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

Abstract

Background: 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. Our aim in 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. Methods: Our analysis used the dataset from a 2-week controlled feeding study mimicking the participants’ habitual diets among 153 post-menopausal women from the Nutrition and Physical Activity Assessment Study Feeding study, conducted in the Women’s Health Initiative (WHI). Fasting serum metabolite profiles, urine metabolites, as well as demographic and Food Frequency Questionnaire (FFQ) data, were used to predict total fat and percent energy from fat using four cross-validated tree-based machine learning models. A LASSO model for regression was used as a way to compare the models to a linear model. Results: The highest cross-validated multiple correlation coefficients (CV-R2) for total fat intake and percent energy intake were 10.2% and 10.4%, respectively. None of the models had a CV-R2 of over 36%. There were no significant differences found between the performance of linear and non-linear models in predicting fat intake. Conclusion: Both linear and non-linear models were shown to be unable to predict total fat and dietary fat intake using serum and urinary metabolites accurately and reliably. Variable importance suggests that tree-based, machine-learning models have the potential to help understand non-linear interactions between biomarkers and dietary intake.