Self-monitoring Of Blood Glucose Measurements Research Articles

Effects of Vildagliptin, a Dipeptidyl Peptidase-4 Inhibitor, on the Parameters of Glucose Metabolism and the Cardio-Ankle Vascular Index in Individuals with Type 2 Diabetes.

DPP-4 inhibitors are frequently used as first-line agents for the treatment of type 2 diabetes in Japan. This study aimed to examine the effects of vildagliptin on glucose metabolism and arterial stiffness. Twenty treatment-naïve patients with type 2 diabetes (8 males and 12 females) received vildagliptin 50 mg twice daily for 6 months. Self-monitored blood glucose measurements and a 75 g OGTT were performed. Arterial stiffness was assessed using the CAVI. After the vildagliptin treatment, a significant decrease in the median HbA1c (from 8.3 to 6.4%) and fasting HOMA-β (from 26.1 to 34.5%), and a marginally significant decrease in the CAVI (from 8.9 to 8.4, p = 0.087) were observed. The glycemic variability parameters also improved, whereas the insulin sensitivity and oxidative stress remained unchanged. Participants with a lower glycemic variability on the 75 g OGTT after vildagliptin treatment showed a significant decrease in their CAVI. The baseline BMI was significantly higher for the participants with a decreased CAVI than in those with no change in their CAVI (24.5 vs. 20.8 kg/m2). After vildagliptin treatment, a decrease in the CAVI was observed, especially in the individuals with improved glycemic variability on the 75 g OGTT. Vildagliptin may be suitable for vascular protection in individuals with high glycemic variability and/or an elevated BMI.

Continuous Glucose Monitoring by Insulin-Treated Pilots Flying Commercial Aircraft Within the ARA.MED.330 Diabetes Protocol: A Preliminary Feasibility Study.

Background and Aims: A preliminary study compared the use of continuous glucose monitoring (CGM) with the use of self-monitored blood glucose (SMBG) by aircraft pilots with insulin-treated diabetes in the United Kingdom, Ireland, and Austria, certified to fly commercial aircraft within the European Aviation Safety Agency ARA.MED.330 protocol. Methods: SMBG and simultaneous interstitial glucose measurements using CGM (Dexcom G6®) were recorded during pre- and in-flight periods. Results: Eight male pilots (seven with type 1 diabetes and one with type 3c diabetes), median age of 48.5 years and median diabetes duration of 11.5 years, participated. The correlation coefficient (R) between 874 contemporaneously recorded SMBG and CGM values was 0.843, P < 0.001. The mean glucose concentration was 8.78 mmol/L (standard deviation [SD] 0.67) using SMBG compared with 8.71 mmol/L (SD 0.85) recorded using CGM. The mean absolute relative difference was 9.39% (SD 3.12). Conclusions: CGM using Dexcom G6 systems is a credible alternative to SMBG for monitoring glucose levels when insulin-treated pilots fly commercial aircraft. The study was registered with Clinical Trials.gov NCT04395378.

Glycemic Control Trajectories and Risk of Perinatal Complications Among Individuals With Gestational Diabetes

