Application of metabolomics methods as an occupational risk assessment tool (literature review)
https://doi.org/10.47470/0869-7922-2025-33-5-337-346
EDN: wsenku
Abstract
The implementation of omix technologies in various fields of medicine has been accelerating in recent years and is extremely relevant for industrial medicine. The identification of biomarkers that identify predictors of pathology, as well as individual sensitivity to harmful working conditions, helps reduce occupational health risks.
The article analyzes 84 literary sources devoted to the problems of biomonitoring in the context of the application of methods of metabolomicsin the field of industrial medicine.
Based on the analysis of data from both foreign and domestic literary sources, including articles in research journals, monographs, and guidelines, data on the use of metabolomics methods in medicine have been collected and analyzed. The prospects of this direction are revealed. The existing advantages and limitations of metabolomicsin the field of occupational health are considered.
The use of metabolomics methods opens up broad prospects in the field of personalized medicine. Monitoring the metabolic parameters of workers engaged in harmful industries makes a significant contribution to ensuring the safety of work and makes it possible to identify premorbid conditions, thereby reducing the likelihood of complications during the course of the disease.
Authors’ contribution:
Orlova O.I. – collecting literary data, writing a test, editing;
Ukolov A.I., Radilov A.S. – editing.
All co-authors – approval of the final version of the article, responsibility for the integrity of all parts of the article.
Conflict of interests. The author declare no conflict of interest.
Funding. The study had no sponsorship.
Received: August 14, 2025 / Revised: September 09, 2025 / Accepted: October 2, 2025 / Published: November 19, 2025
About the Authors
Olga I. OrlovaRussian Federation
Candidate of Chemical Sciences, Leading Researcher, Scientific Research Institute of Hygiene, Occupational Pathology and Human Ecology of the Federal Medical Biological Agency of Russia, Leningrad region, Kuzmolovsky settlement, 188663, Russian Federation
e-mail: orlova@yandex.ru
Anton I. Ukolov
Russian Federation
Candidate of Chemical Sciences, Head of the Department of Toxicology, Scientific Research Institute of Hygiene, Occupational Pathology and Human Ecology of the Federal Medical Biological Agency of Russia, Leningrad region, Kuzmolovsky settlement, 188663, Russian Federation
e-mail: Ukolov.ai@gpech.ru
Andrei S. Radilov
Russian Federation
Doctor of Medical Sciences, Professor, Acting Director, Scientific Research Institute of Hygiene, Occupational Pathology and Human Ecology of the Federal Medical Biological Agency of Russia, 188663, Russian Federation, Leningrad region, Leningrad region, Kuzmolovsky settlement,188663, Russian Federation
e-mail: no-reply@subs.elpub.ru
References
1. Lokhov P.G., Balashova E.E., Trifonova O.P., Maslov D.L., Archakov A.I. Ten years of Russian metabolomics: history of development and main results. Biomedicinskaya khimiya. 2020; 66(4): 279–93. (in Russian)
2. Kadin S.V. Metabolic profiling is the way to precision prevention. Network resource [Metabolicheskoe profilirovanie – put' k precizionnoj profilaktike. Setevoj resurs]. https://www.groupmmc.ru/articles/diagnostika/metabolicheskoe-profilirovanie-put-k-pretsizionnoy-profilaktike / (accessed 05/06/2025). (In Russian)
3. Qiu S., Cai Y., Yao H., Lin C. et al. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduction and Targeted Therapy. 2023; 8: 132.
4. Marciano D.P., Snyder M.P. Personalized Metabolomics. In: High-Throughput Metabolomics: Methods and Protocols, Methods in Molecular Biology, vol. 1978.
5. Bar N., et al. A reference map of potential determinants for the human serum metabolome. Nature. 2020; 588: 135–40.
6. Ogawa T. et al. Novel regulation of cardiac branched-chain amino acid metabolism through AMP deaminase: a possible therapeutic target for diabetic cardiomyopathy. Eur. Heart J. 2020; 41: ehaa946.3619.
7. Wilmanski T. et al. Blood metabolome predicts gut microbiome α-diversity in humans. Nat. Biotechnol. 2019; 37: 1217–28.
