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Background: Passive exposure to the aerosols of electronic cigarettes (e-cigarettes) has been little studied. We assessed this exposure in late pregnancy in a woman and her 3-year-old child, exposed through e-cigarette use by another household member., Methods: This prospective longitudinal case study involved a family unit consisting of an e-cigarette user, a pregnant woman who delivered an infant during the study, and the couple's older 3-year-old son. At 31, 36, and 40 weeks of the pregnancy, we measured biomarkers (nicotine metabolites, tobacco-specific nitrosamines, propanediols, glycerol, and metals) in the urine and hair of all three participants and in the saliva of the adults, in cord blood at delivery, and in the breast milk at the postpartum period., Results: Samples from the e-cigarette user showed quantifiable concentrations of all analytes assessed (maximum urinary cotinine concentration, 4.9 ng/mL). Among samples taken from the mother, nicotine and its metabolites were found mainly in urine and also in saliva and hair, but not in cord blood. During the postpartum period, we found cotinine concentrations of 2.2 ng/mL in the mother's urine and 0.22 ng/mL in breast milk; 1,2-propanediol was generally detected in urine and saliva, but not in cord blood or breast milk. The maximum urinary cotinine concentration in the 3-year-old child was 2.6 ng/mL and propanediols also were detected in his urine. Nitrosamines were not detected in samples taken from the mother or the 3-year-old. Metals found in the refill liquid were detected at low levels in both the mother and the 3-year-old., Conclusions: We detected low but not negligible concentrations of e-cigarette-related analytes (including cord blood and breast milk) in an exposed pregnant non-user and in a 3-year-old child also living in the home. Passive exposure to e-cigarette aerosols cannot be disregarded and should be assessed in larger observational studies., Competing Interests: Declaration of competing interest The members of the family in this study were the primary investigator (Montse Ballbè, the mother), one of the co-authors (Guillem Masana, the e-cigarette user), and their older son (the 3-year-old child). MB and GM have no conflict of interests regarding the results or their implications. Both MB and GM strictly followed the study protocol and were not involved in any of the analytical procedures. MB and GM objectively discussed all the results with the rest of investigators. All the authors declare no conflicts of interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Introduction: The Angiotensin-Converting Enzyme 2 (ACE2) is the main receptor of the SARS-CoV-2. There is contradictory evidence on how the exposure to nicotine may module the concentration of soluble ACE2 (sACE2). The aim of this study was to assess the association between nicotine and sACE2 concentrations in saliva samples., Methods: Pooled analysis performed with data retrieved from two studies (n = 634 and n = 302). Geometric mean (GM) concentrations of sACE2, both total and relative to the total amount of protein in the sample, were compared according to sociodemographic variables and variables associated to nicotine. Multivariable linear regression models were fitted to explore the associations of sACE2 with nicotine adjusting for sex, age and body mass index. Spearman's rank-correlation coefficients were estimated between the concentrations of nicotine and cotinine, and pack-years, the concentration of relative sACE2 and the isoforms of sACE2., Results: We observed a significant increase of 0.108‰ and 0.087 ng/μl in the relative and absolute salivary sACE2 GM concentrations, respectively, between the lowest and highest nicotine levels. Similar results were observed for cotinine. These associations did not change in the multivariable linear models. There was a low correlation of nicotine and cotinine concentration with the concentration of relative salivary sACE2 (r s  = 0.153 and r s  = 0.132, respectively), pack-years (r s  = 0.222 and r s  = 0.235, respectively) and with the concentration of isoform 40 KDa (r s  = 0.193 and r s  = 0.140, respectively)., Conclusion: Salivary nicotine concentration seems to be limitedly associated with the concentration of sACE2., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Previous studies have suggested that exposure to secondhand smoke (SHS) may be associated with greater risk of cognitive impairment. However, no longitudinal study has examined the association of serum cotinine (as objective measure of SHS exposure) and cognitive function in older adults. We used data from 2087 non-smoking adults aged≥65 years participating in the ENRICA-2 cohort and free from limitations in Instrumental Activities of Daily Living. Cognitive function was assessed through the Mini-Mental State Examination (MMSE), the Digit Span Backwards subtest (DSBT), the Luria's motor series subtest from the Frontal Assessment Battery, the Trail Making Test A (TMT-A), the Free and Cued Selective Reminding Test (FCSRT), and the Categorical Verbal Fluency Test (CFT) of the 7 min test. Cross-sectional analyses were performed using multivariable logistic and ordered logistic models, while analyses on changes in cognition over time used multivariable repeated-measures mixed-effects models. Compared to the unexposed, those in the highest exposure group (≥0.161 ng/ml) were more likely to have cognitive impairment (MMSE<24) (odds ratio [OR]:1.64; 95% confidence interval [CI]:1.04-2.60) and lower DSBT scores (OR:1.25; 95%CI:1.00-1.57), as well as a non-significant higher odds of a lower score in the Luria test (OR:1.23; 95%CI:0.92-1.64) or episodic memory impairment (FCSRT<12, OR:1.38; 95%CI:0.90-2.11). In longitudinal analyses, those with baseline cotinine ≥0.161 ng/ml showed an increased risk of cognitive impairment (MMSE<24,OR:2.23; 95%CI:1.14-4.33; p-trend across cotinine categories = 0.028) and decreased DSBT (OR:1.23; 95%CI:1.01-1.51; p-trend across cotinine categories = 0.046). Findings show an increased risk of global cognitive impairment and declines in working memory performance in older adults exposed to SHS. More efforts are needed to protect older adults from SHS in areas not covered by smoke-free legislation., (Copyright © 2022. Published by Elsevier Inc.)

