The Muscle Insulin Sensitivity Index (MISI) has been developed to estimate muscle-specific insulin sensitivity based on oral glucose tolerance test (OGTT) data. To date, the score has been implemented with considerable variation in literature and initial positive evaluations were not reproduced in subsequent studies. In this study, we investigate the computation of MISI on oral OGTT data with differing sampling schedules and aim to standardise and improve its calculation. Seven time point OGTT data for 2631 individuals from the Maastricht Study and seven time point OGTT data combined with a hyperinsulinemic-euglycaemic clamp for 71 individuals from the PRESERVE Study were used to evaluate the performance of MISI. MISI was computed on subsets of OGTT data representing four and five time point sampling schedules to determine minimal requirements for accurate computation of the score. A modified MISI computed on cubic splines of the measured data, resulting in improved identification of glucose peak and nadir, was compared with the original method yielding an increased correlation (ρ = 0.576) with the clamp measurement of peripheral insulin sensitivity as compared to the original method (ρ = 0.513). Finally, a standalone MISI calculator was developed allowing for a standardised method of calculation using both the original and improved methods.

Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals’ challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals’ responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals’ metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

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.

Little is known about the impact of fasting on gene regulation in human adipose tissue. Accordingly, the objective of this study was to investigate the effects of fasting on adipose tissue gene expression in humans. To that end, subcutaneous adipose tissue biopsies were collected from twenty-three volunteers 2h and 26h after consumption of a standardized meal. For comparison, epididymal adipose tissue was collected from C57Bl/6J mice after a 16h fast and in the ab-libitum fed state. Transcriptome analysis was carried out using Affymetrix microarrays. We found that, 1) fasting downregulated numerous metabolic pathways in human adipose tissue, including triglyceride and fatty acid synthesis, glycolysis and glycogen synthesis, TCA cycle, oxidative phosphorylation, mitochondrial translation, and insulin signaling; 2) fasting downregulated genes involved in proteasomal degradation in human adipose tissue; 3) fasting had much less pronounced effects on the adipose tissue transcriptome in humans than mice; 4) although major overlap in fasting-induced gene regulation was observed between human and mouse adipose tissue, many genes were differentially regulated in the two species, including genes involved in insulin signaling (PRKAG2, PFKFB3), PPAR signaling (PPARG, ACSL1, HMGCS2, SLC22A5, ACOT1), glycogen metabolism (PCK1, PYGB), and lipid droplets (PLIN1, PNPLA2, CIDEA, CIDEC). In conclusion, although numerous genes and pathways are regulated similarly by fasting in human and mouse adipose tissue, many genes show very distinct responses to fasting in humans and mice. Our data provide a useful resource to study adipose tissue function during fasting.