Abstract
Introduction
We evaluated the association of preconception nicotine vaping among female and male partners with spontaneous abortion (SAB) incidence, and the extent to which associations vary by cigarette smoking.
Aims and Methods
In a prospective cohort study, 6136 participants assigned female-at-birth and 1688 of their partners assigned male-at-birth reported preconception nicotine vaping and cigarette smoking via online questionnaire. Female partners reported incident pregnancies and outcomes (eg, SAB) on follow-up questionnaires completed every 8 weeks and in early and late pregnancy. We used multivariable Cox proportional hazards regression models estimated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for the association between vaping and SAB incidence, overall and by smoking history.
Results
Mean age was 30 and 32 for females and males, respectively. Among females, 13% reported ever-vaping and 14% reported ever-smoking, while 19% of males reported ever-vaping and 24% reported ever-smoking. Relative to female never-vapers, aHRs were 1.03 for former vaping (95% CI = 0.86% to 1.24%) and 0.91 for current vaping (95% CI = 0.61% to 1.36%). Former and current vaping were also not appreciably associated with SAB rate among ever-smokers. In the couple-based cohort, relative to male never-vapers, aHRs were 1.00 for male former vapers (95% CI = 0.74% to 1.35%) and 0.67 for male current vapers (95% CI = 0.35% to 1.25%). Additional analysis of female participants after stratifying by finer categories of smoking status did not identify any meaningful association with SAB incidence.
Conclusions
The current study found that vaping in either partner during the preconception period was not associated with SAB incidence. Cigarette smoking also did not modify this association.
Implications
The rising prevalence of vaping invokes greater scrutiny on its possible adverse reproductive effects. Studies have linked vaping to adverse birth events, and clear guidance is made to avoid vaping in pregnant people. However, guidance about vaping during preconception is less clear despite research showing how preconception behaviors are linked to adverse pregnancies and birth outcomes. Our cohort study finds little association between preconception vaping and SAB. In the context of established risks of vaping on fetal outcomes, this study highlights the need for additional evidence-based information about preconception vaping to help couples make lifestyle decisions for optimal reproductive outcomes.
Introduction
Spontaneous abortion (SAB), defined as the unintended loss of pregnancy within the first 20 weeks of pregnancy, is a common reproductive outcome with serious physiological and psychological effects. Few risk factors for SAB have been identified. Combustible cigarette smoking, referred to in this paper as “smoking,” has been found to be associated with SAB risk in many studies, including the 2014 Surgeon General’s Report on the Health Consequences of Smoking,1–4 with dose dependent increases in risk.5,6 More specifically, smoking during the preconception period, defined as the months leading up to conception, has been found to affect fetal outcomes including SAB. Of note, increased risk of SAB has been found in couples in which either member of the couple had smoked in the immediate preconception period, suggesting elevated risk can stem from both female and male partner smoke exposure.2,7 Smoking has also been associated with impaired male fertility, including decreases in semen parameters, increased oxidative damage, and altered epigenetic profiles.8 Consequently, it is widely accepted that smoking should be avoided by couples in the preconception period to improve the prospects for a healthy pregnancy.
Usage of electronic cigarettes (e-cigarettes), colloquially known as “vaping,” has increased in the past decade, with global lifetime and current prevalence estimates reaching 15.3% and 7.7% respectively in 2022.9 In the United States alone, prevalence of vaping rose by over 2 million people between 2016 and 2018.10 The increase in vaping can be partially attributed to its perception as a safer alternative to traditional tobacco cigarettes, with one study showing that 84.5% of individuals who vape report they use e-cigarettes for health or smoking cessation purposes.11 However, the 2018 National Academies Science Engineering Medicine (NASEM) report on public health consequences of e-cigarettes has found that although vaping results in lower toxicant exposure compared with smoking, it still directly exposes individuals to harmful and potentially harmful constituents.12 Additionally, given the relative novelty of e-cigarette technology, there is insufficient data on the long-term impacts of vaping, and some short-term studies point to vaping-induced changes in the respiratory epithelia.13 With the rising prevalence of vaping, greater scrutiny has been placed on its possible adverse health effects.
