Long-term real-life glycaemic outcomes with a hybrid closed-loop system compared with sensor-augmented pump therapy in patients with type 1 diabetes.

AIM: To compare glycaemic metrics at 3 and 6 months in patients with type 1 diabetes on a 670G hybrid closed-loop (HCL) system after using a sensor-augmented pump (SAP) for at least 3 months. MATERIALS AND METHODS: A retrospective study from a centre that has the largest number of 670G users in the United States was conducted. Data from 202 SAP users were reviewed. Sixty-one patients were excluded (two for steroid use, four for pregnancy, 27 for previous Enlite use, and 28 for non-continuous use of 670G). Out of 141 patients who met the inclusion criteria, 127 (aged 21-68 years) had complete data. RESULTS: HbA1c levels decreased by 0.4% at 3 months and were maintained at 6 months (7.6 ± 0.07 vs. 7.2 ± 0.08, P < 0.001) with no weight gain at 6 months. Time-in-range (70-180 mg/dL) increased from 59.5% ± 1.1% to 70.2% ± 1.2% and 70.1% ± 1.1% at 3 and 6 months (P < 0.001), respectively. At 6 months, time spent in hypoglycaemia (<70 mg/dL) and time spent in hyperglycaemia (>180 mg/dL) were reduced by 30% (2.2% ± 0.2% vs. 3.2% ± 0.2%; P < 0.05) and 26% (28.3% ± 1.2% vs. 38.1% ± 1.2%; P < 0.001), respectively. More time in auto-mode was associated with improved continuous glucose monitoring metrics, lower HbA1c and decreased glycaemic variability. Time in auto-mode declined in men after 3 months, while women maintained similar auto-mode use throughout the study. CONCLUSIONS: The HCL system improved HbA1c levels and time-in-range, and decreased time spent in hypoglycaemia and hyperglycaemia at 6 months. Auto-mode use was significantly correlated with continuous glucose monitoring metrics and glycaemic outcomes.

Comparison of Cgmanalysis, a Free Open-Source Continuous Glucose Monitoring Data Management and Analysis Software, with Commercially Available CGM Platforms: Data Standardization for Diabetes Technology Research.

Background: Cgmanalysis is an open-source software based on the R programming language for data management and descriptive analysis of data from continuous glucose monitoring (CGM). We sought to validate the summary measures calculated by cgmanalysis against the results from proprietary software associated with four CGM commercially available models. Methods: Two weeks of data from 188 patients with type 1 diabetes using commercially available CGMs. Freestyle Libre Gen 1 (n = 53), Medtronic Guardian 3 (n = 52), Dexcom G6 reported by Dexcom Clarity (n = 48), and Dexcom G6 reported by Tandem (n = 35) were analyzed using proprietary software and cgmanalysis. Agreement was assessed using scatterplots, Bland-Altman plots, and equivalence tests. Results: Good agreement was obtained for all glycemic summary measures for all CGMs assessed. None of the differences between the cgmanalysis package and the manufacturers' software were outside the prespecified bounds of equivalence. Conclusions: Cgmanalysis is a validated open-source software to analyze commercially available CGM data and can be used to standardize diabetes technology research.

Continuous Glucose Monitoring Initiation Within First Year of Type 1 Diabetes Diagnosis Is Associated With Improved Glycemic Outcomes: 7-Year Follow-Up Study.

OBJECTIVE: To evaluate long-term glycemic outcomes of continuous glucose monitoring (CGM) initiation within the first year of type 1 diabetes diagnosis. RESEARCH DESIGN AND METHODS: Patients with type 1 diabetes (N = 396) were divided into three groups: 1) CGM (CGM use within 1 year of diabetes diagnosis and continued through the study), 2) no-CGM (no CGM use throughout the study), and 3) new-CGM (CGM use after 3 years since diabetes diagnosis). Patients were followed up to 7 years. RESULTS: A1c was significantly lower in the CGM compared with the no-CGM group throughout 7 years of follow-up (least squares mean A1c values: 6 months, 7.3% vs. 8.1%; 1 year, 7.4% vs. 8.6%; 2 years, 7.7% vs. 9.1%; 3 years, 7.6% vs. 9.3%; 4 years, 7.4% vs. 9.6%; 5 years, 7.6% vs. 9.7%; 6 years, 7.5% vs. 10.0%; and 7 years, 7.6% vs. 9.8%; for all, P < 0.001) adjusting for age at diagnosis, sex, and insulin delivery method. CONCLUSIONS: CGM initiation within first year of type 1 diabetes diagnosis results in long-term improvement in A1c.

Differentiating Diabetic Ketoacidosis and Hyperglycemic Ketosis Due to Cannabis Hyperemesis Syndrome in Adults With Type 1 Diabetes.

