How valid is fasting plasma glucose as a parameter of glycemic control in non-insulin-using patients with type 2 diabetes?

There is no dose-response relationship between the amount of exercise and improvement in HbA1c in interventions over 12 weeks in patients with type 2 diabetes: a meta-analysis and meta-regression

AIMS

Aerobic exercise is well recognised as an effective treatment for people with type 2 diabetes but the optimal amount of aerobic exercise to improve glycaemic control remains to be determined. Thus, the aim of this meta-analysis and meta-regression was to assess the impact of volume and intensity of aerobic exercise on glycaemic control.

METHODS

Medline, Cochrane, Embase, and Web of Science databases were searched up until 15 December 2020 for the terms "aerobic exercise AND glycaemic control", "type 2 diabetes AND exercise", and "exercise AND glycaemic control AND Type 2 diabetes AND randomised control trial". We included (i) randomised control trials of ≥ 12 weeks, (ii) trials where participants had type 2 diabetes and were aged 18 or over, and (iii) the trial reported HbA1c concentrations pre- and post-intervention. Two reviewers selected studies and extracted data. Data are reported as standardised mean difference (SMD) and publication bias was assessed using funnel plots.

RESULTS

A total of 5364 original titles were identified. Sixteen studies were included in the meta-analysis. Aerobic exercise reduced HbA1c versus control (SMD = 0.56 (95% CI 0.3-0.82), p < 0.001). There were also significant reductions in BMI (SMD = 0.76 (95% CI 0.25-1.27), p < 0.05). There was no dose-response relationship between improvement in HbA1c and the intensity and volume of the intervention (p > 0.05).

CONCLUSIONS

Twelve-week or longer aerobic exercise programmes improve glycaemic control and BMI in adults with type 2 diabetes. Longer or more intense interventions appear to confer no additional benefit on HbA1c.

Suboptimal control for patients with type 2 diabetes in the Central Chronic Medicine Dispensing programme in South Africa

BACKGROUND

In South Africa, the Central Chronic Medicine Dispensing and Distribution (CCMDD) programme allows stable patients with non-communicable diseases, including type 2 diabetes mellitus (T2DM), to collect their medication from a pick-up location near their home, thus avoiding long waiting times and travel expenses. The CCMDD programme aims at improving patient retention and adherence through better access to medicines, resulting in better health outcomes.

AIM

We assessed whether patients with T2DM enrolled in CCMDD achieved the recommended targets for glycaemic, blood pressure (BP) and lipid control.

SETTING

City of Tshwane, South Africa.

METHODS

We reviewed the records of 198 T2DM patients enrolled in CCMDD and assessed their control of haemoglobin A1c (HbA1c), BP and lipids.

RESULTS

Most of the records reviewed belonged to women (64.7%), African (89.9%), hypertensive (82.7%) and to patients exclusively on oral antidiabetic agents (98.5%). Patients were, on average, 57.7 (s.d. = 12.1) years old and had participated in the CCMDD programme for, on average, 2 years. The mean HbA1c was 8% (s.d. = 2). Glycaemic control was achieved by only 29.2% of patients, and 49% of patients had HbA1c between 7% and 9%. Ninety-three patients (66%) had achieved the total cholesterol target, 57.4% achieved BP targets and 6.9% had achieved the low-density lipoprotein cholesterol target.

CONCLUSION

A small group of patients achieved the targets for glycaemic, BP and lipid control. Despite improved accessibility to medication, the CCMDD is not synonymous of improved clinical outcomes. Future research should ascertain the factors associated with suboptimal control for these patients.

Imputing HbA1c from capillary blood glucose levels in patients with type 2 diabetes in Sri Lanka: a cross-sectional study

OBJECTIVE

To develop a population-specific methodology for estimating glycaemic control that optimises resource allocation for patients with diabetes in rural Sri Lanka.

DESIGN

Cross-sectional study.

SETTING

Trincomalee, Sri Lanka.

PARTICIPANTS

Patients with non-insulin-treated type 2 diabetes (n=220) from three hospitals in Trincomalee, Sri Lanka.

OUTCOME MEASURE

Cross-validation was used to build and validate linear regression models to identify predictors of haemoglobin A1c (HbA1c). Validation of models that regress HbA1c on known determinants of glycaemic control was thus the major outcome. These models were then used to devise an algorithm for categorising the patients based on estimated levels of glycaemic control.

RESULTS

Time since last oral intake other than water and capillary blood glucose were the statistically significant predictors of HbA1c and thus included in the final models. In order to minimise type II error (misclassifying a high-risk individual as low-risk or moderate-risk), an algorithm for interpreting estimated glycaemic control was created. With this algorithm, 97.2% of the diabetic patients with HbA1c ≥9.0% were correctly identified.

CONCLUSIONS

Our calibrated algorithm represents a highly sensitive approach for detecting patients with high-risk diabetes while optimising the allocation of HbA1c testing. Implementation of these methods will optimise the usage of resources devoted to the management of diabetes in Trincomalee, Sri Lanka. Further external validation with diverse patient populations is required before applying our algorithm more widely.

The Effect of Diacerein on Type 2 Diabetic Mellitus: A Systematic Review and Meta-Analysis of Randomized Controlled Trials with Trial Sequential Analysis

AIMS

To figure out the effect of diacerein supplementation on type 2 diabetes mellitus (T2DM).

METHODS

An electronic search was processed on Pubmed, Embase, and Cochrane library for randomized controlled trials (RCTs) comparing the efficacy of diacerein with placebo on T2DM. The primary outcome was fasting blood glucose (FBG). Trial sequential analysis (TSA) was used to test the reliability of this pooled outcome. Secondary outcomes were glycosylated hemoglobin A1c (HbA1c), body mass index (BMI), lipid profiles, hematological indexes including hematocrit and platelet count, and systematic inflammatory level expressed as a C-reactive protein (CRP) level. Safety outcome was the rate of complications. The difference in continuous data was measured by mean difference (MD) and 95% confidence interval (CI), while the difference of dichotomous data was calculated by relative risk (RR) and 95% CI. A two-tailed P < 0.05 was regarded as statistically significant.

RESULTS

Five RCTs with 278 participants were included. Compared with control, diacerein provided significant improvement on FBG (MD -0.52; 95% CI (-0.89~-0.14); P < 0.05 was regarded as statistically significant. P < 0.05 was regarded as statistically significant. P < 0.05 was regarded as statistically significant. P < 0.05 was regarded as statistically significant. P < 0.05 was regarded as statistically significant.

CONCLUSION

Based on the current analysis, diacerein as an add-on treatment provided better glycemic control for T2DM but this benefit requires more verification. Compared with control, additional diacerein also lowered body weight and CRP level in T2DM, but increased the rate of gastrointestinal syndromes.

Serum selenoprotein P, but not selenium, predicts future hyperglycemia in a general Japanese population

We aimed to test the hypothesis that selenoprotein P (SELENOP), a hepatokine involved in the development of both insulin resistance and impaired insulin production in mice, is related to future onset of hyperglycemia in humans. 76 healthy non-pregnant human subjects without diabetes underwent oral glucose tolerance test (OGTT) at baseline and 4-years follow-up. Nine subjects developed either impaired glucose tolerance or type 2 diabetes at follow-up. At baseline, SELENOP concentrations correlated negatively with insulinogenic index, but not with homeostasis model assessment-estimated insulin resistance (HOMA-IR). Multivariate analysis showed that baseline SELENOP predicted fasting plasma glucose at follow-up independently of the other parameters. The receiver operating characteristic (ROC) curve analysis showed that baseline concentrations of serum SELENOP, but not of selenium, were a reliable test to predict future onset of glucose intolerance. In conclusion, elevation of circulating SELENOP, but not of circulating selenium, was positively and independently associated with future onset of glucose intolerance in a general Japanese population.

