Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics.

PURPOSE: Develop a diabetes diagnostic tool based on two markers of continuous glucose monitoring (CGM) dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S). METHODS: 5,754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute two individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between eight glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden's index was used to determine "optimal" cut-points for ER and S for health vs. diabetes (case 1); type 1 vs. type 2 (case 2); and low vs. high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC). RESULTS: Optimal cut-off points were determined for ER and S for each of the three cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI: 0.97-0.99) and AUC = 0.99 (95% CI: 0.99-1.00), respectively, (cut-offs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2). CONCLUSIONS: CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing and identifying individuals at higher immunological risk of progressing to type 1 diabetes.

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study.

BACKGROUND: Detection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data. METHODS: Forty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m2 with zero (low risk; N = 21), and ≥2 (high risk; N = 21) Ab, were enrolled in an NIH (National Institutes of Health)-funded TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic features were extracted from two-hour post-SLMM CGM traces, compared across groups, and used in four supervised machine learning Ab risk status classifiers. Recursive Feature Elimination (RFE) algorithm was used for feature selection; classifiers were evaluated through 10-fold cross-validation, using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. RESULTS: The percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with P values of .040, .035, and .028 respectively. The linear SVM (Support Vector Machine) model with RFE features achieved the best performance of classifying low-risk versus high-risk individuals with AUC-ROC = 0.88. CONCLUSIONS: A machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning.

Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m2 with zero (n = 21), one (n = 18), and ≥2 (n = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00-06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly (P = 0.04), overnight CGM incremental AUC (P = 0.005), and T180 for 75 min post-SLMM CGM traces (P = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.

Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 Diabetes.

Background: Use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as adjunct therapy to insulin in type 1 diabetes (T1D) has been previously studied. In this study, we present data from the first free-living trial combining low-dose SGLT2i with commercial automated insulin delivery (AID) or predictive low glucose suspend (PLGS) systems. Methods: In an 8-week, randomized, controlled crossover trial, adults with T1D received 5 mg/day empagliflozin (EMPA) or no drug (NOEMPA) as adjunct to insulin therapy. Participants were also randomized to sequential orders of AID (Control-IQ) and PLGS (Basal-IQ) systems for 4 and 2 weeks, respectively. The primary endpoint was percent time-in-range (TIR) 70-180 mg/dL during daytime (7:00-23:00 h) while on AID (NCT04201496). Findings: A total of 39 subjects were enrolled, 35 were randomized, 34 (EMPA; n = 18 and NOEMPA n = 16) were analyzed according to the intention-to-treat principle, and 32 (EMPA; n = 16 and NOEMPA n = 16) completed the trial. On AID, EMPA versus NOEMPA had higher daytime TIR 81% versus 71% with a mean estimated difference of +9.9% (confidence interval [95% CI] 0.6-19.1); p = 0.04. On PLGS, the EMPA versus NOEMPA daytime TIR was 80% versus 63%, mean estimated difference of +16.5% (95% CI 7.3-25.7); p < 0.001. One subject on SGLT2i and AID had one episode of diabetic ketoacidosis with nonfunctioning insulin pump infusion site occlusion contributory. Interpretation: In an 8-week outpatient study, addition of 5 mg daily empagliflozin to commercially available AID or PLGS systems significantly improved daytime glucose control in individuals with T1D, without increased hypoglycemia risk. However, the risk of ketosis and ketoacidosis remains. Therefore, future studies with SGLT2i will need modifications to closed-loop control algorithms to enhance safety.

A Data-Driven Approach to Classifying Daily Continuous Glucose Monitoring (CGM) Time Series.

