Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 185

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Gut microbiome variation modulates the effects of dietary fiber on host metabolism

Open access
Murga-Garrido, Sofia M., Hong, Qilin, Cross, Tzu-Wen L., Hutchison, Evan R., Han, Jessica, Thomas, Sydney P., Vivas, Eugenio I., Denu, John, Ceschin, Danilo G., Tang, Zheng-Zheng, Rey, Federico E.·2021·Microbiome

Published version of a preprint

Abstract

Background: There is general consensus that consumption of dietary fermentable fiber improves cardiometabolic health, in part by promoting mutualistic microbes and by increasing production of beneficial metabolites in the distal gut. However, human studies have reported variations in the observed benefits among individuals consuming the same fiber. Several factors likely contribute to this variation, including host genetic and gut microbial differences. We hypothesized that gut microbial metabolism of dietary fiber represents an important and differential factor that modulates how dietary fiber impacts the host. Results: We examined genetically identical gnotobiotic mice harboring two distinct complex gut microbial communities and exposed to four isocaloric diets, each containing different fibers: (i) cellulose, (ii) inulin, (iii) pectin, (iv) a mix of 5 fermentable fibers (assorted fiber). Gut microbiome analysis showed that each transplanted community preserved a core of common taxa across diets that differentiated it from the other community, but there were variations in richness and bacterial taxa abundance within each community among the different diet treatments. Host epigenetic, transcriptional, and metabolomic analyses revealed diet-directed differences between animals colonized with the two communities, including variation in amino acids and lipid pathways that were associated with divergent health outcomes. Conclusion: This study demonstrates that interindividual variation in the gut microbiome is causally linked to differential effects of dietary fiber on host metabolic phenotypes and suggests that a one-fits-all fiber supplementation approach to promote health is unlikely to elicit consistent effects across individuals. Overall, the presented results underscore the importance of microbe-diet interactions on host metabolism and suggest that gut microbes modulate dietary fiber efficacy.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s40168-021-01061-6

