Flash Glucose Monitoring System Research Articles

Effect of switch from flash glucose monitoring to flash glucose monitoring with real-time alarms on hypoglycaemia in people with type 1 diabetes mellitus

We aimed to evaluate the utility of the FreeStyle Libre 2 device for reducing time below range level 1 and level 2 compared with the Freestyle Libre device (without alarms) in people with type 1 diabetes mellitus. We conducted longitudinal observational follow-up study of a cohort of 100 people with type 1 diabetes mellitus who had switched from FreeStyle Libre to FreeStyle Libre 2 as part of routine clinical practice. Three months after switching to FreeStyle Libre 2, compared with results with FreeStyle Libre, there were a significant improvements in time below range level 1 (p = 0.02) and level 2 (p <0.001), time in range (p <0.001), time above range level 1 (p = 0.002), glucose management indicator (p= 0.04) and mean glucose (p= 0.04) during follow-up. Furthermore there was a significant direct association between age and change in TIR with a coefficient of 0.23, and a significant inverse association between age and change in TAR-1 with a coefficient of 0.11. Switching to a flash glucose monitoring system with alarms improves time below range, time in range and coefficient of variation in people with type 1 diabetes mellitus.

Multiple basal infusion rates in open-loop insulin delivery systems: is there a metabolic benefit?

To evaluate glycemic control according to the number of daily basal rates (BRs) in type 1 diabetes patients using continuous subcutaneous insulin infusion (CSII). Cross-sectional study of patients treated with an open-loop CSII for at least 6 months and using a flash glucose monitoring system. Patients were divided into 2 groups: group 1 (G1) and group 2 (G2), with ≤4 and >4 BRs/24h, respectively. The groups were compared regarding HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), glucose management indicator (GMI), glucose variability and data related to hypoglycemia. Regression models were performed. The study included 99 patients (n = 55 in G1; n = 44 in G2). Median (Interquartile range) overall age was 30 (17) years, with 19.5 (48) and 51 (77) months of CSII use, respectively. The median number of different BRs was 3 (2) for G1 and 6 (2) for G2. There were no differences concerning age, sex, educational stage, weight, and insulin analog used. G2 had longer disease duration, longer CSII use, and higher total basal daily dose/kg. No significant differences regarding HbA1c, median glucose, GMI, TIR, TAR, and CV were found. G2 patients had more hypoglycemia, more asymptomatic hypoglycemia, and higher TBR. After adjusting for potential confounders, G1 maintained a lower risk of asymptomatic hypoglycemia. Programming open-loop CSII devices with more than 4 BRs does not improve metabolic control. Additionally, it seems to be a risk factor for hypoglycemia and was an independent predictor for asymptomatic hypoglycemia.

Impact of Flash Glucose Monitoring on the Fear of Hypoglycemia Phenomenon in Adults with Type 1 Diabetes.

Objective: To assess the clinical impact of flash glucose monitoring (FGM) systems on fear of hypoglycemia (FoH) and quality of life in adults with type 1 diabetes mellitus (T1DM). Methods: Prospective quasi-experimental study with a 12-month follow-up. People with T1DM (18-80 years old) and self-monitoring by blood capillary glycemia controls were included. The FH15 questionnaire, a survey validated in Spanish in a comparable study population, was used to diagnose FoH with a cutoff point of 28 points. Results: A total of 181 participants were included, with a FoH prevalence of 69% (n = 123). A mean reduction in FH15 score of -4 points (95% confidence interval [-5.5 to -3]; P < 0.001) was observed, along with an improvement in quality of life (EsDQOL-test (Diabetes Quality of Life, Spanish version), -7 points [-10; -4], P < 0.001) and satisfaction with treatment (Diabetes Treatment Satisfaction questionnaire, self-reported version [DTSQ-s] test, +4.5 points [4; 5.5], P < 0.001). At the end of the follow-up, 64.2% of the participants saw an improved FoH intensity, compared to 35.8% who scored the same or higher. This improvement in FoH status was associated with a higher time-in-range at the end of the follow-up (P = 0.003), as well as a lower time spent in hyperglycemia (P = 0.005). In addition, it was linked to participants with a high baseline FoH levels (P < 0.001) and those who were university degree holders (P = 0.07). Conclusions: FGM is associated with an overall reduction of FoH in adults with T1DM and with an increase in their quality of life. Nevertheless, a significant percentage of patients may experience an increase of this phenomenon leading to clinical repercussions and a profound impact on quality of life.

