Glucose Kinetics Research Articles

Characterisation of cotadutide's dual GLP-1/glucagon receptor agonistic effects on glycaemic control using an in vivo human glucose regulation quantitative systems pharmacology model.

Cotadutide is a dual GLP-1 and glucagon receptor agonist with balanced agonistic activity at each receptor designed to harness the advantages on promoting liver health, weight loss and glycaemic control. We characterised the effects of cotadutide on glucose, insulin, GLP-1, GIP, and glucagon over time in a quantitative manner using our glucose dynamics systems model (4GI systems model), in combination with clinical data from a multiple ascending dose/Phase 2a (MAD/Ph2a) study in overweight and obese subjects with a history of Type 2 diabetes mellitus (NCT02548585). The cotadutide PK-4GI systems model was calibrated to clinical data by re-estimating only food related parameters. In vivo cotadutide efficacy was scaled based on in vitro potency. The model was used to explore the effect of weight loss on insulin sensitivity and predict the relative contribution of the GLP-1 and glucagon receptor agonistic effects on glucose. Cotadutide MAD/Ph2a clinical endpoints were successfully predicted. The 4GI model captured a positive effect of weight loss on insulin sensitivity and showed that the stimulating effect of glucagon on glucose production counteracts the GLP-1 receptor-mediated decrease in glucose, resulting in a plateau for glucose decrease around a 200-μg cotadutide dose. The 4GI quantitative systems pharmacology model was able to predict the clinical effects of cotadutide on glucose, insulin, GLP-1, glucagon and GIP given known in vitro potency. The analyses demonstrated that the quantitative systems pharmacology model, and its successive refinements, will be a valuable tool to support the clinical development of cotadutide and related compounds.

Cerebral Glucose Metabolism following TBI: Changes in Plasma Glucose, Glucose Transport and Alternative Pathways of Glycolysis-A Translational Narrative Review.

Traumatic brain injury (TBI) is a major public health concern with significant consequences across various domains. Following the primary event, secondary injuries compound the outcome after TBI, with disrupted glucose metabolism emerging as a relevant factor. This narrative review summarises the existing literature on post-TBI alterations in glucose metabolism. After TBI, the brain undergoes dynamic changes in brain glucose transport, including alterations in glucose transporters and kinetics, and disruptions in the blood-brain barrier (BBB). In addition, cerebral glucose metabolism transitions from a phase of hyperglycolysis to hypometabolism, with upregulation of alternative pathways of glycolysis. Future research should further explore optimal, and possibly personalised, glycaemic control targets in TBI patients, with GLP-1 analogues as promising therapeutic candidates. Furthermore, a more fundamental understanding of alterations in the activation of various pathways, such as the polyol and lactate pathway, could hold the key to improving outcomes following TBI.

Glucose turnover at whole-body and skeletal muscle level in response to parenteral nutrition in male patients with alcoholic liver cirrhosis

Cirrhosis is associated with insulin resistance and impaired glucose tolerance, which may be caused by impairments at different tissue levels (liver, skeletal muscle, and/or beta cell). Here, glucose kinetics at whole-body and skeletal muscle level in patients with cirrhosis (Child-Pugh A and B) were studied during parenteral nutrition using the isotope dilution technique and arteriovenous balance approach across the leg. As opposed to the euglycemic hyperinsulinemic clamp or glucose tolerance tests applied in previous studies, this approach provides a nutrient composition more similar to a normal meal while circumventing any possible portal-systemic shunting, impaired hepatic uptake and incretin effect. We confirmed the presence of hepatic and peripheral insulin resistance in our patient population. Endogenous glucose production was less suppressed in response to parenteral nutrition. However, glucose uptake in skeletal muscle was increased. Our results suggests that in our study participants with cirrhosis, the hepatic and peripheral insulin resistance is compensated for by increased insulin secretion and thus, increased glucose uptake in muscle. Hereby, glucose homeostasis is maintained.