In the realm of reproductive health, studies show that vaping among females is associated with adverse birth outcomes such as preterm birth, low birth weight, and small-for-gestational-age neonates.14 Another study from our group has shown some trend of reduced fecundity in female vapers.15 The 2018 NASEM report links vaping with high nicotine concentration, heavy metals, and volatile organic compounds that have been known to have potential adverse effects on pregnancy.12 Vaping is also associated with declines in semen quality.16
The importance of healthy preconception behaviors in both the male and female partners for healthy pregnancy and delivery has already been established, with many current suggestions including smoking cessation.17,18 However, there are gaps regarding guidance on vaping during the preconception period. The CDC explicitly discourages vaping during pregnancy,19 but makes no mention about the preconception period. Analysis from the 2015 Pregnancy Risk Assessment Monitoring System showed that among females who used e-cigarettes and were planning pregnancy, a higher number vaped in the period >3 months before the planned pregnancy than during pregnancy.20
With the growing body of evidence pointing toward associations between vaping and adverse reproductive health, there is a critical need for evidence on the association of preconception vaping behaviors with pregnancy outcomes to inform guidelines for both male and female partners. In this paper, we examine the extent to which vaping in the preconception period increases the risk of SAB. Because vaping behaviors are closely linked to smoking history,21 we also explored the extent to which the association of preconception vaping with SAB is modified by smoking history.
Materials and Methods
Cohort
We analyzed data from participants in Pregnancy Study Online (PRESTO), a preconception cohort study of individuals residing in the United States and Canada planning for pregnancy.22 The study was approved by Boston Medical Center’s Institutional Review Board, and all participants gave their informed consent.
Eligible participants were 21–45 years old, assigned female at birth, resided in the United States or Canada, and were trying to conceive without fertility treatments. Participants were recruited on a voluntary basis through online advertisements on social networking websites, health-related websites, pregnancy-related websites, and parenting blogs. As a recruitment incentive, 50% of participants were randomly selected to receive a complementary premium subscription to FertilityFriend.com, a menstrual cycle charting and fertility information software. No other direct reimbursement was given; however, participants were entered into lotteries to win $100 and $200 grocery gift cards based on the level of survey completion. All enrollment and primary data collection were completed through the study website (http://presto.bu.edu) and email (bupresto@bu.edu). Participants answered a baseline questionnaire and then optionally invited their partners to respond to a similar baseline questionnaire. Eligible partners were age ≥ 21 years and assigned male at birth. These questionnaires gathered information on socio-economic background, medical histories, anthropometrics, and lifestyle habits, including exercise, smoking, and caffeine intake. Metabolic equivalents for various activities were gauged using the Compendium of Physical Activities.23 Participants also completed follow-up questionnaires bi-monthly for up to a year or until pregnancy or study completion. Those who conceived were invited to complete two additional follow-up questionnaires in early (~8 weeks) and late (~32 weeks) pregnancy.
A total of n = 16 966 eligible female participants had the opportunity to complete follow-up from June 2013 to January 2023. We subsequently excluded n = 46 participants who started their baseline questionnaire >60 days after the eligibility screener or finished their baseline questionnaire >60 days after starting, n = 3500 who did not conceive during the study period, and n = 4175 who were lost to follow-up, yielding n = 9245 eligible females. We then limited our analysis to participants who were enrolled after June 23, 2017, which was when we first included questions about vaping. The final analytic cohort included 6136 females aged 21–45 years and 1688 of their male partners aged ≥21 years.
Outcome Assessment
Female participants provided information regarding their pregnancies on follow-up questionnaires, including the date of their last menstrual period, current pregnancy status, and any intervening pregnancy losses, including SAB, induced abortion, or ectopic pregnancy. Pregnant participants gave details about pregnancy confirmation methods (eg, home pregnancy test, urine test in doctor’s office, blood test in doctor’s office) and, if applicable, pregnancy losses in the initial 8-week period on the early pregnancy questionnaire. SABs that occurred after completion of the early pregnancy questionnaire were identified via the late pregnancy questionnaire. We endeavored to track pregnancy outcomes for known pregnancies by participants who did not complete all required follow-up questionnaires for the study by contacting them via email or phone, by searching for baby registries online, or by acquiring birth certificate data (CA, FL, MA, MI, NY, OH, PA, TX).24 If no birth was recorded and the participant was unable to be contacted, the participant was censored at the date of last contact (generally the week at which they completed the survey indicating that they were pregnant), or at 20 weeks.
Exposure Assessment
We assessed vaping in the baseline questionnaire by asking participants: “Have you ever used e-cigarettes, e-hookahs, vaping pens, personal vaporizers, or any other battery-powered device that simulates smoking?” Subsequent questions following a “Yes” response asked about the age at which vaping began, how much e-liquid is currently vaped per day by mL, and the nicotine concentration used by mg to assess current usage and quantity, clarifying that the initial question was in reference to nicotine-based vaping. Participants were classified as a current vaper if they reported currently vaping >0 mL e-liquid/day, and a former vaper if they reported having ever vaped but currently vaping 0 mL/day. Intensity of vape usage was established at a cutoff of 3 mL, based on the average daily usage amount found in prior studies in the United States and United Kingdom.25,26
Combustible cigarette smoke exposure was also queried at baseline by asking participants whether they currently smoked, if they had formerly smoked, and what their past smoking habits were, including duration, frequency, and quantity smoked, if answers to either prior question were positive. Participants were designated as ever smokers if they answered “Yes” to currently smoking or if they reported smoking at least one cigarette a day for a period of 6 months or longer. If they met the ever-smoker criteria, they were further categorized as current smokers if they self-identified as currently smoking with regular use (one or more cigarettes every day) or occasional use (less than daily smoking). If they said no to current usage, they were categorized as former smokers.
