Citations

Early postprandial glucagon concentrations are higher in type 1 diabetes (T1D) than in individuals with no diabetes (ND). To determine the cause, we infused stable [13C9, 15N1]glucagon before, during, and after a mixed meal in 16 ND and 16 T1D individuals to measure glucagon turnover. In a subcohort of 9 ND and 12 T1D individuals, we estimated [13C9, 15N1]glucagon kinetics during steady state. A linear, single-compartment model described [13C9, 15N1]glucagon kinetics and allowed precise estimation of the volume of distribution (VD) and clearance rate (CL). Model parameters were similar between groups, with the VD of [13C9, 15N1]glucagon at 42.1 ± 3.3 mL/kg, implying that [13C9, 15N1]glucagon distributes in a single compartment and with VD approximating the plasma volume and CL at 10.6 ± 0.9 mL/kg/min. Higher early (0–120 min after meal ingestion) postprandial glucagon concentrations (1,907.9 ± 373.4 vs. −93.6 ± 240.5 pg/mL · 120 min P < 0.001) observed in T1D was due to higher rates of glucagon appearance (3.39 ± 2.8 vs. −3.95 ± 2.0 ng/kg · 120 min, P < 0.04) and disappearance (2.13 ± 2.6 vs. −5.28 ± 2.1 ng/kg · 120 min, P < 0.04) compared with ND. We have determined postprandial glucagon turnover in humans and have demonstrated that changes in postprandial glucagon concentrations in T1D are due to increased rates of glucagon turnover during the early postprandial period. Article Highlights This study was conducted to determine postprandial glucagon metabolism in people with and without type 1 diabetes. We wanted to determine the cause for higher early postprandial glucagon concentrations in type 1 diabetes. We found that higher early postprandial glucagon turnover is the cause of higher early postprandial glucagon concentrations in type 1 diabetes Strategies that decrease early post prandial glucagon fluxes could improve postprandial glucose concentrations in type 1 diabetes.

The advent of continuous glucose monitoring (CGM) in non-insulin-treated individuals raises the possibility of quantifying disposition index (DI), a key metric of β-cell function usually assessed in research settings, in outpatients. A method for DI estimation from postprandial glucose data only (DI G ) was developed and validated against a reference. DI G can be used to assess therapy effectiveness and degree of glucose tolerance in non-insulin-treated individuals, paving the way for its quantification in real-life conditions from CGM devices.

Circulating glucagon concentrations differ between individuals with no diabetes (ND) and those with type 1 diabetes (T1D). We combined an isotope dilution technique using stable tracers [6,22-13C9,15N1]glucagon and [6,14,19,22-13C9,15N1]glucagon with splanchnic and leg catheterization in participants with ND (n = 8; age 23.1 ± 2.9 years, BMI 26.6 ± 3.5 kg/m2, HbA1c 5.0 ± 0.2% [31 ± 2 mmol/mol]) and T1D (n = 6; 29.0 ± 8.8 years, BMI 26.3 ± 5.0 kg/m2, HbA1c 7.9 ± 0.8% [63 ± 8 mmol/mol]) in the overnight fasted state. After baseline period, exogenous glucagon was infused at rates designed to achieve plasma glucagon concentrations spanning the physiological ranges, to determine the effects of rising glucagon concentrations on splanchnic and leg glucagon balance. At baseline, splanchnic glucagon extraction (SGE) was similar (30.7 ± 2.7 vs. 29.1 ± 2.9%) but leg glucagon extraction (LGE) was lower (27.0 ± 4.2 vs. 40.6 ± 3.1%) in participants with T1D versus those with ND. However, with increasing plasma glucagon concentrations, while SGE remained unchanged within and between groups, LGE fell in participants with ND (41 vs. 31 vs. 24%) but did not change in those with T1D. Despite a numerically lower net splanchnic glucagon production in participants with T1D than in those with ND, no changes were observed with increasing glucagon concentrations within the physiological range in both groups. This is the first human study applying novel glucagon isotopes that describes regional glucagon metabolism in participants with ND and T1D. Our observations provide translational relevance for dual hormone closed-loop systems and provide tools for probing the effects of GLP-1, dual, and triple receptor agonists on pancreatic α-cell functions. Article Highlights This study was conducted to assess splanchnic and leg glucagon metabolism in humans using stable glucagon isotopes. We wanted to evaluate whether splanchnic and leg glucagon metabolism differed between participants with no diabetes (ND) and those with type 1 diabetes (T1D) at glucagon concentrations spanning the physiological range. Whereas splanchnic glucagon extraction did not differ between participants with ND and those with T1D, leg glucagon extraction fell in those with ND but did not change in those with T1D as glucagon concentrations increased. Net splanchnic glucagon production did not change with exogenous glucagon infusion. Our study has implications for dual hormone closed-loop control in T1D where glucagon is infused for prevention of hypoglycemia and for investigating the effects of emerging GLP-1, glucose-dependent insulinotropic polypeptide, and glucagon receptor agonists on endogenous glucagon secretion and clearance.