Impact of socioeconomic status on chronic control and complications of type 1 diabetes mellitus in users of glucose flash systems: a follow-up study

BackgroundThis study investigates the association between socioeconomic status (SES) and glycemic control in individuals with type 1 diabetes (T1D) using flash glucose monitoring (FGM) devices within a public health system where these technologies are freely available and utilized according to recommended guidelines.MethodsA follow-up study of 1060 adults (mean age 47.4 ± 15.0 years, 49.0% women) with T1D, receiving care at three Spanish university hospitals that regularly employ the FGM system. SES was assessed using the Spanish Deprivation Index and the average annual net income per person. Glycemic data were collected over a 14-day follow-up period, including baseline glycated hemoglobin (HbA1c) levels prior to sensor placement, the last available HbA1c levels, and FGM-derived glucose metrics. Individuals with sensor usage time < 70% were excluded. Chronic micro and macrovascular complications related to diabetes were documented. Regression models, adjusted for clinical variables, were employed to determine the impact of SES on optimal sensor control (defined as time in range (TIR) ≥ 70% with time below range < 4%) and disease complications.ResultsThe average follow-up was of 2 years. The mean TIR and the percentage of individuals with optimal control were higher in individuals in the highest SES quartile (64.9% ± 17.8% and 27.9%, respectively) compared to those in the lowest SES quartile (57.8 ± 17.4% and 12.1%) (p < 0.001). Regression models showed a higher risk of suboptimal control (OR 2.27, p < 0.001) and ischemic heart disease and/or stroke (OR 3.59, p = 0.005) in the lowest SES quartile. No association was observed between SES and the risk of diabetic nephropathy and retinopathy. FGM system improved HbA1c levels across all SES quartiles. Although individuals in the highest SES quartile still achieved a significantly lower value at the end of the follow-up 55 mmol/mol (7.2%) compared to those in the lowest SES quartile 60 mmol/mol (7.6%) (p < 0.001), the significant disparities in this parameter between the various SES groups were significantly reduced after FGM technology use.ConclusionsSocioeconomic status plays a significant role in glycemic control and complications in individuals with T1D, extending beyond access to technology and its proper utilization. The free utilization of FGM technology helps alleviate the impact of social inequalities on glycemic control.

Comparative Effectiveness of Oral Hypoglycemic Agents for Glycemic Control and Glycemic Variability in Patients with Type 2 Diabetes Mellitus: Using Flash Glucose Monitoring.

The study aimed to compare the effectiveness of oral hypoglycemic agents (OHAs) as monotherapy, dual and quadruple therapy for glycemic control (GC) and glycemic variability (GV) in patients with type-2 diabetes (T2DM) using flash glucose monitoring system (FGM). Diabetes management largely relies on HbA1c monitoring. Glycemic variability (GV) has been an evolving glycemic target for preventing complications related to type 2 diabetes mellitus (T2DM). The purpose of the study was to compare glycemic control (GC) measures and glycemic variability (GV) measures among study groups and to study the relationships between GC and GV indices. Retrospectively, FGM data were collected from 50 T2DM patients. The patients were classified based on prescribed number of OHAs as monotherapy [group 1: dipeptidyl peptidase- 4 (DPP-4) inhibitors (n=10), group 2: sodium-glucose co-transporter-2 (SGLT2) inhibitors (n=10), group 3: sulphonylureas (n=10), group 4: dual therapy (n=10), and group 5: quadruple therapy (n=10)]. Measures of GC and GV were evaluated. Significant differences between study groups were observed in GC and GV measurements. The SGLT2 inhibitors monotherapy group demonstrated optimal GC [eA1c (%): 6.5 ± 2.2; MBG: 140.80 ± 63.94; TIR: 60.60 ± 19.96] and GV (SD: 42.38 ± 34.57; CV: 27.85 ± 6.68; MAGE: 96.76 ± 52.47; MODD: 33.96 ± 22.91) in comparison to other study groups. On using Pearson correlation analysis, mean blood glucose (MBG) and mean amplitude of glycemic excursion (MAGE) showed moderate correlation (r = 0.742)(r2 = 0.551), depicting distinct glucose variabilities at the same mean blood glucose levels. The monotherapy group of SGLT2 inhibitors demonstrated glucose-lowering effects with reduced glycemic variability. Hence, optimum glycemic control is associated with decreased glycemic variability.

