Hemoglobin A1c and mean glucose in patients with type 1 diabetes: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring randomized trial

The Reproducibility and Usefulness of Estimated Average Glucose for Hyperglycemia Management during Health Checkups: A Retrospective Cross-Sectional Study

HbA1c reflects average glucose levels over 3 months, but it does not measure glycemic variability. This study aimed to determine the reproducibility and usefulness of HbA1c-derived estimated average glucose (eAG) and to analyze the factors associated with eAG during health checkups. This cross-sectional retrospective study consecutively selected subjects who had undergone health checkups at 16 health-promotion centers in 13 Korean cities in 2020. The subjects comprised 182,848 healthy subjects with normoglycemia, 109,555 with impaired fasting glucose (IFG), and 35,632 with diabetes. eAG was calculated using Nathan’s regression equation. In all subjects, fasting plasma glucose (FPG) was found to be fairly strongly correlated with eAG (r = 0.811). When the subjects were divided into FPG subgroups, the strength of the correlation decreased among those with normoglycemia and IFG (p < 0.001). Higher eAG levels were associated with older age, females, higher FPG, and lower HDL-C and triglycerides (p < 0.05). The proportion of subjects with a higher value of FPG than eAG was 46.3% in poorly controlled diabetic patients, compared with only 1.5% in normoglycemic subjects. This suggests eAG could help patients to understand their glycemic variability intuitively and healthcare providers to identify patients who might worsen in hyperglycemia control through measuring the difference between eAG and FPG.

Blood glucose monitoring (BGM) still matters for many: Associations of BGM frequency and glycemic control in youth with type 1 diabetes

AIMS

This study aimed to compare three approaches of blood glucose monitoring (BGM) frequency attainment and to examine their associations with glycemic control in youth with type 1 diabetes (T1D).

METHODS

Cross-sectional data was derived from the baseline assessment in three clinical trials. Clinical and demographic characteristics of youth with T1D was obtained by chart review. BGM frequency was assessed by parent-youth interview, chart review, and meter downloads. To examine the relationship between A1c and frequency of BGM we performed analysis of variance.

RESULTS

In youth with T1D (N = 385, 50% female, age 13.6 ± 2.5 years, 74% pump users), the 3 methods of assessing BGM frequency were significantly correlated. Frequency by self-report (6.4 ± 2.3 times/day) was significantly higher than both meter download (5.6 ± 2.4 times/day, p < 0.0001) and clinician report (5.7 ± 2.4 times/day, p < 0.0001). For all methods, more frequent BGM was associated with lower A1c and lower mean glucose (p < 0.0001). For each additional daily blood glucose check, there was a 0.2% decrease in A1c (p < 0.0001).

CONCLUSION

BGM remains a potent predictor of glycemic control, warranting continued targeting in clinical efforts to improve glycemic management in youth with T1D.

Relationships between HbA1c and continuous glucose monitoring metrics of glycaemic control and glucose variability in a large cohort of children and adolescents with type 1 diabetes

AIMS

To evaluate the relationships between HbA1c and Continuous Glucose Monitoring (CGM) metrics in children/adolescents with Type 1 Diabetes (T1D).

METHODS

HbA1c and real-life CGM data of the 12 weeks preceding its measurement were retrospectively collected from 654 children/adolescents with T1D. The relationships between HbA1c and CGM metrics were assessed by Spearman correlation coefficient. Participants were categorized into groups based on HbA1c and CGM metrics values. ANOVA was run across HbA1c and CGM metrics groups in the entire study population and in subjects stratified by CGM type, insulin therapy, age and puberty.

RESULTS

HbA1c was positively correlated with mean glucose, SD, %TAR > 180 mg/dL, %TAR > 250 mg/dL, HBGI and negatively with %TIR, %TBR and %time < 54 mg/dL. HbA1c-based groups were significantly associated with these metrics, but for each group their value widely ranged with a substantial overlap between them. HbA1c and HbA1c-based groups were not associated with %CV and LBGI, as well as %CV and LBGI-based groups had not significantly different HbA1c. Comparable results were found analysing subjects according to age, type of CGM, insulin therapy and puberty.

CONCLUSIONS

The relationships between HbA1c and CGM metrics described in this cohort of paediatric subjects with T1D support the importance of the evaluation of these metrics, in particular %CV and LBGI, independently of HbA1c value.

Current treatment options and challenges in patients with Type 1 diabetes: Pharmacological, technical advances and future perspectives

Type 1 diabetes mellitus imposes a significant burden of complications and mortality, despite important advances in treatment: subjects affected by this disease have also a worse quality of life-related to disease management. To overcome these challenges, different new approaches have been proposed, such as new insulin formulations or innovative devices. The introduction of insulin pumps allows a more physiological insulin administration with a reduction of HbA1c level and hypoglycemic risk. New continuous glucose monitoring systems with better accuracy have allowed, not only better glucose control, but also the improvement of the quality of life. Integration of these devices with control algorithms brought to the creation of the first artificial pancreas, able to independently gain metabolic control without the risk of hypo- and hyperglycemic crisis. This approach has revolutionized the management of diabetes both in terms of quality of life and glucose control. However, complete independence from exogenous insulin will be obtained only by biological approaches that foresee the replacement of functional beta cells obtained from stem cells: this will be a major challenge but the biggest hope for the subjects with type 1 diabetes. In this review, we will outline the current scenario of innovative diabetes management both from a technological and biological point of view, and we will also forecast some cutting-edge approaches to reduce the challenges that hamper the definitive cure of diabetes.

