Objectives: Although smoking is known to have a negative impact in patients with metabolic syndrome (MetS), only a few studies have examined the association between electronic cigarette (e-cig) use and MetS. Methods: Among 22,948 participants in the 6th Korea National Health and Nutrition Examination Survey, 14,738 (13,459 [91.3%] never, 954 [6.5%] ever, and 325 [2.2%] current e-cig users) were selected. The relationship between e-cig exposure and MetS (based on the National Cholesterol Education Program Adult Treatment Panel [NCEP-ATP] III criteria) was evaluated using a multivariable logistic regression analysis. An unweighted analysis was performed to evaluate this association without a sampling weight. A subgroup analysis was performed among active smokers to compare dual users with never e-cig users. Results: Among current e-cig users, 85.0% were dual users, 12.7% were former cigarette users, and 2.2% were only e-cig users. After adjustment for covariates, abdominal obesity and hypertriglyceridemia were significantly associated with current e-cig exposure (odds ratio [OR]: 1.88, 95% confidence interval [CI]: 1.41–2.50 and OR: 1.32, 95% CI: 1.00–1.74 respectively [compared with the never e-cig users group]). Compared with never e-cig users, current e-cig users showed an OR of 1.27 (95% CI: 0.96–1.70, Ptrend = 0.01) for MetS. In the unweighted analysis, the OR for MetS in current e-cig users was 1.40 (95% CI: 1.08–1.81, Ptrend <0.01). Compared with never e-cig users, dual users showed a higher OR for abdominal obesity (OR: 1.71, 95% CI: 1.25–2.34, Ptrend <0.001). Conclusions: Current e-cig exposure was associated with an increased risk of MetS. Dual use of e-cigs and cigarettes was associated with abdominal obesity. Further longitudinal studies and better assessment of e-cig use and type are needed to clarify this relationship.
Since their introduction in 2007, the popularity of electronic cigarettes (e-cigs) has steadily increased among young populations, with 27.5% of high school students and 10.5% of middle school students reporting the use of e-cigs in 2019 [[
As the e-cig-consuming population increases, various adverse health effects have been reported. E-cigs contain several chemical components; however, the actual composition of this product is not well known and there is a significant gap in the data on their health effects. In a crossover single-blind study conducted in 40 healthy individuals, several markers of oxidative stress were not different between e-cig users and traditional cigarette users [[
Metabolic syndrome (MetS) is a term encompassing various metabolic statuses and adult diseases such as hypertriglyceridemia, low high-density lipoprotein (HDL) cholesterol levels, hypertension, diabetes mellitus, and central obesity [[
The South Korean government strongly recommended the suspension of the use of liquid-type e-cigs in October 2019. The results of our analysis on the relationship between e-cig use and metabolic variables can serve as a basis for the development and implementation of policies. We hypothesized that e-cig use may be associated with an increased risk for MetS and evaluated the association between e-cig use and MetS in the Korean general population using a nationwide representative sample.
This cross-sectional study used data from the 6th Korea National Health and Nutrition Examination Survey (KNHANES) from 2013 to 2015. The KNHANES is an annual, nationally representative, population-based survey organized by the Korea Centers for Disease Control and Prevention. Briefly, the survey is designed using a stratified multistage probability sampling method to represent non-institutionalized Korean citizens and consists of health interviews, health examinations, and nutrition surveys. The detailed data profiles have been described previously [[
The study protocol was approved by the Institutional Review Board of the Kosin University Gospel Hospital (no. 2020-06-022), and the study was conducted according to the Declaration of Helsinki. All study procedures were performed in accordance with the Strengthening the Reporting of Observational studies in Epidemiology guidelines. Written informed consent was obtained from all individuals before participation in the survey.
MetS was defined based on the modified Third National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP-ATP III) criteria [[
Data on e-cig use were gathered using a self-reported questionnaire, which included the following items: (a) "Have you ever used an e-cig in your lifetime?" and (b) "Have you used e-cigs within the past 30 days?" Participants who responded "no" to both questions were classified as never e-cig users. Participants who responded "yes" to the first question and "no" to the second question were classified as ever e-cig users. Participants who responded "yes" to both questions were classified as current e-cig users. Data on cigarette use were obtained based on the World Health Organization classification: a current smoker was defined as anyone who had smoked more than 100 cigarettes in their lifetime and smoked currently; a former smoker was defined as anyone who had smoked more than 100 cigarettes in the past and did not smoke currently; and a never smoker was defined as anyone who had ever smoked less than 100 cigarettes and did not smoke currently.
