Influence of smoking and smoking cessation on biomarkers of endothelial function and their association with mortality

Atherosclerosis 292 (2020) 52–59

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Influence of smoking and smoking cessation on biomarkers of endothelial function and their association with mortality

T

Graciela E. Delgadoa, Bernhard K. Krämera,b, Rüdiger Siekmeierc, Babak Yazdania, Winfried Märza,d,e, Jan Leipea,∗,1, Marcus E. Klebera,b,1 a

Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany b European Center for Angioscience ECAS, Medical Faculty Mannheim of the University Heidelberg, Mannheim, Germany c Drug Regulatory Affairs, Pharmaceutical Institute, Bonn University, Bonn, Germany d Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Graz, Austria e SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Augsburg and Mannheim, Germany

HIGHLIGHTS

exhibited higher concentrations of sICAM-1, sE-selectin, sP-selectin. • Smokers exhibited lower concentrations of sL-selectin and sVCAM-1. • Smokers was reduced even after 20 years of smoking abstinence. • sL-selectin was inversely associated with mortality in active smokers. • sL-selectin • sL-selectin significantly improved risk prediction in smokers. ARTICLE INFO

ABSTRACT

Keywords: Endothelial dysfunction Smoking Mortality sL-selectin

Background and aims: Endothelial dysfunction precedes atherosclerosis and smoking is a well-known risk factor for the development of endothelial dysfunction. The aim of our study was to analyse the effect of smoking on circulating markers of endothelial function and to investigate whether such effects have an influence on the potential use of these markers to estimate cardiovascular risk. Methods: Stratified for smoking, levels of sE-/sP-/sL-selectin, von Willebrand (vWF), sICAM-1 and sVCAM-1, their association with mortality using Cox regression, and their accuracy of risk prediction using area-under-theROC-curve and net-reclassification-index were analysed in 1926 participants from the Ludwigshafen Risk and Cardiovascular Health (LURIC) – a prospective case-control study in patients who underwent coronary angiography with a median mortality follow-up of 10.6 years. Results: In smokers, higher concentrations of sICAM-1, sE-selectin sP-selectin, but lower concentrations of sLselectin and sVCAM-1, were detected compared to never-smokers. A direct association with mortality was found for levels of sICAM-1, sVCAM-1 and vWF regardless of smoking. Low sL-selectin levels were inversely associated with mortality in heavy and light smokers, with hazard ratios of 0.72 and 0.67 per 1-SD increase, adjusted for cardiovascular risk factors. Adding sL-selectin to a model based on traditional risk factors significantly improved AUC from 0.725 to 0.752 (p = 0.034) with an NRI of 43% (16.9%–62.3%). Conclusions: Smoking alters the concentration of circulating markers of endothelial function. sL-selectin is decreased in smokers, inversely associated with risk, and could be a useful marker to improve risk prediction.

Corresponding author. Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. E-mail address: [email protected] (J. Leipe). 1 These authors contributed equally to this work. ∗

https://doi.org/10.1016/j.atherosclerosis.2019.11.017 Received 27 August 2019; Received in revised form 22 October 2019; Accepted 14 November 2019 Available online 15 November 2019 0021-9150/ © 2019 Elsevier B.V. All rights reserved.

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1. Introduction

Rhineland-Palatinate” (“Landesärztekammer Rheinland Pfalz”). All patients signed informed written consent before study start.

The vascular endothelium, a monolayer of cells covering the vascular lumen of all vessels, is currently regarded not just as a passive barrier between flowing blood and the vascular wall, but as a highly specialized tissue important for the maintenance of vascular haemostasis under physiological conditions [1,2]. Endothelial dysfunction precedes the morphological changes in the endothelium and is considered to be a precursor of atherosclerosis. Smoking is a well-known risk factor for the development of endothelial dysfunction and consequent damage [2,3] mainly by means of oxidative stress and inflammation [4–7]. Further, smoking was the second leading risk for both deaths (7.1 million) and disability-adjusted life years (DALYs, 182 million) globally in 2017, and between 1990 and 2017 the total number of smoking-attributable deaths has increased by 24.9% [8]. Acute adverse effects of smoking on endothelial function [6] as well as on leukocyte-endothelial adhesion molecules with upregulation of soluble ICAM-1 (sICAM-1) as the most prominent effect have been demonstrated. Other studies have shown that smoking cessation can induce a normalization of circulating adhesion profiles and reduction in risk [9] but data on long-term effects are scarce. The attachment of lymphocytes and monocytes to endothelial cells is usually initiated by soluble mediators (e.g. inflammatory cytokines), which can upregulate adhesion molecules on both leukocytes and the endothelium. Consequently, a multistep cascade mediated by a complex series of interactions between adhesion molecules and their specific ligands leads to loose adhesion of circulating leukocytes, rolling on, firm adhesion to, and transmigration across the endothelium. The first steps (leukocyte capture and rolling) are mediated by P-selectin (CD62P) expressed by endothelial cells, L-selectin (CD62L) expressed on leukocytes and Eselectin (CD62E) expressed by both, while the firm adhesion is mediated by intercellular adhesion molecule-1 (ICAM-1; CD54) and vascular cell adhesion molecule-1 (VCAM-1; CD106) expressed on endothelial cells and their respective ligands. During this process, soluble isoforms of adhesion molecules are shed from cell surfaces in concentrations reflecting the expression of membrane-bound adhesion molecules and the grade of inflammation of the vessel wall making them promising prognostic biomarkers of endothelial inflammation [10,11]. In patients with angina pectoris, an increase of soluble adhesion molecules, including sICAM-1, was shown, and blocking P-selectin was associated with decreased plasma troponin I levels (as a marker of myocardial ischemia) in a phase 2 trial [12], however, the prognostic value of soluble adhesion molecules in predicting (cardiovascular) mortality remains unknown. After transmigration into the vessel wall, leukocytes release various bioactive molecules, which initiate the development of lipid deposits and foam cells as well as proliferation of smooth muscle cells [13]. The aim of our study was to investigate the effect of smoking and smoking cessation on circulating markers of endothelial function and to elucidate whether these markers are associated with mortality in a large cohort of patients with middle-to-high cardiovascular risk that underwent coronary angiography.

