Using Non-Linear, Machine Learning Methodology to Assess the Potential Metabolomic-Based Biomarkers of Total Fat and Percentage Fat Intake Using a Controlled Feeding Study

Understanding and identifying objective dietary biomarkers is a crucial component of nutrition research today. By investigating the relationship between biomarker profiles and dietary intake using machine learning methodologies, there could be a way to more objectively assess study participant nutrient profiles and better understand the relationship between nutrient intake and disease. The aim of this thesis is to assess the utility of non-linear tree-based models in predicting daily intake of total fat and the percent of energy from fat from serum and 24-h urine high dimensional metabolites.

Materials Available

• Abstract

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Project Type(s): Master's Thesis

Author(s): Caroline Lea Nondin

Program(s): Master of Science

Year: 2023

Adviser(s):

• Marian L. Neuhouser