Time Above Range Research Articles

Continuous Glucose Monitoring in Patients Following Simultaneous Pancreas-Kidney Transplantation: Time in Range and Glucose Variability.

Simultaneous pancreas-kidney transplantation (SPKT) can improve long-term patient survival and restore endogenous insulin secretion in recipients with type 1 diabetes (T1D). There are currently few data on glucose fluctuations assessed by continuous glucose monitoring (CGM) after SPKT. Aim: to evaluate CGM-derived time in range (TIR) and glucose variability (GV) in patients with T1D and functioning pancreatic grafts after SPKT. Fifty-four CGM recordings from 43 patients, 15 men and 28 women, aged 34 (31; 39) years were analyzed. Time since SKPT was up to 1 year (group 1, n = 13), from 1 to 5 years (group 2, n = 15), and from 5 to 12 years (group 3, n = 26). TIR (3.9-10 mmol/L), Time Above Range (TAR), Time Below Range (TBR), and GV parameters were estimated. There were no differences in mean glucose (5.5 [5.1; 6.2], 5.9 [5.4; 6.2], and 5.9 [5.6; 6.7] mmol/L), TIR (97.6 [92.8-99.1], 97.2 [93.2; 99.1], and 97.5 [93.4; 99]%); TAR (0, 1.8 [1.3; 3.7], and 2.5 [2; 5]%), TBR (5 [3.3; 12.7], 4.1 [2.2; 10.1], and 3.5 [1.3; 6.5]%) and GV parameters between three groups (all p > 0.05). Thus, recipients with functioning pancreatic grafts demonstrate remarkably high TIR and low GV after SPKT.

Early Insight Into the Retrospective Data of a Case Series on Type 2 Diabetes Mellitus on Alternate-Day Dosing of Oral Semaglutide: Utopia or Reality?

Introduction Oral semaglutide, with a long half-life of seven days, is the first oral-based peptide drug and is used as an antidiabetic for the reduction of glycosylated hemoglobin (HbA1c). Oral semaglutide, like other glucagon-like peptide-1 receptor agonists (GLP1RAs), is costly and has gastrointestinal (GI) side effects, especially with a 14 mg dose. In the real world, some type 2 diabetes mellitus (T2DM) patients on 14 mg oral dose adopt an alternate-day strategy to minimize unwanted GI symptoms. In this study, we analyzed the ambulatory glucose profile (AGP) data of patients with T2DM who were on 14 mg alternate-day oral semaglutide therapy. Methods This retrospective observational study evaluated the AGP data of 10 patients on alternate-day dosing of 14 mg oral semaglutide. The AGP data over a period of 14 days on a single group of patients were analyzed without any control group or randomization and are presented as a case series. AGP monitoring, using Freestyle Libre Pro (Abbott, Illinois, United States), is a standard operating procedure of the endocrinology department for all T2DM patients who were put on oral semaglutide therapy. The AGP data of the glycemic parameters time-in-range (TIR), time-above-range (TAR), and time-below-range(TBR), were compared between the days when oral semaglutide was consumed(days-on-drug) versus the days when oral semaglutide was not consumed (days-off-drug). The statistical analysis was done with Statistical Package for Social Sciences (SPSS) version 21.0 (IBM Corp., Armonk, NY). Results We applied the Shapiro-Wilk test (sample size <50) for normality testing; the TIR values of days-on-drugand days-off-drug showed high p values (p =0.285 and 0.109), respectively. This indicated that TIR values days-on-drug and days-off-drug were normally distributed. Although, the distribution of TAR and TBR values days-on-drug and days-off-drug, were not normal as they hadsmall p values (p< 0.05). Hence, further analysis of the paired set of data was done using the Wilcoxon signed-rank test. It revealed no difference in TIR, TAR, and TBR between the two groups (days-on-drug and days-off-drug). Conclusion Throughout the period of observation, the glycemic metrics (TIR, TAR, and TBR) remained steady with a 14 mg alternate-day oral semaglutide regimen.

