bims-glumda 2025-07-06 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2025–07–06
nineteen papers selected by
Mott Given

Postpartum Continuous Glucose Monitoring to Detect Dysglycemia After Gestational Diabetes.
Comparative Performance Analysis of Manual and Updated Optional Calibration Algorithms for the CareSens Air CGM System.
An AI-based module for interstitial glucose forecasting enabling a "Do-It-Yourself" application for people with type 1 diabetes.
Short-term CGM as a tool to optimize glycemic control and defer intensive insulin therapy in people with poorly controlled type 2 diabetes: a Belgian real-life study.
Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes.
To assess the impact of individualized strategy and continuous glucose monitoring on glycemic control and mental health in pregnant women with diabetes.
Update on Management of Gestational Diabetes Mellitus and the Role of Continuous Glucose Monitor Technology.
Observational Study of the Impact of Infusion Set Replacement in Insulin Pump Users on Glycemic Management.
Continuous Glucose Monitoring Systems Can Meet the Challenge of Glucose Management and Beyond in Individuals with Type 2 Diabetes: An Expert Multidisciplinary Position.
Characterizing Severe and Level 2 Hypoglycemia and Associated Risk Factors in Adults with Type 1 Diabetes Using Hybrid Closed-Loop Insulin Pumps.
Evolution of the Artificial Pancreas: Components and Integration-CGMs, Insulin, and AP Systems.
Improving postprandial hyperglycemia in prediabetic, sedentary office workers with immediately post-lunch, intermediate-intensity exercise: a comprehensive evaluation using a physical activity tracker, a dietary management application, and continuous glucose monitoring.
Inflammatory markers and blood glucose are higher after morning vs afternoon exercise in type 2 diabetes.
The Role of Personality in Shaping Diabetes Management Strategies and Technology Adoption.
First in Human Feasibility Study: Automated Insulin Delivery Utilizing a Self-Adapting Algorithm in Adults With Type 1 and Type 2 Diabetes.
Physical Activity Is Associated With Improved Glycemic Outcomes in Newly Diagnosed Youth With Type 1 Diabetes: 4T Exercise Program.
Sampling Interval Influences Glucose Rate-of-Change Metrics.
Cost-Utility Analysis of the MiniMed™ 780G Advanced Hybrid Closed-Loop System Versus Intermittently Scanned Continuous Glucose Monitoring with Multiple Daily Insulin Injections in People with Type 1 Diabetes in France.
Integration of chemical and physical inputs for monitoring metabolites and cardiac signals in diabetes.

J Diabetes Sci Technol. 2025 Jul 03. 19322968251351819

Postpartum Continuous Glucose Monitoring to Detect Dysglycemia After Gestational Diabetes.

Camila Cabrera, Selassie Ogyaadu, Camilla Levister, Leah Kaplan, Aslihan Ipek, Lauren Ferrara, Carol J Levy, Grenye O'Malley.

   BACKGROUND: Up to one-third of people with gestational diabetes (GDM) will have persistent dysglycemia, and more than half do not complete the recommended postpartum oral glucose tolerance test (OGTT). This study assessed the use of blinded postpartum continuous glucose monitoring (CGM) to detect dysglycemia by assessing return rates, participant experience, and power to predict OGTT results.
METHOD: Blinded CGM was placed on postpartum day 1 to 3 before discharge from the hospital and again at six weeks after pregnancy complicated by GDM and worn at home for up to 10 days. Participants mailed the CGM back and were encouraged to undergo standard of care six-week OGTT.
RESULTS: Fifty women (36 ± 6 years old; 40% non-Hispanic white, 24% non-Hispanic black, 22% Asian, 14% Hispanic; 34% Medicaid insured) were consented. First CGM was completed by 86%, second CGM was completed by 60%, and postpartum OGTT was performed by 68%. Mean first sensor glucose was 121.8 ± 14.1 mg/dL. Dysglycemia on OGTT was diagnosed in seven participants: six with impaired glucose tolerance (18%) and one with diabetes (3%). Percent time <96% in the range 70 to 180 mg/dL predicted abnormal OGTT with positive predictive value of 54% and negative predictive value of 100%. The sensitivity and specificity of CGM to predict postpartum dysglycemia were 100% and 78%, respectively. If given a choice, 94% of participants would prefer CGM over OGTT.
CONCLUSIONS: Postpartum CGM is a reasonable and convenient initial postpartum screen for postpartum dysglycemia with high completion rates, sensitivity, and acceptability ratings. Percent time in range 70 to 180 mg/dL had strong predictive power for OGTT.

Keywords:  continuous glucose monitoring; gestational diabetes; glucose sensor; oral glucose tolerance testing; postpartum

DOI:  https://doi.org/10.1177/19322968251351819
J Diabetes Sci Technol. 2025 Jul 03. 19322968251351318

Comparative Performance Analysis of Manual and Updated Optional Calibration Algorithms for the CareSens Air CGM System.

Nina Jendrike, Manuel Eichenlaub, Manuela Link, Sükrü Öter, Anne Beltzer, Marta Gil Miró, Cornelia Haug, Jung Hee Seo, Moon Hwan Kim, Stefan Pleus, Guido Freckmann.