Relationship of glycated hemoglobin, and fasting and postprandial hyperglycemia in type 2 diabetes mellitus patients in Malaysia

AIMS/INTRODUCTION

Studies on the relative contributions of fasting and postprandial hyperglycemia (FH and PPH) to glycated hemoglobin (HbA1c ) in patients with type 2 diabetes have yielded inconsistent results. We aimed to assess the relationship by using continuous glucose monitoring in a multi-ethnic cohort.

MATERIALS AND METHODS

A total of 100 adults with type 2 diabetes were assessed with 6-day continuous glucose monitoring and HbA1c . Area under the curve (AUC) ≥5.6 mmol/L was defined as AUCTOTAL . AUC equal to or greater than each preprandial glucose for 4-h duration was defined as AUCPPH . The total PPH (AUCTPPH ) was the sum of the various AUCPPH. The postprandial contribution to overall hyperglycemia was calculated as (AUCTPPH / AUCTOTAL ) × 100%.

RESULTS

The present study comprised of Malay, Indian, and Chinese type 2 diabetes patients at 34, 34 and 28% respectively. Overall, the mean PPH significantly decreased as HbA1c advanced (mixed model repeated measures adjusted, beta-estimate = -3.0, P = 0.009). Age (P = 0.010) and hypoglycemia (P = 0.006) predicted the contribution difference. In oral antidiabetic drug-treated patients (n = 58), FH contribution increased from 54% (HbA1c 6-6.9%) to 67% (HbA1c ≥10%). FH predominance was significant in poorly-controlled groups (P = 0.028 at HbA1c 9-9.9%; P = 0.015 at HbA1c ≥10%). Among insulin users (n = 42), FH predominated when HbA1c was ≥10% before adjustment for hypoglycemia (P = 0.047), whereas PPH was numerically greater when HbA1c was <8%.

CONCLUSIONS

FH and PPH contributions were equal in well-controlled Malaysian type 2 diabetes patients in real-world practice. FH predominated when HbA1c was ≥9 and ≥10% in oral antidiabetic drug- and insulin-treated patients, respectively. A unique observation was the greater PPH contribution when HbA1c was <8% despite the use of basal and mealtime insulin in this multi-ethnic cohort, which required further validation.

Single, community-based blood glucose readings may be a viable alternative for community surveillance of HbA1c and poor glycaemic control in people with known diabetes in resource-poor settings

Background

The term HbA_1c (glycated haemoglobin) is commonly used in relation to diabetes mellitus. The measure gives an indication of the average blood sugar levels over a period of weeks or months prior to testing. For most low- and middle-income countries HbA_1c measurement in community surveillance is prohibitively expensive. A question arises about the possibility of using a single blood glucose measure for estimating HbA_1c and therefore identifying poor glycaemic control in resource-poor settings.

Design

Using data from the 2011–2012 US National Health and Nutrition Examination Surveys, we examined the relationship between HbA_1c and a single fasting measure of blood glucose in a non-clinical population of people with known diabetes (n=333). A linear equation for estimating HbA_1c from blood glucose was developed. Appropriate blood glucose cut-off values were set for poor glycaemic control (HbA_1c≥69.4 mmol/mol).

Results

The HbA_1c and blood glucose measures were well correlated (r=0.7). Three blood glucose cut-off values were considered for classifying poor glycaemic control: 8.0, 8.9, and 11.4 mmol/L. A blood glucose of 11.4 had a specificity of 1, but poor sensitivity (0.37); 8.9 had high specificity (0.94) and moderate sensitivity (0.7); 8.0 was associated with good specificity (0.81) and sensitivity (0.75).

Conclusions

Where HbA_1c measurement is too expensive for community surveillance, a single blood glucose measure may be a reasonable alternative. Generalising the specific results from these US data to low resource settings may not be appropriate, but the general approach is worthy of further investigation.

Contributions of Basal Glucose and Postprandial Glucose Concentrations to Hemoglobin A1c in the Newly Diagnosed Patients with Type 2 Diabetes--the Preliminary Study

OBJECTIVE

The aim of this preliminary study is to investigate contributions of basal glucose (BG) and postprandial glucose (PPG) increments to overall hyperglycemia in newly diagnosed patients with type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

We evaluated the relative contributions of BG and PPG to overall hyperglycemia in 59 newly diagnosed T2DM patients according to BG baseline value of 6.1 mmol/L and 24-h glucose profiles of normal glucose tolerance (NGT) subjects obtained by continuous glucose monitoring as baseline, respectively.

RESULTS

When the baseline was 24-h glucose profiles of the NGT subjects, the relative contributions of PPG in the T2DM patients with hemoglobin A1c (HbA1c) levels of ≤ 7.0%, 7.0-9.0%, and >9.0% were 57.58%, 44.69%, and 21.56%, respectively. When the baseline value was equal to 6.1 mmol/L, the relative contributions of PPG in the T2DM patients with HbA1c levels of ≤ 7.0%, 7.0-9.0%, and >9.0% were 77.23%, 53.43%, and 22.78%, respectively. Compared with the 24-h glucose profiles of the NGT subjects as the baseline, the relative contribution of PPG was overestimated by about 10-20% in the T2DM patients with HbA1c levels of ≤ 9.0% when 6.1 mmol/L was chosen as the baseline.

CONCLUSIONS

In the newly diagnosed T2DM patients with mild hyperglycemia, PPG is a predominant contributor, whereas the relative contributions of BG gradually increase from mild to severe hyperglycemia and obviously exceed PPG in the T2DM patients with HbA1c levels of >9.0%. This finding implies that the initial pharmacotherapy may target PPG in those patients with mild hyperglycemia and target BG in those patients with severe hyperglycemia.

Glycemic Variability: Assessing Glycemia Differently and the Implications for Dietary Management of Diabetes

The primary therapeutic target for diabetes management is the achievement of good glycemic control, of which glycated hemoglobin (HbA1c) remains the standard clinical marker. However, glycemic variability (GV; the amplitude, frequency, and duration of glycemic fluctuations around mean blood glucose) is an emerging target for blood glucose control. A growing body of evidence supports GV as an independent risk factor for diabetes complications. Several techniques have been developed to assess and quantify intraday and interday GV. Additionally, GV can be influenced by several nutritional factors, including carbohydrate quality, quantity; and distribution; protein intake; and fiber intake. These factors have important implications for clinical nutrition practice and for optimizing blood glucose control for diabetes management. This review discusses the available evidence for GV as a marker of glycemic control and risk factor for diabetes complications. GV quantification techniques and the influence of nutritional considerations for diabetes management are also discussed.

Lixisenatide as add-on to oral anti-diabetic therapy: an effective treatment for glycaemic control with body weight benefits in type 2 diabetes

BACKGROUND

Achieving recommended glycated haemoglobin (HbA1c ) targets in patients with type 2 diabetes mellitus (T2DM) requires effective control of fasting and post-prandial plasma glucose. As T2DM progresses, oral anti-diabetics are no longer sufficient to maintain glycaemic control. Five phase III studies in the GetGoal clinical trial programme assessed the efficacy of lixisenatide, a once-daily prandial glucagon-like peptide-1 receptor agonist, in combination with oral anti-diabetics in patients with T2DM insufficiently controlled using oral anti-diabetics.

