Diabetes technology & therapeutics最新文献

Optimizing postprandial glucose control in persons with type 1 diabetes (T1D) is challenging. We hypothesized that in free-living individuals, meal composition (high and low glycemic index [HGI and LGI], high and low fat [HF and LF]) may impact insulin requirements. Adults (N = 25) with T1D using open-loop insulin and continuous glucose monitoring were provided a meal-tagging app and prepackaged meals with defined macronutrient content. Data from 463 meals were analyzed. LGI meals required significantly more insulin than HGI meals (P = 0.01). Furthermore, the mean (±standard deviation) carbohydrate-to-insulin ratio (CIR) was significantly different overall among the LGI-LF (5.5 ± 3.4), LGI-HF (4.5 ± 3.8), HGI-LF (7.6 ± 5.1), and HGI-HF (8.7 ± 5.8) meals (P = 0.001). The risk of nocturnal hypoglycemia is associated with daytime hypoglycemia and amount of insulin administered prior to the evening and exercise. This exploratory study designed to examine the impact of different meal types on insulin dosing requirements in free-living adults with T1D emphasizes the need for individualized adjustment of the CIR depending on meal composition.

Background: The advanced hybrid closed-loop (AHCL) algorithm combines automated basal rates and corrections yet requires meal announcement for optimal performance, which poses a challenge for some. We aimed to compare glucose control in adults with type 1 diabetes (T1D) using the MiniMed^TM 780G AHCL system, utilizing simplified meal announcement versus precise carbohydrate (CHO) counting. Methods: In a study involving 14 adults with T1D, we evaluated glycemic control during a 13-week "precise phase," followed by two 3- to 4-week simplified meal announcement phases: "fixed one-step" (preset of one personalized fixed CHO amount) and "multistep" (entry of multiples of one, two, or three of these presets depending on meal size estimate). Results: The mean age was 45.7 ± 12.4, and 10 participants were male (71%). Mean baseline HbA1c was 6.8% ± 1.2% and time in range (TIR) was 67.5% ± 16.7%. Comparing the fixed one-step to the precise study phase, TIR was similar (75.4 ± 13% vs. 77.7 ± 9%, P = 0.12), and glucose management indicator (GMI) was slightly higher (6.8 ± 0.4 vs. 6.6 ± 0, P = 0.01). Furthermore, there was less level 1 and 2 hypoglycemia (1.6 ± 1% vs. 2.8 ± 2%, P = 0.03 and 0.3 ± 5% vs. 0.65 ± 1%, P = 0.08) but slightly more level 1 and 2 hyperglycemia (17.1 ± 8% vs. 15.0 ± 7%, P = 0.05 and 5.5 ± 5% vs. 3.6 ± 3%, P = 0.04). When comparing the multistep with the precise phase, GMI was identical (6.6%) and TIR superior (80.5 ± 10% vs. 77.7 ± 9%, P = 0.02). Additionally, there was less level 1 hypoglycemia (1.9 ± 1% vs. 2.8 ± 2%, P = 0.01) and a trend for less level 2 hypoglycemia (0.4 ± 0.7% vs. 0.65 ± 1%, P = 0.08). Conclusions: A simplified meal announcement strategy for adults using the MiniMed 780G system, relying on three increments of a fixed one-step CHO amount, may offer a way to improve glycemic control and ease self-care. For patients with more limitations, using one fixed one-step CHO amount could be a safe alternative to meeting most consensus glycemic targets.

Introduction: The rise of digital health applications utilizing continuous glucose monitoring (CGM) allows for novel assessments of glucose management and weight changes in people without diabetes. The Signos System incorporates a digital health app paired with a CGM to provide information and prompts aimed to help people without diabetes to manage weight. Objectives: The primary objective of this study was to determine whether the average timing of the latest chronological glucose excursion ("spike") was correlated with amount of weight loss. Methods: This was a retrospective analysis of prospectively obtained glucose and weight data from people without diabetes who enrolled in the Signos System from November 2021 to August 2023. Participants were provided CGMs as well as encouraged to use the Signos app with personalized advice and logging capabilities for weight, food, physical activity, heart rate, sleep, and activities. "Latest spike time" (LST) was retrospectively derived from CGM data and compared with weight changes at 6 months. Results: Nine hundred and twenty-six subjects met the inclusion criteria including sufficient days wearing a CGM and a weight log within 15 days of 6 months from their first weight log. There was a strong correlation between an earlier spike time and increased weight loss. The top quintile of subjects, with an average LST before 5:41 PM, lost over three times as much weight as the bottom quintile of users, with LST after 8:40 PM; this separation was predictable within 1 month of data. Conclusion: In a large population of obese people without diabetes, continuous glucose data, specifically a novel metric "LST," was highly correlated with percentage of total body weight loss at 6 months. This research suggests that for people attempting weight loss, review and alteration of behaviors relating to later glucose excursions may be of specific benefit.

