Viegas S, Zare Jeddi M, N BH, Bessems J, Palmen N, K SG et al. 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).
Louro H, Heinala M, Bessems J, Buekers J, Vermeire T, Woutersen M, et al. 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.
Silins I, Hogberg J. Combined toxic exposures and human health: biomarkers of exposure and effect. Int J Environ Res Public Health. 2011;8(3):629–47.
Article PubMed PubMed Central Google Scholar
Ladeira C, Viegas S. Human biomonitoring – an overview on biomarkers and their application in Occupational and Environmental Health. Biomonitoring. 2016;3:15–24.
Rodríguez-Carrillo A, Mustieles V, Salamanca-Fernández E, Olivas-Martínez A, Suárez B, Bajard L et al. Implementation of effect biomarkers in human biomonitoring studies: a systematic approach synergizing toxicological and epidemiological knowledge. Int J Hyg Environ Health. 2023;249.
Vlaanderen J, Moore LE, Smith MT, Lan Q, Zhang L, Skibola CF, et al. Application of OMICS technologies in occupational and environmental health research; current status and projections. Occup Environ Med. 2010;67(2):136–43.
Article CAS PubMed Google Scholar
Faisandier L, Bonneterre V, De Gaudemaris R, Bicout DJ. Occupational exposome: a network-based approach for characterizing Occupational Health problems. J Biomed Inf. 2011;44(4):545–52.
Dehghani F, Yousefinejad S, Walker DI, Omidi F. Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives. Metabolomics. 2022;18(9):73.
Article CAS PubMed Google Scholar
Sobsey CA, Ibrahim S, Richard VR, Gaspar V, Mitsa G, Lacasse V, et al. Targeted and untargeted proteomics approaches in Biomarker Development. Proteomics. 2020;20(9):e1900029.
Walker DI, Valvi D, Rothman N, Lan Q, Miller GW, Jones DP. The metabolome: a key measure for exposome research in epidemiology. Curr Epidemiol Rep. 2019;6:93–103.
Article PubMed PubMed Central Google Scholar
Bonvallot N, David A, Chalmel F, Chevrier C, Cordier S, Cravedi J-P, et al. Metabolomics as a powerful tool to decipher the biological effects of environmental contaminants in humans. Curr Opin Toxicol. 2018;8:48–56.
Vermeulen R. The Use of High-Resolution Metabolomics in Occupational exposure and Health Research. Ann Work Expo Health. 2017;61(4):395–7.
Article CAS PubMed Google Scholar
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
Article PubMed PubMed Central Google Scholar
(ROBINS-E Development Group, Higgins JM, Rooney R, Taylor A, Thayer K, Silva K, Lemeris R, Akl C, Arroyave A, Bateson W, Berkman T, Demers N, Forastiere P, Glenn F, Hróbjartsson B, Kirrane A, LaKind E, Luben J, Lunn T, McAleenan R, McGuinness A, Meerpohl L, Mehta J, Nachman S, Obbagy R, O’Connor J, Radke A, Savović E, Schubauer-Berigan J, Schwingl M, Schunemann P, Shea H, Steenland B, Stewart K, Straif T, Tilling K, Verbeek K, Vermeulen V, Viswanathan R, Zahm M, Sterne S. J). Risk Of Bias In Non-randomized Studies - of Exposure (ROBINS-E) 2023 [ https://www.riskofbias.info/welcome/robins-e-tool
Wells GA, Wells G, Shea B, Shea B, O’Connell D, Peterson J, et al. editors. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses2014.
McGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): an R package and Shiny web app for visualizing risk-of-bias assessments. Res Synthesis Methods. 2020;n/a(n/a).
Adduri RSR, Vasireddy R, Mroz MM, Bhakta A, Li Y, Chen Z, et al. Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure. Int Arch Occup Environ Health. 2022;95(8):1785–96.
Article CAS PubMed PubMed Central Google Scholar
Assenhöj M, Ward LL, Ghafouri B, Graff P, Ljunggren SA. Metal exposure from additive manufacturing and its effect on the nasal lavage fluid proteome - A pilot study. PLoS ONE. 2021;16(8 August).
Baker MG, Simpson CD, Lin YS, Shireman LM, Seixas N. The Use of metabolomics to identify Biological signatures of Manganese exposure. Annals Work Exposures Health. 2017;61(4):406–15.
Baker MG, Lin YS, Simpson CD, Shireman LM, Searles Nielsen S, Racette BA, et al. The reproducibility of urinary ions in manganese exposed workers. J Trace Elem Med Biol. 2019;51:204–11.
Article CAS PubMed Google Scholar
Carter KA, Simpson CD, Raftery D, Baker MG. Short report: using targeted urine metabolomics to Distinguish between Manganese exposed and unexposed workers in a small Occupational Cohort. Front Public Health. 2021;9.
Chen Z, Han S, Zhang J, Zheng P, Liu X, Zhang Y, et al. Exploring urine biomarkers of early health effects for occupational exposure to titanium dioxide nanoparticles using metabolomics. Nanoscale. 2021;13(7):4122–32.
Article CAS PubMed Google Scholar
Chen Z, Han S, Zhang J, Zheng P, Liu X, Zhang Y, et al. Metabolomics screening of serum biomarkers for occupational exposure of titanium dioxide nanoparticles. Nanotoxicology. 2021;15(6):832–49.
Article CAS PubMed Google Scholar
Chuang KJ, Pan CH, Su CL, Lai CH, Lin WY, Ma CM et al. Urinary neutrophil gelatinase-associated lipocalin is associated with heavy metal exposure in welding workers. Sci Rep. 2015;5.