According to the World Health Organization, about 422 million people worldwide have type 1 or type 2 diabetes (T1D, T2D), with the latter accounting for 90-95% of cases. Safe and effective treatment of patients with diabetes requires accurate and frequent monitoring of their blood sugar levels. Continuous glucose monitoring (CGM) is a monitoring technology developed to address this need, and its use among U.S. T1D patients has increased from 6% in 2011 to 38% in 2018 and continues to increase worldwide in both T1D and T2D. This paper presents a data-driven approach to determine Ω, a finite set of representative daily profiles (motifs) such that almost any daily CGM profile generated by a patient can be matched to one of the motifs in Ω. The training data set (9741 profiles) was used to identify 8 candidate sets of motifs, while the validation data set (14 175 profiles) was used to select the final set Ω. The robustness of Ω was established by using it to successfully classify (match against a representative daily profile in Ω) 99.0% of 42 595 daily CGM profiles in the testing data set. All data sets contained daily CGM profiles from six studies involving T1D and T2D patients using a variety of treatment modes, including daily insulin injections, insulin pumps, or artificial pancreas (AP). The classified profiles can be used in predictive modeling, decision support, and automated control systems (e.g., AP).

Influence of Sleep Stage on LH Pulse Initiation in the Normal Late Follicular Phase and in Polycystic Ovary Syndrome.

OBJECTIVE: During the early follicular phase, sleep-related luteinizing hormone (LH) pulse initiation is positively associated with brief awakenings but negatively associated with rapid eye movement (REM) sleep. The relationship between sleep architecture and LH pulse initiation has not been assessed in other cycle stages or in women with polycystic ovary syndrome (PCOS). DESIGN AND METHODS: We performed concomitant frequent blood sampling (LH pulse analysis) and polysomnography on 8 normal women (cycle day 7-11) and 7 women with PCOS (at least cycle day 7). RESULTS: In the normal women, the 5 min preceding LH pulses contained more wake epochs and fewer REM epochs than the 5 min preceding randomly determined time points (wake: 22.3 vs. 9.1%, p = 0.0111; REM: 4.4 vs. 18.8%, p = 0.0162). However, LH pulse initiation was not related to wake or REM epochs in PCOS; instead, the 5 min preceding LH pulses contained more slow-wave sleep (SWS) than the 5 min before random time points (20.9 vs. 6.7%, p = 0.0089). Compared to the normal subjects, the women with PCOS exhibited a higher REM-associated LH pulse frequency (p = 0.0443) and a lower proportion of wake epochs 0-5 min before LH pulses (p = 0.0205). CONCLUSIONS: Sleep-related inhibition of LH pulse generation during the later follicular phase is normally weakened by brief awakenings and strengthened by REM sleep. In women with PCOS, LH pulse initiation is not appropriately discouraged by REM sleep and may be encouraged by SWS; these abnormalities may contribute to a high sleep-related LH pulse frequency in PCOS.

Recent Exposure to Hypoglycemia Increases Glucose Variability Following a Hyper/Hypoglycemic Metabolic Challenge in T1D.

AIMS: In type 1 diabetes (T1D), repeated hypoglycemic episodes may reduce hormonal defenses and increase the risk for severe hypoglycemia. In this work, we investigate the effect of a structured hyper/hypoglycemic metabolic challenge on the postintervention glucose variability in T1D subjects studied at home. METHODS: Thirty T1D subjects using insulin pump were monitored with blood glucose meters (SMBG) during a 1-month observation period. After 2 weeks of monitoring, participants were admitted at the University of Virginia Clinical Research Unit to undergo an 8-hour metabolic challenge. The intervention was designed to create hyperglycemia shortly followed by hypoglycemia, mimicking a real-life scenario of underbolused meal followed by overcorrection. After the intervention, subjects were monitored for 2 more weeks. Glycemic variability was assessed before and after the challenge using the low blood glucose index (LBGI). Glucagon counterregulation (GCR) response to induced hypoglycemia was also measured. LBGI variation and GCR were linked to prior exposure to hypoglycemia. RESULTS: Subjects significantly exposed to hypoglycemia in the 2 weeks before the intervention had a significant increase of postchallenge LBGI ( P < .001) and lower GCR response ( P < .05). Recent occurrence of hypoglycemia and number of years not using an insulin pump were identified as significant predictors of postchallenge LBGI ( P < .001). CONCLUSION: Glycemic swings, a common result of suboptimal insulin treatment, have a significant impact on future (days) glycemic control in T1D subjects with a recent history of hypoglycemia, as measured in the field. Choice of past insulin therapy may also mediate this effect.

Glucagon - the new 'insulin' in the pathophysiology of diabetes.