Association of hemoglobin A1c stability with mortality and diabetes complications in older adults with diabetes

IntroductionHemoglobin A1c (A1c) treatment goals in older adults should be individualized to balance risks and benefits. It is unclear if A1c stability over time within unique target ranges also affects...

Physical exercise and glycemic management in patients with type 1 diabetes on insulin pump therapy—a cross-sectional study

AimsExercise is an important practice for control in type 1 diabetes (T1D). This study aims to assess de association between exercise and glycemic management in people with T1D and to identify the main barriers to exercise in T1D.MethodsWe evaluated 95 people with T1D treated with insulin pump therapy. Participants answered a questionnaire about 1) exercise habits, 2) usual adjustments in insulin and food intake with exercise and 3) main barriers to exercise.Continuous glucose monitoring (CGM) was used to evaluate time in range (TIR), time below range (TBR) and time above range (TAR) during the last 60 days before the evaluation. CGM data during, before (2 h before) and after (24 h after) the last bout of exercise was also evaluated.ResultsThe mean age was 30.1 ± 12.1 years, and 51.6% were women. Participants that reported practicing exercise (55.8%) had a higher TIR (59.6 ± 16.3 vs. 48.7 ± 15.7, p = 0.012) and a lower TAR (32.6 ± 15.8 vs. 45.4 ± 17.7, p = 0.006). Comparing with the 60 days CGM data, the TBR was lower in the 2 h before exercise (− 1.8 ± 3.8, p = 0.0454) and TAR was lower during (− 16.9 ± 33.6, p = 0.0320) and in the 24 h after (− 8.7 ± 17.2, p = 0.032) the last bout of exercise. The absence of adjustments on insulin and food intake was associated with higher TBR after the exercise (13.44 ± 3.5, p < 0.05). Eating before the exercise and turning off the pump during the exercise were associated with lower TBR after exercise (food booster: − 7.56 ± 3.49, p < 0.05; turning off insulin pump − 8.87 ± 3.52, p < 0.05). The main barriers reported for exercise practicing were fear of hypoglycemia, lack of free time and work schedule.ConclusionExercise was associated with better glycemic management in people with T1D. Addressing common barriers may allow a higher adherence to exercise in T1D.

Comparisons of school-day glycemia in different settings for children with type 1 diabetes using continuous glucose monitoring.

Using continuous glucose monitoring (CGM), we examined patterns in glycemia during school hours for children with type 1 diabetes, exploring differences between school and non-school time. We conducted a retrospective analysis of CGM metrics in children 7-12 years (n=217, diabetes duration 3.5±2.5 years, hemoglobin A1c 7.5±0.8%). Metrics were obtained for weekday school hours (8 AM to 3 PM) during four weeks in fall 2019. Two comparison settings included weekend (fall 2019) and weekday (spring 2020) data when children had transitioned to virtual school due to COVID-19. We used multilevel mixed models to examine factors associated with time in range (TIR) and compare glycemia between in-school, weekends, and virtual school. Though CGM metrics were clinically similar across settings, TIR was statistically higher, and time above range (TAR), mean glucose, and standard deviation (SD) lower, for weekends and virtual school (p<0.001). Hour and setting exhibited a significant interaction for several metrics (p<0.001). TIR in-school improved from a mean of 40.9% at the start of the school day to 58.0% later in school, with a corresponding decrease in TAR. TIR decreased on weekends (60.8 to 50.7%) and virtual school (62.2 to 47.8%) during the same interval. Mean glucose exhibited a similar pattern, though there was little change in SD. Younger age (p=0.006), lower hemoglobin A1c (p<0.001), and insulin pump use (p=0.02) were associated with higher TIR in-school. Although TIR was higher for weekends and virtual school, glycemic metrics improve while in-school, possibly related to beneficial school day routines.

Association between residual islet beta-cell function and achieving the target of time in range in inpatients with type 2 diabetes undergoing antidiabetic treatment: An observation study.

