Omics as Environmental Health study tools
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Keywords

environmental health
"omics"
genomic
proteomic
metabolomic

How to Cite

Muñoz Yañez, C., Rubio Andrade, M., Guangorena Gómez, J. O., Alegría Torres, J. A., & García Vargas, G. G. (2018). Omics as Environmental Health study tools. Spanish Journal of Environmental Health, 18(2), 156–165. Retrieved from https://ojs.diffundit.com/index.php/rsa/article/view/917

Abstract

Deaths caused by environmental pollution are agrowing public health issue. Most of the premature deaths related to pollution are caused by non communicable diseases such as chronic obstructive pulmonary disease, type-2 diabetes, cardiovascular disease and cancer. They are considered complex diseases because of their multicausality and the various mechanisms involved in their emergence and evolution.

Knowledge of disease-causing mechanismsis increasing and the identification of disease-associated biomarkers improving thanks to technological progress, in particular that of the technologiesthat are applied to the measurement and interpretation of molecular components—the so-called “Omics” technologies. These technologies have allowed the cellular causes of some complex diseases to be identified: genetic variants of susceptibility or protection to pollutants (Genomics), as well as changes in the DNA (Epigenomics) and their effects on the process of transcription of specific genes for repair, on metabolism or on the non-coding RNA associated with diseases (Transcriptomics). In addition, Proteomics and Metabolomics do not cease to provide information on proteins and metabolites involved in disease processes. Bioinformatics has evolved parallel to the development of omics, which has allowed the results of the measurements of hundreds of molecules to be interpreted and organized into networks that show the relationships among them.

Omics are mainly used to develop disease risk models based on population studies, but information on genomes, transcriptomes, epigenomes, microbiomes, proteomes and metabolomesis also used to decipher diseases in order to facilitate prognosis and guide patient treatment, thus contributing to personalized, precision medicine. However, their clinical application is still limited by their cost and their technical, regulatory and ethical implications.

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