Exploring the inter-subject variability in the relationship between glucose monitoring metrics and glycated hemoglobin for pediatric patients with type 1 diabetes

OBJECTIVES

Despite the widespread diffusion of continuous glucose monitoring (CGM) systems, which includes both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), an effective application of CGM technology in clinical practice is still limited. The study aimed to investigate the relationship between isCGM-derived glycemic metrics and glycated hemoglobin (HbA1c), identifying overall CGM targets and exploring the inter-subject variability.

METHODS

A group of 27 children and adolescents with type 1 diabetes under multiple daily injection insulin-therapy was enrolled. All participants used the isCGM Abbott's FreeStyle Libre system on average for eight months, and clinical data were collected from the Advanced Intelligent Distant-Glucose Monitoring platform. Starting from each HbA1c exam date, windows of past 30, 60, and 90 days were considered to compute several CGM metrics. The relationships between HbA1c and each metric were explored through linear mixed models, adopting an HbA1c target of 7%.

RESULTS

Time in Range and Time in Target Range show a negative relationship with HbA1c (R²>0.88) whereas Time Above Range and Time Severely Above Range show a positive relationship (R²>0.75). Focusing on Time in Range in 30-day windows, random effect represented by the patient's specific intercept reveals a high variability compared to the overall population intercept.

CONCLUSIONS

This study confirms the relationship between several CGM metrics and HbA1c; it also highlights the importance of an individualized interpretation of the CGM data.

Glucose Time In Range, Time Above Range, and Time Below Range Depend on Mean or Median Glucose or HbA1c, Glucose Coefficient of Variation, and Shape of the Glucose Distribution

Background: Examine the expected relationships between time in range (%TIR), time above range (%TAR), and time below range (%TBR) with median glucose (or %HbA1c) and %coefficient of variation (%CV) of glucose for various shapes of the glucose distribution. Methods: We considered several thresholds defining hypoglycemia and hyperglycemia and examined wide ranges of median glucose and %CV using three models for the glucose distribution: gaussian, log-gaussian, and a modified log-gaussian distribution. Results: There is a linear relationship between %TIR and median glucose for any specified %CV when median glucose is well removed from the threshold for hypoglycemia. %TIR reaches a peak when median glucose is close to 120 mg/dL and declines both at higher and lower median glucose values. There is a nearly linear relationship for %TAR and median glucose for a wider range of glucose (80-220 mg/dL). Risk of hypoglycemia is minimal when %CV is below 20%, but rises exponentially as %CV increases or as median glucose decreases. Similar results were obtained for a wide range of possible shapes of glucose distribution. These simulations are consistent with results from clinical studies. Conclusion: Both %TIR and %TAR are approximately linearly related to mean and median glucose (or %HbA1c). %TAR provides linearity over a wider range than %TIR. Risk of hypoglycemia (%TBR) is critically dependent on both glycemic variability (%CV) and mean or median glucose. These relationships support the use of %TIR, %TAR, and %TBR as metrics of quality of glycemic control for clinical, research, and regulatory purposes.

Usefulness of estimated average glucose (eAG) in glycemic control and cardiovascular risk reduction

OBJECTIVE

One of the 8 regional health authority (RHA) zones in New Brunswick, Canada has implemented eAG since 2010. We sought to evaluate the clinical outcomes of glycemic control and cardiovascular risk levels before and after the eAG implementation in this zone; and to compare the overall outcomes of this zone with other 7 zones of the province.

METHODS

Data (838,407 HbA1c values and 612,314 LDL-c values) was extracted from all adult diabetic patients in the provincial Diabetes Registry from 2008 to 2014. The Kruskal-Wallis statistic was conducted to compare the medians and inter quartile ranges of HbA1c and LDL-c from different zones. The proportion of patients achieving therapeutic targets, the distribution of HbA1c and LDL-c values pre/post the eAG implementation in RHA Zone 1.1 were assessed by Chi-square analysis.

RESULTS

The proportion of patients achieving targets in Zone 1.1 were at an intermediate level among all 8 zones and the trends of Zone 1.1 were no different than other zones. There were statistically significant differences for Zone 1.1 in the distribution of HbA1c (Z = -12.5190, P < 0.001) and LDL-c (Z = 16.4410, P < 0.001) before and after the eAG reported. The proportion of patients with HbA1c < 53 mmol/mol (7.0%) of the RHA Zone 1.1 was significantly lower after eAG reported (49.85% vs. 47.24%, P < 0.001); while the proportion of patients with LDL-c < 2.6 mmol/L showed statistically significant increase (68.56% vs. 71.90%, P < 0.001).

CONCLUSION

The utilization of eAG has demonstrated no significant impact on glycemic control and cardiovascular risk reduction.