Enzymatic Galvanic Redox Potentiometry for In Vivo Biosensing.

Redox potentiometry has emerged as a new platform for in vivo sensing, with improved neuronal compatibility and strong tolerance against sensitivity variation caused by protein fouling. Although enzymes show great possibilities in the fabrication of selective redox potentiometry, the fabrication of an enzyme electrode to output open-circuit voltage (EOC) with fast response remains challenging. Herein, we report a concept of novel enzymatic galvanic redox potentiometry (GRP) with improved time response coupling the merits of the high selectivity of enzyme electrodes with the excellent biocompatibility and reliability of GRP sensors. With a glucose biosensor as an illustration, we use flavin adenine dinucleotide-dependent glucose dehydrogenase as the recognition element and carbon black as the potential relay station to improve the response time. We find that the enzymatic GRP biosensor rapidly responds to glucose with a good linear relationship between EOC and the logarithm of glucose concentration within a range from 100 μM to 2.65 mM. The GRP biosensor shows high selectivity over O2 and coexisting neurochemicals, good reversibility, and sensitivity and can in vivo monitor glucose dynamics in rat brain. We believe that this study will pave a new platform for the in vivo potentiometric biosensing of chemical events with high reliability.

Association between perioperative glucose profiles assessed by the continuous glucose monitoring (CGM) system and prognosis in patients with ST-segment elevation myocardial infarction (STEMI): protocol for a cohort study

IntroductionST-segment elevation myocardial infarction (STEMI) presents a serious cardiovascular condition requiring prompt intervention. Dysglycaemia has been identified as a significant risk factor impacting STEMI prognosis. However, limited research has focused...

Identification of individuals with diabetes who are eligible for continuous glucose monitoring forecasting

Background and objectivesPredicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting. MethodsParticipants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min. ResultsA strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661–0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two. ConclusionsThe identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.

Assessment of osmotic adaptation and seawater readiness in juvenile Chinook salmon reared at the Malkinskiy salmon hatchery

Aim: to assessed the readiness of Chinook salmon fingerlings reared at the Malkinskiy salmon hatchery (Kamchatka region) to seawater run and to reverse water-salt transport; to develop a rapid and effective method to determine the readiness of juvenile Chinook salmon to seawater run.Methods: the dynamics of haemoglobin, glucose, haematocrit, blood osmolarity and body weight were studied in juvenile of Chinook salmon of a different sizes exposed in water with salinities of 30 and 40% (experiments in fresh water under the same conditions served as a control, all experiments were carried out in three repetitions).Novelty: a method is described to assess the readiness of juvenile salmonids with a prolonged freshwater period of life to sea water run.Results: the standard “salinity test” does not always reflect the readiness of juveniles to run, and to clarify their physiological state, it is worth studing the dynamics of blood osmolarity during exposure to water with a salinity of 30% (the critical indicator is 340 mOsm l‑1 after a day of experiment). Blood levels of glucose, hemoglobin and hematocrit, as well as the dynamics of body weight are not suitable for assessing salt resistance because off the excessive sensitivity of these indicators. It was shown that juvenile Chinook weighing ≥ 7 g were physiologically ready for run, whereas juveniles weighing ≤ 5 g were not. The intermediate group (5–7 g) exhibited different physiological states in different years.Practical importance: determining the minimum size of fully transformed smolts and finding ways to increase the salt tolerance of juveniles is the way to increase the profitability of the hatchery.

Impaired plasma glucose clearance is a key determinant of fasting hyperglycemia in people with obesity.