   BACKGROUND: The CE-marked CareSens Air continuous glucose monitoring (CGM) system (CSAir) features a 15-day sensor lifetime, a 2-hour warm-up period and mandatory manual calibrations. During subsequent product development, the algorithm was updated to reduce the warm-up period to 30 minutes and make user-entered calibrations optional. This study compared the CSAir's performance between the manual and updated algorithms.
METHODS: Thirty adults with diabetes wore three CSAir sensors on their upper arms for 15 days. The study included three in-clinic sessions with capillary comparator measurements at 15-minute intervals over seven hours and glucose manipulation in the hypo- or hyperglycemic range. Point accuracy was assessed via mean absolute relative difference (MARD), 20/20 agreement rates (AR) stratified by BG range, and sensor wear time. Further evaluations included clinical point accuracy, alert reliability, technical reliability, safety and user satisfaction.
RESULTS: The CSAir's updated algorithm exhibited improved accuracy compared with the manual calibration algorithm, with a total 20/20 AR of 93.9% (vs 90.1%) and an MARD of 8.7% (vs 9.9%). Accuracy remained stable across measurement ranges and sensor lifetime. Diabetes Technology Society Error Grid analysis revealed high clinical accuracy, with 88.0% and 92.4% of data pairs in zone A for the manual and updated algorithms, respectively. The estimated survival probability was 88.8%. Participants reported positive user satisfaction. No safety concerns were identified.
CONCLUSIONS: Both algorithms of CSAir demonstrated robust performance and reliability with improved accuracy with the updated version. The study results of the CSAir suggest its suitability for nonadjunctive use.

Keywords:  accuracy; algorithm; continuous glucose monitoring; nonadjunctive use; optional calibration; performance

DOI:  https://doi.org/10.1177/19322968251351318
Front Digit Health. 2025 ;7 1534830

An AI-based module for interstitial glucose forecasting enabling a "Do-It-Yourself" application for people with type 1 diabetes.

Antonio J Rodriguez-Almeida, Guillermo V Socorro-Marrero, Carmelo Betancort, Garlene Zamora-Zamorano, Alejandro Deniz-Garcia, María L Álvarez-Malé, Eirik Årsand, Cristina Soguero-Ruiz, Ana M Wägner, Conceição Granja, Gustavo M Callico, Himar Fabelo.

   Introduction: Diabetes mellitus (DM) is a chronic condition defined by increased blood glucose that affects more than 500 million adults. Type 1 diabetes (T1D) needs to be treated with insulin. Keeping glucose within the desired range is challenging. Despite the advances in the mHealth field, the appearance of the do-it-yourself (DIY) tools, and the progress in glucose level prediction based on deep learning (DL), these tools fail to engage the users in the long-term. This limits the benefits that they could have on the daily T1D self-management, specifically by providing an accurate prediction of their short-term glucose level.
Methods: This work proposed a DL-based DIY framework for interstitial glucose prediction using continuous glucose monitoring (CGM) data to generate one personalized DL model per user, without using data from other people. The DIY module reads the CGM raw data (as it would be uploaded by the potential users of this tool), and automatically prepares them to train and validate a DL model to perform glucose predictions up to one hour ahead. For training and validation, 1 year of CGM data collected from 29 subjects with T1D were used.
Results and Discussion: Results showed prediction performance comparable to the state-of-the-art, using only CGM data. To the best of our knowledge, this work is the first one in providing a DL-based DIY approach for fully personalized glucose prediction. Moreover, this framework is open source and has been deployed in Docker, enabling its standalone use, its integration on a smartphone application, or the experimentation with novel DL architectures.

Keywords:  continuous glucose monitoring; deep learning; mHealth; personalized medicine; type 1 diabetes

DOI:  https://doi.org/10.3389/fdgth.2025.1534830
Acta Clin Belg. 2025 Jul 03. 1-12

Short-term CGM as a tool to optimize glycemic control and defer intensive insulin therapy in people with poorly controlled type 2 diabetes: a Belgian real-life study.

Philippe Oriot, Linh Bui, Noémie Klipper Dit Kurz, Mirela Morisca Gavriliu, Maria-Claudia Negrea, Michel P Hermans.

   OBJECTIVE: Continuous glucose monitoring (CGM) benefits type 2 diabetes (T2D) patients on multiple daily insulin injections (MDI), but its role in non-intensive insulin therapy remains underexplored. This study evaluates whether a short-term CGM non-blinded can postpone the escalation to multiple daily insulin injections in people with poorly controlled T2D.
METHODS: This retrospective real-world study analyzed data from 309 adults with T2D in primary care who used a 10 or 14-day CGM (2020-2024). The primary objective was to assess CGM's impact on therapy escalation, particularly to MDI. The secondary objective was to identify factors predicting the intensification of glucose-lowering therapy (GLT).
RESULTS: Among the 309 participants (median age: 65 [56-73] years, diabetes duration: 16 [11-23] years, baseline HbA1c: 8.6% [70 mmol/mol]), 91.3% were deemed unsuitable for MDI based on CGM results (non-MDI GLT group, n = 282). In this group, 76% achieved an HbA1c-GMI differential > 0.5%, and 54% >1.0% after 14 day-CGM. Basal insulin use decreased slightly (70% to 64%, p = 0.13), while twice-daily insulin increased (12% to 18%, p = 0.02). GLTs remained largely unchanged.
CONCLUSION: A short-term CGM prevented MDI escalation in 91.3% of poorly controlled T2D adults, reinforcing its role as a cost-effective strategy. CGM likely improved self-management behaviors, as evidenced by frequent HbA1c-GMI differentials, reflecting better management of hyperglycemia. These findings highlight CGM as a practical behavioral and therapeutic tool in diabetes care.

