Prenatal and childhood mercury (Hg) exposures have been associated with negative impacts on child neurodevelopment. It is unclear if associations persist at the low Hg exposures typical in Western countries. To examine associations between prenatal/childhood blood Hg concentrations and child IQ in Canadian male and female children while considering the potential modifying role of prenatal fish consumption. We analyzed data from the Maternal-Infant Research on Environmental Chemicals study. Hg was measured in first trimester (n = 527), cord (n = 430), and child (at 3-4 years of age, n = 355) blood and examined sex-stratified associations between blood Hg and children's Full Scale IQ (FSIQ), Verbal IQ (VIQ), Performance IQ (PIQ), and General Language Composite (GLC) scores (assessed with WPPSI-III). Prenatal Hg analyses were further stratified by prenatal fish consumption (low: 0-2, moderate: 3-7, or high: ≥8 times/month). Higher cord blood Hg concentrations were associated with lower PIQ (ß = -3.27; 95%CI: 6.44, -0.09) in male children with the lowest prenatal fish consumption. Progressively stronger positive associations were observed with PIQ in male children for moderate (ß = 1.08; 95%CI: 0.10, 2.26) and high (ß = 3.07; 95%CI: 1.95, 4.19) prenatal fish consumption. Cord blood Hg concentrations were positively associated with female children's FSIQ (ß = 1.29; 95% CI: 0.77, 1.81) and PIQ (ß = 2.01; 95% CI: 1.19, 2.83); however, when stratified only in the highest fish consumption subgroup. Among female children, higher child blood Hg concentrations were associated with an approximately 1-point increase in FSIQ, VIQ, and GLC. Prenatal exposure to low levels of Hg was associated with lower PIQ scores in male children with low prenatal fish intake. Positive associations between cord and child blood Hg concentrations and IQ were primarily observed in female children and may be due to beneficial effects of prenatal fish intake.

The body burden of mercury in humans can be measured through hair or blood biomarkers. To compare results from different studies, it is often required to convert mercury in hair to an equivalent level in blood, using a default hair:blood ratio of 250:1 by the World Health Organization (WHO). However, the actual ratio may vary within and between populations. The objectives of this study were to analyze the hair:blood mercury ratio in the general Canadian population, explore factors associated with higher/lower ratios, and determine if the standard ratio of 250:1 is supported. The Canadian Health Measures Survey (CHMS) Cycle 5 (2016-2017) measured total mercury (THg) in both hair and blood of 1168 participants 20-59 years of age. We calculated geometric mean (GM) concentrations of THg for this entire sample and subgroups. The subgroups included biological sex, women of childbearing age, race, hair treatments, categories of blood and hair selenium, urinary arsenobetaine/arsenocholine, categories of blood and hair mercury, and food consumption. We calculated a hair:blood ratio for each participant and determined population-level ratios from the GMs of the distributions. Differences by subgroups, and agreement with the WHO ratio of 250:1, were tested. The combined effect of factors on the THg hair:blood ratio was explored using staged regression analysis. For participants with paired hair and blood mercury measurements, the GM of the hair:blood THg ratio was 293 (95%CI:273-316), and significantly >250. In women of childbearing age, the ratio did not differ from 250. The GMs of the ratio were higher (i.e.>300) for second tertile blood selenium (365, 95%CI:307-433), third and fourth quartiles hair mercury (347, 95%CI:308-390 and 376, 95%CI:336-422), and consumers of shellfish (338, 95%CI:308-371). Shellfish consumption was the only statistically significant factor associated with the hair:blood ratio as identified in the regression model. The mean hair:blood THg ratio among Canadians generally exceeded the default ratio of 250:1. Higher ratios were observed in certain subgroups, such as seafood consumers, and shellfish consumption was the most important variable associated with the ratio. Our results suggest that population-specific hair:blood THg ratios be considered, if possible, when converting mercury levels from hair to blood to better characterize the variation around the conversion.

The Canadian Health Measures Survey (CHMS) employed two laboratory methods to measure each of bisphenol A (BPA) and triclosan in urine. This analysis compares method performance. Method E-475 used GC-MS/MS to measure BPA in recruitment cycles 1-6 and triclosan in recruitment cycles 2-4. Method E-505 used UPLC-MS/MS for BPA and triclosan in biobanked samples from recruitment cycles 4-6. Using unweighted concentrations for samples available from both methods, and removing observations < LOD (BPA Cycles 4-6, n = 3114 and triclosan Cycle 4, n = 651), we compared E-475 and E-505 with descriptive statistics, scatterplots, and Bland Altman analysis. An E-475 variation using isotope dilution (ID) was performed for triclosan. After applying a model to correct E-475 triclosan for ID (E-475m), we compared modeled results to E-505. The geometric mean (GM) for BPA from E-475 vs. E-505 was 1.2 vs. 1.1 μg/L. The E-475/E-505 GM ratio was 1.03, and the lower-upper limits of agreements (LOA) were 0.59-1.81. The GM for triclosan from E-475 vs. E-505 was 31 vs. 20 μg/L. E-475 concentrations were 1.56 times E-505, and the LOAs were 0.87-2.78. The GM for triclosan from E-475m vs. E-505 was 19 vs. 20 μg/L. E-475m concentrations were 0.93 times E-505, and the LOAs were 0.53-1.64. BPA concentrations were comparable with E-475 and E-505. Triclosan concentrations were higher with E-475 than E-505. The E-475 triclosan concentrations became comparable to E-505 after correcting for ID. These results will have implications on whether BPA and triclosan data from the two methods can be combined and compared across CHMS recruitment cycles.

Artificial intelligence (AI) is being broadly integrated into processes to manage and analyze large amounts of data accurately and efficiently. In this work, we explored how AI methods, in particular machine learning (ML), are being implemented in human biomonitoring using a mixed methodology approach that consisted of: (1) a scoping literature review and (2) an international scan of biomonitoring programs to contextualize current practices and perceptions from researchers in the field. We synthesized findings from the review according to the year of publication, biomonitoring study and location, study outcomes, and the most frequent ML methods. We additionally categorized all published ML methods from the review according to three dimensions (paradigm, type of task, and model structure), mapped studies to biomonitoring themes and other applications, and provided details for the more commonly applied ML methods. The international scan was administered through a 30-question online survey and gathered information on current uses, perspectives, and barriers related to AI. Scoping review: We found 286 studies that applied a ML method to human biomonitoring data. Eighty-two ML methods were identified, with the most common being supervised approaches. ML was predominantly applied to predict health-related outcomes based on chemical exposure. International scan: The survey yielded 30 responses from programs across 15 countries. Approximately 27% of respondents reported implementing AI-related methods in the collection and analysis of biomonitoring data, and the primary barrier to adopting these methods was a lack of technical expertise (80%). This exploratory work provides an integrated understanding of ML applications in the human biomonitoring field. ML clearly holds promise for furthering our understanding of chemical exposure in people and will likely undergo continued growth in applications.