To assess whether the beta-cell function of inpatients undergoing antidiabetic treatment influences achieving time in range (TIR) and time above range (TAR) targets. This cross-sectional study included 180 inpatients with type 2 diabetes. TIR and TAR were assessed by a continuous glucose monitoring system, with target achievement defined as TIR more than 70% and TAR less than 25%. Beta-cell function was assessed by the insulin secretion-sensitivity index-2 (ISSI2). Following antidiabetic treatment, logistic regression analysis showed that lower ISSI2 was associated with a decreased number of inpatients achieving TIR (OR=3.10, 95% CI: 1.19-8.06) and TAR (OR=3.40, 95% CI: 1.35-8.55) targets after adjusting for potential confounders. Similar associations still existed in those participants treated with insulin secretagogues (TIR: OR=2.91, 95% CI: 0.90-9.36, P=.07; TAR, OR=3.14, 95% CI: 1.01-9.80) or adequate insulin therapy (TIR: OR=2.84, 95% CI: 0.91-8.81, P=.07; TAR, OR=3.24, 95% CI: 1.08-9.67). Furthermore, receiver operating characteristic curves showed that the diagnostic value of the ISSI2 for achieving TIR and TAR targets was 0.73 (95% CI: 0.66-0.80) and 0.71 (95% CI: 0.63-0.79), respectively. Beta-cell function was associated with achieving TIR and TAR targets. Stimulating insulin secretion or exogenous insulin treatment could not overcome the disadvantage of lower beta-cell function on glycaemic control.

Glycaemia in low-premixed insulin analogue type 2 diabetes patients in a real-world setting: are the CGM targets met?

BackgroundThere are insufficient data on continuous glucose monitoring (CGM) in nonintensive insulin therapy patients. Using CGM and the recommended CGM targets, we wanted to evaluate low-premix insulin analogue therapy (biphasic aspart/NovoMix 30 and biphasic lispro 25/Humalog Mix 25) in real-world type 2 diabetes patients for glycaemic efficacy and especially hypoglycaemia.MethodsThe prospective observational study was performed on 35 patients who were treated with a low-premixed insulin. We used the Dexcom G6 system for CGM (9.6 ± 1 days) to measure the clinically relevant CGM parameters: glycaemic variability (%CV), TBR (time below range) < 3.0 mmol/l = 54 mg/dl (level 2 hypoglycaemia), TBR 3.0–3.8 (= 54–69 mg/dl), TIR (time in range) 3.9–10–0 mmol/l (70–180 mg/dl), TAR (time above range) 10–13.9 mmol/l (180–250 mg/dl) and TAR > 13.9 mmol/l (250 mg/dl). We also assessed clinical and demographic characteristics, laboratory HbA1c, fasting blood glucose, peak postprandial glucose values, and the percentage of hypoglycaemia between 00:00 and 06:00.ResultsIn our patients, the average ± SD age was 70.4 ± 9.2 years, diabetes duration 17.4 ± 7.1 years, 51% were females, average daily insulin dose was 46.4 units (80% received biphasic aspart). The average ± SD TIR was 62.1 ± 12.2%, TBR < 3.0 mmol/l 0.8 ± 2.0%, TBR 3.0–3.8 mmol/l 1.5 ± 1.5%, TAR 10–13.9 mmol/l 29.2 ± 12.4%, TAR > 13.9 mmol/l 6.4 ± 7.2% and %CV 29.9 ± 7.1%. The average time in hypoglycemia was 33.1 min daily in our patients (11.5 min in the level 2 range). In the older/high-risk population, the TBR/TIR/TAR/level 2 TAR targets were met in 40/80/77/80%, respectively. For the general T2D people, level 2 TBR/TBR/TIR/TAR/level 2 TAR would be met in 74/83/34/77/49%. Average fasting blood glucose was 8.0 ± 2.5 mmol/l (144 ± 45 mg/dl), BMI 31.3 ± 5.1 kg/m2, daily insulin dose 46.4 ± 12.1 units, HbA1c 57.4 ± 5.4 mmol/mol (7.4 ± 0.7%). The glycaemic variability goal was met in 80% (with 66% meeting the lower 33% CV goal). 17 ± 12% of hypoglycaemia was nocturnal. People with TBR > 4% were significantly older.ConclusionsMost of our type 2 diabetes patients, treated with low-premixed insulin, did not meet the recommended TBR target for older/high-risk patients while meeting the TIR and TAR targets. Nevertheless, the time spent in (total and nocturnal) hypoglycemia was short. The study indicates that the general type 2 diabetes population targets would mostly be met for TBR and %CV in our patients but not the TIR and TAR targets. CGM appears to be a useful clinical tool in these patients.

