bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2025–08–31
nineteen papers selected by
Mott Given
  1. Validation of a predictive calculator for optimal glycemic control and time-in-tight-range following CGM sensor placement in type 1 diabetes and pancreatic diabetes: a prospective study.
  2. Health Technology Assessment of Continuous Glucose Monitoring Systems for Paediatric Patients.
  3. Exploring Combined Use of Continuous Glucose Monitoring and Anti-Diabetes Medications on Glycaemic Control for People With Type 2 Diabetes Not Using Insulin.
  4. Implementation of a continuous glucose monitoring workflow in a complex primary care clinic.
  5. Does the short-term use of continuous glucose monitoring enhance diabetes self-management behaviour in type 2 diabetes? The DISCO GM Study: A randomised, controlled cross-over study.
  6. Time in Tight Range (TITR) stratified by Coefficient of Variation (CV) in a cohort of patients with type 1 Diabetes Mellitus and Multiple Daily Injections. A real-life study.
  7. The Experience of Patients with Type 1 Diabetes Mellitus with the Use of Glucose Monitoring Systems: A Qualitative Study.
  8. Nocturnal Hypoglycemia: Characterization with Continuous Glucose Monitoring in a Real-World Setting.
  9. Utility of Continuous Glucose Monitors for Improved Detection of Cystic Fibrosis-Related Diabetes.
  10. Divergent Interleukin-6 Responses to Hypoglycemia and Hyperglycemia in Adults With Type 1 Diabetes: Insights From Continuous Glucose Monitoring.
  11. Development of a Prediction Model for Severe Hypoglycemia in Children and Adolescents with Type 1 Diabetes: The Epi-GLUREDIA Study.
  12. Continuous Interstitial Glucose Monitoring through a Long-Term Subcutaneous Implantable Sensor during Pregnancy in Type 1 Diabetes: A Two- Patient Case Report.
  13. Nickel nanoparticle modified conductive hydrogel composite for wearable enzyme-free biosensor toward noninvasive diabetes monitoring.
  14. A Novel Mobile Health App to Educate and Empower Young Adults With Type 1 Diabetes to Exercise Safely: Prospective Single-Arm Pre-Post Noninferiority Clinical Trial.
  15. Enhanced Post-Prandial Glycemic Response Prediction in Type 2 Diabetes with Microbiome Data and Deep Learning.
  16. Global Inequities in Diabetes Technology and Insulin Access and Glycemic Outcomes.
  17. Association between sleep health and glycemic variability in people with type 2 diabetes: An ecological momentary assessment study.
  18. CGM profiling in Roux-en-Y gastric bypass, type 1 diabetes and healthy adults: Unmasking deviations from normoglycaemia.
  19. Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model.
  1. Endocrine. 2025 Aug 26.
    Validation of a predictive calculator for optimal glycemic control and time-in-tight-range following CGM sensor placement in type 1 diabetes and pancreatic diabetes: a prospective study.
    Fernando Sebastian-Valles, Juan Javier López-Hidalgo, Silvia Cañas Sierra, Victor Navas-Moreno, Jose Alfonso Arranz Martín, Miguel Antonio Sampedro-Núñez, Mónica Marazuela.
       BACKGROUND: Continuous glucose monitoring (CGM) has improved diabetes management, yet not all patients benefit equally. We previously developed a predictive calculator using clinical and socioeconomic variables to estimate the likelihood of achieving optimal control after CGM initiation. This study prospectively validated the calculator in a real-world cohort.
    METHODS: A single-center prospective study included 102 adults with type 1 or pancreatic diabetes using multiple daily insulin injections, followed for three months. Optimal control was defined as time in range (TIR, 70-180 mg/dL) > 70% and time below range (TBR, <70 mg/dL) < 4%. Model performance was assessed using ROC analysis and correlation tests.
