CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 089-098
DOI: 10.1055/s-0043-1768723
Section 1: Bioinformatics and Translational Informatics
Survey

Informatics for your Gut: at the Interface of Nutrition, the Microbiome, and Technology

Kate Cooper
1   School of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA
,
Martina Clarke
1   School of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA
,
Jonathan B. Clayton
2   Department of Biology, University of Nebraska at Omaha, Omaha, NE, USA
3   Department of Food Science and Technology, University of Nebraska—Lincoln, Lincoln, NE, USA
4   Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
5   Nebraska Food for Health Center, University of Nebraska—Lincoln, Lincoln, NE, USA
› Institutsangaben

Summary

Background: A significant portion of individuals in the United States and worldwide experience diseases related to or driven by diet. As research surrounding user-centered design and the microbiome grows, movement of the spectrum of translational science from bench to bedside for improvement of human health through nutrition becomes more accessible. In this literature survey, we examined recent literature examining informatics research at the interface of nutrition and the microbiome.

Objectives: The objective of this survey was to synthesize recent literature describing how technology is being applied to understand health at the interface of nutrition and the microbiome focusing on the perspective of the consumer.

Methods: A survey of the literature published between January 1, 2021 and October 10, 2022 was performed using the PubMed database and resulting literature was evaluated against inclusion and exclusion criteria.

Results: A total of 139 papers were retrieved and evaluated against inclusion and exclusion criteria. After evaluation, 45 papers were reviewed in depth revealing four major themes: (1) microbiome and diet, (2) usability,(3) reproducibility and rigor, and (4) precision medicine and precision nutrition.

Conclusions: A review of the relationships between current literature on technology, nutrition and the microbiome, and self-management of dietary patterns was performed. Major themes that emerged from this survey revealed exciting new horizons for consumer management of diet and disease, as well as progress towards elucidating the relationship between diet, the microbiome, and health outcomes. The survey revealed continuing interest in the study of diet-related disease and the microbiome and acknowledgement of needs for data re-use, sharing, and unbiased and rigorous measurement of the microbiome. The literature also showed trends toward enhancing the usability of digital interventions to support consumer health and home management, and consensus building around how precision medicine and precision nutrition may be applied in the future to improve human health outcomes and prevent diet-related disease.



Publikationsverlauf

Artikel online veröffentlicht:
06. Juli 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • Rerefences

  • 1 Ley RE. Obesity and the human microbiome. Curr Opin Gastroenterol 2010 Jan;26(1):5-11. doi: 10.1097/MOG.0b013e328333d751.
  • 2 Devaraj S, Hemarajata P, Versalovic J. The human gut microbiome and body metabolism: implications for obesity and diabetes. Clin Chem 2013 Apr;59(4):617-28. doi: 10.1373/clinchem.2012.187617.
  • 3 Lee CJ, Sears CL, Maruthur N. Gut microbiome and its role in obesity and insulin resistance. Ann N Y Acad Sci 2020 Feb;1461(1):37-52. doi: 10.1111/nyas.14107.
  • 4 Sharma S, Tripathi P. Gut microbiome and type 2 diabetes: where we are and where to go J Nutr Biochem 2019 Jan;63:101-108. doi: 10.1016/j.jnutbio.2018.10.003.
  • 5 Glassner KL, Abraham BP, Quigley EM. The microbiome and inflammatory bowel disease. J Allergy Clin Immunol 2020 Jan;145(1):16-27. doi: 10.1016/j.jaci.2019.11.003.
  • 6 Kostic AD, Xavier RJ, Gevers D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology 2014 May;146(6):1489-99. doi: 10.1053/j.gastro.2014.02.009.
  • 7 Ogilvie RP, Patel SR. The epidemiology of sleep and obesity. Sleep Health 2019 Feb;5(1):84-90. doi: 10.1016/j.sleh.2018.10.010.
  • 8 Chen S, Florax RJ, Snyder S, Miller CC. Obesity and access to chain grocers. Econ Geogr 2010;86(4):431-52. doi: 10.1111/j.1944-8287.2010.01090.x.
  • 9 Kolodinsky JM, Goldstein AB. Time use and food pattern influences on obesity. Obesity (Silver Spring) 2011 Dec;19(12):2327-35. doi: 10.1038/oby.2011.130.
