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

The Usefulness of the Glucose Management Indicator in Evaluating the Quality of Glycemic Control in Patients with Type 1 Diabetes Using Continuous Glucose Monitoring Sensors: A Cross-Sectional, Multicenter Study

The Glucose Management Indicator (GMI) is a biomarker of glycemic control which estimates hemoglobin A1c (HbA1c) based on the average glycemia recorded by continuous glucose monitoring sensors (CGMS). The GMI provides an immediate overview of the patient's glycemic control, but it might be biased by the patient's sensor wear adherence or by the sensor's reading errors. This study aims to evaluate the GMI's performance in the assessment of glycemic control and to identify the factors leading to erroneous estimates. In this study, 147 patients with type 1 diabetes, users of CGMS, were enrolled. Their GMI was extracted from the sensor's report and HbA1c measured at certified laboratories. The median GMI value overestimated the HbA1c by 0.1 percentage points (p = 0.007). The measurements had good reliability, demonstrated by a Cronbach's alpha index of 0.74, an inter-item correlation coefficient of 0.683 and an inter-item covariance between HbA1c and GMI of 0.813. The HbA1c and the difference between GMI and HbA1c were reversely associated (Spearman's r = -0.707; p < 0.001). The GMI is a reliable tool in evaluating glycemic control in patients with diabetes. It tends to underestimate the HbA1c in patients with high HbA1c values, while it tends to overestimate the HbA1c in patients with low HbA1c.

The related factors affecting the relationship between HbA1c and glucose management indicator in adult T2D patients with good glycemic control

PURPOSE

To explore the relationship between glucose management indicator (GMI) and HbA1c and find the affecting factors in adult T2D patients with good glycemic control.

METHODS

Adult T2D patients with both HbA1c < 7% and time in range (TIR) > 70% were retrospectively analyzed. A significant difference between GMI and HbA1c was defined as an absolute value of hemoglobin glycation index (|HGI|, HbA1c minus GMI) ≥ 0.5%. Factors associated with high |HGI| were determined by logistic regression analysis. The performance of possible factors in predicting high |HGI| was verified by ROC curve analysis. And the linear relationship between GMI and HbA1c was also investigated.

RESULTS

Of all the 94 patients (median HbA1c 6.18%, mean GMI 6.34%) included, 28.72% had an |HGI | ≥ 0.5% and only 15.96% had an |HGI | < 0.1%. Standard deviation of blood glucose (SDBG), a glycemic variability index, affected |HGI| (OR = 3.980, P = 0.001), and showed the best performance in predicting high |HGI| (AUC = 0.712, cutoff value = 1.63 mmol/L, P = 0.001). HbA1c was linearly correlated with GMI (β = 0.295, P = 0.004). Their correlation weakened after further adjusting for SDBG (β = 0.232, P = 0.012). Linear correlation between them was closer in patients with smaller SDBG ( < 1.63 mmol/L) than those with larger SDBG (P = 0.004).

CONCLUSIONS

Even in adult T2D patients with good glycemic control, the discrepancy between GMI and HbA1c existed. Their relationship was affected by glycemic variability. SDBG mainly accounted for this consequence.

TRIAL REGISTRATION

Chinese clinical trial registry ( www.chictr.org.cn ), ChiCTR2000034884, 2020-07-23.

Age and Red Blood Cell Parameters Mainly Explain the Differences Between HbA1c and Glycemic Management Indicator Among Patients With Type 1 Diabetes Using Intermittent Continuous Glucose Monitoring

BACKGROUND

Glycated hemoglobin (HbA1c) is the gold standard to assess glycemic control in patients with diabetes. Glucose management indicator (GMI), a metric generated by continuous glucose monitoring (CGM), has been proposed as an alternative to HbA1c, but the two values may differ, complicating clinical decision-making. This study aimed to identify the factors that may explain the discrepancy between them.

METHODS

Subjects were patients with type 1 diabetes, with one or more HbA1c measurements after starting the use of the Freestyle Libre 2 intermittent CGM, who shared their data with the center on the Libreview platform. The 14-day glucometric reports were retrieved, with the end date coinciding with the date of each HbA1c measurement, and those with sensor use ≥70% were selected. Clinical data prior to the start of CGM use, glucometric data from each report, and other simultaneous laboratory measurements with HbA1c were collected.