METHODS

A meta-analysis was performed of the results of five 24-week clinical trials (comprising 2760 patients) concerning lixisenatide or placebo plus oral anti-diabetic therapy. The primary endpoint of these studies was change in HbA1c at week 24. Changes in fasting and post-prandial plasma glucose, and weight were also established as were the odds ratios for hypoglycaemia and composite safety and efficacy endpoints. Meta-analysis outcomes were assessed using a random effects model. All meta-analyses were performed using RevMan, version 5.1.

RESULTS

Lixisenatide was significantly better than placebo in terms of achieving all endpoints in this meta-analysis, including the primary endpoint change in HbA1c at week 24, with p < 0.0001 for all endpoints. The mean number of symptomatic hypoglycaemic events per patient year was increased for patients in the lixisenatide versus placebo groups (p = 0.04). However, compared with patients in the placebo group, patients treated with lixisenatide were more likely to achieve composite efficacy and safety endpoints.

CONCLUSIONS

This meta-analysis demonstrates that lixisenatide in combination with oral anti-diabetic therapy significantly improves outcomes combining efficacy and safety parameters in patients with T2DM.

Lixisenatide plus basal insulin in patients with type 2 diabetes mellitus: a meta-analysis

AIMS

The efficacy of the once-daily prandial GLP-1 receptor agonist lixisenatide plus basal insulin in T2DM was assessed by pooling results of phase III trials.

METHODS

A meta-analysis was performed of results from three trials in the GetGoal clinical program concerning lixisenatide or placebo plus basal insulin with/without OADs. The primary endpoint was change in HbA1c from baseline to week 24. Secondary endpoints were change in PPG, FPG, insulin dose, and weight from baseline to week 24. Hypoglycemia rates and several composite endpoints were assessed.

RESULTS

Lixisenatide plus basal insulin was significantly more effective than basal insulin alone at reducing HbA1c at 24 weeks. Composite and secondary endpoints were improved significantly with lixisenatide plus basal insulin, with the exception of FPG, which showed no significant difference between the groups. Lixisenatide plus basal insulin was associated with an increased incidence of hypoglycemia versus basal insulin alone.

CONCLUSIONS

Lixisenatide plus basal insulin resulted in significant improvement in glycemic control versus basal insulin alone, particularly in terms of controlling PPG. Prandial lixisenatide in combination with basal insulin is a suitable option for treatment intensification in patients with T2DM insufficiently controlled with basal insulin, as these agents have complementary effects on PPG and FPG, respectively.

Glycemic control among patients with type 2 diabetes at a primary health care center in Oman

AIMS

To determine the status of blood sugar control by using fasting blood sugar (FBS) of ≤6.1 mmol/l and glycosyted hemoglobin A1c (HbAc1) of <7% as indictors of glycemic control and to assess the influence of demographic, blood pressure (BP) and lipid characteristics on glycemic control.

METHODS

This retrospective study included all Omani patients with type 2 diabetes (N=177) attended a primary health care center in Al-Dakhiliya region, Oman.

RESULTS

The overall mean age of the cohort was 53±12 years (range: 24-91) with females representing 60% (n=106) of the study sample. The study found that only 9.6% (n=17) and 35% (n=62) of the patients attained optimal FBS and HbAc1 levels, respectively. Higher HbA1c was significantly associated with higher diastolic BP (84 versus 80 mm Hg; p=0.006), higher total cholesterol (5.2 versus 4.7 mmol/l; p=0.002) and higher low-density lipoprotein cholesterol (3.8 versus 3.0 mmol/l; p=0.034).

CONCLUSIONS

The results demonstrated poor glycemic control in Oman type 2 diabetic patients comparable to local and global studies especially in those hypertensive and dyslipidemic patients. Implementation of early and aggressive management of diabetes mellitus at the primary care setting is warranted.

The relationship between fasting plasma glucose and HbA1c during intensive periods of glucose control in antidiabetic therapy

OBJECTIVE

HbA1c measurements are typically less variable than fasting plasma glucose (FPG) for diagnosing diabetes, and for assessment of progress on glucose control therapy. However HbA1c reaches steady-state relative to average plasma glucose over about 120 days. HbA1c thus overestimates average FPG during first three months of starting therapy in newly diagnosed diabetic patients, and care needs to be exercised in interpreting HbA1c measurements during this period. At steady-state excellent regression exists between HbA1c and FPG. We hypothesize that this regression can also be used to obtain reliable estimates of HbA1c relative to FPG at 4 and 8 weeks following the onset of therapy.

MATERIALS AND METHODS

We collected FPG and HbA1c data of type 2 diabetic patients over the first 8 weeks of starting antidiabetic treatment. We fit linear and nonlinear regression models to steady-state data, and estimated how much measured HbA1c deviates at 4 and 8 weeks from these theoretical relations.

RESULTS

If measured HbA1c is decremented by 0.7% (8 mmol/mol) at 4 weeks and 0.3% (3 mmol/mol) at 8 weeks, this corrected HbA1c is a better predictor of the corresponding FPG. Using hyperbolic regression, corrections to HbA1c are 0.5 and 0.1% (5 and 1 mmol/mol), respectively.

CONCLUSION

With the corrections proposed here, HbA1c measurements can be better interpreted in the early weeks of antidiabetic treatment.

Relation of fasting and postprandial and plasma glucose with hemoglobinA1c in diabetics

CONTEXT

Control of plasma glucose could prevent the progression of most of the complications of diabetes and hemoglobinA1c (HbA1c) is the most important criterion controlling these long-term complications.

AIMS

This study was performed to assess the effect of fasting plasma glucose (FPG) and two- hour postprandial plasma glucose (2hpp) levels on HbA1c.

MATERIALS AND METHODS

In this descriptive, cross-sectional study; 300 patients were enrolled, assessed, and followed up at the clinic of the Diabetic Center of the University of Medical Science, Hamadan, Iran. All studied patients were diagnosed type 1 or 2 diabetes mellitus. Sampling was performed; we assessed FPG and 2hpp plasma glucose at baseline and at every two weeks to one month-as needed. HbA1c was assessed at the end of study. Results were analyzed by Pierson Covariance and Multiple Regression methods.

RESULTS

The mean plasma glucose in three groups of HbA1c (good to fair) were 148.5 +/- 56.80 mg/dl at fasting, and 199.70 +/- 53.01 mg/dl at two hours after breakfast (2hpp) and mean concentration of HbA1c were 8.41 +/- 1.1 %. The plasma glucose level and HbA1c were 0.312 for fasting and, 0.416 for 2hpp at covariant value.

CONCLUSION

The postprandial (after breakfast) plasma glucose has closer association to glycosylated hemoglobin than fasting plasma glucose, therefore evaluating postprandial plasma glucose should be our focus.

[Assessment of glycemic control: new insights into the evaluation of the diabetic patient]

Diabetes mellitus is an increasingly prevalent worldwide disease, complicated by development of micro- and macro-angiopathy and associated with high morbidity and mortality. Several studies have demonstrated a direct correlation between improvement of metabolic control and a reduced risk of chronic complications, especially microangiopathy. Glycemic control in type 2 diabetes (DM2) patients focuses primarily on the assessment of three parameters: glycated hemoglobin (HbA1c), fasting plasma glucose (FPG) and postprandial glucose (PPG). Currently, HbA1c is regarded as the gold standard for assessing glycemic control, due to the large experience regarding the reduction of chronic complication risks. However, there is growing evidence that acute glucose fluctuations could also be involved in the pathogenesis of chronic complications. It has therefore been suggested that treatment decisions should not be based exclusively on HbA1c, but should also take into account glycemic variability. Recently, several studies have advocated the use of PPG (either as an isolated value or as a component of glycemic variability) as a more accurate way of estimating glycemic control in patients with diabetes.