  • 10 Jardim TV, Mozaffarian D, Abrahams-Gessel S, Sy S, Lee Y, Liu J, et al. Cardiometabolic disease costs associated with suboptimal diet in the United States: A cost analysis based on a microsimulation model. PLoS Med 2019 Dec 17;16(12):e1002981. doi: 10.1371/journal.pmed.1002981.
  • 11 Adamski M, Gibson S, Leech M, Truby H. Are doctors nutritionists? What is the role of doctors in providing nutrition advice? Nutrition Bulletin 2018 May 18; 43(2):146-52. doi: 10.1111/nbu.12320.
  • 12 Fitzgerald N, Damio G, Segura-Pérez S, Pérez-Escamilla R. Nutrition knowledge, food label use, and food intake patterns among Latinas with and without type 2 diabetes. J Am Diet Assoc 2008 Jun;108(6):960-7. doi: 10.1016/j.jada.2008.03.016.
  • 13 Kretser A, Murphy D, Starke-Reed P. A partnership for public health: USDA branded food products database. J Food Compost Anal 2017 Dec 1;64:10-2. doi: 10.1016/j.jfca.2017.07.019
  • 14 Boland M, Bronlund J. eNutrition-The next dimension for eHealth? Trends in Food Science & Technology 2019 Sep 1;91:634-9. doi: 10.1016/j.tifs.2019.08.001.
  • 15 Anastasiou K, Miller M, Dickinson, K. The relationship between food label use and dietary intake in adults: A systematic review. Appetite 2019 Jul 1;138:280-91. doi: 10.1016/j.appet.2019.03.025.
  • 16 Slavin JL. The challenges of nutrition policymaking. Nutr J 2015 Feb 7;14:15. doi: 10.1186/s12937-015-0001-8.
  • 17 Spronk I, Kullen C, Burdon C, O’Connor H. Relationship between nutrition knowledge and dietary intake. Br J Nutr 2014 May 28;111(10):1713-26. doi: 10.1017/S0007114514000087.
  • 18 Kessler DA. The evolution of national nutrition policy. Annu Rev Nutr 1995;15:xiii-xxvi. doi: 10.1146/annurev.nu.15.070195.005033.
  • 19 Malloy‐Weir L, Cooper M. Health literacy, literacy, numeracy and nutrition label understanding and use: a scoping review of the literature. J Hum Nutr Diet 2017 Jun;30(3):309-25. doi: 10.1111/jhn.12428.
  • 20 Williams P. Consumer understanding and use of health claims for foods. Nutr Rev 2005 Jul;63(7):256-64. doi: 10.1111/j.1753-4887.2005.tb00382.x.
  • 21 McCurdie T, Taneva S, Casselman M, Yeung M, McDaniel C, Ho W, et al. mHealth consumer apps: the case for user-centered design. Biomed Instrum Technol 2012 Fall;Suppl:49-56. doi: 10.2345/0899-8205-46.s2.49.
  • 22 World Health Organization. mHealth: new horizons for health through mobile technologies. 2011 [Available from: https://www.afro.who.int/publications/mhealth-new-horizons-health-through-mobile-technologie].
  • 23 Hoevenaars D, Holla JF, Te Loo L, Koedijker JM, Dankers S, Houdijk H, et al; WHEELS Study Group. Mobile app (WHEELS) to promote a healthy lifestyle in wheelchair users with spinal cord injury or lower limb amputation: usability and feasibility study. JMIR Form Res 2021 Aug 9;5(8):e24909. doi: 10.2196/24909.
  • 24 Kaiser B, Stelzl T, Finglas P, Gedrich K. The Assessment of a Personalized Nutrition Tool (eNutri) in Germany: Pilot Study on Usability Metrics and Users’ Experiences. JMIR Form Res 2022 Aug 4;6(8):e34497. doi: 10.2196/34497.
  • 25 Cifuentes L, Eckel-Passow J, Acosta A. Precision medicine for obesity. Dig Dis Interv 2021 Sep;5(3):239-48. doi: 10.1055/s-0041-1729945.
  • 26 Dualib PM, Taddei CR, Fernandes G, Carvalho CR, Sparvoli LG, Silva IT, et al. Gut microbiota across normal gestation and gestational diabetes mellitus: A cohort analysis. Metabolites 2022 Aug 26;12(9):796. doi: 10.3390/metabo12090796.
