OBJECTIVE: Advanced hybrid closed-loop systems (AHCL) have been shown to improve glycemic control and patient-reported outcomes in type 1 diabetes. The aim was to analyze the outcomes of two commercially available AHCL in real life. RESEARCH DESIGN AND METHODS: A prospective study was performed, including adolescents and adults with type 1 diabetes, AHCL naïve, from 14 centers, who initiated the use of MM780G with SmartGuard or Tandem t:slimX2 with Control-IQ. Baseline and 3-month evaluations were performed, assessing HbA1c, time in different glycemic ranges, and patient-reported outcomes. The primary outcome was the between-group time in range 70–180 mg/dL difference from beginning to end of follow-up. RESULTS: One hundred fifty participants were included, with 75 initiating each system (age: 39.9 ± 11.4 years [16–72]; 64% female; diabetes duration: 21.6 ± 11.9 years). Time in range increased from 61.53 ± 14.01% to 76.17 ± 9.48% (P < 0.001), with no between-group differences (P = 0.591). HbA1c decreased by 0.56% (95% CI 0.44%, 0.68%) (6 mmol/mol, 95% CI 5, 7) (P < 0.001), from 7.43 ± 1.07% to 6.88 ± 0.60% (58 ± 12 to 52 ± 7 mmol/mol) in the MM780G group, and from 7.14 ± 0.70% to 6.56 ± 0.53% (55 ± 8 to 48 ± 6 mmol/mol) in the Control-IQ group (both P < 0.001 to baseline, P = 0.819 between groups). No superiority of one AHCL over the other regarding fear of hypoglycemia or quality of life was found. Improvement in diabetes-related distress was higher in Control-IQ users (P = 0.012). Sleep quality was improved (PSQI: from 6.94 ± 4.06 to 6.06 ± 4.05, P = 0.004), without differences between systems. Experience with AHCL, evaluated by the INSPIRE measures, exceeded the expectations. CONCLUSIONS: The two AHCL provide significant improvement in glucose control and satisfaction, with no superiority of one AHCL over the other.

Aims: To compare glycemic control and maternal–fetal outcomes of women with type 1 diabetes (T1D) using hybrid closed loop (HCL) vs. multiple daily insulin injections (MDI) plus continuous glucose monitoring (CGM). Methods: Multicenter prospective cohort study of pregnant women with T1D in Spain. We evaluated HbA1c and time spent within (TIR), below (TBR) and above (TAR) the pregnancy-specific glucose range 3.5–7.8 mmol/L. Adjusted models were performed for adverse pregnancy outcomes including baseline maternal characteristics and center. Results: 112 women were included (HCL n=59). Women in the HCL group had a longer duration of diabetes and higher rates of prepregnancy care. There were no between-group differences in HbA1c in any trimester. However, in the second trimester, MDI users had a greater decrease in HbA1c (-6.12±9.06 vs. -2.16 ±7.42 mmol/mol, p=0.031). No differences in TIR (3.5-7.8 mmol/L) and TAR were observed between HCL and MDI users, but with a higher total insulin dose in the second trimester (+0.13 IU/Kg/d). HCL therapy was associated with increased maternal weight gain during pregnancy (βadjusted 3.20 kg, 95%CI 0.90-5.50). Regarding neonatal outcomes, newborns of HCL users were more likely to have higher birthweight (βadjusted 279.0 g, 95% CI 39.5-518.5) and macrosomia (ORadjusted 3.18, 95% CI 1.05-9.67) compared to MDI users. These associations disappeared when maternal weight gain or third trimester HbA1c were included in the models. Conclusions: In a real-world setting, HCL users gained more weight during pregnancy and had larger newborns than MDI users, while achieving similar glycemic control in terms of HbA1c and TIR.

