an important role in human civilization for thousands of years. Commonly defined by their specific density (greater than 5.0 g/cm3), heavy metals occur naturally in the environment. Five heavy metals, namely arsenic (which is classified as a metalloid but is often grouped within heavy metals), lead, mercury, cadmium, and chromium, are of considerable public health importance because of their high degree of toxicity (Jarup 2003). In fact arsenic, lead, mercury, and cadmium are ranked within the top seven substances in the Agency for Toxic Substances and Disease Registry’s Substance Priority List, which considers a combination of frequency, toxicity, and potential for human exposure when classifying substances of concern (ATSDR 2019). While metals often occur naturally in the environment, anthropogenic activities such as mining, smelting, industrial production, and agriculture can lead to their increased mobilization and can alter their chemical form, thereby also altering their toxicity. Human activities thus contribute to environmental and occupational exposure to arsenic, lead, mercury, cadmium, and chromium. Exposure to these metals can cause a myriad of health problems such as multiple organ damage, cellular damage, cardiovascular and metabolic diseases, and cancer (Rehman et al. 2018).
In general, concentrations of metals in biological samples are used as biomarkers for assessing the level of concern with metal intoxication. As technologies continue to improve in molecular toxicology, biomarker possibilities have expanded. This chapter discusses circulating miRNAs as potential biomarkers for heavy metal(s) exposure and adverse health outcomes associated with types of heavy metal exposure. The discussion highlights current biofluids used for determining individual heavy metals (arsenic, lead, mercury, cadmium, chromium) and mixed metals exposure, miRNA biogenesis, and studies that have investigated circulating miRNAs associated with heavy metal exposure.
Exposure to Metals
Heavy metal contamination of water, soil, and air can occur as a result of natural phenomena such as soil erosion, atmospheric deposition, and volcanic eruptions (Tchounwou et al. 2012). Furthermore, modern industrial practices have resulted in increased human exposure to heavy metal through mining, smelting, agriculture, industrial processing, by-products, and effluents, as well as in domestic use of metals and metal-containing compounds (Tchounwou et al. 2012). Heavy metal exposure can occur through inhalation, ingestion, or contact with the skin (See Figure 4.1). The effects and the severity of metal toxicity can vary considerably according to the specific metal, its chemical form, its physio-chemical properties, the duration of exposure (acute or chronic), the route of exposure, and individual susceptibility as shaped by genetics, lifestyle factors, and nutritional status (Rajkumar and Gupta 2021). Depending on the dosage and duration of exposure, heavy metals can exert both acute and chronic effects, causing multi-organ maladies such as gastrointestinal and kidney dysfunction, nervous system disorders, skin lesions, vascular damage, immune system dysfunction, birth defects, and cancer (Balali-Mood et al. 2021).
Figure 4.1 Major routes of exposure for heavy metals. The most common sources, modes, and routes of exposure for arsenic (As), cadmium (Cd), chromium (Cr), mercury (Hg), and lead (Pb) are depicted. Large bold blue text indicates the predominant route of exposure for individual metals, while black small text indicates additional exposure routes. For Cd, ingestion is the predominant exposure route in the non-smoking population, while inhalation of cigarette smoke is the predominant exposure source in smokers.
Biomarkers
The term “biomarker” refers to a characteristic that can be measured objectively, accurately, and reproducibly as an indicator of normal biological processes, pathogenic processes, or pharmacologic response to a therapeutic intervention (Califf 2018; Strimbu and Tavel 2010). Biomarkers should be separated from clinical outcome assessment (COA), which encompasses direct measures of how a patient feels, functions, or survives (Califf 2018). This distinction is important, because therapeutics must meet regulatory standards that rely on COAs for measuring the outcomes. Biomarkers, on the other hand, can serve various purposes, such as predicting a COA on the basis of a measurement. To be used in a clinical setting, biomarkers must undergo rigorous scrutiny and be validated for yielding reliable and reproducible results across multiple populations. Subtypes of biomarkers have been defined according to their applications and classified as diagnostic biomarkers, monitoring biomarkers, pharmacodynamic or response biomarkers, predictive biomarkers, prognostic biomarkers, and safety biomarkers (Califf 2018; see also Table 4.1). Importantly, a biomarker may fall into one or more biomarker subtypes.
Biomarkers of Heavy Metal Exposure
Biomarkers that are typically used to assess heavy metal exposure are concentrations of heavy metals in blood, urine, bone, nail, hair, and other tissues and fall into both monitoring and safety biomarker subtypes. These traditional biomarkers are practical and reliable measures of exposure, can be vital when signs and symptoms of metal exposure are lacking, and can be useful for assessing the risk for disease development (Gil and Pla 2001). Typically, blood and urine are used to assess recent exposure, whereas bone, nail, and hair can be used to indicate chronic or past exposure. However, the type of biological tissue examined is highly dependent on the biological half-life of the metal, or the time it takes to excrete the metal from the body. For instance, lead can be retained in bone decades after exposure, whereas the half-life of lead in blood is only a few weeks. However, these traditional biomarkers of exposure are useful only in defining the severity and the duration of exposure (which can be either acute or chronic, depending on the biological sample and metal tested for) and do not have the capacity to determine disease susceptibility or whether adverse health effects can be mitigated. Additionally, metal toxicity can present non-descript and non-specific symptoms, which do not pinpoint a specific metal exposure if the type of exposure is unknown (Hackenmueller et al. 2019). Therefore an immediate need exists to find additional, specific, and reliable biomarkers that play dual roles in identifying exposures to types of heavy metals (i.e., in monitoring and safety) and may be used as early indicators of disease as a result of heavy metal exposure (i.e., for diagnostic purposes). In the last decade or so, ribonucleic acid (RNA) has emerged as a major target for diagnostics and therapeutics (Zampetaki et al. 2018), including in heavy metal exposure. This chapter focuses on microRNAs (miRNAs) as potential biomarkers in heavy metal toxicity and disease.
Table 4.4 Description of biomarker subtypes.
| Biomarker Subtypes | Description |
| Diagnostic | Confirms the presence of a disease or condition with precision and reliability |
| Monitoring | Measured over a span of time and monitors the status of a disease or medical condition due to exposure of an individual to medicinal or environmental agents |
| Pharmacodynamic/Response | Changes in response to exposure to a medicinal or environmental agent |
| Predictive | Predicts whether favourable or unfavourable outcomes are more likely to be experienced by an individual or group of individuals |
| Prognostic | Identifies the likelihood of a clinical event, disease recurrence, or disease progression |
| Safety | Measures before or after exposure to a medicinal or environmental agent to indicate likelihood, presence or extent of toxicity as an adverse event |
This table has been created on the basis of information from Califf (2018).
miRNA Biogenesis
RNAs can be broadly classified into two groups: coding RNAs, which code for proteins and are known as messenger RNAs (mRNAs); and non-coding RNAs (ncRNAs), which comprise the largest class of RNAs and include ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), long non-coding RNAs (lncRNAs, > 200 nucleotides) and small non-coding RNAs (< 200 nucleotides). miRNAs constitute