Information on alcohol consumption and physical activity were gathered from self-administered questionnaires or face-to-face interviews. High-risk alcohol consumption was defined as seven (60 g alcohol) or more drinks for men and five (40 g alcohol) or more drinks for women on a single occasion [[
Body mass index (BMI) was calculated as body weight (kg) divided by height in meter squared (m
Since the KNHANES was designed using a multistage clustered probability sampling method to obtain a nationally representative sample of non-institutionalized Korean citizens, all statistical analyses applied a complex survey design and sampling weight.
The general characteristics of the study participants were categorized according to the status of e-cig use (never, ever, and current). Comparisons between e-cig use groups were performed using a one-way analysis of variance for normally distributed continuous variables and chi-square tests for categorical variables. Subsequently, a post-hoc analysis using Bonferroni correction was performed for multiple comparisons. P-values <0.017 were considered significant in the post-hoc analysis.
The associations between e-cig use and MetS components were evaluated using a multivariable logistic regression analysis with stepwise adjustment for covariates. Model 1 was adjusted for age and sex. Model 2 was adjusted for age, sex, and cigarette use. Model 3 was adjusted for age, sex, cigarette use, and alcohol consumption. Model 4 was adjusted for age, sex, cigarette use, alcohol consumption, physical activity, household income, and education level.
A multivariable logistic model was used to evaluate the association between e-cig use and MetS with stepwise adjustment for the above-mentioned covariates. The associations between e-cig use and MetS components were presented as odds ratios (ORs) with 95% confidence intervals (CIs). An unweighted analysis was also performed to determine whether analyses without sample weighting and clustering altered the associations observed in the weighted analysis. A subgroup analysis of active smokers was also conducted to minimize the effects of traditional cigarette use.
All statistical analyses were performed using IBM SPSS Statistics for Windows, version 25.0 (IBM Corp., Armonk, New York). P-values <0.05 were considered significant.
Table 1 presents the general characteristics of the participants according to the status of e-cig use. Among current e-cig users, 85.0% were dual users, 12.7% were former smokers, and 2.2% were only e-cig users. WC was greater in male current e-cig users than in male never e-cig users. TG levels were the highest in current e-cig users, followed by ever and never e-cig users. Diastolic BP was lower in never e-cig users than in ever and current e-cig users.
Graph
Table 1 Characteristics of the study participants according to electronic cigarette exposure (N = 14,738).
Never users (n = 13,459) Ever users (n = 954) Current users (n = 325) Age (years) 47.6 (0.2) 36.5 (0.4) 35.8 (0.6) <0.001*,† Male 51.1% (0.5) 89.9% (1.0) 88.2% (1.8) <0.001*,† Smoking status <0.001*,† Never 60.8% (0.5) 2.3% (0.5) 2.2% (0.8) Former 20.2% (0.4) 18.8% (1.5) 12.7% (2.0) Current 19.0% (0.5) 78.9% (1.6) 85.0% (2.2) High-risk alcohol consumptiona) <0.001*,† ≥1/week 52.9% (0.6) 54.9% (1.8) 56.2% (3.0) <1/week 47.1% (0.6) 45.1% (1.8) 43.8% (3.0) Physical activityb) 0.33 Yes 52.9% (0.6) 54.9% (1.8) 56.2% (3.0) No 47.1% (0.6) 45.1% (1.8) 43.8% (3.0) Household income <0.01*,† Lowest 11.8% (0.5) 8.7% (1.0) 7.3% (1.6) Lower middle 22.9% (0.6) 22.2% (1.5) 20.1% (2.5) Higher middle 30.9% (0.7) 35.4% (1.8) 39.2% (3.4) Highest 34.4% (0.9) 33.7% (1.7) 33.4% (3.2) Educational