all 80 proteins, none showed a significant association with previous smoking in the multivariable-adjusted model in PIVUS (FDR<5%; data not shown). When assessing dose-response of smoking on protein levels in current and former smokers, only GDF-15 was nominally significant (p< 0.05) in the main model (multivariable-adjusted), while MMP-12, GDF-15, uPAR and HGF were significant in the age- and sex-adjusted model (p < 0.05;

Supplementary Table 3).

We then proceeded to perform sex-stratified analyses to assess the role of sex on the associations between current smoking and protein levels in PIVUS. All ten proteins except MMP-1 were significantly associated (p<0.05) with smoking in women with similar beta coefficients as in the primary analysis. In men, all proteins except IL27-A were significantly associated (p < 0.05) showing similar beta coefficients (Supplementary Table 4). None of the ten proteins showed significantly different associations with current smoking between men and women in a formal sex- interaction test (p>0.05). In the sensitivity analysis excluding all individuals with manifest CVD, we confirmed all ten proteins to be significantly associated with current smoking in PIVUS (FDR<5%).

All of them were successfully replicated in ULSAM (p < 0.05;

Supplementary Table 5).

Table 1

Clinical characteristics of the study samples^a.

Variable PIVUS N¼969 ULSAM N¼717

Sex (% females) 50 0

Current smokers (%) 11 8

Former smokers (%) 41 41

HDL cholesterol (mmol/l) 1.5 (0.4) 1.3 (0.3)

LDL cholesterol (mmol/l) 3.4 (0.9) 3.5 (0.9)

Serum triglycerides (mmol/l) 1.3 (0.6) 1.4 (0.7)

Lipid lowering medication (%) 16 17

BMI (kg/m²) 27.0 (4.2) 26.3 (3.5)

Waist circumference (cm) 91 (11) 96 (10)

Waist/hip-ratio 0.90 (0.07) 0.93 (0.06)

Systolic blood pressure (mmHg) 150 (23) 151 (20) Diastolic blood pressure (mmHg) 79 (10) 81 (10)

Antihypertensive treatment (%) 31 42

Fasting glucose (mmol/l) 5.3 (1.5) 5.9 (1.3)

Prevalent diabetes (%) 8 8

History of myocardial infarction (%) 7 13

History of stroke (%) 3 10

History of congestive heart failure (%) 4 7

History of COPD (%) 8^b 4

aValues are presented as means (standard deviations) or proportions.

bHistory of asthma is included.

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4. Discussion

In the present study, we assessed associations of smoking with levels of 80 circulating CVD-related proteins in two Swedish population-based cohorts. Using a conservative approach including strict correction for multiple testing and independent replication, we identiﬁed ten proteins to be signiﬁcantly associated with cigarette smoking after taking possible confounders into account.

These proteins reﬂect different parts of the atherosclerotic process, including endothelial dysfunction, inﬂammation, neointimal formation, foam cell formation and plaque instability (Fig. 1).

Eight of the proteins were positively associated with smoking, while two were inversely associated. The associations were similar in men and women, and in a subset of individuals without manifest CVD.

4.1. Comparisons with previous studies

Among the ten proteins, GDF-15[15], uPAR[16], MMP-10[17], LOX-1[18]and HGF[19]have previously been shown to be associated with smoking patterns in population-based studies. Exper- imental studies investigating smoking-induced effects on cardiovascular-related proteins are sparse. Upregulation of MMP- 1 in human vascular endothelial cells has been observed following exposure to cigarette smoke condensate. [20] Other in vitrostudies found upregulation of GDF-15[21]and uPAR[22]in human airway epithelium to be associated with smoke exposure.

We are only aware of two prior proteomic studies assessing smoking in relation to proteins with high relevance for CVD, and both of these studies were performed in considerably smaller sample sizes than the present one. Faarvang and colleagues performed profiling of 45 proteins in arterial walls in 11 active smokers and 13 never-smokers, and found differences in extracellular matrix proteins between smokers and non-smokers, implicating a role for smoking in vascular remodeling[23]. Della Corte and colleagues performed mass spectrometry-based proteomics of platelets from 16 smokers and 16 non-smokers, all apparently healthy. Their analyses showed different levels of several proteins related to inflammation and apoptosis, suggesting that smoking may interfere with platelet aggregation and cytoskeleton remodeling[24]. To our knowledge, the present study is the first to investigate the relationship between smoking and many circulating protein

markers relevant for CVD in a large number of individuals from the general population.

Several of the ten proteins have been suggested to be either predictive of future cardiovascular events in asymptomatic individuals[16,25,26] and/or prognostic of clinical outcome in cardiovascular patients[16,25e28]. Besides, several of the proteins we identiﬁed to be associated with smoking have also been linked to a wide range of diseases such as cancer[29], COPD[30]and diabetes [31], reﬂecting other aspects of smoking-induced health effects.

