Practical considerations for continuous glucose monitoring in elite athletes with type 1 diabetes mellitus: A narrative review.

Type 1 diabetes mellitus (T1DM) refers to a metabolic condition where a lack of insulin impairs the usual homeostatic mechanisms to control blood glucose levels. Historically, participation in competitive sport has posed a challenge for those with T1DM, where the dynamic changes in blood glucose during exercise can result in dangerously high (hyperglycaemia) or low blood glucoses (hypoglycaemia) levels. Over the last decade, research and technological development has enhanced the methods of monitoring and managing blood glucose levels, thus reducing the chances of experiencing hyper- or hypoglycaemia during exercise. The introduction of continuous glucose monitoring (CGM) systems means that glucose can be monitored conveniently, without the need for frequent fingerpick glucose checks. CGM devices include a fine sensor inserted under the skin, measuring levels of glucose in the interstitial fluid. Readings can be synchronized to a reader or mobile phone app as often as every 1-5 min. Use of CGM devices is associated with lower HbA1c and a reduction in hypoglycaemic events, promoting overall health and athletic performance. However, there are limitations to CGM, which must be considered when being used by an athlete with T1DM. These limitations can be addressed by individualized education plans, using protective equipment to prevent sensor dislodgement, as well as further research aiming to: (i) account for disparities between CGM and true blood glucose levels during vigorous exercise; (ii) investigate the effects of temperature and altitude on CGM accuracy, and (iii) explore of the sociological impact of CGM use amongst sportspeople without diabetes on those with T1DM.

A concept for human use of real-time and remote monitoring of diabetic subjects using intermittent scanned continuous glucose measurement.

BACKGROUND: Flash glucose monitoring systems like the FreeStyle Libre (FSL) sensor have gained popularity for monitoring glucose levels in people with diabetes mellitus. This sensor can be paired with an off-label converted real-time continuous glucose monitor (c-rtCGM) plus an ad hoc computer/smartphone interface for remote real-time monitoring of diabetic subjects, allowing for trend analysis and alarm generation. OBJECTIVES: This work evaluates the accuracy and agreement between the FSL sensor and the developed c-rtCGM system. As real-time monitoring is the main feature, the system's connectivity was assessed at 5-min intervals during the trials. METHODS: One week of glucose data were collected from 16 type 1 diabetic rats using the FSL sensor and the c-rtCGM. Baseline blood samples were taken the first day before inducing type 1 diabetes with streptozotocin. Once confirmed diabetic rats, FSL and c-rtCGM, were implanted, and to improve data matching between the two monitoring devices, the c-rtCGM was calibrated to the FSL glucometer readings. A factorial design 2 × 3^3 and a second-order regression was used to find the base values of the linear model transformation of the raw data obtained from the sensor. Accuracy, agreement, and connectivity were assessed by median absolute relative difference (Median ARD), range averaging times, Parkes consensus error grid analysis (EGA), and Bland-Altman analysis with a non-parametric approach. RESULTS: Compared to the FSL sensor, the c-rtCGM had an overall Median ARD of 6.58%, with 93.06% of results in zone A when calibration was not carried out. When calibration frequency changed from every 50 h to 1 h, the overall Median ARD improved from 6.68% to 2.41%, respectively. The connectivity evaluation showed that 95% of data was successfully received every 5 min by the computer interface. CONCLUSIONS AND CLINICAL IMPORTANCE: The results demonstrate the feasibility and reliability of real-time and remote subjects with diabetes monitoring using the developed c-rtCGM system. Performing calibrations relative to the FSL readings increases the accuracy of the data displayed at the interface.

Mediterranean diet adherence and glycemic control in children and adolescents with type 1 diabetes.