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 35 references

Research works printed but not linked: 76

  1. Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol. 2014;32(8):834–41. https://doi.org/10.1038/nbt.2942. · DOI · Paper page
  2. Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474(11):1823–36. https://doi.org/10.1042/BCJ20160510. · DOI · Paper page
  3. Deschasaux M, Bouter KE, Prodan A, Levin E, Groen AK, Herrema H, et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat Med. 2018;24(10):1526–31. https://doi.org/10.1038/s41591-018-0160-1. · DOI · Paper page
  4. The GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377:13–27. · DOI · Paper page
  5. Wei B, Liu Y, Lin X, Fang Y, Cui J, Wan J. Dietary fiber intake and risk of metabolic syndrome: a meta-analysis of observational studies. Clin Nutr. 2018;37(6):1935–42. https://doi.org/10.1016/j.clnu.2017.10.019. · DOI
  6. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444(7122):1027–31. https://doi.org/10.1038/nature05414. · DOI · Paper page
  7. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19(5):576–85. https://doi.org/10.1038/nm.3145. · DOI · Paper page
  8. Koh A, Molinaro A, Ståhlman M, Khan MT, Schmidt C, Mannerås-Holm L, et al. Microbially produced imidazole propionate impairs insulin signaling through mTORC1. Cell. 2018;175:947–961.e17. · DOI · Paper page
  9. De Vadder F, Kovatcheva-Datchary P, Goncalves D, Vinera J, Zitoun C, Duchampt A, et al. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell. 2014;156(1-2):84–96. https://doi.org/10.1016/j.cell.2013.12.016. · DOI · Paper page
  10. Kasahara K, Krautkramer KA, Org E, Romano KA, Kerby RL, Vivas EI, et al. Interactions between Roseburia intestinalis and diet modulate atherogenesis in a murine model. Nat Microbiol. 2018;3(12):1461–71. https://doi.org/10.1038/s41564-018-0272-x. · DOI · Paper page
  11. Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell. 2016;165(6):1332–45. https://doi.org/10.1016/j.cell.2016.05.041. · DOI · Paper page
  12. Deehan EC, Duar RM, Armet AM, Perez-Muñoz ME, Jin M, Walter J. Modulation of the gastrointestinal microbiome with nondigestible fermentable carbohydrates to improve human health. Microbiol Spectr. 2017;5:24. · DOI · Paper page
  13. Wang H, Hong T, Li N, Zang B, Wu X. Soluble dietary fiber improves energy homeostasis in obese mice by remodeling the gut microbiota. Biochem Biophys Res Commun. 2018;498(1):146–51. https://doi.org/10.1016/j.bbrc.2018.02.017. · DOI
  14. Sonnenburg ED, Sonnenburg JL. Starving our microbial self: the deleterious consequences of a diet deficient in microbiota-accessible carbohydrates. Cell Metab. 2014;20(5):779–86. https://doi.org/10.1016/j.cmet.2014.07.003. · DOI · Paper page
  15. Chassaing B, Miles-Brown J, Pellizzon M, Ulman E, Ricci M, Zhang L, et al. Lack of soluble fiber drives diet-induced adiposity in mice. Am J Physiol Gastrointest Liver Physiol. 2015;309(7):G528–41. https://doi.org/10.1152/ajpgi.00172.2015. · DOI · Paper page
  16. Kindt A, Liebisch G, Clavel T, Haller D, Hörmannsperger G, Yoon H, et al. The gut microbiota promotes hepatic fatty acid desaturation and elongation in mice. Nat Commun. 2018;9(1):3760. https://doi.org/10.1038/s41467-018-05767-4. · DOI · Paper page
  17. Armet AM, Deehan EC, Thöne JV, Hewko SJ, Walter J. The effect of isolated and synthetic dietary fibers on markers of metabolic diseases in human intervention studies: a systematic review. Adv Nutr. 2020;11(2):420–38. https://doi.org/10.1093/advances/nmz074. · DOI
  18. Healey GR, Murphy R, Brough L, Butts CA, Coad J. Interindividual variability in gut microbiota and host response to dietary interventions. Nutr Rev. 2017;75(12):1059–80. https://doi.org/10.1093/nutrit/nux062. · DOI · Paper page
  19. Hegele RA, Wolever TM, Story JA, Connelly PW, Jenkins DJ. Intestinal fatty acid-binding protein variation associated with variation in the response of plasma lipoproteins to dietary fibre. Eur J Clin Invest. 1997;27(10):857–62. https://doi.org/10.1046/j.1365-2362.1997.2010748.x. · DOI
  20. Korpela K, Flint HJ, Johnstone AM, Lappi J, Poutanen K, Dewulf E, et al. Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. PLoS One. 2014;9(3):e90702. https://doi.org/10.1371/journal.pone.0090702. · DOI · Paper page
  21. Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F, Arora T, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015;22(6):971–82. https://doi.org/10.1016/j.cmet.2015.10.001. · DOI · Paper page
  22. Nguyen NK, Deehan EC, Zhang Z, Jin M, Baskota N, Perez-Muñoz ME, et al. Gut microbiota modulation with long-chain corn bran arabinoxylan in adults with overweight and obesity is linked to an individualized temporal increase in fecal propionate. Microbiome. 2020;8(1):118. https://doi.org/10.1186/s40168-020-00887-w. · DOI · Paper page
  23. Dill-McFarland KA, Tang Z-Z, Kemis JH, Kerby RL, Chen G, Palloni A, et al. Close social relationships correlate with human gut microbiota composition. Sci Rep. 2019;9(1):703. https://doi.org/10.1038/s41598-018-37298-9. · DOI · Paper page
  24. McGill CR, Fulgoni VL, Devareddy L. Ten-year trends in fiber and whole grain intakes and food sources for the United States population: National Health and Nutrition Examination Survey 2001-2010. Nutrients. 2015;7(2):1119–30. https://doi.org/10.3390/nu7021119. · DOI · Paper page
  25. Shan Z, Rehm CD, Rogers G, Ruan M, Wang DD, Hu FB, et al. Trends in dietary carbohydrate, protein, and fat intake and diet quality among US adults, 1999-2016. JAMA. 2019;322(12):1178–87. https://doi.org/10.1001/jama.2019.13771. · DOI · Paper page
