Journal of Diabetes Science and Technology最新文献

Background: We investigated the risk of incident diabetic retinopathy (DR) among high glycator compared to low glycator patients based on the hemoglobin glycation index (HGI). Visit-to-visit variations in HGI also were assessed.

Methods: Glycated hemoglobin (HbA1c) and continuous glucose monitoring data were collected up to 7 years prior to the date of eye examination defining incident DR or no retinopathy (control). Hemoglobin glycation index was calculated as difference in measured HbA1c and an estimated A1c from sensor glucose (eA1c) to define high (HbA1c - eA1c >0%) or low (HbA1c - eA1c <0%) glycator. Stable glycators were defined as ≥75% of visits with same HGI category. Logistic regression was used to assess the association between glycation category and incident DR.

Results: Of 119 adults with type 1 diabetes (T1D), 49 (41%) were stable low glycator (HbA1c - eA1c <0%), 36 (30%) were stable high glycator (HbA1c - eA1c >0%), and 34 (29%) were unstable glycator. Using alternate criteria to define high vs low glycator (consistent difference in HbA1c - eA1c of > 0.4% or <0.4%, respectively), 53% of the adults were characterized as unstable glycator. Compared to low glycators, high glycators did not have a significantly higher risk for incident DR over time when adjusted for age, T1D duration and continuous glucose monitoring (CGM) sensor type (odds ratio [OR] = 1.31, 95% confidence interval [CI] = 0.48-3.62, P = .15).

Conclusions: The risk of diabetic retinopathy was not found to differ significantly comparing high glycators to low glycators in adults with T1D. Moreover, HbA1c - eA1c relationship was not stable in nearly 30% to 50% adults with T1D, suggesting that discordance in HbA1c and eA1c are mostly related either HbA1c measurements or estimation of A1c from sensor glucose rather than physiological reasons.

Background: Sodium glucose cotransporter 2 inhibitors (SGLT2i) prevent heart failure (HF) in patients with type 2 diabetes mellitus (T2DM) but prescription rates are low. The effect of an electronic health record (EHR) alert notifying providers of patients' estimated risk of developing HF on SGTL2i prescriptions is unknown.

Methods: This was a pragmatic, randomized clinical trial that compared an EHR alert and usual care among patients with T2DM and no history of HF or SGLT2i use at a single center. The EHR alert notified providers of their patient's HF risk and recommended HF prevention strategies. Randomization was performed at the provider level across general and subspecialty internal medicine as well as family medicine outpatient clinics. The primary outcome was proportion of SGLT2i prescriptions within 30 days. Proportion of natriuretic peptide (NP) tests within 90 days was also assessed.

Results: A total of 1524 patients (median age 75 years, 45% women, 23% Black) were enrolled between September 28, 2021, and April 29, 2022 from 189 outpatient clinics. SGLT2i were prescribed to 1.2% (9/780) of patients in the EHR alert group and 0% (0/744) of those in the usual care group (P value = 0.009). Natriuretic peptide testing was performed within 90 days among 10.8% (84/780) of patients in the EHR alert group and 7.3% (54/744) of patients in the usual care group (P value = 0.02).

Conclusions: In a single-center trial with low overall SGLT2i use, an EHR alert incorporating HF risk information significantly increased SGLT2i prescriptions and NP testing although the absolute rates were low.

Background: Automated insulin delivery (AID) has revolutionized glucose management. Next-generation AID systems focus on reducing user input, particularly for mealtime dosing, aiming for fully closed loop (FCL) control. Our goal was to assess the safety and feasibility of the next iteration of FCL control, using a miniature neural network to enable implementation within existing hardware capabilities.

Methods: In a randomized crossover trial, six adults with type 1 diabetes completed seven days of usual care and seven days using AIDANET in free-living conditions. AIDANET is designed to enable FCL control, but carbohydrate counting and a novel easy-bolus strategy were enabled for one day each to test the system in hybrid closed loop modalities.