The objective of this study was to evaluate the relative importance of the basal rate of glucose appearance (Ra) in the circulation and the basal rate of plasma glucose clearance in determining fasting plasma glucose concentration in people with obesity and different fasting glycemic statuses. The authors evaluated basal glucose kinetics in 33 lean people with normal fasting glucose (<100 mg/dL; Lean < 100 group) and 206 people with obesity and normal fasting glucose (Ob < 100 group, n = 118), impaired fasting glucose (100-125 mg/dL; Ob 100-125 group, n = 66), or fasting glucose diagnostic of diabetes (≥126 mg/dL; Ob ≥ 126 group, n = 22). Although there was a large (up to three-fold) range in glucose Ra within each group, the ranges in glucose concentration in the Lean < 100, Ob < 100, and Ob 100-125 groups were small because of a close relationship between glucose Ra and clearance rate. However, the glucose clearance rate at any Ra value was lower in the hyperglycemic than the normoglycemic groups. In the Ob ≥ 126 group, plasma glucose concentration was primarily determined by glucose Ra, because glucose clearance was markedly attenuated. Fasting hyperglycemia in people with obesity represents a disruption of the precisely regulated integration of glucose production and clearance rates.

A protocol to isolate, identify, and verify glucose- or carbohydrate-binding receptors.

Sensing, transport, and utilization of glucose is pivotal to the maintenance of energy homeostasis in animals. Although transporters involved in mobilizing glucose across different cellular compartments are fairly well known, the receptors that bind glucose to mediate its effects independently of glucose metabolism remain largely unrecognized. Establishing precise and reproducible methods to identify glucose receptors in the brain or other peripheral organs will pave the way for comprehending the role of glucose signaling pathways in maintaining, regulating, and reprogramming cellular metabolic needs. Identification of such potential glucose receptors will also likely lead to development of effective therapeutics for treatment of diabetes and related metabolic disorders. Commercially available biotin or radiolabeled glucose conjugates have low molecular weight; therefore, they do not provide enough sensitivity and density to isolate glucose receptors. Here, we describe a protocol to isolate, identify, and verify glucose-binding receptor/s using high molecular weight glucose (or other carbohydrate) conjugates. We have produced 30 kDa glucose- (or other carbohydrate-) biotin-polyacrylamide (PAA) conjugates with mole fractions of 80:5:15% respectively. These conjugates are used with biotin-streptavidin biochemistry, In-cell ELISA, and surface plasmon resonance (SPR) methods to isolate, identify, and verify glucose- or carbohydrate-binding receptors. We first demonstrate how streptavidin-coated magnetic beads are employed to immobilize glucose-biotin-PAA conjugates. Then, these beads are used to enrich and isolate glucose-binding proteins from tissue homogenates or from single-cell suspensions. The enriched or isolated proteins are subjected to mass spectrometry/proteomics to reveal the identity of top candidate proteins as potential glucose receptors. We then describe how the In-cell ELISA method is used to verify the interaction of glucose with its potential receptor through stable expression of the receptor in-vitro. We further demonstrate how a highly sensitive SPR method can be used to measure the binding kinetics of glucose with its receptor. In summary, we describe a protocol to isolate, identify, and verify glucose- or carbohydrate-binding receptors using magnetic beads, In-cell ELISA, and SPR. This protocol will form the future basis of studying glucose or carbohydrate receptor signaling pathways in health and in disease.

Physiology-Informed Deep Learning Modeling of Type 1 Diabetes Dynamics: Mapping Data to Virtual Subjects

We consider the problem of creating Digital Twins (DTs) of glucose metabolism in people with Type 1 Diabetes (T1D), coupling a large-scale mean population metabolic model of glucose dynamics developed from first principles, with deep learning (DL) architectures, to map actual patient data to their virtual counterparts. For the neural network component of our proposed strategy, two models were investigated: a Long Short-Term Memory (LSTM) network and a Generative Adversarial Network (GAN). Our best model outperformed significantly the mean population model with respect to evaluation metrics (LSTM vs. metabolic simulator), expressed as median (interquartile range): MAE 35.0 (28.8, 43.8) vs. 79.7 (62.4, 115.5) [mg/dL], RMSE 44.8 (37.2, 56.2) vs. 94.9 (76.3, 128.0) [mg/dL], eNRMSE 0.85 (0.83, 0.88) vs. 0.54 (0.48, 0.72), eFIT 0.25 (0.13, 0.40) vs. 0.00 (0.00, 0.00). We showed that the proposed physiology-informed deep learning approach successfully mapped real patient data to virtual subjects, with the potential to enable in-silico testing of novel therapeutic strategies on a virtual population.

