A1c-Derived Average Glucose Research Articles

Estimating HbA1c from timed Self-Monitored Blood Glucose values

AimsTo analyze mathematical relationships between timed Self-Monitored Blood Glucose (SMBG) and HbA1c, and to identify the SMBG values that correlate most strongly with HbA1c. MethodsWe utilized the average premeal (Pre) and 90-min postmeal (Post) SMBG results from 547 A1c-Derived Average Glucose (ADAG) study participants (285 type 1, 178 type 2 and 84 non-diabetic) to analyze the mathematical relationships with HbA1c levels. Specific times of daily SMBG that best correlate with HbA1c were identified. ResultsLinear regression analyses showed the following correlations for Pre and Post, Pre only and Post only, respectively: HbA1c = 2.488 + 0.018 × Premg/dl + 0.012 × Postmg/dl, R2 = 0.741, P < 0.0001; HbA1c = 2.887 + 0.029 × Premg/dl, R2 = 0.695, P < 0.0001; and HbA1c = 2.815 + 0.025 × Postmg/dl, R2 = 0.657, P < 0.0001.Among patients with type 2 diabetes mellitus (DM), of the 6 individual timepoints, pre-dinner SMBG had the strongest correlation with HbA1c (R2 = 0.577). This was followed by pre-breakfast (R2 = 0.562). Examining combinations of timepoints revealed that pre-breakfast + pre-dinner (R2 = 0.666) performed similarly to the full 6-timepoints (pre-meals + post-meals, R2 = 0.712). ConclusionsWe have established mathematical relationships between HbA1c and timed SMBG values and identified pre-dinner and pre-breakfast as the two SMBG timepoints that best correlate with HbA1c in patients with type 2 DM.

Association between Glycemic Gap and Adverse Outcomes in Critically Ill Patients with Diabetes.

Objectives:Glycemic excursions are commonly seen in patients admitted to the Intensive Care Unit (ICU) and are related to adverse outcomes. Glycemic gap is a marker of this excursion in patients with diabetes. It can be used to predict adverse outcomes in patients with diabetes admitted to the ICU. It is calculated by subtracting A1C-derived average glucose (ADAG) = ([28.7 × HbA1c]-46.7) from plasma glucose at admission. Objective of this study was to correlate glycemic gap and adverse outcomes in patients with type 2 diabetes mellitus (DM) admitted to the ICU.Materials and Methods:We conducted an ambispective study to include patients with type 2 DM admitted to the ICUs from July 2015 to June 2016. The following adverse outcomes were recorded: Multiorgan dysfunction syndrome (MODS), acute respiratory distress syndrome (ARDS), shock, upper gastrointestinal (UGI) bleed, acute kidney injury (AKI), and acute respiratory failure (ARF).Results:A total of 200 patients were enrolled, with a mean age ± standard deviation of 62 ± 11.24 years, and 64.5% were males. The median (interquartile range) duration of hospital stay and ICU stay were 8 (6–12) days and 4 (3–7) days, respectively. The most common primary diagnosis was cardiovascular (39.5%) followed by neurological (16.5%), infection at diagnosis (16.5%), respiratory (14%), gastrointestinal (7.5%), and others (6%). A higher glycemic gap was associated with occurrence of MODS (P < 0.01), ARDS (P = 0.026), shock (P = 0.043), UGI bleed (P = 0.013), AKI (P = 0.01), and ARF (P < 0.01). Glycemic gap cutoffs of 43.31, 45.26, and 39.12 were found to be discriminatory for predicting ICU mortality (area under the receiver operating characteristic [AUROC]=0.631, P = 0.05), MODS (AUROC = 0.725, P < 0.001), and ARF (AUROC = 0.714, P < 0.001).Conclusion:This study showed that higher glycemic gap levels were associated with an increased risk of MODS, ARDS, shock, UGI bleed, AKI, and ARF. Glycemic gap is a tool that can be used to determine prognosis in patients with diabetes admitted to the ICU.

Stress-Induced Hyperglycemia in Diabetes: A Cross-Sectional Analysis to Explore the Definition Based on the Trauma Registry Data.