    RESULTS: Of 102 participants, 85 completed follow-up (median age: 53.6 years; 48% women; median diabetes duration: 12.9 years; baseline HbA1c: 7.6%). Thirty-three (38.8%) achieved optimal control. The calculator showed moderate discrimination (AUC = 0.639) and significant correlations with TIR (p = 0.230, p = 0.023) and time in tight range (TITR, 70-140 mg/dL) (p = 0.271, p = 0.019). Overall accuracy was 61.9%, lower than in the original cohort. Smoking predicted non-completion (p = 0.038).
    CONCLUSIONS: The calculator shows moderate accuracy in predicting glycemic control and TITR after CGM initiation. CGM adherence remains a challenge, warranting further study in publicly funded healthcare settings.
    Keywords:  Continuous glucose monitoring; Socioeconomic status; Time in tight range; Type 1 diabetes
    DOI:  https://doi.org/10.1007/s12020-025-04385-7
  2. Children (Basel). 2025 Aug 19. pii: 1088. [Epub ahead of print]12(8):
    Health Technology Assessment of Continuous Glucose Monitoring Systems for Paediatric Patients.
    Martina Andellini, Riccardo Schiaffini, Massimiliano Angelini, Leandro Pecchia, Matteo Ritrovato.
       OBJECTIVES: Type1 diabetes (T1D) is one of the most common chronic diseases in pediatric age. Continuous glucose monitoring (CGM) has been shown to improve glycaemic control in adults compared to self-monitoring of blood glucose (SMBG); however, evidence about its use in the pediatric field is limited and fragmented and needs to be improved. This paper aims to address all the critical aspects linked to the use of CGM in a pediatric population while also describing a methodology for conducting health technology assessment (HTA) to support the decision-making process.
    METHODS: The use of CGM and SMBG in a pediatric population was compared by using a decision-making support tool (DoHTA method). Twenty-seven Key Performance Indicators (KPIs) were identified, defining safety, clinical effectiveness, organizational, patient perspective, and economic aspects. Performance scores for both monitoring systems were assessed based on these KPIs, leading to a final comparative ranking.
    RESULTS: CGM demonstrated a 29.3% performance advantage over SMBG, highlighting its benefits in terms of clinical effectiveness, patient perspectives, safety, and economic evaluation. No substantial differences were identified in terms of organizational aspects.
    CONCLUSIONS: This study critically evaluates the benefits and drawbacks of the use of CGM in a pediatric population. It integrates the assessment of the clinical effectiveness with the organizational aspects, the cost, the patient perspective, and the safety, providing a valuable proof of evidence as well as a reliable and transferable method for conducting decision-making processes in a hospital setting.
    Keywords:  continuous glucose monitoring; health technology assessment; multicriteria decision analysis; type 1 diabetes
    DOI:  https://doi.org/10.3390/children12081088
  3. Endocrinol Diabetes Metab. 2025 Sep;8(5): e70089
    Exploring Combined Use of Continuous Glucose Monitoring and Anti-Diabetes Medications on Glycaemic Control for People With Type 2 Diabetes Not Using Insulin.
    Poorva M Nemlekar, Katia L Hannah, Courtney R Green, Gregory J Norman.
       INTRODUCTION: Continuous glucose monitoring (CGM) offers a detailed view of glycaemic management, potentially enhancing the effectiveness of non-insulin, anti-diabetes medications. This study aimed to evaluate whether CGM use in combination with anti-diabetes medications is associated with changes in A1c among people with type 2 diabetes not using insulin.
    MATERIALS AND METHODS: This was a retrospective, observational analysis of administrative claims and linked laboratory data from Optum's Clinformatics Data Mart database. The study observation period covered 01/07/2018 through 30/06/2023 with 6-month baseline and follow-up periods. CGM use in conjunction with ≥ 1 of five anti-diabetes medication classes: metformin, sulfonylureas, sodium-glucose cotransporter-2 (SGLT2) inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors and/or glucagon-like peptide-1 receptor agonists (GLP-1 RAs) was required. The primary outcome was change in A1c from baseline. Linear regression models tested the main and interaction effects of CGM and each anti-diabetes medication.
