Polybrominated diphenyl ethers (PBDEs) are brominated flame retardants. Technical mixtures PentaBDE and OctaBDE were phased out in 2004 through voluntary and regulatory actions with DecaBDE remaining in limited use until 2013. Biomonitoring studies have shown widespread presence of PBDEs in the US and worldwide population. While some studies suggest that human serum concentrations are declining over time, it is unclear whether this trend will continue. Our objective was to examine temporal trends of PentaBDEs and their hydroxylated metabolites (OH-PBDEs) between 2008 and 2014 in populations of ethnically diverse, pregnant women residing in Northern California (n = 111). Serum samples were collected and analyzed by high resolution mass spectrometry for five PentaBDE congeners and two OH-PBDEs. We found widespread exposures in participants from all three time points (2008/09, 2011/12, 2014). Temporal patterns varied substantially by congener. BDE-47, -99 and the OH-PBDEs decreased between 2008/09-2011/12 but plateaued between 2011/12-2014. In contrast, BDE-100 decreased across all years, BDE-153 decreased in the latter years, and BDE-28 decreased initially and then increased. These findings indicate that while policies to remove PBDEs from the marketplace have successfully lead to declines in exposures to some PBDE congeners, human exposures to these legacy pollutants could plateau and remain ubiquitous in human populations.

Background

Smokeless tobacco products, such as chewing tobacco or moist snuff, contain many of the same constituents as tobacco smoke and are also known to cause cancer; however, little attention has been paid to indirect exposure of children to tobacco constituents via parental smokeless tobacco use.

Methods

As part of the California Childhood Leukemia Study, we collected dust samples from 6 residences occupied by smokeless tobacco users, 6 residences occupied by active smokers, and 20 tobacco-free residences. Children's potential for exposure to tobacco constituents was assessed using nicotine concentrations in vacuum dust measured by gas chromatography-mass spectrometry.

Results

Median nicotine concentrations for residences with smokeless tobacco users were significantly greater than median nicotine concentrations for tobacco-free homes and similar to median nicotine concentrations in homes of active smokers. Using generalized estimating equations derived from a multivariable marginal model to adjust for a history of parental smoking, income, residence construction date, and mother's age and race/ethnicity, we found nicotine levels from homes of smokeless tobacco users to be 21-fold higher than nicotine levels from tobacco-free homes. Based on mass balance equations, we hypothesize that nicotine is transferred to floors in homes of smokeless tobacco users primarily as a constituent of tobacco that is spilled or expectorated.

Conclusions

Based on our findings, we conclude that children living with smokeless tobacco users may be exposed to nicotine and other constituents of tobacco via contact with contaminated dust and household surfaces.

Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. One of the main shortcomings of current analytical methods and workflows is that they are unable to provide any quantitative information constituting an important obstacle in understanding environmental fate and human exposure. Herein, we present an in silico quantification method using mahine-learning for chemicals analyzed using electrospray ionization (ESI). We considered three data sets from different instrumental setups: (i) capillary electrophoresis electrospray ionization-mass spectrometry (CE-MS) in positive ionization mode (ESI+), (ii) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in ESI+ and (iii) LC-QTOF/MS in negative ionization mode (ESI-). We developed and applied two different machine-learning algorithms: a random forest (RF) and an artificial neural network (ANN) to predict the relative response factors (RRFs) of different chemicals based on their physicochemical properties. Chemical concentrations can then be calculated by dividing the measured abundance of a chemical, as peak area or peak height, by its corresponding RRF. We evaluated our models and tested their predictive power using 5-fold cross-validation (CV) and y randomization. Both the RF and the ANN models showed great promise in predicting RRFs. However, the accuracy of the predictions was dependent on the data set composition and the experimental setup. For the CE-MS ESI+ data set, the best model predicted measured RRFs with a mean absolute error (MAE) of 0.19 log units and a cross-validation coefficient of determination (Q²) of 0.84 for the testing set. For the LC-QTOF/MS ESI+ data set, the best model predicted measured RRFs with an MAE of 0.32 and a Q² of 0.40. For the LC-QTOF/MS ESI- data set, the best model predicted measured RRFs with a MAE of 0.50 and a Q² of 0.20. Our findings suggest that machine-learning algorithms can be used for predicting concentrations of nontargeted chemicals with reasonable uncertainties, especially in ESI+, while the application on ESI- remains a more challenging problem.

