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Fellinger, Paul, Karin Rodewald, Moritz Ferch, Bianca Itariu, Alexandra Kautzky-Willer, and Yvonne Winhofer. "HbA1c and Glucose Management Indicator Discordance Associated with Obesity and Type 2 Diabetes in Intermittent Scanning Glucose Monitoring System." Biosensors 12, no. 5 (2022): 288. http://dx.doi.org/10.3390/bios12050288.
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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.
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Toschi, Elena, Amy Michals, Atif Adam, et al. "Usefulness of CGM-Derived Metric, the Glucose Management Indicator, to Assess Glycemic Control in Non-White Individuals With Diabetes." Diabetes Care 44, no. 12 (2021): 2787–89. http://dx.doi.org/10.2337/dc21-1373.
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OBJECTIVE To assess the relationship between the glucose management indicator (GMI) and HbA1c in non-White individuals with diabetes. RESEARCH DESIGN AND METHODS We performed a retrospective analysis of continuous glucose monitoring metrics in individuals with diabetes divided by race into non-White and White cohorts. RESULTS We evaluated 316 individuals (non-White n = 68; White n = 248). Although GMI was not different (7.6 vs. 7.7; P = not significant) between the cohorts, HbA1c was higher in the non-White cohort (8.7% vs. 8.1%; P = 0.004). HbA1c higher than GMI by ≥0.5% was more frequently observed in the non-White cohort (90% vs. 75%; P = 0.02). In the non-White cohort only, duration of hypoglycemia was longer among those with HbA1c higher than GMI by ≥0.5% compared with those with HbA1c and GMI within 0.5%. CONCLUSIONS A differential relationship between HbA1c and GMI in non-White versus White individuals with diabetes was observed. In non-White individuals, a greater difference between HbA1c and GMI was associated with higher risk of hypoglycemia.
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胡, 坤. "A New Term for Estimating HbA1c by Continuous Glucose Monitoring: Glucose Management Indicator (GMI)." Advances in Clinical Medicine 11, no. 11 (2021): 4866–70. http://dx.doi.org/10.12677/acm.2021.1111714.
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Bergenstal, Richard M., Roy W. Beck, Kelly L. Close, et al. "Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring." Diabetes Care 41, no. 11 (2018): 2275–80. http://dx.doi.org/10.2337/dc18-1581.
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den Braber, Niala, Miriam M. R. Vollenbroek-Hutten, Sacha E. M. Teunissen, Milou M. Oosterwijk, Kilian D. R. Kappert, and Gozewijn D. Laverman. "The Contribution of Postprandial Glucose Levels to Hyperglycemia in Type 2 Diabetes Calculated from Continuous Glucose Monitoring Data: Real World Evidence from the DIALECT-2 Cohort." Nutrients 16, no. 20 (2024): 3557. http://dx.doi.org/10.3390/nu16203557.
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Background/Objectives: Traditional glycemic monitoring in type 2 diabetes is limited, whereas continuous glucose monitoring (CGM) offers better insights into glucose fluctuations. This study aimed to determine the correlations and relative contributions of fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) levels to hyperglycemia. Methods: We utilized CGM and recorded carbohydrate intake data from lifestyle diaries of 59 patients enrolled in the Diabetes and Lifestyle Cohort Twente (DIALECT-2). Correlations between FPG and the glucose management indicator (GMI), FPG and Time Above Range (TAR), PPG and GMI, and PPG and TAR were conducted. Daily and mealtime relative contributions of PPG and FPG to glycated hemoglobin (HbA1c) and GMI were determined, considering two ranges: on target (<7.0%, 53 mmol/mol) and not on target (≥7.0%, 53 mmol/mol). Correlations between mealtime PPG and carbohydrate consumption were examined. Results: FPG and PPG correlated with GMI (r = 0.82 and 0.41, respectively, p < 0.05). The relative contribution of PPG in patients with HbA1c, GMI, and TAR values not on target was lower than in patients with HbA1c, GMI, and TAR values on target. When analyzing different mealtimes, patients with target GMI values had a higher PPG (73 ± 21%) than FPG after breakfast (27 ± 21%, p < 0.001). Individuals with elevated GMI levels had lower PPG after lunch (30 ± 20%), dinner (36 ± 23%), and snacks (34 ± 23%) than FPG. PPG after breakfast positively correlated (r = 0.41, p < 0.01) with breakfast carbohydrate intake. Conclusions: Both PPG and FPG contribute to hyperglycemia, with PPG playing a larger role in patients with better glycemic control, especially after breakfast. Targeting PPG may be crucial for optimizing glucose management.
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Lazar, Sandra, Ovidiu Potre, Ioana Ionita, et al. "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." Biosensors 15, no. 3 (2025): 190. https://doi.org/10.3390/bios15030190.
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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.
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Zaharieva, Dessi P., Ananta Addala, Priya Prahalad, et al. "An Evaluation of Point-of-Care HbA1c, HbA1c Home Kits, and Glucose Management Indicator: Potential Solutions for Telehealth Glycemic Assessments." Diabetology 3, no. 3 (2022): 494–501. http://dx.doi.org/10.3390/diabetology3030037.
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During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland–Altman analyses and Lin’s concordance correlation coefficient (𝜌c) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (𝜌c = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (𝜌c = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.
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Liu, Hongxia, Daizhi Yang, Hongrong Deng, et al. "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." Therapeutic Advances in Endocrinology and Metabolism 11 (January 2020): 204201882093166. http://dx.doi.org/10.1177/2042018820931664.
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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.
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Xu, Yongjin, Yushi Hirota, Ramzi A. Ajjan, et al. "Accurate prediction of HbA1c by continuous glucose monitoring using a kinetic model with patient-specific parameters for red blood cell lifespan and glucose uptake." Diabetes and Vascular Disease Research 18, no. 3 (2021): 147916412110137. http://dx.doi.org/10.1177/14791641211013734.
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Background: A recent kinetic model proposed a new individualized glycaemic marker, calculated HbA1c (cHbA1c), based on kinetic parameters and glucose levels that are specific to each person. The aims of the current work were to validate the accuracy of this glucose metric for clinical use and evaluate data requirements for the estimation of personal kinetic factors. Methods: We retrieved HbA1c and glucose data from a group of 51 Japanese T1D patients under sensor-augmented pump (SAP) therapy. Two patient-specific kinetic parameters were identified by data sections, defined as continuous glucose data between two laboratory HbA1c measurements. The cHbA1c was prospectively validated employing subsequent HbA1c data that were not originally used to determine personal kinetic parameters. Results: Compared to estimated HbA1c (eHbA1c) and glucose management indicator (GMI), cHbA1c showed clinically relevant accuracy improvement, with 20% or more within ±0.5% (±5.5 mmol/mol) of laboratory HbA1c. The mean absolute deviation of the cHbA1c calculation was 0.11% (1.2 mmol/mol), substantially less than for eHbA1c and GMI at 0.54% (5.9 mmol/mol) and 0.47% (5.1 mmol/mol), respectively. Conclusion: Our study shows superior performance of cHbA1c compared with eHbA1c and GMI at reflecting laboratory HbA1c, making it a credible glucose metric for routine clinical use.
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A.Manov, Nazha J., Antonio S., and House J. "Improvement of time in range and glucose management indicator in Type 1 and Type 2 Diabetes Mellitus patients after introduction of continuous Glucose monitoring in internal medicine residency clinic." World Journal of Advanced Research and Reviews 17, no. 3 (2023): 824–30. https://doi.org/10.5281/zenodo.8134576.
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Continuous glucose monitoring (CGM)-derived time in range (TIR) correlates with Glucose management indicator(GMI) which correlates with hemoglobin A1c (HbA1c) among patients with type 1 and type 2 diabetes mellitus. Compare to HbA1c it has better correlation with glucose control, because HbA1c can be influenced by conditions like anemia, Chronic kidney disease, Cirrhosis of the liver etc. With our current project we wanted to introduce CGM not in specialized endocrine clinic, but in Internal medicine Residency clinic. The CGM team had 12 Internal Medicine and Transitional year Residents who were functioning under the supervision of Board Certified Endocrinologist who was a member of the clinic also. Twenty Five patients -85% with type 2 DM and 15% with type 1 DM on multiple injections of Insulin per day-3-4, self-monitored their blood Glucose(SMBG). They were given CGM- Dexcom G6 in the clinic. In the first 2- weeks after the switch the TIR of the blood glucose of the patients-70-180 mg/dl was 18%. Their average blood glucose was 286 mg/dl, GMI was 11.21%. During then first 2- weeks after initiation of CGM the patients were educated in length by our CGM team about their diet, physical activity, how to adjust their Insulin based on their blood glucose levels as well as how to treat the hypoglycemia. Members of the CGM team were contacting the patients twice a week to adjust patients treatment with Insulin and other per oral antidiabetic medications and or injectable – GLP1-RAG if needed after consultation with the Endocrinologist in the clinic based on the shared information between the clinic and the patients. Once a month the patients were seen in the clinic by member of the CGM team as well. The patients were followed for 2- years. After 3- months on CGM and followed for the 2 years thereafter the patients TIR improved from 18 % to 74%, GMI decreased from 11.21% to 7.04% and the average blood glucose decreased from 286 mg/dl to 158 mg/dl. There was also significant reduction of the hypoglycemia. Twenty percent of the patients were able to discontinue their Insulin and be treated only with oral antidiabetic medications plus/minus GLP1-RAG and have GMI less than 7%. We have showed that targeted TIR – above 70% which has been associated recently with diabetic micro and macrovascular complications in diabetic patients can be achieved not only in specialized endocrine clinics , but in Internal Medicine residency clinic and can be adopted by other Internal Medicine Residency programs in USA.