    RESULTS: Overall, 52,394 CGM-naïve adults with non-insulin-treated type 2 diabetes using anti-diabetes medications were identified (4086 CGM users; 48,308 CGM non-users). CGM use was associated with a -0.45% greater A1c change among CGM users compared to CGM non-users (p < 0.0001). After adjusting for covariates, CGM users experienced greater A1c reductions vs. CGM non-users with all medications, but statistically significant interactions showed that for DPP-4 inhibitors, GLP-1 RAs and sulfonylureas, there were greater decreases in A1c for CGM users vs. CGM non-users who were taking the medication compared to CGM users vs. CGM non-users who were not taking the medication. A1c change between CGM users vs. CGM non-users did not vary by metformin or SGLT2 inhibitor use.
    DISCUSSION: The findings suggest that CGM use could augment the glycaemic benefits of anti-diabetes medications in people with non-insulin treated type 2 diabetes. These results support broader adoption of CGM.
    Keywords:  GLP‐1 receptor agonist; continuous glucose monitoring; diabetes mellitus; non‐insulin therapy; type 2
    DOI:  https://doi.org/10.1002/edm2.70089
  4. J Am Assoc Nurse Pract. 2025 Aug 22.
    Implementation of a continuous glucose monitoring workflow in a complex primary care clinic.
    Jack Weisskohl, Dana Burns, Evan Sisson, Kathryn Reid.
       BACKGROUND: Continuous glucose monitors (CGM) are supported by national clinical practice guidelines for glucose monitoring in many people with diabetes. However, CGM data are often underutilized in primary care settings, where most adults with diabetes are treated.
    LOCAL PROBLEM: Despite a growing patient population using CGM in a complex primary care clinic, the clinic lacks a structured workflow process for manually uploading CGM reports to the electronic health record. As a result, CGM data are inconsistently used by primary care providers for clinical decision-making during routine visits.
    METHODS: Using the Plan-Do-Study-Act methodology, workflow processes for registered nurses (RNs), licensed practical nurses (LPNs), doctors of medicine (MDs), family nurse practitioners (FNPs), and clinical pharmacists (PharmDs) were examined and improved to support the project goals.
    INTERVENTIONS: Patients actively using CGM were identified daily. Assigned clinic nurses (n = 3; 1 RN and 2 LPNs) uploaded CGM logs as precharting to the visit, which were then used by providers (n = 3; 1 MD and 2 FNPs) during clinical encounters. When nurses were not available, the MD, FNPs, or PharmD (n = 1) completed the workflow.
    RESULTS: Ambulatory glucose profiles were uploaded to precharting in 43 of 45 patients (96%) with active CGM during the project evaluation period. Providers discussed CGM in 38 (88%) of these cases, using it correctly 100% of the time. The current procedural terminology code 95251 was billed in 35 (92%) of the applicable visits.
    CONCLUSIONS: Interprofessional teamwork to implement clinic workflow process improvements supports the delivery of guideline-driven diabetes care for adults using CGM.
    Keywords:  Continuous glucose monitors; diabetes; implementation; nursing workflow; primary care; quality improvement
    DOI:  https://doi.org/10.1097/JXX.0000000000001187
  5. Diabetes Metab Syndr. 2025 Aug 14. pii: S1871-4021(25)00100-6. [Epub ahead of print]19(7): 103283
    Does the short-term use of continuous glucose monitoring enhance diabetes self-management behaviour in type 2 diabetes? The DISCO GM Study: A randomised, controlled cross-over study.
    Sharon N Parsons, Stephen D Luzio, Sarah C Dowrick, Gareth J Dunseath, Wai Yee Cheung, Sarah L Gibbs, David R Owens, Jeffrey W Stephens.
       INTRODUCTION: There is little evidence on the impact of Continuous Glucose Monitoring (CGM) on self-management behaviour in people with type 2 diabetes using participant reported outcome measures. We aimed to assess whether self-management behaviour, measured by the Diabetes Self-Management Questionnaire (DSMQ), is altered by the short-term use of CGM in people with complex type 2 diabetes.