Eleven novel brominated flame retardants (NBFRs) were analyzed in dust samples from California homes as a part of the Northern California Childhood Leukemia Study (NCCLS) and from the living quarters of California fire stations as a part of the Firefighter Occupational Exposure (FOX) study using high resolution gas chromatography/mass spectrometry. The eleven NBFRs were α- and β-1,2-dibromo-4-(1,2-dibromoethyl)cyclohexane (α- and β-DBE-DBCH), 2-bromoallyl 2,3,6-tribromophenylether (BATE), pentabromotoluene (PBT), pentabromoethylbenzene (PBEB), 2,3-dibromopropyl 2,4,6-tribromophenyl ether (TBP-DBPE), hexabromobenzene (HBB), 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EH-TBB), 1,2-bis(2,4,6-tribromophenoxy) ethane (BTBPE), bis(2-ethylhexyl) tetrabromophthalate (BEH-TEBP), and decabromodiphenylethane (DBDPE). Six of the seven NBFRs that are produced in relatively small quantities (i.e., α-, β-DBE-DBCH, BATE, PBEB, PBT, TBP-DBPE) were measured close to or below the limit of quantitation (0.64 ng/g) in both the NCCLS and FOX samples, and the seventh, HBB, was measured at median concentrations of 1.85 ng/g and 9.40 ng/g in the NCCLS and FOX samples, respectively. The remaining four NBFRs, EH-TBB, BEH-TEBP, BTBPE, and DBDPE, are produced in higher quantities, and were detected at median concentrations of 337 ng/g, 186 ng/g, 22.3 ng/g, and 82.8 ng/g, respectively in the NCCLS samples, and at median concentrations of 2687 ng/g, 2076 ng/g, 28.4 ng/g, and 161 ng/g, respectively, in the FOX samples. Concentrations of NBFRs in the NCCLS and FOX dust samples were several times lower than concentrations of PBDEs previously measured in the same samples. Concentrations of NBFRs in the NCCLS and FOX dust samples were generally comparable to concentrations of NBFRs in other studies of house dust from the US and Canada.

While important advances have been made in high-resolution mass spectrometry (HRMS) and its applications in non-targeted analysis (NTA), the number of identified compounds in biological and environmental samples often does not exceed 5% of the detected chemical features. Our aim was to develop a computational pipeline that leverages data from HRMS but also incorporates physicochemical properties (equilibrium partition ratios between organic solvents and water; K_{solvent-water}) and can propose molecular structures for detected chemical features. As these physicochemical properties are often sufficiently different across isomers, when put together, they can form a unique profile for each isomer, which we describe as the "physicochemical fingerprint". In our study, we used a comprehensive database of compounds that have been previously reported in human blood and collected their K_{solvent-water} values for 129 partitioning systems. We used RDKit to calculate the number of RDKit fragments and the number of RDKit bits per molecule. We then developed and trained an artificial neural network, which used as an input the physicochemical fingerprint of a chemical feature and predicted the number and types of RDKit fragments and RDKit bits present in that structure. These were then used to search the database and propose chemical structures. The average success rate of predicting the right chemical structure ranged from 60 to 86% for the training set and from 48 to 81% for the testing set. These observations suggest that physicochemical fingerprints can assist in the identification of compounds with NTA and substantially improve the number of identified compounds.