8. Coyle S. et al. Predicting dying from lung cancer: Urine metabolites predict the last weeks and days of life. J. Clin. Oncol. 2021; 39: 12030.
9. Di’Narzo A.F. et al. Integrative analysis of the inflammatory bowel disease serum metabolome improves our understanding of genetic etiology and points to novel putative therapeutic targets. Gastroenterology. 2022; 162: 828–43.
10. Ginsberg H.N. et al. Triglyceride-rich lipoproteins and their remnants: metabolic insights, role in atherosclerotic cardiovascular disease, and emerging therapeutic strategies-a consensus statement from the European Atherosclerosis Society. Eur. Heart J. 2021; 42: 4791–806.
11. Postoeva A.V., Dvoryashina I.V., Sel`chenkova E.I. Using the concept of metabolic phenotypes to assess cardiovascular risk: a literature review. Endokrinologiya: novosti, mneniya, obuchenie. 2024; 13(1): 80–8. (in Russian)
12. Mayo-Martínez L., Rupérez F.J, Martos-Moreno G.Á, Unveiling Metabolic Phenotype Alterations in Anorexia Nervosa through Metabolomics. Nutrients. 2021; 13(12): 42–9. https://doi.org/10.3390/nu13124249
13. Viegas S., Zare Jeddi M., Bessems J., Palmen N. Biomonitoring as an underused exposure Assessment Tool in Occupational Safety and Health Context-Challenges and Way Forward. Int J Environ Res Public Health. 2020; 17(16): 5884. https://doi.org/10.3390/ijerph17165884
14. Dennis K.K., Marder E., Balshaw D.M., Cui Y., Lynes M.A. Biomonitoring in the Era of the Exposome. Environmental Health Perspectives. 2017; 125(4): 502–10. https://doi.org/10.1289/EHP474
15. Louro H., Heinala M., Bessems J., Buekers J., Vermeire T., Woutersen M., Human biomonitoring in health risk assessment in Europe: current practices and recommendations for the future. Int J Hyg Environ Health. 2019; 222(5): 727–37.
16. Ladeira C., Viegas S., Human biomonitoring – an overview on biomarkers and their application in Occupational and Environmental Health. Biomonitoring. 2016; 3: 15–24.
17. Rejnyuk V.L., Lukovnikova L.V., Kozlov V.K., Yacelenko Yu.V., Pil`nik E.N. On improving the diagnosis of chemical pathology based on biomonitoring. Medline.ru. 2023; 24: 1033. (In Russian)
18. Rappaport S.M., Barupal D.K., Wishart D., Vineis D.P., Scalbert A. The blood exposome and its role in discovering causes of disease. Environ Health Perspect. 2014; 122: 769–74. https://doi.org/10.1289/ehp.1308015
19. Rappaport S.M., Smith M.T. Environment and disease risks. Science. 2010; 330: 460–1. https://doi.org/10.1126/science.1192603
20. Fedotova A.A., Tyaglik A.B., Sem`yanov A.V. The effect of diet as an exposome factor on brain function. Rossijskij fiziologicheskij zhurnal im. I.M. Sechenova. 2021; 107(4–5): 533–67. (in Russian)
21. Wild C.P. The exposome: from concept to utility. Int J Epidemiol. 2012; 41: 24–32. https://doi.org/10.1093/ije/dyr236
22. Vlaanderen J., Moore L.E., Smith M.T., Lan Q., Zhang L., Skibola C.F., et al. Application of OMICS technologies in occupational and environmental health research; current status and projections. Occup Environ Med. 2010; 67(2): 136–43.
23. Faisandier L., Bonneterre V., De Gaudemaris R., Bicout D.J. Occupational exposome: a network-based approach for characterizing Occupational Health problems. J Biomed Inf. 2011; 44(4): 545–52.
24. Dehghani F., Yousefinejad S., Walker D.I., Omidi F. Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives. Metabolomics. 2022; 18(9): 73–9.
25. Sobsey C.A., Ibrahim S., Richard V.R., Gaspar V., Mitsa G., Lacasse V., et al. Targeted and untargeted proteomics approaches in Biomarker Development. Proteomics. 2020; 20(9): e1900029.
