1,721,022 research outputs found
224-OR: Diurnal Pattern of Insulin Action and Beta-Cell Function in Health and Type 2 Diabetes
No full textInterstitial fluid glucose is not just a shifted-in-time but a distorted mirror of blood glucose: Insight from an in Silico study
Full text linkGlucose sensors measure glucose concentration in the interstitial fluid (ISF), remote from blood. ISF glucose is well known to be "delayed" with respect to blood glucose (BG). However, ISF glucose is not simply a shifted-in-time version of BG but exhibits a more complex pattern.
METHODS:
To gain insight into this problem, one can use linear systems theory. However, this may lose a more clinical readership, thus we use simulation and two case studies to convey our thinking in an easier way. In particular, we consider BG concentration measured after meal and exercise in 12 healthy volunteers, whereas ISF glucose is simulated using a well-accepted model of blood-ISF glucose kinetics, which permits calculation of the equilibration time, a parameter characterizing the system. Two metrics are defined: blood and ISF glucose difference at each time point and time to reach the same glucose value in blood and ISF.
RESULTS:
The simulation performed and the two metrics show that the relationship between blood-ISF glucose profiles is more complex than a pure shift in time and that the pattern depends on both equilibration time and BG.
CONCLUSIONS:
In this in silico study, we have illustrated, with simple case studies, the meaning of the of ISF glucose with respect to BG. Understanding that ISF glucose is not just a shifted-in-time version but a distorted mirror of BG is important for a correct use of continuous glucose monitoring for diabetes management
Circadian variability of insulin sensitivity: Physiological input for in silico artificial pancreas
No full textImpaired Diurnal Pattern of Meal Tolerance and Insulin Sensitivity in Type 2 Diabetes: Implications for Therapy
No full text: To assess the diurnal patterns of postprandial glucose tolerance and insulin sensitivity, 19 subjects with type 2 diabetes (8 women; 60 ± 11 years; BMI 32 ± 5 kg/m2) and 19 anthropometrically matched subjects with no diabetes (ND; 11 women; 53 ± 12 years; BMI 29 ± 5 kg/m2) were studied during breakfast (B), lunch (L), and dinner (D) with identical mixed meals (75 g carbohydrates) on 3 consecutive days in a randomized Latin square design. Three stable isotopes of glucose were ustilized to estimate meal fluxes, and mathematical models were used in estimating indices of insulin action and β-cell function. Postmeal glucose excursions were higher at D versus B and at D versus L in type 2 diabetes (P < 0.05), while in ND they were higher at D versus B (P = 0.025) and at L versus B (P = 0.04). The insulin area under the curve was highest at B compared with L and D in type 2 diabetes, while no differences were observed in ND. Disposition index (DI) was higher at B than at L (P < 0.01) and at D (P < 0.001) in ND subjects, whereas DI was low with unchanging pattern across B-L-D in individuals with type 2 diabetes. Furthermore, between-meal differences in β-cell responsivity to glucose (F) and insulin sensitivity (SI) were concurrent with changes in the DI within groups. Fasting and postmeal glucose, insulin, and C-peptide concentrations, along with estimates of endogenous glucose production (EGP), Rd, SI, F, hepatic extraction of insulin, insulin secretion rate, extracted insulin, and DI, were altered in type 2 diabetes compared with ND (P < 0.011 for all). The data show a diurnal pattern of postprandial glucose tolerance in overweight otherwise glucose-tolerant ND individuals that differs from overweight individuals with type 2 diabetes. The results not only provide valuable insight into management strategies for better glycemic control in people with type 2 diabetes, but also improved understanding of daytime glucose metabolism in overweight individuals without impaired glucose tolerance or overt diabetes
Effects of Hyperglycemia and Hyperinsulinemia on Glucose Turnover during Exercise in Type 1 Diabetes
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A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes
Full text linkBackground: Estimation of insulin sensitivity (S-I) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for S-I estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (S-I(SP)) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of S-I(SP) performing well in daily life unrestrained conditions.Methods: The new S-I(SP) was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70 g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model S-I (S-I(MM)). CGM and CSII data were used for S-I(SP) calculation, which was then validated against the gold standard S-I(MM).Results: S-I(SP) was estimated with good precision (median coefficient of variation <20%) in 100% of the real and 91% of the simulated meals. S-I(SP) and S-I(MM) were highly correlated, both in the simulated and real datasets (R = 0.82 and R = 0.83, P < 0.001), and exhibited a similar intraday pattern.Conclusions: S-I(SP) is suitable for estimating S-I in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump
1744-P: Hepatic and Disposal Insulin Sensitivity with Single Tracer Method: Use of Naturally Abundant 13C-Polysaccharide
No full textHepatic insulin sensitivity in healthy and prediabetic subjects: From a dual- to a single-tracer oral minimal model
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