A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project.
16S rRNA gene sequencing
American Gut Project
Healthy Eating Index
alpha diversity
beta diversity
cohort study
dietary patterns
food frequency questionnaire
gut microbiome
Journal
The American journal of clinical nutrition
ISSN: 1938-3207
Titre abrégé: Am J Clin Nutr
Pays: United States
ID NLM: 0376027
Informations de publication
Date de publication:
09 02 2022
09 02 2022
Historique:
received:
29
06
2021
accepted:
27
09
2021
pubmed:
8
10
2021
medline:
1
3
2022
entrez:
7
10
2021
Statut:
ppublish
Résumé
Individual diet components and specific dietary regimens have been shown to impact the gut microbiome. Here, we explored the contribution of long-term diet by searching for dietary patterns that would best associate with the gut microbiome in a population-based cohort. Using a priori and a posteriori approaches, we constructed dietary patterns from an FFQ completed by 1800 adults in the American Gut Project. Dietary patterns were defined as groups of participants or combinations of food variables (factors) driven by criteria ranging from individual nutrients to overall diet. We associated these patterns with 16S ribosomal RNA-based gut microbiome data for a subset of 744 participants. Compared to individual features (e.g., fiber and protein), or to factors representing a reduced number of dietary features, 5 a posteriori dietary patterns based on food groups were best associated with gut microbiome beta diversity (P ≤ 0.0002). Two patterns followed Prudent-like diets-Plant-Based and Flexitarian-and exhibited the highest Healthy Eating Index 2010 (HEI-2010) scores. Two other patterns presented Western-like diets with a gradient in HEI-2010 scores. A fifth pattern consisted mostly of participants following an Exclusion diet (e.g., low carbohydrate). Notably, gut microbiome alpha diversity was significantly lower in the most Western pattern compared to the Flexitarian pattern (P ≤ 0.009), and the Exclusion diet pattern was associated with low relative abundance of Bifidobacterium (P ≤ 1.2 × 10-7), which was better explained by diet than health status. We demonstrated that global-diet a posteriori patterns were more associated with gut microbiome variations than individual dietary features among adults in the United States. These results confirm that evaluating diet as a whole is important when studying the gut microbiome. It will also facilitate the design of more personalized dietary strategies in general populations.
Sections du résumé
BACKGROUND
Individual diet components and specific dietary regimens have been shown to impact the gut microbiome.
OBJECTIVES
Here, we explored the contribution of long-term diet by searching for dietary patterns that would best associate with the gut microbiome in a population-based cohort.
METHODS
Using a priori and a posteriori approaches, we constructed dietary patterns from an FFQ completed by 1800 adults in the American Gut Project. Dietary patterns were defined as groups of participants or combinations of food variables (factors) driven by criteria ranging from individual nutrients to overall diet. We associated these patterns with 16S ribosomal RNA-based gut microbiome data for a subset of 744 participants.
RESULTS
Compared to individual features (e.g., fiber and protein), or to factors representing a reduced number of dietary features, 5 a posteriori dietary patterns based on food groups were best associated with gut microbiome beta diversity (P ≤ 0.0002). Two patterns followed Prudent-like diets-Plant-Based and Flexitarian-and exhibited the highest Healthy Eating Index 2010 (HEI-2010) scores. Two other patterns presented Western-like diets with a gradient in HEI-2010 scores. A fifth pattern consisted mostly of participants following an Exclusion diet (e.g., low carbohydrate). Notably, gut microbiome alpha diversity was significantly lower in the most Western pattern compared to the Flexitarian pattern (P ≤ 0.009), and the Exclusion diet pattern was associated with low relative abundance of Bifidobacterium (P ≤ 1.2 × 10-7), which was better explained by diet than health status.
CONCLUSIONS
We demonstrated that global-diet a posteriori patterns were more associated with gut microbiome variations than individual dietary features among adults in the United States. These results confirm that evaluating diet as a whole is important when studying the gut microbiome. It will also facilitate the design of more personalized dietary strategies in general populations.
Identifiants
pubmed: 34617562
pii: S0002-9165(22)00152-6
doi: 10.1093/ajcn/nqab332
pmc: PMC8827078
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
432-443Commentaires et corrections
Type : CommentIn
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.