Background: The diagnosis of diabetic hyperglycemia (DH) does not preclude a diabetes patient from having a stress-induced hyperglycemic response. This study aimed to define the optimal level of elevated glucose concentration for determining the occurrence of stress-induced hyperglycemia (SIH) in patients with diabetes. Methods: This retrospective study reviewed the data of all hospitalized trauma patients, in a Level I trauma center, from 1 January 2009 to 31 December 2016. Only adult patients aged ≥20 years, with available data on serum glucose and glycated hemoglobin A1c (HbA1c) levels upon admission, were included in the study. Long-term average glucose levels, as A1c-derived average glucose (ADAG), using the equation, ADAG = ((28.7 × HbA1c) − 46.7), were calculated. Patients with high glucose levels were divided into three SIH groups with diabetes mellitus (DM), based on the following definitions: (1) same glycemic gap from ADAG; (2) same percentage of elevated glucose of ADAG, from which percentage could also be reflected by the stress hyperglycemia ratio (SHR), calculated as the admission glucose level divided by ADAG; or (3) same percentage of elevated glucose as patients with a defined SIH level, in trauma patients with and without diabetes. Patients with incomplete registered data were excluded. The primary hypothesis of this study was that SIH in patients with diabetes would present worse mortality outcomes than in those without. Detailed data of SIH in patients with diabetes were retrieved from the Trauma Registry System. Results: Among the 546 patients with DH, 332 (32.0%), 188 (18.1%), and 106 (10.2%) were assigned as diabetes patients with SIH, based on defined glucose levels, set at 250 mg/dL, 300 mg/dL, and 350 mg/dL, respectively. In patients with defined cut-off glucose levels of 250 mg/dL and 300 mg/dL, SIH was associated with a 3.5-fold (95% confidence interval (CI) 1.61–7.46; p = 0.001) and 3-fold (95% CI 1.11–8.03; p = 0.030) higher odds of mortality, adjusted by sex, age, pre-existing comorbidities, and injury severity score, than the 491 patients with diabetic normoglycemia (DN). However, in patients with a defined cut-off glucose level of 350 mg/dL, adjusted mortality in SIH in DM was insignificantly different than that in DM. According to the receiver operating characteristic (ROC) curve analysis, a blood sugar of 233 mg/dL, a glycemic gap of 79 (i.e., blood sugar of 251 mg/dL), and a SHR of 1.45 (i.e., blood sugar of 250 mg/dL) were identified as cut-offs for mortality outcomes, with AUCs of 0.622, 0.653, and 0.658, respectively. Conclusions: In this study, a cut-off glucose level of 250 mg/dL was selected to provide a better definition of SIH in DM than glucose levels of 300 mg/dL or 350 mg/dL.

Refining Measurement of Hemoglobin A1c.

Glycation is the process by which a monosaccharide, most commonly glucose, attaches nonenzymatically to a protein. Hemoglobin A1c(Hb A1c)3 forms when hemoglobin is glycated at the N-terminal valine of the β-chain. Measurement of Hb A1c is integral to the diagnosis and management of diabetes because it reflects the average glucose (AG) over the preceding 120 days. Further, the mean Hb A1c over time determines the risk of microvascular complications of diabetes. However, a major limitation of its use is that some nonglycemic factors can influence Hb A1c concentration. A few substances (e.g., certain drugs, carbamylated hemoglobin, or labile Hb A1c) have been reported to interfere with Hb A1c measurement, but these interferences have been eliminated from most modern methods. Physiological and pathophysiological conditions reported to change Hb A1c values independently of glucose include advanced age, race, iron deficiency (with or without anemia), hemoglobin variants, and chronic kidney disease (1). In addition, many clinicians have patients with none of these factors; yet, Hb A1c values are inconsistent with glucose concentrations. Evaluation of these patients includes hemoglobin electrophoresis to identify clinically silent hemoglobin variants and measurement of erythrocyte-independent markers of chronic glycemia, such as fructosamine or glycated albumin. In several patients no explanation is identified for the inconsistency between the Hb A1c value and other measures of glycemia. Congruent with this, the A1c-Derived Average Glucose study showed that, although the relationship between Hb A1c and AG was linear, wide variation was observed between individuals (2). An Hb A1c concentration of 6.5% (the threshold for diagnosis) was associated with AG from 125 to 175 mg/dL, while Hb A1c values ranged between 5.5% and 8.0% when AG was 150 mg/dL. Interpretation of Hb A1c values is …

