Journal of Diabetes Science and Technology最新文献

Background: There is room for improvement in the outcome of automated insulin delivery. Our aim was to explore the impact on glucose metrics of algorithmic modifications of a DBLG1 hybrid closed-loop system, targeting the management of meal periods, hypoglycemia and hyperglycemia.

Methods: We performed a two-step analysis of CGM data of all consenting adult patients with type 1 diabetes who were equipped in Europe with DBLG1 between November 1, 2023 and January 31, 2025, comparing three successive versions of the algorithm: v1.12 vs. v1.16 (first step, retrospective comparison), then v1.16 vs. v1.17 (second step, ambispective before/after analysis). Time in range of 70 to 180 mg/dL was the primary endpoint.

Results: The first step (duration 319 days, 937 patients) compared 269 users of 1.12 version and 668 users of 1.16 version. Median TIR improved from 65.3% [IQR 58.4%-72.1%] to 71.3 [63.3%-78.0%]. Time in Tight Range 70 to 140 mg/dL increased from 37.5% [30.6%-43.1%] to 40.4% [31.7%-48.5%]. Time in Hypoglycemia was stable. Time >250 mg/dL decreased from 8.9% to 5.4%, GMI from 7.3% to 7.1%, CV from 30.4% to 27.4%, and GRI from 38.0 to 30.0. The second step (1212 patients, 120 days) showed a further improvement of TIR from 68.8% [59.6%-76.8%] to 70.8% [63.0%-77.6%] when upgrading from v1.16 to v1.17, with marginal changes in other glucose metrics. The incidence rates of severe hypoglycemia or hyperglycemia remained very low.

Conclusion: This large post-market report illustrates the margin of improvement in AID performances through algorithmic refinements that improve the efficacy without deteriorating the safety of closed-loop insulin delivery.

The global burden of diabetes continues to rise, resulting in a concomitant increase in the global demand for insulin replacement therapies. Subcutaneous insulin administration using pen injectors is one of the most common insulin delivery methods; these devices are often preferred over vial and syringe administration by individuals with diabetes owing to their convenience and ease of use, and by clinicians owing to enhanced user adherence and high dose accuracy. However, prefilled insulin pen injectors are commonly made of plastic and many are disposable; consequently, both their production and use are associated with a high carbon footprint and substantial medical waste through poor recycling infrastructures, posing a growing environmental concern. Historically, producing durable insulin pen injectors required high-quality metal components, which are more environmentally taxing despite being reusable. Conversely, plastic has a lower environmental footprint per unit but conventional plastic pen injectors have lacked durability, leading to greater consumption overall. Therefore, reusable insulin pen injectors that can be used repeatedly for several years, while also being easy to use and accurate, are essential for sustainable diabetes management. Novo Nordisk has developed several insulin pen injectors, such as NovoPen (NP) 4 (a reusable pen part of the NP family), FlexTouch (a disposable pen) and, more recently, DuraTouch (DT; a reusable pen). Here, we describe the technical attributes of DT using other pen injectors manufactured by Novo Nordisk as contextual comparators to illustrate how DT may meet users' functional needs, while helping to reduce the environmental burden associated with the use of insulin pen injectors.

The University of Chicago Monogenic Diabetes Registry (UCMDR) developed a participant portal to enhance engagement and data completeness in a large, longitudinal research study. Built in collaboration with the Center for Research Informatics (CRI), the portal integrates with REDCap to provide secure survey access, document exchange, and communication. Since its launch, 40% of invited participants have activated accounts. Among new enrollees, 88% of portal users completed their baseline survey, up from a historical 60%. Portal activation was higher among female participants (P < .01), with no significant differences by age or income. Overall portal feature use was modest, possibly reflecting lower adoption among historical participants accustomed to legacy processes. These findings support the feasibility of research participant portals while highlighting opportunities to improve equitable engagement and functionality.

