Quantifying metabolic resilience; getting the most out of your challenge test data.

Beschrijving

Background: Our concept of health is changing, increasingly we consider the ability of a person to respond and adapt to physical, emotional,
or social challenges, termed resilience, as a more sensitive indicator of
health[1]. Challenge tests, such as oral glucose tolerance tests or mixed
meal challenges tests, are regularly employed in research to assess metabolic resilience [2]. Post-meal trajectories of plasma glucose and insulin
provide insight into insulin resistance or glucose intolerance. However,
methods to effectively process and quantify physiologically relevant features of metabolic health from this data are still lacking, and valuable
information may be lost [3]. In this study we generate a computational
model to quantify features of metabolic resilience from meal challenge
test data.
Methods: We present the Mix Meal Model (M3al Model), a novel computational tool that integrates and processes post-meal plasma glucose,
insulin, triglyceride, and free-fatty acid measurements to characterise
metabolic resilience. We applied the M3al Model to meal challenge test
data from a large population (n=317) of men and women with overweight
or obesity (BMI 24.9-35.8 kg/m2) collected from five different intervention studies identifying postprandial metabolic signatures insulin resistance and elevated liver fat.
Results: The M3al Model provides a surrogate index of hepatic lipid accumulation from plasma trajectories of glucose, insulin, triglycerides, and
free-fatty, confirmed with comparison to measurements of intra-hepatocellular lipid accumulation (ρ = -0.76 p < 0.05) collected using magnetic
resonance spectroscopy. Moreover, the M3al Model estimation of insulin
resistance has a correlation of ρ = 0.65 (p < 0.05) with hyperinsulinemic
euglycemic clamp measure of insulin resistance. The M3al Model predicted measure of beta-cell functionality has a correlation of ρ = 0.61 (p
0.05) with the insulinogenic index. In addition, the M3al Model is generalisable to challenge tests with different macro-nutrient composition.
Conclusion: The M3al Model provides a single computational tool capable of capturing features of metabolic resilience such as insulin resistance,
beta-cell functioning, and hepatic liver fat accumulation from challenge
test data. In this way the M3al Model provides an objective and sensitive
assessment of metabolic resilience for individuals with overweight and
obesity.
References
1. Huber M, Knottnerus JA, Green L, van der Horst H et a. How should we define
health? BMJ. (2011) 343:d4163.
2. Burggraaf J, van Erk MJ, Pellis L, Boessen R et al. Multi-parameter comparison
of a standardized mixed meal tolerance test in healthy and type 2 diabetic
subjects: the PhenFlex challenge. Genes Nutr. (2017)12:21.
3. Vis DJ, Westerhuis JA, Jacobs DM, van Dynhoven JPM et al. Analyzing metabolomics-based challenge tests. Metabolomics. (2015) 11(1):50–63.

Periode	5 mei 2022
Evenementstitel	European Association on the Study of Obesity Congress on Obesity
Evenementstype	Congres
Locatie	Maastricht, NederlandToon op kaart