Introduction

Mycotoxins are fungal secondary metabolites that naturally contaminate food and feed. Direct or cross-contamination of raw materials by mycotoxins throughout the feed and food chains occurs due to changes in temperature, humidity, and growth conditions, occurring both pre- and post-harvest as well as during storage and transportation (Binder 2007; Gagiu et al. 2018). Furthermore, global warming is likely to promote a shortened growth season and increase environmental stress, thus leading to a higher incidence of fungal pathogens and mycotoxin accumulation (Medina et al. 2015; Warnatzsch et al. 2020). Mycotoxins are mutagenic, teratogenic, neurotoxic as well as endocrine disrupters, and which can trigger immune malfunction, leading to serious public health problems (De Ruyck et al. 2015; Lorenz et al. 2019; Qian et al. 2014; Wu et al. 2015). Adverse effects of mycotoxins on human and animal health depends on the class of toxin, its metabolism, toxicokinetics and toxicodynamics, potential for accumulation, exposure concentration, and personal factors such as age, gender, immune system, and health status (Al-Jaal et al. 2019). The mycotoxins commonly found in contaminated food and feed are aflatoxins (AFs), fumonisins (FBs), deoxynivalenol (DON), ochratoxin A (OTA), and zearalenone (ZEN) (Wu et al. 2015; De Ruyck et al. 2015; Gnonlonfin et al. 2013; Raiola et al. 2015). Although some food processing techniques such as milling or fermentation have been proven to mitigate their occurrence (Cheli et al. 2013; Wolf-Hall and Schwarz 2002), they are heat-resistant which can result in humans exposure to mycotoxins through diet. Assessment of mycotoxin exposure is quite challenging due to the broad co-occurrence of mycotoxins over a wide range of food commodities. Biomarker studies will therefore be a growingly important tool to provide further information about mycotoxins exposure, metabolism, and toxico-kinetics in humans.

Chronology of Mycotoxin biomarker in Human Biomonitoring (HBM) Studies

Most epidemiological research has concentrated on specific risk variables and investigated numerous exposure factors. As a result, being exposed to a wide range of endogenous/exogenous chemicals at the same time is not well understood in terms of the effect of combined exposure. Biomonitoring studies have been growing exponentially and have helped reveal the widespread exposure of the population to mycotoxins, mainly AFs, OTA, or trichothecenes depending on geographical location (Marín et al. 2021). The utilization of HBM studies has greatly increased in recent decades to deliver more reliable exposure assessments, with the identification of mycotoxins and their metabolites (Habschied et al. 2021). Human biomarker monitoring is now considered essential for accurate exposure assessments and has led to more comprehensive, exposome-scale assessments of environmental risk factors and etiology of chronic disease.

Biomarkers are defined as specific molecular markers used to measure the extent of exposure to a toxic substance, that can be measured in body fluids or tissues as parent compounds, or as phase I and/or phase II metabolites. Among the quality criteria for biomarkers selection, their content should correlate with dietary intake and their persistence and concentration in fluids/tissues, and should allow their detection with sufficient precision and specificity (Marín et al. 2018). Biomarkers have been divided in three categories: i) biomarkers of exposure, which indicate that the exposure to a particular contaminant has taken place; ii) biomarkers of effect, which are indicators of the biological response to the exposure; iii) biomarkers of susceptibility, which act as indicators of the intrinsic sensitivity of individuals to the toxic agent (Vidal et al. 2018).

The selection of the appropriate representative biomarkers to be analyzed for each mycotoxin is essential. Structurally, mycotoxins can exist in three potential forms (Broekaert et al. 2015). “Unmodified” forms of mycotoxins are those biosynthesized by fungal metabolism and can be considered the ‘parent’ forms that have not been modified in any way. These include but are not limited to AFs, OTA, ZEN, fumonisin B1 (FB1), patulin (PAT) and DON. “Matrix-bound” mycotoxins are those that form non-covalent complexes with matrix compounds. Examples of these include FBs linked to proteins, and OTA linked to polysaccharides. Lastly, “modified” mycotoxins are those classified as having undergone biological or chemical modifications to their structure. These modified forms can be produced by fungi, bacteria, plants, or animals, as well as being formed during food processing. These modified mycotoxins produced by plants are commonly referred to as “masked mycotoxins” (McCormick et al. 2015). Although modified mycotoxins are often less toxic per se than their parent compounds, there is the potential for them to be transformed back to the native form in the digestive system where they may exert the same toxic effects as the parent compounds, thus increasing the overall burden of exposure (Berthiller et al. 2013). Consequently, all forms of mycotoxin need to be considered in HBM of mycotoxin exposure (Al-Jaal et al. 2019).

Biomarkers of exposure are commonly used to substantiate the classic estimates based on food consumption and occurrence data. In addition, they have advantages for directly measuring exposure over time and for assessing individual estimates in certain population sub-groups (Aronson and Ferner 2017). Their value is not only in the sensu stricto exposure assessment, but also in risk benefit assessment when health hazards and benefits of food consumption are weighed and contrasted to each other. Such holistic approach requires qualitative and quantitative analytical measurements of target compounds.

Mycotoxin biomarker evaluation in biological fluids Wait for data from Kemmy

Tables 1 and 2 show the studies undertaken on single and multi-mycotoxin studies in biological matrices over two decades (2000–2010 and 2011–2021), including which mycotoxins were studied, extraction techniques employed, the detection methods used and their LODs/LOQs.

