Linking of metabolomic biomarkers with cardiometabolic health in Chinese population
Linking of metabolomic biomarkers with cardiometabolic health in Chinese population
复制标题
中国人群代谢组生物标志物与心脏代谢健康的联系
DOI：
10.1111/1753-0407.12858
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发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Xu Lin
中科院分区：
文献类型：
--
作者：
Liang Sun;Huaixing Li;Xu Lin
由于营养的快速转变，中国人群中代谢综合征、2型糖尿病和心血管疾病等心脏代谢疾病的患病率正以惊人的速度增加。此外，据推测，包括中国人在内的亚洲人比白人更容易患心脏代谢疾病。早预测、早预防是控制疫情趋势的关键；为此，新型生物标志物的鉴定对于反映环境暴露以及揭示内源性代谢和病理生理机制至关重要。新兴的“组学”技术，尤其是代谢组学，提供了独特的机会来提供新颖的特征或指纹，以了解遗传和非遗传因素对心脏代谢健康的影响。在过去的二十年中，代谢组学方法越来越多地用于各种流行病学研究，主要是在西方人群中。尽管该领域仍处于早期阶段，但一些研究试图鉴定新化合物或确认其代谢物及其与中国人群心脏代谢疾病的关联，包括氨基酸、脂肪酸、酰基肉碱和其他代谢物。尽管在发现用于疾病预测或干预的新型生物标志物方面做出了巨大努力，但当前研究设计、分析平台和数据处理方法的限制仍然是全球代谢组学研究的挑战。因此，未来的研究需要更先进的技术、严格的研究设计、标准化的检测和分析方法以及多组学方法的综合数据，对于评估在临床环境中使用代谢组学的可行性至关重要。最后，代谢组生物标志物的功能作用和潜在的生物学机制应通过未来的机制研究来阐明。
Due to rapid nutrition transitions, the prevalence of cardiometabolic diseases, such as metabolic syndrome, type 2 diabetes, and cardiovascular diseases, has been increasing at an alarming rate in the Chinese population. Moreover, Asians, including Chinese, have been hypothesized to have a higher susceptibility to cardiometabolic diseases than Caucasians. Early prediction and prevention are key to controlling this epidemic trend; to this end, the identification of novel biomarkers is critical to reflect environmental exposure, as well as to reveal endogenous metabolic and pathophysiologic mechanisms. The emerging "omics" technologies, especially metabolomics, offer a unique opportunity to provide novel signatures or fingerprints to understand the effects of genetic and non-genetic factors on cardiometabolic health. During the past two decades, metabolomic approaches have been increasingly used in various epidemiological studies, primarily in Western populations. Although the field is still in its early stages, some studies have tried to identify novel compounds or confirm their metabolites and associations with cardiometabolic diseases in Chinese populations, including amino acids, fatty acids, acylcarnitines and other metabolites. Despite major efforts to discover novel biomarkers for disease prediction or intervention, the limits in current study design, analytical platforms, and data processing approaches are challenges in metabolomic research worldwide. Therefore, future research with more advanced technologies, rigorous study designs, standardized detection and analytic approaches, and integrated data from multiomics approaches are essential to evaluate the feasibility of using metabolomics in clinical settings. Finally, the functional roles and underlying biological mechanisms of metabolomic biomarkers should be elucidated by future mechanistic research.
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