Diabetes technology & therapeutics最新文献

Introduction: Integrating mobile health (mHealth) apps into daily diabetes management allows users to monitor and track their health data, creating a comprehensive system for managing daily diabetes activities and generating valuable real-world data. This analysis investigates the impact of transitioning from traditional self-monitoring of blood glucose (SMBG) to real-time continuous glucose monitoring (rtCGM), alongside the use of a mHealth app, on users' glycemic control. Methods: Data were collected from 1271 diabetes type 1 and type 2 users of the mySugr^® app who made a minimum of 50 SMBG logs 1 month before transitioning to rtCGM and then used rtCGM for at least 6 months. The mean and coefficient of variation of glucose, along with the proportions of glycemic measurements in and out of range, were compared between baseline and 1, 2, 3, and 6 months of rtCGM use. A mixed-effects linear regression model was built to quantify the specific effects of transitioning to a rtCGM sensor in different subsamples. A novel validation analysis ensured that the aggregated metrics from SMBG and rtCGM were comparable. Results: Transitioning to a rtCGM sensor significantly improved glycemic control in the entire cohort, particularly among new users of the mySugr app. Additionally, the sustainability of the change in glucose in the entire cohort was confirmed throughout the observation period. People with type 1 and type 2 diabetes exhibited distinct variations, with type 1 experiencing a greater reduction in glycemic variance, while type 2 displayed a relatively larger decrease in monthly averages.

Objective: Adults with type 1 diabetes (T1D) are increasingly overweight or obese, in part due to intensive insulin therapy. Newer non-insulin medications targeting both hyperglycemia and weight loss are approved for people with type 2 diabetes. These drugs also reduce cardiovascular disease, the major cause of mortality in people with diabetes. We assessed the real-world use of semaglutide and tirzepatide, in adults with T1D followed in a specialty diabetes clinic. Materials and Methods: This retrospective chart review included 100 adults who were prescribed semaglutide or tirzepatide (50 each) and 50 controls frequency matched for age, sex, diabetes duration, body mass index, and glycosylated hemoglobin (HbA1c) and who did not receive any weight loss medications during the study period. Data were collected prior to initiation of weight loss medications (baseline) and then for up to 1 year for each patient. Results: Matching characteristics did not differ between cases and controls. There were declines in weight in both semaglutide (-19.2 ± standard error (SE) 2.9 lbs. [9.1% body weight lost]) and tirzepatide (-49.4 ± SE 3.0 lbs. [21.4% body weight lost]) groups, and HbA1c decreased in both semaglutide (-0.54 ± SE 0.14%, P = 0.0001) and tirzepatide users (-0.68 ± SE 0.16%, P < 0.0001) over 12 months. Weight and HbA1c didn't change in controls. Conclusions: We observed weight loss of 9.1% and 21.4% and improved glucose control in semaglutide and tirzepatide users, respectively, after 1 year of off-label use. As off-label use of these drugs is increasing in patients with T1D, larger, prospective safety and efficacy trials are needed.

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.

This article offers a systematic literature review (SLR) on the use of the MiniMed 780G automated insulin delivery system (MM780G) in people with type 1 diabetes (PwT1D) during Ramadan intermittent fasting. It also presents consensus recommendations on the use of MM780G during the Ramadan period. The SLR was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. The recommendations resulted from a consensus-forming process involving a panel of experts. The process considered evidence found in the SLR as well as the expert opinions. In total, six studies were included in the SLR. The evidence and expert opinions led to recommendations related to (a) pre-Ramadan counseling of MM780G users who plan to fast; (b) suggested MM780G settings, meal announcement strategy, and safety aspects during Ramadan (including a contingency plan); and (c) post-Ramadan transition into and out of Eid-al-Fitr festivities. The SLR findings showed that the MM780G maintains glycemic control at target in PwT1D during Ramadan (meeting continuous glucose monitoring-based clinical targets proposed by the International Consensus on Time-in-Range) while ensuring low rates of hypoglycemia and diabetic ketoacidosis. Automated insulin delivery also helps PwT1D fast more days of Ramadan compared with users of other less advanced modalities of treatment. Pre-Ramadan guidance on specific aspects of the MM780G along with the International Diabetes Federation and Diabetes and Ramadan International Alliance counseling guidelines is recommended. There is still a challenge with post-Iftar hyperglycemia, which could potentially be mitigated by following the recommendations outlined in this article.

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.

Objective: Autonomic neuropathy is associated with dysglycemia that is difficult to control. We investigated if transcutaneous vagus nerve stimulation (tVNS) could improve glycemic levels. Methods: We randomized 145 individuals with type 1 diabetes (T1D) (n = 70) or type 2 diabetes (T2D) (n = 75) and diabetic autonomic neuropathy (DAN) to self-administered treatment with active cervical tVNS (n = 68) or sham (n = 77) for 1 week (4 daily stimulations) and 8 weeks (2 daily stimulations), separated by a wash-out period of at least 2 weeks. Continuous glucose monitoring (CGM) indices were measured for 104 participants starting 5 days prior to intervention periods, during the 1-week period, and at end of the 8-week period. Primary outcomes were between-group differences in changes in coefficient of variation (CV) and in time in range (TIR 3.9-10 mmol/L). Secondary outcomes were other metrics of CGM and HbA1c. Results: For the 1-week period, median [interquartile range] changes of CV from baseline to follow-up were -1.1 [-4.3;2.0] % in active and -1.5 [-4.4;2.5] % in sham, with no significance between groups (P = 0.54). For TIR, the corresponding changes were 2.4 [-2.1;7.4] % in active and 5.1 [-2.6;8.8] in sham group (P = 0.84). For the 8-week treatment period, changes in CV and TIR between groups were also nonsignificant. However, in the subgroup analysis, persons with T1D receiving active tVNS for 8 weeks had a significant reduction in CV compared with the T1D group receiving sham stimulation (estimated treatment effect: -11.6 [95% confidence interval -20.2;-2.0] %, P = 0.009). None of the changes in secondary outcomes between treatment groups were significantly different. Conclusions: Overall, no significant changes were observed in CGM metrics between treatment arms, while individuals with T1D and DAN decreased their CV after 8 weeks of tVNS treatment.

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.

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.

Objective: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Methods: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. Results: The analysis included 836 participants (healthy: n = 282; prediabetes: n = 133; T2DM: n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (P < 0.001). Statistically significant differences (P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Conclusion: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.