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A.Manov, J. Nazha, S. Antonio, and J. House. "Improvement of time in range and glucose management indicator in Type 1 and Type 2 Diabetes Mellitus patients after introduction of continuous Glucose monitoring in internal medicine residency clinic." World Journal of Advanced Research and Reviews 17, no. 3 (2023): 824–30. http://dx.doi.org/10.30574/wjarr.2023.17.3.0478.
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Continuous glucose monitoring (CGM)-derived time in range (TIR) correlates with Glucose management indicator(GMI) which correlates with hemoglobin A1c (HbA1c) among patients with type 1 and type 2 diabetes mellitus. Compare to HbA1c it has better correlation with glucose control, because HbA1c can be influenced by conditions like anemia, Chronic kidney disease, Cirrhosis of the liver etc. With our current project we wanted to introduce CGM not in specialized endocrine clinic, but in Internal medicine Residency clinic. The CGM team had 12 Internal Medicine and Transitional year Residents who were functioning under the supervision of Board Certified Endocrinologist who was a member of the clinic also. Twenty Five patients -85% with type 2 DM and 15% with type 1 DM on multiple injections of Insulin per day-3-4, self-monitored their blood Glucose(SMBG). They were given CGM- Dexcom G6 in the clinic. In the first 2- weeks after the switch the TIR of the blood glucose of the patients-70-180 mg/dl was 18%. Their average blood glucose was 286 mg/dl, GMI was 11.21%. During then first 2- weeks after initiation of CGM the patients were educated in length by our CGM team about their diet, physical activity, how to adjust their Insulin based on their blood glucose levels as well as how to treat the hypoglycemia. Members of the CGM team were contacting the patients twice a week to adjust patients treatment with Insulin and other per oral antidiabetic medications and or injectable – GLP1-RAG if needed after consultation with the Endocrinologist in the clinic based on the shared information between the clinic and the patients. Once a month the patients were seen in the clinic by member of the CGM team as well. The patients were followed for 2- years. After 3- months on CGM and followed for the 2 years thereafter the patients TIR improved from 18 % to 74%, GMI decreased from 11.21% to 7.04% and the average blood glucose decreased from 286 mg/dl to 158 mg/dl. There was also significant reduction of the hypoglycemia. Twenty percent of the patients were able to discontinue their Insulin and be treated only with oral antidiabetic medications plus/minus GLP1-RAG and have GMI less than 7%. We have showed that targeted TIR – above 70% which has been associated recently with diabetic micro and macrovascular complications in diabetic patients can be achieved not only in specialized endocrine clinics , but in Internal Medicine residency clinic and can be adopted by other Internal Medicine Residency programs in USA.
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Leelarathna, Lalantha, Roy W. Beck, Richard M. Bergenstal, Hood Thabit, and Roman Hovorka. "Glucose Management Indicator (GMI): Insights and Validation Using Guardian 3 and Navigator 2 Sensor Data." Diabetes Care 42, no. 4 (2019): e60-e61. http://dx.doi.org/10.2337/dc18-2479.
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Welsh, John, Robert Dowd, and David A. Price. "Should Target Glucose Values Be Increased to Avoid Severe Hypoglycemia? Real-World Data Say “No.”." Journal of the Endocrine Society 5, Supplement_1 (2021): A462. http://dx.doi.org/10.1210/jendso/bvab048.943.
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Abstract Early studies such as the Diabetes Control and Complications Trial showed a strong inverse relationship between A1C and the risk of severe hypoglycemia in type 1 diabetes. This risk has historically limited insulin therapy intensification efforts, and some treatment guidelines (e.g., Rosenzweig et al., J Clin Endocrinol Metab 105:969, 2020) suggest that A1C values &lt;7% confer an increased risk of hypoglycemia. Nowadays, real-time continuous glucose monitoring (CGM) systems can flatten and attenuate the relationship between overall glucose control and hypoglycemia (Oliver et al., Diabetes Care 43:53, 2020). The glucose management indicator (GMI) is an estimate of A1C derived from the CGM system’s mean estimated glucose value (EGV) (Bergenstal et al., Diabetes Care 41:2275, 2018). We analyzed real-world evidence of the relationship between the GMI and exposure to hypoglycemia. Data were from an anonymized convenience sample of US-based users of the G6 CGM system (Dexcom, Inc., San Diego, CA) who used a mobile device to upload EGVs in the third quarter of 2020. Only data from people who had uploaded ≥80% of possible values were included. Each person’s GMI was calculated as GMI = 3.31 + (0.02392 × mean EGV [mg/dL]). Each person’s exposure to hypoglycemia was estimated as the percentage of EGVs &lt;70 mg/dL or &lt;54 mg/dL (%&lt;70 and %&lt;54, respectively). Patients were grouped into 6 categories according to GMI values &lt;6.5%, 6.5 to 6.9%, 7.0 to 7.4%, 7.5 to 7.9%, 8.0 to 8.4%, and ≥8.5%. Mean %&lt;70 mg/dL and %&lt;54 mg/dL were both inversely correlated with GMI, decreasing monotonically as the GMI category increased. GMI category, %&lt;70, and %&lt;54 are as follows: (&lt;6.5%: 5.27%, 1.13%); (6.5 to 6.9%: 2.84%, 0.59%); (7.0 to 7.4%: 1.95%, 0.41%); (7.5 to 7.9%: 1.46%, 0.31%); (8.0 to 8.4%: 1.14%, 0.25%); (≥8.5%: 0.69%, 0.17%). However, in all GMI categories except for the “&lt;6.5%” category, the extent of hypoglycemic exposure was below the consensus targets proposed by Battelino et al. (Diabetes Care 42:1593, 2019) of &lt;4% for EGVs &lt;70 mg/dL and &lt;1% for EGVs &lt;54 mg/dL. The approach of elevating A1C targets to reduce hypoglycemia risk is not supported by real-world evidence for CGM users who have GMI or A1C values ≥6.5%. CGM users can safely strive for A1C values &lt;7.0%.
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Mayeda, Laura, Ronit Katz, Iram Ahmad, et al. "Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease." BMJ Open Diabetes Research & Care 8, no. 1 (2020): e000991. http://dx.doi.org/10.1136/bmjdrc-2019-000991.
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ObjectiveCompared with hemoglobin A1c (HbA1c), continuous glucose monitoring (CGM) may better capture risk of diabetes complications in patients with chronic kidney disease (CKD), including diabetic peripheral neuropathy (DPN). We hypothesized that glucose time in range (TIR), measured by CGM, is associated with DPN symptoms among participants with type 2 diabetes mellitus (type 2 DM) and moderate-to-severe CKD.Research design and methodsWe enrolled 105 people with type 2 DM treated with insulin or sulfonylurea, 81 participants with CKD (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2) and 24 matched control participants with eGFR ≥60 mL/min/1.73 m2. Each participant wore a CGM for two 6-day periods. Calculated glycemic measures included TIR (glucose 70–180 mg/dL) and glucose management indicator (GMI). DPN symptoms were assessed using the Michigan Neuropathy Screening Instrument (MNSI) questionnaire, with a positive MNSI score defined as ≥2 symptoms.ResultsParticipants with CKD had a mean age of 68 years, diabetes duration 20 years, eGFR 38 mL/min/1.73 m2 and HbA1c 7.8%, 61 mmol/mol. Sixty-two participants reported ≥2 DPN symptoms, 51 (63%) with CKD and 11 (46%) controls. Less TIR and higher GMI were associated with higher risk of MNSI questionnaire score ≥2 (OR 1.25 (95% CI 1.02 to 1.52) per 10% lower TIR, and OR 1.79 (95% CI 1.05 to 3.04) per 1% higher GMI, adjusting for age, gender and race). Similar results were observed when analyses were restricted to participants with CKD. In contrast, there was no significant association of HbA1c with DPN symptoms.ConclusionsSymptoms of DPN were common among participants with long-standing type 2 DM and CKD. Lower TIR and higher GMI were associated with DPN symptoms.
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Foti Randazzese, Simone, Bruno Bombaci, Serena Costantino, Ylenia Giorgianni, Fortunato Lombardo, and Giuseppina Salzano. "Discordance between Glucose Management Indicator and Glycated Hemoglobin in a Pediatric Cohort with Type 1 Diabetes: A Real-World Study." Children 11, no. 2 (2024): 210. http://dx.doi.org/10.3390/children11020210.
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The introduction of continuous glucose monitoring (CGM) systems in clinical practice has allowed a more detailed picture of the intra- and interdaily glycemic fluctuations of individuals with type 1 diabetes (T1D). However, CGM-measured glucose control indicators may be occasionally inaccurate. This study aims to assess the discrepancy between the glucose management indicator (GMI) and glycated hemoglobin (HbA1c) (ΔGMI-HbA1c) within a cohort of children and adolescents with T1D, exploring its correlation with other CGM metrics and blood count parameters. In this single-center, cross-sectional study, we gathered demographic and clinical data, including blood count parameters, HbA1c values, and CGM metrics, from 128 pediatric subjects with T1D (43% female; mean age, 13.4 ± 3.6 years). Our findings revealed higher levels of the coefficient of variation (CV) (p < 0.001) and time above range > 250 mg/dL (p = 0.033) among subjects with ΔGMI-HbA1c > 0.3%. No association was observed between blood count parameters and ΔGMI-HbA1c. In conclusion, despite the advancements and the widespread adoption of CGM systems, HbA1c remains an essential parameter for the assessment of glycemic control, especially in individuals with suboptimal metabolic control and extreme glycemic variability.
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Préau, Yannis, Sébastien Galie, Pauline Schaepelynck, Martine Armand, and Denis Raccah. "Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §." Sensors 21, no. 18 (2021): 6131. http://dx.doi.org/10.3390/s21186131.
