William A. Watson

Endocrine-disrupting chemicals (EDCs), including bisphenols, phthalates, and parabens, are widely used in plastics, personal care, and household products and are linked to adverse health outcomes such as cancer, metabolic disease, and infertility. Although product labels disclose ingredients, they often fail to capture the full range of consumer chemical exposures. Building on prior evidence that participants in the Million Marker (MM) biomonitoring program reduced some (but not all) EDC metabolites, we hypothesized that undisclosed ingredients, mislabeling, and contamination contribute to persistent exposures. We conducted nontargeted chemical analyses of 113 personal care and cleaning products across 12 categories, spanning a range of label-based hazard ratings, using high-resolution GC × GC-MS with machine-learning–assisted identification. We detected 3,141 unique chemicals, including 303 with confirmed, probable, or tentative identification. Nearly all products (98%) contained at least one unlabeled chemical, and 85% contained at least one with known or suspected health hazards, with products averaging more than three times as many chemicals as listed on labels. Over half of the products (57.5%) failed testing based on undisclosed contaminants, including many marketed as “natural,” “non-toxic,” or “fragrance-free,” and 26% contained chemicals that directly contradicted label claims. These findings reveal substantial gaps in product transparency and demonstrate that ingredient labels alone cannot reliably convey product safety, underscoring the need for nontargeted testing of finished products to reduce harmful chemical exposures.

Characterization of chemicals in household products is important for understanding this potential source of chemical exposure. Increasingly, suspect screening and nontargeted analysis techniques are used to characterize as many chemical signatures as possible. Solids such as household products are most conveniently prepared using solvent extraction, revealing what chemicals are contained within the product matrix but providing no information about the potential of those chemicals to leave the matrix and cause actual exposure. In this work, the profile and relative abundances of "extractable" chemical signatures found after solvent extraction are compared to those "emittable" to the headspace for 81 household products analyzed by two-dimensional gas chromatography time-of-flight mass spectrometry. This study retrospectively fuses data collected in separate efforts over 3.8 years and 13 analytical batches. Management of the data is made possible by recent developments in processing systems for complex data such as Highlight. Compounds were generically classified as aromatic heteroatom, aromatic hydrocarbon, glycol, hydrocarbon, long chain heteroatom, nonaromatic heteroatom, and unknown/unclassified. Class-based retention time and abundance trends were observed. Liquid extraction resulted in the greatest number of features and the highest relative abundances, while low temperature emission conditions produced the smallest number of features and lowest relative abundances.

Non-targeted analysis (NTA) using high-resolution mass spectrometry allows scientists to detect and identify a broad range of compounds in diverse matrices for monitoring exposure and toxicological evaluation without a priori chemical knowledge. NTA methods present an opportunity to describe the constituents of a sample across a multidimensional swath of chemical properties, referred to as “chemical space.” Understanding and communicating which region of chemical space is extractable and detectable by an NTA workflow, however, remains challenging and non-standardized. For example, many sample processing and data analysis steps influence the types of chemicals that can be detected and identified. Accordingly, it is challenging to assess whether analyte non-detection in an NTA study indicates true absence in a sample (above a detection limit) or is a false negative driven by workflow limitations. Here, we describe the need for accessible approaches that enable chemical space mapping in NTA studies, propose a tool to address this need, and highlight the different ways in which it could be implemented in NTA workflows. We identify a suite of existing predictive and analytical tools that can be used in combination to generate scores that describe the likelihood a compound will be detected and identified by a given NTA workflow based on the predicted chemical space of that workflow. Higher scores correspond to a higher likelihood of compound detection and identification in a given workflow (based on sample extraction, data acquisition, and data analysis parameters). Lower scores indicate a lower probability of detection, even if the compound is truly present in the samples of interest. Understanding the constraints of NTA workflows can be useful for stakeholders when results from NTA studies are used in real-world applications and for NTA researchers working to improve their workflow performance. The hypothetical ChemSpaceTool suggested herein could be used in both a prospective and retrospective sense. Prospectively, the tool can be used to further curate screening libraries and set identification thresholds. Retrospectively, false detections can be filtered by the plausibility of the compound identification by the selected NTA method, increasing the confidence of unknown identifications. Lastly, this work highlights the chemometric needs to make such a tool robust and usable across a wide range of NTA disciplines and invites others who are working on various models to participate in the development of the ChemSpaceTool. Ultimately, the development of a chemical space mapping tool strives to enable further standardization of NTA by improving method transparency and communication around false detection rates, thus allowing for more direct method comparisons between studies and improved reproducibility. This, in turn, is expected to promote further widespread applications of NTA beyond research-oriented settings.

To determine the morningness-eveningness (ME) distribution of emergency medicine (EM) residents. A voluntary, modified ME questionnaire was administered to all EM residents in the United States at the time of the 1995 American Board of Emergency Medicine's annual In-Training Examination. Seventy-eight percent (2,047/2,614) of the surveys were returned. ME scores ranged from 24 to 76, with a median score of 49 (interquartile range 44, 56). The scores were distributed differently from those of the normal population (p < 0.001), being skewed toward eveningness. There was a correlation (r = 0.13, p < 0.0001) between resident age and ME score, with older residents being more morning-oriented. Males were more morning-oriented than females (p = 0.005), and respondents with children living at home also were significantly more morning-oriented (p < 0.001). Stepwise logistic regression showed that the influence of age, gender, and children was cumulative (r = 0.19) but accounted for only 4% of the observed variability. EM residents are distributed differently from the normal population in terms of their ME preferences, tending slightly toward eveningness. The importance of this distribution in EM residents in unknown. A longitudinal follow-up of this cohort may help to determine the association of ME preference with overall practice satisfaction, tolerance of shift work, and career longevity.