What is the relative contribution of blood glucose levels at different time points of the day to HbA1c in Type 1 diabetes?

Basal Hyperglycemia Contributes More Than 50% to Time in Range in Pregnant Women With Type 1 Diabetes

OBJECTIVES

To evaluate the relative contribution of basal hyperglycemia (BHG) and postprandial hyperglycemia (PHG) to the time in range (TIR) categories and adverse pregnancy outcomes in pregnant women with type 1 diabetes mellitus (T1DM).

MATERIALS AND METHODS

This observational study included 112 pregnancies with T1DM from the CARNATION study who wore continuous glucose monitoring (CGM) devices during pregnancy. The data from CGM were analyzed for TIR (range, 3.5-7.8 mmol/L), areas under the curve of PHG, area under the curve of BHG, and BHG and PHG contribution rates. The contribution rates of BHG and PHG to the different levels of TIR (<60%, 60-78%, ≥78%) and adverse pregnancy outcomes were analyzed.

RESULTS

The participants' average age was 28.8 ± 3.9 years with a diabetes duration of 8.4 ± 6.2 years. All women experienced a mean TIR of 75.6 ± 19.0% and a mean glycated hemoglobin of 6.2 ± 1.1% during pregnancy. The BHG contribution accounted for 74.9% (36.8, 100), 69.2% (13.4, 100), and 66.5% (10.0, 100) (P < .001) and PHG accounted for 25.1% (0, 63.2), 30.8% (0, 86.6), and 33.5% (0, 90.0) (P < .001) when participants experienced the TIR<60%, 60%-78%, and ≥78%, respectively. Participants with higher BHG contribution rates tended to have more adverse pregnancy outcomes.

CONCLUSION

Basal hyperglycemia was the major contributor to TIR during pregnancy. Along with controlling PHG, pregnant women with T1DM who did not reach the target of TIR may benefit more from the optimization of insulin regimens focusing on reducing basal glucose.

Short-acting glucagon-like peptide-1 receptor agonists as add-on to insulin therapy in type 1 diabetes: A review

A large proportion of patients with type 1 diabetes do not reach their glycaemic target of glycated hemoglobin (HbA1c) <7.0% (53 mmol/mol) and, furthermore, an increasing number of patients with type 1 diabetes are overweight and obese. Treatment of type 1 diabetes is based on insulin therapy, which is associated with well-described and unfortunate adverse effects such as hypoglycaemia and increased body weight. Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are the focus of increasing interest as a possible adjunctive treatment to insulin in type 1 diabetes because of their glucagonostatic and extrapancreatic effects. So far, the focus has mainly been on the long-acting GLP-1RAs, but the risk-benefit ratio emerging from studies evaluating the effect of long-acting GLP-1RAs as adjunctive therapy to insulin therapy in patients with type 1 diabetes has been disappointing. This might be attributable to a lack of glucagonostatic effect of these long-acting GLP-1RAs in type 1 diabetes, alongside development of tachyphylaxis to GLP-1-induced retardation of gastric emptying. In contrast, the short-acting GLP-1RAs seem to have a preserved and sustained effect on glucagon secretion and gastric emptying in patients with type 1 diabetes, which could translate into effective lowering of postprandial glucose excursions; however, these observations regarding short-acting GLP-1RAs are all derived from small open-label trials and should thus be interpreted with caution. In the present paper we review the potential role of GLP-1RAs, in particular short-acting GLP-1RAs, as add-on to insulin in the treatment of type 1 diabetes.