Background: Estimation of insulin sensitivity (S I ) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for S I estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (S I SP ) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of S I SP performing well in daily life unrestrained conditions. Methods: The new S I SP was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70 g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model S I (S I MM ). CGM and CSII data were used for S I SP calculation, which was then validated against the gold standard S I MM . Results: S I SP was estimated with good precision (median coefficient of variation <20%) in 100% of the real and 91% of the simulated meals. S I SP and S I MM were highly correlated, both in the simulated and real datasets ( R = 0.82 and R = 0.83, P < 0.001), and exhibited a similar intraday pattern. Conclusions: S I SP is suitable for estimating S I in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump.

While the triple tracer isotope dilution method has enabled accurate estimation of carbohydrate turnover after a mixed meal, use of the simple carbohydrate glucose as the carbohydrate source limits its translational applicability to everyday meals that typically contain complex carbohydrates. Hence, utilizing the natural enrichment of [13C]polysaccharide in commercially available grains, we devised a novel tracer method to measure postprandial complex carbohydrate turnover and indices of insulin action and β-cell function and compared the parameters to those obtained after a simple carbohydrate containing mixed meal. We studied healthy volunteers after either rice ( n = 8) or sorghum ( n = 8) and glucose ( n = 16) containing mixed meals and modified the triple tracer technique to calculate carbohydrate turnover. All meals were matched for calories and macronutrient composition. Rates of meal glucose appearance (2,658 ± 736 vs. 4,487 ± 909 μM·kg−1·2 h−1), endogenous glucose production (−835 ± 283 vs. −1,123 ± 323 μM·kg−1·2 h−1) and glucose disappearance (1,829 ± 807 vs. 3,606 ± 839 μM·kg−1·2 h−1) differed ( P < 0.01) between complex and simple carbohydrate containing meals, respectively. Interestingly, there were significant increase in indices of insulin sensitivity (32.5 ± 3.5 vs. 25.6 ± 3.2 10−5(dl·kg−1·min−2)/pM, P = 0.006) and β-cell responsivity (disposition index: 1,817 ± 234 vs. 1,236 ± 159 10−14(dl·kg−1·min−2)/pM, P < 0.005) with complex than simple carbohydrate meals. We present a novel triple tracer approach to estimate postprandial turnover of complex carbohydrate containing mixed meals. We also report higher insulin sensitivity and β-cell responsivity with complex than with simple carbohydrates in mixed meals of identical calorie and macronutrient compositions in healthy adults.

Insulin and nutrients have profound effects on proteome homeostasis. Currently no reliable methods are available to measure postprandial protein turnover. A triple-tracer method was developed using phenylalanine stable isotope tracers to estimate appearance rates of ingested (R a meal ) and endogenous phenylalanine and the rate of phenylalanine disposal (R d ). This was compared with the “traditional” dual-tracer method, using one (1-CM)- and two (2-CM)-compartment models. For both methods, [ 13 C 6 ]phenylalanine was given orally, and [ 15 N]phenylalanine was constantly infused; the triple-tracer method added [ 2 H 5 ]phenylalanine, infused at rates to mimic meal [ 13 C 6 ]phenylalanine appearance. Additionally, incorporation of meal-derived phenylalanine into specific proteins was measured after purification by two-dimensional electrophoresis. The triple-tracer approach reduced modeling errors, allowing improved reconstruction of R a meal with a tracer-to-tracee ratio that was more constant and better estimated R d . The 2-CM better described phenylalanine kinetics and R d than 1-CM. Thus, the triple-tracer approach using 2-CM is superior for measuring non-steady-state postprandial protein turnover. This novel approach also allows measurement of postprandial synthesis rates of specific plasma proteins. We offer a valid non-steady-state model to measure postprandial protein turnover and synthesis of plasma proteins that can safely be applied in adults, children, and pregnant women.

