Lower Glycemic Variability Research Articles

Glycemic Variability and Disordered Eating Among Adolescents and Young Adults with Type 1 Diabetes: The Role of Disinhibited Eating.

Background and Aims: Disordered eating behaviors (DEB) are common among individuals with type 1 diabetes (T1D). Glycemic variability, potentially harmful in T1D, may reveal distinct characteristics between those with higher versus lower variability, particularly concerning DEB. Our aim was to evaluate the prevalence of DEB and associated risk factors among adolescents and young adults with T1D and to investigate unique factors associated with DEB across different levels of glycemic variability. Methods: An observational, cross-sectional study was conducted with 147 individuals with T1D, aged 13-21 years. Data were collected from medical charts, personal technological devices for assessing glycemic variability, and self-reported questionnaires, including assessments of DEB. Results: DEB were found in 62 (42.1%) individuals, and 41.5% achieved the glycemic variability (% coefficient of variation) target ≤36%. Among individuals with low glycemic variability, DEB were positively associated with diabetes distress (odds ratio [OR]: 1.14 [95% confidence interval or CI: 1.05-1.22], P < 0.001), longer diabetes duration (OR: 1.34 [95% CI: 1.05-1.70], P = 0.016) and lower socioeconomic-status (OR: 0.53 [95% CI: 0.31-0.90], P = 0.019). Among those with high glycemic variability, body mass index Z score (OR: 3.82 [95% CI: 1.48-9.85], P = 0.005), HbA1c (OR: 4.12 [95% CI: 1.33-12.80], P = 0.014), disinhibited eating (OR: 1.57 [95% CI: 1.14-2.15], P = 0.005), and tendency to lower socioeconomic status (OR: 0.75 [95% CI: 0.56-1.01], P = 0.065). Discussion: DEB are prevalent among adolescents and young adults with T1D and are associated with various risk factors. Factors associated with DEB vary across different levels of glycemic variability. Both low and high glycemic variability are associated with specific risk factors for DEB. One notable risk factor is diabetes-specific disinhibited eating among individuals with high glycemic variability, in contrast to those with low glycemic variability. Given these different risk factors, it may be prudent to adjust intervention programs to reduce DEB among T1D adolescents according to their glycemic variability levels.

Glycemic variability in chronic calcific pancreatitis with diabetes mellitus and its possible determinants

AimsTo study glycemic patterns and variability in patients with pancreatic diabetes or type 3c Diabetes mellitus (DM) due to chronic pancreatitis and its subtypes and assess the role of pancreatic enzyme replacement therapy (ERT) in modulating glycemic variability. MethodsPatients having type 3c DM due to chronic pancreatitis, and on insulin underwent Flash continuous-glucose-monitoring (CGM) for 14 days. Parameters were compared between patients with fibrocalculous pancreatic diabetes (FCPD) and non-FCPD-chronic calcific pancreatitis (non-FCPD) and between the recipients and non-recipients of pancreatic enzyme-replacement-therapy (ERT). ResultsOut of 54 patients with pancreatic diabetes, 35 patients had chronic calcific pancreatitis. They underwent CGM, median HbA1c 9.20 % (77 mmol/mol) and mean Time-In-Range (TIR) being 41.21 % (23.48). Only 5 (15.2 %) patients achieved target TIR>70 % while 16 (48.5 %) patients had high glycemic-variability [Coefficient-of-variation (CV) > 36 %]. Patients with FCPD (n = 14) had higher hypoglycemia-indices like Time-Below-Range (18.92 % vs 8.20 %; p = 0.03) and Low-Blood-Glucose-Index (18.14 % vs 6.04 %; p = 0.02) compared to non-FCPD (n = 21). HbA1c% and hyperglycemic excursions were similar in both groups. Recipients of ERT (n = 20) had lower glycemic-variability [Standard Deviation (SD) 52.15 % vs 68.14 % and CV 32.59 % vs 41.79 %, p < 0.05 for both) than non-recipients. ERT-recipients had no serious hypoglycemia within the 14 days. On subgroup analysis, lower glycemic-variability and hypoglycemia with ERT were seen only in FCPD but not in non-FCPD subgroup (50.13 vs 77.91, 30.09 vs 48.36 for SD and CV respectively, p < 0.05). ConclusionPatients with type 3c DM due to chronic pancreatitis have high frequency of hyperglycemic and hypoglycemic excursions, with those with FCPD having a particularly higher risk of hypoglycemia and glycemic-variability. Those receiving pancreatic ERT had lesser glycemic variability and hypoglycemia. The small sample size and lack of objective markers of documentation of exocrine pancreatic insufficiency like fecal elastase highlight the need for further larger studies in this field.