G2P2C — A modular reinforcement learning algorithm for glucose control by glucose prediction and planning in Type 1 Diabetes

Developing diagnostic and treatment solutions for medical applications is often challenging due to the complex dynamics, partial observability, high inter- and intra-population variability, and the presence of unknown delays and disturbances. A characteristic case is the control of glucose concentration in people with Type 1 Diabetes (T1D) through the administration of exogenous insulin. The above complexities, enhanced by the significant cognitive burden associated with the estimation of optimal insulin dosing related to daily activities such as food intake and exercise, call for advanced insulin administration solutions towards a fully automated Artificial Pancreas System (APS). Reinforcement Learning (RL) is currently being explored in the development of APSs thanks to its demonstrated potential in problems characterized by complex dynamics and uncertainties. Despite the progress, RL algorithms in T1D still require manual estimation and announcement of meal carbohydrate (CHO) content or rely on small meal scenarios. In this study, we proposed G2P2C, a modular deep RL algorithm, which aims to fully automate glucose control in T1D, eliminating the need for CHO estimation and announcement. G2P2C was designed based on the state-of-the-art Proximal Policy Optimization (PPO) algorithm, augmented by two novel optimization phases: (i) model learning and (ii) planning. The former integrated an auxiliary learning task to learn a glucose dynamics model. The latter fine-tuned the learned control strategy to a short-time horizon by simulating glucose trajectories into the future. We evaluated the performance of G2P2C in-silico on a challenging meal protocol (180 g of CHO per day) for 20 subjects (10 adults and 10 adolescents) using an open-source version of a T1D simulator approved by the United States Food and Drug Administration (FDA). G2P2C was compared with state-of-the-art RL algorithms and two basal-bolus (BB) clinical treatment strategies, which involve manual meal announcement and CHO estimation with automated correction insulin boli for elevated glucose. G2P2C obtained statistically significant (P<0.05) reward improvements compared to PPO in 18 out of 20 subjects, while maintaining a lower failure rate. In addition, G2P2C achieved a time in range of 73% and 64% for the adult and adolescent cohorts, respectively, outperforming BB strategies in the adult cohort although no meal announcement was performed. The control performance and algorithmic characteristics of G2P2C show promise as a candidate algorithm for glucose control in APSs. We released the codebase of G2P2C (https://github.com/chirathyh/G2P2C) and an online demonstration tool (https://capsml.com/), where users can perform custom simulations to compare G2P2C with BB strategies, under the MIT license.

Prenatal cortisol exposure impairs adrenal function but not glucose metabolism in adult sheep.

Adverse environmental conditions before birth are known to programme adult metabolic and endocrine phenotypes in several species. However, whether increments in fetal cortisol concentrations of the magnitude commonly seen in these conditions can cause developmental programming remains unknown. Thus, this study investigated the outcome of physiological increases in fetal cortisol concentrations on glucose-insulin dynamics and pituitary-adrenal function in adult sheep. Compared with saline treatment, intravenous fetal cortisol infusion for 5 days in late gestation did not affect birthweight but increased lamb body weight at 1-2 weeks after birth. Adult glucose dynamics, insulin sensitivity and insulin secretion were unaffected by prenatal cortisol overexposure, assessed by glucose tolerance tests, hyperinsulinaemic-euglycaemic clamps and acute insulin administration. In contrast, prenatal cortisol infusion induced adrenal hypo-responsiveness in adulthood with significantly reduced cortisol responses to insulin-induced hypoglycaemia and exogenous adrenocorticotropic hormone (ACTH) administration relative to saline treatment. The area of adrenal cortex expressed as a percentage of the total cross-sectional area of the adult adrenal gland was also lower after prenatal cortisol than saline infusion. In adulthood, basal circulating ACTH but not cortisol concentrations were significantly higher in the cortisol than saline-treated group. The results show that cortisol overexposure before birth programmes pituitary-adrenal development with consequences for adult stress responses. Physiological variations in cortisol concentrations before birth may, therefore, have an important role in determining adult phenotypical diversity and adaptability to environmental challenges.

