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Metabolomics and proteomics in occupational medicine: a comprehensive systematic review

Journal of Occupational Medicine and Toxicology volume 19, Article number: 38 (2024) Cite this article

Abstract

Background

Occupational biomonitoring is essential for assessing health risks linked to workplace exposures. The use of ‘omics’ technologies, such as metabolomics and proteomics, has become crucial in detecting subtle biological alterations induced by occupational hazards, thereby opening novel avenues for biomarker discovery.

Aims

This systematic review aims to evaluate the application of metabolomics and proteomics in occupational health.

Methods

Following the PRISMA guidelines, we conducted a comprehensive search on PubMed, Scopus, and Web of Science for original human studies that use metabolomics or proteomics to assess occupational exposure biomarkers. The risk of bias was assessed by adapting the Cochrane Collaboration tool and the Newcastle-Ottawa Quality Assessment Scale.

Results

Of 2311 initially identified articles, 85 met the eligibility criteria. These studies were mainly conducted in China, Europe, and the United States of America, covering a wide range of occupational exposures. The findings revealed that metabolomics and proteomics approaches effectively identified biomarkers related to chemical, physical, biomechanical, and psychosocial hazards. Analytical methods varied, with mass spectrometry-based techniques emerging as the most prevalent. The risk of bias was generally low to moderate, with specific concerns about exposure measurement and confounding factors.

Conclusions

Integrating metabolomics and proteomics in occupational health biomonitoring significantly advances our understanding of exposure effects and facilitates the development of personalized preventive interventions. However, challenges remain regarding the complexity of data analysis, biomarker specificity, and the translation of findings into preventive measures. Future research should focus on longitudinal studies and biomarker validation across diverse populations to improve the reliability and applicability of occupational health interventions.

Introduction

Occupational biomonitoring plays a pivotal role in occupational medicine, serving as a fundamental approach to assessing and managing the health risks associated with workplace exposures [1]. Biomonitoring is generally recognised as a comprehensive method of assessing risk because it considers all routes of exposure while allowing for health surveillance [1, 2]. Occupational exposure to work-related hazards poses significant health risks, such as occupational diseases requiring robust biological monitoring methods [3]. It involves the measurement of biological markers in samples collected from workers, such as blood, urine, hair, and others. Biomarkers are any biological parameters or substances that indicate exposure to a specific substance or adverse health effects [2]. This methodology not only aids in the early detection of occupational diseases but also contributes to formulating effective preventive strategies and regulatory policies [4].

Several types of biomarkers are used in occupational health, including biomarkers of exposure, effects, and susceptibility [3]. Exposure biomarkers quantify the presence of the parent substance, its metabolites, or resulting DNA adducts and measure the internal, effective dose [5]. Effect biomarkers, on the other hand, signal cellular responses or early signs of exposure, including molecular and cellular alterations [3, 5]. Although these classifications may overlap, exposure biomarkers tend to be more specific, whereas effect biomarkers may not directly identify the agent responsible [1, 3, 5]. Susceptibility biomarkers reflect an individual’s innate capacity to respond to specific exposures.

Metabolomics and proteomics have emerged as pivotal techniques in discerning subtle biological changes induced by environmental and occupational exposures [6]. Metabolomics involves the comprehensive analysis of small molecule metabolites within a biological system. At the same time, proteomics studies the entire set of proteins expressed by a genome, cell, tissue, or organism [6]. These techniques are instrumental in unraveling the complexities of the exposome, which encompasses all environmental exposures throughout an individual’s lifetime, including occupational exposures [7]. These omics technologies capture the dynamic molecular changes, offering a holistic view of an organism’s physiological state under specific conditions. Integrating metabolomics and proteomics in occupational health, particularly in industrial hygiene, could revolutionize the current biomonitoring practices by providing comprehensive exposure profiles and identifying novel biomarkers linked to occupational hazards [8, 9].

Metabolomics and proteomics broadly encompass targeted and untargeted approaches [9, 10]. Targeted metabolomics and proteomics focus on quantifying and analysing a predefined set of known metabolites or proteins, offering high specificity and sensitivity for the biomarkers of interest. In contrast, untargeted metabolomics and proteomics provide a global overview of all metabolites or proteins in a sample. They can reveal unexpected alterations and discover new biomarkers, although they need more precision in targeted methods [9]. Both approaches have their strengths, with targeted methods being advantageous for hypothesis-driven research and untargeted methods being more suitable for exploratory or discovery research [10]. Biomarker development in occupational health using proteomics and metabolomics typically progresses through three key phases: discovery, verification, and validation. The discovery phase focuses on identifying a large number of candidate biomarkers through in-depth, untargeted analyses with the aim of quantifying as many possible biomarkers as possible. The verification phase is then dedicated to confirming that the identified biomarkers show significant differences in abundance between disease and control groups through rigorous quantitative measurements. The validation phase assesses the reproducibility and clinical relevance of the biomarker in different populations and settings, ensuring that it meets the standards required for real-world application [9].

