Basu, Ananda

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

Abstract Context Higher gluconeogenesis (GNG) contributes to higher nocturnal endogenous glucose production (EGP) in type 2 diabetes (T2D). Studies using 13C magnetic resonance spectroscopy (MRS) have confirmed lower hepatic glycogen content in subjects with T2D than in subjects with no diabetes (ND). Objective We determined the role of glycogen loading (GL) vs nonglycogen loading (NGL) on the contribution of GNG to nocturnal EGP in T2D. Methods In total, 14 subjects with T2D and 15 matched subjects with ND were studied on 2 occasions, with GL (60% carbohydrate) vs NGL (40% carbohydrate) isocaloric meals for 3 days, in random order in the overnight state. [6,6-2H2] glucose was infused to measure EGP, deuterium labelled water was used to measure GNG, and 13C MRS scans were performed in fed and fasted states to measure hepatic glycogen content. Results Hepatic glycogen content and nocturnal EGP were higher (P < .05) in GL vs NGL in both cohorts. The % GNG to EGP averaged ∼50% in subjects with ND throughout the night after both meals. In contrast, % GNG to nocturnal EGP in T2D was lower with GL vs NGL and matched the pattern observed in subjects with ND with GL lowering overnight rates of GNG in subjects with T2D. Conclusion Selective targeting of GNG at night with appropriate medications could reduce nocturnal and early morning fasting hyperglycemia and hepatic insulin resistance in people with T2D.

Background: Regadenoson is a selective A2A receptor agonist that is administered as a fixed dose bolus with rapid onset (∼30 sec), and short duration (∼2-4 min) of action. The aim of this study was to evaluate the feasibility of real-time myocardial contrast echocardiography (MCE) for quantification of coronary flow reserve (CFR) in healthy humans during regadenoson vasodilator stress. Methods: Fifteen subjects [3 males, age: 45.8±5.2 years, BMI 23.8±2.3 kg/m2] with normal LV wall motion and no CAD underwent real-time MCE. Three standard apical LV views were acquired during a continuous intravenous (IV) infusion of Definity (1.3 ml in 60 cc 0.9% saline at 200 ml/hr) at rest and during vasodilator stress (regadenoson 400 ug IV bolus). Following transient microbubble destruction, microbubble velocity (β, sec -1 ), myocardial blood volume (A, dB) and myocardial blood flow (MBF, dB/sec) were determined. Videodensitometric quantification using a 16-segment model at end-systole was performed off-line. Results: Quantification of reserve parameters was feasible in all (15/15) patients, 188/240 segments (78%)[82%, 80% and 70% of 4-, 2-, 3-chambers apical views] and 43/45 (95%) coronary territories [one each of Cx and RCA territories were not feasible]. Augmentation from baseline to vasodilator stress was observed for both ß (s -1 ) [0.65± 0.25 vs 0.79± 0.21, P=0.031] and MBF (dB/sec) [2.85± 1.43 vs 4.65± 2.06, P= 0.002], however A (dB) did not change [06.62± 2.57 vs 6.79±2.1, P=0.463]. CFR measurements from all views were similar (Fig 1). There was no significant difference between CFR measured in the 3 coronary territories, although some heterogeneity was noted in the posterior circulation CFR (Fig 2). Conclusion: Real-time MCE during Regadenoson vasodilator stress is feasible, and is a useful technique for the rapid noninvasive quantification of CFR in healthy humans.

