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中华损伤与修复杂志(电子版) ›› 2022, Vol. 17 ›› Issue (06) : 540 -544. doi: 10.3877/cma.j.issn.1673-9450.2022.06.014

综述

机器学习与多组学结合推动精准营养的研究进展
王璐1, 王宇2, 曾俊2, 陈伟3, 江华4,()   
  1. 1. 637000 南充,川北医学院研究生院;610072 成都,四川省医学科学院·四川省人民医院急诊医学与灾难医学研究所;610072 成都,四川省医学科学院·四川省人民医院急救中心
    2. 610072 成都,四川省医学科学院·四川省人民医院急诊医学与灾难医学研究所;610072 成都,四川省医学科学院·四川省人民医院急救中心;610072 成都,四川省急危重症临床研究中心
    3. 610072 成都,四川省急危重症临床研究中心;100730 北京协和医院临床营养科
    4. 610072 成都,四川省医学科学院·四川省人民医院急诊医学与灾难医学研究所;610072 成都,四川省医学科学院·四川省人民医院急救中心;610072 成都,四川省急危重症临床研究中心;100730 北京协和医院临床营养科
  • 收稿日期:2022-09-05 出版日期:2022-12-01
  • 通信作者: 江华
  • 基金资助:
    四川省重点研发项目(2019YFS0534,2020YFS0392,2021YFS0378)

Research progress of precision nutrition promoted by the combination of machine learning and multi-omics

Lu Wang1, Yu Wang2, Jun Zeng2, Wei Chen3, Hua Jiang4,()   

  1. 1. Graduate School of North Sichuan Medical College, Nanchong 637000, China; Institute of Emergency and Disaster Medicine, Sichuan Academy of Medicial Sciences, Sichuan Provincial People′s Hospital, Chengdu 610072, China; Department of First-Aid Centre, Sichuan Academy of Medicial Sciences, Sichuan Provincial People′s Hospital, Chengdu 610072, China
    2. Institute of Emergency and Disaster Medicine, Sichuan Academy of Medicial Sciences, Sichuan Provincial People′s Hospital, Chengdu 610072, China; Department of First-Aid Centre, Sichuan Academy of Medicial Sciences, Sichuan Provincial People′s Hospital, Chengdu 610072, China; Sichuan Provincial Emergency and Critical Care Clinical Research Center, Chengdu 610072, China
    3. Sichuan Provincial Emergency and Critical Care Clinical Research Center, Chengdu 610072, China; Department of Parenteral and Enteral Nutrition, Peking Union Medical College Hospital, Beijing 100730, China
    4. Institute of Emergency and Disaster Medicine, Sichuan Academy of Medicial Sciences, Sichuan Provincial People′s Hospital, Chengdu 610072, China; Department of First-Aid Centre, Sichuan Academy of Medicial Sciences, Sichuan Provincial People′s Hospital, Chengdu 610072, China; Sichuan Provincial Emergency and Critical Care Clinical Research Center, Chengdu 610072, China; Department of Parenteral and Enteral Nutrition, Peking Union Medical College Hospital, Beijing 100730, China
  • Received:2022-09-05 Published:2022-12-01
  • Corresponding author: Hua Jiang
引用本文:

王璐, 王宇, 曾俊, 陈伟, 江华. 机器学习与多组学结合推动精准营养的研究进展[J/OL]. 中华损伤与修复杂志(电子版), 2022, 17(06): 540-544.

Lu Wang, Yu Wang, Jun Zeng, Wei Chen, Hua Jiang. Research progress of precision nutrition promoted by the combination of machine learning and multi-omics[J/OL]. Chinese Journal of Injury Repair and Wound Healing(Electronic Edition), 2022, 17(06): 540-544.

在大数据的时代背景下,精准营养已逐渐成为临床营养研究的一个重要趋势。精准营养综合了基因组学、代谢组学、表型组学等多组学的研究理论和方法,并引入机器学习等计算科学技术,使得人们对各种疾病/病理生理条件下营养代谢紊乱的认识达到了新高度,也为发现新的营养干预靶点以及干预的模式提供了全新范式。本文就精准营养中机器学习模型建立的数据准备及算法选择方法,结合机器学习在多组学研究中的应用成果进行综述。在大数据背景下,应用机器学习的精准营养研究不仅能够为每例患者营养支持方案制定策略提供科学依据,同时还可以探索各个营养素之间的联系甚至因果关系,因此机器学习是精准营养研究中必不可少的技术手段。然而,本文也提出目前机器学习在精准营养中的应用受限于数据的异质性高、算法的特异性低等挑战,还有很多未知领域需要探索。