Continuous Glucose Monitoring in Intensive Care Unit Patients with Diabetes: Insights and Outcomes from a Single-center Study in India

Abstract Introduction: The use of continuous glucose monitoring (CGM) for in-hospital glucose control has been widely studied. However, its application in intensive care unit (ICU) settings is debatable owing to its perceived limitations. In ICU patients with diabetes, glycemic excursions, including frequent hypoglycemic episodes, are more prevalent. This retrospective study was undertaken to evaluate the utility of CGM in the management of diabetes patients admitted to the ICU. Methods: We collected data of patients with diabetes mellitus who were admitted to the ICU at our institution and were advised CGM by the intensivist. A retrospective analysis was conducted, examining demographic, clinical, and laboratory data, along with glucose meter readings and CGM data. The results are presented as numbers, percentages, or median values, as appropriate. Results: Out of 65 patients in the ICU, the median values of percentage time in range, time below range, and time above range were 59%, 2%, and 36%, respectively. The median HbA1C and capillary glucose on admission were 8.8% and 246 mg/dl, respectively. During the ICU stay, the CGM derived average glucose level was 167 mg/dl (percent coefficient of variation [%CV] =25.96) and the glucose management indicator was 7%. In total, 65 episodes of hypoglycemia were detected, all confirmed by capillary glucose tests. A total of 27 patients experienced hypoglycemia, of which 12 had nocturnal hypoglycemia. Conclusion: The use of CGM in the ICU improved glycemic control and helped to detect hypoglycemia, particularly nocturnal with acceptably low glycemic variability as measured by percentage CV. Further studies are required to corroborate and confirm these findings.

Simultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database

BackgroundStress hyperglycemia and glycemic variability (GV) can reflect dramatic increases and acute fluctuations in blood glucose, which are associated with adverse cardiovascular events. This study aimed to explore whether the combined assessment of the stress hyperglycemia ratio (SHR) and GV provides additional information for prognostic prediction in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU).MethodsPatients diagnosed with CAD from the Medical Information Mart for Intensive Care-IV database (version 2.2) between 2008 and 2019 were retrospectively included in the analysis. The primary endpoint was 1-year mortality, and the secondary endpoint was in-hospital mortality. Levels of SHR and GV were stratified into tertiles, with the highest tertile classified as high and the lower two tertiles classified as low. The associations of SHR, GV, and their combination with mortality were determined by logistic and Cox regression analyses.ResultsA total of 2789 patients were included, with a mean age of 69.6 years, and 30.1% were female. Overall, 138 (4.9%) patients died in the hospital, and 404 (14.5%) patients died at 1 year. The combination of SHR and GV was superior to SHR (in-hospital mortality: 0.710 vs. 0.689, p = 0.012; 1-year mortality: 0.644 vs. 0.615, p = 0.007) and GV (in-hospital mortality: 0.710 vs. 0.632, p = 0.004; 1-year mortality: 0.644 vs. 0.603, p < 0.001) alone for predicting mortality in the receiver operating characteristic analysis. In addition, nondiabetic patients with high SHR levels and high GV were associated with the greatest risk of both in-hospital mortality (odds ratio [OR] = 10.831, 95% confidence interval [CI] 4.494–26.105) and 1-year mortality (hazard ratio [HR] = 5.830, 95% CI 3.175–10.702). However, in the diabetic population, the highest risk of in-hospital mortality (OR = 4.221, 95% CI 1.542–11.558) and 1-year mortality (HR = 2.013, 95% CI 1.224–3.311) was observed in patients with high SHR levels but low GV.ConclusionsThe simultaneous evaluation of SHR and GV provides more information for risk stratification and prognostic prediction than SHR and GV alone, contributing to developing individualized strategies for glucose management in patients with CAD admitted to the ICU.

A meta-analysis of the relationship between glycaemic variability and the mortality of patients with heart failure.

Recent findings indicate that fluctuations in blood glucose could potentially increase the risk of unfavourable outcomes in individuals with cardiovascular conditions. The objective of the research was to assess the correlation between glycaemic variability (GV) and the mortality of patients with heart failure (HF) through a comprehensive review and meta-analysis. Longitudinal follow-up studies comparing the mortality risk between HF patients with higher and lower GV were identified by searching Medline, Embase, Web of Science, and Cochrane Library databases. The results were combined using a random-effects model that accounted for the potential variability. The meta-analysis included nine cohort studies involving 76843 patients diagnosed with HF, out of which 35853 patients died within a follow-up period of up to 86months. The combined findings indicated that a significant increase in GV was linked to an elevated risk of mortality in patients with HF during the follow-up period (RR 2.18, 95% CI 1.61 to 2.96, P<0.001, I2=83%). The relationship between GV and mortality in HF patients was not significantly influenced by the patients' diabetic status (diabetic or non-diabetic), type of GV (acute or long-term GV), study design (prospective or retrospective), country of the study (Asian or non-Asian), follow-up durations, or the scores of study quality (P-values for subgroup differences all >0.05). A high GV could be a risk factor of mortality of patients with HF.