Short-term glycemic variability in non-diabetic, non-obese dogs assessed by common glycemic variability indices

Glycemic variability (GV) refers to swings in blood glucose levels and is an emerging measure of glycemic control in clinical practice. It is associated with micro- and macrovascular complications and poor clinical outcomes in diabetic humans. Although an integral part of patient assessment in human patients, it is to a large extent neglected in insulin-treated diabetic dogs.This prospective pilot study was performed to describe canine within-day GV in non-diabetic dogs with the aim to provide a basis for the interpretation of daily glucose profiles, and to promote GV as an accessible tool for future studies in veterinary medicine.Interstitial glucose concentrations of ten non-diabetic, non-obese beagles were continuously measured over a 48-h period using a flash glucose monitoring system. GV was assessed using the common indices MAGE (mean amplitude of glycemic excursion), GVP (Glycemic variability percentage) and CV (coefficient of variation).A total of 2260 sensor measurements were obtained, ranging from 3.7 mmol/L (67 mg/dL) to 8.5 mmol/L (153 mg/dL). Glucose profiles suggested a meal-dependent circadian rhythmicity with small but significant surges during the feeding periods. No differences in GV indices were observed between day and night periods (p > 0.05). The MAGE (mmol/L), GVP (%) and CV (%) were 0.86 (± 0.19), 7.37 (± 1.65), 6.72 (± 0.89) on day one, and 0.83 (± 0.18), 6.95 (± 1.52), 6.72 (± 1.53) on day two, respectively.The results of this study suggest that GV is low in non-diabetic dogs and that glucose concentrations are kept within narrow ranges.

A comparative observational study of carbohydrate intake and continuous blood glucose levels in relation to performance in ultramarathon

Ultra-endurance events have gained global participation, whereas the critical factors of competition results remain to be well elucidated. This study used a nutritional approach to evaluate the association of competition results with carbohydrate intake and blood glucose control during a 100-mile ultramarathon. This observational study was conducted in the 2021 LAKE BIWA 100, which covered 100 miles (169 km) and 10,500 m elevation. The course was divided into 9 segments by aid station. According to the competition results, 22 participants (18 men and 4 women) were divided into higher finishers (n = 7), lower finishers (n = 9), and non-finishers (n = 6). The participants self-recorded their overall dietary intake throughout the race. Glucose levels were monitored every 15 min by a flash glucose monitoring system. Running speed in each segment was standardized to the average of the top five finishers for each gender. Among finishers, the carbohydrate intakes were significantly higher in the higher finishers than in the lower finishers during overall segments, especially in the first half of the race (p < 0.05). There was a significant positive correlation between running speed and carbohydrate intake in the lower finishers (rho = 0.700, p = 0.036). Two-way ANOVA analysis revealed that lowering glucose levels in each segment were more frequently observed in the lower finishers compared to the higher finishers (p = 0.012). Compared to the higher finishers, the lower finishers exhibited significantly greater fluctuations (⊿highest-lowest) in glucose levels (p < 0.001). The fluctuations in glucose levels were significantly and negatively correlated with the running speed of the finishers (rho =  − 0.612, p = 0.012). Faster runners consume high amounts of carbohydrates and maintain glucose levels during the 100-mile ultramarathon on the trail, especially at the beginning. Lowering and fluctuating glucose levels during the race are associated with lower running speed in endurance athletes.

Study of Glycemic Variability in Well-controlled Type 2 Diabetic Patients Using Continuous Glucose Monitoring System.