Table 1 Case studies and analytical techniques used for the determination of mycotoxins in 2000–2010
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Table 2 Case studies and analytical techniques used for the determination of mycotoxins in 2011–2021
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Human Blood, Serum, Plasma

In the most recent decade (2011–2021), HBM studies of mycotoxin biomarkers in blood have been determined predominantly through the analysis of the parent compounds (OTA, OTα, AFB1), as presented in Table 3. In some studies, HBM was carried out by analysing protein adducts due to the complexity of blood, plasma, and serum samples, whereby the components of the matrix, such as proteins, can bind to the biomarker of interest and interfere with the analyte’s retention time. These matrix effects have also been found to reduce the extraction efficiency of the analytes, as well as negatively impacting the sensitivity of the method through signal suppression (SSE), when mass spectrometry (MS) detectors are used. Mycotoxin contamination is generally heterogenous, and most of the methods currently used in analysing these toxins in human blood focus on testing single mycotoxins and/or their metabolites, or, structurally related mycotoxins that belong to a single family, such as the AFs, which includes: AFB1, AFB2, AFG1, AFG2 AFB1-lys (Lauer et al. 2019; Leroy et al. 2018; Shirima et al. 2015), OTA and OTα (Ali et al. 2018; Malir et al. 2019; Sueck et al. 2019), CIT and DH-CIT (Malir et al. 2019; Degen et al. 2018), ZEN and its metabolites (De Santis et al. 2019a; Mauro et al. 2018; Fleck et al. 2016). Regarding AFs, HBM of plasma and serum is often carried out by analysing AFB1 or by determining AFB1-albumin adducts. Albumin adducts are generally selected due to their half-life being approximately 2–3 months and hence, the presence of these adducts in plasma and serum can demonstrate long-term chronic exposure to AFBI. Furthermore, AFB1-albumin adducts are stable in serum matrices stored at – 80 °C for over 25 years and therefore can be re-analyzed at future time points (Scholl and Groopman 2008). In studies on human exposure to AFB1 by De Santis et al. (2019a), AFB1 was extracted using liquid–liquid extraction (LLE) of blood serum prior to analysis with liquid chromatography-high resolution mass spectrometry (LC-HRMS). OTA has been established as a good biomarker of exposure because of its persistence in blood matrices. The methodologies for OTA and OTα detection may include LLE or solid phase extraction (SPE) of plasma and/or serum samples, with detection by liquid chromatography fluorescence detector (LC-FLD) (Prati et al. 2016; Woo and El-Nezami 2016) or liquid chromatography tandem mass spectrometry (LC–MS/MS) (Sueck et al. 2019). However, LLE has limited benefits due to incomplete matrix component removal which can negatively influence the sensitivity of analysis (Devreese et al. 2012). Furthermore, in most cases, samples are usually dried followed by a reconstitution step, normally in a smaller volume to concentrate the analytes of interest, with the caveat that this also concentrates any matrix components present. Ali et al. (2018) carried out studies on plasma samples to evaluate OTA and OTα exposure in Bangladesh students (Ali et al. 2018). OTA and OTα were determined using high-performance liquid chromatography with fluorescence detection (HPLC-FLD) and entailed sample clean-up with LLE. They revealed that OTA was detected in all plasma samples (100%) at a range of 0.20–6.63 ng/mL, with OTα detected in 95% of the samples at 0.10–0.79 ng/mL. OTA is considered a risk factor for hepatocellular carcinoma (HCC) and cirrhosis, with OTA known to pose a risk to liver disease patients due to its ability to trigger DNA damage. Also, OTA has a relatively long half-life in human blood and can therefore accumulate in the kidneys (Studer-Rohr et al. 2000) (Fig. 1).

Table 3 Summary of benefits and challenges of various omics technologies
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Fig. 1
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Global map of mycotoxin prevalence from the determination of mycotoxin biomarkers in biological samples: a 2010–2010; b 2011–2021. AFB Aflatoxin-B1, ZEN Zearalenone, OTA ochratoxin A, DON deoxynivalenol, FB1 fumonisin B1, fumonisin B2, CIT citrinin, NEO neosolaniol

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To date, there have only been a few reports of multi-mycotoxin detection in plasma and serum samples, with most of these methods only developed over the last 4–5 years (De Santis et al. 2019a; Fan et al. 2019). Approximately half of these studies can analyze more than ten mycotoxins simultaneously, with sufficiently low LOQs to allow for HBM studies (Slobodchikova and Vuckovic 2018).

Urine

Since the evolution of high-performance LC–MS/MS and gas chromatography–tandem mass spectrometry (GC–MS/MS) instruments, a clear progression regarding the development and application of simultaneous multi-detection methods in HBM studies has been observed (Table 2). Present-day studies reveal that most of the mycotoxin determination in urine performed over the past decade (2011–2021) were performed on LC–MS/MS (QTRAP), and LC-HRMS systems (De Girolamo et al. 2022). Urine biomarkers are vulnerable to variations in daily mycotoxin intake. A primary challenge in urine mycotoxin biomarker analysis is the exceedingly low analyte concentrations that are detected after dietary exposure, i.e., in the range of few μg L−1 (ppb), demanding that sampling must be carried out within 24 h. Therefore, highly sensitive, and accurate methods employing various extraction techniques must be combined to obtain the required sensitivity and selectivity to detect and quantify compounds of interest in urine samples. However, one caveat of using clean-up techniques such as SPE and/or immunoaffinity chromatography (IAC) is that although it affords lower detection limits, it may also reduce the number of analytes due to selectivity of the chemistry or antibody used in those clean-up techniques.

From the relatively few studies performed on single mycotoxin analysis in urine samples between 2000 and 2010, OTA was the most studied, mainly utilizing LLE alongside SPE or IAC as the extraction and sample preparation techniques. SPE has been the most widely used sample clean-up procedure for the analysis of mycotoxins in urine samples. It was employed in a single mycotoxin extraction in combination with LLE in the sample purification strategy for the detection of OTA using LC-FLD (Quigley et al. 2016) (Table 1). However, LLE alone has been used as an extraction and sample clean-up technique in other single-toxin studies, such as in the identification of DON-GlcA metabolites (Tchana et al. 2010), CIT analysis (Ahn et al. 2010) and has been used together with enzymatic hydrolysis (EH) in the detection of OTA (Föllmann et al. 2016). Direct IAC clean-up procedures have also been employed in the analysis of OTA (Magoha et al. 2014) and single FB (FB1) analysis (Magoha et al. 2014; Sadeghi et al. 2009). Sample clean-up using IAC has been widely applied in the analysis of CIT in human urine samples using HPLC–MS/MS analysis (Ezekiel et al. 2014; Martins et al. 2019; Muñoz et al. 2014). After extraction and purification, HPLC with FLD or ESI–MS/MS are the most utilized techniques in the detection of these mycotoxins as part of single-toxin assessment studies.