RESULTS

A total of 646 HbA1c values and their corresponding glucometric reports were obtained from 339 patients. The absolute difference between HbA1c and GMI was <0.3% in only 38.7% of cases. Univariate analysis showed that the HbA1c-GMI value was associated with age, diabetes duration, estimated glomerular filtration rate, mean corpuscular volume (MCV), red cell distribution width (RDW), and time with glucose between 180 and 250 mg/dL. In a multilevel model, only age and RDW, positively, and MCV, negatively, were correlated to HbA1c-GMI.

CONCLUSION

The difference between HbA1c and GMI is clinically relevant in a high percentage of cases. Age and easily accessible hematological parameters (MCV and RDW) can help to interpret these differences.

Discordance between mean glucose and time in range in relation to HbA1c in individuals with type 1 diabetes: results from the GOLD and SILVER trials

AIMS/HYPOTHESIS

Previous studies have shown that individuals with similar mean glucose levels (MG) or percentage of time in range (TIR) may have different HbA1c values. The aim of this study was to further elucidate how MG and TIR are associated with HbA1c.

METHODS

Data from the randomised clinical GOLD trial (n=144) and the follow-up SILVER trial (n=98) of adults with type 1 diabetes followed for 2.5 years were analysed. A total of 596 paired HbA1c/continuous glucose monitoring measurements were included. Linear mixed-effects models were used to account for intra-individual correlations in repeated-measures data.

RESULTS

In the GOLD trial, the mean age of the participants (± SD) was 44±13 years, 63 (44%) were female, and the mean HbA1c (± SD) was 72±9.8 mmol/mol (8.7±0.9%). When correlating MG with HbA1c, MG explained 63% of the variation in HbA1c (r=0.79, p<0.001). The variation in HbA1c explained by MG increased to 88% (r=0.94, p value for improvement of fit <0.001) when accounting for person-to-person variation in the MG-HbA1c relationship. Time below range (TBR; <3.9 mmol/l), time above range (TAR) level 2 (>13.9 mmol/l) and glycaemic variability had little or no effect on the association. For a given MG and TIR, the HbA1c of 10% of individuals deviated by >8 mmol/mol (0.8%) from their estimated HbA1c based on the overall association between MG and TIR with HbA1c. TBR and TAR level 2 significantly influenced the association between TIR and HbA1c. At a given TIR, each 1% increase in TBR was related to a 0.6 mmol/mol lower HbA1c (95% CI 0.4, 0.9; p<0.001), and each 2% increase in TAR level 2 was related to a 0.4 mmol/mol higher HbA1c (95% CI 0.1, 0.6; p=0.003). However, neither TIR, TBR nor TAR level 2 were significantly associated with HbA1c when accounting for MG.

CONCLUSIONS/INTERPRETATION

Inter-individual variations exist between MG and HbA1c, as well as between TIR and HbA1c, with clinically important deviations in relatively large groups of individuals with type 1 diabetes. These results may provide important information to both healthcare providers and individuals with diabetes in terms of prognosis and when making diabetes management decisions.

The Glucose Management Indicator: Time to Change Course?

Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.

Intermittently Scanned Continuous Glucose Monitoring Performance in Patients With Liver Cirrhosis

AIM

To evaluate the use of intermittently scanned continuous glucose monitoring (isCGM) in patients with liver cirrhosis (LC).

METHODS

Observational study including 30 outpatients with LC (Child-Pugh B/C): 10 without diabetes (DM) (G1), 10 with newly diagnosed DM by oral glucose tolerance test (G2), and 10 with a previous DM diagnosis (G3). isCGM (FreeStyle Libre Pro) was used for 56 days (four sensors/patient). Blood tests were performed at baseline and after 28 and 56 days.

