Abstract
Background
The use of e-cigarettes is increasing in the US but there is still a paucity of research on the metabolic effects of e-cigarette use. The goal of this work was to determine the association between e-cigarette use and self-reported prediabetes in adult never cigarette smokers.
Method
The 2017 cross sectional Behavioral Risk Factor Surveillance System (BRFSS) survey data was used for the analysis. Current e-cigarette users reported daily or someday use of e-cigarettes and former e-cigarette users reported no current use of e-cigarettes. Participants who reported a history of diabetes, gestational prediabetes/ diabetes were excluded. Odds ratios were calculated to determine the association between e-cigarette use and self-reported prediabetes in never cigarette smokers after adjusting for potential confounders.
Results
There were a total of 154,404 participants that met the inclusion criteria. Of those participants, there were 143,952 never, 1,339 current and 7,625 former e-cigarette users. Current e-cigarette users had an increased odds of reporting a diagnosis of prediabetes 1.97 (95% CI 1.25–3.10) compared to never e-cigarette users. After stratifying by gender, men and women had an increased odds ratio of reporting a diagnosis of prediabetes 2.36 (95% CI 1.26 – 4.40) and 1.88 (95% CI 1.00 – 3.53) respectively when compared to never e-cigarette users. There was no association between former e-cigarette use and a self-reported diagnosis of prediabetes.
Conclusion
Our findings show that e-cigarette use may be associated with self-reported prediabetes. Further evaluation is needed in prospective studies.
Keywords: e-cigarette, prediabetes, electronic nicotine delivery systems
1. INTRODUCTION
The use of e-cigarettes is increasing rapidly especially among young adults in the United States (US), and an estimated one-third of current e-cigarette users are never or former cigarette smokers (McMillen et al., 2015). E-cigarettes are perceived by the public to be safer than cigarettes(Pearson et al., 2012), but studies have shown that e-cigarette aerosol can contain nicotine and other toxicants that are found in cigarette smoke(Fagan et al., 2017; Goniewicz et al., 2018). The deleterious effects of e-cigarette use on metabolic parameters have not yet been studied thoroughly in humans; however, animal models show that e-cigarette liquids, even without nicotine, can induce weight and metabolic changes such as hyperglycemia that are similar to cigarette smoking (Verhaegen and Van Gaal, 2017).
Prediabetes is a state of intermediate hyperglycemia, and defined by the American Diabetes Association as a fasting plasma glucose of 100 to 125mg/dL or 2-h plasma glucose value during a 75-g oral glucose tolerance test of 140 to 199 mg/dL, or hemoglobin A1c (HbA1c) levels of 5.7–6.4% (Association, 2019). An estimated 84.1 million people age 18 years or older in the US had prediabetes in 2015 based on their fasting glucose or HbA1c level (Centers for Disease Control and Prevention, 2017) and about 5–10% of people with prediabetes become diabetic annually(Tabák et al., 2012).
Prediabetes has been shown to be associated with multiple comorbidities, including stroke, cardiovascular disease, coronary heart disease, chronic kidney disease, erectile dysfunction, polyneuropathy, sensory/small fiber neuropathy and diabetic retinopathy (Huang et al., 2016; Tabák et al., 2012)
Cigarette smoking and dual use of cigarette and e-cigarettes have been shown to be associated with prediabetes and increased HbA1c levels in adults without a history of diabetes (Aeschbacher et al., 2014; Choi et al., 2018). Nicotine that can be found in e-cigarette liquids (Fagan et al., 2017) has been shown to negatively affect pancreatic beta cells, increase HbA1c levels and induce hyperinsulinemia(Clair et al., 2011; Eliasson et al., 1996; Xie et al., 2009). Furthermore, e-liquids with or without nicotine have been shown to induce hyperglycemia and affect glucose homeostasis in rats (El Golli et al., 2016a).
The goal of this study was to examine the association between e-cigarette use and self-reported prediabetes in a representative population of adult never cigarette smokers in the United States who have no known history of diabetes. Based on the fact that e-cigarette liquids and nicotine found in e-cigarette liquids can affect glucose homeostasis, we hypothesized that e-cigarette use will be associated with an increased odds of self-reported prediabetes after adjusting for other potential confounding factors.