The world has changed tremendously for patients suffering from diabetes mellitus with the development of cutting-edge technologies like continuous glucose monitoring and flash glucose monitoring systems. Now, the details of constant fluctuations of glucose in their blood can be monitored not only by medical professionals but also by patients, and this is called glycemic variability (GV). Traditional metrics of glycemic control measurement, such as glycated hemoglobin (HbA1c), fail to reflect various short-term glycemic changes like postprandial hyperglycemia and hypoglycemic episodes, paving the way to the occurrence of various diabetic complications even in asymptomatic, well-controlled diabetic patients. This need for advanced management of diabetes and effective monitoring of these swings in blood glucose can be met by using a continuous glucose monitoring system (CGMS). To evaluate the extent of GV in well-controlled type 2 diabetes mellitus (T2DM) patients using a flash CGMS and to assess the correlation between GV and HbA1c. A hospital-based prospective observational study was carried out from May 2020 to Oct 2021 at the Department of Medicine, SMS Hospital, Jaipur, Rajasthan (India), after approval from the Ethics Committee of the institution. A total of 30 patients with well-controlled T2DM (HbA1c was ≥6.5, but ≤7.5) were included in the study using simple random techniques after written informed consent from patients. Patients were studied for glycemic excursions over a period of 7 days by using FreeStyle® Libre Pro™, which is a flash glucose monitoring system. The CGM sensor was attached to the left upper arm of the patient on day 0 and removed on day 7. The data recorded in the sensor was then retrieved using pre-installed computer software and analyzed using standard CGM metrics like standard deviation (SD), percentage coefficient of variation (%CV), time above range (TAR), time below range (TBR), and time in range (TIR), out of which %CV was used to quantify GV. %CV has been used to cluster patients into four cohorts from best to worst, namely: best/low CV ≤ 10%, intermediate CV from 10 to 20%, high CV from 20 to 30%, and very high CV of >30%. Scatterplots are used to establish correlations between various parameters. Data from a total of 30 patients were analyzed using CGMS and thus used for calculating standard CGM metrics; glucose readings every 15 minutes were recorded consecutively for 7-day periods, making it a total of 672 readings for each patient. Interpreting the CGM data of all 30 patients, the following results were found: the mean blood glucose of all cases is 134.925 ± 22.323 mg/dL, the mean SD of blood glucose of all cases is 35.348 ± 9.388 mg/dL, the mean of %CV of all cases is 26.376 ± 6.193%. CGM parameters of time are used in the form of percentages, and the following results were found: the mean of TAR, TBR, and TIR is 14.425 ± 13.211, 5.771 ± 6.808, and 82.594 ± 12.888%, respectively. Clustering the patients into cohorts, the proportion of patients exhibiting best/low %CV (10%) is 0, intermediate %CV (10-20%) is 16.67% (five out of 30 patients), high %CV (20-30%) is 50% (15 out of 30 patients) and very high %CV (>30%) is 33.33% (10 out of 30 patients). Also, there is no significant correlation found between HbA1c and %CV (ρ = 0.076, p-value = 0.690); a significant negative correlation was found between %CV and TIR (ρ = -0.604, p < 0.001S); a positive correlation of %CV with TAR and TBR is significant (ρ = 0.816, p-value of <0.001). Using a flash CGMS device and considering %CV as the parameter and primary measure of GV, the study demonstrated the overall instability of a person's glycemic control, making note of unrecognized events of hypoglycemia and hyperglycemia in asymptomatic well-controlled T2DM patients, revealing the overall volatile glycemic control. The most important finding of this study is that even those diabetics who are considered well-controlled experience a great degree of GV as assessed by CGM-derived metrics. This study also demonstrated that there is no significant correlation between HbA1c and GV, suggesting that patients may not have optimal control of their diabetes despite having "normal HbA1c" values; hence, GV can be considered an HbA1c-independent danger factor, having more harmful effects than sustained hyperglycemia in the growth of diabetic complications. So, by using CGM-derived metrics, the measurement of GV has the potential to complement HbA1c data. In this manner, a more comprehensive assessment of glycemic excursions can be provided for better treatment decisions, thereby facilitating optimal glycemic control, which is essential for reducing overall complications and promoting good quality of life.

Evaluation of association between time in range, a continuous glucose monitoring metric, and cardiac autonomic neuropathy in type 2 diabetes patients.

Time in range (TIR), a metric of continuous glucose monitoring (CGM) provides better information regarding the individual's glycemic variability than a static measure like glycated hemoglobin (HbA1c). TIR is emerging as an independent risk factor for diabetic complications, both microvascular and macrovascular complications independent of HbA1c. Hence, this study evaluates the association between TIR and cardiac autonomic neuropathy (CAN) in type 2 diabetic patients. A total of 42 patients with type 2 diabetes mellitus were enrolled in this study and underwent a 3-day CGM using the "FreeStyle Libre Pro Flash Glucose Monitoring System Sensor" along with tests for CAN within the 3 days of attaching the CGM. Out of 42 patients, 36 patients (85.7%) were diagnosed with CAN (early CAN 57.1% and definite CAN 28.6%) and the mean TIR was 64.4% ±23.5%. Out of those with TIR <70%, 42.9% were affected with definite CAN compared to only 14.3% among those with TIR >70%. Patients with more severe CAN were found to have a lower TIR (P = 0.115). The study found a high prevalence of cardiac autonomic neuropathy (CAN) of around 85.7% in type 2 diabetes patients. Lower TIR values were associated with a higher incidence of definite CAN (42.9% vs. 14.3% in TIR <70% vs. >70% groups). The findings suggest TIR is inversely associated with the presence and severity of cardiac autonomic neuropathy in type 2 diabetic patients and also a potential link between TIR and CAN severity.

EE19 Cost Utility Analysis of the Flash Glucose Monitoring System in the Management of People With Type 2 Diabetes Mellitus on Basal Insulin Therapy—An Italian Healthcare System Perspective

EE19 Cost Utility Analysis of the Flash Glucose Monitoring System in the Management of People With Type 2 Diabetes Mellitus on Basal Insulin Therapy—An Italian Healthcare System Perspective

Scans per day as predictors of optimal glycemic control in people with type 1 diabetes mellitus using flash glucose monitoring: what number of scans per day should raise a red flag?