Ourﬁndings in sex-stratiﬁed analyses and in individuals without manifest CVD suggest that smoking may play an important role in subclinical atherosclerosis during a long period of time in both men and women. We have previously studied the association of these proteins with plaque prevalence in carotid arteries in the PIVUS cohort.[32]MMP-12 and GDF-15 were associated with the number of carotid arteries with plaque, although none of them was significant after adjustment for established cardiovascular risk factors.

Asymptomatic peripheral artery disease (PAD; deﬁned as ankle- brachial index<0.9) is considered as another marker of subclinical atherosclerosis[33]. In fact, a large cohort study, enrolling nearly two million people, found smoking to be more strongly associated with incident PAD than myocardial infarction and ischemic stroke.

[34]Among the proteins we found to be associated with smoking, LOX-1[35]and MMP-10[36]are previously known to be linked to PAD.

We observed a lack of association of previous smoking with protein levels. This observation of a weaker association of former than current smoking is consistent with previous studies of the effects of smoking on cardiovascular health. For example, a recent meta-analysis reported more than two-fold and 1.4-fold increased risk of cardiovascular mortality in current and former smokers, respectively, when compared to never smokers, and the excess risk among former smokers decreased continuously with time since smoking cessation[37].

4.2. Biological mechanisms

Cigarette smoke is known to interfere with all major steps of atherosclerosis and thrombosis, although the exact underlying mechanisms are not fully understood [3]. The ten smoking- associated proteins found in the present study reﬂect essential parts of the atherosclerosis process, and helps elucidating the role Table 2

Proteins signiﬁcantly associated with current smoking in PIVUS and ULSAM^a.

Protein markers PIVUS (N¼969) ULSAM (N¼717)

Age- and sex-adjusted Multivariable-adjusted^b Age-adjusted Multivariable-adjusted^c

Beta 95% CI p-value Beta 95% CI p-value Beta 95% CI p-value Beta 95% CI p-value

MMP-12 0.74 0.55, 0.94 3.97E-13 0.72 0.52, 0.92 3.17E-12 0.50 0.23, 0.77 2.84E-04 0.43 0.15, 0.70 0.002

GDF-15 0.67 0.47, 0.87 3.86E-11 0.67 0.49, 0.86 3.90E-12 0.35 0.08, 0.62 0.010 0.43 0.16, 0.69 0.001

UPAR 0.68 0.48, 0.88 4.79E-11 0.71 0.50, 0.91 1.44E-11 0.56 0.30, 0.83 4.02E-05 0.52 0.24, 0.79 2.48E-04

TRAIL-R2 0.58 0.38, 0.78 1.93E-08 0.64 0.44, 0.84 3.82E-10 0.35 0.08, 0.61 0.011 0.38 0.11, 0.64 0.006

LOX-1 0.61 0.41, 0.82 2.83E-09 0.64 0.44, 0.84 1.06E-09 0.42 0.15, 0.69 0.002 0.35 0.07, 0.63 0.016

HGF 0.38 0.18, 0.58 2.62E-04 0.47 0.28, 0.66 2.18E-06 0.43 0.16, 0.69 0.002 0.41 0.14, 0.69 0.003

ESM-1 0.41 0.61,0.21 7.78E-05 0.49 0.69,0.28 3.92E-06 0.32 0.59,0.05 0.019 0.37 0.65,0.09 0.009

MMP-10 0.46 0.26, 0.66 9.84E-06 0.46 0.25, 0.67 1.50E-05 0.30 0.03, 0.56 0.031 0.29 0.01, 0.57 0.041

IL27-A 0.29 0.50,0.09 0.005 0.30 0.51,0.09 0.005 0.53 0.80,0.26 1.20E-04 0.56 0.84,0.28 1.08E-04

MMP-1 0.26 0.05, 0.46 0.014 0.26 0.05, 0.47 0.015 0.39 0.12, 0.66 0.005 0.33 0.05, 0.61 0.021

Beta, beta coefficient; CI, confidence interval; MMP-12, matrix metalloproteinase-12; GDF-15, growth/differentiation factor 15; UPAR, urokinase plasminogen activator surface receptor; TRAIL-R2, TNF-related apoptosis-inducing ligand receptor 2; LOX-1, lectin-like oxidized LDL receptor 1; HGF, hepatocyte growth factor; ESM-1, endothelial cell-specific molecule 1; MMP-10, matrix metalloproteinase-10; IL27-A, interleukin-27 subunit alpha; MMP-1, matrix metalloproteinase-1.

aListing only proteins that were signiﬁcantly associated in the discovery sample (PIVUS) at false discovery rate<5% (corresponding to an alpha of 0.0183), and successfully replicated in ULSAM with the same effect direction andp<0.05.

b Adjusted for age, sex, alcohol consumption, education level, body mass index (BMI), total daily energy intake, physical activity, fasting glucose level, history of CVD (MI, CHF and/or stroke), asthma, chronic bronchitis, COPD and diabetes mellitus.

c Adjusted for the same variables as in PIVUS except sex (as all were men) and total energy intake (as that information was unavailable).