HbA1c and Mean Glucose Derived from Short-Term Continuous Glucose Monitoring Assessment Do Not Correlate in Patients With HbA1c >8%

Optimum therapy for patients with diabetes depends on both acute and long-term changes in plasma glucose, generally assessed by glycated hemoglobin (HbA1c) levels. However, the correlation between HbA1c and circulating glucose has not been fully determined. Therefore, we carefully examined this correlation when glucose levels were assessed by continuous glucose monitoring (CGM). Fifty-one patients (70% female, 30% male) were examined; among them were 28 with type 1 diabetes and 23 with type 2 diabetes. Clinically determined HbA1c levels were compared with blood glucose determined by CGM during a short time period. Changes in HbA1c levels up to 8.0% showed a clear and statistically strong correlation (R = 0.6713; P<.0001) with mean blood glucose levels measured by CGM, similar to that observed in the A1c-derived Average Glucose study in which patients were monitored for a longer period. However, we found no statistical correlation (R = 0.0498; P = .83) between HbA1c and CGM-assessed glucose levels in our patient population when HbA1c was >8.0%. Short-term CGM appears to be a good clinical indicator of long-term glucose control (HbA1c levels); however, cautions should be taken while interpreting CGM data from patients with HbA1c levels >8.0%. Over- or underestimation of the actual mean glucose from CGM data could potentially increase the risks of inappropriate treatment. As such, our results indicate that a more accurate analysis of CGM data might be useful to adequately tailor clinical treatments. ADAG = A1c-Derived Average Glucose CGM = continuous glucose monitoring %CV = percent coefficient of variation HbA1c = glycated hemoglobin.

Simulation of a computed HbA1c using a weighted average glucose.

BackgroundThe A1c-derived average glucose examined the link between the glycated haemoglobin and the estimated average glucose, and provided a linear relation between them. Other studies proved that, over a period of 4 months, plasma glucose in the preceding 30 days contribute to about 50 % to the glycated haemoglobin value while the other 50 % is due to the remaining 3 months altogether.Technical details of the methodIn this technical note, we propose a weighted method assuming that the contribution of glucose to glycated haemoglobin over 3 months is chronologically 20 %, 30 % and 50 % respectively. A comparison is made with the linear regression method which uses the same estimated average glucose over the whole period. Results yielded by the weighted method are also compared to those given by the model proposed by Ladyzyński et al.FindingsA simulation is carried out on data assumed to come from a first individual with nearly the same level of glucose over 3 months, a second individual who starts with high levels of glucose and then reaches a stabilised low level by the last month, and finally, a third case who had just been diagnosed with diabetes during the last month whereas he/she had a normal glycaemia during the preceding 2 months. The weighted method gives more realistic values of HbA1c (7.36 %, 6.80 %, 8.49 %) than the linear regression method without weights which gives the same value (7.45 %) for the three cases. Another comparison shows that the three values given by the weighted method are slightly smaller than the corresponding values given by the model of Ladyzynski et al. (7.62 %, 7.02 %, 8.8 %) but the relative variation is nearly the same for the three values (≈3 %).ConclussionWithout regular self-testing and day-to-day insights, a sole HbA1c value can be confusing and misleading. For physicians and patients, a clear understanding of the relationship between the weighted average glucose and HbA1c is necessary in order to set an appropriate daily control depending on whether the glucose is stabilized over the whole period, at the beginning, at the end; or still under recurrent episodes of high and low levels. The measured HbA1c at a biological laboratory gives no indication on glucose variation. Moreover, low values of glucose may cancel high values and lead to a “good” average glucose and ideal glycated haemoglobin.

Glucose control in critically ill diabetic: Not so sweet.

Glucose control in critically ill diabetic: Not so sweet.

Estimation of the Relative Contribution of Postprandial Glucose Exposure to Average Total Glucose Exposure in Subjects with Type 2 Diabetes.