In multi-detection studies, SPE was used in combination with IAC in the purification of DON, DOM-1, α-ZOL, β-ZOL, OTA (Silva et al. 2010); and with hydrophilic-lipophilic balance (HLB) SPE cartridges during the clean-up of DON, AFB, AFM, OTA, ZEN and α-ZEL (Braun et al. 2021). IACs have also been used as the sample clean-up technique in the multi-detection analysis of AFM1, OTα, OTA, FB1, and FB2 (Keskin et al. 2009) (Table 1). This technique has also been employed in the simultaneous detection of multiple mycotoxins such as AFB1, AFB2, AFG1, AFG2, DON, HT-2, T-2, ZEN, OTA, FB1, and FB2, with detection carried out using an LC-QTRAP-MS/MS (Table 2). Furthermore, purification of DON, DOM-1, α-ZOL, β-ZOL and OTA was conducted using IAC in tandem with SPE, with analysis carried out using an LC-QTRAP-MS/MS (Muñoz et al. 2010). Similarly, LLE was applied during urine sample preparation for the simultaneous multi-analysis of AFM1, DON, FB1, FB2, OTA, and ZEN (Blaszkewicz et al. 2013). Other multi-detection methodologies include the use of dispersed liquid–liquid microextraction (DLLME) in the detection of DON, DOM-1, DON-3G, DON-3-GlcA, DON-15-GlcA, ZEN, ZEN-14-GlcA, OTA, CIT, and FB1 (Martins et al. 2019); HT-2, DON, NEO, T-2, NEO/HT-2, T-2/HT-2, DON/NEO/HT- 2 (Niknejad et al. 2021) (as shown in Table 2).

DLLME is a clean-up technique that has emerged within the last decade, involving the dispersion of drops of solvent in an aqueous sample (Quigley et al. 2016). DLLME is more environmentally friendly and faster than LLE and SPE due to its lower requirement of organic solvent use, subsequently less waste produced, and less labor intensive in nature. In addition, SPE entails the use of expensive solid phase extraction cartridges. Moreover, the simple and quick DLLME procedures yield very high enrichment factors (Quigley et al. 2016). Various analytical methods; ranging from HPLC-FLD, HPLC–ESI–MS/MS (Quigley et al. 2016; Magoha et al. 2014; Keskin et al. 2009; Dashti et al. 2009) (Table 1) to LC-QTRAP-MS/MS, LC–MS/MS, HPLC-FD, GC–MS/MS, and LC-HRMS (Table 2), have utilized this technique to detect, mycotoxins in urine samples,.

Breastmilk

To evaluate the potential risks regarding mycotoxin exposure of infants through breastmilk, various groups have established analytical procedures to assess AFs, OTA, FB1 and ZEN contamination in breastmilk. (Tables 1 and 2). The state-of-the-art techniques still mainly entail the monitoring of a single mycotoxin and its metabolites. However, attention should be drawn to the fact that most of the analytical detection methods employed, such as ELISA or LC-FLR, fall short of the specificity facilitated by the use of LC–MS/MS for the detection of mycotoxins in breastmilk (Magoha et al. 2014). So far, more than 90% of the data generated on breastmilk analysis does not include the specificity of the LC–MS/MS approach in primary analysis or as a validation tool for the data obtained by rapid methods that could be useful during primary screening (Warth et al. 2017). The methods mainly used in the detection of mycotoxins in breast milk to date include LC-FLD (Tchana et al. 2010; Magoha et al. 2014; Muñoz et al. 2014; Keskin et al. 2009; Muñoz et al. 2010; Polychronaki et al. 2007; Dostal et al. 2008), thin layer chromatography (TLC) (Atanda et al. 2007), or enzyme-linked immunosorbent assays (ELISA) (Sadeghi et al. 2009; Massart et al. 2016; Dashti et al. 2009). Some of these methods permit low detection levels even before the emergence of highly sensitive triple quadrupole mass spectrometers in the past 15 years. Established LC-FLD protocols often demand a more labor-intensive sample preparation procedure due to the mycotoxin(s) of choice not fluorescing and therefore requiring derivatization. Recently, LC–MS/MS based techniques have been introduced in this area, with a study by Braun et al. (2020a, 2021) using LC–MS/MS to identify and quantify 34 and 46 mycotoxins and metabolites simultaneously in breastmilk and infant food, respectively,.

LLE, IAC, and SPE are the most extensively used clean-up procedures employed for breastmilk analysis (Tables 1 and 2), while some studies involve the use of LLE-SPE protocols, QuEChERs, and dilute-and-shoot methodologies. Owning to the low signal intensity attained by dilute-and-shoot approaches, due to the fact that the matrix and subsequently analytes present are diluted, they are regarded as not appropriate for routine mycotoxin assessment in biological matrices, hence, SPE, LLE, etc., are considered necessary to achieve the detection levels required. This was in part due to the majority of analyses being carried out using LC-FLR or ELISA. However, sensitive LC–MS/MS methods for the analysis of multiple mycotoxins and metabolites in breast milk have recently been developed, such as those used by Braun et al. (2022). Furthermore, a sensitive LC–MS/MS method for the analysis of multiple chemical exposures such as synthetic and natural xenobiotics in breast milk has been developed by Jamnik et al. (2022), with the potential for the detection of trace-level chemical contamination in low-volume samples, and therefore could be adopted for the simultaneous determination of numerous mycotoxins and their metabolites in matrices such as breast milk and infant food (Braun et al. 2020a, b, 2022; Jamnik et al. 2022).