  • 27 Lymberopoulos E, Gentili GI, Budhdeo S, Sharma N. COVID-19 severity is associated with population-level gut microbiome variations. Front Cell Infect Microbiol 2022 Aug 23;12:963338. doi: 10.3389/fcimb.2022.963338.
  • 28 Jian Z, Zeng L, Xu T, Sun S, Yan S, Zhao S, et al. The intestinal microbiome associated with lipid metabolism and obesity in humans and animals. J Appl Microbiol 2022 Nov;133(5):2915-30. doi: 10.1111/jam.15740.
  • 29 Mennella JA, Li Y, Bittinger K, Friedman ES, Zhao C, Li H, et al. The macronutrient composition of infant formula produces differences in gut microbiota maturation that associate with weight gain velocity and weight status. Nutrients 2022 Mar 15;14(6):1241. doi: 10.3390/nu14061241.
  • 30 Boonma P, Shapiro JM, Hollister EB, Badu S, Wu Q, Weidler EM, et al. Probiotic VSL# 3 treatment reduces colonic permeability and abdominal pain symptoms in patients with irritable bowel syndrome. Front Pain Res (Lausanne) 2021 Sep 22;2:691689. doi: 10.3389/fpain.2021.691689.
  • 31 Guzzardi MA, Ederveen TH, Rizzo F, Weisz A, Collado MC, Muratori F, et al. Maternal pre-pregnancy overweight and neonatal gut bacterial colonization are associated with cognitive development and gut microbiota composition in pre-school-age offspring. Brain Behav Immun 2022 Feb;100:311-20. doi: 10.1016/j.bbi.2021.12.009.
  • 32 Jacobs JP, Gupta A, Bhatt RR, Brawer J, Gao K, Tillisch K, et al. Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome 2021 Nov 30;9(1):236. doi: 10.1186/s40168-021-01188-6.
  • 33 Oliphant K, Ali M, D’Souza M, Hughes PD, Sulakhe D, Wang AZ, et al. Bacteroidota and Lachnospiraceae integration into the gut microbiome at key time points in early life are linked to infant neurodevelopment. Gut Microbes 2021 Jan-Dec;13(1):1997560. doi: 10.1080/19490976.2021.1997560.
  • 34 Bel Lassen P, Attaye I, Adriouch S, Nicolaou M, Aron-Wisnewsky J, Nielsen T, et al. Protein intake, metabolic status and the gut microbiota in different ethnicities: Results from two independent cohorts. Nutrients 2021 Sep 10;13(9):3159. doi: 10.3390/nu13093159.
  • 35 Ahrens AP, Culpepper T, Saldivar B, Anton S, Stoll S, Handberg EM, et al. A six-day, lifestyle-based immersion program mitigates cardiovascular risk factors and induces shifts in gut microbiota, specifically lachnospiraceae, ruminococcaceae, faecalibacterium prausnitzii: a pilot study. Nutrients 2021 Sep 29;13(10):3459. doi: 10.3390/nu13103459.
  • 36 Lymberopoulos E, Gentili GI, Alomari M, Sharma N. Topological data analysis highlights novel geographical signatures of the human gut microbiome. Front Artif Intell 2021 Aug 18;4:680564. doi: 10.3389/frai.2021.680564.
  • 37 Schütte K, Schulz C, Vilchez-Vargas R, Vasapolli R, Palm F, Simon B, et al. Impact of healthy aging on active bacterial assemblages throughout the gastrointestinal tract. Gut Microbes 2021 Jan-Dec;13(1):1966261. doi: 10.1080/19490976.2021.1966261.
  • 38 Sorensen K, Cawood AL, Gibson GR, Cooke LH, Stratton RJ. Amino acid formula containing synbiotics in infants with cow’s milk protein allergy: A systematic review and meta-analysis. Nutrients 2021 Mar 14;13(3):935. doi: 10.3390/nu13030935.
  • 39 Burchill E, Lymberopoulos E, Menozzi E, Budhdeo S, McIlroy JR, Macnaughtan J, et al. The unique impact of COVID-19 on human gut microbiome research. Front Med (Lausanne) 2021 Mar 16;8:652464. doi: 10.3389/fmed.2021.652464.
