Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models
Shauna D. O’Donovan,
Milena Rundle,
E. Louise Thomas,
Jimmy D. Bell,
Gary Frost,
Doris M. Jacobs,
Anne Wanders,
Ryan de Vries,
Edwin C.M. Mariman,
Marleen A. van Baak,
Luc Sterkman,
Max Nieuwdorp,
Albert K. Groen,
Ilja C.W. Arts,
Natal A.W. van Riel,
Lydia A. Afman
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 Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands; Corresponding author
Milena Rundle
Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
E. Louise Thomas
Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, the United Kingdom
Jimmy D. Bell
Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, the United Kingdom
Gary Frost
Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
Doris M. Jacobs
Science & Technology, Unilever Foods Innovation Center, Wageningen, the Netherlands
Anne Wanders
Science & Technology, Unilever Foods Innovation Center, Wageningen, the Netherlands
Ryan de Vries
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
Edwin C.M. Mariman
Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
Marleen A. van Baak
Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
Luc Sterkman
Caelus Pharmaceuticals, Zegveld, the Netherlands
Max Nieuwdorp
Vascular Medicine, Amsterdam UMC Locatie, AMC, Amsterdam, the Netherlands
Albert K. Groen
Vascular Medicine, Amsterdam UMC Locatie, AMC, Amsterdam, the Netherlands
Ilja C.W. Arts
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
Natal A.W. van Riel
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
Lydia A. Afman
Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
Summary: The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual’s metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.