Background: Foot temperature monitoring can prevent diabetic foot ulceration (DFU). This technical report determined the validity of the Feetsee device (InvCam) in measuring temperature differences between feet compared with current reference standard: FlirA615, with Food and Drug Administration-approved TotalVision software (RefCam).

Methods: Measurements were taken in 107 participants (mean age: 48 years; 49% female) across three groups: people with diabetes and active foot ulcer (DFU, n = 38; 57 ± 12.2 years), diabetes without ulcers (diabetes mellitus DM, n = 35; 51.5 ± 16.3 years), and healthy young controls without diabetes (C, n = 34; 34.2 ± 10.7 years).

Results: Strong agreement was shown in foot temperature measurements between the devices with no systematic bias, an intraclass correlation coefficient of 0.95 and typical error of 0.31 deg.

Conclusions: The InvCam system demonstrated validity for foot temperature measurement compared with the current reference standard and offers the advantage over current systems of being non-contact, eliminating the risk of infection and cross-contamination.

Introduction: Current continuous glucose monitors (CGM) sensing glucose in the subcutaneous tissue have a significant time lag (τ). This delay could result in severe hypo/hyperglycemia and lower time in range (TIR). Dermal sensing can greatly reduce time lag.

Methods: In a clinical study conducted at two US-based clinical centers, subjects with type 1 diabetes mellitus (DM) wore a novel dermal CGM + Abbott-Libre 3 or Dexcom-G7. All were compared to a YSI-glucose analyzer. Time lag kinetics for all sensors were modeled using the two-compartment model and compared to published data. Time lag data and its potential effect on TIR were also analyzed.

Results: Data from 55 subjects showed fast kinetics for the dermal CGM. In total, 93% of the Laxmi sensors had a τ of 0-2 minutes, whereas commercial CGMs had a varying distribution of τ (-10 to 10+ minutes). This reduction in τ by 10 minutes has profound effects on errors in insulin administration in both open-loop and in a proportional-integral-derivative (PID) model of automated insulin delivery (AID). To evaluate the effect of tau on TIR, we used an in silico PID controller in a well-accepted model (UVA type 1 diabetes simulator) over a variety of conditions. We observed that tau greatly affects TIR and the distribution of the time out of range parameters.

Conclusion: Dermal sensing has a time lag close to 0. Individuals with DM can have lower glucose targets with a system that eliminates fear of hypoglycemia, resulting in higher TIR and better control of DM.

Objective: Continuous glucose monitoring (CGM) is increasingly applied in populations without diabetes, yet existing reference ranges are largely derived from middle-aged or older adults. This study characterized CGM metrics in young adults without diabetes and examined variation by sex, age, body mass index (BMI), and physical activity (PA).

Method: Participants wore an unmasked Dexcom G7 CGM for up to 10 days under free-living conditions. Glycemic metrics were derived using the iglu R package and summarized as median [IQR]. Associations with sex, age, BMI, and PA were evaluated using Wilcoxon tests, Spearman correlations, and quantile regressions.

Results: A total of 105 participants (age = 21 years [range: 18-26], BMI 23 kg/m² [21-25]; 72% female; 72% non-White) provided ≥48hr of CGM data. Compared with females, males had higher mean sensor glucose (110 [103-119] vs 104 [99-108] mg/dL; P < .01), eA1c (5.4[5.2-5.8] vs 5.2[5.1-5.4]; P < .01), area under the curve (110[102-119] vs 103[99-108]; P = .01), and daily episodes >140 mg/dL (1.6[1.0-2.6] vs 1.3[0.7-2.0]; P = .03). Age correlated with CV (r = .20, P = .04). BMI was inversely correlated with CV (r = -.35), MAGE (r = -.35), and MODD (r = -.27), all P < .001. Physical activity was modestly associated with reduced glycemic burden.

