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.

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.

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.

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.

AutoDecon, a deconvolution algorithm for identification and characterization of luteinizing hormone secretory bursts: description and validation using synthetic data.

Hormone signaling is often pulsatile, and multiparameter deconvolution procedures have long been used to identify and characterize secretory events. However, the existing programs have serious limitations, including the subjective nature of initial peak selection, lack of statistical verification of presumed bursts, and user-unfriendliness of the application. Here we describe a novel deconvolution program, AutoDecon, which addresses these concerns. We validate AutoDecon for application to serum luteinizing hormone (LH) concentration time series using synthetic data mimicking real data from normal women and then comparing the performance of AutoDecon with the performance of the widely employed hormone pulsatility analysis program Cluster. The sensitivity of AutoDecon is higher than that of Cluster ( approximately 96% vs. 80%, P=0.001). However, Cluster had a lower false-positive detection rate than did AutoDecon (6% vs. 1%, P=0.001). Further analysis demonstrated that the pulsatility parameters recovered by AutoDecon were indistinguishable from those characterizing the synthetic data and that sampling at 5- or 10-min intervals was optimal for maximizing the sensitivity rates for LH. Accordingly, AutoDecon presents a viable nonsubjective alternative to previous pulse detection algorithms for the analysis of LH data. It is applicable to other pulsatile hormone concentration time series and many other pulsatile phenomena. The software is free and downloadable at http://mljohnson.pharm.virginia.edu/home.html.

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.

Tripartite control of growth hormone secretion in women during controlled estradiol repletion.

CONTEXT: Studies of how aging attenuates GH secretion are confounded by differences in sex-steroid milieus, abdominal visceral fat mass (AVF), and IGF-I concentrations and limited in interpretability by the use of pharmacological doses of secretagogues. HYPOTHESIS: In a controlled estrogenic milieu, near-physiological secretagogue drive will unmask distinct influences of age, AVF, and IGF-I on GH secretion. LOCATION: The study was conducted at an academic medical center. SUBJECTS: Subjects included 10 healthy pre- (PRE) and 10 postmenopausal (POST) women. PROCEDURE: In a defined estradiol (E(2)) milieu, we compared GH secretion after submaximal stimulation with GH-releasing peptide (GHRP)-2 (ghrelin analog), GHRH, and l-arginine (an inhibitor of somatostatin outflow). ANALYSIS: We related GH responses to age stratum (dichotomous variable) and AVF and IGF-I concentrations (continuous variables). RESULTS: In the face of comparable concentrations of E(2), testosterone, and SHBG: 1) age (P < 0.001) and secretagogue type (P < 0.001) independently determined GH secretion; 2) GH responses in POST subjects were only 26-33% of those in PRE (P < or = 0.002) across all secretagogues; 3) POST women lost the PRE order of secretagogue potency (GHRP-2 > GHRH = l-arginine); and 4) in the combined cohorts, higher AVF predicted reduced l-arginine-stimulated GH secretion (R(2) = 0.46, P = 0.0013), whereas higher IGF-I concentrations forecast increased GHRP-2 and GHRH drive (R(2) > or = 0.52, P < or = 0.013). CONCLUSION: A paradigm of near-physiological secretagogue drive in an E(2)-clamped milieu unmasks tripartite deficits in peptide-signaling pathways in healthy POST, compared with PRE, women. Post hoc analyses indicate that both greater visceral adiposity and lower IGF-I concentrations mark this triple regulatory defect.

Leptin secretory burst mass correlates with body mass index and insulin in normal women but not in women with polycystic ovary syndrome.

Leptin secretion exhibits a pulsatile, circadian pattern and may play a role in reproduction. No previous studies have compared leptin secretory burst characteristics in normal eumenorrheic women and women with polycystic ovary syndrome (PCOS) who are appropriately matched for body mass index (BMI). To determine if leptin secretory burst characteristics and/or the relationships of BMI, insulin, or testosterone to these characteristics differ between PCOS and normal women, we studied 9 normal eumenorrheic women and 9 women with PCOS. Each woman underwent blood sampling every 10 minutes for 24 hours to measure leptin and insulin under controlled conditions. Leptin secretory bursts were identified and characterized using multiparameter deconvolution procedures (Deconv), and the 24-hour periodicity of leptin was characterized with cosinor analysis. Relationships between BMI, area under the curve (AUC) insulin, and testosterone and leptin secretory burst characteristics in PCOS and normal women were sought using linear regression. There were no significant differences in mean serum leptin concentrations or in secretory burst characteristics between PCOS and normal women. Although the 24-hour serum leptin concentration correlated with BMI in both normal and PCOS women, leptin secretory burst mass correlated with BMI only in normal women. Similarly, the 24-hour serum leptin concentration correlated with serum insulin AUC in both normal and PCOS women; but insulin AUC correlated with leptin burst mass only in normal women. Although there was a strong trend toward a correlation between both mean 24-hour serum leptin concentration and leptin secretory burst mass with the serum testosterone concentration in normal women, such trends were not seen in PCOS women. Both normal and PCOS women exhibited a diurnal rhythm of leptin secretion with the peak occurring at night. However, neither the peak amplitude nor the timing of the peak amplitude differed between normal and PCOS women. The presence of strong relationships between BMI and insulin with both mean serum leptin and leptin secretory burst mass in normal women as opposed to PCOS women suggests that the mechanisms subserving leptin secretion differ in these 2 groups.

Distinctive inhibitory mechanisms of age and relative visceral adiposity on growth hormone secretion in pre- and postmenopausal women studied under a hypogonadal clamp.

BACKGROUND: Aging, body composition, and sex steroids jointly determine GH production. However, the actions of any given factor are confounded by the effects of the other two. HYPOTHESIS: Age and abdominal visceral fat (AVF) mass govern GH secretion via individually distinctive mechanisms, which can be unmasked by short-term sex steroid deprivation. DESIGN/SUBJECTS: In a university setting, healthy pre- and postmenopausal volunteers underwent GnRH agonist-induced down-regulation for 6 wk to deplete ovarian sex steroids. GH secretion was evaluated by frequent blood sampling, saline vs. dual secretagogue infusions, an irregularity statistic, variable waveform deconvolution analysis, and a simplified feedback model. Computerized tomography was used to estimate AVF mass. OUTCOMES/MEASURES: In the sex steroid-deficient milieu, postmenopausal compared with premenopausal women exhibited 1) lower concentrations of IGF-I (P = 0.028) and GH (P < 0.05); 2) reduced pulsatile, but elevated basal, GH secretion (P < 0.05); 3) more irregular GH patterns (P = 0.027); 4) an attenuated GH response to simultaneous GHRH/GH-releasing peptide-2 stimulation (P < 0.01); and 5) more rapid onset of GH release within secretory bursts (P < 0.01). In contrast, AVF negatively forecast GH responses to L-arginine/GH-releasing peptide-2 (r2= 0.45; P < 0.001) and L-arginine/GHRH (r2= 0.57; P = 0.007). From these marked contrasts, model-based analyses predicted distinguishable mechanisms by which aging and AVF alter pulsatile GH production. CONCLUSION: Under limited confounding by sex steroids, age and body composition modulate GH secretion via highly selective peptidyl pathways in healthy women.

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.

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.

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.

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.