We hypothesized that the relative contribution of fasting plasma glucose (FPG) versus postprandial plasma glucose (PPG) to glycated haemoglobin (HbA1c) could be calculated using an algorithm developed by the A1c-Derived Average Glucose (ADAG) study group to make HbA1c values more clinically relevant to patients. The algorithm estimates average glucose (eAG) exposure, which can be used to calculate apparent PPG (aPPG) by subtracting FPG. The hypothesis was tested in a large dataset (comprising 17 studies) from the vildagliptin clinical trial programme. We found that 24 weeks of treatment with vildagliptin monotherapy (n = 2523) reduced the relative contribution of aPPG to eAG from 8.12% to 2.95% (by 64%, p < 0.001). In contrast, when vildagliptin was added to metformin (n = 2752), the relative contribution of aPPG to eAG insignificantly increased from 1.59% to 2.56%. In conclusion, glucose peaks, which are often prominent in patients with type 2 diabetes, provide a small contribution to the total glucose exposure assessed by HbA1c, and the ADAG algorithm is not robust enough to assess this small relative contribution in patients receiving combination therapy.

Usefulness of Glycemic Gap to Predict ICU Mortality in Critically Ill Patients With Diabetes.

Stress-induced hyperglycemia (SIH) has been independently associated with an increased risk of mortality in critically ill patients without diabetes. However, it is also necessary to consider preexisting hyperglycemia when investigating the relationship between SIH and mortality in patients with diabetes. We therefore assessed whether the gap between admission glucose and A1C-derived average glucose (ADAG) levels could be a predictor of mortality in critically ill patients with diabetes.We retrospectively reviewed the Acute Physiology and Chronic Health Evaluation II (APACHE-II) scores and clinical outcomes of patients with diabetes admitted to our medical intensive care unit (ICU) between 2011 and 2014. The glycosylated hemoglobin (HbA1c) levels were converted to the ADAG by the equation, ADAG = [(28.7 × HbA1c) − 46.7]. We also used receiver operating characteristic (ROC) curves to determine the optimal cut-off value for the glycemic gap when predicting ICU mortality and used the net reclassification improvement (NRI) to measure the improvement in prediction performance gained by adding the glycemic gap to the APACHE-II score.We enrolled 518 patients, of which 87 (17.0%) died during their ICU stay. Nonsurvivors had significantly higher APACHE-II scores and glycemic gaps than survivors (P < 0.001). Critically ill patients with diabetes and a glycemic gap ≥80 mg/dL had significantly higher ICU mortality and adverse outcomes than those with a glycemic gap <80 mg/dL (P < 0.001). Incorporation of the glycemic gap into the APACHE-II score increased the discriminative performance for predicting ICU mortality by increasing the area under the ROC curve from 0.755 to 0.794 (NRI = 13.6%, P = 0.0013).The glycemic gap can be used to assess the severity and prognosis of critically ill patients with diabetes. The addition of the glycemic gap to the APACHE-II score significantly improved its ability to predict ICU mortality.

An Elevated Glycemic Gap is Associated With Adverse Outcomes in Diabetic Patients With Community-Acquired Pneumonia.

Several studies argue against the association between admission hyperglycemia and adverse outcomes in infected diabetic patients. When investigating the association, it is necessary to consider preexisting hyperglycemia. The objective of this study was to assess whether stress-induced hyperglycemia, determined by the glycemic gap between admission glucose levels and A1c-derived average glucose levels adversely affects outcomes in diabetic patients admitted to hospital with community-acquired pneumonia (CAP).We retrospectively analyzed the glycemic gap and adverse outcomes of diabetic patients hospitalized because of CAP from June 1, 2007 to August 31, 2012 in single medical center in Taiwan.A total of 203 patients admitted with principal diagnosis of CAP and available data of glycemic gap.Patients with glycemic gaps ≥40 mg/dL had greater AUROC values for the development of adverse outcomes compared with acute hyperglycemia and long-term glycemic controls. Patients with an elevated glycemic gap had an odds ratio of 3.84 for the incidence of combined adverse outcomes. Incorporation of the glycemic gap into pneumonia severity index, CURB-65 or SMART-COP scores, increased the discriminative performance of predicting the development of adverse outcomes.Glycemic gaps were associated with adverse outcomes of diabetic CAP patients. The discriminative performance of the calculated glycemic gaps was comparable with those of current clinical scoring systems and may further increase the AUROC of each system.