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.

Serum 25-hydroxyvitamin D level is associated with short-term glycemic variability metrics derived from continuous glucose monitoring in T2DM

This study aims to investigate the association between 25-hydroxyvitamin D (25OHD) and continuous glucose monitoring-assessed short-term glycemic variability (GV) and HbA1c among patients with type 2 diabetes mellitus (T2DM). We conducted a cross-sectional study recruiting 325 patients. The association between 25OHD and GV metrics (mean amplitude of glycemic excursions [MAGE], coefficient of variation [CV], standard deviation of sensor glucose [SD], and TIR) and HbA1c were analyzed using multivariable linear and logistic regression analyses. The 25OHD level and GV metrics showed significant differences among HbA1c groups (P < 0.01). CV, MAGE, SD and HbA1c decreased, and TIR increased with ascending 25OHD tertiles (P < 0.05). Serum 25OHD was inversely associated with CV (β = − 0.211 [− 0.350 to − 0.071], P < 0.01) and HbA1c (β = − 0.061 [− 0.114 to − 0.031], P < 0.01), and further multivariable analyses confirmed these results (P < 0.05). However, no association of HbA1c and 25OHD was found with the highest tertile of CV. These findings revealed that increased GV and HbA1c were both associated with lower 25OHD, and the relationship between HbA1c and 25OHD was attenuated with higher glucose CV in T2DM. Taken together, the analyses suggest that increasing vitamin D status has effects on improvements in long-term glycemic control and low glycemic variability.

Glycaemic control in comorbid patients: an important element of CVD progression prevention

There is a clear and specific bidirectional relationship between diabetes mellitus and cardiovascular disease. It is known that cardiovascular disease in patients with diabetes occurs 2–5 times more often than in people without diabetes. CVD itself, and it is cardiovascular outcomes, are the main cause of death in patients with diabetes mellitus, both in men and women. In diabetes mellitus, there is a high risk of coronary heart disease, myocardial infarction, arterial hypertension, and acute cerebrovascular accident, and patients with diabetes may experience painless acute myocardial infarction associated with the presence of autonomic cardiac neuropathy. Much more often in diabetes mellitus there are various rhythm disturbances, including paroxysmal forms of atrial fibrillation, which increase the risk of death by 1.8–2 times. Currently, numerous international clinical studies have convincingly demonstrated that improved glycemic control causes a significant reduction in the risk of late macroand microvascular complications of diabetes mellitus. The concept of dysglycemia includes disorders of glycated hemoglobin (HbA1c), fasting glycemia, postprandial glycemia, hypoglycemia, and glycemic variability. Dysglycemia increases the risk of developing type 2 diabetes mellitus and cardiovascular diseases, and their poor prognosis. HbA1c is the “gold standard” for monitoring glycemic control, but this indicator does not provide complete information about daily and intraday changes in glucose levels. Variability (not level) of fasting glucose determines cardiovascular mortality in patients with type 2 diabetes mellitus. Achieving glucose stability may become an additional therapeutic goal for the management of this category of patients with diabetes mellitus, and low glycemic variability is currently assessed as an additional target. Algorithms of specialized medical care for patients with diabetes mellitus recommend that patients with type 2 diabetes mellitus carry out self-monitoring, depending on the type of treatment taken and the degree of carbohydrate metabolism compensation. An important aspect of the technical impact on the adherence of patients to self-control and treatment of diabetes mellitus is the availability of convenient communication between the patient and the doctor, in particular, the possibility of contact remotely via a computer and mobile phone. In conclusion, the possibilities of a new model of the Contour® glucometer line, the Contour® Plus One glucometer, are considered.

Glycemic Variability and the Risk of Diabetic Peripheral Neuropathy: A Meta-Analysis.

