Performance of individually measured vs population-based C-peptide kinetics to assess β-cell function in the presence and absence of acute insulin resistance

Altered Insulin Clearance after Gastric Bypass and Sleeve Gastrectomy in the Fasting and Prandial Conditions

Background: The liver has the capacity to regulate glucose metabolism by altering the insulin clearance rate (ICR). The decreased fasting insulin concentrations and enhanced prandial hyperinsulinemia after Roux-en-Y gastric-bypass (GB) surgery and sleeve gastrectomy (SG) are well documented. Here, we investigated the effect of GB or SG on insulin kinetics in the fasting and fed states. Method: ICR was measured (i) during a mixed-meal test (MMT) in obese non-diabetic GB (n = 9) and SG (n = 7) subjects and (ii) during a MMT combined with a hyperinsulinemic hypoglycemic clamp in the same GB and SG subjects. Five BMI-matched and non-diabetic subjects served as age-matched non-operated controls (CN). Results: The enhanced ICR during the fasting state after GB and SC compared with CN (p < 0.05) was mainly attributed to augmented hepatic insulin clearance rather than non-liver organs. The dose-response slope of the total insulin extraction rate (InsExt) of exogenous insulin per circulatory insulin value was greater in the GB and SG subjects than in the CN subjects, despite the similar peripheral insulin sensitivity among the three groups. Compared to the SG or the CN subjects, the GB subjects had greater prandial insulin secretion (ISR), independent of glycemic levels. The larger post-meal ISR following GB compared with SG was associated with a greater InsExt until it reached a plateau, leading to a similar reduction in meal-induced ICR among the GB and SG subjects. Conclusions: GB and SG alter ICR in the presence or absence of meal stimulus. Further, altered ICR after bariatric surgery results from changes in hepatic insulin clearance and not from a change in peripheral insulin sensitivity.

The relationship between insulin and glucagon concentrations in non-diabetic humans

Abnormal postprandial suppression of glucagon in Type 2 diabetes (T2DM) has been attributed to impaired insulin secretion. Prior work suggests that insulin and glucagon show an inverse coordinated relationship. However, dysregulation of α-cell function in prediabetes occurs early and independently of changes in β-cells, which suggests insulin having a less significant role on glucagon control. We therefore, sought to examine whether hepatic vein hormone concentrations provide evidence to further support the modulation of glucagon secretion by insulin. As part of a series of experiments to measure the effect of diabetes-associated genetic variation in TCF7L2 on islet cell function, hepatic vein insulin and glucagon concentrations were measured at 2-minute intervals during fasting and a hyperglycemic clamp. The experiment was performed on 29 nondiabetic subjects (age = 46 ± 2 years, BMI 28 ± 1 Kg/m2 ) and enabled post-hoc analysis, using Cross-Correlation and Cross-Approximate Entropy (Cross-ApEn) to evaluate the interaction of insulin and glucose. Mean insulin concentrations rose from fasting (33 ± 4 vs. 146 ± 12 pmol/L, p < 0.01) while glucagon was suppressed (96 ± 8 vs. 62 ± 5 ng/L, p < 0.01) during the clamp. Cross-ApEn was used to measure pattern reproducibility in the two hormones using glucagon as control mechanism (0.78 ± 0.03 vs. 0.76 ± 0.03, fasting vs. hyperglycemia) and using insulin as a control mechanism (0.78 ± 0.02 vs. 0.76 ± 0.03, fasting vs. hyperglycemia). Values did not differ between the two scenarios. Cross-correlation analysis demonstrated a small in-phase coordination between insulin and glucagon concentrations during fasting, which inverted during hyperglycemia. This data suggests that the interaction between the two hormones is not driven by either. On a minute-to-minute basis, direct control and secretion of glucagon is not mediated (or restrained) by insulin.

Measurement of Pulsatile Insulin Secretion: Rationale and Methodology

Pancreatic β-cells are responsible for the synthesis and exocytosis of insulin in response to an increase in circulating glucose. Insulin secretion occurs in a pulsatile manner, with oscillatory pulses superimposed on a basal secretion rate. Insulin pulses are a marker of β-cell health, and secretory parameters, such as pulse amplitude, time interval and frequency distribution, are impaired in obesity, aging and type 2 diabetes. In this review, we detail the mechanisms of insulin production and β-cell synchronization that regulate pulsatile insulin secretion, and we discuss the challenges to consider when measuring fast oscillatory secretion in vivo. These include the anatomical difficulties of measuring portal vein insulin noninvasively in humans before the hormone is extracted by the liver and quickly removed from the circulation. Peripheral concentrations of insulin or C-peptide, a peptide cosecreted with insulin, can be used to estimate their secretion profile, but mathematical deconvolution is required. Parametric and nonparametric approaches to the deconvolution problem are evaluated, alongside the assumptions and trade-offs required for their application in the quantification of unknown insulin secretory rates from known peripheral concentrations. Finally, we discuss the therapeutical implication of targeting impaired pulsatile secretion and its diagnostic value as an early indicator of β-cell stress.