Dudka I, Kossowska B, Senhadri H, Latajka R, Hajek J, Andrzejak R, et al. Metabonomic analysis of serum of workers occupationally exposed to arsenic, cadmium and lead for biomarker research: a preliminary study. Environ Int. 2014;68:71–81.
Article CAS PubMed Google Scholar
Gao S, Zhuo Z, Hutchinson J, Su L, Christiani DC. Metabolomic profiling identifies plasma sphingosine 1-phosphate levels associated with welding exposures. Occup Environ Med. 2021;78(4):255–61.
Gao S, Quick C, Guasch-Ferre M, Zhuo Z, Hutchinson JM, Su L, et al. The association between inflammatory and oxidative stress biomarkers and plasma metabolites in a longitudinal study of healthy male welders. J Inflamm Res. 2021;14:2825–39.
Article PubMed PubMed Central Google Scholar
Hu G, Wang T, Liu J, Chen Z, Zhong L, Yu S, et al. Serum protein expression profiling and bioinformatics analysis in workers occupationally exposed to chromium (VI). Toxicol Lett. 2017;277:76–83.
Article CAS PubMed Google Scholar
Kossowska B, Dudka I, Bugla-Płoskońska G, Szymańska-Chabowska A, Doroszkiewicz W, Gancarz R, et al. Proteomic analysis of serum of workers occupationally exposed to arsenic, cadmium, and lead for biomarker research: a preliminary study. Sci Total Environ. 2010;408(22):5317–24.
Article CAS PubMed Google Scholar
Kozłowska L, Santonen T, Duca RC, Godderis L, Jagiello K, Janasik B et al. HBM4EU Chromates Study: urinary metabolomics study of workers exposed to Hexavalent Chromium. Metabolites. 2022;12(4).
Long C, Hu G, Zheng P, Chen T, Su Z, Zhang Y, et al. Analysis of serum metabolome of workers occupationally exposed to hexavalent chromium: a preliminary study. Toxicol Lett. 2021;349:92–100.
Article CAS PubMed Google Scholar
Peng F, Yu L, Zhang C, Liu Q, Yan K, Zhang K, et al. Analysis of serum metabolome of laborers exposure to welding fume. Int Arch Occup Environ Health. 2023;96(7):1029–37.
Article CAS PubMed Google Scholar
Shen S, Zhang R, Zhang J, Wei Y, Guo Y, Su L et al. Welding fume exposure is associated with inflammation: a global metabolomics profiling study. Environ Health Global Access Sci Sour. 2018;17(1).
Wei Y, Wang Z, Chang CY, Fan T, Su L, Chen F et al. Global Metabolomic Profiling Reveals an Association of Metal Fume Exposure and plasma unsaturated fatty acids. PLoS ONE. 2013;8(10).
Yang JF, Feng PY, Ling ZM, Khan A, Wang X, Chen YL et al. Nickel exposure induces gut microbiome disorder and serum uric acid elevation. Environ Pollut. 2023;324.
Zhai R, Su S, Lu X, Liao R, Ge X, He M, et al. Proteomic profiling in the sera of workers occupationally exposed to arsenic and lead: identification of potential biomarkers. Biometals. 2005;18(6):603–13.
Article CAS PubMed Google Scholar
Bello D, Chanetsa L, Christophi CA, Singh D, Setyawati MI, Christiani DC, et al. Biomarkers of oxidative stress in urine and plasma of operators at six Singapore printing centers and their association with several metrics of printer-emitted nanoparticle exposures. Nanotoxicology. 2022;16(9–10):913–34.
Article CAS PubMed Google Scholar
Chen Z, Shi J, Zhang Y, Zhang J, Li S, Guan L et al. Screening of serum biomarkers of coal workers’ pneumoconiosis by Metabolomics Combined with Machine Learning Strategy. Int J Environ Res Public Health. 2022;19(12).
Jia S, Setyawati MI, Liu M, Xu T, Loo J, Yan M, et al. Association of nanoparticle exposure with serum metabolic disorders of healthy adults in printing centers. J Hazard Mater. 2022;432:128710.
Article CAS PubMed Google Scholar
Miao R, Ding B, Zhang Y, Xia Q, Li Y, Zhu B. Proteomic profiling change during the early development of silicosis disease. J Thorac Dis. 2016;8(3):329–41.
Article PubMed PubMed Central Google Scholar
Ostroff RM, Mehan MR, Stewart A, Ayers D, Brody EN, Williams SA et al. Early detection of malignant pleural mesothelioma in asbestos-exposed individuals with a noninvasive proteomics-based Surveillance Tool. PLoS ONE. 2012;7(10).
Peng F, Dai J, Qian Q, Cao X, Wang L, Zhu M, et al. Serum metabolic profiling of coal worker’s pneumoconiosis using untargeted lipidomics. Environ Sci Pollut Res. 2022;29(56):85444–53.
Sauvain JJ, Hemmendinger M, Suárez G, Creze C, Hopf NB, Jouannique V et al. Malondialdehyde and anion patterns in exhaled breath condensate among subway workers. Part Fibre Toxicol. 2022;19(1).
Wang H, Zhou S, Liu Y, Yu Y, Xu S, Peng L, et al. Exploration study on serum metabolic profiles of Chinese male patients with artificial stone silicosis, silicosis, and coal worker’s pneumoconiosis. Toxicol Lett. 2021;356:132–42.
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