Zhang Boyao, Schmidlin Thierry
Institute of Immunology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
NPJ Metab Health Dis. 2024 Sep 23;2(1):25. doi: 10.1038/s44324-024-00028-z.
Traditional risk factors and biomarkers of cardiovascular diseases (CVD) have been mainly discovered through clinical observations. Nevertheless, there is still a gap in knowledge in more sophisticated CVD risk factor stratification and more reliable treatment outcome prediction, highlighting the need for a more comprehensive understanding of disease mechanisms at the molecular level. This need has been addressed by integrating information derived from multiomics studies, which provides systematic insights into the different layers of the central dogma in molecular biology. With the advancement of technologies such as NMR and UPLC-MS, metabolomics have become a powerhouse in pharmaceutical and clinical research for high-throughput, robust, quantitative characterisation of metabolic profiles in various types of biospecimens. In this review, we highlight the versatile value of metabolomics spanning from targeted and untargeted identification of novel biomarkers and biochemical pathways, to tracing drug pharmacokinetics and drug-drug interactions for more personalised medication in CVD research (Fig. 1).
心血管疾病(CVD)的传统风险因素和生物标志物主要是通过临床观察发现的。然而,在更复杂的CVD风险因素分层和更可靠的治疗结果预测方面,仍存在知识空白,这凸显了在分子水平上更全面了解疾病机制的必要性。通过整合多组学研究获得的信息,这一需求得到了满足,多组学研究为分子生物学中心法则的不同层面提供了系统的见解。随着核磁共振(NMR)和超高效液相色谱-质谱联用(UPLC-MS)等技术的进步,代谢组学已成为药物和临床研究的强大工具,可对各种生物样本中的代谢谱进行高通量、稳健、定量的表征。在本综述中,我们强调了代谢组学的多用途价值,从靶向和非靶向鉴定新型生物标志物和生化途径,到追踪药物药代动力学和药物-药物相互作用,以在CVD研究中实现更个性化的用药(图1)。