Degrees Of Insulin Sensitivity Research Articles

534-P: Sustained Improvement of Whole-Body Oxidative Capacity but Not Insulin Sensitivity after Detraining of People with Type 2 Diabetes

High-intensity interval training (HIIT) improves skeletal muscle mitochondrial oxidative capacity in humans with different degrees of insulin sensitivity, whereas it increases insulin sensitivity along with number of circulating small extracellular vesicle (SEV) mainly in insulin resistant individuals with or without T2D (T2D, IR) . In order to examine the sustainability of metabolic effects induced by HIIT, this study tested whether the observed effects on insulin sensitivity, oxidative capacity and SEV number remain and/or differ between insulin sensitive (IS) , IR and T2D humans. Twenty T2D and age- and BMI-matched IR and 12 IS individuals (HbA1c in %: 7.2±0.2, 5.4±0.1, 5.4±0.1; M-value in mg*kg-1*min-1: 3.0±0.4, 4.2±0.4, 7.4±0.4) performed a 12-week HIIT cycling protocol for 3 d/week. Whole-body insulin sensitivity was assessed by hyperinsulinemic-euglycemic clamps, physical fitness (VO2max) by spiroergometry and myocellular pathways of insulin sensitivity by immunoblotting of muscle biopsies at baseline, 72 h after the last HIIT bout and 4 weeks (detraining) later. SEV were isolated from serum by size exclusion chromatography. Compared to HIIT, insulin sensitivity decreased in T2D after detraining (p<0.01) , instead VO2max decreased in T2D and IR (p<0. and p<0.05) , although it remained higher than baseline (p<0.0 and p=0.05) . Of note, HIIT-induced reduction of the nuclear factor-κB protein (p<0.vs. baseline) disappeared in IR after detraining, whereas decreased protein kinase Cε activity was sustained in T2D (p<0.vs. HIIT and p<0.vs. baseline) , who also showed higher circulating SEV number (p<0.vs. baseline) . In conclusion, HIIT-induced increases in physical fitness and SEV number, but not insulin sensitivity are maintained at least after short-term detraining in T2D. Characterization of SEV cargo might help to better understand the differences in metabolic responses to detraining in individuals with and without T2D Disclosure L.Mastrototaro: None. J.Szendroedi: Advisory Panel; Boehringer Ingelheim International GmbH. M.Roden: Advisory Panel; Eli Lilly and Company, Research Support; Boehringer Ingelheim International GmbH, Nutricia, Speaker's Bureau; Novo Nordisk. M.Apostolopoulou: n/a. N.Saatmann: None. C.Granata: None. S.Hartwig: None. Y.Karusheva: None. S.Gancheva: None. S.Lehr: None. H.Al-hasani: Consultant; Bayer AG.

Нейросетевое прогнозирование гликемии у пациентов с сахарным диабетом на основе смешанных временных рядов с перспективой применения в составе интеллектуальной инсулиновой помпы

The paper considers the prospect of using a neural network self-learning algorithm for personalizing insulin therapy within the software and hardware complex of an intelligent insulin pump (IIP). IIP is an autonomous automatic insulin control system that can completely simulate the secretion of insulin by the pancreas of a healthy person. The required dose calculation is carried out on the basis of biological data from sensors installed on the patient's body. The algorithm for calculating the dose is an intelligent self-learning neural network unit that allows you to achieve fine personalization of therapy with the possibility of flexible readjustment in accordance with the dynamically changing biological parameters of the patient in real time. The article discusses several possible neural network paradigms for predicting blood sugar in the short-term (3 minutes) and medium-term (30 minutes) periods based on a mixed time series that includes measurements of blood glucose, active insulin and active carbohydrates at intervals of 3 minutes. The advantage of using MLP (multilayer perceptron) networks over other paradigms, in particular, LSTM networks (long short-term memory networks), is shown. The results of computational experiments using a neural network model on real data of two volunteers with different degrees of insulin sensitivity are presented. The impossibility of unifying the model for patients with different sensitivities has been proven, which confirms the need to personalize therapy for insulin-dependent diabetes. On the basis of the results of the experiments, recommendations are given on the construction and training of neural network models for predicting blood glucose levels in patients, as well as prospects and directions for further research.

Adipose tissue depot-specific intracellular and extracellular cues contributing to insulin resistance in obese individuals.