To effectively manage type 1 diabetes (T1D) insulin is essential, with dosages based on lifestyle. The Mediterranean diet has demonstrated its advantages in preventing and enhancing the management of chronic diseases. Our objective was to investigate the potential mediation of sensor activity on the relationship between adherence to the Mediterranean diet and glycemic control in children and adolescents. A total of 150 children and adolescents (mean age = 13.09, SD = 3.54; 44% female) with T1D were recruited. Adherence to the Mediterranean diet was assessed using the KIDMED questionnaire which evaluates 16 items and gives higher scores when adherence is higher. Glycemic control and the duration of sensor activity were evaluated with data from flash glucose monitoring. The data confirmed our hypothesis by revealing that adherence to the Mediterranean diet positively influenced glycemic control (direct effect = 1.505; P < 0.01) and that this relationship was mediated by the duration of sensor activity (indirect effect = 0.531; P < 0.01).  Conclusions: Our results support the increased utilization of glycemic control devices, as they contribute to improve glycemic control and mediate on the positive relationship between adherence to the Mediterranean diet and adequate glycemic control. Furthermore, our findings highlight the importance of incorporating Mediterranean diet recommendations to achieve better glycemic control in children and adolescents with T1D. What is Known: • The Mediterranean diet and glycemic control have proven benefits in improving cardiovascular health in the general population. Scarce evidence exists of these benefits among children and adolescents with T1D. What is New: • Adherence to the Mediterranean diet and greater use of glucose monitoring devices in children and adolescents with T1D are related to better glycemic control. These variables can be enhanced by psychoeducational interventions such as structured diabetes education programs or peer group-based sessions, which highlights the importance of focusing on these aspects.

Effect of Christmas Holidays on Type 1 Diabetes Mellitus in Users of Glucose Flash Systems.

OBJECTIVE: Christmas holidays can impact weight and glycemic control in type 2 diabetes, but their effect on type 1 diabetes (T1D) remains understudied. This study assessed how Christmas holidays affect individuals with T1D who use flash continuous glucose monitoring systems. METHODS: This retrospective study involved 812 adults diagnosed with T1D recruited from 3 hospitals. Clinical, anthropometric, and socioeconomic data were collected. Glucose metrics from 14 days before January 1st, and before December 1st and February 1st as control periods, were recorded. Analyses adjusted for multiple variables were conducted to assess the holiday season's impact on glycemic control. RESULTS: The average time in range during the holidays (60.0 ± 17.2%) was lower compared to December (61.9 ± 17.2%, P < .001) and February (61.7 ± 17.7%, P < .001). Time above range (TAR > 180 mg/dL) was higher during Christmas (35.8 ± 18.2%) compared to December (34.1 ± 18.3%, P < .001) and February (34.2 ± 18.4%, P < .001). Differences were also observed in TAR >250 mg/dL, coefficient of variation, and average glucose (P < .05). No differences were found in time below range or other metrics. Linear regression models showed that the holidays reduced time in range by 1.9% (ß = -1.92, P = .005) and increased TAR >180 mg/dL by 1.8% (ß = 1.75, P = .016). CONCLUSION: Christmas holidays are associated with a mild and reversible deterioration in glucose metrics among individuals with T1D using flash continuous glucose monitoring, irrespective of additional influencing factors. These discoveries can be useful to advise individuals with diabetes during the festive season and to recognize potential biases within studies conducted during this timeframe.

Use of glucose sensors for post-discharge care triaging of insulin-treated patients with type 2 diabetes: a feasibility study.

The use of glucose sensors to triage post-discharge follow-up was investigated among hospital inpatients with type 2 diabetes. Feasibility, utility and patient satisfaction with this model of care were studied. Feasibility was 36.5%, with 90/198 (45.5%) inpatients discharged with glucose sensors but 9.0% unable to use glucose sensors effectively. Follow-up plans were altered in 76.3% of the patients able to use the sensor technology. Patient satisfaction was high and was improved on follow-up after 6 months.

Patient and nursing experience of flash glucose monitoring following kidney transplantation.

Flash glucose monitoring (FGM) is increasingly used for blood glucose assessment due to ease of use and is now subsidized in Australia for blood glucose measurement for patients with Type 1 Diabetes Mellitus. Dysglycaemia is common following kidney transplantation and is associated with worse outcomes and there are data to support the use of FGM post-transplant to better detect and manage changes in blood glucose levels. There is, however, no data on patient or staff perceptions of FGM, or resource implications in this setting. We prospectively evaluated patients and nursing staff experiences of FGM compared to traditional capillary glucose measurement in the immediate post-transplant setting, along with resource utilization, cost of testing, staff time taken to test and accuracy. Twenty-one kidney transplant recipients had a FGM sensor applied in the post-operative period and results compared to capillary blood glucose monitoring (CBGM) measured at least four times a day. Six-hundred-fifty-six glucose measurements were obtained, median per patient of 30 readings (IQR 10). Pearson's correlation between FGM and CBGM readings is 0.95 (p < .001). FGM readings were lower than CBGM by an average of 1.2 mmol/L (SD 0.7). Using a 5-point preference questionnaire (with ratings varying from strongly disagree-strongly agree), both patients and nurses were highly satisfied with the usability and convenience of FGM, with all preferring FGM over CBGM. Average time to perform FGM was 3.6 s versus 64 s for CBGM. In average, cost of FGM was $58 less than traditional testing per patient. FGM is an accurate, convenient and cost-effective tool that may support optimal management of glycaemic control in the post-transplant period.