    METHODS: Open, single-centre, randomised crossover study lasting 36 weeks. Participants were aged >18 years, diagnosed with type 2 diabetes >1 year and HbA1c ≥ 9%/75 mmol/mol. All were receiving care from a specialist diabetes team. Following basic diabetes self-management education and a 10 day period of blinded CGM, participants were randomised to one of two sequences. Sequence 1: 12 weeks routine diabetes care followed by 12 weeks CGM use; Sequence 2: 12 weeks CGM followed by 12 weeks routine diabetes care. Both sequences undertook a 12 week follow up period with no CGM use.
    RESULTS: Fifty-one participants were randomised, 25 to sequence 1, 26 to sequence 2. At baseline, 62.7% were male, mean age 59.7 years, mean (SD) HbA1c 10.7% (1.07)/93 mmol/mol (11.74) and 88.2% were prescribed insulin therapy. DSMQ mean total score pre-CGM was 7.0 (1.37). Following CGM use, DSMQ total and subset scores improved, with total score increasing significantly (mean difference 0.62, 95% CI 0.27, 0.98; p = 0.001). Present quality of life, HbA1c and %Time in Range also significantly improved following CGM use.
    CONCLUSION: In people with complex type 2 diabetes, the introduction of CGM can significantly improve diabetes self-management behaviour and other important outcomes.
    Keywords:  Continuous glucose monitoring; Patient reported outcome measures; Self-management; Type 2 diabetes
    DOI:  https://doi.org/10.1016/j.dsx.2025.103283
  6. Endocrine. 2025 Aug 26.
    Time in Tight Range (TITR) stratified by Coefficient of Variation (CV) in a cohort of patients with type 1 Diabetes Mellitus and Multiple Daily Injections. A real-life study.
    Sandra Herranz-Antolín, Verónica Esteban-Monge, María Covadonga López-Virgos, Sofía Ramos-Garrido, Clara Coton-Batres, Silvia Lallena-Pérez, Miguel Torralba.
       OBJETIVE: To analyze the Time in Tight Range (TITR) (70-140 mg/dL) and the relationship between TITR-Time in Range (TIR) and assess their possible differences according to Coefficient of Variation (CV) in a cohort of patients with type 1 Diabetes Mellitus (DM) and Multiple Daily Injections in real life.
    PATIENTS AND METHODS: 355 adult users of Continuous Glucose Monitoring (CGM) with at least one HbA1c (October 1, 2023-October 1, 2024) and glucose data in the 90 days prior were included.
    RESULTS: Age 46.9 years (SD 13.6); 57.2% male; time of evolution 21.6 years (SD 12.6). Mean TITR was 38.4% (SD 14.6) and 20.3% had a TITR ≥ 50%. The correlation TITR-TIR was strong (β = 0.83; CI 95% 0.8-0.87; R2 Adjusted 0.89; p < 0.001) and varied according to CV [CV ≤ 36% (β = 0.88; CI 95% 0.83-0.93; R2 Adjusted 0.89; p < 0.001); CV > 36% (β = 0.84; CI 95% 0.81-0.87; R2 Adjusted 0.93; p < 0.001)]. The cutoff value for TIR to discriminate TITR ≥ 50% varied according to CV [(CV ≤ 36% 75.9% (sensitivity 98%, specificity 94%, AUC 0.99, p < 0.001); CV > 36% 70.5% (sensitivity 100%, specificity 98%, AUC 0.99, p < 0.001)]. The variables that were independently associated with TITR in CV ≤ 36% group were TIR (β = 0.74; CI 95% 0.57-0.9; p < 0.001) and mean glucose (β = -0.11; CI 95% -0.21 to -0.01; p = 0.045). However, in CV > 36% group were time of evolution (β = 0.04; CI 95% 0.01-0.07; p = 0.008), HbA1c (β = -0.63; CI 95% -1.22 to -0.4; p = 0.036; CV (β = 0.33; CI 95% 0.24-0.41; p < 0.001) and TIR (β = 0.84; CI 95% 0.74-0.93; p < 0.001).
    CONCLUSIONS: The correlation between TITR-TIR was strong and higher in patients with CV > 36%. Cutoff value for TIR to discriminate TITR ≥ 50% and factors that were associated with TITR also differ depending on CV. It is essential to take glycemic variability into account when interpreting metabolic control data.
