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

Area of Science:

• Metabolic Health
• Computational Biology
• Obesity Research

Background:

• Metabolic deterioration in overweight/obesity is highly variable between individuals.
• This heterogeneity complicates nutritional intervention assessment.
• Assessing multiple metabolic health aspects simultaneously is challenging.

Purpose of the Study:

• To apply a physiology-based computational model, the Mixed Meal Model, for in silico characterization of individual metabolic health.
• To validate the model's ability to quantify key metabolic parameters.
• To demonstrate the utility of personalized models in evaluating dietary interventions.

Main Methods:

• Generation of 342 personalized Mixed Meal Models using data from overweight/obesity intervention studies.
• Comparison of model-derived insulin resistance metric with hyperinsulinemic-euglycemic clamp.
• In silico assessment of liver fat accumulation and beta-cell functionality.

Main Results:

• Strong correlation found between model-derived insulin resistance and hyperinsulinemic-euglycemic clamp (ρ = 0.67, p < 0.05).
• The model successfully quantified liver fat accumulation and beta-cell function.
• Personalized models demonstrated effectiveness in evaluating dietary intervention impacts.

Conclusions:

• The Mixed Meal Model provides a robust in silico tool for characterizing individual metabolic health.
• The model accurately reflects key metabolic parameters, including insulin resistance.
• Personalized Mixed Meal Models can individualize the assessment of nutritional interventions for metabolic health.