Feasibility and acceptability of the use of flash glucose monitoring encountered by Indigenous Australians with type 2 diabetes mellitus: initial experiences from a pilot study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is highly prevalent within the Indigenous Australian community. Novel glucose monitoring technology offers an accurate approach to glycaemic management, providing real-time information on glucose levels and trends. The acceptability and feasibilility of this technology in Indigenous Australians with T2DM has not been investigated. OBJECTIVE: This feasibility phenomenological study aims to understand the experiences of Indigenous Australians with T2DM using flash glucose monitoring (FGM). METHODS: Indigenous Australians with T2DM receiving injectable therapy (n = 8) who used FGM (Abbott Freestyle Libre) for 6-months, as part of a clinical trial, participated in semi-structured interviews. Thematic analysis of the interviews was performed using NVivo12 Plus qualitative data analysis software (QSR International). RESULTS: Six major themes emerged: 1) FGM was highly acceptable to the individual; 2) FGM's convenience was its biggest benefit; 3) data from FGM was a tool to modify lifestyle choices; 4) FGM needed to be complemented with health professional support; 5) FGM can be a tool to engage communities in diabetes management; and 6) cost of the device is a barrier to future use. CONCLUSIONS: Indigenous Australians with T2DM had positive experiences with FGM. This study highlights future steps to ensure likelihood of FGM is acceptable and effective within the wider Indigenous Australian community.

Relationships of habitual daily alcohol consumption with all-day and time-specific average glucose levels among non-diabetic population samples.

BACKGROUND: Alcohol consumption is a prevalent behavior that is bi-directionally related to the risk of type 2 diabetes. However, the effect of daily alcohol consumption on glucose levels in real-world situations in the general population has not been well elucidated. This study aimed to clarify the relationship between alcohol consumption and all-day and time-specific glucose levels among non-diabetic individuals. METHODS: We investigated 913 non-diabetic males and females, aged 40-69 years, during 2018-2020 from four communities across Japan. The daily alcohol consumption was assessed using a self-report questionnaire. All-day and time-specific average glucose levels were estimated from the interstitial glucose concentrations measured using the Flash glucose monitoring system for a median duration of 13 days. Furthermore, we investigated the association between all-day and time-specific average glucose levels and habitual daily alcohol consumption levels, using never drinkers as the reference, and performed multiple linear regression analyses after adjusting for age, community, and other diabetes risk factors for males and females separately. RESULTS: All-day average glucose levels did not vary according to alcohol consumption categories in both males and females. However, for males, the average glucose levels between 5:00 and 11:00 h and between 11:00 and 17:00 h were higher in moderate and heavy drinkers than in never drinkers, with the difference values of 4.6 and 4.7 mg/dL for moderate drinkers, and 5.7 and 6.8 mg/dL for heavy drinkers. Conversely, the average glucose levels between 17:00 and 24:00 h were lower in male moderate and heavy drinkers and female current drinkers than in never drinkers; the difference values of mean glucose levels were -5.8 for moderate drinkers, and -6.1 mg/dL for heavy drinkers in males and -2.7 mg/dL for female current drinkers. CONCLUSIONS: Alcohol consumption was associated with glucose levels in a time-dependent biphasic pattern.

A head-to-head comparison between Guardian Connect and FreeStyle Libre systems and an evaluation of user acceptability of sensors in patients with type 1 diabetes.

