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Supplementary Figure 2 from Neuropilin-1 Upholds Dedifferentiation and Propagation Phenotypes of Renal Cell Carcinoma Cells by Activating Akt and Sonic Hedgehog Axes

Supplementary Figure 1 from Neuropilin-1 Upholds Dedifferentiation and Propagation Phenotypes of Renal Cell Carcinoma Cells by Activating Akt and Sonic Hedgehog Axes

Supplementary Figure Legends 1-2 from Neuropilin-1 Upholds Dedifferentiation and Propagation Phenotypes of Renal Cell Carcinoma Cells by Activating Akt and Sonic Hedgehog Axes

To evaluate whether short-term treatment with a selective 11β-Hydroxysteroid dehydrogenase-1 (11β-HSD1) inhibitor, AZD4017, would block hepatic cortisol production and thereby decrease hepatic fat in patients with nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH), with or without type 2 diabetes (T2D).This was a randomized, double-blind, placebo-controlled, phase 2 study conducted at two sites. Key inclusion criteria were the presence of NAFLD or NASH on magnetic resonance imaging (MRI) or recent biopsy positive for NASH. Enrolled patients were randomly assigned (1:1) to AZD4017 or placebo for 12 weeks. Primary outcomes were between-group differences in mean change from baseline to week 12 in liver fat fraction (LFF) and conversion ofA total of 93 patients were randomized; 85 patients completed treatment. The mean (standard deviation [SD]) change in LFF was -0.667 (5.246) and 0.139 (4.323) in the AZD4017 and placebo groups (P = 0.441). For patients with NASH and T2D, the mean (SD) change in LFF was significantly improved in the AZD4017 versus the placebo group (-1.087 [5.374] vs. 1.675 [3.318]; P = 0.033). Conversion ofAlthough the study did not meet one of the primary outcomes, AZD4017 blocked the conversion of

To assess the diurnal patterns of postprandial glucose tolerance and insulin sensitivity, 19 subjects with type 2 diabetes (8 women; 60 ± 11 years; BMI 32 ± 5 kg/m2) and 19 anthropometrically matched subjects with no diabetes (ND; 11 women; 53 ± 12 years; BMI 29 ± 5 kg/m2) were studied during breakfast (B), lunch (L), and dinner (D) with identical mixed meals (75 g carbohydrates) on 3 consecutive days in a randomized Latin square design. Three stable isotopes of glucose were ustilized to estimate meal fluxes, and mathematical models were used in estimating indices of insulin action and β-cell function. Postmeal glucose excursions were higher at D versus B and at D versus L in type 2 diabetes (P < 0.05), while in ND they were higher at D versus B (P = 0.025) and at L versus B (P = 0.04). The insulin area under the curve was highest at B compared with L and D in type 2 diabetes, while no differences were observed in ND. Disposition index (DI) was higher at B than at L (P < 0.01) and at D (P < 0.001) in ND subjects, whereas DI was low with unchanging pattern across B-L-D in individuals with type 2 diabetes. Furthermore, between-meal differences in β-cell responsivity to glucose (F) and insulin sensitivity (SI) were concurrent with changes in the DI within groups. Fasting and postmeal glucose, insulin, and C-peptide concentrations, along with estimates of endogenous glucose production (EGP), Rd, SI, F, hepatic extraction of insulin, insulin secretion rate, extracted insulin, and DI, were altered in type 2 diabetes compared with ND (P < 0.011 for all). The data show a diurnal pattern of postprandial glucose tolerance in overweight otherwise glucose-tolerant ND individuals that differs from overweight individuals with type 2 diabetes. The results not only provide valuable insight into management strategies for better glycemic control in people with type 2 diabetes, but also improved understanding of daytime glucose metabolism in overweight individuals without impaired glucose tolerance or overt diabetes.