Despite the increasing amount of research, there still needs to be a systematic consolidation of evidence on the effectiveness of these techniques in occupational settings [8]. The use of omics technologies in occupational health is still in its early stages. More comprehensive literature on the subject needs to be provided, and most reviews have focused on metabolomics [8, 11, 12]. This review aims to fill this gap by evaluating the application of metabolomic and proteomic methods in occupational health, with a specific focus on identifying biomarkers of occupational exposure and effect. This analysis is essential for improving biomonitoring practices, shaping regulatory frameworks, and protecting worker health. The main objective of this review is to assess the applicability of metabolomics and proteomics techniques for the biological monitoring of workers, provide guidance for practice on the current evidence, and identify areas where further research is needed.

Methodology

The review followed the systematic review methodology outlined in the PRISMA statement [13]. To address the research question, the PECO framework was structured as follows:

  • Population: Workers in various occupational settings.

  • Exposure: Occupational hazards.

  • Comparator: Workers not exposed to occupational hazards or exposed to different levels/types of occupational hazards.

  • Outcome: Discovery and validation of occupational biomarkers through metabolomic and proteomic techniques.

The research question was then formulated as: “Can metabolomics and proteomics techniques be used for biological monitoring of workers exposed to occupational hazards compared to those not exposed or exposed to different levels/types of hazards?” Any original research article was eligible for review without any restrictions on the date of publication. Reviews, notes, book chapters, letters, editorials, conference papers, and articles published outside of journals were excluded. The study’s inclusion criteria were restricted to peer-reviewed journal articles that employed metabolomics or proteomics techniques in occupational settings. The study focused on identifying or assessing exposure and effect biomarkers associated with occupational exposure. Exclusion criteria comprised studies that did not use these techniques specifically, studies unrelated to occupational health and exposure biomarker discovery, and studies on biomarkers of diseases, treatment, prognostic, or follow-up. Also, English, Portuguese, Spanish, or French studies were included. The study search was conducted on PubMed, Scopus, and Web of Science using specific keywords and Boolean operators between December 2023 and January 2024 (Supplementary Material – Table S1). Table 1 presents the search expression in each database.

Table 1 Electronic databases and query expressions
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The database search identified a total of 2311 articles (Fig. 1). After removing duplicates, using EndNote® software, 2135 studies were left for the title and abstract screening phase.

Fig. 1
figure 1

Flow diagram of literature search for ‘Metabolomics and Proteomics in Occupational Medicine: A Comprehensive Systematic Review’ adapted from PRISMA 2020

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Two independent reviewers screened the titles and abstracts of the systematic search, read the full texts of potentially eligible studies, extracted data, and assessed the quality of the studies. A third reviewer was consulted in case of disagreement.

During this phase, 113 articles were selected for an in-depth full-text review. Most of the studies were excluded at this stage due to their focus on disease and treatment biomarker research, mainly in oncology, neurologic and degenerative diseases (n = 711). Additionally, a significant number of articles were excluded because they involved animal or laboratory studies (n = 489), were reviews, meta-analyses, or opinion articles (n = 432), focused on ecological assessments (n = 278), or were related to non-occupational exposures (n = 112). During the full-text screening phase, articles were excluded if they were not related to occupational exposures (6 articles), were not human studies (5 articles), were reviews or protocols (8 and 2 articles, respectively), or did not evaluate exposure biomarkers (7 articles). As a result, only 85 articles qualified for inclusion in the review.

Essential information about the included studies was collected using a standardized data extraction form. Relevant parameters analysed included: country, occupational exposure, population, the proteomic and metabolomic technique used, biological matrix, analytical methods, primary outcomes, and conclusions. In our analysis, the ‘country’ refers to the country of origin of the first author of each article, whereas the term ‘population’ encompasses the number of participants in the studies, including both exposed and non-exposed individuals, as well as their respective professions.

A risk of bias analysis was conducted to assess the quality and reliability of the reviewed studies. This approach was adapted from the Cochrane Collaboration framework [14] and the Newcastle-Ottawa Quality Assessment Scale [15]. These criteria included a clear definition of sample inclusion criteria, a detailed description of study subjects, valid and reliable exposure measurement, identification and management of confounding factors, accurate and reliable outcome measurement, and appropriate statistical analysis. Two independent reviewers evaluated each study and assigned a risk of bias score for each domain. Any discrepancies were resolved through consensus. To summarize the risk-of-bias assessments, a summary plot (Fig. 3) and a traffic-light plot (Supplementary Material – Table S2) were created using the Robvis® tool [16].

