정밀영양: 개인 간 대사 다양성을 이해하기 위한 접근 = Precision nutrition: approach for understanding intra-individual biological variation

다국어 초록 (Multilingual Abstract)

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation.
This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

참고문헌 (Reference)

1 Snijders C, "“Big Data”: big gaps of knowledge in the field of internet science" 7 (7): 1-5, 2012

2 Bi Q, "What is machine learning? A primer for the epidemiologist" 188 (188): 2222-2239, 2019

3 Tomás-Navarro M, "Volunteer stratification is more relevant than technological treatment in orange juice flavanone bioavailability" 62 (62): 24-27, 2014

4 Galmés S, "Vitamin E metabolic effects and genetic variants : a challenge for precision nutrition in obesity and associated disturbances" 10 (10): 1919-, 2018

5 Bashiardes S, "Towards utilization of the human genome and microbiome for personalized nutrition" 51 : 57-63, 2018

6 Heidemann C, "Total and high-molecularweight adiponectin and resistin in relation to the risk for type 2 diabetes in women" 149 (149): 307-316, 2008

7 Claus SP, "The gut microbiota : a major player in the toxicity of environmental pollutants" 2 (2): 16003-, 2016

8 Comuzzie AG, "The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome" 86 (86): 4321-4325, 2001

9 Aller R, "The effect of single-nucleotide polymorphisms at the ADIPOQ gene locus rs1501299 on metabolic parameters after 9 mo of a high-protein/low-carbohydrate versus a standard hypocaloric diet" 65 : 44-49, 2019

10 Filippi E, "The adiponectin gene SNP+276G>T associates with early-onset coronary artery disease and with lower levels of adiponectin in younger coronary artery disease patients(age

11 Welter D, "The NHGRI GWAS Catalog, a curated resource of SNP-trait associations" 42 (42): D1001-D1006, 2014

12 Grillo A, "Sodium intake and hypertension" 11 (11): 1970-, 2019

13 Kirk D, "Precision nutrition : a systematic literature review" 133 : 104365-, 2021

14 Zeevi D, "Personalized nutrition by prediction of glycemic responses" 163 (163): 1079-1094, 2015

15 Shukla SK, "Personalized medicine going precise : from genomics to microbiomics" 21 (21): 461-462, 2015

16 Ayodele TO, "New Advances in Machine Learning" IntechOpen 19-48, 2010

17 Levy M, "Microbiota-modulated metabolites shape the intestinal microenvironment by regulating NLRP6 inflammasome signaling" 163 (163): 1428-1443, 2015

18 L’heureux A, "Machine learning with big data : challenges and approaches" 5 : 7776-7797, 2017

19 Zhu A, "Inter-individual differences in the gene content of human gut bacterial species" 16 (16): 82-, 2015

20 Dedić N, "Innovations in Enterprise Information Systems Management and Engineering. ERP Future 2016. Lecture Notes in Business Information Processing" Springer 6-114, 2016

21 Berry SE, "Human postprandial responses to food and potential for precision nutrition" 26 (26): 964-973, 2020

22 Cornelis MC, "Genetic polymorphism of the adenosine A2A receptor is associated with habitual caffeine consumption" 86 (86): 240-244, 2007

23 World Health Organization, "Fact sheets, 2020" World Health Organization

24 Gardner CD, "Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion : the DIETFITS randomized clinical trial" 319 (319): 667-679, 2018

25 Norman JM, "Disease-specific alterations in the enteric virome in inflammatory bowel disease" 160 (160): 447-460, 2015

26 Mozaffarian D, "Dietary and policy priorities for cardiovascular disease, diabetes, and obesity : a comprehensive review" 133 (133): 187-225, 2016

27 Meyer KA, "Diet and gut microbial function in metabolic and cardiovascular disease risk" 16 (16): 93-, 2016

28 Korem T, "Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses" 25 (25): 1243-1253.e5, 2017

29 Salmenniemi U, "Association of adiponectin level and variants in the adiponectin gene with glucose metabolism, energy expenditure, and cytokines in offspring of type 2 diabetic patients" 90 (90): 4216-4223, 2005

30 Oh DK, "Adiponectin in health and disease" 9 (9): 282-289, 2007

31 Laney D, "3D data management: controlling data volume, velocity, and variety" META Group Inc 2001