Biological aging acceleration associated with electronic cigarette use in the United States

Abstract

Background:

Smoking combustible cigarettes accelerates biological aging and contributes to subsequent aging-related cardiovascular decline. However, the aging-related health consequences of electronic cigarette (e-cigarette) use (vaping) remain largely unknown. This study aims to investigate whether e-cigarette use is associated with biological aging acceleration using a nationally representative sample.

Methods:

This cross-sectional study included 8,595 participants aged 20–79 from two cycles (2015–2016 and 2017–2018) of the National Health and Nutrition Examination Survey. Biological aging was measured using PhenoAge, a validated epigenetic biomarker of aging. Its acceleration (PhenoAgeAccel) was quantified by the residual of PhenoAge regressed on chronological age. E-cigarette use was categorized as current (N = 417), former (N = 1,253), or nonuse (N = 6,925). Linear regression models were used to assess the association between e-cigarette use and PhenoAgeAccel, adjusting for combustible cigarette use, sex, race/ethnicity, education, body mass index, alcohol use, other tobacco product use, and secondhand smoke exposure.

Results:

In the fully adjusted model, current e-cigarette use was associated with 1.20 (95% confidence interval: 0.41, 2.10) years higher PhenoAgeAccel than never use. Similarly, individuals with current cigarette use exhibited 1.50 (95% confidence interval: 1.00, 2.00) years higher PhenoAgeAccel than those who had never smoked at least 100 cigarettes in their lifetime.

Conclusions:

This work provides the first epidemiologic evidence revealing the association between e-cigarette use and accelerated biological aging, after adjusting for smoking status. The findings underscore the potential value of public health interventions targeting reduced tobacco product use. Future longitudinal studies are needed to elucidate the underlying mechanisms.

What this study adds

Given the relatively short time that e-cigarettes have been introduced to the market, limited epidemiologic evidence is available regarding their health effects. To our knowledge, this is the first study to investigate the potential aging effects of e-cigarette use at the population level. The findings demonstrate a significant association between e-cigarette use and accelerated biological aging, after adjustment for active and passive combustible cigarette use, as well as other tobacco product use. This study provides novel epidemiologic evidence about the adverse health effects of e-cigarette use and supports stricter tobacco control policies and further research.

Introduction

The human body experiences the accumulated effects of endogenous and exogenous exposures over time, and long-lasting, unrepaired damage influences the normal maintenance of biological functions at all levels.¹ These aging-related conditions are affected by the natural aging process, during which there is a progressive decline in organ function.^2,3 The concept of biological aging has emerged to represent the aging-dependent functional decline in the biological process.⁴ Zhao et al² pointed out that biological aging can be of great importance in explaining aging-related cardiovascular disease (CVD), as intrinsic biological aging is a better indicator reflecting personal physiological function at the molecular and cellular levels.⁵ Hamczyk et al⁶ also emphasized the application value of biological aging, other than chronological age, in the early prevention and intervention for vascular diseases. DNA methylation (DNAm)-based phenotypic age (PhenoAge) is one aging biomarker developed based on clinical data from the third National Health and Nutrition Examination Survey (NHANES), and it has demonstrated promising performance in the prediction of various aging-related health outcomes, including CVD, cancers, and all-cause mortality.⁷

Combustible cigarette use is an established risk factor for several aging-related diseases, including CVD,⁸ accounting for approximately 10% of CVD incidence.⁹ PhenoAge is now widely used in health studies examining the effects of cigarettes on biological aging. Yang et al⁸ observed that PhenoAge acceleration was associated with both, cumulative smoking and smoking-related loci. Sheng et al¹⁰ demonstrated that both age at smoking initiation and the time window of smoking initiation were important contributing factors influencing PhenoAge. Recent studies have begun exploring the potential mediating role of PhenoAge acceleration in the relationship between smoking and cardiovascular outcomes,^10,11 providing mechanistic insights linking tobacco use to cardiovascular health.

Since 2016, the U.S. Food and Drug Administration has regulated electronic cigarettes (e-cigarettes) and other electronic nicotine delivery systems as “tobacco products,” although e-cigarettes do not contain tobacco.^12–14 Despite their rising prevalence,¹⁵ there is limited evidence focusing on aging-related health effects of e-cigarette use (vaping), given their recent introduction to the market. To our knowledge, no epidemiologic evidence at the population level has linked e-cigarette use to accelerated biological aging to date. This issue particularly raises concern as evidence shows that the increasing prevalence of e-cigarette use is accompanied by a decrease in combustible cigarette use.¹⁶ Given the popularity of e-cigarettes, scientific research is needed to demonstrate the adverse health effects of e-cigarette use. This study aims to investigate whether e-cigarette use is associated with accelerated biological aging, contributing novel epidemiological evidence on the negative health consequences of e-cigarette use and providing the scientific basis supporting stricter regulations and public health interventions on e-cigarettes.