2.2. Laboratory procedures Fasting blood samples were obtained by venipuncture in the early morning before the angiography. A detailed summary of analytic methods has been reported previously [14]. The lipoproteins were separated using a combined ultracentrifugation-precipitation method (βquantification). Cholesterol was measured with enzymatic reagents from WAKO on a WAKO 30 R or Olympus AU640 analyser. Triglycerides were quantified with an enzymatic assay on a Hitachi 717 analyser (Roche). NT-pro-BNP was measured by electro-chemiluminescence on an Elecsys 2010 (Roche Diagnostics). Serum cotinine was measured by a radioimmunoassay (Nikotin Metabolit RIA, DPC Biermann). Highsensitive C-reactive protein (hsCRP) was measured by immunonephelometry (N-High-Sensitive CRP, Dade Behring). Soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1) were determined using ELISA assays (R&D Systems). sE-selectin, sP-selectin and sL-selectin concentrations were measured using the Human sE-selectin, sP-selectin and sL-selectin assays (R&D Systems), respectively. Von Willebrand factor antigen was determined using the STA Liatest vWF (Stago Diagnostica/Roche). Cellular adhesion molecules were only measured in a subset of LURIC samples (N = 1945) and those were used for the statistical analyses. 2.3. Definition of clinical variables and endpoints The presence of a visible luminal narrowing (> 20% stenosis) in at least one of 15 coronary segments was used to define coronary artery disease (CAD) according to the classification of the American Heart Association [14]. Diabetes mellitus was defined according to the 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD [15] as increased fasting (≥126 mg/dl) and/or post-challenge (2 h after the 75 g glucose load > 200 mg/dl) glucose and/or elevated glycated hemoglobin (≥6.5%) and/or history of diabetes. Hypertension was defined as a systolic and/or diastolic blood pressure ≥140 and/or ≥90 mm Hg or a history of hypertension. Smoking status was assessed based on a questionnaire and verified by measurement of serum cotinine concentration. A commonly used cut-off to define active smoking is 15 μg/L [16,17] and we used this value to reclassify self-reported non- or exsmokers as active smokers. For 19 smokers the amount of smoking, defined as cigarettes smoked per day, was not available and these were excluded leading to a final sample size of 1926. This included 682 (35.4%) never-smokers, 827 (42.9%) ex-smokers and 417 (21.7%) active smokers. Information on vital status was obtained from local registries. Death certificates, medical records of local hospitals, and autopsy data were reviewed independently by two experienced clinicians who were blinded to patient characteristics and who classified the causes of death. In cases of disagreement or uncertainty concerning the coding of a specific cause of death the decision was made by a principal investigator (W.M.). Information for vital status is complete for all participants but the cause of death of 10 deceased was unknown and these patients were included in calculations of all-cause mortality but not in calculations considering different causes of death.

2. Patients and methods 2.1. Subjects The Ludwigshafen Risk and Cardiovascular Health (LURIC) study included 3316 individuals who had been hospitalized for coronary angiography at the Klinikum Ludwigshafen, a tertiary care center in Southwestern Germany [14]. Clinical indications for angiography were chest pain or a positive non-invasive stress test suggestive of myocardial ischemia. Individuals suffering from acute illnesses other than acute coronary syndrome, chronic non-cardiac diseases and a history of malignancy within the past five years were excluded. The study was approved by the ethics committee at the “State Chamber of Physicians of

2.4. Statistical procedures Study participants were categorized into five groups: heavy smokers (defined as smoking ≥ 20 cigarettes per day), light smokers, former smokers that quit smoking less than 20 years ago, former smokers that quit smoking more than 20 years ago and never-smokers. Continuous variables were compared between groups using ANOVA with nonnormally distributed variables being logarithmically transformed 53

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before entering analyses. We used the trend test of Tukey, Ciminera and Heyse [18] as implemented in the R package ‘tukeytrend’ v0.6 to calculate a p value for linear trend. Categorical variables were compared between groups using the χ [2] test and we used the Cochrane-Armitage test to calculate a p value for linear trend. For correlation analyses, the Spearmans' rho is reported. The association with mortality was investigated using Cox proportional hazards regression. All statistical analysis was performed using SPSS 24.0 (IBM SPSS, USA) and R v3.6.1 [19] (http://www.rproject.org). Estimated marginal means were calculated using the Rpackage ‘emmeans’ v1.4.1 and the p values shown for pairwise comparisons were adjusted for multiple testing using the Bonferroni method, hazard ratio plots were drawn using the R package ‘rms’ v5.1–3.1 and Kaplan-Meier curves were drawn using the R package ‘survminer’ v0.4.6. Harrells' C was calculated using the ‘rcorrcens’ function as implemented in the R package ‘Hmisc’ v4.2-0, the AUCs were calculated and compared using the R-package ‘pROC’ v1.15.3 and the net-reclassification-index was calculated using the R package ‘nricens’ v1.6.