RESULTS

No differences were found in the baseline characteristics, except for higher age in G3. There were significant differences between G1, G2 and G3 in glucose management indicator (GMI) (5.28 ± 0.17, 6.03 ± 0.59, 6.86 ± 1.08%, P < .001), HbA1c (4.82 ± 0.39, 5.34 ± 1.26, 6.97 ± 1.47%, P < .001), average glucose (82.79 ± 7.06, 113.39 ± 24.32, 149.14 ± 45.31mg/dL, P < .001), time in range (TIR) (70.89 ± 9.76, 80.2 ± 13.55, 57.96 ± 17.96%, P = .006), and glucose variability (26.1 ± 5.0, 28.21 ± 5.39, 35.31 ± 6.85%, P = .004). There was discordance between GMI and HbA1c when all groups were considered together, with a mean difference of 0.35% (95% SD 0.17, 0.63). In G1, the mean difference was 0.46% (95% SD 0.19, 0.73) and in G2 0.69% (95% SD 0.45, 1.33). GMI and HbA1c were concordant in G3, with a mean difference of -0.10 % (95% SD [-0.59, 0.38]).

CONCLUSION

Disagreements were found between the GMI and HbA1c levels in patients with LC. isCGM was able to detect abnormalities in glycemic control that would not be detected by monitoring with HbA1c, suggesting that isCGM can be useful in assessing glycemic control in patients with LC.

A pregnancy-specific Glucose management indicator derived from continuous glucose monitoring in pregnant women with type 1 diabetes

OBJECTIVE

Glucose management indicator (GMI) is a core metric derived from continuous glucose monitoring (CGM) and is widely used to evaluate glucose control in patients with diabetes. No study has explored the pregnancy-specific GMI. This study aimed to derive a best-fitting model to calculate GMI from mean blood glucose (MBG) obtained from CGM among pregnant women with type 1 diabetes mellitus (T1DM).

METHODS

A total of 272 CGM data and corresponding laboratory HbA1c from 98 pregnant women with T1DM in the CARNATION study were analysed in this study. Continuous glucose monitoring data were collected to calculate MBG, time-in-range (TIR), and glycaemic variability parameters. The relationships between the MBG and HbA1c during pregnancy and postpartum were explored. Mix-effect regression analysis with polynomial terms and cross-validation method was conducted to investigate the best-fitting model to calculate GMI from MBG obtained by CGM.

RESULTS

The pregnant women had a mean age of 28.9 ± 3.8 years, with a diabetes duration of 8.8 ± 6.2 years and a mean body mass index (BMI) of 21.1 ± 2.5 kg/m2 . The HbA1c levels were 6.1 ± 1.0% and 6.4 ± 1.0% during pregnancy and at postpartum (p = 0.024). The MBG levels were lower during pregnancy than those at postpartum (6.5 ± 1.1 mmol/L vs. 7.1 ± 1.5 mmol/L, p = 0.008). After adjusting the confounders of haemoglobin (Hb), BMI, trimesters, disease duration, mean amplitude of glycaemic excursions and CV%, we developed a pregnancy-specific GMI-MBG equation: GMI for pregnancy (%) = 0.84-0.28* [Trimester] + 0.08 * [ BMI in kg/m2 ] + 0.01 * [Hb in g/mL] + 0.50 * [MBG in mmol/L].

CONCLUSIONS

We derived a pregnancy-specific GMI equation, which should be recommended for antenatal clinical care.

CLINICAL TRIAL REGISTRY NUMBER

ChiCTR1900025955.

Factors associated with hemoglobin glycation index in adults with type 1 diabetes mellitus: The FGM-Japan study

AIMS/INTRODUCTION

The discrepancy between HbA1c and glucose exposure may have significant clinical implications; however, the association between the hemoglobin glycation index (HGI) and clinical parameters in type 1 diabetes remains controversial. This study aimed to find the factors associated with HGI (laboratory HbA1c - predicted HbA1c derived from the continuous glucose monitoring [CGM]).

MATERIALS AND METHODS

We conducted a cross-sectional study of adults with type 1 diabetes (n = 211, age 50.9 ± 15.2 years old, female sex = 59.2%, duration of CGM use = 2.1 ± 1.0 years). All subjects wore the CGM for 90 days before HbA1c measurement. Data derived from the FreeStyle Libre sensor were used to calculate the glucose management indicator (GMI) and glycemic variability (GV) parameters. HGI was defined as the difference between the GMI and the laboratory HbA1c levels. The participants were divided into three groups according to the HGI tertile (low, moderate, and high). Multivariate regression analyses were performed.