Fig. 2
figure 2

Occupational risks assessed in the primary studies included in ‘metabolomics and proteomics in occupational medicine: a comprehensive systematic review.’

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Fig. 3
figure 3

Summary plot of risk of bias assessment for ‘metabolomics and proteomics in occupational medicine: a systematic review’

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Results

The initial search yielded 2311 articles, of which 2135 remained after removing duplicates. After screening the titles and abstracts, 113 articles were selected for full-text review. Finally, 85 studies met the inclusion criteria and were included in this systematic review (Fig. 1).

Table 2 Summary of studies reviewed on occupational exposure to chemical risks
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Table 3 Summary of studies reviewed on occupational exposure to physical risks
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Table 4 Summary of studies reviewed on occupational exposure to biomechanical risks
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Table 5 Summary of studies reviewed on occupational exposure to psychosocial risk
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Geographic distribution

The included studies were conducted in different countries, with a significant concentration in China (36%), Europe (23%) and the United States of America (22%). European countries contributed notably, with Sweden accounting for 11% and smaller proportions from Poland, Italy, Portugal and others. Other contributions from Asia are substantial, with studies from South Korea (6%), India, Singapore, and Taiwan. Brazil and Australia represent the studies from the Southern Hemisphere (3%).

Occupational exposures

The results of our systematic review reflect a diverse landscape of occupational exposures examined in the included studies. The studies cover a spectrum of chemical, physical, biomechanical, and psychosocial hazards encountered in different work environments.

Chemical exposures (Table 2; Fig. 2) predominate (85%), with a significant number of studies examining the effects of airborne particles such as silica dust [40, 44], chemical elements such as arsenic, chromium, lead, and mercury [17,18,19,20,21,22,23, 25, 28,29,30,31, 35, 36], and volatile organic compounds such as benzene [56, 59, 62,63,64,65, 68,69,70,71] and trichloroethylene [58, 60, 61, 66, 72]. Metabolomic and proteomic analyses have also helped to identify potential biomarkers in workers exposed to pesticides [48, 49, 51, 53], welding fumes [24, 26, 27, 32,33,34], and firefighting chemical exposures [45,46,47,48].

Physical hazards (11%) such as heat exposure [93], hyperbaric pressure [94, 95], noise [96,97,98], ionizing radiation [99], and segmental vibration [96, 97] were also investigated and indicated in Table 3. In particular, one study used proteomics to assess a range of workplace hazards, including extreme temperatures, noise, and dust, as well as exposure to chemical vapours, second-hand cigarette smoke, and diesel exhaust, based on self-reports by participants in the UK Biobank [82] with research highlighting the molecular and cellular responses to these stressors. For example, the proteomic response to noise exposure has revealed changes in serum proteins that may indicate noise-induced hearing loss [93].

The review also identifies studies on biomechanical hazards (2%) in Table 4, such as those related to physical activity in mining [99] or the ergonomic challenges faced by farmers [98], where proteomic analysis has provided insights into the systemic inflammatory responses induced by such activities.

Notably, psychosocial factors (2%) such as stress from night shifts [100] and sleep quality impact of underground environments have also been investigated [101], listed in Table 5, with metabolomic profiling providing a window into the biochemical changes associated with these factors.

Proteomics and metabolomics in occupational health biomonitoring

Targeted approaches, which focus on a predefined set of proteins or metabolites, have allowed for quantifying specific biomarkers that indicate exposure to particular chemicals or stressors. The use of targeted proteomics has enabled the identification of proteins altered by exposure to toxic substances such as benzene [62, 63, 65], and trichloroethylene [58, 60, 61, 66]. Targeted metabolomics was applied to benzene-exposed workers [56], indicating the contribution of specific metabolites to benzene toxicity.

In contrast, untargeted methodologies broadly profile the proteome or metabolome. They are the most frequent strategy in this review, uncovering potential biomarkers and metabolic pathways affected by workplace hazards, whether chemical [34, 36, 44, 50, 55], physical [89,90,91, 93, 96], or psychosocial [101] exposures.

Some studies have employed both targeted and untargeted methods, resulting in a comprehensive and reliable dataset [86, 94]. This approach combines the specificity of targeted analyses with the broader scope of discovery offered by untargeted methods. Researchers enhance the reliability and relevance of potential biomarkers by validating findings from untargeted screens with targeted methods.

