Citations

People with NIDDM are resistant to insulin. The present studies sought to determine whether the ability of glucose to regulate its own metabolism in the presence of basal insulin concentrations is impaired. To address this question, basal insulin concentrations were maintained constant with an exogenous insulin infusion, while endogenous hormone secretion was inhibited by somatostatin. The integrated glycemic response above baseline during identical prandial glucose infusions was greater (1,411 ± 94 vs. 938 ± 45 mmol/l per 5 h; P < 0.01) in the diabetic subjects than in the nondiabetic subjects, indicating a decrease in net glucose effectiveness. [6-3H]glucose also was infused to determine whether the decrease in net glucose effectiveness was due to a decrease in the ability of glucose to stimulate its own uptake and/or to suppress its own production. Despite identical rates of tracer infusion, the increment in plasma concentration of [6-3H]glucose was higher (4.50 ± 0.29 vs. 3.16 ± 0.21 × 105 dpm/ml per 5 h; P < 0.05) in the diabetic subjects than in the nondiabetic subjects. This was due to both a decrease (P < 0.05) in the ability of glucose to stimulate its own disappearance via mass action and to a greater (P < 0.01) inhibitory effect of glucose on its own clearance. The increase in glucose concentration resulted in prompt and comparable suppression of endogenous glucose production in both groups. Under these optimized conditions, indexes of glucose effectiveness calculated with both the “cold” and “hot” minimal models also were lower (P < 0.05) in the diabetic subjects than in the nondiabetic subjects and were highly correlated (r = 0.94–0.99; P < 0.001) with the indexes of glucose effectiveness calculated from the increments above baseline of glucose and [6-3H]glucose concentration. We conclude that the ability of glucose to regulate its own metabolism in the presence of basal insulin concentrations is abnormal in people with NIDDM.

The accuracy of continuous interstitial fluid (ISF) glucose sensing is an essential component of current and emerging open- and closed-loop systems for type 1 diabetes. An important determinant of sensor accuracy is the physiological time lag of glucose transport from the vascular to the interstitial space. We performed the first direct measurement of this phenomenon to our knowledge in eight healthy subjects under an overnight fasted condition. Microdialysis catheters were inserted into the abdominal subcutaneous space. After intravenous bolus administrations of glucose tracers, timed samples of plasma and ISF were collected sequentially and analyzed for tracer enrichments. After accounting for catheter dead space and assay noise, the mean time lag of tracer appearance in the interstitial space was 5.3–6.2 min. We conclude that in the overnight fasted state in healthy adults, the physiological delay of glucose transport from the vascular to the interstitial space is 5–6 min. Physiological delay between blood glucose and ISF glucose, therefore, should not be an obstacle to sensor accuracy in overnight or fasting-state closed-loop systems of insulin delivery or open-loop therapy assessment for type 1 diabetes.

Background: In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. Subjects and Methods: In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. Results: Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40–45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35–40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. Conclusions: Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose–insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

Background: Currently, patients with type 1 diabetes decide on the amount of insulin to administer based on several factors, including current plasma glucose value, expected meal input, and physical activity (PA). One future therapeutic modality for patients with type 1 diabetes is the artificial endocrine pancreas (AEP). Incorporation of PA could enhance the efficacy of AEP significantly. We compared the main technologies used for PA quantitation. Subjects and Methods: Data were collected during inpatient studies involving healthy control subjects and type 1 diabetes. We report PA quantified from accelerometers (acceleration units [AU]) and heart rate (HR) monitors during a standardized activity protocol performed after a dinner meal at 7 p.m. from nine control subjects (four were males, 37.4±12.7 years old, body mass index of 24.8±3.8 kg/m 2 , and fasting plasma glucose of 4.71±0.63 mmol/L) and eight with type 1 diabetes (six were males, 45.2±13.4 years old, body mass index of 25.1±2.9 kg/m 2 , and fasting plasma glucose of 8.44±2.31 mmol/L). Results: The patient-to-patient variability was considerably less when examining AU compared with HR monitors. Furthermore, the exercise bouts and rest periods were more evident from the data streams when AUs were used to quantify activity. Unlike the AU, the HR measurements provided little insight for active and rest stages, and HR data required patient-specific standardizations to discern any meaningful pattern in the data. Conclusions: Our results indicated that AU provides a reliable signal in response to PA, including low-intensity activity. Correlation of this signal with continuous glucose monitoring data would be the next step before exploring inclusion as input for AEP control.