Glycemic variability (GV) has been related to complications in patients with diabetes. The aim of the systematic review and meta-analysis was to investigate whether GV is also associated with the incidence of diabetic peripheral neuropathy (DPN). A systematic search of Medline, Web of Science, Embase, and Cochrane Library database was conducted to identify relevant observational studies with longitudinal follow-up. The Newcastle-Ottawa Scale was used for study quality evaluation. A random-effects model was utilized to pool the results, accounting for heterogeneity. Ten observational studies including 72 565 patients with diabetes were included. The quality score was 8-9, indicating generally good quality of the included studies. With a mean follow-up duration of 7.1 years, 11 532 patients (15.9%) were diagnosed as DPN. Compared to patients with low GV, patients with high GV were associated with an increased risk incidence of DPN (risk ratio: 1.51, 95% confidence interval: 1.23 to 1.85, p<0.001; I2=78%). In addition, subgroup analysis showed consistent results in patients with type 1 and type 2 diabetes, and in studies evaluating the short-term and long-term GV (p for subgroup difference=0.82 and 0.53). Finally, results of subgroup analysis also suggested that the association between GV and risk of DPN were not significantly affected by study design, follow-up durations, diagnostic methods for DPN, adjustment of mean glycated hemoglobin A1c, or study quality scores (p for subgroup difference all>0.05). A high GV may be associated with an increased incidence of DPN.

Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

ObjectiveTo evaluate the impact of glucose variability on the relationship between the GRI and other glycemic metrics in a cohort of pediatric and adult patients with type 1 diabetes (T1D) using intermittent scanning continuous glucose monitoring (isCGM).MethodsWe performed a cross-sectional study of 202 patients with T1D under intensive insulin treatment (25.2% CSII) using isCGM. Clinical, metabolic, and glycemic metrics were collected, and the GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. The correlation between the GRI and other classical glycometrics in relation to the coefficient of variation (CV) was evaluated.ResultsA total of 202 patients were included (53% male; 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution (TIR 59.0 ± 17.0%; CV 39.8 ± 8.0%; GMI 7.3 ± 1.1%). The mean GRI was 54.0 ± 23.3 with a CHypo and CHyper component of 5.7 ± 4.8 and 23.4 ± 14.3, respectively. A strong negative correlation was observed between the GRI and TIR (R = −0.917; R2 = 0.840; p < 0.001), showing differences when dividing patients with low glycemic variability (CV < 36%) (R = −0.974; R2 = 0.948; p < 0.001) compared to those with greater CV instability (≥36%) (R = −0.885; R2 = 0.784; p < 0.001). The relationship of GRI with its two components was strongly positive with CHyper (R = 0.801; R2 = 0.641; p < 0.001) and moderately positive with CHypo (R = 0.398; R2 = 0.158; p < 0.001). When the GRI was evaluated with the rest of the classic glycemic metrics, a strong positive correlation was observed with HbA1c (R = 0.617; R2 = 0.380; p < 0.001), mean glucose (R = 0.677; R2 = 0.458; p < 0.001), glucose standard deviation (R = 0.778; R2 = 0.605; p < 0.001), TAR > 250 (R = 0.801; R2 = 0.641; p < 0.001), and TBR < 54 (R = 0.481; R2 = 0.231; p < 0.001).ConclusionsThe GRI correlated significantly with all the glycemic metrics analyzed, especially with the TIR. Glycemic variability (GV) significantly affected the correlation of the GRI with other parameters and should be taken into consideration.

Efficacy of Fasting in Type 1 and Type 2 Diabetes Mellitus: A Narrative Review.

Over the last decade, studies suggested that dietary behavior modification, including fasting, can improve metabolic and cardiovascular markers as well as body composition. Given the increasing prevalence of people with type 1 (T1DM) and type 2 diabetes mellitus (T2DM) and the increasing obesity (also in combination with diabetes), nutritional therapies are gaining importance, besides pharmaceutical interventions. Fasting has demonstrated beneficial effects for both healthy individuals and those with metabolic diseases, leading to increased research interest in its impact on glycemia and associated short- and long-term complications. Therefore, this review aimed to investigate whether fasting can be used safely and effectively in addition to medications to support the therapy in T1DM and T2DM. A literature search on fasting and its interaction with diabetes was conducted via PubMed in September 2022. Fasting has the potential to minimize the risk of hypoglycemia in T1DM, lower glycaemic variability, and improve fat metabolism in T1DM and T2DM. It also increases insulin sensitivity, reduces endogenous glucose production in diabetes, lowers body weight, and improves body composition. To conclude, fasting is efficient for therapy management for both people with T1DM and T2DM and can be safely performed, when necessary, with the support of health care professionals.