Glycemic control is the cornerstone of gestational diabetes management. Glycemic control trajectories account for differences in longitudinal patterns throughout pregnancy; however, studies on glycemic control trajectories are scarce. To examine whether glycemic control trajectories from gestational diabetes diagnosis to delivery were associated with differential risk of perinatal complications. This population-based cohort study included individuals with gestational diabetes with longitudinal electronic health record data from preconception to delivery who received prenatal care at Kaiser Permanente Northern California (KPNC) and were enrolled in KPNC's telemedicine-based gestational diabetes care program between January 2007 and December 2017. Data analysis was conducted from September 2021 to January 2022. Glycemic control trajectories were derived using latent class modeling based on the American Diabetes Association's recommended self-monitoring of blood glucose measurements. Optimal glycemic control was defined as at least 80% of all measurements meeting the targets at KPNC clinical settings. Multivariable Poisson regression models were used to estimate the associations of glycemic control trajectories with cesarean delivery, preterm birth, shoulder dystocia, large- and small-for-gestational-age, and neonatal intensive care unit admission and stay of 7 days or longer. Among a total of 26 774 individuals (mean [SD] age, 32.9 [5.0] years; 11 196 Asian or Pacific Islander individuals [41.8%], 1083 Black individuals [4.0%], 7500 Hispanic individuals [28.0%], and 6049 White individuals [22.6%]), 4 glycemic control trajectories were identified: stably optimal (10 528 individuals [39.3%]), rapidly improving to optimal (9151 individuals [34.2%]), slowly improving to near-optimal (4161 individuals [15.5%]), and slowly improving to suboptimal (2934 individuals [11.0%]). In multivariable models with the rapidly improving to optimal trajectory group as the reference group, glycemic control trajectories were associated with perinatal complications with a gradient across stably optimal to slowly improving to suboptimal. For individuals in the stably optimal trajectory group, there were lower risks of cesarean delivery (adjusted relative risk [aRR], 0.93 [95% CI, 0.89-0.96]), shoulder dystocia (aRR, 0.75 [95% CI, 0.61-0.92]), large-for-gestational age (aRR, 0.74 [95% CI, 0.69-0.80]), and neonatal intensive care unit admission (aRR, 0.90 [95% CI, 0.83-0.97]), while for patients in the slowly improving to suboptimal glycemic control trajectory group, risks were higher for cesarean delivery (aRR, 1.18 [95% CI, 1.12-1.24]; (P for trend < .001), shoulder dystocia (aRR, 1.41 [95% CI, 1.12-1.78]; P for trend < .001), large-for-gestational-age (aRR, 1.42 [95% CI, 1.31-1.53]; P for trend < .001), and neonatal intensive care unit admission (aRR, 1.33 [95% CI, 1.20-1.47]; P for trend < .001). The risk of small-for-gestational-age was higher in patients in the stably optimal group (aRR, 1.10 [95% CI, 1.02-1.20]) and lower in the slowly improving to suboptimal group (aRR, 0.63 [95% CI, 0.53-0.75]). These findings suggest that slowly improving to near-optimal and slowly improving to suboptimal glycemic control trajectories were associated with increased risk of perinatal complications. Future interventions should help individuals achieve glycemic control early after gestational diabetes diagnosis and throughout pregnancy to decrease the risk of perinatal complications.

763-P: Real-World Use of the MiniMed 780G Advanced Hybrid Closed-Loop (AHCL) System with the Guardian 4 Sensor and the Guardian Sensor 3

Objective: The MiniMed™ 780G system with the adjunctive Guardian™ sensor 3 that requires calibration has been commercially available in Europe, since October 2020. Since October 2021, the system has been available with the non-adjunctive Guardian™ 4 sensor, which does not require calibration. In this study, glycemic outcomes during real-world MiniMed™ 780G system use with both sensors were evaluated. Methods: Analyses were based on data collected on Dec 10, 2021, from consenting system users (N=674) residing in Europe with ≥days of sensor glucose (SG) data from both sensors. Glycemic outcomes including mean±SD of the glucose management indicator (GMI) and percentage of time spent in target range of 70-180mg/dL (TIR) , at &amp;lt;54mg/dL and &amp;lt;70mg/dL (TBR) and at &amp;gt;180mg/dL (TAR) were assessed. The time spent in AHCL, number of AHCL exits/week and self-monitoring of blood glucose (SMBG) measurements/day were also determined. Results: System use with the Guardian™ 4 sensor versus Guardian™ sensor 3 demonstrated 94±9% vs 93±11% of time in AHCL, 73.1±11.4% vs 74.6±9.8% TIR, 0.11±0.16% vs 0.51±0.58% TBR&amp;lt;54, 1.6±1.4% vs 2.4±1.8% TBR&amp;lt;70, and 25.3±11.7% vs 23.1±10.0% TAR&amp;lt;180. During the night (12AM-6AM) , greater glycemic control was observed: with 81.6±13.3% vs 82.8±11.0% TIR, 0.09±0.22% vs 0.42±0.69% TBR&amp;lt;54, 1.1±1.5% vs 1.8±1.9% TBR&amp;lt;70 and 17.3±13.3% vs 15.4±11.0% TAR&amp;gt;180. The GMI was 6.98±0.43% vs 6.84±0.38%. Closed-loop exits were 0.83±1.02 vs 1.1±1.06/week and SMBG measurements were 0.9±0.9 vs 3.3±0.9/day. Conclusion: Real-world MiniMed™ 780G system use findings indicate reduced burden related to SMBG measurements and closed-loop exits, with the Guardian™ 4 sensor, while maintaining favorable glycemic outcomes of the MiniMed™ 780G + Guardian™ sensor 3 system. Disclosure R.A. Vigersky: Employee; Medtronic. J. Castañeda: Employee; Medtronic. A. Arrieta: Employee; Medtronic. T.L. Cordero: Employee; Medtronic, Medtronic. A.S. Rhinehart: Employee; Medtronic. Stock/Shareholder; Medtronic. J. Shin: Employee; Medtronic.

