Smoking habit in parents and exposure to environmental tobacco smoke in elementary school children of Milan

Children with smoking parents are potentially exposed to Environmental Tobacco Smoke (ETS). The aims of this study were: 1) to assess ETS exposure in Milan schoolchildren, by measuring urinary cotinine (COT-U), 2) to compare the parents' perception of children ETS exposure, with the actual ETS exposure measured by COT-U, 3) to explore the factors influencing COT-U, including smoking bans at home, the season, and children characteristics.One-hundred school children (7-11 years) and their parents were recruited for the study in Spring 2018 (n = 81) and in Winter 2019 (n = 94), 75 children participated to both campaigns, for a sum of 175 observations. A questionnaire was submitted to parents to collect information about smoking habits in the house. COT-U was measured by LC-MS/MS in spot urine sample collected in the morning from children.Detectable COT-U levels were found in 42% and 57% of children, in spring and winter, in contrast with 17% and 13% of parents acknowledging ETS exposure. Children living with smokers or e-cigarette users (vapers) (30% of the participants) had higher COT-U levels than children not living with smokers or vapers (median 0.67, 0.46, and0.1 μg/L in spring, and 0.98, 0.85, and 0.11 μg/L in winter, respectively). Increasingly higher COT-U levels were observed in children living in homes where smoking was completely banned, allowed in the external parts of the home, or allowed in some rooms. The multiple regression analysis confirmed the positive significant effect of living with smokers, a partial smoking ban and absence of smoking ban at home, the winter season, and BMI as determinants of COT-U.ETS exposure resulted in measurable urinary cotinine in children. Smoking parents underestimate exposure to ETS of their children. Living with smokers is a determinant of COT-U, only slightly mitigated by adopting partial smoking ban

Prenatal and childhood exposure to per-/polyfluoroalkyl substances (PFASs) and its associations with childhood overweight and/or obesity: a systematic review with meta- analyses

Background. Per-/polyfluoroalkyl substances (PFASs) are persistent organic pollutants and suspected endocrine disrupters. Objective. The aim of this work was to conduct a systematic review with meta-analysis to summarise the associations between prenatal or childhood exposure to PFASs and childhood overweight/obesity. Methods. The search was performed on the bibliographic databases PubMed and Embase with text strings containing terms related to prenatal, childhood, overweight, obesity, and PFASs. Only papers describing a biomonitoring study in pregnant women or in children up to 18 years that assessed body mass index (BMI), waist circumference (WC), or fat mass in children were included. When the estimates of the association between a PFAS and an outcome were reported from at least 3 studies, a meta-analysis was conducted; moreover, to correctly compare the studies, we developed a method to convert the different effect estimates and made them comparable each other. Results. In total, 354 and 565 articles were retrieved from PubMed and Embase, respectively, resulting in a total of 613 articles after merging duplicates. The papers included in this systematic review were 31: 18 evaluating prenatal exposure to PFASs, 11 childhood exposure, and 2 both. Overall, results were conflicting, with positive, negative, and null associations. 17 papers were included in meta-analyses (12 prenatal, 3 children, and 2 both). The method implemented for data conversion allowed a suitable comparison of different effect estimates. Meta-analyses evaluating the associations between prenatal exposure to PFOA, PFOS, PFNA, PFHxS, and the outcomes BMI, WC, and Dual-Energy X-ray Absorptiometry (DXA) showed no significant results. Meta-analyses for the associations between childhood exposure to PFASs and the outcomes BMI showed no significant results except for a negative association between PFNA and BMI (pooled estimate from a random effect model: -0.045; 95%CI: -0.087, -0.002), however, more studies are required to confirm the strength of this association. Conclusion. To increase the reliability of meta-analyses in environmental epidemiology we suggest the conversion of effect estimates to compare different studies. The pooled evidence of the meta-analyses of the present study suggests that there is no overall association between exposure to PFASs and childhood overweight/obesity

Development, validation and application of an HPLC-MS/MS method to quantify urinary mercapturic acids