The Exposome Concept for Mycotoxin Exposure in Humans

From Human Biomonitoring to the Exposome Study

Figure 2 compares advances in mycotoxin biomarker identification and analytical techniques in human biomonitoring over two decades (2000–2010 and 2011–2021), and the evolution of this towards the exposome concept. Initially, use of LC-HRMS was a valuable tool in the analysis of small molecules such as endogenous metabolites and environmental contaminants, with the benefit of being able to screen for as many of these as possible. The number of analytes identified was dependent on several factors, such as the number of databases the analyst has access to, the sample clean-up and instrumentation the analysis was performed on and therefore the overall sensitivity of the final methodology. In this instance, sample clean-up must be kept to a minimum to include as many analytes as possible, with the drawback of not removing much matrix components, usually leading to signal suppression (SSE). Furthermore, with HBM analysis of biological fluids where sample volumes are usually limited, and with the levels of biomarkers, and especially metabolites present usually at ultra-trace levels (low or sub ppb), detection using LC-HRMS is not ideal. Therefore, targeted multi-analyte methods using triple quadrupole mass spectrometers (QqQ) has become the instrument of choice for HBM studies, with advances in the technology of triple quadrupoles facilitating higher sensitivity, linearity, reduced data processing, and ultimately, providing quantitative data (Jamnik et al. 2022).

Fig. 2
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Illustration comparing advances in mycotoxin biomarker identification and analytical techniques in human biomonitoring for two decades (2000–2010 and 2011–2021), and evolution towards the exposome concept

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Originally, exploration of biological fluids for HBM focused more on single chemical classes, such as the analysis of mycotoxins and their metabolites in matrices including (infant) feces and breast milk. In these studies, the main analytes detected were the emerging mycotoxins, which included AME, BEA and the ENNs, mainly B and B1, with FB1 and OTA also detected. This work, albeit important in the data that was generated, fell short of the type of methodologies required to explore chemical exposure across several different classes of endogenous chemicals, which was typically was done using several different single-class methods, making it labor and time intensive and expensive. The switch from HBM to exposome studies requires the inclusion of many classes of compounds, incorporating both natural and synthetic xenobiotics such as pesticides, pharmaceuticals, plasticizers, plant toxins and industrial pollutants that people can be exposed to through their environment, lifestyle, and diet. However, it is difficult to design appropriate methodologies capable of detecting multiple compounds with varying physiochemical properties in biological matrices. This was achieved through the work carried by Jamnik et al. (2022), in which 80 highly diverse xenobiotics were analyzed in urine, infant serum/plasma, and breast milk; with detection limits generally in the pg-ng mL−1 range. Using this method, as many as 27 naturally occurring compounds and 29 xenobiotics were simultaneously detected in at least one sample in the plasma of extremely premature infants (n = 21) and breastmilk (n = 86), respectively, highlighting the need for this type of exposome studies due to the diverse number of compounds detected at such an early life stage, with infants generally more sensitive to toxin exposure.

The hypotheses for these studies are often developed with limited data concerning the detailed background of the exposure, including the physical, chemical or lifestyle components. Moreover, humans are exposed to so many essential nutrients and stressors (essential and toxic elements) from chemical (essential and toxic substances) to physical and lifestyle sources on a daily basis. While essential nutrients come from the same dietary origin, most stressors are related to various origins such as their macro-environment involving climate, ultraviolent (UV) radiation, or from micro-environment like air pollutants or food contaminants (Bocato et al. 2019). Furthermore, variations in human genetics could also change health risks by modulating feedback responses to compounds. Hence, only a few studies on the exposure-effect interactions and other related mechanisms for single or multi-mycotoxins are available (Crovella et al. 2018). In 2005, a cancer epidemiologist, Professor Chris Wild proposed for the first time the word “exposome”, as the complementary environmental constituent to the genome, in assessing the risk of a certain disease (Wild 2005). The exposome was initially defined as a series of environmental exposures of an individual throughout their lifetime (Wild 2005). Recently, the term “exposome” has changed from concept to reality with an advancement in the definition to “cumulative measures of environmental influences and related biological responses throughout the lifespan of an individual, such as exposure to diet, environment, lifestyle, and endogenous processes” (Johnson et al. 2017).

The concept was intended to enhance a wider evaluation of exposure in epidemiological studies through the discovery of novel biomarkers that exist as potential indicators of environmental influence on human health, as well as to explore the range of natural and synthetic xenobiotics individuals are exposed to.

Exposome Concept

The exposome concept was developed to emphasize the need for an all-inclusive data on environmental exposure. Wild (2005) defined the three intersecting domains within the exposome that explains the various factors to be considered:

  1. (1)

    A common external environment (external exposome) involving factors which include urban–rural environment and climatic conditions.

  2. (2)

    Specific or personal environment such as dietary lifestyle, physical activity, work-life, exposure to chemicals, tobacco and socioeconomic factors including education.

  3. (3)

    Internal environment (biological responses) including endogenous biological chemicals in response to stress and aging, epigenetics and gene expression, inflammation, gut microbiome.