concentrations of HDL-C compared to never-smokers. Further, no differences were observed for markers of diabetes like HbA1c or markers of heart failure like NT-proBNP. As expected, the frequency of patients with CAD was significantly higher in active and ever smokers compared to never-smokers. After adjusting for age and sex, the odds ratio for CAD more than doubled for active and former smokers combined as compared to never-smokers [2.32 (1.91–2.82)]. Regarding inflammatory markers, in heavy smokers, hsCRP was almost twice as high, and similarly IL-6 (2.79 vs. 4.15 ng/L, p < 0.001), as well as IgE levels (26 vs. 59.5 IU/mL, p < 0.001), were significantly higher as compared to never-smokers. Interestingly, these three parameters were still slightly elevated in participants that quit smoking more than 20 years ago as compared to participants that never smoked. Active smokers showed significantly higher numbers of leukocytes and the leukocyte count was still elevated 20 years after smoking cessation. 3.2. Association of smoking with markers of endothelial function When analysing markers of endothelial function, for sE-selectin, sPselectin and sICAM-1, we found increased concentrations in smokers, however, their levels were lower with longer smoking abstinence, and reached almost the concentrations of never-smokers in patients abstinent for more than 20 years (Table 2). Contrary, sVCAM-1 and sLselectin levels were significantly decreased in (681 mg/L vs. 782 mg/L and 689 mg/L vs. 705 mg/L in heavy smokers vs. never-smokers). While the concentrations of sVCAM-1 increased in the groups of participants with longer smoking abstinence, the concentrations of sL-selectin were even lower in light smokers and ex-smokers. We noticed strong direct correlations for sICAM-1, sP-selectin and sE-selectin with markers of smoking severity (cigarettes per day, packyears and plasma cotinine) while sL-selectin was inversely correlated with packyears

3. Results 3.1. Study demographics Stratified according to smoking status, study demographics, cardiovascular risk factors, blood pressure, lipid markers, concentrations of different markers of endothelial (dys-) function and leukocyte subpopulations were assessed (Tables 1 and 2). Regarding patient characteristics, active smokers (heavy and light) were younger and more often male. Heavy smokers had lower blood pressure than never-smokers. While BMI and LDL-C levels were comparable, heavy smokers showed significantly higher concentrations of triglycerides but lower Table 1 Study descriptives according to smoking status. Smoking status Variable

c heavy vs never

p

64.8 (10.3) 53.2 27.1 (4.09) 144 (23.4) 81.4 (11.4) – 80.8 (19.2) 68.9 76.7 36.4

< 0.001 < 0.001 0.109 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.006

< 0.001 < 0.001 0.147 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.127

< 0.001 < 0.001 0.262 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.190

116 (31.6) 37.7 (11) 146 (106–200) 6.23 (1.15) 3.5 (1.25–8.66) 3.45 (1.95–7.12) 35 (12–87) 192 (160–224) 337 (124–993)

121 (34.8) 41.1 (10.8) 135 (101–191) 6.22 (1.25) 2.66 (1.12–6.81) 2.79 (1.56–5.34) 26 (11–70) 176 (150–209) 246 (107–738)

0.725 < 0.001 0.000 0.102 < 0.001 < 0.001 < 0.001 < 0.001 0.206

0.852 < 0.001 0.000 0.137 < 0.001 < 0.001 < 0.001 < 0.001 0.271

0.760 < 0.001 0.000 0.217 < 0.001 < 0.001 < 0.001 < 0.001 0.169

6.67 (1.92) 26.4 (8) 59.8 (9.55) 6.5 (5.4–7.6) 2.9 (1.9–4.3) 0.7 (0.5–0.9)

6.54 (2.03) 28 (8.59) 59.3 (9.79) 6 (5–7.1) 2.4 (1.7–3.6) 0.7 (0.5–0.9)

< 0.001 0.042 0.170 0.413 0.089 0.980

< 0.001 0.060 0.227 0.543 0.090 0.927

< 0.001 0.068 0.195 0.125 0.025 0.451

Light

Quit < 20y

Quit ≥20y

Never

(n = 260)

(n = 157)

(n = 481)

(n = 346)

(n = 682)

Age (years) Female sex (%) BMI (kg/m2) SysBP (mmHg) DiaBP (mmHg) Packyears eGFR (ml/min/1.73m2) CAD (%) Hypertension (%) T2DM (%)

54.2 (10.1) 20.0 26.8 (4.09) 133 (23.5) 77.9 (11.5) 37.5 (29–50) 90.7 (20.6) 81.2 59.2 31.5

58.1 (9.8) 28.0 26.5 (3.82) 139 (24.3) 80.3 (11) 13.5 (7.5–20) 87.1 (20.4) 76.4 68.2 35.0

60.8 (10.5) 18.5 27.6 (3.89) 140 (23.6) 80.9 (11.6) 30 (13.3–45) 84.7 (20.8) 86.9 70.9 38.3

67.6 (7.86) 6.94 27.7 (3.7) 146 (22.6) 81.6 (10.7) 14.5 (5–25) 79.1 (18.7) 89.3 79.2 45.7

