Abstract
Background:
Cigarette smoking remains one of the leading public health threats worldwide. Electronic cigarettes (e-cigs) provide an alternative to conventional cigarette smoking; however, the evidence base of risks and benefits of e-cig use is new and growing. In this cross-sectional pilot study, the effect of e-cig use on biological profiles in saliva and gingival crevicular fluid (GCF) was assessed and compared with the profiles of cigarette smokers (CS), dual users, and non-users. The systemic inflammatory mediators between e-cig users (EC) and these other groups were also assessed.
Methods:
This pilot cross-sectional study recruited volunteer participants consisting of four groups, non-smokers (NS), CS, EC, and dual EC and cigarette smokers (DS). Saliva and GCF samples were collected and analyzed for biomarkers of inflammation, oxidative stress, anti-inflammatory lipid mediators, tissue injury and repair, and growth factors with immunoassay (enzyme-linked immunosorbent assay and Luminex).
Results:
Smoking status was confirmed via salivary cotinine. Prostaglandin E2 level was significantly increased in CS compared with EC and DS, but not significantly different in EC and DS groups compared with non-smokers (NS). Statistically significant differences were observed between groups of EC and NS (myeloperoxidase [MPO], matrix metalloproteinase-9) as well as between DS and EC for biomarkers of inflammatory mediators (receptor for advanced glycation end products [RAGE], MPO, uteroglobin/CC-10); between groups of DS and NS for extracellular newly identified RAGE binding protein and between CS and NS for MPO. No statistically significant differences in biomarkers of immunity (S100A8, S100A9, galectin-3), tissue injury and repair (Serpine1/PAI-1) and growth factors (brain-derived neurotrophic factor, fibroblast growth factors, platelet-derived growth factor-AA, vascular endothelial growth factor, and others) were found between any of groups.
Conclusion:
Statistically significant differences in measurable health outcomes were found between different smoking status groups, suggesting that smoking/vaping produces differential effects on oral health.
Keywords: biomarkers, growth factors, inflammation oxidative stress, vaping
1 |. INTRODUCTION
Cigarette smoking is a leading cause of chronic diseases, mainly through inflammation.1 Cigarette smoke contains many toxicants and is associated with adverse health effects, primarily chronic obstructive pulmonary disease (COPD) and other systemic disease, heart disease, and cancer.2 It is also a major risk factor for oral health concerns including periodontal diseases and is associated with increased dental implant failures.3,4 Mechanistically, the effects of cigarette smoking on human health have been extensively investigated at the tissue, cellular, and molecular levels, and found to be associated with increased inflammatory responses.5,6
In contrast to traditional cigarettes, electronic cigarettes (e-cigs) are handheld devices using a battery heating element that, in the absence of combustion, aerosolizes an e-liquid composed of a mixture of propylene glycol and/or vegetable glycerin, flavorings and nicotine in varying concentrations (0 to 24 mg/mL).7 Chronic use of these devices has unexpectedly accelerated rapidly in the recent decade, especially among adolescents and young adults.8
Enchanting flavors, the “e-cigarette experience”, and the presumed safety of e-cig use due to elimination of combustion-produced toxicants,9 continue to attract new users. Inconsistent understanding of the risks and benefits of the many new nicotine delivery products exists among healthcare practitioners and the general public.10–12 Substantial research gaps exist in the evidence-base with respect to the effects of e-cigs on human health, including oral health.
Widely varying research questions, hypotheses, study designs, and findings contribute to this inconsistency. For example, the effects of nicotine-free e-cigs and traditional cigarettes on pulmonary function and nitric oxide release in exhaled air of smokers versus non-smokers were compared, and it was found that short-term use of e-cigs or vaping did not result in immediate adverse effects in non-smokers (NS).13 In addition, replacement of conventional cigarettes with nicotine-containing e-cigs caused arterial stiffness and oxidative stress, albeit to a lesser extent.14 In contrast, e-cigs with flavorings produced increased inflammatory responses, oxidative stress, and even DNA damage in human lung cells and periodontal cells/tissue.9,15–17 However, the effects of e-cigs or vaping on systemic biomarkers of inflammatory mediators in human subjects remain understudied. The general research question guiding the present pilot study was, “Are there any differences in oral health biomarkers of oxidative stress, inflammation, and growth factors in saliva and gingival crevicular fluid among smokers, e-cig users, dual users, and non-users?”.
In this cross-sectional pilot study, two oral fluids, saliva and gingival crevicular fluid (GCF), were used to investigate biomarker profiles, including inflammatory mediators, oxidative stress, tissue injury, and repair mediators and growth factors among cigarette smokers (CS), electronic-cigarette users (EC), dual smokers (DS), and NS, highlighting clinical evidence on the biological effects of these increasingly popular tobacco products, and introducing perspectives for public health, future translational research, and regulatory research for public health policy.