Data Analysis
Descriptive statistics of baseline demographic characteristics were presented in mean ± SD and percentages for continuous and categorical variables, respectively. We fit Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of preconception vaping categorized as ever vs never; current, former vs never, and current amount of vaping liquid used in mL, with SAB incidence. Multivariate Cox models included covariates that have been associated with adverse pregnancy outcomes, such as SAB, including female age, body mass index, race, educational attainment, current smoking status, baseline alcohol consumption, history of SAB, and diabetes. A covariate must have resulted in at least a 10% change in HR to be included in our final analytic model.
We performed stratified analyses examining whether current vaping was associated with the incidence of SAB among female never and ever cigarette smokers. We then conducted additional analyses examining combinations of current, former, and never vaping and current, former, and never smoking to see how effects differed based on recency of exposure versus overall exposure. We assessed interaction by performing stratified analysis for queried variables and assessing the difference in model output using the relative excess risk due to interaction statistic. We also performed Cox regression analyses that incorporated vaping data from both partners, controlling for potential confounders in our multivariable models. The couple-based model included the same covariates used in the previous model for both females and males, along with a history of erectile dysfunction for males.
To account for missing data, we multiply imputed covariate data using the fully conditional specification method. Most covariates had no missingness; for those with missing data, missingness was rare, ranging from 1% (smoking status) to 2% (use of fertility treatment to conceive index pregnancy). We imputed gestational age at SAB for <1% of participants with SAB. The date of the first positive pregnancy test was missing for 3% of participants. We performed all analyses using SAS statistical software (version 9.4, SAS Institute).
Results
The mean age was 30 years for females and 32 years for males, with approximately 85% identifying as non-Hispanic White. Of the 6136 participants in the female-based cohort, 13% reported ever vaping with 2% current vaping, and 14% reported ever smoking with 3% current smoking. Of the males in the couple cohort, 19% reported ever vaping with 4.4% current vaping, and 24% reported ever smoking with 9% current smoking (Table 1).
Table 1.
Demographic Characteristics of PRESTO Participants by Vaping History at Baseline, 2017–2023
| Characteristics | Current vapers | Former vapers | Never vapers | Total |
|---|---|---|---|---|
| Female participants | ||||
| Number of females (%) | 127 (2.1) | 648 (10.6) | 5361 (87.4) | 6136 |
| Age, years (mean ± SD) | 30 ± 4 | 29 ± 4 | 30 ± 4 | 30 ± 4 |
| <25 | 11 (8.7) | 68 (10.5) | 298 (5.6) | 377 (6.1) |
| 25–29 | 54 (42.5) | 264 (40.7) | 2012 (37.5) | 2330 (38.0) |
| 30–34 | 50 (39.4) | 254 (39.2) | 2311 (43.1) | 2615 (42.6) |
| 35–39 | 11 (8.7) | 59 (9.1) | 686 (12.8) | 756 (12.3) |
| ≥40 | 1 (0.79) | 3 (0.46) | 54 (1.0) | 58 (0.95) |
| Physical activity (total MET-hours per week, mean ± SD) | 30.2 ± 25.8 | 32.3 ± 23.2 | 34.6 ± 23.5 | 34.3 ± 23.5 |