    Keywords:  Coefficient of variation; Continuous glucose monitoring; Multiple daily injections; Time in range; Time in tight range
    DOI:  https://doi.org/10.1007/s12020-025-04379-5
  7. Nurs Rep. 2025 Aug 12. pii: 294. [Epub ahead of print]15(8):
    The Experience of Patients with Type 1 Diabetes Mellitus with the Use of Glucose Monitoring Systems: A Qualitative Study.
    Anxela Soto-Rodriguez, Ana Fernández-Conde, Raquel Leirós-Rodríguez, Álvaro Toubes Opazo, Nuria Martinez-Blanco.
      Aim: The purpose of this study was to explore the broad experience of continuous glucose monitoring from the perspective of patients diagnosed with type 1 diabetes mellitus, including not only their emotions and feelings but also the lifestyle changes, perceptions, and social aspects associated with its use. Design: This is a phenomenological qualitative study. Patient or Public Contribution: The sample consisted of 10 adult patients diagnosed with type 1 diabetes who had been using the continuous glucose monitoring system for at least 6 months and were patients of the Endocrinology and Nutrition Service of the University Hospital Complex of Ourense. Methods: The recorded interviews were conducted in November 2024. The conversations were audio-recorded with the participants' consent, and then transcribed for thematic analysis. Results: Three main categories were identified: "experience prior to continuous glucose monitoring" (accessibility, prior knowledge, and expectations), "experience with the use of continuous glucose monitoring" (perception of healthcare support, concerns, strengths, and alarm management), and "experience regarding the disease" (self-management of the disease and safety). Despite the fact that diabetes mellitus is a complex chronic disease, all participants provided a positive assessment of their progress and improved control through continuous glucose monitoring. Conclusions: All participants felt more secure and protected with continuous glucose monitoring, improving their quality of life. The main concern among the subjects was the possibility of the sensor failing. They positively valued the alarm system in case of hypoglycemia. The CGM is a highly effective tool for the management and self-control of diabetes and promotes the relationship between patients and professional health. Impact: The findings of this study have important implications for clinical care, highlighting the need for more training and more health education at the first level of health care, such as health centers.
    Keywords:  blood glucose self-monitoring; glucose metabolism disorders; nursing; qualitative research; quality of life
    DOI:  https://doi.org/10.3390/nursrep15080294
  8. Diabetes Technol Ther. 2025 Aug 25.
    Nocturnal Hypoglycemia: Characterization with Continuous Glucose Monitoring in a Real-World Setting.
    Michael Mitter, Delphine Theodorou, Bernhard Ruch, Timor Glatzer, Josip Zivkovic.
      Background: Nocturnal hypoglycemia is a common complication in people with diabetes. The use of continuous glucose monitoring (CGM) has reduced the frequency of hypoglycemia and improved its clinical characterization; however, available CGM data mostly focus on overall or diurnal hypoglycemia in people with type 1 diabetes (T1D). This real-world study compared the frequency and duration of nocturnal versus diurnal hypoglycemia in people with T1D or type 2 diabetes (T2D) and evaluated the subsequent impact of nocturnal hypoglycemia on daytime glucose profiles. Methods: Between January 2010 and September 2023, CGM data during the first month of use were collected retrospectively from people with T1D (n = 3696) or T2D (n = 441) using multiple daily insulin injections. Hypoglycemic events were identified as CGM readings: <70 mg/dL or <54 mg/dL for at least 15 min. The incidence and duration of hypoglycemic events were calculated for diurnal and nocturnal periods. The effect of nocturnal hypoglycemic events on glycemia the following day was assessed. Results: Nocturnal hypoglycemia occurred less frequently than diurnal events in both persons with T1D (median [interquartile range, IQR] 0.54 [0.0, 1.5] vs. 2.25 [0.64, 5.25] events per week) and those with T2D (median [IQR] 0 [0.0, 0.52] vs. 0.30 [0.0, 1.21] events per week). In T1D, nocturnal hypoglycemia events had a 65-min median duration compared with 40 min for diurnal events (P < 0.001). Similar trends were observed in T2D, with nocturnal hypoglycemia events lasting 57 min versus 40 min diurnally (P < 0.001). Significant changes in multiple glycemic parameters were observed during days following nocturnal hypoglycemic events versus days following nights without hypoglycemia. These findings suggest a heightened risk of morning hypoglycemia following nocturnal hypoglycemia. Conclusion: These results showed that nocturnal hypoglycemia is characterized by longer duration and slower recovery than daytime hypoglycemia, with significant effects on next-day glycemic control, which emphasizes the need for improved prevention strategies.
