Literature DB >> 31225589

The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models-Part I: Overview of Current Methods.

Riley L Hughes1, Maria L Marco2, James P Hughes3, Nancy L Keim1,4, Mary E Kable1,5.   

Abstract

Health care is increasingly focused on health at the individual level. In the rapidly evolving field of precision nutrition, researchers aim to identify how genetics, epigenetics, and the microbiome interact to shape an individual's response to diet. With this understanding, personalized responses can be predicted and dietary advice can be tailored to the individual. With the integration of these complex sources of data, an important aspect of precision nutrition research is the methodology used for studying interindividual variability in response to diet. This article stands as the first in a 2-part review of current research investigating the contribution of the gut microbiota to interindividual variability in response to diet. Part I reviews the methods used by researchers to design and carry out such studies as well as the statistical and bioinformatic methods used to analyze results. Part II reviews the findings of these studies, discusses gaps in our current knowledge, and summarizes directions for future research. Taken together, these reviews summarize the current state of knowledge and provide a foundation for future research on the role of the gut microbiome in precision nutrition. Published by Oxford University Press on behalf of the American Society for Nutrition 2019.

Entities:  

Keywords:  dietary response; effect modification; gut microbiome; interindividual variability; metabolism; methods; personalized nutrition; precision nutrition; prediction

Mesh:

Year:  2019        PMID: 31225589      PMCID: PMC6855943          DOI: 10.1093/advances/nmz022

Source DB:  PubMed          Journal:  Adv Nutr        ISSN: 2161-8313            Impact factor:   8.701


  109 in total

1.  Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

Authors:  Zhenqiu Liu; William Hsiao; Brandi L Cantarel; Elliott Franco Drábek; Claire Fraser-Liggett
Journal:  Bioinformatics       Date:  2011-10-07       Impact factor: 6.937

2.  Host Genotype and Gut Microbiome Modulate Insulin Secretion and Diet-Induced Metabolic Phenotypes.

Authors:  Julia H Kreznar; Mark P Keller; Lindsay L Traeger; Mary E Rabaglia; Kathryn L Schueler; Donald S Stapleton; Wen Zhao; Eugenio I Vivas; Brian S Yandell; Aimee Teo Broman; Bruno Hagenbuch; Alan D Attie; Federico E Rey
Journal:  Cell Rep       Date:  2017-02-14       Impact factor: 9.423

3.  Prior Dietary Practices and Connections to a Human Gut Microbial Metacommunity Alter Responses to Diet Interventions.

Authors:  Nicholas W Griffin; Philip P Ahern; Jiye Cheng; Andrew C Heath; Olga Ilkayeva; Christopher B Newgard; Luigi Fontana; Jeffrey I Gordon
Journal:  Cell Host Microbe       Date:  2016-12-29       Impact factor: 21.023

Review 4.  The Microbiome-Gut-Brain Axis in Health and Disease.

Authors:  Timothy G Dinan; John F Cryan
Journal:  Gastroenterol Clin North Am       Date:  2017-01-04       Impact factor: 3.806

5.  Diet dominates host genotype in shaping the murine gut microbiota.

Authors:  Rachel N Carmody; Georg K Gerber; Jesus M Luevano; Daniel M Gatti; Lisa Somes; Karen L Svenson; Peter J Turnbaugh
Journal:  Cell Host Microbe       Date:  2014-12-18       Impact factor: 21.023

6.  Enterolignan-producing phenotypes are associated with increased gut microbial diversity and altered composition in premenopausal women in the United States.

Authors:  Meredith A J Hullar; Samuel M Lancaster; Fei Li; Elizabeth Tseng; Karlyn Beer; Charlotte Atkinson; Kristiina Wähälä; Wade K Copeland; Timothy W Randolph; Katherine M Newton; Johanna W Lampe
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-12-26       Impact factor: 4.254

Review 7.  MLST revisited: the gene-by-gene approach to bacterial genomics.

Authors:  Martin C J Maiden; Melissa J Jansen van Rensburg; James E Bray; Sarah G Earle; Suzanne A Ford; Keith A Jolley; Noel D McCarthy
Journal:  Nat Rev Microbiol       Date:  2013-09-02       Impact factor: 60.633

8.  Variable responses of human microbiomes to dietary supplementation with resistant starch.

Authors:  A Venkataraman; J R Sieber; A W Schmidt; C Waldron; K R Theis; T M Schmidt
Journal:  Microbiome       Date:  2016-06-29       Impact factor: 14.650

9.  Ecological robustness of the gut microbiota in response to ingestion of transient food-borne microbes.

Authors:  Chenhong Zhang; Muriel Derrien; Florence Levenez; Rémi Brazeilles; Sonia A Ballal; Jason Kim; Marie-Christine Degivry; Gaëlle Quéré; Peggy Garault; Johan E T van Hylckama Vlieg; Wendy S Garrett; Joël Doré; Patrick Veiga
Journal:  ISME J       Date:  2016-03-08       Impact factor: 10.302

10.  Pre-treatment microbial Prevotella-to-Bacteroides ratio, determines body fat loss success during a 6-month randomized controlled diet intervention.