Results: The mean glucose during usual care was 168 ± 24.3 mg/dL, compared to 161.3 ± 16.7 mg/dL using the AIDANET system. Time-in-range (TIR) 70 to 180 mg/dL was 63.3% ± 14.9% in usual care compared to 66.4% ± 8.3% using AIDANET, while time-below-range (TBR) 70 mg/dL remained within acceptable margins (0.9 ± 1 vs 1.6 ± 1.8). There were no serious adverse events during the study. The hybrid bolusing options provided safe glycemic control, with carbohydrate counting achieving 57.1% TIR with 0.6% TBR, and Easy Bolus achieving 70.5% TIR with 1.5% TBR.

Conclusion: This pilot-feasibility study demonstrates that the AIDANET system provides safe glycemic control. The small sample size (n = 6) limits overall generalizability, and further larger, statistically powered trials to validate these results are warranted.

Background: While automated insulin delivery (AIDs) systems have significantly improved glycemic control for individuals with type 1 diabetes (T1D), there remains a need for identifying and acting upon complex physiologic and behavioral patterns which consistently lead to hypo- and hyperglycemia. Prior methods have lacked the ability to automatically identify and extract patterns across mixed-type multidimensional data (eg, insulin, glucose, activity) without instilling bias from stipulations on time-lagged coupling, pattern length, or pre-defining patterns.

Methods: We introduce a new pattern-detection technique-Block-based Recurrence Quantification Analysis (BlockRQA)-and preliminary results using BlockRQA in an AID on both in silico and in an outpatient feasibility study. We first introduce the BlockRQA algorithm, which extends Recurrence Quantification Analysis for use in categorical and continuous time-series data, while maintaining interpretable patterns in the domain of interest, in contrast to prior state-of-the-art approaches which require embeddings. Next, we demonstrate the feasibility of utilizing these patterns and BlockRQA with an existing AID system (BlockRQA+AID) to identify and dose for patterns leading to hyperglycemia in individuals with T1D.

Results: We demonstrate how BlockRQA+AID can improve glucose outcomes in patterns leading to hyperglycemia in silico. And we show real-world results using BlockRQA+AID to reduce hyperglycemic events (>250 mg/dL) via an interim safety analysis of a small outpatient pilot study. For all cases, we show BlockRQA efficiently identifies, aggregates, and scores behavioral patterns which can be targeted for clinical intervention.

Conclusions: The BlockRQA is a powerful pattern recognition tool that may be used to identify glucose outcome patterns to guide AID dosing.

Background: Type 1 diabetes is treated with exogenous insulin using multiple daily injections or insulin pumps. However, both strategies require carbohydrate counting for prandial insulin dosing, which is both burdensome and error prone.

Methods: We conducted a pilot, randomized, controlled study to eliminate carbohydrate counting in adults (n = 12, 7 females, age 39.5 [15.1], HbA1c 7.4% [0.6]) using an automated insulin and pramlintide fully closed-loop system. The interventions included five arms during which participants underwent 14 hours of outpatient, free-living, supervised experiments of (1) faster aspart with carbohydrate counting (control), faster aspart and pramlintide without carbohydrate counting at (2) 8 µg/U and (3) 10 µg/U ratios, and aspart and pramlintide without carbohydrate counting at (4) 8 µg/U and (5) 10 µg/U ratios.

Results: The median time in target range (3.9-10.0 mmol/L) with the control arm was 78.6 [65.3-92.9], compared with 76.2 [64.6-86.9] and 78.8 [68.8-86.0] with the fully closed-loop faster aspart and pramlintide systems at 8 and 10 µg/U ratios, respectively, and compared with 65.9 [59.9-83.6] and 77.4 [72.1-82.7] with the fully closed-loop aspart and pramlintide systems at 8 and 10 µg/U ratios, respectively. Times spent below 3.9 and 3.0 mmol/L were numerically higher with the fully closed-loop aspart and pramlintide systems than the control arm. None of the differences were statistically significant.

Conclusions: This study suggests that automated insulin and pramlintide systems have the potential to alleviate carbohydrate counting without degrading time in range. A longer and larger study is underway.

