Basu, Ananda

Abstract 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.

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.

Abstract Context A better understanding of nocturnal regulation of glucose homeostasis will provide the framework for designing rational therapeutic strategies to improve the management of overnight glucose in patients with type 2 diabetes (T2D). Objective To establish the nocturnal pattern and regulation of glucose production (EGP) in humans and to determine whether the pattern is dysregulated in people with T2D. Design Subjects were infused with [3-3H] glucose overnight. Arterial blood samples were drawn for hormones and analytes to estimate EGP throughout the night. Deuterium-labeled water was provided to measure gluconeogenesis (GNG) using the hexamethylenetetramine method of Landau. Setting Mayo Clinic Clinical Research Trials Unit, Rochester, MN, USA. Participants and Interventions A total of 43 subjects [23 subjects with T2D and 20 nondiabetic (ND) subjects comparable for age and body mass index] were included in this study. Main Outcome(s) Measure(s) Glucose and EGP. Results Plasma glucose, C-peptide, and glucagon concentrations were higher throughout the night, whereas insulin concentrations were higher in subjects with T2D vs ND subjects at 1:00 and 4:00 am but similar at 7:00 am. EGP was higher in the subjects with T2D than in the ND subjects throughout the night (P &lt; 0.001). Glycogenolysis (GGL) fell and GNG rose, resulting in significantly higher (P &lt; 0.001) rates of GNG at 4:00 and 7:00 am and significantly (P &lt; 0.001) higher rates of GGL at 1:00, 4:00, and 7:00 am in T2D as compared with ND. Conclusions These data imply that optimal therapies for T2D for nocturnal/fasting glucose control should target not only the absolute rates of EGP but also the contributing pathways of GGL and GNG sequentially.

Objective: The objective was to investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes. Methods: Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m 2 (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based “effectiveness index” (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes. Results: Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG. Conclusion: Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.

People with type 2 diabetes have defects in both α- and β-cell function. To determine whether lack of suppression of glucagon causes hyperglycemia when insulin secretion is impaired but not when insulin secretion is intact, twenty nondiabetic subjects were studied on two occasions. On both occasions, a “prandial” glucose infusion was given over 5 h while endogenous hormone secretion was inhibited. Insulin was infused so as to mimic either a nondiabetic ( n = 10) or diabetic ( n = 10) postprandial profile. Glucagon was infused at a rate of 1.25 ng ⋅ kg−1⋅ min−1, beginning either at time zero to prevent a fall in glucagon (nonsuppressed study day) or at 2 h to create a transient fall in glucagon (suppressed study day). During the “diabetic” insulin profile, lack of glucagon suppression resulted in a marked increase ( P < 0.002) in both the peak glucose concentration (11.9 ± 0.4 vs. 8.9 ± 0.4 mmol/l) and the area above basal of glucose (927 ± 77 vs. 546 ± 112 mmol ⋅ l−1⋅ 6 h) because of impaired ( P < 0.001) suppression of glucose production. In contrast, during the “nondiabetic” insulin profile, lack of suppression of glucagon resulted in only a slight increase ( P< 0.02) in the peak glucose concentration (9.1 ± 0.4 vs. 8.4 ± 0.3 mmol/l) and the area above basal of glucose (654 ± 146 vs. 488 ± 118 mmol ⋅ l−1⋅ 6 h). Of interest, when glucagon was suppressed, glucose concentrations differed only minimally during the nondiabetic and diabetic insulin profiles. These data indicate that lack of suppression of glucagon can cause substantial hyperglycemia when insulin availability is limited, therefore implying that inhibitors of glucagon secretion and/or glucagon action are likely to be useful therapeutic agents in such individuals.

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: Reliability of continuous glucose monitors (CGM) is a prerequisite for therapeutic dosing of insulin without the need for confirmatory blood glucose meter measurements. Interference of CGMs with commonly prescribed substances has not been extensively evaluated. Methods: We sought to undertake a novel pilot study to determine the susceptibility of FDA-approved CGM systems (Medtronic Guardian Sof-Sensor, Dexcom G4 Platinum) to erroneous readings in the presence of common medications. CGMs were placed on the abdomen of healthy subjects 48 hours prior to study. Subjects were admitted to the Clinical Research Trials Unit (CRTU) on the evening before study and fed a standard supper. The following morning, an oral medication was administered in the fasted state and blood was sampled for 9 hours. CGM values were compared to ambient glucose (measured with YSI) to observe variations in CGM readings. Microdialysis catheters were also placed in the abdomen to sample interstitial fluid (ISF) for drug concentrations. Results: Nineteen healthy drug-naïve subjects without diabetes participated in the study. A drug/substance was tested up to a maximum of nine times on separate occasions. Comparison of CGM glucose patterns to actual plasma glucose concentrations show several drugs, including lisinopril, albuterol, and acetaminophen, appear to interfere with commonly used CGM devices. Wine also interfered with CGM readings. Conclusions: We conclude there is some evidence of CGM interference with lisinopril, albuterol, acetaminophen, atenolol, and red wine. Future studies are required to address interference with newer sensors being approved or in the process of approval.

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.