Biological matrix

Based on the analysis of the presented data, the most commonly used biological matrix was serum, which was used in 27 studies (24%), followed by plasma in 22 studies (20%) and urine in 14 studies (12%). The analysis also included other biological matrices such as nasal lavage fluid (NLF) (7 trials, 6%), combinations of plasma and urine (5 trials, 4%), combinations of serum and urine (4 trials, 4%), exhaled breath condensate (EBC) (3 trials, 3%), and combinations of serum and faeces (1 trial, 1%), saliva and urine (1 trial, 1%), and whole blood (1 trial, 1%).

Analytical methods

The analytical methods include a range of mass spectrometry-based techniques, including liquid chromatography-mass spectrometry (LC-MS) [27, 28, 91], gas chromatography-mass spectrometry (GC-MS) [49, 96], and tandem mass spectrometry (MS/MS) [55], which have been used primarily for their high sensitivity and specificity in detecting minor biomolecular changes.

In particular, nuclear magnetic resonance (NMR) spectroscopy has been used in several studies [25, 75, 77, 85, 94], capitalizing on its non-destructive nature and its ability to provide detailed information on the structure, dynamics, reaction state, and chemical environment of molecules. The use of high-resolution mass spectrometry (HRMS) [45, 69] and ultra-high performance liquid chromatography (UHPLC) [44, 68] was also reported, providing superior resolution and throughput essential for biomarker discovery.

Electrophoresis techniques, including two-dimensional gel electrophoresis (2-DE) [29, 52, 98] and matrix-assisted laser desorption/ionization (MALDI-TOF MS) [58, 83, 95], were used to separate and identify proteins based on their isoelectric points and molecular weights. These methods provided a comprehensive profile of protein expression and post-translational modifications.

Immunological and biochemical assays, such as enzyme-linked immunosorbent assays (ELISA) [65, 92], have been widely used to validate the presence of specific proteins and peptides, providing a high degree of quantitative accuracy. Innovative techniques such as isobaric tags for relative and absolute quantitation (iTRAQ) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) [24, 59, 79] allowed multiplexed protein quantification, increasing the depth of proteomic analysis.

Innovative techniques like SOMAscan™ [41] showed high accuracy in detecting malignant mesothelioma, emphasizing the role of targeted proteomics in early disease detection among at-risk workers.

Risk of bias

The reviewed studies were evaluated for risk of bias to ensure the integrity and reliability of the findings (Fig. 3). The inclusion criteria were well-defined, reducing the risk of selection bias. Most studies provided detailed descriptions of the study subjects and settings, minimizing the risk of bias related to contextual factors that could influence the outcomes.

The measurement of exposure showed varying levels of risk. Although many studies used valid and reliable methods, indicating a low risk of measurement bias, some had unclear methodologies, contributing to a moderate risk. The identification and handling of confounding factors presented a mixed risk profile. Several studies explicitly outlined strategies to address potential confounders, ensuring a low risk of bias, whereas others did not, resulting in a high risk of confounding bias.

The outcomes were measured with a low risk of bias, indicating consistent, precise, and reliable results. Most studies used appropriate statistical analysis, contributing to a low risk of bias in data interpretation.

Overall, the risk of bias assessment indicates that although most studies demonstrate a low risk of bias in several key domains, there are areas where the risk is unclear or high, particularly concerning the measurement of exposure and confounding variables.

Discussion

Overview

The reviewed studies highlight the potential of metabolomic and proteomic techniques in identifying biomarkers for occupational exposures. They reveal the complexity and challenges of using these methods in occupational health. Although these techniques offer a promising avenue for early detection and prevention strategies in workplace-related diseases, other areas for future development emerge. Currently, no single analytical method can universally identify and quantify the full range of biomarkers in different biological samples with high sensitivity and specificity. This highlights the need for continued efforts to develop standardised protocols and methodologies to improve the reliability and comparability of biomarker studies in occupational health.

The search strategy identified 85 relevant studies, indicating a still limited but growing body of research in this expanding field. There was a significant increase in publications, with a peak in 2023 suggesting a growing but still emergent recognition of these methods within diverse occupational settings. The diverse geographical distribution also underlines the recognition of these technologies in advancing occupational health. This trend underscores the potential of ‘omics’ technologies to enhance our understanding of occupational health risks, although it also highlights the need for further research and validation.

Using proteomics and metabolomics in occupational health settings has improved our understanding of workplace exposures and their potential health impacts. These analytical methods can identify subtle changes in biological systems resulting from exposure to various occupational hazards. The integration of ‘omics’ technologies provides a comprehensive view of an organism’s biochemical state, enabling the detection of specific biomarkers that reflect the complex interplay between environmental exposures and biological responses [6].

