Glucose Coefficient Of Variation Research Articles

Hyperglycemia and Other Glycemic Measures Throughout Therapy for Pediatric Acute Lymphoblastic Leukemia and Lymphoma.

Transient hyperglycemia during induction chemotherapy is associated with increased morbidity and mortality in patients with acute lymphoblastic leukemia (ALL). Treatment with glucocorticoids, asparaginase, and stress are the proposed causal factors. Although these risks are not exclusive to induction, glycemic control throughout the remainder of ALL/lymphoma (ALL/ALLy) therapy has not been described. Furthermore, prior research has been limited to transient hyperglycemia. This study aimed to characterize glycemic control throughout ALL/ALLy and to evaluate risk factors and outcomes associated with increased mean glucose and glucose coefficient of variation (glucose CV) during induction chemotherapy. The records for 220 pediatric/young adult patients, age 1 to 26 years, who underwent treatment for ALL/ALLy from 2010 to 2014 at Children's Hospital Colorado were retrospectively reviewed. Measures of glycemic control were calculated for each cycle. For the cycle with the highest mean glucose, induction (n=208), multivariable models were performed to identify potential risk factors and consequences of increased glucose. Highest mean glucose by cycle were induction 116mg/dL, pretreatment 108mg/dL, delayed intensification 96mg/dL, and maintenance 93mg/dL; these cycles also had the most glycemic variability. During induction, patients with Down syndrome, or who were ≥12 years and overweight/obese, had higher mean glucoses; age and overweight/obese status were each associated with increased glucose CV. In multivariable analysis, neither induction mean glucose nor glucose CV were associated with increased hazard of infection, relapse, or death.

Glucose variability is a marker for COVID-19 severity and mortality.

We aimed to investigate the association between glucose coefficient of variation (CV) and mortality and disease severity in hospitalized patients with coronavirus disease-19 (COVID-19). Retrospective cohort study in a tertiary center of patients with COVID-19 admitted to designated departments between March 11th, 2020, and November 2nd, 2020. We divided patients based on quartiles of glucose CV after stratification to those with and without diabetes mellitus (DM). Main outcomes were length of stay and in-hospital mortality. The cohort included 565 patients with a mean age of 67.71 ± 15.45 years, and 62.3% were male. Of the entire cohort, 44.4% had DM. The median glucose CV was 32.8% and 20.5% in patients with and without DM, respectively. In patients with DM, higher glucose CV was associated with a longer hospitalization in the unadjusted model (OR = 2.7, 95% CI [1.3,5.6] for Q4), and when adjusted for age, sex, comorbidities, and laboratory markers, this association was no longer statistically significant (OR = 1.3, 95% CI [0.4,4.5] for Q4). In patients with and without DM, higher glucose CV was associated with higher rates of in-hospital mortality in the unadjusted model, but adjustment for comorbidities and laboratory markers eliminated the association (OR = 0.5, 95% CI [0.1,3.4] for Q4 in patients with DM). Higher glucose CV was associated with increased in-hospital mortality and length of stay, but this association disappeared when the adjustment included laboratory result data. Glucose CV can serve as a simple and cheap marker for mortality and severity of disease in patients with COVID-19.

Cross-sectional association of metrics derived from continuous glucose monitoring with cognitive performance in older adults with type 2 diabetes.

To examine the association between continuous glucose monitoring (CGM)-derived metrics and cognitive performance in older adults with type 2 diabetes (T2D). A total of 100 outpatients with T2D aged 70 years or older were analysed. Participants underwent CGM for 14 days. As CGM-derived metrics, mean sensor glucose (SG), glucose coefficient of variation (CV), time in range (TIR; 70-180 mg/dl), time above range (TAR; > 180 mg/dl) and time below range (TBR; < 70 mg/dl), were calculated. Participants underwent cognitive tests, including the Japanese version of the Montreal Cognitive Assessment (MoCA-J), a delayed word-recall test from the Alzheimer's Disease Assessment Scale-cognitive subscale, a digit symbol substitution test, a letter word fluency test, a trail-making test (TMT) and digit span test (DSP). In multiple regression analyses adjusted for confounders, a higher mean SG was associated with a lower performance in MoCA-J and TMT part B (TMT-B) (P < .05). A higher TAR was associated with a lower performance in TMT-B and DSP-backward (P < .05). By contrast, a higher TIR was associated with better function in TMT-B and DSP-backward (P < .05). Furthermore, CV and TBR were not associated with any cognitive function. Hyperglycaemia metrics and TIR derived from CGM are associated with cognitive functions, especially with executive function and working memory, in older adults with T2D.