Improved HbA1c and reduced glycaemic variability after 1-year intermittent use of flash glucose monitoring

Flash glucose monitoring (FGM) was introduced in China in 2016, and it might improve HbA1c measurements and reduce glycaemic variability during T1DM therapy. A total of 146 patients were recruited from October 2018 to September 2019 in Liaocheng. The patients were randomly divided into the FGM group or self-monitoring blood glucose (SMBG) group. Both groups wore the FGM device for multiple 2-week periods, beginning with the 1st, 24th, and 48th weeks for gathering data, while blood samples were also collected for HbA1c measurement. Dietary guidance and insulin dose adjustments were provided to the FGM group patients according to their Ambulatory Glucose Profile (AGP) and to the SMBG group patients according to their SMBG measurements taken 3–4 times daily. All of the participants underwent SMBG measurements on the days when not wearing the FGM device. At the final visit, HbA1c, time in range (TIR), duration of hypoglycaemia and the number of diabetic ketoacidosis (DKA) events were taken as the main endpoints. There were no significant difference in the baseline characteristics of the two groups. At 24 weeks, the HbA1c level of the FGM group was 8.16 ± 1.03%, which was much lower than that of the SMBG group (8.68 ± 1.01%) (p = 0.003). The interquartile range (IQR), mean blood glucose (MBG), and the duration of hypoglycaemia in the FGM group also showed significant declines, compared with the SMBG group (p < 0.05), while the TIR increased in the FGM group [(49.39 ± 17.54)% vs (42.44 ± 15.49)%] (p = 0.012). At 48 weeks, the differences were more pronounced (p < 0.01). There were no observed changes in the number of episodes of DKA by the end of the study [(0.25 ± 0.50) vs (0.28 ± 0.51), p = 0.75]. Intermittent use of FGM by T1DM patients can improve their HbA1c and glycaemic control without increasing the hypoglycaemic exposure in insulin-treated individuals with type 1 diabetes in an developing country.

Clinical characteristics and management of type 1 diabetes in Spain. The SED1 study

ObjectivesTo determine the sociodemographic and clinical profile of a representative sample of people with type 1 diabetes mellitus (DM1) in Spain and identify factors associated with glycemic control. Material and methodsA cross-sectional observational study was carried out in adults and children with DM1 treated in 75 Spanish public hospitals, geographically distributed in order to be representative of the Spanish population. Within each center, the patients were included on a consecutive basis as they visited the clinic. They were interviewed, and their clinical histories were reviewed. A descriptive statistical analysis was made, and factors associated with HbA1c were analysed using multivariate linear regression analysis. ResultsA total of 647 patients were included: 55.3% females, aged 36.6 ± 14.4 years, 97.2% Caucasians, BMI 24.7 ± 4.4 kg/m2 (12.1% ≥ 30 kg/m2), and 74.0% had secondary / university education. A total of 20.2% were active smokers. The mean time from the diagnosis of DM1 was 17.9 ± 12.0 years. A total of 48.7% presented comorbidities: 19.3% retinopathy and 16.4% hypothyroidism. As regards treatment for DM1, 76.5% received basal-bolus insulin therapy and 20.7% continuous subcutaneous insulin infusion (CSII); 51.0% of the patients used an insulin/carbohydrate ratio (ICR), with 4.6 ± 1.6 self-monitored capillary blood glucose (SMCBG) measurements a day, and 24.8% used continuous glucose monitoring (CGM). The mean HbA1c value was 7.6 ± 1.1% (30% below 7%). Metabolic control improved (lower HbA1c) with more daily SMCBG (B = −0.053; p = 0.009), a higher educational level (B = 0.461; P < 0.001), greater number of hypoglycemia episodes (B = −0.253; P = 0.018) and carbohydrate counting (B = −0.190; P = 0.048), and worsened the longer the duration of the disease (B = 0.010; P = 0.010), higher total dose of insulin (B = 0.010; P < 0.0001), poorer adherence to diet (B = 0.650; P < 0.0001) and a family history of DM (B = −0.233; P = 0.007). ConclusionsThe management of patients with DM1 in Spain, as well as the treatment they receive, is similar to that seen in other Western countries. Blood glucose control is associated with educational level, disease duration, and the characteristics of treatment and self-care.

Study protocol: Safety and efficacy of smart watch integrated do-it-yourself continuous glucose monitoring in adults with Type 1 diabetes, a randomised controlled trial.