885-P: Impact on Glycaemic Variability in Newly Onset T2DM Patients Initiating Dapagliflozin Plus Metformin vs. Metformin Alone, a Randomized Open Label Clinical Study—The MAGNNIFY Trial

Background: The aim of this study is to compare the effect of dapagliflozin (DAPA) in addition to metformin (MET) at maximum tolerated dose in glycaemic variability (GV) on drug naïve T2DM patients. ClinicalTrials.gov Identifier: NCT04090580 Methods: Newly diagnosed T2DM patients with HbA1c ≥ 7.5% - ≤ 12%, BMI &amp;gt; 25 - &amp;lt;45 kg/m2, were randomized 1:1 to receive DAPA 10 mg + MET or MET alone for 12 weeks. Both groups were monitored using a CGM system for 7 days at baseline and week 12. Primary outcome was defined as the mean difference of MAGE (Mean Amplitude of Glycaemic Excursions). Additional secondary endpoints included mean difference of serum insulin, HbA1c, weight, and time in range. Results: 80 patients completed follow-up at week 12 (DAPA+MET, n=41; MET, n=39). Baseline characteristics included mean age 52.2±10.4 years, weight 80.6±16.5 kg, SBP 133.3±19.3 mmHg, HbA1c 9.3±1.5 %, eGFR 100.5±15.4 ml/min/1.73m2, MAGE 4.2±1.4 mmol/L. Patients treated with DAPA+MET had lower GV at week 12 measured by MAGE (-0.79 [-0.86 DAPA+MET vs -0.06 MET; p=0.018]) and higher time in range (+22.9% [35.8 DAPA+MET vs 12.9 MET; p=0.002]). DAPA group had lower insulin plasma levels (-4.5 µU/mL [-2.8 DAPA+MET vs +1.6 MET; p=0.029]) and body weight (-3.2 kg [-3.22 DAPA+MET vs -0.6 MET; p&amp;lt;0.001]); nominal reductions were observed in HbA1c (-1.82 DAPA+MET vs -1.61 MET; p=0.919) and SBP (-4.96 mmHg [-4.35 DAPA+MET vs +0.60 MET; p=0.277]. Conclusions: Patients with new onset T2DM treated with DAPA + MET during a 12-week period had improvements in glycaemic variability measured by MAGE (-19.63%) and achieved longer periods within target range for glycaemic control in comparison with patients treated only with MET. Plasmatic insulin levels, and weight reduction were also significantly reduced with DAPA; nominal reductions in HbA1c and SBP were observed. All these findings support the early dapagliflozin use in patients with newly onset T2DM. Disclosure A.P.Guerrero-castillo: None. S.C.Juarez-comboni: Employee; AstraZeneca. F.J.Gómez-perez: None. M.A.Gomez-samano: Research Support; AstraZeneca. A.Benitez-renteria: Employee; AstraZeneca. D.Cuevas-ramos: None. M.López-carrasco: None. A.Silva: Stock/Shareholder; AstraZeneca. G.X.Brito: None. L.Palacios-baez: None. I.Manjarrez-martínez: None. S.I.Rodriguez-carranza: None. Funding AstraZeneca Mexico

263-LB: Food Intake Impacts on Insulin Secretion, Resistance, and Glucose Variability in Free-Living Individuals with Type 2 Diabetes Mellitus

Changes in eating habits and physical activity are the cornerstone of treatment of type 2 diabetes mellitus (T2D). Dietary composition has important effects on the postprandial glucose and insulin response, but little is known about its impact in a real-life context. We analyzed the self-reported daily food composition of 13 individuals with obesity and T2D not on insulin (7F/6M, age 48±9, BMI 33±2, A1C 7±1) with ASA24 (NIH), while wearing continuous glucose monitor (Dexcom G4) for 3±1 days. They also underwent an oral glucose tolerance test to explore correlations with standard indices of insulin secretion and sensitivity. The mean consumption per meal of total calorie was: 534±110Kcal, carbohydrate: 51±19g, total grains: 1.78±0.66g (refined 1.61±0.59g, whole 0.17±0.18g), total fat: 25±6g (10±3.17g monounsaturated fat), and protein: 24±7g. We observed a positive correlation between average intake of total and monounsaturated fat with the insulin secretion index (ρ= 0.630, p=0.02,) and Disposition Index (DI) (p=0.001, ρ=0.821). An inverse relationship between the consumption of total and refined grains was noted with the insulin sensitivity indexes (p=0.01, ρ= -0.663); as well as with whole grains and DI (p=0.03, ρ= -0.601). We documented glycemic variability (GV) related to meal nutrient intake through macro program: CGM peak and nadir analysis, Microsoft Excel. Total and monounsaturated fats correlated with lower GV (p=0.02), while total and refined grains were associated with greater GV (p&amp;lt;0.03). Glucose nadir after a meal and glucose at the time of the meal inversely correlated with insulin secretion indexes and with DI (p&amp;lt;0.05). These data suggest, in a real-life context, the impact of food composition on modulation of GV, with fats being associated with greater insulin secretion and refined grains with decreased insulin sensitivity, reinforcing the importance of the effect of diet on management of diabetes. Disclosure A. Comarella: None. R. Belfort-DeAguiar: Research Support; Fractyl Health, Inc., Silver Palate Kitchens. Funding National Institutes of Health (DK098286-02)