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of smoking in the development of atherosclerosis. Impaired endothelial function in the initial phase of atherosclerosis can be induced by cigarette smoking [3,38], which is supported by increased levels of GDF-15, LOX-1 and ESM-1 in smokers in the present study. All three proteins have been shown to promote endothelial dysfunction in prior literature[15,27,39]. However, in contrast to the other proteins, ESM-1 showed association in the opposite direction and was signiﬁcantlylowerin current smokers.

This may reﬂect compensatory mechanisms following smoking exposure, or just highlight the limitation of any cross-sectional observational study, which cannot deﬁnitely disentangle causal mechanisms.

Further, cigarette smoking may induce inflammation [40], neointimal formation[41]and foam cell formation[42]during the progression of atherosclerosis. GDF-15 [43], LOX-1 [39], ESM-1 [44] and uPAR [41] are all involved in inflammatory processes, including recruitment of various inflammatory cells. IL-27 is an anti-inflammatory cytokine and showed an inverse association with smoking in the present study[45]. LOX-1[39], ESM-1[44], uPAR [41] and TRAIL-R2 [46]^, [47] mediate proliferation and migration of vascular smooth muscle cells (VSMCs) leading to neointimal formation. LOX-1[39], uPAR[41]and TRAIL-R2[48]all promote lipid accumulation in macrophages eventually resulting in the formation of foam cells. On the other hand, IL-27 (inversely associated in our study) induces cholesterol efflux from macrophages, and therefore inhibits foam cell formation.[49]Smokers have also demonstrated more vulnerable plaques which may explain their increased risk of thrombosis [3]. MMP-1, MMP-10 and MMP-12 induce plaque instability by degrading components in the extracellular matrix (ECM)[50,51], while uPAR may activate degradation of ECM indirectly[41]. Moreover, ESM-1[44]and HGF [52] are both involved in angiogenesis and may contribute to plaque neovascularization which in turn creates vulnerable plaques.[53].

4.3. Strengths and limitations

The strengths of the present study include the large study samples analyzed simultaneously using a highly sensitive and highly specific protein profiling technique, that made it possible to assess a wide range of protein markers known to be relevant to CVD. Further, strict multiple testing correction and replication in an independent study sample minimized the risk of false positive findings (estimated FDR in the validation sample was 0.4%). Our study also had several limitations. Since all study participants were elderly individuals of Scandinavian descent, the generalizability of ourfindings to other age and ethnic groups is unknown. As in any community-based study of the elderly, there is a risk of survival bias. The fact that more than 30% of the initial ULSAM participants had deceased before the age 77 investigation highlights this pos- sibility, which could lead to a surviving subset of individuals with lower risk of developing CVD. Further, we did not have information about snuff use, which potentially could act as a confounder of the observed associations. However, since our study sample consisted of elderly individuals (where snuff use is more uncommon[54]), and as the associations were similar in men and women (the latter have dramatically lower use of snuff[54]), we believe that it is unlikely that the associations were confounded to any larger extent.

Finally, although the nature of the exposure and outcome of this study makes reverse causation less likely and even though we have tried to adjust for the most plausible confounders, the fact that it is an observational, cross-sectional study means that no causal associations of smoking with the examined proteins can beﬁrmly established.

In conclusion, we found ten proteins reﬂecting essential parts of the atherosclerosis process to be associated with cigarette smoking.

Some of these proteins are more recently discovered and their roles in atherosclerosis are still largely unknown. Our study helps un- derstand different pathways by which smoking may increase risk of Fig. 1.Possible mechanisms connecting smoking and atherosclerosis highlighted by ourﬁndings.

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atherosclerosis and development of cardiovascular disease. Addi- tional studies are warranted to conﬁrm ourﬁndings in younger populations and other ethnic groups, to identify protein markers causally affected by cigarette smoke and to explore potential therapeutic targets of smoking-induced health effects.

Conﬂict of interest

The authors declared they do not have anything to disclose regarding conﬂict of interest with respect to this manuscript.

Financial support

This study was conducted with support from the Swedish Research Council (grant no. 2012-1397 and 2015-02907), G€oran Gustafsson Foundation, Swedish Heart-Lung Foundation (grant no.

20140422), Knut och Alice Wallenberg Foundation (grant no.

2013.0126), and the European Research Council (ERC-StG-335395).

Author contributions

Biying Huang made substantial contributions to the design of the study, drafted the manuscript and performed the analysis together with Dr. Ingelsson. Dr. Per Svensson, Dr. JohanArnl€€ ov, Dr.

Johan Sundstr€om and Dr. Lars Lind contributed to the study design and data collection. Dr. Ingelsson conceived and designed the study, oversaw the analysis and writing, and takes full responsibility for the data integrity, results and conclusions.

Acknowledgements

The authors would like to thank the participants of the PIVUS and ULSAM cohorts for their generous contribution.

Appendix A. Supplementary data

Supplementary data related to this article can be found athttp://

dx.doi.org/10.1016/j.atherosclerosis.2016.09.014.

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