Factors associated with hypoglycemia unawareness and severe hypoglycemia in type 1 diabetes mellitus patients.

Several factors are associated with hypoglycemia unawareness and severe hypoglycemia, but few large studies have analyzed Japanese patients with type 1 diabetes. The aim of this study was to analyze the risk factors for hypoglycemia unawareness and severe hypoglycemia in Japanese type 1 diabetes patients. A self-administered questionnaire investigated events, complications and treatments associated with hypoglycemia in patients with type 1 diabetes. Multiple logistic regression analysis of factors associated with hypoglycemia unawareness and severe hypoglycemia requiring medical treatment was carried out. The coefficient of variation (CV) of blood glucose levels was determined using blood samples collected at six outpatient visits. Of the 1,619 participants, 44.2% and 10.4% experienced hypoglycemia unawareness and severe hypoglycemia, respectively. Mean HbA1c levels in patients with hypoglycemia unawareness were lower than those in patients without hypoglycemia unawareness. The type 1 diabetes subtype, glycated hemoglobin (HbA1c) level, CV of blood glucose levels and history of severe hypoglycemia requiring medical treatment were significant independent variables predicting the presence of hypoglycemia unawareness. The glucose CV and a history of hypoglycemia unawareness were significant independent variables predicting severe hypoglycemia requiring medical treatment. In stratified analyses of patients divided into four groups according to glucose CV and HbA1c levels, the high-glucose-CV/low-HbA1c group had the highest odds ratios for hypoglycemia unawareness (2.60) and severe hypoglycemia requiring medical treatment (2.55). The ambulant glucose CV correlated with both hypoglycemia unawareness and severe hypoglycemia. Patients with high glucose CV and low HbA1c are at high risk of such adverse events, and their treatment strategies should be reviewed.

Momentary Partner Involvement in Diabetes Self-Care and Continuously Measured Glucose: A Dynamic Analysis.

This study examined the dynamic, real-time associations between partner involvement in diabetes self-care and continuous glucose monitor (CGM) metrics in adults with type 2 diabetes. For 1 week, 63 participants wore Dexcom G4 CGMs and provided momentary reports of partner involvement in diabetes self-care five times per day. Dynamic structural equation models were used to estimate the reciprocal lagged effects of partner involvement on next-hour CGM metrics (and vice versa). Partner involvement predicted improved next-hour glucose control for five of six CGM metrics in analyses adjusted for time-varying covariates. The hour after partner involvement, the model predicted a 26.34 mg/dl decrease in glucose level (standardized β = -0.19), 30% greater odds of meeting target time in target range ( β = 0.07), 48% higher odds of target time below target range (TBR; β = 0.04; the only nonsignificant effect), 47% greater odds of target time above target range (β = 0.11), a 4.20 unit decrease in glucose standard deviation ( β = -0.19), and a 0.01 unit decrease in glucose coefficient of variation ( β = -0.08; all p values < .05). There was less consistent support for the reverse pathway, with only two metrics significantly related to next-hour partner involvement: glucose level ( β = 0.15) and TBR ( β = 0.21), such that having higher levels and meeting target TBR were significantly predictive of next-hour partner involvement. This is the first study showing that partner involvement in daily diabetes management predicts short-term glucose control. More research is needed to understand how partners influence glycemic control and evaluate interventions that promote their involvement in diabetes care.

573-P: Continuous Glucose Monitoring Measures Impact Cognitive Function in Long-Duration Type 1 Diabetes

As people with type 1 diabetes (T1D) live longer, they are increasingly presented with greater health risks tied to aging-related cognitive decline. We previously reported that participants of the Joslin 50-year Medalist Study (“Medalists”) , with T1D ≥50 years, have impaired cognitive function compared to controls without diabetes, and similar to those with type 2 diabetes. While glycemic control has been shown to impact cognitive function, the impact of day-to-day continuous glucose monitoring (CGM) measures is unclear. CGM provides measures of glycemic variability uncaptured by HbA1c alone. A 2-week block of CGM data was procured from a subset of Medalists (n=64) who also had undergone a cognitive battery testing psychomotor function (Grooved Pegboard) , verbal learning and memory (RAVLT- immediate and delayed recall) , working memory (Wechsler) and executive function (WASI) . In a cross-sectional study, log-transformed CGM metrics were tested for associations with cognitive domains in linear regression models adjusted for duration of diabetes, education status and sex. Better psychomotor function was associated with a higher time in range 70 - 180 mg/dL (p=0.04) , and with lower: glucose coefficient of variation (p=0.02) , time above range (TAR) &amp;gt;180 -250 mg/dL (p=0.01) , TAR &amp;gt; 250 mg/dL (p=0.07) , mean glucose (p=0.07) and glucose management indicator (p=0.07) . Better delayed recall associated with lower time below range &amp;lt; 70 mg/dL (p=0.02) and better immediate recall with lower TAR &amp;gt; 180 -250 mg/dl (p=0.08) . Lower HbA1c previously associated with better executive function in the Medalists, but not with other cognitive domains. This CGM study shows that glucose variability and hyperglycemia impact psychomotor function, while hypoglycemia impacts verbal learning and memory. Thus, better day-to-day glucose management in conjunction with long-term HbA1c control, are potentially important to prevent cognitive decline in aging populations with long-duration T1D. Disclosure H.Shah: None. M.Yu: None. T.Boumenna: None. J.Gauthier: None. R.Tham: None. A.Adam: None. G.L.King: Advisory Panel; Medtronic, Research Support; Janssen Pharmaceuticals, Inc. Funding Beatson Foundation

