"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

Evaluation of HbA1c from CGM traces in an Indian population

Introduction

The development of continuous glucose monitoring (CGM) over the last decade has provided access to many consecutive glucose concentration measurements from patients. A standard method for estimating glycated hemoglobin (HbA1c), already established in the literature, is based on its relationship with the average blood glucose concentration (aBG). We showed that the estimates obtained using the standard method were not sufficiently reliable for an Indian population and suggested two new methods for estimating HbA1c.

Methods

Two datasets providing a total of 128 CGM and their corresponding HbA1c levels were received from two centers: Health Centre, Savitribai Phule Pune University, Pune and Joshi Hospital, Pune, from patients already diagnosed with diabetes, non-diabetes, and pre-diabetes. We filtered 112 data-sufficient CGM traces, of which 80 traces were used to construct two models using linear regression. The first model estimates HbA1c directly from the average interstitial fluid glucose concentration (aISF) of the CGM trace and the second model proceeds in two steps: first, aISF is scaled to aBG, and then aBG is converted to HbA1c via the Nathan model. Our models were tested on the remaining 32 data- sufficient traces. We also provided 95% confidence and prediction intervals for HbA1c estimates.

Results

The direct model (first model) for estimating HbA1c was HbA1cmmol/mol = 0.319 × aISFmg/dL + 16.73 and the adapted Nathan model (second model) for estimating HbA1c is HbA1cmmol/dL = 0.38 × (1.17 × ISFmg/dL) - 5.60.

Discussion

Our results show that the new equations are likely to provide better estimates of HbA1c levels than the standard model at the population level, which is especially suited for clinical epidemiology in Indian populations.

Intermittent-scanned continuous glucose monitoring with low glucose alarms decreases hypoglycemia incidence in middle-aged adults with type 1 diabetes in real-life setting

OBJECTIVE

There is limited real-life data demonstrating that hypo-/hyperglycemic alarms added to continuous glucose monitoring (CGM) improve metabolic control in adults with type 1 diabetes (T1D). We evaluated the usefulness of switching from a flash or intermittent-scanned continuous glucose monitoring (is-CGM) device without low or higher glucose alarms to a is-CGM device with alarms to prevent hypoglycemia in adults with T1D.

METHODS

Individuals with T1D and fearful of hypoglycemia, prone to hypoglycemia unawareness, and/or experiencing severe hypoglycemia while using is-CGM Free Style Libre 1 (FSL1) were switched to FSL2 with individually-programmable low glucose alarms. The primary endpoint was the changes in % time below range (TBR%) <70 mg/dl [3.9 mmol/l] and <54 mg/dl [3.0 mmol/l] after 12 weeks on FSL2 compared with FSL1. Secondary endpoints were changes in % time in range (TIR% 70-180 mg/dl [3.9-10.0 mmol/l]), % time above range (TAR%) >180 [10.0 mmol/l], mean interstitial glucose, glycemic management indicator (GMI), interstitial glucose coefficient of variation (CV%), hemoglobin A1c, and sensor's scans/day.

RESULTS

We included 108 individuals (57.4 % men), aged 58.2 ± 17.3 [95 % CI: 55.0 to 61.5] years, with mean diabetes duration 25 ± 14.6 [95 % CI: 22.1 to 27.7] years. Among individuals, 40 (37.0 %) had hypoglycemia awareness with Clarke's score ≥4 and 19 (17.5 %) had a history of severe hypoglycemia. The median low glucose alarm threshold was 70 [IQR: 65-70] mg/dl (3.9 [IQR: 3.6-3.9] mmol/L). By comparison of first 12 weeks on FSL2 vs. last 12 weeks on FSL1, TBR% <70 mg/dl decreased from 4.5 ± 4.4 to 2.3 ± 2.8 % (p < 0.001), TBR% <54 mg/dl decreased from 1.4 ± 2.2 to 0.3 ± 0.9 % (p < 0.001). TIR% was not significantly different (51.5 ± 14.9 vs. 52.9 ± 16 % (p = 0.13)), nor was TAR% (43.8 ± 16.2 vs. 44.7 ± 16.5 % (p = 0.5)). CV% decreased from 39.4 ± 6.9 to 37.9 ± 6.1 % (p < 0.001). Those at risk for hypoglycemia (TBR >4 % and >1 %, respectively, at baseline) showed a significant decrease in the incidence of hypoglycemia <70 and <54 mg/dl (p < 0.0001). Patients' satisfaction with hypoglycemia alarms was high, since all individuals opted to pursue using individual alarm beyond the study period.