Simultaneously taking into consideration a large set of exposures in human exposome-health studies is a significant evolution, with this only achieved through several independent analyses to cover the various chemical exposures. On a daily basis, humans are simultaneously exposed to a broad-spectrum of external factors and biological health effects and are dependent on how resilient individuals are to withstanding the effects of such exposures. In the exposome concept, the history of lifelong exposures is studied which therefore calls for changes in exposure to be considered (Siroux et al. 2016). The primary potential advantage of adopting the exposome concept is the reduction of the risk of biased reporting (i.e., assessing different exposures and only presenting the most important relationships) and the number of false positives in epidemiological studies. While the health status is the result of a network of multiple factors, common epidemiological studies focus only on the major ones, and therefore do not paint a full picture. On the contrary, in exposome-health studies, there is provision for insights into the association between different exposures (Siroux et al. 2016). Exposomics plays a growingly important role in discovering, mitigating, and systematically monitoring exposures and improving etiological health studies between exposure and health (Dennis et al. 2016). In 2017, the European Human Biomonitoring Initiative (HBM4EU) was established to increase public awareness regarding exposure to environmental and other anthropogenic chemicals, with the number of chemicals people are exposed to on the increase. Furthermore, there is the unknown toxicological effects caused by exposure to a ‘cocktail’ of such compounds, with many of them being endocrine disrupters, mutagenic and/or carcinogenic (Fareed et al. 2022; Jamnik et al. 2022).

Separating exposures that genuinely affect the health outcome from other related exposures and identifying synergistic effects amongst exposures can be rather difficult. For this reason, biological responses in health and disease employ “omics” strategies combined with phenotypic traits as this helps to reduce variation in genetic polymorphism. Through this, health professionals can better identify exposures, health-effect relationships, and risk groups (Li et al. 2017). As such, as well as identifying the exposure and health risks based on food consumption, these studies can also help to elucidate which other external xenobiotics people are exposed to during their lifetime.

Targeted and Nontargeted Exposomics

Studies on human exposome can either be on a panel of pre-determined “targets”, (targeted exposomics; TE), or employ non-targeted methodologies (non-targeted exposomics; NTE). TE studies are used when there is some knowledge of which xenobiotics potentially may be present and this is usually carried out using LC–MS/MS platforms, whereas NTE studies are useful when you want the full possible range of analytes individuals may have been exposed to. This is usually performed using LC-HRMS instrumentation. While the latter may be the future for exposomic studies, currently it is the use of targeted, multi-analyte methods that are the method of choice.

TE studies involve screening for known chemicals and/or their metabolites, with accurate quantification of the compounds found to be present in samples. In order to carry out such analyses, the number of ‘targets’ included is determined by several factors including access to analytical standards, the sample clean-up technique and instrumentation used for analysis. The last two factors dictate the overall sensitivity of the final methodology. In order to incorporate as many analytes as possible, sample clean-up must be kept to a minimum. Biological samples such as urine and plasma/serum are usually extracted using LLE with an organic solvent, facilitating the migration of the analytes of interest from the matrix to the solvent, and in the case of plasma/serum, deproteinisation of the sample. In most instances, instead of a dilution step, the sample is dried and reconstituted to concentrate the analytes of interest, referred to as ‘dilute, evaporate and shoot’ (da Silva et al. 2019; Greer et al. 2021), with the benefit that the same extraction procedure can be employed for targeted and untargeted exposomics.

Considering the internal exposome (body components), targeted or non-targeted proteins, expressed genes, metabolites, or microbes are assessed in plasma, blood, urine, feces by employing various “omics” high-throughput programs in HBM studies. This approach utilizes the concept of “systems biology” (Badimon et al. 2017). Systems biology approaches comprised a quantitative analysis of large networks of functional and molecular modulations that occur in multiple levels of biological processes. It also provides advanced methodologies to obtain conceptual understanding by combining molecular data acquisition on a broad level, through multi-omics programs (transcriptomic, proteomic, metabolomic/lipidomic/adductomic, and metallomic), and integrating the data using mining approaches (van Ommen et al. 2017).

The mycotoxin aspects of the human exposome have been well documented with respect to regulated mycotoxins exposure and their health impacts, although much less information is known their modified forms, and the emerging mycotoxins. However, studies by Krausová et al. (2022) in infant feces, Braun et al. (2021, 2022) in breast milk and Braun et al. (2021) in infant food, detected emerging mycotoxins including sterigmatocystin (STG), alternariol monomethyl ether (AME), beauvericin (BEA) and the enniatins, mainly EnnB and EnnB1, as well as some of the regulated mycotoxins such as OTA and FB1. In a study by Krausová et al. (2022) and the Emerging mycotoxins, AME as well as FB1 were detected in the majority of stool samples from the Nigerian cohort (80%), compared to none from an Austrian sample set, highlighting how geographical location can affect someone’s exposure.

Most studies on mycotoxin exposure have centered on dietary modeling strategies (De Ruyck et al. 2015; De Ruyck et al. 2020), whereas HBM studies applied to mycotoxins have mainly focused on monitoring of the general population for risk assessment purposes (Hendrickx et al. 2015; Louro et al. 2019). The development of an accurate, sensitive, and reliable multi-biomarker method not only to simultaneously identify various kinds of mycotoxins, but also industrial pollutants greatly helps in understanding the possible links between environment exposure and human health. The study by Jamnik et al. (2022) was an important step forward, analysing 80 diverse xenobiotics, both natural and synthetic, using LC–MS/MS. The method included a number of mycotoxins, with AME again being detected amongst other chemicals. This has been further expanded by the work of Flasch et al. (2022) who developed a method composing 251 analytes, including endogenous human metabolites and xenobiotics, including estrogenic compounds and human estrogens, with the method applied to urine samples from sub-Saharan Africa (high-exposure) and Europe (low-exposure). Due to the diverse chemical properties of the analytes, chromatographic separation was performed in parallel using hydrophilic interaction liquid chromatography (HILIC) for the human metabolites, and reverse phase chromatography (RP) for the xenobiotics. However, instead of using a triple quadrupole for detection, analysis was conducted on a Q Exactive HF quadrupole-Orbitrap mass spectrometer with fast polarity-switching in full scan mode (Flasch et al. 2022), Due to the combination of the quadrupole and Orbitrap, this can be considered a form of LC–MS/MS. This was a significant step forward in exposome studies due to the number of analytes detected, the ability to simultaneously analyze both polar and non-polar analytes and the fast run time of 15 min. However, drawbacks of this approach are the high cost of such an analytical set-up as well as the use of ammonium fluoride (NH4F) as an additive to enhance the method sensitivity, which limits the use of instrumentation. However, the use of both HILIC (Hydrophilic interaction chromatography) and RP (reverse-phase) coupled to a triple quadrupole instrument, or performed as two separate runs, opens up the possibility of analysing a larger range of xenobiotics, both natural and synthetic. The use of HILIC in particular affords separation of polar compounds, which gives more of an insight into the biotransformation products of contaminants, which have been metabolized to more polar compounds to facilitate their excretion.