Authors:  M F Hjorth; H M Roager; T M Larsen; S K Poulsen; T R Licht; M I Bahl; Y Zohar; A Astrup
Journal:  Int J Obes (Lond)       Date:  2017-09-08       Impact factor: 5.095

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  22 in total

1.  Impact of a 7-day homogeneous diet on interpersonal variation in human gut microbiomes and metabolomes.

Authors:  Leah Guthrie; Sean Paul Spencer; Dalia Perelman; Will Van Treuren; Shuo Han; Feiqiao Brian Yu; Erica D Sonnenburg; Michael A Fischbach; Timothy W Meyer; Justin L Sonnenburg
Journal:  Cell Host Microbe       Date:  2022-05-27       Impact factor: 31.316

2.  An Overview of Current Knowledge of the Gut Microbiota and Low-Calorie Sweeteners.

Authors:  Riley L Hughes; Cindy D Davis; Alexandra Lobach; Hannah D Holscher
Journal:  Nutr Today       Date:  2021 May-Jun

Review 3.  Diet and the Microbiota-Gut-Brain Axis: Sowing the Seeds of Good Mental Health.

Authors:  Kirsten Berding; Klara Vlckova; Wolfgang Marx; Harriet Schellekens; Catherine Stanton; Gerard Clarke; Felice Jacka; Timothy G Dinan; John F Cryan
Journal:  Adv Nutr       Date:  2021-07-30       Impact factor: 8.701

Review 4.  Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease.

Authors:  Baiba Vilne; Juris Ķibilds; Inese Siksna; Ilva Lazda; Olga Valciņa; Angelika Krūmiņa
Journal:  Front Microbiol       Date:  2022-04-11       Impact factor: 6.064

Review 5.  A Guide to Diet-Microbiome Study Design.

Authors:  Abigail J Johnson; Jack Jingyuan Zheng; Jea Woo Kang; Anna Saboe; Dan Knights; Angela M Zivkovic
Journal:  Front Nutr       Date:  2020-06-12

6.  Effect of probiotics on obesity-related markers per enterotype: a double-blind, placebo-controlled, randomized clinical trial.

Authors:  Eun-Ji Song; Kyungsun Han; Tae-Joong Lim; Sanghyun Lim; Myung-Jun Chung; Myung Hee Nam; Hojun Kim; Young-Do Nam
Journal:  EPMA J       Date:  2020-02-07       Impact factor: 6.543

7.  Dietary Moutan Cortex Radicis Improves Serum Antioxidant Capacity and Intestinal Immunity and Alters Colonic Microbiota in Weaned Piglets.

Authors:  Miaomiao Bai; Hongnan Liu; Shanshan Wang; Qingyan Shu; Kang Xu; Jian Zhou; Xia Xiong; Ruilin Huang; Jinping Deng; Yulong Yin; Zheng'an Liu
Journal:  Front Nutr       Date:  2021-06-17

8.  Positive influence of gut microbiota on the effects of Korean red ginseng in metabolic syndrome: a randomized, double-blind, placebo-controlled clinical trial.

Authors:  Eunhak Seong; Shambhunath Bose; Song-Yi Han; Eun-Ji Song; Myeongjong Lee; Young-Do Nam; Hojun Kim
Journal:  EPMA J       Date:  2021-06-03       Impact factor: 8.836

9.  Age- and duration-dependent effects of whey protein on high-fat diet-induced changes in body weight, lipid metabolism, and gut microbiota in mice.

Authors:  Serena Boscaini; Raul Cabrera-Rubio; Oleksandr Nychyk; John Roger Speakman; John Francis Cryan; Paul David Cotter; Kanishka N Nilaweera
Journal:  Physiol Rep       Date:  2020-08

Review 10.  Immunometabolism: new insights and lessons from antigen-directed cellular immune responses.

Authors:  Renata Ramalho; Martin Rao; Chao Zhang; Chiara Agrati; Giuseppe Ippolito; Fu-Sheng Wang; Alimuddin Zumla; Markus Maeurer
Journal:  Semin Immunopathol       Date:  2020-06-09       Impact factor: 9.623

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