2 |. MATERIALS AND METHODS
2.1 |. Ethics statements
2.1.1 |. Institutional review board
The clinical aspect of the study protocol was approved by the Institutional Review Board of the University of Rochester Medical Center (URMC) (RSRB00057001). This study was approved by the human subjects’ ethics board of the University of Rochester and was conducted in accordance with the Helsinki Declaration of 1975, as revised in 2013.
2.1.2 |. Institutional biosafety approvals
All laboratory experiments performed in this study were approved and in accordance with the University of Rochester Institutional Biosafety Committee.
2.1.3 |. Scientific rigor statement
The approach to creating the experiment was unbiased and all study procedures and analyses ensure that our data are reproducible.
2.2 |. Participants, demographics and vaping, smoking assessment
Participants were recruited from the local population of a large metropolitan area of Western New York. The study participants were recruited/enrolled from June 2015 through March 2018 at a university-based dental setting through word-of-mouth and flyers posted in and around the university campus. Patients with inflammatory diseases, those who required antibiotic prophylaxis for routine dental procedures and those who had taken antibiotics in the past 3 months were excluded from the study. Participants selected based on their traditional cigarette and e-cig status (NS, CS, EC, and DS) provided informed written consent and information about demographic variables (age, sex, race, and ethnicity).
Participants were asked to refrain from vaping or smoking at least in the morning of sample collection. Detailed information about the use of different tobacco products (cigarettes, e-cigs, or both) was collected. The study participants were categorized into four groups: NS, CS, EC, and DS (dual use of cigarettes and e-cigs). An initial sample size of 48 was calculated for n = 12 in each group and Bonferroni correction for comparisons was used with significance level at 0.01 and power of 0.90. Permutation randomization facilitated additional exploratory analyses appropriate for this sample size, and useful for hypothesis generation and observation of potential real world effect sizes. There are no closed form sample size/power/effect size calculations for these tests; however, Monte Carlo simulation is used to calculate effect sizes for multiple outcomes. Specifically, 0.05 significance level permutation tests for this study have 80% power to determine an Omega square of 15% for cotinine, 20% for prostaglandin E2 (PGE2), and 35% for interleukin-1β (IL-1β).
2.3 |. Study design
This preliminary cross-sectional study was designed to analyze various biomarkers in directly collected biological samples from the study participants. In this study, saliva and GCF were used as the sample source for assessment. There are many advantages of using biofluids, such as saliva and GCF.18,19 The sample collection is fast, easy, inexpensive, non-invasive, and easy to store and ship. Furthermore, biomarkers in saliva and GCF can reflect the current physiological state of an individual.20,21
2.3.1 |. Collection of saliva
All participants were instructed not to eat or use any oral hygiene measures for at least 2 hours before biological sample collection. Participants rinsed their mouths with water at the beginning of the appointment, and saliva was collected by having participants spit into an ice-cooled graduated tube. Saliva was allowed to collect passively in the participant’s mouth, and then expectorated every 30 seconds until 5 mL of saliva were obtained. Supernatant was collected by centrifugation at 4,000 × g for 15 minutes at 4°C and stored at −80°C until analysis. All procedures described below were conducted according to pre-optimized protocols as per manufacturer’s instructions, and consistent with industry standards.
2.3.2 |. Gingival crevicular fluid sampling
The GCF was collected from two randomly selected quadrants of the mouth. After the selected site was isolated with a cotton roll and gently air-dried, a paper strip* was gently inserted into the gingival crevice until light resistance was felt and left for 30 seconds. Salivary absorbents were used to avoid contamination of the strips with saliva. The paper strips were immediately transferred to the Eppendorf tubes, placed on ice, and subsequently stored at −80°C until analysis. All strips from each participant were pooled and their contents eluted with 250 μL of phosphate-buffered saline. The total protein content of GCF was determined using a protein assay kit.†
2.3.3 |. Measurement of salivary cotinine
Cotinine is the major proximate metabolite of nicotine. Salivary cotinine was measured to determine smoking and e-cig status and tobacco smoke exposure by using a high sensitivity, quantitative enzyme immunoassay kit.‡
2.3.4 |. Measurement of salivary biomarkers by ELISA
The levels of PGE2 and IL-1β (an endogenous anti-inflammatory lipid mediator) were measured quantitatively in saliva by commercially available ELISA kits.§
2.3.5 |. Measurement of biomarkers and growth factors in GCF by multiplex panel assay
Biomarkers of immunity, oxidative stress, tissue injury/repair, and growth factors were measured in GCF samples by using magnetic immunoassay kits.¶ The concentrations of biomarkers and growth factors in GCF were normalized to either per microgram or per milligram of total protein. Data are presented as means (±SD) and medians with interquartile ranges (pg/mg or pg/μg of protein). Out of range values were designated as the lowest detectable value. All samples were assayed in duplicate.
