Tolstikov Vladimir, Moser A James, Sarangarajan Rangaprasad, Narain Niven R, Kiebish Michael A
BERG, Precision Medicine Division, Framingham, MA 01701, USA.
Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA.
Metabolites. 2020 May 29;10(6):224. doi: 10.3390/metabo10060224.
Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10-20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.
组学技术的广泛应用正在改变我们对人群健康的理解,并有望为基于患者生物学特征选择治疗干预措施提供精确指导。与目前使用10 - 20种分析物相比,利用数百种分析物对人类健康进行诊断评估,将在解构疾病状态下人类生物学的复杂性方面提供更高的准确性。目前,诸如胆固醇、肌酐和尿素氮等传统生化测量用于评估健康状况;然而,代谢组学能够实时捕捉表征人类表型及其复杂代谢过程的一组全面的分析物。与传统临床分析物不同,代谢组学谱受到人口统计学和环境因素的显著影响,这些因素会影响正常值范围并增加错误生物标志物发现的风险。本综述探讨了临床代谢组学不断发展的领域所带来的挑战和机遇,并强调了研究设计和生物信息学的特点,这些对于最大化跨人口群体的代谢组学数据的效用是必要的。