A computational model of postprandial adipose tissue lipid metabolism derived using human arteriovenous stable isotope tracer data

Abstract

Given the association of disturbances in non-esterified fatty acid (NEFA) metabolism with the development of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, computational models of glucose-insulin dynamics have been extended to account for the interplay with NEFA. In this study, we use arteriovenous measurement across the subcutaneous adipose tissue during a mixed meal challenge test to evaluate the performance and underlying assumptions of three existing models of adipose tissue metabolism and construct a new, refined model of adipose tissue metabolism. Our model introduces new terms, explicitly accounting for the conversion of glucose to glyceraldehye-3-phosphate, the postprandial influx of glycerol into the adipose tissue, and several physiologically relevant delays in insulin signalling in order to better describe the measured adipose tissues fluxes. We then applied our refined model to human adipose tissue flux data collected before and after a diet intervention as part of the Yoyo study, to quantify the effects of caloric restriction on postprandial adipose tissue metabolism. Significant increases were observed in the model parameters describing the rate of uptake and release of both glycerol and NEFA. Additionally, decreases in the model’s delay in insulin signalling parameters indicates there is an improvement in adipose tissue insulin sensitivity following caloric restriction.

Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis

“…Our findings might also be relevant to understand the development of steatosis, non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) associated with Metabolic Syndrome (Rozendaal et al, 2018b ). Increased flux of FFA and glycerol from lipolysis of white adipose tissue (O'Donovan et al, 2019 ) has been associated with liver steatosis and NAFLD, also contributing to impaired postprandial repression of endogenous glucose production occurring in Type 2 Diabetes (Perry et al, 2015 ; Roden and Shulman, 2019 ).…”

“…For example, the model by de Winter et al ( 2006 ) is composed of three differential equations to simulate glucose, insulin and HbA1c (glycated hemoglobin) over time in patients with diabetes. Dynamic metabolic models calibrated to time-series data have been developed for biological systems such as yeast (e.g., Rizzi et al, 1997 ; van Riel et al, 1998 ) and human metabolism (e.g., Rozendaal et al, 2018a ; O'Donovan et al, 2019 ). In silico dynamic models often lack the multi level layers of regulation that control metabolism.…”

Metabolic flux between organs measured by arteriovenous metabolite gradients

“…Ivanisevic et al (2015) performed AV comparisons at the metabolomics level in 20 human subjects and found active release of TCA cycle intermediates from the arm 26 . O'Donovan et al (2019) measured AV differences in NEFAs, glucose, and glycerol in human adipose tissue, showing that NEFA release from adipose tissue is highly associated with the development of insulin resistance 31 . Recently, to understand human cardiac metabolism, Murashige et al (2020) compared the heart and leg AV differences of ~300 metabolites in 110 human subjects 32 .…”

A systems biology analysis of lipolysis and fatty acid release from adipocytes in vitro and from adipose tissue in vivo

“…For a more thorough review of models in diabetes, we refer to [22]. There have also been other efforts to understand adipose tissue lipolysis in more detail, for example experimentally in [23] and using mathematical modelling in [24,25], but without detailed intracellular components. In summary, none of the existing models have been developed to elucidate the mechanisms of control of intracellular lipolysis.…”