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The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style Libre 1 (FSL1) to Dexcom G4 (DG4) biosensors over 1 year. Patients (n = 21, 43 ± 15 years, BMI 25 ± 5, HbA1c 8.1 ± 1.0%) had severe hypoglycemia and/or HbA1c ≥ 8%. AGP metrics (time-in-range (TIR) 70–180 mg/dL, time-below-range (TBR) <70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50 ± 17 vs. 45 ± 16, p = 0.036), and lower TBR < 70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], p = 0.0007), TBR < 54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], p = 0.007) and %CV (39 ± 5 vs. 45 ± 8, p = 0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR < 70 mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs. “non-responders” warrant to be investigated.
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Guzman, Guillermo Edinson, María Fernanda Escobar, Oriana Arias-Valderrama, María Angélica Guerra, and Veline Martínez. "Clinical Experience of Using Telemedicine for the Management of Patients Using Continuous Subcutaneous Insulin Infusion in a Highly Complex Latin American Hospital." International Journal of Environmental Research and Public Health 20, no. 9 (2023): 5719. http://dx.doi.org/10.3390/ijerph20095719.
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Introduction: Continuous subcutaneous insulin infusion (CSII) has emerged as a potential solution for diabetes management during the pandemic, as it reduces the need for in-person visits and allows for remote monitoring of patients. Telemedicine has also become increasingly important in the management of diabetes during the pandemic, as it allows healthcare providers to provide remote consultations and support. Here, we discuss the implications of this approach for diabetes management beyond the pandemic, including the potential for increased access to care and improved patient outcomes. Methods: We performed a longitudinal observational study between 1 March 2020 and 31 December 2020 to evaluate glycemic parameters in diabetic patients with CSII in a telehealth service. Glycemic parameters were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability control. Results: A total of 36 patients were included in the study, with 29 having type 1 diabetes and 6 having type 2 diabetes. The study found that the proportion of patients achieving target glucose variability and GMI remained unchanged during follow-up. However, in patients with type 2 diabetes, the time in target range increased from 70% to 80%, and the time in hyperglycemia decreased from 2% to 0%. Conclusions: The results of this study suggest that telemedicine is a strategy for maintaining glycemic control in patients using CSII. However, the lack of access to the internet and adequate telemonitoring devices make it difficult to use on a large scale in emerging countries like ours.
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Piloya-Were, Thereza, Lucy W. Mungai, Antoinette Moran, et al. "Can HbA1c Alone Be Safely Used to Guide Insulin Therapy in African Youth with Type 1 Diabetes?" Pediatric Diabetes 2023 (April 24, 2023): 1–7. http://dx.doi.org/10.1155/2023/1179830.
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Introduction. The relationship of HbA1c versus the mean blood glucose (MBG) is an important guide for diabetes management but may differ between ethnic groups. In Africa, the patient’s glucose information is limited or unavailable and the management is largely guided by HbA1c. We sought to determine if the reference data derived from the non-African populations led to an appropriate estimation of MBG from HbA1c for the East African patients. Methods. We examined the relationship of HbA1c versus MBG obtained by the continuous glucose monitoring in a group of East African youth having type 1 diabetes in Kenya and Uganda (n = 54) compared with the data obtained from A1c-derived average glucose (ADAG) and glucose management indicator (GMI) studies. A self-identified White (European heritage) population of youth (n = 89) with type 1 diabetes, 3–18 years old, living in New Orleans, LA, USA metropolitan area (NOLA), was studied using CGM as an additional reference. Results. The regression equation for the African cohort was MBG (mg/dL) = 32.0 + 16.73 × HbA1c (%), r = 0.55, p < 0.0001 . In general, the use of the non-African references considerably overestimated MBG from HbA1c for the East African population. For example, an HbA1c = 9% (74.9 mmol/mol) corresponded to an MBG = 183 mg/dL (10.1 mmol/L) in the East African group, but 212 mg/dL (11.7 mmol/L) using ADAG, 237 mg/dL (13.1 mmol/L) using GMI and 249 mg/dL (13.8 mmol/L) using NOLA reference. The reported occurrence of serious hypoglycemia among the African patients in the year prior to the study was 21%. A reference table of HbA1c versus MBG from the East African patients was generated. Conclusions. The use of non-African-derived reference data to estimate MBG from HbA1c generally led to the overestimation of MBG in the East African patients. This may put the East African and other African patients at higher risk for hypoglycemia when the management is primarily based on achieving target HbA1c in the absence of the corresponding glucose data.
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Amadou, Coralie, Vincent Melki, Jennifer Allain, et al. "Performance and patients’ satisfaction with the A7+TouchCare insulin patch pump system: A randomized controlled non-inferiority study." PLOS ONE 18, no. 8 (2023): e0289684. http://dx.doi.org/10.1371/journal.pone.0289684.
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Background We assessed the performance and patient satisfaction of a new insulin patch pump, the A7+TouchCare (Medtrum), compared with the Omnipod system. Methods This multicenter, randomized, open-label, controlled study enrolled 100 adult patients with type 1 or type 2 diabetes mellitus (A1C ≥ 6.5% and ≤ 9.5%, i.e., 48 to 80 mmol/mol) who were assigned with the Omnipod or with the A7+TouchCare pump for 3 months. The primary study outcome was the glucose management indicator (GMI) calculated with continuous glucose monitoring (CGM). Results Premature withdrawals occurs respectively in 2 and 9 participants in the Omnipod and TouchCare groups. In the Per Protocol analysis, the difference in GMI between groups was 0.002% (95% confidence interval -0.251; 0.255). The non-inferiority was demonstrated since the difference between treatments did not overlap the pre-defined non-inferiority margin (0.4%). There was no significant difference in CGM parameters between groups. On average, patients in both groups were satisfied/very satisfied with the insulin pump system. Patients preferred Omnipod as an insulin management system and especially the patch delivery system but preferred the A7+TouchCare personal diabetes manager to control the system. Conclusions This study showed that the A7+TouchCare insulin pump was as efficient as the Omnipod pump in terms of performance and satisfaction. Clinical trail registration The study was registered in the ClinicalTrials.gov protocol register (NCT04223973).
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Christou, Maria A., Panagiota A. Christou, Daphne N. Katsarou, et al. "Effect of Body Weight on Glycaemic Indices in People with Type 1 Diabetes Using Continuous Glucose Monitoring." Journal of Clinical Medicine 13, no. 17 (2024): 5303. http://dx.doi.org/10.3390/jcm13175303.
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Background/Objectives: Obesity and overweight have become increasingly prevalent in different populations of people with type 1 diabetes (PwT1D). This study aimed to assess the effect of body weight on glycaemic indices in PwT1D. Methods: Adult PwT1D using continuous glucose monitoring (CGM) and followed up at a regional academic diabetes centre were included. Body weight, body mass index (BMI), waist circumference, glycated haemoglobin (HbA1c), and standard CGM glycaemic indices were recorded. Glycaemic indices were compared according to BMI, and correlation and linear regression analysis were performed to estimate the association between measures of adiposity and glycaemic indices. Results: A total of 73 PwT1D were included (48% normal weight, 33% overweight, and 19% obese). HbA1c was 7.2% (5.6–10), glucose management indicator (GMI) 6.9% (5.7–8.9), coefficient of variation (CV) for glucose 39.5% ± 6.4, mean glucose 148 (101–235) mg/dL, TIR (time in range, glucose 70–180 mg/dL) 66% (25–94), TBR70 (time below range, 54–69 mg/dL) 4% (0–16), TBR54 (<54 mg/dL) 1% (0–11), TAR180 (time above range, 181–250 mg/dL) 20% ± 7, and TAR250 (>250 mg/dL) 6% (0–40). Glycaemic indices and achievement (%) of optimal glycaemic targets were similar between normal weight, overweight, and obese patients. BMI was associated negatively with GMI, mean glucose, TAR180, and TAR250 and positively with TIR; waist circumference was negatively associated with TAR250. Conclusions: CGM-derived glycaemic indices were similar in overweight/obese and normal weight PwT1D. Body weight and BMI were positively associated with better glycaemic control. PwT1D should receive appropriate ongoing support to achieve optimal glycaemic targets whilst maintaining a healthy body weight.
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Parise, Martina, Linda Tartaglione, Antonio Cutruzzolà, et al. "Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study." Journal of Medical Internet Research 23, no. 4 (2021): e24552. http://dx.doi.org/10.2196/24552.
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Background Telemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers. Objective During the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period. Methods This prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability. This metric was generated using specific computer programs that automatically upload data from the devices used to monitor blood or interstitial glucose levels. If needed, we changed the ongoing treatment at the first virtual visit. Results Among 209 eligible patients with T1D, 166 completed 2 virtual visits, 35 failed to download glucose data, and 8 declined the visit. Among the patients not included in the study, we observed a significantly lower proportion of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) users (n=7/43, 16% vs n=155/166, 93.4% and n=9/43, 21% vs n=128/166, 77.1%, respectively; P<.001) compared to patients who completed the study. TIR significantly increased from the first (62%, SD 18%) to the second (65%, SD 16%) virtual visit (P=.02); this increase was more marked among patients using the traditional meter (n=11; baseline TIR=55%, SD 17% and follow-up TIR=66%, SD 13%; P=.01) than among those using CGM, and in those with a baseline GMI of ≥7.5% (n=46; baseline TIR=45%, SD 15% and follow-up TIR=53%, SD 18%; P<.001) than in those with a GMI of <7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; P=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95% CI were 5 (95% CI 0.7-8.0) (P=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters. Conclusions These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.
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Pluchino, Kristen M., Yiduo Wu, Alain D. Silk, Jisun Yi, and Courtney H. Lias. "Comment on Bergenstal et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018;41:2275–2280." Diabetes Care 42, no. 2 (2018): e28-e28. http://dx.doi.org/10.2337/dc18-2366.
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Kim, Hwa Young, So Hyun Shin, Hyunju Lee, and Jaehyun Kim. "Changes in metrics of continuous glucose monitoring during COVID-19 in Korean children and adolescents with type 1 diabetes mellitus." Annals of Pediatric Endocrinology & Metabolism 30, no. 1 (2025): 38–44. https://doi.org/10.6065/apem.2448036.018.
