Journal of Diabetes Science and Technology最新文献

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.

The introduction of continuous glucose monitoring (CGM) has been considered a transformative monitoring tool in diabetes management. However, its adoption remains limited in the Gulf region, especially for patients with type 2 diabetes, due to cost, lack of reimbursement strategies, variability in healthcare infrastructure, and lack of trained health care providers (HCPs). The lack of regional guidelines tailored to the unique demographic, cultural, and health care needs of the Gulf population has resulted in low adoption and inconsistent use of CGM in clinical practice, leaving many patients without adequate advanced glucose monitoring options. This expert opinion statement evaluates the evidence for real-time CGM in the management of patients with type 2 diabetes and provides region-specific recommendations to guide HCPs in optimizing CGM use, improving patient outcomes, and addressing barriers to implementation in the Gulf region.

Background: Automated insulin delivery (AID) systems are limited by the short wear time of insulin infusion sets, which typically need replacement every 2 to 3 days, significantly shorter than the 14-day lifespan of continuous glucose monitoring (CGM) sensors. Infusion set failure remains a major obstacle to AID reliability and patient adherence. This study examined the roles of insertion trauma and biomaterial composition in causing acute inflammatory responses using both swine and mouse models.

Methodology: We evaluated three commercial CGM sensors (Abbott Libre 2, Dexcom G7, Medtronic Guardian 3) and two Teflon-based IIS catheters (Medtronic QuickSet and i-Port Advance). In swine, tissue was histologically analyzed one day after implantation to assess neutrophil extracellular trap (NET) formation. In a murine air pouch model, we isolated material-specific immune responses by reducing mechanical injury. Lavage fluids collected at 1 and 3 days postimplantation were examined for immune cell infiltration and cytokine expression using flow cytometry and MSD multiplex assays.

Results: NETs were observed at all insertion sites, indicating that tissue trauma, rather than the material itself, is the primary trigger of early NET formation. However, Teflon catheters caused a more prolonged inflammatory response, with increased recruitment of macrophages and mast cells, and higher levels of TNF-α and KC/GRO. In contrast, polyurethane-based sensors induced minimal immune activation, suggesting greater biocompatibility. The findings were consistent across models, although some species-specific differences were noted.

Conclusion: These findings underscore the importance of minimizing insertion trauma and selecting biocompatible materials to promote device-tissue integration, prolong wear time, and enhance AID system performance.

Background: This descriptive observational study reports on continuous glucose monitoring (CGM) data, using a novel glucose biosensor (Abbott Libre Sense Glucose Sport Biosensor), during professional game play and during daily life in elite European football players.

Methods: Eighteen healthy male elite football players (age: 27.5 ± 5.1 years; height 180.1 ± 7.2 cm, weight 74.2 ± 9.1 kg, UEFA Champions League club) participated, with a subset examined for a single game for active (n = 10) and reserve (n = 4) players. Group comparisons used unpaired t-tests or Wilcoxon rank-sum tests; within-group differences used repeated measures one-way analysis of variance or Friedman test. Descriptive statistics were summarized for 24-hour data for daytime (06:00 am-10:59 pm) and nighttime (11:00 pm-05:59 am).

Results: Higher mean CGM glucose was observed during-game in active compared with reserve players (159 ± 23 vs 133 ± 25 mg/dL, P = .09), with significantly higher time above range (TAR, 72.8 ± 32.02 vs 29.7 ± 37.9%, P = .04) and lower time in range (TIR, 26.7 ± 31.9 vs 70.3 ± 37.9%, P = .04). In the 90 minute pre- to 180 minute post-game period, TAR (57.3 ± 26.6% vs 16.1 ± 20.2%, P = .02) and mean iG (149 ± 19 vs 123 ± 14 mg/dL, P = .02) remained higher for active players. For all 18 players, TIR was 89.4 ± 11.7 and 91.6 ± 13.7%, TAR was 5.9 ± 6.7 and 2.9 ± 5.7%, and time below range was 4.5 ± 10.5 and 5.3 ± 13.2% for day and night, respectively.

Conclusions: This observational study suggests that elite European footballers may have significant increases in glycemia, as measured by CGM, supporting the notion that mild hyperglycemia can occur during and after active competition in healthy and metabolically normal athletes, perhaps because of competition stress.

Background: To evaluate a new fully closed-loop (FCL) system in people with type 1 diabetes (T1D) with high-carbohydrate and high-fat unannounced meals.

Methods: After a 1-week Control-IQ run-in period at home with mealtime insulin boluses, ten adults with T1D used the Tandem Freedom FCL System in the hotel setting for 72 hours without meal announcement or mealtime insulin boluses. Participants consumed high-carbohydrate and high-fat meals during their stay. Exercise challenges occurred each day. A Wilcoxon signed-rank test for nonparametric data compared outcomes between periods.

Results: Mean participant age was 38.6 years, duration of diabetes 15.9 years, total daily insulin 0.71 units/kg/d, and HbA_1c 7.3%. There were no diabetic ketoacidosis (DKA) or severe hypoglycemia events. During the hotel study, FCL was active 97.3% of the time, and median meal size was 70.8 g carbohydrate and 53.2 g fat for breakfast, 53.8 g carbohydrate and 40.0 g fat for lunch, and 96.1 g carbohydrate and 53.1 g fat for dinner. Median time in range (TIR) 70 to 180 mg/dL was 61.0% [58.9, 73.0] without any meal announcement or mealtime insulin boluses during the 72-hour FCL period, compared to 56.3% [50.9, 64.0] with their home pump with mealtime insulin boluses during the at-home run-in week (+9.0%, P = .23). Overnight TIR was 95.9% [83.8, 100.0] for FCL versus 69.6% [57.6, 77.8] for the run-in period (+26.1%, P = .01). Time <70 mg/dL was low at 0.4% during FCL.

Conclusions: FCL insulin delivery with the Tandem Freedom System was safe and effective in adults with T1D with high-carbohydrate, high-fat meals.

Presented is a series of narrative reviews that summarize published information regarding the effect or potential effect of interfering substances on the accuracy of continuous glucose monitoring (CGM) devices. While drawing together what is currently known regarding this topic, the future direction in this field and clinical implications posed by polypharmacy on CGM performance are considered. This first in a series of four review articles classifies commercially available CGMs by glucose measurement principle before reviewing what is currently known regarding substance interference mechanisms and design approaches that may serve to reduce interfering effects. Points covered include the following: minimally invasive electrochemical CGMs, which may be classified by first-, second-, or third-generational design (these models are at risk of interference from electroactive substances, or substances that can interfere with the enzymatic biorecognition process); non-invasive fluid sampling CGMs, which draw glucose across the skin barrier but are similarly reliant on the electrochemical measurement of an enzymatic reaction product; and minimally invasive implantable CGMs, which exhibit different interfering substance behaviors to other CGM classes, using a non-enzyme-based glucose-recognition agent coupled to optical detection. An understanding of substance-interfering mechanisms allows consideration of the potential impact on clinical accuracy of substances that are routinely prescribed, can be purchased over the counter, or are new to market.