2. METHOD AND MATERIALS
2.1. Data source
The 2017 cross-sectional Behavioral Risk Factor Surveillance System (BRFSS) survey data was used for the analysis (Centers for Disease Control and Prevention (CDC), 2017,-b). BRFSS is a collaborative project between the Centers for Disease Control and Prevention (CDC) and all states in the US and participating US territories. The BRFSS survey is designed to collect data on health-related risk behaviors, chronic health conditions, and use of preventive services from the noninstitutionalized adult population (≥ 18 years) residing in the US. It includes a core set of questions that is used by all the states and optional modules that can be included by the different states. Data in BRFSS is self-reported and collected using landlines and cellphones. To collect the data, landlines numbers are randomly selected using a disproportionate stratified sample design and data is collected from a randomly selected adult in the household. Also, cellphone numbers are randomly selected using cellphone sampling frames and data is collected from an adult who answers the cellphone and who resides in a private or college housing. More information about BRFSS design can be found elsewhere(Centers for Disease Control and Prevention (CDC), 2017,-a)
The prediabetes questionnaire was an optional module in the 2017 survey, therefore only the 47 states and US territories that reported data on the prediabetes module were used for the analysis (N = 355,585).
2.2. Exclusion criteria
Participants who were current or former smokers of conventional cigarettes (N =149,892) or who had a history of diabetes (N=48,677), gestational prediabetes (N=2,979) or gestational diabetes (N=2,581) were excluded from the analysis.
2.3. Data and definitions
Data was collected on age, race, gender, marital status, education level, tobacco and alcohol use, food and vegetable consumption, exercise patterns and health status. Never smokers of cigarettes reported having smoked less than 100 cigarettes in their lifetime. E-cigarette use was assessed with the following question: “Have you ever used an e-cigarette or other electronic vaping product, even just one time, in your entire life?” Never e-cigarette users reported “no” to the question and ever e-cigarette users reported “yes” to the question. Ever e-cigarette users were then asked the following question: “Do you now use e-cigarettes or other electronic “vaping” products every day, some days, or not at all”. Current e-cigarette users reported currently using e-cigarettes every day or some days. Former e-cigarette users reported ever using an e-cigarette but not currently using e-cigarettes.
Prediabetes was defined as a “yes” to the question: “Ever been told by a doctor or other health professional that you have pre-diabetes or borderline diabetes?” and participants who tested their blood sugar in the past three years reported “yes” to the following question: “Had a test for high blood sugar or diabetes in the past three years?”
2.4. Statistical analysis
Descriptive statistics for never smokers of cigarettes who were current, former or never e-cigarette users are shown in Table 1. Multivariable logistic regression was used to examine the association between e-cigarette use and self-reported prediabetes after controlling for all the confounders listed in Table 1. For the secondary analysis, subgroup analysis with only participants that reported a test for high blood sugar or diabetes in the past three years was performed. Additionally, e-cigarette use was stratified based on gender, and the association between e-cigarette use and self-reported prediabetes was examined after adjusting for all the confounders in Table 1.
Table 1:
Demographics and medical history of never smokers stratified by e-cigarette use
