Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Proc Nutr Soc. 2023 Sep;82(3):359-369. doi: 10.1017/S0029665123003038. Epub 2023 Jun 7.
The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.
精准营养的总体目标是用更针对个体的建议来取代“一刀切”的饮食建议,以改善慢性病的预防或管理。随着基因组学、蛋白质组学、代谢组学和肠道微生物组测量等技术的进步,人们对精准营养的兴趣日益浓厚。精准营养计划在精准医学中有三个主要应用。首先,它们旨在通过人工智能、可穿戴设备或采用组学技术来更精确地描述饮食,从而提供更“精准”的饮食评估。其次,精准营养使我们能够了解饮食影响疾病风险的潜在机制,并确定由于基因-饮食或微生物群-饮食相互作用而更容易患病的个体。第三,精准营养可用于“个性化营养”建议,机器学习算法可以将组学谱数据与其他个人和临床指标整合起来,以改善疾病风险。蛋白质组学和代谢组学特别提供了发现食物或营养素摄入、饮食和疾病的蛋白质组学或代谢组学特征以及发现饮食-疾病相互作用的潜在机制的新生物标志物的能力。尽管为了提高这些方法的可重复性、成本效益和效果,还必须克服一些挑战,但精准营养方法在营养研究和临床应用方面具有巨大的潜力。