组学与营养学个性化趋势研究进展
Advances of Omics and Personalized Nutrition Trends
DOI: 10.12677/HJFNS.2020.91011, PDF,  被引量   
作者: 邹 婧:北京林业大学,生物科学与技术学院,北京;毛建平*:北京军事科学院,军事医学研究院,北京
关键词: 营养个性化营养基因组宏基因组肠道菌群代谢组学Nutrition Personalized Nutrition Nutritional Genomics Metagenomics Intestinal Flora Metabolomics
摘要: 营养学个性化是快速发展的研究方向,个性化营养对防治慢性病已经变成大众的迫切健康需求。利用组学大数据和整体分析的技术优势,近年来从蛋白组学、营养基因组学、肠道菌宏基因组学和营养代谢组学的诸多研究进展,给个性化营养发展提供了新思路和新手段。分析营养图谱结构可确立精准膳食、依据个体基因数据可选择健康膳食、调整个体饮食习惯及补充肠道菌而改变肠道菌生态以强化营养、对代谢特征物高通量分析而监管个人膳食营养等方面正在指导营养膳食实现个性化,结合网络分析、数据集成、智能整合和交互在线,使得个性化营养从算法和技术上日臻完善,实现个人健康营养快捷指导。营养学个性化需要在临床、行为学、心理学、计算机科学、生物学和营养学多专业全面合作,以提供精准性和系统性指导服务。
Abstract: The research on personalized nutrition is fast-growing. Personalized nutrition is a desired demand for people health to prevent chronic diseases in era. The Omics have the advantages of overall systematic analysis upon big data, from which the recent researches in proteomics, individual nutritional genomics, individual metagenomics on gut microbiome, and nutritional metabolomics were summarized. There after come some new progresses and means for personalized nutrition. Combining aspects such as: the analysis of the nutritional spectrum of nutrition molecular figures to establish suitable diet, tuning selective nutritious diet based on personal genetic data, enhancing the nutrition by changing the intestinal flora after dietary adjustment or supplementation of intestinal bacteria, and regulating individuals diet under high-throughput analysis the metabolic components, personalized nutrition has been come in practical use. Network analysis, omics’ data consolidation, human-machine interaction were either needed for guiding people nutrition personalization conveniently. Moreover, comprehensive integration of clinical, behavioral, psychological, computational, biological and nutritional was perfect for providing accurate, systematic healthy nutrition services to individuals.
文章引用:邹婧, 毛建平. 组学与营养学个性化趋势研究进展[J]. 食品与营养科学, 2020, 9(1): 87-94. https://doi.org/10.12677/HJFNS.2020.91011

参考文献

[1] Ordovas, J.M., Ferguson, L.R., Tai, E.S. and Mathers, J.C. (2018) Personalised Nutrition and Health. British Medical Journal, 361, k2173.
[Google Scholar] [CrossRef] [PubMed]
[2] 孙丽翠, 王琴, 刘轶群, 等. 蛋白质组学技术在营养学研究中的应用[J]. 卫生研究, 2013, 42(6): 1036-1040.
[3] Jain, S., Rustagi, A., Kumar, D., et al. (2019) Meeting the Challenge of Developing Food Crops with Improved Nutritional Quality and Food Safety: Leveraging Proteomics and Related Omics Techniques. Biotechnology Letters, 41, 471-481.
[Google Scholar] [CrossRef] [PubMed]
[4] Timson, D.J. (2016) The Molecular Basis of Galactosemia-Past, Present and Future. Gene, 589, 133-141.
[Google Scholar] [CrossRef] [PubMed]
[5] Kakkoura, M.G., Loizidou, M.A., Demetriou, C.A., et al. (2017) The Synergistic Effect between the Mediterranean Diet and GSTP1 or NAT2 SNPs Decreases Breast Cancer Risk in Greek-Cypriot Women. European Journal of Nutrition, 56, 545-555.
[Google Scholar] [CrossRef] [PubMed]
[6] Pavlidis, C., Lanara, Z., Balasopoulou, A., et al. (2015) Meta-Analysis of Genes in Commercially Available Nutrigenomic Tests Denotes Lack of Association with Dietary Intake and Nutrient-Related Pathologies. OMICS A Journal of Integrative Biology, 19, 512-520.
[Google Scholar] [CrossRef] [PubMed]
[7] Bray, M.S., Loos, R.J., McCaffery, J.M., et al. (2016) NIH Working Group Report-Using Genomic Information to Guide Weight Management: From Universal to Precision Treatment. Obesity, 24, 14-22.
[Google Scholar] [CrossRef] [PubMed]
[8] Locke, A.E., Kahali, B., Berndt, S.I., et al. (2015) Genetic Studies of Body Mass Index Yield New Insights for Obesity Biology. Nature, 518, 197-206.
[Google Scholar] [CrossRef] [PubMed]
[9] Claussnitzer, M., Dankel, S.N., Kim, K.H., et al. (2015) FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. The New England Journal of Medicine, 373, 895-907.