Adipose tissue dysregulation in obesity strongly influences systemic metabolic homeostasis and is often linked to insulin resistance (IR). However, the molecular mechanisms underlying adipose tissue dysfunction in obesity are not fully understood. Herein, a proteomic analysis of subcutaneous (SC) and omental (OM) fat from lean subjects and obese individuals with different degrees of insulin sensitivity was performed to identify adipose tissue biomarkers related to obesity‐associated metabolic disease. Our results suggest that dysregulation of both adipose tissue extracellular matrix (ECM) organization and intracellular trafficking processes may be associated with IR in obesity. Thus, abnormal accumulation of the small leucine‐rich proteoglycan, lumican, as observed in SC fat of IR obese individuals, modifies collagen I organization, impairs adipogenesis and activates stress processes [endoplasmic reticulum and oxidative stress] in adipocytes. In OM fat, IR is associated with increased levels of the negative regulator of the Rab family of small GTPases, GDI2, which alters lipid storage in adipocytes by inhibiting insulin‐stimulated binding of the Rab protein, Rab18, to lipid droplets. Together, these results indicate that lumican and GDI2 might play depot‐dependent, pathogenic roles in obesity‐associated IR. Our findings provide novel insights into the differential maladaptive responses of SC and OM adipose tissue linking obesity to IR.

Wnt Signaling Genes in Adipose Tissue and Skeletal Muscle of Humans With Different Degrees of Insulin Sensitivity.

The β-catenin-dependent Wnt signaling plays a role in adipogenesis, myogenesis, and glucose homeostasis. The aim of this study was to assess adipose tissue and skeletal muscle expression of Wnt/β-catenin signaling genes in a young healthy population according to insulin sensitivity and its regulation by hyperinsulinemia and free fatty acids. We examined 117 male volunteers. The participants were divided into subgroups of high-insulin sensitivity (IS) and low-IS on the basis of a 2-hour euglycemic clamp. In 20 subjects, the clamp was prolonged to 6 hours. After 1 week, another 6-hour clamp, with Intralipid/heparin infusion, was performed. Tissue biopsies were performed before each clamp and after 6-hour clamps. Additionally, we collected muscle biopsies from another group of 16 male subjects for cell cultures. Myotubes were treated with insulin separately and in combination with palmitate. We found decreased adipose tissue WNT10B, FZD1/8, LRP5, DVL2, CTNN1B, TCF7L2, and AXIN2 and increased muscle WNT10B, FZD1/8, LRP6, DVL1, GSK3B, CTNNB1, TCF7L2, AXIN2, MYC, and CCND1 expression in the low-IS group. Hyperinsulinemia resulted in a decrease in adipose tissue FZD4, LRP5/6, TCF7L2, and AXIN2 and an increase in muscle FZD1/8, DVL1/2/3, TCF7L2, AXIN2, and MYC expression. These changes disappeared after free fatty acid elevation. In myotubes, insulin increased the expression of FZD1, DVL2, CTNNB1, and TCF7L2, whereas palmitate abolished these effects. The association of β-catenin-dependent Wnt signaling with insulin resistance is tissue specific. Observed changes might reflect a compensatory mechanism to increase muscle glucose uptake and to generate new fat cells in insulin-resistant conditions.

Effects of hyperinsulinemia on lipoprotein lipase, angiopoietin-like protein 4, and glycosylphosphatidylinositol-anchored high-density lipoprotein binding protein 1 in subjects with and without type 2 diabetes mellitus

Our aims were to compare the systemic effects of insulin on lipoprotein lipase (LPL) in tissues from subjects with different degrees of insulin sensitivity. The effects of insulin on LPL during a 4-hour hyperinsulinemic, euglycemic clamp were studied in skeletal muscle, adipose tissue, and postheparin plasma from young healthy subjects (YS), older subjects with type 2 diabetes mellitus (DS), and older control subjects (CS). In addition, we studied the effects of insulin on the expression of 2 recently recognized candidate genes for control of LPL activity: angiopoietin-like protein 4 (ANGPTL4) and glycosylphosphatidylinositol-anchored high-density lipoprotein binding protein 1. As an effect of insulin, LPL activity decreased by 20% to 25% in postheparin plasma and increased by 20% to 30% in adipose tissue in all groups. In YS, the levels of ANGPTL4 messenger RNA in adipose tissue decreased 3-fold during the clamp. In contrast, there was no significant change in DS or CS. Regression analysis showed that the ability of insulin to reduce the expression of ANGPTL4 was positively correlated with M-values and inversely correlated with factors linked to the metabolic syndrome. Expression of glycosylphosphatidylinositol-anchored high-density lipoprotein binding protein 1 tended to be higher in YS than in DS or CS, but the expression was not affected by insulin in any of the groups. Our data imply that the insulin-mediated regulation of LPL is not directly linked to the control of glucose turnover by insulin or to ANGPTL4 expression in adipose tissue or plasma. Interestingly, the response of ANGPTL4 expression in adipose tissue to insulin was severely blunted in both DS and CS.