  26. Kasubuchi M, Hasegawa S, Hiramatsu T, Ichimura A, Kimura I. Dietary gut microbial metabolites, short-chain fatty acids, and host metabolic regulation. Nutrients. 2015;7(4):2839–49. https://doi.org/10.3390/nu7042839. · DOI · Paper page
  27. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685–8. https://doi.org/10.1038/s41587-020-0548-6. · DOI · Paper page
  28. Gibson GR, Hutkins R, Sanders ME, Prescott SL, Reimer RA, Salminen SJ, et al. Expert consensus document: The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics. Nat Rev Gastroenterol Hepatol. 2017;14(8):491–502. https://doi.org/10.1038/nrgastro.2017.75. · DOI · Paper page
  29. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. https://doi.org/10.1186/gb-2011-12-6-r60. · DOI · Paper page
  30. Bouhnik Y, Raskine L, Simoneau G, Paineau D, Bornet F. The capacity of short-chain fructo-oligosaccharides to stimulate faecal bifidobacteria: a dose-response relationship study in healthy humans. Nutr J. 2006;5(1). https://doi.org/10.1186/1475-2891-5-8. · DOI
  31. Cantu-Jungles TM, Hamaker BR. New view on dietary fiber selection for predictable shifts in gut microbiota. mBio. 2020;11:e02179–19.
  32. Venkataraman A, Sieber JR, Schmidt AW, Waldron C, Theis KR, Schmidt TM. Variable responses of human microbiomes to dietary supplementation with resistant starch. Microbiome. 2016;4(1):33. https://doi.org/10.1186/s40168-016-0178-x. · DOI · Paper page
  33. Heaver SL, Johnson EL, Ley RE. Sphingolipids in host–microbial interactions. Curr Opin Microbiol. 2018;43:92–9. https://doi.org/10.1016/j.mib.2017.12.011. · DOI · Paper page
  34. Boini KM, Xia M, Koka S, Gehr TW, Li PL. Sphingolipids in obesity and related complications. Front Biosci (Landmark Ed). 2017;22:96-116. https://doi.org/10.2741/4474. · DOI
  35. Johnson EL, Heaver SL, Waters JL, Kim BI, Bretin A, Goodman AL, et al. Sphingolipids produced by gut bacteria enter host metabolic pathways impacting ceramide levels. Nat Commun. 2020;11(1):2471. https://doi.org/10.1038/s41467-020-16274-w. · DOI · Paper page
  36. DiNicolantonio JJ, McCarty MF, OKeefe JH. Role of dietary histidine in the prevention of obesity and metabolic syndrome. Open Heart. 2018;5(2):e000676. https://doi.org/10.1136/openhrt-2017-000676. · DOI
  37. Molinaro A, Bel Lassen P, Henricsson M, Wu H, Adriouch S, Belda E, et al. Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology. Nat Commun. 2020;11(1):1–0. · DOI · Paper page
  38. Nishitani S, Takehana K, Fujitani S, Sonaka I. Branched-chain amino acids improve glucose metabolism in rats with liver cirrhosis. Am J Physiol Gastrointest Liver Physiol. 2005;288(6):G1292–300. https://doi.org/10.1152/ajpgi.00510.2003. · DOI · Paper page
  39. Connelly MA, Wolak-Dinsmore J, Dullaart RPF. Branched chain amino acids are associated with insulin resistance independent of leptin and adiponectin in subjects with varying degrees of glucose tolerance. Metab Syndr Relat Disord. 2017;15(4):183–6. https://doi.org/10.1089/met.2016.0145. · DOI
  40. Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. 2014;10(12):723–36. https://doi.org/10.1038/nrendo.2014.171. · DOI · Paper page
  41. Tremblay F, Lavigne C, Jacques H, Marette A. Role of dietary proteins and amino acids in the pathogenesis of insulin resistance. Annu Rev Nutr. 2007;27(1):293–310. https://doi.org/10.1146/annurev.nutr.25.050304.092545. · DOI · Paper page
  42. Brial F, Le Lay A, Dumas M-E, Gauguier D. Implication of gut microbiota metabolites in cardiovascular and metabolic diseases. Cell Mol Life Sci. 2018;75(21):3977–90. https://doi.org/10.1007/s00018-018-2901-1. · DOI · Paper page
  43. de Mello VD, Paananen J, Lindström J, Lankinen MA, Shi L, Kuusisto J, et al. Indolepropionic acid and novel lipid metabolites are associated with a lower risk of type 2 diabetes in the Finnish Diabetes Prevention Study. Sci Rep. 2017;7(1):46337. https://doi.org/10.1038/srep46337. · DOI · Paper page
  44. Jump RLP, Polinkovsky A, Hurless K, Sitzlar B, Eckart K, Tomas M, et al. Metabolomics analysis identifies intestinal microbiota-derived biomarkers of colonization resistance in clindamycin-treated mice. PLoS One. 2014;9(7):e101267. https://doi.org/10.1371/journal.pone.0101267. · DOI
  45. Zhang H, Kovacs-Nolan J, Kodera T, Eto Y, Mine Y. γ-Glutamyl cysteine and γ-glutamyl valine inhibit TNF-α signaling in intestinal epithelial cells and reduce inflammation in a mouse model of colitis via allosteric activation of the calcium-sensing receptor. Biochim Biophys Acta. 2015;1852(5):792–804. https://doi.org/10.1016/j.bbadis.2014.12.023. · DOI · Paper page
  46. Geurts L, Lazarevic V, Derrien M, Everard A, Van Roye M, Knauf C, et al. Altered gut microbiota and endocannabinoid system tone in obese and diabetic leptin-resistant mice: impact on apelin regulation in adipose tissue. Front Microbiol. 2011;13:2–149.
  47. Kim K-A, Gu W, Lee I-A, Joh E-H, Kim D-H. High fat diet-induced gut microbiota exacerbates inflammation and obesity in mice via the TLR4 signaling pathway. PLoS One. 2012;7(10):e47713. https://doi.org/10.1371/journal.pone.0047713. · DOI · Paper page
  48. Rauschert S, Uhl O, Koletzko B, Kirchberg F, Mori TA, Huang R-C, et al. Lipidomics reveals associations of phospholipids with obesity and insulin resistance in young adults. J Clin Endocrinol Metab. 2016;101(3):871–9. https://doi.org/10.1210/jc.2015-3525. · DOI · Paper page
