TY - JOUR
T1 - Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models
AU - O'Donovan, Shauna D.
AU - Rundle, Milena
AU - Thomas, E. Louise
AU - Bell, Jimmy D.
AU - Frost, Gary
AU - Jacobs, Doris M.
AU - Wanders, Anne
AU - de Vries, Ryan
AU - Mariman, Edwin C. M.
AU - van Baak, Marleen A.
AU - Sterkman, Luc
AU - Nieuwdorp, Max
AU - Groen, Albert K.
AU - Arts, Ilja C. W.
AU - van Riel, Natal A. W.
AU - Afman, Lydia A.
N1 - Publisher Copyright: © 2024 The Author(s)
PY - 2024/4/19
Y1 - 2024/4/19
N2 - 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.
AB - 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.
KW - Human metabolism
KW - Nutrition
UR - http://www.scopus.com/inward/record.url?scp=85187558715&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2024.109362
DO - 10.1016/j.isci.2024.109362
M3 - Article
C2 - 38500825
SN - 2589-0042
VL - 27
JO - iScience
JF - iScience
IS - 4
M1 - 109362
ER -