Associations of glucose control with insulin sensitivity and pancreatic beta-cell responsiveness in newly presenting type 2 diabetes

The pathophysiology of glucose intolerance in newly diagnosed, untreated T2DM

AIMS

The two predominant pathophysiological defects resulting in glucose intolerance are beta-cell dysfunction and insulin insensitivity. This study aimed to re-examine beta-cell function and insulin sensitivity across a continuum from normal glucose tolerance (NGT) to early type 2 diabetes (T2DM) employing highly specific insulin, C-peptide and intact proinsulin assays.

MATERIALS AND METHODS

A total of 104 persons with NGT, 85 with impaired glucose tolerance (IGT) and 554 with newly diagnosed T2DM were investigated. Following an overnight fast, all underwent a 4-h standardised mixed meal tolerance test (MTT), and on a second day, a sub-group underwent a frequently sampled insulin-modified intravenous glucose tolerance test (FSIVGTT) over a 3-h period. The participants were stratified according to fasting glucose and BMI for analysis.

RESULTS

The MTT revealed that increasing FPG was accompanied by progressively elevated and delayed postprandial glucose peaks. In parallel, following an initial compensatory increase in fasting and postprandial insulin responses there followed a progressive demise in overall beta-cell secretory capacity. FSIVGTT demonstrated a major reduction in the early insulin response to IV glucose in persons with IGT accompanied by a dramatic fall in insulin sensitivity. Beyond pre-diabetes, ever-increasing fasting and postprandial hyperglycaemia resulted predominantly from a progressively decreasing beta-cell secretory function.

CONCLUSION

This study utilising improved assay technology re-affirms that beta-cell dysfunction is evident throughout the spectrum of glucose intolerance, whereas the predominant fall in insulin sensitivity occurs early in its evolution.

Mixed-meal tolerance test to assess residual beta-cell secretion: Beyond the area-under-curve of plasma C-peptide concentration

AIMS

Residual beta-cell secretion in type 1 diabetes is commonly assessed by area-under-curve of plasma C-peptide concentration (AUCCpep ) following mixed-meal tolerance test (MMTT). We aimed to investigate alternative measures of beta-cell responsiveness.

METHODS

We analyzed data from 32 youth (age 7 to 17 years) undergoing MMTT within 6 months of type 1 diabetes diagnosis. We related AUCCpep with (a) validated mechanistic index of postprandial beta-cell responsiveness MI accounting for glucose level during MMTT, and (b) pragmatic marker calculated as baseline plasma C-peptide concentration corrected for baseline plasma glucose concentration.

RESULTS

Postprandial responsiveness MI was correlated with age and BMI SDS (Rs = 0.66 and 0.44, P < 0.01 and P < 0.05) and was more correlated with glycated hemoglobin than AUCCpep (Rs = 0.79, P = 0.04). The pragmatic marker was highly correlated with AUCCpep (Rs = 0.94, P < 0.01).

CONCLUSIONS

Postprandial responsiveness MI may be more relevant to glucose control than AUCCpep . Baseline C-peptide corrected for baseline glucose appears to be a suitable surrogate of AUCCpep if MMTT is not performed.

Normal meal tolerance test is preferable to the glucagon stimulation test in patients with type 2 diabetes that are not in a hyperglycemic state: Comparison with the change of C-peptide immunoreactivity

AIMS/INTRODUCTION

The aim of the present study was to evaluate the properties of the glucagon stimulation test (GST) and the normal meal tolerance test (NMTT) in patients with type 2 diabetes.

MATERIALS AND METHODS

We enrolled 142 patients with type 2 diabetes, and carried out a GST and a NMTT. We carried out the NMTT using a calorie-controlled meal based on an intake of 30 kcal/kg ideal bodyweight/day. We calculated the change in C-peptide immunoreactivity (ΔCPR) by subtracting fasting CPR from the CPR 6 min after the 1-mg glucagon injection (GST) or 120 min after the meal (NMTT).