Abstract Context Circulating lactate concentration is an important determinant of exercise tolerance. Objective This work aimed to determine the role of hyperglycemia on lactate metabolism during exercise in individuals with type 1 diabetes (T1D). Methods The protocol at the University of Virginia compared 7 T1D participants and 7 participants without diabetes (ND) at euglycemia (5.5 mM) or hyperglycemia (9.2 mM) in random order in T1D and at euglycemia in ND. Intervention included [1-13C] lactate infusion, exercise at 65% maximal oxygen uptake (VO2max), euglycemia, and hyperglycemia visits. The main outcome measure was lactate turnover before, during, and after 60 minutes of exercise at 65% VO2max. Results A 2-compartment model with loss only from the peripheral compartment described lactate kinetics. Volume of distribution of the accessible compartment was similar between T1D and ND individuals (P = .76) and concordant with plasma volume (∼40 mL/kg). Circulating lactate concentrations were higher (P < .001) in T1D participants during exercise at hyperglycemia than euglycemia. Exercise-induced lactate appearance did not differ (P = .13) between hyperglycemia and euglycemia. However, lactate clearance (CL) was lower (P = .03) during hyperglycemia than euglycemia in T1D participants. There were no differences in any of the aforementioned parameters between T1D and ND participants during euglycemia. Conclusion Hyperglycemia modulates lactate metabolism during exercise by lowering CL, leading to higher circulating lactate concentrations in T1D individuals. This novel observation implies that exercise during hyperglycemia can lead to higher circulating lactate concentrations thus increasing the likelihood of reaching the lactate threshold sooner in T1D, and has high translational relevance both for providers and recreationally active people with T1D.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Background Hypoglycemia results in a reduction in spontaneous cardiac baroreflex sensitivity (scBRS) and a shift in the baroreflex working range to higher heart rates; this effect may be mediated by the carotid chemoreceptors. We hypothesized changes in baroreflex control of heart rate with hypoglycemia would be blunted in carotid body resected patients when compared with healthy controls. Methods Five patients (3M/2F) with bilateral carotid body resection for glomus tumors and ten healthy controls (7M/3F) completed a 180‐min hyperinsulinemic, hypoglycemic (~3.3 µmol/mL) clamp. Changes in heart rate, blood pressure, and scBRS were assessed. Results scBRS was reduced from baseline during hypoglycemia (p<0.01) and was lower in carotid body resected patients when compared with controls (p=0.01). Hypoglycemia‐mediated reductions in systolic and mean blood pressure were significantly greater in patients when compared with controls (p<0.05). Despite lower blood pressures, increases in heart rate with hypoglycemia were blunted in patients (p<0.01). Conclusion Reductions in scBRS with hypoglycemia cannot be exclusively attributed to the carotid chemoreceptors; however, the chemoreceptors play a role in resetting the baroreflex working range to higher heart rates during hypoglycemia such that a rise in heart rate is not observed in carotid body resected patients. These data support a contribution of the carotid chemoreceptors to baroreflex control of blood pressure during hypoglycemia and highlight potential widespread effects of carotid body resection in humans. Funding DK090541, HL120570

Cardio‐vagal tone (CVT) and heart rate variability (HRV) are reduced during hypoglycemia. Carotid body (CB) peripheral chemoreceptors are multi‐modal; therefore we hypothesized a correlation between CB activity and CVT during hypoglycemia. Seven healthy adults (4M/3F) underwent two 180 min hyperinsulemic (2mU/kg FFM/min), hypoglycemic (3.33 mmol/L) clamps one week apart, randomized to normoxia (arterial P O2 (P a O 2 ) 111 ± 6.3 mmHg) or hyperoxia (P a O 2 345 ± 80.6 mmHg) to desensitize the CB. CVT was assessed via the high frequency (HF) component of HRV from 5 min periods of 5‐lead ECG recording under baseline (euglycemia) and hypoglycemia. HF values decreased significantly from baseline during hypoglycemia, however this reduction was attenuated under hyperoxia (Normoxia (N): −69% vs. Hyperoxia (H): −41%, p=0.04). Heart rate increased significantly during hypoglycemia (N: 59.7 bpm ± 2.4 vs. 71.9 ± 4.0; H: 60.6 bpm ± 1.9 vs. 68.5 ± 3, p<0.001), however, this increase was not different between normoxia and hyperoxia, despite differences in CVT and HRV. These data indicate that the CB chemoreceptors may be partially responsible for the reduction in CVT associated with hypoglycemia, and they provide evidence for a role of the CB chemoreceptors in parasympathetic tone to the heart. Future studies are required to determine whether inhibition of the CB may serve to improve CVT function during hypoglycemia. T32 DK07352, F32 DK84624‐01 A1, CTSA 1 UL 1 RR024150 , R01DK 29953

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Quantification of disturbances in glucose-insulin homeostasis has been the cornerstone of appraising insulin resistance and detecting early-stage diabetes. Metabolic homeostasis arises from feedback and feed-forward interactions among (at least) all four of glucose, insulin, glucagon, and cortisol. Quantifying such tetrapartite interactions in the fasting (endogenously regulated) state overnight could elucidate very early regulatory disruption. In the present study, healthy subjects without diabetes (ND; n = 20) and patients with Type 2 diabetes (T2D; n = 21) were investigated by repeated overnight blood sampling of all four of glucose, insulin, glucagon, and cortisol concentrations. To obviate confounding by hormone-specific disappearance rates, analyses were performed at the level of production (glucose) or secretion (insulin, glucagon, and cortisol) rates estimated by regularized deconvolution analysis. Then, a novel method for quantifying the loss of homeostasis among glucose, insulin, and glucagon (and, when available, cortisol) secretion patterns was developed. Potential early stage prediabetic candidates were identified. The new methodology avoids many of the difficulties encountered in the conventional estimation of insulin-glucose sensitivity or resistance, while incorporating the dynamics of the key coregulators under fasting conditions.