105-LB: Glucose Variability with Second-Generation Basal Insulin Analogs Glargine 300 U/mL and Degludec 100 U/mL, Evaluated by CGM in People with T1D—The InRange Randomized Controlled Trial

Background: InRange has previously demonstrated that insulin glargine 300 U/mL (Gla-300) is non-inferior to insulin degludec 100 U/mL (IDeg-100) in terms of time in glucose range 70--180 mg/dL (primary endpoint) and total glucose coefficient of variation (CV) as measured by CGM in people with T1D. Methods: InRange (NCT04075513) was a multicenter, randomized, active-controlled, parallel-group, 12-week open-label trial comparing Gla-300 vs. IDeg-100 in adults with T1D using 20-day CGM profiles (≥10 days evaluable) . Here we present data for within- and between-day CV. Results: Overall, 343 participants were randomized (172 Gla-300, 171 IDeg-100) : mean ± SD age was 42.8 ± 13.3 years and T1D duration was 20.5 ± 12.8 years. Both within-day and between-day glucose CV at Week 12 were similar with Gla-300 and IDeg-100 (Table) . Within-day glucose CV at Week 12 was below the threshold for glycemic stability (36%) in both groups and was also similar in both treatment groups irrespective of glycemic stability at baseline (CV ≤36% or &amp;gt;36%) . The LS mean within-day glucose CV of participants with CV &amp;gt;36% at baseline reached 33.67 (Gla-300) and 35.38 (IDeg-100) at Week 12. Conclusion: Using clinically relevant CGM metrics, InRange shows that Gla-300 and IDeg-100 provide similar within- and between-day glucose variability in people with T1D. Disclosure R. M. Bergenstal: Advisory Panel; Hygieia, Medtronic, Roche Diabetes Care, Zealand Pharma A/S, Consultant; Abbott Diabetes, Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur SA, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, Onduo LLC, Sanofi, United HealthCare Services, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi. T. Battelino: Advisory Panel; Abbott Diabetes, Indigo Diabetes, Lilly Diabetes, Medtronic, Novo Nordisk, Sanofi, Research Support; Medtronic, Novartis Pharmaceuticals Corporation, Novo Nordisk, Zealand Pharma A/S, Speaker’s Bureau; Abbott Diabetes, Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk, Sanofi, Stock/Shareholder; DreaMed Diabetes, Ltd. S. Edelman: Consultant; Abbott Diabetes, Lilly Diabetes, Sanofi-Aventis U. S., Xeris Pharmaceuticals, Inc. P. Choudhary: Advisory Panel; Abbott Diabetes, Lilly Diabetes, Medtronic, Research Support; Novo Nordisk, Speaker’s Bureau; Dexcom, Inc., Glooko, Inc., Insulet Corporation, Sanofi. T. Danne: Consultant; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Lilly, Medtronic, Novo Nordisk, Roche Pharmaceuticals, Sanofi, Ypsomed AG, Research Support; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Lilly, Medtronic, Novo Nordisk, Roche Pharmaceuticals, Sanofi, Ypsomed AG, Stock/Shareholder; DreaMed Diabetes, Ltd. E. Renard: Consultant; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Insulet Corporation, LifeScan, Lilly, Novo Nordisk, Roche Diabetes Care, Sanofi, Tandem Diabetes Care, Inc. J. Westerbacka: Employee; Sanofi, Stock/Shareholder; Sanofi. B. Mukherjee: Employee; Sanofi, Stock/Shareholder; Sanofi. P. Picard: n/a. V. Pilorget: None. Funding Sanofi

Maintenance of plasma glucose variability after an acute session of aerobic exercise despite changes in insulin and glucagon-like peptide-1 levels in type 2 diabetes.