| White, non-Hispanic, % | 104 (81.9) | 534 (82.4) | 4657 (86.9) | 5295 (86.3) |
| Body mass index (BMI) | ||||
| <18.5 | 2 (1.6) | 5 (0.77) | 88 (1.6) | 95 (1.6) |
| 18.5–24.9 | 47 (37.0) | 239 (36.9) | 2610 (48.7) | 2896 (47.2) |
| 25.0–29.9 | 29 (22.8) | 182 (28.1) | 1356 (25.3) | 1567 (25.5) |
| 30.0–34.9 | 24 (18.9) | 105 (16.2) | 673 (12.6) | 802 (13.1) |
| ≥35.0 | 25 (19.7) | 117 (18.1) | 634 (11.8) | 776 (12.7) |
| Education ≥16 years, % | 58 (45.7) | 402 (62.0) | 4471 (83.4) | 4931 (80.4) |
| Annual household Income | ||||
| <$50 K | 40 (31.5) | 144 (22.2) | 622 (11.6) | 806 (13.1) |
| $50 k–$99 k | 54 (42.5) | 258 (39.8) | 1752 (32.7) | 2064 (33.6) |
| $100 k–$149 k | 22 (17.3) | 157 (24.2) | 1640 (30.6) | 1819 (29.6) |
| ≥$150 k | 11 (8.7) | 89 (13.7) | 1347 (25.1) | 1447 (23.6) |
| History of tobacco, nicotine substitute, and cannabis use | ||||
| Current smoker, % | 17 (13.4) | 88 (13.6) | 87 (1.6) | 192 (3.1) |
| Former smoker, % | 67 (52.8) | 184 (28.4) | 429 (8.0) | 680 (11.1) |
| Used nicotine gum or substitute in the past 2 months | 9 (7.1) | 17 (2.6) | 6 (0.11) | 32 (0.52) |
| Cannabis use, ever | 39 (30.7) | 240 (37.0) | 679 (12.7) | 958 (15.6) |
| Sleep duration | ||||
| <7 h/day, % | 40 (31.5) | 180 (27.8) | 1052 (19.6) | 1272 (20.7) |
| 7–8 h/day, % | 82 (64.6) | 406 (62.7) | 3975 (74.2) | 4463 (72.7) |
| ≥9 h/day, % | 5 (3.9) | 62 (9.6) | 334 (6.2) | 401 (6.5) |
| Reproductive characteristics | ||||
| Cycles of attempt time at study entry (mean ± SD) | 4.0 ± 6.5 | 2.9 ± 4.8 | 2.6 ± 4.3 | 2.6 ± 4.4 |
| History of preterm birth, % | 8 (6.3) | 33 (5.1) | 251 (4.7) | 292 (8.4) |
| History of SAB, % | 50 (39.4) | 207 (31.9) | 1404 (26.2) | 1661 (27.1) |
| Medical History | ||||
| History of diagnosed diabetes, % | 3 (2.4) | 8 (1.2) | 53 (0.99) | 64 (1.0) |
| Male partners of participants | ||||
| Number of males (%) | 73 (4.4) | 236 (14.2) | 1359 (81.5) | 1668 |
| Age, years (mean ± SD) | 32 ± 5 | 31 ± 4 | 32 ± 5 | 32 ± 5 |
| <25 | 5 (6.9) | 6 (2.5) | 40 (2.9) | 51 (3.1) |
| 25–29 | 18 (24.7) | 75 (31.8) | 385 (28.3) | 478 (28.7) |
| 30–34 | 30 (41.1) | 108 (45.8) | 583 (42.9) | 721 (43.2) |
| 35–39 | 15 (20.6) | 36 (15.3) | 258 (19.0) | 309 (18.5) |
| ≥40 | 5 (6.9) | 11 (4.7) | 93 (6.8) | 109 (6.5) |
| Physical activity (total MET-hours per week, mean ± SD) | 26.0 ± 20.4 | 32.6 ± 23.2 | 35.0 ± 24.0 | 34.3 ± 23.8 |
| White, non-Hispanic, % | 56 (76.7) | 199 (84.3) | 1162 (85.5) | 1417 (85.0) |
| Body mass index (BMI) | ||||
| <18.5 | 1 (1.4) | 2 (0.85) | 11 (0.81) | 14 (0.84) |
| 18.5–24.9 | 13 (17.8) | 63 (26.7) | 502 (36.9) | 578 (34.7) |
| 25.0–29.9 | 25 (34.3) | 92 (39.0) | 533 (39.2) | 650 (39.0) |
| 30.0–34.9 | 20 (27.4) | 50 (21.2) | 192 (14.1) | 262 (15.7) |
| ≥35.0 | 14 (19.2) | 29 (12.3) | 121 (8.9) | 164 (9.8) |
| Education ≥16 years, % | 29 (39.7) | 145 (61.4) | 1041 (76.6) | 1215 (72.8) |
| History of nicotine, tobacco, and cannabis use | ||||
| Current smoker, % | 22 (30.1) | 60 (25.4) | 67 (4.9) | 149 (8.9) |
| Former smoker, % | 31 (42.5) | 71 (30.1) | 141 (10.4) | 243 (14.6) |
| Chewing tobacco | 9 (12.3) | 18 (7.6) | 49 (3.6) | 76 (4.6) |
| Used nicotine gum or substitute in the past 2 months | 13 (17.8) | 18 (7.6) | 12 (0.88) | 43 (2.6) |
| Cannabis use, ever | 28 (38.4) | 112 (47.5) | 233 (17.1) | 373 (22.4) |
| Sleep duration | ||||
| Male sleep <7 h/day, % | 39 (53.4) | 93 (39.4) | 393 (28.9) | 525 (31.5) |