The incidence, prevalence, mortality, and health expenditure associated with diabetes continue to grow, despite efforts. The use of multianalyte sensors, which detect glucose as well as key analytes such as ketones, lactate, insulin, uric acid, and electrolytes, may provide additional information to guide earlier identification and management of diabetes and its complications. We undertook a narrative review using a systematic approach in May 2023, with a bridge search undertaken in April 2024. Four biomedical databases were searched: MEDLINE (Ovid), Embase, Emcare, and Cochrane Library. Searches for gray literature were conducted on ClinicalTrials.gov, Google Scholar, and websites of relevant organizations. Included studies incorporated articles on multianalyte sensors in diabetes and single-analyte sensors proposing integration into multianalyte diabetes management, with no limits placed on publication date and study design. Data were screened and extracted using Covidence^TM software. Overall, 11 articles were included, of which 7 involved multianalyte sensors (involving glucose and other analytes) and 4 single-analyte sensors (measuring non-glucose substances for proposed future integration into multianalyte systems). Analytes examined were ketones (n = 3), lactate (n = 4), uric acid (n = 3), insulin (n = 1), and potassium (n = 1). Results demonstrated that in vitro and in vivo measurements of multi- and single-analyte sensors accurately and reliably corresponded with human capillary and serum samples. While the literature on this topic is sparse, our review demonstrated that measurement of glucose and other analytes can be feasibly undertaken using multi- and single-analyte sensors. More studies in humans are needed to establish clinical utility in diabetes self-management and assist with technological improvements.

Background: For people with type 1 diabetes (T1D), ensuring fast and effective recovery from hypoglycemia while avoiding posthypoglycemic hyperglycemia (rebound hyperglycemia, RH) can be challenging. The objective of this study was to investigate the frequency of RH across different treatment modalities and its impact on glycemic control. Methods: This cross-sectional real-world study included adults with T1D using continuous glucose monitoring and attending the outpatient clinic at Steno Diabetes Center Copenhagen. RH was defined as ≥1 sensor glucose value (SG) >10.0 mmol/L (180 mg/dL) starting within 2 h of an antecedent SG <3.9 mmol/L (70 mg/dL). The severity of the RH events was calculated as area under the curve (AUC) and separately for users of multiple daily injections (MDIs), unintegrated insulin pumps, sensor augmented pumps (SAPs), and automated insulin delivery (AID), respectively. Results: Across the four groups, SAP and AID users had the highest incidence of RH (2.06 ± 1.65 and 2.08 ± 1.49 events per week, respectively) and a similar percentage of hypoglycemic events leading to RH events (41.3 ± 22.8% and 39.6 ± 20.1%, respectively). The AID users with RH events were significantly shorter compared with MDI users (122 ± 72 vs. 185 ± 135 min; P < 0.0001). Overall, severity of RH was inversely associated with more advanced technology (P < 0.001) and inversely associated (P < 0.001) with time in target range (TIR). Conclusions: Groups with insulin suspension features experienced the highest frequency of RH; however, AID users tended to experience shorter and less severe RH events. The association between the severity of RH events and TIR suggests that RH should be assessed and used in the guidance of hypoglycemia management.

Background: Despite new pharmacotherapy, most patients with long-term type 2 diabetes are still hyperglycemic. This could have been solved by insulin with its unlimited potential efficacy, but its dynamic physiology demands frequent titrations which are overdemanding. This report provides a real-life account for a scalable transformation of diabetes care in a community-based endocrinology center by harnessing artificial intelligence-based autonomous insulin titration. Methods: The center embedded the d-Nav^® technology and its dedicated clinical support. Reported outcomes include treatment efficacy/safety in the first 600 patients and use of cardiorenal-risk reduction pharmacotherapy. Findings: Patients used d-Nav for 8.2 ± 3.0 months with 82% retention. Age was 67.1 ± 11.5 years and duration of diabetes was 19.8 ± 11.0 years. During the last 3 years before d-Nav, glycated hemoglobin (HbA1c) had been overall higher than 8% and at the beginning of the program it was as high as 8.6% ± 2.1% with 29.3% of the patients with HbA1c >9%. With d-Nav, HbA1c decreased to 7.3% ± 1.2% with 5.7% of patients with HbA1c >9%. During the first 3 months, d-Nav reduced total daily dose of insulin in one of every five patients due to relatively low glucose levels to minimize the risk of hypoglycemia. Glucagon like peptide 1 (GLP-1) receptor agonists or dual GLP-1 and Glucose-dependent insulinotropic polypeptide (GIP) receptor agonists were prescribed in about a half of the patients and sodium glucose cotransporter 2 inhibitor in a third. The frequency of hypoglycemia (<54 mg/dL) was 0.4 ± 0.6/month and severe hypoglycemia 1.7/100-patient-years. Interpretation: The use of d-Nav allowed for improvement in overall diabetes management with appropriate use of both insulin and noninsulin pharmacologic agents in a scalable way.