The association between glucose fluctuation with sarcopenia in elderly patients with type 2 diabetes mellitus.

Growing evidence shows that sarcopenia is more prevalent in patients with type 2 diabetes mellitus (T2DM) than in the normal population. However, currently, data on the relationship between blood glucose fluctuation and sarcopenia in elderly patients with T2DM are still limited. In this study, 280 patients ≥ 60 years with T2DM were divided into sarcopenic group and non-sarcopenic group, according to the diagnostic criteria of the 2019 Asian Working Group for Sarcopenia. They wore MeiQi to acquire the indexes including time in range (TIR), time above range (TAR), time below range (TBR), mean amplitude of glycemic excursion (MAGE), coefficient of Variation (CV), blood glucose standard deviation (SD), largest amplitude of glycemic excursions (LAGE) and mean glucose (MG). The prevalence rate of sarcopenia was statistically analyzed and the different indicators of glucose fluctuation between the two groups were compared. We analyzed the indexes of glucose fluctuation and appendicular skeletal muscle mass index (ASMI), handgrip strength, the time of five times sit to stand test (FTSST) with Spearman's correlation analysis. Logistic regression was used to analyze the influence factors for sarcopenia. The prevalence of sarcopenia was 15.36%. TIR, MG and TAR were correlated with ASMI, handgrip strength, the time of FTSST. MG and TAR were risk factors for sarcopenia, while TIR was the protective factor of sarcopenia. After adjusting mixing factors, logistic regression analysis showed that TIR was an independent protective factor. The result of the Chi-square test showed that the incidence of sarcopenia in different TIR ranges was different: the proportion of patients with sarcopenia was 40.48% (TIR ≤50%), 20.41% (50%<TIR≤70%) and 8.47% (TIR >70%). TIR is associated with sarcopenia in elderly T2DM patients. Furtherly, the incidence rate of sarcopenia decreases with the increase of TIR.

Hypoglycemia and glycemic variability of people with type 1 diabetes with lower and higher physical activity loads in free-living conditions using continuous subcutaneous insulin infusion with predictive low-glucose suspend system

IntroductionMaintaining glycemic control during and after physical activity (PA) is a major challenge in type 1 diabetes (T1D). This study compared the glycemic variability and exercise-related diabetic management strategies of...

Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Effect of Gplife Advance Diabetic Support on Time in Range in Type 2 Diabetes Mellitus Patients: A Case Report

This case report documents 20 patients suffering from uncontrolled type 2 diabetes mellitus, presenting complaints of fluctuating blood glucose levels. The parameters like fasting and postprandial blood glucose, HbA1c and C- peptide levels of patients were evaluated. These patients were then advised an adjuvant therapy of Gplife advanced diabetic support tablets along with their daily insulin injections. After 60 days of treatment, the therapy was discontinued, and patients were subjected to continuous glucose monitoring for 8 days and time in range (TIR) percentage was calculated. Also, the fasting and postprandial blood glucose, HbA1c and C-peptide levels were estimated. The average TIR percentage was found to be 93.2%, whereas the time above range (TAR) and time below range (TBR) was 6.8 % and 0 % respectively, which indicates that the patients had a negligible incidence of hyper and hypoglycemia after 60 days of treatment. There was a significant decrease by 61 % in HbA1c levels, 40.05 % in fasting blood glucose levels and 34.29 % in postprandial blood glucose levels (p&lt;0.001). C-peptide levels increased by 1.38 folds after 60 days of treatment. Also, there were no evidence of diabetic comorbidities. Thus, indicating a marked improvement in the diabetic condition and overall quality of life in patients.

