A Guide to Dietary Pattern-Microbiome Data Integration

Yuni Choi, Susan L. Hoops, Calvin J. Thoma, Abigail J. Johnson

Research output: Contribution to journalReview articlepeer-review

23 Scopus citations

Abstract

The human gut microbiome is linked to metabolic and cardiovascular disease risk. Dietary modulation of the human gut microbiome offers an attractive pathway to manipulate the microbiome to prevent microbiome-related disease. However, this promise has not been realized. The complex system of diet and microbiome interactions is poorly understood. Integrating observational human diet and microbiome data can help researchers and clinicians untangle the complex systems of interactions that predict how the microbiome will change in response to foods. The use of dietary patterns to assess diet-microbiome relations holds promise to identify interesting associations and result in findings that can directly translate into actionable dietary intake recommendations and eating plans. In this article, we first highlight the complexity inherent in both dietary and microbiome data and introduce the approaches generally used to explore diet and microbiome simultaneously in observational studies. Second, we review the food group and dietary pattern-microbiome literature focusing on dietary complexity - moving beyond nutrients. Our review identified a substantial and growing body of literature that explores links between the microbiome and dietary patterns. However, there was very little standardization of dietary collection and assessment methods across studies. The 54 studies identified in this review used ≥7 different methods to assess diet. Coupled with the variation in final dietary parameters calculated from dietary data (e.g., dietary indices, dietary patterns, food groups, etc.), few studies with shared methods and assessment techniques were available for comparison. Third, we highlight the similarities between dietary and microbiome data structures and present the possibility that multivariate and compositional methods, developed initially for microbiome data, could have utility when applied to dietary data. Finally, we summarize the current state of the art for diet-microbiome data integration and highlight ways dietary data could be paired with microbiome data in future studies to improve the detection of diet-microbiome signals.

Original languageEnglish (US)
Pages (from-to)1187-1199
Number of pages13
JournalJournal of Nutrition
Volume152
Issue number5
DOIs
StatePublished - May 1 2022

Bibliographical note

Funding Information:
The authors' responsibilities were as follows-YC: conducted research and literature review, analyzed data, created figures, and wrote the paper; SLH: reviewed and revised the paper; CJT: reviewed and revised the paper; AJJ: conceptualized and wrote the paper, analyzed data, and created figures; and all authors: read and approved of the final manuscript.

Publisher Copyright:
© 2022 The Author(s).

Keywords

  • alpha diversity
  • beta diversity
  • dietary diversity
  • dietary patterns
  • epidemiology
  • gut microbiome

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