With the development of clinical big data, precision nutrition has gradually become an important trend in clinical nutrition research. Precision nutrition integrates the research theories and methods of genomics, metabolomics, phenoomics and other omics, and introduces computational science and technology such as machine learning, which makes people′s understanding of nutritional metabolic disorders under various diseases/pathophysiological conditions reach a new height, and also provides a new paradigm for the discovery of new nutritional intervention targets and intervention modes. This paper reviews the data preparation and algorithm selection methods of machine learning model building in precision nutrition, combined with the application of machine learning in multi-omics research. Under the background of big data, precision nutrition research with the application of machine learning can not only provide scientific basis for the formulation of nutrition support plan for each patient, but also explore the relationship and even causal relationship between various nutrients. Therefore, machine learning is an essential technical means in precision nutrition research. However, this paper also points out that the current application of machine learning in precision nutrition is limited by high heterogeneity of data and low specificity of algorithms, and there are still many unknown areas to explore.

[1]
Businesswire. 10-Key-Trends-Food-Nutrition-Health-2019[EB/OL]. (2019-07-25) [2022-05-13].

URL    
[2]
刘琰,陈伟. 精准营养新定义:理念与落实[J]. 中华预防医学杂志2022, 56(2): 151-153.
[3]
何俏,时景璞. 临床真实世界研究中的实验性研究设计[J]. 中华流行病学杂志2018, 39(4): 519-523.
[4]
Zeevi D, Korem T, Zmora N, et al. Personalized Nutrition by Prediction of Glycemic Responses[J]. Cell, 2015, 163(5): 1079-1094.
[5]
Ferguson LR, De Caterina R, Görman U, et al. Guide and Position of the International Society of Nutrigenetics/Nutrigenomics on Personalised Nutrition: Part 1 - Fields of Precision Nutrition[J]. J Nutrigenet Nutrigenomics, 2016, 9(1): 12-27.
[6]
马国耀,孙勇韬,马玉玲. 数据采集模板化技术在医疗大数据集成建设中的应用[J]. 中国卫生信息管理杂志2016, 13(4): 414-416.
[7]
周光华,李岳峰. 数据挖掘技术在卫生统计信息工作中的应用研究[J]. 中国卫生信息管理杂志2012, 9(6): 82-86.
[8]
Wu S, Roberts K, Datta S, et al. Deep learning in clinical natural language processing: a methodical review[J]. J Am Med Inform Assoc, 2020, 27(3): 457-470.
[9]
Chen L, Song L, Shao Y, et al. Using natural language processing to extract clinically useful information from Chinese electronic medical records[J]. Int J Med Inform, 2019, 124: 6-12.
[10]
Patel TA, Puppala M, Ogunti RO, et al. Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods[J]. Cancer, 2017, 123(1): 114-121.
[11]
阮彤,高炬,冯东雷,等. 基于电子病历的临床医疗大数据挖掘流程与方法[J]. 大数据2017, 3(5): 83-98.
[12]
Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research[J]. J Am Med Inform Assoc, 2013, 20(1): 144-151.
[13]
熊兴江,马敬东,徐承中. 电子健康档案数据质量评估与治理的综述研究[J]. 中国卫生信息管理杂志2018, 15(6): 637-642.
[14]
王宇. 基于机器学习建立创伤代谢组学快速精准分析技术的研究[D]. 成都:电子科技大学,2019.
[15]
Lei C, Wang Y, Zhao J, et al. A patient specific forecasting model for human albumin based on deep neural networks[J]. Compute Methods Programs Biomed, 2020, 196: 105555.
[16]
Tchagna Kouanou A, Mih Attia T, Feudjio C, et al. An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection[J]. J Healthc Eng, 2021, 2021: 4733167.
[17]
Westerhuis JA, van Velzen EJ, Hoefsloot HC, et al. Multivariate paired data analysis: multilevel PLSDA versus OPLSDA[J]. Metabolomics, 2010, 6(1): 119-128.
[18]
Zhang F, Tapera TM, Gou J. Application of a new dietary pattern analysis method in nutritional epidemiology[J]. BMC Med Res Methodol, 2018, 18(1): 119.
[19]
Kirk D, Catal C, Tekinerdogan B. Precision nutrition: A systematic literature review[J]. Comput Biol Med, 2021, 133: 104365.
[20]
Jones P, Mirkes EM, Yates T, et al. Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data[J]. Sensors (Basel), 2019, 19(20): 4504.
[21]