| Male sleep 7–8 h/day, % | 32 (43.8) | 129 (54.7) | 921 (67.8) | 1082 (64.9) |
| Male sleep ≥9 h/day, % | 2 (2.7) | 14 (5.9) | 45 (3.3) | 61 (3.7) |
| Reproductive characteristics | ||||
| Intercourse once per week or less, % | 30 (41.1) | 120 (50.8) | 646 (47.5) | 796 (47.7) |
| Previously impregnated a female partner, % | 48 (65.8) | 120 (50.9) | 613 (45.1) | 781 (46.8) |
| History of infertility, % | 16 (21.9) | 19 (8.1) | 130 (9.6) | 165 (9.9) |
| Erectile dysfunction, ever % | 3 (4.1) | 3 (1.3) | 40 (2.9) | 46 (2.8) |
| History of diagnosed diabetes, % | 1 (1.4) | 2 (0.85) | 23 (1.7) | 26 (1.6) |
| History of diagnosed hypertension, % | 6 (8.2) | 15 (6.4) | 70 (5.2) | 91 (5.5) |
Females who never vaped experienced an unadjusted rate of 18 SABs per 1000 gestational weeks at risk, while females who had ever vaped experienced 17 SABs per 1000 gestational weeks. In adjusted Cox models, the aHR comparing ever versus never vapers was 1.01 (95% CI = 0.85% to 1.20%) (Table 2). Relative to never vapers, aHRs were 1.03 (95% CI = 0.86% to 1.24%) for former vapers and 0.91 (95% CI = 0.61% to 1.36%) for current vapers. Among current vapers, we did not see strong evidence for a monotonic association based on amount of vaping, quantified by amount of vaping liquid consumed: <3 mL: aHR = 0.94, 95% CI = 0.60% to 1.47% and ≥3 mL: aHR = 0.76, 95% CI = 0.31% to 1.84%.
Table 2.
Vaping Status and Risk of SAB Among Individual Female Participants Surveyed in PRESTO Study
| Number of SAB | Number of gestational weeks at risk | SAB per 1000 gestational weeks at risk | Crude HR (95% CI) | Adjusted HR (95% CI)* | ||
|---|---|---|---|---|---|---|
| Female ever vaping | Never vapers | 1212 | 68 250 | 18 | ref | ref |
| Ever vapers | 166 | 9644 | 17 | 0.97 (0.82–1.14) | 1.01 (0.85–1.20) | |
| Total | 1378 | 77 894 | 18 | |||
| Female current vaping | Never vapers | 1212 | 68 250 | 18 | ref | ref |
| Former vapers | 141 | 8001 | 18 | 0.90 (0.61–1.34) | 1.03 (0.86–1.24) | |
| Current vapers | 25 | 1643 | 15 | 0.98 (0.82–1.17) | 0.91 (0.61–1.36) | |
| Total | 1378 | 77 894 | 18 | |||
| Female current vaping amount | 0 ml | 1353 | 76 251 | 18 | ref | ref |
| <3 ml | 20 | 1289 | 16 | 0.93 (0.60–1.44) | 0.94 (0.60–1.47) | |
| > = 3 ml | 5 | 354 | 14 | 0.81 (0.34–1.96) | 0.76 (0.31–1.84) | |
| Total | 1378 | 77 894 | 18 | |||
BMI = body mass index; CI = confidence interval; HR = hazard ratio; SAB = spontaneous abortion.
*Adjusted with female age, education, race and ethnicity, female current smoking status, BMI, alcohol use, and history of SAB.
Among female participants who had never smoked combustible cigarettes, the aHR for current (vs. never) vapers was 0.66 (95% CI = 0.22% to 1.93%) and the aHR for former vapers was 1.11 (95% CI = 0.88% to 1.40%) (Table 3). In the ever-smoker group, current vaping (aHR = 0.97, 95% CI = 0.60% to 1.55%) and former vaping (aHR = 0.92, 95% CI = 0.68% to 1.26%) were not strongly associated with rates of SAB. Compared with never-vaping, never-smoking subjects, SAB rate was not much different in never-vaping, ever-smoking participants (aHR = 0.93, 95% CI = 0.77% to 1.12%) and in ever-smoking, ever-vaping participants (aHR = 0.83, 95% CI = 0.60% to 1.16%). Further distinction of vaping and smoking status between never, former, and current usage also did not show any clear association between SAB risk and vaping and combustible cigarette smoking (Table S1).
Table 3.