2.4 |. Data and statistical analysis
2.4.1 |. Permutation test
For each outcome, the null hypothesis that type-of-smoker does not make a difference in individual responses was tested using a Monte Carlo permutation test22 based on linear regressions; 10,000 random reassignments of type-of-smoker for the tests were used. Permutation tests are valid for small-n samples and do not require distribution assumptions. Under the null hypothesis, type-of-smoker is independent of outcome responses; consequently, repeatedly randomly reassigning individuals a type-of-smoker class and obtaining the regression results for each reassignment provides a distribution of the test statistic that reflect the null hypothesis. The P value is calculated as the proportion of test statistics that are at least as extreme as the observed value. Permutation tests do not depend on sampling distributions and are thereby internally valid for samples obtained from any data generating process. Moreover, permutation tests produced the best type I error rates when compared with probabilities generated by classical ANOVA and Kruskal–Wallis test.23 This provides information regarding whether type-of-smoker status among the sampled individuals is related to their outcomes. Continuous variables are presented as the mean ± SD and medians and interquartile ranges unless otherwise indicated. The level of statistical significance used was P ≤0.05.
3 |. RESULTS
3.1 |. Demographic and clinical findings
Table 1 summarizes the demographic characteristics of the study participants. A total of 48 study participants were recruited into the study. The mean age of participants in the EC group was (34.92 ± 11.45 years) compared with NS (35.67 ± 12.46 years), CS (40.25 ± 15.96 years), and DS (39.42 ± 11.81 years) groups. Female participants were comparatively underrepresented in the EC group (16.7%) and overrepresented in the NS group (83.3%). A majority of the participants were white and non-Hispanic. Salivary cotinine levels were found to be higher in EC, CS, and DS groups versus NS (Tables 2 and 3), with DS statistically significantly higher than NS.
TABLE 1.
Demographic characteristics of study participants
| Non-smokers (n = 12) | Cigarette smokers (n = 12) | E-cig users (n = 12) | Dual smokers (n = 12) | |
|---|---|---|---|---|
| Age, years | ||||
| Mean (SD) | 35.67 (12.46) | 40.25 (15.96) | 34.92 (11.45) | 39.42 (11.81) |
| Range (years) | (22 to 57) | (24 to 75) | (22 to 53) | (25 to 62) |
| Sex, n (%) | ||||
| Male | 2 (16.7%) | 5 (41.7%) | 10 (83.3%) | 7 (58.3%) |
| Female | 10 (83.3%) | 7 (58.3%) | 2 (16.7%) | 5 (41.7%) |
| Race (n) | ||||
| White | 5 | 7 | 8 | 4 |
| African American | 4 | 2 | 1 | 4 |
| Asian | 0 | 3 | 1 | 2 |
| American Indian | 1 | 0 | 0 | 1 |
| Other (mixed) | 2 | 0 | 2 | 1 |
| Ethnicity, n | ||||
| Hispanic | 1 | 1 | 0 | 0 |
| Non-Hispanic | 11 | 11 | 12 | 12 |
TABLE 2.
Salivary biomarkers of inflammatory mediators among non-smokers, cigarette smokers, e-cig users, and dual smokers
| Biomarkers | NS | CS | EC | DS | |
|---|---|---|---|---|---|
| Cotinine (ng/mL) | Mean, SD | 0.56, 0.64 | 142.61, 174.11 | 180.22, 273.42 | 298.97, 432.67 |
| Median, IQR | 0.42, 0.15 | 61.29, 225.79 | 95.47, 105.92 | 132.79, 409.17 | |
| IL-1β (pg/mL) | Mean, SD | 284.86, 223.24 | 350.69, 364.94 | 182.31, 100.90 | 167.85, 61.45 |
| Median, IQR | 224.67, 287.90 | 214.33, 408.2 | 148.15, 151.50 | 159.85, 24.09 | |
| PGE2 (pg/mL) | Mean, SD | 331.82, 223.55 | 588.72, 207.87 | 344.19, 172.22 | 362.44, 212.09 |
| Median, IQR | 276.65, 210.21 | 574.61, 223.59 | 336.64, 303.76 | 337.85, 165.35 |
Data are presented as mean, SD and median, interquartile range (IQR) (n = 6–12).
CS = cigarette smokers; DS = dual smokers; EC = e-cig users; IL-1β = interleukin-1β; NS = non-smokers; PGE2 = prostaglandin E2.
TABLE 3.