"Mind the gap please…": estimated vs. measured A1c from continuous measurement of interstitial glucose over a 3-month period in patients with type 1 diabetes

Introduction: Glycated hemoglobin (A_1c) is the measurement of choice to estimate the glycemic exposure over the last 3 months prior to sampling. The Free Style Libre® is a continuous glucose monitoring device which provides an estimated A_1c (eA1c) from average interstitial glucose using Nathan's ADAG equation. The objective of this study was to compare eA1c and A_1c in type 1 diabetes patients (T1D) over a period of 3 months.Materials and methods: Data were collected from patient charts between July 2016 and October 2017. 3-months recordings with >70% of data available were analyzed. eA1c was recorded at each visit and the corresponding A_1c value measured by high performance liquid chromatography in a single reference lab.Results: A total of 344 reports from 170 T1D were studied, 3 categories were identified: eA1c = A_1c: 13% of reports. eA1c > A_1c: 57% of reports, positive difference (eA1c - A_1c) of +0.74 ± 0.5% (P < 0.0001). eA1c < A_1c: 30% of reports, negative difference (eA1c - A_1c) of -0.5 ± 0.3% (P < 0.0001).Conclusion: eA1c value was generally overestimated compared to measured A_1c in this T1D cohort. This lesser concordance may result from differences in measured glucose source and/or frequency to calculate eA1c compared to ADAG, but also from using the reverse equation which is a source of potential bias. Another explanation could be a different rate of hypoglycemia between groups, or an asymmetric distribution of A1c patients' phenotypes with differential hyper- or hypoglycation intrinsic propensity.

Role of Composite Glycemic Indices: A Comparison of the Comprehensive Glucose Pentagon Across Diabetes Types and HbA1c Levels

Background: Complex changes of glycemia that occur in diabetes are not fully captured by any single measure. The Comprehensive Glucose Pentagon (CGP) measures multiple aspects of glycemia to generate the prognostic glycemic risk (PGR), which constitutes the relative risk of hypoglycemia combined with long-term complications. We compare the components of CGP and PGR across type 1 and type 2 diabetes. Methods: Participants: n = 60 type 1 and n = 100 type 2 who underwent continuous glucose monitoring (CGM). Mean glucose, coefficient of variation (%CV), intensity of hypoglycemia (INT_hypo), intensity of hyperglycemia (INT_hyper), time out-of-range (TOR <3.9 and >10 mmol/L), and PGR were calculated. PGR (median, interquartile ranges [IQR]) for diabetes types, and HbA1c classes were compared. Results: While HbA1c was lower in type 1 (type 1 vs. type 2: 8.0 ± 1.6 vs. 8.6 ± 1.7, P = 0.02), CGM-derived mean glucoses were similar across both groups (P > 0.05). TOR, %CV, INT_hypo, and INT_hyper were all higher in type 1 [type 1 vs. type 2: 665 (500, 863) vs. 535 (284, 823) min/day; 39% (33, 46) vs. 29% (24, 34); 905 (205, 2951) vs. 18 (0, 349) mg/dL × min²; 42,906 (23,482, 82,120) vs. 30,166 (10,276, 57,183) mg/dL × min², respectively, all P < 0.05]. Across each HbA1c class, the PGR remained consistently and significantly higher in type 1. While mean glucose remained the same across HbA1c classes, %CV, TOR, INT_hyper, and INT_hypo were significantly higher for type 1. Even within the same HbA1c class, the variation (IQR) of each parameter in type 1 was wider. The PGR increased across diabetes groups; type 2 on orals versus type 2 on insulin versus type 1 (PGR: 1.6 vs. 2.2 vs. 2.9, respectively, P < 0.05). Conclusion: Composite indices such as the CGP capture significant differences in glycemia independent of HbA1c and mean glucose. The use of such indices must be explored in both the clinical and research settings.

Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes

Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an "artificial pancreas" that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.

A Review of Continuous Glucose Monitoring Data Interpretation in the Age of Automated Insulin Delivery

Using a continuous glucose monitor (CGM) improves glycemic control in patients with type 1 diabetes. The ambulatory glucose profile (AGP) has been recommended as a standard method for reporting CGM data. However, in recently developed automated insulin delivery (AID) systems, a standard format for reporting data has not yet been developed. Instead, reports are specific to each system being used. Currently, the only FDA approved AID system is a hybrid closed-loop insulin pump. In these systems, the patient is still required to announce a meal, respond to alerts, and keep the system in automated insulin delivery. The integrated pump and sensor information provides insights into how the system is performing, and how to make changes to tunable parameters, such as carbohydrate to insulin ratios. The reports also offer a window into human behavior related to performing diabetes tasks, responding to alarms, reasons for exiting HCL, and how glycemic goals are being met. This article reviews the pump and CGM data provided by several of the current closed-loop systems with a focus on systems that are currently approved in the United States (MiniMed™ 670G, Tandem Basal:IQ) and those used by patients using do-it-yourself systems. A step-wise approach to reviewing the nuances of these systems is provided. The comparison may reinforce the importance of the continued need for streamlining a standard report for providers to be able to interpret the CGM data of these systems.

Clinical Implications of Real-time and Intermittently Scanned Continuous Glucose Monitoring

Two types of continuous glucose monitoring (CGM) systems are now available: real-time CGM (rtCGM) and intermittently scanned (isCGM). Current rtCGM systems automatically transmit a continuous stream of glucose data to the user, provide alerts and active alarms, and transmit glucose data (trend and numerical) in real time to a receiver, smart watch, or smartphone. The current isCGM system provides the same type of glucose data but requires the user to purposely scan the sensor to obtain information, and it does not have alerts and alarms. Both CGM technologies have significant advantages over self-monitoring of blood glucose; however, differences in the features and capabilities of the two approaches must be considered when guiding patient selection of the system that meets their individual needs.

Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

The Comprehensive Glucose Pentagon: A Glucose-Centric Composite Metric for Assessing Glycemic Control in Persons With Diabetes

BACKGROUND

Composite metrics have the potential to provide more complete and clinically useful information about glycemic control than traditional individual metrics such as hemoglobin A1C, %/time/area under curve of hypoglycemia and hyperglycemia.

