Accuracy and Functionality of Select Continuous Glucose Monitoring Systems Are Not Impacted by Implantable Cardioverter Defibrillator Devices.

BACKGROUND: Increasing numbers of individuals with diabetes are adopting use of continuous glucose monitoring (CGM) in their daily self-management. Many of these individuals have advanced heart disease. Implantable cardioverter defibrillator (ICD) devices can effectively reduce arrhythmic death and all-cause mortality in individuals with advanced heart disease. However, the potential impact of ICD devices on CGM system accuracy and functionality has not been well studied. METHODS: This evaluation assessed whether FreeStyle Libre (FL) CGM systems can coexist and function within the same patient in the presence of wireless interference devices, including current ICD devices. Interferer sources included Wi-Fi devices, Bluetooth devices, cellular mobile devices, implantable medical devices, Bluetooth Low-Energy (BLE) devices, BLE accessory devices and BLE mobile devices, and ICD-programmer interferers. Five testing methodologies were used to evaluate the accuracy and functionality of the CGM systems when exposed to ICD functions: high-energy emergency shocking, pacing modes, anti-tachycardia pacing mode (ATP), and DC Fibber mode. RESULTS: All acceptance criteria and testing requirements were met for the CGM and ICD system for wireless coexistence evaluation. CONCLUSIONS: Our findings demonstrated that coexisting ICD devices and FL CGM systems provide safe and effective wireless communications with functional and accurate transfer of data during scenarios expected in clinical use.

Is It Time to Move Beyond TIR to TITR? Real-World Data from Over 20,000 Users of Continuous Glucose Monitoring in Patients with Type 1 and Type 2 Diabetes.

The growing use of continuous glucose monitoring (CGM) has been supported by expert consensus and clinical guidelines on glycemic management in diabetes with time in range (TIR 70-180 mg/dL) representing a key CGM-derived glucose metric. Time in tight range (TITR) has also been proposed for clinical use, spanning largely normal glucose levels of 70-140 mg/dL. However, keeping such narrow glucose ranges can be challenging, and understanding the factors modulating TITR can help achieve these tight glycemic targets. Our real-life study aimed to evaluate the relationship between average glucose (AG) and TIR/TITR in a large cohort (n = 22,006) of CGM users, divided into four groups: self-identified as having type 1 diabetes (T1D) treated with insulin using multiple daily injections (MDI) or pumps; type 2 diabetes (T2D) on MDI or insulin pumps; T2D on basal insulin only; and T2D not on insulin treatment. The T2D groups, regardless of treatment type, displayed the highest TIR and TITR values, associated with lowest glycemic variability measured as glucose coefficient of variation (CV; 23-30%). The T1D group showed the lowest TIR and TITR, associated with the highest CVs (36-38%). Overall, higher CV was associated with lower TIR and TITR for AG values below 180 and 140 mg/dL, respectively, with the reverse holding true for AG values above these thresholds. The discordance between AG and TIR/TITR was less pronounced in T2D compared with T1D, attributed to lower CV in the former group. It was also observed that TITR has advantages over TIR for assessing glycemia status and progress toward more stringent A1C, particularly when approaching normal glucose levels. The data detail how CV affects the AG relationship with TIR/TITR, which has implications for CGM interpretation. In many instances TITR, rather than TIR, may be preferable to employ once AG falls below 140 mg/dL and near-normal glucose levels are required clinically.

Time in Range, Time in Tight Range, and Average Glucose Relationships Are Modulated by Glycemic Variability: Identification of a Glucose Distribution Model Connecting Glycemic Parameters Using Real-World Data.