PURPOSE OF REVIEW: Autoimmune destruction of the ß cells is considered the key abnormality in type 1 diabetes mellitus and insulin replacement the primary therapeutic strategy. However, a lack of insulin is accompanied by disturbances in glucagon release, which is excessive postprandially, but insufficient during hypoglycaemia. In addition, replacing insulin alone appears insufficient for adequate glucose control. This review focuses on the growing body of evidence that glucagon abnormalities contribute significantly to the pathophysiology of diabetes and on recent efforts to target the glucagon axis as adjunctive therapy to insulin replacement. RECENT FINDINGS: This review discusses recent (since 2013) advances in abnormalities of glucagon regulation and their link to the pathophysiology of diabetes; new mechanisms of glucagon action and regulation; manipulation of glucagon in diabetes treatment; and analytical and systems biology tools to study glucagon regulation. SUMMARY: Recent efforts 'resurrected' glucagon as a key hormone in the pathophysiology of diabetes. New studies target its abnormal regulation and action that is key for improving diabetes treatment. The progress is promising, but major questions remain, including unravelling the mechanism of loss of glucagon counterregulation in type 1 diabetes mellitus and how best to manipulate glucagon to achieve more efficient and safer glycaemic control.

Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.

Stress-induced hyperglycemia is common in critically ill patients, where elevated blood glucose and glycemic variability have been found to contribute to infection, slow wound healing, and short-term mortality. Early clinical studies demonstrated improvement in mortality and morbidity resulting from intensive insulin therapy targeting euglycemia. Follow-up clinical studies have shown mixed results suggesting that the risk of hypoglycemia may outweigh the benefits of aggressive glycemic control. None of the prior studies clarify whether euglycemic targets are in themselves harmful, or if the danger lies in the inadequacy of the available methods for achieving desired glycemic outcomes. In this paper, we use a recently developed simulation model of stress hyperglycemia to demonstrate that given an insulin protocol glycemic outcomes are specific to the patient population under consideration, and that there is a need to optimize insulin therapy at the population level. Next, we use the simulator to demonstrate that the performance of Adaptive Proportional Feedback (APF), a popular format for computerized insulin therapy, is sensitive to its parameters, especially to the parameters that govern the aggressiveness of adaptation. Finally, we propose a framework for simulation-based protocol optimization using an objective function that penalizes below-range deviations more heavily than comparable deviations above.

The level of circulating octanoate does not predict ghrelin O-acyl transferase (GOAT)-mediated acylation of ghrelin during fasting.

BACKGROUND: Acyl-ghrelin is a 28-amino acid peptide released from the stomach. Ghrelin O-acyl transferase (GOAT) attaches an 8-carbon medium-chain fatty acid (MCFA) (octanoate) to serine 3 of ghrelin. This acylation is necessary for the activity of ghrelin. Animal data suggest that MCFAs provide substrate for GOAT and an increase in nutritional octanoate increases acyl-ghrelin. OBJECTIVES: To address the question of the source of substrate for acylation, we studied whether the decline in ghrelin acylation during fasting is associated with a decline in circulating MCFAs. METHODS: Eight healthy young men (aged 18-28 years, body mass index range, 20.6-26.2 kg/m(2)) had blood drawn every 10 minutes for acyl- and desacyl-ghrelin and every hour for free fatty acids (FFAs) during the last 24 hours of a 61.5-hour fast and during a fed day. FFAs were measured by a highly sensitive liquid chromatography-mass spectroscopy method. Acyl- and desacyl-ghrelin were measured in an in-house assay; the results were published previously. Ghrelin acylation was assessed by the ratio of acyl-ghrelin to total ghrelin. RESULTS: With the exception of MCFAs C8 and C10, all other FFAs, the MCFAs (C6 and C12), and the long-chain fatty acids (C14-C18) significantly increased with fasting (P < .05). There was no significant association between the fold change in ghrelin acylation and circulating FFAs. CONCLUSIONS: These results suggest that changes in circulating MCFAs are not linked to the decline in ghrelin acylation during fasting and support the hypothesis that acylation of ghrelin depends at least partially on the availability of gastroluminal MCFAs or the regulation of GOAT activity.