THE ASSESSMENT OF COMPENSATION OF CARBOHYDRATE METABOLISM IN PATIENTS WITH TYPE 2 DIABETES MELLITUS WITH METABOLIC SYNDROME BEYOND THE LIMITS OF GLYCATED HEMOGLOBIN.

The aim: To investigate glycemic variability in type 2 diabetes patients with metabolic syndrome (MS) and to assess its effect on diabetes compensation. Materials and methods: We used traditional indicators of glycemia variability according to the recommendations of the American Diabetes Association Professional Practice Committee. We proved that patients with type 2 diabetes mellitus with MS reliably have worse CGM indicators: Time in range TIR: (3.9-10.0 mmol/l) - 53.30±5.90%; Time above range (TAR): (time above range) (>10.1 mmol/l) - 43.33±5.96%; Time above TAR range (>13.9 mmol/l) - 22.1±3.91%; Glucose Variability СV - 44.10±4.89% compared to patients with type 2 diabetes without MS, which proves the negative effect of insulin resistance on compensation of diabetes. Results: Determination of the level of EI in the blood, calculation of the Caro index, HOMA-IR are informative for verifying the presence of IR in patients with type 2 diabetes with MS. For optimal diabetes control, in addition to HbA1c, we must consider CGM data and % Time in range (TIR). Conclusions: TIR should be used as a target point and an indicator of glycemic control in routine clinical practice. TIR provides accurate data on a patient's glycemic status and helps better control diabetes.

Continuous Glucose Monitoring in Non-Insulin Type 2 Diabetes

Abstract Diabetes mellitus, a global health concern, is characterized by complex pathophysiology and presents diverse clinical challenges. Effective management of diabetes hinges on key principles such as glycemic control, lifestyle modifications, and adherence. In non-insulin-requiring Type 2 diabetes (T2D), persistent elevated HbA1c levels remain a challenge. Continuous glucose monitoring (CGM) is pivotal, serving as a cornerstone for optimizing therapy, mitigating hypoglycemia, and reducing the financial burden. Unlike traditional self-monitoring of blood glucose (SMBG), CGM offers continuous, pain-free data, aiding treatment decisions. This review explores CGM’s multifaceted role in non-insulin requiring T2D, scrutinizing HbA1c reduction, glycemic variability, time in range (TIR), exercise, dietary management, early comorbidity detection, and cost-effectiveness. CGM empowers users to monitor and manage their glycemic levels, making it an effective tool for HbA1c reduction. Glycemic variability poses risks, and CGM provides valuable metrics like time below range (TBR), time in range (TIR), and time above range (TAR). CGM effectively minimizes glycemic variability and improves TIR in non-insulin requiring T2D. Additionally, CGM aids in real-time decision-making for physical activity and dietary choices, enhancing the effectiveness of lifestyle modifications. It also assists healthcare providers in identifying early signs of comorbidities, particularly cardiovascular disease, and diabetic retinopathy, through monitoring glycemic variability. While CGM devices may incur costs, studies suggest their cost-effectiveness, considering long-term benefits and complications prevention. This review underscores CGM’s importance in T2D management, even for non-insulin-requiring individuals. Recommendations include CGM use for newly diagnosed people with T2D, those with uncontrolled diabetes, and those adjusting treatments. Personalized glycemic control goals are proposed, aligning CGM’s role in managing hypoglycemia, hyperglycemia, and glycemic variability in T2D. As CGM technology advances and becomes more accessible, it is poised to play an increasingly pivotal role in diabetes management. Crucially, CGM should be employed in consultation with healthcare providers, considering regional disparities in cost-effectiveness and accessibility influenced by insurance coverage and government interventions.

Objective sleep characteristics and continuous glucose monitoring profiles of type 2 diabetes patients in real-life settings.