LDL-C (mg/dl) HDL-C (mg/dl) TG (mg/dl) HbA1c (%) hsCRP (mg/L) IL-6 (ng/L) IgE (IU/mL) LpPLA2 (nmol/min/mL) NT-proBNP (pg/mL)

119 (30.9) 34.9 (9.21) 158 (116–230) 6.1 (1.24) 5.66 (2.08–10.4) 4.15 (2.17–8.32) 59.5 (18.5–200) 199 (164–224) 272 (77.8–752)

123 (33.4) 38.2 (11.8) 150 (111–205) 6.09 (1.1) 5.39 (1.84–9.72) 3.76 (2.09–7.14) 40 (16–110) 194 (166–224) 332 (114–1190)

116 (31.9) 37.3 (9.95) 156 (115–208) 6.2 (1.31) 3.46 (1.38–8.68) 3.17 (1.88–5.89) 35 (15–93) 186 (158–214) 286 (94.5–866)

Leukocytes (/nl) Lymphocytes (%) Neutrophiles (%) Monocytes (%) Eosinophiles (%) Basophiles (%)

8.0 (1.96) 26.8 (7.43) 60.3 (8.29) 6.2 (5.3–7.1) 2.8 (1.9–4.12) 0.7 (0.5–1)

7.8 (2.64) 27.4 (8.22) 59.4 (9.58) 6 (5.2–7.1) 2.6 (1.7–3.8) 0.7 (0.5–0.9)

7.02 (1.93) 26.7 (7.98) 60 (9.13) 6.2 (5.2–7.4) 2.8 (1.8–3.9) 0.7 (0.5–1)

a

b linear

pa

Heavy

p

ANOVA; b linear trend test of Tukey, Ciminera and Heyse for continuous variables and Cochrane-Armitage-test for categorical variables; c t-test heavy smokers vs. never-smokers; d to convert values for LDL and HDL cholesterol to millimoles per liter, multiply by 0.02586; to convert values for triglycerides to millimoles per liter, multiply by 0.01129. 54

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Table 2 Endothelial adhesion molecules according to smoking status. Smoking status Variable

sICAM-1 (mg/L) sVCAM-1 (mg/L) vWF (U/dL) sE-Selectin (mg/L) sP-Selectin (mg/L) sL-Selectin (mg/L) a b c

Heavy

Light

Quit < 20y

Quit≥20y

Never

(n = 260)

(n = 157)

(n = 481)

(n = 346)

(n = 682)

285 (237–336) 681 (566–850) 150 (110–196) 38.2 (26.8–50.1) 55.5 (45.2–70.7) 689 (574–791)

261 (223–323) 709 (596–879) 157 (118–198) 34.2 (25–44.3) 54.6 (44.6–66) 654 (535–773)

240 (204–281) 761 (631–933) 156 (116–200) 34.4 (25–46.7) 52.1 (40–66) 648 (560–753)

228 (198–275) 775 (650–948) 166 (126–210) 32.1 (23.7–42.7) 49.9 (39.5–62.2) 643 (554–751)

227 (197–269) 782 (638–957) 152 (118–201) 31 (22.7–42.3) 47.9 (38.4–60.3) 705 (602–807)

pa

p

< 0.001 < 0.001 0.135 < 0.001 < 0.001 0.001

< 0.001 < 0.001 0.181 < 0.001 < 0.001 0.002

linear

b

pheavy

vs. never

c

< 0.001 < 0.001 0.228 < 0.001 < 0.001 0.019

ANOVA. Linear trend test of Tukey, Ciminera and Heyse. t-test heavy smokers vs. never-smokers.

(Supplemental Fig. 1). We also performed analyses in the subgroup of patients without CAD at baseline and obtained similar results (Supplemental Table 1). Only the association of hsCRP with smoking status was no longer significant, whereas we observed significantly lower NT-proBNP in heavy smokers with stepwise increases to the groups of never-smokers. Further, we analysed correlations between the endothelial markers and the inflammation markers total leukocytes, hsCRP and IL-6 (Supplemental Fig. 2). All endothelial markers showed direct associations with inflammation markers except for sL-selectin, which was inversely correlated with hsCRP and IL-6, although the correlation was only modest. These inverse associations were stronger in active smokers than in never-smokers.

mortality included the following categories: sudden cardiac death (n = 149, 7.7%), fatal myocardial infarction (n = 73, 3.8%), death due to congestive heart failure (n = 97, 5.0%), death after intervention to treat CAD (n = 21, 1.1%), fatal stroke (n = 40, 2.1%), and other causes of death due to CAD (n = 10, 0.5%). Smoking constitutes a significant risk factor for 10-year mortality in our study, with hazard ratios (95%CI) of 2.35 (1.77–3.12) and 2.22 (1.61–3.05) for heavy smokers and light smokers versus never-smokers, respectively, in a Cox regression model adjusted for age and sex. When we analysed the association of sE-/sP-/sL-selectin, vWF, sICAM-1 and sVCAM-1 with all-cause mortality adjusted for age, sex, LDL-C, HDL-C, BMI, hypertension, type 2 diabetes mellitus (T2DM) and medication (intake of lipid and/or blood pressure lowering drugs, aspirin, coumarin, glucocorticoids), we found that increasing concentrations of sICAM-1, sVCAM-1 and vWF were associated with increased risk of all-cause mortality in both active smokers and never-smokers (Fig. 1). The HR (95% CI) per 1-SD increase in heavy smokers were 1.24 (1.02–1.49), 1.38 (1.11–1.71) and 1.32 (1.11–1.57) for sICAM-1, sVCAM-1 and vWF, respectively. The HR (95% CI) for light smokers were 1.39 (1.05–1.85), 1.06 (0.75–1.51) and 1.09 (0.86–1.38). sE-selectin and sP-selectin were associated with increased risk only in the light smokers with HR of 1.51 (1.20–1.91) and 1.42 (1.09–1.87). However, for sL-selectin, we observed an inverse association with mortality risk only for active smokers [0.72 (0.54–0.97) and 0.67 (0.46–0.97) for heavy and light smokers, respectively]. Regarding cardiovascular mortality, the associations were similar as compared to all-cause mortality (Supplemental Fig. 3). Again, sL-selectin levels showed an inverse association with risk only in smokers, but this association was only statistically significant for the light smokers. There was also an inverse association of sE-selectin with cardiovascular risk that reached statistical significance, with a HR (95% CI) of 0.68 (0.53–0.88), but only in the never-smokers. In summary, whereas sICAM-1, sVCAM-1 and vWF levels were consistently associated with increased mortality in smokers and never-smokers, sE-selectin levels were only associated with increased risk in light smokers and sL-selectin was even protective in smokers. We then investigated the association of endothelial marker with