135-LB: A Variable Impact of the Wave 1 and Wave 2 Lockdowns of the COVID-19 Pandemic on Glycemic Control—A Longitudinal Analysis of FGM Adult Users From Poland

WHO declared COVID19 pandemic in March 2020. In Poland, the particularly severe restrictions related to COVID19 pandemic were implemented during Wave 1. All activities were prohibited except necessary everyday ones. From late May to July 2020 restrictions were gradually lifted. In October 2020, due to Wave 2 of COVID19 pandemic some restrictions were reimplemented, however, their impact on everyday life was smaller than during the Wave 1. The aim of the study was to analyze the impact of different phases of COVID19 pandemic on glycemic control in patients with diabetes using FGM. Longitudinal data of 469 adults aged 18-64 were analyzed. Analyzed time periods were every first 45 days of each quarter of the year 2020. Q1 stands for the first pre-pandemic quarter of 2020, Q2 correspondences to the severe lockdown of Wave 1, Q3 to time of most restrictions lifted, and Q4 to less strict lockdown of Wave 2. Mean glycemic indices are shown in Table 1. In summary, Wave 1 and Wave 2 of COVID19 pandemic had different impact on glycemic control in patients with diabetes. The severe lockdown of the first wave resulted in adults with diabetes in improvement of glycemic indices, particularly TIR. Common findings during both lockdowns were reduction in TBR and lower glycemic variability as compared to the preceding periods. Disclosure J. Hohendorff: Speaker's Bureau; Abbott, Novo Nordisk, Ascensia Diabetes Care, Dexcom, Inc., Bayer Inc. K. Kao: Employee; Abbott Diabetes. L. Brandner: Employee; Abbott Diabetes. M. Malecki: Advisory Panel; Abbott Diabetes. Speaker's Bureau; AstraZeneca, AlfaSigma. Advisory Panel; Novo Nordisk, Lilly, Sanofi. Speaker's Bureau; Merck &amp; Co., Inc. Research Support; Medtronic. Advisory Panel; Dexcom, Inc. Speaker's Bureau; BIOTON S. A., Servier Laboratories, KRKA. Advisory Panel; Bayer Inc.

252-OR: Continuous Glucose Monitoring in Patients with Post-bariatric Hypoglycemia Reduces Hypoglycemia and Glycemic Variability

Post-bariatric hypoglycemia (PBH) is an increasingly recognized complication of bariatric surgery, with prevalence of up to 36% after gastric bypass. Hypoglycemia typically follows postprandial glycemic spikes. Continuous glucose monitors (CGM) can alert patients to high or low sensor glucose (SG), allowing treatment and prevention of severe hypoglycemia. The aim of this study (NCT03353415) was to determine whether CGM, currently indicated only for diabetes, could reduce hypoglycemia in PBH. In an open label, nonrandomized, pre-post design, sensor glucose data were collected using Dexcom CGM in two sequential phases: (1) masked (no access to SG or alarms); and (2) unmasked (access to SG and alarms for low or rapidly declining SG). Data were available for paired analysis for 22 individuals with PBH (mean±SD age 51.1±13.0 yrs, 20F/2M, BMI 30.6±6.2 kg/m2, 82% with level 3 hypoglycemia and 100% with neuroglycopenia). Participants with PBH spent a lower percentage of time in hypoglycemia with unmasked vs masked CGM (&amp;lt;60 mg/dL, median [MAD]: 0.7 [0.8] vs 1.4 [1.7]%, p=0.03; &amp;lt;70 mg/dL: 2.9 [2.5] vs 4.7 [4.8]%, p=0.04), with similar trends overnight. Likewise, there was more time between 70-180 mg/dL (94.8 [3.9] vs 90.8 [5.2]%, p=0.004), and lower glycemic variability (mean amplitude of glycemic excursion: 74.0 [17.7] vs 79.6 [17.8] mg/dL, p=0.04) during unmasked wear. During the day (6:00 AM-11:59 PM), participants spent more time in normoglycemia with unmasked CGM (94.2 [4.8] vs 90.9 [6.2]%, p=0.005), largely due to reduced hyperglycemia (&amp;gt;180 mg/dL: 1.9 [2.2] vs 3.9 [3.6]%, p=0.02). Availability of real-time CGM data and alarms is associated with reductions in low SG, elevated SG, and glycemic variability. This suggests CGM helps patients detect hyperglycemic peaks and imminent hypoglycemia, allowing dietary modification and self-treatment to reduce hypoglycemia. CGM may thus improve safety in PBH, particularly for patients with hypoglycemia unawareness. Disclosure C.J.Cummings: None. A.Jiang: None. A.L.Sheehan: None. R.Ferraz-bannitz: None. A.Puleio: None. D.C.Simonson: Stock/Shareholder; Phase V Technologies, Inc., GI Windows. J.Dreyfuss: None. M.Patti: Consultant; MBX Biosciences, AstraZeneca, Hanmi Pharm. Co., Ltd., Other Relationship; Fractyl Health, Inc. Funding Diabetes Research Center (P30DK036836)

