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

doi:10.1016/j.isci.2024.109362

Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models

iScience. 2024 Feb 29;27(4):109362. doi: 10.1016/j.isci.2024.109362. eCollection 2024 Apr 19.

Authors

Shauna D O'Donovan^{1

2

3}, Milena Rundle⁴, E Louise Thomas⁵, Jimmy D Bell⁵, Gary Frost⁴, Doris M Jacobs⁶, Anne Wanders⁶, Ryan de Vries², Edwin C M Mariman⁷, Marleen A van Baak⁷, Luc Sterkman⁸, Max Nieuwdorp⁹, Albert K Groen⁹, Ilja C W Arts¹⁰, Natal A W van Riel^{2

3}, Lydia A Afman¹

Affiliations

¹ Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands.
² Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
³ Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands.
⁴ Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK.
⁵ Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, the United Kingdom.
⁶ Science & Technology, Unilever Foods Innovation Center, Wageningen, the Netherlands.
⁷ Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands.
⁸ Caelus Pharmaceuticals, Zegveld, the Netherlands.
⁹ Vascular Medicine, Amsterdam UMC Locatie, AMC, Amsterdam, the Netherlands.
¹⁰ Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands.

Abstract

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.

Keywords: Human metabolism; Nutrition.