Methods

Study participants

Starting from the 2015–2016 cycle, the NHANES collected e-cigarette use data in its “Smoking—Cigarette Use” questionnaire. We used the two full cycles (2015–2016 and 2017–2018) before the coronavirus disease 2019 (COVID-19) pandemic for analysis, as COVID-19 infection was considered to have an impact on health and e-cigarette use habits.¹⁷ Participants aged over 80 were excluded because their chronological age in years was suppressed for confidentiality reasons. Among 10,485 adults with age in years available, we excluded participants with missing laboratory and self-reported information (n = 1,634), abnormal values of laboratory readings (one outlier for alkaline phosphatase and one outlier for white blood cell count), infinite biological aging calculations (n = 16), and extreme biological aging accelerations exceeding ±20 (n = 238) years. A total of 8,595 adult participants aged 20–79 were included in the analyses.

E-cigarette and cigarette use

In this study, “tobacco product use” refers to the use of products classified as “tobacco products” by Food and Drug Administration, including e-cigarettes, cigarettes, cigars, and smokeless tobacco.¹⁸

The primary exposure of interest was e-cigarette use status, classified as “never use,” “former use,” and “current use.” Participants who reported both e-cigarette use history by responding to “Ever used an e-cigarette?” and recent use to “How many days used an e-cigarette?” during the past 30 days were classified as having “current use.” Ever use of e-cigarette without use in the past 30 days was defined as “former use.” Never use of e-cigarette was categorized as “never use.”

Combustible cigarette use status was included in the analyses as a parallel exposure. Participants were categorized as having “current use” if they reported both established smoking history of at least 100 cigarettes in lifetime and recent smoking in the past 30 days, “former use” if they only reported smoking history, and “no established use” if they never smoked or had smoked no more than 100 cigarettes in lifetime. The classification scheme is presented in Figure S1, https://links.lww.com/EE/A489.

Use intensity was measured by the number of days a tobacco product was used in the past 30 days. However, this information had limited variability and could not be used to assess dose–response relationships. Most participants (75.0%) reported no use of e-cigarettes in the past 30 days, while 72.6% of participants who reported current cigarette use smoked every day in the past 30 days. The distribution of use intensity is presented in Figure S2, https://links.lww.com/EE/A489.

Biological aging and acceleration

Biological aging was assessed by PhenoAge, which was calculated based on nine clinical biomarkers and chronological age using the formula as follows.¹⁹ The units and mean levels of each biomarker can be found in Table S1, https://links.lww.com/EE/A489.

$$\begin{array}{c}{\displaystyle }PhenoAge=141.50+\frac{In(-0.00553\times ln(1-M))}{0.09165}\end{array}$$

where:

$$\begin{array}{c}{\displaystyle }M=1-exp\left(\frac{-1.51714\times exp\left(xb\right)}{0.0076927}\right)\end{array}$$

And:

$$\begin{array}{c}{\displaystyle }\begin{array}{cc}& {\displaystyle }xb=(-19.907-0.0336\times albumin+0.0095\\ & {\displaystyle }\times creatinine+0.1953\times glucose+0.0954\\ & {\displaystyle }\times ln(C-reactiveprotein/10)-0.0120\\ & {\displaystyle }\times lymphocytepercent+0.0268\\ & {\displaystyle }\times meancellvolume+0.3306\times redcelldistributionwidth\\ & {\displaystyle }+0.00188\times alkalinephosphatase+0.0554\\ & {\displaystyle }\times whitebloodcellcount+0.0804\\ & {\displaystyle }\times chronologicalage)\end{array}\end{array}$$

PhenoAge Acceleration (PhenoAgeAccel) is a more intuitive indicator that shows biological aging acceleration considering the chronological age. It is the residual from a linear regression model in which PhenoAge is regressed on chronological age.¹⁹ The absolute value of PhenoAgeAccel reflects the intensity of deviation between biological and chronological age. A positive value suggests accelerated biological aging, while a negative value suggests decelerated biological aging. To reduce the influence of participants with extreme physiological conditions, we excluded participants whose PhenoAgeAccel values exceeded 20 years.²⁰

Covariates

Covariates were selected a priori based on prior knowledge and published literature regarding potential confounders of the association between e-cigarette use and biological aging acceleration.^8,21 Chronological age was self-reported. Body mass index (kg/m²) was classified into “under 25,” “25–29,” and “30 and above.” Alcohol use was considered as alcoholic beverage use during the past 12 months. Sex (female and male), race and ethnicity (Mexican-American, non-Hispanic Black, non-Hispanic White, other Hispanic, and others), and educational attainment (high school or less, some college, and college graduate or above) were obtained from the questionnaire.