Keywords:  Continuous glucose monitoring; GMI; HbA1c; insulin; primary care; type 2 diabetes

DOI:  https://doi.org/10.1080/17843286.2025.2528030
PLOS Digit Health. 2025 Jun;4(6): e0000918

Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes.

Helene Bei Thomsen, Livie Yumeng Li, Anders Aasted Isaksen, Benjamin Lebiecka-Johansen, Charline Bour, Guy Fagherazzi, William P T M van Doorn, Tibor V Varga, Adam Hulman.

Non-Hispanic white (White) populations are overrepresented in medical studies. Potential healthcare disparities can happen when machine learning models, used in diabetes technologies, are trained on data from primarily White patients. We aimed to evaluate algorithmic fairness in glucose predictions. This study utilized continuous glucose monitoring (CGM) data from 101 White and 104 Black participants with type 1 diabetes collected by the JAEB Center for Health Research, US. Long short-term memory (LSTM) deep learning models were trained on 11 datasets of different proportions of White and Black participants and tailored to each individual using transfer learning to predict glucose 60 minutes ahead based on 60-minute windows. Root mean squared errors (RMSE) were calculated for each participant. Linear mixed-effect models were used to investigate the association between racial composition and RMSE while accounting for age, sex, and training data size. A median of 9 weeks (IQR: 7, 10) of CGM data was available per participant. The divergence in performance (RMSE slope by proportion) was not statistically significant for either group. However, the slope difference (from 0% White and 100% Black to 100% White and 0% Black) between groups was statistically significant (p = 0.02), meaning the RMSE increased 0.04 [0.01, 0.08] mmol/L more for Black participants compared to White participants when the proportion of White participants increased from 0 to 100% in the training data. This difference was attenuated in the transfer learned models (RMSE: 0.02 [-0.01, 0.05] mmol/L, p = 0.20). The racial composition of training data created a small statistically significant difference in the performance of the models, which was not present after using transfer learning. This demonstrates the importance of diversity in datasets and the potential value of transfer learning for developing more fair prediction models.

DOI: https://doi.org/10.1371/journal.pdig.0000918
Front Endocrinol (Lausanne). 2025 ;16 1470473

To assess the impact of individualized strategy and continuous glucose monitoring on glycemic control and mental health in pregnant women with diabetes.

Mengxue Liu, Tong Chen, Shuai Wang, Na Li, Dan Liu.

   Objective: To assess the impact of individualized strategy and continuous glucose monitoring (CGM) on glycemic control and mental health(anxiety, depression, pregnancy-related anxiety and diabetes specific quality of life during pregnancy) in patients with diabetes in pregnancy (DIP).
Methods: In this study, 80 pregnant women diagnosed with type 2 diabetes mellitus (T2DM) complicated with pregnancy or gestational diabetes mellitus (GDM) were enrolled. Participants were randomly assigned to either CGM group or self-monitoring of blood glucose (SMBG) group. Blood glucose was regularly monitored for 14 days to guide and adjust hypoglycemic treatment (lifestyle or hypoglycemic agents) of the patients in time. Baseline characteristics were collected after enrollment. Self-rating anxiety scale (SAS), self-rating depression scale (SDS), pregnancy-related anxiety questionnaire (PAQ), diabetes specific quality of life scale (DSQL) were used to evaluate the anxiety, depression, pregnancy-related anxiety and quality of life. Glycemic parameters and scale scores were collected before and after individualized strategy.
Results: FBG and 2hPBG significantly decreased post-intervention in both groups (P<0.001). In the CGM group, the scores of SAS (39.59 ± 7.10 vs 37.15 ± 6.28), PAQ (24.15 ± 6.45 vs 22.59 ± 5.65) and DSQL (47.44 ± 9.01 vs 43.20 ± 9.00) after individualized strategy were significantly lower than those before individualized strategy (P<0.05). The SAS scale scores and PAQ scale scores were positively correlated with blood glucose levels (P<0.05).
Conclusion: The individualized strategy encompasses an insulin titration protocol guided by CGM, coupled with structured lifestyle modifications that address dietary patterns, physical activity and more, combined with short-term glucose monitoring can exert a positive effect on glycemic improvement in the short term and meet the requirements of glycemic control in pregnancy, which has important clinical significance. The combined use of individualized strategy and CGM improves glycemic control and may have protective effects on psychological well-being.
Clinical Trial Registration: https://www.chictr.org.cn, identifier ChiCTR2200060719.