Observed value difference and P value of salivary inflammatory mediators between groups of non-smokers, cigarette smokers, e-cig users, and dual smokers
| Biomarkers | CS-NS | EC-NS | DS-NS | EC-CS | DS-CS | DS-EC |
|---|---|---|---|---|---|---|
| Cotinine (SD = 115)a | ||||||
| Observed difference | 142.05 | 179.65 | 298.40* | 37.61 | 156.36 | 118.75 |
| P value | 0.22 | 0.12 | 0.005 | 0.75 | 0.18 | 0.32 |
| IL-1β (SD = 107)a | ||||||
| Observed difference | 65.83 | −102.55 | −117.02 | −168.38 | −182.85 | −14.46 |
| P value | 0.56 | 0.36 | 0.28 | 0.11 | 0.08 | 0.89 |
| PGE2 (SD = 92)a | ||||||
| Observed difference | 256.89* | 12.37 | 30.61 | −244.52* | −226.28* | 18.25 |
| P value | 0.004 | 0.90 | 0.73 | 0.006 | 0.01 | 0.84 |
CS-NS = CS group compared with NS; DS-CS = DS group compared with CS; DS-EC = DS group compared with EC; DS-NS = DS group compared with NS; EC-CS, EC group compared with CS; EC-NS, EC group compared with NS.
Standard deviation of null distributions for permutation tests. Under the null each difference has the same standard deviation.
P <0.01.
3.2 |. Biomarkers of systemic inflammation and anti-inflammatory lipid mediators in saliva
Saliva levels of PGE2 and IL-1β were measured to determine whether the systemic inflammatory response was different among EC, CS, DS, and NS (Tables 2 and 3). The levels of PGE2 in saliva were significantly higher in CS compared with NS, EC, and DS (Tables 2 and 3). The other inflammatory marker (IL-1β) was not significantly affected in any of the groups.
3.3 |. Biomarkers of inflammatory mediators, oxidative stress, and tissue injury and repair in GCF
The assessment included inflammatory mediators (galectin-3, RAGE, EN-RAGE, matrix metalloproteinase-9, S100A8, S100A9, uteroglobin/CC-10), oxidative stress marker (MPO), and tissue injury and repair factor (Serpine1/PAI-1) in GCF samples (Tables 4 and 5). The level of MPO reduced statistically significantly in CS and EC compared with NS, as well as in EC comparing to DS. EN-RAGE was almost significantly different in DS with NS. Galectin-3, S100A8, S100A9 and Serpine1/PAI-1 levels in the EC group were not statistically significantly different as compared with CS, DS, and NS groups. However, RAGE and uteroglobin were statistically significantly lower in EC compared with DS. S100A8 and S100A9 were not statistically significantly different between the groups (Tables 4 and 5).
TABLE 4.
GCF biomarkers of inflammatory mediators and growth factors among non-smokers, cigarette smokers, e-cig users, and dual smokers
| Biomarkers (pg/μg protein) | NS | CS | EC | DS | |
|---|---|---|---|---|---|
| Inflammatory mediators | |||||
| EN-RAGE | Mean, SD | 53.47, 18.03 | 115.84, 145.39 | 79.48, 43.01 | 156.22, 175.13 |
| Median, IQR | 45.98, 20.15 | 64.84, 107.71 | 62.25, 53.46 | 99.56, 127.83 | |
| RAGE | Mean, SD | 16.01, 17.02 | 7.38, 3.57 | 2.22, 2.50 | 30.76, 33.64 |
| Median, IQR | 10.47, 20.86 | 8.48, 4.32 | 2.22, 3.54 | 21.81, 40.53 | |
| MMP-9 | Mean, SD | 95.09, 50.41 | 48.08, 35.41 | 38.07, 26.67 | 83.92, 105.54 |
| Median, IQR | 78.45, 67.02 | 38.04, 42.69 | 28.45, 33.09 | 50.02, 55.76 | |
| S100A8 | Mean, SD | 92.38, 115.38 | 19.39, 28.29 | 20.14, 14.51 | 43.86, 62.37 |
| Median, IQR | 56.37, 115.76 | 6.48, 31.41 | 17.37, 15.15 | 6.29, 57.46 | |
| S100A9 | Mean, SD | 1,333.69, 2,606.67 | 516.54, 517.08 | 606.8, 606.21 | 2,264.34, 3,992.99 |