3.3. Multivariate linear regression models As there were highly significant differences in possible confounding variables between the different smoking status groups we calculated multivariate linear regression models to identify parameters strongly associated with the concentration of endothelial marker (exemplified by sICAM-1 and sL-selectin) in the groups of active smokers (heavy smokers and light smokers combined) and never-smokers (Supplemental Table 2 and 3). We then adjusted for markers associated with sICAM-1 and/or sLselectin concentration (age, sex, BMI, HDL-C, T2DM, eGFR, antihypertensive medication, glucocorticoids, coumarins and lipid-lowering medication) to calculate estimated marginal means for the endothelial markers. Similar to the results obtained by unadjusted analysis, concentrations of sICAM-1, sE-selectin and sP-selectin were higher in smokers as compared to former smokers and never-smokers, while concentrations of sVCAM-1 and sL-selectin were lower (Table 3). 3.4. Markers of endothelial function and mortality Among the 1926 participants included in the current analyses, 626 (32.5%) died during a median follow-up of 10.6 years (range 0.01–11.9 years), 391 (20.5%) from cardiovascular causes. Cardiovascular

Table 3 Estimated marginal means with 95% CI, adjusted for age, sex, HDL-C,T2DM, eGFR, BMI, glucocorticoids, antihypertensive medication, antiplatelet medication and lipid-lowering medication.

sICAM-1 (mg/L) sVCAM-1 (mg/L) sE-Selectin (mg/L) sP-Selectin (mg/L) sL-Selectin (mg/L) vWF (U/dL)

Heavy smokers

Light smokers

Quit < 20y

Quit > 20y

Never

p heavy

322 (295–350) 796 (724–869) 42.3 (36.3–48.4) 57.8 (51.7–64.0) 682 (636–728) 208 (193–222)

301 (271–331) 817 (737–896) 42.0 (35.4–48.7) 58.4 (51.7–65.1) 680 (630–731) 206 (190–222)

272 (247–297) 832 (766–898) 39.7 (34.2–45.3) 55.3 (49.7–61.0) 701 (659–743) 201 (188–215)

269 (241–296) 821 (749–894) 36.9 (30.8–43.0) 52.3 (46.1–58.4) 711 (665–757) 195 (180–210)

258 (232–283) 853 (787–920) 37.7 (32.1–43.3) 52.4 (46.8–58.1) 730 (688–772) 197 (183–210)

< 0.001 0.017 0.032 0.011 0.001 0.051

55

vs.

never

p light

vs.

never

< 0.001 0.248 0.495 0.017 0.005 0.261

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Fig. 1. Association of markers of endothelial dysfunction with 10-year mortality. Cox regression showing the increase in all-cause mortality risk per 1-SD increase, adjusted for age, sex, LDL-C, HDL-C, BMI, hypertension, T2DM, intake of lipid and/ or blood pressure lowering drugs, aspirin, coumarin and glucocorticoids. Clipped 95% confidence intervals are shown as arrows.

3.5. sL-selectin and risk prediction

short-term mortality (within one year after study begin). Due to the lower number of events, we only analysed the groups of active smokers combined and the group of never-smokers. vWF was associated with increased mortality risk both in active smokers and never-smokers (Supplemental Fig. 4). For sICAM-1, sVCAM-1 and sE-selectin, the association with increased risk reached statistical significance only for the active smokers. To examine the association of endothelial markers with mortality in detail, we stratified them into quartiles and calculated Kaplan-Meier curves (Supplemental Figs. 5–10). Higher concentrations of sVCAM-1, sP-selectin and vWF were associated with increased mortality in active smokers. The inverse association of sL-selectin with risk was only apparent in active smokers, with the mortality risk almost linearly decreasing with rising sL-selectin concentration. We modelled the six endothelial markers as restricted cubic splines and created hazard ratio plots with adjustment for age and sex (Supplemental Fig. 11). sICAM-1, sVCAM-1, sP-selectin and vWF showed increasing risk with increasing concentration of the respective marker for all smoking groups, sE-selectin showed no association except for the group of long-term quitters and sL-selectin showed inverse associations with risk for all groups except for the never-smokers. We further sought to determine the association of the endothelial markers with mortality in a full model including all of the markers simultaneously. The analyses revealed sVCAM-1 to be an independent predictor of increased risk in all groups except for the light smokers (Table 4). In active smokers, both heavy and light, we observed sLselectin to be independently associated with risk. Of note, sL-selectin was by far the strongest predictor in smokers with HRs (95%CI) of 0.62 (0.45–0.85) and 0.52 (0.34–0.78) per 1-SD increase for heavy smokers and light smokers, respectively.