| Never e-cigarette user n= 143,952 % (95% CI %) | Current e-cigarette user n = 1,339 % (95% CI %) | Former e-cigarette user n= 7,625 % (95% CI %) | |
|---|---|---|---|
| Gender | |||
| Male | 43.1 (42.5 – 43.6) | 68.8 (64.7 – 72.9) | 56.6 (54.5 – 58.7) |
| Female | 56.9 (56.4 – 57.5) | 31.2 (27.1 – 35.3) | 43.4 (41.3 – 45.5) |
| Age groups | |||
| 18 to 24 years | 14 (13.6 – 14.5) | 67.3 (63.1 – 71.6) | 52 (49.9 – 54.1) |
| 25 to 34 years | 17.3 (16.9 – 17.8) | 21.2 (17.5 – 24.8) | 28.3 (26.5 – 30.2) |
| 35 to 44 years | 16.9 (16.5 – 17.4) | 6.9 (4.3 – 9.4) | 9.8 (8.6 – 10.9) |
| 45 to 54 years | 18.4 (18 – 18.8) | 2.5 (1.2 – 3.7) | 5.6 (4.8 – 6.5) |
| 55 years and older | 33.3 (32.8 – 33.8) | 2.2 (1.3 – 3.1) | 4.3 (3.7 – 4.9) |
| Race and ethnicity | |||
| White only, Non-Hispanic | 62.5 (61.9 – 63.1) | 60.2 (55.6 – 64.8) | 57.7 (55.6 – 59.8) |
| Black only, Non-Hispanic | 13.7 (13.3 – 14.2) | 13.7 (10.2 – 17.1) | 14.6 (13.1 – 16.2) |
| Other race or multiracial, Non-Hispanic | 7.8 (7.5 – 8.2) | 10.4 (7.7 – 13.2) | 8.3 (7.2 – 9.4) |
| Hispanic | 15.9 (15.4 – 16.4) | 15.7 (12.1 – 19.2) | 19.4 (17.5 – 21.2) |
| Education level | |||
| Did not graduate High School | 10.3 (9.9 – 10.8) | 11.2 (7.8 – 14.7) | 7 (5.7 – 8.2) |
| Graduated High School | 25.3 (24.8 – 25.8) | 43.6 (38.9 – 48.2) | 30.7 (28.7 – 32.7) |
| Attended College or Technical School | 29.2 (28.7 – 29.7) | 33.6 (29.5 – 37.7) | 38.8 (36.8 – 40.8) |
| Graduated from College or Technical School | 35.1 (34.6 – 35.6) | 11.6 (9.2 – 14.1) | 23.5 (21.9 – 25.1) |
| Other factors | |||
| Currently married | 55.3 (54.7 – 55.8) | 13.6 (10.7 – 16.6) | 21.8 (20.2 – 23.4) |
| Body Mass Index (BMI)a | 27.6 (27.6 – 27.7) | 26.4 (25.8 – 27.0) | 26.6 (26.4 – 26.9) |
| Consumed fruits or vegetables at least once a day | 90.3 (90 – 90.7) | 82.8 (78.6 – 87) | 84 (82.3 – 85.8) |
| Participated in 150 minuites (or vigorous equivalent minutes) of physical activity per week | 51.6 (51 – 52.2) | 55.1 (50.3 – 59.9) | 52 (49.9 – 54.2) |
| Heavy alcohol drinkera,b | 3.4 (3.2 – 3.6) | 9.6 (7 – 12.2) | 9.7 (8.5 – 10.9) |
| Currently using smokeless tobacco products | 2.3 (2.2 – 2.5) | 7.6 (5.5 – 9.7) | 5.6 (4.8 – 6.4) |
| Strength Activity Frequency per Weeka | 1.4 (1.3 – 1.4) | 2.2 (2.0 – 2.4) | 1.9 (1.8 – 2.0) |
|
Medical history History of COPD |
2.5 (2.3 – 2.6) | 1.5 (0.6 – 2.4) | 2.3 (1.7 – 2.9) |
| Currently has Asthma | 7.5 (7.2 – 7.8) | 10.1 (7.3 – 12.8) | 9.9 (8.7 – 11.1) |
| History of heart diseasec | 3.5 (3.3 – 3.7) | 1.4 (0.3 – 2.6) | 0.8 (0.6 – 1.1) |
| History of high cholesterol | 26.4 (25.9 – 26.9) | 10.7 (7.5 – 14) | 14 (12.6 – 15.5) |
| History of high blood pressure | 26 (25.5 – 26.4) | 13.6 (10.4 – 16.9) | 14.2 (12.8 – 15.5) |
| Tested blood sugar in past 3 years | 57.7 (57.1 – 58.3) | 33.9 (29.5 – 38.2) | 40.4 (38.3 – 42.4) |
| History of depressiond | 13.2 (12.8 – 13.5) | 24.1 (20.5 – 27.7) | 20.1 (18.5 – 21.6) |
| Outcome | |||
| History of prediabetes | 9.4 (9.1 – 9.7) | 5.6 (3.3 – 7.8) | 5.2 (4.3 – 6.1) |
Mean (95% CI)
Men having more than 14 drinks per week, and women having more than 7 drinks per week
Participants reported ever been told by a by a doctor, nurse, or other health professional that they have heart attack and/or angina and/or coronary heart disease
Participants reported ever been told by a doctor, nurse, or other health professional that they have a depressive disorder, including depression, major depression, dysthymia, or minor depression
Weights were incorporated into the analysis to account for probability of selection and also to adjust for nonresponse bias and non-coverage errors as detailed elsewhere (Centers for Disease Control and Prevention (CDC), 2017,-c). Weights and analysis for the subpopulations were calculated as detailed elsewhere (Lumley, 2010). All participants with missing values were removed from the analysis. All analyses were conducted using R version 3.4.2.