Deng HF, Sun MW, Wang Y, et al. Evaluating machine learning models for sepsis prediction: A systematic review of methodologies[J]. iScience, 2021, 25(1): 103651.
[22]
Khorraminezhad L, Leclercq M, Droit A, et al. Statistical and Machine-Learning Analyses in Nutritional Genomics Studies[J]. Nutrients, 2020, 12(10): 3140.
[23]
Lee YC, Christensen JJ, Parnell LD, et al. Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions[J]. Front Genet, 2022, 12: 783845.
[24]
Li ZJ, Chen W, Jiang H, et al. Effects of Postoperative Parenteral Nutrition Enhanced by Multivitamin on Metabolic Phenotype in Postoperative Gastric Cancer Patients[J]. Mol Nutr Food Res, 2018, 62(12): e1700757.
[25]
Tan Q, Wang Y, Zhang G, et al. The metabolic effects of multi-trace elements on parenteral nutrition for critically ill pediatric patients: a randomized controlled trial and metabolomic research[J]. Transl Pediatr, 2021, 10(10): 2579-2593.
[26]
Shen X, Wang C, Liang N, et al. Serum Metabolomics Identifies Dysregulated Pathways and Potential Metabolic Biomarkers for Hyperuricemia and Gout[J]. Arthritis Rheumatol, 2021, 73(9): 1738-1748.
[27]
Ulaszewska MM, Weinert CH, Trimigno A, et al. Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies[J]. Mol Nutr Food Res, 2019, 63(1): e1800384.
[28]
Fan J, Meng Q, Guo G, et al. Effects of early enteral nutrition supplemented with arginine on intestinal mucosal immunity in severely burned mice[J]. Clin Nutr, 2010, 29(1): 124-130.
[29]
Xu D, Lu Q, Deitch EA. Elemental diet-induced bacterial translocation associated with systemic and intestinal immune suppression[J]. JPEN J Parenter Enteral Nutr, 1998, 22(1): 37-41.
[30]
Okamoto K, Fukatsu K, Hashiguchi Y, et al. Lack of preoperative enteral nutrition reduces gut-associated lymphoid cell numbers in colon cancer patients: a possible mechanism underlying increased postoperative infectious complications during parenteral nutrition[J]. Ann Surg, 2013, 258(6): 1059-1064.
[31]
Heneghan AF, Pierre JF, Tandee K, et al. Parenteral nutrition decreases paneth cell function and intestinal bactericidal activity while increasing susceptibility to bacterial enteroinvasion[J]. JPEN J Parenter Enteral Nutr, 2014, 38(7): 817-824.
[32]
Danneskiold-Samsøe NB, Dias de Freitas Queiroz Barros H, Santos R, et al. Interplay between food and gut microbiota in health and disease[J]. Food Res Int, 2019, 115: 23-31.
[33]
Wu H, Tremaroli V, Schmidt C, et al. The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study[J]. Cell Metab, 2020, 32(3): 379-390. e3.
[34]
Gou W, Ling CW, He Y, et al. Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes[J]. Diabetes Care, 2021, 44(2): 358-366.
[35]
Wang K, Zeng Q, Li KX, et al. Efficacy of probiotics or synbiotics for critically ill adult patients: a systematic review and meta-analysis of randomized controlled trials[J]. Burns Trauma, 2022, 10: tkac004.
[36]
吴桐,王鸿超,陆文伟,等. 肥胖人群肠道菌群特征分析及机器学习模型[J]. 微生物学通报2020, 47(12): 4328-4337.
[37]
Wang S, Zhang L, Wang D, et al. Gut Microbiota Composition is Associated with Responses to Peanut Int ervention in Multiple Parameters Among Adults with Metabolic Syndrome Risk[J]. Molr Nutr Food Res, 2021: 2001051.
[38]
Sak J, Suchodolska M. Artificial Intelligence in Nutrients Science Research: A Review[J]. Nutrients, 2021, 13(2): 322.
[39]
Morgenstern JD, Rosella LC, Costa AP, et al. Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology[J]. Adv Nutr, 2021, 12(3): 621-631.
[40]
Oliveira Chaves L, Gomes Domingos AL, Louzada Fernandes D, et al. Applicability of machine learning techniques in food intake assessment: A systematic review[J]. Crit Rev Food Sci Nutr, 2021, 29: 1-18.
[41]
Morgenstern JD, Rosella LC, Costa AP, et al. Development of Machine Learning Prediction Models to Explore Nutrients Predictive of Cardiovascular Disease Using Canadian Linked Population-Based Data [J]. Appl Physiol Nutr Metab, 2022, 47(5): 529-546.
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