27-OR: Residual Beta-Cell Function Associates with Lower Glycaemic Variability and More Time in Range in Individuals with Type 1 Diabetes

Introduction: A significant proportion of individuals with type 1 diabetes (T1D) have long-term preservation of beta-cell function. Residual beta-cell function (RBCF) is associated with fewer long-term complications and hypoglycemic events. Yet, little is known about the influence of RBCF on daily glycemic control. Therefore, we investigated the associations between RBCF and daily markers of glycemic control in individuals with T1D. Methods: In this cross-sectional study 500 individuals with T1D were included (41.0 ±14years), 64% female, HbA1c 55.6 ±12mmol/mol, median T1D duration 14 years [IQR: 6-29years]. As a validated marker of RBCF, postprandial urinary C-peptide/creatinine ratios (UCPCR) were measured. Time in euglycemic range (TIR) (3.9-10mmol/L) and hypoglycemic range (TUR) (&amp;lt;3.9-10mmol/L) were monitored 14 days prior to the study visit, with a continuous glucose monitoring device. Spearman correlations were calculated in Rstudio. Results: The median UCPCR was 0.03 (IQR: 0.00-0.88) nmol/mmol and strongly correlated with duration of T1D (r = -0.651, p &amp;lt; 0.001). Higher UCPCR correlated with longer TIR (r = 0.330, p &amp;lt; 0.001), lower mean glucose levels (r =-0.318, p=0.005) and shorter TUR (r= -0.237, p &amp;lt;0.001) and lower glucose variation (-0.356, p &amp;lt;0.001). In line, higher UCPCR associated with lower HbA1c levels (r= -0.183, p&amp;lt;0.001) and less 24-hour insulin use (r = -0.183, p = &amp;lt;0.001). The associations remained significant in linear regression models when adjusted for sex, age, plasma glucose and duration of diabetes. Conclusion: UCPCR correlates with more time in range, shorter time under range, and lower HbA1c levels in individuals with T1D. Beneficial effects of beta-cell preservation in T1D may therefore be attributable to less intermittent glucotoxicity and hypoglycaemic episodes. Disclosure C.Fuhri snethage: None. M.Nieuwdorp: Advisory Panel; caelus health. N.Hanssen: Consultant; Novo Nordisk, Research Support; Novo Nordisk. T.J.Mcdonald: None. E.Rampanelli: None. P.De groen: None. A.W.M.Schimmel: None. D.Van raalte: Consultant; Boehringer Ingelheim and Eli Lilly Alliance, AstraZeneca, Merck &amp; Co., Inc., Research Support; Boehringer Ingelheim and Eli Lilly Alliance, AstraZeneca, Merck &amp; Co., Inc. C.B.Brouwer: None. R.A.Oram: Consultant; Janssen Research &amp; Development, LLC, Research Support; Randox R &amp; D. B.O.Roep: None. Funding DNF DON Grant 2020 (2020.10.002)