The present study aimed to evaluate glucose variability and hormonal responses during and after an aerobic exercise session performed after breakfast in type 2 diabetes patients treated with metformin. In this quasi-experimental study individuals underwent clinical and laboratory evaluations and maximal exercise test. After two weeks an aerobic exercise session (30 minutes at 60%-70% of the peak heart rate) was performed. At rest, during and after the exercise session, glucose variability (mean amplitude glucose excursions, glucose coefficient of variation, and glucose standard deviation) and levels of plasma glucose, insulin, glucagon, and glucagon-like-peptide-1 were evaluated. Thirteen patients were enrolled in the study. Plasma glucose increased at 15 minutes during the exercise session (244.6 ± 61.9 mg/dL), and decreased at 60 min after exercise (195.6 ± 50.0 mg/dL). Glucose variability did not show any difference before and after exercise. Insulin levels at 15 min [27.1 μU/mL (14.2-42.1)] and 30 min [26.3 μU/mL (14.6-37.4)] during the exercise were higher than those at fasting [11.2 μU/mL (6.7-14.9)] but decreased 60 minutes after exercise (90 minutes) [16.6 μU/mL (8.7-31.7)]. Glucagon levels did not show any difference. GLP-1 levels increased at 30 min [7.9 pmol/L (7.1-9.2)] during exercise and decreased 60 min after exercise (90 minutes) [7.7 pmol/L (6.8-8.5)]. Subjects with type 2 diabetes presented expected changes in insulin, glucagon and GLP-1 levels after breakfast and a single aerobic exercise session, not accompanied by glycemic variability changes.

Contribution of glycemic variability to hypoglycemia, and a new marker for diabetes remission after Roux-en-Y gastric bypass surgery.

Diabetes remission after bariatric surgery was reported to be characterized by increased risk of hypoglycemia. This study aimed to examine the contribution of glycemic variability (GV) to the risk of hypoglycemia, and the suitability of time in range (TIR), an emerging glycemic marker, to define diabetes remission after Roux-en-Y gastric bypass (RYGB). A single referral center in China. This retrospective study included 175 individuals with type 2 diabetes and obesity who underwent RYGB. Subjects were classified as remission/nonremission according to the current standard. Each participant underwent continuous glucose monitoring both before and 1 year after RYGB. GV was measured by glucose coefficient of variation (CV), and hypoglycemia was assessed by time below range (TBR). Complete remission was achieved in 88 of the 175 participants following RYGB. In the remission group, significant correlations between postoperative CV and TBRs (r = .353-.442, both P ≤ .001) were found. Excess hypoglycemia (TBR<54 , ≥1%, TBR<70, ≥4%) increased with ascending CV quintiles (both P for trend <.05) and was significantly more frequent in subjects with glucose CV >32.2% (both P <.05). Compared with glycated hemoglobin A1C, TIR showed stronger correlation with glucose CV and risk of hypoglycemia. Increased glycemic variability was linked to excess risk of hypoglycemia after RYGB. Furthermore, TIR may serve as a new metric, in addition to HbA1C, for defining diabetes remission.

Impacts of glycemic variability on the relationship between time in range and estimated glycated hemoglobin in patients with type 1 diabetes mellitus