  • 40 Balakrishnan B, Selvaraju V, Chen J, Ayine P, Yang L, Ramesh Babu J, et al. Ethnic variability associating gut and oral microbiome with obesity in children. Gut Microbes 2021 Jan-Dec;13(1):1-15. doi: 10.1080/19490976.2021.1882926.
  • 41 Gershuni V, Li Y, Elovitz M, Li H, Wu GD, Compher CW. Maternal gut microbiota reflecting poor diet quality is associated with spontaneous preterm birth in a prospective cohort study. Am J Clin Nutr 2021 Mar 11;113(3):602-11. doi: 10.1093/ajcn/nqaa361.
  • 42 Diener C, Reyes-Escogido MD, Jimenez-Ceja LM, Matus M, Gomez-Navarro CM, Chu ND, et al. Progressive shifts in the gut microbiome reflect prediabetes and diabetes development in a treatment-naive Mexican cohort. Front Endocrinol (Lausanne) 2021 Jan 8;11:602326. doi: 10.3389/fendo.2020.602326.
  • 43 Oluwagbemigun K, O‘Donovan, AN, Berding K, Lyons K, Alexy U, Schmid M, et al. Long-term dietary intake from infancy to late adolescence is associated with gut microbiota composition in young adulthood. Am J Clin Nutr 2021 Mar 11;113(3):647-56. doi: 10.1093/ajcn/nqaa340.
  • 44 Nicholson K, Bjornevik K, Abu-Ali G, Chan J, Cortese M, Dedi B, et al. The human gut microbiota in people with amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener 2021 May;22(3-4):186-94. doi: 10.1080/21678421.2020.1828475.
  • 45 Kamp KJ, Cain KC, Utleg A, Burr RL, Raftery D, Lun, RA, et al. Bile acids and microbiome among individuals with irritable bowel syndrome and healthy volunteers. Biol Res Nurs 2021 Jan;23(1):65-74. doi: 10.1177/1099800420941255.
  • 46 Sporrel K, Wang S, Ettema DD, Nibbeling N, Krose BJ, Deutekom M, et al. Just-in-Time Prompts for Running, Walking, and Performing Strength Exercises in the Built Environment: 4-Week Randomized Feasibility Study. JMIR Form Res 2022 Aug 1;6(8):e35268. doi: 10.2196/35268.
  • 47 Castle EM, Dijk G, Asgari E, Shah S, Phillips R, Greenwood J, et al. The Feasibility and User-Experience of a Digital Health Intervention Designed to Prevent Weight Gain in New Kidney Transplant Recipients—The ExeRTiOn2 Trial. Front Nutr 2022 May 23;9:887580. doi: 10.3389/fnut.2022.887580.
  • 48 Dinour LM. Infant Feeding Tracker Applications: Cross-Sectional Analysis of Use. J Nutr Educ Behav 2022 Sep;54(9):835-43. doi: 10.1016/j.jneb.2022.03.012.
  • 49 Kamath S, Kappaganthu K, Painter S, Madan A. Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study. JMIR Form Res 2022 Mar 21;6(3):e33329. doi: 10.2196/33329.
  • 50 Dening J, George ES, Ball K, Islam SM. User-centered development of a digitally-delivered dietary intervention for adults with type 2 diabetes: The T2Diet study. Internet Interv 2022 Feb 12;28:100505. doi: 10.1016/j.invent.2022.100505.
  • 51 Akam EY, Nuako AA, Daniel AK, Stanford FC. Racial disparities and cardiometabolic risk: new horizons of intervention and prevention. Curr Diab Rep 2022 Mar;22(3):129-36. doi: 10.1007/s11892-022-01451-6.
  • 52 Curtis RG, Bartel B, Ferguson T, Blake HT, Northcott C, Virgara R, et al. Improving user experience of virtual health assistants: scoping review. J Med Internet Res 2021 Dec 21;23(12):e31737. doi: 10.2196/31737.
  • 53 Kennedy M, Kumar R, Ryan NM, Bennett J, Fuentes GL, Gould GS. Codeveloping a multibehavioural mobile phone app to enhance social and emotional well-being and reduce health risks among Aboriginal and Torres Strait Islander women during preconception and pregnancy: a three-phased mixed-methods study. BMJ Open 2021 Nov 24;11(11):e052545. doi: 10.1136/bmjopen-2021-052545.