Diverse exposures and biomarker research

Exposure to chemical hazards accounted for most of the studies found in this review, demonstrating that the growing concern about chemical exposure in the workplace highlights the critical role of proteomic and metabolomic studies in discovering sensitive and specific biomarkers for real-world occupational biomonitoring. Research in occupational medicine using metabolomics and proteomics has identified particular biomarkers associated with chemical exposure. These findings provide valuable insights for early detection and prevention strategies. For instance, Guardiola et al. [55] identified metabolomic changes in plasma that could predict hepatic hemangiosarcoma in vinyl chloride monomer-exposed workers. These findings suggest that metabolic alterations resulting from chemical exposures can be quantified, which helps to understand disease progression and could be used in occupational health surveillance.

Benzene is one of the most studied chemical agents identified in this systematic review, and exposure in occupational settings has been associated with considerable health risks. Joo et al. [62, 63] suggested that plasma proteins such as T cell receptor β chain and matrix metalloproteinase-13 may represent potential markers for the hematotoxic effects induced by benzene. Huang et al. [59] identified several serum proteins, including plasminogen, apolipoprotein B100, and platelet basic protein, as potential indicators of chronic exposure. These proteins suggest immunosuppressive effects and alterations in lipid metabolism. Liang et al. [65] validated the increase in serum plasminogen levels as a potential biomarker of low-dose benzene exposure. Li et al. [64] and Rothman et al. [69]. have linked oxidative stress and mitochondrial dysfunction to differentially expressed proteins and metabolic alterations from benzene exposure. The biomarkers aid in assessing benzene exposure and shed light on the pathophysiological pathways of benzene, including fatty acid oxidation, inflammation, and the fibrinolytic system. Rigorous biomonitoring practices are necessary in industrial settings to aid in assessing and managing hematotoxicity and other health risks in workers exposed to benzene.

Current systematic research on occupational exposure to metals and metal fumes, as outlined in recent studies, has identified specific biomarkers through proteomic and metabolomic analyses. Adduri et al. [17]. established a blood plasma biomarker signature for beryllium exposure with high sensitivity and specificity. Baker et al. [19, 20]. comprehensively assessed workers exposed to manganese in steel foundries. Their findings revealed the presence of metabolites associated with amino acid metabolism. However, the efficacy of these metabolites as biomarkers for exposure varied among individuals. First, they conducted a global metabolomics study to identify unique metabolites between individuals exposed to manganese at a steel foundry and those not exposed. Workers wore personal inhalable dust samplers and provided end-of-shift urine samples, which underwent metabolomic profiling. Fifteen ions showed significant differences between the exposed and unexposed groups in the initial set. A subsequent set validated nine ions, some showing a dose-response relationship. However, the last research [20] indicates that the profiles’ predictive power varies across different worker populations, possibly due to exposure heterogeneity. The importance of context-specific biomarkers that consider the complexities of exposure scenarios and the biological relevance of identified metabolites is emphasized. It is crucial to identify context-aware biomarkers, given the diverse exposures across occupational settings. This highlights the significance of tailored assessments in the field of occupational health.

Welders exposed to metal fumes showed proteomic changes and renal injury markers in a study by Chuang et al. [24], while Gao et al. [26, 27] and Peng et al. [32] linked welding exposures to changes in metabolites associated with inflammation pathways. Also, longitudinal studies, such as by Wei et al. [34], underscore the systemic impacts of metal fumes on fatty acid profiles, suggesting prolonged health risks even post-exposure. These biomarkers facilitate the identification of exposures and offer insights into the underlying biological mechanisms that may link exposure to welding fumes with cardiovascular diseases, dyslipidemia, and neoplastic conditions, thereby contributing to the assessment of occupational health risks.

In the field of non-chemical occupational hazards such as noise, physical exertion, and night shifts, recent studies underscore significant metabolic and proteomic changes triggered by these exposures. Research has unveiled specific metabolic disturbances associated with night shifts, hinting at a connection with chronic low-grade inflammation, notably among male workers [100]. Moreover, investigations into the impacts of deep underground working conditions reveal modifications in amino acid and neurotransmitter levels, potentially influencing sleep regulation [101]. The physical demands inherent in activities like hand-arm vibration [96, 97] and extensive noise exposure in industrial settings are linked to various biomarkers signalling physiological stress and possible injuries [91, 93]. These insights are invaluable as they expand our comprehension of how non-chemical exposures might contribute to cardiovascular diseases, dyslipidaemia, and cancer, thereby enhancing the framework for occupational health safety and preventive measures. However, whilst ‘omics’ techniques appear capable of enriching our knowledge of biological effects, isolating the occupational component from other exposures that may contribute to biochemical changes remains challenging. This bottom-up strategy of assessing internal changes has yet to demonstrate whether it will yield any practical biomarkers that clinicians can rely on in their daily practice.