Passive smoking exposure was assessed using household smoking exposure and recent exposure to secondhand smoke (in the past 7 days) in multiple indoor environments. Ever use of other tobacco products was also assessed, including cigars and smokeless tobacco.

Statistical analyses

All analyses were conducted using R, version 4.4.1 and weighted according to the NHANES analytical protocol and tutorial,²² using the Full sample 2 year interview weights combining two full cycles (2015–2016 and 2017–2018) to generate nationally representative estimates of accelerated aging consequences in relation to e-cigarette use. Survey-weighted descriptive analyses were performed to present the characteristics across different use groups. Multivariate linear regressions were performed to assess the associations between e-cigarette use and accelerated biological aging. The beta (β) and its 95% confidence interval (CI) were reported, where β represents the weighted estimated mean difference in PhenoAge acceleration between current/former use and nonuse among each use category. Models were adjusted for potential confounders including current smoking status, sex, race and ethnicity, educational attainment, body mass index, alcohol use, other tobacco product use, and passive smoking exposure. Considering that the majority of participants with current e-cigarette use (~78%) were aged 50 years or younger, we conducted a sensitivity analysis restricted to this age group. To further minimize confounding by cigarette use and other tobacco products, additional analyses were conducted among participants who had no established smoking history, and further among participants who had no established use of cigarettes and had never used cigars or smokeless tobacco.

Results

Characteristics of the participants are shown in Table 1. The general population had a median age of 47.0 (95% CI: 46.0, 49.0) years. Participants currently using e-cigarettes were younger (median = 34.0 years, 95% CI: 31.0, 38.0) compared with those who never used e-cigarettes (mean = 50.0 years, 95% CI: 49.0, 52.0). Participants with current e-cigarette use had a higher proportion of males, lower educational levels, and a greater prevalence of current alcohol use compared to the other two e-cigarette use groups. 57.8% (95% CI: 51.7%, 63.8%) of those with current e-cigarette use and 51.4% (95% CI: 47.3%, 55.6%) of those with former e-cigarette use reported current cigarette use, indicating a high prevalence of concurrent use of both electronic and combustible cigarettes.

Table 1.

Participant characteristics among e-cigarette use groups (N = 8,595)