Type 1 diabetes (T1D) management requires regular monitoring of glucose levels and judicious therapeutic administration of insulin to prevent both microvascular and macrovascular complications. Few people with diabetes are able to sustain the self-monitored blood glucose measurements needed for optimal care, and those that do, typically experience significant diabetes-related distress. Both intermittently scanned continuous glucose monitoring (isCGM) and continuous glucose monitoring (CGM) offer alternatives to reduce the overall burden, but both still have limitations. Given the expense of CGM, smart watch integrated do-it-yourself (DIY)-CGM has been developed as an alternative to commercial isCGM and CGM technologies. This study has been designed to evaluate the clinical efficacy of smart watch integrated DIY-CGM compared with isCGM in adults with T1D. This multicentre, randomised, crossover study will be conducted in New Zealand and aims to recruit 60 adults with established T1D who currently use isCGM. DIY-CGM will be compared to usual care with isCGM. Participants will be randomised to either arm of the study for 8weeks followed by a 4-week washout period before crossing over to the other study arm for a further 8weeks. The primary endpoint is glucose time in range (TIR) defined as percentage of time interstitial glucose is spent between 3.9 to 10mmol/L for the entire intervention period. Secondary endpoints include diabetes-related quality of life, distress, and sleep quality in participants and their partners. The results of this study will provide clinical trial data regarding smart watch integrated DIY-CGM versus isCGM for improving glycaemic control in adults with T1D, and also report a variety of key secondary outcomes, including changes in subjective outcome measures for both people with diabetes and their partners. Australian New Zealand Clinical Trials Registry (ACTRN12621000648820,31 May 2021); World Health Organisation International Clinical Trial Registry Platform (U1111-1262-2784, 3rd December 2020).

Continuous Glucose Monitoring (CGM) Readings During Patient-Reported Symptomatic Hypoglycemia: Assessment of the Advanced Technologies and Treatments for Diabetes Consensus Definition of CGM-Recorded Hypoglycemia.

Aim: Continuous glucose monitoring (CGM) is widely used in clinical practice and research to detect hypoglycemia. A consensus definition of CGM-recorded hypoglycemia is made by a group of international experts under the auspice of the Advanced Technologies and Treatments for Diabetes (ATTD). The purpose of this study is to compare the definition with patient-reported hypoglycemia. Methods: In a prospective, observational study of 186 patients with type 1 diabetes using blinded Medtronic iPro 2 CGM for 6 days, every patient-reported symptomatic hypoglycemic event and interstitial glucose (IG) values at the registration time were classified according to the ATTD definition of CGM-recorded hypoglycemia. For comparison between CGM and self-monitored blood glucose (SMBG) values, the International Hypoglycemia Study Group (IHSG) classification of hypoglycemia and chi-square test were used. Results: A total of 321 events of symptomatic hypoglycemia were reported by 68% of the patients, corresponding to 2.0 ± 2.3 events (mean ± standard deviation) per patient-week. A total of 206 (64%) events met the CGM consensus definition. In the remaining 115 (36%) not-confirmed events, 5 events had an IG <3.9 mmol/L, which lasted <15 min. The overall mean IG value was 3.6 ± 1.1 mmol/L (median 3.1, range 2.2-10.4). In symptomatic hypoglycemic events with both CGM and SMBG data, SMBG confirmed significantly more symptomatic hypoglycemic events than CGM (P < 0.001). Conclusion: CGM-recorded hypoglycemia according to the consensus definition is present at two thirds of all patient-reported events when recorded by the Medtronic iPro 2 system. The recommended minimum duration of a hypoglycemic event of 15 min is supported by the study. SMBG measurements detect significantly more symptomatic hypoglycemic events than Medtronic iPro 2 CGM.

How the COVID-19 outbreak affected patients with diabetes mellitus?

BackgroundGlobal COVID-19 outbreak has been such a stressful experience for most of the people. Using a web-based cross-sectional study, we aimed to evaluate the acute stress response, depression, and anxiety in patients with diabetes mellitus (DM) during the COVID-19 pandemic, and to examine the effect of these psychiatric problems on diet habits and glycemic controls of patients.MethodsThis web-based survey of COVID-19 was sent to the patients through the Whatsapp platform. All participants reported their demographic data, diabetes-related information, changes in self-monitoring blood glucose measurements, physical parameters, and eating habits after COVID-19, then completed Hospital Anxiety and Depression Scale (HADS) and the Impact of Event Scale, Revised (IES-R) questionnaires which assessed acute stress sypmtoms, anxiety, and depression.ResultsThree hundred and four patients with DM [(141 type 1 DM (T1D) and 163 type 2 (T2D)] were included in the study. In our study, female gender, higher BMI and weight, decreased in financial income after outbreak, presence of diabetic complications and comorbid diseases (i.e., retinopathy, neuropathy, diabetic foot, hypertension, dyslipidemia), worsened glycemic levels, increased carbohydrate consumption, and snacking were associated with higher anxiety and depression scores. Depression was higher in patients with T2D and duration of illness was correlated with acute stress level.ConclusionsIt is important to be aware of the possibility of acute stress, depression, and anxiety after pandemic in patients with DM whose glycemic control is impaired. Psychological problems should not be ignored beyond physical inactivity and worsening eating habits.