Clinical Specimens for Exposome Studies

For every compound that may be present in the human body, a particular fluid or tissue needs to be selected as an exposure marker (internal dose or effect). In matrix selection, various factors such as which matrices can be collected practically, sample volume, and which matrix is important for evaluating specific chemical exposures. The same also applies for the selection of biological fluids for assessing the general impacts on the metabolome, proteome, adductome, and inflammatory biomarkers. Plasma/serum, blood, and urine alone or in combination are the most frequently used in HBM and exposome studies (Wallace et al. 2016). Other biological samples, such as nails and hair present fundamental advantages for HBM and exposome research (Barbosa et al. 2005). These matrices are easy to obtain, inexpensive to collect, and are non-invasive. However, the most significant potential of these matrices is the ability to provide a more prolonged exposure timeline (Barbosa et al. 2005). One of the most intriguing advances is the use of exhaled gasses and aerosols as a simple and non-invasive biological specimen for assessing some of the human exposure (Pleil et al. 2019). This technique may be a promising approach for exposome studies to support data derived from the analysis of more regularly analyzed matrices such as plasma or serum, blood, breastmilk, and urine. Future studies in this area are required to improve sample collection methods and to deal with the challenges surrounding extended sample storage time (Bosch et al. 2015).

Omics Tools Involved in the Internal Exposome

The benefits and challenges of omics technologies are presented in Table 3.

Epigenomics

Epigenetics is the study of heritable alterations of gene expression through various mechanisms such as DNA methylation, non-coding RNAs (ncRNAs), and protein post-translational modifications (PTMs) without altering the underlying DNA sequence (Dai et al. 2017). Epigenetic alterations have gained much attention to decipher the toxicological mechanism of these mycotoxins due to the evidence of their involvement in mycotoxin-induced toxicity (Zhu et al. 2021). Moreover, epigenetic marks may also serve as biomarkers of exposure due to their stable and robust changes associated with exposures, specificity to a particular exposure, dose–response relationships, and available technologies to measure such changes (Ladd-Acosta. 2015).

For instance, DNA methylation, histone modifications and regulation of non-coding RNA reportedly play an important role in AFB1-induced disease and carcinogenesis (Dai et al. 2017; Wu et al. 2013). For FB1 exposure, several epigenetic consequences such as DNA and RNA methylation alterations, post-translational histone modifications and miRNA profile fluctuations have been reported (Yu et al. 2021; Arumugam et al. 2020; Chuturgoon et al. 2014; Arumugam and Chuturgoon 2021; Huang et al. 2019) which may contribute to its detrimental effects leading to health problems and deadly diseases (Arumugam and Chuturgoon 2021).

With the increasing and convincing evidence confirming the epigenetic consequences after mycotoxin exposure, future research opportunities should be directed towards investigating toxicoepigenetics to unraveling comprehensive epigenetic profile after mycotoxin exposures to identify epigenetic targets that may be involved in the detoxification of mycotoxin after exposure. The rapid advance in current and emerging sequencing technology platforms will definitely accelerate understanding of how epigenetics plays vital roles for toxicological mechanisms which can reveal potential biomarkers for mycotoxin exposure.

Transcriptomics

Transcriptomics is the study of the changes in the expression of all mRNAs in a population of cells, tissues, or organisms to identify perturbed biological systems and in obtaining a holistic view of the feedback of a system to an exposure (Studer-Rohr et al. 2000). Current transcriptomic analysis techniques such as oligonucleotide microarrays and various types of fast and cost-effective new generation sequencing (NGS) platforms have remarkably contributed to advance the understanding in the field of toxicology (Joseph 2017). The advanced NGS platforms accelerate investigation of the transcriptome differences between subpopulations with various attributes or exposures to determine not only toxicological implications of mycotoxin exposure for human and animal health, but also screen for potential gene markers for specific exposure. Transcriptomic approach has been applied for exposome mostly for DON, followed by AFs and ZEA (Cimbalo et al. 2022). These studies were conducted in various model systems such as human cell lines and animal cells such as pigs (Hasuda et al. 2022). Deoxynivalenol induces apoptosis and inflammation in the liver: analysis using precision-cut liver slices. Most of transcriptomic studies on mycotoxin exposome focused on expression changes of genes involved in immune system, mitochondrion, DNA damage and transcription and inflammation, cellular processes, intestinal processes and neurotoxic markers, lipid biosynthesis and hormonal modifications (Cimbalo et al. 2022). For instance, transcriptomic analysis of murine C2C12 cell model exposed to DON revealed effects on myoblast differentiation, the strong down-regulation of integrin αv and β5, cytoskeleton and extracellular matrix (ECM) genes with inhibition of myotubes formation (Jia et al. 2021). DON exposure also results in expression changes of genes involved in inflammation, metabolic disturbance, intracellular ROS generation, inflammasome activation and alternation of protein synthesis (Tremblay-Franco et al. 2021). While there is an increase in transcriptomic studies over the last few years, most of the studies were conducted using in vitro system. Moreover, most of the available publications reported on toxicological effects at molecular level and specific biomarkers are yet to be confirmed.