Background/objectives: While exposure to secondhand smoke (SHS) is a well-established problem, exposure to third-hand smoke (THS) is scanty known and needs to be studied. The objective of this work is to characterize salivary cotinine concentrations among people who self-reported exposure to SHS and THS at home., Methods: Cross-sectional study of a representative sample (n = 736) of the adult population (≥16 years) from the city of Barcelona carried out in 2013-2014. A questionnaire on tobacco use and passive exposure was administered, and a saliva sample was collected for cotinine determination. For this study, the information of the non-smoker participants who provided saliva sample (n = 519) was used. The geometric means (GM) and geometric standard deviations (GSD) of the cotinine concentration were compared according to the type of self-reported exposure at home: (1) Not exposed to SHS or THS; (2) Exposed to SHS and THS; and (3) Only exposed to THS. We used log-linear models to compare the cotinine concentration of each exposed group with respect to the unexposed group, adjusting for sex, age, educational level, and tobacco exposure in other settings., Results: The GM of the salivary cotinine concentration was 0.34 ng/ml (GSD = 0.16) among individuals reporting SHS and THS exposure, 0.22 ng/ml (GSD = 0.15) among those reporting only THS exposure and 0.11 ng/ml (GSD = 0.04) among those who declared not to be exposed to SHS nor THS (p-value for trend <0.001). The regression model showed a statistically significant increase in cotinine concentration among those exposed to SHS and THS (188% higher, 95% CI: 153%; 223%), and only exposed to THS (106% higher, IC95. %: 74.5%; 137.0%) when comparing with the unexposed group. No statistically significant differences in cotinine concentration were observed between those exposed to SHS and THS compared to the THS group (-25.8%, 95% CI: -69.5%; 17.9%)., Conclusions/recommendations: People exposed to third-hand smoke at home had quantifiable cotinine levels in saliva. No differences in cotinine levels were found between those exposed to second-hand and third-hand smoke at home. The reduction of exposure to third-hand smoke at home should be put into the agenda of tobacco control., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Aims: The objectives of the present study were to describe smoking prevalence and compare the smoking attributes of adult smokers according to the type of tobacco product consumed., Methods: Repeated cross-sectional surveys (2004-2005 and 2011-2012) of a representative sample of the adult (≥16 years) population in Barcelona, Spain, were used to assess self-reported tobacco consumption, smoking attributes, and salivary cotinine concentration. The survey conducted in 2004-2005 included information on 1245 subjects and the survey in 2011-2012 on 1307 individuals., Results: Smoking prevalence decreased over the study period (from 26.6% to 24.1% in self-reported daily smokers). The prevalence of daily smokers who reported the use of manufactured cigarettes declined from 23.7% in 2004-2005 to 17.3% in 2011-2012. The prevalence of roll-your-own cigarette users increased from 0.4% to 3.7%. According to data obtained in 2011-2012, the proportion of self-reported roll-your-own cigarette users was higher among men (19.8% vs. 9.5% of women), participants aged 16-44 years (22.9% vs. 5.8% of participants aged 45-65 years and 4.0% of participants aged ≥65 years), and participants with secondary and university education (17.7% and 18.5% vs. 7.9% of participants with less than primary and primary education). We did not observe differences in cotinine concentrations according to the type of tobacco product consumed., Conclusions: Systematic collection of data on smoking prevalence and smoker attributes from representative samples of the population is necessary for policymakers to develop efficient tobacco control interventions. Considering the increase of roll-your-own cigarette users and the unclear health consequences of their use, policymakers should aim to implement tax policies to equalize the prices of different types of tobacco products., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Smoke-free laws are expected to reduce smoking habits and exposure to secondhand smoke. The objective of this study was the measurement of tobacco specific carcinogens (TSNAs) in oral fluid to assess the most suitable biomarker of cancer risk associated with tobacco smoke., Methods: TSNAs, N'-nitrosonornicotine (NNN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), as well as nicotine and cotinine were measured in oral fluid samples from 166 smokers and 532 non-smokers of the adult population of Barcelona, Spain. A simple method with an alkaline single liquid-liquid extraction with dichloromethane/isopropanol was used and lower limits of quantification for cotinine, NNN, NNK and NNAL were set at 0.10ng/mL, 1.0, 2.0 and 0.50pg/mL