Keywords:  anxiety; continuous glucose monitoring; depression; diabetes in pregnancy; gestational diabetes mellitus; quality of life

DOI:  https://doi.org/10.3389/fendo.2025.1470473
Diabetes Technol Ther. 2025 Jul 03.

Update on Management of Gestational Diabetes Mellitus and the Role of Continuous Glucose Monitor Technology.

Amber Lachaud, Celeste Durnwald.

  Gestational diabetes mellitus (GDM) complicates 5%-25% of pregnancies worldwide and is the most prevalent metabolic complication of pregnancy. Risk factors for GDM include maternal obesity, advanced maternal age, family history of type 2 diabetes mellitus (T2DM), diagnosis of Polycystic ovarian syndrome (PCOS), and a prior history of GDM. GDM has both implications for the pregnant person and the offspring with increased risks of adverse pregnancy outcomes as well as increased chance of developing T2DM later in life. The first-line treatment for GDM includes behavior modification followed by pharmacologic therapy with insulin being preferred medication of choice. Standard of care for the management of continuous glucose monitors (CGM) currently includes self-monitored blood glucoses or finger sticks 4× per day and this can increase stress and anxiety in pregnancies. Continuous glucose monitorings have been used commonly in nonpregnant diabetic patients and patients with type 1 diabetes but their use in patients with GDM are increasing. Although there are no specific Continuous glucose monitoring targets for patients with GDM, CGMs have been used to help determine normative data in patients without GDM, which has helped provide expert opinion on GDM targets. In research studies, CGMs have also been used to explore glycemic profiles for patients early in pregnancy who go on to develop GDM as well as looking at adverse pregnancy outcomes in patients with higher Continuous glucose monitoring metrics. Using CGMs has the potential to provide more information about glycemia, ultimately leading to treatment recommendations in patients with GDM with the ultimate goal to improve adverse pregnancy outcomes and improve health and well-being at large.

Keywords:  adverse pregnancy outcome; continuous glucose monitoring; continuous glucose monitors; gestational diabetes; pregnancy

DOI:  https://doi.org/10.1089/dia.2025.0148
J Diabetes Sci Technol. 2025 Jun 28. 19322968251345837

Observational Study of the Impact of Infusion Set Replacement in Insulin Pump Users on Glycemic Management.

Kai Yoshimura, Shin Urai, Kaori Hozumi, Mei Nakatsuji, Shuichiro Saito, Seiji Nishikage, Akane Yamamoto, Tomofumi Takayoshi, Wataru Ogawa, Yushi Hirota.

   BACKGROUND: Although several studies have evaluated the impact of prolonged infusion set use in insulin pump users on glycemic management with the use of continuous glucose monitoring (CGM), real-world assessments without intervention have been unavailable.
METHODS: This retrospective observational study recruited individuals with type 1 diabetes who received insulin pump therapy with real-time CGM. Insulin pump and CGM logs were extracted from the Medtronic CareLink system, and a dataset was constructed programmatically, counting tracking days from infusion set replacement every 24 hours up to day 4. The primary outcome was mean sensor glucose (SG) level, and the impact of infusion set usage duration on glycemic management was assessed.
RESULTS: The study enrolled 45 individuals with a median age of 40 (interquartile range = 32-51) years and median body mass index of 22.5 (21.2-23.8) kg/m². Mean SG was significantly higher on day 4 (median of 151.9 [136.5-173.7] mg/dL) than on day 2 (144.4 [124.0-162.9] mg/dL, P = .024). Similarly, time above range (TAR), time in range (TIR), and time in tight range (TITR) had worsened on day 4 compared with day 2. The TAR increased from a median of 22.0% (7.3%-35.8%) on day 2 to 27.6% (17.7%-44.9%) on day 4, whereas TIR decreased from 74.2% (59.9%-87.1%) to 66.5% (52.0%-79.8%) and TITR decreased from 47.6% (37.1%-67.5%) to 42.2% (34.6%-54.1%).
CONCLUSIONS: Our evaluation of the real-world impact of prolonged infusion set use revealed an association between longer use and worsening of glycemic management.

Keywords:  continuous glucose monitoring; infusion set; insulin pump therapy; observational study; type 1 diabetes

DOI:  https://doi.org/10.1177/19322968251345837
Diabetes Ther. 2025 Jun 30.

Continuous Glucose Monitoring Systems Can Meet the Challenge of Glucose Management and Beyond in Individuals with Type 2 Diabetes: An Expert Multidisciplinary Position.

Malgorzata Mysliwiec, Leszek Czupryniak, Ryszard Gellert, Piotr Jankowski, Agnieszka Mastalerz-Migas, Marek Rekas, Krzysztof Strojek, Mieczyslaw Walczak.