AIMS: A user-calibrated real-time continuous glucose monitoring (rt-CGM) system is compared to a factory-calibrated flash glucose monitoring (FGM) system and assessed in terms of accuracy and acceptability in patients with type 1 diabetes (T1D). METHODS: Ten participants with T1D were enroled from a specialist diabetes centre in Singapore and provided with the Guardian Connect with Enlite Sensor (Medtronic, Northridge, CA, USA) and first-generation Freestyle Libre System (Abbott Diabetes Care, Witney, UK), worn simultaneously. Participants had to check capillary blood glucose four times per day. At the end of week 1 and week 2, participants returned for data download and were given a user evaluation survey. RESULTS: Accuracy evaluation between Guardian Connect and Freestyle Libre includes the overall mean absolute relative difference value (9.7 ± 11.0% vs. 17.5 ± 10.9%), Clarke Error Grid zones A + B (98.6% vs. 98.1%), sensitivity (78.9% vs. 63.4%), and specificity (93.4% vs. 81.0%). Notably, time below range (<3.9 mmol/L) was 10.5% for FGM versus 2% for rt-CGM. From the evaluation survey, 90% of participants perceived rt-CGM to be accurate versus 40% for FGM, although the majority found both devices to be easy to use, educational, and useful in improving glycaemic control. However, due to the cost of sensors, only 30% were keen to use either device for continuous monitoring. CONCLUSIONS: Although rt-CGM was superior to FGM in terms of accuracy, the value of glucose trends in both devices is still useful in diabetes self-management. Patients and clinicians may consider either technology depending on their requirements.

Cost-effectiveness of professional-mode flash glucose monitoring in general practice among adults with type 2 diabetes: Evidence from the GP-OSMOTIC trial.

AIM: To assess the cost-effectiveness of professional-mode flash glucose monitoring in adults with type 2 diabetes in general practice compared with usual clinical care. METHODS: An economic evaluation was conducted as a component of the GP-OSMOTIC trial, a pragmatic multicentre 12-month randomised controlled trial enrolling 299 adults with type 2 diabetes in Victoria, Australia. The economic evaluation was conducted from an Australian healthcare sector perspective with a lifetime horizon. Health-related quality of life (EQ-5D) and total healthcare costs were compared between the intervention and the usual care group within the trial period. The 'UKPDS Outcomes Model 2' was used to simulate post-trial lifetime costs, life expectancy and quality-adjusted life years (QALYs). RESULTS: No significant difference in health-related quality of life and costs was found between the two groups within the trial period. Professional-mode flash glucose monitoring yielded greater QALYs (0.03 [95% CI: 0.02, 0.04]) and a higher cost (A$3807 [95% CI: 3604, 4007]) compared with usual clinical care using a lifetime horizon under the trial-based monitoring frequency, considered not cost-effective (incremental cost-effectiveness ratio = A$120,228). The intervention becomes cost-effective if sensor price is reduced to lower than 50%, or monitoring frequency is decreased to once per year while maintaining the same treatment effect on HbA1c . CONCLUSIONS: Including professional-mode flash glucose monitoring every 3 months as part of a management plan for people with type 2 diabetes in general practice is not cost-effective, but could be if the sensor price or monitoring frequency can be reduced.

Glucose Awareness to Motivate and Enable Solutions (GAMES) in diabetes mellitus using flash glucose monitoring: A clinical programme.

AIMS: This real-world observational clinical programme evaluated short and medium-term effects of intermittent flash glucose monitoring on HbA1c, glycaemic variability and lifestyle behavioural changes. METHODS: Two first-generation Libre flash glucose monitoring sensors were provided 3-4 months apart with a food, activity diary, user evaluation survey and treatment modification after each sensor wear. T-tests were used to compare glucose variables within each sensor (week 1 vs. week 2) and between sensors (1st sensor vs. 2nd sensor). EasyGV software was used to calculate glycaemic variability. RESULTS: From 42 type 1 diabetes and 120 type 2 diabetes participants, there was no statistically significant change in mean HbA1c for participants with type 1 diabetes at 3-4 months after the 1st sensor but there was a statistically significant HbA1c reduction for participants with type 2 diabetes [-4 mmol/mol (-0.4%), p = 0.008], despite no statistically significant differences in carbohydrate intake, exercise frequency and duration. Greater reduction was seen in those with baseline HbA1c> 86 mmol/mol (10%) in both type 1 [-12 mmol/mol (-1.1%), p = 0.009] and type 2 diabetes [-11 mmol/mol (-1.0%), p = 0.001). Both type 1 and type 2 diabetes showed improvements in Glucose Management Indicator and percentage time-above-range when comparing week 1 versus week 2 of the same sensor. Higher scan frequency resulted in improved glycaemic parameters and certain measures of glycaemic variability. The majority of participants (85%) agreed that flash glucose monitoring is a useful device but only 60% were keen to use it for daily monitoring. CONCLUSION: Constant feedback from flash glucose monitoring improves glycaemic parameters within the first week of wear. Intermittent use 3-4 months apart resulted in greater improvements for those with higher baseline HbA1c.