level <0.001*,† Middle school or lower 18.0% (0.5) 5.9% (0.8) 4.5% (1.1) High school 27.9% (0.6) 28.2% (1.6) 31.1% (2.8) College or more 54.1% (0.8) 65.9% (1.7) 64.4% (2.9) Body mass index <0.001*,† <18.5 kg/m2 4.3% (0.2) 4.1% (0.8) 5.4% (1.6) 18.5–22.9 kg/m2 40.6% (0.5) 29.9% (1.6) 29.9% (2.7) 23.0–24.9 kg/m2 22.9% (0.4) 23.7% (1.5) 19.0% (2.3) 25.0≥ kg/m2 32.2% (0.5) 42.2% (1.9) 45.8% (3.2) Metabolic variables WC (cm) Men 85.5 (0.1) 86.1 (0.4) 87.4 (0.7) 0.01† Women 77.2 (0.2) 74.8 (0.9) 76.0 (1.7) 0.03* Triglyceride (mg/dL) 136.4 (1.5) 173.1 (4.5) 185.3 (10.4) <0.001*,† Fasting glucose (mg/dL) 98.7 (0.2) 97.6 (0.8) 98.4 (1.2) 0.41 HDL-cholesterol (mg/dL) Men 48.2 (0.2) 47.5 (0.5) 47.1 (0.7) 0.14 Women 56.3 (0.2) 59.3 (1.4) 59.3 (1.8) 0.03 SBP (mmHg) 116.3 (0.2) 116.7 (0.5) 115.8 (0.7) 0.55 DBP (mmHg) 75.7 (0.1) 77.6 (0.4) 77.5 (0.6) <0.001*,†
1 Data are presented as weighted percentages (standard errors [SEs]) for categorical variables or weighted means (SEs) for continuous variables, unless otherwise stated.
- 2 P-values were calculated using a one-way analysis of variance for continuous variables and chi-square test for categorical variables.
- 3 Post-hoc analyses with Bonferroni's correction were performed between *never vs. ever, †never vs. current, and ‡ever vs. current e-cig users. A P-value <0.017 was considered significant.
- 4 a) Heavy alcohol consumption was defined as the consumption of ≥7 drinks in men and ≥5 drinks in women on an occasion.
- 5 b) Adequate physical activity was defined as 1) at least 150–300 minutes of moderate-intensity physical activity per week, 2) 75–150 minutes of vigorous-intensity physical activity per week, or 3) an equivalent combination of moderate- and vigorous-intensity aerobic activities.
- 6 WC: waist circumference, HDL: high-density lipoprotein, SBP: systolic blood pressure, DBP: diastolic blood pressure.
The associations between the five MetS components and e-cig use status are shown in Table 2. Current e-cig users showed a significantly higher OR for abdominal obesity and hypertriglyceridemia than never e-cig users (OR: 1.88, 95% CI: 1.41–2.50, P
Graph
Table 2 Association between metabolic syndrome components and electronic cigarette exposure.
Never user (n = 13,459) Ever user (n = 954) Current user (n = 325) Reference OR (95% CI) OR (95% CI) Abdominal obesity Model 1 1 1.48 (1.25–1.75) 1.91 (1.44–2.52) <0.001 Model 2 1 1.45 (1.21–1.74) 1.87 (1.41–2.48) <0.001 Model 3 1 1.44 (1.20–1.72) 1.89 (1.42–2.51) <0.001 Model 4 1 1.42 (1.19–1.70) 1.88 (1.41–2.50) <0.001 High triglycerides Model 1 1 1.64 (1.41–1.92) 1.80 (1.37–2.36) <0.001 Model 2 1 1.22 (1.30–1.44) 1.30 (1.00–1.71) <0.01 Model 3 1 1.20 (1.01–1.42) 1.32 (1.00–1.74) <0.01 Model 4 1 1.20 (1.10–1.41) 1.32 (1.00–1.74) 0.01 High fasting glucose Model 1 1 1.03 (0.86–1.24) 1.20 (0.90–1.60) 0.26 Model 2 1 0.90 (0.75–1.09) 1.04 (0.79–1.39) 0.72 Model 3 1 0.89 (0.73–1.07) 1.05 (0.79–1.40) 0.67 Model 4 1 0.89 (0.74–1.08) 1.05 (0.78–1.40) 0.67 Low HDL-cholesterol Model 1 1 1.35 (1.13–1.61) 1.40 (1.06–1.85) <0.001 Model 2 1 1.13 (0.94–1.37) 1.15 (0.87–1.54) 0.16 Model 3 1 1.15 (0.95–1.39) 1.14 (0.86–1.52) 0.15 Model 4 1 1.15 (0.95–1.39) 1.14 (0.86–1.52) 0.16 High blood pressure Model 1 1 1.29 (1.08–1.54) 0.96 (0.73–1.26) 0.13 Model 2 1 1.23 (1.03–1.47) 0.90 (0.68–1.20) 0.49 Model 3 1 1.19 (1.00–1.42) 0.91 (0.68–1.21) 0.59 Model 4 1 1.19 (0.97–1.42) 0.91 (0.68–1.21) 0.60
- 7 a) Metabolic syndrome was defined in accordance with the modified Third National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP-ATP III) criteria and the abdominal obesity criteria of the Korean Society for the Study of Obesity.
