Dalamaga Maria
Department of Biological Chemistry, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527, Athens, Greece.
Metabol Open. 2024 May 31;22:100290. doi: 10.1016/j.metop.2024.100290. eCollection 2024 Jun.
Metabolomics, a cutting-edge omics technique, is a rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and the discovery of novel metabolite signatures predictive of disease risk, aiding in earlier disease detection, prognosis and prediction of treatment response. The capacity of this omics approach to simultaneously quantify thousands of metabolites, i.e. small molecules less than 1500 Da in samples, positions it as a promising tool for research and clinical applications in personalized medicine. Clinical metabolomics studies have proven valuable in understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers predictive of disease risk. Liquid chromatography-mass spectrometry is the predominant analytical method used in metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, clinical chemistry data, imaging, health records, and other pertinent health-related data may yield significant advances beneficial for both public health initiatives, clinical applications and precision medicine, particularly in rare disorders and multimorbidity. This special issue has gathered original research articles in topics related to clinical metabolomics as well as research articles, reviews, perspectives and highlights in the broader field of translational and clinical metabolic research. Additional research is necessary to identify which metabolites consistently enhance clinical risk prediction across various populations and are causally linked to disease progression.
代谢组学是一种前沿的组学技术，是生物医学研究中一个快速发展的领域，专注于阐明致病机制以及发现预测疾病风险的新型代谢物特征，有助于疾病的早期检测、预后评估和治疗反应预测。这种组学方法能够同时对数千种代谢物进行定量分析，即在样本中对分子量小于1500道尔顿的小分子进行定量分析，使其成为个性化医疗研究和临床应用的一种有前景的工具。临床代谢组学研究已被证明在理解心脏代谢紊乱方面具有重要价值，有可能发现预测疾病风险的诊断生物标志物。液相色谱-质谱联用是代谢组学中使用的主要分析方法，尤其是非靶向分析。代谢组学与广泛的基因组数据、蛋白质组学、临床化学数据、影像学、健康记录以及其他相关的健康相关数据相结合，可能会取得重大进展，有利于公共卫生举措、临床应用和精准医学，特别是在罕见疾病和多种疾病并存的情况下。本期特刊收集了与临床代谢组学相关主题的原创研究文章，以及转化和临床代谢研究更广泛领域的研究文章、综述、观点和亮点。还需要进行更多研究，以确定哪些代谢物能够在不同人群中持续增强临床风险预测能力，并与疾病进展存在因果关系。