To test whether the addition of sL-selectin might increase the accuracy of risk prediction, we defined a basic risk model based on age, sex, LDL-C, HDL-C, BMI, hypertension and T2DM and added sL-selectin for the prediction of all-cause mortality in active smokers, former smokers and never-smokers (Table 5). While there was no effect on the area-under-the-ROC-curve (AUC) or the net-reclassification-index (NRI) for the never-smokers or the former smokers, the addition of sL-selectin substantially improved the AUC in smokers and led to an NRI of 43%. 4. Discussion 4.1. Main results In the current study, we investigated the effects of smoking on the various parameters of endothelial function as well as the prognostic potential of these markers in active smokers, former smokers and neversmokers in a large cohort of patients that had been referred to coronary angiography. First, we demonstrate elevated concentrations of sICAM1, sP-selectin and sE-selectin and decreased concentrations of sVCAM-1 and sL-selectin in active smokers as compared to never-smokers. Second, only for some of the markers (sICAM-1 and sP-selectin) we observed a continuous decline of the concentrations in ex-smokers until the level of never-smokers dependent on the time since smoking cessation. Third, higher concentrations of sICAM-1, sVCAM-1 and vWF were associated with a higher mortality risk. Higher sL-selectin concentration was associated with reduced risk, but only in active smokers. sL-selectin emerged as the strongest predictor of risk in smokers even in models adjusted for all the other endothelial markers and significantly improved risk prediction in active smokers when added to a basic risk model including cardiovascular risk factors. 56

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Table 4 Multivariate Cox regression model for all-cause mortality.a Heavy smokers

Age (years) Female sex LDL-C (mg/dl) HDL-C (mg/dl) BMI (kg/m2) Hypertension Diabetes mellitus Antihypertensive medication Antiplatelet medication Coumarins Glucocorticoids Lipid-lowering medication sICAM-1 (mg/L) sVCAM-1 (mg/L) sE-Selectin (mg/L) sL-Selectin (mg/L) sP-Selectin (mg/L) vWF:Ag (U/dL) a

Light smokers

Quit < 20y

Quit ≥20y

Never-smokers

HR (95%CI)

p

HR (95%CI)

p

HR (95%CI)

p

HR (95%CI)

p

HR (95%CI)

p

1.19 0.43 0.90 1.03 0.88 1.16 1.90 1.16 0.89 1.15 1.27 0.69 1.43 1.34 0.68 0.62 1.33 1.22

0.221 0.044 0.414 0.816 0.377 0.596 0.018 0.702 0.716 0.853 0.779 0.152 0.032 0.034 0.035 0.003 0.029 0.170

1.76 1.23 0.98 1.03 1.02 0.83 1.36 0.67 1.86 3.35 1.68 0.80 1.18 1.12 1.28 0.52 1.38 1.11

0.008 0.591 0.926 0.892 0.904 0.659 0.333 0.481 0.154 0.101 0.528 0.539 0.389 0.522 0.107 0.002 0.043 0.516

1.96 0.73 1.11 1.05 0.87 1.12 1.81 1.38 1.11 2.24 1.49 1.06 1.03 1.25 0.89 0.83 1.15 1.16

< 0.001 0.166 0.219 0.552 0.112 0.586 0.001 0.298 0.629 0.002 0.364 0.730 0.713 0.036 0.255 0.085 0.081 0.088

1.97 0.97 0.94 0.92 1.14 0.78 1.36 1.39 0.59 1.23 1.32 1.02 1.16 1.35 0.98 0.73 1.21 0.99

< 0.001 0.931 0.532 0.448 0.200 0.289 0.105 0.367 0.017 0.547 0.566 0.934 0.118 0.001 0.864 0.004 0.056 0.959

1.99 0.73 1.18 0.87 1.06 1.20 1.65 1.66 0.95 0.93 1.45 1.17 1.01 1.39 0.87 0.91 0.99 1.25

< 0.001 0.078 0.066 0.150 0.463 0.410 0.003 0.119 0.794 0.795 0.478 0.351 0.941 < 0.001 0.304 0.301 0.910 0.003

(0.90–1.58) (0.19–0.98) (0.71–1.15) (0.80–1.32) (0.66–1.17) (0.66–2.04) (1.12–3.23) (0.55–2.46) (0.49–1.63) (0.26–5.20) (0.24–6.64) (0.41–1.15) (1.03–2.00) (1.02–1.75) (0.48–0.97) (0.45–0.85) (1.03–1.73) (0.92–1.61)

(1.16–2.69) (0.57–2.66) (0.69–1.41) (0.71–1.47) (0.69–1.52) (0.36–1.91) (0.73–2.55) (0.22–2.02) (0.79–4.35) (0.79–14.2) (0.34–8.35) (0.40–1.62) (0.81–1.74) (0.79–1.61) (0.95–1.74) (0.34–0.78) (1.01–1.89) (0.81–1.52)