- 8 b) Model 1 was adjusted for age and sex; Model 2 additionally adjusted for conventional cigarette exposure; Model 3 was additionally adjusted for alcohol consumption; and Model 4 was additionally adjusted for physical activity, household income, and education level.
- 9 c) The P-value for trend was measured using a logistic regression analysis considering electronic cigarette exposure as a continuous variable.
- 10 OR: odds ratio, CI: confidence interval.
The associations between MetS and e-cig use status are shown in Table 3. The prevalence rates of MetS were 23.6%, 26.8%, and 25.9% in never, ever, and current e-cig users, respectively (P
Graph
Table 3 Association between metabolic syndrome and electronic cigarette exposure.
Never user (n = 13,459) Ever user (n = 954) Current user (n = 325) Reference OR (95% CI) OR (95% CI) Prevalence of MetS, % (SE) 23.6 (0.4) 26.8 (1.6) 25.9 (0.2) 0.08 Model 1 1 1.51 (1.26–1.82) 1.53 (1.16–2.01) <0.001 Model 2 1 1.28 (1.05–1.54) 1.27 (0.96–1.69) 0.01 Model 3 1 1.26 (1.04–1.52) 1.28 (0.96–1.70) 0.01 Model 4 1 1.25 (1.03–1.51) 1.27 (0.96–1.69) 0.01
- 11 a) Model 1 was adjusted for age and sex; Model 2 was additionally adjusted for conventional cigarette exposure; Model 3 was additionally adjusted for alcohol consumption; and Model 4 was additionally adjusted for physical activity, household income, and education level.
- 12 b) The P-value for trend was measured using a logistic regression analysis considering electronic cigarette exposure as a continuous variable.
- 13 OR: odds ratio, CI: confidence interval.
Graph
Table 4 Unweighted analysis of the association between metabolic syndrome and electronic cigarette exposure.
Never user (n = 13,459) Ever user (n = 954) Current user (n = 325) Reference OR (95% CI) OR (95% CI) Model 1 1 1.47 (1.25–1.73) 1.72 (1.33–2.22) <0.001 Model 2 1 1.22 (1.03–1.44) 1.40 (1.08–1.82) <0.01 Model 3 1 1.20 (1.01–1.42) 1.40 (1.08–1.82) <0.01 Model 4 1 1.20 (1.01–1.42) 1.40 (1.08–1.81) <0.01
- 14 a) Model 1 was adjusted for age and sex; Model 2 was additionally adjusted for conventional cigarette exposure; Model 3 was additionally adjusted for alcohol consumption; and Model 4 was additionally adjusted for physical activity, household income, and education level.
- 15 b) The P-value for trend was measured using a logistic regression analysis considering electronic cigarette exposure as a continuous variable.
- 16 OR: odds ratio, CI: confidence interval.
Results of the subgroup analysis conducted among active smokers to determine the association between e-cig use status and MetS are shown in Table 5. Dual use of e-cigs and cigarettes was associated with an increased OR for abdominal obesity. The OR for abdominal obesity in dual users was 1.71 (95% CI: 1.25–2.34) compared with that in never e-cig users.
Graph
Table 5 Association between metabolic syndrome and electronic cigarette exposure among active smokers (n = 3,278).