The change in glycaemic control immediately after COVID-19 vaccination in people with type 1 diabetes.

AIMS: Evidence suggests that some people with type 1 diabetes mellitus (T1DM) experience temporary instability of blood glucose (BG) levels after COVID-19 vaccination. We aimed to assess this objectively. METHODS: We examined the interstitial glucose profile of 97 consecutive adults (age ≥ 18 years) with T1DM using the FreeStyle Libre® flash glucose monitor in the periods immediately before and after their first COVID-19 vaccination. The primary outcome measure was percentage (%) interstitial glucose readings within the target range 3.9-10 mmol/L for 7 days prior to the vaccination and the 7 days after the vaccination. Data are mean ± standard error. RESULTS: There was a significant decrease in the % interstitial glucose on target (3.9-10.0) for the 7 days following vaccination (mean 52.2% ± 2.0%) versus pre-COVID-19 vaccination (mean 55.0% ± 2.0%) (p = 0.030). 58% of individuals with T1DM showed a reduction in the 'time in target range' in the week after vaccination. 30% showed a decrease of time within the target range of over 10%, and 10% showed a decrease in time within target range of over 20%. The change in interstitial glucose proportion on target in the week following vaccination was most pronounced for people taking metformin/dapagliflozin + basal bolus insulin (change -7.6%) and for people with HbA1c below the median (change -5.7%). CONCLUSION: In T1DM, we have shown that initial COVID-19 vaccination can cause temporary perturbation of interstitial glucose, with this effect more pronounced in people talking oral hypoglycaemic medication plus insulin, and when HbA1c is lower.

Flash Glucose Monitoring in Israel: Understanding Real-World Associations between Self-Monitoring Frequency and Metrics of Glycemic Control.

OBJECTIVE: Flash glucose monitoring has been widely used in Israel for diabetes treatment and since 2018, the cost is reimbursed for all people with type 1 diabetes nationally. In the current study, we present the daily scanning behavior for FreeStyle Libre users in Israel and how this was associated with a range of metrics for glycemic assessment. METHODS: Deidentified data from FreeStyle Libre readers were collected between September 2014 and October 2020. Scan-rate data from Israel was extracted and sorted into 10 equal-sized groups based on scan frequency. The glucose parameters derived for each group were: estimated HbA1c (eA1c), time in range (TIR) between 70 and 180 mg/dL, and time with glucose levels of <70 mg/dL, <54 mg/dL, and >180 mg/dL. RESULTS: The data set for Israel included 12 370 readers, with data from 131 639 separate glucose sensors representing 152 million automatically recorded individual glucose readings. Users performed an average of 15 daily glucose scans, ranging from a mean of 4.1 scans per day (lowest, 10%), rising to a mean of 38.7 scans/day (highest, 10%) (median, 12; IQR, 8-18 for all readers). As the scan rates increased, the eA1c decreased from 7.6% to 6.7% (P < .001). Mean TIR increased from 56.9% to 70.0% with increasing scan rates (P < .001). Concordantly, time with glucose levels of >180 mg/dL and <54 mg/dL decreased from 37.2% to 23.6% (P < .001) and from 2.23% to 1.99%, respectively, as scan frequency increased. CONCLUSION: In Israel, people with diabetes under real-world conditions record higher rates of FreeStyle Libre scanning. These are associated with improvements in TIR, eA1c, and reduced time with glucose levels of >180 mg/dL or <54 mg/dL.

[A Case of Reactive Hypoglycemia Exacerbated Before Menstruation Improved by the Use of Continuous Glucose Monitoring].