  49. Son G, Kremer M, Hines IN. Contribution of gut bacteria to liver pathobiology. Gastroenterol Res Pract. 2010;2010:1–13. https://doi.org/10.1155/2010/453563. · DOI · Paper page
  50. Krautkramer KA, Reiter L, Denu JM, Dowell JA. Quantification of SAHA-dependent changes in histone modifications using data-independent acquisition mass spectrometry. J Proteome Res. 2015;14(8):3252–62. https://doi.org/10.1021/acs.jproteome.5b00245. · DOI
  51. Gilep AA, Sushko TA, Usanov SA. At the crossroads of steroid hormone biosynthesis: the role, substrate specificity and evolutionary development of CYP17. Biochim Biophys Acta. 2011;1814(1):200–9. https://doi.org/10.1016/j.bbapap.2010.06.021. · DOI
  52. Lindberg R, Burkhart B, Ichikawa T. The structure and characterization of type I P-45015, gene as major steroid 15a-hydroxylase and its comparison with type I1 P-4501~~ gene. J Biol Chem. 1989;264(11):6465–71. https://doi.org/10.1016/S0021-9258(18)83371-3. · DOI
  53. Singh V, Yeoh BS, Chassaing B, Xiao X, Saha P, Aguilera Olvera R, et al. Dysregulated microbial fermentation of soluble fiber induces cholestatic liver cancer. Cell. 2018;175:679–694.e22. · DOI · Paper page
  54. Smits SA, Marcobal A, Higginbottom S, Sonnenburg JL, Kashyap PC. Individualized responses of gut microbiota to dietary intervention modeled in humanized mice. mSystems. 2016;1:e00098–16. · DOI
  55. Bailén M, Bressa C, Martínez-López S, González-Soltero R, Montalvo Lominchar MG, San Juan C, et al. Microbiota features associated with a high-fat/low-fiber diet in healthy adults. Front Nutr. 2020;7:583608. https://doi.org/10.3389/fnut.2020.583608. · DOI
  56. Meddens SFW, de Vlaming R, Bowers P, et al. Genomic analysis of diet composition finds novel loci and associations with health and lifestyle. Mol Psychiatry. 2020. https://doi.org/10.1038/s41380-020-0697-5. · DOI
  57. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079–94. https://doi.org/10.1016/j.cell.2015.11.001. · DOI · Paper page
  58. Rodriguez J, Hiel S, Neyrinck AM, Le Roy T, Pötgens SA, Leyrolle Q, et al. Discovery of the gut microbial signature driving the efficacy of prebiotic intervention in obese patients. Gut. 2020;69(11):1975–87. https://doi.org/10.1136/gutjnl-2019-319726. · DOI · Paper page
  59. Reeves PG, Nielsen FH, Fahey GC. AIN-93 purified diets for laboratory rodents: final report of the American Institute of Nutrition Ad Hoc Writing Committee on the Reformulation of the AIN-76A Rodent Diet. J Nutr. 1993;123(11):1939–51. https://doi.org/10.1093/jn/123.11.1939. · DOI · Paper page
  60. Herd P, Carr D, Roan C. Cohort Profile: Wisconsin longitudinal study (WLS). Int J Epidemiol. 2014;43(1):34–41. https://doi.org/10.1093/ije/dys194. · DOI · Paper page
  61. Romano KA, Dill-McFarland KA, Kasahara K, Kerby RL, Vivas EI, Amador-Noguez D, et al. Fecal aliquot straw technique (FAST) allows for easy and reproducible subsampling: assessing interpersonal variation in trimethylamine-N-oxide (TMAO) accumulation. Microbiome. 2018;6(1):91. https://doi.org/10.1186/s40168-018-0458-8. · DOI
  62. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79(17):5112–20. https://doi.org/10.1128/AEM.01043-13. · DOI · Paper page
  63. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11(12):2639–43. https://doi.org/10.1038/ismej.2017.119. · DOI · Paper page
  64. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6(3):610–8. https://doi.org/10.1038/ismej.2011.139. · DOI · Paper page
  65. Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online. Chichester: Wiley; 2017. p. 1–15. · DOI · Paper page
  66. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40. https://doi.org/10.1093/bioinformatics/btp616. · DOI · Paper page
  67. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. https://doi.org/10.1186/gb-2010-11-3-r25. · DOI · Paper page
  68. Maile TM, Izrael-Tomasevic A, Cheung T, Guler GD, Tindell C, Masselot A, et al. Mass spectrometric quantification of histone post-translational modifications by a hybrid chemical labeling method. Mol Cell Proteomics. 2015;14(4):1148–58. https://doi.org/10.1074/mcp.O114.046573. · DOI
  69. Thomas SP, Haws SA, Borth LE, Denu JM. A practical guide for analysis of histone post-translational modifications by mass spectrometry: best practices and pitfalls. Methods. 2020;184:53–60. https://doi.org/10.1016/j.ymeth.2019.12.001. · DOI
  70. Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29. · DOI · Paper page
  71. McHardy IH, Goudarzi M, Tong M, Ruegger PM, Schwager E, Weger JR, et al. Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome. 2013;1(1):17. https://doi.org/10.1186/2049-2618-1-17. · DOI · Paper page
  72. Tang Z-Z, Chen G, Hong Q, Huang S, Smith HM, Shah RD, et al. Multi-omic analysis of the microbiome and metabolome in healthy subjects reveals microbiome-dependent relationships between diet and metabolites. Front Genet. 2019;10:454. https://doi.org/10.3389/fgene.2019.00454. · DOI · Paper page
  73. Lin W, Shi P, Feng R, Li H. Variable selection in regression with compositional covariates. Biometrika. 2014;101(4):785–97. https://doi.org/10.1093/biomet/asu031. · DOI · Paper page
  74. Meinshausen N, Bühlmann P. Stability selection: stability selection. J R Stat Soc Series B Stat Methodol. 2010;72:417–73. · DOI · Paper page
  75. Zhang J, Wei Z, Chen J. A distance-based approach for testing the mediation effect of the human microbiome. Bioinformatics. 2018;34(11):1875–83. https://doi.org/10.1093/bioinformatics/bty014. · DOI
  76. Aitchison J. The statistical analysis of compositional data. J R Stat Soc. 1982;44:139–77. · DOI · Paper page

