Glycemic Metrics Derived From Intermittently Scanned Continuous Glucose Monitoring

Differences Between Glycated Hemoglobin and Glucose Management Indicator in Real-Time and Intermittent Scanning Continuous Glucose Monitoring in Adults With Type 1 Diabetes

BACKGROUND

This study demonstrates the difference between glucose management indicator (GMI) and glycated hemoglobin (HbA1c) according to sensor mean glucose and HbA1c status using 2 continuous glucose monitoring (CGM) sensors in people with type 1 diabetes.

METHODS

A total of 275 subjects (117 Dexcom G6 [G6] and 158 FreeStyle Libre 1 [FL]) with type 1 diabetes was included. The G6 and FL sensors were used, respectively, over 90 days to analyze 682 and 515 glycemic profiles that coincide with HbA1c.

RESULTS

The mean HbA1c was 6.6% in Dexcom G6 and 7.2% in FL profiles. In G6 profiles, GMI was significantly higher than HbA1c irrespective of mean glucose (all P < .001, mean difference: 0.58% ± 0.49%). The GMI was significantly higher than the given HbA1c when HbA1c was below 8.0% (all P < .001). The discordance was the highest at 0.9% for lower HbA1c values (5.0%-5.9%). The proportion of discordance >0.5% improved from 60.1% to 30.9% when using the revised GMI equation in G6 profiles. In FL profile, the overall mean difference between GMI and HbA1c was 0 (P = .966). The GMI was significantly lower by 0.9% than HbA1c of 9.0% to 9.9% and higher by 0.5% in HbA1c of 5.0% to 5.9% (all P < .001).

CONCLUSIONS

The GMI is overestimated in G6 users, particularly those with well-controlled diabetes, but the GMI and HbA1c discordance improved with a revised equation from the observed data. The FL profile showed greater discordance for lower HbA1c levels or higher HbA1c levels.

Risk factors for hypoglycemia in patients with type 2 diabetes mellitus after intensive insulin therapy and blood glucose monitoring strategy

Background

To explore the risk factors for hypoglycemia in patients with type 2 diabetes mellitus (T2DM) after intensive insulin therapy and the blood glucose monitoring strategy.

Methods

A total of 172 T2DM patients diagnosed from March 2019 to March 2021 were randomly divided into training (n=115) and test sets (n=57), and given intensive insulin therapy. After treatment, the training set was divided into hypoglycemia (n=35) and non-hypoglycemia groups (n=80). Univariate and multivariate logistic regression analyses were performed. Then 120 patients were divided into instantaneous scanning glucose test system monitoring (observation) and glucometer monitoring (control) groups. The diagnostic values of the two methods were validated using receiver operator characteristic curves.

Results

Course of disease, body mass index, fasting C-peptide and creatinine were independent risk factors for hypoglycemia, while instantaneous scanning glucose test system monitoring and glucometer monitoring were protective factors (P<0.05). The model had high predictive value. The observation group had shorter time of blood glucose reaching the standard, smaller insulin dose, and lower risk of hypoglycemia than those of the control group (P<0.05).

Conclusion

During intensive insulin therapy by multiple subcutaneous injections, the clinical effect of instantaneous scanning glucose test system on T2DM patients is better than that of glucometer.

Rebound Hypoglycemia and Hyperglycemia in Type 1 Diabetes

AIMS

The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics.

METHODS

Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes.

RESULTS

A total of 10 977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ± 1.6 mmol/l before Rhypo; 12.8 ± 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly (P < .001) correlated with Rhypo (Spearman's rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13).

CONCLUSIONS

The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions.

The Frequency of Intermittently Scanned Glucose and Diurnal Variation of Glycemic Metrics

BACKGROUND

The relation between the frequency of intermittently scanned continuous glucose monitoring (isCGM) and diurnal variation of time in range (TIR) and time below range (TBR) is unknown.

METHOD

A total of 163 persons with type 1 diabetes who used isCGM had glucose data for 60 days downloaded. Mean TIR and median TBR were calculated for 15-minute periods and presented for daytime and nighttime. The values for tertiles of scanning frequency were compared.

RESULTS

The 1st tertile (n = 53) of the population scanned <10 times; the 2nd tertile (n = 56) 10-13 times, and the 3rd tertile (n = 54) >13 per 24 hours. TIR (%, mean ± (SD)) increased significantly from the 1st to the 3rd scan tertile both during the day (43.8 ± 14.8, 52.0 ± 12.3, 62.1 ± 12.8) and the night (44.5 ± 17.3, 52.3 ± 18.5, 64.0 ± 13.9; P < .0001). In contrast, TBR (median, (IQR)) was not significantly associated with scan tertiles during daytime (3.5% (1.1-7.8), 4.4% (1.8-6.1), 3.5% (2.1-6.1); P = .85) or nighttime (3.8% (1.4-13.7), 5.0% (1.6-9.6), 5.7% (3.6-10.9); P = .24). In a multiple regression model, a 50% increase in 24-hour scanning frequency was associated with a 7.8 percentage point increase in TIR (95% CI, 5.6-10.0).

CONCLUSIONS

Increased scanning frequency was associated with a higher TIR both during daytime and nighttime with no change in TBR.

Variation of glucose time in range in type 1 diabetes

INTRODUCTION

The aim of the study was to assess the variation of glucose time in range (TIR) for persons with type 1 diabetes who perform intermittently scanned continuous glucose monitoring (isCGM).

METHODS

Glucose data for 8 weeks were analysed for 166 persons. TIR was calculated over four consecutive 2 weeks periods. Sixty-one of the persons had two downloads with an interval of >3 months.

RESULTS

A total of 140 individuals (84%) used multiple daily injection, and 26 (16%) used continuous insulin infusion. The within-individual standard deviation (SD) for TIR was 6.3% corresponding to 95% limits of agreement for the difference between two TIR values of ±17.6%. Mean TIR calculated from the first and last 2 weeks was 52.2 ± 17.1% and 53.7 ± 16.4%, respectively (difference 1.5%, SD of the difference 10.4%, p = .07). For persons with two downloads separated by months, the SD of the difference in TIR was 12.6%.

CONCLUSIONS

The 95% limit of agreement for TIR is vast for persons using isCGM. It is difficult to draw firm conclusions regarding systematic differences when individual TIR from 2 weeks are compared. This may not be valid for users of insulin pumps with closed-loop insulin delivery.

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.