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Purpose: There are limited data regarding changes in glucose control in pediatric patients with type 1 diabetes (T1D) affected by coronavirus disease 2019 (COVID-19). This study aimed to evaluate changes in the metrics of a continuous glucose monitoring (CGM) system during COVID-19 infection in children and adolescents with T1D.Methods: Eighteen patients with T1D (<18 years of age) were included in this retrospective study. The effects of COVID-19 on CGM metrics were assessed at 5 time points (2 weeks before COVID-19 [time 1], 1 week before COVID-19 [time 2], during COVID-19 [time 3], 1 week after COVID-19 [time 4], and 2 weeks after COVID-19 [time 5]).Results: All participants had at least 1 symptom of COVID-19 and did not need to be hospitalized. The glucose management indicator (GMI) was higher at time 3 (7.7%±1.4%) compared to time 1 (7.1%±1.1%; P=0.016) and time 5 (7.0%±1.2%; P=0.008). According to the insulin delivery method, the GMI at time 3 was significantly higher than that at time 5 in patients treated with multiple daily injections (MDI) (median and interquartile range, 8.0% [6.1%–8.5%] vs. 7.1% [5.8%–7.9%]; P=0.020) but not in those treated with continuous subcutaneous insulin infusion (CSII).Conclusion: Pediatric patients with T1D and mild COVID-19 showed worsening glycemic control during COVID-19 infection, but it returned to preinfection levels within 2 weeks of infection. CSII is more effective in maintaining stable glycemic control during COVID-19 infection than is MDI therapy.
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Munawar, Faizan, John Donovan, Etain Kiely, and Konrad Mulrennan. "Temporal Analysis of Glycaemic Variability Metrics." International Journal of Integrated Care 25 (April 9, 2025): 365. https://doi.org/10.5334/ijic.icic24451.
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Introduction/Background: Glycaemic Variability (GV) is a widely used measure in managing type 1 diabetes mellitus, describing the fluctuations in blood glucose (BG) levels over time. High GV is linked to chronic complications like micro- and macrovascular diseases. Factors contributing to GV include both external factors (diet, activity, medications) and internal factors (glucose absorption, insulin sensitivity). High GV has also been linked to an increased risk of hypoglycaemia and reduced quality of life. Therefore, minimising GV is an important goal in diabetes management. GV can be measured using various statistics, such as standard deviation, coefficient of variation (CV%), glucose management indicator (GMI), which estimates lab-tested HbA1c, average daily risk range (ADRR) measuring daily risk, high BG index (HBGI) and low BG index (LBGI) for hyperglycaemia hypoglycaemia risk, J-index for glucose control quantification, time in range (TIR), time outside range (TOR), and Glycaemia Risk Index (GRI) summarising glycaemia quality, among other methods. Methods: This study analyses the OhioT1DM dataset using GV metrics from continuous glucose monitoring (CGM), employing a rolling window approach. Each metric assesses a different aspect of GV, quantifying BG control. GMI estimates average BG over 3 months, while ADRR, LBGI, and HBGI classify hypoglycaemia and hyperglycaemia risks into different levels. Additionally, the J-index assesses glucose control using mean and standard deviation, while time in range measures the duration within the target range. GRI provides a comprehensive risk summary. Analysing these metrics collectively offers insights into type 1 diabetes management for individuals using CGM and insulin pump therapy. Each statistic is calculated over a 14-day rolling window, shifting one day at a time. This method captures trends and trajectories for individual statistics effectively. Subsequently, various time series forecasting algorithms are explored including Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX) and Support Vector Regression (SVR) to predict future values, followed by a comparative assessment of these algorithms. Results: Subjects present conflicting results in various diabetes management statistics. While some show GMI within the target range, indicating satisfactory medium to long-term control, the J-index and HBGI suggest inadequate control and high hyperglycaemia risk. This demonstrates the necessity for a comprehensive assessment of metrics for diabetes control evaluation. The conflicting results might stem from statistical biasness towards hypoglycaemia or hyperglycaemia. GRI resolves this by combining both risks of hypoglycaemia and hyperglycaemia. Additionally, analysis by rolling window reveals trends towards an increased risk of hypoglycaemia and hyperglycaemia among specific subjects. Closer examination of the trend lines demonstrates similar trajectories between several metrics. Conclusion: This study holds potential to significantly influence diabetes self-management by offering valuable insights into disease management. Employing various measures of GV allows for a comprehensive analysis of BG control and enhances the understanding of self-management practices. The utilisation of a rolling window not only reveals trends and trajectories but also aids in predicting future values and assessing the risk of complications among individuals with diabetes. The comparison of foundational forecasting algorithms serves as a crucial basis for further investigations and analyses in the respective field.
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Bergenstal, Richard M., Roy W. Beck, Kelly L. Close, et al. "Response to Comment on Bergenstal et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018;41:2275–2280." Diabetes Care 42, no. 2 (2018): e29-e30. http://dx.doi.org/10.2337/dci18-0061.
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Rutkowska, Katarzyna, Agnieszka Łoś-Stegienta, Michał Bagiński, et al. "Impact of the FreeStyle Libre 2® System on Glycaemic Outcomes in Patients with Type 1 Diabetes—Preliminary Study." Diagnostics 14, no. 16 (2024): 1777. http://dx.doi.org/10.3390/diagnostics14161777.
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We aimed to evaluate glycaemic control in patients with type 1 diabetes during the first three months of use of the flash glucose monitoring (FGM) system. Methods: We conducted a study of a cohort of 81 people with type 1 diabetes mellitus who used the FreeStyle Libre 2 (FSL2) sensor continuously for 3 months. Patients had not used a CGM before. The effectiveness of using the FSL2 system was assessed using AGP reports at two time points (3–4 weeks and 11–12 weeks of system use). Results: Eight weeks after using FSL2, compared with results from 3–4 weeks of use, there were no differences in the glucose management indicator, time spent in range, above range and below range, or glucose variability. In the first month of FGM use, patients scanned the sensor significantly more often than in the following two months (p = 0.021). No significant differences were found in the change of the evaluated parameters when comparing patients by duration of diabetes and treatment method. Conclusions: Short-term use of FSL2 promotes a significant reduction in GMI in patients with more time spent in hyperglycaemia (especially > 250 mg/dL). In this short period of use, no other changes in glycaemic control parameters are observed.
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Selvin, Elizabeth. "The Glucose Management Indicator: Time to Change Course?" Diabetes Care, January 31, 2024. http://dx.doi.org/10.2337/dci23-0086.
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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.
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Meek, Claire L., Denice S. Feig, Eleanor M. Scott, Rosa Corcoy, and Helen R. Murphy. "Lack of Validity of the Glucose Management Indicator in Type 1 Diabetes in Pregnancy." Diabetes Care, April 2, 2025. https://doi.org/10.2337/dc24-2494.
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OBJECTIVE The glucose management indicator (GMI) is widely used as a replacement for HbA1c, but information in pregnancy is very limited. We assessed the accuracy of GMI and associations with pregnancy outcomes in type 1 diabetes. RESEARCH DESIGN AND METHODS We compared HbA1c, continuous glucose monitoring (CGM) metrics, GMI at 12, 24, and 34 weeks’ gestation and outcomes in 220 women from the CGM in pregnant women with type 1 diabetes (CONCEPTT) trial using logistic/linear regression and Bland-Altman plots. RESULTS GMI equations performed less accurately in pregnancy, with higher bias, especially in first and third trimesters. GMI and mean CGM glucose had equivalent predictive capability over pregnancy outcomes. GMI did not offer additional predictive capability over time-in-range (63–140 mg/dL; 3.5–7.8 mmol/L), time-above-range (&gt;140 mg/dL; &gt;7.8 mmol/L), and average CGM glucose concentrations. CONCLUSIONS GMI is not an accurate replacement for HbA1c in pregnancy in women with type 1 diabetes.
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Monzon, Alexandra D., Susana R. Patton, and Mark Clements. "An Examination of the Glucose Management Indicator in Young Children with Type 1 Diabetes." Journal of Diabetes Science and Technology, June 7, 2021, 193229682110231. http://dx.doi.org/10.1177/19322968211023171.
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Background: Previous studies utilizing glucose data from continuous glucose monitors (CGM) to estimate the Glucose Management Indicator (GMI) have not included young children or determined appropriate GMI formulas for young children with type 1 diabetes (T1D). Methods: We extracted CGM data for 215 children with T1D (0-6 years) from a repository. We defined sampling periods ranging from the 3-27 days prior to an HbA1c measurement and compared a previously established GMI formula to a young child-specific GMI equation based on the sample’s CGM data. We examined associations between HbA1c, GMI values, and other CGM metrics for each sampling period. Results: The young child-specific GMI formula and the published GMI formula did not evidence significant differences when using 21-27 days of CGM data. The young child-specific GMI formula demonstrated higher correlations to laboratory HbA1c when using 18 or fewer days of CGM data. Overall, the GMI estimate and HbA1c values demonstrate a strong relationship in young children with T1D. Conclusions: Future research studies may consider utilizing the young child-specific GMI formula if the data collection period for CGM values is under 18 days. Further, researchers and clinicians may consider changing the default number of days of data used to calculate glycemic metrics in order to maximize validity of CGM-derived metrics.
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Fang, Michael, Dan Wang, Mary R. Rooney, et al. "Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes." Clinical Chemistry, February 4, 2023. http://dx.doi.org/10.1093/clinchem/hvac210.