Objective: This study is to investigate the relationship between time in range (TIR) and glucose management indicator (GMI), and the impact of glycemic variability (GV) on their relationship in patients with type 1 diabetes mellitus (T1DM). Methods: The CGM data were collected from a multicenter randomized clinical trial of adults (≥18 years old) with T1DM, including 83 T1DM patients, respectively from the Third Affiliated Hospital of Sun Yat-sen University (72 cases), Drum Tower Hospital Affiliated to Nanjing University School of Medicine (2 cases), and the First Affiliated Hospital of University of Science and Technology of China (9 cases). All subjects wore the iProTM2 system for 14 days at baseline (0-2 weeks), 3 months (12-14 weeks), and 6 months (24-26 weeks). Data derived from iProTM2 sensor was used to calculate CGM parameters. Correlation between TIR and GMI was explored according to different stratification of glycemic variability assessed by glucose coefficient of variation (CV). Predicted TIR in the fixed GMI value was calculated via the linear regression equations performed in the respective interquartile group of CV. Results: From November 2017 to June 2021, a total of 233 CGM data were collected with 83 collected from baseline, 80 from the 3-month follow-up, 70 from the 6-month follow-up. Patients including 27 males had a median (Q1, Q3) age of 30.69 (25.22, 38.43) years, with a diabetes duration of 10.05(4.46, 13.92) years. The median (Q1, Q3) and effective wearing time of available CGM data was 13.92 (13.02, 14.00) days and 91.61% (84.96%, 95.94%), and the value of TIR, GMI and CV was 60.34%±13.03%, 7.14%±0.61% and 41.01%±7.64%, respectively. There was a strong negative correlation between TIR and GMI (r=-0.822, P<0.001). Multiple linear regression analysis showed that the predictive value of TIR calculated from a given GMI was 8.352% higher when CV was up to standard (36%) than that when CV was down to standard. Based on the multiple linear regression equations generated from quartiles of CV, the predicted TIR value was decreased across the ascending quartiles with 69.98 % in the lowest quartile of CV (≤35.91%), 64.57 % in 25th-50th quartile of CV (35.91%<CV≤40.08%), 60.96% in 50th-75th quartile of CV (40.08%<CV≤45.86%) and 56.44% in the highest quartile of CV (>75th quartile, CV>45.86%) when GMI was set as 7%. Conclusions: There is a strong correlation between TIR and GMI in adult patients with T1DM in patients with type 1 diabetes mellitus. CV influenced the relationship between TIR and GMI.

Relationship between blood glucose variability and muscle composition in ICU patients receiving nutrition support: A pilot study.

Many critically ill patients experience increased blood glucose variability (BGV). The objective of the current pilot study was to assess the relationship between muscle composition (defined as average Hounsfield units (HU)) among ICU patients with an abdominal CT scan within seven days of intubation, and BGV (defined as coefficient of variation (CV)) calculated from blood glucose levels measured each morning while intubated. The first serum blood glucose measurement obtained each day during intubation was recorded, blood glucose CV ((mean/SD)∗100) was calculated. Cross-sectional muscle area (CSA; cm2) at the third lumbar region was identified using the-29 to+150 HU range; muscle composition was calculated as the average HU. BGV predictors were determined using linear regression. Eighty-two patients were included (53% female), with a median age of 64 (25th, 75 percentile (IQR): 51, 70) years. The median CV was 29% (IQR: 20, 37); 40% of subjects required insulin. The median CSA was 100.4cm2 (IQR: 84.0, 120.8) and muscle composition was 20.4 HU (IQR: 12.2, 29.4). Patients received only 36% of estimated calorie requirements. Insulin administration, history of diabetes, and muscle composition were significant BGV predictors. Among these adult intubated ICU patients, higher muscle composition was associated with lower BGV. Future research is needed to corroborate these findings, determine other factors associated with poor muscle quality, and identify methods to describe muscle composition for all ICU patients.

Hybrid Closed-Loop Control with Ultrarapid Lispro Compared with Standard Insulin Aspart and Faster Insulin Aspart: An In Silico Study.

There is room for improvement in the performance of closed-loop regulation algorithms during the prandial period. This in silico study evaluated the efficiency and safety of ultrarapid lispro insulin using the Diabeloop DBLG1® algorithm. We modeled the insulin profile of URLi according to literature data and integrated it to the model used within a simulation platform built from a 60 patients' virtual cohort. We then ran the DBLG1® algorithm in silico with various meal intakes using modeled URLi, Aspart and Faster Aspart. The primary endpoints were glucose metrics (time in 70-180 mg/dL range and time below range). When insulin time constant values were tuned, time in 70-180 mg/dL range was 69.4 [61.1-75.6] (Aspart) vs 74.7 [65.5-81.5] (URLi). Glucose coefficient of variation was reduced from 34.1 [29.7-37.8] to 28.4 [25.7-34.6]. Time below 70 mg/dL and 54 mg/dL were significantly reduced with URLi, whether or not DBLG1 was specifically tuned to this insulin. Metrics with Faster Aspart were intermediate and did not significantly differ from URLi. This simulation study performed on a virtual T1D population suggests that the use of URLi within an unmodified closed-loop DBLG1 regulation algorithm is safe and, with DBLG1 being tuned to this specific insulin type, improved the regulation performances as compared with Aspart. This fact supports the use of such an insulin in clinical investigations.

Association of glycemic variability assessed by continuous glucose monitoring with subclinical diabetic polyneuropathy in type 2 diabetes patients.