METHODS

Using five key metrics that are derived from continuous glucose monitoring, we developed a new, multicomponent composite metric, the Comprehensive Glucose Pentagon (CGP) that demonstrates glycemic control both numerically and visually. Two of its axes are composite metrics-the intensity of hypoglycemia and intensity of hyperglycemia. This approach eliminates the use of the surrogate marker, hemoglobin A1C (A1C), and replaces it with glucose-centric metrics.

RESULTS

We reanalyzed the data from two randomized control trials, the STAR 3 and ASPIRE In-Home studies using the CGP. It provided new insights into the effect of sensor-augmented pumping (SAP) in the STAR 3 trial and sensor-integrated pumping with low-glucose threshold suspend (SIP+TS) in the ASPIRE In-Home trial.

CONCLUSIONS

The CGP has the potential to enable health care providers, investigators and patients to better understand the components of glycemic control and the effect of various interventions on the individual elements of that control. This can be done on a daily, weekly, or monthly basis. It also allows direct comparison of the effects on different interventions among clinical trials which is not possible using A1C alone. This new composite metric approach requires validation to determine if it provides a better predictor of long-term outcomes than A1C and/or better predictor of severe hypoglycemia than the low blood glucose index (LBGI).

Associations between glycaemic control and activation of the renin-angiotensin-aldosterone system in participants with type 2 diabetes mellitus and hypertension

Introduction

Hyperglycaemia increases succinate concentrations and succinate receptor activation in the kidney resulting in renin release. The aim of our study was to determine if there is an association between glycaemic control as evidenced by glycated haemoglobin values and activation of the renin-angiotensin-aldosterone system in patients with type 2 diabetes mellitus and hypertension.

Methods

A cross-sectional study was conducted at Galway University Hospitals between December 2014 and March 2015. Participants ( n = 66) were identified following interrogation of the electronic database for patients with type 2 diabetes mellitus. Baseline clinical demographics, aldosterone, plasma renin activity, direct renin concentration, urea and electrolytes, glycated haemoglobin, cholesterol, urine sodium and albumin creatinine ratio were recorded.

Results

There was a significant positive linear correlation between glycated haemoglobin and renin (both plasma renin activity [ P = 0.002] and direct renin concentration [ P = 0.008]) and between serum creatinine and aldosterone measured using both radioimmunoassay ( P = 0.008) and immunochemiluminometric assay ( P = 0.008). A significant negative linear correlation was demonstrated between serum sodium and plasma renin activity ( P = 0.005) and direct renin concentration ( P = 0.015) and between estimated glomerular filtration rate and aldosterone measured using radioimmunoassay ( P = 0.02) and immunochemiluminometric assay ( P = 0.016). A significant negative linear correlation existed between urine sodium and plasma renin activity ( P = 0.04) and aldosterone measured using radioimmunoassay ( P = 0.045).

Conclusions

There is a direct positive association between glycaemic control and renin. We advocate for renin measurement to be part of the diabetologist's armamentarium to assess, guide and optimize therapeutic strategies in patients with diabetes.

The Fallacy of Average: How Using HbA1c Alone to Assess Glycemic Control Can Be Misleading

HbA_1c is a valuable metric for comparing treatment groups in a randomized trial, for assessing glycemic trends in a population over time, or for cross-sectional comparisons of glycemic control in different populations. However, what is not widely appreciated is that HbA_1c may not be a good indicator of an individual patient's glycemic control because of the wide range of mean glucose concentrations and glucose profiles that can be associated with a given HbA_1c level. To illustrate this point, we plotted mean glucose measured with continuous glucose monitoring (CGM) versus central laboratory-measured HbA_1c in 387 participants in three randomized trials, showing that not infrequently HbA_1c may underestimate or overestimate mean glucose, sometimes substantially. Thus, if HbA_1c is to be used to assess glycemic control, it is imperative to know the patient's actual mean glucose to understand how well HbA_1c is an indicator of the patient's glycemic control. With knowledge of the mean glucose, an estimated HbA_1c (eA1C) can be calculated with the formula provided in this article to compare with the measured HbA_1c. Estimating glycemic control from HbA_1c alone is in essence applying a population average to an individual, which can be misleading. Thus, a patient's CGM glucose profile has considerable value for optimizing his or her diabetes management. In this era of personalized, precision medicine, there are few better examples with respect to the fallacy of applying a population average to a specific patient rather than using specific information about the patient to determine the optimal approach to treatment.

Racial differences in neighborhood disadvantage, inflammation and metabolic control in black and white pediatric type 1 diabetes patients

BACKGROUND

Racial variation in the relationship between blood glucose and hemoglobin A1c (HbA1c) complicates diabetes diagnosis and management in racially mixed populations. Understanding why HbA1c is persistently higher in blacks than whites could help reduce racial disparity in diabetes outcomes.

OBJECTIVE

Test the hypothesis that neighborhood disadvantage is associated with inflammation and poor metabolic control in a racially mixed population of pediatric type 1 diabetes patients.

METHODS

Patients (n = 86, 53 white, 33 black) were recruited from diabetes clinics. Self-monitored mean blood glucose (MBG) was downloaded from patient glucose meters. Blood was collected for analysis of HbA1c and C-reactive protein (CRP). Patient addresses and census data were used to calculate a concentrated disadvantage index (CDI). High CDI reflects characteristics of disadvantaged neighborhoods.