This article is the work product of the Continuous Ketone Monitoring Consensus Panel, which was organized by Diabetes Technology Society and met virtually on April 20, 2021. The panel consisted of 20 US-based experts in the use of diabetes technology, representing adult endocrinology, pediatric endocrinology, advanced practice nursing, diabetes care and education, clinical chemistry, and bioengineering. The panelists were from universities, hospitals, freestanding research institutes, government, and private practice. Panelists reviewed the medical literature pertaining to ten topics: (1) physiology of ketone production, (2) measurement of ketones, (3) performance of the first continuous ketone monitor (CKM) reported to be used in human trials, (4) demographics and epidemiology of diabetic ketoacidosis (DKA), (5) atypical hyperketonemia, (6) prevention of DKA, (7) non-DKA states of fasting ketonemia and ketonuria, (8) potential integration of CKMs with pumps and automated insulin delivery systems to prevent DKA, (9) clinical trials of CKMs, and (10) the future of CKMs. The panelists summarized the medical literature for each of the ten topics in this report. They also developed 30 conclusions (amounting to three conclusions for each topic) about CKMs and voted unanimously to adopt the 30 conclusions. This report is intended to support the development of safe and effective continuous ketone monitoring and to apply this technology in ways that will benefit people with diabetes.

Postprandial glucose turnover in adolescents with type 1 diabetes (T1D) has not been investigated. To do so, we studied adolescents (n=15, age 11.5±0.5 yrs, BMI 19±2.3 kg/m2, HbA1c 7.5±1%, diabetes duration 4.6±3 yrs) and adults (n=15, age 36.3±9.4 yrs, BMI 27.1±2.1 kg/m2, HbA1c 6.9±1.1%, diabetes duration 18.1±9.6 yrs) with T1D after a mixed meal. Healthy adults (n=14, age 26.9 ± 7 yrs, BMI 24.9±2.5 kg/m2, HbA1c 5.0±0.3%) served as controls. T1D subjects administered their customary insulin bolus dose at meal onset. Rates of endogenous glucose production (EGP) , meal glucose appearance (MRa) and glucose disappearance (Rd) were calculated by the triple tracer method with stable isotopes ([1-13C] glucose, [2-13C] glucose and [6, 6-2H2] glucose) . Integrated glucose excursions did not differ between adolescents and adults with T1D during early (0-120 min) and total (0-300 min) postprandial periods. While basal EGP at start of the meal was higher (p Disclosure F. Ruchi: None. D. Romeres: None. S. Gururaj: None. M. D. Deboer: Research Support; Dexcom, Inc., Tandem Diabetes Care, Inc. R. Basu: Consultant; Sparrow Pharmaceuticals Inc, Research Support; Abbott Diabetes, AstraZeneca. A. Basu: Speaker's Bureau; Zealand Pharma A/S. Funding DK 124886, DK 085516, DK 029953

Use of continuous glucose monitoring (CGM) facilitates better management of type 2 diabetes (T2D) in outpatient clinics in academic medical centers. In a Quality Improvement project, we evaluated the effects of telehealth aided by CGM on HbA1c and percent time in range (TIR 70-180 mg/dl) in patients with T2D over a 6-month duration. Diabetes management advice based on intermittent CGM use was provided via phone by an academic endocrinologist to the clinical pharmacist at a rural community clinic. Inadequately controlled (HbA1c > 8%) T2D patients (n=27) on at least one antidiabetic drug were included and were monitored over a 6 month period, baseline (V1) , 3 months (V2) and 6 months (V3) . All patients received standard of care (SOC) treatment. On each visit, 12 patients (age 59 8y, BMI 36.2 3.6kg/m2, HbA1C 9.3 1.3%) received a professional CGM for 2 weeks. Thereafter, the devices were removed and analyzed. The PCP implemented medication changes based on recommendations by the endocrinologist to the clinical pharmacist. The other 15 patients (age 60 8y, BMI 32 4 kg/m2, HbA1C 9.1 1.6%) underwent the same schedule without CGM or telehealth consultations. There were no significant differences in age or HbA1c between groups at baseline. HbA1c (%) decreased (Δ -1.0 ± 1.4; p=0.03) , and CGM TIR (%) increased (Δ +24.3 ± 38.7; p=0.05) at V3 vs. V1 in the telehealth group. However, weight, CGM time below and above ranges (%) did not differ (p>0.05) statistically. In contrast, there were no significant differences in HbA1c (Δ +0.4 ± 1.7; p=0.3) between V3 and V1 in the SOC without telehealth group. Our results show that intermittent CGM use coupled with tele-consultation with diabetes expert improves glucose control and care quality in patients with T2D in community based health practice. This strategy could potentially mitigate existing health care disparities between suburban/rural-based community health practices and academic medical centers, thus closing the gaps in quality of care. Disclosure S. Gururaj: None. A. Asfa: None. L. C. Hanley: None. Y. R. Yadav: None. R. T. Bashore: None. R. Basu: Consultant; Sparrow Pharmaceuticals Inc, Research Support; Abbott Diabetes, AstraZeneca. A. Basu: Speaker’s Bureau; Zealand Pharma A/S. Funding Virginia Department of Health (GS11963)