604-P: GlucoMen Day CGM System Accuracy Assessment in Individuals with Type 1 Diabetes

Continuous glucose monitors (CGMs) are increasingly becoming indispensable in everyday diabetes management. This study assessed the accuracy of the new GlucoMen Day® CGM (Waveform Cascade, launched by A. Menarini Diagnostics) by comparing it to a laboratory reference instrument and a self-monitoring blood glucose (SMBG) device. Eight individuals with type 1 diabetes (3 females, age 41.6 ± 13.3 years, BMI 28.0 ± 6.1 kg/m2, HbA1c 55.6 ± 12.2 mmol/mol, diabetes duration 13.9 ± 6.5 years) spent 14 days at home simultaneously wearing two CGMs in the abdominal subcutaneous adipose tissue while also performing a minimum of five SMBG measurements (GlucoMen Day® Meter, A. Menarini Diagnostics, IT) per day. On days four and ten of sensor wear, the participants underwent a 5-hour meal and insulin challenge at the research center. The plasma glucose concentration was determined in 20-min intervals with a laboratory reference instrument (YSI 2300 Stat Plus, Yellow Springs, OH). The CGM accuracy was assessed by calculating the mean absolute relative difference (MARD) and by performing the consensus error grid analysis (CEG). The overall MARDs calculated for YSI and SMBG were 9.7 % and 13.1 %, respectively. The CEG analysis showed that 98% of all data points were in the clinically acceptable zones A and B. The study data indicate that the CGM accuracy meets the current clinical requirements for state-of-the-art continuous glucose monitors.View largeDownload slideView largeDownload slide DisclosureA. Simic: None. M. T. Taucher: None. D. A. Hochfellner: None. T. Poettler: None. F. Aberer: None. J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker's Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH.FundingA. Menarini Diagnostics

Características clínicas y manejo de la diabetes tipo 1 en España. Estudio SED1

ObjectivesTo determine the sociodemographic and clinical profile of a representative sample of people with type 1 diabetes mellitus (DM1) in Spain and identify factors associated with glycemic control. Material and methodsA cross-sectional observational study was carried out in adults and children with DM1 treated in 75 Spanish public hospitals, geographically distributed in order to be representative of the Spanish population. Within each center, the patients were included on a consecutive basis as they visited the clinic. They were interviewed, and their clinical histories were reviewed. A descriptive statistical analysis was made, and factors associated with HbA1c were analyzed using multivariate linear regression analysis. ResultsA total of 647 patients were included: 55.3% females, aged 36.6±14.4 years, 97.2% Caucasians, BMI 24.7±4.4kg/m2 (12.1% ≥30kg/m2), and 74.0% had secondary / university education. A total of 20.2% were active smokers. The mean time from the diagnosis of DM1 was 17.9±12.0 years. A total of 48.7% presented comorbidities: 19.3% retinopathy and 16.4% hypothyroidism. As regards treatment for DM1, 76.5% received basal-bolus insulin therapy and 20.7% continuous subcutaneous insulin infusion (CSII); 51.0% of the patients used an insulin/carbohydrate ratio (ICR), with 4.6±1.6 self-monitored capillary blood glucose (SMCBG) measurements a day, and 24.8% used continuous glucose monitoring (CGM). The mean HbA1c value was 7.6±1.1% (30% below 7%). Metabolic control improved (lower HbA1c) with more daily SMCBG (B=−0.053; p=0.009), a higher educational level (B=0.461; P<0.001), greater number of hypoglycemia episodes (B=−0.253; P=0.018) and carbohydrate counting (B=−0.190; P=0.048), and worsened the longer the duration of the disease (B=0.010; P=0.010), higher total dose of insulin (B=0.010; P<0.0001), poorer adherence to diet (B=0.650; P<0.0001) and a family history of DM (B=−0.233; P=0.007). ConclusionsThe management of patients with DM1 in Spain, as well as the treatment they receive, is similar to that seen in other Western countries. Blood glucose control is associated with educational level, disease duration, and the characteristics of treatment and self-care.