Characteristics		Overall	Never e-cigarette use	Former e-cigarette use	Current e-cigarette use
Weighted N		191,538,043	148,521,461	32,056,522	10,960,060
Chronological age (median years)		47.0 (46.0, 49.0)	50.0 (49.0, 52.0)	35.0 (34.0, 37.0)	34.0 (31.0, 38.0)
Sex (%)	Female	52.2% (51.1%, 53.4%)	54.0% (52.9%, 55.2%)	47.6% (44.7%, 50.5%)	41.2% (34.2%, 48.2%)
	Male	47.8% (46.6%, 48.9%)	46.0% (44.8%, 47.1%)	52.4% (49.5%, 55.3%)	58.8% (51.8%, 65.8%)
Race and ethnicity (%)	Non-Hispanic White	63.8% (59.1%, 68.5%)	62.6% (57.4%, 67.7%)	68.3% (63.7%, 72.9%)	67.4% (61.8%, 72.9%)
	Non-Hispanic Black	10.6% (8.0%, 13.2%)	10.8% (8.0%, 13.6%)	10.0% (7.4%, 12.5%)	10.0% (7.0%, 12.9%)
	Mexican-American	9.0% (6.3%, 11.7%)	9.4% (6.4%, 12.3%)	8.0% (5.5%, 10.5%)	6.8% (4.5%, 9.1%)
	Other Hispanic	6.6% (5.0%, 8.2%)	7.1% (5.3%, 8.9%)	4.9% (3.5%, 6.4%)	5.0% (3.0%, 7.1%)
	Others	10.0% (8.2%, 11.8%)	10.2% (8.2%, 12.2%)	8.8% (7.2%, 10.4%)	10.9% (6.2%, 15.6%)
Education attainment (%)	High school or less	35.6% (32.5%, 38.8%)	33.2% (30.2%, 36.3%)	40.5% (36.8%, 44.1%)	54.2% (46.4%, 62.1%)
	Some college	32.0% (29.9%, 34.0%)	30.3% (28.1%, 32.4%)	39.1% (35.4%, 42.7%)	34.2% (28.8%, 39.6%)
	College graduate or above	32.4% (28.3%, 36.5%)	36.5% (32.3%, 40.7%)	20.5% (15.3%, 25.7%)	11.5% (6.1%, 17.0%)
BMI (%)	under 25	26.8% (24.7%, 29.0%)	26.1% (23.7%, 28.6%)	28.5% (25.3%, 31.7%)	31.8% (26.6%, 37.0%)
	25–29	31.5% (30.0%, 33.1%)	32.8% (31.1%, 34.5%)	27.1% (24.0%, 30.3%)	27.7% (22.0%, 33.3%)
	30 and above	41.6% (39.2%, 44.0%)	41.1% (38.6%, 43.6%)	44.4% (39.9%, 48.9%)	40.5% (33.8%, 47.3%)
Alcohol use (%)	Current use	72.4% (70.8%, 73.9%)	69.8% (67.7%, 71.8%)	80.3% (77.7%, 82.9%)	84.3% (79.7%, 88.9%)
	No current use	27.6% (26.1%, 29.2%)	30.2% (28.2%, 32.3%)	19.7% (17.1%, 22.3%)	15.7% (11.1%, 20.3%)
Smoking (%)	No established use	57.3% (55.4%, 59.3%)	67.9% (66.0%, 69.7%)	22.0% (18.4%, 25.7%)	17.8% (13.9%, 21.7%)
	Former use	24.4% (22.9%, 25.9%)	24.0% (22.1%, 25.8%)	26.5% (23.5%, 29.5%)	24.4% (19.5%, 29.4%)
	Current use	18.3% (16.7%, 19.8%)	8.2% (7.1%, 9.3%)	51.4% (47.3%, 55.6%)	57.8% (51.7%, 63.8%)
Cigar use (%)	Never use	56.9% (55.0%, 58.8%)	64.5% (62.3%, 66.6%)	30.5% (27.2%, 33.7%)	31.9% (27.7%, 36.0%)
	Ever use	43.1% (41.2%, 45.0%)	35.5% (33.4%, 37.7%)	69.5% (66.3%, 72.8%)	68.1% (64.0%, 72.3%)
Smokeless tobacco use (%)	Never use	78.3% (76.2%, 80.4%)	83.5% (81.5%, 85.5%)	59.0% (53.9%, 64.0%)	64.2% (59.3%, 69.0%)
	Ever use	21.7% (19.6%, 23.8%)	16.5% (14.5%, 18.5%)	41.0% (36.0%, 46.1%)	35.8% (31.0%, 40.7%)
Household smoking exposure (%)	No	89.8% (88.3%, 91.3%)	93.9% (92.7%, 95.1%)	76.7% (72.8%, 80.7%)	72.3% (64.2%, 80.3%)
	Yes	10.2% (8.7%, 11.7%)	6.1% (4.9%, 7.3%)	23.3% (19.3%, 27.2%)	27.7% (19.7%, 35.8%)
Secondhand smoke exposure (%)	No	74.3% (71.7%, 76.9%)	81.3% (78.9%, 83.6%)	52.6% (47.9%, 57.3%)	43.4% (37.2%, 49.6%)
	Yes	25.7% (23.1%, 28.3%)	18.7% (16.4%, 21.1%)	47.4% (42.7%, 52.1%)	56.6% (50.4%, 62.8%)

Survey-weighted percentages with 95% confidence intervals were reported for categorical variables, and survey-weighted medians with 95% confidence intervals were reported for age.

BMI indicates body mass index.

Differences in PhenoAgeAccel were observed across categories of both e-cigarette and cigarette use, as shown in Table 2. Participants with current e-cigarette use had a mean PhenoAgeAccel of 0.7 (95% CI: −0.2, 1.6) years, while those who never vaped had a mean PhenoAgeAccel of −1.8 (95% CI: −2.2, −1.5) years. Almost null acceleration on PhenoAge (PhenoAgeAccel mean=0.03, 95% CI: −0.4, 0.5) was observed among those who used e-cigarettes in the past. Similarly, those with current cigarette use had a mean PhenoAgeAccel of 0.6 (95% CI: 0.2, 1.1) years, compared with −2.0 (95% CI: −2.4, −1.6) years among participants who had never smoked at least 100 cigarettes in their lifetime and −1.4 years (95% CI: −1.9, −0.9) among those who formerly smoked.