Background Glucagon serves as an important regulatory hormone for regulating blood glucose concentration with tight feedback control exerted by insulin and glucose. There are critical gaps in our understanding of glucagon kinetics, pancreatic α cell function and intra-islet feedback network that are disrupted in type 1 diabetes. This is important for translational research applications of evolving dual-hormone (insulin + glucagon) closed-loop artificial pancreas algorithms and their usage in type 1 diabetes. Thus, it is important to accurately measure glucagon kinetics in vivo and to develop robust models of glucose-insulin-glucagon interplay that could inform next generation of artificial pancreas algorithms. Methods Here, we describe the administration of novel 13C15N heavy isotope-containing glucagon tracers—FF glucagon [(Phe 6 13C9,15N; Phe 22 13C9,15N)] and FFLA glucagon [(Phe 6 13C9,15N; Phe 22 13C9,15N; Leu 14 13C6,15N; Ala 19 13C3)] followed by anti-glucagon antibody-based enrichment and LC–MS/MS based-targeted assays using high-resolution mass spectrometry to determine levels of infused glucagon in plasma samples. The optimized assay results were applied for measurement of glucagon turnover in subjects with and without type 1 diabetes infused with isotopically labeled glucagon tracers. Results The limit of quantitation was found to be 1.56 pg/ml using stable isotope-labeled glucagon as an internal standard. Intra and inter-assay variability was Conclusions The methods described in this study for simultaneous detection and quantitation of glucagon tracers have clinical utility for investigating glucagon kinetics in vivo in humans.

OBJECTIVE Continuous glucose monitoring (CGM) improves diabetes management, but its reliability in individuals on hemodialysis is poorly understood and potentially affected by interstitial and intravascular volume variations. RESEARCH DESIGN AND METHODS We assessed the accuracy of a factory-calibrated CGM by using venous blood glucose measurements (vBGM) during hemodialysis sessions and self-monitoring blood glucose (SMBG) at home. RESULTS Twenty participants completed the protocol. The mean absolute relative difference of the CGM was 13.8% and 14.4%, when calculated on SMBG (n = 684) and on vBGM (n = 624), and 98.7% and 100% of values in the Parkes error grid A/B zones, respectively. Throughout 181 days of CGM monitoring, the median time in range (70–180 mg/dL) was 38.5% (interquartile range 29.3–57.9), with 28.7% (7.8–40.6) of the time >250 mg/dL. CONCLUSIONS The overall performance of a factory-calibrated CGM appears reasonably accurate and clinically relevant for use in practice by individuals on hemodialysis and health professionals to improve diabetes management.

The prevalence and incidence of young-onset diabetes are increasing in many parts of the world, with the most rapid increase occurring in Asia, where one in five people with diabetes are diagnosed below the age of 40 years. Accumulation of glycaemic burden from an early age significantly increases the lifetime risks of developing complications from diabetes. Despite impending health threats, young people fare worse in the control of blood glucose and other metabolic risk factors. Challenges in the management of young-onset diabetes are compounded by heterogeneity of the underlying causes, pathophysiology and clinical phenotypes in this group. Effective characterization of people with diabetes has implications in steering the choice of glucose-lowering drugs, which, in turn, determines the clinical outcome. Medical nutritional therapy is key to effective management of people with diabetes but dietary adherence is often suboptimal among younger individuals. A recently published consensus report on nutritional therapy addresses dietary management in people with prediabetes as well as diabetes, and summarizes clinical evidence regarding macronutrient and micronutrient composition as well as eating patterns in people with diabetes. For people with type 1 diabetes, automated insulin delivery systems have rapidly evolved since the concept was first introduced at the National Institute of Health and the Juvenile Diabetes Research Foundation in 2005. The subsequent development of a type 1 diabetes simulator, developed using detailed human physiology data on carbohydrate metabolism replaced the need for pre-clinical animal studies and facilitated the seamless progression to artificial pancreas human clinical trials.