Continuous Glucose Monitoring Versus Self-Monitoring of Blood Glucose to Assess Glycemia in Gestational Diabetes

Background: Gestational diabetes mellitus (GDM) management using self-monitoring blood glucose (SMBG) does not normalize pregnancy outcomes. Objective: We aimed to conduct an observational study to explore if continuous glucose monitoring (CGM) could identify elevated glucose levels not apparent in women with GDM managed using SMBG. Study Design: A 7-day masked-CGM (iPro; Medtronic) was performed within 2 weeks of GDM diagnosis, immediately post-GDM education, but before insulin commencement as determined by SMBG. CGM data regarding hyperglycemia (sensor glucose >126 mg/dL [06:00-00:00 h] and >99 mg/dL [00:00-06:00 h] for >10% of time), time with health care professionals, treatment, and pregnancy outcome were collected. Comparisons (Mann-Whitney test) were performed between subjects subsequently commenced on insulin versus those continued with diet and lifestyle measures alone. Results: Ninety women of mean (standard deviation) gestational age weeks 27(1) were studied. Those prescribed insulin (n = 34) compared with those managed with diet and lifestyle alone (n = 56) had a greater time in hyperglycemia (P = 0.0001). Of those not prescribed insulin, 35/56 (61%) breached CGM cutoffs between 00:00 and 06:00 h; 11/56 (20%) breached 6.00-00.00 h CGM cutoffs for >10% of the time; and 21/45 (47%) with optimal CGM glucose levels during the daytime spent >10% time in hyperglycemia between 00.00 and 06:00 h. In contrast, SMBG measurements exceeded the clinical targets of <120 mg/dL postdinner in 5.4% and <100 mg/dL fasting in 0% of the subjects. Conclusions: CGM provides a more comprehensive assessment of nocturnal hyperglycemia than SMBG and could improve targeting of interventions in GDM. Larger studies to better define CGM targets are required, which once established will inform studies aimed at targeting nocturnal glucose levels.

Using Iterative Learning for Insulin Dosage Optimization in Multiple-Daily-Injections Therapy for People With Type 1 Diabetes.

In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements. Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements. Upon termination of the 20 weeks long in-silico trial, in a scenario characterized by meal carbohydrate (CHO) normally distributed with mean μ = [50, 75, 75] grams and standard deviation σ = [5, 7, 7] grams, our strategy produced statistically significant improvements in time in range (70--180) [mg/dl], from 66.9(33.1) % to 93.6(6.7) %, p = 0.02. Iterative learning shows potential to improve glycemic regulation over time by driving blood glucose closer to the recommended glycemic targets. Decision support systems (DSSs) and automated therapy advisors such as the one proposed here are expected to improve glycemic outcomes reducing the burden on patients on MDI therapy.

Efficacy and safety of flash glucose monitoring in patients with type 1 and type 2 diabetes: a systematic review and meta-analysis.

IntroductionFlash glucose monitoring (FGM) is a factory-calibrated sensor-based technology for the measurement of interstitial glucose. We performed a systematic review and meta-analysis to assess its efficacy and safety in patients with type 1 and type 2 diabetes.Research design and methodsPubMed, CENTRAL, Scopus and Web of Science were searched in July 2019. Twelve studies with a follow-up longer than 8 weeks, evaluating 2173 patients on prandial insulin, multiple daily insulin injections or continuous subcutaneous insulin infusion were included. The following data were extracted: HbA1c, time in range, time above 180 mg/dL, time below 70 mg/dL, frequency of hypoglycemic events, number of self-monitoring of blood glucose (SMBG) measurements, total daily insulin dose, patient-reported outcomes, adverse events, and discontinuation rate. A comparison with SMBG was conducted.ResultsFGM use was associated with a reduction in HbA1c (−0.26% (−3 mmol/mol); p=0.002) from baseline to the last available follow-up, which correlated with HbA1c levels at baseline (−0.4% (−4 mmol/mol) for each 1.0% (11 mmol/mol) of HbA1c above 7.2% (55 mmol/mol)). Also, a decrease in time below 70 mg/dL was found (−0.60 hours/day; p=0.04). Favorable findings in patient-reported outcomes and no device-related serious adverse events were reported. When compared with SMBG, FGM was characterized by no statistically different change in HbA1c (p=0.09), with lower number of SMBG measurements per day (−3.76 n/day; p<0.001) and risk of discontinuation (relative risk=0.42; p=0.001). A limited number of studies, with a heterogeneous design and usually with a short-term follow-up and without specific training, were found.ConclusionsThe present review provides evidence for the use of FGM as an effective strategy for the management of diabetes.