| Median, IQR | 36.82, 2,614.97 | 300.56, 896.99 | 362.79, 615.43 | 600.95, 2057.05 | |
| Galectin-3 | Mean, SD | 4.36, 2.35 | 3.59, 2.58 | 3.42, 1.64 | 6.62, 7.62 |
| Median, IQR | 3.22, 2.5 | 2.49, 3.28 | 3.29, 3.29 | 3.47, 4.92 | |
| Uteroglobin/CC-10 | Mean, SD | 1.64, 2.44 | 0.98, 1.08 | 0.27, 0.16 | 2.52, 4.04 |
| Median, IQR | 0.28, 2.33 | 0.52, 0.64 | 0.23, 0.13 | 0.29, 5.27 | |
| Oxidative stress | |||||
| MPO | Mean, SD | 216.11, 92.69 | 70.24, 57.59 | 40.37, 34.82 | 151.44, 166.24 |
| Median, IQR | 219.41, 122.01 | 58.65, 54.25 | 25.47, 54.43 | 121.67, 166.37 | |
| Tissue injury and repair | |||||
| Serpine1/PAI-1 | Mean, SD | 1.41, 1.47 | 0.66, 0.37 | 0.52, 0.47 | 2.03, 4.10 |
| Median, IQR | 0.74, 1.74 | 0.6, 0.62 | 0.43, 0.39 | 0.47, 2.14 | |
| Growth factors (pg/mg protein) | |||||
| BDNF | Mean, SD | 130.52, 313.40 | 36.60, 80.40 | 5.52, 11.54 | 72.57, 207.26 |
| Median, IQR | 2.35, 6.61 | 6.78, 16.33 | 1.72, 3.05 | 3.38, 18.95 | |
| basic-FGF | Mean, SD | 60.98, 137.11 | 21.77, 38.66 | 6.99, 10.42 | 64.27, 172.54 |
| Median, IQR | 4.32, 6.14 | 4.8, 14.6 | 3.9, 3.2 | 4.31, 10.02 | |
| β-NGF | Mean, SD | 86.41, 190.95 | 9.41, 12.93 | 2.89, 5.98 | 82.57, 249.09 |
| Median, IQR | 1.14, 1.41 | 1.98, 25.29 | 0.8, 0.97 | 1.57, 3.7 | |
| SCF | Mean, SD | 197.40, 432.82 | 187.93, 324.65 | 9.10, 18.36 | 328.50, 738.27 |
| Median, IQR | 3.99, 3.0 | 38.71, 368.89 | 2.29, 1.83 | 24.92, 85.52 | |
| BMP-2 | Mean, SD | 293.48, 675.01 | 422.60, 606.33 | 49.15, 70.95 | 434.65, 1,153.92 |
| Median, IQR | 18.28, 11.45 | 126.82, 1,099.13 | 14.54, 81.88 | 30.9, 80.18 | |
| HGF | Mean, SD | 498.28, 675.63 | 476.06, 572.47 | 287.57, 317.74 | 490.06, 1,128.87 |
| Median, IQR | 255.46, 266.65 | 301.6, 305.2 | 191.94, 260.3 | 118.97, 99.89 | |
| PDGF-AA | Mean, SD | 193.76, 353.49 | 104.36, 111.90 | 38.83, 49.87 | 134.28, 261.47 |
| Median, IQR | 47.52, 75.12 | 78.28, 114.6 | 26.27, 16.16 | 23.02, 131.53 | |
| TGF-α | Mean, SD | 125.08, 218.66 | 55.15, 27.89 | 36.4, 31.52 | 147.10, 397.02 |
| Median, IQR | 40.18, 17.02 | 60.27, 40.27 | 24.71, 14.04 | 26.56, 28.26 | |
| EGF | Mean, SD | 213.11, 228.50 | 174.80, 132.38 | 81.5, 76.36 | 157.73, 236.21 |
| Median, IQR | 126.04, 141.97 | 181.51, 215.53 | 54.86, 80.3 | 70.75, 146.69 | |
| PlGF | Mean, SD | 49.75, 97.28 | 18.81, 37.18 | 1.97, 3.02 | 71.22, 170.08 |
| Median, IQR | 1.36, 97.87 | 3.25, 12.72 | 0.75, 2.02 | 3.18, 25.11 | |
| VEGF | Mean, SD | 664.26, 888.14 | 604.22, 558.74 | 302.40, 210.91 | 804.05, 1,523.71 |
| Median, IQR | 261.56, 272.67 | 379.65, 671.78 | 221.41, 356.85 | 280.18, 479.13 | |
Data are presented as mean, SD and median, interquartile range (IQR) (n = 2 to 12).
BDNF = brain-derived neurotrophic factor; BMP = bone morphogenetic protein; CS = cigarette smokers; DS = dual smokers; EC = e-cig users; EGF = epidermal growth factor; EN-RAGE = extracellular newly identified RAGE binding protein; FGF = fibroblast growth factors; GCF = gingival crevicular fluid; HGF = hepatocyte growth factor; MMP-9 = matrix metallopeptidase-9; MPO = myeloperoxidase; NGF = nerve growth factor; NS = non-smokers; PAI-1 = plasminogen activator inhibitor-1; PDGF = platelet-derived growth factor; PlGF = placenta growth factor; RAGE= receptor for advanced glycation end products; S100A8 = S100 calcium-binding protein A8; S100A9 = S100 calcium-binding protein A9; SCF = stem cell factor; TGF = transforming growth factor; VEGF = vascular endothelial growth factor.
TABLE 5.