Four-hour infusion of hydrocortisone does not suppress the nocturnal increase of circulating acyl- or desacyl-ghrelin concentrations in healthy young adults.

BACKGROUND: Ghrelin is a 28-amino acid peptide released from the stomach. Ghrelin is found in the circulation in two forms: acyl- and desacyl-ghrelin. Acyl- and desacyl-ghrelin concentrations increase at night, when cortisol concentrations are low. Acute ghrelin administration increases ACTH and cortisol concentrations and a feedback loop between the ghrelin and ACTH-cortisol axis has been postulated. A previous study showed that exogenously induced hypercortisolism for 5 days decreased plasma ghrelin concentrations. OBJECTIVE: The objective of the study was to determine whether a 4-hour infusion of hydrocortisone given at a time of low endogenous cortisol concentrations (11:00 pm to 3:00 am) acutely suppresses acyl- and desacyl-ghrelin. METHODS: Eight healthy young men aged (mean ± SD) 21.5 ± 2.7 years with a body mass index of 22.4 ± 2.5 kg/m(2) were studied in a single-blind, placebo-controlled study during two separate overnight admissions on the Clinical Research Unit. The volunteers received either a 4-hour (11:00 pm to 3:00 am) infusion of hydrocortisone or a saline infusion. The hydrocortisone infusion rate was 0.3 mg/kg·h for the initial 3 minutes, 0.24 mg/kg·h for 9 minutes, and then 0.135 mg/kg·h until the end of the infusion. Plasma acyl- and desacyl-ghrelin concentrations (in-house two site sandwich assay) and ACTH, cortisol, insulin, GH, and glucose levels were measured every 10 minutes for 16 hours (5:00 pm to 9:00 am). RESULTS: The mean differences (lower 95% limit; upper 95% limit) between the saline infusion and hydrocortisone infusion for acyl- and desacyl-ghrelin concentrations were not significantly different from zero. The infusion period (11:00 pm to 3:00 am) was as follows: acyl-ghrelin, 0.22 (-7.39; 7.83) (P = 1.00); desacyl-ghrelin, -3.36 (-17.66; 10.95) (P = 1.00). The postinfusion period (3:00-7:00 am) was as follows: acyl-ghrelin, 8.68 (1.07; 16.28); (P = .056); desacyl-ghrelin, 8.75 (-5.56; 23.05) (P = .403). CONCLUSIONS: A short-term increase in circulating cortisol concentrations by exogenous hydrocortisone infusion does not suppress circulating nocturnal acyl- or desacyl-ghrelin concentrations. Thus, it is likely that the diurnal pattern of ghrelin secretion is under circadian control and not directly regulated by cortisol.

Age-dependent decline in acyl-ghrelin concentrations and reduced association of acyl-ghrelin and growth hormone in healthy older adults.

BACKGROUND: Acyl-ghrelin is thought to have both orexigenic effects and to stimulate GH release. A possible cause of the anorexia of aging is an age-dependent decrease in circulating acyl-ghrelin levels. OBJECTIVES: The purpose of the study was to compare acyl-ghrelin and GH concentrations between healthy old and young adults and to examine the relationship of acyl-ghrelin and GH secretion in both age groups. METHODS: Six healthy older adults (age 62-74 y, body mass index range 20.9-29 kg/m(2)) and eight healthy young men (aged 18-28 y, body mass index range 20.6-26.2 kg/m(2)) had frequent blood samples drawn for hormone measurements every 10 minutes for 24 hours. Ghrelin was measured in an in-house, two-site sandwich ELISA specific for full-length acyl-ghrelin. GH was measured in a sensitive assay (Immulite 2000), and GH peaks were determined by deconvolution analysis. The acyl-ghrelin/GH association was estimated from correlations between amplitudes of individual GH secretory events and the average acyl-ghrelin concentration in the 60-minute interval preceding each GH burst. RESULTS: Twenty-four-hour mean (±SEM) GH (0.48 ± 0.14 vs 2.2 ± 0.3 µg/L, P < .005) and acyl-ghrelin (14.7 ± 2.3 vs 27.8 ± 3.9 pg/mL, P < .05) levels were significantly lower in older adults compared with young adults. Twenty-four-hour cortisol concentrations were higher in the old than the young adults (15.1 ± 1.0 vs 10.6 ± 0.9 µg/dL, respectively, P < .01). The ghrelin/GH association was more than 3-fold lower in the older group compared with the young adults (0.16 ± 0.12 vs 0.69 ± 0.04, P < .001). CONCLUSIONS: These results provide further evidence of an age-dependent decline in circulating acyl-ghrelin levels, which might play a role both in the decline of GH and in the anorexia of aging. Our data also suggest that with normal aging, endogenous acyl-ghrelin levels are less tightly linked to GH regulation.