State Estimation in Type 2 Diabetes Using The Continuous-Discrete Unscented Kalman Filter

Using a nonlinear model for the glucose-insulin dynamics in type 2 diabetes, formulated in continuous-time as a stochastic differential equation, we seek to estimate the system states and parameters based only on discrete-time self-monitored blood glucose measurements of fasting glucose and the known exogenous insulin dose. This is done by means of continuous-discrete unscented Kalman filtering. The results are compared to an implementation of a continuous-discrete extended Kalman filter. Simulations show that it is possible to estimate all states with good accuracy using the CD-UKF, while it is also possible to estimate one unknown parameter at the same time. Further simulations show that increasing the sample rate makes it possible to estimate more parameters, given that the meal intake of the patient is known perfectly.

Modelling potential cost savings from use of real-time continuous glucose monitoring in pregnant women with Type 1 diabetes.

AimTo investigate potential cost savings associated with the use of real‐time continuous glucose monitoring (RT‐CGM) throughout pregnancy in women with Type 1 diabetes.MethodsA budget impact model was developed to estimate, from the perspective of National Health Service England, the total costs of managing pregnancy and delivery in women with Type 1 diabetes using self‐monitoring of blood glucose (SMBG) with and without RT‐CGM. It was assumed that the entire modelled cohort (n = 1441) would use RT‐CGM from 10 to 38 weeks’ gestation (7 months). Data on pregnancy and neonatal complication rates and related costs were derived from published literature, national tariffs, and device manufacturers.ResultsThe cost of glucose monitoring was £588 with SMBG alone and £1820 with RT‐CGM. The total annual costs of managing pregnancy and delivery in women with Type 1 diabetes were £23 725 648 with SMBG alone, and £14 165 187 with SMBG and RT‐CGM; indicating potential cost savings of approximately £9 560 461 from using RT‐CGM. The principal drivers of cost savings were the daily cost of neonatal intensive care unit (NICU) admissions (£3743) and the shorter duration of NICU stay (mean 6.6 vs. 9.1 days respectively). Sensitivity analyses showed that RT‐CGM remained cost saving, albeit to lesser extents, across a range of NICU costs and durations of hospital stay, and with varying numbers of daily SMBG measurements.ConclusionsRoutine use of RT‐CGM by pregnant women with Type 1 diabetes, would result in substantial cost savings, mainly through reductions in NICU admissions and shorter duration of NICU care.

The association between socio-economic status and diabetes care and outcome in children with diabetes type 1 in Germany: The DIAS study (diabetes and social disparities).

To evaluate the association between socioeconomic status (SES) and diabetes outcomes in German children and adolescents. A total of 1829 subjects <18 years old with type 1 diabetes mellitus from 13 German diabetes centers were included from June 2013 until June 2014. Data were collected within the multicenter DPV (Diabetes Prospective Follow-up) registry. SES was measured with a composite index. Multivariable regression models were applied to analyze the association of SES and outcomes adjusted for age, sex, diabetes duration, and migration status. Low SES was significantly associated with worse diabetes outcomes: higher hemoglobin A1C (HbA1c) (64.3 mmol/mol), lower proportion of insulin pump therapy (43.6%), fewer daily self-monitored blood glucose (SMBG) measurements (5.7), more inpatient days per patient-year (5.8) compared to patients with medium/high SES (HbA1c: 61.3 mmol/mol, P < 0.001/59.8 mmol/mol, P < 0.0001; proportion of pump therapy: 54.5%, P < 0.01/ 54.9%, P < 0.01; SMBG: 6.0, P < 0.01/ 6.1, P < 0.01; inpatient days: 4.5, P < 0.0001/3.4, P < 0.0001). The inclusion of migration status in the models resulted in only minor changes in the outcomes. Despite free health care, low SES is associated with unfavorable diabetes outcomes in Germany. The poorer diabetes outcomes of children with diabetes have been attributed to their migration status and may be partly explained by low SES. Both factors must become part of targeted diabetes care in children and adolescents with type 1 diabetes.

The Effects and Effect Sizes of Real-Time Continuous Glucose Monitoring on Patient-Reported Outcomes: A Secondary Analysis of the HypoDE Study.