3. RESULTS
In our cohort, there were 143,952 never e-cigarette users, 1,339 current e-cigarette users and 7,625 former e-cigarette users. Descriptive statistics and medical history of never, current and former e-cigarette users are shown in Table 1. Among the never smokers used for the analysis, 55.4% (95% CI 54.9 – 56.0) were female, 55.8% (95% CI 55.3 – 56.4) had tested their blood sugar in the past 3 years and 9.0 (95% CI 8.7 – 9.3) had self-reported prediabetes. Among the participants who had a blood sugar test in the past 3 years, 58.9% (95% CI 58.2 – 59.6) were female and 14.0% (95% CI 13.5 – 14.5) had self-reported prediabetes.
Compared to never e-cigarette users, the odds ratio for self-reported prediabetes was 1.97 (95% CI 1.25–3.10) for current e-cigarette users and 1.07 (95% CI 0.84–1.37) for former e-cigarette users. After stratifying by gender, among men, the odds ratio of self-reported prediabetes was 2.36 (95% CI 1.26 – 4.40) for current e-cigarette users and 1.22 (95% CI 0.86 – 1.74) for former e-cigarette users compared to never e-cigarette users. Among women, the odds ratio of self-reported prediabetes was 1.88 (95% CI 1.00 – 3.53) for current e-cigarette users and 1.00 (95% CI 0.71 – 1.40) for former e-cigarette users compared to never e-cigarette users.
Current e-cigarette users who have had a blood sugar test in the past 3 years also had increased odds of self-reported prediabetes compared to never e-cigarette users (Table 2). After stratifying by gender, male current e-cigarette users had increased odds of self-reported prediabetes compared to male never e-cigarette users. There was no statistically significant difference in the odds of self-reported prediabetes for females who were current e-cigarette compared to never e-cigarette users (Table 2).
Table 2:
Results of the multivariable regression analyses for never smokers who had a blood sugar test within the past 3 yearsa
| Odds ratio (95 % CI) | |
|---|---|
| Entire cohort (n=71,541) | |
| Never e-cigarette user (n=68,719) | Ref |
| Current e-cigarette user (n=352) | 1.96 (1.13 – 3.40) |
| Former e-cigarette user (n=2,470) | 1.20 (0.91 – 1.58) |
| Male cohort (n=28,070) | |
| Never e-cigarette user (n=26,607) | Ref |
| Current e-cigarette user (n=213) | 2.34 (1.13 – 4.86) |
| Former e-cigarette user (n=1,250) | 1.39 (0.92 – 2.10) |
| Female cohort (n=43,471) | |
| Never e-cigarette user (n=42,112) | Ref |
| Current e-cigarette user (n=139) | 1.76 (0.79 – 3.92) |
| Former e-cigarette user (n=1,220) | 1.11 (0.77 – 1.59) |
Adjusted for all confounders listed in Table 1
1. DISCUSSION
Among never cigarette smokers, current e-cigarette users had an increased odds of self-reported prediabetes compared to never e-cigarette users. There was no association between former e-cigarette use and self-reported prediabetes. Stratified analysis showed higher odds of self- reported prediabetes for male current e-cigarette users compared to never e-cigarette users. Subgroup analysis with participants who have tested their blood sugar in the past 3 years showed similar odds of self-reported prediabetes for current e-cigarette users compared to never e-cigarette users. Stratified analysis with female e-cigarette users, however, showed no association between current e-cigarette use and self-reported prediabetes compared to never e-cigarette use. The lack of association between diabetes risk or elevated HbA1c levels and cigarette or dual use of cigarettes and e-cigarette in women has been reported previously (Beziaud et al., 2004; Choi et al., 2018); therefore, it is not surprising that current e-cigarette use may not be associated with prediabetes in female e-cigarette users compared to never e-cigarette user. The gender difference found may be due to the fact that men are generally less insulin sensitive than women, develop type 2 diabetes at a lower BMI than women and are at a higher risk for prediabetes and diabetes (Geer and Shen, 2009; Logue et al., 2011; Yang et al., 2019)
The association we found between e-cigarette use and prediabetes might be due to the nicotine found in e-cigarette liquids(Fagan et al., 2017). Long-term use of nicotine-containing gum has been shown to be associated with insulin resistance and hyperinsulinemia in middle aged men, and the degree of insulin sensitivity correlated negatively to plasma cotinine levels (Eliasson et al., 1996). Also, cotinine has been shown to be associated with higher HbA1c levels in adults without diabetes (Clair et al., 2011). In vitro studies have shown that nicotine in the presence of palmitate enhanced the production of reactive oxygen species and impaired glucose uptake in skeletal myocytes (Tatebe and Morita, 2011). Fetal and neonatal nicotine exposure in rats resulted in dysglycaemia, pancreatic beta cell dysfunction, increased apoptosis, and loss of beta cell mass(Xie et al., 2009).