Insulin Pulse Characteristics and Insulin Action in Non-diabetic Humans

OBJECTIVE

Pulsatile insulin secretion is impaired in diseases such as type 2 diabetes that are characterized by insulin resistance. This has led to the suggestion that changes in insulin pulsatility directly impair insulin signaling. We sought to examine the effects of pulse characteristics on insulin action in humans, hypothesizing that a decrease in pulse amplitude or frequency is associated with impaired hepatic insulin action.

METHODS

We studied 29 nondiabetic subjects on two occasions. On 1 occasion, hepatic and peripheral insulin action was measured using a euglycemic clamp. The deuterated water method was used to estimate the contribution of gluconeogenesis to endogenous glucose production. On a separate study day, we utilized nonparametric stochastic deconvolution of frequently sampled peripheral C-peptide concentrations during fasting to reconstruct portal insulin secretion. In addition to measuring basal and pulsatile insulin secretion, we used approximate entropy to measure orderliness and Fourier transform to measure the average, and the dispersion of, insulin pulse frequencies.

RESULTS

In univariate analysis, basal insulin secretion (R2 = 0.16) and insulin pulse amplitude (R2 = 0.09) correlated weakly with insulin-induced suppression of gluconeogenesis. However, after adjustment for age, sex, and weight, these associations were no longer significant. The other pulse characteristics also did not correlate with the ability of insulin to suppress endogenous glucose production (and gluconeogenesis) or to stimulate glucose disappearance.

CONCLUSIONS

Overall, our data demonstrate that insulin pulse characteristics, considered independently of other factors, do not correlate with measures of hepatic and peripheral insulin sensitivity in nondiabetic humans.

Model-Based Assessment of C-Peptide Secretion and Kinetics in Post Gastric Bypass Individuals Experiencing Postprandial Hyperinsulinemic Hypoglycemia

Assessment of insulin secretion is key to diagnose postprandial hyperinsulinemic hypoglycemia (PHH), an increasingly recognized complication following bariatric surgery. To this end, the Oral C-peptide Minimal Model (OCMM) can be used. This usually requires fixing C-peptide (CP) kinetics to the ones derived from the Van Cauter population model (VCPM), which has never been validated in PHH individuals. The objective of this work was to test the validity of the OCMM coupled with the VCPM in PHH subjects and propose a method to overcome the observed limitations. Two cohorts of adults with PHH after gastric bypass (GB) underwent either a 75 g oral glucose (9F/3M; age=42±9 y; BMI=28.3±6.9 kg/m²) or a 60 g mixed-meal (7F/3M; age = 43 ± 11 y; BMI=27.5±4.2 kg/m²) tolerance test. The OCMM was identified on CP concentration data with CP kinetics fixed to VCPM (VC approach). In both groups, the VC approach underestimated CP-peak and overestimated CP-tail suggesting CP kinetics predicted by VCPM to be inaccurate in this population. Thus, the OCMM was identified using CP kinetics estimated from the data (DB approach) using a Bayesian Maximum a Posteriori estimator. CP data were well predicted in all the subjects using the DB approach, highlighting a significantly faster CP kinetics in patients with PHH compared to the one predicted by VCPM. Finally, a simulation study was used to validate the proposed approach. The present findings question the applicability of the VCPM in patients with PHH after GB and call for CP bolus experiments to develop a reliable CP kinetic model in this population.