Background: Time in range (TIR), time in tight range (TITR), and average glucose (AG) are used to adjust glycemic therapies in diabetes. However, TIR/TITR and AG can show a disconnect, which may create management difficulties. We aimed to understand the factors influencing the relationships between these glycemic markers. Materials and Methods: Real-world glucose data were collected from self-identified diabetes type 1 and type 2 diabetes (T1D and T2D) individuals using flash continuous glucose monitoring (FCGM). The effects of glycemic variability, assessed as glucose coefficient of variation (CV), on the relationship between AG and TIR/TITR were investigated together with the best-fit glucose distribution model that addresses these relationships. Results: Of 29,164 FCGM users (16,367 T1D, 11,061 T2D, and 1736 others), 38,259 glucose readings/individual were available. Comparing low and high CV tertiles, TIR at AG of 150 mg/dL varied from 80% ± 5.6% to 62% ± 6.8%, respectively (P < 0.001), while TITR at AG of 130 mg/dL varied from 65% ± 7.5% to 49% ± 7.0%, respectively (P < 0.001). In contrast, higher CV was associated with increased TIR and TITR at AG levels outside the upper limit of these ranges. Gamma distribution was superior to six other models at explaining AG and TIR/TITR interactions and demonstrated nonlinear interplay between these metrics. Conclusions: The gamma model accurately predicts interactions between CGM-derived glycemic metrics and reveals that glycemic variability can significantly influence the relationship between AG and TIR with opposing effects according to AG levels. Our findings potentially help with clinical diabetes management, particularly when AG and TIR appear mismatched.

Personalized Glycated Hemoglobin in Diabetes Management: Closing the Gap with Glucose Management Indicator.

Glycated hemoglobin (HbA1c) has played a central role in the management of diabetes since the end of the landmark Diabetes Control and Complications Trial 30 years ago. However, it is known to be subject to distortions related to altered red blood cell (RBC) properties, including changes in cellular lifespan. On occasion, the distortion of HbA1c is associated with a clinical pathological condition affecting RBCs, however, the more frequent scenario is related to interindividual RBC variations that alter HbA1c-average glucose relationship. Clinically, these variations can potentially lead to over- or underestimating glucose exposure of the individual to the extent that may put the person at excess risk of over- or undertreatment. Furthermore, the variable association between HbA1c and glucose levels across different groups of people may become an unintentional driver of inequitable health care delivery, outcomes, and incentives. The subclinical effects within the normal expected physiological range of RBCs can be large enough to alter clinical interpretation of HbA1c and addressing this will help with individualized care and decision making. This review describes a new glycemic measure, personalized HbA1c (pA1c), that may address the clinical inaccuracies of HbA1c by taking into account interindividual variability in RBC glucose uptake and lifespan. Therefore, pA1c represents a more sophisticated understanding of glucose-HbA1c relationship at an individual level. Future use of pA1c, after adequate clinical validation, has the potential to refine glycemic management and the diagnostic criteria in diabetes.

Real-life 24-week changes in glycemic parameters among European users of flash glucose monitoring with type 1 and 2 diabetes and different levels of glycemic control.

AIM: To evaluate real-life changes of glycemic parameters among flash glucose monitoring (FLASH) users who do not meet glycemic targets. METHODS: De-identified data were obtained between 2014 and 2021 from patients using FLASH uninterrupted for a 24-week period. Glycemic parameters during first and last sensor use were examined in four identifiable groups: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) on basal-bolus insulin, T2DM on basal insulin, and T2DM without insulin treatment. Within each group, subgroup analyses were performed in persons with initial suboptimal glycemic regulation (time in range (TIR; 3.9-10 mmol/L) < 70%, time above range (TAR; >10 mmol/L) > 25%, or time below range (TBR; <3.9 mmol/L) > 4%). RESULTS: Data were obtained from 1,909 persons with T1DM and 1,813 persons with T2DM (1,499 basal-bolus insulin, 189 basal insulin, and 125 non-insulin users). In most of the performed analyses, both overall and in the various subgroups, significant improvements were observed in virtually all predefined primary (TIR) and secondary endpoints (eHbA1c, TAR, TBR and glucose variability). CONCLUSIONS: 24-weeks FLASH use in real life by persons with T1DM and T2DM with suboptimal glycemic regulation is associated with improvement of glycemic parameters, irrespective of pre-use regulation or treatment modality.

Safety and Functional Integrity of Continuous Glucose Monitoring Sensors When Used During Radiologic Procedures Under High Exposure Conditions.