CONCLUSION

Switching from FSL1 to FSL2 with low glucose alarms reduced the frequency of hypoglycemia in middle-age adults with T1D, particularly in those who were prone to hypoglycemia awareness or severe hypoglycemia.

Evaluation of continuous glucose monitoring-derived person-specific HbA1c in the presence and absence of complications in type 1 diabetes

AIM

To evaluate the accuracy of a novel kinetic model at predicting HbA1c in a real-world setting and to understand and explore the role of diabetes complications in altering the glucose-HbA1c relationship and the mechanisms involved.

MATERIALS AND METHODS

Deidentified HbA1c and continuous glucose monitoring values were collected from 93 individuals with type 1 diabetes. Person-specific kinetic variables were used, including red blood cell (RBC) glucose uptake and lifespan, to characterize the relationship between glucose levels and HbA1c. The resulting calculated HbA1c (cHbA1c) was compared with glucose management indicator (GMI) for prospective agreement with laboratory HbA1c.

RESULTS

The cohort (42 men and 51 women) had a median age (IQR) of 61 (43, 72) years and a diabetes duration of 21 (10, 33) years. A total of 24 459 days of continuous glucose monitoring (CGM) data were available and 357 laboratory HbA1c were used to assess the average glucose-HbA1c relationship. cHbA1c had a superior correlation with laboratory HbA1c compared with GMI with a mean absolute deviation of 1.7 and 6.7 mmol/mol, r²  = 0.85 and 0.44, respectively. The fraction within 10% of absolute relative deviation from laboratory HbA1c was 93% for cHbA1c and 63% for GMI. Macrovascular disease had no effect on the model's accuracy, whereas microvascular complications resulted in a trend towards higher HbA1c, secondary to increased RBC glucose uptake.

CONCLUSIONS

cHbA1c, which takes into account RBC glucose uptake and lifespan, accurately reflects laboratory HbA1c in a real-world setting and can aid in the management of individuals with diabetes.

Level of Agreement and Correlation Between the Estimated Hemoglobin A1c Results Derived by Continuous or Conventional Glucose Monitoring Systems Compared with the Point-of-Care or Laboratory-Based Measurements: An Observational Study

INTRODUCTION

Hemoglobin A1C (HbA1c) is an important marker for diabetes care management. With the increasing use of new technologies such as continuous glucose monitoring (CGM) and point-of-care testing (POCT), patients and their physicians have been able to monitor and continuously check their blood glucose levels in an efficient and timely manner. This study aimed to investigate the level of agreement between the standard laboratory test for HbA1c (Lab-HbA1c) with point-of-care testing (POCT-HbA1c) and glucose monitoring index (GMI) derived by intermittently scanned CGM (isCGM) or estimated average glucose (eAG) derived by conventional self-monitored blood glucose (SMBG) devices.

METHODS

A cross-sectional study was conducted at the Diabetes Treatment Center, Prince Sultan Military Medical City, Saudi Arabia, between May and December 2020 with 81 patients with diabetes who used the isCGM system (n = 30) or conventional finger-pricking SMBG system (n = 51). At the same visit, venous and capillary blood samples were taken for routine HbA1c analysis by the standard laboratory and POCT methods, respectively. Also, for isCGM users, the GMI data for 28 days (GMI-28) and 90 days (GMI-90) were obtained, while for SMBG users, eAG data for 30 days (eAG-30) and 90 days (eAG-90) were calculated. The limits of agreement in different HbA1c measurements were evaluated using a Bland-Altman analysis. Pearson correlation and multivariate linear regression analyses were also performed.

RESULTS

Based on the Bland-Altman analysis, HbA1c levels for 96.7% and 96.1% of the patients analyzed by the POCT and the standard laboratory methods were within the range of the 95% limit of agreement in both isCGM and conventional SMBG users, respectively. About 93.3% of the GMI measurements were within the 95% limit of agreement. Also, about 94.12% of the eAG-30 and 90.2% of the eAG-90 measurements were within the 95% limit of agreement. Moreover, the correlation analysis revealed a statistically significant positive correlation and linear regression among Lab-HbA1c, POCT-HbA1c, GMI, and eAG in both conventional SMBG and isCGM users (all p < 0.001). These positive results persisted significantly after adjusting for different factors (all p < 0.001).

CONCLUSION

GMI derived by isCGM or eAG derived by conventional SMBG systems, as well as the POCT-HbA1c measurements, showed a high level of agreement; therefore, we recommend them as potential methods for diabetes monitoring, especially when a rapid result is needed or with patients with uncontrolled diabetes or on intensive insulin therapy.