Joint and distinct risk factors associated with micro- and macrovascular complications in a cohort of type 2 diabetic patients cared through disease management

We analysed the risk factors associated with diabetic complications in the cohort of patients assisted by a type 2 diabetes mellitus (T2DM) shared-care program. We analysed registry data from 16,136 T2DM patients. Of them, 4,781 had microangiopathy, 3,469 CV events. They were 70.5 ± 17.1 years old, 50% were male, disease duration 13.3 ± 7.8 years, BMI 28.7 ± 4.9 kg/m², HbA1c 7.08 ± 1.23%, FBG 134.7 ± 35.7 mg/dl, 2hPPBG 163.9 ± 47.8 mg/dl, 12.5% smokers. Cholesterol 202.5 ± 37.6 mg/dl, HDL 51.4 ± 20.4 mg/dl, LDL 126.5 ± 36.0 mg/dl, triglyceride 146.2 ± 72.4 mg/dl, SBP 137.8 ± 14.2 mmHg, DBP 80.7 ± 10.8 mmHg, 10-year CV risk score 13.7 ± 9.1; 70.4% had no microangiopathy-i.e. renal, retinal, peripheral nerve disease-and 78.5% of patients had no CV events. Age-adjusted risk factors associated with diabetic complications were male gender, HbA1c, 2hPPBG, HDL, and triglyceride. FBG and SBP were associated with microangiopathy, whereas smoking with cardiovascular events. Optimal targets were reached in: FBG 17%, 2hPPBG 8%, HbA1c 21%, cholesterol 17%, HDL 8%, LDL 5%, triglyceride 20%, SBP 13%, DBP 30%. Drug profiles showed 13% using metformin, 28% sulphonilureas, 26% bitherapy, 4% insulin; 12% statins, 16% anti-platelets, 27% anti-hypertensives, 2% anti-coagulants. T2DM patients showed an acceptable CV risk profile. Joint risk factors for diabetic complications were male gender, HbA1c, 2hPPBG, HDL, and triglyceride. Distinct risk factors were FBG and SBP for micro- and smoking for macrovascular disease. A targeted-to-treat approach needs more attention in the care of T2DM patients.

Diabetes treatment patterns and goal achievement in primary diabetes care (DiaRegis) - study protocol and patient characteristics at baseline

BACKGROUND

Patients with type 2 diabetes are at an increased risk for disease and treatment related complications after the initial approach of oral mono/dual antidiabetic therapy has failed. Data from clinical practice with respect to this patient group are however scarce. Therefore we set up a registry in primary care documenting the course and outcomes of this patient group.

METHODS

Diabetes Treatment Patterns and Goal Achievement in Primary Diabetes Care (DiaRegis) is a prospective, observational, German, multicenter registry including patients with type-2 diabetes in which oral mono/dual antidiabetic therapy has failed. Data were recorded at baseline and will be prospectively documented during visits at 6 ± 1, 12 ± 2 and 24 ± 2 months. The primary objective is to estimate the proportion of patients with at least 1 episode of severe hypoglycemia within one year.

RESULTS

313 primary care offices included 4,048 patients between June 2009 and March 2010 of which 3,810 patients fulfilled the in- and exclusion criteria. 46.7% of patients were female; patients had a median diabetes duration of 5.5 years and most were obese with respect to BMI or waist circumference. HbA1c at baseline was 7.4%, fasting plasma glucose 142 mg/dl and postprandial glucose 185 mg/dl. Co-morbidity in this patient population was substantial with 17.9% having coronary artery disease, 14.4% peripheral neuropathy, 9.9% heart failure and 6.0% peripheral arterial disease. 68.6% of patients received oral monotherapy, 31.4% dual oral combination therapy. The most frequent antidiabetic agent used as monotherapy was metformin (79.0%) followed by sulfonylureas (14.8%).

CONCLUSIONS

DiaRegis is a large, prospective registry in primary diabetes care to document the course and outcomes of patients with type-2 diabetes in which the initial approach of oral mono/dual antidiabetic therapy has failed. The two year follow-up will allow for a prospective evaluation of these patients during multiple adjustments of therapy.

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.

The relationship between self-monitored blood glucose values and glycated haemoglobin in insulin-treated patients with Type 2 diabetes

AIMS

Self-monitoring of blood glucose (SMBG) is an important self-management tool for insulin-treated patients with Type 2 diabetes mellitus (T2DM). Its value in estimating glycaemic control in insulin-treated T2DM patients remains unclear. The relationship between glycated haemoglobin (HbA(1c)) and SMBG measures in T2DM patients treated with premixed insulin lispro mixtures or basal insulin glargine was examined.

METHODS

HbA(1c) and plasma equivalent glucose (PGe) data derived from SMBG profiles were pooled from five randomized clinical trials of patients with T2DM on one or more oral glucose-lowering medication +/- 0-2 insulin injections per day switching to insulin lispro mixtures (N = 317) or glargine (N = 306). Patients generated seven-point SMBG profiles three times in a 2-week period prior to each HbA(1c) measurement. Pearson's correlation coefficients (r) were calculated for PGe values and HbA(1c). Receiver-operating characteristic (ROC) curves determined the ability of sets of PGe to estimate HbA(1c) (< or > 7.0%).

RESULTS

Mean +/- standard deviation age was 57.5 +/- 9.5 years, body mass index 31.3 +/- 5.6 kg/m(2), 52.5% were male and HbA(1c) overall was 7.4 +/- 1.0% at end-point. Among individual SMBG measures, r for HbA(1c) ranged from 0.34 to 0.49. For means of two or more PGe measures, r for HbA(1c) ranged from 0.51 to 0.59. Correlations were similar for either regimen. ROC curves were consistent with the correlation data.

CONCLUSIONS

These data provide patients and clinicians information on the relationship between HbA(1c) and SMBG measurements in patients with T2DM, and support the value of frequent blood glucose measurements for assessing overall glycaemic control.

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.

Markers of glycemic control in the mouse: comparisons of 6-h- and overnight-fasted blood glucoses to Hb A1c

The present studies examined the relationship between fasting blood glucose and Hb A(1c) in C57BL/6J, DBA/2J, and KK/HlJ mice with and without diabetes mellitus. Daily averaged blood glucose levels based on continuous glucose monitoring and effects of 6-h vs. overnight fasting on blood glucose were determined. Daily averaged blood glucose levels were highly correlated with Hb A(1c), as determined with a hand-held automated device using an immunodetection method. R(2) values were 0.90, 0.95, and 0.99 in KK/HIJ, C57BL/6J, and DBA/2J, respectively. Six-hour fasting blood glucose correlated more closely with the level of daily averaged blood glucose and with Hb A(1c) than did blood glucose following an overnight fast. To validate the immunoassay-determined Hb A(1c), we also measured total glycosylated hemoglobin using boronate HPLC. Hb A(1c) values correlated well with total glycosylated hemoglobin in all three strains but were relatively lower than total glycosylated hemoglobin in diabetic DBA/2J mice. These results show that 6-h fasting glucose provides a superior index of glycemic control and correlates more closely with Hb A(1c) than overnight-fasted blood glucose in these strains of mice.

