Closed-loop insulin delivery for treatment of type 1 diabetes

Type 1 diabetes is one of the most common endocrine problems in childhood and adolescence, and remains a serious chronic disorder with increased morbidity and mortality, and reduced quality of life. Technological innovations positively affect the management of type 1 diabetes. Closed-loop insulin delivery (artificial pancreas) is a recent medical innovation, aiming to reduce the risk of hypoglycemia while achieving tight control of glucose. Characterized by real-time glucose-responsive insulin administration, closed-loop systems combine glucose-sensing and insulin-delivery components. In the most viable and researched configuration, a disposable sensor measures interstitial glucose levels, which are fed into a control algorithm controlling delivery of a rapid-acting insulin analog into the subcutaneous tissue by an insulin pump. Research progress builds on an increasing use of insulin pumps and availability of glucose monitors. We review the current status of insulin delivery, focusing on clinical evaluations of closed-loop systems. Future goals are outlined, and benefits and limitations of closed-loop therapy contrasted. The clinical utility of these systems is constrained by inaccuracies in glucose sensing, inter- and intra-patient variability, and delays due to absorption of insulin from the subcutaneous tissue, all of which are being gradually addressed.Supported by the Juvenile Diabetes Research Foundation (#22-2006-1113, #22-2007-1801, #22-2009-801), Diabetes UK (BDA07/0003549, BDA07/0003551), European Commission Framework Programme 7 (247138), NIDDK (DK085621), and NIHR Cambridge Biomedical Research Centre

A local glucose-and oxygen concentration-based insulin secretion model for pancreatic islets

<p>Abstract</p> <p>Background</p> <p>Because insulin is the main regulator of glucose homeostasis, quantitative models describing the dynamics of glucose-induced insulin secretion are of obvious interest. Here, a computational model is introduced that focuses not on organism-level concentrations, but on the quantitative modeling of local, cellular-level glucose-insulin dynamics by incorporating the detailed spatial distribution of the concentrations of interest within isolated avascular pancreatic islets.</p> <p>Methods</p> <p>All nutrient consumption and hormone release rates were assumed to follow Hill-type sigmoid dependences on local concentrations. Insulin secretion rates depend on both the glucose concentration and its time-gradient, resulting in second-and first-phase responses, respectively. Since hypoxia may also be an important limiting factor in avascular islets, oxygen and cell viability considerations were also built in by incorporating and extending our previous islet cell oxygen consumption model. A finite element method (FEM) framework is used to combine reactive rates with mass transport by convection and diffusion as well as fluid-mechanics.</p> <p>Results</p> <p>The model was calibrated using experimental results from dynamic glucose-stimulated insulin release (GSIR) perifusion studies with isolated islets. Further optimization is still needed, but calculated insulin responses to stepwise increments in the incoming glucose concentration are in good agreement with existing experimental insulin release data characterizing glucose and oxygen dependence. The model makes possible the detailed description of the intraislet spatial distributions of insulin, glucose, and oxygen levels. In agreement with recent observations, modeling also suggests that smaller islets perform better when transplanted and/or encapsulated.</p> <p>Conclusions</p> <p>An insulin secretion model was implemented by coupling local consumption and release rates to calculations of the spatial distributions of all species of interest. The resulting glucose-insulin control system fits in the general framework of a sigmoid proportional-integral-derivative controller, a generalized PID controller, more suitable for biological systems, which are always nonlinear due to the maximum response being limited. Because of the general framework of the implementation, simulations can be carried out for arbitrary geometries including cultured, perifused, transplanted, and encapsulated islets.</p

Indices of insulin sensitivity and secretion from a standard liquid meal test in subjects with type 2 diabetes, impaired or normal fasting glucose

<p>Abstract</p> <p>Background</p> <p>To provide an initial evaluation of insulin sensitivity and secretion indices derived from a standard liquid meal tolerance test protocol in subjects with normal (NFG), impaired fasting glucose (IFG) or type 2 diabetes mellitus.</p> <p>Methods</p> <p>Areas under the curve (AUC) for glucose, insulin and C-peptide from pre-meal to 120 min after consumption of a liquid meal were calculated, as were homeostasis model assessments of insulin resistance (HOMA2-IR) and the Matsuda index of insulin sensitivity.</p> <p>Results</p> <p>Subjects with NFG (n = 19), IFG (n = 19), and diabetes (n = 35) had mean ± SEM HOMA2-IR values of 1.0 ± 0.1, 1.6 ± 0.2 and 2.5 ± 0.3 and Matsuda insulin sensitivity index values of 15.6 ± 2.0, 8.8 ± 1.2 and 6.0 ± 0.6, respectively. The log-transformed values for these variables were highly correlated overall and within each fasting glucose category (r = -0.91 to -0.94, all p < 0.001). Values for the product of the insulin/glucose AUC ratio and the Matsuda index, an indicator of the ability of the pancreas to match insulin secretion to the degree of insulin resistance, were 995.6 ± 80.7 (NFG), 684.0 ± 57.3 (IFG) and 188.3 ± 16.1 (diabetes) and discriminated significantly between fasting glucose categories (p < 0.001 for each comparison).</p> <p>Conclusion</p> <p>These results provide initial evidence to support the usefulness of a standard liquid meal tolerance test for evaluation of insulin secretion and sensitivity in clinical and population studies.</p

