The novel mu-opioid antagonist, GSK1521498, reduces ethanol consumption in C57BL/6J mice.

RATIONALE Using the drinking-in-the-dark (DID) model, we compared the effects of a novel mu-opioid receptor antagonist, GSK1521498, with naltrexone, a licensed treatment of alcohol dependence, on ethanol consumption in mice. OBJECTIVE We test the ability of GSK1521498 to reduce alcohol consumption and compare its intrinsic efficacy to that of naltrexone by comparing the two drugs at doses matched for equivalent receptor occupancy. METHODS Thirty-six C57BL/6J mice were tested in a DID procedure. In 2-day cycles, animals experienced one baseline, injection-free session, and one test session when they received two injections, one of test drug and one placebo. All animals received GSK1521498 (0, 0.1, 1 and 3 mg/kg, i.p., 30 min pre-treatment) and naltrexone (0, 0.1, 1 and 3 mg/kg, s.c. 10 min pre-treatment) in a cross-over design. Receptor occupancies following the same doses were determined ex vivo in separate groups by autoradiography, using [3H]DAMGO. Binding in the region of interest was measured integrally by computer-assisted microdensitometry and corrected for non-specific binding. RESULTS Both GSK1521498 and naltrexone dose-dependently decreased ethanol consumption. When drug doses were matched for 70-75 % receptor occupancy, GSK1521498 3 mg/kg, i.p., caused a 2.5-fold greater reduction in alcohol consumption than naltrexone 0.1 mg/kg, s.c. Both GSK1521498 and naltrexone significantly reduced sucrose consumption at a dose of 1 mg/kg but not 0.1 mg/kg. In a test of conditioned taste aversion, GSK1521498 (3 mg/kg) reduced sucrose consumption 24 h following exposure to a conditioning injection. CONCLUSIONS Both opioid receptor antagonists reduced alcohol consumption but GK1521498 has higher intrinsic efficacy than naltrexone

Analysis of Insulin Doses of Chinese Type 2 Diabetic Patients with Intensive Insulin Treatment

BACKGROUND: To investigate the daily insulin doses and the ratio of basal insulin to total daily insulin in Chinese type 2 diabetic patients who received basal bolus insulin therapy. METHODOLOGY/PRINCIPAL FINDINGS: Totally 2480 patients prescribed with pre-meal bolus insulin and bedtime basal insulin were included. The mean daily insulin doses was 38.22 ± 14.92 IU/day, the mean daily insulin doses per weight was 0.58 ± 0.22 IU/kg, the mean bolus insulin dose was 0.44 ± 0.17 IU/kg and the mean basal insulin dose was 0.13 ± 0.08 IU/kg. The mean basal/total daily insulin ratio (BD/TDD) was 0.23 ± 0.08. In most patients (47.94%), the BD/TDD was between 0.20 and 0.30. Diabetic duration, BMI, HbA1c, fasting and postprandial blood glucose level were positively associated with daily insulin dose, while age was negatively associated with daily insulin dose. Diabetic duration, BMI, HbA1c, fasting blood glucose level, and using metformin were positively associated with BD/TDD ratio, while age, postprandial C peptide, postprandial blood glucose level and CRE level were negatively associated with BD/TDD ratio. CONCLUSIONS/SIGNIFICANCE: The daily insulin doses of intensive treatment in Chinese type 2 diabetic patients was 38.22 IU/day, the mean daily insulin doses per weight was 0.58 IU/kg, mean BD/TDD ratio was 0.23

Controversy about the relative efficacy of dipeptidyl peptidase IV inhibitors.

