Dynamic network model of glucose counterregulation in subjects with insulin-requiring diabetes

[Evaluation of fructosamine as a parameter of blood glucose control in diabetic pregnant women]

OBJECTIVE

To evaluate the alternative parameters to monitor glycemia in pregnant women with diabetes studying the relationship between fructosamine testing and self monitoring of blood glucose in pregnant women with diabetes.

MATERIALS AND METHODS

Serum fructosamine levels and the self monitoring of blood glucose over 14 days before the collection of fructosamine were evaluated in 47 diabetic pregnant women.

RESULTS

Seventy-one fructosamine levels and 2,238 glucose measurements (CGs) were analysed. Levels of fructosamine correlated with high blood glucose index (HBGI) and the standard deviation of glycemias (r = 0.28; p = 0.021 and r = 0.26; p = 0.03, respectively). The comparison between the mothers of the newborns with appropriated or large birthweight and those who gave birth to small newborns for their gestational age (SGA) showed that the latter had a lower glycemic mean (105 vs. 114 and 119 mg/dL), a higher low blood glucose index (5.8 vs. 1.3 and 0.7) and a higher percentage of hyperglycemias (11 vs. 0 and 0%) even when the fructosamine falls within the reference values (242 vs. 218 and 213 μmol/l).

CONCLUSION

The levels of fructosamine can be used as further parameter to aid self monitoring of blood glucose to evaluate hyperglycemias and glycemic variability, however, this can underestimate hypoglycemias in pregnant women carrying small-for-gestational age fetuses.

The university of Virginia/Padova type 1 diabetes simulator matches the glucose traces of a clinical trial

BACKGROUND

In 2008, the Food and Drug Administration (FDA) accepted our type 1 diabetes mellitus (T1DM) simulator (S2008), equipped with 100 in silico adults, 100 adolescents, and 100 children, as a substitute for preclinical trials for certain insulin treatments, including closed-loop algorithms. Hypoglycemia was well described in the simulator, but recent closed-loop trials showed a much larger frequency of hypoglycemia events in patients compared with the in silico ones. In order to better describe the distribution of glucose concentration observed in clinical trials, the simulator has recently been updated, and modifications have been accepted by the FDA (S2013). The aim of this study is to assess the validity of the S2013 simulator against clinical data and compare its performance with that of the S2008.

SUBJECTS AND METHODS

The database consists of 24 T1DM subjects who received dinner (70.7±3.3 g of carbohydrate) and breakfast (52.9±0.1 g of carbohydrate) in two occasions (open- and closed-loop), for a total of 96 postmeal glucose profiles. Measured plasma glucose profiles were compared with those simulated in 100 in silico adults, and the continuous glucose error grid analysis (CG-EGA) was used to assess the validity of the simulated traces. Moreover, the most common outcome metrics have been compared.

RESULTS

The frequency of hypoglycemia episodes predicted by the S2013 well reproduces that observed during clinical trials as proven by the CG-EGA. In addition, the outcome metrics provided by the S2013 are similar to those observed in clinical trials in a set of T1DM subjects.

CONCLUSIONS

We demonstrated that the virtual subjects of the S2013 are representative of the T1DM population observed in a clinical trial. We conclude that the S2013 is a valid tool usable to test the robustness of closed-loop control algorithms for artificial pancreas.