AimsThis study aimed to determine the minimum frequency of flash glucose monitoring (FGM) scans necessary for optimal glycemic control in patients with type 1 diabetes (T1D).MethodsData were collected from 692 patients (47.5% female, with a median age of 47.4 years) who used FGM systems daily and recorded their clinical variables and device data.ResultsLogistic regression models showed that performing more than 12 scans per day was associated with improved T1D control (OR = 4.22, p < 0.001) and a reduction in HbA1c (7.6 vs 7.0%, 60–53 mmol/mol p < 0.001). However, those performing less than 6 scans showed no improvement in HbA1c (7.9 vs 7.8%, 63–61 mmol/mol p = 0.514). Thirteen daily scans were determined as the optimal cutoff point for predicting optimal glycemic control using a maximally selected rank algorithm. Significant reductions were observed in mean glucose (< 0.001), coefficient of variation (< 0.001), HbA1c (< 0.001), and an increase in TIR (< 0.001) in patients who performed more than 12 daily scans.ConclusionsThe results suggest that a higher frequency of daily scans by T1D patients using FGM systems leads to improved chronic glycemic control. The minimum recommended frequency for optimal control is 13 scans per day, and more than 6 daily scans are needed to improve HbA1c.

Flash glucose monitoring system in gestational diabetes: a study of accuracy and usability.

Studies of flash glucose monitoring systems (FGMSs) in pregnancy are insufficient, especially in gestational diabetes (GD). Our aim was to evaluate Freestyle Libre's usability and accuracy (compared to self-monitoring of blood glucose [SMBG]) for GD patients in real-life conditions. This is a prospective study with pregnant women diagnosed with GD (n = 24 for the usability analysis; n = 19 for the accuracy analysis). The study duration was up to 28 days (lifetime of two sensors). Participants executed a minimum of four daily FGMS readings obtained immediately after capillary SMBG. Analytical accuracy was assessed with mean absolute relative difference (MARD) and mean absolute difference (MAD); clinical accuracy was assessed with Surveillance Error Grid (SEG). Usability was evaluated with a user acceptability questionnaire. The mean pregestational BMI was 25.21 ± 5.15 kg/m2 (mean ± SD), the mean gestational age was 30.31 ± 2.02 weeks, and the mean glucose values were 76.63 ± 7.49 mg/dL. A total of 1339 SMBG-FGMS pairs of values were obtained. Analytical accuracy was good with an overall MARD of 14.07% and an in-target MARD of 13.79%. The number of SMBG-FMGS pairs for above-target values was low (122 of 1339) with an associated MARD of 17.95%. Clinical accuracy of the FGMS was demonstrated, with 94.4% of values in the no-risk or slight, lower risk zones of the SEG. FGMS accuracy was unaffected by pregestational BMI or gestational age. The user acceptability questionnaire showed high levels of satisfaction, with 95.8-100% preferring FGMS to SMBG. No unexpected or severe adverse effects occurred. FGMS showed good performance in GD regarding accuracy and usability. Larger studies are needed to corroborate our results, verify the analytical accuracy of above-target values as this glucose range might lead to initiation or adjustment of pharmacological therapy, and ultimately establish definitive recommendations regarding prescription of FGMS for GD patients.

Analytical Performance of the Factory-Calibrated Flash Glucose Monitoring System FreeStyle Libre2TM in Healthy Women.

Continuous glucose monitoring (CGM) is used clinically and for research purposes to capture glycaemic profiles. The accuracy of CGM among healthy populations has not been widely assessed. This study assessed agreement between glucose concentrations obtained from venous plasma and from CGM (FreeStyle Libre2TM, Abbott Diabetes Care, Witney, UK) in healthy women. Glucose concentrations were assessed after fasting and every 15 min after a standardized breakfast over a 4-h lab period. Accuracy of CGM was determined by Bland-Altman plot, 15/15% sensor agreement analysis, Clarke error grid analysis (EGA) and mean absolute relative difference (MARD). In all, 429 valid CGM readings with paired venous plasma glucose (VPG) values were obtained from 29 healthy women. Mean CGM readings were 1.14 mmol/L (95% CI: 0.97 to 1.30 mmol/L, p < 0.001) higher than VPG concentrations. Ratio 95% limits of agreement were from 0.68 to 2.20, and a proportional bias (slope: 0.22) was reported. Additionally, 45% of the CGM readings were within ±0.83 mmol/L (±15 mg/dL) or ±15% of VPG, while 85.3% were within EGA Zones A + B (clinically acceptable). MARD was 27.5% (95% CI: 20.8, 34.2%), with higher MARD values in the hypoglycaemia range and when VPG concentrations were falling. The FreeStyle Libre2TM CGM system tends to overestimate glucose concentrations compared to venous plasma samples in healthy women, especially during hypoglycaemia and during glycaemic swings.