Introduction: This study aimed to compare efficacy and safety of ultra-rapid-acting insulin analogs (URAIs; faster aspart [FAsp], ultra-rapid lispro [URLi], and technosphere insulin [TI]) with rapid-acting insulin analogs (RAI) in individuals with type 1 (T1D) or type 2 diabetes (T2D). Methods: Searching for randomized control trial comparing the effects of URAI versus RAI that lasted at least 12 weeks, we initially selected 15 studies for analysis. Three studies involving TI were excluded due to a high degree of heterogeneity. The final meta-analysis included only 12 studies with either FAsp or URLi. Results: Mealtime URAI significantly reduced overall early 1 h postprandial glycemia in individuals with T1D (-20.230 mg/dL [95% confidence interval, 95% CI -24.040 to -16.421]; P < 0.001; I² = 33.42%) and those with T2D (-9.138 mg/dL [95% CI -12.612 to -5.663]; P < 0.001; I² = 0%). However, the significant reduction in 2 h postprandial glucose remained only in individuals with T1D (-17.620 mg/dL [95% CI -26.047 to -9.193]; P < 0.001; I² = 65.88%). These benefits were lost when URAI was administered postmeal. At 24-26 weeks, there was no significant difference in HbA1c between groups, but at 52 weeks, a slight reduction in HbA1c with mealtime URAI was observed (-0.080% [95% CI -0.147 to -0.013]; P = 0.019; I² = 0%). No difference in weight or the rate of severe or confirmed hypoglycemia was observed. Only individuals with T1D showed a small, but significant increase in early 1-h hypoglycemia with URAI (1.468 [95% CI 1.235 to 1.747]; P < 0.001; I² = 0%). Conclusion: Mealtime URAI improves 1 and 2 h postprandial glycemic control compared to RAI without increasing hypoglycemia or weight gain.

Objective: Examine gestational safety, glycemic and health outcomes, of a hybrid closed-loop (HCL) system without pregnancy-specific glucose targets. Research Design: This was a pilot feasibility investigator-initiated, two-site, single-blind, randomized controlled trial of sensor-augmented pump therapy (SAPT) versus HCL therapy in type 1 diabetes pregnancies. Participants were enrolled in the first trimester and randomized at 14-18 weeks of gestation and used SAPT or HCL until 4-6 weeks postpartum. We compared continuous glucose monitoring (CGM) metrics, severe hypoglycemia (SH), diabetic ketoacidosis (DKA), adverse skin reactions, and pregnancy outcomes between groups. Results: Baseline characteristics were similar between groups (n = 11 HCL and n = 12 SAPT). There was no SH or DKA episode after randomization. Time spent <54 mg/dL did not differ between groups. Time spent <63 mg/dL decreased in both groups, significantly in the HCL group (3.5% [1.3% standard error] second trimester and 2.8% [1.3%] third trimester vs. 7.9% [1.3%] run-in phase, P < 0.05 for both). Mean sensor glucose was lower with SAPT compared to HCL therapy in the third trimester (119 [4] mg/dL SAPT vs. 132 [4] mg/dL HCL, P < 0.05). Third trimester time-in-range (TIR; 63-140 mg/dL) increased with SAPT (68.2% [3.1%] vs. 64.3% [3.1%] run-in phase, P < 0.05). Gestational health outcomes did not differ between groups. The HCL group used assistive techniques, such as fake carbohydrate boluses and exiting HCL overnight. Conclusions: CGM within group differences were seen for time <63 mg/dL favoring HCL therapy and TIR favoring SAPT (third trimester vs. baseline). Safety and adverse pregnancy outcomes were similar between groups.

Aims: To compare glycemic control and maternal-fetal outcomes of women with type 1 diabetes (T1D) using hybrid closed loop (HCL) versus multiple daily insulin injections (MDI) plus continuous glucose monitoring. 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 of 3.5-7.8 mmol/L. Adjusted models were performed for adverse pregnancy outcomes, including baseline maternal characteristics and center. Results: One hundred twelve women were included (HCL n = 59). Women in the HCL group had a longer duration of diabetes and higher rates of prepregnancy care. There was no between-group difference 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 difference in TIR (3.5-7.8 mmol/L) and TAR was observed between HCL and MDI users, but with a higher total insulin dose in the second trimester [+0.13 IU/kg·day)]. HCL therapy was associated with increased maternal weight gain during pregnancy (β_adjusted = 3.20 kg, 95% confidence interval [CI] 0.90-5.50). Regarding neonatal outcomes, newborns of HCL users were more likely to have higher birthweight (β_adjusted = 279.0 g, 95% CI 39.5-518.5) and macrosomia (OR_adjusted = 3.18, 95% CI 1.05-9.67) compared to MDI users. These associations disappeared when maternal weight gain or third trimester HbA1c was 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.