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Hemoglobin A1C (HbA1c) is used as an index of average blood glucose measurement over a period of months and is a mainstay of blood glucose monitoring. This metric is easy to measure and relatively inexpensive to obtain, and it predicts diabetes-related microvascular complications. However, HbA1c provides only an approximate measure of glucose control; it does not address short-term glycemic variability (GV) or hypoglycemic events. Continuous glucose monitoring (CGM) is a tool which helps clinicians and people with diabetes to overcome the limitations of HbA1c in diabetes management. Time spent in the glycemic target range and time spent in hypoglycemia are the main CGM metrics that provide a more personalized approach to diabetes management. Moreover, the glucose management indicator (GMI), which calculates an approximate HbA1c level based on the average CGM-driven glucose level, facilitates individual decision-making when the laboratory-measured HbA1c and estimated HbA1c are discordant. GV, on the other hand, is a measure of swings in blood glucose levels over hours or days and may contribute to diabetes-related complications. In addition, addressing GV is a major challenge during the optimization of glycemia. The degree of GV is associated with the frequency, duration, and severity of the hypoglycemic events. Many factors affect GV in a patient, including lifestyle, diet, the presence of comorbidities, and diabetes therapy. Recent evidence supports the use of some glucose-lowering agents to improve GV, such as the new ultra-long acting insulin analogs, as these agents have a smoother pharmacodynamic profile and improve glycemic control with fewer fluctuations and fewer nocturnal hypoglycemic events. These newer glucose-lowering agents (such as incretin hormones or sodium–glucose cotransporter 2 inhibitors) can also reduce the degree of GV. However, randomized trials are needed to evaluate the effect of GV on important diabetes outcomes. In this review, we discuss the role of HbA1c as a measure of glycemic control and its limitations. We also explore additional glycemic metrics, with a focus on time (duration) in glucose target range, time (duration) in hypoglycemia, GV, GMI, and their correlation with clinical outcomes.

The discordance between glycated haemoglobin (HbA1c) and the glucose management indicator (GMI) has been proposed as a marker of vascular risk in diabetes. This study evaluated whether the GMI/ HbA1c ratio independently predicts diabetic retinopathy (DR) in adults with type 1 diabetes (T1D) using continuous glucose monitoring. We conducted a multicenter cross-sectional study involving 1,070 adults using flash glucose monitoring. Participants were stratified as high glycators (ratio < 0.9) or non-high glycators based on the GMI/HbA1c ratio. DR status was assessed by ophthalmologic evaluation. Multivariable logistic regression and 1:1 propensity score matching were used to assess independent associations with DR, adjusting for age, sex, diabetes duration, smoking, hypertension, LDL cholesterol, BMI and insulin dose. While high glycators had a higher crude DR prevalence (31.3% vs. 23.1%, p  = 0.020), the GMI/HbA1c ratio was not independently associated with DR in adjusted models (OR 1.19; 95% CI: 0.34–4.15; p  = 0.785) or in the matched cohort (OR 1.23; 95% CI: 0.76–1.99; p  = 0.391). Absolute HbA1c remained the strongest glycemic predictor. These findings suggest that the GMI/HbA1c ratio may aid in interpreting discordant glycemic profiles, serving as a contextual tool in clinical practice, but it lacks independent prognostic value for DR.

Background Glucose management indicator (GMI) is a useful metric for the clinical management of diabetic patients using continuous glucose monitoring (CGM). In adults, a marked discordance between HbA1c and GMI has been reported. To date, no studies have evaluated this discordance in children/adolescents with type 1 diabetes (T1D). Methods HbA1c and real‐life CGM data of the 12 weeks preceding HbA1c measurement were collected from 805 children/adolescents. The absolute difference between HbA1c and GMI was calculated for both the 12‐week and 4‐week periods preceding HbA1c measurement and the proportion of discordant patients was defined according to specific thresholds in the entire study population and in subjects stratified by type of CGM, insulin therapy, gender, age and puberty. Regression analyses were performed with HbA1c‐GMI discordance as dependent variable and patients' characteristics as independent ones. A new GMI equation for children and adolescent was derived from the linear regression analysis between mean glucose and HbA1c. Results HbA1c‐GMI discordance calculated on the 12‐week period was <0.1, ≥0.5 and ≥1.0 in 24.8, 33.9 and 9.2% of the subjects, respectively. No significant differences in the proportion of discordant patients were found comparing patients stratified by type of CGM, insulin therapy, gender, age and puberty. GMI‐HbA1c discordance was not significantly explained by age, gender, BMI, type of CGM, insulin therapy, hemoglobin, anemia and autoimmune diseases (R2 = 0.012, p = 0.409). HbA1c‐GMI discordance calculated on the 4‐week period was comparable. GMI (%) equation derived for this cohort was: 3.74 + 0.022x (mean glucose in mg/dl). Conclusions GMI could be meaningfully discordant respect to HbA1c in more than a third of children/adolescents with T1D. This discrepancy should be taken into careful consideration when the two indices are directly compared in daily clinical practice.