Proteomics

Proteomics is the comprehensive analysis of proteins in biological organisms not only intracellular and extracellular proteins, but also their interactions and modifications. It is one of omics cascades that can identify functions of the gene products in a holistic view (Wilkins et al. 1996). Advanced analytical tools such as MALDI-TOF–MS/MS and LC–MS/MS coupling with computational tools provide robustness and high-throughput approaches for proteomics analysis to identify proteins of interest (Chandramouli and Qian 2009). The multiple mechanisms of biological systems responsible for the effects linked to the exposure to various chemicals, including mycotoxins may be evaluated through essential components of cellular occurrences (Kennedy 2002). Proteomics can help identify and quantify proteins (e.g., signaling molecules and enzymes) in biochemical pathways affected to mycotoxin exposure which is useful to understand molecular mechanism underlying physiological conditions and/or disease states. There were several reports studying the effects of single and multiple mycotoxins exposure in animal models. For instance, an exposure of aflatoxin B1 in duck revealed the activation of ATP-dependent chromatin remodeling enzyme that affected to cellular process, replication, transcription, recombination, repair and developmental processes (Tansakul et al. 2019). A study of protective effect from tea polyphenol to shrimp quality after induction with aflatoxin B1 indicated that enzymes related to gluconeogenesis, glycolysis, and the pentose phosphate pathways were found to be potential markers for the shrimp muscle quality (Huang et al. 2021). An alteration of proteins involved in DNA repair and reproduction showed significant impact to obese rats to enhance ovarian sensitivity to zearalenone exposure (González-Alvarez et al. 2021). Additionally, the study of mycotoxin exposure in human at protein level was only found in in vitro model, for example, a study of protective effect of zinc in an aflatoxin-induced cytotoxicity in human liver carcinoma (HepG2) cells revealed that it could alleviate the toxicity through oxidative stress, energy metabolism, DNA damage, and cell apoptosis biochemical pathways (Zhu et al. 2020). Multiple exposure of aflatoxin M1 and ochratoxin A in human colorectal adenocarcinoma (Caco-2) cells showed synergistic effect on inflammatory response through gap junctions, calcium signaling, and platelet activation pathways (Gao et al. 2020). Proteomic analysis explored biochemical pathways involved in cell adhesion and cytoskeletal modulation processes in response to deoxynivalenol exposure in epidermoid carcinoma (A431) cells (Del Favero et al. 2018). Mycotoxins were found to dose-dependently upregulate the mRNA of the inducible NO synthase proteins in a Caco-2 cell model but were unable to trigger the production of these proteins, as degradation of these proteins was prompted by stimulating their ubiquitylation (Dellafiora and Dall'Asta 2017). However, it is worth noting that numerous cellular processes take place at the levels of proteins and these cell components are linked to disease development (Bocato et al. 2019). Therefore, proteins in human subjects may be detected as biomarkers and used to discover early stages of disease pathways such as cancer. DON-dependent alterations at the proteomic level have also been studied. DON may act on the phosphoproteome by altering the phosphorylation sequences regarding the states and sites of diverse proteins of differentiated intestinal epithelial cells (Zhang et al. 2016). Also, DON at sub-toxic levels generally found in food and feed, may reduce synthesis of mucin glycoproteins in the goblet cells by stimulating a decrease in the amount of mRNA encoding for the intestinal membrane-related and the mucin secreted (Dellafiora and Dall'Asta 2017). These findings provide the scientific evidence of the protein alterations related to cellular processes, intestinal processes, immune system, mitochondrion, and reproductive system under mycotoxin exposures. The identified proteins and pathways could be targeted as protein biomarkers affected to mycotoxin exposure, as well as protein biomarkers for protecting and/or alleviating their toxicities. To date, the study of human proteomics in in vivo and ex vivo model is still limited. Future prospect of proteomic for mycotoxin exposome may expand to investigation of interactions among host-diet-gut microbes to identify the biological effects of toxicology and mechanism of toxicological action. The information obtained from proteomics analysis could be implemented to find early stage of biomarkers for acute and chronic mycotoxic effects.

Metabolomics

The metabolome comprises a complete set of small molecules found endogenously in cells and organs of biological organism as well as exogenous metabolites (i.e., diets, xenobiotics, gut microbe metabolites) found in the organisms. Metabolomics is part of the omics cascades that is the end point of the biochemical processes and correlate to phenotype. Genetic variation and environmental factors also influence to the metabolome changes (Bino et al. 2004). Thus, metabolomics analysis is a useful tool to identify and measure potential biomarkers for monitoring health and disease states, particularly, upon mycotoxin exposure. Metabolomics study employs a combination of sample preparation, analytical tools and computational analysis. The analytical tools are mainly based on nuclear magnetic resonance (NMR) and mass spectrometry (MS) in coupling with separation technique such as gas chromatography (GC) and liquid chromatography (LC). With the advanced technology, high resolution of mass analyzer is developed to facilitate higher resolution of molecular chemical identification for example quadrupole time-of-flight (qTOF), Fourier transformation (Orbitrap and ion cyclotron resonance) (Del Favero et al. 2018; Monnin et al. 2018; Sun et al. 2018).

Currently, targeted metabolomics is widely used for detecting mycotoxin contaminants in foods, feeds and agricultural products (Arce-López et al. 2020) as well as for monitoring biomarkers of mycotoxin exposure in the population (Richard-Forget et al. 2021). Apart from this, an untargeted metabolomics analysis provides a powerful and unbiased approach for mycotoxin exposome research. It can identify and measure the metabolic response of hosts from mycotoxin exposure to help discovering biomarkers of effect. For example, urine metabolite profiles from piglets fed with 5 mycotoxins (AFB1, FUM1, DON, OTA and ZEA) contaminated in boluses showed increasing creatinine and p-cresol levels compared to the unexposed piglets which indicated kidney malfunction and perturbed gut microbiota, respectively (De Pascali et al. 2017). Ji et.al. reported a combinatorial effect of mycotoxins through a comparative urine metabolite profile from mice between single mycotoxin (either DON or ZEA) and dual mycotoxins (DON + ZEA) exposure. It was found that a single mycotoxin exposure had more metabolic responses than dual mycotoxin exposures (Ji et al. 2018). Another combinatorial effect was also studied in mice exposed between a single ochratoxin (OTA) and dual (AFM1 + OTA) mycotoxins. It revealed that the dual exposure enhanced renal impairment and lysophosphatidylcholine more than the single exposure (Wang et al. 2021). Moreover, combinatorial effect of xenoestrogens (genistein and zearalenone) and drug to human breast cancer (MCF-7) cells was examined using metabolomics and functional assay. It revealed that metabolites involved in amino acid metabolism and mTOR pathways were significantly altered in the combinatorial treatment which correlated to proliferation of the cancer cells. This could be noted that xenoestogens could be an impact to therapeutic response (Warth et al. 2018). In human in vitro study, metabolic profiling of liver carcinoma (HepG2) cells after exposure of 20 mycotoxins revealed cellular alterations and metabolic signatures to identify toxic properties of the xenobiotics (Gerdemann et al. 2022).