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Abstract Background The glucose management indicator (GMI) is an estimated measure of hemoglobin A1c (HbA1c) recommended for the management of persons with diabetes using continuous glucose monitoring (CGM). However, GMI was derived primarily in young adults with type 1 diabetes, and its performance in patients with type 2 diabetes is poorly characterized. Methods We conducted a prospective cohort study in 144 adults with obstructive sleep apnea and type 2 diabetes not using insulin (mean age: 59.4 years; 45.1% female). HbA1c was measured at the study screening visit. Participants simultaneously wore 2 CGM sensors (Dexcom G4 and Abbott Libre Pro) for up to 4 weeks (2 weeks at baseline and 2 weeks at the 3-month follow-up visit). GMI was calculated using all available CGM data for each sensor. Results Median wear time was 27 days (IQR: 23–29) for the Dexcom G4 and 28 days (IQR: 24–29) for the Libre Pro. The mean difference between HbA1c and GMI was small (0.12–0.14 percentage points) (approximately 2 mmol/mol). However, the 2 measures were only moderately correlated (r = 0.68–0.71), and there was substantial variability in GMI at any given value of HbA1c (root mean squared error: 0.66–0.69 percentage points [7 to 8 mmol/mol]). Between 36% and 43% of participants had an absolute difference between HbA1c and GMI ≥0.5 percentage points (≥5 mmol/mol), and 9% to 18% had an absolute difference &gt;1 percentage points (&gt;11 mmol/mol). Discordance was higher in the Libre Pro than the Dexcom G4. Conclusions GMI may be an unreliable measure of glycemic control for patients with type 2 diabetes and should be interpreted cautiously in clinical practice. Clinicaltrials.gov Registration Number: NCT02454153.
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JHUANG, AN-TING, SAM BACON, SAMIRA KAMRUDIN, NELS THOMPSON, and CALLAHAN CLARK. "942-P: A1C-Glucose Management Indicator (GMI) Discordance among Continuous Glucose Monitor (CGM)–Wearing Adults with T2D." Diabetes 72, Supplement_1 (2023). http://dx.doi.org/10.2337/db23-942-p.
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ADA Standards of Medical Care considers 14-day CGM-based GMI an A1c surrogate for clinical management. To date, supporting evidence is from T1D or insulin-treated T2D populations. As CGM demand and access grows, quantifying A1c-GMI discordance in diverse T2D populations can inform practice. We evaluated this discordance, stratified by T2D drugs and A1c. CGM-wearing adults with T2D, an overlapping A1c and GMI date (10/2019 to 3/2022), and CGM data sufficiency (DS) ≥ 70% were included. Prior 3 month pharmacy claims defined drug histories. We calculated the Pearson correlation coefficient (r) and A1c-GMI differences by subgroup. A1c-GMI pairs existed for 2,760 people (mean [SD] age 55 [9]; 46% F; 58% had A1c &lt;7%). Overall, pairs highly correlated (56% differed by &lt;0.5%; r=0.80). Non-insulin T2D drug users (N=1,674) had slightly higher concordance than basal insulin (N=437) and basal-bolus insulin users (N=317), with 58%, 50%, and 56% discordance &lt;0.5% (r=0.79, 0.77, 0.77). On average, A1c underestimated GMI for non-insulin users and overestimated GMI for basal and basal-bolus insulin users. Concordance decreased at A1c extremes, with 61-76% concordance for A1cs from 5.7% to 8%. Sensitivity analyes with lower DS had similar results. A1c-GMI discordance varied by T2D drug class and A1c, highlighting how CGM-derived measures can provide personalized insights for diverse T2D populations. Disclosure A.Jhuang: Employee; UnitedHealth Group, Stock/Shareholder; UnitedHealth Group. S.Bacon: Employee; Optum Labs, Research Support; Level2. S.Kamrudin: Employee; UnitedHealth Group. N.Thompson: Employee; Level2, Stock/Shareholder; UnitedHealth Group. C.Clark: Employee; UnitedHealth Group, Stock/Shareholder; UnitedHealth Group.
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Lenters‐Westra, Erna, Marion Fokkert, Eric S. Kilpatrick, et al. "Managing discordance between HbA1c and glucose management indicator." Diabetic Medicine, March 23, 2025. https://doi.org/10.1111/dme.70023.
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AbstractAimsThe assessment of haemoglobin A1c (HbA1c) continues to play an essential role in diabetes care; however, major advances in new technologies widen the armament available to clinicians to further refine treatment for their patients. Whilst HbA1c remains a critical glycaemic marker, advances in technologies such as Continuous Glucose Monitoring (CGM) now offer real‐time glucose monitoring, allowing a more instant assessment of glycaemic control. Discrepancies between laboratory‐measured HbA1c and Glucose Management Indicator (GMI) values are a significant clinical issue. In this article, we present a checklist of potential sources of error for both GMI and HbA1c values and provide suggestions to mitigate these sources in order to continue to improve diabetes care.MethodsWe identified key literature pertaining to GMI measurement, HbA1c measurement, and potential factors of discordance between the two. Using these sources, we explore the potential factors leading to discordance and how to mitigate these when found.ResultsWe have constructed a quick reference checklist covering the main sources of discordance between HbA1c and GMI, with accompanying narrative text for more detailed discussion. Discordance can arise due to various factors, including CGM accuracy, sensor calibration, red blood cell turnover and other physiological conditions.ConclusionsGMI will likely continue to be used in the upcoming years by both persons with diabetes and their health care providers, and so it is important for users of CGM devices to be equipped with the knowledge to understand the potential causes of discordance between GMI and HbA1c values.
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BERGENSTAL, RICHARD M., YONGJIN XU, TIMOTHY DUNN, PRATIK CHOUDHARY, and RAMZI AJJAN. "165-OR: Improving A1C and CGM Average Glucose Alignment—The Updated Glucose Management Indicator (GMI)." Diabetes 74, Supplement_1 (2025). https://doi.org/10.2337/db25-165-or.
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Introduction and Objective: The not-infrequently observed disconnect between A1C and GMI, particularly at low and high A1C levels, can create clinical management difficulties. Our aim was to evaluate the agreement of an updated GMI metric (uGMI) with A1C and potential use in diabetes management. Methods: The GDAC study (n=257) collected CGM data and bi-monthly A1C for 26 weeks for those with diabetes across different race groups. GMI was calculated by an empirical known formula employing average glucose (AG) levels, GMI(%)=0.02392*AG+3.31, while the uGMI formula accounts for population-based red blood cell factors, uGMI(%) = (15.36/AG+0.0426)-1. Analysis was replicated in a real-world dataset (n=2,074). Results: GDAC showed a disconnect between original GMI and A1C with regression slope value up to 25% outside unity, a difference more pronounced in the Black group at 40%. Using uGMI, regression slopes improved to within 2 and 6% of unity in the non-Black and Black groups, respectively. For the same AG, the Black group displayed higher A1C values than the non-Black group, at all HbA1c ranges.The misalignment between GMI and A1C was more pronounced in real-world data with values up to 50% outside unity, improving to within 3% of unity with uGMI. Conclusion: The uGMI metric is superior to the original GMI at reflecting A1C, promising to refine interpretation of CGM data and improve clinical decision-making. Disclosure R.M. Bergenstal: Advisory Panel; Abbott. Research Support; Abbott. Consultant; Abbott. Research Support; Dexcom, Inc. Consultant; Dexcom, Inc., Eli Lilly and Company. Research Support; Eli Lilly and Company. Consultant; Novo Nordisk. Research Support; Novo Nordisk. Consultant; Roche Diabetes Care, Sanofi, Medtronic. Research Support; Medtronic, Insulet Corporation, Hemsley Charitable Trust. Other Relationship; MannKind Corporation. Consultant; Medscape. Research Support; National Institute of Diabetes and Digestive and Kidney Diseases, Tandem Diabetes Care, Inc, UnitedHealth Group. Consultant; Vertex Pharmaceuticals Incorporated, Ascensia Diabetes Care, American Diabetes Association, Hygieia, embecta, Senseonics, Zealand Pharma A/S. Y. Xu: Employee; Abbott. T. Dunn: Employee; Abbott. Other Relationship; Omada Health. P. Choudhary: Speaker's Bureau; Abbott. Advisory Panel; Dexcom, Inc. Consultant; Insulet Corporation. Advisory Panel; Ypsomed AG, embecta, Sanofi. Speaker's Bureau; Lilly Diabetes, Medtronic. Advisory Panel; Roche Diabetes Care. Consultant; Cambridge Mechatronics. R. Ajjan: Research Support; Abbott. Speaker's Bureau; Abbott, Boehringer-Ingelheim. Advisory Panel; AstraZeneca, Novo Nordisk.
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Oriot, Philippe, Claire Viry, Antoine Vandelaer, et al. "Discordance Between Glycated Hemoglobin A1c and the Glucose Management Indicator in People With Diabetes and Chronic Kidney Disease." Journal of Diabetes Science and Technology, April 25, 2022, 193229682210920. http://dx.doi.org/10.1177/19322968221092050.
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Introduction: Assessment of glucose exposure via glycated hemoglobin A1c (HbA1c) has limitations for interpretation in individuals with diabetes and chronic kidney disease (CKD). The glucose management indicator (GMI) derived from continuous glucose monitoring (CGM) data could be an alternative. However, the concordance between HbA1c measured in laboratory and GMI (HbA1c-GMI) is uncertain in individuals with CKD. The purpose of this study is to analyze this discrepancy. Material and method: We performed a multicentric, retrospective, observational study. A group of individuals with diabetes and CKD (n = 170) was compared with a group of individuals with diabetes without CKD (n = 185). All individuals used an intermittently scanned continuous glucose monitoring (isCGM). A comparison of 14-day and 90-day glucose data recorded by the isCGM was performed to calculate GMI and the discordance between lab HbA1c and GMI was analyzed by a Bland-Altman method and linear regression. Results: HbA1c-GMI discordance was significantly higher in the CKD group versus without CKD group (0.78 ± 0.57 [0.66-0.90] vs 0.59 ± 0.44 [0.50-0.66]%, P < .005). An absolute difference >0.5% was found in 68.2% of individuals with CKD versus 42.2% of individuals without CKD. We suggest a new specific formula to estimate HbA1c from the linear regression between HbA1c and mean glucose CGM, namely CKD-GMI = 0.0261 × 90-day mean glucose (mg/L) + 3.5579 ( r2 = 0.59). Conclusions: HbA1c-GMI discordance is frequent and usually in favor of an HbA1c level higher than the GMI value, which can lead to errors in changes in glucose-lowering therapy, especially for individuals with CKD. This latter population should benefit from the CGM to measure their glucose exposure more precisely.