RESULTS

Mean ΔCPR for the GST was 2.0 ng/mL, and for the NMTT was 3.1 ng/mL. A total of 104 patients had greater ΔCPR in the NMTT than the GST, and the mean ΔCPR was significantly greater in the NMTT than the GST (P < 0.05). To exclude any influence of antidiabetic drugs, we examined 42 individuals not taking antidiabetic agents, and found the mean ΔCPR was significantly greater in the NMTT than the GST (GST 2.4 ng/mL, NMTT 4.3 ng/mL; P < 0.05). To consider the influence of glucose toxicity, we carried out receiver operating characteristic analyses with fasting plasma glucose and glycated hemoglobin. The optimal cut-off levels predicting GST ΔCPR to be larger than NMTT ΔCPR were fasting plasma glucose 147 mg/dL and glycated hemoglobin 9.0% (fasting plasma glucose: sensitivity 0.64, specificity 0.76, area under the curve 0.73; glycated hemoglobin: sensitivity 0.56, specificity 0.71, area under the curve 0.66).

CONCLUSIONS

The NMTT is a reliable insulin secretion test in patients with type 2 diabetes, except for those in a hyperglycemic state.

Postprandial C-peptide to glucose ratio as a predictor of β-cell function and its usefulness for staged management of type 2 diabetes

Aims/Introduction

Type 2 diabetes is characterized by progressive deterioration of β‐cell function. Recently, it was suggested that the C‐peptide‐to‐glucose ratio after oral glucose ingestion is a better predictor of β‐cell mass than that during fasting. We investigated whether postprandial C‐peptide‐to‐glucose ratio (PCGR) reflects β‐cell function, and its clinical application for management of type 2 diabetes.

Materials and Methods

We carried out a two‐step retrospective study of 919 Korean participants with type 2 diabetes. In the first step, we evaluated the correlation of PCGR level with various markers for β‐cell function in newly diagnosed and drug‐naïve patients after a mixed meal test. In the second step, participants with well‐controlled diabetes (glycated hemoglobin <7%) were divided into four groups according to treatment modality (group I: insulin, group II: sulfonylurea and/or dipeptityl peptidase IV inhibitor, group III: metformin and/or thiazolidinedione and group IV: diet and exercise group).

Results

In the first step, PCGR was significantly correlated with various insulin secretory indices. Furthermore, PCGR showed better correlation with glycemic indices than homeostatic model assessment of β‐cell function (HOMA‐β). In the second step, the PCGR value significantly increased according to the following order: group I, II, III, and IV after adjusting for age, sex, body mass index and duration of diabetes. The cut‐off values of PCGR for separating each group were 1.457, 2.870 and 3.790, respectively (P < 0.001).

Conclusions

We suggest that PCGR might be a useful marker for β‐cell function and an ancillary parameter in the choice of antidiabetic medication in type 2 diabetes.

Post-meal β-cell function predicts the efficacy of glycemic control in patients with type 2 diabetes inadequately controlled by metformin monotherapy after addition of glibenclamide or acarbose

BACKGROUND

This study aimed to explore parameters which will predict good control of HbA1c after adding a second anti-diabetic drug in patients with type 2 diabetes mellitus (T2DM) inadequately controlled with metformin monotherapy.

METHODS

Fifty-one patients (M/F: 25/26, mean age: 53.7 ± 8.2 years, mean glycated hemoglobin [HbA1c] 8.4 ± 1.2%) with T2DM inadequately controlled with metformin were randomized to add-on glibenclamide or acarbose for 16 weeks. Before and after combination therapy, the subjects underwent a 2-hour liquid mixed meal tolerance test to determine insulin secretion (HOMA-β, insulinogenic index, and disposition index [DI]) and insulin sensitivity (HOMA-IR and Matsuda insulin sensitivity index).

RESULTS

At baseline, there was a significant inverse relationship between DI120 and HbA1c (p = 0.001) in all subjects. The addition of glibenclamide and acarbose improved HbA1c significantly from 8.6 ± 1.6% to 7.4 ± 1.2% (p < 0.001), and from 8.2 ± 0.8% to 7.5 ± 0.8% (p < 0.001), respectively. In the glibenclamide group, DI120 significantly increased from 51.2 ± 24.2 to 74.9 ± 41.9 (p < 0.05), and in the acarbose group, from 62.5 ± 31.4 to 91.7 ± 36.2 (p < 0.05), respectively. Multiple regression analyses showed that both baseline HbA1c and DI120 independently predicted reduction of HbA1c as well as final HbA1c after combination therapy.