BACKGROUND: We investigated wearable components of the Abbott Diabetes Care FreeStyle Libre® (continuous glucose monitoring [CGM 1), FreeStyle Libre® 2 (CGM 2), and FreeStyle Libre® 3 (CGM 3) systems in simulated diagnostic radiologic procedures. METHODS: Sensors were loaded with simulated glucose data and exposed to X-ray scanning, computed tomography (CT), and magnetic resonance imaging (MRI) to simulate radiotherapeutic procedures. The exposure settings were representative of maximum in clinical settings. After the simulations, bench tests were used to assess data integrity and responsiveness of sensors to various concentrations of aqueous glucose. RESULTS: All sensors passed all acceptance criteria following each session of X-ray, CT, and MRI exposures. During the 3 T MRI simulation, the displacement forces for the CGM 1, CGM 2, and CGM 3 sensors were 0.132, 0.109, and 0.063 N, respectively, which are more than 100× smaller than the force of 15.97 N required to dislodge the sensor from the body. Data stored in the sensors prior to the exposures remained intact. CONCLUSION: The sensors maintained functionality following a series of high exposure conditions in both X-ray and CT scanning systems, and the sensors were easily visible and identifiable when scanned using clinically relevant scanning parameters. Therefore, patients can continue to wear and use their sensors during and after imaging. The nonclinical MRI testing demonstrated that the sensors can be worn under the specified MRI conditions.

Observaciones en el mundo real sobre la efectividad del sistema flash de monitorización de glucosa en la práctica clínica en Chile / Real-world observations of the effectiveness of the flash glucose monitoring in clinical practice in Chile

OBJETIVO: Evaluar el tiempo en rango de glucosa y su asociación con otras medidas del control glicémico establecidas por el consenso internacional del tiempo en rango en usuarios de vida real del sistema flash de monitorización de glucosa FreeStyle LibreTM en Chile. MÉTODOS: Se analizaron los datos provenientes de la base de datos Freestyle Libre™ entre diciembre de 2014 y enero de 2022. Las lecturas se dividieron en 10 grupos (deciles) del mismo tamaño (cada decil contenía aproximadamente 498 usuarios) en función del tiempo en rango. Para cada decil se calculó la media de determinaciones diarias, el promedio de glucosa, la HbA1c, la desviación estándar de glucosa, el coeficiente de variación de la glucosa, el tiempo en rango, el tiempo de glucosa (porcentaje) por encima de 250 mg/dL (TA250), el tiempo de glucosa (porcentaje) por encima de 180 mg/dL (TA180), el tiempo por debajo (porcentaje) de 70 mg/dL (TB70) y el tiempo por debajo (porcentaje) de 54 mg/dL (TB54). RESULTADOS: Desde diciembre de 2014 hasta enero de 2022 hubo 4984 lectores. El grupo con el mayor tiempo en rango mostró significativamente una menor glucosa promedio que el grupo con el tiempo en rango más bajo (decil 1: media 248,3 mg/dL, decil 10: media 113,2 mg/L, diferencia ­135,1 mg/dL, p<0.05). Asimismo, el mayor tiempo en rango se asoció con una menor desviación estándar (decil 1: media 93,7mg/dL, decil 10: media 26,7mg/L, diferencia: -67,0 mg/ dL, p<0,05), menor coeficiente de variación (decil 1: media 37,8%, decil 10: media 23,3%, diferencia: -14,5%, p<0,05), menor TA250 (decil 1: media 46,5%, decil 10: media 0,2%, diferencia: -46,3%, p<0.05), menor TA180 (decil 1: media 73,9%, decil 10: media 3,8%, diferencia: -70,1%, p<0.05), menor TB70 (decil 5: mediana 6,13%, decil 10: mediana 1,70%, diferencia: -4,43%, p<0.05) y menor TB54 (decil 5: mediana 1,79%, decil 10: mediana 0,12%, diferencia: -1,67%, p<0.05). El mayor tiempo en rango se asoció también significativamente con más determinaciones diarias (decil 1: media 11,4, decil 10: media 16,6, diferencia: 5,2, p<0,05). La frecuencia media de las determinaciones entre todos los lectores fue de 14,7 determinaciones diarias. CONCLUSIONES: En los pacientes con diabetes en Chile, el empleo del sistema flash de monitorización demuestra la asociación entre el mayor tiempo en rango, la reducción de la variabilidad de la glucosa y un menor riesgo de hiperglucemias e hipoglicemias y también con un mayor compromiso.