Relationship between measured average glucose by continuous glucose monitor and HbA1c measured by three different routine laboratory methods

ObjectivesThe relationship between glycated hemoglobin A1c (HbA1c) and average glucose has been described by the empirically derived estimated average glucose (eAG) equation in the A1c-Derived Average Glucose (ADAG) study, with extensive calibration efforts in four secondary reference HbA1c methods. It is not known if this relationship is preserved when HbA1c is measured by routine laboratory methods under routine conditions. Design and methodsWe measured average glucose (mAG) by six days of continuous glucose monitoring in 45 adults with stable HbA1c (<1% HbA1c change in the preceding two months). Subjects with medical conditions that may confound HbA1c measurement, including anemia and hemoglobinopathy, were excluded. HbA1c was measured using Bio-Rad Variant II (cation-exchange HPLC), Bio-Rad in2it (boronate affinity HPLC) and Roche Tina-quant (immunoassay) methods. ResultsThe average differences between eAG derived from the routine HbA1c methods and mAG were 10.4% (Variant II), 6.0% (Tina-quant) and 1.0% (in2it). The regression coefficients between the mAG and HbA1c are different between in2it (mAG, mmol/L=0.58×%HbA1c+2.3), Tina-quant and Variant II (both mAG, mmol/L=0.66×%HbA1c+1.9). However, the 95% confidence intervals of the slope and bias of these methods overlap. The correlation between mAG and HbA1c was greatest when measured using the Variant II (r2=0.84), followed by Tina-quant (r2=0.82) and in2it (r2=0.71). ConclusionsThe relationship between HbA1c and measured average glucose is method-dependent despite improved HbA1c standardization. The differences in relationship may reflect as discrepant eAG and home glucose monitoring results.

Validation of a hemoglobin A1c model in patients with type 1 and type 2 diabetes and its use to go beyond the averaged relationship of hemoglobin A1c and mean glucose level.

BackgroundGlycated hemoglobin A1c (HbA1c) has been used as an index of glycemic control in the management, guidance, and clinical trials of diabetic patients for the past 35 years. The aim of this study was to validate the HbA1c model in patients with type 1 and type 2 diabetes and to use it to support interpretation of HbA1c in different clinical situations.MethodsThe HbA1c model was identified in 30 patients (15 with type 1 diabetes and 15 with type 2 diabetes) by estimating the overall glycation rate constant (k), based on results of continuous glucose monitoring. The model was validated by assessing its ability to predict HbA1c changes in cultures of erythrocytes in vitro and to reproduce results of the A1C-Derived Average Glucose (ADAG) study. The model was used to simulate the influence of different glucose profiles on HbA1c.ResultsThe mean k was equal to 1.296 ± 0.216 × 10−9 l mmol−1 s−1 with no difference between type 1 and type 2 diabetes. The mean coefficient of variation of k was equal to 16.7%. The model predicted HbA1c levels in vitro with a mean absolute difference less than 0.3% (3.3 mmol/mol). It reproduced the linear relationship of HbA1c and mean glucose levels established in the ADAG study. The simulation experiments demonstrated that during periods of unstable glycemic control, glycemic profiles with the same mean glucose might result in much different HbA1c levels.ConclusionsPatients with type 1 and type 2 diabetes are characterized by the same mean value of k, but there is considerable interindividual variation in the relationship of HbA1c and mean glucose level. Results suggest that reciprocal changes in glycation rate and the life span of erythrocytes exist in a wide range of HbA1c values. Thus, for an average patient with diabetes, no modifications of parameters of the glycation model are required to obtain meaningful HbA1c predictions. Interpreting HbA1c as a measure of the mean glucose is fully justified only in the case of stable glycemia. The model and more frequent tests of HbA1c might be used to decrease ambiguity of interpreting HbA1c in terms of glycemic control.

Does Race Alter the Relationship Between HbA1c and Glucose in Type 2 Diabetes?

