Population.

Eligible studies included pregnant humans and in vivo mammalian models of pregnancy (e.g., murine and rat models). Studies assessing health outcomes in offspring following antenatal exposure were eligible. Ex vivo tissue studies and non-mammalian models were excluded. Studies examining postnatal-only exposure were also excluded.

Concept.

The core concept was antenatal vape exposure (nicotine-containing or nicotine-free) including both active and passive aerosol exposure in animal models. Studies examining exclusive vaping (EV), dual use (vaping plus combustible cigarette use), combustible cigarette comparison, nicotine-free exposure, and passive exposure were eligible. Studies evaluating exposure to specific device components (e.g., flavouring constituents) within in vivo mammalian models were also included.

Outcomes.

Eligible outcomes included maternal, fetal, neonatal, and infant health outcomes.

Outcome domains included:

• Pregnancy complications (e.g., preterm birth, fetal growth restriction)
• Anthropometric measures (e.g., birth weight, fetal and placental weight, biometric indices)
• Maternal physiological endpoints (e.g., autonomic function, endothelial function)
• Neonatal and infant outcomes (e.g., breastfeeding, neonatal mortality, NICU admission)
• Organ system outcomes (respiratory, metabolic, neurological, cardiovascular, renal, immune, neurobehavioural)

Context.

No geographic restrictions were applied. Studies were included if published in English or where an English translation was available.

Anthropometric data.

Eighteen studies examined anthropometric data; primarily birthweight, weanling weight, and crown–rump length. Nicotine-containing vapour exposure was consistently associated with growth restriction, only Church et al. reported no significant difference in fetal weight [43]. Orzabal et al. demonstrated 25–47% fetal weight reduction and 12–24% reduction in crown–rump length, relative to controls [37–39]. Ten other studies noted similar restrictions in growth [31–34,36,40–42,54]. All but one associated this with nicotine-containing vapes [44]. Findings from nicotine-free vapour exposure were inconsistent. One study identified a 16–18% weight reduction at postnatal day (PND) 21, with similar effects seen in another study [35,65]. Conversely, Aslander et al. identified a higher body weight in females and Chen et al. found a higher PND 20 weanling weight [52,54]. Two studies found no difference between the non-nicotine vaping cohorts and controls [37,43]. In summary, most studies reported reduced fetal or postnatal growth following exposure to nicotine-containing vapour. Associations with non-nicotine vapes remain inconclusive.

Three studies reported skeletal abnormalities, including shorter long bones and impaired craniofacial development, less pronounced without nicotine [34,44,63]. Notably, one study found that the ratio of propylene glycol (PG) to vegetable glycerin (VG) influenced outcomes, with skeletal abnormalities observed at a 30PG/70VG ratio but not at 50PG/50VG [44]. Orzabal et al. identified maternal amino acid dysregulation, especially in growth and nitrogen metabolism pathways, which were more pronounced in nicotine exposed dams [38].

Metabolic health.

Five studies identified systemic and hepatic metabolic dysregulation [38,45,48,51,53]. Aboaziza et al. reported increased adiposity and reduced lean mass after both nicotine and non-nicotine exposure, persisting into adulthood in the nicotine-free group, suggesting a predisposition to obesity [45]. Two studies corroborated these findings, identifying increased weight gain during adolescence and higher body mass at 6 and 12 months [48,51]. Li et al. demonstrated that both nicotine-containing and nicotine-free vapour exposure impaired hepatic metabolic function in dams and pups [53]. Dams from both groups exhibited increased expression of glucose transporter GLUT4 and an increased HOMA-IR index, an indicator of insulin resistance [53]. The nicotine-free group also demonstrated elevated liver triglycerides and plasma non-esterified fatty acids (NEFA), suggesting increased lipolysis [53,66]. In addition, nicotine-exposed pups had increased expression of FOXO1, a hepatic gluconeogenesis marker, and elevated liver triglycerides suggesting increased lipogenesis [53,67]. Nicotine-free pups had increased mRNA expression of glucose transporters GLUT2 and GLUT4 and elevated FOXO1. Orzabal et al. noted increased levels of branched-chain amino acids in the nicotine-exposed group, which is frequently linked to insulin resistance and amino acid dysregulation [68]. These findings suggest potential long-term metabolic and epigenetic changes following in utero exposure to nicotine and non-nicotine containing vapour which may predispose to both obesity and insulin resistance.