Risk of bias

Evaluating bias risk in the reviewed studies was a central step to ensure the reliability and validity of the results. The Cochrane Collaboration framework and the Newcastle-Ottawa Quality Assessment Scale were adapted for comprehensive and rigorous analysis of the risk of bias. We opted for this methodology to assess the risk of bias due to the heterogeneity of the studies and to better align with the evaluation needs. This decision was further justified by the identified limitations of the ROBINS-E tool, which include challenges in addressing multiple biases, confusion in distinguishing co-exposures from confounders, and the impracticality of its application to diverse observational studies, as discussed by Bero et al. [102]. Clear inclusion criteria were established to evaluate the sample selection, description of study subjects, and the measurement of exposure and outcomes.

The risk of bias across the included studies was evaluated by considering several critical factors, such as the size of study populations, control groups, participant matching, and study design. The sample sizes of some of the studies included in this review were relatively small, which may limit the generalisability of the results and increase the risk of type II errors [18, 34, 37, 43, 46, 59]. The selection of appropriate control groups varied, with some studies employing well-matched controls while others lacked adequate comparators, potentially introducing selection bias. For instance, the study by Carter et al. [21] compared 20 manganese-exposed workers with 17 crane operators, which were well-matched based on occupational exposure. The studies also varied in handling confounding factors, with some providing thorough adjustments [25, 31]. In contrast, others did not adequately account for potential confounders, potentially impacting the associations’ validity. In addition to these challenges, an issue identified in some studies, particularly those by Gao et al. [26, 27], is the absence of explicit control groups. Although this absence gives rise to valid concerns regarding the internal validity of these studies, it is crucial to contextualise this within the design of the studies themselves. Gao et al. employed observational methods, focusing on within-group comparisons over time. However, the absence of a rigorous control group makes it challenging to distinguish the effects of occupational exposure from other temporal confounding factors, limiting the robustness and generalisability of the findings. Furthermore, the review identifies shortcomings in participant matching in terms of demographics and baseline characteristics, which needed to be more consistently reported. This may affect the comparability between exposed and non-exposed groups. Also, exposure assessments often relied on self-reported data or indirect measures, which could introduce misclassification bias.

These factors underscore the need for more robust study designs, larger sample sizes, and comprehensive exposure and confounder assessments to enhance the reliability of biomarker research in occupational health.

Limitations

One of the apparent limitations of this review is the potential under-representation of studies with negative results, which biases the overall results towards positive associations. Additionally, the heterogeneity of different methodologies and outcomes between studies limits the ability to perform meta-analyses, reducing the comparability of results. Furthermore, the rapid development of omics technologies may have led to the exclusion of the latest methods, which were not published or indexed during the review period. These findings require a cautious interpretation of some studies’ results and emphasize the importance of rigorous study design and analysis in future research to minimize potential biases.

However, it is noteworthy that, to the best of our knowledge, this is the first systematic review to consolidate findings from both proteomics and metabolomics studies in occupational health, providing a comprehensive perspective on the subject. Additionally, this review underscores the imperative for further research to validate and expand occupational biomonitoring using these methods.

Current state and future directions

This body of evidence underscores the complexity of metabolic responses to occupational hazards.

The integration of high-throughput metabolomic and proteomic techniques generates substantial quantities of data, which are frequently challenging to interpret, reproduce, and validate, despite their potential to offer valuable insights for developing targeted interventions. Given that the majority of the working population is generally in good health, it is anticipated that any biological changes caused by occupational hazards will be relatively subtle. Consequently, adhering to the highest standards of research practice is essential in order to enhance the sensitivity, validity, and specificity of the findings. Therefore, standardised procedures are essential in research involving high-throughput omics techniques.

The reviewed studies utilize various biological matrices, including blood, urine, and nasal lavage fluid, each with advantages and limitations. The matrix choice impacts the biomarkers detected and their interpretation, indicating a need for standardized protocols to compare results across studies. Serum and plasma are frequently used, with serum often obtained after coagulation and plasma typically requiring anticoagulants like EDTA or sodium citrate. Processing conditions vary, with most samples stored at sub-zero temperatures to preserve analytes. Blood samples are mainly collected in the morning, centrifuged, and subjected to specific protocols for storage and analysis.