Assessing Interaction Between Vaping and Cigarette Smoking on the Risk of SAB
| Number of SABs | Number of gestational weeks at risk | SAB per 1000 gestational weeks at risk | Crude HR (95% CI) | Adjusted HR (95% CI)* | |
|---|---|---|---|---|---|
| Never-smokers | |||||
| Never vapers | 1077 | 60 645 | 18 | ref | ref |
| Former vapers | 79 | 4282 | 18 | 1.03 (0.82–1.30) | 1.11 (0.88–1.40) |
| Current vapers | 4 | 399 | 10 | 0.62 (0.21–1.81) | 0.66 (0.22–1.93) |
| Ever-smokers | |||||
| Never vapers | 135 | 7605 | 17 | ref | Ref |
| Former vapers | 62 | 3719 | 17 | 0.92 (0.68–1.24) | 0.92 (0.68–1.26) |
| Current vapers | 21 | 1244 | 18 | 0.98 (0.62–1.55) | 0.97 (0.60–1.55) |
| Never vapers | |||||
| Never smokers | 1077 | 60 645 | 18 | ref | Ref |
| Ever smokers | 135 | 7605 | 18 | 1.00 (0.84–1.20) | 0.93 (0.77–1.12) |
| Ever vapers | |||||
| Never smokers | 83 | 4681 | 18 | ref | Ref |
| Ever smokers | 83 | 4963 | 17 | 0.93 (0.69–1.26) | 0.83 (0.60–1.16) |
BMI = body mass index; CI = confidence interval; HR = hazard ratio; SAB = spontaneous abortion.
*Adjusted with female age, education, race and ethnicity, female current smoking status, BMI, alcohol use, and history of SAB.
In the couple-based analyses (Table 4), we observed little difference in rates of SAB comparing male ever versus never vapers (aHR = 0.90, 95% CI = 0.63% to 1.28%). Relative to male never vapers, aHRs were 1.00 for male former vapers (95% CI = 0.74% to 1.35%) and 0.67 for male current vapers (95% CI = 0.35% to 1.25%), though associations were imprecise. Finally, differences in the amount of vaping by male partners were not appreciably associated with rates of SAB.
Table 4.
Risk of SAB and Vaping in a Combined-Couple Model With Female and Male Partner Data
| Number of SABs | Number of gestational weeks at risk | SAB per 1000 gestational weeks at risk | Crude HR (95% CI) | Adjusted HR (95% CI)** | Combined HR (95% CI)*** | ||
|---|---|---|---|---|---|---|---|
| Female ever vaping | Never vapers | 359 | 19 110 | 19 | ref | ref | ref |
| Ever vapers | 39 | 2433 | 16 | 0.87 (0.62–1.21) | 0.86 (0.61–1.22) | 0.90 (0.63–1.28) | |
| Male ever vaping | Never vapers | 329 | 17 526 | 19 | ref | ref | ref |
| Ever vapers | 69 | 4017 | 17 | 0.91 (0.70–1.18) | 0.96 (0.73–1.26) | 0.94 (0.71–1.25) | |
| Female current vaping | Never vapers | 359 | 19 110 | 19 | ref | ref | ref |
| Former vapers | 31 | 1992 | 16 | 0.83 (0.58–1.20) | 0.84 (0.58–1.23) | 0.89 (0.60–1.31) | |
| Current vapers | 8 | 441 | 18 | 1.03 (0.51–2.07) | 0.96 (0.47–1.95) | 1.15 (0.54–2.46) | |
| Male current vaping | Never vapers | 329 | 17 526 | 19 | ref | ref | ref |
| Former vapers | 57 | 3019 | 19 | 0.99 (0.74–1.31) | 1.03 (0.77–1.39) | 1.002 (0.74–1.35) | |
| Current vapers | 12 | 998 | 12 | 0.67 (0.38–1.19) | 0.70 (0.39–1.27) | 0.67 (0.35–1.25) | |
| Female current vaping in mL | 0 ml | 390 | 21 102 | 18 | ref | ref | ref |
| 1: <3 mL | 6 | 323 | 19 | 1.10 (0.49–2.44) | 1.08 (0.48–2.43) | 1.32 (0.57–3.07) | |
| 2: ≥3 mL | 2 | 118 | 17 | 0.91 (0.23–3.66) | 0.76 (0.19–3.08) | 0.86 (0.20–3.71) | |
| Male current vaping in mL | 0 ml | 386 | 20 545 | 19 | ref | ref | Ref |
| 1: <3 mL | 8 | 639 | 13 | 0.70 (0.35–1.42) | 0.75 (0.37–1.54) | 0.71 (0.34–1.49) | |
| 2: ≥3 mL | 4 | 359 | 11 | 0.61 (0.23–1.63) | 0.62 (0.23–1.67) | 0.60 (0.21–1.66) | |
**Adjusted for female age and BMI, race and ethnicity, education, current smoking status, baseline alcohol consumption, history of SAB, and history of diabetes.
***Adjusted for female age and BMI, race and ethnicity, education, current smoking status, baseline alcohol consumption, history of SAB, and history of diabetes.