Assessment of individual and standardized glucagon kinetics in healthy humans

Impaired glucose tolerance arises out of impaired postprandial insulin secretion and delayed suppression of glucagon. These defects occur early and independently in the pathogenesis of prediabetes. Quantification of the contribution of α-cell dysfunction to glucose tolerance has been lacking because knowledge of glucagon kinetics in humans is limited. Therefore, in a series of experiments examining the interaction of glucagon suppression with insulin secretion we studied 51 nondiabetic subjects (age = 54 ± 13 yr, BMI = 28 ± 4 kg/m²). Glucose was infused to mimic the systemic appearance of an oral challenge. Somatostatin was used to inhibit endogenous hormone secretion. 120 min after the start of the experiment, glucagon was infused at 0.65 ng/kg/min. The rise in glucagon concentrations was used to estimate its kinetic parameters [volume of distribution (V_d), half-life (t_1/2), and clearance rate (CL)]. A single-exponential model provided the best fit for the data, and individualized kinetic parameters were estimated: V_d = 8.2 ± 2.7 L, t_1/2 = 4 ± 1.1 min, CL = 1.4 ± 0.33 L/min. Stepwise linear regression was used to correlate V_d with BMI and sex (R²_adj = 0.44), whereas CL similarly correlated with lean body mass or BSA (both R² = 0.28). This enabled the development of a population-based model using anthropometric characteristics to predict V_d and CL. These data demonstrate that it is feasible to derive glucagon kinetic parameters from anthropometric characteristics, thereby enabling quantitation of the rate of glucagon appearance in the systemic circulation in large populations.NEW & NOTEWORTHY State-of-the-art measurement of insulin secretion in humans is accomplished by deconvolution of peripheral C-peptide concentrations using population-derived parameters of C-peptide kinetics. In contrast, knowledge of the kinetic parameters of glucagon in humans is lacking so that measurement of glucagon secretion to date is largely qualitative. This series of experiments enabled measurement of glucagon kinetics in 51 subjects, and subsequently, stepwise linear regression was used to correlate these parameters with anthropometric characteristics. This enabled the development of a population-based model using anthropometric characteristics to predict the volume of distribution and the rate of clearance. This is a necessary first step in the development of a model to quantitate of glucagon secretion and action (and its contribution to glucose tolerance) in large populations.

Population insulin sensitivity from sparsely sampled oral glucose tolerance tests

OBJECTIVE

This work aimed to estimate population-level insulin sensitivity (S_I) from 2-hour oral glucose tolerance tests (OGTT) with less than 7 samples.

RESEARCH DESIGN AND METHODS

The current methodology combines the OGTT mathematical model developed by Dalla Man et al., with nonlinear multilevel (NLML) statistical model to estimate population-level insulin sensitivity (S_I) from sparsely sampled datasets (3 or 4 samples per subject obtained in 120 min). To validate our novel methodology of population S_I estimation, we simulated 50 virtual subjects. We simulated 10 observations per subject over 240 minutes. After estimating their S_I using the OGTT model, the virtual subjects were split into two groups, subjects with S_I above the average and ones with below average. Subsequently, the simulated data were analyzed using statistical software and employing a t-test. The mean estimates of population S_I for the two groups of virtual subjects and their respective 95% CI were compared to the estimates obtained with our novel NLML group S_I estimates obtained using the 3 and 4 time points per subject. To further validate the performance of the novel NLML model, a set of 34 prediabetic and 30 diabetic subjects with T2D was used. As outlined above for the in-silico subjects, differences between the prediabetic and T2D subjects in regard to S_I was assessed using the classical two-stage approach (individual S_I estimation followed by statistical comparison of the two groups). The average estimates obtained with the classical two-stage approach were compared to the group estimated obtained with the NLML approach using 3 (0, 60, and 120 minutes) points per subject obtained in 120 minutes.

RESULTS

Unique and identifiable individual estimates of S_I were obtained for all virtual subjects. In comparison to the subjects with above average S_I (n=25), the subjects with simulated below average S_I (n=25) exhibited significantly lower insulin sensitivity (P<0.001). Our novel NLML population model confirmed these findings (4-point OGTT: P<0.001; 3-point OGTT: P<0.001). In a similar fashion to the one outlined for the virtual subjects, the median insulin sensitivities estimated with the classical two-stage approach were different between the prediabetic (n=34) and T2D subjects (n=32, P=0.004). Using 3 points per subject, our novel NLML model confirmed these findings (P<0.001).

CONCLUSIONS

The population estimates of S_I from OGTT data is an effective tool to assess population insulin sensitivity and assess differences that may not be possible when calculating individual S_I or when less than 7 samples are available.

Diabetes-associated genetic variation in TCF7L2 alters pulsatile insulin secretion in humans