OBJECTIVE: To evaluate glucose time in range and its association with other metrics of glucose control established by the International Consensus on TIR amongst real-life patients using the Flash Glucose Monitoring system FreeStyle LibreTM in Chile. METHODS: Data from the Freestyle Libre™ database between December 2014 and January 2022 were analyzed. Readers were divided into 10 groups (deciles) of the same size (each decile had approximately 498 users) according to time in range. For each decile of time in range, the mean of daily scans, average glucose, estimated HbA1c, glucose standard deviation, glucose coefficient of variation, time in range, glucose time (percentage) above 250 mg/dL (TA250), and glucose time (percentage) above 180 mg/dL (TA180), and the median of glucose time (percentage) below 70 mg/dL (TB70) and glucose time (percentage) below 54 mg/dL (TB54), were calculated. RESULTS: From December 2014 to January 2022, there were 4984 readers. The group with the highest TIR showed significantly lower average glucose than the group with the lowest TIR (decile 1: mean 248.3 mg/dL, decile 10: mean 113.2 mg/L, difference: ­135.1 mg/dL, p<0.05). In addition, more time in range was associated with a lower glucose standard deviation (decile 1: mean 93.7 mg/dL, decile 10: mean 26.7 mg/L, difference: -67.0 mg/dL, p<0.05), lower glucose coefficient of variation (decile 1: mean 37.8%, decile 10: mean 23.3%, difference: -14.5%, p<0.05), lower TA250 (decile 1: mean 46.5%, decile 10: mean 0.2%, difference: -46.3%, p<0.05),lower TA180 (decile 1: mean 73.9%, decile 10: mean 3.8%, difference: -70.1%, p<0.05), lower TB70 (decile 5: median 6.13%, decile 10: median 1.70%, difference: -4.43%, p<0.05) and lower TB54 (decile 5: median 1.79%, decile 10: median 0.12%, difference: -1.67%, p<0.05). Greater TIR was also associated with significantly more daily scans (decile 1: mean 11.4, decile 10: mean 16.6, difference: 5.2, p<0.05). Mean scan frequency amongst all readers was 14.7 daily scans. CONCLUSIONS: In patients with diabetes from Chile, the use of the flash glucose monitoring system demonstrates the association between greater TIR, reduced glucose variability, and reduced risk of hyperglycemia and hypoglycemia, and also its association with greater engagement.

Evaluation of continuous glucose monitoring-derived person-specific HbA1c in the presence and absence of complications in type 1 diabetes.

AIM: To evaluate the accuracy of a novel kinetic model at predicting HbA1c in a real-world setting and to understand and explore the role of diabetes complications in altering the glucose-HbA1c relationship and the mechanisms involved. MATERIALS AND METHODS: Deidentified HbA1c and continuous glucose monitoring values were collected from 93 individuals with type 1 diabetes. Person-specific kinetic variables were used, including red blood cell (RBC) glucose uptake and lifespan, to characterize the relationship between glucose levels and HbA1c. The resulting calculated HbA1c (cHbA1c) was compared with glucose management indicator (GMI) for prospective agreement with laboratory HbA1c. RESULTS: The cohort (42 men and 51 women) had a median age (IQR) of 61 (43, 72) years and a diabetes duration of 21 (10, 33) years. A total of 24 459 days of continuous glucose monitoring (CGM) data were available and 357 laboratory HbA1c were used to assess the average glucose-HbA1c relationship. cHbA1c had a superior correlation with laboratory HbA1c compared with GMI with a mean absolute deviation of 1.7 and 6.7 mmol/mol, r2  = 0.85 and 0.44, respectively. The fraction within 10% of absolute relative deviation from laboratory HbA1c was 93% for cHbA1c and 63% for GMI. Macrovascular disease had no effect on the model's accuracy, whereas microvascular complications resulted in a trend towards higher HbA1c, secondary to increased RBC glucose uptake. CONCLUSIONS: cHbA1c, which takes into account RBC glucose uptake and lifespan, accurately reflects laboratory HbA1c in a real-world setting and can aid in the management of individuals with diabetes.

Real world hypoglycaemia related to glucose variability and Flash glucose scan frequency assessed from global FreeStyle Libre data.

