Citations

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.

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.

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.

To determine whether type 2 diabetes mellitus alters systemic and regional free fatty acid ([3H]palmitate) metabolism, 14 nondiabetic (ND) and 14 type 2 diabetic (D) subjects underwent hyperinsulinemic-hyperglycemic (∼9.3 mM) clamps. The subjects were matched for age, body mass index, percent body fat, and fat-free mass. D subjects had more ( P < 0.05) visceral fat than ND. During somatostatin, replacement growth hormone, and glucagon infusions, insulin was infused to achieve moderate (∼75 pmol/l) and high (∼150 pmol/l) physiological insulin levels. D subjects had greater ( P < 0.02) systemic and regional (splanchnic and leg) palmitate release than ND subjects during both insulin infusion intervals. The relative contributions of splanchnic, leg, and nonsplanchnic upper body regions to systemic palmitate release did not differ between groups, although the last contributed the most (∼75%) to systemic palmitate release. Visceral fat area correlated with systemic palmitate flux ( r = 0.45, P < 0.03) during both insulin infusions. We conclude that type 2 diabetes is associated with a generalized impairment in insulin suppression of lipolysis compared with equally obese ND individuals.

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.