Context The effect of physiological changes in night-time cortisol and glucagon on endogenous glucose production (EGP) and nocturnal glycemia are unknown. Objective To determine the effects of changes in cortisol and glucagon on EGP during the night. Design Two overnight protocols were conducted. In Protocol 1, endogenous cortisol was blocked with metyrapone and hydrocortisone infused either at constant (constant) or increasing (variable) rates to mimic basal or physiological nocturnal cortisol concentrations. In Protocol 2, endogenous glucagon was blocked with somatostatin and exogenous glucagon was infused at either basal or elevated rates to mimic nocturnal glucagon concentrations observed in nondiabetic (ND) and type 2 diabetes (T2D) individuals. EGP was measured using [3-3H] glucose and gluconeogenesis estimated with 2H2O in all studies. Setting Mayo Clinic Clinical Research Trials Unit, Rochester, MN, US. Participants In Protocol 1, 34 subjects (17 ND and 17 T2D) and in Protocol 2, 39 subjects (21 ND and 18 T2D) were studied. Main Outcome Measures Endogenous glucose production Results EGP, gluconeogenesis, and glycogenolysis were higher with variable than with constant cortisol at 7 am in T2D subjects. In contrast, nocturnal EGP did not differ in ND subjects between variable and constant cortisol. While elevated glucagon increased EGP, glycogenolysis, and gluconeogenesis in ND, the data in T2D subjects indicated that EGP and gluconeogenesis but not glycogenolysis were higher during the early part of the night. Conclusion Nocturnal hyperglucagonemia, but not physiological rise in cortisol, contributes to nocturnal hyperglycemia in T2D due to increased gluconeogenesis.

People with diabetes mellitus are more vulnerable to viral infections including influenza and the ongoing COVID-19 pandemic. Fatal pneumonia and cytokine storm is a contributor to the mortality. We hypothesized that aberrant glycemic control induces an immune dysregulation, which compromises the anti-viral immunity and promotes inflammation by altering the balance of the protective T-regulatory cells (Tregs) and IL-17 responses. Indeed, comparison of the peripheral blood lymphocytes in a limited cohort of healthy controls (HC; n=4) and people with diabetes (n=9) showed profound immune dysregulation. As compared to HC (66±21 x10^3/ml), the number of circulating Tregs were significantly (p In summary, altered glycemic control, as indicated by higher HbA1c levels, significantly correlates with compromised IFNg production in the CD8 and Th1 cells, which is important for anti-viral immunity, as well as a marked proinflammatory shift shown as reduced Tregs levels and enhanced IL-17 production by both CD4 and CD8 cells. This immune-dysregulation in diabetes may thus contribute to a greater susceptibility to viral infections and infection-induced pathology. Disclosure V. Sabapathy: Stock/Shareholder; Self; 10x Genomics, Apple, AstraZeneca, Bayer U. S., Gilead Sciences, Inc. M. Dogan: None. R. Venkatadri: None. R. Arbab: None. J. Z. Ma: None. A. Basu: None. R. Basu: None. R. Sharma: Other Relationship; Self; SlateBio Inc. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK104963); University of Virginia

We have recently established a method utilizing the natural abundance of [13C] polysaccharide enabling measurement of enrichment of [13C] glucose in healthy humans. We now compare complex carb (CC) meal with a single tracer infusion of [6,6-2H2] glucose at -120 min to the established simple carb (SC) triple tracer protocol with infusion of [6,6-2H2] glucose at -120 min and [2-13C] glucose at 0 min with meal ingested at time 0. Six type 2 diabetes subjects (4M, age 65±6 y; BMI 33±4 kg/m2; HbA1c 50±10 mM/mol) were studied in random order with SC labeled with [1-13C] glucose and CC meal with sorghum grain. All meals had ~75 g carbohydrates with matched macronutrient content. Steele’s non-steady state equations were used to calculate glucose fluxes: rates of meal glucose appearance (Rameal), glucose disappearance (Rd) and endogenous glucose production (EGP) (Results in Fig1). Glucose, insulin and C-peptide concentrations achieved as well as iAUC of Rameal and Rd were significantly lower with CC than SC meal (p Disclosure Y. R. Yadav: None. A. Asfa: None. F. Ruchi: None. A. Weaver: None. C. M. Amarasekarage: None. C. Lane: None. A. Basu: None. R. Basu: None. Funding National Institutes of Health (R01DK029953 to R.B.), (R01DK085516 to A.B.), (DK059637, DK020593)