Introduction Mercapturic acids are metabolic end products of some occupational and environmental toxicants such as volatile organic compounds. They are metabolites formed by the conjugation of an electrophilic compound with glutathione. These electrophilic metabolic intermediates are believed to be the active species able to react with DNA and responsible for the genotoxicity associated with parent compounds [1]. Mercapturates can be found in urine and, therefore, they can be considered useful non-invasive biomarkers of exposure. Although several analytical methods were reported for the analysis of single or small groups of mercapturates [2], only two papers describes the analysis of several mercapturates [3,4]. The aim of this work was to set up a LC-MS/MS method able to determine mercapturic acids derived from different toxicants. Experimental For the preparation of standard solution, the majority of standard compounds were purchased from Toronto Research Chemicals (Ontario, Canada), along with relative isotopically labelled standards. The complete list of analytes is reported in Table 1. The simple sample preparation developed includes dilution with formic acids (0.2 M), addition of an internal standard mixture of 16 deuterated analogs and filtration with 0.45 \u3bcm regenerated cellulose membrane filter (Agilent Technologies, Santa Clara, California). Analysis were carried out using a hybrid triple quadrupole/linear ion trap mass spectrometer (QTRAP 5500, AB Sciex, Monza, Italy) interfaced with an ultrahigh pressure liquid chromatograph (UHPLC, Agilent 1220, Cernusco sul Naviglio, Italy) equipped with a Betasil C18 column (150 x2.1 mm, 5 \u3bcm; Thermo Fisher Scientific, Rodano, Italy) and a pre-column BETASIL C18 (10 x 2,1 mm, 5\u3bc; Thermo Fisher Scientific, Rodano, Italy). Chromatographic separation was performed using a linear gradient with an aqueous mobile phase composed by an aqueous solution of ammonium formiate 5 mM and 0.1% formic acid and an organic mobile phase composed by acetonitrile. A complete validation was carried out: linearity, sensitivity, accuracy, precision, selectivity, matrix effect, recovery and process efficiency were evaluated according to both FDA guidelines and the considerations reported in the review written by Gonz\ue1lez and co-workers [5,6]. The method was then applied to the analysis of urine samples from adult subjects with different smoking habits: non-smokers, electronic cigarette smokers, and traditional tobacco smokers. Results Results from linearity assays showed that correlation coefficients (R2) were close to 1 for most of compounds, demonstrating optimal linear responses for the considered concentrations ranges, although a polynomial regression was necessary for AAMA since it showed a saturation at high concentrations. Limits of quantitation (LOQ) values were between 0.15 and 1 \u3bcg/L, except for HEMA and AAMA (1.93 and 1.30 \u3bcg/L respectively). Precision, evaluated as relative standard deviations (RDS), was below 15% for most analytes in both intra-day and inter-day tests. Accuracy was between 85 and 110 % of expected values, with few exceptions exceeding 120% at the lowest concentrations. Selectivity was verified by injection of a blank sample (synthetic urine) showing no chromatographic peak having an area at 20% of LOQ at the relative retention time and mass transition of compounds of interest. The same condition was verified analysing a blank sample immediately after the injection of the standard mixture at the highest concentration of the calibration curve, indicating the absence of carry-over. Results from the matrix effect, recovery and process efficiency tests were suitable in most of the cases, with some exceptions that were partially corrected using the internal standards. Results from urine samples of individuals with different smoking habit showed significant differences between smokers and non-smokers: 11 different mercapturic acids were significantly higher (P-value 640.005) in traditional tobacco smokers than in non-smokers (an example is illustrated in Figure 1). Conclusion In this work, we developed a method useful to quantify mercapturic acids in urine samples. The method was subjected to a complete validation and showed to be suitable for most of the considered analytes. Despite some critical issues with some analytes (in particular HEMA), it demonstrated to be an useful tool for fast determination of mercapturates. The first application carried out using human urine samples suggests that mercapturic acids are suitable biomarkers for toxicants in tobacco smoke. References 1. B.M. De Rooij, J.N.M. Commandeur, N.P.E; Biomarkers, 3 (1998), pp 239-303. 2. P.I. Mathias, C. B'hymer; Biomarkers, 21 (2016), pp 293-315. 3. K.U. Alwis, B.C. Blount, A.S. Britt, D. Patel, D.L. Ashley; Analytica Chimica Acta, 750 (2012), pp 152-160. 4. N. Pluym, G. Gilch, G. Scherer, M. Scherer; Analytical and Bioanalytical Chemistry, 407 (2015), pp 5463-5476. 5. FDA. Guidance for Industry - Bioanalytical Method Validation. (2013) Available at: https://www.fda.gov/downloads/Drugs/Guidances/ucm368107.pdf 6. O. Gonz\ue1lez, M.E. Blanco, G. Iriarte, L. Bartolom\ue9, M.I. Maguregui, R.M. Alonso; Journal of Chromatography A, 1353 (2014), pp10-27

Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy

Pooled quality controls (QCs) are usually implemented within untargeted methods to improve the quality of datasets by removing features either not detected or not reproducible. However, this approach can be limiting in exposomics studies conducted on groups of exposed and nonexposed subjects, as compounds present at low levels only in exposed subjects can be diluted and thus not detected in the pooled QC. The aim of this work is to develop and apply an untargeted workflow for human biomonitoring in urine samples, implementing a novel separated approach for preparing pooled quality controls. An LC-MS/MS workflow was developed and applied to a case study of smoking and non-smoking subjects. Three different pooled quality controls were prepared: mixing an aliquot from every sample (QC-T), only from non-smokers (QC-NS), and only from smokers (QC-S). The feature tables were filtered using QC-T (T-feature list), QC-S, and QC-NS, separately. The last two feature lists were merged (SNS-feature list). A higher number of features was obtained with the SNS-feature list than the T-feature list, resulting in identification of a higher number of biologically significant compounds. The separated pooled QC strategy implemented can improve the nontargeted human biomonitoring for groups of exposed and nonexposed subjects