Transaldolase (TA) exchange overestimates gluconeogenesis measured with deuterated water (2H2O). However, it is unknown whether TA differs in people with type 2 diabetes (T2DM).2H2O was ingested, and [1-13C]acetate and [3-3H]glucose were infused in T2DM ( n = 10) and healthy nondiabetic (ND; n = 8) subjects. TA was assessed from the ratio of13C3 to13C4 glucose enrichment (13C3/13C4) measured by13C NMR. Glucose turnover was measured before (∼16-h fast) and during hyperglycemic (∼10 mM) moderate-dose insulin (∼0.35 mU·kg−1·min−1) clamp.13C3/13C4 in T2DM vs. ND was <1.0 and not different at baseline and clamp, indicating equivalent TA. To determine whether incomplete triose phosphate isomerase (TPI) exchange contributed to asymmetric13C3/13C4, [U-13C]glycerol was infused in lieu of [1-13C]acetate during a separate visit in a subset of ND ( n = 7) subjects. Ratio of13C3/13C4 obtained following either tracer was <1.0 at baseline and during clamp, indicating that TPI exchange was essentially complete and did not contribute to asymmetric glucose enrichment. Uncorrected and corrected rates of gluconeogenesis were no different ( P = not significant) in T2DM vs. ND both at baseline and during clamp. TA correction resulted in equivalent estimates of corrected gluconeogenesis in T2DM and ND that were ∼25–35% lower than uncorrected gluconeogenesis both at baseline and during the clamp. The asymmetric enrichment of glucose from13C-gluconeogenic tracers is attributable to TA exchange and can be utilized to correct for TA exchange. In conclusion, TA exchange does not differ between T2DM and ND under fasting or hyperglycemic clamp conditions, and the2H2O method continues to provide an accurate estimation of gluconeogenesis.

Abstract Carbohydrate metabolism in humans is regulated by insulin secretion from pancreatic β‐cells and glucose disposal by insulin‐sensitive tissues. Insulin facilitates glucose utilization in peripheral tissues and suppresses hepatic glucose production. Any defects in insulin action predispose an individual to glucose intolerance and Type 2 diabetes mellitus. Early detection of defects in insulin action could provide opportunities to prevent or delay progression of the disease state. There are different approaches to assess insulin action. Initial methods, such as peripheral insulin concentration and simple indices, have several limitations. Subsequently, researchers developed methodologies using intravenous glucose infusion to determine glucose fluxes. However, these methodologies are limited by being non‐physiological. Newer, innovative techniques that have been developed are more sophisticated and physiological. By modelling glucose kinetics using isotope dilution techniques, several robust parameters can be obtained that are physiologically relevant and sound. This brief review summarizes most of the non‐physiological and physiological methodologies used to measure the variables of insulin action.

We recently demonstrated a diurnal pattern to insulin action (i.e., insulin sensitivity [SI]) in healthy individuals with higher SI at breakfast than at dinner. To determine whether such a pattern exists in type 1 diabetes, we studied 19 subjects with C-peptide–negative diabetes (HbA1c 7.1 ± 0.6%) on insulin pump therapy with normal gastric emptying. Identical mixed meals were ingested during breakfast, lunch, and dinner at 0700, 1300, and 1900 h in randomized Latin square of order on 3 consecutive days when measured daily physical activity was equal. The triple tracer technique enabled measurement of glucose fluxes. Insulin was administered according to the customary insulin:carbohydrate ratio for each participant. Although postprandial glucose excursions did not differ among meals, insulin concentration was higher (P &lt; 0.01) and endogenous glucose production less suppressed (P &lt; 0.049) at breakfast than at lunch. There were no differences in meal glucose appearance or in glucose disappearance between meals. Although there was no statistical difference (P = 0.34) in SI between meals in type 1 diabetic subjects, the diurnal pattern of SI taken across the three meals in its entirety differed (P = 0.016) from that of healthy subjects. Although the pattern in healthy subjects showed decreasing SI between breakfast and lunch, the reverse SI pattern was observed in type 1 diabetic subjects. The results suggest that in contrast to healthy subjects, SI diurnal pattern in type 1 diabetes is specific to the individual and cannot be extrapolated to the type 1 diabetic population as a whole, implying that artificial pancreas algorithms may need to be personalized.

Recently, we proposed a model describing the suppression of endogenous glucose production (EGP) during a meal. It assumes that EGP suppression depends on glucose concentration and its rate of change and on delayed insulin action. Hepatic insulin sensitivity (SILmeal) can be derived from EGP model parameters. This model was shown to adequately describe EGP profiles measured with multiple tracer techniques; however, SILmealhas never been compared directly with its euglycemic hyperinsulinemic clamp counterpart (SILclamp). To do so, 62 subjects with different degrees of glucose tolerance underwent a triple-tracer mixed meal. Fifty-seven subjects also underwent a labeled ([3-3H]glucose) euglycemic hyperinsulinemic clamp. From the triple-tracer meal data, virtually model-independent estimates of EGP were obtained using the tracer-to-tracee clamp technique, and the EGP model was identified in each subject. Model fit was satisfactory, and SILmealwas estimated with good precision. Correlation between SILclampand SILmealwas good ( r = 0.72, P < 0.001); however, SILmealwas lower than SILclamp(4.60 ± 0.64 vs. 8.73 ± 1.07 10−4dl·kg−1·min−1per μU/ml, P < 0.01). This difference may be due to different ranges of insulin explored during the two tests (ΔIclamp= 15.60 ± 1.61 vs. ΔImeal= 83.37 ± 10.71 μU/ml) as well as steady- vs. non-steady-state glucose and insulin profiles. In conclusion, the new EGP model provides an estimate of hepatic insulin sensitivity during a meal that is in good agreement with that derived in the same individuals with a hyperinsulinemic clamp. When used in conjunction with the minimal model, the approach potentially enables estimation of hepatic insulin sensitivity from a single-tracer labeled meal or oral glucose tolerance test.