Pharmacokinetics modeling of exogenous glucagon in type 1 diabetes mellitus patients.

BACKGROUND: Insulin-induced hypoglycemia is as a critical barrier in the treatment of type 1 diabetes mellitus patients and may lead to unconsciousness, brain damage, or even death. Clinically, glucagon is used as a rescue drug to treat severe hypoglycemic episodes. More recently, in a bihormonal closed-loop glucose control, glucagon has been used subcutaneously along with insulin for protection against hypoglycemia. In this context, small doses of glucagon are frequently administered. The efficacy and safety of such systems, however, require precise information on the pharmacokinetics of the glucagon transport from the administrative site to the circulation, which is currently lacking. The goal of this work is to address this need by developing and validating a mathematical model of exogenous glucagon transport to the plasma. MATERIALS AND METHODS: Eight pharmacokinetic models with various levels of complexity were fitted to nine clinical datasets. An optimal model was chosen in two consecutive steps. At Step 1, all models were screened for parameter identifiability (discarding the unidentifiable candidates). At Step 2, the remaining models are compared based on Bayesian information criterion. RESULTS: At Step 1, two models were removed for higher parameter fractional SDs. Another three were discarded for location of their optimal parameters on the parameter search boundaries. At Step 2, an optimal model was selected based on the Bayesian information criterion. It has a simple linear structure, assuming that glucagon is injected into one compartment, from where it enters a pool for a slower release into a third, plasma compartment. In the first and third compartments, glucagon is cleared at a rate proportional to its concentration. CONCLUSIONS: A linear kinetic model of glucagon intervention has been developed and validated. It is expected to provide guidance for glucagon delivery and the construction of preclinical simulation testing platforms.

Association of Basal hyperglucagonemia with impaired glucagon counterregulation in type 1 diabetes.

Glucagon counterregulation (GCR) protects against hypoglycemia, but is impaired in type 1 diabetes (T1DM). A model-based analysis of in vivo animal data predicts that the GCR defects are linked to basal hyperglucagonemia. To test this hypothesis we studied the relationship between basal glucagon (BasG) and the GCR response to hypoglycemia in 29 hyperinsulinemic clamps in T1DM patients. Glucose levels were stabilized in euglycemia and then steadily lowered to 50 mg/dL. Glucagon was measured before induction of hypoglycemia and at 10 min intervals after glucose reached levels below 70 mg/dL. GCR was assessed by CumG, the cumulative glucagon levels above basal; MaxG, the maximum glucagon response; and RIG, the relative increase in glucagon over basal. Analysis of the results was performed with our mathematical model of GCR. The model describes interactions between islet peptides and glucose, reproduces the normal GCR axis and its impairment in diabetes. It was used to identify a control mechanism consistent with the observed link between BasG and GCR. Analysis of the clinical data showed that higher BasG was associated with lower GCR response. In particular, CumG and RIG correlated negatively with BasG (r = -0.46, p = 0.012 and r = -0.74, p < 0.0001 respectively) and MaxG increased linearly with BasG at a rate less than unity (p < 0.001). Consistent with these results was a model of GCR in which the secretion of glucagon has two components. The first is under (auto) feedback control and drives a pulsatile GCR and the second is feedback independent (basal secretion) and its increase suppresses the GCR. Our simulations showed that this model explains the observed relationships between BasG and GCR during a three-fold simulated increase in BasG. Our findings support the hypothesis that basal hyperglucagonemia contributes to the GCR impairment in T1DM and show that the predictive power of our GCR animal model applies to human pathophysiology in T1DM.

Average daily risk range as a measure of glycemic risk is associated with mortality in the intensive care unit: a retrospective study in a burn intensive care unit.