CONCLUSIONS

In patients with T2DM inadequately controlled with metformin, add-on oral anti-diabetic agent with glibenclamide or acarbose resulted in the significant HbA1c reduction and improvement of β-cell function. Subjects with greater baseline β-cell function reserve displayed better glycemic response in the combination therapy of metformin with glibenclamide or acarbose.

TRIAL REGISTRATION

This study was registered in the ClinicalTrials.gov with registration number of NCT00417729.

Beta cell response to a mixed meal in nigerian patients with type 2 diabetes

BACKGROUND

The pathophysiology of type2 diabetes involves both insulin resistance and poor beta cell function. Studies have been done in several populations to assess the relative importance of these mechanisms in individual patients. In our environment studies to assess beta cell function have been done with glucagon stimulation or an oral glucose tolerance test. This study was done to assess the response of the beta cell to a standardized mixed meal and its relationship with glycaemic control in patients with type2 diabetes.

METHODS

Ninety patients with type 2 diabetes were recruited into the study. Weight, height, body mass index and waist circumference were measured. Blood samples were analysed for fasting plasma glucose (FPG) and fasting C peptide (FCP) and glycated haemoglobin (HbA1c). Patients were given their usual drugs for management of their diabetes and then served with a standard meal calculated to contain 50 g of carbohydrate, made up of 53 % carbohydrate, 17 % of protein and 30 % of lipids, providing 500 kcal. Blood samples 2 hours after the start of the meal were analysed for postprandial glucose (PPG) and postprandial C peptide (PCP). Fasting (M0) and postprandial beta cell responsiveness (M1) were calculated.

RESULTS

The mean FPG and PPG were 7.51+/- 3.39 mmol/l and 11.02+/-4.03 mmol/l respectively while the mean glycated haemoglobin (HbA1c) was 9.0+/-2.5 %. The mean fasting C peptide was 1.44+/-1.80ug/ml. Many of the patients (56.7 %) had low FCP levels. The mean postprandial C peptide was 4.0+/-2.8 ng/ml. There were significant correlations between M1, HbA1c and PPG (p = 0.015, 0.024, 0.001 respectively) and also between M0, HbA1c, PPG and FPG (p = 0.001, 0.002, 0.001). HbA1c decreased across increasing tertiles of M0 (p < 0.001) and also M1 (p = 0.002). In step-wise linear regression analysis, M0 and M1 significantly predicted HbA1c.

CONCLUSIONS

Many of the patients had low C peptide levels with poor beta cell response to the meal. The patients had poor glycaemic control and poor beta cell function. Both fasting and postprandial beta cell responsiveness were significant determinants of blood glucose and glycated haemoglobin levels. It is likely that putting these patients on insulin may have led to better glycaemic control in them.

Comparative pharmacodynamic and pharmacokinetic characteristics of subcutaneous insulin glulisine and insulin aspart prior to a standard meal in obese subjects with type 2 diabetes

AIMS

A multinational, randomized, double-blind, two-way crossover trial to compare the pharmacokinetic and pharmacodynamic properties of bolus, subcutaneously administered insulin glulisine (glulisine) and insulin aspart (aspart) in insulin-naÏve, obese subjects with type 2 diabetes.

METHODS

Thirty subjects [9/21 females/males; mean ± SD age: 60.7 ± 7.7 years; body mass index (BMI): 33.5 ± 3.3 kg/m(2) ; duration of diabetes: 6.8 ± 4.6 years; HbA1c: 7.1 ± 0.8%] were included in the analysis. They fasted overnight and then received a 0.2 U/kg subcutaneous dose of glulisine or aspart 2 min before starting a standardized test meal, 7 days apart, according to a randomization schedule. Blood samples were taken every 15 min, starting 20 min before the meal and ending 6 h postprandially.