Observed value difference and P value of different GCF mediators between groups of non-smokers, cigarette smokers, e-cig users, and dual smokers
| Inflammatory mediators | CS-NS | EC-NS | DS-NS | EC-CS | DS-CS | DS-EC |
|---|---|---|---|---|---|---|
| EN-RAGE (SD = 52)a | ||||||
| Observed difference | 62.36 | 26.01 | 102.75* | −36.36 | 40.39 | 76.75 |
| P value | 0.25 | 0.64 | 0.05 | 0.50 | 0.46 | 0.14 |
| RAGE (SD = 14)a | ||||||
| Observed difference | −8.63 | −13.79 | 14.75 | −5.16 | 23.38 | 28.54* |
| P value | 0.56 | 0.33 | 0.29 | 0.73 | 0.09 | 0.04 |
| MMP-9 (SD = 28)a | ||||||
| Observed difference | −47.01 | −57.02* | −11.17 | −10.01 | 35.84 | 45.85 |
| P value | 0.09 | 0.04 | 0.70 | 0.73 | 0.22 | 0.11 |
| MPO (SD = 52)a | ||||||
| Observed difference | −145.87† | −175.74† | −64.67 | −29.87 | 81.21 | 111.07* |
| P value | 0.002 | 0.000 | 0.22 | 0.57 | 0.11 | 0.03 |
| S100A8 (SD = 43)a | ||||||
| Observed difference | −72.99 | −72.24 | −48.52 | .7511 | 24.47 | 23.72 |
| P value | 0.09 | 0.09 | 0.27 | 0.99 | 0.59 | 0.59 |
| S100A9 (SD = 1180)a | ||||||
| Observed difference | −817.15 | −726.89 | 930.65 | 90.26 | 1,747.80 | 1,657.54 |
| P value | 0.50 | 0.55 | 0.45 | 0.94 | 0.15 | 0.19 |
| Galectin-3 (SD = 1.9)a | ||||||
| Observed difference | −.7746 | −.9382 | 2.264 | −.1636 | 3.03 | 3.20 |
| P value | 0.71 | 0.64 | 0.24 | 0.93 | 0.11 | 0.09 |
| Uteroglobin/CC-10 (SD = 1.09)a | ||||||
| Observed difference | −.6678 | −1.37 | .8730 | −.7036 | 1.54 | 2.24* |
| P value | 0.55 | 0.22 | 0.44 | 0.55 | 0.16 | 0.03 |
| Tissue injury and repair | ||||||
| Serpine1/PAI-1 (SD = 0.96)a | ||||||
| Observed difference | −.7483 | −.8938 | .6152 | −.1454 | 1.36 | 1.51 |
| P value | 0.47 | 0.41 | 0.53 | 0.87 | 0.19 | 0.13 |
CS-NS = CS group compared with NS; DS-CS = DS group compared with CS; DS-EC = DS group compared with EC; DS-NS = DS group compared with NS; EC-CS = EC group compared with CS; EC-NS = EC group compared with NS.
Standard deviation of null distributions for permutation tests. Under the null each difference has the same standard deviation.
P ≤0.05.
P <0.01.
3.4 |. Level of growth factors in GCF samples
The levels of growth factors, including brain-derived neurotrophic factor (BDNF), fibroblast growth factors (FGF), nerve growth factor (NGF), stem cell factor (SCF), bone morphogenetic protein-2 (BMP-2), hepatocyte growth factor (HGF), platelet-derived growth factor-AA (PDGF-AA), transforming growth factor-α (TGF-α), epidermal growth factor (EGF), placenta growth factor (PlGF), and vascular endothelial growth factor (VEGF) in GCF samples are shown in Tables 4 and 6. All the growth factors measured values were lower in EC compared with each of the other groups, but these differences did not reach statistical significance (Tables 4 and 6).
TABLE 6.