Aims/IntroductionDiabetic peripheral neuropathy is a common diabetes‐related microvascular complication. The relationship between peripheral nerve function and glucose variability is unclear. We investigated the association of glucose variability with subclinical diabetic polyneuropathy in a large‐scale sample of patients with type 2 diabetes.Materials and MethodsWe enrolled 509 individuals with type 2 diabetes who were screened for diabetic peripheral neuropathy and monitored using a continuous glucose monitoring system. Multiple glycemic variability parameters, including the mean amplitude of glycemic excursions, glucose standard deviation (SDgluc) and glucose coefficient of variation were calculated from 3‐day glucose profiles obtained from continuous glucose monitoring. All participants underwent nerve conduction studies, and the composite Z‐scores for nerve conduction parameters were calculated.ResultsMultivariate logistic regression analyses showed that SDgluc and the conventional risk factor hemoglobin A1c (HbA1c) were independently associated with abnormal nerve function, and the corresponding odds ratios (95% confidence interval) were 1.198 (1.027–1.397, SDgluc) and 1.182 (1.061–1.316, HbA1c), respectively. The composite Z‐score of nerve conduction velocity and response amplitude obviously decreased with greater SDgluc, and the composite Z‐score of distal latency significantly increased with increasing tertiles of SDgluc (all P trend <0.05). After adjusting for age, sex, body mass index, diabetes duration and HbA1c, SDgluc was independently associated with nerve conduction velocity (β = −0.124, P = 0.021).ConclusionsThe SDgluc is a significant independent contributor to subclinical diabetic polyneuropathy, in addition to conventional risk factors including diabetes duration and HbA1c.

Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §.

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style Libre 1 (FSL1) to Dexcom G4 (DG4) biosensors over 1 year. Patients (n = 21, 43 ± 15 years, BMI 25 ± 5, HbA1c 8.1 ± 1.0%) had severe hypoglycemia and/or HbA1c ≥ 8%. AGP metrics (time-in-range (TIR) 70–180 mg/dL, time-below-range (TBR) <70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50 ± 17 vs. 45 ± 16, p = 0.036), and lower TBR < 70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], p = 0.0007), TBR < 54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], p = 0.007) and %CV (39 ± 5 vs. 45 ± 8, p = 0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR < 70 mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs. “non-responders” warrant to be investigated.

Blood glucose-related indicators are associated with in-hospital mortality in critically ill patients with acute pancreatitis

Acute pancreatitis (AP) results in potentially harmful blood glucose fluctuations, affecting patient prognosis. This study aimed to explore the relationship between blood glucose-related indicators and in-hospital mortality in critically ill patients with AP. We extracted data on AP patients from the Multiparameter Intelligent Monitoring in Intensive Care III database. Initial glucose (Glucose_initial), maximum glucose (Glucose_max), minimum glucose (Glucose_min), mean glucose (Glucose_mean), and glucose variability (glucose standard deviation [Glucose_SD] and glucose coefficient of variation [Glucose_CV]) were selected as blood glucose-related indicators. Logistic regression models and the Lowess smoothing curves were used to display the association between significant blood glucose-related indicators and in-hospital mortality. Survivors and non-survivors showed significant differences in Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV (P < 0.05). Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV were risk factors for in-hospital mortality in AP patients (OR > 1; P < 0.05). According to the Lowess smoothing curve, the overall trends of blood glucose-related indicators showed a non-linear correlation with in-hospital mortality. Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV were associated with in-hospital mortality in critically ill patients with AP.

Association of HbA1c With All-cause Mortality Across Varying Degrees of Glycemic Variability in Type 2 Diabetes.

ContextThe interaction of glycated hemoglobin A1c (HbA1c) and glycemic variability in relation to diabetes-related outcomes remains unknown.ObjectiveTo evaluate the relationship between HbA1c and all-cause mortality across varying degrees of glycemic variability in patients with type 2 diabetes.Design, Setting, and PatientsThis was a prospective study conducted in a single referral center. Data of 6090 hospitalized patients with type 2 diabetes was analyzed. Glucose coefficient of variation [coefficient of variation (CV)] was obtained as the measure of glycemic variability by using continuous glucose monitoring for 3 days. Cox proportional hazards regression models were used to estimate hazard ratios and 95% CIs for all-cause mortality.ResultsDuring a median follow-up of 6.8 years, 815 patients died. In patients with the lowest and middle tertiles of glucose CV, HbA1c ≥ 8.0% was associated with 136% (95% CI 1.46-3.81) and 92% (95% CI 1.22-3.03) higher risks of all-cause mortality, respectively, as compared with HbA1c 6.0%-6.9%, after adjusting for confounders. However, a null association of HbA1c with mortality was found in patients with the highest tertile of glucose CV.ConclusionsHbA1c may not be a robust marker of all-cause mortality in patients with high degree of glycemic variability. New metrics of glycemic control may be needed in these individuals to achieve better diabetes management.