The timing of blood sampling can significantly affect results due to diurnal variations in metabolic processes. Numerous studies have shown that several biochemical components exhibit significant diurnal variations [103, 104]. These variations require that the time of day of sample collection be carefully considered and documented to ensure consistency and reliability of data. Diurnal and circadian rhythms affect the expression of genes and the levels of certain metabolites, meaning that metabolic profiles can vary throughout the day. This variation can affect the interpretation of results and the comparability of data between studies [104]. Therefore, studies should carefully describe the conditions of sample processing and freezing, including the exact time of sample collection, temperature during processing, and storage protocols, to preserve the integrity of the metabolites and ensure reproducibility [103]. Future research should continue to refine these techniques and validate the potential biomarkers used in clinical and occupational settings. The complexity of metabolomic and proteomic analyses, including the need for high-resolution instruments and sophisticated data analysis tools, poses a challenge. This complexity necessitates further development in analytical methodologies to enhance sensitivity, specificity, and ability to effectively process and interpret large datasets while remaining economically viable and cost-effective.

Integrating these large-scale analytical methods with bioinformatics tools [28] and machine learning approaches has enabled a deeper understanding of the biochemical pathways affected by various occupational exposures [38]. These technologies can enhance the processing and interpretation of complex datasets, allowing the identification of patterns and biomarkers that may not be evident through traditional methods. This set of approaches has shown potential in identifying biomarkers of occupational hazards, paving the way for predictive, preventive, and personalized medicine in occupational health.

Future research should focus on longitudinal studies to track the progression of biomarker changes over time, providing insights into the dynamic nature of occupational exposures and their health effects. Given the multitude of occupational hazards and non-occupational exposures, including environmental factors, lifestyle, and pollutants, which collectively form a complex mixture, establishing a causal relationship with a single agent is often a challenging endeavour. It is, therefore, imperative that prospective studies, accounting for all relevant past exposures, are conducted to elucidate these complex interactions. Additionally, integrating these omics techniques with clinical outcomes will facilitate a more profound understanding of the correlations between these biomarkers and disease states, thereby enhancing their applicability in preventive medicine and occupational health. However, it is crucial to consider that not all biological alterations caused by exposure result in health damage or significant risks, which introduces complexity to the relationship between exposure and disease outcomes. This complexity underscores the challenge of developing useful biomarkers that can inform decisions in clinical practice, identify workers at risk, and prove beneficial for regulatory and risk management frameworks.

Integrating proteomics and metabolomics into occupational medicine is promising but faces significant hurdles to routine clinical application. The primary challenge is to simplify the complex metabolomic data, which typically involves the analysis of numerous metabolites, and to reduce this complexity to a manageable number of key biomarkers that can reliably indicate occupational exposure or disease. The goal of utilising omics techniques to identify a biomarker and validate a cost-effective, accessible test for specific exposure health surveillance remains elusive. Simplification is the key to effective commercialisation and implementation of these technologies. Clear communication of their potential and limitations is essential, especially to industrial and regulatory stakeholders. Targeted collaborations between research institutions and industry are necessary to translate exploratory research into practical, impactful solutions for occupational health [105].

In summary, while metabolomic and proteomic analyses offer promising tools for advancing occupational health research, addressing these challenges through technological advancements, standardization, and integration with computational tools will be crucial for their successful application.

Conclusion

In conclusion, this review highlights metabolomics and proteomics as promising tools in occupational medicine with potential for transformative impact as further research and validation are conducted. Omics technologies are recognized for their capacity to elucidate the mechanistic links between various occupational exposures—such as chemical, physical, and psychosocial hazards—and biological alterations. These methods are pivotal in advancing our deep understanding of the molecular interactions triggered by these occupational hazards. However, the transition from discovering potential biomarkers to their practical application in clinical settings is still in its early stages. This systematic review has identified a deficiency in validation studies, underscoring the critical developmental needs of omics technologies to produce validated, sensitive, and clinically applicable biomarkers. Despite the capabilities of these technologies to generate comprehensive and detailed data, the challenge remains in effectively interpreting this data and developing reliable biomarkers—an area that continues to demand significant research effort.

Longitudinal research is essential to validate biomarkers across diverse populations and settings and to integrate omics findings with clinical outcomes for enhanced preventive medicine. Ultimately, metabolomics and proteomics may provide the basis for personalised, predictive occupational health monitoring and interventions. If the challenges posed by large-scale, subtle data can be effectively interpreted and overcome, these technologies have the potential to advance our approach to biomonitoring, paving the way for more tailored and proactive occupational health strategies.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors acknowledge the editorial support, namely, the constructive review of the manuscript and raised comments.

Funding

This research was funded by the Doctoral Program in Occupational Safety and Health of the University of Porto, grant number demsso.cdopcl.PD9986.