The UVA/PADOVA Type 1 Diabetes Simulator: New Features

Recent studies have provided new insights into nonlinearities of insulin action in the hypoglycemic range and into glucagon kinetics as it relates to response to hypoglycemia. Based on these data, we developed a new version of the UVA/PADOVA Type 1 Diabetes Simulator, which was submitted to FDA in 2013 (S2013). The model of glucose kinetics in hypoglycemia has been improved, implementing the notion that insulin-dependent utilization increases nonlinearly when glucose decreases below a certain threshold. In addition, glucagon kinetics and secretion and action models have been incorporated into the simulator: glucagon kinetics is a single compartment; glucagon secretion is controlled by plasma insulin, plasma glucose below a certain threshold, and glucose rate of change; and plasma glucagon stimulates with some delay endogenous glucose production. A refined statistical strategy for virtual patient generation has been adopted as well. Finally, new rules for determining insulin to carbs ratio (CR) and correction factor (CF) of the virtual patients have been implemented to better comply with clinical definitions. S2013 shows a better performance in describing hypoglycemic events. In addition, the new virtual subjects span well the real type 1 diabetes mellitus population as demonstrated by good agreement between real and simulated distribution of patient-specific parameters, such as CR and CF. S2013 provides a more reliable framework for in silico trials, for testing glucose sensors and insulin augmented pump prediction methods, and for closed-loop single/dual hormone controller design, testing, and validation.

Pancreatic network control of glucagon secretion and counterregulation

Glucagon counterregulation (GCR) is a key protection against hypoglycemia compromised in insulinopenic diabetes by an unknown mechanism. In this work, we present an interdisciplinary approach to the analysis of the GCR control mechanisms. Our results indicate that a pancreatic network which unifies a few explicit interactions between the major islet peptides and blood glucose (BG) can replicate the normal GCR axis and explain its impairment in diabetes. A key and novel component of this network is an alpha-cell auto-feedback, which drives glucagon pulsatility and mediates triggering of pulsatile GCR by hypoglycemia via a switch-off of the beta-cell suppression of the alpha-cells. We have performed simulations based on our models of the endocrine pancreas which explain the in vivo GCR response to hypoglycemia of the normal pancreas and the enhancement of defective pulsatile GCR in beta-cell deficiency by switch-off of intrapancreatic alpha-cell suppressing signals. The models also predicted that reduced insulin secretion decreases and delays the GCR. In conclusion, based on experimental data we have developed and validated a model of the normal GCR control mechanisms and their dysregulation in insulin deficient diabetes. One advantage of this construct is that all model components are clinically measurable, thereby permitting its transfer, validation, and application to the study of the GCR abnormalities of the human endocrine pancreas in vivo.

Evidence for acyl-ghrelin modulation of growth hormone release in the fed state

CONTEXT

The timing and frequency of GH secretory episodes is regulated by GHRH and somatostatin. This study provides evidence for amplification of these GH pulses by endogenous acyl-ghrelin.

DESIGN

Blood was sampled every 10 min for 26.5 h during a fed admission with standardized meals and also during the final 24 h of a 61.5-h fast. GH secretion profiles were derived from deconvolution of 10-min sampling data, and full-length acyl-ghrelin levels were measured using a newly developed two-site sandwich assay.

SETTING

The study was conducted at a university hospital general clinical research center.

PARTICIPANTS

Participants included eight men with mean (+/- sd) age 24.5 +/- 3.7 yr (body mass index 24 +/- 2.1 kg/m(2)).

RESULTS

Correlations were computed between amplitudes of individual GH secretory events and the average acyl-ghrelin concentration in the 60-min interval preceding each GH burst. In the fed state, the peak correlations were positive for all subjects and significantly higher than in the fasting state when acyl-ghrelin levels declined [mean (+/- sem): 0.7 (0.04) vs. 0.29 (0.08), P = 0.017]. In addition, long-term fasting was associated with an increase in the GH secretory pulse mass and amplitude but not frequency [fed vs. fasting pulse mass: 0.22 (0.05) vs. 0.44 (0.06) microg/liter, P = 0.002; amplitude: 5.2 (1.3) vs. 11.8 (1.9) microg/liter/min, P = 0.034; pulses per 24 h: 19.4 (0.5) vs. 22.0 (1.4), P = 0.1].

CONCLUSION

Our data support the hypothesis that under normal conditions in subjects given regular meals endogenous acyl-ghrelin acts to increase the amplitude of GH pulses.