Respiratory outcomes.

Eleven studies reported adverse effects on respiratory development, with more severe outcomes with nicotine exposure [32,33,36,38,39,52,54,61–64]. Across several studies, lung structural and histological alterations were observed, with features indicative of fibrotic remodelling, emphysematous changes, and allergic/asthmatic phenotypes. Fibrosis, characterised by increased lung stiffness, reduced compliance, tissue deposition, and a lower lung tissue-to-airspace ratio, was described in five studies [33,36,52,63,64]. Orzabal et al. noted an emphysema-like phenotype [39], two additional studies supported these findings, observing decreased lung fibrillar collagen [32,52]. Two described asthma-like features, including eosinophilia, neutrophilic inflammation, and elevated airway resistance, less pronounced in nicotine-free offspring [36,61].

Immune dysregulation and inflammation were noted. Nicotine mediated upregulation of inflammatory markers, with greater predisposition to allergic inflammation and eosinophilia, was reported in two studies [32,61]. Chen et al. found altered respiratory cytokine profiles across nicotine and nicotine-free groups. Dams exhibited elevated TNF-α, IL-1β, and IL-6, indicating heightened pulmonary inflammation, while pups had reduced IL-1β, suggesting a dysregulated postnatal immune response [54]. Lung-specific genetic dysregulation was also reported, again more marked with nicotine-containing vapour. Orzabal et al. identified 3,306 dysregulated genes in fetal lung tissue [39], supported by Ozekin et al. and Noel et al., who reported 2,563 and 75 dysregulated genes respectively [36,63]. Affected biological pathways included mitochondrial dysfunction, protein degradation, metabolic regulation, DNA damage response, lung development, and inflammatory signalling [63]. Together, these studies report structural, immunological and epigenetic alterations in pulmonary development, with generally greater effects from nicotine-containing vapes when compared to an unexposed control group.

Neurological, developmental, and behavioural outcomes.

Ten studies investigated neurodevelopmental and behavioural outcomes following antenatal vape exposure, broadly categorised into cognitive, neurochemical, metabolic and epigenetic impacts. Several studies reported cognitive dysfunction, characterised by impaired memory and increased hyperactivity, in offspring irrespective of nicotine exposure [31,41,43,48,51,58,59]. All nicotine-containing studies also reported reduced anxiety-like behaviours, whereas nicotine-free findings were mixed [58,65].

Oxidative stress emerged as a recurrent mechanism. Al-Sawalha et al. reported elevated hippocampal superoxide dismutase levels, indicating compensation for oxidative damage [59]. While Walyat et al. found elevated levels of reactive oxygen species were associated with greater infarct sizes following hypoxic ischaemic injury (HII) [42]. Sifat et al. reported similar findings with nicotine-exposed offspring demonstrating vulnerability to HII, with larger infarcts, delayed reflex recovery and impaired neuronal variability when compared to an unexposed control group [41]. Church et al. noted reduced neuronal anti-inflammatory cytokines in nicotine and non-nicotine groups, suggesting impairment mediated by multiple interacting pathways [43]. Awada et al. identified increased hypothalamic expression of glucose metabolism proteins (GLUT-1, AMPAR, PPARγ) in both nicotine and non-nicotine groups and Sifat et al. observed increased neuronal glucose uptake and ATP production, suggesting altered cerebral energy metabolism [41,60]. Mills & Corbin reported altered expression of clock genes (Arnt1, Per1, Per3, Clock, Cry1, Cry2), which regulate circadian rhythms, alongside evidence of neuronal damage (silver staining), increased astrocyte activation and astrocyte–endothelial interactions, and reduced SIRT1 with elevated NOX1, Aβ1–40, and Aβ1–42 in both nicotine and non-nicotine groups. Importantly, these changes persisted to 12 months, suggesting ongoing neuroinflammation and oxidative stress that may underlie chronic neurological dysfunction [48,49].