RESULTS

HbA1c and MBG were higher (p < 0.0001) in blacks [10.4% (90.3 mmol/mol), 255 mg/dL] than whites [8.9% (73.9 mmol/mol), 198 mg/dL). CDI was higher in blacks (p < 0.0001) and positively correlated with HbA1c (r = 0.40, p = 0.0002) and MBG (r = 0.35, p = 0.0011) unless controlled for race. CDI was positively associated with CRP by linear regression within racial groups. CRP was not different between racial groups, and was not correlated with MBG, but was positively correlated with HbA1c when controlled for race (p = 0.04).

CONCLUSIONS

Neighborhood disadvantage was associated with inflammation and poor metabolic control in pediatric type 1 diabetes patients. Marked racial differences in potential confounding factors precluded differentiation between genetic and environmental effects. Future studies should recruit patients matched for neighborhood characteristics and treatment regimen to more comprehensively assess racial variation in HbA1c.

Hemoglobin A1C, mean glucose, and persistence of glycation ratios in insulin-treated diabetes

OBJECTIVE

Determine the relationship between mean glucose (MG), as assessed by continuous glucose monitoring (CGM), and hemoglobin A1c (A1C) in insulin-requiring adults in a clinical practice setting and examine the persistence of this relationship over time.

METHODS

In this retrospective record review in a diabetes practice, a linear regression model was developed using data sets from all patients with ≥1 available download of a Dexcom SevenPlus CGM device in which there was >50% utilization in the 60 days prior to a laboratory A1C. Persistence of the MG to A1C relationship was examined in patients with ≥2 data sets available.

RESULTS

A total of 139 patients had ≥1 data set available for evaluation, and 101 patients had ≥2 data sets (range, 2 to 6; total, 279). The slope of the MG versus A1C curve was 19.5 mg/dL for each 1% change in A1C, with an intercept of 17.7 mg/dL. Although 88% of the measured MG values were within 15% of the A1C-predicted MG, there was substantial variation in individuals, with differences as large as ±26%. The MG to A1C (MG:A1C) ratio, which is a measure of glycation, was normally distributed, with a median of 21.6. Spearman correlation coefficients for the MG:A1C ratio on repeated measures ranged from 0.52 to 0.73, demonstrating persistence.

CONCLUSION

The relationship between MG and A1C is linear in a population but can vary between individuals. The MG:A1C ratio was normally distributed, tended to persist in individuals over time, and thus could be useful to identify apparent high and low glycators. Glycemic goals may need to be modified in such patients.

Design Considerations for Artificial Pancreas Pivotal Studies

The development of artificial pancreas systems has evolved to the point that pivotal studies designed to assess efficacy and safety are in progress or soon to be initiated. These pivotal studies are intended to provide the necessary data to gain clearance from the U.S. Food and Drug Administration, coverage by payers, and adoption by patients and clinicians. Although there will not be one design that is appropriate for every system, there are certain aspects of protocol design that will be considerations in all pivotal studies designed to assess efficacy and safety. One key aspect of study design is the intervention to be used by the control group. A case can be made that the control group should use the currently available best technology, which is sensor-augmented pump therapy. However, an equally, if not more, compelling case can be made that the control intervention should be usual care. In this Perspective, we elaborate on this issue and provide a pragmatic approach to the design of clinical trials of artificial pancreas systems.

Improved Accuracy of Continuous Glucose Monitoring Systems in Pediatric Patients with Diabetes Mellitus: Results from Two Studies

OBJECTIVE

This study was designed to evaluate accuracy, performance, and safety of the Dexcom (San Diego, CA) G4(®) Platinum continuous glucose monitoring (CGM) system (G4P) compared with the Dexcom G4 Platinum with Software 505 algorithm (SW505) when used as adjunctive management to blood glucose (BG) monitoring over a 7-day period in youth, 2-17 years of age, with diabetes.

RESEARCH DESIGN AND METHODS

Youth wore either one or two sensors placed on the abdomen or upper buttocks for 7 days, calibrating the device twice daily with a uniform BG meter. Participants had one in-clinic session on Day 1, 4, or 7, during which fingerstick BG measurements (self-monitoring of blood glucose [SMBG]) were obtained every 30 ± 5 min for comparison with CGM, and in youth 6-17 years of age, reference YSI glucose measurements were obtained from arterialized venous blood collected every 15 ± 5 min for comparison with CGM. The sensor was removed by the participant/family after 7 days.

RESULTS

In comparison of 2,922 temporally paired points of CGM with the reference YSI measurement for G4P and 2,262 paired points for SW505, the mean absolute relative difference (MARD) was 17% for G4P versus 10% for SW505 (P < 0.0001). In comparison of 16,318 temporally paired points of CGM with SMBG for G4P and 4,264 paired points for SW505, MARD was 15% for G4P versus 13% for SW505 (P < 0.0001). Similarly, error grid analyses indicated superior performance with SW505 compared with G4P in comparison of CGM with YSI and CGM with SMBG results, with greater percentages of SW505 results falling within error grid Zone A or the combined Zones A plus B. There were no serious adverse events or device-related serious adverse events for either the G4P or the SW505, and there was no sensor breakoff.

CONCLUSIONS

The updated algorithm offers substantial improvements in accuracy and performance in pediatric patients with diabetes. Use of CGM with improved performance has potential to increase glucose time in range and improve glycemic outcomes for youth.