(1.55–2.47) (0.47–1.14) (0.94–1.32) (0.89–1.24) (0.73–1.03) (0.75–1.66) (1.29–2.52) (0.75–2.54) (0.72–1.71) (1.33–3.77) (0.63–3.50) (0.76–1.48) (0.87–1.22) (1.02–1.54) (0.73–1.09) (0.67–1.03) (0.98–1.34) (0.98–1.38)

(1.57–2.48) (0.48–1.97) (0.77–1.15) (0.75–1.13) (0.93–1.38) (0.49–1.24) (0.94–1.98) (0.68–2.86) (0.38–0.91) (0.63–2.38) (0.51–3.38) (0.69–1.49) (0.96–1.39) (1.13–1.62) (0.79–1.22) (0.60–0.90) (0.99–1.47) (0.82–1.21)

(1.61–2.47) (0.52–1.03) (0.99–1.41) (0.73–1.05) (0.90–1.25) (0.78–1.86) (1.19–2.28) (0.88–3.16) (0.65–1.39) (0.54–1.60) (0.52–4.05) (0.84–1.61) (0.86–1.17) (1.16–1.66) (0.67–1.13) (0.77–1.08) (0.82–1.19) (1.08–1.45)

Continuous variables were Z-transformed before entering analyses.

4.2. Impact of smoking on adhesion molecules

differences between active smokers and never-smokers essentially remained the same after adjustment for confounders. In our study, sLselectin concentrations were significantly lower in active smokers as compared to never-smokers. Interestingly, while other endothelial markers like sICAM-1 almost reach the same levels as in the neversmokers with longer time since smoking cessation this was not the case for sL-selectin, whose concentrations were even lower in ex-smokers as compared to active smokers, which might suggest a direct or indirect sustained effect of smoking on the level of this adhesion molecule. Contrary to the other selectins as well as ICAM-1, whose expression is strongly stimulated by cytokines such as TNFα and IL-1 or lipopolysaccharide, L-selectin is constitutively expressed. Removal of inflammatory stimuli upon smoking cessation might therefore explain the relatively rapid decline of sICAM-1, sE-selectin and sP-selectin concentrations while this may have no effect on sL-selectin. Our data are partially in line with data from a large cohort study, the Multi-Ethnic Study of Atherosclerosis, demonstrating lower sL-selectin levels in current smokers [30]. A difference to our data, that in this study ex-smokers had similarly low levels like active smokers (not even lower levels as we observed), might be due to different study population characteristics such as patients without a history of clinical cardiovascular disease were included. Like for sL-selectin, we observed lower concentrations of sVCAM-1 in active smokers. Previous studies on the association of smoking on sVCAM-1 reported conflicting results with some studies observing higher expression in smokers (with peripheral artery disease), some observing lower expression and others no difference at all [31]. Our data showing higher concentrations of the inflammation markers hsCRP, IL-6 and IgE that remained elevated even in study participants that quit smoking more than 20 years ago are in line with previous observations. Elevated levels of hsCRP in active smokers were

It has been demonstrated that smoking is associated with vascular endothelial inflammation and damages the epithelial junctions making them more permeable [20]. Even moderate cigarette smoking influences the plasma concentrations of cell adhesion molecules like e.g. sICAM-1 [21]. Smoking cessation has been shown to be a highly effective measure to lower sICAM-1 [9,22,23]. A few studies examined the effect of nicotine replacement therapy on endothelial function in smokers that quit smoking and found improvement of endothelial function and reduction of sICAM-1 concentration [24,25] suggesting that it is not primarily the nicotine that harms the endothelium but other noxious substances contained in tobacco smoke. In line with these results, we observe higher concentrations of sICAM-1 in active smokers (higher in heavy smokers than in light smokers) that gradually decline with increasing time since smoking cessation until they reach a concentration comparable to never-smokers. We observed similar trends for sE-selectin and sP-selectin. This is somewhat consistent with observations in cardiovascular high-risk patients showing that a reduction or cessation of smoking (along with other measures of cardiovascular risk management) was associated with a reduction of sP-selectin [26]. Another study found elevated levels of sP-selectin, but only in 35 female smokers compared to 99 female non-smokers [27]. A reason that we found higher sP-selectin levels in smokers irrespective of sex and age might be due to higher numbers of individuals tested in our study. Regarding sE-selectin, elevated serum levels were described for diabetic and non-diabetic smokers with a positive correlation with the packyears [28], although initial studies with smaller numbers of cases failed to find differences [29]. Contrary to sE-selectin and sP-selectin, we observed lower concentrations of sVCAM-1 and sL-selectin in active smokers. The Table 5 Improvement in risk prediction. Active smokers

Former smokers

Never-smokers

Harrell's C

AUC

Harrell's C

AUC

Harrell's C

AUC

Base Base + sL-selectin p Base vs. Base+sL-selectin

0.686 0.708

0.725 (0.674–0.775) 0.752 (0.702–0.801) 0.034

0.716 0.717

0.770 (0.738–0.803) 0.771 (0.738–0.803) 0.838

0.735 0.735

0.778 (0.739–0.816) 0.778 (0.739–0.816) 0.756

Continuous NRI

0.434 (0.169–0.623)

0.074 (−0.048 - 0.260)

57

−0.074 (−0.169 - 0.268)

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shown in a number of studies [32,33] and those studies that investigated the changes over time in ex-smokers usually report no shortterm effects on hsCRP but persisting elevation up to 20 years after smoking cessation [33,34]. Similarly, persistent leucocytosis has been shown in persons that had quit smoking [35,36]. However, a suppression of the innate immune response by smoking has been demonstrated by a number of studies [37–39] and decreased concentrations of sVCAM-1 and sL-selectin might contribute to this suppression.

receptor 1 (ESR1) to a promoter element upstream of the CYP2A6 gene and therefore increase cotinine metabolism [53]. Data on air pollution was not available for our study participants and we did not measure CYP2A6 concentration or activity. However, we observed no correlation between estradiol and cotinine in our study. Another limitation is that blood parameters were only measured once at baseline. Strengths of our study are the high number of individuals, the detailed characterization of the study participants, the availability of cotinine measurements to correct the classification into smokers and non-smokers and the long follow-up on mortality.