  The increasing prevalence of type 2 diabetes (T2D) can be considered a global healthcare emergency, with far-reaching burdens on the health and well-being of people with diabetes, their carers and families, and the mounting costs within each national healthcare economy. Although application of diabetes technologies, such as insulin pumps, continuous glucose monitoring (CGM) systems, and a range of connected devices, is starting to have an impact on the outcomes of care for people with type 1 diabetes (T1D), similar application for people with T2D is lagging behind. This is a purely cost-based decision, since evidence from numerous randomized controlled trials (RCTs) and real-world studies has shown the significant clinical impact of diabetes technologies for people with T2D, whether they are on insulin therapy or not. Amongst available technologies, it is the lack of widespread access to CGM devices for people with T2D that is most pressing, as these systems have the potential to bring a quantum change in the way people with T2D and their healthcare professionals (HCPs) are supported to manage the adverse impact both of hyperglycemia and hypoglycemia. Central to improving diabetes care for people with T2D is the demonstration in many studies that CGM can actively support healthy behavioral changes to meal planning and physical activity, with concomitant improvements in mental health and quality of life. In this expert opinion, we review the significant evidence base on which application of CGM in people with T2D is founded, and make the case for wider access for every person with diabetes as early as possible after diagnosis, in order to mitigate the global impact of T2D.

Keywords:  Continuous glucose monitoring; Expert opinion; Primary care; Type 2 diabetes

DOI:  https://doi.org/10.1007/s13300-025-01769-w
Diabetes Technol Ther. 2025 Jul 02.

Characterizing Severe and Level 2 Hypoglycemia and Associated Risk Factors in Adults with Type 1 Diabetes Using Hybrid Closed-Loop Insulin Pumps.

Micah Devore, Emily Hepworth, Annika Agni, Wen Ye, Stephanie A Amiel, Simon J Fisher, Yu Kuei Lin.

  Background: Despite advances in diabetes technologies, severe hypoglycemia (SH) and level 2 hypoglycemia (Lv2Hypo) persist among hybrid closed-loop (HCL) insulin pump users. This study assessed the relationship of impaired awareness of hypoglycemia (IAH) and other patient characteristics with SH and Lv2Hypo in HCL insulin pump users with type 1 diabetes. Methods: A cross-sectional survey assessed 6-month SH history, hemoglobin A1C, IAH (using the Hypoglycemia Awareness Questionnaire), and 30-day continuous glucose monitoring (CGM) data among adult HCL insulin pump users recruited from a national U.S. type 1 diabetes patient registry. Analyses included logistic regression, t-tests, and Chi-square tests. Results: Of 601 participants (female: 54%; mean age: 43), IAH and higher glucose coefficients of variation (CVs) were associated with both SH and spending ≥1% of time in Lv2Hypo (Ps < 0.05). Individuals with SH were further characterized as having spent more time with glucose levels >180 mg/dL and >250 mg/dL and having a lower education level (Ps < 0.05). CGM hypoglycemia measures were not associated with SH. Age and diabetes duration were not associated with experiencing SH or spending ≥1% of time in Lv2Hypo. Participants who both experienced SH and spent ≥1% of time in Lv2Hypo showed trends toward exhibiting the most severe IAH and the highest glucose CVs. Conclusions: Among adults with type 1 diabetes using HCL insulin pumps, IAH and higher glucose CVs are risk factors of experiencing SH and Lv2Hypo. Hyperglycemia and lower education level are also associated with a higher risk for experiencing SH.

Keywords:  hybrid closed-loop insulin pumps; hypoglycemia; impaired awareness of hypoglycemia; type 1 diabetes

DOI:  https://doi.org/10.1089/dia.2025.0126
J Diabetes Sci Technol. 2025 Jul;19(4): 883-894

Evolution of the Artificial Pancreas: Components and Integration-CGMs, Insulin, and AP Systems.

Richard M Bergenstal, Adam Heller, Marc D Breton, Robert Vigersky, Susan A Brown, Gregory P Forlenza, Jennifer L Sherr, Roman Hovorka, Steven J Russell, Edward R Damiano, Melissa S Putman, Roy W Beck, Eric Renard, Rayhan Lal, Claudio Cobelli, Eyal Dassau, Halis K Akturk, Michael A Weiss, Jay S Skyler.

  The landmark Diabetes Control and Complications Trial (DCCT) showed that glucose control is critical to reducing the risk of diabetes-related complications. This chapter outlines a series of innovations and investigations that followed the DCCT, aimed at minimizing the risk of hypoglycemia while further improving glucose control. The chapter presents an example of innovations in wired enzyme technology that facilitated the movement from capillary glucose monitoring to continuous glucose monitoring (CGM) and ultimately, the first-factory calibrated CGM system. The next glycemic management innovation was to connect CGM data to an insulin pump containing an algorithm able to adjust insulin delivery based on the changing glucose levels and trends. The key features of automated insulin delivery (AID) systems, currently approved in the United States, are presented. The AID summary table includes type of pump, type and function of the insulin delivery algorithm, the data management system, and the indications for use. The next section explores the innovation of alternative routes of insulin delivery to move toward the goal of a fully automated insulin delivery system. The main trials in developing and implementing an implantable intraperitoneal programmable system are summarized. The last section explores if sensor input in addition to glucose levels such as continuous sensing of ketone, lactate, or insulin levels may provide valuable feedback to move us closer to a fully autonomous AID system. Much of this diabetes innovation and investigation work has been supported by the National Institute of Diabetes and Digestive and Kidney Diseases over that last 75 years.