Longitudinal assessment of neuroanatomical and cognitive differences in young children with type 1 diabetes: association with hyperglycemia

Significant regional differences in gray and white matter volume and subtle cognitive differences between young diabetic and nondiabetic children have been observed. Here, we assessed whether these differences change over time and the relation with dysglycemia. Children ages 4 to <10 years with (n = 144) and without (n = 72) type 1 diabetes (T1D) had high-resolution structural MRI and comprehensive neurocognitive tests at baseline and 18 months and continuous glucose monitoring and HbA1c performed quarterly for 18 months. There were no differences in cognitive and executive function scores between groups at 18 months. However, children with diabetes had slower total gray and white matter growth than control subjects. Gray matter regions (left precuneus, right temporal, frontal, and parietal lobes and right medial-frontal cortex) showed lesser growth in diabetes, as did white matter areas (splenium of the corpus callosum, bilateral superior-parietal lobe, bilateral anterior forceps, and inferior-frontal fasciculus). These changes were associated with higher cumulative hyperglycemia and glucose variability but not with hypoglycemia. Young children with T1D have significant differences in total and regional gray and white matter growth in brain regions involved in complex sensorimotor processing and cognition compared with age-matched control subjects over 18 months, suggesting that chronic hyperglycemia may be detrimental to the developing brain.

Continuous glucose monitoring system: Is it really accurate, safe and clinically useful?

The continuous glucose monitoring system (CGM) has been used for constant checking of glucose level by measuring interstitial glucose concentrations, since the early days of the 21st century. It can potentially improve diabetes care if used carefully with the understanding of the characteristics of this system. Although there is a time lag of approximately 5–15 min between blood and interstitial glucose levels, the system is considered the most suitable device for meticulous glucose control and prevention of hypoglycemia. A large number of studies have examined its accuracy, safety and clinical effectiveness. The continuous glucose‐error grid analysis (CG‐EGA), designed by WL Clarke, evaluates the clinical accuracy of CGM. It examines ‘temporal’ characteristics of the data, analyzing pairs of reference and sensor readings as a process in time represented by a ‘bidimensional’ time series and taking into account inherent physiological time lags. Investment in CG‐EGA is clearly meaningful, even though there are other methodologies for evaluation. The use of each method complementarily is the most effective way to prove the accuracy of the device. The device has improved gradually, and real‐time CGM, which allows real‐time monitoring of blood glucose level, is already available commercially. The use of real‐time CGM could potentially lead to over‐ or undertreatment with insulin. Patient education through proper and effective handling of the new device is essential to improve diabetes care. (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2012.00197.x, 2012)

The relationship between estimated average glucose and fasting plasma glucose

BACKGROUND

Estimated average glucose (eAG) is a value calculated from hemoglobin A1c (HbA1c) that reflects average glycemic status over the preceding few months. A linear relationship between HbA1c and eAG was demonstrated by the International HbA1c-Derived Average Glucose (ADAG) Trial in 2008. We investigated the relationship between fasting plasma glucose (FPG) and eAG.

METHODS

This retrospective study was conducted by reviewing the medical records of 6443 subjects, including 5567 diabetic patients and 876 non-diabetic subjects. The levels of HbA1c and FPG were reviewed and eAG was calculated using the regression equation published by the ADAG trial: eAGmmol/L=1.59×HbA1c(NGSP, %)-2.59[eAGmg/dL= 28.7×HbA1c(NGSP, %)-46.7].

RESULTS

In all subjects, FPG showed a moderate correlation with eAG (r=0.672, p<0.001). When diabetic and non-diabetic subjects were divided into subgroups according to FPG level, the correlation between eAG and FPG decreased in both diabetic [FPG ≥10.0 mmol/L (180 mg/dL), r=0.425; FPG 7.2-9.9 mmol/L (130-179 mg/dL), r=0.373; FPG <7.2 mmol/L (130 mg/dL), r=0.202] and non-diabetic [FPG 5.6-6.9 mmol/L (100-125 mg/dL), r=0.363; FPG <5.6 mmol/L (100 mg/dL), r=0.186] subgroups as the FPG level decreased. The differences between eAG and FPG were statistically significant (p<0.001). Only 81% (4487/5567) of diabetic patients had a lower FPG level than eAG level.

CONCLUSIONS

Our results suggest that the relationship between eAG and FPG may depend on glycemic control, thereby enhancing our understanding of eAG.

Alterations in white matter structure in young children with type 1 diabetes

OBJECTIVE

To investigate whether type 1 diabetes affects white matter (WM) structure in a large sample of young children.

RESEARCH DESIGN AND METHODS

Children (ages 4 to <10 years) with type 1 diabetes (n = 127) and age-matched nondiabetic control subjects (n = 67) had diffusion weighted magnetic resonance imaging scans in this multisite neuroimaging study. Participants with type 1 diabetes were assessed for HbA1c history and lifetime adverse events, and glucose levels were monitored using a continuous glucose monitor (CGM) device and standardized measures of cognition.

RESULTS

Between-group analysis showed that children with type 1 diabetes had significantly reduced axial diffusivity (AD) in widespread brain regions compared with control subjects. Within the type 1 diabetes group, earlier onset of diabetes was associated with increased radial diffusivity (RD) and longer duration was associated with reduced AD, reduced RD, and increased fractional anisotropy (FA). In addition, HbA1c values were significantly negatively associated with FA values and were positively associated with RD values in widespread brain regions. Significant associations of AD, RD, and FA were found for CGM measures of hyperglycemia and glucose variability but not for hypoglycemia. Finally, we observed a significant association between WM structure and cognitive ability in children with type 1 diabetes but not in control subjects.

CONCLUSIONS

These results suggest vulnerability of the developing brain in young children to effects of type 1 diabetes associated with chronic hyperglycemia and glucose variability.