Distribution of blood glucose and the correlation between blood glucose and hemoglobin A1c levels in diabetic outpatients

Tight glycemic control is important for the prevention of microvascular complications in diabetic patients. We examined the reliability of using blood glucose levels measured at various time-points relative to a meal as an index of glycemic control in Japanese diabetic outpatients. Basic procedures followed: We examined the correlation between the fasting blood glucose (FBG) level; the one-hour (1-h), two-hour (2-h), and three-hour (3-h) post breakfast blood glucose (PBBG) levels, the 1 h, 2 h, and 3 h post lunch blood glucose (PLBG) levels and the hemoglobin A1c (HbA1c) levels in Japanese diabetic outpatients. A total of 11451 patient-visits to the Marunouchi Hospital between January 2002 and December 2002 were included in the study. The main findings: The blood glucose levels measured at all of the above time-points were significantly correlated with the HbA1c level. As calculated using local polynomial regression fitting, the FPG, 1-h, 2-h, and 3-h PBBG levels that corresponded to an HbA1c level of 6.5% were 132 mg/dL, 174 mg/dL, 170 mg/dL, and 143 mg/dL, respectively. The FPG and 2-h PBBG levels exhibited a good sensitivity and specificity for predicting a glycemic control corresponding to an HbA1c<5.8%, while the FPG and 3-h PBBG levels exhibited fair sensitivity and specificity for predicting glycemic control corresponding to an HbA1c<6.5%. The principal conclusions: The FBG, 2-hPBBG, and 3-hPBBG levels can be used as rough estimates of glycemic control in Japanese diabetic outpatients.

The utility of fasting plasma glucose in predicting glycosylated hemoglobin in type 2 diabetes

BACKGROUND

Access to glycosylated hemoglobin (HbA1c) assays in clinical practice remains limited. We investigated the relationship of fasting plasma glucose and HbA1c to determine optimal glucose levels for predicting HbA1c.

PATIENTS AND METHODS

We retrospectively analyzed data on 2888 patients with type 2 diabetes mellitus aged >or=20 years using a linear regression of HbA1c against fasting plasma glucose. A receiver-operating characteristic analysis was used to determine optimal cut-points for fasting glucose in relation to HbA1c, area under the curve, sensitivity and specificity, and 95% confidence intervals (CI) for each cut-point.

RESULTS

The mean (standard deviation) for the age of patients was 52+/-11.6 years. The average HbA1c was 8.9+/-2.46% and mean fasting plasma glucose was 10.1+/-3.62 mmol/L. The prevalence of HbA1c >or=7.0% and >6.5% was 76% and 82%, respectively. Overall, fasting plasma glucose and HbA1c were linearly correlated (r=0.62, P=0.001). A fasting plasma glucose of >9.0 mmol/L predicted HbA1c >or=7.0% with an area under the curve = 0.807 (95% CI, 0. 0.794 to 0.821), while fasting plasma glucose >8.2 mmol/L predicted HbA1c >6.5%, with an area under the curve = 0.805 (95% CI, 0.791 to 0.818). The sensitivity of both cut-points was 64.5% and 70.7%, the specificity was 82.7% and 76.4%, the positive likelihood ratio was 3.73 and 2.99, and the positive predictive value was 92.2% and 93.2%, respectively.

CONCLUSION

When HbA1c determination is not available, fasting plasma glucose levels may be used to identify patients with uncontrolled type 2 diabetes and initiate timely intensification of therapy to avoid long- term complications of diabetes.

A comparison of associations of alanine aminotransferase and gamma-glutamyltransferase with fasting glucose, fasting insulin, and glycated hemoglobin in women with and without diabetes

Associations between biomarkers of nonalcoholic fatty liver disease (NAFLD) alanine aminotransferase (ALT), and gamma-glutamyltransferase (GGT), with 3 separate measures of glucose homeostasis: fasting glucose, fasting insulin and glycated hemoglobin (HbA1c) were studied and compared between women with and without diabetes in order to gain insight into the documented associations between NAFLD, insulin resistance and diabetes. Data from the British Women's Health and Heart Study, a random sample of British women aged 60-79 years (N = 3394; 3086 without diabetes and 308 with diabetes) was used. Associations of ALT and GGT with fasting glucose and HbA1c and of ALT with fasting insulin (and homeostasis model assessment of insulin resistance [HOMA]) are stronger in women with diabetes compared to women without diabetes (P for interaction < 0.001). GGT is associated with fasting insulin (and HOMA) to the same extent in all women, irrespective of diabetes status. Results excluding hyperinsulinemic women, i.e., in the highest fourth of the fasting insulin distribution, were similar to those obtained for all non-diabetic women as were results excluding women in the highest quartile of the alcohol consumption distribution and for women with ALT and GGT levels within the normal range. Associations did not differ substantially between obese and non-obese non-diabetic women.

CONCLUSION

elevation of liver enzymes and hepatic insulin resistance as reflected by fasting insulin occur in the early stages of insulin resistance and highlight the central role of the liver in insulin resistance in the general population.

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.

Relationship between HbA1c and indices of glucose tolerance derived from a standardized meal test in newly diagnosed treatment naive subjects with Type 2 diabetes

AIMS

To determine the relationship between HbA1c and other indices of glycaemic status derived during a standardized meal tolerance test (MTT) in newly diagnosed treatment-naive subjects with Type 2 diabetes (T2DM).

METHODS

T2DM subjects (n = 262) consumed a standard MTT in the morning after a 10-h overnight fast. Frequent samples for plasma glucose (PG) were collected over the 4-h test period. The relationship between HbA1c and other glycaemic indices derived from the MTT were explored. The postprandial glucose exposure was calculated as the area under the incremental plasma glucose curve above the fasting level for the test period (AUC1). Excess hyperglycaemia was calculated as the AUC0-4 h above the arbitrary PG concentrations of 6.0 mmol/l (AUC2) and 5.5 mmol/l (AUC3), respectively [upper limit of fasting normoglycaemia according to World Health Organization (WHO) and American Diabetes Association (ADA), respectively]. Fasting hyperglycaemia was also estimated, being the difference between each of the above and the postprandial excursion. The participants were divided into three subgroups according to HbA1c (Group 1, <or= 7.0%; Group 2, 7.1-9.0%; Group 3, > 9.0%) and the relative contribution calculated of the postprandial glucose and fasting hyperglycaemia to the excess hyperglycaemia above the designated international thresholds for fasting plasma glucose.

RESULTS

HbA1c was more strongly correlated with the fasting plasma glucose (r = 0.85, P < 0.001) than the overall postprandial glucose exposure (r = 0.539, P = 0.003). The contribution of fasting hyperglycaemia to excess hyperglycaemia using the WHO criteria for normal fasting plasma glucose for the three groups (Groups 1, 2 and 3) was 50.4%, 54.3% and 69.8%, respectively, and 57.8%, 58.8% and 71.4% using the ADA criteria.

CONCLUSIONS

The contribution of fasting hyperglycaemia to excess hyperglycaemia increases as glycaemic control deteriorates, becoming dominant with an HbA1c in excess of 7.0%. These findings indicate which therapeutic approach needs to be adopted based on the HbA1c of the person with T2DM.

Fasting and post-prandial glycemia and their correlation with glycated hemoglobin in Type 2 diabetes

OBJECTIVE

The relative contribution of fasting and post-prandial glucose to glycated hemoglobin (HbA1c) is controversial. In the present study, we assessed the relationship with HbA1c of fasting and post-prandial glucose measured in a more naturalistic setting, through home glucose self-monitoring or with a continuous glucose monitoring system (CGM).

MATERIALS AND METHODS

A consecutive series of 300 patients with Type 2 diabetes were enrolled in the study, provided that they performed blood glucose self-monitoring. HbA1c and fasting plasma glucose (FPG) were measured at enrolment.