A diverse range of epigenetic modifications were also observed. In nicotine-exposed pups, Archie et al. reported reduced expression of proteins critical for blood–brain barrier integrity (ZO-1, claudin-5, occludin), astrocyte function (GFAP), glucose transport (GLUT-1) and water homeostasis (AQP4) [31,69]. Decreased cerebral glucose transporter expression was also noted by Sifat et al. [41]. Neurodevelopmental disruption was not confined to nicotine groups. Awada et al. identified upregulation of neuroinflammatory genes (PPARγ, LepRb, MC4R, AMPK, POMC, SLC2A1) in both nicotine and non-nicotine-exposed pups [60]. Similarly, Nguyen et al. reported increased global DNA methylation and downregulation of epigenetic regulators (DNMT1, DNMT3A, HDAC1, MECP) in carrier-aerosol exposed pups [58]. These findings suggest that both nicotine-containing and nicotine-free vapour exposure disrupts neurodevelopment through overlapping cognitive, oxidative, metabolic, and epigenetic pathways.

Cardiovascular outcomes.

Eight studies assessed the effect of antenatal exposure to both nicotine and non-nicotine-containing vapes on cardiovascular development and function when compared to an unexposed control group [35,40,45–50]. Both nicotine-containing and nicotine-free exposures were associated with vascular alterations across studies, with oxidative stress repeatedly implicated as a key mechanism.

Two studies identified increased arterial stiffness, evidenced by elevated pulse wave velocity (PWV), with Aboaziza et al. noting persistence into adulthood [45,50]. Cerebrovascular dysfunction was observed by Burrage et al. and Mills et al. who reported reduced endothelial-dependent dilation of the middle cerebral artery (MCA) [35,46–49]. Partial restoration of vascular function with oxidative stress modulators further implicates this pathway [46,48,49]. Histological assessment revealed altered aortic structure; nicotine exposure reduced elastin and elastin-to-collagen ratio, while non-nicotine exposure was associated with increased collagen content and impaired reactivity [45]. Reynolds et al. reported increased maternal cardiac arrhythmias, accelerated ventricular repolarization, and elevated cardiac output in nicotine-containing vape–exposed groups, with raised 3-methoxytyramine levels suggesting a mechanism of sympathetic dominance [40]. These findings suggest that antenatal vape exposure, with or without nicotine, may be associated with long-term cardiovascular dysregulation in animal models, with oxidative stress implicated as a potential mechanism.

Other outcomes.

Five studies examined effects beyond major organ systems. Alarabi et al. identified disrupted haemostasis, in nicotine-exposed pups, with shortened bleeding/occlusion times and altered platelet gene and miRNA expression [57]. Yu et al. noted impaired bone marrow stem cell function with reduced proliferation, migration, and wound healing capacity, and molecular changes suggesting a fibrotic shift but only in nicotine-exposed pups [56].

Two studies reported increased pup resorption and smaller litter sizes, irrespective of nicotine content [48,49]. Orzabal et al. provided a potential mechanistic explanation, demonstrating marked reductions in maternal uterine (−49.5%) and fetal umbilical (−65.3%) artery blood flow following nicotine exposure, consistent with impaired placental perfusion as a driver of growth restriction and fetal loss, although this was only seen in the nicotine-exposed group [37]. Overall, these studies report alterations in haematological, immune, and placental function, particularly with nicotine-containing vapour.

Low birth weight.

Sixteen studies examined vaping and LBW, with mixed findings. Only two studies employed biochemical validation [79,85], the others relied on self-report. Nine studies reported an increased risk of LBW among exclusive vapers versus non-users. Six were large Pregnancy Risk Assessment Monitoring System (PRAMS) analyses. Reported adjusted risk ratios (aRR) for LBW included 1.38 (95% CI 1.09–1.75), while adjusted odds ratios (aOR) ranged from 1.53 (95% CI 1.06–2.22) to 1.68 (95% CI 1.15–2.47), an adjusted prevalence ratio (aPR) of 1.33 (95% CI 1.06–1.66) was reported for LBW [70,73,75–77]. One study additionally reported a lower prevalence of LBW among individuals who quit vaping during pregnancy compared with ongoing vapers (aPR 0.53, 95% CI 0.47–0.60) [78]. Galbo et al. reported higher odds of a composite adverse birth outcome, which included LBW, identifying a 62% increase among exclusive vapers (aOR 1.62, 95% CI 1.16–2.26) [74]. However, repeated reliance on a single surveillance dataset (PRAMS), which is based on retrospective self-report, may limit external replication across independent cohorts. Bailey et al. found a significantly lower birth weight, length and head circumference in the EV cohort compared with non-users [72]. In a large US retrospective cohort study spanning 70 hospitals (n = 29,103), Azar et al. found exclusive vapers had higher odds of LBW compared with non-users (aOR 1.6 [95% CI 1.03–2.61]) [71].