New methods of continuous glucose monitoring (CGM) data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.0), these new methods, which we refer to as CGM Data Analysis 2.0, can provide a more detailed understanding of glucose fluctuations and trends and enable more personalized and effective diabetes management strategies once translated into practical clinical solutions.

Machine learning (ML) uses computer systems to develop statistical algorithms and statistical models that can draw inferences from demographic data, structured behavioral data, continuous glucose monitor (CGM) tracings, laboratory data, cardiovascular and neurological physiology measurements, and images from a variety of sources. ML is becoming increasingly used to diagnose complications of diabetes based on these types of datasets. In this article, we review the current status, barriers to progress, and future prospects for using ML to diagnose seven complications of diabetes, including five traditional complications, one set of other systemic complications, and one prediction that can result in favorable or unfavorable outcomes. The complications include (1) diabetic retinopathy, (2) diabetic nephropathy, (3) peripheral neuropathy, (4) autonomic neuropathy, (5) diabetic foot ulcers, and (6) other systemic complications. The prediction is for outcomes in hospitalized patients with diabetes. ML for these purposes is in its infancy, as evidenced by only a limited number of products having received regulatory clearance at this time. However, as multicenter reference datasets become available, it will become possible to train algorithms on increasingly larger and more complex datasets and patterns so that diagnoses and predictions will become increasingly accurate. The use of novel choices of images and imaging technologies will contribute to progress in this field. ML is poised to become a widely used tool for the diagnosis of complications and predictions of outcomes and glycemia in people with diabetes.

Digital twin is a new concept that is rapidly gaining recognition especially in the medical field. Indeed, being a virtual representation of real-world entities and processes, a digital twin can be used to accurately represent the patients' disease, clarify the treatment target, and realize personalized and precise therapies. However, despite being a revolutionary concept, the diffusion of digital twins in type 1 diabetes (T1D) is still limited. In this systematic review, we analyzed structure, operating conditions, and characteristics of digital twins being developed for T1D. Our search covered published documents until March 2024: 220 publications were identified, 37 of which were duplicated entries; in addition, 173 publications were removed after inspection of titles, abstracts, and keywords; and finally, 11 publications were fully reviewed, of which 8 were deemed eligible for inclusion. We found that all eight methodologies are not comprehensive multi-scale virtual replicas of the individual with T1D, but they all focus on describing glucose-insulin metabolism, aiming to simulate glucose concentration resultant from therapeutic interventions. In this review, we will compare and analyze different factors characterizing these digital twins, such as operating principles (mathematical model, twinning procedure, validation and assessment) and the key aspects for practical adoption (inclusion of physical activity, data required for twinning, open-source availability). We will conclude the paper listing which, in our opinion, are the current limitations and future directives of digital twins in T1D, hoping that this article can be helpful to researchers working on diabetes technologies to further develop the use of such an important instrument.

Objective: To explore insulin pump-associated severe adverse events (SAEs) involving intensive care unit (ICU) admissions and deaths and examine the impact of the COVID-19 pandemic on these SAEs.

Methods: Qualitative template analysis of narrative data in reported insulin pump-associated SAEs occurring between May 1, 2019, and January 31, 2021, involving MiniMed 670G, MiniMed 630G, Omnipod, Omnipod DASH, and t:slim X2 insulin pumps.

Results: Over the 21-month measurement period, 460 SAEs involving an ICU admission and 288 SAEs involving a death were reported to the Food and Drug Administration. Problems with the pump or pod reservoir/cartridge were among the most frequently cited potential root causes in SAEs involving ICU admissions and deaths overall. However, problems with the infusion set or site and the pump battery or power emerged in the top three potential root causes of SAEs involving an ICU admission, whereas the patient sleeping at the time of the event and the tasks of changing the pod/infusion set, including reservoir/cartridge and programming the pump emerged in the top three for SAEs involving a death. The median monthly number of reported SAEs involving ICU admissions and deaths decreased during the pandemic, but their potential root causes were unchanged.

Conclusions: Although insulin pumps are generally safe, SAEs related to their components and external factors can and do occur. By learning from the potential root causes of insulin pump-associated SAEs, providers and patients can implement corrective actions to prevent future events, thereby reducing harm.