Keywords:  artificial pancreas; automated insulin delivery; continuous glucose monitoring; insulin formulation; intraperitoneal insulin delivery; metabolic sensors

DOI:  https://doi.org/10.1177/19322968251342239
Diabetol Int. 2025 Jul;16(3): 504-512

Improving postprandial hyperglycemia in prediabetic, sedentary office workers with immediately post-lunch, intermediate-intensity exercise: a comprehensive evaluation using a physical activity tracker, a dietary management application, and continuous glucose monitoring.

Keiko Koide, Koichiro Azuma, Yoshihito Atsumi.

   Aim/introduction: This study investigated if immediately post-lunch exercise may improve postprandial hyperglycemia in individuals with prediabetes.
Materials and methods: The study consisted of a control phase involving no exercise and an exercise phase involving exercise. During both phases, participants were assessed for their AUC, RCMC and %TITR using CGM-derived postprandial data; they were also assessed for physical activity using a physical activity tracker and for energy intake using a dietary management application.
Results: Of the 43 males included, 23 were available for analysis. Their AUC values were significantly lower at post-lunch 1 h in the exercise phase than in the control phase with their %TITR values being significantly higher in the exercise phase than in the control phase. Their cumulative AUC values were significantly lower for post-lunch 2, 3, and 4 h in the exercise phase, with the cumulative %TITR values being also significantly higher. Their RCMC values were significantly lower for post-lunch 0-1 and 3-4 h, and significantly higher for post-lunch 1-2 h, in the exercise phase than in the control phase, with no difference for post-lunch 2-3 h between the phases. They exhibited monophasic or biphasic glucose profiles in the exercise phase with significantly different AUC and %TITR values for post-lunch 0-4 h, but no difference in HR reserve (HRR), energy intake or its composition.
Conclusion: In those with prediabetes, postprandial hyperglycemia improved with immediately post-lunch exercise, with significant improvements in cumulative AUC and %TITR values. Further study is required to clarify why they exhibited disparate glucose profiles.

Keywords:  Continuous glucose monitoring; Dietary management application; Physical activity tracker; Postprandial hyperglycemia; Prediabetes

DOI:  https://doi.org/10.1007/s13340-025-00812-2
Diabetologia. 2025 Jun 28.

Inflammatory markers and blood glucose are higher after morning vs afternoon exercise in type 2 diabetes.

Martin J Keller, Aidan J Brady, Jonathan A B Smith, Mladen Savikj, Kirstin MacGregor, Maxence Jollet, Sofia B Öberg, Carolina Nylén, Marie Björnholm, Anette Rickenlund, Marcus Carlsson, Kenneth Caidahl, Anna Krook, Nicolas J Pillon, Juleen R Zierath, Harriet Wallberg-Henriksson.

   AIMS/HYPOTHESIS: The aim of this study was to investigate effects of time of day on glucose management in individuals with type 2 diabetes undertaking high-intensity interval exercise. Additionally, the association between regular eating behaviour and mean amplitude of glycaemic excursions was examined. Specifically, the primary outcome was to determine the effect of the intervention on 24 h glucose levels.
METHODS: A crossover trial was conducted, comprising 12 men and 12 women with type 2 diabetes and 12 men and 12 women without diabetes. Participants performed high-intensity interval exercise sessions in the morning (09:00 hours) or afternoon (16:00 hours) on separate days at least 7 days apart. Standardised meals were provided the day before exercise, on the day of exercise and on the day after exercise. Continuous glucose monitoring was used to estimate blood glucose levels.
RESULTS: The 24 h glucose profile did not differ between morning and afternoon exercise across cohorts. However, morning exercise increased blood glucose during the 2 h post-exercise period in men (p<0.05) and women (p<0.01) with type 2 diabetes, but blood glucose was unaltered following afternoon exercise. Glycaemic variability (assessed using the mean amplitude of glycaemic excursions) was reduced during the 3 day meal intervention in men (p<0.001) and women (p<0.05) with type 2 diabetes, but not in individuals without diabetes. Participants exhibited higher morning cortisol levels (p<0.001) compared with afternoon cortisol levels, independently of diagnosis. Individuals with type 2 diabetes exhibited higher levels of the inflammation marker C-reactive protein (p<0.001) and the heart failure marker NT-proBNP (p<0.001) in the morning than in the afternoon.
CONCLUSIONS/INTERPRETATION: In type 2 diabetes, afternoon high-intensity interval exercise appears to be more effective than morning high-intensity interval exercise for maintaining glucose management. Further research is needed to explore how elevated morning cortisol levels and inflammatory markers influence the exercise response and affect glucose regulation. Additionally, consistent meal timing and controlled energy intake are recommended for reducing the mean amplitude of glycaemic excursions.
TRIAL REGISTRATION: ClinicalTrials.gov NCT05115682.