RESULTS

The female sex ratio, HbA1c, and % coefficient of variation (%CV) significantly increased over the HGI tertile, while eGFR and Hb decreased over the HGI tertile. In multivariate analysis, the factors associated with HGI were %CV and eGFR, after adjusting for HbA1c level and sex (R²  = 0.44).

CONCLUSIONS

This study demonstrated that HGI is associated with female sex, eGFR, and some glycemic variability indices, independently of HbA1c. Minimizing glycemic fluctuations might reduce HGI. This information provides diabetic health professionals and patients with personalized diabetes management for adults with type 1 diabetes.

Comparison of Point-of-Care and Laboratory Glycated Hemoglobin A1c and Its Relationship to Time-in-Range and Glucose Variability: A Real-World Study

Introduction The main objective of the current study was to perform a comparison of point-of-care testing for hemoglobin A1c (POCT-HbA1c) versus the standard laboratory method (Lab HbA1c) and their relationship to time-in-range (TIR) and glucose variability (GV) among patients with diabetes mellitus (DM) presented to the outpatient diabetes clinics. Methods This single-center cross-sectional study was carried out on diabetic patients (aged ≥14 years of both genders) who undergo routine follow-up at our institution and whose physicians ordered HbA1c analysis for routine care. The included patients were those using the intermittently scanned continuous glucose monitoring (isCGM) Abbott's FreeStyle Libre system for at least three months and regular CGM users with at least 70% use. Results We included 97 diabetic patients (41 female and 56 male), with a median age of 25 years (Interquartile range= 18) and a mean DM duration of 10.33±5.48 years. The mean values of Lab-HbA1c and POCT HbA1c were 8.82%±0.85% and 8.53%±0.89%, respectively. The TIR, time below range, and time above range were 33.47±14.38 minutes (47.78%±14.32%), 5.44±2.58 minutes (8.41%±4.42%), and 28.8±8.27 minutes (43.81%±13.22%), respectively. According to the Bland-Altman plot analysis, the POCT-HbA1c values are consistent with the standard Lab-HbA1c values (SD of bias= 0.55, and 95% CI= -0.78 to 1.4). The univariate linear regression analysis showed a statistically significant relationship between laboratory HbA1c and POCT HbA1c (R2= 0.637, p <0.001), TIR (R2= 0.406, p <0.001), and GV (R2= 0.048, p= 0.032). After adjusting for age, gender, disease duration, diabetes type, and percentage of sensor data in a multivariable linear regression model, the linear associations remained significant (all p < 0.05). Conclusion The current findings show that TIR and GV can be used as endpoints and valuable parameters for the therapy of DM.

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.

Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated With Insulin: Real-World Evidence From the DIALECT-2 Cohort

OBJECTIVE

To investigate glucose variations associated with glycated hemoglobin (HbA1c) in insulin-treated patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Patients included in Diabetes and Lifestyle Cohort Twente (DIALECT)-2 (n = 79) were grouped into three HbA1c categories: low, intermediate, and high (≤53, 54-62, and ≥63 mmol/mol or ≤7, 7.1-7.8, and ≥7.9%, respectively). Blood glucose time in range (TIR), time below range (TBR), time above range (TAR), glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by continuous glucose monitoring (CGM) using the FreeStyle Libre sensor and compared between HbA1c categories.

RESULTS

CGM was performed for a median (interquartile range) of 10 (7-12) days/patient. TIR was not different for low and intermediate HbA1c categories (76.8% [68.3-88.2] vs. 76.0% [72.5.0-80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4-19.1] vs. 0.7% [0.3-6.1] and 8.2% [5.7-17.6] vs. 20.4% [11.6-27.0], respectively, P < 0.05). Patients in the highest HbA1c category had lower TIR (52.7% [40.9-67.3]) and higher TAR (44.1% [27.8-57.0]) than the other HbA1c categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06 ± 0.06/h vs. 0.03 ± 0.03/h; P = 0.002) and longer (88.0 [45.0-195.5] vs. 53.4 [34.4-82.8] minutes; P < 0.001) TBR episodes during the night than during the day.

CONCLUSIONS

In this study, a high HbA1c did not reduce the occurrence of nocturnal hypoglycemia, and low HbA1c was not associated with the highest TIR. Optimal personalization of glycemic control requires the use of newer tools, including CGM-derived parameters.

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.