Consensus guidelines for glycemic monitoring in type 1/type 2 & GDM

Stringent monitoring of blood glucose in diabetes plays an important role as the treatment of the disease itself. Blood glucose monitoring (BGM) strategies such as measurement of Hb1Ac, Self-Monitoring of Blood Glucose (SMBG) and Continuous Glucose Monitoring (CGM) plays a vital role in achieving the important goal of preventing long term complications of diabetes. Although the use of BGM is recommended by various international guidelines in T1DM and T2DM, there is no consensus on the utility of BGM in India. So, there is a need to develop a guidance for uniform monitoring mechanism among the care givers taking into account the variations and challenges that are unique to Indian population. A committee was established that comprised of physicians, researchers and other healthcare professionals having expertise in diabetes treatment to oversee the formulation of guidelines on different monitoring and treatment aspects of diabetes. Extensive literature searches were conducted to identify and analyze the evidence available on BGM. An initial draft of BGM guidelines was presented to core members who discussed the subject matter and presented their opinion. This was then taken to wider expert audience to invite their comments that were incorporated in the initial draft. The first compilation was presented at a conference attended by nearly 200 experts. Again, their opinion was sought and the next version was prepared which was sent to core committee members for the final inputs. The Indian consensus guideline on BGM using Hb1Ac, SMBG and CGM as the primary tools was then finalized.

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.

Ethnic variation in the correlation between random serum glucose concentration and glycated haemoglobin

AIMS

To determine if the relationship between serum glucose concentration and glycated haemoglobin is different between African-Americans and whites.

METHODS

Retrospective cross-sectional study comparing the association between glycated haemoglobin and serum glucose levels, based upon ethnicity. Two databases were evaluated: (i) 4215 African-American and 6359 white outpatients who had simultaneous glycated haemoglobin, random serum glucose and creatinine concentration measurements between 2000 and 2007 at the North Carolina Baptist Hospital and (ii) 1021 white and 312 African-American Diabetes Heart Study (DHS) participants.

RESULTS

In North Carolina Baptist Hospital clinic attendees, a given glycated haemoglobin was associated with higher serum glucose concentrations in African-Americans compared with whites. In a multivariate model with glycated haemoglobin as the outcome variable, racial differences remained significant after adjustment for serum glucose, age, gender and kidney function. For individuals with a serum glucose between 5.6 and 8.3 mmol/l, the glucose : glycated haemoglobin ratio was 1.03 +/- 0.16 mmol/l/% in white individuals and 0.99 +/- 0.17 mmol/l/% in African-Americans (P < 0.0001). For a glycated haemoglobin value of 7.0%, there was a 0.98-mmol/l difference in predicted serum glucose concentration in 50-year-old African-American men, relative to white. Results were replicated in the DHS, where in a best-fit linear model, after adjustment for glucose, African-American race was a significant predictor of glycated haemoglobin (P < 0.0001).

CONCLUSIONS

African-Americans have higher glycated haemoglobin values at given serum glucose concentrations relative to whites. This finding may contribute to the observed difference in glycated haemoglobin values reported between these race groups.

Optimizing diabetes management through glucose profiling: a case-based approach

It is well documented that tight glucose control prevents the microvascular complications of diabetes, and many studies suggest that postprandial hyperglycemia may be associated with macrovascular complications. Maintaining target glucose values is challenging, as therapies are often not targeted to individual glucose excursion patterns. Postprandial SMBG values may be more tightly correlated to HbA1c than are fasting values. Studies of patients with pregnancies complicated by diabetes demonstrate that using SMBG around meals significantly improves glucose control and pregnancy outcomes. Adopting this model in type 2 diabetes may help achieve better glycemic control.

Relationship of A1C to glucose concentrations in children with type 1 diabetes: assessments by high-frequency glucose determinations by sensors

OBJECTIVE

Despite the standing of A1C as the most validated and widely used measure for average glycemic control over time, the relationship between A1C and glucose concentrations is not completely understood. The purpose of this Diabetes Research in Children Network (DirecNet) study was to use continuous glucose monitoring data to examine the relationship between A1C and glucose in type 1 diabetes.

RESEARCH DESIGN AND METHODS

Forty-eight youth enrolled in studies of the Navigator continuous glucose monitor were encouraged to wear the Navigator sensor at home continuously. A1C was measured at baseline, at 3 months, and at 6 months. Sensor glucose data were directly transmitted via the Internet, assuring that essentially all glucose values were analyzed.