BACKGROUND: Although tight glycemic control has been associated with improved outcomes in the intensive care unit (ICU), glycemic variability may be the influential factor in mortality. The main goal of the study was to relate blood glucose (BG) variability of burn ICU patients to outcomes using a sensitive measure of glycemic variability, the average daily risk range (ADRR). METHOD: Data from patients admitted to a burn ICU were used. Patients were matched by total body surface area (TBSA) and injury severity score (ISS) to test whether increased BG variability measured by ADRR was associated with higher mortality risk and whether we could identify ADRR-based classifications associated with the degree of risk. RESULTS: Four ADRR classifications were identified: low risk, medium-low, medium-high, and high. Mortality progressively increased from 25% in the low-risk group to over 60% in the high-risk group (p < .001). In a post hoc analysis, age also contributed to outcome. Younger (age < 43 years) survivors and nonsurvivors matched by TBSA and ISS had no significant difference in age, mean BG or standard deviation of BG; however, nonsurvivors had higher ADRR (p < .01). CONCLUSIONS: Independent of injury severity, glycemic variability measured by the ADRR was significantly associated with mortality in the ICU. When age was considered, ADRR was the only measure of glycemia significantly associated with mortality in younger patients with burns.

Models of glucagon secretion, their application to the analysis of the defects in glucagon counterregulation and potential extension to approximate glucagon action.

This review analyzes an interdisciplinary approach to the pancreatic endocrine network-like relationships that control glucagon secretion and glucagon counterregulation (GCR). Using in silico studies, we show that a pancreatic feedback network that brings together several explicit interactions between islet peptides and blood glucose reproduces the normal GCR axis and explains its impairment in diabetes. An α-cell auto-feedback loop drives glucagon pulsatility and mediates triggering of GCR by hypoglycemia by a rapid switch-off of ß-cell signals. The auto-feedback explains the enhancement of defective GCR in ß-cell deficiency by a switch-off of signals in the pancreas that suppress α cells. Our models also predict that reduced ß-cell activity decreases and delays the GCR. A key application of our models is the in silico simulation and testing of possible scenarios to repair defective GCR in ß-cell deficiency. In particular, we predict that partial suppression of hyperglucagonemia may repair the impaired GCR. We also outline how the models can be extended and tested using human data to become a part of a larger construct including the regulation of the hepatic glucose output by the pancreas, circulating glucose, and incretins. In conclusion, a model of the normal GCR control mechanisms and their dysregulation in insulin-deficient diabetes is proposed and partially validated. The model components are clinically measurable, which permits its application to the study of the abnormalities of the human endocrine pancreas and their role in the progression of many diseases, including diabetes, metabolic syndrome, polycystic ovary syndrome, and others. It may also be used to examine therapeutic responses.

Validation of a deconvolution procedure (AutoDecon) for identification and characterization of fasting insulin secretory bursts.

BACKGROUND: Insulin secretion is pulsatile, and has been shown to be altered in both physiologic and pathophysiologic conditions. The identification and characterization of such pulses have been challenging, partially because of the low concentrations of insulin during fasting and its short half-life. Existing pulse detection algorithms used to identify insulin pulses either cannot separate hormone pulses into their secretory burst and clearance components, or have been limited by both the subjective nature of initial peak selection and a lack of statistical verification of bursts. METHODS: To address these concerns, we have developed AutoDecon, a novel deconvolution computer program. RESULTS: AutoDecon was applied to synthetic insulin concentration-time series modeled on data derived from normal fasting subjects and simulated to reflect several sampling frequencies, sampling durations, and assay replicates. The operating characteristics of AutoDecon were compared to those obtained with Cluster, a standard pulse detection algorithm. AutoDecon performed considerably better than Cluster with regard to sensitivity and secretory burst detection rates for true positives, false positives, and false negatives. As expected, given the short half-life of insulin, sampling at 30-second intervals is required for optimal analytical results. The choice of sampling duration is more flexible and relates to the number of replicates assayed. CONCLUSION: AutoDecon represents a viable alternative to standard pulse detection algorithms for the appraisal of fasting insulin pulsatility.

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.