A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project. Issue 2 (7th October 2021)

• Record Type: Journal Article
• Authors: Cotillard, Aurélie; Cartier-Meheust, Agnès; Litwin, Nicole S; Chaumont, Soline; Saccareau, Mathilde; Lejzerowicz, Franck; Tap, Julien; Koutnikova, Hana; Lopez, Diana Gutierrez; McDonald, Daniel; Song, Se Jin; Knight, Rob; Derrien, Muriel; Veiga, Patrick
• Is Part Of: American journal of clinical nutrition. Volume 115:Issue 2(2022)
• Page Start: 432
• Page End: 443
• Publication Date: 2021-10-07
• Subjects: dietary patterns -- gut microbiome -- alpha diversity -- beta diversity -- American Gut Project -- cohort study -- food frequency questionnaire -- Healthy Eating Index -- 16S rRNA gene sequencing; Diet therapy -- Periodicals; Nutrition -- Periodicals; Dietetics -- Periodicals; 613.205
• DOI: 10.1093/ajcn/nqab332
• Languages: English
• ISSNs: 0002-9165

Abstract

ABSTRACT: 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.