Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models
© 2024 The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
iScience - 27(2024), 4 vom: 19. März, Seite 109362 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
O'Donovan, Shauna D [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 20.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.isci.2024.109362 |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 | ||
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| 700 | 1 | |a Thomas, E Louise |e verfasserin |4 aut | |
| 700 | 1 | |a Bell, Jimmy D |e verfasserin |4 aut | |
| 700 | 1 | |a Frost, Gary |e verfasserin |4 aut | |
| 700 | 1 | |a Jacobs, Doris M |e verfasserin |4 aut | |
| 700 | 1 | |a Wanders, Anne |e verfasserin |4 aut | |
| 700 | 1 | |a de Vries, Ryan |e verfasserin |4 aut | |
| 700 | 1 | |a Mariman, Edwin C M |e verfasserin |4 aut | |
| 700 | 1 | |a van Baak, Marleen A |e verfasserin |4 aut | |
| 700 | 1 | |a Sterkman, Luc |e verfasserin |4 aut | |
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| 700 | 1 | |a Afman, Lydia A |e verfasserin |4 aut | |
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