Discussion
In this prospective study, we saw no consistent association between preconception nicotine vaping in either partner and SAB incidence. Additionally, there was no appreciable difference in the association of vaping with SAB incidence by history of combustible cigarette use.
Our investigation of the effects of preconception vaping on the risk of SAB was guided by previous studies linking vaping to adverse reproductive outcomes. Research on animal models has shown that components of e-cigarette liquids can alter sperm morphology and the seminiferous epithelium, potentially affecting implantation and subsequent pregnancy.27 In a previous publication using the same data source as the current study, current vaping was associated with reduced fecundability when compared with never having used e-cigarettes.14 From the data we examined, however, preconception vaping did not meaningfully affect the risk of SAB. Within this context, it is possible that the effects of vaping are not so deleterious to induce SAB but still have adverse influences that are not seen within the scope of our analysis.
The previously established link between combustible smoking and SAB also formed the basis of our question. Our results demonstrated that vaping was not strongly associated with increased risk of SAB despite both e-cigarettes and cigarettes exposing users to some of the same hazardous chemicals, including nicotine, tobacco-specific nitrosamines, diethylene glycol, trace elements, polycyclic aromatic hydrocarbons, pesticides, and carbonyl compounds.28 One possible explanation for this difference is that the amount of exposure to these hazardous chemicals is substantially less in vaping. A study has shown that former smokers who switched completely to vaping had lower exposure to biomarkers of potential harm.29,30 Though the main components of vaping liquid, propylene glycol and glycerin, have been linked to toxicity in oral and intravenous administration, these toxicities were demonstrated at concentrations that are not commonly achievable by normal vaping behaviors and were not directly evaluated in relation to reproduction.12 Still, more research on specific, long-term evidence of vaping toxicities on reproduction is necessary to strengthen this conclusion.
Given the close association between vaping and smoking, we also examined data combining both factors on the risk of SAB. Our results do not show any important change in the risk of SAB with vaping in females who have a current or former history of smoking. At the same time, they do not show any change in SAB risk for females with a history of vaping who also smoke compared with controls. We included this portion of the study to see how the interactions between the two risk factors affected SAB risk, and to see whether we needed to disentangle further these commonly linked factors. In contrast to prior studies, our findings from PRESTO showed a small but imprecise negative association between smoking use and SAB (aHR = 0.70, 95% CI = 0.55% to 1.15%) (Table S1). However, this finding may be explained by increased intentional smoking cessation in the preconception period by participants, as surveys about smoking in the preconception period show lower reported prevalences of smoking among females who identified as actively “trying for pregnancy” when compared with females who did not.19 This difference is reflected in our study, which found a prevalence of smoking of 3% among female participants, in contrast with the 2021 US female smoking prevalence of 10%.31 Moreover, people planning for pregnancy also improve lifestyle patterns the longer they are trying to conceive.32
Due to the specific preconception focus of our study, we have not included an analysis of vaping during early pregnancy and the risk of SAB. However, we acknowledge that this is also a critical exposure period for SAB risk, as combustible cigarette smoking during pregnancy has been shown to increase SAB risk.4
There are limitations to our study, as well as possible sources of bias. As we only assessed vaping and smoking status at the baseline questionnaire before conception, our study did not account for post-survey changes in either behavior. This also left variance in the timing of last exposure relative to conception between participants. If a participant was unaware of being pregnant and the pregnancy ended in a SAB without the participant becoming aware, this event also would not have been detected by our surveys. The overall size of the study cohort was large, but the number of e-cigarette users (n = 127 female, n = 73 male) was relatively small, which may affect the detection of small increases in HR. The reliability of self-reported data on vaping is also a possible limitation of our study. However, previous studies have justified the use of self-reporting to measure vape usage,33 as well as the use of self-reported online surveys to measure data on smoking.34
Though we quantified the amount of vaping liquid used by current vapers, data on the daily frequency of vaping or specific nicotine concentration of liquid used, which can vary widely, was not available. The length of time of vaping was also not available, nor was the precise exposure duration during preconception for each participant. Additionally, there are many variations in vaping systems that change vapor volume, concentration, and delivery that were not accounted for.35 The voluntary preconception nature of PRESTO may also introduce self-selection bias of subjects who engage in less risky preconception behaviors overall compared with the general population. We also acknowledge the possibility of the Hawthorne effect changing subject behaviors, given the usage of follow-up surveys as a proxy for observation.