Understanding the clinical implications of differences between glucose management indicator and glycated haemoglobin

Laboratory measured glycated haemoglobin (HbA1c) is the gold standard for assessing glycaemic control in people with diabetes and correlates with their risk of long-term complications. The emergence of continuous glucose monitoring (CGM) has highlighted limitations of HbA1c testing. HbA1c can only be reviewed infrequently and can mask the risk of hypoglycaemia or extreme glucose fluctuations. While CGM provides insights in to the risk of hypoglycaemia as well as daily fluctuations of glucose, it can also be used to calculate an estimated HbA1c that has been used as a substitute for laboratory HbA1c. However, it is evident that estimated HbA1c and HbA1c values can differ widely. The glucose management indicator (GMI), calculated exclusively from CGM data, has been proposed. It uses the same scale (% or mmol/mol) as HbA1c, but is based on short-term average glucose values, rather than long-term glucose exposure. HbA1c and GMI values differ in up to 81% of individuals by more than ±0.1% and by more than ±0.3% in 51% of cases. Here, we review the factors that define these differences, such as the time period being assessed, the variation in glycation rates and factors such as anaemia and haemoglobinopathies. Recognizing and understanding the factors that cause differences between HbA1c and GMI is an important clinical skill. In circumstances when HbA1c is elevated above GMI, further attempts at intensification of therapy based solely on the HbA1c value may increase the risk of hypoglycaemia. The observed difference between GMI and HbA1c also informs the important question about the predictive ability of GMI regarding long-term complications.

Utilizing the New Glucometrics: A Practical Guide to Ambulatory Glucose Profile Interpretation

Traditional continuous glucose monitoring and flash glucose monitoring systems are proven to lower glycated haemoglobin levels, decrease the time and impact of hypoglycaemia or hyperglycaemia and, consequently, improve the quality of life for children and adults with type 1 diabetes mellitus (T1DM) and adults with type 2 diabetes mellitus (T2DM). These glucose-sensing devices can generate large amounts of glucose data that can be used to define a detailed glycaemic profile for each user, which can be compared with targets for glucose control set by an International Consensus Panel of diabetes experts. Targets have been agreed upon for adults, children and adolescents with T1DM and adults with T2DM; separate targets have been agreed upon for older adults with diabetes, who are at higher risk of hypoglycaemia, and women with pregestational T1DM during pregnancy. Along with the objective measures and targets identified by the International Consensus Panel, the dense glucose data delivered by traditional continuous glucose monitoring and flash glucose monitoring systems is used to generate an ambulatory glucose profile, which summarizes the data in a visually impactful format that can be used to identify patterns and trends in daily glucose control, including those that raise clinical concerns. In this article, we provide a practical guide on how to interpret these new glucometrics using a straightforward algorithm, and clear visual examples that demystify the process of reviewing the glycaemic health of people with T1DM or T2DM such that forward-looking goals for diabetes management can be agreed.

Effect of glycemic gap upon mortality in critically ill patients with diabetes

AIMS/INTRODUCTION

Hyperglycemia, hypoglycemia, and blood glucose fluctuation are associated with the outcome in critically ill patients, but the target of blood glucose control is debatable especially in patients with diabetes regarding the status of blood glucose control before admission to ICU. This study aimed to investigate the association between the glycemic gap which is calculated as the mean blood glucose level during the first 7 days after admission to ICU minus the A1C-derived average glucose and the outcome of critically ill patients with diabetes.

METHOD

This study was undertaken in two intensive care units (ICUs) with a total of 30 beds. Patients with diabetes who were expected to stay for more than 24 h were enrolled, the HbA1c was tested within 3 days after admission and converted to the A1C-derived average glucose (ADAG) by the equation: ADAG = [(HbA1c * 28.7) - 46.7 ] * 18-1 , arterial blood glucose measurements were four per day routinely during the first 7 days after admission, the APACHE II score within the first 24 h, the mean blood glucose level (MGL), standard deviation (SD), and coefficient of variation (CV) during the first 7 days were calculated for each person, the GAPadm and GAPmean were calculated as the admission blood glucose and MGL minus the ADAG, respectively, the incidence of moderate hypoglycemia (MH) and severe hypoglycemia (SH), the total dosage of glucocorticoids and average daily dosage of insulin within 7 days, the duration of renal replacement therapy (RRT), ventilator-free hours, and non-ICU stay days within 28 days were also collected. The enrolled patients were divided into a survival group and a nonsurvival group according to survival or not at 28 days and 1 year after admission, and the relationship between parameters derived from blood glucose and mortality in the enrolled critically ill patients was explored.