To investigate the association between objective sleep parameters and glycaemic variability determined by continous glucose monitoring (CGM) among patients with type 2 diabetes, given the significant role of sleep in glycaemic control. In this study, CGM was carried out in 28 patients with T2D (aged 62.3 ± 4.8 years, 57% women). Sleep characteristics were assessed by actigraphy within the CGM period. CGM-derived outcomes included glucose level, and percentages of time in range (TIR) and time above range (TAR) during the monitoring period. Associations between intraindividual night-to-night variations in sleep characteristics and overall CGM outcomes were analysed using linear regression. Associations between sleep characteristics during each night and time-matched CGM outcomes were analysed using linear mixed models. A total of 249 person-days of CGM, coupled with 221 nights of sleep characteristics, were documented. Greater standard deviation (SD) of objective sleep duration (minutes) between measurement nights was associated with higher glucose level (coefficient 0.018 mmol/L [95% confidence interval {CI} 0.004, 0.033], P=0.017), smaller proportion of TIR (% in observation period; coefficient -0.20% [95% CI -0.36, -0.03], P=0.023), and greater proportion of TAR (coefficient 0.22% [95% CI 0.06, 0.39], P=0.011). Later sleep midpoint (minutes from midnight) was associated with greater SD of glucose during the same sleep period (coefficient 0.002 minutes [95% CI 0.0001, 0.003], P=0.037), longer nocturnal sleep duration was associated with smaller coefficient of variation of glucose level in the upcoming day (-0.015% [95% CI -0.03, -0.001], P=0.041). Objectively determined sleep duration and sleep midpoint, as well as their daily variability, are associated with CGM-derived glucose profiles in T2D patients.

Time in Range in Children with Type 1 Diabetes before and during a Diabetes Camp-A Ceiling Effect?

The aim of this study was to assess and compare the time in range (TIR) of children with type 1 diabetes (T1D) before and during a diabetes summer camp using different therapy modalities. A retrospective analysis of continuous glucose monitoring (CGM) data collected from 26 children with T1D (mean age: 11.0 ± 1.4 years; 62% female; 62% on insulin pump; Hb1Ac 7.3 ± 0.8% (56.3 ± 8.7 mmol/mol) before and during a 14-day summer camp. CGM methods: 50% intermittently scanned CGM (isCGM) and 50% real-time CGM (rtCGM). No child was using a hybrid closed loop system. Mean TIR during camp was significantly higher than before camp ((67.0 ± 10.7%) vs. 58.2% ± 17.4%, p = 0.004). There was a significant reduction in time above range (TAR) (p = 0.001) and increase in time below range (TBR) (p &lt; 0.001), Children using isCGM showed a more pronounced improvement in TIR during camp compared to rtCGM-users (p = 0.025). The increase in TIR strongly correlated with numbers of scans per day in isCGM-users (r = 0.751, p = 0.003). Compared to isCGM-users, rtCGM-users showed significantly less TBR. The TIR target was met by 30.8% of participants during camp. Glycemic control improved significantly during the camp. However, on average, the therapy goal (TIR &gt; 70%) could not be achieved despite great professional effort.

The AH-HA! Project: Transforming Group Diabetes Self-Management Education Through the Addition of Flash Glucose Monitoring.

Background: The majority of individuals referred to diabetes self-management education and support (DSMES) programs do not access this resource. Of those who do, attrition is high, with anecdotal reports pointing to the didactic and impersonal nature of these programs contributing to low utilization and completion rates. In an effort to develop a more engaging form of DSMES for adults with type 2 diabetes (T2D), we constructed a nondidactic "discovery learning"-based DSMES program centered on real-time flash glucose monitoring (FGM). Methods: In this single-arm pilot study, 35 adults with T2D duration 1-5 years, ages 21-75 years, not using insulin and HbA1c ≥8.0% were introduced to FGM and participated in five weekly group sessions. DSMES content was personalized, emerging from the concerns and questions arising from participants' FGM discoveries. The primary outcome was glycemic change as assessed by blinded FGM at baseline and month 3. Secondary outcomes included psychosocial and behavioral measures. Results: There was a significant gain in percentage time in range (% TIR) 70-180 mg/dL from baseline (55%) to month 3 (74%), and a parallel drop-in percentage time above range (TAR) >180 mg/dL from 44% to 25% (Ps = 0.01). Overall well-being rose significantly (P = 0.04), whereas diabetes distress showed a nonsignificant drop. Participants reported improvements in healthy eating (P < 0.001) and physical activity, although the latter did not reach statistical significance. Conclusions: These findings support a new approach to DSMES, a method that integrates FGM with a highly interactive and engaging patient-driven "discovery learning" approach to education.