Table 2.

Biological aging and acceleration among different tobacco product use groups (N = 8,595), Unit: years

Groups		Unweighted N	Weighted N	PhenoAgeAccel (mean)	PhenoAge (mean)	Chronological age (median)
Vaping	Never use	6,925	148,521,461	−1.8 (−2.2, −1.5)	47.2 (46.3, 48.1)	49.0 (48.2, 49.9)
	Former use	1,253	32,056,522	0.03 (−0.4, 0.5)	38.3 (37.3, 39.3)	38.8 (38.0, 39.6)
	Current use	417	10,960,060	0.7 (−0.2, 1.6)	37.6 (35.0, 40.1)	37.6 (35.2, 40.0)
Smoking	No established use	5,023	109,818,417	−2.0 (−2.4, −1.6)	42.9 (42.0, 43.8)	45.1 (44.3, 46.0)
	Former use	1,922	46,756,316	−1.4 (−1.9, −0.9)	50.9 (49.4, 52.4)	52.2 (50.8, 53.6)
	Current use	1,650	34,963,310	0.6 (0.2, 1.1)	44.5 (43.1, 45.8)	44.1 (42.9, 45.4)

Survey-weighted medians with 95% confidence intervals were reported for non-normally distributed continuous variables, whereas survey-weighted means with 95% confidence intervals were reported for normally distributed continuous variables.

The results from linear regression models on PhenoAgeAccel can be found in Table 3. In the fully adjusted model, we observed the association between e-cigarette use and accelerated biological aging. Current e-cigarette use was significantly associated with 1.20 (95% CI: 0.41, 2.10) years higher PhenoAgeAccel compared with never using e-cigarettes, while former e-cigarette use was associated with 0.73 (95% CI: 0.38, 1.10) years higher PhenoAgeAccel. Likewise, current combustible cigarette use was associated with 1.50 (95% CI: 1.00, 2.00) years higher PhenoAgeAccel compared with no established use of cigarettes. However, no such significant association was observed for former cigarette use.

Table 3.

Linear regression model on PhenoAgeAccel (N = 8,595), Unit: years

Variable	Mean difference (95% CI)	P value
Vaping
Never use	Reference	/
Former use	0.73 (0.38, 1.10)	<0.001
Current use	1.20 (0.41, 2.10)	0.007
Smoking
No established use	Reference	/
Former use	−0.24 (−0.69, 0.21)	0.30
Current use	1.50 (1.00, 2.00)	<0.001

The model was adjusted for sex, race and ethnicity, education attainment, BMI, alcohol use, other tobacco product use, and passive smoking exposure.

All estimates were survey-weighted and presented with 95% confidence intervals.

The sensitivity analysis among participants aged 50 years or younger (Table S2, https://links.lww.com/EE/A489) showed a consistent pattern that current and former e-cigarette use had a higher PhenoAgeAccel compared to never e-cigarette use with comparable effect estimates. The associations remained positive and were stronger for current e-cigarette use when analysis was restricted to participants with no established use of cigarettes (Table S3, https://links.lww.com/EE/A489) and further restricted to those who had no established cigarette use and never used other tobacco products (Table S4, https://links.lww.com/EE/A489).

Discussion

In this study, we first systematically investigated the association between e-cigarette use and accelerated biological aging using a nationally representative sample and found that PhenoAgeAccel was significantly higher among participants with former and current e-cigarette use compared with those who never used e-cigarettes, after adjusting for combustible cigarette use, secondhand smoke exposure, and the use of other tobacco products. The findings suggest that e-cigarette use may be distinctly associated with biological aging acceleration and add to the growing body of evidence linking e-cigarette use to biological aging at the population level. Given the well-established associations between PhenoAgeAccel and both CVD and cardiovascular mortality,^23,24 our results suggest that e-cigarette use may be relevant to cardiovascular health through its association with biological aging, warranting further investigation.

Previous studies examining the relationship between tobacco product use and accelerated biological aging have primarily focused on combustible cigarette use, with limited attention to e-cigarettes. Richmond et al²⁵ discovered a different and weaker DNA methylation profile for e-cigarette use (N = 116) compared with combustible smoking (N = 117). Song et al²⁶ contributed valuable insight that cigarette smoking (N = 16) and e-cigarette use (N = 14) both had significantly faster epigenetic age in the lung. Additionally, exposure to toxicants such as volatile organic compounds via e-cigarette use was also associated with accelerated epigenetic aging in the lungs.²⁷ However, the small sample size in past studies limits the generalizability of the findings. Although evidence from these studies remains preliminary, our results are consistent with their findings and demonstrate a significant association between e-cigarette use and accelerated biological aging, as reflected by the epigenetic aging biomarker PhenoAge.

