Invited Perspective: Metal Mixtures and Child Health: The Complex Interplay of Essential and Toxic Elements

Prenatal exposure to multiple metallic and metalloid trace elements and the risk of bacterial sepsis in extremely low gestational age newborns: A prospective cohort study

“…It has been posited that non-essential metals and metalloids gain entry into human cells by "mimicking" and potentially competing with nutritionally-essential trace elements (22). For this reason, epidemiologic studies of exposure mixtures encompassing both non-and nutritionally-essential trace elements have become popular under the premise that nutritional interventions could counteract environmental metal/metalloid toxicity (23). Although few epidemiologic studies to date have considered the role of complex trace element mixtures in the development of immune-mediated and infectious diseases (23), nutritionally-essential elements are recognized for their important roles in the immune system.…”

“…For this reason, epidemiologic studies of exposure mixtures encompassing both non-and nutritionally-essential trace elements have become popular under the premise that nutritional interventions could counteract environmental metal/metalloid toxicity (23). Although few epidemiologic studies to date have considered the role of complex trace element mixtures in the development of immune-mediated and infectious diseases (23), nutritionally-essential elements are recognized for their important roles in the immune system. Zinc, for example, modulates the inflammatory response by binding to and inhibiting IKK-β, a protein important for cytokine-activated intracellular signaling.…”

Associations of heavy metal mixtures with blood pressure among U.S. adults in NHANES 2017–2018 by four statistical models

“…[ 4 ] The analysis of health outcomes specific to metal mixtures is challenging by nonlinear relationships and interactions between toxic metals and essential trace elements. [ 5 ] The development of innovative statistical methods for analyzing mixtures has greatly facilitated the exploration of the health effects of multiple pollutant exposures, such as Bayesian kernel machine regression (BKMR) and quantile g-computation (QG-C). BKMR can be used to estimate the exposure-response function, including nonlinear exposure-response curves, linear functions with main effects and their interaction, as well as nonlinear functions incorporating a synergistic interaction between them.…”