Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model
Shauna D. O’Donovan,
Balázs Erdős,
Doris M. Jacobs,
Anne J. Wanders,
E. Louise Thomas,
Jimmy D. Bell,
Milena Rundle,
Gary Frost,
Ilja C.W. Arts,
Lydia A. Afman,
Natal A.W. van Riel
Affiliations
Shauna D. O’Donovan
Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands; Corresponding author
Balázs Erdős
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
Doris M. Jacobs
Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
Anne J. Wanders
Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
E. Louise Thomas
Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
Jimmy D. Bell
Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
Milena Rundle
Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
Gary Frost
Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
Ilja C.W. Arts
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
Lydia A. Afman
Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
Natal A.W. van Riel
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
Summary: Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.