Investigation of urine metabolites related to tobacco smoke chemicals using an untargeted metabolomic approach

Although thousands of different chemicals have been identified in cigarette smoke, the characterization of urinary metabolites derived from those compounds is still not completely achieved. The aim of this work was to perform an untargeted metabolomic experiment on a pilot cross-sectional study conducted on subjects with different smoking habits. Urine samples were collected from 67 adults; including 38 non-smokers, 7 electronic cigarette smokers, and 22 traditional tobacco smokers. Samples were analyzed by liquid chromatography/time-of flight mass spectrometer operating in data dependent mode. Data were processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The ANOVA test was used to detect significant features among groups. The software BEAMS (University of Birmingham) was implemented for grouping adducts and isotopes, and to perform a first annotation. Annotation was completed by comparing fragmentation patterns with on-line databases as Metlin, and using the software MS-FINDER. One hundred and seventeen features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites, for which we were able to putatively annotate about half. Among these, the identification of the glucuronide conjugated of 3-hydroxycotinine supports the validity of the proposed approach. Furthermore, several metabolites, mostly as sulfate conjugates, derived from chemicals known to be present in tobacco smoke, were annotated, among which the metabolite of methoxyphenol, acrolein, 1,3-butadiene, and crotonaldeide

A workflow for data integration, analysis, and metabolite annotation for untargeted metabolomics

Metabolomics is the youngest of the \u201comics\u201d disciplines and it is regarded as a promising approach to understand the metabolic changes that can occur in particular conditions and to identify new biomarkers. We present here a workflow for data integration, analysis, and metabolite annotation to be applied to untargeted metabolomic experiments. Data acquired with LC-MS/MS, operating in data dependent mode, are processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The data-table obtained is elaborated and submitted to statistical analysis using the on-line software MetaboAnalyst. Multivariate analysis, in particular principal component and partial least squares discriminant analysis are performed for data visualization. Univariate analysis, in particular T-test for pairwise and ANOVA for multi-groups comparison, are performed to detect significant features among groups. The software BEAMS, developed by the University of Birmingham, is then implemented for grouping adducts and isotopes, and to perform a first annotation. Metabolite annotation is finally completed by comparing the fragmentation pattern obtained from each parent ion corresponding to a significant feature with data stored in on-line databases as Metlin, and with the help of the software MS-FINDER, which performs in-silico fragmentation. We applied this workflow to an untargeted metabolomic experiment performed on 67 urine samples obtained from adult subjects with different smoking habits: non-smokers, electronic cigarette smokers, and traditional tobacco smokers. 117 features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites. We were able to putatively annotate compound classes of most of the significant metabolites (level 3 according to the \u201cProposed minimum reporting standards\u201d; Sumner et al., 2007) and to putatively annotate some of them (level 2). Among them, the glucuronide conjugated of 3-hydroxycotinine supports the validity of the proposed approach

Untargeted metabolomics in urine to investigate smoking exposure

Background: Although thousands of different chemicals have been identified in cigarette smoke, the characterization of urinary metabolites derived from those compounds is still not completely achieved. The aim of this work was to perform an untargeted metabolomic experiment on a pilot cross-sectional study conducted on subjects with different smoking habits. Methods: Urine samples were collected from 67 adults; including 38 non-smokers, 7 electronic cigarette smokers, and 22 traditional tobacco smokers. Samples were analyzed by liquid chromatography/time-of flight mass spectrometer operating in data dependent mode. Data were processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The ANOVA test was used to detect significant features among groups. The software BEAMS (University of Birmingham) was implemented for grouping adducts and isotopes, and to perform a first annotation. Annotation was completed by comparing fragmentation patterns with on-line databases as Metlin, and using the software MS-FINDER. Results: One hundred and seventeen features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites, of which we were able to putatively annotate about half. The identification of the mercapturic acids of acrolein, 1,3-butadiene, and crotonaldeide, chemicals known to be present in tobacco smoke, supports the validity of the proposed approach. With a lower level of confidence, we annotated the glucuronide conjugated of 3-hydroxycotinine and the sulfate conjugate of methoxyphenol; finally, with the lowest degree of confidence, several other sulfate conjugates of small molecules were annotated. Short discussion/conclusions: The proposed approach seems to be useful for the investigation of exposure to toxicants in humans