Observed value difference and P value of various GCF growth factors between groups of non-smokers, cigarette smokers, e-cig users, and dual smokers
| Growth factors | CS-NS | EC-NS | DS-NS | EC-CS | DS-CS | DS-EC |
|---|---|---|---|---|---|---|
| BDNF (SD = 87)a | ||||||
| Observed difference | −93.92 | −125.00 | −57.95 | −31.08 | 35.97 | 67.06 |
| P value | 0.29 | 0.14 | 0.57 | 0.69 | 0.66 | 0.52 |
| Basic-FGF (SD = 56)a | ||||||
| Observed difference | −39.20 | −53.98 | 3.29 | −14.78 | 42.49 | 57.27 |
| P value | 0.52 | 0.36 | 0.94 | 0.77 | 0.49 | 0.34 |
| β-NGF (SD = 88)a | ||||||
| Observed difference | −76.99 | −83.51 | −3.83 | −6.51 | 73.15 | 79.67 |
| P value | 0.41 | 0.38 | 0.85 | 0.78 | 0.43 | 0.40 |
| SCF (SD = 282)a | ||||||
| Observed difference | −9.46 | −188.30 | 131.10 | −178.83 | 140.57 | 319.40 |
| P value | 0.95 | 0.50 | 0.66 | 0.53 | 0.63 | 0.26 |
| BMP-2 (SD = 459)a | ||||||
| Observed difference | 129.12 | −244.32 | 141.17 | −373.45 | 12.04 | 385.50 |
| P value | 0.79 | 0.60 | 0.75 | 0.42 | 0.97 | 0.42 |
| HGF (SD = 362)a | ||||||
| Observed difference | −22.22 | −210.70 | −8.21 | −188.49 | 14.00 | 202.49 |
| P value | 0.96 | 0.58 | 0.98 | 0.63 | 0.97 | 0.60 |
| PDGF-AA (SD = 106)a | ||||||
| Observed difference | −89.41 | −154.93 | −59.48 | −65.52 | 29.92 | 95.44 |
| P value | 0.44 | 0.14 | 0.59 | 0.57 | 0.77 | 0.41 |
| TGF-α (SD = 119)a | ||||||
| Observed difference | −69.91 | −88.67 | 22.02 | −18.75 | 91.94 | 110.70 |
| P value | 0.55 | 0.45 | 0.77 | 0.78 | 0.44 | 0.39 |
| EGF (SD = 88)a | ||||||
| Observed difference | −38.30 | −131.61 | −55.37 | −93.30 | −17.06 | 76.23 |
| P value | 0.68 | 0.14 | 0.55 | 0.31 | 0.86 | 0.40 |
| PlGF (SD = 55)a | ||||||
| Observed difference | −30.94 | −47.79 | 21.46 | −16.84 | 52.41 | 69.25 |
| P value | 0.56 | 0.41 | 0.68 | 0.75 | 0.37 | 0.23 |
| VEGF (SD = 474)a | ||||||
| Observed difference | −60.03 | −361.85 | 139.78 | −301.82 | 199.81 | 501.64 |
| P value | 0.90 | 0.48 | 0.78 | 0.55 | 0.67 | 0.32 |
CS-NS = CS group compared with NS; DS-CS = DS group compared with CS; DS-EC = DS group compared with EC; DS-NS= DS group compared with NS; EC-CS = EC group compared with CS; EC-NS = EC group compared with NS.
Standard deviation of null distributions for permutation tests. Under the null each difference has the same standard deviation.
4 |. DISCUSSION
The main objective of this study was to evaluate the effects of e-cigarette vaping, cigarette smoking, and dual use on levels of biomarkers of inflammation, oxidative stress, and tissue injury/repair and growth factors in human saliva and GCF. In this cross-sectional pilot study, several biomarkers belonging to different panels were assessed to potentially characterize them as specific biomarkers of EC, CS, or dual smokers. The biomarkers selected have been shown to be associated with systemic inflammation, oxidative stress, tissue injury/repair, and angiogenesis in the pathogenesis of respiratory, cardiovascular, and oral diseases.24–26
Several types of inflammatory and redox/oxidative stress biomarkers associated with both systemic diseases and oral diseases have been detected in saliva and GCF, such as IL-1β, IL-6, IL-8, tumor necrosis factor-α, matrix metalloproteinase-8 (MMP-8), and reduced glutathione GSH levels.27,28
E-cigs are battery-operated devices that deliver nicotine, flavorings, and other constituents by heating e-liquid. The composition of e-liquids varies greatly due to the broad range of nicotine concentrations and flavoring chemicals, thus resulting in different aerosol mixtures inhaled by EC.9 Since cotinine levels could represent current individual smoking activity,29,30 salivary cotinine was assessed in NS, CS, EC, and DS to confirm reported smoking status. Not surprisingly, it was found that CS, EC, and DS had higher cotinine levels compared with NS. Cotinine levels of EC were higher than CS, but lower than DS, suggesting their systemic nicotine exposure was unlike that of CS.
A previous study showed that e-cig vapor exposure in vitro produced inflammatory responses in human periodontal ligament fibroblasts and in human gingival epithelium progenitor cells.16 This translational study extended these findings with cross-sectional research of EC, CS, and dual smokers. Higher levels of inflammatory markers were observed, such as PGE2 and IL-1β in CS compared with EC and NS. In contrast, several inflammatory mediators including IL-1β were increased systemically in EC versus NS.31 This discrepancy may be due to the differential nature of the study, sample collections, and differences in cohorts (i.e., products used including nicotine and e-cig flavors). Nevertheless, these mediators play an important role in inflammatory pathways that can lead to inflammatory conditions in the lungs and oral health by smoking. Further, the findings from the present study are consistent with previous clinical studies that have shown the elevated levels of PGE2 and IL-1β in CS compared with EC in periodontal disease.32,33 These results suggest that chronic e-cig use/vaping may be associated with the development of chronic systemic and oral disease, although less so than the effects of smoking traditional cigarettes. In addition, e-cig use was hypothesized to perhaps not cause dramatic oxidative stress as has been observed with traditional smoking, such as the levels of MPO. The results of the present study show a slight decrease in the levels of MPO in EC compared with CS, suggesting that e-cig does not trigger overwhelming oxidant responses at least in oral fluids.