Effect of 5'-adenosine monophosphate-activated protein kinase agonists on insulin and glucose dynamics in experimentally induced insulin dysregulation in horses.

5'-adenosine monophosphate-activated protein kinase (AMPK) agonists, particularly resveratrol (RES), have not been extensively evaluated for their effect on insulin dysregulation (ID) in horses. Evaluate the effects of treatment with RES (10 mg/kg PO q12h), metformin (MET; 30 mg/kg PO q12h), and aspirin (ASP; 20 mg/kg PO q24h) on experimentally induced ID. Thirty-three healthy, adult, light-breed horses. Unblinded, placebo-controlled, experimental trial evaluating effects of AMPK agonists (RES, MET, and ASP) on experimentally induced ID. Horses were randomly assigned to a treatment group (RES, MET/ASP, RES/ASP, RES/MET/ASP, or placebo [CON]) after induction of ID with dexamethasone (0.08 mg/kg PO q24h for 7 days). Frequently sampled insulin-modified IV glucose tolerance tests (FSIGTT) and oral sugar tests (OST) were performed at baseline, 7 days after ID, and ID plus 7 days of treatment. Minimal model and OST variables were compared between (1-way ANOVA) and within (1-way ANOVA for repeated measures) groups over time to determine effects of treatment on ID. Administration of dexamethasone for 14 days resulted in significantly altered insulin and glucose dynamics (SI, DI, basal [glucose], and [insulin]) and produced clinical signs of laminitis in 5 out of 33 (15%) of horses included in the study. Combination therapy with RES, MET, and ASP did not significantly improve insulin and glucose dynamics in horses with experimentally induced ID. Metabolic testing before glucocorticoid administration should be considered in horses with clinical signs of metabolic syndrome.

An extended minimal model of OGTT: estimation of α- and β-cell dysfunction, insulin resistance, and the incretin effect.

Loss of insulin sensitivity, α- and β-cell dysfunction, and impairment in incretin effect have all been implicated in the pathophysiology of type 2 diabetes (T2D). Parsimonious mathematical models are useful in quantifying parameters related to the pathophysiology of T2D. Here, we extend the minimum model developed to describe the glucose-insulin-glucagon dynamics in the isoglycemic intravenous glucose infusion (IIGI) experiment to the oral glucose tolerance test (OGTT). The extended model describes glucose and hormone dynamics in OGTT including the contribution of the incretin hormones, glucose-dependent insulinotropic polypeptide (GIP), and glucagon-like peptide-1 (GLP-1), to insulin secretion. A new function describing glucose arrival from the gut is introduced. The model is fitted to OGTT data from eight individuals with T2D and eight weight-matched controls (CS) without diabetes to obtain parameters related to insulin sensitivity, β- and α-cell function. The parameters, i.e., measures of insulin sensitivity, a1, suppression of glucagon secretion, k1, magnitude of glucagon secretion, γ2, and incretin-dependent insulin secretion, γ3, were found to be different between CS and T2D with P values < 0.002, <0.017, <0.009, <0.004, respectively. A new rubric for estimating the incretin effect directly from modeling the OGTT is presented. The average incretin effect correlated well with the experimentally determined incretin effect with a Spearman rank test correlation coefficient of 0.67 (P < 0.012). The average incretin effect was found to be different between CS and T2D (P < 0.032). The developed model is shown to be effective in quantifying the factors relevant to T2D pathophysiology.NEW & NOTEWORTHY A new extended model of oral glucose tolerance test (OGTT) has been developed that includes glucagon dynamics and incretin contribution to insulin secretion. The model allows the estimation of parameters related to α- and β-cell dysfunction, insulin sensitivity, and incretin action. A new function describing the influx of glucose from the gut has been introduced. A new rubric for estimating the incretin effect directly from the OGTT experiment has been developed. The effect of glucose dose was also investigated.