A 40-year-old female patient was referred to our department with a complaint of postprandial hypoglycemia. We performed a 75g oral glucose tolerance test, and the patient was diagnosed as having impaired glucose tolerance with a 1-hour blood glucose of 245 mg/dl and a 2-hour blood glucose of 196 mg/dl. The patient also showed hypoglycemia with a 6-hour blood glucose of 46 mg/dl, and delayed hypersecretion of insulin, which was diagnosed as reactive hypoglycemia. The patient was diagnosed as having reactive hypoglycemia with delayed hypersecretion of insulin. She was given dietary guidance to avoid simple carbohydrates, and voglibose 0.6 mg was started for glucose intolerance and reactive hypoglycemia. The frequency of hypoglycemic symptoms decreased for a while, but gradually increased again. An interview revealed that the frequency of hypoglycemia was high at 2-3 days before menstruation, and Flash Glucose Monitoring (FGM) was applied to check the blood glucose fluctuation before and after menstruation. Her postprandial hyperglycemia worsened with FGM, and reactive hypoglycemia appeared 3 days before menstruation, while postprandial hyperglycemia improved and reactive hypoglycemia disappeared 4 days after menstruation. The frequency of hypoglycemia was reduced by instructing the patient to take voglibose before menses and to eat a supplementary meal after lunch a few days before menses. There have been no reports on the evaluation of reactive hypoglycemia exacerbated before menstruation by FGM. The menstrual cycle should be considered in the diagnosis, evaluation, and treatment of reactive hypoglycemia.

Moving with technological advancements: blood glucose monitoring from a district nurse's perspective.

Capillary blood glucose monitoring is a standard safety protocol before administering insulin. Over the past 12 months, there has been a notable increase in patients under the district nursing service using a flash glucose sensor (FGS), which is a portable technological device inserted into the skin via a stamp-like mechanism. The device sits in the interstitial fluid under the skin; the device can be scanned using a sensor to obtain glucose readings, which can eliminate the need for capillary finger pricking. From experience, some people opt for this device, considering the pain and inconvenience associated with capillary finger pricking. Despite some patients already utilising FGS, some community teams may still have to take a capillary finger prick before insulin administration, depending on local trust policy. Interestingly, while looking into the reasons for this, one discovered some contradictory concerns over the safety of FGS due to a difference in time lag, where interstitial fluid readings differ from blood glucose readings. However, new national guidelines reflect the push towards this technological innovation that could revolutionise patient care in glucose monitoring and diabetes management.

Factors associated with glycemic variability in children with type 1 diabetes mellitus based on flash glucose monitoring system. / 基于扫描式葡萄糖监测系统的儿童1åž‹ç³–å°¿ç— æ‚£è€ è¡€ç³–æ³¢åŠ¨çš„å½±å“å› ç´ .

OBJECTIVES: Patients with classical type 1 diabetes mellitus (T1DM) require lifelong dependence on exogenous insulin therapy due to pancreatic beta-cell destruction and absolute insulin deficiency. T1DM accounts for about 90% of children with diabetes in China, with a rapid increase in incidence and a younger-age trend. Epidemiological studies have shown that the overall glycated haemoglobin (HbA1c) and compliance rate are low in Chinese children with T1DM. Optimal glucose control is the key for diabetes treatment, and maintaining blood glucose within the target range can prevent or delay chronic vascular complications in patients with T1DM. Therefore, this study aims to investigate the glycemic control of children with T1DM from Hunan and Henan Province with flash glucose monitoring system (FGMS), and to explore factors associated with glycemic variability. METHODS: A total of 215 children with T1DM under 14 years old were enrolled continuously in 16 hospitals from August 2017 to August 2020. All subjects wore a FGMS device to collect glucose data. Correlation of HbA1c, duration of diabetes, or glucose scan rates with glycemic variability was analyzed. Glucose variability was compared according to the duration of diabetes, HbA1c, glucose scan rates and insulin schema. RESULTS: HbA1c and duration of diabetes were positively correlated with mean blood glucose, standard deviation of glucose, mean amplitude of glucose excursions (MAGE), and coefficient of variation (CV) of glucose (all P<0.01). The glucose scan rates during FGMS wearing was significantly positively correlated with time in range (TIR) (P=0.001) and negatively correlated with MAGE and mean duration of hypoglycemia (all P<0.01). Children with duration ≤1 year had lower time below range (TBR) and MAGE when compared with those with duration >1 year (all P<0.05). TIR and TBR in patients with HbA1c ≤7.5% were higher (TIR: 65% vs 45%, TBR: 5% vs 4%, P<0.05), MAGE was lower (7.0 mmol/L vs 9.4 mmol/L, P<0.001) than those in HbA1c >7.5% group. Compared to the multiple daily insulin injections group, TIR was higher (60% vs 52%, P=0.006), MAGE was lower (P=0.006) in the continuous subcutaneous insulin infusion group. HbA1c was lower in the high scan rates (≥14 times/d) group (7.4% vs 8.0%, P=0.046), TIR was significantly higher (58% vs 47%, P<0.001), and MAGE was lower (P<0.001) than those in the low scan rate (<14 times/d) group. CONCLUSIONS: The overall glycemic control of T1DM patients under 14 years old in Hunan and Henan Province is under a high risk of hypoglycemia and great glycemic variability. Shorter duration of diabetes, targeted HbA1c, higher glucose scan rates, and CSII are associated with less glycemic variability.