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Changes in glycemic control from 1996 to 2006 among adults with type 2 diabetes: a longitudinal cohort study

<p>Abstract</p> <p>Background</p> <p>Our objectives were to examine temporal changes in HbA1c and lipid levels over a 10-year period and to identify predictors of metabolic control in a longitudinal patient cohort.</p> <p>Methods</p> <p>We identified all adults within our hospital network with T2DM who had HbA1c's measured in both 1996 and 2006 (longitudinal cohort). For patients with no data in 2006, we used hospital and social security records to distinguish patients lost to follow-up from those who died after 1996. We compared characteristics of the 3 baseline cohorts (longitudinal, lost to f/u, died) and examined metabolic trends in the longitudinal cohort.</p> <p>Results</p> <p>Of the 4944 patients with HbA1c measured in 1996, 1772 (36%) had an HbA1c measured in 2006, 1296 (26%) were lost to follow-up, and 1876 (38%) had died by 2006. In the longitudinal cohort, mean HbA1c decreased by 0.4 ± 1.8% over the ten-year span (from 8.2% ± 1.7% to 7.8% ± 1.4%) and mean total cholesterol decreased by 49.3 (± 46.5) mg/dL. In a multivariate model, independent predictors of HbA1c decline included older age (OR 1.41 per decade, 95% CI: 1.3-1.6, p < 0.001), baseline HbA1c (OR 2.9 per 1% increment, 2.6 - 3.2, p < 0.001), and speaking English (OR 2.1, 1.4-3.1, p < 0.001).</p> <p>Conclusions</p> <p>Despite having had diabetes for an additional 10 years, patients in our longitudinal cohort had better glycemic and cholesterol control in 2006 than 1996. Greatest improvements occurred in patients with the highest levels in the baseline year.</p

Translating HbA1c measurements into estimated average glucose values in pregnant women with diabetes

Aims/hypothesis This study aimed to examine the relationship between average glucose levels, assessed by continuous glucose monitoring (CGM), and HbA1c levels in pregnant women with diabetes to determine whether calculations of standard estimated average glucose (eAG) levels from HbA1c measurements are applicable to pregnant women with diabetes. Methods CGM data from 117 pregnant women (89 women with type 1 diabetes; 28 women with type 2 diabetes) were analysed. Average glucose levels were calculated from 5–7 day CGM profiles (mean 1275 glucose values per profile) and paired with a corresponding (±1 week) HbA1c measure. In total, 688 average glucose–HbA1c pairs were obtained across pregnancy (mean six pairs per participant). Average glucose level was used as the dependent variable in a regression model. Covariates were gestational week, study centre and HbA1c. Results There was a strong association between HbA1c and average glucose values in pregnancy (coefficient 0.67 [95% CI 0.57, 0.78]), i.e. a 1% (11 mmol/mol) difference in HbA1c corresponded to a 0.67 mmol/l difference in average glucose. The random effects model that included gestational week as a curvilinear (quadratic) covariate fitted best, allowing calculation of a pregnancy-specific eAG (PeAG). This showed that an HbA1c of 8.0% (64 mmol/mol) gave a PeAG of 7.4–7.7 mmol/l (depending on gestational week), compared with a standard eAG of 10.2 mmol/l. The PeAG associated with maintaining an HbA1c level of 6.0% (42 mmol/mol) during pregnancy was between 6.4 and 6.7 mmol/l, depending on gestational week. Conclusions/interpretation The HbA1c–average glucose relationship is altered by pregnancy. Routinely generated standard eAG values do not account for this difference between pregnant and non-pregnant individuals and, thus, should not be used during pregnancy. Instead, the PeAG values deduced in the current study are recommended for antenatal clinical care

Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study

Abstract AIMS/HYPOTHESIS: Real-life glycaemic profiles of healthy individuals are poorly studied. Our aim was to analyse to what extent individuals without diabetes exceed OGTT thresholds for impaired glucose tolerance (IGT) and diabetes. METHODS: In the A1C-Derived Average Glucose (ADAG) study, 80 participants without diabetes completed an intensive glucose monitoring period of 12 weeks. From these data, we calculated the average 24 h glucose exposure as time spent above different plasma glucose thresholds. We also derived indices of postprandial glucose levels, glucose variability and HbA(1c). RESULTS: We found that 93% of participants reached glucose concentrations above the IGT threshold of 7.8 mmol/l and spent a median of 26 min/day above this level during continuous glucose monitoring. Eight individuals (10%) spent more than 2 h in the IGT range. They had higher HbA(1c), fasting plasma glucose (FPG), age and BMI than those who did not. Seven participants (9%) reached glucose concentrations above 11.1 mmol/l during monitoring. CONCLUSIONS/INTERPRETATION: Even though the non-diabetic individuals monitored in the ADAG study were selected on the basis of a very low level of baseline FPG, 10% of these spent a considerable amount of time at glucose levels considered to be 'prediabetic' or indicating IGT. This highlights the fact that exposure to moderately elevated glucose levels remains under-appreciated when individuals are classified on the basis of isolated glucose measurements