Keywords:  Circadian biology; Continuous glucose monitoring; Diet; High-intensity interval exercise; Inflammation; Sex differences; Type 2 diabetes

DOI:  https://doi.org/10.1007/s00125-025-06477-5
Stud Health Technol Inform. 2025 Jun 26. 328 515-519

The Role of Personality in Shaping Diabetes Management Strategies and Technology Adoption.

Tahmienh Aldaghi, Martina Vlasakova, Robert Bem, Anna Holubova, Martin Haluzik, Karel Hana, Jan Muzik.

  As a chronic metabolic disease, diabetes mellitus necessitates ongoing self-management to control blood sugar levels and avoid complications. Although diabetes management relies heavily on technologies like insulin pumps and continuous glucose monitors (CGMs), patient use of these devices may differ depending on personality factors. This study investigated the association between diabetes types, personality types, and preferences for diabetes management technology. The 56 patients receiving intense insulin therapy completed the Myers-Briggs Type Indicator (MBTI) to evaluate personality qualities. Multinomial logistic regression was employed for statistical analysis after technology utilization data was gathered. According to the findings, introverts are more likely to utilize CGMs. Insulin pumps were preferred by extroverts, indicating their inclination for proactive, hands-on care. There were no significant relationships between diabetes type, technology use, or other aspects of personality. These results imply that personality variables affect technology preferences and could be helpful in customizing diabetes care plans. This study highlights the importance of considering psychological issues when choosing diabetes solutions.

Keywords:  Continuous glucose monitoring; Diabetes management technologies; Insulin pumps; MBTI; Personality types; Technology preferences

DOI:  https://doi.org/10.3233/SHTI250773
J Diabetes Sci Technol. 2025 Jul 03. 19322968251349528

First in Human Feasibility Study: Automated Insulin Delivery Utilizing a Self-Adapting Algorithm in Adults With Type 1 and Type 2 Diabetes.

Tom Wilkinson, Solita Donnelly, Claire Lever, Jonathan Williman, Renee Meier, Alisa Boucsein, Shirley Jones, Dave Ballagh, Reon van Rensburg, Rachael Sampson, Enrique Campos-Náñez, Steve Patek, Ryan Paul, Benjamin Wheeler, Martin de Bock.

   BACKGROUND: This feasibility study assessed a novel self-adapting closed-loop system which does not require carbohydrate announcement, in adults with type 1 and type 2 diabetes.
METHODS: Single-arm study, comprising a 14-day run-in using participants' usual insulin therapy with a blinded continuous glucose monitor (CGM), followed by 12 weeks use of the novel closed-loop system. The algorithm adjusted its own parameters after 4, 6, 8, and 10 weeks of use.
RESULTS: Thirty-two participants with type 1 and 10 participants with type 2 diabetes were enrolled. Mean time in range (TIR; % CGM readings = 70-180 mg/dL) was 37.7% at baseline and 55.9% during the intervention period in type 1 diabetes; 17.6% at baseline and 51.5% during the intervention period in type 2 diabetes. Median time <70 mg/dL during the intervention period was 1.1% in type 1 and 0.0% in type 2 diabetes. Median TIR was 65% following the fourth algorithm adaptation. Median daily insulin delivered by manual bolus was 1.0 units in type 1 and 0.0 units in type 2 diabetes, consistent with no meal announcement. There were four serious adverse events: worsening retinopathy, severe hypoglycemia following a period of paused automation, and two hospitalizations unrelated to the device.
CONCLUSIONS: A closed-loop algorithm that adjusts its own parameters and requires no meal announcement was feasible in a cohort of adults with type 1 and type 2 diabetes. Clinical benefits were most apparent with the fully adapted algorithm.

Keywords:  automated insulin delivery; fully automated closed-loop system; type 1 diabetes; type 2 diabetes; unannounced meals

DOI:  https://doi.org/10.1177/19322968251349528
Diabetes Care. 2025 Jul 01. pii: dc250765. [Epub ahead of print]

Physical Activity Is Associated With Improved Glycemic Outcomes in Newly Diagnosed Youth With Type 1 Diabetes: 4T Exercise Program.

4T Study Group

OBJECTIVE: The Teamwork, Targets, Technology, and Tight Range (4T) Exercise Program evaluated physical activity patterns across the first year of type 1 diabetes diagnosis and whether physical activity was associated with changes in glucose outcomes in the 24 h following physical activity.
RESEARCH DESIGN AND METHODS: The 4T Exercise Program started newly diagnosed youth with type 1 diabetes on a continuous glucose monitoring (CGM) system and physical activity tracker around 1 month postdiagnosis. A subset of youth opted to participate in up to four quarterly structured exercise education sessions to increase their knowledge around safe physical activity.
RESULTS: Ninety-eight youth with type 1 diabetes (median [interquartile range (IQR)] age of 13 [12-15] years, 45% female, 44% non-Hispanic White) completed the study. Compared with sedentary days, days with ≥10 min of vigorous intensity physical activity were associated with an increase in time in range (TIR) of 2.3% (1.4-3.2%; P < 0.001), a decrease in time above range (TAR) of 3.1% (2.2-4.0%; P < 0.001), and an increase in time below range (TBR) of 0.8% (0.6-0.9%; P < 0.001) in the 24 h following physical activity. From 1-3 months to 10-12 months postdiagnosis, the median (IQR) step count increased by 1,134 (445-1,519) steps per day (P < 0.001), while daily moderate-to-vigorous physical activity increased by 11 (2-23) min per day (P < 0.001).
CONCLUSIONS: In the 24 h following physical activity as compared with sedentary days, TIR improved, TAR was lower, and TBR remained within clinical target recommendations. For youth with new-onset type 1 diabetes, each structured exercise education session was associated with a further 0.79% increase in TIR.