Never user (n = 2,263) Ever user (n = 774) Current user (n = 271) Reference OR (95% CI) OR (95% CI) Metabolic syndrome 1 1.14 (0.92–1.43) 1.13 (0.82–1.55) 0.25 Abdominal obesity 1 1.28 (1.04–1.58) 1.71 (1.25–2.34) <0.001 High triglyceride 1 1.11 (0.91–1.36) 1.24 (0.91–1.70) 0.13 High fasting glucose 1 0.92 (0.73–1.15) 1.02 (0.73–1.42) 0.78 Low HDL-cholesterol 1 1.13 (0.90–1.41) 1.13 (0.82–1.55) 0.29 High blood pressure 1 1.15 (0.94–1.41) 0.78 (0.56–1.07) 0.57
- 17 a) In all analyses, age, sex, alcohol consumption, physical activity, household income, and education level were adjusted as multivariables.
- 18 b) The P-value for trend was measured using a logistic regression analysis considering electronic cigarette exposure as a continuous variable.
- 19 OR: odds ratio, CI: confidence interval, HDL: high-density lipoprotein.
In the present study, we observed that e-cig use was significantly associated with an increased OR for MetS. The OR for MetS was the highest in current e-cig users and the lowest in never e-cig users. This significant difference might be attributable to the increased ORs for abdominal obesity, high TG levels, and low HDL-cholesterol levels. To the best of our knowledge, this is the first study to evaluate the association between e-cig use and MetS in the general population. These results indicate that e-cigs may play a role in inducing several metabolic abnormalities, and these findings could form a basis for further research on the types, chemical components, and consumption patterns of e-cigs that are responsible for this relationship.
Interestingly and importantly, 85% of current e-cig users continued to use cigarettes. Dual users were found to have a higher OR for abdominal obesity than former smokers, current e-cig users, and never e-cig users. This high proportion of dual use is consistent with the findings from other countries. In the United States, nearly 93% of e-cig users consume cigarettes concurrently [[
The association between cigarette use and metabolic abnormalities has been widely evaluated since Facchini et al. reported the effect of cigarette smoke on insulin resistance in 1992 [[
This study showed an association between e-cig use and MetS. However, the underlying mechanisms of this relationship are still not well understood, and the specific e-cig components responsible for these results remain unknown. It is plausible to assume that the negative impacts of e-cig use on metabolism are similar to those of cigarette use as e-cigs also contain varying levels of toxicants such as nicotine, based on the device, as well as additive materials such as flavors and solvent carriers [[
Although our study findings represent a significant advancement in our understanding of the health-related adverse effects of e-cig use, there are several limitations to our study. First, considering the cross-sectional nature of this study, the results should be interpreted with caution as the causal relationships are unclear. Moreover, prospective studies must be conducted on the effects of e-cig use on various metabolic abnormalities in the human body. Second, data on the use of e-cigs and cigarettes were lacking. Although the study findings were adjusted for smoking status as a covariate, data on other potential confounders such as cigarette pack-years (the 2013–2015 KNHANES only assessed pack-years of smoking in current smokers but not in former smokers), types of e-cigs, flavors of e-cigs, consumption pattern, and indoor use of e-cigs were unavailable in the KNHANES.
E-cigs are considered safer than traditional cigarettes; however, being less harmful does not guarantee safety. Although the data from animal studies provide useful insights on the effects of e-cig use, caution is required while interpreting the results of these studies due to the differences in materials and methods, species, and time of exposure. Since an increasing number of people are consuming e-cigs and since many people are dual users, research on health-related concerns is currently in progress. Therefore, efforts are needed to determine the real-world effects of e-cig use on the human body, especially in terms of metabolic abnormalities. In addition, the effect of e-cig use on metabolic outcomes needs to be evaluated according to the flavors and types of e-cigs in future studies.
E-cig exposure was associated with an increased risk of MetS, and abdominal obesity, low HDL-cholesterol levels, and high TG levels were thought to be the main contributors to this relationship. The dual use of e-cigs and traditional cigarettes showed an increased OR for abdominal obesity. Our results support the hypothesis that e-cig use is associated with MetS and are in line with the results of several previous studies emphasizing that e-cigs are not as safe as previously believed. Further studies are needed to clarify the underlying mechanisms contributing to these findings.
By Taeyun Kim; Hyunji Choi; Jihun Kang and Jehun Kim
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