The euglycemic hyperinsulinemic clamp examination: variability and reproducibility

The aim was to examine the reproducibility of the euglycemic hyperinsulinemic clamp method. From a random population sample of 60-year-old clinically healthy men, 32 subjects with varying degrees of insulin sensitivity were recruited. Conventional 2-h clamp examinations were carried out at an interval of 2 weeks. Insulin was infused intravenously (priming for 10 min and thereafter 1.0 mU/kg body wt/min). Glucose was infused concomitantly aiming at a whole blood glucose of 5 mmol/L. The glucose infusion rate (GIR) was adjusted for body weight or fat free mass (FFM), the latter measured with dual-energy X-ray absorptiometry. During the final hour of each examination (60-120 min) the mean whole blood glucose concentrations were 5.06+/-0.15 and 5.09+/-0.17 mmol/L, respectively. Of the different time intervals studied, the glucose infusion rate during the final hour (GIR60-120) showed the highest correlation and lowest coefficient of variation (GIR60-120 adjusted by FFM: r=0.70, coefficient of variation=14.7%). Adjustment of GIR for weight instead of FFM underestimated insulin sensitivity in obese men. GIR60-120 adjusted for FFM tended to increase during the second examination. The measurement error was constant across all GIR. In summary, the euglycemic hyperinsulinemic clamp method has a coefficient of variation around 15%. The glucose infusion rate should be adjusted for fat free mass.

Hyperinsulinemia is related to erythrocyte phospholipid composition and membrane fluidity changes in obese nondiabetic women.

It has been suggested that changes in the properties of cell membranes are involved in an altered insulin action. However, the influence of changes in the distribution of phospholipid classes has not been explored. We investigated 69 obese nondiabetic normoglycemic women (17 patients with impaired glucose tolerance) with varying degrees of insulin sensitivity to determine the phospholipid composition and fluid state of their erythrocyte plasma membranes. The fasting plasma insulin, the homeostasis model analysis of insulin resistance (HOMA), and the integrated area under the insulin curve (AUC-I) after an oral glucose challenge were used as markers of insulin resistance. Results were divided into normal glucose tolerance (NGT) and impaired glucose tolerance. There was a positive correlation in NGT group between the membrane sphingomyelin (SM) content and the fasting plasma insulin (r = 0.523; P < 0.0001), HOMA value (r = 0.483; P < 0.0005), and AUC-I (r = 0.352; P < 0.05) and negative correlations between membrane fluidity determined with two fluorescent probes and plasma fasting insulin (r = 0.320; r = -0.365; P < 0.05) and HOMA value (r = 0.321; r = -0.382; P < 0.05). There were also correlations between SM and the three markers of insulin resistance in the impaired glucose tolerance group. There was no correlation between insulin resistance and other membrane components. Stepwise multiple regression analysis in the NGT group confirmed that the membrane SM content was an independent predictor of plasma fasting insulin, HOMA values, and AUC-I variations. Sphingomyelin could be one of the membrane parameters contributing to insulin resistance.

Body weight, skeletal muscle morphology, and enzyme activities in relation to fasting serum insulin concentration and glucose tolerance in 48-year-old men.

Tissue samples were taken from the gastrocnemius muscle of 26 randomly selected, glucose-tolerant, 48-yr-old men. Hexokinase, phosphorylase, lactate dehydrogenase (LDH), succinate dehydrogenase, and lipoprotein lipase activity (LPLA), as well as the area per fiber type and capillary density, were determined. Mean fiber area correlated positively with relative body weight (r equals 0.53, P less than 0.01), but capillary density did not. The result is that, in cases of high body weight, each capillary supplies a larger muscle fiber area. Serum insulin concentration in the fasting state correlated positively with body weight (r equals 0.77, P less than 0.001) and with mean fiber area per capillary (r equals 0.87; P less than 0.001). Only during the latter part of an oral glucose tolerance test (OGTT) did blood glucose concentrations correlate with relative body weight and mean fiber area per capillary (r equals 0.42, r equals 0.51, P less than 0.05). A stepwise multiple regression analysis showed that the different muscle morphology measurements could account for 3/4 of the variation in the fasting serum insulin concentration, the fasting insulin/glucose ratio, and the blood glucose concentration at 120 min in the OGTT. Of the intracellular enzymes, only LDH (r equals -0.71, P less than 0.001) correlated with the mean fiber area per capillary. LPLA correlated with capillary density (r equals 0.66, P less than 0.001), and, long with the muscle morphology measurements, could account for 3/4 of the variation in serum triglyceride concentrations. The results show that a large mean muscle fiber area/capillary ratio indicates a morphologic imbalance, which is related to both glucose tolerance and various degrees of insulin sensitivity.