Non-targeted analysis (NTA), including both suspect screening analysis (SSA) and unknown compound analysis, has gained increasing popularity in various fields for its capability in identifying new compounds of interests. Current major challenges for NTA SSA are that (1) tremendous effort and resources are needed for large-scale identification and confirmation of suspect chemicals and (2) suspect chemicals generally show low matching rates during identification and confirmation processes. To narrow the gap between these challenges and smooth implementation of NTA SSA methodology in the biomonitoring field, we present a thorough SSA workflow for the large-scale screen, identification, and confirmation of industrial chemicals that may pose adverse health effects in pregnant women and newborns. The workflow was established in a study of 30 paired maternal and umbilical cord serum samples collected at delivery in the San Francisco Bay area. By analyzing LC-HRMS and MS/MS data, together with the assistance of a combination of resources including online MS/MS spectra libraries, online in silico fragmentation tools, and the EPA CompTox Chemicals Dashboard, we confirmed the identities of 17 chemicals, among which monoethylhexyl phthalate, 4-nitrophenol, tridecanedioic acid, and octadecanedioic acid are especially interesting due to possible toxicities and their high-volume use in industrial manufacturing. Similar to other previous studies in the SSA field, the suspect compounds show relatively low MS/MS identification (16%) and standard confirmation (8%) rates. Therefore, we also investigated origins of false positive features and unidentifiable suspected features, as well as technical obstacles encountered during the confirmation process, which would promote a better understanding of the flaw of low confirmation rate and encourage gaining more effective tools for tackling this issue in NTA SSA.

Our proof-of-concept study develops a suspect screening workflow to identify and prioritize potentially ubiquitous chemical exposures in matched maternal/cord blood samples, a critical period of development for future health risks. We applied liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF/MS) to perform suspect screening for ∼3500 industrial chemicals on pilot data from 30 paired maternal and cord serum samples (n = 60). We matched 662 suspect features in positive ionization mode and 788 in negative ionization mode (557 unique formulas overall) to compounds in our database, and selected 208 of these for fragmentation analysis based on detection frequency, correlation in feature intensity between maternal and cord samples, and peak area differences by demographic characteristics. We tentatively identified 73 suspects through fragmentation spectra matching and confirmed 17 chemical features (15 unique compounds) using analytical standards. We tentatively identified 55 compounds not previously reported in the literature, the majority which have limited to no information about their sources or uses. Examples include (i) 1-(1-acetyl-2,2,6,6-tetramethylpiperidin-4-yl)-3-dodecylpyrrolidine-2,5-dione (known high production volume chemical) (ii) methyl perfluoroundecanoate and 2-perfluorooctyl ethanoic acid (two PFAS compounds); and (iii) Sumilizer GA 80 (plasticizer). Thus, our workflow demonstrates an approach to evaluating the chemical exposome to identify and prioritize chemical exposures during a critical period of development.

Context

Currently no direct evidence links in utero dichlorodiphenyltrichloroethane (DDT) exposure to human breast cancer. However, in utero exposure to another xenoestrogen, diethylstilbestrol, predicts an increased breast cancer risk. If this finding extends to DDT, it could have far-reaching consequences. Many women were heavily exposed in utero during widespread DDT use in the 1960s. They are now reaching the age of heightened breast cancer risk. DDT exposure persists and use continues in Africa and Asia without clear knowledge of the consequences for the next generation.

Hypothesis

In utero exposure to DDT is associated with an increased risk of breast cancer.

Design

This was a case-control study nested in a prospective 54-year follow-up of 9300 daughters in the Child Health and Development Studies pregnancy cohort (n = 118 breast cancer cases, diagnosed by age 52 y and 354 controls matched on birth year).

Setting and participants

Kaiser Foundation Health Plan members who received obstetric care in Alameda County, California, from 1959 to 1967, and their adult daughters participated in the study.

Main outcome measure

Daughters' breast cancer diagnosed by age 52 years as of 2012 was measured.

Results

Maternal o,p'-DDT predicted daughters' breast cancer (odds ratio fourth quartile vs first = 3.7, 95% confidence interval 1.5-9.0). Mothers' lipids, weight, race, age, and breast cancer history did not explain the findings.

Conclusions

This prospective human study links measured DDT exposure in utero to risk of breast cancer. Experimental studies are essential to confirm results and discover causal mechanisms. Findings support classification of DDT as an endocrine disruptor, a predictor of breast cancer, and a marker of high risk.