  • 54 Wong J, Foussat AC, Ting S, Acerbi E, van Elburg RM, Chien CM. A chatbot to engage parents of preterm and term infants on parental stress, parental sleep, and infant feeding: usability and feasibility study. JMIR Pediatr Parent 2021 Oct 26;4(4):e30169. doi: 10.2196/30169.
  • 55 Subramaniam S, Dhillon JS, Wan Ahmad WF. Behavioral Theory-Based Framework for Prediabetes Self-Care System—Design Perspectives and Validation Results. Int J Environ Res Public Health 2021 Aug 31;18(17):9160. doi: 10.3390/ijerph18179160.
  • 56 Seo LM, Petersen CL, Halter RJ, Kotz DF, Fortuna KL, Batsis JA. Usability Assessment of a Bluetooth-Enabled Resistance Exercise Band Among Young Adults. Health Technol (Berl) 2021 Apr;5(1):4. doi: 10.21037/ht-20-22.
  • 57 Kempler JV, Love P, Bolton KA, Rozman M, Spence AC. Exploring the Use of a Web-Based Menu Planning Tool in Childcare Services: Qualitative Cross-sectional Survey Study.JMIR Form Res 2022 Jul 18;6(7):e35553. doi: 10.2196/35553.
  • 58 Liu Z, Liu L, Weng S, Guo C, Dang Q, Xu H, et al. Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nat Commun 2022 Feb 10;13(1):816. doi: 10.1038/s41467-022-28421-6.
  • 59 Mirzayi C, Renson A; Genomic Standards Consortium; Massive Analysis and Quality Control Society; Zohra F, Elsafoury S, Geistlinger L, Kasselman LJ, Eckenrode K, van de Wijgert J, et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med 2021 Nov;27(11):1885-92. doi: 10.1038/s41591-021-01552-x.
  • 60 Robeson MS, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, et al. RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Comput Biol 2021 Nov 8;17(11):e1009581. doi: 10.1371/journal.pcbi.1009581.
  • 61 Dillon MR, Bolyen E, Adamov A, Belk A, Borsom E, Burcham Z, et al. Experiences and lessons learned from two virtual, hands-on microbiome bioinformatics workshops. PLoS Comput Biol 2021 Jun 24;17(6):e1009056. doi: 10.1371/journal.pcbi.1009056.
  • 62 Matchado MS, Lauber M, Reitmeier S, Kacprowski T, Baumbach J, Haller D, et al. Network analysis methods for studying microbial communities: A mini review. Comput Struct Biotechnol J 2021 May 4;19:2687-98. doi: 10.1016/j.csbj.2021.05.001.
  • 63 Cao Q, Sun X, Rajesh K, Chalasani N, Gelow K, Katz B, et al. Effects of rare microbiome taxa filtering on statistical analysis. Front Microbiol 2021 Jan 12;11:607325. doi: 10.3389/fmicb.2020.607325.
  • 64 Secci R, Hartmann A, Walter M, Grabe HJ, Van der Auwera-Palitschka S, Kowald A, et al. Biomarkers of geroprotection and cardiovascular health: An overview of omics studies and established clinical biomarkers in the context of diet. Crit Rev Food Sci Nutr 2021 Oct 14:1-21. doi: 10.1080/10408398.2021.1975638.
  • 65 Amato KR, Arrieta MC, Azad MB, Bailey MT, Broussard JL, Bruggeling CE, et al. The human gut microbiome and health inequities. Proc Natl Acad Sci U S A 2021 Jun 22;118(25):e2017947118. doi: 10.1073/pnas.2017947118.
  • 66 Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, et al. Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment. Front Microbiol 2021 Feb 19;12:634511. doi: 10.3389/fmicb.2021.634511.
  • 67 Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, et al. Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions. Front Microbiol 2021 Feb 22;12:635781. doi: 10.3389/fmicb.2021.635781.
  • 68 Usability of consumer products and products for public use — Part 2: Summative test method. [Available at: https://www.iso.org/obp/ui/#iso:std:iso:ts:20282:-2:ed-2:v1:en. Accessed March 13, 2023].
  • 69 Lange KW, Nakamura Y. Lifestyle factors in the prevention of COVID-19. Glob Health J 2020 Dec;4(4):146-52. doi: 10.1016/j.glohj.2020.11.002.