632-P: Flash Glucose Monitoring in Hypoglycemia Monitor in Type 1 Diabetes

Aims: Hypoglycemia is an acute complication of patients with type 1 diabetes. This study investigated flash glucose monitoring in hypoglycemia monitor in patients with type 1 diabetes. Methods: A total of 120 patients with type 1 diabetes were recruited and randomly divided to implement FreeStyle LibreTM as flash glucose monitoring group (n=60) and retrospective continuous glucose monitoring system (CGMS) as control group (n=60). Both groups carried out CGMS at least two times a year and filled three questionnaires including Diabetes Monitoring and Treatment Satisfaction Questionnaire (DMTSQ), Diabetes Specific Quality of Life (DQoL) and Chinese Version Hypoglycemia Fear Survey II (CFH II). Clinical Data, CGM metrics and Questionnaire scores were collected at first visit and one year follow up. Results: The result showed that the time below range (TBR) and the glucose coefficient of variation (CV) were significantly higher in flash glucose monitoring group than control group (all P &amp;lt;0.05). TBR was prolonged with the increase of CV (P &amp;lt;0.05) and low blood glucose index (LBGI) (P &amp;lt;0.05) in both two groups. After multiple stepwise linear regression, CV and LBGI were independent risk factors for TBR (Standardized Coefficients β=0.665, β=0.762, respectively, All P&amp;lt;0.001 ). After one year followed up, the scores of DQoL and DMTSQ had no significant difference, but number of hypoglycemia times per month and scores of behavior part of CFH II significantly decreased in flash glucose monitoring group compared with a year ago (all P&amp;lt;0.05). Conclusions: Glucose CV and LBGI were independent risk factors for TBR. And Flash glucose monitoring decreased number of hypoglycemia times per month and fear behavior of hypoglycemia in patients with type 1 diabetes after one year. Disclosure J. Li: None. F. Liu: None. Funding National Key Research and Development Program of China (2017YFC1309601)

150-OR: Intermittently Scanned Continuous Glucose Monitoring and Automated Bolus Calculation in Type 1 Diabetes Treated with Multiple Daily Insulin Injections: A Randomized Controlled Trial

In a randomized controlled multicenter trial, 170 adults with type 1 diabetes ≥1 year, multiple daily insulin injections, HbA1c &amp;gt;7.0% (53 mmol/mol), were randomized to usual care (n=42), carbohydrate counting with automated bolus calculation (ABC) (n=41), intermittently scanned continuous glucose monitoring (isCGM) (n=48), or ABC+isCGM (n=39). HbA1c, blinded CGM and patient-reported outcomes were recorded at baseline and after 26 weeks. Primary outcome was change in time in range (TIR) with isCGM vs. usual care. Baseline characteristics were comparable across intervention arms; median (Q1;Q3) diabetes duration 18 (10;28) years, HbA1c 8.1 (7.7;8.7) % (65 (61;72) mmol/mol) and mean (SD) BMI 26.7 (3.9) kg/m2. Changes in TIR, hypo- and hyperglycemia were similar in the intervention arms compared to usual care. Daytime TIR increased 22 (95% CI: 1;47) % with ABC and nighttime hyperglycemia increased 258 (40;814) % with isCGM compared to usual care. With ABC+isCGM, HbA1c was reduced 0.4 (0.1;0.7) % (4 (1;8) mmol/mol) and glucose coefficient of variation (CV) was reduced 11 (4;17) % compared to usual care. Treatment satisfaction improved with isCGM and ABC+isCGM. With ABC+isCGM, empowerment and present life quality also improved. Within the ABC and isCGM arms, we observed HbA1c reductions of 0.3 and 0.3% (4 and 3 mmol/mol), respectively, however not significantly different from usual care. Statistical significance was maintained after adjustment for multiple testing with regards to CV and treatment satisfaction with ABC+isCGM, and treatment satisfaction with isCGM only. In conclusion, although no significant improvement in TIR was achieved, simultaneous intervention with ABC+isCGM improved the coefficient of variation and treatment satisfaction compared to usual care, and HbA1c was clinically relevant reduced, in adult technology-naïve persons with type 1 diabetes and suboptimal HbA1c. Disclosure A. L. Secher: None. U. Pedersen-bjergaard: Advisory Panel; Self; Novo Nordisk A/S, Sanofi-Aventis, Consultant; Self; Abbott, Speaker’s Bureau; Self; Novo Nordisk A/S. O. L. Svendsen: None. B. Gade-rasmussen: None. T. P. Almdal: None. L. H. Raimond: None. D. Vistisen: Stock/Shareholder; Self; Novo Nordisk A/S. K. Nørgaard: Advisory Panel; Self; Medtronic, Other Relationship; Self; Novo Nordisk Inc., Zealand Pharma A/S, Speaker’s Bureau; Self; Medtronic.