RESULTS

Subjects had a median of 112 h/week of Navigator data in the first 3 months and 115 h/week in the second 3 months. The slope of mean glucose over the previous 3 months versus A1C was only 18 mg/dl per 1.0% A1C. Individually, there was substantial variation in the relationship between mean glucose and A1C. A1C was not associated with glucose lability after controlling for mean glucose. Measures of an individual's rate of glycation were moderately correlated at the 3- and 6-month visits.

CONCLUSIONS

As the chemistry of glycation would predict, we found no evidence to contradict the simple hypothesis that A1C directly reflects mean glucose over time. There is, however, substantial variability in individual mean glucose concentrations for a given A1C. Transforming reliable A1C values into calculated mean glucose values would, when applied to an individual, introduce substantial error.

How much do forgotten insulin injections matter to hemoglobin a1c in people with diabetes? A simulation study

BACKGROUND

Forgotten or omitted insulin injections are an important contributing factor to poor glycemic control in people with type 1 diabetes. This study uses mathematical modeling and examines the impact on hemoglobin A1c (HbA1c) levels if insulin injections are forgotten. The simulation concerns people with type 1 diabetes on intensive insulin therapy.

METHODS

Five sets of blood glucose profiles with and without a forgotten injection were obtained. The difference to HbA1c was calculated using an HbA1c estimator on the profiles and was multiplied by the frequency of forgotten events. A frequency of 2.1 forgotten injections per week was found in the literature.

RESULTS

Calculations showed that forgetting 2.1 meal-related injections per week would lead to an increase in HbA1c of at least 0.3-0.4% points, and similarly 0.2-0.3% points related to forgotten injections of the long-acting insulin. In case of even more pronounced nonadherence (e.g., if 39% of all injections are forgotten) there is a possible increase of HbA1c of 1.8% points.

CONCLUSIONS

The magnitude of the possible improvement in HbA1c agrees well with other studies in the relation between adherence and HbA1c levels. The estimated numbers suggest that missing injections are an important reason for suboptimal treatment.

Use of maternal GHb concentration to estimate the risk of congenital anomalies in the offspring of women with prepregnancy diabetes

OBJECTIVE

We sought to determine the absolute risk of having a congenital anomaly in relation to periconceptional GHb concentration among women with prepregnancy diabetes.

RESEARCH DESIGN AND METHODS

Two reviewers independently retrieved all cohort studies through a systematic literature search between January 1985 and May 2006. For each study, the absolute risk of having a pregnancy affected by a major or minor structural anomaly (diagnosed either antenatally or up to 28 days after conception) was calculated according to the number of SDs of GHb above the mean for nondiabetic, nonpregnant control subjects. A multilevel logistic-normal model was used to pool the data, which were expressed in tabular and graphic formats.

RESULTS

In seven cohort studies, there were 117 anomalies among 1,977 pregnancies. At a periconceptional GHb concentration 0 SD above normal, the absolute risk of a pregnancy affected by a congenital anomaly was approximately 2% (95% CI 0.0-4.4). At 2 SD above normal, the risk was 3% (0.4-6.1), and at 8 SD it was approximately 10% (2.3-17.8). For each 1-SD unit increase in GHb, the associated risk of a congenital malformation increased by an odds ratio of 1.2 (95% CI 1.1-1.4). The risk in relation to A1C followed the same pattern.

CONCLUSIONS

Using data from a limited number of published studies, a practical aid was developed to optimize use of the GHb and A1C concentrations for estimating the absolute risk of a congenital anomaly in the offspring of women with prepregnancy diabetes.

