Evaluation of the relationship between glycated hemoglobin A1c and mean glucose levels derived from the professional continuous flash glucose monitoring system

To What Extent Is HbA1c Associated with Glycemic Variability in Patients with Type 1 Diabetes? A Retrospective, Noninterventional Study

BACKGROUND

Glycemic variability (GV) is a novel parameter used in evaluating the quality of diabetes management. Current guidelines recommend the use of GV indexes alongside the traditional parameter to evaluate glycemic control: hemoglobin A1c (HbA1c). This study aims to evaluate the extent to which HbA1c explains the GV phenomena in patients with Type 1 diabetes (T1DM).

METHODS

In 147 patients with T1DM, associations between HbA1c and several GV indexes were analyzed.

RESULTS

Patients with an HbA1c < 7% had a lower median standard deviation of glycemia (60 vs. 48; p < 0.001), a lower coefficient of variation (34.1 vs. 38.0; p < 0.001), and a significantly increased median time in range (78 vs. 58; p < 0.001). HbA1c was positively correlated with the coefficient of variation (r = 0.349; p < 0.001) and the standard deviation (r = 0.656; p < 0.001) but reversely correlated with a lower time in range (r = -0.637; p < 0.001).

CONCLUSIONS

HbA1c only partially explains the GV phenomena in patients with T1DM. The HbA1c value is associated more strongly with the time in range and standard deviation than with the coefficient of variation.

Correlation between glucose measurement parameters of continuous flash monitoring and HbA1c. Real life experience in Asturias

INTRODUCTION

Despite continuous glucose monitoring having been proven useful in patients with type 1 diabetes mellitus, A1C remains the gold standard for assessing disease management.

MATERIAL AND METHODS

Descriptive, retrospective study which included 252 patients, 40.5% male, mean age 44.91±14.57 years, mean duration of diabetes 22.21±13.12 years, 88.1% on basal-bolus insulin therapy and 11.9% users of continuous subcutaneous insulin infusion. Glucose measurement, analytical and anthropometric data were obtained.

RESULTS

The mean time in range was 60.18±15.60% and was associated with A1C after adjusting for age, gender, duration of diabetes, BMI, insulin regimen, %CV and time below range (ß: -0.548; p<0.01). The glucose management indicator (GMI) was 7.19±0.69% and was also associated with A1C (ß: 0.957; p<0.01) regardless of age, gender, duration of diabetes, BMI, insulin treatment, %CV and time in range. The average difference between A1C and GMI was 0.17±0.65% (-2.70-3.40%), being higher as A1C increased, in a linear and significant manner, without being influenced by the duration of diabetes or CV.

CONCLUSIONS

Although we found a positive correlation between continuous glucose monitoring glucose measurement parameters and A1C, there is still not enough evidence to replace one parameter with another.

HbA1c and Glucose Management Indicator Discordance Associated with Obesity and Type 2 Diabetes in Intermittent Scanning Glucose Monitoring System

Glucose management indicator (GMI) is frequently used as a substitute for HbA1c, especially when using telemedicine. Discordances between GMI and HbA1c were previously mostly reported in populations with type 1 diabetes (T1DM) using real-time CGM. Our aim was to investigate the accordance between GMI and HbA1c in patients with diabetes using intermittent scanning CGM (isCGM). In this retrospective cross-sectional study, patients with diabetes who used isCGM >70% of the time of the investigated time periods were included. GMI of four different time spans (between 14 and 30 days), covering a period of 3 months, reflected by the HbA1c, were investigated. The influence of clinical- and isCGM-derived parameters on the discordance was assessed. We included 278 patients (55% T1DM; 33% type 2 diabetes (T2DM)) with a mean HbA1c of 7.63%. The mean GMI of the four time periods was between 7.19% and 7.25%. On average, the absolute deviation between the four calculated GMIs and HbA1c ranged from 0.6% to 0.65%. The discordance was greater with increased BMI, a diagnosis of T2DM, and a greater difference between the most recent GMI and GMI assessed 8 to 10 weeks prior to HbA1c assessment. Our data shows that, especially in patients with increased BMI and T2DM, this difference is more pronounced and should therefore be considered when making therapeutic decisions.

Glucose patterns following alcohol and illicit drug use in young adults with type 1 diabetes: A flash glucose monitoring study

Introduction

To assess the effects of alcohol and illicit drug use in young adults (age 18–35) with type 1 diabetes (T1D) on flash glucose monitor sensor glucose (SG) readings.

Methods

Twenty young adults with T1D were enrolled from a tertiary referral hospital outpatient department in Melbourne, Australia for a 6‐week prospective observational study using flash glucose monitoring (FGM). Glucometrics comparing substance using days (SUEDs) to those without substance use (non‐SUEDS) were analysed. The primary outcomes were the difference in mean SG values, its standard deviation and minutes/24‐h period out of range (SG <3.9 mmol/L or >10.0 mmol/L) between matched SUEDs vs non‐SUEDs. An interaction model with the primary effect of HbA1c on SG values was also performed.

Results

There were no differences in the primary outcome measures between SUEDS and non‐SUEDs. However, there were differences in the regression coefficients for HbA1c and glucometrics between non‐SUEDs and SUEDs for mean SG, time out of range and time with SG > 10 mmol/L. This difference was also identified between non‐SUEDS and days of ≥40 g alcohol for mean SG.

Conclusions

While there was no difference between glucometrics for SUEDs and non‐SUEDs on primary outcomes, HbA1C was found to be a less reliable predictor of glucose patterns in the 24‐h period following substance use than control days. Young adults with T1D need to monitor and respond to their glucose levels following substance use and engage in harm minimisation practices irrespective of baseline glucose control.