Trivedi Drupad K, Hollywood Katherine A, Goodacre Royston
Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
New Horiz Transl Med. 2017 Mar;3(6):294-305. doi: 10.1016/j.nhtm.2017.06.001.
Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.
当前的临床实践聚焦于少数与患者病理生理学直接相关的生化指标,因此只能描绘出患者非常有限的代谢组,并且未能考虑这些小分子之间的相互作用。这种信息的匮乏可能会阻碍临床医生在足够的时间内做出最佳治疗干预措施,从而影响对患者的治疗效果。以前,各种后基因组学(“组学”)方法已被用于治疗干预。代谢组学如今已成为一种成熟的“组学”方法,作为一种发现生物标志物的新方法被广泛采用,并且与基因组学(尤其是单核苷酸多态性和全基因组关联研究)协同使用,有潜力提供对病理潜在原因的系统理解。在这篇综述中,我们讨论了代谢组学方法在临床科学中的相关性及其发现生物标志物的潜力,这可能有助于指导临床干预。尽管代谢组学是一种在分子水平上发现生物标志物的强大且具有潜在高通量的方法,但将其真正转化到临床是一个极其缓慢的过程。借助巧妙的数据挖掘以及深度学习和人工智能的新型便携式和可穿戴技术,有可能更快地应用通过代谢组学发现的生物标志物;我们还将着眼于精准医学的未来进行讨论,在未来代谢组学可以惠及大众。