AIM: Flash glucose monitoring provides a range of glucose metrics. In the current study, we aim to identify those that indicate that glycaemic targets can be consistently met and contrast the total (t-CV) and within-day coefficient of variation (wd-CV) to guide the assessment of glucose variability and hypoglycaemia exposure. METHODS: De-identified data from Flash readers were collected. The readers were sorted into 10 equally sized groups of scan frequency followed by quartiles of estimated A1c (eA1c). A similar grouping was performed for the total coefficient of variation (t-CV) and within-day coefficient of variation (wd-CV). In addition, analysis of the association of time below 54 mg/dl and glucose variability measured by t-CV and wd-CV was performed. RESULTS: The dataset included 1 002 946 readers. Readers sorted by 10 equal groups of scan rate and quartiles by eA1c, t-CV and wd-CV represented 25 074 readers per group. The association of lower eA1c with higher time in range and reduced time above range was clear. The correlation of eA1c quartiles and time below range was not consistent. An association between glucose variability and hypoglycaemia was found. Both wd-CV and t-CV were associated with time below range. For achieving the consensus target of <1% time below 54 mg/dl, the associated wd-CV and t-CV values were 33.5% and 39.5%, respectively. CONCLUSIONS: The type of CV reported by the different continuous glucose monitoring systems should be acknowledged. CV <36% might not be adequate to ensure low hypoglycaemia exposure. To our knowledge, the majority of continuous glucose monitoring reports the t-CV. Appropriate thresholds should be used to identify patients that would probably meet time below range targets (t-CV <40% or wd-CV <34%).

Glycaemic measures for 8914 adult FreeStyle Libre users during routine care, segmented by age group and observed changes during the COVID-19 pandemic.

AIM: To evaluate the impact of the stay-at-home policy on different glucose metrics for time in range (%TIR 3.9-10 mmol/L), time below range (%TBR < 3.9 mmol/L) and time above range (%TAR > 10 mmol/L) for UK adult FreeStyle Libre (FSL) users within four defined age groups and on observed changes during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Data were extracted from 8914 LibreView de-identified user accounts for adult users aged 18 years or older with 5 or more days of sensor readings in each month from January to June 2020. Age-group categories were based on self-reported age on LibreView accounts (18-25, 26-49, 50-64 and ≥65 years). RESULTS: In January, prior to the COVID-19 pandemic, the 65 years or older age group had the highest %TIR (57.9%), while the 18-25 years age group had the lowest (51.2%) (P < .001). Within each age group, TIR increased during the analysed months, by 1.7% (26-49 years) to 3.1% (≥65 years) (P < .001 in all cases). %TBR was significantly reduced only in the 26-49 years age group, whereas %TAR was reduced by 1.5% (26-49 years) to 3.0% (≥65 years) (P < .001 in both cases). The proportion of adults achieving both of the more than 70% TIR and less than 4% TBR targets increased from 11.7% to 15.9% for those aged 65 years or older (P < .001) and from 6.0% to 9.1% for those aged 18-25 years (P < .05). Mean daily glucose-sensor scan rates were at least 12 per day and remained stable across the analysis period. CONCLUSIONS: Our data show the baseline glucose metrics for FSL users in the UK across different age groups under usual care. During lockdown in the UK, the proportion of adults achieving TIR consensus targets increased among FSL users.

Interindividual variability in average glucose-glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice.