Studies suggest that Indian Americans (IA) living in US have higher basal insulin resistance than other ethnic groups thereby increasing risk for T2DM. To characterize glucose metabolism between IA to Indians (I) living in India, we studied thirty-one Indians recruited at the Chellaram Hospital in Pune, India and twenty Indian Americans at Mayo Clinic, MN. All underwent a 3-hour 9-point OGTT with analyses of glucose, insulin and C-peptide. The oral minimal model was used to estimate β-cell function (φtotal) and insulin sensitivity (SI), with assessment of β-cell responsivity (Disposition Index: DI). Insulin clearance was calculated as AUC of insulin secretion rate over insulin concentrations. Both I and IA were similar with respect to age (33±7 vs. 39±13y), BMI (25.0±1.8 vs. 24.7± 3.2kg/m2) and sex (13F and 9F) respectively. Results are in fig 1. Fasting and iAUC glucose did not differ between groups (p=0.478), while integrated C-peptide and Insulin concentrations were higher in I than IA (p Disclosure V. Purandare: None. A. Galderisi: None. M. Maertino: None. A. Basu: None. C. Cobelli: None. A. Unnikrishnan: Advisory Panel; Self; Sanofi-Aventis, Other Relationship; Self; Novo Nordisk A/S, Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes. R. Basu: None. Funding National Institutes of Health (R01DK029953 to R.B.), (R01DK085516 to A.B.)

The triple tracer method has enabled accurate and concurrent measurements of glucose turnover as well as indices of insulin action and beta cell function in people with diabetes. We have previously utilized the natural enrichment of 13C polysaccharide in commercially available grains, and compared indices obtained after a complex carbohydrate (CC) meal to those obtained after a simple carbohydrate (SC) mixed meal in healthy humans. We have now completed studies in 6 subjects with type 2 diabetes (4M, age 65±6 y; BMI 33±4 kg/m2; HbA1c 50±10 mM/mol) after ~75 gm of either sorghum (naturally enriched 13C) or glucose labeled with 13C mixed meals containing ~75 gm carbs and matched protein and fat in random order. Results in figure. Glucose, insulin and C-peptide concentrations achieved were significantly lower with CC meal as compared to SC meal (p Disclosure D. Romeres: None. A. Basu: None. Y. R. Yadav: None. C. Cobelli: None. C. Dalla man: Research Support; Self; Becton, Dickinson and Company, Sanofi-Aventis Deutschland GmbH. R. Basu: None. Funding National Institutes of Health (R01DK029953 to R.B.), (R01DK085516 to A.B.), (DK059637, DK020593)

Inappropriately elevated postprandial (PP) glucagon concentrations contribute to hyperglycemia in type 1 diabetes (T1D). To define the underlying mechanisms, we infused 13C15N glucagon tracer in healthy and T1D subjects during a mixed meal (75 gm carb, 15% fat, 35% protein; 8 kcal/kg) to estimate PP glucagon turnover. We show data from 12 healthy (age 26 ± 6 yrs, BMI 24.6±2.8 kg/m2, fasting plasma glucose 4.8±0.3 mM, HbA1c 5.0±0.3%) and 4, C-peptide negative T1D (age 37 ± 15 yrs, BMI 27.5 ± 2.3 kg/m2, fasting plasma glucose 11.1 ± 2.9 mM, HbA1c 7.7 ± 0.9%) subjects completed thus far. Arterialized venous samples were drawn periodically for measurements of 13C15N glucagon (tandem mass spectrometry), glucagon (Mercodia), and glucose (YSI) concentrations throughout the study. Systemic glucagon turnover was calculated by non-steady-state Steele’s equation after determining volume of distribution of glucagon. Integrated PP glucagon concentrations were higher (p Disclosure F. Ruchi: None. Y. R. Yadav: None. M. Schiavon: None. A. Weaver: None. C. Cobelli: None. C. Dalla man: Research Support; Self; Becton, Dickinson and Company, Sanofi-Aventis Deutschland GmbH. R. Basu: None. A. Basu: None. Funding National Institutes of Health (DK085516 to A.B.), (DK029953 to R.B.); National Mouse Metabolic Phenotyping Center (DK059637); Vanderbilt Diabetes Research and Training Center (DK020593)