[Google Scholar] [CrossRef
[10] Jerko, M., Barbara, J.S., Audery, R., et al. (2015) Food4ME Study: Validity and Reliability of Food Choice Questionnaire in 9 European Countries. Food Quality and Preference, 45, 26-32.
[Google Scholar] [CrossRef
[11] Li, S.X., Imamura, F., Ye, Z., et al. (2017) Interaction between Genes and Macronutrient Intake on the Risk of Developing Type 2 Diabetes: Systematic Review and Findings from European Prospective Investigation into Cancer (EPIC)-InterAct. The American Journal of Clinical Nutrition, 106, 263-275.
[Google Scholar] [CrossRef] [PubMed]
[12] Norman, J.M., Handley, S.A., Baldridge, M.T., et al. (2015) Disease Specific Alterations in the Enteric Virome in Inflammatory Bowel Disease. Cell, 160, 447-460.
[Google Scholar] [CrossRef] [PubMed]
[13] Ding, T. and Schloss, P.D. (2014) Dynamics and Associations of Microbial Community Types across the Human Body. Nature, 509, 357-360.
[Google Scholar] [CrossRef] [PubMed]
[14] Cassidy, A. and Minihane, A.-M. (2017) The Role of Metabolism (and the Microbiome) in Defining the Clinical Efficacy of Dietary Flavonoids. The American Journal of Clinical Nutrition, 105, 10-22.
[Google Scholar] [CrossRef] [PubMed]
[15] Routy, B., et al. (2018) Gut Microbiome Influences Efficacy of PD-1-Based Immunotherapy against Epithelial Tumors. Science, 359, 91-97.
[Google Scholar] [CrossRef] [PubMed]
[16] Niv, Z., Gili, Z.-S., Jotham, S., et al. (2018) Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features. Cell, 174, 1388-1405.
[Google Scholar] [CrossRef] [PubMed]
[17] Zhao, L., Zhang, F., Ding, X., et al. (2018) Gut Bacteria Selectively Promoted by Dietary Fibers Alleviate Type 2 Diabetes. Science, 359, 1151-1156.
[Google Scholar] [CrossRef] [PubMed]
[18] Zdemir, V. and Kolker, E. (2016) Precision Nutrition 4.0: A Big Data and Ethics Foresight Analysis-Convergence of Agrigenomics, Nutrigenomics, Nutriproteomics, and Nutrimetabolomics. OMICS A Journal of Integrative Biology, 20, 69-75.
[Google Scholar] [CrossRef] [PubMed]
[19] Kowalski, G.M., Souza, D.P.D., Burch, M.L., et al. (2015) Application of Dynamic Metabolomics to Examine in Vivo, Skeletal Muscle Glucose Metabolism in the Chronically High-Fat Fed Mouse. Biochemical & Biophysical Research Communications, 462, 27-32.
[Google Scholar] [CrossRef] [PubMed]
[20] Zheng, H., Yde, C.C., Dalsgaard, T.K., et al. (2015) Nuclear Magnetic Resonance-Based Metabolomics Reveals that Dairy Protein Fractions Affect Urinary Urea Excretion Differently in Overweight Adolescents. European Food Research & Technology, 240, 489-497.
[Google Scholar] [CrossRef
[21] Li, K., Brennan, L., McNulty, B.A., et al. (2016) Plasma Fatty Acid Patterns Reflect Dietary Habits and Metabolic Health: A Cross-Sectional Study. .Molecular Nutrition & Food Research, 60, 2043-2052.
[Google Scholar] [CrossRef] [PubMed]
[22] Houston, M. (2018) The Role of Noninvasive Cardiovascular Testing, Applied Clinical Nutrition and Nutritional Supplements in the Prevention and Treatment of Coronary Heart Disease. Therapeutic Advances in Cardiovascular Disease, 12, 85-108.
[Google Scholar] [CrossRef] [PubMed]
[23] Wittenbecher, C., Mühlenbruch, K., Kröger, J., et al. (2015) Amino Acids, Lipid Metabolites, and Ferritin as Potential Mediators Linking Red Meat Consumption to Type 2 Diabetes. The American Journal of Clinical Nutrition, 101, 1241-1250.
[Google Scholar] [CrossRef] [PubMed]
[24] Mathews, A.T., Famodu, O.A., Olfert, M.D., et al. (2017) Efficacy of Nutritional Interventions to Lower Circulating Ceramides in Young Adults: FRUVEDomic Pilot Study. Physiological Reports, 5, e13329.
[Google Scholar] [CrossRef] [PubMed]
[25] Kovatcheva-Datchary, P., Nilsson, A., Akrami, R., et al. (2015) Dietary Fiber-Induced Improvement in Glucose Metabolism Is Associated with Increased Abundance of Prevotella. Cell Metabolism, 22, 971-982.
[Google Scholar] [CrossRef] [PubMed]