RESULTS

Five hundred and two patients were enrolled and divided into a survival group (n = 310) and a nonsurvival group (n = 192). It was shown that the two groups had a comparable level of HbA1c, the nonsurvivors had a greater APACHE II, MGL, SD, CV, GAPadm , GAPmean , and a higher incidence of hypoglycemia. A lesser duration of ventilator-free, non-ICU stay, and a longer duration of RRT were recorded in the nonsurvival group, who received a lower carbohydrate intake, a higher daily dosage of insulin and glucocorticoid. GAPmean had the greatest predictive power with an AUC of 0.820 (95%CI: 0.781-0.850), the cut-off value was 3.60 mmol/L (sensitivity 78.2% and specificity 77.3%). Patients with a low GAPmean tended to survive longer than the high GAPmean group 1 year after admission.

CONCLUSIONS

Glycemic GAP between the mean level of blood glucose within the first 7 days after admission to ICU and the A1C-derived average glucose was independently associated with a 28 day mortality of critically ill patients with diabetes, the predictive power extended to 1 year. The incidence of hypoglycemia was associated with mortality either.

Improved Estimation of Glycated Hemoglobin from Continuous Glucose Monitoring and Past Glycated Hemoglobin Data

Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination (R²), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R² = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. -0.152%, P < 0.0001], and overall 95% CI = -0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R² = 0.83, bias = 0.026%, 95% CI = -0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients (R² = 0.76, bias = -0.069%, 95% CI = -0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.

Intermittently Scanned Glucose Values for Continuous Monitoring: Cross-Sectional Analysis of Glycemic Control and Hypoglycemia in 1809 Children and Adolescents with Type 1 Diabetes

Background and Objective: Intermittent scanning continuous glucose monitoring (iscCGM) is increasingly used for glycemic monitoring in diabetes care. In this cross-sectional real-world analysis, iscCGM data were compared to traditional parameters of glycemic control in young people with type 1 diabetes. Methods: Using the DPV registry, most recent data from children and adolescents aged <18 years with uploaded iscCGM sensor profiles with at least 14 days of data and a > 50% completeness were evaluated using recommended parameters of sensor metrics. Analysis was performed stratified by age group, glycemic control, and type of therapy; data were taken from DPV data pool in February 2020. Results: Glucose sensor profiles and clinical data from 1809 individuals (mean age 13.4 years, 53% male, and mean diabetes duration 5.02 years) were evaluated in this study. More than 50% of this population (n = 965) reached the current German treatment target of hemoglobin A1c (HbA1c) <7.5%. In this target, the mean scanning frequency was higher than in groups with HbA1c >7.5 or >8.0% (12.0 vs. 10.2 vs 7.6 times per day). The group of preschool children had the highest frequency of scanning (16.6 vs. 13.3 times per day in school kids and 7.9 in adolescents), the lowest HbA1c level, and the lowest risk for hypoglycemia (low blood glucose index 0.8 vs. 1.0 vs 1.2). Conclusion: Real-world data will help to determine the value of iscCGM to improve clinical and patient-related outcomes in pediatric diabetology. Not only the use of a device but also the intensity of use seems to have a high and direct impact on glycemic control.

Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

AIMS

Our aim was to investigate the impact of glycemic variability (GV) on the relationship between glucose management indicator (GMI) and laboratory glycated hemoglobin A1c (HbA1c).

METHODS

Adult patients with type 1 diabetes mellitus (T1D) were enrolled from five hospitals in China. All subjects wore the iPro^™2 system for 14 days before HbA1c was measured at baseline, 3 months and 6 months. Data derived from iPro^™2 sensor was used to calculate GMI and GV parameters [standard deviation (SD), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE)]. Differences between GMI and laboratory HbA1c were assessed by the absolute value of the hemoglobin glycation index (HGI).

RESULTS

A total of 91 sensor data and corresponding laboratory HbA1c, as well as demographic and clinical characteristics were analyzed. GMI and HbA1c were 7.20 ± 0.67% and 7.52 ± 0.73%, respectively. The percentage of subjects with absolute HGI 0 to lower than 0.1% was 21%. GMI was significantly associated with laboratory HbA1c after basic adjustment (standardized β = 0.83, p < 0.001). Further adjustment for SD or MAGE reduced the standardized β for laboratory HbA1c from 0.83 to 0.71 and 0.73, respectively (both p < 0.001). In contrast, the β remained relatively constant when further adjusting for CV. Spearman correlation analysis showed that GMI and laboratory HbA1c were correlated for each quartile of SD and MAGE (all p < 0.05), with the corresponding correlation coefficients decreased across ascending quartiles.

CONCLUSIONS

This study validated the GMI formula using the iPro^™2 sensor in adult patients with T1D. GV influenced the relationship between GMI and laboratory HbA1c.