Conclusion: CGM revealed sex differences in young adults-males exhibited higher mean glucose and excursions-while both sexes maintained normoglycemic patterns. Age, BMI, and PA were linked to variability indices. Findings provide CGM reference data for young adults and highlight the importance of biological and behavioral factors in glycemic regulation.

Background: In people with type 1 diabetes (T1D) admitted to hospital, adverse glycemic events (AGE), both hypoglycemia and hyperglycemia, bestow risk for adverse outcomes. Continuous glucose monitoring (CGM) use is increasingly common amongst people with T1D. We investigated AGE frequency in hospital, based on CGM versus point-of-care (POC) blood glucose measures.

Methods: In this multi-center retrospective analysis of non-critically ill hospitalized adults with T1D who continued wearing their unmasked CGM (FreeStyle Libre 1/2, Dexcom G5/G6, Medtronic Guardian 3) during admission and received standard ward-based POC testing, we compared CGM- and POC-based AGE detection of hypoglycemia (<70 mg/dL) and hyperglycemia (>180 mg/dL).

Results: In 253 admissions, 127 837 CGM and 5508 POC glucose measures were analyzed, yielding 1391 CGM-detected hyperglycemia AGE and 317 CGM-detected hypoglycemia AGE. For CGM-detected AGE with a concurrent POC AGE evident, CGM detected hyperglycemia a median [interquartile range, IQR] of 70 minutes [22, 166] before POC and at lower glucose concentrations (187 vs 223 mg/dL, P < .0001) and detected hypoglycemia a median [IQR] of 38 minutes [14, 65] before POC and at higher glucose concentrations (67 vs 56 mg/dL, P < .0001). A quarter of CGM-detected AGE were not detected by POC. Only 3% of POC-detected AGE were not detected by CGM.

Conclusions: Almost all AGE in hospital were detected by CGM, with few detected by POC alone. Compared to POC, CGM detected AGE earlier, with a lesser glycemic extreme, although unmasked CGM use may have influenced these results. Detecting AGE in hospital appears superior with CGM compared to POC glucose alone in people with T1D.

There are a plethora of medical journals, also for the diabetes indication. Only a limited number of these journals are listed in databases like PubMed. A number of other diabetes journals approach potential authors and ask for submission of manuscripts. They promise rapid publication; however, one wonders what kind of impact these journals have and how serious they are at handling the review process and so on. One wonders what the economical basis (= business model) for these journals is, the publication fee might be considerable. Apparently, some journals pretend to publish manuscripts; however, this does not happen in reality, despite the fact that the authors have paid the publication fee. In the same line of thinking, the quality of the publications in these journals is at least questionable.

Importance and aims:Diabetes can lead to microvascular and macrovascular complications. Modeling the complex relationships between risk factors has motivated the use of Artificial Intelligence (AI) to develop predictive models. Recent advancements, including foundation models and generative AI, have significantly changed how this technology is applied across various contexts. In this review, we summarize the current state of research on AI for predictive diabetes complications, investigating the present and future implications of these innovations.

Methods: We conducted the literature search on PubMed, Scopus, Ovid MEDLINE, CINAHL, and IEEE databases. Our analysis focused on predicted complications, population characteristics, use of AI-based approaches, models' performance, predictor variables, and feature importance evaluation results.

Results: The 49 studies selected in our analysis considered different conditions as prediction outcomes. Eye-related complications were included in 29 studies (59%), emerging as the most frequent predicted diseases. Among the 48 studies employing AI algorithms specifically for the prediction task, 26 (54%) developed only Machine Learning models, 4 (8%) only Deep Learning models, and 18 (38%) applied both approaches. Foundation models and recent AI innovations included in the query were not used by any study. Moreover, only five studies (10%) dealt with unstructured data (signals and images). In the feature importance evaluation, age and glycated hemoglobin consistently emerged as important predictors.

Conclusions: Despite the extensive existing literature on AI for predicting diabetes complications, several emerging challenges persist. These include the effective utilization of unstructured data and the integration of recent advancements introduced by foundation models and generative AI.