OBJECTIVE Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P &lt; 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P &lt; 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P &lt; 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

Background: Several clinical trials have been performed to assess safety and efficacy of closed-loop control. Some included physical activity (PA), with the goal of challenging the control algorithms with a rapid change in insulin sensitivity while reducing the risk of hypoglycemia. However, the question remains as to the necessity to inform the control algorithm on the imminent PA. The aim here is to assess in silico (i) if it is necessary to announce upcoming PA and (ii) if this is the case, what is the safest strategy of basal insulin reduction in the context of the closed-loop control. Methods: We modified the University of Virginia/Padova type 1 diabetes simulator to incorporate the effect of PA based on a study in healthy subjects that demonstrated an almost doubling of insulin sensitivity during PA versus rest. Two in silico experiments, including a PA session, have been simulated on the virtual adult population: One in the absence of and one with different degrees of reductions and durations of basal insulin infusion rates. Results: Most in silico subjects experienced hypoglycemia in the absence of basal insulin adjustment. We show that, in the absence of patient-specific information, a safe and effective strategy is to reduce basal insulin by 50% starting 90 min before exercise and by 30% during exercise. Conclusions: Our results suggest that control algorithms could benefit by knowing an upcoming PA. Ideally, the control algorithm should be informed about the patient-specific basal insulin reduction pattern. An alternative strategy that has been proposed here has been deemed safe and effective in in silico experiments.

Background: Glucose sensors measure glucose concentration in the interstitial fluid (ISF), remote from blood. ISF glucose is well known to be “delayed” with respect to blood glucose (BG). However, ISF glucose is not simply a shifted-in-time version of BG but exhibits a more complex pattern. Methods: To gain insight into this problem, one can use linear systems theory. However, this may lose a more clinical readership, thus we use simulation and two case studies to convey our thinking in an easier way. In particular, we consider BG concentration measured after meal and exercise in 12 healthy volunteers, whereas ISF glucose is simulated using a well-accepted model of blood–ISF glucose kinetics, which permits calculation of the equilibration time, a parameter characterizing the system. Two metrics are defined: blood and ISF glucose difference at each time point and time to reach the same glucose value in blood and ISF. Results: The simulation performed and the two metrics show that the relationship between blood–ISF glucose profiles is more complex than a pure shift in time and that the pattern depends on both equilibration time and BG. Conclusions: In this in silico study, we have illustrated, with simple case studies, the meaning of the of ISF glucose with respect to BG. Understanding that ISF glucose is not just a shifted-in-time version but a distorted mirror of BG is important for a correct use of continuous glucose monitoring for diabetes management.

Abstract Context: Early postprandial hyperglycemia and delayed hypoglycemia remain major problems in current management of type 1 diabetes (T1D). Objective: Our objective was to investigate the effects of pramlintide, known to suppress glucagon and delay gastric emptying, on postprandial glucose fluxes in T1D. Design: This was a single-center, inpatient, randomized, crossover study. Patients: Twelve patients with T1D who completed the study were analyzed. Interventions: Subjects were studied on two occasions with or without pramlintide. Triple tracer mixed-meal method and oral minimal model were used to estimate postprandial glucose turnover and insulin sensitivity (SI). Integrated liver insulin sensitivity was calculated based on glucose turnover. Plasma glucagon and insulin were measured. Main Outcome Measure: Glucose turnover and SI were the main outcome measures. Results: With pramlintide, 2-hour postprandial glucose, insulin, glucagon, glucose turnover, and SI indices showed: plasma glucose excursions were reduced (difference in incremental area under the curve [iAUC], 444.0 mMmin, P = .0003); plasma insulin concentrations were lower (difference in iAUC, 7642.0 pMmin; P = .0099); plasma glucagon excursions were lower (difference in iAUC, 1730.6 pg/mlmin; P = .0147); meal rate of glucose appearance was lower (difference in iAUC: 1196.2 μM/kg fat free mass [FFM]; P = .0316), endogenous glucose production was not different (difference in iAUC: −105.5 μM/kg FFM; P = .5842), rate of glucose disappearance was lower (difference in iAUC: 1494.2 μM/kg FFM; P = .0083). SI and liver insulin sensitivity were not different between study visits (P &gt; .05). Conclusions: Inhibition of glucagon and gastric emptying delaying reduced 2-hour prandial glucose excursions in T1D by delaying meal rate of glucose appearance.