26. Walker D.I., Valvi D., Rothman N., Lan Q., Miller G.W., Jones D.P. The metabolome: a key measure for exposome research in epidemiology. Curr Epidemiol Rep. 2019; 6: 93–103.
27. Nikolskiy I., Siuzdak G., Patti G.J. Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry. Bioinformatics. 2015; 31: 2017–23.
28. Liang B., Zhong Y., Chen K., Zeng L., Li G., Zheng J., et al. Serum plasminogen as a potential biomarker for the effects of low-dose benzene exposure. Toxicology. 2018; 410: 59–64.
29. Hong W.X., Liu W., Zhang Y., Huang P., Yang X., Ren X., et al. Identification of serum biomarkers for occupational medicamentosa-like dermatitis induced by trichloroethylene using mass spectrometry. Toxicol Appl Pharmacol. 2013; 273(1): 121–9.
30. Guo X., Zhang L., Wang J., Zhang W., Ren J., Chen Y., et al. Plasma metabolomics study reveals the critical metabolic signatures for benzene-induced hematotoxicity. JCI Insight. 2022; 7(2): e154999.
31. Tlegenov A.Sh., Aby`lajuly` Zh., Bogenbaj G.A. Metabolomic research: the views of a clinician [Metabolomicheskie issledovaniya: vzglyady` klinicista]. Vestnik kazanskogo nacional`nogo medicinskogo universiteta. 2017; 1: 158–60. (in Russian)
32. Vitale G.A., Geibel C., Minda V., Wang M., Aron A.T., Petras D. Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products. Nat Prod Rep. 2024; 41(6): 885–904. https://doi.org/10.1039/d3np00050h
33. Yu T., Park Y., Johnson J.M., Jones D. P. apLCMS – adaptive processing of high-resolution LC/MS data. Bioinformatics. 2009; 25 (15): 1930–36. https://doi.org/10.1093/bioinformatics/btp291
34. Uppal K., Soltow Q.A., Strobel F.H. et al. xMSanalyser: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data. BMC Bioinformatics. 2013; 14: 15–25. https://doi.org/10.1186/1471-2105-14-15
35. Walker D.I., Uppal K., Zhang L., Vermeulen R., Smith M., Hu W. High-resolution metabolomics of occupational exposure to trichloroethylene. International Journal of Epidemiology. 2016; 45(5): 1517–27. https://doi.org/10.1093/ije/dyw218
36. Dehghani F., Yousefinejad S., Walker D.I., Omidi F. Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives. Metabolomics. 2022; 18(9): 73–80.
37. Ivanisevic J., Want E.J. From samples to insights into metabolism: uncovering biologically relevant information in LC-HRMS metabolomics data. Meta. 2019; 9(12): 308–15.
38. Naser F.J., Mahieu N.G., Wang L., Spalding J.L., Johnson S.L., Patti G.J. Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome. Anal Bioanal Chem. 2018; 410(4): 1287–97. https://doi.org/10.1007/s00216-017-0768-x
39. Chen L., Lu W., Wang L., Xing X., Chen Z., Teng X. Metabolite discovery through global annotation of untargeted metabolomics data. Nat Methods. 2021; 18(11): 1377–85. https://doi.org/10.1038/s41592-021-01303-3
40. Stricker T., Bonner R., Lisacek F., Hopfgartner G. Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification. Anal Bioanal Chem. 2021; 413: 503–17. [PubMed: 33123762]
41. Wang L., et al. Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics. Anal. Chem. 2019; 91: 1838–46. [PubMed: 30586294]