  • 70 Tavakol Z, Ghannadi S, Tabesh MR, Halabchi F, Noormohammadpour P, Akbarpour S, et al. Relationship between physical activity, healthy lifestyle and COVID-19 disease severity; a cross-sectional study. Z Gesundh Wiss 2023;31(2):267-75. doi: 10.1007/s10389-020-01468-9.
  • 71 De Frel DL, Atsma DE, Pijl H, Seidell JC, Leenen PJ, Dik WA, et al. The impact of obesity and lifestyle on the immune system and susceptibility to infections such as COVID-19. Front Nutr 2020 Nov 19;7:597600. doi: 10.3389/fnut.2020.597600.
  • 72 International Foundation for Gastrointestinal Disorders. IBS Facts and Statistics. [Available at: https://aboutibs.org/what-is-ibs/facts-about-ibs/. Accessed 11/29/, 2022].
  • 73 Cheng FS, Pan D, Chang B, Jiang M, Sang LX. Probiotic mixture VSL# 3: An overview of basic and clinical studies in chronic diseases. World J Clin Cases 2020 Apr 26;8(8):1361-84. doi: 10.12998/wjcc.v8.i8.1361. Erratum in: World J Clin Cases 2021 Jul 16;9(20):5752-3.
  • 74 Centers for Disease Control. Type 2 Diabetes. 2021; [Available at: https://www.cdc.gov/diabetes/basics/type2.html. Accessed 11/28/, 2022].
  • 75 Hartstra AV, Bouter KE, Bäckhed F, Nieuwdorp M. Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care 2015 Jan;38(1):159-65. doi: 10.2337/dc14-0769.
  • 76 Mokkala K, Houttu N, Vahlberg T, Munukka E, Rönnemaa T, Laitinen K. Gut microbiota aberrations precede diagnosis of gestational diabetes mellitus. Acta Diabetol 2017 Dec;54(12):1147-9. doi: 10.1007/s00592-017-1056-0.
  • 77 Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet 2014 Mar 22;383(9922):1068-83. doi: 10.1016/S0140-6736(13)62154-6.
  • 78 Morozova N, Weisskopf MG, McCullough ML, Munger KL, Calle EE, Thun MJ, et al. Diet and amyotrophic lateral sclerosis. Epidemiology 2008 Mar;19(2):324-37. doi: 10.1097/EDE.0b013e3181632c5d.
  • 79 Ngo ST, Steyn FJ, McCombe PA. Body mass index and dietary intervention: implications for prognosis of amyotrophic lateral sclerosis. J Neurol Sci 2014 May 15;340(1-2):5-12. doi: 10.1016/j.jns.2014.02.035.
  • 80 Koren O, Goodrich JK, Cullender TC, Spor A, Laitinen K, Bäckhed HK, et al. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 2012 Aug 3;150(3):470-80. doi: 10.1016/j.cell.2012.07.008.
  • 81 Cortez RV, Taddei CR, Sparvoli LG, Ângelo AG, Padilha M, Mattar R, et al. Microbiome and its relation to gestational diabetes. Endocrine 2019 May;64(2):254-64. doi: 10.1007/s12020-018-1813-z.
  • 82 Crusell MK, Hansen TH, Nielsen T, Allin KH, Rühlemann MC, Damm P, et al. Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. Microbiome 2018 May 15;6(1):89. doi: 10.1186/s40168-018-0472-x.
  • 83 Ye G, Zhang L, Wang M, Chen Y, Gu S, Wang K, et al. The gut microbiota in women suffering from gestational diabetes mellitus with the failure of glycemic control by lifestyle modification. J Diabetes Res 2019 Oct 23;2019:6081248. doi: 10.1155/2019/6081248.
  • 84 Lauer M, Jorgenson L. Implementation Updates for the New NIH Data Management and Sharing Policy. 2022; [Available at: https://nexus.od.nih.gov/all/2022/08/05/implementation-updates-for-the-new-nih-data-management-and-sharing-policy/. Accessed 11/28, 2022].
  • 85 Chuong KH, Mack DR, Stintzi A, O’Doherty KC. Human microbiome and learning healthcare systems: integrating research and precision medicine for inflammatory bowel disease. OMICS 2018 Feb;22(2):119-26. doi: 10.1089/omi.2016.0185.
