(PO18-13-22) Representing Diet in a Tree-Based Format for Interactive and Exploratory Assessment of Dietary Intake Data

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Presenting Author(s)

Nicole Litwin, PhD, RD
– Manager, Scientific Impact, Crohn's & Colitis Foundation, Wellington, Colorado, United States

Co-Author(s)

KC

Kalen Cantrell
– Center for Microbiome Innovation, University of California San Diego
AC

Aurélie Cotillard, PhD
– Senior Omics Statistician, Danone Research, Palaiseau, Ile-de-France, France
MD

Muriel Derrien, PhD
– Danone Nutricia Research, Paris-Saclay, Ile-de-France, France
Abigail J. Johnson, PhD, RDN
– Assistant Professor, University of Minnesota, School of Public Health, Minneapolis, Minnesota, United States
RK

Rob Knight, PhD
– Center for Microbiome Innovation & Department of Pediatrics, School of Medicine & Department of Bioengineering & Department of Computer Science and Engineering & Scripps Institution of Oceanography - University of California San Diego, La Jolla, California, United States
FL

Franck Lejzerowicz, PhD
– Center for Microbiome Innovation & Department of Pediatrics, School of Medicine - University of California San Diego, La Jolla, California, United States
DM

Daniel McDonald
– University of California San Diego
BN

Brent Nowinski
– Center for Microbiome Innovation, University of California San Diego
SS

Se Jin Song, PhD
– Center for Microbiome Innovation - University of California San Diego, La Jolla, California, United States
JT

Julien Tap, PhD
– Danone Research, Palaiseau, Ile-de-France, France
PV

Patrick Veiga, PhD
– Danone Research, Palaiseau, Ile-de-France, France

Disclosure(s):

Nicole Litwin, PhD, RD: No relevant financial relationship(s) with ineligible companies to disclose.

Objectives: We assessed the utility of representing dietary intake data in hierarchical tree structures that consider relationships among foods.

Methods: Dietary intake was collected from 1909 adults (≥ 18 years) using a food frequency questionnaire (FFQ; VioScreen) from the American Gut Project. FFQ food items were formatted into hierarchical tree structures based on 1) USDA’s Food Nutrient and Database for Dietary Studies (FNDDS) classifications, 2) nutrient content, and 3) molecular compound information detected via mass spectrometry to capture the non-nutrient composition of foods. Next, we compared how well representing dissimilarities (or distances) between individuals based on their diet corresponded with indices such as the Healthy Eating Index (HEI-2015), when those distances are calculated using tree-based versus non-tree-based metrics. We performed an Adonis test (PERMANOVA) to measure the amount of variation explained (R²) in these diet-based distances by HEI-2015.

Results: We observed that dietary ordinations generated using tree-based relationships between foods have better agreement with HEI than ordinations generated without considering relatedness between foods. The variation explained by HEI-2015 increased by 35% when using the FNDDS tree compared to using a non-tree based quantitative metric (Bray-Curtis (not tree-based) R² = 0.02931 vs. Weighted UniFrac (tree-based) R²=0.03969), by >20% when using the nutrient tree ( vs. Weighted UniFrac R²=0.03627), and only marginally (6%) when using the molecular compound tree (vs. Weighted UniFrac R² = 0.03116).

Conclusions: We show that tree-based measurements of dietary similarity lead to better agreement with diet indices (e.g. HEI) than when relationships among foods are not considered. We also show that representing dietary intake in a tree-like structure can offer interactive visualizations of data that can be used to inform hypotheses regarding dietary characteristics.

Funding Sources: Danone Nutricia Research