Ultrarapid Insulin Use Can Reduce Postprandial Hyperglycemia and Late Hypoglycemia, Even in Delayed Insulin Injections: A Connected Insulin Cap-Based Real-World Study.

Objectives: Reaching optimal postprandial glucose dynamics is a daily challenge for people with type 1 diabetes (T1D). This study aimed to analyze the postprandial hyperglycemic excursion (PHEs) and late postprandial hypoglycemia (LPH) risk according to prandial insulin time and type. Research Design and Methods: Real-world, retrospective study in T1D using multiple daily injections (MDI) analyzing 5 h of paired continuous glucose monitoring and insulin injections data collected from the connected cap Insulclock®. Meal events were identified using the rate of change detection methodology. Postprandial glucometrics and LPH (glucose <70 mg/dL 2-5 h after a meal) were evaluated according to insulin injection time and rapid (RI) or ultrarapid analog, Fiasp® (URI), use. Results: Meal glycemic excursions (n = 2488), RI: 1211, 48.7%; UR: 1277, 51.3%, in 82 people were analyzed according to injection time around the PHE: -45 to -15 min; -15 to 0 min; and 0 to +45 min. In 63% of the meals, insulin was injected after the PHE started. Lower PHE was observed with URI versus RI (glucose peak-baseline; mg/dL; mean ± standard deviation): 106.7 ± 35.2 versus 111.2 ± 40.3 (P = 0.003), particularly in 0/+45 injections: 111.6 ± 40.2 versus 118.1 ± 43.3; (P = 0.002). One third (29.1%) of participants added a second (correction) injection. The use of URI and avoiding a second injection were independently associated with less LPH risk, even in delayed injections (0/+45), (-36%, odds ratio [OR] 0.641; confidence interval [CI]: 0.462-0.909; P = 0.012) and -56% (OR 0.641; CI: 0.462-0.909 P = 0.038), respectively. Conclusions: URI analog use as prandial insulin reduces postprandial hyper- and hypoglycemia, even in delayed injections.

Disturbances in Dynamics of Glucose, Insulin, and C-Peptide in Blood after a Normalized Intake of a Mixed Meal in Obesity and Type 2 Diabetes Mellitus

Disturbances in Dynamics of Glucose, Insulin, and C-Peptide in Blood after a Normalized Intake of a Mixed Meal in Obesity and Type 2 Diabetes Mellitus

Risk of hypoglycemia in type 1 diabetes management: An in-silico sensitivity analysis to assess and rank the quantitative impact of different behavioral factors

Background and Objective: In type 1 diabetes (T1D), a quantitative evaluation of the impact on hypoglycemia of suboptimal therapeutic decision (e.g. incorrect estimation of the ingested carbohydrates, inaccurate insulin timing, etc) is unavailable. Clinical trials to measure sensitivity to patient actions would be expensive, exposed to confounding factors and risky for the participants. In this work, a T1D patient decision simulator (T1D-PDS), realistically reproducing blood glucose dynamics in a large virtual population, is used to perform extensive in-silico trials and the so-derived data employed to implement a sensitivity analysis that ranks different behavioral factors based on their impact on a clinically meaningful parameter, the time below range (TBR). Methods: Eleven behavioral factors impacting on hypoglycemia are considered. The T1D-PDS was used to perform multiple 2-week simulations involving 100 adults, by testing about 3500 different perturbations for nominal behavior. A local linear approximation of the function linking the TBR and the factors were computed to derive sensitivity indices (SIs), quantifying the impact of each factor on TBR variations. Results: The obtained ranking quantifies importance of factors w.r.t. the others. Factors apparently related to hypoglycemia were correctly placed on the top of the ranking, including systematic (SI=2.05%) and random (SI=1.35%) carb-counting error, hypotreatment dose (SI=-1.21%), insulin bolus time w.r.t. mealtime (SI=1.09%). Conclusions: The obtained SIs allowed to rank behavioral factors based on their impact on TBR. The behavioral factors identified as most influential can be prioritized in patient training.