In contrast, seven studies identified no association between LBW and EV [79–85]. These were derived from independent prospective cohorts and non-PRAMS datasets [80–84], and two included biochemical validation [79,85]. A secondary analysis of PRAMS data found no association between vaping and birthweight (−57.8g [95% CI −134.3, 18.6]) and two small UK studies (EV n = 10 and n = 27) also supported this, but conclusions were limited by sample size [80,81,84]. McDonnell et al. (EV n = 218) in a prospective cohort study including 218 exclusive vapers, observed no difference in birth weight (BW EV 3470 ± 555 g vs BW non-use 3471 ± 504 g p = 0.97) [83], consistent with Lin et al. who, using Population Assessment of Tobacco and Health (PATH) data, found no association with composite “high-risk birth” outcome, including LBW, among exclusive vapers (aOR 1.19 [95% CI 0.38–3.73]) [82]. Pesola et al. analysed data from the Pregnancy Trial of E-cigarette and Patches (PREP) and identified no association between a composite EV and nicotine replacement therapy (NRT) group and LBW (aRR 0.64 [95% CI 0.24–1.72]) [85], replicated by Ashford et al. [79]. Both studies incorporated biochemical validation using salivary/urinary cotinine (a nicotine metabolite), anabasine (a tobacco alkaloid biomarker) and exhaled carbon monoxide (CO), although saliva sample return rates were only 55% in Pesola et al. [79,85].

Most comparative analyses reported lower effect estimates for EV compared with CC or dual use groups [70,71,73,76,80–83,85]. Three PRAMS studies showed reduced risk versus CC and dual (concurrent vaping and smoking) users, though vapers still exceeded non-user risk (aRR 1.38 vs CC 1.78 and dual 2.01) [70,76,81]. A UK prospective observational cohort study (EV n = 218) reported a similar pattern [83]. Four studies using different data sources found lower odds of LBW when EV groups were compared to CC groups [71,73,80,82]. Pesola et al. reported higher birthweights in EV/NRT than CC and dual (difference 0.15 kg, [95% CI 0.05–0.25]), supported by biochemical validation [85]. Three studies reported similar effect estimates for EV compared with CC or dual use groups [75,78,79].

In summary, findings were mixed. Nine studies identified increased LBW risk [70–78] and while seven found none [79–85]. Studies reporting no association more frequently incorporated biochemical validation and were more often conducted in independent prospective cohorts. By contrast, PRAMS-based studies dominated the positive associations. Comparative analyses more consistently reported lower effect estimates for EV compared with combustible cigarette or dual use groups.

Small for gestational age.

Fifteen studies examined the association between vaping during pregnancy and the risk of small for gestational age (SGA) infants, yielding mixed results. Nine studies reported an increased risk compared to non-use [72,73,75,76,78,86,87,90,92]. Bailey et al. identified reduced third trimester ultrasound growth parameters (estimated weight, head circumference and femur length) in regular vapers [72]. Cardenas et al. identified a 5.1-fold increase in SGA risk for exclusive vapers (aRR 5.1 [95% CI 1.2–22.2]) with biochemical validation, though only recent use was measured so smoking in early pregnancy may have contributed [90]. Nanninga et al. also reported increased risk, though conclusions were limited by a small sample (EV n = 10) and lack of validation [86]. Several large PRAMS analyses reported increased risk (aRR 1.52–2.4), though these rely on self-reported exposure with potential for recall bias and exposure misclassification [73,75,76,78,87,92]. In contrast, six PRAMS-based studies reported no increased risk when compared to non-use [70,77,81,88,93]. McDonnell et al. (EV n = 218), also found no association, however, exposure was self-reported [83].

