Aquatic organisms are exposed to ever-changing complex mixtures of chemicals throughout their lifetime. Component-Based Mixture Risk Assessment (CBMRA) is a well-established methodology for water contaminant-mixture management, the use of which is growing due to improved access to reference ecotoxicity data and extensive monitoring datasets. It enables the translation of measured exposure concentrations of chemicals into biological effect values, and thus to quantitatively estimate the risk of the whole water sample (i.e., as a mixture). However, many factors can bias the final risk decision by impacting the risk metric components; thus, a careful design of the CBMRA is needed, taking into primary consideration the specific features of the dataset and mixture risk assessment assignments. This study systematically addressed the effects of the most common approaches used for handling the concentrations of chemicals below the limit of detection/quantification (LOD/LOQ) in CBMRA. The main results included: i) an informed CBMRA procedure that enables the tracking of the risk decisions triggered by substances below LOD/LOQ, ii) a conceptual map and guidance criteria to support the selection of the most suitable approach for specific scenarios and related interpretation; iii) a guided implementation of the informed CBMRA on dataset of pesticide concentrations in Italian rivers in 2020 (702,097 records).

Previous studies on the association between metals and dyslipidemia are not completely consistent. There are few studies investigating the relationship between mixed metal exposure and dyslipidemia as well as the effects of metals on dyslipidemia in community-dwelling elderly. To evaluate the correlations and interaction effect between the urinary concentrations of metals and the risk of dyslipidemia in community-dwelling elderly. We designed a case-control study to assess the correlation between urine metals and dyslipidemia in elderly people in the Yinchuan. The urinary levels of 13 metals, including calcium, vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, and thallium, were measured by inductively coupled plasma-mass spectrometry (ICP-MS), and the blood biochemical analyzer was used to measure the blood lipid levels of 3384 senior individuals from four different areas of Yinchuan city. Logistic regression and restricted cubic splines (RCS) were used to explore the correlation and dose-response relationship between urinary metals and the risk of dyslipidemia. Least absolute shrinkage and selection operator (LASSO) regression was used to select metals, and then weighted quantile sum (WQS) regression was used to explore the weight of each metal in mixed metals. Bayesian kernel machine regression (BKMR) was used to explore the interactions between metals on dyslipidemia risk. (1) After selection by LASSO regression, in the multi-metal model, compared with the lowest quartile, the adjusted ORs (95%CI) of the highest quartiles were 0.47 (0.37-0.60) for Fe, 1.43 (1.13-1.83) for Zn, 1.46 (1.11-1.92) for As, 0.59 (0.44-0.80) for Se, 1.53 (1.18-2.00) for Mo, and 1.36 (1.07-1.73) for Te. (2) In the WQS regression model, Fe and Mo accounted for the largest weight in the negative and positive effects of dyslipidemia, respectively. (3) In the BKMR model, there may be a positive interaction between Te and Se on dyslipidemia. Among the mixed metals, Fe, As, Se, Mo, and Te were associated with the prevalence of dyslipidemia, with Fe and Mo contributing the most. There may be certain interactions between Te and Se.

UNASSIGNED: Trace elements exist widely in the natural environment and mostly enter the human body through drinking water or various types of food, which has raised increasing health concerns. Exposure to a single or a few trace elements has been previously reported to be associated with oral cancer risk, but studies on other elements and combined effects are limited. This study aimed to comprehensively evaluate the independent and joint effects of 33 trace elements on oral cancer risk.
UNASSIGNED: The concentrations of 33 trace elements from the serum samples of 463 cases and 1,343 controls were measured using inductively coupled plasma mass spectrometry (ICP-MS). Propensity score matching was used to minimize the impact of potential confounders. Conditional logistic regression was utilized to evaluate the association of each element individually with oral cancer risk. Quantile g-computation and Bayesian kernel machine regression (BKMR) models were used to assess the joint effect of the overall element mixture and interactions.
UNASSIGNED: In single-element models, essential elements (Cu, Se, Zn, Sr, and Cr) and non-essential elements (As, Li, Th, Ce, Ti, and Sc) showed significant association with oral cancer risk. In multiple-element models, a quartile increase in overall non-essential elements was observed for a significant inverse association with oral cancer risk (β = -3.36, 95% CI: -4.22 to -2.51). The BKMR analysis revealed a potential beneficial joint effect of essential metals on the risk of oral cancer. Among these, higher levels of serum Zn and V exhibited an adverse effect, while serum Sr, Se, and Cu displayed favorable effects when all other essential elements were fixed at 25th or 50th percentiles. Of note, Se performed complex interactions among essential metals. As for non-essential elements, there were greater effect estimates for serum Th, Li, and Y when all other elements were held at the 75th percentile.
UNASSIGNED: This study provides supportive evidence that the overall mixture effect of essential and non-essential elements might be associated with oral cancer risk, especially for serum Zn, V, Cu, Sr, Se, Th, Li, and Y. Extensive prospective studies and other experiments are warranted to confirm our findings.

OBJECTIVE: There is growing evidence that single substances present below their individual thresholds of effect may still contribute to combined effects. In component-based mixture risk assessment (MRA), the risks can be addressed using information on the mixture components. This is, however, often hampered by limited availability of ecotoxicity data. Here, the possible use of ecotoxicological threshold concentrations of no concern (i.e. 5th percentile of statistical distribution of ecotoxicological values) is investigated to fill data gaps in MRA.
METHODS: For chemicals without available aquatic toxicity data, ecotoxicological threshold concentrations of no concern have been derived from Predicted No Effect Concentration (PNEC) distributions and from chemical toxicity distributions, using the EnviroTox tool, with and without considering the chemical mode of action. For exposure, chemical monitoring data from European rivers have been used to illustrate four realistic co-exposure scenarios. Based on those monitoring data and available ecotoxicity data or threshold concentrations when no data were available, Risk Quotients for individual chemicals were calculated, to then derive a mixture Risk Quotient (RQmix).
RESULTS: A risk was identified in two of the four scenarios. Threshold concentrations contribute from 24 to 95% of the whole RQmix; thus they have a large impact on the predicted mixture risk. Therefore they could only be used for data gap filling for a limited number of chemicals in the mixture. The use of mode of action information to derive more specific threshold values could be a helpful refinement in some cases.