RESULTS

Both fasting plasma and capillary glucose showed a significant correlation with HbA1c (r=0.66 and 0.61, respectively; p<0.001). When home glucose monitoring was considered, both mean fasting and post-prandial glucose showed a significant correlation with HbA1c (r=0.71 and 0.73, respectively). In patients in the lower tertile of body mass index (BMI), HbA1c showed a significant correlation at multivariate analysis with post-prandial glucose, but not with fasting glucose. In patients with HbA1c >7%, both fasting and post-prandial glucose showed a significant correlation, after adjustment for age and BMI, with HbA1c (both p<0.01); conversely, in those with HbA1c < or =7%, such a correlation could be observed for fasting (p<0.01), but not for post-prandial glucose.

CONCLUSION

In conclusion, both fasting and post-prandial glucose contribute to the determination of HbA1c . Home glucose self-monitoring appears to provide a more accurate assessment of metabolic control than a single plasma glucose measurement in experimental conditions. Fasting glucose could provide a greater contribution to HbA1c in patients with lower HbA1c, while post-prandial glucose seems to play a major role in leaner Type 2 diabetic subjects.

[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.

Tackling Hyperglycemia: A More Comprehensive Approach

OBJECTIVE

To review the components of glycemic control in patients with diabetes and discuss the factors contributing to development of complications.

METHODS

The findings in various published reports are summarized, particularly in reference to metabolic alterations, treatment, and long-term outcomes in diabetes.

RESULTS

Hemoglobin A1c, an integrated marker of overall glucose exposure, is the standard for monitoring glycemic control. The results in the United Kingdom Prospective Diabetes Study indicated that each 1% decrease in hemoglobin A1c reduced the risk for mortality associated with diabetes by 21% and the risk for myocardial infarction by 14%. Nevertheless, fasting plasma glucose and postprandial glucose levels are well-defined targets for therapy. Numerous epidemiologic studies also support the concept that excessive glucose excursion after ingestion of an oral glucose load is associated with an independent risk for cardiovascular disease that is greater than the one associated with the fasting plasma glucose level. Hyperglycemia apparently activates different metabolic pathways that may contribute to development of microangiopathy and macroangiopathy. Intensive treatment to maintain strict glycemic control has been shown to yield better control of diabetes-related complications in comparison with conventional therapy.

CONCLUSION

The complexity of the metabolic alterations in diabetes must be fully recognized, and a comprehensive approach to management should be considered.

Patient characteristics do not predict poor glycaemic control in type 2 diabetes patients treated in primary care

Many diabetic patients in general practice do not achieve good glycaemic control. The aim of this study was to assess which characteristics of type 2 diabetes patients treated in primary care predict poor glycaemic control (HbA1c > or = 7%). Data were collected from the medical records. 1641 patients were included who had mean HbA1c 7.1(SD 1.7)% , and 42% had HbA1c > or = 7%. On univariate analysis younger age; longer duration of diabetes; higher levels of blood glucose at diagnosis; most recent fasting blood glucose (FBG), total cholesterol, and triglyceride; higher body mass index (BMI); treatment with oral hypoglycaemic agents (OHA); treatment with insulin; more GP-visits for diabetes in the last year; and lower educational level were associated with poor control. Both in multiple linear regression and in multiple logistic regression higher levels of FBG (odds ratio (OR): = 1.6, 95% confidence interval (CI): 1.49, 1.70), treatment with OHA (OR: 2.1, 95% CI: 1.41, 3.04), treatment with insulin (OR: 7.2, 95% CI: 4.18, 12.52), lower educational level (OR: 1.26, 95% CI: 1.01, 1.56) were independently associated with poor levels of HbA1c. When FBG levels were excluded from the model, higher blood glucose at diagnosis, higher values for triglyceride and total cholesterol, and younger age predicted poor glycaemic control, but these variables explained only 15% of the variation in HbA1c. In conclusion prediction of poor glycaemic control from patient characteristics in diabetic patients in general practice is hardly possible. FBG appeared to be a strong predictor of HbA1c, which underlines the usefulness of this simple test in daily diabetes care. The worse metabolic control in those treated with either OHA or insulin suggests that current treatment regimes might be not sufficiently applied to reach the targets of care. Providers of diabetes care should be attentive to patients with lower educational level.

Postprandial hyperglycaemia in type 2 diabetes: pathophysiological aspects, teleological notions and flags for clinical practice

Type 2 diabetes subjects carry an excess risk for micro- and macrovascular disease and a higher cardiovascular morbidity and mortality rate. The beneficial impact of tight glycaemic control-evidenced by the integrated marker of fasting glucose and postprandial glucose values, the HbA1c-for the prevention of microvascular complications is definitely confirmed. Over the past few years, several studies have identified postprandial hyperglycaemia as a better predictor of cardiovascular or even of all-cause mortality, as well as an independent risk factor for atherosclerosis. The continuous glucose monitoring could offer a rationale means for the detection of postprandial hyperglycaemia and ultimately for its effective management. Advances in technology keep a promise for a reliable, convenient and closer to the idea of the artificial endocrine pancreas glucose sensor. Subcutaneous glucose levels charted by one of the new sensors were found to be well correlated with venous glucose measurements. Intervention for a healthy lifestyle is frequently hampered by patients' poor compliance. The availability of diverse antidiabetic agents provides options for targeting the glycaemic goal and a choice more fitted to the particularized pathophysiology of each individual subject. Drugs targeting postprandial glycaemia may prove to represent the 'sine qua non' for the 'return' of postprandial glucose values at a 'non-deleterious' threshold, either as monotherapy for the early stages of the disease or as combination therapy later in the progression of diabetes.

The relationship of postprandial glucose to HbA1c

The gold standard for the assessment of the overall glycemic control is the determination of HbA1c. There are, however, insufficient data to determine reliably the relative contribution of fasting and postprandial plasma glucose to HbA1c. Increasing evidence suggests that excessive excursions of postprandial glucose might be important for the development of micro- and macroangiopathic complications. With respect to the treatment options, one important question to be answered is whether premeal, postmeal or fasting plasma glucose, alone or in combination, will be necessary in adjusting the therapy to achieve optimal HbA1c levels while minimizing hypoglycemia. HbA1c is difficult to predict from fasting plasma glucose. There are indications that there is a shift in the relative contribution from postprandial glucose at good to fair HbA1c levels (<7.3% to <9.2%) to fasting plasma glucose at high HbA1c (>9.3%). There is also a better correlation of afternoon and evening plasma glucose with HbA1c than with prebreakfast and prelunch plasma glucose values. Since the definition on how to define postprandial glucose is still a matter of debate and since postprandial glucose depends on the premeal blood glucose level and, on the time of the meal, its size and composition and the therapeutic strategy, the data so far available are inconclusive and the best correlation of HbA1c is with the area under the glucose profiles. Continuous glucose monitoring under daily life conditions will be the key to definitely unravel the relationship among HbA1c and fasting, premeal, postprandial and postabsorptive plasma glucose.

