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Protocols (Poster Session)

(P30-058-24) Protocol for the Metabol(om)ic Response to Omnivorous Dietary Patterns Using Randomized Controlled Feeding Trials (MET-OMNIV) Research Consortium and Dataset

Monday, July 1, 2024
12:45 PM – 1:45 PM CT
Location: Poster Board 56
Disclosure(s):
Aaron Yerke, PhD: No relevant financial relationship(s) with ineligible companies to disclose.


Objectives: We present the protocol for a new data pooling initiative to investigate metabolomic markers of animal-based protein foods (ABPF) and omnivorous dietary patterns using metabolomics and machine learning. This initiative will strengthen scientific understanding of how consuming ABPF and omnivorous dietary patterns influence metabolomic and cardiometabolic responses of human research participants. Additionally, this protocol calls for a formalized collaboration in the form of a research consortium, called Metabol(om)ic Response to Omnivorous Dietary Patterns Using Randomized Controlled Feeding Trials (MET-OMNIV) to curate and expand this dataset.

Methods: Data from 4 randomized controlled feeding trials (RCfT) will be pooled that met the following criteria: 1) foods and beverages provided, 2) crossover design with 4-6 week feeding phases, 3) primary objective to investigate cardiometabolic effects of consuming ABPF in the context of various dietary patterns, mostly weight-maintenance, and 4) detailed descriptions of foods and dietary patterns, including specifics to protein source and degree and type of processing. Data related to participant characteristics, menus, and cardiometabolic measures at baselines and post-feeding will be harmonized.

Results: Data from 4 randomized controlled feeding trials (RCfT) will be pooled that met the following criteria: 1) foods and beverages provided, 2) crossover design with 4-6 week feeding phases, 3) primary objective to investigate cardiometabolic effects of consuming ABPF in the context of various dietary patterns, mostly weight-maintenance, and 4) detailed descriptions of foods and dietary patterns, including specifics to protein source and degree and type of processing. Data related to participant characteristics, menus, and cardiometabolic measures at baselines and post-feeding will be harmonized.

Conclusions: This pooled dataset will provide an excellent resource for those who wish to study plasma metabolomic markers of food consumption, especially the consumption of ABPF.

Funding Sources: Beef Checkoff
Agricultural Research Service SCINet and AI Center of Excellence ORISE Fellowship Program
Agriculture Research Service, USDA