AIM: Glycated haemoglobin (HbA1c) can fail to reflect average glucose levels, potentially compromising management decisions. We analysed variability in the relationship between mean glucose and HbA1c in individuals with diabetes. MATERIALS AND METHODS: Three months of continuous glucose monitoring and HbA1c data were obtained from 216 individuals with type 1 diabetes. Universal red blood cell glucose transporter-1 Michaelis constant KM and individualized apparent glycation ratio (AGR) were calculated and compared across age, racial and gender groups. RESULTS: The mean age (range) was 30 years (8-72) with 94 younger than 19 years, 78 between 19 and 50 years, and 44 were >50 years. The group contained 120 women and 96 men with 106 white and 110 black individuals. The determined KM value was 464 mg/dl and AGR was (mean ± SD) 72.1 ± 7 ml/g. AGR, which correlated with red blood cell lifespan marker, was highest in those aged >50 years at 75.4 ± 6.9 ml/g, decreasing to 73.2 ± 7.8 ml/g in 19-50 years, with a further drop to 71.0 ± 5.8 ml/g in the youngest group (p <0 .05). AGR differed between white and black groups (69.9 ± 5.8 and 74.2 ± 7.1 ml/g, respectively; p < .001). In contrast, AGR values were similar in men and women (71.5 ± 7.5 and 72.5 ± 6.6 ml/g, respectively; p = .27). Interestingly, interindividual AGR variation within each group was at least four-fold higher than average for between-group variation. CONCLUSIONS: In this type 1 diabetes cohort, ethnicity and age, but not gender, alter the HbA1c-glucose relationship with even larger interindividual variations found within each group than between groups. Clinical application of personalized HbA1c-glucose relationships has the potential to optimize glycaemic care in the population with diabetes.

Frequency of flash glucose monitoring and glucose metrics: real-world observational data from Saudi Arabia.

BACKGROUND: This real-world data study analyzed glucose metrics from FreeStyle Libre® flash glucose monitoring in relation to scanning frequency, time in range (TIR) and estimated A1c (eA1c) in Saudi Arabia. METHODS: Anonymized reader data were analyzed according to scanning frequency quartiles, eA1c categories (<7%,≥7%‒≤9% or>9%) and TIR categories (<50%,≥50%‒≤70% or>70%). Sensors, grouped by reader, were required to have≥120 h of operation. Differences in scanning frequency, eA1c, TIR, time in hypoglycemia and hyperglycemia, and glucose variability (standard deviation [SD] and coefficient of variation [CV]) were analyzed between groups. RESULTS: 6097 readers, 35,747 sensors, and 40 million automatic glucose measurements were analyzed. Patients in the highest scanning frequency quartile (Q4, mean 32.0 scans/day) had lower eA1c (8.47%), greater TIR (46.4%) and lower glucose variation (SD 75.0 mg/dL, CV 38.2%) compared to the lowest quartile (Q1, mean 5.2 scans/day; eA1c 9.77%, TIR 32.8%, SD 94.9 mg/dL, CV 41.3%). Lower eA1c and higher TIR were associated with greater scanning frequency, lower glucose variability and less time in hyperglycemia. CONCLUSIONS: Higher scanning frequency in flash glucose users from Saudi Arabia is associated with lower eA1c, higher TIR, lower glucose variability and less time in hypoglycemia or hyperglycemia.

Addressing shortfalls of laboratory HbA1c using a model that incorporates red cell lifespan.

Laboratory HbA1c does not always predict diabetes complications and our aim was to establish a glycaemic measure that better reflects intracellular glucose exposure in organs susceptible to complications. Six months of continuous glucose monitoring data and concurrent laboratory HbA1c were evaluated from 51 type 1 diabetes (T1D) and 80 type 2 diabetes (T2D) patients. Red blood cell (RBC) lifespan was estimated using a kinetic model of glucose and HbA1c, allowing the calculation of person-specific adjusted HbA1c (aHbA1c). Median (IQR) RBC lifespan was 100 (86-102) and 100 (83-101) days in T1D and T2D, respectively. The median (IQR) absolute difference between aHbA1c and laboratory HbA1c was 3.9 (3.0-14.3) mmol/mol [0.4 (0.3-1.3%)] in T1D and 5.3 (4.1-22.5) mmol/mol [0.5 (0.4-2.0%)] in T2D. aHbA1c and laboratory HbA1c showed clinically relevant differences. This suggests that the widely used measurement of HbA1c can underestimate or overestimate diabetes complication risks, which may have future clinical implications.

Flash Glucose Monitoring in the Netherlands: Increased monitoring frequency is associated with improvement of glycemic parameters.

