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The impact of new research technologies on our understanding of environmental causes of disease: the concept of clinical vulnerability

机译:新研究技术对我们对疾病环境原因的理解的影响:临床易感性概念

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摘要

In spite of decades of epidemiological research, the etiology and causal patterns for many common diseases, such as breast and colon cancer or neurodegenerative diseases, are still largely unknown. Such chronic diseases are likely to have an environmental origin. However, "environmental" risks have been often elusive in epidemiological studies. This is a conundrum for current epidemiological research. On the other side, the relative contribution of genes to chronic diseases, as emerging from GWAS, seems to be modest (15-50% increase in disease risk). What is yet to be explored extensively is a model of disease based on long-term effects of low doses of environmental exposures, incorporating both genetic and acquired susceptibility ("clinical vulnerability"), and the cumulative effects of different exposures. Such a disease model would be compatible with the weak associations found by GWAS and the still elusive role of many (low-level) environmental exposures. We also propose that the introduction of "-omic" high-throughput technologies, such as transcriptomics, proteomics and metabolomics, may provide, in the next years, powerful tools to investigate early effects of environmental exposures and understand the etiology of common diseases better, according to the "clinical vulnerability model". The development of "-omics", in spite of current limitations and lack of sound validation, could greatly contribute to the elucidation of the disease model we propose.
机译:尽管进行了数十年的流行病学研究,但许多常见疾病(例如乳腺癌和结肠癌或神经退行性疾病)的病因和病因模式仍然很大程度上未知。此类慢性疾病很可能是环境原因造成的。但是,在流行病学研究中,“环境”风险通常难以捉摸。这是当前流行病学研究的难题。另一方面,从GWAS中发现的基因对慢性疾病的相对贡献似乎不大(疾病风险增加15-50%)。尚待广泛研究的是一种基于低剂量环境暴露的长期影响的疾病模型,该模型结合了遗传和获得性敏感性(“临床易感性”)以及不同暴露的累积影响。这种疾病模型将与GWAS发现的弱关联以及许多(低级)环境暴露仍然难以捉摸的作用兼容。我们还建议,引入“-组学”高通量技术,例如转录组学,蛋白质组学和代谢组学,可以在未来几年内提供强大的工具,以调查环境暴露的早期影响并更好地了解常见疾病的病因,根据“临床脆弱性模型”。尽管目前存在局限性且缺乏合理的验证,“-组学”的发展仍可能极大地有助于阐明我们提出的疾病模型。

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