To assess real-world safety and effectiveness of dapagliflozin in people living with type 1 diabetes mellitus (T1DM). We conducted a multicenter retrospective study in Spain including data from 250 people living with T1DM receiving dapagliflozin as add-on therapy to insulin (80.8 % on-label use). The number of diabetic ketoacidosis (DKA) events was calculated over a 12-month follow-up (primary outcome). Changes in body weight, HbA1c, total daily insulin dose, and continuous glucose monitoring (CGM) metrics from baseline (at dapagliflozin prescription) to 12 months were also evaluated. A total of five DKA events (2.4 % [95 % CI 0.3;4.5] were reported in patients with a 12-month follow-up, n = 207): two events related to insulin pump malfunction, two events related to concomitant illnesses, and one event related to insulin dose omission. DKA events were more frequent among insulin pump users than among participants on multiple daily injections (7.7 % versus 1.2 %). Four of the reported DKA events occurred within the first six months after initiation of dapagliflozin. No deaths or persistent sequelae due to DKA were reported. No severe hypoglycemia episodes were reported. Significant reductions in mean body weight (-3.3 kg), HbA1c (-0.6 %), and total daily insulin dose (-8.6 %), P < 0.001, were observed 12 months after dapagliflozin prescription. Significant improvements in TIR (+9.3 %), TAR (-7.2 %), TBR (-2.5 %), and coefficient of variation (-5.1 %), P < 0.001, were also observed in the subgroup of patients with available CGM data. Finally, an improvement in urinary albumin-to-creatinine ratio (UACR) was found among participants with UACR ≥ 30 mg/g at baseline (median decrease of 99 mg/g in UACR, P = 0.001). The use of dapagliflozin in people living with T1DM has an appropriate safety profile after careful selection of participants and implementation of strategies to reduce the risk of DKA (i.e., prescribed according to the recommendations of the European Medicines Agency), and also leads to clinical improvements in this population.

Objectives: Reaching optimal postprandial glucose dynamics is a daily challenge for people with type 1 diabetes (T1D). This study aimed to analyze the postprandial hyperglycemic excursion (PHEs) and late postprandial hypoglycemia (LPH) risk according to prandial insulin time and type. Research Design and Methods: Real-world, retrospective study in T1D using multiple daily injections (MDI) analyzing 5 h of paired continuous glucose monitoring and insulin injections data collected from the connected cap Insulclock ^® . Meal events were identified using the rate of change detection methodology. Postprandial glucometrics and LPH (glucose <70 mg/dL 2–5 h after a meal) were evaluated according to insulin injection time and rapid (RI) or ultrarapid analog, Fiasp ^® (URI), use. Results: Meal glycemic excursions ( n = 2488), RI: 1211, 48.7%; UR: 1277, 51.3%, in 82 people were analyzed according to injection time around the PHE: −45 to −15 min; −15 to 0 min; and 0 to +45 min. In 63% of the meals, insulin was injected after the PHE started. Lower PHE was observed with URI versus RI (glucose peak-baseline; mg/dL; mean ± standard deviation): 106.7 ± 35.2 versus 111.2 ± 40.3 ( P = 0.003), particularly in 0/+45 injections: 111.6 ± 40.2 versus 118.1 ± 43.3; ( P = 0.002). One third (29.1%) of participants added a second (correction) injection. The use of URI and avoiding a second injection were independently associated with less LPH risk, even in delayed injections (0/+45), (−36%, odds ratio [OR] 0.641; confidence interval [CI]: 0.462–0.909; P = 0.012) and −56% (OR 0.641; CI: 0.462–0.909 P = 0.038), respectively. Conclusions: URI analog use as prandial insulin reduces postprandial hyper- and hypoglycemia, even in delayed injections.

Diabetes is a complex and rapidly growing disease with heterogeneous clinical presentations. Recent advances in molecular and genetic technologies have led to the identification of various subtypes of diabetes. These advancements offer the potential for a more precise, individualized approach to treatment, known as precision medicine. Recognizing high-risk phenotypes and intervening early and intensively is crucial. A staging system for type 1 diabetes has been proposed and accepted globally. In this article, we will explore the different methods for categorizing and classifying type 2 diabetes (T2D) based on clinical characteristics, progression patterns, risk of complications, and the use of molecular techniques for patient grouping. We, as a team of experts, will also present an easy-to-follow treatment plan and guidance for non-specialists, particularly primary care physicians, that integrates the classification and staging of diabetes. This will help ensure that the most suitable therapy is applied to the different types of T2D at each stage of the disease’s progression.