Estimating the intake of moderation foods and evaluating associations with metabolomic and microbiome measures

Principal Investigator

Name
Kaelyn Burns

Degrees
Ph.D., M.S.

Institution
National Cancer Institute

Position Title
Postdoctoral Fellow

Email
kaelyn.burns@nih.gov

About this CDAS Project

Study
IDATA (Learn more about this study)

Project ID
IDATA-99

Initial CDAS Request Approval
May 11, 2026

Title
Estimating the intake of moderation foods and evaluating associations with metabolomic and microbiome measures

Summary
Ultra-processed foods (UPF) are defined using the Nova classification system, which classifies foods and beverages by their extent and purpose of industrial processing. Nova is agnostic to the nutrient content of foods, which contributes to uncertainty as to whether UPF are associated with disease through aspects of the food processing itself or due to their nutrient content. Conversely, the moderation food classification system is a novel method that classifies foods and beverages based on dietary components that should be limited (i.e., added sugar, saturated fat, sodium, refined grains, alcohol) according to the Dietary Guidelines for Americans due to associations between overconsumption of these nutrients with adverse health outcomes.

We propose to derive moderation food and non-moderation food intake variables in IDATA using data from the DHQ, ASA24, and 4DFR. Given the rich dietary data available, IDATA provides the unique opportunity to evaluate the performance of the DHQ for estimating moderation food intake, with ASA24 as the reference instrument. We also propose to evaluate associations between moderation food and non-moderation intake with recovery biomarkers, metabolomics profiles, and the oral microbiome composition, to investigate mechanisms by which the intake of moderation foods may be related to health outcomes.

Aims

Aim 1: Estimate the intake of moderation foods and non-moderation foods, in grams and calories, using data from the DHQ, ASA24, and 4DFR.

Aim 2: Evaluate the performance of the DHQ for estimating moderation food and non-moderation food intake using a measurement error model.

Aim 3: Evaluate associations between moderation food and non-moderation food intake and recovery biomarkers of energy, sodium, potassium, and protein.

Aim 4: Evaluate associations between moderation food and non-moderation food intake with metabolites and derive multi-metabolite scores predictive of moderation and non-moderation food intake.

Aim 5: Evaluate associations between moderation food and non-moderation food intake with the oral microbiome composition and diversity.

Collaborators

Kaelyn Burns National Cancer Institute
Emily Vogtmann National Cancer Institute
Hyokyoung Hong National Cancer Institute
Christian Abnet National Cancer Institute
Rashmi Sinha National Cancer Institute
Yukiko Yano National Cancer Institute
Erikka Loftfield National Cancer Institute
Lisa Kahle Information Management Services