We recently developed a novel stable glucagon tracer, [13C15N]-glucagon, to study glucagon kinetics and turnover in humans. Here, [13C15N]-glucagon was used to model glucagon kinetics during steady-state conditions in 5 nondiabetic subjects with complete data thus far (age 27±7 y; BMI 25.3±2.4 kg/m2, Mean±SD) who underwent two separate protocols: bolus injection (1497±564 pg/kg) and stop-from-constant infusion (138±33 pg/kg/min) of [13C15N]-glucagon. Arterialized blood was frequently sampled to measure the decay of [13C15N]-glucagon concentrations using tandem mass spectrometry. A linear, single compartment model was necessary and sufficient to reliably describe [13C15N]-glucagon kinetics and estimate both glucagon volume of distribution and clearance rate. The model well predicted the data (Fig. 1) and model parameters were estimated with precision (CV = 6±3 %). Volume of glucagon distribution was 37±9 mL/kg while clearance rate was 8.1±1.5 mL/kg/min. Results suggest that glucagon distributes in a single compartment with a volume of distribution approximately equal to the plasma volume. Additional nondiabetic and subjects with type 1 diabetes are currently under study. This model will be key to study postprandial glucagon turnover. Disclosure M. Schiavon: None. F. Ruchi: None. Y. R. Yadav: None. R. Basu: None. C. Cobelli: None. C. Dalla man: Research Support; Self; Becton, Dickinson and Company, Sanofi-Aventis Deutschland GmbH. A. Basu: None. Funding National Institutes of Health (DK085516 to A.B.), (DK029953 to R.B.); Italian Ministry of Education, University and Research (Law 232/2016 to M.S. and C.D.M.)

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.

Assessment of glycaemic outcomes in the management of Type 1 and Type 2 diabetes has been revolutionized in the past decade with the increasing availability of accurate, user-friendly continuous glucose monitoring (CGM). This advancement has brought a need for new techniques to appropriately analyse and understand the voluminous and complex CGM data for application in research-related goals and clinical guidance for individuals. Traditionally, HbA1c was established using the Diabetes Control and Complications Trial (DCCT) and other trials as the ultimate measure of glycaemic control in terms of efficacy and, by default, risk of microvascular complications of diabetes. However, it is acknowledged that HbA1c alone is inadequate at describing an individual's daily glycaemic variation and risks for hypo- and hyperglycaemia, and it does not provide the guidance needed to decrease those risks. CGM data provide means by which to characterize an individual's daily glycaemic excursions on a different time scale measured in minutes rather than months. As a consequence, clinical reports, such as the ambulatory glucose profile, increasingly include summary statistics related to averages (mean glucose, time in range) as well as markers related to glycaemic variability (coefficient of variation, standard deviation). However, there is a need to translate those metrics into specific risks that can be addressed in an actionable plan by individuals with diabetes and providers. This review presents several clinical scenarios of glycaemic outcomes from CGM data that can be analysed to describe glycaemic variability and its attendant risks of hyperglycaemia and hypoglycaemia, moving towards relevant interpretation of the complex CGM data streams.

Exercise effects (EE) on whole body glucose rate of disappearance (Rd) occur through insulin- independent (IIRd) and insulin-dependent (IDRd) mechanisms. Quantifying these processes in vivo would allow a better understanding of the physiology of glucose regulation. This is of particular importance in individuals with type 1 diabetes (T1D) since such a knowledge may help to improve glucose management. However, such a model is still lacking. Here, we analyzed data from 6 T1D and 6 nondiabetic (ND) subjects undergoing a labeled glucose clamp study during, before and after a 60 min exercise session at 65% VO2max on three randomized visits: euglycemia-low insulin, euglycemia-high insulin and hyperglycemia-low insulin. We tested a set of models, all sharing a single-compartment description of glucose kinetics, but differing in how exercise is assumed to modulate glucose disposal. Model selection was based on parsimony criteria. The best model assumed an exercise-induced immediate effect on IIRd and a delayed effect on IDRd. It predicted that exercise increases IIRd, compared to rest, by 66% - 82% and 67% - 97% in T1D and ND, respectively, not significantly different between the two groups. Conversely, the exercise effect on IDRd ranged between 81% - 155% in T1D and it was significantly higher than ND, which ranged between 10% - 40%. The exaggerated effect observed in IDRd can explain the higher hypoglycemia risk related to T1D individuals. This novel exercise model could help in informing safe and effective glucose management during and after exercise in T1D individuals.