Hemoglobin Alc (HbAlc) is the standard measurement of glycemic control, and the HbAlc value can be used to estimate average glucose using a formula. Several studies suggest that the relationship between average glucose and HbAlc may be different for Blacks. This project enrolled non-Hispanic black and white individuals with type 2 diabetes and evaluated the relationship between HbAlc and blood glucose. 22 black and 29 white adults with type 2 diabetes were included in the analysis. Approximately 42 measurements (fasting and postprandial glucose) were collected over three months and compared to HbAl1 of the third month. The effect of race was evaluated by ANCOVA and X2 analysis testing the slope and intercepts simultaneously for HbA1c and its relationship to fasting glucose and to postprandial glucose. The relationship between HbAlc and glucose was not statistically significantly different between Blacks and Whites (ANCOVA: P = 0.968 for fasting glucose, P = 0.428 for postprandial glucose), allowing us to calculate estimated fasting and postprandial glucose disregarding race. For fasting glucose, the linear regression is FGmgiadl = (18.939 X HbAlc%) - 1.864, R2 = 0.586, P < 0.0001. For postprandial glucose, the linear regression is In(PPG mg,dl) (1.261 X In(HbA1c%)) + 2.555, R2 = 0.614, P < 0.0001. Predicted values for postprandial glucose based on HbA1c were similar to estimated average glucose values reported by ADAG. This study reinforces the A1c-Derived Average Glucose (ADAG) group finding that the relationship between HbA1c and glucose is similar in non-Hispanic black and white adults with type 2 diabetes.

Empirically Establishing Blood Glucose Targets to Achieve HbA1c Goals

OBJECTIVETo determine the average fasting, postprandial, and bedtime self-monitored blood glucose (SMBG) concentrations associated with specified HbA1c levels using data from the A1c-Derived Average Glucose (ADAG) study.RESEARCH DESIGN AND METHODSThe ADAG study was a multicenter observational study that used continuous glucose monitoring and SMBG testing to determine the relationship between mean average glucose and HbA1c. We used the SMBG data from 470 of the ADAG study participants (237 with type 1 diabetes and 147 with type 2 diabetes) to determine the average fasting, premeal, 90-min postmeal, and bedtime blood glucose (BG) for predefined target HbA1c groups between 5.5 and 8.5% (37–69 mmol/mol). t Tests were used to compare mean BG values between type 1 and type 2 diabetes groups.RESULTSThe average fasting BG needed to achieve predefined HbA1c target levels of 5.5–6.49% (37–47 mmol/mol), 6.5–6.99% (48–52 mmol/mol), 7.0–7.49% (52–58 mmol/mol), 7.5–7.99% (58–64 mmol/mol), and 8.0–8.5% (64–69 mmol/mol) were 122 mg/dL with 95% CI 117–127, 142 mg/dL (135–150), 152 mg/dL (143–162), 167 mg/dL (157–177), and 178 mg/dL (164–192), respectively. Postmeal BG to achieve the HbA1c level of 6.5–6.99% (48–52 mmol/mol) and 7.0–7.49% (52–58 mmol/mol) were 139 mg/dL (134–144) and 152 mg/dL (147–157), respectively. Bedtime BG was 153 mg/dL (145–161) and 177 mg/dL (166–188), respectively.CONCLUSIONSWe have determined the average BG at premeal, postmeal, and bedtime to achieve a variety of HbA1c targets. These results, based on empirical data, will help patients and providers set realistic day-to-day SMBG targets to achieve individualized HbA1c goals.

Ethnic Differences in Glycemic Markers in Patients With Type 2 Diabetes

OBJECTIVERecent studies have reported hemoglobin A1c (HbA1c) differences across ethnic groups that could limit its use in clinical practice. The authors of the A1C-Derived Average Glucose study have advocated to report HbA1c in estimated average glucose (AG) equivalents. The aim of this study was to assess the relationships between HbA1c and the mean of three 7-point self-monitored blood glucose (BG) profiles, and to assess whether estimated AG is an accurate measure of glycemia in different ethnic groups.RESEARCH DESIGN AND METHODSWe evaluated 1,879 participants with type 2 diabetes in the DURABLE trial who were 30 to 80 years of age, from 11 countries, and, according to self-reported ethnic origin, were Caucasian, of African descent (black), Asian, or Hispanic. We performed logistic regression of the relationship between the mean self-monitored BG and HbA1c, and estimated AG, according to ethnic background.RESULTSBaseline mean (SD) HbA1c was 9.0% (1.3) (75 [SD, 14] mmol/mol), and mean self-monitored BG was 12.1 mmol/L (3.1) (217 [SD, 55] mg/dL). In the clinically relevant HbA1c range of 7.0–9.0% (53–75 mmol/mol), non-Caucasian ethnic groups had 0.2–0.5% (2–6 mmol/mol) higher HbA1c compared with Caucasians for a given BG level. At the mean self-monitored BG levels ≤11.6 mmol/L, estimated AG overestimated the actual average BG; at levels >11.6 mmol/L, estimated AG underestimated the actual BG levels.CONCLUSIONSFor a given degree of glycemia, HbA1c levels vary among different ethnic groups. Ethnicity needs to be taken into account when using HbA1c to assess glycemic control or to set glycemic targets. Estimated AG is not a reliable marker for mean glycemia and therefore is of limited clinical value.