608-P: Relationship between Time-in-Range and Estimated HbA1c Derived from Continuous Glucose Monitoring System in Adults with Type 1 Diabetes Mellitus

Aims: Time-in-range (TIR) and estimated glycated hemoglobin (eHbA1c), two key metrics derived from continuous glucose monitoring system (CGMS), are valuable for glycemic control. This study is to investigate the relationship between TIR and eHbA1c, and the impact of glycemic variability on their relationship. Methods: The CGMS data were collected from baseline and follow-up visits in a clinical trial (NCT03522870) and yearly visits in the Guangdong Type 1 Diabetes Translational Medicine Study. Adult patients with type 1 diabetes (≥18 yrs) who had available CGM data for &amp;gt; 3 days were included. Correlation between TIR and eHbA1c was explored according to different stratification of glycemic variability assessed by glucose coefficient of variation (CV). Predicted TIR in the fixed eHbA1c value was calculated via the linear regression equations performed in the respective interquartile group of CV. Results: From March 2014 to July 2020, a total of 260 CGMS data were collected. The mean wearing time of available CGM data was 11.8±3.8 days, and the mean value of TIR, eHbA1c, and CV was 62.9±14.1%, 7.2±0.8%, and 39.4±8.1%, respectively. There was a strong negative correlation between TIR and eHbA1c (R=-0.83, P&amp;lt;0.001). For patients with stable glycemic variability (CV&amp;lt;36%), the correlation between the two metrics was stronger (R=-0.95) while in the unstable group the coefficient was decreased (R=-0.80). Based on the regression equations, the predicted %TIR value was decreased across the ascending quartiles with 73.0% in the lowest quartile of CV (&amp;lt;25th quartile, &amp;lt;33.8%), 67.4% in 25th - 50th quartile (33.8-39.2%), 61.4% in 50th - 75th quartile (39.2-44.9%) and 58.5% in the highest quartile (&amp;gt;75th quartile, CV &amp;gt;44.9%) when eHbA1c was set as 7%. Conclusions: There is a strong correlation between TIR and eHbA1c in adult patients with type 1 diabetes. Patients with lower glycemic variability usually have higher %TIR in the same eHbA1c level. Disclosure Y. Zhou: None. J. Weng: None. H. Liu: None. D. Yang: None. H. Ai: None. J. Lv: None. X. Mai: None. W. Xu: None. B. Yao: None. J. Yan: None. Funding National Key Research and Development Program of China (2017YFC1309600)

525-P: Is It All In Your Head? Associations between Subjective and Objective Measures of Glycemic Variability

CGM offers insights into daily glucose fluctuations. However, little is known how these insights impact feelings of diabetes distress in everyday life. We analyzed associations between subjective experiences of distress due to glucose fluctuations and CGM-derived parameters. In the DIA-LINK study, 203 people with type 1 diabetes completed ecological momentary assessment while using unblinded CGM over 17 days. Every evening, participants rated diabetes distress they experienced over a day using 5 adapted questions from the PAID questionnaire (feeling overwhelmed, alone, guilty, deprivation, diabetes takes up too much mental/physical energy). Also, they rated their daily distress specifically due to glucose fluctuations from 0 (not at all) to 10 (very much). Mean daily scores of these distress ratings were calculated per participants. Glucose parameters were calculated for the study period (mean glucose, coefficient of variation (CV) of glucose, % 180 mg/dl). Correlations were analyzed using Pearson’s r (Table 1). Ratings of distress were highly associated. CGM-derived glucose CV was neither associated with overall distress nor with specific distress due to glucose variability. This may indicate that glucose fluctuations can be a source of diabetes distress, but subjective experiences seem to be more important than objectively measured glucose fluctuations. Disclosure L. Priesterroth: None. D. Ehrmann: Speaker’s Bureau; Self; Berlin-Chemie AG. N. Hermanns: Advisory Panel; Self; Abbott, Research Support; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH. A. J. Schmitt: None. B. Kulzer: Advisory Panel; Self; Berlin-Chemie AG, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Research Support; Self; Berlin-Chemie AG, Novo Nordisk, Speaker’s Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk. Funding German Center for Diabetes Research (DZD) (82DZD01102)