Combined Heavy Metal Exposure and Metabolic Health: Epidemiological Evidence, Mechanistic Pathways, and Mixture Toxicology

ABSTRACT

Heavy metals in the environment are known pollutants whose relevance is increasingly being recognized due to their role in the global prevalence of metabolic diseases. In fact, people are usually exposed to mixtures of various metals, such as arsenic, cadmium, lead, mercury, and others, rather than single metal compounds, which happens due to pollution of the environment, occupation, and ingestion of foods and water that contain toxins. There is evidence that suggests that these metal exposures can be more dangerous than expected from studies of single exposures, as all of them act through the same mechanisms, such as oxidative stress, inflammation, mitochondrial impairment, and disruption of endocrine balance. Thus, in this article, a systems biology approach will be utilized to understand the metabolic effects of heavy metal mixtures and assess associated risks. The combination of multiple metals is associated with more stress to the system when compared to the stress caused by exposure to a single metal. This is due to factors such as insulin resistance, toxic effect on the β cell, which secretes insulin, dyslipidemia, and type 2 diabetes development risk. Multi‐metal exposure does not only happen through different exposure routes but rather through molecular nodes common for regulating redox balance, mitochondria, inflammation, endocrine function, and epigenetics. The introduction of mixture modeling, biomarker profiling, and multi‐omics approaches based on the exposome, understanding how multi‐metal exposures influence coordinated metabolic disruption, will be enhanced. Metals in mixtures exhibit cumulative and interactive effects that traditional single‐metal risk assessment models overlook, leading to an underestimation of metabolic risk. It is, therefore, necessary to shift towards risk frameworks based on mixtures that integrate state‐of‐the‐art statistical modeling with exposomic and multi‐omics platforms. The strategy would bolster preventive environmental health approaches and improve the identification of people with greater susceptibility to environment‐driven metabolic disease.