An Elevated Gap between Admission and A1C-Derived Average Glucose Levels Is Associated with Adverse Outcomes in Diabetic Patients with Pyogenic Liver Abscess

ObjectivesTo assess whether chronic glycemic control and stress-induced hyperglycemia, determined by the gap between admission glucose levels and A1C-derived average glucose (ADAG) levels adversely affects outcomes in diabetic patients with pyogenic liver abscess (PLA).MethodsClinical, laboratory, and multi-detector computed tomography (MDCT) findings of 329 PLA patients (2004–2010) were retrospectively reviewed. HbA1C levels were used to determine long-term glycemic control status, which were then converted to estimated average glucose values. For the gap between admission glucose levels and ADAG levels, we used receiver operating characteristic (ROC) curve to determine the optimal cut-off values predicting adverse outcomes. Univariate and multivariate logistic regressions were used to identify predictors of adverse outcomes.ResultsDiabetic PLA patients with poorer glycemic control had significantly higher Klebsiella pneumoniae (KP) infection rates, lower albumin levels, and longer hospital stays than those with suboptimal and good glycemic control. The ROC curve showed that a glycemic gap of 72 mg/dL was the optimal cut-off value for predicting adverse outcomes and showed a 22.3% relative increase in adverse outcomes compared with a glycemic gap<72 mg/dL. Multivariate analysis revealed that an elevated glycemic gap≥72 mg/dL was important predictor of adverse outcomes.ConclusionsA glycemic gap≥72 mg/dL, rather than admission hyperglycemia or chronic glycemic control, was significantly correlated with adverse outcomes in diabetic PLA patients. Poorer chronic glycemic control in diabetic PLA patients is associated with high incidence of KP infection, hypoalbuminemia and longer hospital stay.

Using Hemoglobin A1c to Derive Mean Blood Glucose in Peritoneal Dialysis Patients

Background: Although hemoglobin A1c (HbA1c) has been widely used as a clinical assessment tool for outcome analyses related to glycemic control, the relationship between HbA1c and average blood glucose (BG) specific to peritoneal dialysis (PD) patients with diabetes has not been characterized. We sought to develop HbA1c-BG equation models for PD patients. Methods: We examined associations between HbA1c and random serum BG values over time in a contemporary 5-year (2001-2006) cohort of DaVita PD patients with diabetes. We identified 850 patients (mean age: 58 ± 13 years, 56% male) with 4,566 paired measurements of HbA1c and BG. The bootstrapping method was used to estimate average BG and corresponding HbA1c. Results: Linear regression analyses yielded the following HbA1c-BG equations: (1) BG (mg/dl) = 24.1 + 28.6 × HbA1c - 12.2 × albumin [adjusted R² (R²_adj = 0.454)], (2) BG = 55.3 + 28.8 × HbA1c - 10.2 × albumin - 3.3 × Hb (R²_adj = 0.457), and (3) BG = 69.5 + 28.7 × HbA1c - 10.1 × albumin - 3.7 × Hb - 0.1 × age + race/ethnicity (-10.1 African Americans, -5.4 other race/ethnicities; R²_adj = 0.457). All models showed greater explanatory power of BG variation than previously established HbA1c-BG equation models defined within non-PD cohorts [R²_adj = 0.446 for both the Diabetes Control and Complications Trial (DCCT) and the A1c-Derived Average Glucose (ADAG) equations]. Conclusions: The association between HbA1c and BG in PD patients is different than that of patients with normal kidney function. Our analysis suggests that equations incorporating serum albumin and/or Hb values better estimate the HbA1c-BG relationship in PD patients compared to equations using HbA1c alone.