Associations Between Features of Glucose Exposure and A1C: The A1C-Derived Average Glucose (ADAG) Study

OBJECTIVE: Various methods are used to quantify postprandial glycemia or glucose variability, but few have been compared and none are standardized. Our objective was to examine the relationship among common indexes of postprandial glycemia, overall hyperglycemia, glucose variability, and A1C using detailed glucose measures obtained during everyday life and to study which blood glucose values of the day provide the strongest prediction of A1C. RESEARCH DESIGN AND METHODS: In the A1C-Derived Average Glucose (ADAG) study, glucose levels were monitored in 507 participants (268 type 1 diabetic, 159 type 2 diabetic, and 80 nondiabetic subjects) with continuous glucose monitoring (CGM) and frequent self-monitoring of blood glucose (SMBG) during 16 weeks. We calculated several indexes of glycemia and analyzed their intercorrelations. The association between glucose measurements at different times of the day (pre- and postprandial) and A1C was examined using multiple linear regression. RESULTS: Indexes of glucose variability showed strong intercorrelation. Among postprandial indexes, the area under the glucose curve calculated from CGM 2 h after a meal correlated well with the 90-min SMBG postprandial measurements. Fasting blood glucose (FBG) levels were only moderately correlated with indexes of hyperglycemia and average or postprandial glucose levels. Indexes derived with SMBG strongly correlated with those from CGM. Some SMBG time points had a stronger association with A1C than others. Overall, preprandial glucose values had a stronger association with A1C than postprandial values for both diabetes types, particularly for type 2 diabetes. CONCLUSIONS: Indexes of glucose variability and average and postprandial glycemia intercorrelate strongly within each category. Variability indexes are weakly correlated with the other categories, indicating that these measures convey different information. FBG is not a clear indicator of general glycemia. Preprandial glucose values have a larger impact on A1C levels than postprandial values

Protocol and baseline data from The Inala Chronic Disease Management Service evaluation study: a health services intervention study for diabetes care

Background: Type 2 Diabetes Mellitus is one of the most disabling chronic conditions worldwide, resulting in significant human, social and economic costs and placing huge demands on health care systems. The Inala Chronic Disease Management Service aims to improve the efficiency and effectiveness of care for patients with type 2 diabetes who have been referred by their general practitioner to a specialist diabetes outpatient clinic. Care is provided by a multidisciplinary, integrated team consisting of an endocrinologist, diabetes nurse educators, General Practitioner Clinical Fellows (general practitioners who have undertaken focussed post-graduate training in complex diabetes care), and allied health personnel (a dietitian, podiatrist and psychologist)

Insulin resistance in type 1 diabetes: what is ‘double diabetes’ and what are the risks?

In this review, we explore the concept of ‘double diabetes’, a combination of type 1 diabetes with features of insulin resistance and type 2 diabetes. After considering whether double diabetes is a useful concept, we discuss potential mechanisms of increased insulin resistance in type 1 diabetes before examining the extent to which double diabetes might increase the risk of cardiovascular disease (CVD). We then go on to consider the proposal that weight gain from intensive insulin regimens may be associated with increased CV risk factors in some patients with type 1 diabetes, and explore the complex relationships between weight gain, insulin resistance, glycaemic control and CV outcome. Important comparisons and contrasts between type 1 diabetes and type 2 diabetes are highlighted in terms of hepatic fat, fat partitioning and lipid profile, and how these may differ between type 1 diabetic patients with and without double diabetes. In so doing, we hope this work will stimulate much-needed research in this area and an improvement in clinical practice