Luseogliflozin Additively Enhances the Glucose-Lowering Effect of an Incretin Modulator in a High-Carbohydrate Diet.

Background and objectives Sodium/glucose co-transporter 2 inhibitors (SGLT2i) have been shown to have a glucose-lowering effect related to carbohydrate intake. It has also been reported that the combined effect of incretin modulators and SGLT2i is useful in improving blood glucose and reducing blood glucose variability. However, there have been no reports examining the effects of these two drugs together and while considering carbohydrate intake in an outpatient setting. In the present study, Hi-Speed Food Analysis was used to assess the exact intake of carbohydrates, and the glucose-lowering effects of luseogliflozin, an SGLT2i, and incretin modulators were examined under high- and low-carbohydrate intakes. Methods Thirty-five enrolled diabetic patients continued their regular medications for one week. All patients took luseogliflozin in the second week for seven days. During the two weeks, ingested carbohydrates were accurately calculated by Hi-Speed Food Analysis. The glucose-lowering effect of luseogliflozin with and without incretin modulators was checked according to the amount of ingested carbohydrates. A general linear model (GLM) was used to analyze the effect of luseogliflozin with or without an incretin modulator, with carbohydrate intake as a confounding factor. Results Luseogliflozin had an additive effect in patients who had taken the incretin modulator. There was a significant decrease in the time above range (TAR) with glucose above 140 mg/dL as expressed as TAR(min140), and this effect was affected by carbohydrate intake. Conclusions The glucose-lowering effect of luseogliflozin was enhanced with high-carbohydrate intake more than with low-carbohydrate intake. In this study, the observed number was small;however, combined treatment with an incretin modulator and luseogliflozin had an additive effect in high- versus low-carbohydrate intake, indicating the possible effectiveness of the combined therapy.

Continuous Glucose Monitoring Sensor Glucose Data Analysis of Time in Range, Time Above Range, Time Below Range and Waveform Comparison Study of Time in Range Curve Against both Average Daily Glucose Curve and Glucose Fluctuation Curve for a 3.3 Year Period based on GH-Method: Math-Physical Medicine (No. 500)

The author applies quantitative pattern and trend analysis tools using his collected continuous glucose monitoring (CGM) sensor data during a 3.3 year period from 5/8/2018 to 8/25/2021. Special attention has been placed on applying the American Diabetes Association (ADA) 2020 Guidelines for Time In Range (TIR) % with TIR average glucose values, Time Above Range (TAR) % with TAR average glucose value, and Time Below Range (TBR) % with TBR average glucose value. The purpose of this article is to study the correlations between the TIR curve and both average daily glucose curve (eAG) and glucose fluctuation curve (GF). GF is defined as the difference between the maximum glucose and the minimum glucose within one day or 24 hours duration. This GF term expresses the same meaning as the glycemic variability (GV) used by some diabetes research scientists. However, the author prefers GF over GV since GF describes the amplitude of glucose excursion in an exact and most direct way. Depending on the object of his research project, he sometimes selects the GF value within a meal’s PPG waveform of a 3-hours duration. Furthermore, the “primary range” of TIR is defined between 70 mg/dL and 180 mg/dL while a “secondary range” of TIR is defined between 70 mg/dL and 140 mg/dL. Of course, both the TIR’s and TAR’s percentages and their averaged values of secondary range (difference of 70 mg/dL) are smaller than the primary range results due to the secondary range’s narrower glucose range of 40 mg/dL (110-70 or 180-140). In summary, there are five noticeable findings from this study: (1) TIR: His TIR percentage (89%) and average TIR value (121 mg/dL) of the primary range, along with the TIR percentage (72%) and average TBR value (114 mg/dL) of the secondary range represent the majority of the data. This means that his type 2 diabetes (T2D) is quite well under control. (2) TBR: His TBR percentage (5%) and average TBR value (65 mg/dL) of the primary range, along with the same TBR percentage (5%) and the same average TBR value (65 mg/dL) of the secondary range contain rather small amounts. This means that his risk of having hypoglycemia (insulin shock) is relatively low.