Hypoglycemia measured by flash glucose monitoring system predicts liver-related events in chronic liver disease patients

Impaired glucose tolerance, glucose fluctuations, and hypoglycemia have been observed in patients with chronic liver disease (CLD). The flash glucose monitoring (FGM) system, which recognises continuous and dynamic glucose changes in real time, is used in daily clinical practice. This study aimed to examine the association between glucose fluctuations and hypoglycemia, as measured by the FGM system, and liver-related events. Seventy-two patients with CLD and type 2 DM who had their blood glucose measured using Freestyle Libre Pro between April 2017 and July 2018 at our institution were enrolled in this retrospective study. We assessed the results of the FGM system measurements and liver-related events, as defined by gastrointestinal bleeding, infection, ascites, encephalopathy, and liver-related death. The standard deviation (SD) of mean glucose as measured by the FGM system was 41.55 mg/dl, and hypoglycemia was observed in 48.6% (35/72) of the patients. Liver-related event-free survival was not significant when stratified based on SD; however, the event-free survival was significantly lower when stratified by hypoglycemia (p = 0.007). In a multivariate analysis using the Cox proportional hazards model, Child–Pugh class B [Hazards ratio (HR) 2.347 (95% confidence interval (CI): 1.042–5.283), p = 0.039] and hypoglycemia [HR 2.279 (95% CI: 1.064–4.881), p = 0.034] were identified as factors contributing to event-free survival. Hypoglycemia, as determined by the FGM system, was identified as a significant factor that was closely associated with liver-related events. In addition to measuring glucose levels, the FGM system is useful in predicting the occurrence of liver-related events.

Correlation Between Blood Glucose Indexes Generated by the Flash Glucose Monitoring System and Diabetic Vascular Complications.

To discuss the relationship between time in range (TIR) which is deprived of the FGMS and the risk of diabetic vascular complications and to provide a theoretical foundation for the clinical application of TIR and other FGMS-deprived indexes. Patients with T2DM who wore the FGMS sensor continuously were enrolled. Relevant indexes such as TIR, time below range (TBR), time above range (TAR), a standard deviation of blood glucose (SDBG), coefficient of variation of blood glucose (CV), and mean amplitude of glycemic excursion (MAGE) generated by the FGMS were recorded, and the risk of diabetic vascular complications were followed up for one year. The TIR was measured by continuous glucose monitoring at baseline, and patients were grouped according to TIR every 20%. Finally, the Cox proportional hazards regression model was used to estimate the association of different levels of TIR with different rates of diabetic vascular complications. TIR was negatively correlated with HbA1C, CV, SDBG, and amplitude of glycemic excursion (MV), wherein, the lower the TIR, the higher the HbA1C, CV, SDBG, and MV. TIR in the diabetic microvascular complication was significantly lower than that in the non-microvascular complication group, and the difference was statistically significant. TIR <40% was identified as a risk factor for DN, DPN, and DR according to the risk assessment. The mean TAR in the DN group was significantly higher than that in the non-DN group. TAR, CV, SD, MAGE, and HbA1C in the DR group were significantly higher than those in the non-DR group. TAR, ABG, CV, SD, MAGE, and HbA1C in the DPN group were significantly higher than those in the non-DPN group. The relationships between the TIR and the prevalence and risk of diabetic vascular complications and the HbA1C may be negative. Other CGM-deprived indexes such as CV and MV should be integrated into glycemic control and diabetes complication prediction.

Glucose metabolism profile recorded by flash glucose monitoring system in patients with hypopituitarism during prednisone replacement.

Commonly used glucocorticoids replacement regimens in patients with hypopituitarism have difficulty mimicking physiological cortisol rhythms and are usually accompanied by risks of over-treatment, with adverse effects on glucose metabolism. Disorders associated with glucose metabolism are established risk factors of cardiovascular events, one of the life-threatening ramifications. To investigate the glycometabolism profile in patients with hypopituitarism receiving prednisone (Pred) replacement, and to clarify the impacts of different Pred doses on glycometabolism and consequent adverse cardiovascular outcomes. Twenty patients with hypopituitarism receiving Pred replacement [patient group (PG)] and 20 normal controls (NCs) were recruited. A flash glucose monitoring system was used to record continuous glucose levels during the day, which provided information on glucose-target-rate, glucose variability (GV), period glucose level, and hypoglycemia occurrence at certain periods. Islet β-cell function was also assessed. Based on the administered Pred dose per day, the PG was then regrouped into Pred > 5 mg/d and Pred ≤ 5 mg/d subgroups. Comparative analysis was carried out between the PG and NCs. Significantly altered glucose metabolism profiles were identified in the PG. This includes significant reductions in glucose-target-rate and nocturnal glucose level, along with elevations in GV, hypoglycemia occurrence and postprandial glucose level, when compared with those in NCs. Subgroup analysis indicated more significant glucose metabolism impairment in the Pred > 5 mg/d group, including significantly decreased glucose-target-rate and nocturnal glucose level, along with increased GV, hypoglycemia occurrence, and postprandial glucose level. With regard to islet β-cell function, PG showed significant difference in homeostasis model assessment (HOMA)-β compared with that of NCs; a notable difference in HOMA-β was identified in Pred > 5 mg/d group when compared with those of NCs; as for Pred ≤ 5 mg/d group, significant differences were found in HOMA-β, and fasting glucose/insulin ratio when compared with NCs. Our results demonstrated that Pred replacement disrupted glycometabolic homeostasis in patients with hypopituitarism. A Pred dose of > 5 mg/d seemed to cause more adverse effects on glycometabolism than a dose of ≤ 5 mg/d. Comprehensive and accurate evaluation is necessary to consider a suitable Pred replacement regimen, wherein, flash glucose monitoring system is a kind of promising and reliable assessment device. The present data allows us to thoroughly examine our modern treatment standards, especially in difficult cases such as hormonal replacement mimicking delicate natural cycles, in conditions such as diabetes mellitus that are rapidly growing in worldwide prevalence.