Exercise effects (EE) on whole body glucose rate of disappearance (Rd) occur through insulin-independent (IIRd) and insulin-dependent (IDRd) mechanisms. Quantifying these processes in vivo would allow a better understanding of the physiology of glucose regulation. This is of particular importance in individuals with type 1 diabetes (T1D) since such a knowledge may help to improve glucose management. However, such a model is still lacking. Here, we analyzed data from six T1D and six nondiabetic (ND) subjects undergoing a labeled glucose clamp study during, before, and after a 60-min exercise session at 65% V̇o(2max) on three randomized visits: euglycemia-low insulin, euglycemia-high insulin, and hyperglycemia-low insulin. We tested a set of models, all sharing a single-compartment description of glucose kinetics, but differing in how exercise is assumed to modulate glucose disposal. Model selection was based on parsimony criteria. The best model assumed an exercise-induced immediate effect on IIRd and a delayed effect on IDRd. It predicted that exercise increases IIRd, compared with rest, by 66%–82% and 67%–97% in T1D and ND, respectively, not significantly different between the two groups. Conversely, the exercise effect on IDRd ranged between 81% and 155% in T1D and it was significantly higher than ND, which ranged between 10% and 40%. The exaggerated effect observed in IDRd can explain the higher hypoglycemia risk related to individuals with T1D. This novel exercise model could help in informing safe and effective glucose management during and after exercise in individuals with T1D. NEW & NOTEWORTHY Here, we present a new mathematical model describing the effect of moderate physical activity on insulin-mediated and noninsulin-mediated glucose disposal in subjects with and without diabetes. We believe that this represents a step-forward in the knowledge of type 1 diabetes pathophysiology, and an useful tool to design safe and effective insulin-therapies.

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.

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.

Quantifying the exercise effect (EE) on glucose turnover is fundamental for type 1 diabetes (T1D) management. We recently proposed a model describing EE on insulin-dependent and -independent glucose utilization in subjects with and without T1D (ND). However, to date, a model of EE on endogenous glucose production (EGP) is still lacking. To do so, we analysed existing data on 6 T1D (age 29±3 yrs, BMI 28.3±2.0 kg/m2, VO2 max 28.7±2.9 ml/kg/min, mean±SE) and 6 ND (age=28.2±4.2 yr, BMI=23.6 ±1.0 kg/m2, VO2 max 36.6±2.9 ml/kg/min) subjects studied with a glucose clamp, during, before and after a 60 min aerobic exercise session at 65% VO2max on three visits: euglycemia-low insulin (V1); euglycemia-high insulin (V2) and hyperglycemia-low insulin (V3) in random order. EGP was measured with the isotope dilution technique using [6,6-2H2]glucose. A battery of models was tested against data to assess if EE on EGP is mediated by insulin, glucose or both. The best model (Fig 1) provided a good description of the data in both groups. Results show that EE on EGP is delayed and insulin mediated. Further studies are needed to validate the model in real life conditions thus making it usable for optimization of insulin therapy. Disclosure D. Romeres: None. M. Schiavon: None. R. Visentin: None. A. Basu: Consultant; Spouse/Partner; GENFIT. Research Support; Spouse/Partner; AstraZeneca. C. Cobelli: None. R. Basu: Consultant; Self; GENFIT. Research Support; Self; AstraZeneca. C. Dalla Man: Research Support; Self; Sanofi-Aventis Deutschland GmbH. Funding Italian Ministry of Education (232/2016); National Institute of Diabetes and Digestive and Kidney Diseases (DK029953 to R.B.), (DK085516 to A.B.)

