The Mixed Meal Model; towards precision nutrition in overweight and obesity.

1. Introduction
An energy imbalance in overweight and obesity can induce disturbances in the metabolic crosstalk between the liver, skeletal muscle, and adipose tissue giving rise to the development of dyslipidemia, insulin resistance, ultimately a loss of glycaemic control [1]. While specific nutritional interventions interventions have been shown to ameliorate the effects of these co-morbidities, and in some cases allow the recovery of a metabolically healthy state, the considerable inter-individual heterogeneity has greatly impeded the identification of effective intervention and treatment options for overweight/obesity[2]. In this study we generate a novel physiology based computational model to quantify features of metabolic resilience from meal challenge test data, allowing for a personalised assessment of intervention response.
2. Approach
We construct the Mixed Meal Model, a novel physiology based computational model of postprandial glucose and lipid metabolism which can be personalised using post-meal time series of plasma glucose, insulin, triglyceride, and free-fatty acid concentrations. A population of 342 personalised Mixed Meal Models were generated using data from three independent intervention studies (caloric restriction, improved macronutrient quality, or a combination of both) identifying postprandial metabolic signatures insulin resistance and elevated liver fat.
3. Results
The Mixed Meal Model could capture the diverse individual responses to the standardised meals included in this study. Moreover, personalised parameter estimates quantified features of metabolic health from the meal response data with the model parameter describing lipid metabolism (k11) producing a strong correlation (ρ = -0.76, p < 0.05) with an independent measure of hepatic fat accumulation. The Mixed Meal Model derived estimation of insulin sensitivity (k5) produced a correlation of ρ = 0.65 (p < 0.05) with hyperinsulinemic euglycemic clamp measure of insulin resistance and the model predicted measure of beta-cell functionality (k6) has a correlation of ρ = 0.61 (p < 0.05) with the insulinogenic index. In addition, we use the personalised models to assess the impact of a given dietary intervention on metabolic health and the individual level.
4. Conclusion
The Mixed Meal Model provides an objective and sensitive assessment of metabolic resilience for individuals with overweight and obesity.
References
1. JO Hill, HR Wyatt, JC Peters. Energy Balance and Obesity. Circulation, 126(1):126-132 (2012)..
2. JM Ordovas, LR Ferguson, ES Tai, JC Mathers. Personalised Nutrition and Health. BMJ :236 (2018).
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