The association between e-cigarette use and prediabetes might also be due to the sugar, aldehydes and other flavorants found in the e-liquid(Fagan et al., 2017). In vitro studies have shown that e-liquid with or without nicotine promoted oxidative stress leading to the disruption of plasma and liver biochemical parameters, thereby altering the liver tissue histology in rats (El Golli et al., 2016b). Furthermore, e-liquids without nicotine induced hyperglycemia in rats, and the mechanism of glucose metabolism disruption was different from that of nicotine containing e-liquid and nicotine alone (El Golli et al., 2016a).
One of the strengths of the study was the large number of respondents available for the analysis, who were nationally representative of the non-institutionalized adults in the US. Another strength was the inclusion of only never smokers of cigarettes, thereby removing the confounding effect of cigarette use on prediabetes. Some of the limitations were the cross-sectional nature of the analysis, the inability to establish a causal relationship between e-cigarette use and self-reported prediabetes by participants and the lack of information on the amount, duration and the last time e-cigarettes were used by participants. Other limitations were the inability to verify the diagnosis of prediabetes from HbA1c levels or glucose levels, the lack of biochemical confirmation of cigarette and e-cigarette use which may lead to the under reporting of use, which may bias the results of the analysis and the potential for recall bias and diagnosis misclassification bias by the participants. Also, there may have been undiagnosed prediabetes in some participants; however, subgroup analysis with participants who had a test for blood sugar in the past 3 years likely reduced the number of participants with undiagnosed prediabetes in the study.
2. CONCLUSION
In conclusion, current e-cigarette use was associated with self-reported prediabetes. Our findings are a cause for concern because of the growing use of e-cigarettes, and further evaluation is needed in larger prospective studies.
Highlights.
Participants were never cigarette smokers (less than 100 cigarettes in lifetime)
Participants had no history of diabetes, gestational prediabetes or diabetes
There was an association between current e-cigarette use and prediabetes
There was no association between former e-cigarette use and prediabetes
Role of Funding Source
Dr. Cheryl Oncken receives support from the NCI grant R01CA207491 and NIH grant R01HD069314
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Declarations of interest: none
Conflict of interest statement
No conflict declared.
Conflict of interest statement
None
REFERENCES
- Aeschbacher S, Schoen T, Clair C, Schillinger P, Schonenberger S, Risch M, Risch L, Conen D, 2014. Association of smoking and nicotine dependence with pre-diabetes in young and healthy adults. Swiss medical weekly 144, w14019. [DOI] [PubMed] [Google Scholar]
- Association AD, 2019. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2019. Diabetes Care 42(Supplement 1), S13–S28. [DOI] [PubMed] [Google Scholar]
- Beziaud F, Halimi JM, Lecomte P, Vol S, Tichet J, 2004. Cigarette smoking and diabetes mellitus. Diabetes Metab 30(2), 161–166. [DOI] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention, 2017. National Diabetes Statistics Report, 2017, in: Centers for Disease Control and Prevention, U.S. Dept of Health and Human Services; (Eds.). Atlanta, GA. [Google Scholar]
- Centers for Disease Control and Prevention (CDC), 2017,-aBehavioral Risk Factor Surveillance System Overview. Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta, Georgia: U.S. [Google Scholar]
- Centers for Disease Control and Prevention (CDC), 2017,-bBehavioral Risk Factor Surveillance System Survey Data. Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta, Georgia: U.S.. [Google Scholar]
- Centers for Disease Control and Prevention (CDC), 2017,-cThe Behavioral Risk Factor Surveillance System, Complex Sampling Weights and Preparing 2017 BRFSS Module Data for Analysis U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta, Georgia. [Google Scholar]