Comparisons with CC and dual use were variable. Seven studies found lower risk of SGA in EV groups [70,73,76,81,83,88,92]. Hawkins et al. reported EV risk similar to non-smokers (aRR 0.78 [95% CI 0.48–1.27]) but lower than CC (aRR 2.30, [95% CI 2.01–2.63]) and dual use (aRR 1.93, [95% CI 1.31–2.83]) [81]. Similar patterns were identified by five other studies [70,73,76,88,92]. However, six of these seven analyses were derived from PRAMS data. McDonnell et al. observed significantly lower SGA rates in EV (11%) vs CC (28% P < 0.001) and dual use (30.7% p < 0.001) groups, providing findings from an independent prospective cohort [83]. Four studies reported no difference between EV and CC/dual use [75,77,78,93]. Again, all were based on the PRAMS dataset.

Four studies reported higher SGA risk with EV compared to CC or dual use [86,88,90,91]. Cardenas et al. found elevated risk in vapers (aRR 5.1, 95% CI 1.2–22.2) compared to CC (aRR 2.6, [95% CI 0.9–7.2]) and dual use (aRR 2.5, [95% CI 0.7–8.8]) [90]. This prospective cohort study had independent data and biochemical validation but a small sample (EV n = 6) and does not consider the effects of smoking earlier in pregnancy. Cardenas et al. also identified higher SGA rates in dual users who switched to EV (RR 3.2, [95% CI 1.5–6.6]), when compared to smokers who quit (RR 2.9 [95% CI 1.4, 6.3]) [91]. Nanninga et al. also observed increased risk, although generalisability is limited by sample size (EV n = 10) [86]. Wang et al. reported similar findings in a PRAMS study, showing higher odds of SGA in their EV group (aOR 2.4 [95% CI 1.0–5.7]) compared with the CC (aOR 2.3 [95% CI 1.8–2.9]) or dual use groups (aOR 2.3 [95% CI 1.3–4.1]) [87].

Overall, findings were mixed. Several studies reported increased SGA risk among exclusive vapers compared with non-users, while others reported no association. Comparative analyses variably reported lower, similar, or higher effect estimates for EV relative to combustible cigarette or dual use groups. Across studies, exposure was frequently self-reported, comparator definitions varied, and biochemical validation was uncommon. A substantial proportion of analyses were derived from PRAMS data. These methodological characteristics should be considered when interpreting patterns of association.

Preterm birth.

Fifteen studies assessed associations between vaping and PTB; only one used biochemical validation [85]. All six studies reporting increased PTB risk were derived from PRAMS data [70,74–78]. Three studies reported higher risk among vapers Ammar et al. (aRR 1.29, [95% CI 1.00–1.65]), Kim et al. (OR 1.86, [95% CI 1.11–3.12]), and Regan & Bombard et al. (adjusted Prevalence Ratio [aPR] 1.69, [95% CI 1.20–2.39]) [70,75,77]. Regan & Pereira found lower PTB risk in quitters than ongoing EV users (aPR 0.70 [95% CI 0.61–0.81]) [78]. Galbo et al. included PTB in a composite adverse outcome, reporting increased odds with vaping (aOR 1.62, [95% CI 1.16–2.26]) [74].

In contrast, nine studies, using a range of study designs, found no association [73,76,81,82,84–88]. These studies included prospective cohorts, PRAMS analyses, and independent datasets. Five PRAMS studies showed no significant association [73,76,81,87,88]. Nanninga (n = 10) reported 0% PTB, though sample size was small (n = 10) [86]. Opondo et al. also found no association (aRR 1.29 [95% CI 0.54–2.04]) [84]. Lin et al. included PTB within a composite “high-risk birth” outcome and found no association (aOR 1.19 [95% CI 0.38–3.73]) [82]. Pesola et al. reported reduced PTB risk in EV/NRT users vs non-users (aRR 0.29 [95% CI 0.12–0.70]), though the non-user group was very small (n = 25) [85].

Comparative analyses examining EV relative to CC or dual use reported variable findings. No study reported higher estimate of risk for PTB in EV groups compared with CC groups. Five reported overlapping confidence intervals between EV and CC use [70,75,78,85,86]. Six studies spanning two US data sets, PRAMS and PATH, reported lower effect estimates for EV compared with combustible cigarette use [81,82,87,88]. Two studies found EV risk to be lower than both CC (aOR 1.30, [95% CI 1.21–1.40], prevalence 4.8% vs 13%) and dual use (aOR 1.43, [95% CI 1.01–2.08], prevalence 4.8% vs 13.1%) [73,76].