Nevertheless, the current report found little association between vaping in either partner on SAB incidence, including among never and former users of combustible cigarettes. The results imply that vaping may not adversely affect rates of SAB. However, it is important to consider these results in the context with research linking vaping with impaired fecundity and adverse birth outcomes. Other studies have shown associations between vaping and detrimental cardiovascular and respiratory health impacts on users, which, although not tied specifically to SAB, may affect the overall well-being of the pregnant person and lead to pregnancy complications.36 Additionally, more direct evidence is needed on the specific potential reproductive impacts of chemical exposures from vaping. Understanding the long-term impacts of vaping on SAB and the wider realm of reproduction constitutes an important need in the realm of public health.
Conclusions
In this study to evaluate the association between preconception vaping in either partner with the incidence of SAB, we found that vaping in either partner during the preconception period was not strongly associated with SAB incidence. Additionally, cigarette smoking did not appear to modify the association between vaping and SAB Incidence in a meaningful way. In the context of multiple studies that point to adverse effects of vaping upon pregnancy outcomes, including SAB, our study highlights the need for more accurate, evidence-based information to guide decision-making in lifestyle behaviors for couples trying to conceive. Though the current study does not demonstrate an association between vaping and SAB, vaping is not risk-free and does expose users to toxins with the potential for negative fetal outcomes.
Supplementary Material
Contributor Information
Austen D Le, Department of Medical Education, Stanford University School of Medicine, Stanford, CA.
Chiyuan Amy Zhang, Department of Urology, Stanford University School of Medicine, Stanford, CA.
Abby L Chen, Department of Medical Education, Stanford University School of Medicine, Stanford, CA.
Satvir Basran, Department of Urology, Stanford University School of Medicine, Stanford, CA.
Nicolas Seranio, Department of Urology, Stanford University School of Medicine, Stanford, CA.
Michael Scott, Department of Urology, Stanford University School of Medicine, Stanford, CA.
Shufeng Li, Department of Urology, Stanford University School of Medicine, Stanford, CA.
Elizabeth E Hatch, Department of Epidemiology, Boston University School of Public Health, Boston, MA.
Kenneth J Rothman, Department of Epidemiology, Boston University School of Public Health, Boston, MA.
Amelia K Wesselink, Department of Epidemiology, Boston University School of Public Health, Boston, MA.
Alyssa F Harlow, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA; Department of Population and Public Health Sciences, Institute for Addiction Science, University of Southern California, Los Angeles, CA.
Lauren A Wise, Department of Epidemiology, Boston University School of Public Health, Boston, MA.
Michael L Eisenberg, Department of Urology, Stanford University School of Medicine, Stanford, CA.
Author Contributions
Austen Le (Formal analysis, Visualization, Writing—original draft [lead], Writing—review & editing [equal]), Chiyuan Amy Zhang (Data curation [lead], Formal analysis [supporting]), Abby L. Chen (Validation [equal], Writing—original draft [supporting], Writing—review & editing [equal]), Satvir Basran (Project administration [lead], Writing—original draft, Writing—review & editing [supporting]), Nicolas Seranio (Conceptualization [equal], Writing—review & editing [supporting]), Michael Scott (Conceptualization [equal], Writing—review & editing [supporting]), Shufeng Li (Data curation, Formal analysis [supporting]), Elizabeth Hatch (Methodology [equal], Writing—review & editing [supporting]), Kenneth J. Rothman (Methodology [equal], Writing—review & editing [supporting]), Amelia K. Wesselink (Visualization, Writing—review & editing [supporting]), Alyssa Fitzpatrick Harlow (Methodology, Validation [equal], Writing—review & editing [supporting]), Lauren A. Wise (Funding acquisition, Methodology [equal], Writing—review & editing [supporting]), Michael L. Eisenberg (Conceptualization, Funding acquisition [equal], Supervision [lead], Writing—review & editing [supporting])
Funding
This work was funded by grants from the National Institutes of Health and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01-HD105863 to LAW and MLE; R01-HD086742 to LAW).
Declaration of Interests
LAW serves as a consultant for AbbVie, Inc and the Gates Foundation. She also receives in-kind donations for primary data collection in Pregnancy Study Online (PRESTO) from Swiss Precision Diagnostics (home pregnancy tests) and Kindara.com (fertility apps). All of these relationships are for work unrelated to this manuscript. MLE serves as an advisor for Next, Doveras, Hannah, Illumicell, VSeat, HisTurn, and Legacy. All other authors have no conflicts of interest to declare.
Data Availability
Data regarding any of the subjects in the study have not been previously published unless specified. PRESTO participants did not consent to share their personal data with outside parties. Analytic code will be made available to the editors of the journal for review or query upon request. We used the STROBE cohort study checklist when writing this report (von Elm et al., 2019).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data regarding any of the subjects in the study have not been previously published unless specified. PRESTO participants did not consent to share their personal data with outside parties. Analytic code will be made available to the editors of the journal for review or query upon request. We used the STROBE cohort study checklist when writing this report (von Elm et al., 2019).