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal

    Carlos Ochoa-Leite, Sara Rodrigues & José Torres Costa

  2. Associate Laboratory i4HB - Institute for Health and Bioeconomy, University Institute of Health Sciences - CESPU, Gandra, 4585-116, Portugal

    Carlos Ochoa-Leite & Ricardo Jorge Dinis-Oliveira

  3. UCIBIO - Research Unit on Applied Molecular Biosciences, Translational Toxicology Research Laboratory, University Institute of Health Sciences (1H-TOXRUN, IUCS-CESPU), Gandra, 4585-116, Portugal

    Carlos Ochoa-Leite & Ricardo Jorge Dinis-Oliveira

  4. Occupational Medicine Office and Cancer Biology and Epigenetics Group, Research Center of IPO Porto (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC Raquel Seruca), Porto, Portugal

    Carlos Ochoa-Leite

  5. Occupational Medicine Office, Portuguese Oncology Institute of Porto (IPO Porto), Porto, 4200-072, Portugal

    Ana Sofia Ramos, Flávio Ribeiro & João Barbosa

  6. Faculty of Nutrition and Food Sciences, University of Porto – Rua do Campo Alegre, Porto, 823, 4150-180, Portugal

    Sara Rodrigues

  7. Faculty of Medicine, University of Porto, Porto, 4200-319, Portugal

    Ana Sofia Ramos & José Torres Costa

  8. Department of Pathology & Molecular Immunology, ICBAS-School of Medicine & Biomedical Sciences, University of Porto, Porto, 4050-313, Portugal

    Carmen Jerónimo

  9. Cancer Biology and Epigenetics Group, Research Center of IPO Porto (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC Raquel Seruca), Porto, Portugal

    Carmen Jerónimo

  10. Associate Laboratory i4HB – Institute for Health and Bioeconomy, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, Porto, 4050-313, Portugal

    Paula Guedes de Pinho

  11. UCIBIO – Applied Molecular Biosciences Unit, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, Porto, 4050-313, Portugal

    Paula Guedes de Pinho

  12. Department of Public Health and Forensic Sciences and Medical Education, Faculty of Medicine, University of Porto, Porto, 4200-319, Portugal

    Ricardo Jorge Dinis-Oliveira

  13. FOREN – Forensic Science Experts, Dr. Mário Moutinho Avenue, no. 33-A, Lisbon, 1400-136, Portugal

    Ricardo Jorge Dinis-Oliveira

Authors
  1. Carlos Ochoa-Leite
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  2. Sara Rodrigues
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  3. Ana Sofia Ramos
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  4. Flávio Ribeiro
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  5. João Barbosa
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  6. Carmen Jerónimo
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  7. Paula Guedes de Pinho
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  8. Ricardo Jorge Dinis-Oliveira
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  9. José Torres Costa
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Contributions

Carlos Ochoa-Leite (C.O.L.) conceptualised and designed the study, led the writing of the manuscript, and revised it critically for important intellectual content. He also analysed the data and interpreted the findings within the context of occupational medicine. C.O.L. approved the final version of the manuscript and agreed to be accountable for all aspects of the work.Sara Rodrigues (S.R.) contributed to the study’s design and assisted in literature search and drafting and revising the manuscript. S.R. has approved the final manuscript.Ana Sofia Ramos (A.S.R.) was involved in data extraction, analysis, and interpretation of the results, particularly focusing on integrating metabolomics and proteomics data. A.S.R. assisted in drafting the manuscript and revising it. She approved the final version for publication.Flávio Ribeiro (F.R.) assessed the risk of bias. F.R. contributed to the drafting and revision of the manuscript and has approved the final version.João Barbosa (J.B.) assessed the risk of bias. J.B. contributed to the drafting and revision of the manuscript and has approved the final version.Carmen Jerónimo (C.J.) was involved in the study design. She assisted in drafting and revising the manuscript critically for important intellectual content. C.J. approved the final version.Paula Guedes de Pinho (P.G.d.P.) provided expertise in metabolomics data interpretation and was involved in drafting and revising the manuscript. P.G.d.P. approved the final version to be published.Ricardo Jorge Dinis-Oliveira (R.J.D.O.) conceptualised and designed the study, played a crucial role in interpreting data, and critically revised the manuscript for intellectual content. He approved the final manuscript.José Torres Costa (J.T.C.) contributed to the study’s design and the interpretation of data and assisted in drafting and revising. J.T.C. approved the final version.

Corresponding authors

Correspondence to Carlos Ochoa-Leite or Ricardo Jorge Dinis-Oliveira.

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Ochoa-Leite, C., Rodrigues, S., Ramos, A.S. et al. Metabolomics and proteomics in occupational medicine: a comprehensive systematic review. J Occup Med Toxicol 19, 38 (2024). https://doi.org/10.1186/s12995-024-00436-3

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Keywords

Journal of Occupational Medicine and Toxicology

ISSN: 1745-6673