Diabetes control and complications: the role of glycated haemoglobin, 25 years on

The long-term complications of diabetes have major consequences for individual subjects and growing healthcare delivery and cost implications for society. Evidence for the benefits of good glycaemic control, as monitored by glycated haemoglobin measurements, has been developed in the 25 years since they were introduced to the point where HbA(1c) assays play central roles in patient management, clinical guidance and audit, and clinical trial design. In this review this evidence is examined and three classes of uncertainty identified that diminish confidence in the effectiveness of these roles for HbA(1c). 1 Analytical variability between different methods for HbA(1c) has restricted the application of clinical targets and this problem has recently been addressed by reference method standardization. There are two approaches to this which result in different HbA(1c) values and this discrepancy needs to be resolved. 2 Biological variability in HbA(1c) values between individuals also restricts its predictive role when applied to populations. The correlations between HbA(1c) measurements and various components of glycaemia (overall, fasting, postprandial) are still uncertain and differences in protein glycation and de-glycation are greater between subjects than often thought. The influence of variability in erythrocyte life span is an area where research is needed, especially in diabetic subjects. 3 Clinical variability is the most important and complex area of uncertainty. A predictive link between HbA(1c) and clinical outcomes is not as clear-cut as often stated. The correlation with the development of microvascular disease is well established in Type 1 diabetes, but in Type 2 subjects (90% of those with diabetes) the evidence that HbA(1c) monitoring is of value in predicting or preventing macrovascular disease is not strong, although it is the major cause of morbidity and early death in this group. It is recommended that, as a matter of urgency, these issues be examined, particularly within the context of self-care in diabetes.

Contributions of Weekly Mean Blood Glucose Values to Hemoglobin A1c in Insulin-Treated Type 2 Diabetes: The Diabetes Outcomes in Veterans Study (DOVES)

BACKGROUND

Daily self-monitored blood glucose testing is recommended for patients with insulin-treated type 2 diabetes. However, most patients do not test frequently enough for optimal glycemic control. Less frequent testing may be sufficient for assessing glycemic control among stable patients as well as improving patient compliance. The study objective was to evaluate the weekly contribution of glucose readings to hemoglobin (Hb)A1c during an 8-week period of intensified self-monitored blood glucose testing.

METHODS

The authors randomly selected stable, insulin-treated subjects with type 2 diabetes. Subjects monitored their blood glucose four times daily for 8 weeks; the authors then downloaded glucose meters and measured an HbA1c. Mean blood glucose values were calculated for each of the 8 weeks. Multiple linear regression analyses examined the contribution of these mean values to the HbA1c.

RESULTS

A total of 182 subjects completed the monitoring protocol; mean HbA1c was 7.63 +/- 1.42%, mean glucose was 9.78 +/- 2.27 mmol/L, the regression correlation was 0.77, P < 0.001. A fitted multiple linear model using all 8 weekly mean blood glucose values showed large variation in their independent contributions to the HbA1c. Mean blood glucose values from consecutive weeks were highly correlated and did not provide independent information about glycemic control. Stepwise regression showed that the mean blood glucose values from weeks 4, 6, and 8 significantly and equally influenced HbA1c.

CONCLUSIONS

Glycemic control can be efficiently assessed by reviewing at least 5 weeks' worth of monitoring results, focusing on alternate weeks and giving less weight to more remote readings.

Improving glycaemic control in patients with Type 2 diabetes mellitus without insulin therapy

AIMS

In general practice at least 30% of those with Type 2 diabetes do not achieve good glycaemic control. We studied the effect of improving oral glucose-lowering medication in a primary care setting in patients treated with oral hypoglycaemic agents without satisfactory glycaemic control.

METHODS

We provided flowcharts to general practitioners and outreach visits by trained facilitators, who checked adherence to the protocol. Fifty-two Dutch general practices with 2140 Type 2 diabetes mellitus (DM) patients recruited 288 patients < or = 75 years old inadequately controlled (HbA1c >7%) by diet or oral medication. Outcome measures were decrease of HbA1c, number of patients with HbA1c < or = 7%, and non-compliance rate.

RESULTS

After a mean of 3.3 consultations over 14 weeks, 209 patients were following the protocol fully with a reduction in HbA1c from 8.7% to 6.7% (P<0.001). One hundred and fifty-eight patients (55%) achieved HbA1c < or =7%, and 51 (18%) persisted with HbA1c >7% unless fasting blood glucose < or =7 mmol/l (n=18) or a maximum of medication (n=33). Seventy-nine patients (27%) did not adhere to the protocol, mostly due to loss of motivation and non-attendance.

CONCLUSIONS

A simple flowchart and relatively little support by trained facilitators results in improved glycaemic control.

Quality of recording of data from patients with type 2 diabetes is not a valid indicator of quality of care. A cross-sectional study

BACKGROUND

The quality of recording of clinical data in diabetes care in general practices is very variable. It has been suggested that better recording leads to improved glycaemic control.

OBJECTIVES

The purpose of this study was to assess the completeness of recording by GPs of data from type 2 diabetes patients; to compare recorded and missing data; and to investigate the association between completeness and glycaemic control.

METHODS

A cross-sectional survey was carried out in 52 general practices. Medical records were scrutinized for the presence of 11 variables. Examining patients through an active approach completed incomplete records. We compared recorded and unrecorded items. Completeness of recording was determined at both patient and practice levels.

RESULTS

Fifty-two general practices with 1641 type 2 diabetes patients cared for by the GP participated. The frequency of absence of any particular item ranged from 20 to 70%. Weight, systolic blood pressure and HbA(1c) were slightly lower in patients with those items missing on their files, and more such patients were non-smokers (P < 0.05). The percentage of patients with unrecorded variables that exceeded target values ranged from 39 to 75. Neither at practice level nor at patient level was any association between the completeness of the data recording and HbA(1c) found.

CONCLUSION

Records often were incomplete, which hampers a systematic approach to care of diabetic patients. However, the lack of association between completeness of data recording and control of glycaemia indicates that improved recording is not a valid indicator of good quality of care.

Unrecognized hypo- and hyperglycemia in well-controlled patients with type 2 diabetes mellitus: the results of continuous glucose monitoring

The aim of this study was to determine the prevalence and extent of glycemic excursions (hypo- and hyperglycemic) in elderly patients with well-controlled type 2 diabetes using a Continuous Glucose Monitor System (CGMS) (Medtronic MiniMed). Elderly patients (>65 years old) with type 2 diabetes were recruited if their glycosylated hemoglobin (HbA1c) was <7.5% and if their oral hypoglycemic therapy included a sulfonylurea. Patients were asked to undergo two consecutive 72-h periods of continuous glucose monitoring at baseline and then again at 1 month (total 288 h). Patients were asked to record four self-monitored capillary blood glucose levels each day for calibration of the monitor and also to record meal times, exercise, and symptoms of hypoglycemia. The number of hyperglycemic (>144 mg/dL), hypoglycemic (<50 mg/dL), and borderline-hypoglycemic (50-65 mg/dL) events were determined (an event was defined as a glucose value that persisted for at least 15 min with or without symptoms). Twenty-five patients (21 men, four women) 73.9 +/- 4.4 years old with an HbA1c of 6.2 +/- 0.8% were each monitored for an average of 187.57 h. The mean glucose values were: fasting, 139 +/- 40 mg/dL; 2 h post-breakfast, 167 +/- 58 mg/dL; 2 h post-lunch, 157 +/- 53 mg/dL; and 2 h post-dinner, 149 +/- 49 mg/dL. Twenty patients (80%) experienced a total of 103 hypoglycemic events, and 14 of these patients experienced 54 events where the glucose levels were </=40 mg/dL. Twenty-four patients (96%) experienced borderline-hypoglycemia (n = 229 events). Patients experienced a mean of 0.62 +/- 0.72 episodes of hypoglycemia (interstitial glucose <50 mg/dL) per day (four to five episodes overall), 0.35 +/- 0.6 episodes per day where the interstitial glucose was </=40 mg/dL (two to three episodes overall), and 1.37 +/- 1.22 episodes of borderline-hypoglycemia (nine to 10 episodes overall). Each episode of hypoglycemia persisted for 78 +/- 73 min, and borderline-hypoglycemia for 45 +/- 11 min. Patients were hypoglycemic 3.3% of the time and borderline-hypoglycemic 3.7% of the time. No episode of hypoglycemia was recorded by any patient in his or her daily diary. High postprandial glucose values (>144 mg/dL 2 h postprandial) were recorded after 57% of all meals (breakfast 60%, lunch 57.5%, dinner 55.2%). The CGMS was generally well tolerated, but 52% of patients could not be studied for the full 12 days of monitoring. Thus hypoglycemia and excessive postprandial glycemic excursions are common in well-controlled patients with type 2 diabetes treated with a sulfonylurea with or without metformin. The CGMS is a useful research and clinical tool to assess glycemia in patients with type 2 diabetes but is not tolerated by all subjects.