RESULTS

The area under the absolute glucose concentration-time curve between 0 and 1 h after insulin injection and maximal glucose concentration was significantly lower with glulisine than with aspart (p = 0.0455 and 0.0337, respectively). However, for the total study period, plasma glucose concentration was similar for glulisine and aspart. Peak insulin concentration was significantly higher for glulisine than for insulin aspart (p < 0.0001). Hypoglycaemic events (≤ 70 mg/dl with or without symptoms) occurred in 13 and 16 subjects treated with glulisine and aspart, respectively, but there were no cases of severe hypoglycaemia requiring intervention.

CONCLUSIONS

Glulisine was associated with lower glucose levels during the first hour after a standard meal; the remaining glucose profiles were otherwise equivalent, with higher insulin levels observed throughout the study period.

Postprandial insulin and triglycerides after different breakfast meal challenges: use of finger stick capillary dried blood spots to study postprandial dysmetabolism

BACKGROUND

High levels of insulin and lipids following a meal are recognized risk factors for atherosclerosis. Monitoring such risk factors in the general population is hampered by the inconvenience of venipuncture blood collection, particularly for both premeal and postmeal analyses. This study examined insulin and triglyceride levels in dried blood spots (DBSs) collected after different breakfast meal challenges to assess the potential of this method for risk assessment.

METHODS

Glucose levels were measured using a glucose meter, and insulin and triglycerides were determined in DBS samples collected from 19 healthy volunteers before and at four time points up to 2.5 h after consuming each of five typical breakfast meals varying in nutritional composition.

RESULTS

At 2 h, glucose was within normal postprandial values (<140 mg/dl) for all meals; significantly lower glucose was seen after meal 2 (the lowest carbohydrate content) compared to the other meals. Insulin returned to normal fasting levels (<15 microIU/ml) in significantly more subjects (90%) after meal 2 and significantly fewer subjects (31%) after meal 4 (highest carbohydrate content) than the other meals. Triglycerides were elevated to a similar extent in all subjects, with no significant differences between meals; levels were still rising at 2.5 h.

CONCLUSIONS

Subjects were able to collect blood spots with minimum disruption to their normal daily activities. Relative ease of collection, analyte stability in dried blood, and the close correlation with serum levels that we have previously demonstrated makes DBS a convenient and simple tool for assessing the individual impact of different diets on postprandial dysmetabolism.

Glycemic variability correlates strongly with postprandial beta-cell dysfunction in a segment of type 2 diabetic patients using oral hypoglycemic agents

OBJECTIVE

Glucose fluctuations trigger activation of oxidative stress, a main mechanism leading to secondary diabetes complications. We evaluated the relationship between glycemic variability and beta-cell dysfunction.

RESEARCH DESIGN AND METHODS

We conducted a cross-sectional study in 59 patients with type 2 diabetes (aged 64.2 +/- 8.6 years, A1C 6.5 +/- 1.0%, and BMI 29.8 +/- 3.8 kg/m(2)[mean +/- SD]) using either oral hypoglycemic agents (OHAs) (n = 34) or diet alone (nonusers). As a measure of glycemic variability, the mean amplitude of glycemic excursions (MAGE) was computed from continuous glucose monitoring data recorded over 3 consecutive days. The relationships between MAGE, beta-cell function, and clinical parameters were assessed by including postprandial beta-cell function (PBCF) and basal beta-cell function (BBCF) obtained by a model-based method from plasma C-peptide and plasma glucose during a mixed-meal test as well as homeostasis model assessment of insulin sensitivity, clinical factors, carbohydrate intake, and type of OHA.

RESULTS

MAGE was nonlinearly correlated with PBCF (r = 0.54, P < 0.001) and with BBCF (r = 0.31, P = 0.025) in OHA users but failed to correlate with these parameters in nonusers (PBCF P = 0.21 and BBCF P = 0.07). The stepwise multiple regression analysis demonstrated that PBCF and OHA combination treatment were independent contributors to MAGE (R(2) = 0.50, P < 0.010), whereas insulin sensitivity, carbohydrate intake, and nonglycemic parameters failed to contribute.

CONCLUSIONS

PBCF appears to be an important target to reduce glucose fluctuations in OHA-treated type 2 diabetes.