respectively. The NNN/cotinine ratio was also calculated., Results: NNN was the most abundant TSNA present in oral fluid with a significant difference between smokers and non-smokers (mean concentrations of 118 and 5.3pg/mL, respectively, p<0.001). NNK and NNAL were detectable in fewer samples. NNN and cotinine concentrations had a moderate correlation within both groups (Spearman's rank correlation coefficient of 0.312, p<0.001 in smokers and 0.279, p=0.022 in non-smokers). NNN/cotinine ratio was significantly higher (p<0.001) in non-smokers than in smokers, in line with equivalent findings for the NNAL/cotinine ratio in urine., Conclusions: TSNAs are detectable in oral fluid of smokers and non-smokers. NNN is the most abundant, in line with its association with esophageal and oral cavity cancers. The NNN/cotinine ratio confirms the relative NNN increase in second hand smoke. Findings provide a new oral fluid biomarker of cancer risk associated with exposure to tobacco smoke., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: There is scarce evidence about passive exposure to the vapour released or exhaled from electronic cigarettes (e-cigarettes) under real conditions. The aim of this study is to characterise passive exposure to nicotine from e-cigarettes' vapour and conventional cigarettes' smoke at home among non-smokers under real-use conditions., Methods: We conducted an observational study with 54 non-smoker volunteers from different homes: 25 living at home with conventional smokers, 5 living with nicotine e-cigarette users, and 24 from control homes (not using conventional cigarettes neither e-cigarettes). We measured airborne nicotine at home and biomarkers (cotinine in saliva and urine). We calculated geometric mean (GM) and geometric standard deviations (GSD). We also performed ANOVA and Student's t tests for the log-transformed data. We used Bonferroni-corrected t-tests to control the family error rate for multiple comparisons at 5%., Results: The GMs of airborne nicotine were 0.74 μg/m(3) (GSD=4.05) in the smokers' homes, 0.13 μg/m(3) (GSD=2.4) in the e-cigarettes users' homes, and 0.02 μg/m(3) (GSD=3.51) in the control homes. The GMs of salivary cotinine were 0.38 ng/ml (GSD=2.34) in the smokers' homes, 0.19 ng/ml (GSD=2.17) in the e-cigarettes users' homes, and 0.07 ng/ml (GSD=1.79) in the control homes. Salivary cotinine concentrations of the non-smokers exposed to e-cigarette's vapour at home (all exposed ≥ 2 h/day) were statistically significant different that those found in non-smokers exposed to second-hand smoke ≥ 2 h/day and in non-smokers from control homes., Conclusions: The airborne markers were statistically higher in conventional cigarette homes than in e-cigarettes homes (5.7 times higher). However, concentrations of both biomarkers among non-smokers exposed to conventional cigarettes and e-cigarettes' vapour were statistically similar (only 2 and 1.4 times higher, respectively). The levels of airborne nicotine and cotinine concentrations in the homes with e-cigarette users were higher than control homes (differences statistically significant). Our results show that non-smokers passively exposed to e-cigarettes absorb nicotine., (Copyright © 2014. Published by Elsevier Inc.)

Objective: We assessed and characterized the relationship among biomarkers of secondhand smoke (SHS) exposure in non-smokers according to their exposure at home as measured by airborne markers., Methods: We conducted an observational study on exposure to SHS at home using airborne markers (nicotine and benzene) and biomarkers from the non-smokers living in these homes. We selected 49 non-smoking volunteers from different homes: 25 non-smokers living with at least one smoker and 24 non-smokers living in smoke-free homes. We installed two passive devices to measure nicotine and benzene concentrations in the main room of the house (i.e., the living room). One week later, the researcher returned to the volunteer's home to collect the two devices, obtain saliva and urine samples, and administer a SHS questionnaire., Results: Salivary and urinary cotinine concentrations highly correlated with air nicotine concentrations measured at the volunteers'homes (rsp=0.738 and rsp=0.679, respectively). The concentrations of airborne markers of SHS and biomarkers in non-smokers increased with increasing self-reported intensity and duration of SHS exposure at home during the previous week (p<0.05). The multivariable regression model showed a significant association with nicotine in air at home (β=0.126, p=0.002 for saliva and β=0.115, p=0.010 for urine)., Conclusions: Our findings suggest that, even in countries with comprehensive smoke-free legislation, exposure to SHS at home continues to be the main source of exposure for non-smokers who live in non-smoke-free homes. Therefore, public health policies should promote smoke-free homes., (Copyright © 2014 Elsevier Inc. All rights reserved.)