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BOVEE, LAURA, JORDAN PERLMAN, TED GOOLEY, and IRL B. HIRSCH. "107-OR: Association of Diabetic Retinopathy with A1C and Glucose Management Indicator Discordance." Diabetes 72, Supplement_1 (2023). http://dx.doi.org/10.2337/db23-107-or.
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Background: Past analyses have shown significant discordance between glycated hemoglobin (A1C) and glucose management indicator (GMI). This study uses a glycation ratio (GR) defined as GMI/A1C and evaluates the association of this ratio with microvascular complications in 661 patients with diabetes. Methods: A retrospective review was done of patients with diabetes using CGM who were seen at the University of Washington (UW) Diabetes Care Center from 2012-2019. All patients had A1C and sensor data obtained fewer than 4 weeks apart. Positive discordance was defined as GMI/A1c &lt;0.9. Results: This study included 661 patients of whom 90% had type 1 diabetes with a mean duration of current CGM use of 24.7 days (+/- 7.9 days). Positive discordance was found in 29.9% of participants and was associated with retinopathy (OR 2.05, CI 1.33-3.14) and nephropathy (OR 2.83, CI 1.68-4.77). The data were modeled as a continuous nonlinear function revealing an inverse relationship between GMI/A1C and diabetic retinopathy. Discussion: In this study positive discordance conferred a more than two-fold increased risk for diabetic retinopathy and nephropathy. Modeling of GMI/A1C as a continuous variable upheld this relationship for diabetic retinopathy. These data suggest that GMI/A1C ratios may allow for identification of patients at increased risk for microvascular complications. Disclosure L.Bovee: None. J.Perlman: None. T.Gooley: None. I.B.Hirsch: Consultant; Abbott Diabetes, Lifecare, Inc., Hagar, Research Support; Beta Bionics, Inc., Insulet Corporation, Dexcom, Inc.
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CHEN, DANRUI, ZHIGU LIU, BEISI LIN, et al. "946-P: Impacts of MAGE and SD on the Relationship between TIR and Glucose Management Indicator in T2D Patients." Diabetes 72, Supplement_1 (2023). http://dx.doi.org/10.2337/db23-946-p.
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Aims: Previous studies proved that glucose coefficient of variation (CV), an index of glycemic variability (GV), affected the relationship between TIR and glucose management indicator (GMI) in patients with diabetes. This study aimed to explore whether other GV indexes, including MAGE and SD of glucose, affect the relationship between TIR and GMI in T2D patients. Methods: Data were collected from T2D patients who received 48-h to 72-h CGM with iPro2 between January 2018 and October 2019. GV indexes, TIR, and GMI were derived from iPro2. A linear regression equation was generated with values of TIR and GMI. Patients were then divided into 3 subgroups according to the values of MAGE or SD. Predicted TIR in given GMI was calculated via the linear regression equations in the respective tertiles groups of MAGE or SD. Results: A total of 118 T2D patients who were 57.2±11.6 years old, with a median diabetes duration of 6.0 years, HbA1c of 6.9±1.2%, and TIR of 87.5±12.9% were included. There was a strong negative correlation between TIR and GMI (r=-0.762, P&lt;0.001). The linear regression equation was TIR%=-21.2× GMI%+224.6. Multiple linear regression analysis showed that both MAGE and SD have impacts on the relationship between TIR and GMI (P&lt;0.001). The predicted TIR decreased when MAGE or SD increased with different given GMI (Table 1). Conclusions: MAGE and SD influenced the relationship between TIR and GMI in T2D patients. Disclosure D.Chen: None. Z.Liu: None. B.Lin: None. D.Yang: None. J.Yan: None. Y.Yang: None. W.Xu: None.
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SHAH, VIRAL, TIMOTHY B. VIGERS, LAURA PYLE, STEPHANIE DUBOSE, PETER CALHOUN, and RICHARD M. BERGENSTAL. "88-OR: Discordance between Glucose Management Indicator (GMI) and A1C in Well-Controlled Type 1 Diabetes (T1D) and Nondiabetic Population." Diabetes 71, Supplement_1 (2022). http://dx.doi.org/10.2337/db22-88-or.
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Glucose management indicator (GMI) was developed using a linear regression formula to estimate A1C from mean glucose. The study population that was used to develop and validate GMI had a mean A1C of 7.3 ± 0.8%. Therefore, we hypothesize that GMI may overestimate A1C in those with A1C &lt;6.5%. Non-pregnant adults (≥18 years) with T1D for ≥2 years and using Dexcom G6 for ≥6 months from Barbara Davis Center for Diabetes (n=93, 40.6 ± 12.6 years, female 58.4%, A1C 5.9 ± 0.4%) and subjects from “Glucose Sensor Profile in Healthy Non-diabetic Subjects” study (N=153, 31.2± 21.0 years, female 66.0%, A1C 5.1 ± 0.3%) were included. All participants used Dexcom G6 continuous glucose monitor (CGM) . Up to days (healthy participants) and 15 days (T1D patients) of CGM data was compared with laboratory A1C. Discordance in GMI and A1C is presented by A1C &lt;5.7% (healthy participants) and A1C between 5.7-6.4% (T1D patients) . The degree of discordance between GMI and A1C was higher in those with A1C&lt;6.5% compared to original GMI development cohort [Figure 1A]. The differences between A1C and GMI were largely negative in both A1C categories (A1C &lt;5.7% and A1C of 5.7-6.4%) . On average, GMI was 0.59% higher in the A1C &lt; 5.7% group and 0.49% higher in the A1C 5.7 - 6.4% group (both p &lt; 0.0001) [Figure 1B and 1C]. The current GMI formula may overestimate A1C and may need to be re-assessed for those with A1C &lt;6.5%. Disclosure V.Shah: Advisory Panel; Medscape, Sanofi, Consultant; Dexcom, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Novo Nordisk. T.B.Vigers: None. L.Pyle: None. S.Dubose: None. P.Calhoun: None. R.M.Bergenstal: Advisory Panel; Hygieia, Medtronic, Roche Diabetes Care, Zealand Pharma A/S, Consultant; Abbott Diabetes, Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur SA, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, Onduo LLC, Sanofi, United HealthCare Services, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi.
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Yoo, Jee Hee, Sun Joon Moon, Cheol-Young Park, and Jae Hyeon Kim. "Differences Between Glycated Hemoglobin and Glucose Management Indicator in Real-Time and Intermittent Scanning Continuous Glucose Monitoring in Adults With Type 1 Diabetes." Journal of Diabetes Science and Technology, July 29, 2024. http://dx.doi.org/10.1177/19322968241262106.
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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.
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CACCELLI, MILENA, CIGDEM OZKAN, HALA ZAKARIA, et al. "1117-P: Comparison between Glucose Management Indicator (GMI) with HbA1c among Patients in a Hybrid Therapeutics Care Model." Diabetes 73, Supplement_1 (2024). http://dx.doi.org/10.2337/db24-1117-p.
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GMI is frequently used as a substitute for HbA1c, especially when using remote monitoring, and the difference between them has a clinical relevance¹. In this study, GMI and Hba1c, along with other glycemic parameters, are examined in patients with Type 1 diabetes, Type 2 diabetes (T2D), and prediabetes using the Freestyle libre sensor during hybrid (both physical and continuous remote monitoring) therapeutic care (GluCare.Health).A retrospective observational study included 45 adults who used CGM &gt; 70 % in 3 months. Time in range (TIR), average glucose (AG), GMI, and percentage coefficient of variation (% CV) were collected from each patient's 90-day Libreview report, along with their HbA1c. Patients were monitored in-clinic and remotely through GluCare.health. HbA1c and GMI were calculated and categorized into four categories: &lt;0.1, 0.1-0.4, 0.5-0.9, and &gt; 1.Mean patient's age was 41.7 ± 16.8 years. HbA1c and GMI were different in 86.4 % of the patients. Significant differences in AG, TIR, %CV, HbA1c and GMI were observed among the 3 groups (p&lt;0.001) (Table 1). Significance between categories of HbA1c - GMI across all cohorts, with T1D showing a higher proportion of variation compared to T2D and prediabetes (p = 0.034). HbA1c-GMI can be used in immediate decision making to manage diabetes when practicing a hybrid care model that uses continuous remote monitoring together with physical visits. Disclosure M. Caccelli: None. C. Ozkan: None. H. Zakaria: None. J. Kattan: None. R. Sultan: None. Y. Said: None. A. Hashemi: None. I. Almarzooqi: None.
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SILLER, ALEJANDRO F., JAMES K. SICKLER, CAROLINA VILLEGAS, et al. "1181-P: Differences in Hemoglobin A1c and CGM-Derived Glucose Management Indicator (GMI) in Youth with Type 1 Diabetes." Diabetes 73, Supplement_1 (2024). http://dx.doi.org/10.2337/db24-1181-p.