Effect of flash glucose monitoring in adults with type 1 diabetes: a nationwide, longitudinal observational study of 14,372 flash users compared with 7691 glucose sensor naive controls.

AIMS/HYPOTHESIS: The aim of this work was to evaluate changes in glycaemic control (HbA1c) and rates of severe hypoglycaemia over a 2 year period after initiation of flash glucose monitoring (FM) in type 1 diabetes. METHODS: Using data from the Swedish National Diabetes Registry, 14,372 adults with type 1 diabetes with a new registration of FM during 2016-2017 and with continued FM for two consecutive years thereafter, and 7691 control individuals using conventional self-monitoring of blood glucose (SMBG) during the same observation period, were included in a cohort study. Propensity sores and inverse probability of treatment weighting (IPTW) were used to balance FM users with SMBG users. Changes in HbA1c and events of severe hypoglycaemia were compared. RESULTS: After the start of FM, the difference in IPTW change in HbA1c was slightly greater in FM users compared with the control group during the follow-up period, with an estimated mean absolute difference of -1.2 mmol/mol (-0.11%) (95% CI -1.64 [-0.15], -0.75 [-0.07]; p < 0.0001) after 15-24 months. The change in HbA1c was greatest in those with baseline HbA1c ≥70 mmol/mol (8.5%), with the estimated mean absolute difference being -2.5 mmol/mol (-0.23%) (95% CI -3.84 [-0.35], -1.18 [-0.11]; p = 0.0002) 15-24 months post index. The change was also significant in the subgroups with initial HbA1c ≤52 mmol/mol (6.9%) and 53-69 mmol/mol (7.0-8.5%). Risk of severe hypoglycaemic episodes was reduced by 21% for FM users compared with control individuals using SMBG (OR 0.79 [95% CI 0.69, 0.91]; p = 0.0014)]. CONCLUSIONS/INTERPRETATION: In this large cohort, the use of FM was associated with a small and sustained improvement in HbA1c, most evident in those with higher baseline HbA1c levels. In addition, FM users experienced lower rates of severe hypoglycaemic events compared with control individuals using SMBG for self-management of glucose control.

Flash glucose monitoring and automated bolus calculation in type 1 diabetes treated with multiple daily insulin injections: a 26 week randomised, controlled, multicentre trial.

AIMS/HYPOTHESIS: We aimed to compare the effects of intermittently scanned continuous glucose monitoring (isCGM) and carbohydrate counting with automated bolus calculation (ABC) with usual care. METHODS: In a randomised, controlled, open-label trial carried out at five diabetes clinics in the Capital Region of Denmark, 170 adults with type 1 diabetes for ≥1 year, multiple daily insulin injections and HbA1c > 53 mmol/mol (7.0%) were randomly assigned 1:1:1:1 with centrally prepared envelopes to usual care (n = 42), ABC (n = 41), isCGM (n = 48) or ABC+isCGM (n = 39). Blinded continuous glucose monitoring data, HbA1c and patient-reported outcomes were recorded at baseline and after 26 weeks. The primary outcome was change in time in range using isCGM vs usual care. RESULTS: Baseline characteristics were comparable across arms: mean age 47 (SD 13.7) years, median (IQR) diabetes duration 18 (10-28) years and HbA1c 65 (61-72) mmol/mol (8.1% [7.7-8.7%]). Change in time in range using isCGM was comparable to usual care (% difference of 3.9 [-12-23], p = 0.660). The same was true for the ABC and ABC+isCGM arms and for hypo- and hyperglycaemia. Also compared with usual care, using ABC+isCGM reduced HbA1c (4 [95% CI 1, 8] mmol/mol) (0.4 [0.1, 0.7] %-point) and glucose CV (11% [4%, 17%]) and improved treatment satisfaction, psychosocial self-efficacy and present life quality. Treatment satisfaction also improved by using isCGM alone vs usual care. Statistical significance was maintained after multiple testing adjustment concerning glucose CV and treatment satisfaction with ABC+isCGM, and treatment satisfaction with isCGM. Discontinuation was most common among ABC only users, and among completers the ABC was used 4 (2-5) times/day and the number of daily isCGM scans was 5 (1-7) at study end. CONCLUSIONS/INTERPRETATION: isCGM alone did not improve time in range, but treatment satisfaction increased in technology-naive people with type 1 diabetes and suboptimal HbA1c. The combination of ABC+isCGM appears advantageous regarding glycaemic variables and patient-reported outcomes, but many showed resistance towards ABC. TRIAL REGISTRATION: ClinicalTrials.gov NCT03682237. FUNDING: The study is investigator initiated and financed by the Capital Region of Denmark.