In contrast with EC, CS displayed a modest increase in the levels of EN-RAGE and RAGE. Smoking-associated inflammation may be triggered by interaction of RAGE and EN-RAGE. EN-RAGE, a member of the S100 protein family of EF-hand calcium-binding proteins, is an endogenously produced inflammatory ligand of RAGE.34 RAGE is a member of the immunoglobulin superfamily of cell surface molecules and expressed in multiple tissues, including cardiac, neural tissue, and monocyte-derived macrophages.35 The binding of RAGE by EN-RAGE activates inflammatory cascades, including the proinflammatory NF-κB signaling pathway, a well-known pathway involved in the pathogenesis of COPD and periodontitis.9,36 Hence, our results imply that EN-RAGE and RAGE are involved in CS-induced chronic inflammation and tissue injury.
Smoking-induced endothelial dysfunction may lead to the activation of inflammatory markers within vascular tissue.37,38 Growth factors in particular may play an important role in the regulation of inflammation and angiogenesis. The results of the present study show that growth factors including BDNF, FGF, NGF, SCF, BMP-2, HGF, PDGF-AA, TGF-α, EGF, PlGF, and VEGF, all presented statistically non-significant decreases in EC compared with CS. These decreases may result from e-cig generating aerosols from e-liquid without combustion and tobacco tar. Further, non-significant alterations of growth factors suggest lower inflammatory cascades by e-cig vaping.
The variations on the effects of vaping and smoking on periodontal biomarker profile as measured here in saliva and GCF may be due to younger population of participants, smoking/vaping history and duration, oral hygiene of the participants. It remains to be determined whether these biomarkers are affected in other biological fluids, such as plasma by e-cig vaping, and correlated with clinical conditions.
4.1 |. Limitations
The lack of control of the overall P value may be a limitation of this study, which did not have an a priori set of tests. Thus, Bonferroni-type adjustments are not really well suited. However, the expected number of false rejections was examined using a significance level of 0.05 assuming true values of all tested quantities are equal to zero and independent tests.39 There may be statistical artifacts of the present analysis due to small cell sizes, or because there are indeed unexpected differences that need further research. In this small cross-sectional pilot study, generalizability is limited because data were collected from a convenience sample at a single time point, which only allows for assessment of associations between the independent and dependent variables. Another caveat for generalizability is the use of permutation method in the data analysis. This limits external validity, which can only be estimated by addressing the probability that similar populations share the same relevant characteristics with the present sample.23 The present study did not assess when a participant last used a cigarette/e-cigarette. Future longitudinal studies with larger samples can address the above limitations and allow for specific hypothesis testing.
5 |. CONCLUSIONS
The results of this pilot study suggest that some of the measurable inflammatory biomarkers are affected by type of smokers, which can inform future translational and regulatory research. Larger clinical cross-sectional and longitudinal studies are required to evaluate the deleterious effects of e-cig vaping on oral health, including periodontal health.
ACKNOWLEDGMENTS
We thank our participants and our research assistants and nurses’ initial recruitment of participants, and Ms. Janice Gerloff, Ms. Samantha McDonough (Department of Environmental Medicine, University of Rochester, Medical Center, Rochester, NY), and Dr. Naushad Ahmad Khan (Department of Environmental Medicine, University of Rochester, Medical Center, Rochester, NY) for technical assistance. Thanks to Drs. Rita Cacciato (Eastman Institute for Oral Health, University of Rochester, Medical Center, Rochester, NY), Deborah Ossip (Department of Public Health Sciences, University of Rochester, Medical Center, Rochester, NY), and Luke Peppone (Department of Surgery, University of Rochester, Medical Center, Rochester, NY) for their help in the study. The authors report no conflicts of interest related to this study. This work was supported in part by a National Institutes of Health (NIH) Grant, NIH 1R01HL135613. Also, in part was supported by the National Cancer Institute of the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) Center for Tobacco Products under Award Number U54CA228110.
Funding information
National Institutes of Health, Grant/Award Number: 1R01HL135613; National Cancer Institute; Center for Tobacco Products, Grant/Award Number: U54CA228110
Footnotes
PerioPaper strip, Oraflow, Plainview, NY.
Micro BCA Protein Assay Kit, Thermo Scientific, Waltham, MA.
Salivary Cotinine ELISA Kit, Salimetrics, Carlsbad, CA.
PGE-2 and IL-1β ELISA Kit, Cayman, Ann Arbor, MI.
Human XL 9-plex and customized 12-plex magnetic Luminex immunoassay kits, R&D systems, Minneapolis, MN.
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