4.3. Association of adhesion molecules with mortality A number of studies has linked the presence of both coronary and systemic endothelial dysfunction to an increased risk for future cardiovascular events and mortality [1]. Higher concentrations of sICAM-1 and sVCAM-1 were reported in critically ill patients that died during follow-up as compared to survivors [40] and higher sVCAM-1 was a predictor of mortality in an elderly population [41]. In our study, increasing concentrations of sVCAM-1, sICAM-1 and von Willebrand factor were associated with increasing mortality risk in both active smokers and never-smokers. For sL-selectin we observed an inverse association with mortality risk only for the active smokers. Modelling sL-selectin as restricted cubic spline, the association of sL-selectin with mortality was only apparent in active smokers with the mortality risk almost linearly decreasing with rising sL-selectin concentration in light smokers and a slightly curved association in heavy smokers (Supplemental Fig. 11). Accordingly, adding sL-selectin to a basic risk prediction model only improved the accuracy of risk prediction for active smokers. Studies investigating the role of sL-selectin as a biomarker of cardiovascular disease and as a predictor of cardiovascular events have reported conflicting results. In animal models, a protective role of this molecule was demonstrated [42], and consistent with this, lower levels of serum L-selectin were observed in patients with cardiovascular risk factors [43], CAD [44] and peripheral artery disease (PAD) [45]. In trials of the multiple sclerosis drug natalizumab patients with low L-selectin levels have been found to be at an increased risk of developing natalizumab-associated progressive multifocal leukoencephalopathy [46,47] but this association could not be replicated in another study [48]. However, other studies showed higher levels of L-selectin being associated with acute stroke [49], and unstable angina [50,51]. In conclusion, we have shown that smokers exhibit alterations in the concentration of soluble cell adhesion molecules as compared to neversmokers that were independent of possible confounding factors. High concentrations of sICAM-1, sVCAM-1 and vWF were associated with increased mortality risk in both active smokers and non-smokers. These associations remained largely unchanged after adjustment for cardiovascular risk factors. Regarding sL-selectin, we observed decreased concentrations in active smokers and an inverse association with mortality, but only in active smokers. In active smokers, sL-selectin was the strongest risk predictor even after adjustment for all the other markers of endothelial function and measurement of sL-selectin might improve cardiovascular risk prediction in this group of persons already at high cardiovascular risk.

Financial support This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the ERA-Net Cofund action N° 727565 (OCTOPUS project) and the German Ministry of Education and Research (grant number 01EA1801A). The sponsors had no role in the design and conduct of the study; collection, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. Author contributions G.E.D., M.E.K., B.K.K., J.L., and W.M. conceived and designed the study; G.E.D. and M.E.K. performed the statistical analyses; G.E.D., M.E.K. and J.L. wrote the manuscript. All authors provided important intellectual content and critically revised the manuscript. Declaration of competing interest W.M. reports grants from Siemens Healthineers, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from AMGEN, grants from Astrazeneca, grants and personal fees from Sanofi, grants and personal fees from Alexion Pharmaceuticals, grants and personal fees from BASF, grants and personal fees from Abbott Diagnostics, grants and personal fees from Numares AG, grants and personal fees from Berlin-Chemie, grants and personal fees from Akzea Therapeutics, grants from Bayer Vital GmbH, grants from bestbion dx GmbH, grants from Boehringer Ingelheim Pharma GmbH Co KG, grants from Immundiagnostik GmbH, grants from Merck Chemicals GmbH, grants from MSD Sharp and Dohme GmbH, grants from Novartis Pharma GmbH, grants from Olink Proteomics, other from Synlab Holding Deutschland GmbH, all outside the submitted work. B.K.K. reports lecture fees and/or advisory board memberships and/or study participation from Astellas, Bayer, Boehringer Ingelheim, Chiesi, Riepharm, Pfizer, Servier, and Vifor Pharma. He is the current president of the German Hypertension Society DHL. M.E.K. reports lecture fees from Bayer outside the submitted work. G.E.D, J.L., B.Y. and R.S. declare no competing interest. Acknowledgements We thank all participants of the LURIC study as well as the study team who were either temporarily or permanently involved in patient recruitment as well as sample and data handling, in addition to the laboratory staff at the Ludwigshafen General Hospital and the Universities of Freiburg and Ulm, Germany.

4.4. Strengths and limitations As possible limitations, we have to mention that the LURIC participants represent a cohort with medium-to-high cardiovascular risk and so our results cannot be generalized to healthy populations. Our participants were all of European ancestry and differences in the concentration of cell adhesion molecules have been shown for different ethnicities [30]. Further, there are environmental factors that could potentially influence smoking biomarkers, as well as endothelial function, like e.g. air pollution that influences the measurement of exhaled carbon monoxide [52] or hormones like estradiol that induce CYP2A6 expression via direct binding of the transcription factor estrogen

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