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Introduction: The relationship of A1c vs. GMI across race/ethnicity has significant differences between Non-Hispanic White (NHW) vs. Non-Hispanic Black (NHB) adults; however, data in ethnically diverse children are lacking. We assessed the correlation of A1c vs. GMI in a large, diverse pediatric population with T1D. Methods: T1D youth with ≥70% CGM use over the prior 14 days at the time of A1c were included. Delta (defined as GMI minus A1c) was categorized as ≤-0.5%, &gt;-0.5 to &lt;0.5%, ≥0.5% with absolute value of ≥0.5% considered clinically significant. Proportional odds regression was used to evaluate if race/ethnicity was associated with larger difference category. Results: In 969 youth (age 11.6 ± 3.8y; 53% female; T1D duration 4.4 ± 3.4y), mean A1c was 7.71% and mean GMI was 7.98%. Delta overall and across race/ethnicity are presented in Table 1. Hispanic (OR 2.17; 95% CI 1.44-3.26; p&lt;0.001) and NHW (OR 2.22; 95% CI 1.53-3.22; p&lt;0.001) youth had significantly higher likelihood of having Delta (GMI - A1c) of ≥0.5% compared to NHB youth. Conclusion: In this diverse pediatric population with T1D, NHW and Hispanic youth were more likely to have a clinically significant discrepancy in which GMI is greater than A1c as compared to NHB youth. Clinical relevance of the A1c vs. GMI relationship, including which best predicts risk of micro- and macrovascular complications, warrants further study. Disclosure A.F. Siller: None. J.K. Sickler: None. C. Villegas: None. X.C. Huang: None. C.G. Minard: None. M.J. Redondo: None. D. DeSalvo: Advisory Panel; Insulet Corporation. Consultant; Dexcom, Inc.
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LIU, ZHIGU, BEISI LIN, DANRUI CHEN, et al. "965-P: The Related Factors Affecting the Relationship between HbA1c and Glucose Management Indicator in Adult T2D Patients with Good Glycemic Control." Diabetes 72, Supplement_1 (2023). http://dx.doi.org/10.2337/db23-965-p.
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Aim: Glucose Management Indicator (GMI) is correlated with HbA1c, while studies reported the discrepancy between them. Factors affecting this discrepancy, especially in patients with good glycemic control, remain unclear. This study was designed to explore the related factors affecting the relationship between HbA1c and GMI in adult T2D patients with good glycemic control. Methods: Adult T2D patients with good glycemic control who received HbA1c test and CGM were retrospectively analyzed. GMI and glycemic variability (GV) indices including SD and MAGE were derived from CGMS. The absolute value of hemoglobin glycation index (HbA1c minus GMI) (|HGI|) was used to quantify the difference between HbA1c and GMI. Linear regression and correlation analyses were used to analyze the correlation between GV indices and |HGI|, HbA1c and GMI and whether GV affected their relationship. Results: Eighty-four patients (median HbA1c 6.6%, median GMI 6.4%) were included. |HGI| was higher than 0.5% in 40% of the patients (n=34). |HGI| was linearly correlated with SD and MAGE (β = 0.291 and 0.294, P&lt;0.05). HbA1c was linearly correlated with GMI (β=0.525, P&lt;0.001). This correlation remained after adjusting for sex, age, diabetes course, BMI, hemoglobin level and with chronic diabetic complications or not (Model 1, β=0.496, P&lt;0.001). Further adjusting for SD (Model 2) or MAGE (Model 3) based on Model 1, the correlation between HbA1c and GMI became weaker (β=0.398 and 0.425, respectively). The correlation between HbA1c and GMI was closer in the patients with normal SD (&lt;1.4mmol/L) than those with abnormal SD (r=0.563 vs. r=0.505). A similar result could be found in patients with normal MAGE (&lt;3.9mmol/L) and abnormal MAGE (r=0.579 vs. r=0.514). Conclusion: HbA1c was positively correlated with GMI. But even in adult T2D patients with good glycemic control, the correlation between HbA1c and GMI was significantly affected by GV. SD or MAGE accounted for this discrepancy. Disclosure Z.Liu: None. B.Lin: None. D.Chen: None. H.Lin: None. D.Yang: None. J.Yan: None. B.Yao: None. W.Xu: None.
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Leelarathna, L., H. Thabit, R. Hovorka, and M. Evans. "Estimated HbA 1c and glucose management indicator (GMI): are they the same?" Diabetic Medicine, November 2020. http://dx.doi.org/10.1111/dme.14423.
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Hansen, Klavs Würgler, and Bo Martin Bibby. "Glycemic Metrics Derived From Intermittently Scanned Continuous Glucose Monitoring." Journal of Diabetes Science and Technology, December 3, 2020, 193229682097582. http://dx.doi.org/10.1177/1932296820975822.
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Background: Glucose data from intermittently scanned continuous glucose monitoring (isCGM) is a combination of scanned and imported glucose values. The present knowledge of glycemic metrics originate mostly from glucose data from real-time CGM sampled every five minutes with a lack of information derived from isCGM. Methods: Glucose data obtained with isCGM and hemoglobin A1c (HbA1c) were obtained from 169 patients with type 1 diabetes. Sixty-one patients had two observations with an interval of more than three months. Results: The best regression line of HbA1c against mean glucose was observed from 60 days prior to HbA1c measurement as compared to 14, 30, and 90 days. The difference between HbA1c and estimated HbA1c (=glucose management indicator [GMI]) first observed correlated with the second observation (R2 0.61, P < .001). Time in range (TIR, glucose between 3.9 and 10 mmol/L) was significantly related to GMI (R2 0.87, P < .001). A TIR of 70% corresponded to a GMI of 6.8% (95% confidence interval, 6.3-7.4). The fraction of patients with the optimal combination of TIR >70% and time below range (TBR) <4% was 3.6%. The fraction of patients with TBR>4% was four times higher for those with high glycemic variability (coefficient of variation [CV] >36%) than for those with lower CV. Conclusion: The individual difference between HbA1c and GMI was reproducible. High glycemic variability was related to increased TBR. A combination of TIR and TBR is suggested as a new composite quality indicator.
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RIVELINE, JEAN-PIERRE, GAETAN PREVOST, ANAIS ANDRIEU, et al. "1020-P: Evolution over Time of the Discrepancy between HbA1c and Glucose Management Indicator—Findings from a Franco-Belgian Cohort of 347 Patients." Diabetes 73, Supplement_1 (2024). http://dx.doi.org/10.2337/db24-1020-p.
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A discrepancy between laboratory-measured HbA1c and Glucose Management Indicator (GMI), estimated from continuous glucose monitoring, is frequently encountered in clinical practice. However, its evolution over time is not yet known. Methodology: We conducted a multicenter retrospective study (9 centers) that collected pairs of HbA1c and GMI (calculated over 90 days) at T0, T1 year, T2 years of follow-up in patients with diabetes, all users of FreeStyleLibre®. The primary study endpoint was the analysis of the mean HbA1c-GMI differences at the 3 time points. Glucose data, clinical parameters, and complications were also analyzed. Patients were classified based on the HbA1c-GMI discrepancy: positive (PosD, HbA1c-GMI&gt;+0.3%), neutral (NullD, HbA1c-GMI from -0.3 to +0.3%), negative (NegD, HbA1c-GMI&lt; -0.3%) at each time point, and with the average differences over the 3 time points. Group comparisons were assessed using ANOVA. Result: We included 347 patients (82% type 1 diabetes), mean age of 51±17 years, diabetes duration 20±13 years, HbA1c 7.6±1.0%, 90±9% CGM data collected, Time in Range 70-180 mg/dl (TIR) 57±17%, GMI 7.4±0.8%. The mean HbA1c-GMI differed over time (T0: 0.27%, T1 year: 0.16%, T2 years: 0.04%, P&lt;0.0001). Considering the mean HbA1c-GMI differences over the 3 time points for all patients, PosD individuals were statistically older, had higher BMI and HbA1c compared to NegD patients. At T0, the patients were distributed as follows: 168 PosD (48.4%), 129 NullD (37.2%), 50 NegD (14.4%). The 121 patients (only 34.8% of the cohort) who stayed in the same group at the three time-points were 44.6% PosD, 38% NullD and 17.4% NegD. Conclusion: In only 1/3 of patients does the difference between HbA1c and GMI appear to be stable over time. This should be taken into account when analyzing the supposed poor prognosis associated with PosD. Disclosure J. Riveline: Board Member; Abbott, Novo Nordisk A/S, Sanofi, Eli Lilly and Company, Medtronic, Dexcom, Inc., Insulet Corporation, Air Liquide, AstraZeneca. G. Prevost: Board Member; Abbott. A. Andrieu: None. M. Joubert: Consultant; Abbott, Medtronic, Dexcom, Inc. P. Oriot: Research Support; Abbott. A. Penfornis: Speaker's Bureau; Sanofi, Dexcom, Inc., Diabeloop SA. Board Member; AstraZeneca. Speaker's Bureau; Novo Nordisk, Lilly Diabetes. Board Member; Novo Nordisk, Bayer Inc. Advisory Panel; Abbott, Sanofi. J. Philips: Consultant; Sanofi, Novo Nordisk, Abbott, Avazzia, Boehringer-Ingelheim, Eli Lilly and Company. J. Julla: Speaker's Bureau; Lilly Diabetes, Novo Nordisk. Board Member; Sanofi. E. Cosson: Advisory Panel; Abbott, AstraZeneca, Lilly Diabetes, Novo Nordisk, Sanofi, Roche Diagnostics, Novartis AG, Amgen Inc. Funding Abbott Diabetes Care
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Hiroshi, Bando. "The Importance of Time in Range (TIR) for Continuous Glucose Monitoring (CGM) in the Clinical Practice for Diabetes." Edelweiss Journal of Biomedical Research and Review, July 29, 2021, 12–13. http://dx.doi.org/10.33805/2690-2613.119.
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As to the development of treatment for diabetes, Continuous Glucose Monitoring (CGM) has been recently prevalent rapidly. By the analysis of real-time CGM, Ambulatory Glucose Profile (AGP) has been used. It includes time in range (TIR, 70-180 mg/dL), time above range (TAR, >181mg/dL), time below range (TBR, <69 mg/dL), Glycemic Variability (GV), Glucose Management Indicator (GMI), Glycemic variability, Coefficient Of Variation (CV%) and so on. TIR value indicating approximately 70% seems to correlate closely with the HbA1c level of 6.77.0%. Marked discordance of HbA1c values has been found between laboratory HbA1c and estimated HbA1c (eA1c) using GMI from CGM.