In summary, findings were mixed. Some PRAMS-based analyses reported increased PTB risk among exclusive vapers compared with non-use. However, a larger number of studies, including prospective cohorts and independent datasets, reported no association. Comparative analyses reported either overlapping or more frequently lower associations between EV and preterm birth relative to smoking or dual use.

Maternal health outcomes.

The association between vaping and maternal mental health was explored by two studies, both using PRAMS data. Choi et al. found no association between vaping and postpartum depression (PPD) compared with non-use (aOR 1.09, [95% CI 0.98–1.29]) [95]. These findings contrast with Wen and Kwak et al., who reported higher PPD prevalence among exclusive vapers compared with non-users (13.2% vs 9.7%). The prevalence of PPD was similar between exclusive vapers and smokers (13.2% vs 13.4%), but lower in vapers compared with dual users (13.2% vs 19.8%) [94].

Maternal gestational weight gain (GWG), which when inadequate is linked to PTB, SGA, and LBW, was examined by Wen et al. via PRAMS data [100]. They reported no association between EV and low GWG compared with non-use (aOR 0.99, [95% CI 0.78–1.27]) and observed a lower likelihood of inadequate GWG compared with CC users [99]. Nanninga et al. examined a composite adverse maternal outcome (Hypertension [HTN], Gestational diabetes [GDM] and Postpartum haemorrhage [PPH]) and found no association with EV compared with non-use (n = 4 p = 0.27) [86]. This was no different when compared to CC or dual users. Overall, evidence on the association between vaping in pregnancy and adverse maternal health outcomes is limited, predominantly derived from PRAMS data, and does not demonstrate consistent associations.

Maternal outcomes remain one of the clearest gaps in the human literature. Most studies focused on fetal or neonatal outcomes, while maternal physiological endpoints were rarely assessed. This is important because animal studies suggest vascular, metabolic and placental mechanisms that could plausibly relate to maternal complications, including endothelial dysfunction, altered arterial stiffness, hypertensive disorders, impaired placental perfusion and gestational metabolic disturbance. Future human studies should therefore incorporate maternal vascular, autonomic, metabolic and placental endpoints alongside fetal and neonatal outcomes, to assess whether the mechanistic pathways identified in animal studies translate to human pregnancy.

Fetal and neonatal outcomes.

Pregnancy loss was assessed in three studies. Lin et al., using PATH data (EV n = 41), found no association between vaping and fetal death compared with non-use (aOR 0.33, [95% CI 0.04–2.83]) or between EV and CC groups (aOR 0.56 [95% CI 0.15–2.15]). They did however, observe an association between mint/menthol-flavoured e-liquids and fetal death (aOR 3.27, [95% CI 1.17–9.19] p < 0.05), though case numbers were unreported and adjustment for smoking history was not described [82]. Nanninga et al. reported higher miscarriage rates in their EV group (30% p = 0.01) when compared to non-users and CC users, but their small sample size (EV n = 10), with three using non-nicotine vapes) and lack of biochemical validation limit interpretability [86]. In contrast Le et al. found no association between vaping and miscarriage (aHR 0.64 [95% CI 0.22–1.89]) in a large multi-national cohort study (EV n = 127) [97].

Two small UK studies by Froggatt et al. examined infant neurobehaviour. Their 2020 case-control study (EV n = 10, self-reported exposure) identified neurobehavioural disruption in vapers’ neonates, with abnormal reflexes (p = 0.036), reduced motor maturity (p = 0.036) and borderline self-regulation (p = 0.057). Outcomes were more adverse in CC users compared with exclusive vapers [80]. A 2021 prospective cohort study (EV n = 15, CO validated), found no difference in fetal mouth movements (FMM) between the EV and non-use groups, but heavy CC use (11–20 cigarettes/day) increased FMM (p = 0.038) [96]. Authors noted that diurnal variation limits FMM as a neurobehavioural measure, but both studies reported more pronounced adverse findings in combustible cigarette users.

