The Mixed Meal Model; quantifying metabolic resilience in overweight and obesity

Beschrijving

Introduction
While once thought of as simply the absence of disease, our concept of health is increasing begin redefined as the ability of a person to respond and adapt to physical, emotional, or social challenges, termed resilience. Challenge tests, such as oral glucose tolerance tests or mixed meal challenges tests, are regularly employed in nutritional research to assess metabolic resilience. The post-meal trajectories of plasma glucose, insulin, and triglycerides can provide insights into disturbances in the metabolic crosstalk between the liver, skeletal muscle, and adipose tissue seen in overweight and obesity that give rise to the development of dyslipidemia, insulin resistance, and ultimately a loss of glycaemic control. However, methods to effectively process and quantify physiologically relevant features of metabolic health from this dynamic multi-variate data are still lacking, and valuable information may be lost. In this study we generate a novel mechanistic model to quantify features of metabolic resilience from meal challenge test data.

Approach
We construct the Mixed Meal Model, a 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 dietary intervention studies (caloric restriction, improved macronutrient quality, or a combination of both) identifying postprandial metabolic signatures insulin resistance and elevated liver fat.

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 hepatic fat accumulation measured with magnetic resonance spectroscopy (MRS). The Mixed Meal Model derived estimation of insulin sensitivity (k5) produced a correlation of ρ = 0.65 (p < 0.05) with hyperinsulinemic euglycemic clamp, the gold standard 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, for some individuals the personalised Mixed Meal Models could infer a reduction in hepatic fat content following a period of caloric restriction using meal responses alone, confirmed using MRS measure of liver fat.

Conclusion
The Mixed Meal Model provides an objective and sensitive assessment of metabolic resilience for individuals with overweight and obesity.

Periode	8 okt. 2022 → 12 okt. 2022
Evenementstitel	International Conference on Systems Biology
Evenementstype	Congres
Conferentienummer	21
Locatie	Berlin, DuitslandToon op kaart