We have recently developed a novel isotope dilution method using nonradioactive, stable human Glucagon (Phe 6 13C9, 15N; Phe 22 13C9, 15N) tracer to measure glucagon turnover. In this study we combined this method with organ catheterization technique to estimate the effects of increasing circulating glucagon concentrations, spanning the physiological range, on splanchnic and leg glucagon extraction. We present data from the first 6 healthy subjects (age 25±3.6 yrs; BMI 26.7±4.5 kg/m2; fasting glucose 4.6±0.5 mM; HbA1c 5.1±0.3%) completed thus far. After IRB approval and informed consent, subjects were admitted in the morning after an overnight fast to Interventional Radiology, where catheters were placed in the right femoral artery and vein under local anesthesia with aseptic precautions. Under fluoroscopic guidance, a hepatic venous catheter was positioned through the femoral vein. Glucagon tracer was infused intravenously and Indocyanine Green infused via the femoral artery sheath to measure splanchnic and femoral plasma flows. After a 90 min period for basal sampling, exogenous glucagon was infused at 0.65 ng/kg/min for 60 minutes (low) followed by 1.5 ng/kg/min (high) for another 60 minutes. Plasma samples were collected periodically from femoral artery, femoral vein and hepatic vein for concentrations of Glucagon tracer (Tandem Mass Spectrometry), glucagon, glucose and Indocyanine Green (Spectrophotometry) concentrations. Arterial glucagon concentrations were 45.2±9.6 pg/ml (basal), 56.3±13.3 pg/ml (low) and 81.4±15.5 pg/ml (high). While fractional splanchnic glucagon extraction remained relatively unchanged (24.0±12.5% vs. 27.7±15.6% vs. 23.5±10.1%: basal vs. low vs. high), fractional leg glucagon extraction tended to increase (13.5±3.8% vs. 16.0±7.2% vs. 18.5±9.4%: basal vs. low vs. high) with increasing glucagon concentrations. These initial data suggest differing mechanisms of glucagon clearance across the splanchnic and leg tissues in humans. Disclosure F. Ruchi: None. Y.R. Yadav: None. D. Romeres: None. S. Sawleh: None. L.M. Benson: None. K.L. Johnson: None. C. Dalla Man: Research Support; Self; Sanofi-Aventis Deutschland GmbH. C. Cobelli: None. D.J. McCormick: None. L.R. Wilkins: None. R. Basu: Consultant; Self; GENFIT. Research Support; Self; AstraZeneca. A. Basu: Consultant; Spouse/Partner; GENFIT. Research Support; Spouse/Partner; AstraZeneca. Funding National Institutes of Health (R01DK029953 to R.B.), (R01DK085516 to A.B.), (DK059637, DK020593)

We have recently demonstrated successful estimation of postprandial glucose fluxes using a novel triple tracer technique after ingestion of a mixed meal containing complex carbohydrates (CC) that is naturally enriched with 13C polysaccharide ([13C]-PS). We also reported higher insulin sensitivity (SI) with CC than simple carbohydrate (SC) containing mixed meals. The current analysis was designed to determine if [13C]-PS per se could directly measure hepatic (SIH) and disposal SI (SID) during the CC containing mixed meal. Sixteen healthy subjects (9M, age=33±2 y, BMI=23±1 kg/m2) received, in random order, either SC (labeled with [13C]-glucose) or CC (naturally containing [13C]-PS) mixed meals. All meals were isocaloric and contained 50g carbohydrates with similar macronutrient composition. Plasma glucose, [13C]-glucose and insulin were measured and the Single Tracer Oral Minimal Model applied to estimate SIH and SID. The model well predicted the data. SIH was significantly higher, while SID was numerically but not statistically higher in CC than SC meals (Figure 1). This implies that the higher SI with CC meals is likely due to higher hepatic insulin sensitivity. Taken together, the data suggest that a single, naturally occurring [13C]-PS tracer can measure postprandial SI and its components without the need for the triple tracer method. Disclosure C. Dalla Man: Research Support; Self; Sanofi-Aventis Deutschland GmbH. R. Visentin: None. M. Schiavon: None. A. Basu: Consultant; Spouse/Partner; GENFIT. Research Support; Spouse/Partner; AstraZeneca. C. Cobelli: None. R. Basu: Consultant; Self; GENFIT. Research Support; Self; AstraZeneca. Funding National Institutes of Health (DK085516, DK94331, DK29953); Italian Minister for Education