Evaluating once- and twice-daily self-monitored blood glucose testing strategies for stable insulin-treated patients with type 2 diabetes : the diabetes outcomes in veterans study

OBJECTIVE

To evaluate once- and twice-daily self-monitored blood glucose testing strategies in assessing glycemic control and detecting hypoglycemia or hyperglycemia in patients with stable insulin-treated type 2 diabetes.

RESEARCH DESIGN AND METHODS

Subjects with stable insulin-treated type 2 diabetes monitored blood glucose four times daily (prebreakfast, prelunch, predinner, and bedtime) for 8 weeks. We correlated mean blood glucose values with HbA(1c) measured after 8 weeks and determined the number of hypoglycemic (< or =3.33 mmol/l) and hyperglycemic (> or =22.20 mmol/l) readings captured at the various testing times.

RESULTS

A total of 150 subjects completed the monitoring period; their average age was 67 years, 90% were men, and the mean HbA(1c) at baseline was 8.0 +/- 1.8%. The overall correlation of glucose testing and HbA(1c) was 0.79 (P < 0.0001). Mean blood glucose values for each of the four once-daily testing strategies were significantly correlated with HbA(1c) (r = 0.65-0.70, P < 0.0001), as were mean blood glucose values for each of the six twice-daily testing strategies (r = 0.73-0.75, P < 0.0001). The prebreakfast/prelunch measurements captured the largest proportion (63.6%) of the hypoglycemic readings, the predinner/bedtime measurements captured the largest proportion (66.2%) of hyperglycemic readings, and the prelunch/predinner measurements captured the largest proportion (57.7%) of all out-of-range readings.

CONCLUSIONS

Twice-daily testing strategies, particularly prelunch/predinner, effectively assess glycemic control and capture a substantial proportion of out-of-range readings. However, personal testing strategies will vary depending on an individual's risk for hypoglycemia and hyperglycemia.

Morning hyperglycemic excursions: a constant failure in the metabolic control of non-insulin-using patients with type 2 diabetes

OBJECTIVE

To determine whether, over daytime, one or several hyperglycemic excursions exist that can be general failures in the glycemic control of patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

In 200 non-insulin-using patients with type 2 diabetes, diurnal plasma glucose and insulin profiles were studied. Plasma glucose concentrations were measured after an overnight fast (at 8:00 A.M. immediately before breakfast), during the postprandial period (at 11:00 A.M. and 2:00 P.M.), and during the postabsorptive period (at 5:00 P.M., extended postlunch time).

RESULTS

In the population considered as a whole, prelunch glucose concentrations (12.0 mmol/l) were found to be significantly increased (P < 0.0001) when compared with those observed at 8:00 A.M. (8.8 mmol/l), at 2:00 P.M. (10.5 mmol/l), and at 5:00 P.M. (8.6 mmol/l). Similar significant excursions (P < 0.0001) in prelunch glucose were observed within subsets of patients selected from the following criteria: 1) body weight, 2) HbA(1c), 3) categories of treatment, and 4) residual beta-cell function. From the calculation of areas under the daytime glucose curves, the relative contributions of postprandial and fasting glucose to the total glucose increment were found to be similar.

CONCLUSIONS

High plasma glucose excursions over morning periods seem to be a permanent failure in non-insulin-using patients with type 2 diabetes, whatever the clinical (BMI), biological (HbA(1c)), therapeutic, and pathophysiological (residual beta-cell function) status. Midmorning glucose testing should be recommended for detecting such abnormalities and for correcting them with appropriate therapies.

HbA(1c) levels are genetically determined even in type 1 diabetes: evidence from healthy and diabetic twins

HbA(1c), a measure of blood glucose regulation, reflects glucose levels in the preceding months. In diabetes, HbA(1c) levels predict the risk of microvascular complications. The aim of this study was to determine whether genetic factors could influence HbA(1c) levels in normal subjects and type 1 diabetic patients. We performed a classical twin study of HbA(1c) in healthy nondiabetic female twins and 42 monozygotic (MZ) and 47 dizygotic (DZ) pairs. Interclass correlations (r) were higher in MZ (r = 0.77) compared with DZ (r = 0.53) twin pairs, suggesting a substantial genetic effect; this was confirmed by quantitative genetic model fitting. Additive genetic effects (heritability) explained 62% (95% CI 47-75) of population variance in HbA(1c); the remainder was attributable to the influence of unique environment (23% [15-36]) and age (14% [5-28]). Multivariate modeling showed that genetic factors also have a substantial influence on fasting glucose levels (51%). However, HbA(1c) heritability could not be explained by genes in common with fasting glucose. In the patients with type 1 diabetes, HbA(1c) levels were correlated in 33 MZ twins concordant for diabetes (r = 0.68; P < 0.001) but also in 45 MZ twins discordant for the disease (r = 0.52; P < 0.001). These significant correlations for HbA(1c) in both concordant and discordant pairs indicate a diabetes-independent familial effect. Thus, HbA(1c) levels are largely genetically determined and independent of the genes influencing fasting glucose. Even in type 1 diabetes, familial (i.e., diabetes-independent) factors influence protein glycation, implying that familial factors may explain, in part, the risk for microvascular complications, as indicated by high HbA(1c) levels.

Home testing of fructosamine improves glycemic control in patients with diabetes

OBJECTIVE

To determine whether weekly fructosamine testing at home by patients with type 2 diabetes, combined with therapeutic intervention when necessary on the basis of the results, would lead to improved glycemic control in comparison with usual care during a 3-month period.

METHODS

In a prospective study, 25 patients with glycosylated hemoglobin (HbA1c) values above 8.0% were randomized into 2 groups. Both groups, a glucose-only testing group (14 patients with an initial mean HbA1c of 9.4 +/- 0.9%) and a combined glucose plus fructosamine testing group (11 patients with an initial mean HbA1c of 9.2 +/- 0.7%), received therapeutic intervention at the time of randomization. Both groups were instructed to perform blood glucose testing up to four times per day. The combined glucose plus fructosamine testing group was also instructed to perform weekly fructosamine testing in addition to the glucose testing and to telephone the investigator if their home-testing fructosamine value exceeded 350 mmol/L (approximately equivalent to HbA1c of 7.8%), whereupon the investigator implemented further interventions. Both groups returned in 3 months, at which time HbA1c testing was repeated in order to determine whether glycemic control had changed.

RESULTS

The study results after 3 months showed that the HbA1c values in the combined glucose plus fructosamine testing group decreased from 9.2 +/- 0.7% to 8.0 +/- 0.5% (P<0.0001). In contrast, the HbA1c values in the glucose-only testing group declined from 9.4 +/- 0.9% to 9.1 +/- 1.3%, a difference that was not significant.

CONCLUSION

In the 3 months after a change in therapy for type 2 diabetes, weekly home testing of fructosamine, combined with therapeutic interventions based on the results, led to a more rapid and significant improvement in glycemic control than did the usual regimen of glucose-only testing.