Improving metabolic control in sub-optimally controlled subjects with Type 1 diabetes: comparison of two treatment algorithms using insulin glargine

This study assessed the incidence of severe hypoglycaemia with two insulin glargine titration algorithms: Algorithm 1 (increments of at least 10%, but not exceeding 4U) versus Algorithm 2 (1-6U increments). In this multicenter (n=409), multinational (n=54), open-label, 24-week randomized trial in 2442 subjects with sub-optimally controlled Type 1 diabetes (T1DM), mean prior insulin therapy duration was 14.6+/-10.3 years. The incidence of severe hypoglycaemia was similar with Algorithms 1 and 2 (16.6events/100 patient-years versus 14.4events/100 patient-years). There were similar rates of both symptomatic and nocturnal hypoglycaemia. HbA(1c) and fasting blood glucose (FBG) decreased significantly (baseline to endpoint; p<0.001), and comparably with Algorithms 1 and 2 (HbA(1c): -0.64% versus -0.72%; FBG: -57mg/dL versus -59mg/dL). Mean basal insulin dose increased with both algorithms (+5.7U versus +5.9U). In a diverse population with longstanding T1DM, transfer from any insulin regimen, including basal-bolus or premixed insulin to an insulin glargine-based regimen resulted in significant improvements in glycaemic control, with low rates of severe hypoglycaemia, irrespective of the titration algorithm used.

A simple way to estimate mean plasma glucose and to identify Type 2 diabetic subjects with poor glycaemic control when a standardized HbA1c assay is not available

AIMS

To evaluate the relationship between HbA(1c) and fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) levels, and to estimate the mean plasma glucose (mPG) derived from FPG and PPG that would predict Type 2 diabetic subjects with poor glycaemic control.

METHODS

FPG, PPG and HbA(1c) values from 565 Type 2 diabetic patients (247 men and 318 women) were recorded. Linear regression analysis and Pearson's correlation was used to determine the relationship between HbA(1c), FPG and PPG. FPG and PPG were included as explanatory variables of HbA(1c) in linear regression analysis.

RESULTS

The American Diabetes Association's objective of achieving an HbA(1c) level < 7.0% was obtained in 26.2% of the patients. The coefficients of FPG and PPG which determined HbA(1c) were similar. Therefore, mPG was calculated using the equation (FPG + PPG)/2. Pearson's correlation coefficient for HbA(1c) and FPG, PPG and mPG were 0.723 (P < 0.0001), 0.734 and 0.761 (P < 0.0001), respectively. A mPG cut-off value of 10 mmol/l predicted an HbA(1c) > 7% in the whole population, with a sensitivity of 84.2% and specificity of 80.4%. The area was high (0.90) in receiver-operating characteristic (ROC) curve analysis performed to examine the performance of mPG to predict HbA(1c) > 7%.

CONCLUSIONS

The mPG derived from FPG and PPG correlates strongly with HbA(1c). We therefore suggest that using a cut-off of 10 mmol/l for mPG may be appropriate in diabetes management in the primary-care setting, where most management of Type 2 diabetes occurs.

[Contribution of post-breakfast plasma glucose to the glycemic control of type 2 diabetic patients]

Epidemiological studies have documented that postprandial hyperglycemia is the main risk factor for cardiovascular diseases. It has been established that glycated hemoglobin (HbA1C) provides an integrated measure of plasma glucose (PG) of the last 2-3 months. However, the relative contribution of fasting PG (FPG) and postprandial PG (PPG) to the HbA1C value is controversial.

OBJECTIVE

To evaluate FPG and PPG contributions to the HbA1C value in patients with type 2 diabetes mellitus (DM2).

METHODS

53 subjects with stable DM2 were studied. They were treated with oral anti-diabetic agents (n = 27) and/or insulin (n = 26). Each subject went to 3 visits at 2-month-intervals. On each visit, FPG, PPG (2 h after breakfast and lunch), and HbA1C were measured and we provided breakfast and lunch according to their meal habits. PG was measured by glucose-oxidase and HbA1C by ion-exchange chromatography. Statistical analysis was performed by correlation coefficients at a < 0.05 P value.

RESULTS

Correlations were stronger between HbA1C and post-breakfast PG (r: 0.66-0.48), mean FPG (r: 0.64-0.41), glucose area under the curve (r: 0.64-0.46), and mean PPG (r: 0.59-0.41).

CONCLUSIONS

Measurement of post-breakfast PG showed to be another valuable tool for type 2 diabetic glucose control monitoring.