Cited by

See all 185

Metadata

Authors
Murga-Garrido, S. M., Hong, Q., Cross, T.-W. L., Hutchison, E. R., Han, J., Thomas, S. P., Vivas, E. I., Denu, J., Ceschin, D. G., Tang, Z.-Z., & Rey, F. E.
Venue
Microbiome
Published
2021-05-20
Volume
9
Issue
1
Pages
117-117
ISSN
2049-2618 (ISSN-L)
OpenAlex
W3161190141
Cited by
185
References
35
FWCI
7.45
Type
journal article
Language
en
Open access
gold
Licence
Funders
  • National Institute of Diabetes and Digestive and Kidney Diseases (DK108259)
  • Foundation for the National Institutes of Health (GM140464)
  • Food and Nutrition Service (2018-08000)
  • Fondation Leducq
  • Agencia Nacional de Promoción Científica y Tecnológica (PICT-2017-4497)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Gut microbiome variation modulates the effects of dietary fiber on host metabolism. https://app.cochat.ai/papers/10.1186/s40168-021-01061-6
The paper
Murga-Garrido, S. M., Hong, Q., Cross, T.-W. L., Hutchison, E. R., Han, J., Thomas, S. P., Vivas, E. I., Denu, J., Ceschin, D. G., Tang, Z.-Z., & Rey, F. E. (2021). Gut microbiome variation modulates the effects of dietary fiber on host metabolism. Microbiome. https://doi.org/10.1186/s40168-021-01061-6

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.