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.

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).

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.

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.

Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis.

OBJECTIVE: To compare the clinical accuracy of two different continuous glucose sensors (CGS) during euglycemia and hypoglycemia using continuous glucose-error grid analysis (CG-EGA). RESEARCH DESIGN AND METHODS: FreeStyle Navigator (Abbott Laboratories, Alameda, CA) and MiniMed CGMS (Medtronic, Northridge, CA) CGSs were applied to the abdomens of 16 type 1 diabetic subjects (age 42 +/- 3 years) 12 h before the initiation of the study. Each system was calibrated according to the manufacturer's recommendations. Each subject underwent a hyperinsulinemic-euglycemic clamp (blood glucose goal 110 mg/dl) for 70-210 min followed by a 1-mg.dl(-1).min(-1) controlled reduction in blood glucose toward a nadir of 40 mg/dl. Arterialized blood glucose was determined every 5 min using a Beckman Glucose Analyzer (Fullerton, CA). CGS glucose recordings were matched to the reference blood glucose with 30-s precision, and rates of glucose change were calculated for 5-min intervals. CG-EGA was used to quantify the clinical accuracy of both systems by estimating combined point and rate accuracy of each system in the euglycemic (70-180 mg/dl) and hypoglycemic (<70 mg/dl) ranges. RESULTS: A total of 1,104 data pairs were recorded in the euglycemic range and 250 data pairs in the hypoglycemic range. Overall correlation between CGS and reference glucose was similar for both systems (Navigator, r = 0.84; CGMS, r = 0.79, NS). During euglycemia, both CGS systems had similar clinical accuracy (Navigator zones A + B, 88.8%; CGMS zones A + B, 89.3%, NS). However, during hypoglycemia, the Navigator was significantly more clinically accurate than the CGMS (zones A + B = 82.4 vs. 61.6%, Navigator and CGMS, respectively, P < 0.0005). CONCLUSIONS: CG-EGA is a helpful tool for evaluating and comparing the clinical accuracy of CGS systems in different blood glucose ranges. CG-EGA provides accuracy details beyond other methods of evaluation, including correlational analysis and the original EGA.

Gender modulates sequential suppression and recovery of pulsatile growth hormone secretion by physiological feedback signals in young adults.

The basic mechanisms that drive the renewal of GH pulses in the human are not understood. Recent ensemble models predict that pulse regeneration requires quenching of an ongoing GH pulse by somatostatin outflow and evocation of a new burst by rebound GHRH release. We reasoned that related principles might explain why women consistently maintain higher-amplitude GH secretory bursts than men. Accordingly, the present study tests the hypothesis that gender modulates the successive dynamics of GH feedback and escape in the morning fasting, when GH pulses are larger in women. To this end, we infused single iv pulses of recombinant human (rh) GH (0, 1, and 3 microg/kg) in eight young men and six women on separate randomly ordered mornings fasting and quantitated serial inhibition and recovery of GH secretion by frequent sampling, immunochemiluminometry, a deconvolution procedure, and regularity analysis. Statistical contrasts revealed gender-comparable peak concentrations and kinetics of rhGH. However, women differed from men by way of: (1) 3.5- and 4.0-fold less feedback suppression of GH secretory-burst mass; (2) more irregular patterns of GH release during negative feedback; and (3) 12-and 14-fold greater postnadir rebound-like GH secretion after rhGH pulses. Mechanistic analyses based on a minimal feedback construct predicted that women generate higher endogenous secretagogue stimulation per unit somatostatin outflow than men. In summary, negative feedback induced by near-physiological GH pulses unmasks prominent gender-related contrasts in hypothalamo-pituitary autoregulation in young adults. A frugal but sufficient explanation of the ensemble outcomes is that women sustain greater hypothalamo-pituitary agonist input than men.

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.

Determinants of dual secretagogue drive of burst-like growth hormone secretion in premenopausal women studied under a selective estradiol clamp.

The present study tests the hypothesis that estradiol (E(2)), compared with placebo (Pl), amplifies combined-secretagogue stimulation of GH secretion in premenopausal women studied at comparable IGF-I and testosterone concentrations. To this end, 13 women underwent GnRH agonist-induced gonadal down-regulation followed by graded transdermal addback of E(2) or Pl and randomly ordered iv infusions of saline or paired secretagogues on separate morning fasting. GH secretion was assessed by frequent blood sampling, immunochemiluminometry, and variable-waveform deconvolution analysis. Two-way ANOVA revealed that specific secretagogue combination (P < 0.001), E(2) status (P = 0.012), and their interaction (P = 0.038) jointly determined GH secretory-burst mass. Compared with Pl, the E(2)-clamped milieu elevated mean fasting GH concentrations (P = 0.032), the mass of GH secreted in bursts (P = 0.037), and maximal stimulation by paired l-arginine/GH-releasing peptide (GHRP)-2 (P = 0.028). E(2) also markedly accelerated the initial release of GH induced by GHRH/GHRP-2 (P < 0.001) and l-arginine/GHRH (P < 0.01). By linear regression analysis, E(2) concentrations positively forecast 41% of intersubject variability in GH secretion stimulated by combined l-arginine/GHRP-2 (P = 0.018), whereas abdominal visceral-fat mass negatively predicted 49% of that due to l-arginine/GHRH (P = 0.012). These data indicate that pulsatile GH secretion in young women studied at constant IGF-I and testosterone concentrations is dictated 3-fold jointly by secretagogue pair, E(2) availability, and intraabdominal adiposity. Moreover, the rapidity of GH release is controlled 2-fold jointly by E(2) and GHRH.

Complementary secretagogue pairs unmask prominent gender-related contrasts in mechanisms of growth hormone pulse renewal in young adults.

The present study examines the thesis that pulsatile GH secretion is controlled simultaneously by three principal signals; viz., GHRH, GH-releasing peptide (GHRP, ghrelin), and somatostatin (SS). According to this ensemble notion, no single regulatory peptide acts alone or can be interpreted in isolation. Therefore, to investigate gender-specific control of pulsatile GH secretion, we designed dual-effector stimulation paradigms in eight young men and six women as follows: 1) L-arginine/GHRH (to clamp low SS and high GHRH input); 2) L-arginine/GHRP-2 (to clamp low SS and high GHRP drive); 3) GHRH/GHRP-2 (to clamp high GHRH and high GHRP feedforward); vs. 4) saline (unclamped). Statistical comparisons revealed that: 1) fasting pulsatile GH secretion was 7.6-fold higher in women than men (P < 0.001); 2) L-arginine/GHRH and L-arginine/GHRP-2 evoked, respectively, 4.6- and 2.2-fold greater burst-like GH release in women than men (P < 0.001 and P = 0.015); and 3) GHRH/GHRP-2 elicited comparable GH secretion by gender. In the combined cohorts, estradiol concentrations positively predicted responses to L-arginine/GHRP-2 (r2= 0.49, P = 0.005), whereas testosterone negatively predicted those to L-arginine/GHRH (r2= 0.56, P = 0.002). Based upon a simplified biomathematical model of three-peptide control, the current outcomes suggest that women maintain greater GHRH potency, GHRP efficacy, and opposing SS outflow than men. This inference upholds recent clinical precedence and yields valid predictions of sex differences in self-renewable GH pulsatility.

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.