Developing an HbA1c-Based Equation to Estimate Blood Glucose in Maintenance Hemodialysis Patients

OBJECTIVEHemoglobin A1c (HbA1c) has been widely used as a clinically important assessment tool for outcome analyses related to glycemic control. However, because of special conditions in dialysis patients, including the uremic milieu, there is no HbA1c blood glucose (BG) equation specific for patients on dialysis. In this study, we sought to develop HbA1c-BG equation models for hemodialysis patients.RESEARCH DESIGN AND METHODSWe examined associations between HbA1c and random serum BG over time in a contemporary cohort of diabetic patients with hemodialysis treated in DaVita dialysis clinics. We identified 11,986 patients (63 ± 12 years old and 49% male) with 69,764 paired measurements of HbA1c and BG over the course of 5 years (2001–2006). Bootstrapping method was used to estimate average BG and corresponding HbA1c levels. The association was adjusted by patient factors using linear regression.RESULTSLinear regression analyses yielded the following three regression equations: BG = 59.2 + 29.4 × HbA1c − 20.8 × Alb (R2 = 0.483); BG = 104.8 + 29.7 × HbA1c − 18.4 × Alb − 4.7 × Hb (R2 = 0.486); and BG = 82.9 + 30.7 × HbA1c − 16.5 × Alb − 5.4 × Hb + 0.3 × age + race (R2 = 0.491). All our models showed stronger association than previous equation models (R2 = 0.468 in the Diabetes Control and Complications Trial and A1c-Derived Average Glucose equations).CONCLUSIONSThe association between HbA1c and BG in hemodialysis patients is different than that of patients with normal kidney function. Our analysis suggests that equations including serum albumin or hemoglobin are better for hemodialysis patients.

Metabolic Management in Overweight Subjects with Naive Impaired Fasting Glycaemia by Means of a Highly Standardized Extract From Cynara scolymus: A Double‐blind, Placebo‐controlled, Randomized Clinical Trial

The aim of this study is to evaluate the efficacy of a dietary supplementation with an extract from Cynara scolymus (Cs) on the glucose pattern in a group of patients with naïve impaired fasting glycaemia (IFG). A randomized, double-blind, placebo-controlled trial has been performed in 55 overweight subjects with IFG (fasting blood glucose [FBG]: 6.11 ± 0.56 mmol/l). These subjects were randomly assigned to supplement their diet with either an extract from Cs (600 mg/d) (26 subjects) or placebo (29 matched subjects) for 8 weeks. The decrease of FBG was the primary endpoint. The assessment of Homeostatic Metabolic Assessment (HOMA), glycosylated haemoglobin, A1c-Derived Average Glucose (ADAG), lipidic pattern and anthropometric parameters were the secondary endpoints. The within groups and percent changes from baseline were analyzed by the signed rank test. The comparison between groups was performed by Wilcoxon's two sample test. The supplemented group had significant decreases of: FBG (-9.6%), HOMA (-11.7%), glycosylated haemoglobin (-2.3%), ADAG (-3.1%) and lipidic pattern. The placebo group did not show any significant difference. Compared with the placebo, the supplemented group showed a significant difference in FBG, HOMA and lipidic pattern. These data demonstrate the efficacy of Cs extract on the reduction of glycometabolic parameters in overweight subjects with IFG.

A semi-mechanistic model of the relationship between average glucose and HbA1c in healthy and diabetic subjects

HbA1c is the most commonly used biomarker for the adequacy of glycemic management in diabetic patients and a surrogate endpoint for anti-diabetic drug approval. In spite of an empirical description for the relationship between average glucose (AG) and HbA1c concentrations, obtained from the A1c-derived average glucose (ADAG) study by Nathan et al., a model for the non-steady-state relationship is still lacking. Using data from the ADAG study, we here develop such models that utilize literature information on (patho)physiological processes and assay characteristics. The model incorporates the red blood cell (RBC) aging description, and uses prior values of the glycosylation rate constant (KG), mean RBC life-span (LS) and mean RBC precursor LS obtained from the literature. Different hypothesis were tested to explain the observed non-proportional relationship between AG and HbA1c. Both an inverse dependence of LS on AG and a non-specificity of the National Glycohemoglobin Standardization Program assay used could well describe the data. Both explanations have mechanistic support and could be incorporated, alone or in combination, in models allowing prediction of the time-course of HbA1c changes associated with changes in AG from, for example dietary or therapeutic interventions, and vice versa, to infer changes in AG from observed changes in HbA1c. The selection between the alternative mechanistic models require gathering of new information.