AIMS: To evaluate the association between Flash Glucose Monitoring (FLASH) frequency and glycemic parameters during real-life circumstances in the Netherlands. METHODS: Obtained glucose readings were de-identified and uploaded to a dedicated database when FLASH reading devices were connected to internet. Data between September 2014 and March 2020, comprising 16,331 analyzable readers (163,762 sensors) were analyzed. Scan rate per reader was determined and each reader was sorted into 20 equally sized rank ordered groups (n = 817 each). RESULTS: Users performed a median of 11.5 [IQR 7.7-16.7] scans per day. Those in the lowest and highest ventiles scanned on average 3.7 and 40.0 times per day and had an eHbA1c of 8.6% (71 mmol/mol) and 6.9% (52 mmol/mol), respectively. Increasing scan rates were associated with more time in target range (3.9-10 mmol/L), less time in hyperglycemia (>10 mmol/L), and a lower standard deviation of glucose. An eHbA1c of 7.0% (53 mmol/mol) translated in approximately 65% time in target range, 30% time in hyperglycemia and 5% time in hypoglycemia (<3.9 mmol/L). CONCLUSIONS: These outcomes among Dutch FLASH users suggest that with higher scan rate glycemic control improves.

Accurate prediction of HbA1c by continuous glucose monitoring using a kinetic model with patient-specific parameters for red blood cell lifespan and glucose uptake.

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.

Personal Glycation Factors and Calculated Hemoglobin A1c for Diabetes Management: Real-World Data from the Diabetes Prospective Follow-up (DPV) Registry.

Background: Glycated hemoglobin A1c (HbA1c) is a key biomarker in the glycemic management of individuals with diabetes, but the relationship with glucose levels can be variable. A recent kinetic model has described a calculated HbA1c (cHbA1c) that is individual specific. Our aim was to validate the routine clinical use of this glucose metric in younger individuals with diabetes under real-life settings. Materials and Methods: We retrieved HbA1c and glucose data from the German-Austrian-Swiss-Luxembourgian diabetes follow-up (DPV) registry, which covers pediatric individuals with type 1 diabetes (T1D). The new glycemic measure, cHbA1c, uses two individual parameters identified by data sections that contain continuous glucose data between two laboratory HbA1c measurements. The cHbA1c was prospectively validated using longitudinal HbA1c data. Results: Continuous glucose monitoring data from 352 T1D individuals in 13 clinics were analyzed together with HbA1c that ranged between 4.9% and 10.6%. In the prospective analysis, absolute deviations of estimated HbA1c (eHbA1c), glucose management indicator (GMI), and cHbA1c compared with laboratory HbA1c were (median [interquartile range]): 1.01 (0.50, 1.75), 0.46 (0.21, 084) and 0.26 (0.12, 0.46), giving an average bias of 0.6, 0.4 and 0.0, respectively, in National Glycohemoglobin Standardization Program (NGSP) % unit. For eHbA1c and GMI only 25% and 54% of subjects were within ±0.5% of laboratory HbA1c values, whereas 82% of cHbA1c were within ±0.5% of laboratory HbA1c results. Conclusions: Our data show the superior performance of cHbA1c compared with eHbA1c and GMI at reflecting laboratory HbA1c. These data indicate that cHbA1c can be potentially used instead in laboratory HbA1c, at least in younger individuals with T1D.

A Kinetic Model for Glucose Levels and Hemoglobin A1c Provides a Novel Tool for Individualized Diabetes Management.

BACKGROUND: Regular assessment of glycated hemoglobin (HbA1c) is central to the management of patients with diabetes. Estimated HbA1c (eHbA1c) from continuous glucose monitoring (CGM) has been proposed as a measure that reflects laboratory HbA1c. However, discrepancies between the two markers are common, limiting the clinical use of eHbA1c. Therefore, developing a glycemic maker that better reflects laboratory HbA1c will be highly relevant in diabetes management. METHODS: Using CGM data from two previous clinical studies in 120 individuals with diabetes, we derived a novel kinetic model that takes into account red blood cell (RBC) turnover, cross-membrane glucose transport, and hemoglobin glycation processes to individualize the relationship between glucose levels and HbA1c. RESULTS: Using CGM data and two laboratory HbA1c measurements, kinetic rate constants for RBC glycation and turnover were calculated. These rate constants were used to project future HbA1c, creating a new individualized glycemic marker, termed calculated HbA1c (cHbA1c). In contrast to eHbA1c, the new glycemic marker cHbA1c gave an accurate estimation of laboratory HbA1c across individuals. The model and data demonstrated a non-linear relationship between laboratory HbA1c and steady-state glucose and also showed that glycation status is modulated by age. CONCLUSION: Our kinetic model offers mechanistic insights into the relationship between glucose levels and glycated hemoglobin. Therefore, the new glycemic marker does not only accurately reflect laboratory HbA1c but also provides novel concepts to explain the mechanisms for the mismatch between HbA1c and average glucose in some individuals, which has implications for future clinical management.