From the scientific evidence, metabolomics provides biochemical information and identify potential biomarkers for diagnostic, prognostic, monitoring and therapeutic aspects. Urine biomarkers are promising as they can provide a non-invasive approach to monitor mycotoxin exposome. Future prospect of metabolomic for mycotoxin exposome may expand to investigation of the dynamic change among human genetic, human lifestyle (diets, drugs, exercises, etc.) and gut microbiome to identify key biomarkers for preventive and personalized medicines.

From a Multi-omics Approach to System Biology

Over recent years, multiple omics layers, i.e., genomics, transcriptomics, proteomics, metabolomics, have started to be integrated to creating a more holistic comprehension of cells, organisms, and communities, in relation to their growth, development, adaptation, and progression to disease (Martins et al. 2019; Xu et al. 2018). The acquisition of multi-omics data has become an essential component of present-day molecular biology and technology. This can be attributed to technological advancements, such as HRMS and NGS technologies, which is provide high-throughput data production (Fondi and Liò 2015).

In addition, the wide uptake by the scientific community of high-performance computing and advanced bioinformatics have helped in providing comprehensive models to disclose the biological cascade and the ultimate physiological effects driven by an event, i.e., exposure to multiple factors. Such a multi-omics approach may provide insights on how and to what extent the “exposome” impairs cell functions and integrity at the various omics levels, leading to a more efficient and precise identification and characterization of health hazards (Dellafiora and Dall'Asta 2017). For example, transcriptome and proteome integrative analyses confirmed the genes and proteins involved in cellular pathways (i.e., cell cycle, DNA repair and histone modification), and calcium signaling pathway of bovine spermatozoa exposed to AFB1 and deciphered its negative effect on embryo formation (Komsky-Elbaz et al. 2020). Integration of transcriptome and metabolome data from jejunal explant of pig exposed to DON-contaminated feed revealed that alteration of energy metabolism, protein synthesis, oxidative stress, and inflammasome activation were significant and found to be linked to intestinal damage. This integrative multi-omics analysis provides higher numbers of significant data compared to a single omics data analysis, resulting in obtaining more biological information (Tremblay-Franco et al. 2021).

The challenge in understanding the role of the exposome on health is due not only to the lifelong, large number of chemical exposures, but also due to their complex interactions with physiological processes in the cells (Vermeulen et al. 2020). An opportunity to unravel such complexity is offered by a systems biology approach, which is a multidisciplinary research field that involves the joint contribution of many disciplines to unravel the biology of living systems through the integration of multiple kinds of quantitative molecular measurements and with appropriately planned mathematical models (Tricco et al. 2016). A successful systems biology observation requires that data from multi-omics should be carefully generated from the same group of samples to enable direct comparison under the same condition and fed into computational modeling to define connections and interactions at a comprehensive level (Warth et al. 2017; Vermeulen et al. 2020).

Apart from mathematical modeling, database among different omics layers plays important role in the interpretation into system biology (Eils et al. 2006). Not only the multi-omics of host responded to the toxins, but the multi-omics among host-gut microbiome-toxin are also found to be promising for implementation in terms of mitigation strategy such as agricultural practice and supplementary products to control the contamination. In view of the increasingly wide uptake of advanced omics techniques within the scientific field, the multi-omics integration in exposome studies will offer a large opportunity of growth for food safety research in the years to come.

Challenges and Conclusion

The interest and applications of exposome studies is rising, but several challenges and limitations have yet to be solved. Changes over time and variability between subjects are associated with multiple intrinsic and extrinsic factors. In contrast with the genome, systems biology-based results are dynamic and have the potential to vary in different cells and tissues throughout the lifespan of an individual. This may cause the outcomes from studies to be irreproducible with uncertainty about the significance of the measurements made. The method for recording and quantifying prolonged exposure to all external stressors remains a great challenge as the timing of exposure to toxic substances during vulnerable periods impacts the observed health effects. A potential alternative would be to define critical windows, or life stage exposome snapshots (LEnS) that captures data on repeated exposures during the essential periods of the lifetime as proposed by Shaffer et al. (2017) (Dellafiora and Dall'Asta 2017). For exposome studies, biological influences must be evaluated through the application of a system- and pathway-based perspective to (1) detect unknown relevant biomarkers, (2) distinguish between exposures and biological feedbacks, (3) pinpoint biologically important exposures. Lastly, there are statistical inference problems with the use of omic techniques in exposome studies involving the repeated testing of null hypothesis. The numerous comparisons made in these studies may lead to both false-positive results (type 1 errors), and false-negative results (type 2 errors) if the statistical power is inadequate. To manage false findings, the proportion of the positive results that are allowed to be false (generally 5%) must be defined.

Emerging work on exposome-health studies presents distinctive opportunities of bringing mycotoxins into more mainstream health related studies. Moving forward, it is necessary to establish solid connections between related disciplines including mycotoxicology, epidemiology, statistics, and biomonitoring, particularly for those populations and diseases where mycotoxins may be pertinent.