Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Nat Metab. 2023 Oct;5(10):1656-1672. doi: 10.1038/s42255-023-00903-x. Epub 2023 Oct 23.
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
代谢组学流行病学是对代谢物与健康相关特征之间关系的高通量研究。这一新兴且快速发展的领域增进了我们对疾病病因的理解，并推动了精准医学的发展。随着该领域的不断发展，代谢组学流行病学可能会发现具有疾病风险预测能力的诊断生物标志物，有助于更早地发现疾病和改善预后。在这篇综述中，我们讨论了代谢组学流行病学在一系列疾病（包括心血管代谢疾病、癌症、阿尔茨海默病和 COVID-19）方面取得的关键进展，重点讨论了其潜在的临床应用。简要讨论了代谢组学流行病学中的核心原则，包括研究设计、因果推断方法和多组学整合。总结了实现代谢组学流行病学研究结果临床转化所需的未来方向，强调了其对公共卫生的影响。需要进一步的工作来确定哪些代谢物能够在不同人群中重复改善临床风险预测，并与疾病进展有因果关系。