OBJECTIVE: To differentiate diabetic ketoacidosis (DKA) from hyperglycemic ketosis due to cannabis hyperemesis syndrome (HK-CHS) in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS: Of 295 adults with type 1 diabetes who were seen with DKA-related ICD-10 codes, 68 patients with 172 DKA events meeting the inclusion criteria were analyzed. Cannabis use was defined as a positive urine test result for cannabis. Linear mixed models were used to define HK-CHS (pH ≥7.4 with bicarbonate ≥15 mmol/L [mEq/L]), and sensitivity and specificity were calculated using the receiver operating characteristic (ROC) curve. RESULTS: Cannabis users had significantly higher pH (7.42 ± 0.01 vs. 7.09 ± 0.02) and bicarbonate (19.2 ± 0.61 vs. 9.1 ± 0.71 mmol/L) (P < 0.0001) compared with nonusers. The area under the ROC curve for a positive cannabis urine test result predicting HK-CHS was 0.9892. CONCLUSIONS: In patients who present with DKA and higher pH, especially pH ≥7.4, cannabis use should be considered in the differential diagnosis.

Fracture risk assessment (FRAX) without BMD and risk of major osteoporotic fractures in adults with type 1 diabetes.

OBJECTIVE: To evaluate the association between Fracture Risk Assessment Tool (FRAX) without bone mineral density (BMD) and risk for major osteoporotic fractures (MOF) in type 1 diabetes. METHODS: Subjects with type 1 diabetes and without diabetes from the 'Coronary Artery Calcification in Type 1 Diabetes' study were included. Risk for MOF was calculated using FRAX-based clinical risk factors and without BMD at visit 3 (2006-2008). Incident fractures were defined as fractures that occurred between visit 3 and visit 4 (2013-2017). Survival models were used to study the predictability of new MOF by diabetes status. RESULTS: 346 type 1 diabetes (mean age 43.3 ± 9, BMI 26.4 ± 5, diabetes duration 29.4 ± 8.6 years, A1c 7.8 ± 1.1) and 411 controls (mean age 46.9 ± 9 years, BMI 26.3 ± 5 kg/m2, A1c 5.5 ± 0.4) were analyzed in this study. In unadjusted survival analysis, the FRAX score without BMD was significantly associated with MOF (HR 1.08, 95% CI: 1.04-1.13, p < 0.0001), and remained significantly associated after adjustment for age and sex (HR 1.09, 95% CI: 1.04-1.15, p = 0.0007) and type 1 diabetes (HR 1.08, 95% CI: 1.04-1.12, p = 0.0002). In the fully adjusted model (adjusted for age, sex and type 1 diabetes), the FRAX score without BMD was the only variable significantly associated with risk of MOF (HR 1.08, 95% CI: 1.02-1.14, p = 0.006). CONCLUSION: Clinical use of FRAX without BMD is useful tool in identifying adults with type 1 diabetes at higher risk for MOF risk and may help clinicians to guide therapeutic decision-making in this high fracture risk population.

Bone Mineral Density across the Lifespan in Patients with Type 1 Diabetes.

CONTEXT: Fracture risk in people with type 1 diabetes (T1D) is higher than their peers without diabetes. OBJECTIVE: To compare bone mineral density (BMD) across the lifespan in individuals with T1D and age- and sex-matched healthy controls. DESIGN: Cross-sectional. SETTING: Subjects (5-71 years) with T1D and matched controls from ongoing research studies at Barbara Davis Center for Diabetes. PATIENTS OR OTHER PARTICIPANTS: Participants with lumbar spine BMD by dual X-ray absorptiometry (DXA) were divided into 2 groups: children ≤20 years and adults >20 years. INTERVENTION: None. MAIN OUTCOME MEASURES: Comparison of BMD by diabetes status across age groups and sex using a linear least squares model adjusted for age and body mass index (body mass index (BMI) for adults; and BMI z-score in children). RESULTS: Lumbar spine BMD from 194 patients with T1D and 156 controls were analyzed. There was no difference in age- and BMI-adjusted lumbar spine BMD between patients with T1D and controls: among male children (least squares mean ± standard error of the mean [LSM ± SEM]; 0.80 ± 0.01 vs 0.80 ± 0.02 g/cm2, P = .98) or adults (1.01 ± 0.03 vs 1.01 ± 0.03 g/cm2, P = .95), and female children (0.78 ± 0.02 vs 0.81 ± 0.02 g/cm2, P = .23) or adults (0.98 ± 0.02 vs 1.01 ± 0.02 g/cm2, P = .19). Lumbar spine (0.98 ± 0.02 vs 1.04 ± 0.02 g/cm2, P = .05), femoral neck (0.71 ± 0.02 vs 0.79 ± 0.02 g/cm2, P = .003), and total hip (0.84 ± 0.02 vs 0.91 ± 0.02, P = .005) BMD was lower among postmenopausal women with T1D than postmenopausal women without diabetes. CONCLUSION: Across age groups, lumbar spine BMD was similar in patients with T1D compared with age- and sex-matched participants without diabetes, except postmenopausal females with T1D had lower lumbar spine, femoral neck, and total hip BMD.