134-LB: Real-World Impact of Intermittently Scanned Continuous Glucose Monitoring on Hemoglobin A1c in People with Type 2 Diabetes in Canada That Use Basal Insulin—A Prospective Chart Review

The change in HbA1c after initiating FreeStyle Libre or FreeStyle Libre 2 Flash Glucose Monitoring System (FSL and FSL2) in adults with type 2 diabetes (T2D) in a real-world setting was evaluated over a 3-6 month period. Nine sites in Canada collected medical record data prospectively for adults who had started FSL or FSL2 in the last 3 months and with the following criteria: a basal insulin regimen with or without additional glucose lowering medications, and HbA1c 8.0-12.0%. Preliminary data were analysed from 79/138 medical records (n=138; 6 records ineligible, 53 no follow up, ineligible HbA1c or had stopped FSL or FSL2). Participants had the following baseline characteristics (mean±SD): HbA1c 8.9%±0.8, age 67.3±11.5 years (57% ≥65 years), BMI 30.1±6.2 (n=65), insulin use duration 5.3±3.9 years, and 54% were males. The most commonly used non-insulin glucose lowering medications included metformin (87.3%), sodium-glucose co-transporter-2 (SGLT2) inhibitors (62.0%) and sulphonylureas (46.8%). By the end of the study (3-6 months after the index date), mean HbA1c was reduced by 0.6%±1.1 to 8.3%±1.0 (p&amp;lt;0.0001). Observed changes in antihyperglycemic medications from baseline included increased use of GLP-1 agonists from 44.3% to 51.9% and decreased use of DPP4i from 24.1% to 16.5%. This prospective chart review study found that initiating intermittently scanned continuous glucose monitoring in individuals with T2D treated with basal insulin plus other glucose lowering medications was associated with significantly improved HbA1c over a 3-6 month period. Disclosure A. Abitbol: Advisory Panel; Abbott Diabetes, Lilly Diabetes, Dexcom, Inc., Novo Nordisk Canada Inc., Janssen Pharmaceuticals, Inc., Research Support; Abbott Diabetes, Lilly Diabetes, Zucara Therapeutics, Novo Nordisk Canada Inc., Moderna, Inc., Senseonics, Speaker's Bureau; Boehringer Ingelheim (Canada) Ltd., Lilly Diabetes, Amgen Canada, Dexcom, Inc., Novo Nordisk Canada Inc., HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Merck &amp; Co., Inc., Sanofi. A. B. Jain: Advisory Panel; Abbott, Amgen Canada, Dexcom, Inc., AstraZeneca, Novo Nordisk, Bayer Inc., Insulet Corporation, Takeda Canada, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Research Support; Abbott, Amgen Canada, Novo Nordisk, Speaker's Bureau; Abbott, Amgen Canada, Dexcom, Inc., AstraZeneca, Novo Nordisk, Bausch Health, Canada, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Pfizer Inc. M. Tsoukas: Speaker's Bureau; Novo Nordisk Canada Inc., Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Scientific Affairs, LLC, AstraZeneca, Sanofi. J. Sigalas: None. B. P. Galm: Advisory Panel; Abbott, Boehringer Ingelheim (Canada) Ltd., Novo Nordisk Canada Inc., Pfizer Inc., Research Support; Abbott, Speaker's Bureau; Abbott, Dexcom, Inc., Pfizer Inc. J. Lee: Advisory Panel; Amgen Canada, Research Support; Novo Nordisk, Lilly, Abbott, Speaker's Bureau; Amgen Canada, Canadian Collaborative Research Network, AstraZeneca, Pfizer Inc. K. S. Qureshy: Advisory Panel; Novo Nordisk, Other Relationship; Novo Nordisk, Novo Nordisk, Research Support; Abbott Diagnostics, Novo Nordisk, Speaker's Bureau; AstraZeneca. C. Collins: None. V. C. Woo: Advisory Panel; Abbott, Speaker's Bureau; Dexcom, Inc. Funding Abbott Diabetes Care

Hip sensor location for flash interstitial glucose monitoring system provides adequate clinical accuracy in non-diabetic hypoglycemic dogs.