    Keywords:  nocturnal hypoglycemia; real-world data; type 1 diabetes; type 2 diabetes
    DOI:  https://doi.org/10.1177/15209156251369884
  9. Pediatr Pulmonol. 2025 Aug;60(8): e71267
    Utility of Continuous Glucose Monitors for Improved Detection of Cystic Fibrosis-Related Diabetes.
    Claire E Moore, Wenya Chen, Stacy Bichl, Alexis Wong, Carolyn Heyman, Sarayu Ratnam, Monica E Bianco.
       BACKGROUND: Cystic fibrosis-related diabetes (CFRD) can be associated with decline in pulmonary function and nutritional status. Earlier diagnosis of CFRD than offered by annual recommended oral glucose tolerance test (OGTT) and earlier initiation of insulin may help prevent clinical decline. This retrospective study investigates the utility of continuous glucose monitoring (CGM) for detection of hyperglycemia in patients with cystic fibrosis (CF).
    METHODS: In this single-center, retrospective study, we analyzed data from 18 patients with CF over age 10 who had an abnormal OGTT and subsequently had at least 24 h of CGM data. EasyGV software was used to calculate multiple measures of CGM variability. Differences in OGTT and CGM measures were explored across four glucose-tolerance groups: indeterminate, fasting hyperglycemia, impaired glucose tolerance (with or without fasting hyperglycemia), and CFRD.
    RESULTS: Multiple CGM measures correlated with components of the OGTT. Across glucose-tolerance groups, significant differences were observed for the OGTT 2-h glucose (p = 0.002), mean of daily differences from CGM (p = 0.03), and standard deviation from CGM (p = 0.02). Approaching significance was the lability index (p = 0.05) from the CGM data. Glucose management indicator (GMI), continuous overlapping net glycemic action (CONGA), glycemic risk assessment in diabetes equation (GRADE), and average daily risk range (ADRR) showed negative correlations with change in forced expiratory volume over 1 s (FEV1) over the year before OGTT.
    CONCLUSION: Markers of glycemic variability may be important variables distinguishing between degrees of abnormal glucose tolerance, including CFRD. This area warrants further research with a larger sample size.
    Keywords:  continuous glucose monitoring; cystic fibrosis; cystic fibrosis‐related diabetes; oral glucose tolerance test; screening
    DOI:  https://doi.org/10.1002/ppul.71267
  10. J Diabetes Sci Technol. 2025 Aug 23. 19322968251370298
    Divergent Interleukin-6 Responses to Hypoglycemia and Hyperglycemia in Adults With Type 1 Diabetes: Insights From Continuous Glucose Monitoring.
    Theocharis Koufakis, Areti Kourti, Paraskevi Karalazou, Katerina Thisiadou, Kali Makedou, Ioanna Zografou.
      
    Keywords:  continuous glucose monitoring; inflammation markers; interleukin-6; type 1 diabetes
    DOI:  https://doi.org/10.1177/19322968251370298
  11. Nutrients. 2025 Aug 12. pii: 2610. [Epub ahead of print]17(16):
    Development of a Prediction Model for Severe Hypoglycemia in Children and Adolescents with Type 1 Diabetes: The Epi-GLUREDIA Study.
    Antoine Harvengt, Marie Bastin, Cédric Toussaint, Maude Beckers, Thibault Helleputte, Philippe Lysy.