Four studies assessed neonatal intensive care unit (NICU) admissions. Pesola et al. identified no increased risk of NICU admission in the combined EV/nicotine replacement therapy (EV/NRT) group compared with non-users (aRR 0.57 [95% CI 0.21–1.59]), and equivalent risk compared with CC (6.8%), dual (9.6%), and EV/NRT (9%) groups [85]. An Irish prospective observational cohort study replicated these findings [83] Azar et al. found no association between EV and NICU admission compared with non-use (aOR 0.6 [95% CI 0.37–1.01]) alongside significantly reduced odds of neonatal antibiotic use among exclusive vapers compared with non-users (aOR 0.4 [95% CI 0.21–0.78]). In direct comparisons, combustible cigarette users had higher odds than exclusive vapers of NICU admission (aOR 1.8, 95% CI 1.03–2.99) and neonatal antibiotic use (aOR 2.7 [95% CI 1.35–5.36]) [71]. A Kentucky-based cohort study observed no differences in neonatal respiratory distress (OR 0.75 [95% CI 0.26, 2.21]) and NICU transfer (OR 1.08 [95% CI 0.47, 2.50]) between vaping, smoking and dual use [79].

Three studies examined breastfeeding [83,84,98]. McBride et al. identified reduced odds of breastfeeding beyond 3 months in vapers compared to non-users (OR 0.63 [95% CI 0.44–0.89]) [98]. McDonnell et al. (EV n = 218) similarly observed reduced breastfeeding at discharge (48.6% vs. 61.1% in non-users, p = 0.03), but higher rates compared with CC (27.2%, p < 0.001) and dual users (29.7%, p < 0.001) [83]. In contrast, Opondo et al. found no association (aRR 1.03 [95% CI 0.98 to 1.08]), however, interpretation is limited by small sample size (EV n = 27) and reliance on self-reported exposure [84].

Together, these studies suggest potential neurobehavioural differences associated with EV compared with non-use, however, findings are inconsistent and limited by small sample sizes, retrospective exposure assessment, and limited biochemical validation. Evidence relating to NICU admission was inconsistent, with most studies reporting no statistically significant differences between EV and non-use, and numerically lower point estimates compared with smokers.

Methodological characteristics of included human studies.

Across human studies, substantial methodological heterogeneity was observed. Exposure was most frequently classified using retrospective self-report, commonly during the third trimester only, with limited and inconsistent quantification of vaping frequency or dose. Biochemical validation of exposure was rare, incorporated in only a small number of prospective cohorts. Comparator groups varied considerably and included non-use, combustible cigarette smoking, dual use, and in some cases nicotine replacement therapy. However, definitions of EV and dual use were not uniform across studies. Adjustment for confounding was inconsistently reported, and prior smoking history was variably accounted for. A large proportion of positive associations were derived from repeated analyses of PRAMS surveillance data, whereas independent prospective cohorts were fewer in number and often smaller in sample size. These methodological characteristics complicate direct comparison across studies and limit interpretation of apparent consistency or inconsistency in reported associations.

The structured appraisal, presented in S4 Appendix, highlighted substantial variation in exposure classification, confounder adjustment and residual confounding from previous tobacco use across human studies. In several PRAMS analyses, EV referred to self-reported e-cigarette use without concurrent combustible cigarette smoking during the last 3 months of pregnancy, rather than lifelong EV or biochemically confirmed abstinence from tobacco. Other studies included former smokers, quitters, or participants with unclear prior smoking history within lower-exposure comparison groups. Non-use was also inconsistently defined, ranging from no e-cigarette use to no reported nicotine or tobacco use. Dual use was not always analysed separately, and previous smoking was inconsistently captured. These differences limit direct comparison across studies, particularly where apparent EV groups included former smokers or individuals who had switched from combustible cigarettes earlier in pregnancy.

Confounder adjustment also varied substantially. Commonly adjusted variables included maternal age, race or ethnicity, education, BMI, parity, prenatal care, insurance status, WIC participation, income or area-level deprivation, prior preterm birth and pregnancy complications. However, adjustment for prior smoking intensity, switching behaviour, dual use, partner or household smoking, alcohol, cannabis and other substance use was inconsistent. This variation limits comparison across studies reporting adjusted effect estimates and may contribute to residual confounding.