Glycemic dispersion: a new index for screening high glycemic variability

ObjectiveFor patients with diabetes, high-frequency and -amplitude glycemic variability may be more harmful than continuous hyperglycemia; however, there is still a lack of screening indicators that can quickly and easily assess the level of glycemic variability. The aim of this study was to investigate whether the glycemic dispersion index is effective for screening high glycemic variability.MethodsA total of 170 diabetes patients hospitalized in the Sixth Affiliated Hospital of Kunming Medical University were included in this study. After admission, the fasting plasma glucose, 2-hour postprandial plasma glucose, and glycosylated hemoglobin A1c were measured. The peripheral capillary blood glucose was measured seven times in 24 h, before and after each of three meals and before bedtime. The standard deviation of the seven peripheral blood glucose values was calculated, and a standard deviation of > 2.0 was used as the threshold of high glycemic variability. The glycemic dispersion index was calculated and its diagnostic efficacy for high glycemic variability was determined by the Mann–Whitney U test, receiver operating characteristic (ROC) curve and, Pearson correlation analysis.ResultsThe glycemic dispersion index of patients with high glycemic variability was significantly higher than that of those with low glycemic variability (p < 0.01). The best cutoff value of the glycemic dispersion index for screening high glycemic variability was 4.21. The area under the curve (AUC) was 0.901 (95% CI: 0.856–0.945) and had a sensitivity of 0.781 and specificity of 0.905. It was correlated with the standard deviation of blood glucose values (r = 0.813, p < 0.01).ConclusionsThe glycemic dispersion index had good sensitivity and specificity for screening high glycemic variability. It was significantly associated with the standard deviation of blood glucose concentration and is simple and easy to calculate. It was an effective screening indicator of high glycemic variability.

Role of glucose variability on linear growth in children with type 1 diabetes.

Linear growth is impaired in children with type 1 diabetes (T1D) and poor metabolic control. A good metabolic control is a key therapeutic goal to prevent vascular complications and also to ensure appropriate anthropometric development during childhood. In this study, we aimed to identify and characterize the effects of glycemic variability on linear growth in children with T1D. Data from 144 prepubertal children with T1D were evaluated. Anthropometric measurements (weight, weight-SDS, height, height-SDS, BMI, BMI-SDS) were collected and glycosylated hemoglobin (HbA1c) was measured at admission and every 4 months over a 2-year period. Glycemic variability indexes (glycemic coefficient of variation (CV), glycemic CV percentage (CV%), and the product between HbA1c-mean and HbA1c-SDS/100 (M*SDS-HbA1c/100)) were calculated. According to height-SDS changes after 2 years of follow-up, the study population was divided into three tertile groups and differences across groups were investigated for variables of interest. The three groups were similar in terms of age, gender, and follow-up period. After 2 years, all prepubertal children showed a significant positive trend of anthropometric data. Across the three tertile groups, HbA1c-SDS, CV, CV%, and M*SDS-HbA1c significantly decreased from the first to the third tertile of height-SDS. During follow-up, children with lower Δheight-SDS values reported higher values of HbA1c-SDS, CV, CV%, and M*SDS-HbA1c than subjects with higher linear growth. Glycemic variability correlates with linear growth in children with T1D. Low glycemic variability indexes were reported in higher height-SDS tertiles. Δheight-SDS is inversely correlated with glycemic CV, CV%, and M*SDS-HbA1c.

Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Application of the New "Points in Range" Metrics in the Assessment of In-Hospital Glycemic Control.

Introduction Capillary blood glucose (CBG) monitoring remains the most used testing form in hospitals and allows for"points in range (PIR)" metric calculation. This study was conceived to evaluate the metabolic control in patients with diabetes mellitus (DM) at a hospital through PIR metrics. Methods This was an observational cross-sectional study conducted on October 9, 2020, that included non-critical adults admitted to Centro Hospitalar Universitário do Porto (except pregnant/postpartum women) with DM under CBG monitoring and a minimum of 24 hours of hospitalization. Glycemic control was evaluated by previous day CBG monitoring. Results The study sample consisted of 110 patients with DM (93.6% type 2) with a median number of CBG tests of 4.00 (1.00) and a median CBG of 166.20 (69.41) mg/dL, SD 41.93 ± 27.20 mg/dL, and variation coefficient of 22.56 ± 12.51%. Points below range were 0.5%, with 0% below 54 mg/dL. The points in ranges 70-140 mg/dL and 140-180 mg/dL were32.8% and 22.0%, respectively, and the total number of patients with all points in range 70-180 mg/dL was 19 (17.3%), with only 3 (2.7%) having all points in range 140-180 mg/dL and 10 (9.1%)in range70-140 mg/dL. Regarding points above range (PAR), 29.9% and 14.8%pointswere at levels 1 and 2 hyperglycemia, respectively, and 15 (13.6%) patients had all points above 180 mg/dL. Correlations were identified between PAR and the total number of CBG assessments (ρ = 0.689, p < 0.001). Conclusion We conclude that in-hospital glycemic control remains suboptimal: only few have adequate control according to the PIR metrics despite low glycemic variability. PIR metrics are a new, valuable, simple and valid way to take better advantage of CBG monitoring at no added cost.