A randomized cross-over trial of the postprandial effects of three different diets in patients with type 2 diabetes

BACKGROUND

In the clinic setting both fasting levels of glucose and the area under the curve (AUC) of glucose, by determination of HbA1c levels, are used for risk assessments, in type 2 diabetes (NIDDM). However little is known about postprandial levels, and hence AUC, regarding other traditional risk factors such as insulin and blood-lipids and how this is affected by different diets.

OBJECTIVE

To study postprandial effects of three diets, during a single day, in NIDDM.

METHODS

A low-fat diet (45-56 energy-% from carbohydrates), and a low-carbohydrate diet (16-24 energy-% from carbohydrates) was compared with a Mediterranean-style diet (black coffee for breakfast and the same total-caloric intake as the other two diets for lunch with red wine, 32-35 energy-% from carbohydrates) in a randomized cross-over design. Total-caloric intake/test-day at the clinic from food was 1025-1080 kCal in men and 905-984 kCal in women. The test meals were consumed at a diabetes ward under supervision.

RESULTS

Twenty-one participants were recruited and 19 completed the studies. The low-carbohydrate diet induced lower insulin and glucose excursions compared with the low-fat diet (p<0.0005 for both AUC). The insulin-response following the single Mediterranean-style lunch-meal was more pronounced than during the low-fat diet lunch (insulin increase-ratio of the low-fat diet: 4.35 ± 2.2, of Mediterranean-style diet: 8.12 ± 5.2, p = 0.001) while postprandial glucose levels were similar. The increase-ratio of insulin correlated with the elevation of the incretin glucose-dependent insulinotropic-polypeptide following the Mediterranean-style diet lunch (Spearman, r = 0.64, p = 0.003).

CONCLUSIONS

The large Mediterranean-style lunch-meal induced similar postprandial glucose-elevations as the low-fat meal despite almost double amount of calories due to a pronounced insulin-increase. This suggests that accumulation of caloric intake from breakfast and lunch to a single large Mediterranean style lunch-meal in NIDDM might be advantageous from a metabolic perspective.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01522157 NCT01522157.

Closed-loop basal insulin delivery over 36 hours in adolescents with type 1 diabetes: randomized clinical trial

OBJECTIVE

We evaluated the safety and efficacy of closed-loop basal insulin delivery during sleep and after regular meals and unannounced periods of exercise.

RESEARCH DESIGN AND METHODS

Twelve adolescents with type 1 diabetes (five males; mean age 15.0 [SD 1.4] years; HbA1c 7.9 [0.7]%; BMI 21.4 [2.6] kg/m(2)) were studied at a clinical research facility on two occasions and received, in random order, either closed-loop basal insulin delivery or conventional pump therapy for 36 h. During closed-loop insulin delivery, pump basal rates were adjusted every 15 min according to a model predictive control algorithm informed by subcutaneous sensor glucose levels. During control visits, subjects' standard infusion rates were applied. Prandial insulin boluses were given before main meals (50-80 g carbohydrates) but not before snacks (15-30 g carbohydrates). Subjects undertook moderate-intensity exercise, not announced to the algorithm, on a stationary bicycle at a 140 bpm heart rate in the morning (40 min) and afternoon (20 min). Primary outcome was time when plasma glucose was in the target range (71-180 mg/dL).

RESULTS

Closed-loop basal insulin delivery increased percentage time when glucose was in the target range (median 84% [interquartile range 78-88%] vs. 49% [26-79%], P = 0.02) and reduced mean plasma glucose levels (128 [19] vs. 165 [55] mg/dL, P = 0.02). Plasma glucose levels were in the target range 100% of the time on 17 of 24 nights during closed-loop insulin delivery. Hypoglycemia occurred on 10 occasions during control visits and 9 occasions during closed-loop delivery (5 episodes were exercise related, and 4 occurred within 2.5 h of prandial bolus).

CONCLUSIONS

Day-and-night closed-loop basal insulin delivery can improve glucose control in adolescents. However, unannounced moderate-intensity exercise and excessive prandial boluses pose challenges to hypoglycemia-free closed-loop basal insulin delivery.

The impact of blood glucose and HbA(1c) goals on glycaemic control in children and adolescents with Type 1 diabetes

AIMS

To evaluate parents' goals and parents' perceptions of physicians' goals for blood glucose and HbA(1c) in children and adolescents with Type 1 diabetes.

METHODS

In a cross-sectional observational assessment, parents (80% mothers) of 153 children/adolescents (56% female), aged 12.9 ± 2.3 years (range 8-16 years) with Type 1 diabetes for 6.3 ± 3.5 years, completed surveys regarding their goals and their perceptions of physicians' goals for their child's blood glucose and HbA(1c) levels.

RESULTS

Children/adolescents had a mean HbA(1c) of 69 ± 16 mmol/mol (8.4 ± 1.4%) and checked blood glucose levels 3.8 ± 1.2 times/day; 23% received pump therapy. Almost half of parents reported a blood glucose goal of 130 (80-180) mg/dl [7.2 (4.4-10) mmol/l]; 75% of parents reported a HbA(1c) goal of 42-64 mmol/mol (6-8%). HbA(1c) was significantly lower when parents reported HbA(1c) goals ≤ 64 mmol/mol (≤ 8%) vs. > 64 mmol/mol (> 8%) [67 ± 14 mmol/mol (8.3 ± 1.2%) vs. 76 ± 20 mmol/mol (9.1 ± 1.8%), respectively, P = 0.02]. Parents' blood glucose and HbA(1c) goals were tightly linked with parents' perceptions of physicians' blood glucose and HbA(1c) goals (69% concordant, P < 0.0001; 88% concordant, P < 0.0001, respectively).