  • 86 Kuntz TM, Gilbert JA. Introducing the microbiome into precision medicine. Trends Pharmacol Sci 2017 Jan;38(1):81-91. doi: 10.1016/j.tips.2016.10.001.
  • 87 Petrosino JF. The microbiome in precision medicine: the way forward. Genome Med 2018 Feb 22;10(1):12. doi: 10.1186/s13073-018-0525-6.
  • 88 Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, et al. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 2020 Oct;17(10):635-48. doi: 10.1038/s41575-020-0327-3.
  • 89 Lam KN, Alexander M, Turnbaugh, PJ. Precision medicine goes microscopic: engineering the microbiome to improve drug outcomes. Cell Host Microbe 2019 Jul 10;26(1):22-34. doi: 10.1016/j.chom.2019.06.011.
  • 90 NIH Nutrition Research Task Force (NRTF). 2020-2030 Strategic Plan for NIH Nutrition Research. NIH NIDDK 2020; [Available at : https://dpcpsi.nih.gov/onr/strategic-plan].
  • 91 Heart Disease and Stroke. 2020; [Available at: https://www.cdc.gov/chronicdisease/resources/publications/factsheets/heart-disease-stroke.htm. Accessed 05/19, 2021].
  • 92 Centers for Disease Control. Chronic Diseases in America. 2022; [Available at: https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm. Accessed 11/8, 2022].
  • 93 Fraser AM. Malnutrition in older adults in the united states. In: Preedy V, Patel VB, editors. Handbook of Famine, Starvation, and Nutrient Deprivation; 2017. p. 1-20.
  • 94 Streicher M, van Zwienen‐Pot J, Bardon L, Nage, G, The R, Meisinger C, et al. Determinants of incident malnutrition in community‐dwelling older adults: a MaNuEL multicohort meta‐analysis. J Am Geriatr Soc 2018 Dec;66(12):2335-43. doi: 10.1111/jgs.15553.
  • 95 Gupta RS, Warren CM, Smith BM, Jiang J, Blumenstock JA, Davis MM, et al. Prevalence and severity of food allergies among US adults. JAMA Netw Open 2019 Aug 2;2(8):e199144. doi: 10.1001/jamanetworkopen.2019.9144.
  • 96 Vierk KA, Koehler KM, Fein SB, Street DA. Prevalence of self-reported food allergy in American adults and use of food labels. J Allergy Clin Immunol 2007 Jun;119(6):1504-10. doi: 10.1016/j.jaci.2007.03.011.
  • 97 Cha E, Kim KH, Lerner HM, Dawkins CR, Bello MK, Umpierrez, et al. Health literacy, self-efficacy, food label use, and diet in young adults. Am J Health Behav 2014 May;38(3):331-9. doi: 10.5993/AJHB.38.3.2.
  • 98 Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med 2011 Jul 19;155(2):97-107. doi: 10.7326/0003-4819-155-2-201107190-00005.
  • 99 Silk KJ, Sherry J, Winn B, Keesecker N, Horodynski MA, Sayir A. Increasing nutrition literacy: testing the effectiveness of print, web site, and game modalities. J Nutr Educ Behav 2008 Jan-Feb;40(1):3-10. doi: 10.1016/j.jneb.2007.08.012.
  • 100 Monsivais P, Aggarwal A, Drewnowski A. Time spent on home food preparation and indicators of healthy eating. Am J Prev Med 2014 Dec;47(6):796-802. doi: 10.1016/j.amepre.2014.07.033.
  • 101 Drichoutis AC, Lazaridis P, Nayga Jr RM. Nutrition knowledge and consumer use of nutritional food labels. Eur Rev Agric Econ 2005 Mar 1;32(1):93-118. 10.1093/erae/jbi003.
  • 102 Poussin C, Sierro N, Boué S, Battey J, Scotti E, Belcastro V, Peitsch, et al. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov Today 2018 Sep;23(9):1644-57. doi: 10.1016/j.drudis.2018.06.005.
  • 103 Schloss PD. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 2018 Jun 5;9(3):e00525-18. doi: 10.1128/mBio.00525-18.
  • 104 Babaei P, Shoaie S, Ji B, Nielsen J. Challenges in modeling the human gut microbiome. Nat Biotechnol 2018 Aug 6;36(8):682-6. doi: 10.1038/nbt.4213.
  • 105 Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M. Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18. Erratum in: Sci Data 2019 Mar 19;6(1):6.