42. Sindelar M., Patti G.J. Chemical Discovery in the Era of Metabolomics. J. Am. Chem. Soc. 2020; 142: 9097−105.
43. Souza A.L., Patti G.J. A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing Methods. Mol Biol. 2021; 2276: 357–82. https://doi.org/10.1007/978-1-0716-1266-8_27
44. Marciano D.P., Snyder M.P. Personalized metabolomics. In: D’Alessandro A., ed. High-Throughput Metabolomics. Methods in Molecular Biology, vol. 1978. New York: Humana; 2019. https://doi.org/10.1007/978-1-4939-9236-2_27
45. Guo P., Furnary T., Vasiliou V., Yan Q., Nyhan K., Jones D.P. Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review. Environ Int. 2022; 162: 107159. https://doi.org/10.1016/j.envint.2022.107159
46. Barhoum A., Garcia-Betancourt M.L., Rahier H., van Assche G. Chapter 9. Physicochemical characterization of nanomaterials: Polymorph, composition, wettability, and thermal stability. In: Barhoum A., Makhlouf A.S.H., eds. Emerging Applications of Nanoparticles and Architectural Nanostructures: Current Prospects and Future Trends. Amsterdam: Elsevier; 2018: 255–78.
47. Zhang A., Sun H., Yan G., Wang P., Wang X. Metabolomics for biomarker discovery: moving to the clinic. BioMed Res Int. 2015; 2015: 354671.
48. Goodacre R., Vaidyanathan S., Dunn W.B., Harrigan G.G., Kell D.B. Metabolomics by Numbers: Acquiring and Understanding Global Metabolite Data. Trends Biotechnol. 2004; 22: 245–52. https://doi.org/10.1016/j.tibtech.2004.03.007
49. González-Riaño C., Dudzik D., García A., Gil-De-La-Fuente A., Gradillas A., Godzien J., López-Gonzálvez Á., Rey-Stolle F., Rojo D., Ruperez F.J., et al. Recent Developments along the Analytical Process for Metabolomics Workflows. Anal. Chem. 2020; 92: 203–26. https://doi.org/10.1021/acs.analchem.9b04553
50. Alseekh S., Aharoni A., Brotman Y., Contrepois K., D’Auria J. et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nature Methods. 2021; 18: 747–56.
51. Tsugawa H., Cajka T., Kind T., Ma Y., Higgins B., Ikeda K., Kanazawa M. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015; 12(6): 523–6.
52. Krumsiek J., Suhre K., Evans A.M., Mitchell M.W., Mohney R.P., Milburn M.V. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 2012; 8(10): e1003005.
53. Zhou B., Wang J., Ressom H.W. MetaboSearch: tool for mass-based metabolite identification using multiple databases. PLoS One. 2012; 7(6): e40096.
54. Smith C.A., O’Maille G., Want E. J., Qin C., Trauger S.A., et al. METLIN A Metabolite Mass Spectral Database. Ther Drug Monit. 2005; 27: 747–51.
55. Jain K.K., Sharipov K.O. Fundamentals of personalized medicine. Medicine of the 21st century: omix technologies, New knowledge, Competencies and innovations [Osnovy` personalizirovannoj mediciny`. Medicina XXI veka: omiks-texnologii, novy`e znaniya, kompetencii i innovacii]. Moscow: Izdatel`stvo "Litterra"; 2020. (in Russian)
56. Khamis M.M., Adamko D.J., El-Aneed A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom Rev. 2015; 36(2): 115–34. https://doi.org/10.1002/mas.21455
57. Dehghani F., Yousefinejad S., Walker D.I., Omidi F. Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives. Metabolomics. 2022; 18(9): 73–8.
58. Biological control of industrial exposure to harmful substances. Methodological recommendations. Moscow; 07.12.1990 n 5205-90. (in Russian)
59. Göen T., Schaller K.-H., Drexler H. Biological reference values for chemical compounds in the work area (BARs): an approach for evaluating biomonitoring data. Int Arch Occup Environ Health. 2012; 85: 571–8.
60. Faisandier L., Bonneterre V., De Gaudemaris R., Bicout D.J. Occupational exposome: a network-based approach for characterizing Occupational Health problems. J Biomed Inf. 2011; 44(4): 545–52.
Review
For citations:
Orlova O.I., Ukolov A.I., Radilov A.S. Application of metabolomics methods as an occupational risk assessment tool (literature review). Toxicological Review. 2025;33(5):337-346. (In Russ.) https://doi.org/10.47470/0869-7922-2025-33-5-337-346. EDN: wsenku





