Références
Cell Host Microbe. 2019 Jun 12;25(6):789-802.e5
pubmed: 31194939
Public Health Nutr. 2014 Mar;17(3):510-8
pubmed: 23290469
Stat Methods Med Res. 2019 Sep;28(9):2834-2847
pubmed: 30045678
mSystems. 2018 May 15;3(3):
pubmed: 29795809
Nat Methods. 2018 Oct;15(10):796-798
pubmed: 30275573
PLoS One. 2010 Nov 29;5(11):e15046
pubmed: 21151493
F1000Res. 2017 Jun 16;6:926
pubmed: 28690835
Gut Microbes. 2017 Mar 4;8(2):172-184
pubmed: 28165863
BMJ. 2018 Jun 13;361:bmj.k2173
pubmed: 29898881
Science. 2016 Apr 29;352(6285):560-4
pubmed: 27126039
J Nutr. 2020 Apr 1;150(4):861-872
pubmed: 31851320
J Transl Med. 2017 Apr 8;15(1):73
pubmed: 28388917
mSystems. 2017 Mar 7;2(2):
pubmed: 28289731
J Nutr. 2014 Mar;144(3):399-407
pubmed: 24453128
J Acad Nutr Diet. 2014 Apr;114(4):613-21
pubmed: 24462267
mSystems. 2017 Mar 7;2(2):
pubmed: 28289733
mSystems. 2020 Mar 17;5(2):
pubmed: 32184365
J Microbiol Biotechnol. 2016 Oct 28;26(10):1723-1735
pubmed: 27381339
Gut. 2021 Jul;70(7):1287-1298
pubmed: 33811041
J Nutr. 2019 Sep 1;149(9):1575-1584
pubmed: 31187868
Nutrients. 2019 Feb 12;11(2):
pubmed: 30759766
Int J Obes (Lond). 2017 Jul;41(7):1099-1105
pubmed: 28286339
Int J Epidemiol. 2001 Apr;30(2):309-17
pubmed: 11369735
Nat Commun. 2019 Jun 20;10(1):2719
pubmed: 31222023
Gastroenterology. 2017 Apr;152(5):1023-1030.e2
pubmed: 28065788
mSystems. 2019 Jun 25;4(4):
pubmed: 31239397
Nutr Metab Cardiovasc Dis. 2013 Mar;23(3):250-6
pubmed: 22647416
Nutr Rev. 2018 Oct 1;76(10):747-764
pubmed: 30053192
Nat Rev Microbiol. 2013 Apr;11(4):227-38
pubmed: 23435359
Genome Biol. 2014;15(12):550
pubmed: 25516281
J Nutr. 2012 Feb;142(2):284-91
pubmed: 22190026
Gut. 2020 Jul;69(7):1258-1268
pubmed: 32075887
Am J Clin Nutr. 2019 Oct 1;110(4):1003-1014
pubmed: 31504105
Int J Food Microbiol. 2011 Jan 31;145(1):267-72
pubmed: 21276631
MMWR Morb Mortal Wkly Rep. 2016 Oct 28;65(42):1166-1169
pubmed: 27787492
Gut. 2016 Nov;65(11):1812-1821
pubmed: 26416813
ISME J. 2012 Mar;6(3):610-8
pubmed: 22134646
Nat Microbiol. 2018 Jan;3(1):8-16
pubmed: 29255284
Curr Dev Nutr. 2019 Jul 03;3(8):nzz079
pubmed: 31528836
Cell. 2014 Nov 6;159(4):789-99
pubmed: 25417156
Science. 2016 Apr 29;352(6285):565-9
pubmed: 27126040
Nat Commun. 2020 Oct 15;11(1):5206
pubmed: 33060586
Curr Opin Lipidol. 2002 Feb;13(1):3-9
pubmed: 11790957
Microbiome. 2018 Apr 25;6(1):77
pubmed: 29695307
Br J Nutr. 2009 Feb;101(4):598-608
pubmed: 18577300
Am J Clin Nutr. 2019 May 1;109(5):1472-1483
pubmed: 31051503
Nat Med. 2021 Feb;27(2):321-332
pubmed: 33432175
Nature. 2012 Aug 9;488(7410):178-84
pubmed: 22797518
Nutr Rev. 2004 May;62(5):177-203
pubmed: 15212319
Front Microbiol. 2018 May 07;9:890
pubmed: 29867803
Adv Nutr. 2019 Nov 1;10(Suppl_4):S275-S283
pubmed: 31728495
Nutrients. 2018 Mar 17;10(3):
pubmed: 29562591
Heliyon. 2017 Jun 07;3(6):e00315
pubmed: 28626807
Proc Nutr Soc. 2012 Nov;71(4):599-609
pubmed: 22863306
Nat Biotechnol. 2019 Aug;37(8):852-857
pubmed: 31341288
Cell. 2018 Nov 1;175(4):962-972.e10
pubmed: 30388453
BMC Nutr. 2017 Mar 4;3:20
pubmed: 32153802