While real-time continuous glucose monitoring (rtCGM) has proven its efficacy for glycemic control and avoidance of hypoglycemia, evidence on its effects on patient-reported outcomes is still inconclusive. This secondary analysis of the HypoDE study analyzed effect sizes of rtCGM on patient-reported outcomes and compared them with the effect sizes for glycemic outcomes. The intervention group using rtCGM (n = 75) and the control group using self-monitored blood glucose measurements (n = 66) in the HypoDE study were compared. Effect sizes for the patient-reported outcome measures were calculated as the standardized between-group difference in baseline-adjusted follow-up scores (Cohen's d). rtCGM had significant effects with medium effect sizes on satisfaction with the glucose monitoring device (d = 0.50; 95% confidence interval [CI] 0.84-0.17), fear of hypoglycemia (d = 0.32; 95% CI 0.66 to -0.01), and hypoglycemia-related distress (d = 0.41; 95% CI 0.74-0.08). However, nonsignificant effects and rather small effect sizes were found for general diabetes distress (d = 0.21; 95% CI 0.54 to -0.12), hypoglycemia unawareness (d = 0.03; 95% CI 0.37 to -0.30), and self-reported health status (d = 0.27; 95% CI 0.60 to -0.08), while the effect sizes for the hypoglycemia-related glycemic endpoints were large (d > 1.0). Based on these effect sizes for patient-reported outcomes, a much larger sample size would result in detecting significant differences in patient-reported outcomes. Effect sizes of rtCGM on patient-reported outcomes are substantially smaller than those on glycemic outcomes. Studies on the efficacy of rtCGM previously had a glycemic endpoint with sample size calculations based on the larger effect sizes for glycemic outcomes. Thus, previous studies were neither powered to detect effects on patient-reported outcomes nor was the participant recruitment tailored to specific patient-reported outcomes. To demonstrate whether rtCGM affects patient-reported outcomes, future studies should consider this comparison of effect sizes.

Glycemic and metabolic features in gestational diabetes: singleton versus twin pregnancies.

A number of data on gestational diabetes mellitus (GDM) in singleton pregnancy is available, however, little is known about the glycemic characteristics of twin pregnancy with GDM. The aim of this study was to compare the severity of dysglycemia between twin and singleton pregnancies with GDM (T-GDM and S-GDM). We retrospectively analyzed pregnancies with GDM defined by the Japan Diabetes Society criteria (T-GDM, n = 20; S-GDM, n = 451) in our hospital. During the study period, women with GDM underwent self-monitoring of blood glucose measurements as well as dietary management. Insulin treatment was initiated when dietary treatment did not achieve the glycemic goal. The glycemic and metabolic characteristics were compared between T-GDM and S-GDM, as follows: gestational week at the diagnosis of GDM, 75 g oral glucose tolerance test (OGTT) results, HbA1c, insulin secretion (i.e. insulinogenic index [IGI] and Insulin Secretion-Sensitivity Index-2 [ISSI-2]), and insulin requirement before delivery. The rate of one abnormal OGTT value in T-GDM was similar to that in S-GDM (60% vs. 71%). There were no significant differences in gestational week and levels of HbA1c at diagnosis, levels of IGI and ISSI-2 between T-GDM and S-GDM (median, 20 weeks vs. 17 weeks, 5.0% vs. 5.2%, 0.58 vs. 0.71, 1.7 vs. 1.8, respectively). The rate of insulin treatment and a median dosage of insulin needed before delivery was comparable between the two groups (T-GDM vs. S-GDM: 45% vs. 32% and 14 vs. 13 unit/day). Our data suggested that the severity of dysglycemia in T-GDM was similar to that in S-GDM during pregnancy.

Associations of dietary macronutrient and fibre intake with glycaemia in individuals with Type 1 diabetes.

To study the association between dietary intake and glycaemia in Type 1 diabetes. Data on energy and nutrient intakes, and the mean and coefficient of variation of self-monitored blood glucose measurements were obtained from records completed by 1000 adults with Type 1 diabetes. Associations between these measures of glycaemia and dietary intake were investigated using generalized linear regression, with and without macronutrient substitution. In the first set of analyses, fibre intake was associated with lower mean self-monitored blood glucose values (β = -0.428, 95% CI -0.624 to -0.231; P<0.001). In these same analyses, carbohydrate (β = 0.011, 95% CI 0.002 to 0.020; P=0.014), alcohol (β = 0.013, 95% CI 0.003 to 0.023; P=0.009) and monounsaturated fatty acid intakes (β=0.012, 95% CI 0.001 to 0.023; P=0.029) were associated with higher variability in blood glucose measurements. In the macronutrient substitution analyses, substituting proteins for either carbohydrates (β = -0.026, 95% CI -0.040 to -0.013; P<0.001), fats (β = -0.018, 95% CI -0.033 to -0.004; P=0.014), or alcohol (β = -0.026, 95% CI -0.045 to -0.006; P=0.010), or fats for carbohydrates (β=-0.009, 95% CI -0.017 to -0.001; P=0.030), were all associated with lower variability in the measured blood glucose values. After adjusting for fibre intake, no significant results were observed in analyses of mean self-monitored blood glucose. This observational, cross-sectional study indicates that dietary fibre is associated with lower mean blood glucose concentrations in people with Type 1 diabetes. Glycaemic excursions were reduced when protein was substituted for other macronutrients and when fat replaced carbohydrate, after adjusting for fibre intake.