Defense Against Cannibalism: The SdpI Family of Bacterial Immunity/Signal Transduction Proteins

The SdpI family consists of putative bacterial toxin immunity and signal transduction proteins. One member of the family in Bacillus subtilis, SdpI, provides immunity to cells from cannibalism in times of nutrient limitation. SdpI family members are transmembrane proteins with 3, 4, 5, 6, 7, 8, or 12 putative transmembrane α-helical segments (TMSs). These varied topologies appear to be genuine rather than artifacts due to sequencing or annotation errors. The basic and most frequently occurring element of the SdpI family has 6 TMSs. Homologues of all topological types were aligned to determine the homologous TMSs and loop regions, and the positive-inside rule was used to determine sidedness. The two most conserved motifs were identified between TMSs 1 and 2 and TMSs 4 and 5 of the 6 TMS proteins. These showed significant sequence similarity, leading us to suggest that the primordial precursor of these proteins was a 3 TMS–encoding genetic element that underwent intragenic duplication. Various deletional and fusional events, as well as intragenic duplications and inversions, may have yielded SdpI homologues with topologies of varying numbers and positions of TMSs. We propose a specific evolutionary pathway that could have given rise to these distantly related bacterial immunity proteins. We further show that genes encoding SdpI homologues often appear in operons with genes for homologues of SdpR, SdpI’s autorepressor. Our analyses allow us to propose structure–function relationships that may be applicable to most family members

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

The uncoupling protein 1 gene, UCP1, is expressed in mammalian islet cells and associated with acute insulin response to glucose in African American families from the IRAS Family Study

BACKGROUND: Variants of uncoupling protein genes UCP1 and UCP2 have been associated with a range of traits. We wished to evaluate contributions of known UCP1 and UCP2 variants to metabolic traits in the Insulin Resistance and Atherosclerosis (IRAS) Family Study. METHODS: We genotyped five promoter or coding single nucleotide polymorphisms (SNPs) in 239 African American (AA) participants and 583 Hispanic participants from San Antonio (SA) and San Luis Valley. Generalized estimating equations using a sandwich estimator of the variance and exchangeable correlation to account for familial correlation were computed for the test of genotypic association, and dominant, additive and recessive models. Tests were adjusted for age, gender and BMI (glucose homeostasis and lipid traits), or age and gender (obesity traits), and empirical P-values estimated using a gene dropping approach. RESULTS: UCP1 A-3826G was associated with AIR(g )in AA (P = 0.006) and approached significance in Hispanic families (P = 0.054); and with HDL-C levels in SA families (P = 0.0004). Although UCP1 expression is reported to be restricted to adipose tissue, RT-PCR indicated that UCP1 is expressed in human pancreas and MIN-6 cells, and immunohistochemistry demonstrated co-localization of UCP1 protein with insulin in human islets. UCP2 A55V was associated with waist circumference (P = 0.045) in AA, and BMI in SA (P = 0.018); and UCP2 G-866A with waist-to-hip ratio in AA (P = 0.016). CONCLUSION: This study suggests a functional variant of UCP1 contributes to the variance of AIR(g )in an AA population; the plausibility of this unexpected association is supported by the novel finding that UCP1 is expressed in islets

Recombinant Human Growth Hormone and Rosiglitazone for Abdominal Fat Accumulation in HIV- Infected Patients with Insulin Resistance: A Randomized, Double-Blind, Placebo-Controlled, Factorial Trial

Background: Recombinant human growth hormone (rhGH) reduces visceral adipose tissue (VAT) volume in HIV-infected patients but can worsen glucose homeostasis and lipoatrophy. We aimed to determine if adding rosiglitazone to rhGH would abrogate the adverse effects of rhGH on insulin sensitivity (SI) and subcutaneous adipose tissue (SAT) volume. Methodology/Principal Findings: Randomized, double-blind, placebo-controlled, multicenter trial using a 262 factorial design in which HIV-infected subjects with abdominal obesity and insulin resistance were randomized to rhGH 3 mg daily, rosiglitazone 4 mg twice daily, combination rhGH + rosiglitazone, or double placebo (control) for 12 weeks. The primary endpoint was change in SI by frequently sampled intravenous glucose tolerance test from entry to week 12. Body composition was assessed by whole body magnetic resonance imaging (MRI) and dual Xray absorptiometry (DEXA). Seventy-seven subjects were randomized of whom 72 initiated study drugs. Change in SI from entry to week 12 differed across the 4 arms by 1-way ANCOVA (P = 0.02); by pair-wise comparisons, only rhGH (decreasing SI; P = 0.03) differed significantly from control. Changes from entry to week 12 in fasting glucose and glucose area under the curve on 2- hour oral glucose tolerance test differed across arms (1-way ANCOVA P = 0.004), increasing in the rhGH arm relative to control. VAT decreased significantly in the rhGH arms (217.5% in rhGH/rosiglitazone and 222.7% in rhGH) but not in the rosiglitazone alone (22.5%) or control arms (21.9%). SAT did not change significantly in any arm. DEXA results were consistent with the MRI data. There was no significant rhGH x rosiglitazone interaction for any body composition parameter. Conclusions/Significance: The addition of rosiglitazone abrogated the adverse effects of rhGH on insulin sensitivity and glucose tolerance while not significantly modifying the lowering effect of rhGH on VAT