BACKGROUNDMetabolic disorders such as type 2 diabetes have been associated with a decrease in insulin pulse frequency and amplitude. We hypothesized that the T allele at rs7903146 in TCF7L2, previously associated with β cell dysfunction, would be associated with changes in these insulin pulse characteristics.METHODSTwenty-nine nondiabetic subjects (age 46 ± 2, BMI 28 ± 1 kg/m2) participated in this study. Of these, 16 were homozygous for the C allele at rs7903146 and 13 were homozygous for the T allele. Deconvolution of peripheral C-peptide concentrations allowed the reconstruction of portal insulin secretion over time. These data were used for subsequent analyses. Pulse orderliness was assessed by approximate entropy (ApEn), and the dispersion of insulin pulses was measured by a frequency dispersion index (FDI) after a Fast Fourier Transform (FFT) of individual insulin secretion rates.RESULTSDuring fasting conditions, the CC genotype group exhibited decreased pulse disorderliness compared with the TT genotype group (1.10 ± 0.03 vs. 1.19 ± 0.04, P = 0.03). FDI decreased in response to hyperglycemia in the CC genotype group, perhaps reflecting less entrainment of insulin secretion during fasting.CONCLUSIONDiabetes-associated variation in TCF7L2 is associated with decreased orderliness and pulse dispersion, unchanged by hyperglycemia. Quantification of ApEn and FDI could represent novel markers of β cell health.FUNDINGThis work was funded by US NIH (DK78646, DK116231), University of Padova research grant CPDA145405, and Mayo Clinic General Clinical Research Center (UL1 TR000135).

Association between allelic variants in the glucagon-like peptide 1 and cholecystokinin receptor genes with gastric emptying and glucose tolerance

BACKGROUND

Nutrient-mediated release of cholecystokinin and glucagon-like peptide-1 (GLP-1) regulates gastric emptying (GE) via duodenogastric feedback mechanisms; GLP-1 also regulates postprandial insulin secretion. Some patients with functional upper gastrointestinal symptoms have impaired glucose tolerance during enteral dextrose infusion. Our hypothesis was that variants in CCK, GLP-1, and TCF7L2 (transcription factor 7-like 2 locus), which is associated with greatest genetic risk for development of type 2 diabetes mellitus, are associated with GE and independently with glucose tolerance. Our aims were to evaluate the associations between these GE, glucose tolerance, and these single nucleotide polymorphisms (SNPs).

METHODS

Genetic variants, scintigraphic GE of solids, plasma glucose, insulin, and GLP-1 during enteral dextrose infusion (75gm over 2 hours) were measured. GE and enteral dextrose infusion were, respectively, evaluated in 44 (27 controls and 17 patients with functional dyspepsia or nausea) and 42 (28 controls, 14 patients) participants; of these, 51 participants consented to assessment of SNPs. Four functional SNPs were studied: rs6923761 and rs1042044 at GLP-1 receptor, rs7903146 (TCF7L2), and rs1800857 (CCK receptor).

KEY RESULTS

Gastric emptying was normal in 38, rapid in 4, and delayed in two participants; 38 had normal, and four had impaired glucose tolerance. The T allele at rs7903146 (TCF7L2) was non-significantly associated (P = .14) with faster GE. The associations between SNPs and demographic variables, GE t_half , glucose tolerance and plasma GLP1 levels were not significant.

CONCLUSIONS & INFERENCES

There is a trend toward an association between faster GE and the diabetes-associated allele at rs7903146 in TCF7L2. However, these SNPs were not associated with plasma glucose or GLP1 concentrations during enteral dextrose infusion.

Assessment of pulsatile insulin secretion derived from peripheral plasma C-peptide concentrations by nonparametric stochastic deconvolution

The characteristics of pulsatile insulin secretion are important determinants of type 2 diabetes pathophysiology, but they are understudied due to the difficulties in measuring pulsatile insulin secretion noninvasively. Deconvolution of either peripheral C-peptide or insulin concentrations offers an appealing alternative to hepatic vein catheterization. However, to do so, there are a series of methodological challenges to overcome. C-peptide has a relatively long half-life and accumulates in the circulation. On the other hand, peripheral insulin concentrations reflect relatively fast clearance and hepatic extraction as it leaves the portal circulation to enter the systemic circulation. We propose a method based on nonparametric stochastic deconvolution of C-peptide concentrations, using individually determined C-peptide kinetics, to overcome these limitations. The use of C-peptide (instead of insulin) concentrations allows estimation of portal (and not post-hepatic) insulin pulses, whereas nonparametric stochastic deconvolution allows evaluation of pulsatile signals without any a priori assumptions of pulse shape and occurrence. The only assumption required is the degree of smoothness of the (unknown) secretion rate. We tested this method first on simulated data and then on 29 nondiabetic subjects studied during euglycemia and hyperglycemia and compared our estimates with the profiles obtained from hepatic vein insulin concentrations. This method produced satisfactory results both in the ability to fit the data and in providing reliable estimates of pulsatile secretion, in agreement with hepatic vein measurements. In conclusion, the proposed method enables reliable and noninvasive measurement of pulsatile insulin secretion. Future studies will be needed to validate this method in people with type 2 diabetes.