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Topical Area: Dietary Patterns

Dietary Patterns (Poster Session)

(P14-066-23) Dietary Patterns Identified Through DNA Metabarcoding and Their Association With Geographic Location and Health Indicators

Sunday, July 23, 2023

11:45 AM – 12:45 PM ET

Location: Poster Board 66

Presenting Author(s)

BN

Benjamin C. Neubert, B.S. (he/him/his)

Ph.D. Candidate
Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, USA, Duke University

Co-Author(s)

BP

Brianna L. Petrone, B.S.

MD-PhD Student
Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA; Medical Scientist Training Program, Duke University School of Medicine, Durham, North Carolina, USA
SJ

Sharon Jiang, MS

Lab Research Analyst
Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA
LD

Lara R. Dugas, Ph.D., MPH

Associate Professor
Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
Maywood, Illinois, United States
CC

Candice Choo-Kang, MPH

Project Coordinator
Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
BL

Brian T. Layden, M.D., Ph.D.

Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA; Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois, USA
Chicago, Illinois, United States
AL

Amy Luke, Ph.D.

Public Health Sciences, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
Maywood, Illinois, United States
PB

Pascal Bovet, M.D., MPH

Unit for Prevention and Control of Cardiovascular Disease, Ministry of Health, Victoria, Republic of Seychelles; University Center for Primary Care and Public Health, Lausanne, Switzerland
EL

Estelle V. Lambert, Ph.D.

Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa
DR

Dale Rae, Ph.D.

Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa
KB

Kweku Bedu-Addo, Ph.D.

Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
JP

Jacob C. Plange-Rhule, Ph.D.

Ph.D. Candidate
Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
TF

Terrence Forrester, M.D., Ph.D.

Solutions for Developing Countries, University of the West Indies, Mona, Kingston, Jamaica
JG

Jack A. Gilbert, Ph.D.

Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, California; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California
LD

Lawrence A. David, Ph.D.

Associate Professor
Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA

Disclosure(s):

Benjamin C. Neubert, B.S.: No relevant financial relationship(s) with ineligible companies to disclose.

Objectives: To identify plant-based dietary patterns using sequencing-based dietary assessment and their epidemiological associations.

Methods: We analyzed data from 624 individuals from the United States, Ghana, and South Africa (16≤Age≤71, 52% Male). Stool samples, demographic information, and BMI data were gathered from participants. We applied DNA sequencing to the chloroplast trnL-P6 marker from residual DNA in human stool, termed DNA metabarcoding. This method enables the assessment of the plant component of individuals’ diet within stool. Random forest models trained on DNA metabarcoding data were used to evaluate the predictive capacity of DNA metabarcoding for participant country of origin on held-out samples. The area under the receiver operating characteristic curve (ROC AUC) for 100 random 80-20 training and test splits was used to assess model performance. To assess American dietary patterns, principal component analysis was applied to DNA metabarcoding data using Aitchison distances (n=266). Multiple linear regression performed in R was used to identify associations between American dietary patterns, demographic variables, and BMI in a subset of 438 international individuals where confounder variables were available.

Results: DNA metabarcoding was highly predictive of the participant’s country of origin (mean ROC AUC = 0.975). The staple crop dietary pattern (7.9% variance explained) was associated with significantly increased BMI after adjusting for age, sex, smoking status, alcohol intake, and country of origin (β = 0.342; P < 0.0001). The leafy green vegetable dietary pattern (7.0% variance explained) was associated with significantly decreased BMI after adjustment (β = -0.236; P = 0.012).

Conclusions: We applied dietary pattern analysis techniques for the first time to DNA metabarcoding data in humans, identifying significant associations between staple crop species, leafy green vegetables, and BMI in a global cohort of individuals. Further, we found DNA metabarcoding data to be highly predictive of the country of origin in participants using machine learning.

Funding Sources: National Institutes of Health, Burroughs Wellcome Fund, and The Chan Zuckerberg Initiative