Metabolomics enables precision medicine: "A White Paper, Community Perspective".
Metabolomics enables precision medicine: "A White Paper, Community Perspective".
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DOI：
10.1007/s11306-016-1094-6
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发表时间：
2016
期刊：
影响因子：
3.6
通讯作者：
Kaddurah-Daouk, Rima
中科院分区：
文献类型：
--
作者：
Beger, Richard D.;Dunn, Warwick;Schmidt, Michael A.;Gross, Steven S.;Kirwan, Jennifer A.;Cascante, Marta;Brennan, Lorraine;Wishart, David S.;Oresic, Matej;Hankemeier, Thomas;Broadhurst, David I.;Lane, Andrew N.;Suhre, Karsten;Kastenmueller, Gabi;Sumner, Susan J.;Thiele, Ines;Fiehn, Oliver;Kaddurah-Daouk, Rima
关键词：
代谢组学是对细胞、组织和体液中存在的生化物质（或小分子）的代谢组学的综合研究。在全球或“组学”水平上的代谢研究是一个快速发展的领域，有可能对医学实践产生深远的影响。在代谢组学的中心，是一个人的代谢状态提供了一个人的整体健康状况的密切代表的概念。这种代谢状态反映了基因组编码的内容，并通过饮食，环境因素和肠道微生物组进行了修改。代谢谱以基因表达分析通常不明显的方式提供了从正常生理学到不同病理生理学的生化状态的可量化读数。今天，临床医生仅捕获代谢组中包含的信息的很小一部分，因为他们通常仅测量一组狭窄的血液化学分析物来评估健康和疾病状态。实例包括测量葡萄糖以监测糖尿病，测量胆固醇和高密度脂蛋白/低密度脂蛋白比率以评估心血管健康，测量BUN和肌酐以评估肾脏疾病，以及测量一组代谢物以诊断新生儿中潜在的先天性代谢缺陷。我们预计，目前医学界使用的狭窄范围的化学分析将在未来被揭示更全面的代谢特征的分析所取代。该特征有望描述反映健康状态变化模式的整体生化畸变，更准确地描述特定疾病及其进展，并极大地有助于鉴别诊断。这种未来的代谢特征将：（1）提供不同疾病状态的预测、预后、诊断和替代标志物;（2）告知疾病的潜在分子机制;（3）允许疾病的亚分类，以及基于受影响的代谢途径对患者进行分层;（4）揭示药物反应表型的生物标志物，提供预测受试者对治疗的反应的变化的有效手段（药物代谢组学）;（5）为每个特定基因型定义代谢型，提供遗传变异的功能读数;（6）提供监测疾病（如癌症）的反应和复发的手段;（7）描述人类性能应用和极端环境中的分子景观。重要的是，最近开发了复杂的代谢组学分析平台和信息学工具，使测量血液、其他体液和组织中的数千种代谢物成为可能。这些工具还能够更有力地分析对治疗的反应。人们对疾病的机制有了新的认识，包括神经精神疾病、心血管疾病、癌症、糖尿病和一系列病理。美国国立卫生研究院（NIH）通过药物代谢组学研究网络及其与药物基因组学研究网络的合作伙伴关系支持的一系列突破性研究说明了患者在基线、治疗前、治疗期间和治疗后的代谢型如何能够告知治疗结果和对药物的反应性变化（例如，他汀类药物、抗抑郁药、抗高血压药和抗血小板疗法）。这些研究沿着其他几项研究也阐明了代谢组学数据如何补充和告知遗传数据，以确定治疗反应变化的种族，性别和性别基础，这说明了药物代谢组学和药物基因组学是如何互补的，是精准医学的有力工具。我们的代谢组学社区认为，将代谢组学数据纳入精准医学计划是及时的，将提供一个非常有价值的数据层，补充和告知这些重要计划获得的其他数据。我们的代谢组学学会，通过其“精准医学和药物代谢组学任务组”，与我们的代谢组学社区的投入，在整个，已经制定了这份白色文件，我们讨论的价值和方法，包括代谢组学数据在大型精准医学计划。这份白色白皮书为选择最先进的代谢组学平台和方法提供了建议，这些平台和方法提供了最广泛的生化覆盖范围，考虑了关键的样本收集和保存以及测量标准化等重要主题。我们预计，我们的代谢组学社区将在大型精准医学计划中具有代表性，以提供有关样本采集/保存，最佳组学技术选择以及有关数据收集，解释和传播的关键问题的输入。我们强烈建议为精准医学计划收集和建立样本库，这将考虑到大规模代谢表型研究的需求。
Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
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影响因子：
3.6
作者：
Dunn, Warwick B.;Lin, Wanchang;Broadhurst, David;Begley, Paul;Brown, Marie;Zelena, Eva;Vaughan, Andrew A.;Halsall, Antony;Harding, Nadine;Knowles, Joshua D.;Francis-McIntyre, Sue;Tseng, Andy;Ellis, David I.;O'Hagan, Steve;Aarons, Gill;Benjamin, Boben;Chew-Graham, Stephen;Moseley, Carly;Potter, Paula;Winder, Catherine L.;Potts, Catherine;Thornton, Paula;McWhirter, Catriona;Zubair, Mohammed;Pan, Martin;Burns, Alistair;Cruickshank, J. Kennedy;Jayson, Gordon C.;Purandare, Nitin;Wu, Frederick C. W.;Finn, Joe D.;Haselden, John N.;Nicholls, Andrew W.;Wilson, Ian D.;Goodacre, Royston;Kell, Douglas B.
通讯作者：
Kell, Douglas B.
影响因子：
5.2
作者：
Cacciatore S;Loda M
通讯作者：
Loda M
影响因子：
16.6
作者：
Draisma, Harmen H. M.;Pool, Rene;Boomsma, Dorret I.
通讯作者：
Boomsma, Dorret I.
影响因子：
--
作者：
Cooper-DeHoff, Rhonda M.;Hou, Wei;Johnson, Julie A.
通讯作者：
Johnson, Julie A.
影响因子：
13.1
作者：
Cajka, Tomas;Fiehn, Oliver
通讯作者：
Fiehn, Oliver