Aims For end-stage renal disease (ESRD) patients with diabetes on haemodialysis, diabetes control is difficult to achieve. Hypoglycaemia is a major problem in these frailty subjects. Continuous glucose monitoring (CGM) devices appear therefore to be a good tool to help patients monitor their glycaemic control and to help practitioners optimize treatment. We aimed to compare the laboratory value of Hba1c with the sensor-estimated value of Hba1c (= glucose management indicator, GMI) in ESRD patients with type 2 diabetes (T2D) (with or without insulin treatment) on haemodialysis. Secondly, we aimed to identify CGM-derived monitoring parameters [time in range, time in hypo/hyperglycaemia, glycaemic variability (coefficient of variation, CV)] to identify patients at risk of frequent hypo- or hyperglycaemia. Methods The FSLPRO-DIAL pilot study (NCT04641650) was a prospective monocentric cohort study including 29 subjects with T2D who achieve the protocol. Inclusion criteria were: age ≥ 18 years, haemodialysis duration for at least 3 months, type 2 diabetes with no change in treatment for at least 3 months. Demographic data and blood sample were collected at the day of inclusion. Freestyle Libre pro IQ sensor (blinded CGM) was inserted for 14 days. After this period, all CGMs data were collected and analysed. Results Data were available for 27 patients. Mean age was 73 ± 10, mean BMI 27.2 kg/m 2 , mean duration of diabetes 16.9 years and mean dialysis duration 2.9 years. Twenty-four subjects were treated with insulin. Mean HbA1c was 6.6% (SD 1.2), and mean GMI was 6.7% (SD 0.9) (no significant difference, p  = 0.3). Twelve subjects (44.4%) had a discordance between HbA1c and GMI of < 0.5%, 11 (40.8%) had a discordance between 0.5 and 1%, and only 4 (14.8%) had a discordance of > 1%. Mean time in range (70–180 mg/dl) was 71.9%, mean time below range (< 70 mg/dl) was 5.6%, and mean time above range (> 180 mg/dl) was 22.1%. Mean CV was 31.8%. For 13 out of 27 patients, we reduced antidiabetic treatment by stopping treatments or reducing insulin doses. Conclusion In this pilot study, there was no global significant difference between HbA1c and GMI in this particular cohort with very well-controlled diabetes. However, the use of the sensor enabled us to identify an excessive time in hypoglycemia in this fragile population and to adapt their treatment.

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.

Nicotine inhibits glucose metabolism. In this national cross-sectional analysis of 388 young adults with type 1 diabetes and above target glycemic control, vaping was the most common route of nicotine use, and heavy nicotine use plus higher type 1 diabetes distress was related to worse objective measures of glycemic control. Trial registration: ClinicalTrials.govNCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473. •Young adults with type 1 diabetes and high HbA1c are a vulnerable population.•One in four young adults with type 1 diabetes reported nicotine use in past month.•Young adults with type 1 diabetes primarily reported only nicotine vaping.•Higher nicotine use was significantly associated with worse glycemic control.•Heavy nicotine use plus higher diabetes distress related to worse glycemic control.

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.

Background Since the arrival of continuous glucose monitoring (CGM), the relationship between the glucose management indicator (GMI) and HbA1c has been a topic of considerable interest in diabetes research. This study aims to explore the association between the GMI/HbA1c ratio and the presence of preclinical carotid atherosclerosis in type 1 diabetes (T1D). Methods Individuals with T1D and no prior history of cardiovascular disease were recruited from two centers. Carotid ultrasonography was performed using a standardized protocol and carotid plaques were defined as intima-media thickness ≥ 1.5 mm. CGM-derived data were collected from a 14-day report. A GMI/HbA1c ratio < 0.90 was selected to identify “fast-glycator” phenotype. Results A total of 584 participants were included (319 women, 54.6%), with a mean age of 48.8 ± 10.7 years and a mean diabetes duration of 27.5 ± 11.4 years. Carotid plaques were present in 231 subjects (39.6%). Approximately 43.7% and 13.4% of participants showed absolute differences of ≥ 0.5 and ≥ 1.0 between 14-day GMI and HbA1c, respectively. Among patients ≥ 48 years, the fast-glycator phenotype was independently associated with presence of plaques (OR 2.27, 95%CI: 1.06–4.87), even after adjusting for non-specific and T1D-specific risk factors and statin treatment. No significant association was observed in younger subjects (p for interaction < 0.05). Conclusions Fast-glycator phenotype is independently associated with atherosclerosis in T1D individuals aged ≥ 48 years, suggesting an age-related increase in the glycation risk. These findings highlight the potential of the GMI/HbA1c ratio for cardiovascular risk stratification in this population. Graphical abstract

Background Advanced hybrid closed loop (AHCL) system provides both automated basal rate and correction boluses to keep glycemic values in a target range. Objectives To evaluate the real-world performance of the MiniMed™ 780G system among different age groups of Egyptian patients with type 1diabetes. Methods One-hundred seven AHCL system users aged from 3 to 71 years were enrolled. Data uploaded by patients were aggregated and analyzed. The mean glucose management indicator (GMI), percentage of time spent within glycemic ranges (TIR), time below range (TBR) and time above range (TAR) were determined. Results Six months after initiating Auto Mode, patients spent a mean of 85.31 ± 22.04% of the time in Auto Mode (SmartGuard) and achieved a mean GMI of 6.95 ± 0.58% compared with 7.9 ± 2.1% before AHCL initiation (p < 0.001). TIR 70–180 mg/dL was increased post-AHCL initiation from 63.48 ± 10.14% to 81.54 ± 8.43% (p < 0.001) while TAR 180–250 mg/dL, TAR > 250 mg/dL, TBR < 70 mg/dL and TBR < 54 mg/dL were significantly decreased (p < 0.001). After initiating AHCL, TIR was greater in children and adults compared with adolescents (82.29 ± 7.22% and 83.86 ± 9.24% versus 78.4 ± 7.34%, respectively; p < 0.05). The total daily dose of insulin was increased in all age groups primarily due to increased system-initiated insulin delivery including auto correction boluses and basal insulin. Conclusions MiniMed ™ 780G system users across different age groups achieved international consensus-recommended glycemic control with no serious adverse effects even in challenging age group as children and adolescents.

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.