- Choi D-W, Jeon J, Lee SA, Han K-T, Park E-C, Jang S-I, 2018. Association between Smoking Behavior Patterns and Glycated Hemoglobin Levels in a General Population. International journal of environmental research and public health 15(10), 2260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Clair C, Bitton A, Meigs JB, Rigotti NA, 2011. Relationships of cotinine and self-reported cigarette smoking with hemoglobin A1c in the U.S.: results from the National Health and Nutrition Examination Survey, 1999–2008. Diabetes care 34(10), 2250–2255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- El Golli N, Dkhili H, Dallagi Y, Rahali D, Lasram M, Bini-Dhouib I, Lebret M, Rosa JP, El Fazaa S, Allal-El A, 2016a. Comparison between electronic cigarette refill liquid and nicotine on metabolic parameters in rats. Life Sciences 146, 131–138. [DOI] [PubMed] [Google Scholar]
- El Golli N, Jrad-Lamine A, Neffati H, Rahali D, Dallagi Y, Dkhili H, Ba N, El May MV, El Fazaa S, 2016b. Impact of e-cigarette refill liquid with or without nicotine on liver function in adult rats. Toxicology Mechanisms and Methods 26(6), 433–440. [DOI] [PubMed] [Google Scholar]
- Eliasson B.r., Taskinen M-R, Smith U, 1996. Long-term Use of Nicotine Gum Is Associated With Hyperinsulinemia and Insulin Resistance. Circulation 94(5), 878–881. [DOI] [PubMed] [Google Scholar]
- Fagan P, Pokhrel P, Herzog TA, Moolchan ET, Cassel KD, Franke AA, Li X, Pagano I, Trinidad DR, Sakuma K.-l.K., Sterling K, Jorgensen D, Lynch T, Kawamoto C, Guy MC, Lagua I, Hanes S, Alexander LA, Clanton MS, Graham-Tutt C, Eissenberg T, Workgroup AC, 2017. Sugar and Aldehyde Content in Flavored Electronic Cigarette Liquids. Nicotine & Tobacco Research 20(8), 985–992. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Geer EB, Shen W, 2009. Gender differences in insulin resistance, body composition, and energy balance. Gend Med 6 Suppl 1(Suppl 1), 60–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Goniewicz ML, Smith DM, Edwards KC, Blount BC, Caldwell KL, Feng J, Wang L, Christensen C, Ambrose B, Borek N, van Bemmel D, Konkel K, Erives G, Stanton CA, Lambert E, Kimmel HL, Hatsukami D, Hecht SS, Niaura RS, Travers M, Lawrence C, Hyland AJ, 2018. Comparison of Nicotine and Toxicant Exposure in Users of Electronic Cigarettes and Combustible Cigarettes. JAMA Network Open 1(8), e185937–e185937. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang Y, Cai X, Mai W, Li M, Hu Y, 2016. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ 355, i5953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Logue J, Walker JJ, Colhoun HM, Leese GP, Lindsay RS, McKnight JA, Morris AD, Pearson DW, Petrie JR, Philip S, Wild SH, Sattar N, Group, o.b.o.T.S.D.R.N.E., 2011. Do men develop type 2 diabetes at lower body mass indices than women? Diabetologia 54(12), 3003–3006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lumley T, 2010. Complex surveys : a guide to analysis using R. Hoboken, N.J. : John Wiley, [2010] ©2010. [Google Scholar]
- McMillen RC, Gottlieb MA, Shaefer RMW, Winickoff JP, Klein JD, 2015. Trends in Electronic Cigarette Use Among U.S. Adults: Use is Increasing in Both Smokers and Nonsmokers. Nicotine & Tobacco Research 17(10), 1195–1202. [DOI] [PubMed] [Google Scholar]
- Pearson JL, Richardson A, Niaura RS, Vallone DM, Abrams DB, 2012. e-Cigarette Awareness, Use, and Harm Perceptions in US Adults. American Journal of Public Health 102(9), 1758–1766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M, 2012. Prediabetes: a high-risk state for diabetes development. Lancet (London, England) 379(9833), 2279–2290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tatebe J, Morita T, 2011. Enhancement of TNF-α Expression and Inhibition of Glucose Uptake by Nicotine in the Presence of a Free Fatty Acid in C2C12 Skeletal Myocytes. Horm Metab Res 43(01), 11–16. [DOI] [PubMed] [Google Scholar]
- Verhaegen A, Van Gaal L, 2017. Do E-cigarettes induce weight changes and increase cardiometabolic risk? A signal for the future. Obesity Reviews 18(10), 1136–1146. [DOI] [PubMed] [Google Scholar]
- Xie X, Liu Q, Wu J, Wakui M, 2009. Impact of cigarette smoking in type 2 diabetes development. Acta Pharmacologica Sinica 30, 784. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang YM, Shin B-C, Son C, Ha IH, 2019. An analysis of the associations between gender and metabolic syndrome components in Korean adults: a national cross-sectional study. BMC Endocr Disord 19(1), 67–67. [DOI] [PMC free article] [PubMed] [Google Scholar]