Metabolic syndrome associated with higher glycemic variability in type 1 diabetes: A multicenter cross-sectional study in china.

AimsThe comorbidity of metabolic syndrome (MetS) and type 1 diabetes mellitus (T1DM) is an obstacle to glucose control in patients with T1DM. We compared glycemic profiles using continuous glucose monitoring (CGM) systems in patients with T1DM with or without MetS.MethodsThis was a multicenter cross-sectional study of patients with T1DM (N = 207) with or without MetS. CGM data were collected from study enrollment until discharge during a 1-week study session. We analyzed baseline HbA1c, average glucose, estimated HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), coefficient of variation (CV), postprandial glucose excursions (PPGE) and other glycemic variability (GV) metrics. Logistic regression was developed to investigate the association between MetS and CGM metrics.ResultsThe results showed higher average baseline HbA1c levels, and a higher percentage of patients with baseline HbA1c levels ≥7.5%, in the T1DM with MetS group. Furthermore, MetS was associated with GV, which indicated a higher CV in patients with T1DM with MetS. However, our results showed that TAR, TIR, TBR and other GV metrics were comparable between the two groups. The T1DM with MetS group also had a higher proportion of patients with high CV (≥ 36%) than the group without MetS. In multivariable logistic regression analysis, the presence of MetS was a risk factor for high CV (≥ 36%) in our study participants.ConclusionsT1DM patients with MetS in our study had better β-cell function. However, MetS was associated with worse glycemic control characterized by higher GV and HbA1c levels. Efforts should be expanded to improve treatment of MetS in patients with T1DM to achieve better glycemic control.

Flash Glucose Monitoring System facilitates sustainable improvements in glycemic control in patients with type 1 diabetes: A 12-month follow-up study in real life

Background and aimsExamine the glycemic control on Type 1 Diabetes (T1D) wearing the Flash Glucose Monitoring (FGM) system for a one-year period of time. MethodsThis prospective study done using 187 patients with T1D (14–40yrs) who self-tested their glucose levels by FGM. Continuous glucose monitoring (CGM) metrics were gathered i.e., Glucose Variability (GV) (%), mean Time in Range (TIR), Time Above Range (TAR), Time Below Range (TBR), and average duration of hypoglycemic events at the 3, 6, and 12 month time periods. ResultsAt 6th, 9th and 12th months, for values of GV, % in target, TAR and %>250 mg/dL, no significant changes (p > 0.05) were noted compared to 3 months. However, significant changes from the baseline were evident for the values of the mean glucose level at the 3rd (p = 0.028), 9th (p = 0.048) and 12th months (p = 0.022). When the mean glucose value at 3 months was compared to the same at 6, 9, and 12 month period, no significant changes (p > 0.05) were seen. When compared with baseline values, low glucose events at 3 months (p = 0.028), 6 months (p = 0.048), 9 months (p = 0.022) and 12 months (p = 0.038) showed significant changes. However, the percentage below 70 mg/dL (barring the value at 12 months, p = 0.046), no significant changes were observed. The HbA1c revealed significant drop in 3, 6, 9 and 12 months compared to baseline values. ConclusionSignificant improvement was noted in CGM metrics when patients switched from conventional finger pricking method over to FGM system, and the effect was observed during the entire study period.