临床代谢组学:有用的见解、观点和挑战

Maria Dalamaga
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引用次数: 0

摘要

代谢组学是一种前沿的全息技术,是生物医学研究中一个快速发展的领域,其研究重点是阐明致病机制和发现可预测疾病风险的新型代谢物特征,从而帮助更早地发现疾病、预后和预测治疗反应。这种全息方法能够同时量化样本中成千上万种代谢物(即小于 1500 Da 的小分子),因此是个性化医学研究和临床应用的理想工具。临床代谢组学研究已被证明对了解心脏代谢疾病很有价值,有可能发现预测疾病风险的诊断生物标记物。液相色谱-质谱法是代谢组学(尤其是非靶向代谢组学)的主要分析方法。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、健康记录和其他相关的健康数据相结合,可能会取得重大进展,有利于公共卫生计划、临床应用和精准医疗,尤其是罕见疾病和多病的治疗。本特刊收集了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这一更广泛领域的研究文章、综述、观点和亮点。要确定哪些代谢物能持续增强不同人群的临床风险预测并与疾病进展有因果关系,还需要进行更多的研究。
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Clinical metabolomics: Useful insights, perspectives and challenges

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.

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来源期刊
Metabolism open
Metabolism open Agricultural and Biological Sciences (General), Endocrinology, Endocrinology, Diabetes and Metabolism
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审稿时长
40 days
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