| Authors | O'Donovan, S., Rundle, M., Thomas, E.L., Bell, J.D., Frost, G., Jacobs, D.M., Wanders, A., de Vries, R., Mariman, E.C.M., va Baak, M.A., Sterkman, L., Nieuwdorp, M., Groem, A.K., Arts, I.C.W., van Riel, N. and Afman, L.A. |
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| 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 characterise an individual’s metabolic health in silico. A population of 342 personalised 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-euglycamic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalised Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level. |
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