DOI: https://doi.org/10.2337/dc25-0765
J Diabetes Sci Technol. 2025 Jun 30. 19322968251353540

Sampling Interval Influences Glucose Rate-of-Change Metrics.

Ermina Bach, Sanne Fisker, Mia Christensen, Bo M Bibby, Klavs W Hansen.



Keywords:  automated insulin delivery; continuous glucose monitoring; glucose rate of change; type 1 diabetes

DOI:  https://doi.org/10.1177/19322968251353540
Diabetes Technol Ther. 2025 Jul 02.

Cost-Utility Analysis of the MiniMed™ 780G Advanced Hybrid Closed-Loop System Versus Intermittently Scanned Continuous Glucose Monitoring with Multiple Daily Insulin Injections in People with Type 1 Diabetes in France.

Helene Hanaire, Asli Zeynep Ozdemir Saltik, Richard F Pollock, Neesha Nanu, Clea Sambuc, Apolline Grangeon, Simona De Portu, Pierre Koch, Ohad Cohen, Charles Thivolet.

  Background and Aims: Advanced hybrid closed-loop (AHCL) automated insulin delivery systems such as the MiniMed™ 780G have been shown to result in substantial improvements in disease management in people living with type 1 diabetes. The aim of the analysis was to assess the cost utility of the MiniMed 780G system compared with intermittently scanned continuous glucose monitoring (is-CGM) and multiple daily insulin injections (MDI) in people living with type 1 diabetes in France, to estimate the incremental cost-utility ratio (ICUR) and inform decision-making. Methods: The analysis was performed using the CORE Diabetes Model (version 9.5) and clinical input data were sourced from a randomized controlled trial, with glycated hemoglobin reductions of 1.54% (16.8 mmol/mol) and 0.2% (2.18 mmol/mol) assumed for the MiniMed 780G arm and is-CGM + MDI arm, respectively. The analysis was conducted from a national payer perspective over a 40-year time horizon; future costs and clinical outcomes were discounted at 2.5% per annum. Results: In the base case analysis, use of the MiniMed 780G system was associated with a mean gain in quality-adjusted life expectancy of 2.26 quality-adjusted life years (QALYs) compared with is-CGM + MDI (16.33 QALYs vs. 14.07 QALYs), while mean direct lifetime costs were EUR 78,509 higher (EUR 215,037 vs. EUR 136,528), resulting in an ICUR of EUR 34,732 per QALY gained. Findings from sensitivity analyses showed that analyses were robust to changes in assumptions in most input parameters. Conclusions: In people with type 1 diabetes in France not achieving glycemic target levels at baseline, the use of the MiniMed 780G system was projected to lead to substantial improvements in quality-adjusted life expectancy compared with continued use of is-CGM + MDI, with an ICUR of EUR 34,732 per QALY gained.

Keywords:  France; automated insulin delivery; costs and cost analysis; type 1 diabetes

DOI:  https://doi.org/10.1089/dia.2025.0100
Nat Biomed Eng. 2025 Jul 02.

Integration of chemical and physical inputs for monitoring metabolites and cardiac signals in diabetes.

An-Yi Chang, Muyang Lin, Lu Yin, Maria Reynoso, Shichao Ding, Ruixiao Liu, Yuma Dugas, Ana Casanova, Geonho Park, Zhengxing Li, Hao Luan, Nelly Askarinam, Fangyu Zhang, Sheng Xu, Joseph Wang.

The development of closed-loop systems towards effective management of diabetes requires the inclusion of additional chemical and physical inputs that affect disease pathophysiology and reflect cardiovascular risks in patients. Comprehensive glycaemic control information should account for more than a single glucose signal. Here, we describe a hybrid flexible wristband sensing platform that integrates a microneedle array for multiplexed biomarker sensing and an ultrasonic array for blood pressure, arterial stiffness and heart-rate monitoring. The integrated system provides a continuous evaluation of the metabolic and cardiovascular status towards improving glycaemic control and alerting patients to cardiovascular risks. The multimodal platform offers continuous glucose, lactate and alcohol monitoring, along with simultaneous ultrasonic measurements of blood pressure, arterial stiffness and heart rate, to support understanding of the interplay between interstitial fluid biomarkers and physiological parameters during common activities. By expanding the continuous monitoring of patients with diabetes to additional biomarkers and key cardiac signals, our integrated multiplexed chemical-physical health-monitoring platform holds promise for addressing the limitations of existing single-modality glucose-monitoring systems towards enhanced management of diabetes and related cardiovascular risks.

DOI: https://doi.org/10.1038/s41551-025-01439-z