Flash Glucose Monitoring Can Accurately Reflect Postprandial Glucose Changes in Healthy Adults in Nutrition Studies.

OBJECTIVE: This study investigated the accuracy of a flash glucose monitoring system (FGMS) in a postprandial setting. METHODS: Ten fasted adults without diabetes wore the FGMS sensors then consumed a standard breakfast. Their glucose levels were subsequently recorded for 2 hours, both by the FGMS and by measuring capillary glucose levels using the glucose oxidase method. The accuracy of the FGMS data was assessed using the accuracy limits stated in ISO 15197:2013. RESULTS: FGMS measurements were mostly lower than glucose oxidase measurements (mean absolute relative difference ± SD: 25.4 ± 17.0%, p < 0.001). However, the maximum difference from baseline captured by the two methods was not significantly different (mean ± SD, glucose oxidase: 58.5 ± 18.9 mg/dl; FGMS, 54.4 ± 28.9 mg/dl, p = 0.366). CONCLUSIONS: FGMS could track the incremental glycaemic excursions after meals in adults without diabetes, yet further studies with greater sample sizes are needed to confirm this finding.

Flash glucose monitoring data analysed by detrended fluctuation function on beta-cell function and diabetes classification.

AIM: We aimed to use data-driven glucose pattern analysis to unveil the correlation between the metrics reflecting glucose fluctuation and beta-cell function, and to identify the possible role of this metric in diabetes classification. MATERIALS AND METHODS: In total, 78 participants with type 1 diabetes and 59 with type 2 diabetes were enrolled in this study. All participants wore a flash glucose monitoring system, and glucose data were collected. A detrended fluctuation function (DFF) was utilized to extract glucose fluctuation information from flash glucose monitoring data and a DFF-based glucose fluctuation metric was proposed. RESULTS: For the entire study population, a significant negative correlation between the DFF-based glucose fluctuation metric and fasting C-peptide was observed (r = -0.667; P <.001), which was larger than the correlation coefficient between the fasting C-peptide and mean amplitude of plasma glucose excursions (r = -0.639; P < .001), standard deviation (r = -0.649; P <.001), mean blood glucose (r = -0.519; P < .001) and time in range (r = 0.593; P < .001). As glucose data analysed by DFF revealed a clear bimodal distribution among the total participants, we randomly assigned the 137 participants into discovery cohorts (n = 100) and validation cohorts (n = 37) for 10 times to evaluate the consistency and effectiveness of the proposed metric for diabetes classification. The confidence interval for area under the curve according to the receiver operating characteristic analysis in the 10 discovery cohorts achieved (0.846, 0.868) and that for the 10 validation cohorts was (0.799, 0.862). In addition, the confidence intervals for sensitivity and specificity in the discovery cohorts were (75.5%, 83.0%), (81.3%, 88.5%) and (71.8%, 88.3%), (76.5%, 90.3%) in the validation cohorts, indicating the potential capacity of DFF in distinguishing type 1 and type 2 diabetes. CONCLUSIONS: Our study first proposed the possible role of data-driven analysis acquired glucose metric in predicting beta-cell function and diabetes classification, and a large-scale, multicentre study will be needed in the future.