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

High-protein diet with excess leucine prevents inactivity-induced insulin resistance in women

Background and aimsMuscle inactivity leads to muscle atrophy and insulin resistance. The branched-chain amino acid (BCAA) leucine interacts with the insulin signaling pathway to modulate glucose metabolism. We have tested the ability of a high-protein BCAA-enriched diet to prevent insulin resistance during long-term bed rest (BR). MethodsStable isotopes were infused to determine glucose and protein kinetics in the postabsorptive state and during a hyperinsulinemic-euglycemic clamp in combination with amino acid infusion (Clamp+AA) before and at the end of 60 days of BR in two groups of healthy, young women receiving eucaloric diets containing 1 g of protein/kg per day (n=8) or 1.45 g of protein/kg per day enriched with 0.15 g/kg per day of BCAAs (leucine/valine/isoleucine=2/1/1) (n=8). Body composition was determined by Dual X-ray Absorptiometry. ResultsBR decreased lean body mass by 7.6±0.3% and 7.2±0.8% in the groups receiving conventional or high protein-BCAA diets, respectively. Fat mass was unchanged in both groups.At the end of BR, percent changes of insulin-mediated glucose uptake significantly (p=0.01) decreased in the conventional diet group from 155±23% to 84±10% while did not change significantly in the high protein-BCAA diet group from 126±20% to 141±27% (BR effect, p=0.32; BR/diet interaction, p=0.01; Repeated Measures ANCOVA). In contrast, there were no BR/diet interactions on proteolysis and protein synthesis Clamp+AA changes in the conventional diet and the high protein-BCAA diet groups. ConclusionA high protein-BCAA enriched diet prevented inactivity-induced insulin resistance in healthy women.

ReplayBG: a digital twin-based methodology to identify a personalized model from type 1 diabetes data and simulate glucose concentrations to assess alternative therapies.

Design and assessment of new therapies for type 1 diabetes (T1D) management can be greatly facilitated by in silico simulations. The ReplayBG simulation methodology here proposed allows "replaying" the scenario behind data already collected by simulating the glucose concentration obtained in response to alternative insulin/carbohydrate therapies and evaluate their efficacy. ReplayBG is based on the concept of digital twin and works in two steps. First, a personalized model of glucose-insulin dynamics is identified using data of insulin, carbohydrate, and continuous glucose monitoring (CGM). Then, this model is used to simulate the glucose concentration that would have been obtained by "replaying" the same portion of data using a different therapy. The validity of the methodology was evaluated using data from 100 virtual subjects generated with the UVa/Padova T1D Simulator (T1DS). In particular, the glucose concentration traces simulated by ReplayBG are compared with those provided by T1DS in five different scenarios of meal intake and insulin amount modifications. To further evaluate the methodology, we compared ReplayBG with a state-of-the-art methodology for the scope. Two case studies using real data are also presented to provide actual examples of ReplayBG use. ReplayBG simulates with high accuracy the effect of the considered insulin and carbohydrate treatment alterations, performing significantly better than state-of-art method in almost all considered situations. The good performance of ReplayBG in the two case studies using real data supports simulation results. ReplayBG proved to be a reliable and robust tool to retrospectively explore the effect of new treatments for T1D on the glucose dynamics. It is freely available as open source software at https://github.com/gcappon/replay-bg. ReplayBG offers a new approach to preliminary evaluate new therapies for T1D management before clinical trials.