Real-world flash glucose monitoring in Brazil: can sensors make a difference in diabetes management in developing countries?

BACKGROUND: New technologies are changing diabetes treatment and contributing better outcomes in developed countries. To our knowledge, no previous studies have investigated the comparative effect of sensor-based monitoring on glycemic markers in developing countries like Brazil. The present study aims to evaluate the use of intermittent Continuous Glucose Measurements (iCGM) in a developing country, Brazil, regarding (i) frequency of glucose scans, (ii) its association with glycemic markers and (iii) comparison with these findings to those observed in global population data. METHODS: Glucose results were de-identified and uploaded to a dedicated database when Freestyle Libre™ readers were connected to an internet-ready computer. Data between September 2014 and Dec 2018, comprising 688,640 readers and 7,329,052 sensors worldwide, were analysed (including 17,691 readers and 147,166 sensors from Brazil). Scan rate per reader was determined and each reader was sorted into 20 equally-sized rank ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated A1c, time above, below and within range identified as 70-180 mg/dL. RESULTS: In Brazil, reader users performed an average of 14 scans per day, while around the world, reader users performed an average of 12 scans per day (p < 0.01). In Brazil dataset, those in the lowest and in the highest groups scanned on average 3.6 and 43.1 times per day had an estimated A1c of 7.56% (59 mmol/mol) and 6.71% (50 mmol/mol), respectively (p < 0.01). Worldwide, the lowest group and the highest groups scanned 3.4 times/day and 37.8 times/day and had an eA1c of 8.14% (65 mmol/mol) and 6.70% (50 mmol/mol), respectively (p < 0.01). For the scan groups in both populations, the time spent above 180 mg/dL decreased as the scan frequency increased. In both Brazil and around the world, as scan frequency increased, time in range (TIR) increased. In Brazil, TIR increased from 14.15 to 16.62 h/day (p < 0.01). Worldwide, TIR increased from 12.06 to 16.97 h/day (p < 0.01). CONCLUSIONS: We conclude that Brazilian users have a high frequency of scans, more frequent than global data. Similarly to the world findings, increased scan frequency is associated with better glycemic control.

Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: A European analysis of over 60 million glucose tests.

AIMS: Randomised controlled trials demonstrate that using flash glucose monitoring improves glycaemic control but it is unclear whether this applies outside trial conditions. We investigated glucose testing patterns in users worldwide under real life settings to establish testing frequency and association with glycaemic parameters. METHODS: Glucose results were de-identified and uploaded onto a dedicated database once readers were connected to an internet-ready computer. Data between September 2014 and May 2016, comprising 50,831 readers and 279,446 sensors worldwide, were analysed. Scan rate per reader was determined and each reader was sorted into twenty equally-sized rank-ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated HbA1c, time above, below and within range identified as 3.9-10.0 mmol/L. RESULTS: Users performed a mean of 16.3 scans/day [median (IQR): 14 (10-20)] with 86.4 million hours of readings and 63.8 million scans. Estimated HbA1c gradually reduced from 8.0% to 6.7% (64 to 50 mmol/mol) as scan rate increased from lowest to highest scan groups (4.4 and 48.1 scans/day, respectively; p < .001). Simultaneously, time below 3.9, 3.1 and 2.5 mmol/L decreased by 15%, 40% and 49%, respectively (all p < .001). Time above 10.0 mmol/L decreased from 10.4 to 5.7 h/day (44%, p < .001) while time in range increased from 12.0 to 16.8 h/day (40%, p < .001). These patterns were consistent across different countries. CONCLUSIONS: In real-world conditions, flash glucose monitoring allows frequent glucose checks with higher rates of scanning linked to improved glycaemic markers, including increased time in range and reduced time in hyper and hypoglycaemia.