CONCLUSIONS

There was a significant association between lower parent HbA(1c) goals and lower child/adolescent HbA(1c) . Further, parents appear to set glycaemic goals based upon their perceptions of physician goals. Future studies should assess the relationship between parents' perceptions of health-care providers' goals and health-care providers' actual goals and the impact of unified family/provider goal-setting on glycaemic control.

Using multiple measures of glycemia to support individualized diabetes management: recommendations for clinicians, patients, and payers

By the year 2030, the diabetes pandemic will likely affect more than 10% of the world's population. The personal, public health, and economic crises implicit in this trend call for decisive action. Yet, escalating dilemmas thwart full realization of current therapies. First, controversial studies, such as the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Trial, have amplified calls to individualize glycated hemoglobin (A1C) targets in the absence of adequate infrastructures for supporting personalized care. Second, costlier medications and technologies addressing more nuanced aspects of metabolic dysfunction are expanding options for diabetes management amidst growing disparities between "affordable" and "best" care. Third, common clinical quandaries, such as discrepancies between A1C and self-monitoring of blood glucose data, as well as misconceptions about long-term glycemic assessment, compound entrenched cycles of inadequate self-care, delayed intervention, and suboptimal glycemic outcomes. Because individual, clinical, and public policy responses to these conflicting forces are based largely on methodologies for glucose measurement, a panel of clinical experts from Europe and North America was convened to reexamine our glucose measuring tools and determine ways in which they can be better applied toward more purposeful processes of glycemic management. Among the main issues addressed were the need for caution in interpreting A1C for individual patients, the role of alternative biomarkers in identifying aspects of glycemic dysregulation not captured by A1C, and the value of using patients' own glucose data to consolidate therapeutic, educational, and behavior-change objectives.

Motherhood, apple pie, hemoglobin A(1C), and the DCCT

OBJECTIVE

To review recent literature on the limitations of hemoglobin A(1c) (HbA(1c)) as a marker of glycemic control.

METHODS

English-language literature published between 1985 and 2011 was reviewed specific to analyses of major trials relating glycemic control to complications of diabetes mellitus, as expressed through HbA(1c) as a marker of glycemic control.

RESULTS

HbA(1c) has been accepted as the most fundamental biomarker in diabetes, if not all of medicine, as it clearly predicts risk for diabetes-related complications. What is not generally appreciated is that HbA(1c) is a crude marker of glycemia with many limitations. It is now accepted that HbA(1c) does not reflect mean glucose for many people, and even for those it does, any level could represent a wide range of glycemia. While we have learned HbA(1c) is not a perfect biomarker, we also know that in the Diabetes Control and Complications Trial, HbA(1c) could only explain 11% of the variation in retinopathy risk between the conventional and intensive therapy groups. This important finding suggests that other glycemic and nonglycemic factors may be responsible for the pathogenesis of diabetes-related complications. One candidate is glycemic variability, which must be differentiated from postprandial hyperglycemia since hypoglycemia can also result in inflammatory activation. Importantly, although it is clear that in insulin-requiring patients glycemic variability is associated with hypoglycemia, we require a definitive prospective trial to confirm glycemic variability's association with one or more vascular complications.

CONCLUSIONS

What is abundantly clear is that the HbA(1c) message, as we know it, is too simplistic. While certain wholesome concepts such as motherhood and apple pie are accepted by all, the HbA(1c) message may be more complex than originally appreciated, and it may be time to reevaluate our most basic premise in diabetes.

Is there a relationship between mean blood glucose and glycated hemoglobin?

Measurement of hemoglobin A1c (HbA1c) is considered the gold standard for monitoring chronic glycemia of diabetes patients. Hemoglobin A1c indicates an average of blood glucose levels over the past 3 months. Its close association with the risk for the development of long-term complications is well established. However, HbA1c does not inform patients about blood glucose values on a daily basis; therefore, frequent measurements of blood glucose levels are necessary for the day-to-day management of diabetes. Clinicians understand what HbA1c means and how it relates to glucose, but this is not the case with patients. Therefore, the translation of the HbA1c results into something more familiar to patients seemed a necessity. The scope of this article is to review the literature to search for enough scientific evidence to support the idea of a close relationship between HbA1c and mean blood glucose (MBG), and to justify the translation of HbA1c into something that reflects the MBG. Most studies confirm a close relationship between HbA1c and MBG, although different studies result in different linear equations. Factors affecting this relationship may limit the usefulness and applicability of a unique mathematical equation to all diabetes populations.

Innovations in technology for the treatment of diabetes: clinical development of the artificial pancreas (an autonomous system)

The Food and Drug Administration in collaboration with the National Institutes of Health presented a public workshop to facilitate medical device innovation in the development of the artificial pancreas (or autonomous system) for the treatment of diabetes mellitus on November 10, 2010 in Gaithersburg, Maryland. The purpose of the workshop was to discuss four aspects of artificial pancreas research and development, including: (1) the current state of device systems for autonomous systems for the treatment of diabetes mellitus; (2) challenges in developing this expert device system using existing technology; (3) clinical expectations for these systems; and (4) development plans for the transition of this device system toward an outpatient setting. The patients discussed how clinical science, system components, and regulatory policies will all need to harmonize in order to achieve the goal of seeing an AP product brought forward to the marketplace for patients to use.