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A network biology model of micronutrient related health

机译:微量营养素相关健康的网络生物学模型

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Micronutrients are involved in specific biochemical pathways and have dedicated functions in the body, but they are also interconnected in complex metabolic networks, such as oxidative-reductive and inflammatory pathways and hormonal regulation, in which the overarching function is to optimise health. Post-genomic technologies, in particular metabolomics and proteomics, both of which are appropriate for plasma samples, provide a new opportunity to study the metabolic effects of micronutrients in relation to optimal health. The study of micronutrient-related health status requires a combination of data on markers of dietary exposure, markers of target function and biological response, health status metabolites, and disease parameters. When these nutrient-centred and physiology/health-centred parameters are combined and studied using a systems biology approach with bioinformatics and multivariate statistical tools, it should be possible to generate a micronutrient phenotype database. From this we can explore external factors that define the phenotype, such as lifestage and lifestyle, and the impact of genotype, and the results can also be used to define micronutrient requirements and provide dietary advice. New mechanistic insights have already been developed using biological network models, for example genes and protein-protein interactions in the aetiology of type 2 diabetes mellitus. It is hoped that the challenge of applying this approach to micronutrients will, in time, result in a change from micronutrient oriented to a health oriented views and provide a more holistic understanding of the role played by multiple micronutrients in the maintenance of homeostasis and prevention of chronic disease, for example through their involvement in oxidation and inflammation.
机译:微量营养素参与特定的生化途径并在体内具有专门的功能,但它们也通过复杂的代谢网络相互连接,例如氧化还原和炎症途径以及荷尔蒙调节,其中的主要功能是优化健康。基因组后技术,特别是代谢组学和蛋白质组学,两者均适用于血浆样品,为研究微量营养素与最佳健康相关的代谢效应提供了新的机会。与微量营养素相关的健康状况的研究需要结合饮食暴露,目标功能和生物学反应,健康状况代谢物和疾病参数的数据。当将这些以营养素为中心的参数和以生理/健康为中心的参数结合起来并使用带有生物信息学和多元统计工具的系统生物学方法进行研究时,应该有可能生成微量营养素表型数据库。由此,我们可以探索定义表型的外部因素,例如生命周期和生活方式,以及基因型的影响,其结果还可以用于确定微量营养素需求并提供饮食建议。使用生物网络模型已经开发出新的机理见解,例如2型糖尿病病因中的基因和蛋白质-蛋白质相互作用。希望将这种方法应用于微量营养素的挑战将及时导致从以微量营养素为导向的观点转变为以健康为导向的观点,并使人们对多种微量营养素在维持体内稳态和预防食道癌中的作用有更全面的了解。慢性疾病,例如通过参与氧化和炎症。

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