Flash glucose monitoring systems (FGMS) are frequently used for interstitial glucose monitoring in dogs with diabetes mellitus and are typically placed between the scapulae. We aimed to evaluate the variability between glucose measurements from FGMS placed in 2 locations (between the scapulae and over the hip) in non-diabetic dogs during rapidly induced hypoglycemia. 24 apparently healthy colony dogs that were subjects in a teaching laboratory. Prospective interventional study. FGMS sensors (FreeStyle Libre 14-day system) were placed between the scapulae and over the hip of all dogs. Regular insulin was administered (0.3 u/kg IV) and subsequent hypoglycemia was corrected. Before insulin administration and every 10 minutes over 90 minutes, interstitial glucose was recorded from both locations, and blood glucose was measured with a point-of-care blood glucose monitor (AlphaTRAK 2). There was a constant bias of 5.6 mg/dL (95% limits of agreement: -26.3 to 37.5 mg/dL) between locations, but the proportional bias was not apparent. There was a correlation between FGMS locations (r = 0.731, P = < .001). Sensor site B was clinically accurate with 100% of paired samples within Parkes error grid zones A (83%) and B (17%) but did not meet the criteria for analytical accuracy. In this model of induced hypoglycemia in healthy dogs, variation between measurements from FGMS locations was unlikely to have affected the clinical outcome. Placement of FGMS over the hip may be an acceptable alternative to placement between the scapulae, but the utility in hyperglycemic dogs is unknown.

EPS4.03 An audit of diagnosing cystic fibrosis-related diabetes using newly developed diagnostic criteria using flash glucose monitoring system (Freestyle Libre®) at a regional adult cystic fibrosis centre

Objectives: In 2022, the West Midlands Adult Cystic Fibrosis Centre began using Freestyle Libre flash glucose monitoring system (FGM) as a first line method for cystic fibrosis related diabetes (CFRD) screening, as an alternative to oral glucose tolerance testing (OGTT). There are no published criteria for the diagnosis of CFRD using FGM. Using the WHO criteria, the Brompton’s paediatric clinical guideline and clinical judgement we devised diagnostic criteria to standardise the diagnosis of CFRD using FGM. Diagnostic criteria - CFRD >2 readings >11.1, impaired glucose tolerance (IGT) >2 readings 7.8–11 or <3.5; over a 2 week period. The aim of this study was to compare the number of patients diagnosed with CFRD and IGT using our new FGM diagnostic criteria compared to using our previous non standardised practice of using clinical judgement. We aimed to assess if there was any difference in the number of patients being diagnosed with CFRD or IGT using our new criteria.

Systems compared: Capillary glucose self-monitoring and Flash glucose monitoring system. Cross-sectional study

Introduction: In recent years, with the rapid advancement of technology, tools capable of minimising discomfort for the diabetic patient are becoming increasingly popular, thereby increasing adherence to treatment. The use of technology improves glycometabolic compensation and quality of life. Objective: The aim of the present study is to verify, in the group using the Flash Glucose Monitoring system, improved diabetes management and satisfaction in insulin-treated patients. Materials and Methods: A retrospective cross-sectional study was performed through the administration of the “Diabetes Treatment Satisfaction Questionnaire”. The study was performed from January to July 2019. 82 validated questionnaires were administered in the Italian version. Participants were 47 women (39 Type 1 Diabetes Mellitus and 8 Type 2 Diabetes Mellitus) and 35 men (19 Type 1 Diabetes Mellitus and 16 Type 2 Diabetes Mellitus). Results: Results show that subjects with the Flash Glucose Monitoring system have greater satisfaction than those using the Self-Monitoring of Blood Glucose system. Patients using Flash Glucose Monitoring showed 83.9% satisfaction, while patients using Self-Monitoring of Blood Glucose showed 33.3% complete satisfaction. The average glycosylated haemoglobin of the group of subjects using Flash Glucose Monitoring was 53.2 mmol/mol, while that of subjects with self-monitoring of capillary blood glucose was 60.3 mmol/mol. Conclusions: The adoption of new technologies has benefits for the patient that positively affect quality of life. The study showed better glycometabolic compensation in the group of patients using Flash Glucose Monitoring. Diabetes carries significant costs to public health, and prevention and improvement of care could be a primary goal for the community and health systems.