Our study features enhanced generalizability to the adult population in the United States using the largest sample size for biological aging in relation to e-cigarette use to date. This significantly strengthens the public health implications of our findings, particularly given the increasing prevalence of e-cigarette use among US adults.²⁸ Additionally, previous studies have raised concerns about potential confounding by past smoking history and concurrent cigarette use, as some individuals may have recently switched from cigarette use to e-cigarette use or may be dual users.^26,29,30 Indeed, evidence supports that former smoking history and concurrent smoking may confound or distort the relationship between e-cigarette use and health outcomes.^29,31 We included both variables of vaping and smoking status as parallel exposures in the models, allowing for estimation of their individual associations with biological aging, with additional adjustment for secondhand smoke exposure and use of other tobacco products. Beyond that, the association between e-cigarette and PhenoAge remained significant in the sensitivity analyses among those who never smoked with larger effect estimates, further supporting the robustness of our findings.

Multiple biomarkers of aging have been proposed in the past decades, and a valid biomarker of biological aging can have significant importance in predicting aging-related morbidity and mortality.³² For example, GrimAge is another DNAm-based biomarker that incorporated smoking pack-years in its calculation,³³ indicating the inherent association between biological aging and tobacco use. Furthermore, telomere length can serve as an effective predictor of lifespan at a molecular level, and an accelerated pace of its shortening may suggest a faster aging process.³⁴ One systematic review supports the negative effect of smoking on telomere length, showing that individuals who ever used cigarettes had a significantly shorter telomere length of −0.11 (95% CI: −0.16, −0.07) compared with those who never used cigarettes.³⁵ This study used PhenoAge as the sole biological aging biomarker due to its promising application value in population health research and data availability. Compared with other biological markers of aging requiring high-dimensional molecular data, PhenoAge can be calculated based on standard biochemical assays, making it more accessible and clinically applicable. Nevertheless, further studies may integrate multiple biomarkers of aging complementing PhenoAge to provide a more comprehensive assessment of aging impact in relation to the use of e-cigarettes and other emerging tobacco products.

This study also has some other limitations. The cross-sectional study design of NHANES limits the establishment of temporality between e-cigarette use and biological aging. Future studies with longitudinal designs are needed to explore the long-term effects of e-cigarette use on biological aging and potential mechanisms underlying their relationship. In addition, the self-reported use of tobacco products may introduce potential bias from misclassification. However, as a long-established national representative survey, NHANES has consistently maintained standardized protocols for data collection and is able to provide reliable data. Therefore, the potential misclassification is likely to be nondifferential with minimal impact. Our study also included combustible cigarette use as a parallel variable to e-cigarette use. The consistency of our results regarding cigarette use with previous findings provides support for the validity of our exposure classification and increases confidence in the findings regarding e-cigarette use. Nevertheless, residual confounding may remain due to unmeasured or incompletely captured use of tobacco products. Also, the limited variability in use intensity did not allow assessment of a potential dose-response relationship.

Age, along with other nonmodifiable factors such as genetic heritage, is a traditional risk factor for various aging-related diseases such as cardiovascular and chronic lung diseases.^36,37 Unlike chronological age, accelerated biological aging and tobacco use are modifiable factors associated with disease burden. Liu et al³⁸ demonstrated that multiple factors contribute to the modification of biological aging, including smoking status, suggesting that the pace of biological aging processes can be mitigated by quitting smoking. We observed that biological aging was strongly associated with current but not former cigarette use, a pattern consistent with a potential benefit of tobacco use cessation for biological aging processes. The findings contribute to the scientific evidence supporting stricter tobacco control regulations and more informed tobacco cessation decisions.

In summary, this study reported a novel association between e-cigarette use and biological aging acceleration in a nationally representative population, after adjusting for combustible cigarette use, secondhand smoke exposure, and other tobacco product use. The results highlight the potential health implications of e-cigarette use, complementing the scientific evidence for stricter tobacco control regulations. Future studies may consider a longitudinal design to better understand the long-term aging effects of e-cigarette use and its underlying mechanisms.

Supplementary Material

ee9-10-e536-s001.pdf ^{(199.9KB, pdf)}