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LEI, MENGYUN, JING LV, XIAODONG MAI, et al. "1821-PUB: Relationship between Time in Range and Glucose Management Indicator in Adolescents and Children with Type 1 Diabetes Mellitus." Diabetes 72, Supplement_1 (2023). http://dx.doi.org/10.2337/db23-1821-pub.
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Objective: To investigate the relationship between time in range (TIR, 3.9-10.0mmol/L) and glucose management indicator (GMI) in adolescents and children with type 1 diabetes mellitus (T1DM) and explore the impact of coefficient of variation (CV) on their relationship. Methods: Data derived from continuous glucose monitoring (CGM) and other clinical data (including age, duration of T1DM, and laboratory-measured HbA1c) were obtained from the annual follow-up of the Guangdong T1DM Translational Medicine Study. The patients under 18 years old were included. The patients were divided into CV≤36% group and CV&gt;36% group by the attainment of CV. The relationship between TIR and GMI of both groups was assessed with correlation coefficient. Further, patients were divided into 4 groups by the interquartile range of CV. The linear regression model was used to calculate the TIR predicted value corresponding to the same GMI in 4 groups. Results: The 56 eligible datasets collected from May 2014 to August 2021 were included. The median age, duration of T1DM, and laboratory-measured HbA1c were 14.00 (12.00, 16.00) years, 4.15 (1.66, 5.29) years, and 9.00 (7.65, 11.23) %, respectively. The median TIR, GMI, CV, and valid number of days the CGM device was worn were 60.73 (42.59, 77.72) %, 7.37 (6.78, 8.56) %, 30.38 (24.87, 34.94) %, and 3.39 (2.97, 4.84) days, respectively. TIR and GMI were highly linear correlated (R2=0.89, p&lt;0.001), and a significantly higher Spearman’s correlation coefficient was observed in the CV≤36% group than in CV&gt;36% group ((R2=0.92, p&lt;0.001) vs. (R2=0.63, p=0.004)). When the GMI was 7%, the corresponding TIR predicted values gradually decreased with the increase of CV, which were 75.66% (CV≤24.87%), 73.48% (24.87%&lt;cv≤30.38%), 69.16% (30.38%34.94%). Conclusions: TIR and GMI were highly linear correlated in adolescents and children with T1DM. With constant GMI, the less the glycemic fluctuation, the higher the TIR.&lt;/cv≤30.38%), 69.16% (30.38% Disclosure M.Lei: None. J.Lv: None. X.Mai: None. H.Deng: None. C.Wang: None. D.Yang: None. X.Yang: None. W.Xu: None. J.Yan: None. Funding Science and Technology Planning Project of Guangzhou (202102010154)
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Pleus, Stefan, Ulrike Kamecke, Delia Waldenmaier, et al. "Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics." Journal of Diabetes Science and Technology, June 8, 2020, 193229682093182. http://dx.doi.org/10.1177/1932296820931825.
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Background: International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system. Method: A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics. Results: Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%). Conclusion: Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.
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Montaser, Eslam, Sebastián E. Abad, and Viral N. Shah. "Changes in A1C versus GMI Across Glycemic Categories in Clinical Trials of Type 1 Diabetes." Journal of Clinical Endocrinology & Metabolism, April 2, 2025. https://doi.org/10.1210/clinem/dgaf211.
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Abstract Context The glucose management indicator (GMI) is an estimated A1C derived from sensor glucose. Though it is being used to approximate A1C in clinical trials, there is no data on direction and magnitude of change in GMI vs A1C after an intervention. Objective To evaluate the magnitude and direction of changes in A1C compared to GMI across different baseline glycemic categories in type 1 diabetes (T1D) clinical trials. Methods Baseline and 3-month central lab measured A1C and estimated GMI from sensor glucose were collected from T1D clinical trials (DCLP3, DCLP5, and WISDM), encompassing children, adolescents, adults, and older adults. Magnitude and direction of changes (baseline- 3 months) in A1C versus GMI were compared overall across the studies and by stratified baseline A1C (&lt;7%, 7-9%, &gt;9%). Results A modest correlation was found between changes in A1C and GMI (r = 0.34). Participants with baseline A1C &gt;9% had larger reductions in A1C compared to GMI [-1.2 (-2.1 to -0.6) vs. -0.6 (-0.94 to 0), p&lt;0.01]. Those with baseline A1C between 7–9% showed a greater decline in A1C than GMI [-0.4 (-0.9 to -0.1) vs. -0.12 (-0.49 to 0.21), p&lt;0.01]. No significant difference was observed for baseline A1C &lt;7%. Conclusions Change in GMI is influenced by the baseline A1C of the participants and it underestimates the true change in A1C. Use of GMI as an endpoint in clinical trials may not reliably capture efficacy of an intervention in T1D trials or real-world studies.
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Takeishi, Soichi. "Abstract P158: Hypoglycemia Prediction From The Coefficient Of Variation For Each Hba1c Value." Circulation 145, Suppl_1 (2022). http://dx.doi.org/10.1161/circ.145.suppl_1.p158.
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It has also been reported that the coefficient of variation (CV) corresponding to the number of hypoglycemia cases decreases as HbA1c values decrease. Therefore, when an association between the CV and hypoglycemia is considered, the possibility that the CV varies according to HbA1c values should be considered. If hypoglycemia can be predicted from both HbA1c values and the CV, the relationship between glycemic variability and hypoglycemia can be assessed in detail. Thus, we studied a formula that could predict hypoglycemia based on HbA1c values and the CV. Materials and Methods This was a prospective observational study. One hundred and one outpatients with type 2 diabetes mellitus underwent HbA1c testing, wore a flash glucose monitor (FGM: FreeStyle Libre Pro, Abbott Diabetes Care, Alameda, CA, USA), and did not change diabetic treatment at the hospital visit. The CV and mean glucose levels were calculated using the FGM data over 24-h х 13 days. The glucose management indicator (GMI) was calculated using the mean glucose levels, and we compared the GMI to the HbA1c values mainly to detect differences between sensor glucose levels (SG) and blood glucose levels. It has been previously reported that the HbA1c value minus (-) GMI >0.5% is associated with the risk of hypoglycemia, and the GMI - HbA1c value >0.5% is associated with the risk of hyperglycemia. Therefore, we assessed the difference between the GMI and HbA1c value with real numbers. We calculated the “percentage of mean absolute deviation to mean glucose levels” (Metric1) as a new metric. Hypoglycemia (<70 mg/dL) absence could be significantly predicted from the CV, HbA1c value, and “glucose management indicator (GMI) minus HbA1c value” (ΔA1c) (Nagelkerke=0.68, p<0.001). When a response variable was hypoglycemia absence and explanatory variables were the CV, HbA1c value, and ΔA1c, the optimal predicted value for the logistic regression analysis was 0.35 (sensitivity: 90%, specificity: 84%; area under the curve: 0.93, p<0.001) (predicted values=1÷(1+e -(-0.28хCV+2.18хHbA1c+1.85хΔA1c-7.48) ) [e: Napier's constant]), where CVs corresponding to HbA1c values of 6%, 7%, 8%, 9%, and 10% were 22.0%, 29.8%, 37.5%, 45.3%, and 53.0%, respectively. The CV correlated with Metric1 (r=0.99, p<0.001, Metric1=0.82хCV-0.006 [formula1]). The CV should be reduced more to prevent hypoglycemia as HbA1c values decrease. For avoiding hypoglycemia, an “alarm threshold using ‘mean glucose levels (Mean) corresponding to the HbA1c values’, and ‘Metric 1 corresponding to the CV calculated using formula 1’ ” (Mean ± Mean х Metric 1 ÷ 100) should be used for personal continuous glucose monitoring, and all glucose levels should be kept within the alarm threshold.
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Gómez Medina, Ana, Camilo A. González, Oscar M. Muñoz, et al. "HbA1c overestimates the glucose management indicator: a pilot study in patients with diabetes, chronic kidney disease not on dialysis, and anemia using isCGM." Therapeutic Advances in Endocrinology and Metabolism 15 (January 2024). http://dx.doi.org/10.1177/20420188241252546.
Full textAbstract:
Introduction: There are multiple mechanisms by which HbA1c values can be altered in chronic kidney disease (CKD), which limits its usefulness as a strategy to assess glycemic control in this population. Methods: Concordance and agreement study between two diagnostic tests: HbA1c and glucose management indicator (GMI) measured by intermittently scanned continuous glucose monitoring (isCGM), based in a prospective cohort of patients with diabetes, CKD (glomerular filtration rate between 15 and 60 ml/min/1.73 m²), and anemia. The isCGM was performed for 3 months, and the GMI was compared with the HbA1c levels taken at the end of isCGM. Agreement was evaluated using Bland–Altman graph analysis and Lin’s concordance correlation coefficient (CCC). The concordance of the measures with good glycemic control (<7%) was also evaluated. Results: A total of 74 patients were enrolled (median age 68.5 years, 51.3% female, 64.9% with CKD stage 3, hemoglobin 11.1 ± 1.2 g/l). The Bland–Altman analysis shows a mean difference between GMI and HbA1c of 0.757 ± 0.687% (95% limits of agreement: −0.590 and 2.105). Difference was greater as the values of GMI and HbA1c increased. The agreement was poor [CCC 0.477; 95% confidence interval (CI): 0.360–0.594], as well as the concordance of values with good glycemic control according to GMI versus HbA1c (67.5% versus 29.7%, p < 0.001) (Kappa 0.2430; 95% CI: 0.16–0.32). Conclusion: The HbA1c overestimates the GMI values with highly variable ranges of difference, which prevents a precise correction factor. isCGM probably is a safer option for monitoring and decision-making in this population, especially in patients treated with insulin where the risk of hypoglycemia is greater.