Electronic cigarette use has raised concern worldwide regarding potential health risks and its position in tobacco cessation strategies. As part of any toxicity assessment, the chemical characterization of e-liquids and their related vapors are among fundamental data to be determined. Considering the lack of available reference methods, we developed and validated several analytical procedures in order to conduct a multicomponent analysis of six e-liquid refills and their resultant vapor emissions (generated by a smoking machine), and compared them with tobacco smoke. We combined several techniques including gas-chromatography, high and ultra-performance liquid chromatography and inductively coupled plasma with mass spectrometry or ultraviolet and flame ionization detection in order to identify the main e-liquid constituents (propylene glycol, glycerol and nicotine), as well as multiple potentially harmful components (trace elements, polycyclic aromatic hydrocarbons (PAHs), pesticides and carbonyl compounds). Regarding propylene glycol, glycerol and nicotine concentrations, the six tested e-liquids comply with the advertised composition and contain only traces of pollutants. Noticeable lower concentrations of trace elements (≤3.4 pg/mL puff), pesticides (<LOQ), PAHs (≤4.1 pg/mL puff) and carbonyls (≤2.11 ng/mL puff) were measured in e-vapors compared to those in cigarette smoke (up to 45.0 pg/mL puff, 8.7 pg/mL puff, 560.8 pg/mL puff and 1540 ng/mL puff, respectively). Although an accurate characterization of electronic cigarette emissions requires further analytical optimizations, our results have shown that vaping exposes the user to lesser amounts of selected toxic components of concern found in some representative French e-cigarette products than does smoking typical conventional cigarettes.

The incidence of neurodevelopmental and neurodegenerative diseases worldwide has dramatically increased over the last decades. Although the aetiology remains uncertain, evidence is now growing that exposure to persistent organic pollutants during sensitive neurodevelopmental periods such as early life may be a strong risk factor, predisposing the individual to disease development later in life. Epidemiological studies have associated environmentally persistent organic pollutant exposure to brain disorders including neuropathies, cognitive, motor, and sensory impairments; neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD); and neurodegenerative diseases including Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). In many ways, this expands the classical “Developmental Origins of Health and Disease” paradigm to include exposure to pollutants. This model has been refined over the years to give the current “three-hit” model that considers the individual’s genetic factors as a first “hit.” It has an immediate interaction with the early-life exposome (including persistent organic pollutants) that can be considered to be a second “hit.” Together, these first two “hits” produce a quiescent or latent phenotype, most probably encoded in the epigenome, which has become susceptible to a third environmental “hit” in later life. It is only after the third “hit” that the increased risk of disease symptoms is crystallised. However, if the individual is exposed to a different environment in later life, they would be expected to remain healthy. In this review, we examine the effect of exposure to persistent organic pollutants and particulate matters in early life and the relationship to subsequent neurodevelopmental and neurodegenerative disorders. The roles of those environmental factors which may affect epigenetic DNA methylation and therefore influence normal neurodevelopment are then evaluated.

Urine and plasma have been used to date for the biomonitoring of exposure to pollutants and are still the preferred fluids for this purpose; however, these fluids mainly provide information on the short term and may present a high level of variability regarding pesticide concentrations, especially for nonpersistent compounds. Hair analysis may provide information about chronic exposure that is averaged over several months; therefore, this method has been proposed as an alternative to solely relying on these fluids. Although the possibility of detecting pesticides in hair has been demonstrated over the past few years, the unknown linkage between exposure and pesticides concentration in hair has limited the recognition of this matrix as a relevant tool for assessing human exposure. Based on a rat model in which there was controlled exposure to a mixture of pesticides composed of lindane, β-hexachlorocyclohexane, β-endosulfan, p,p′-DDT, p,p′-DDE, dieldrin, pentachlorophenol, diazinon, chlorpyrifos, cyhalothrin, permethrin, cypermethrin, propiconazole, fipronil, oxadiazon, diflufenican, trifluralin, carbofuran, and propoxur, the current work demonstrates the association between exposure intensity and resulting pesticide concentration in hair. We also compared the results obtained from a hair analysis to urine and plasma collected from the same rats. Hair, blood, and urine were collected from rats submitted to 90-day exposure by gavage to the aforementioned mixture of common pesticides at different levels. We observed a linear relationship between exposure intensity and the concentration of pesticides in the rats’ hair (R _Pearson 0.453–0.978, p < 0.01). A comparison with results from urine and plasma samples demonstrated the relevance of hair analysis and, for many chemicals, its superiority over using fluids for differentiating animals from different groups and for re-attributing animals to their correct groups of exposure based on pesticide concentrations in the matrix. Therefore, this study strongly supports hair analysis as a reliable tool to be used during epidemiological studies to investigate exposure-associated adverse health effects.