      Background: Severe hypoglycemia (SH) is a critical complication in children and adolescents with type 1 diabetes (T1D), associated with cognitive impairment, coma, and significant psychosocial burden. Despite advances in glucose monitoring, predicting SH remains challenging, as most models focus on milder hypoglycemic events. Objective: To develop a machine learning model for early prediction of SH using continuous glucose monitoring (CGM) data in children and adolescent T1D patients. Methodology: This retrospective study analyzed CGM data from 67 patients (37 SH episodes, 1430 non-SH segments). Glycemic curves were segmented into 5-day windows, and 21 features were extracted, including glycemic mean, variability, time below range (TBR < 60 mg/dL), and PCA components of glucose trends. A support vector machine (SVM) model was trained using repeated cross-validation to predict SH 15 min before onset. Model performance was evaluated using sensitivity, specificity, balanced classification rate (BCR), and area under the ROC curve (AUC). Results: The model achieved robust performance, with a median AUC of 90% (IQR: 87-93%) and median BCR of 84% (IQR: 80-89%). Sensitivity and specificity exceeded 80%, demonstrating reliable detection of impending SH. However, the positive predictive value (PPV) was low (12%), with false alarms frequently triggered during descending glucose trends or near-hypoglycemic values (end glucose <54 mg/dL). SH episodes were stratified into two subgroups: group 1 (<45 mg/dL, n = 26) and group 2 (>52 mg/dL, n = 15). Notably, false alarms occurred at a median interval of 25 days, minimizing alarm fatigue. Conclusions: These findings confirm the feasibility of SH prediction in clinical practice, prioritizing high-risk events over milder hypoglycemia. By alerting patients and medical teams early on, this tool could facilitate individualized treatment adjustments, reduce the risk of serious hypoglycemic events, and thus contribute to more personalized management of pediatric diabetes, while improving patients' quality of life.
    Keywords:  continuous glucose monitoring; machine learning; predictive modeling; severe hypoglycemia; type 1 diabetes
    DOI:  https://doi.org/10.3390/nu17162610
  12. Endocr Metab Immune Disord Drug Targets. 2025 Aug 18.
    Continuous Interstitial Glucose Monitoring through a Long-Term Subcutaneous Implantable Sensor during Pregnancy in Type 1 Diabetes: A Two- Patient Case Report.
    N Betella, V Resi, A Gaglio, A Ciafardini, F Mazzoleni, W Vena, E Orsi, A C Bossi.
       INTRODUCTION: Optimal glycaemic control is crucial during pregnancy in women with type 1 diabetes mellitus (T1D). Current guidelines, based on positive data from the CONCEPTT (Continuous Glucose Monitoring in Pregnant Women With Type 1 Diabetes Trial), on maternal glycaemic control and fetal outcomes, recommend offering real-time Continuous Glucose Monitoring (rt-CGM) as a standard method in all pregnancies of women with type 1 diabetes (T1D).
    CASE PRESENTATION: In these two clinical cases, we describe for the first time the gestational outcomes in two patients with T1D who chose to maintain a long-term implantable subcutaneous sensor (Eversense XL®) as a CGM method during their pregnancies. The first case concerns a 33- year-old young woman with a 25-year history of T1D on Continuous Subcutaneous Insulin Infusion (CSII), who faced her first pregnancy with a suboptimal preconception glycaemic control; the second case describes the second pregnancy of a 37-year-old patient with a more recently diagnosed T1D, on multiple daily injection (MDI) therapy who achieved adequate glycaemic compensation before planned conception.
    CONCLUSION: The subcutaneous sensor replacement was carried out at the beginning and end of the 2nd trimester, respectively, without any complications, allowing optimal monitoring and adjustment of insulin dose and achieving optimal glucose targets throughout the pregnancy until delivery.
    Keywords:  Type 1 diabetes; glucose monitoring; pregnancy; subcutaneous implantable sensor.
    DOI:  https://doi.org/10.2174/0118715303393466250807050121
  13. Talanta. 2025 Aug 20. pii: S0039-9140(25)01223-8. [Epub ahead of print]297(Pt B): 128732
    Nickel nanoparticle modified conductive hydrogel composite for wearable enzyme-free biosensor toward noninvasive diabetes monitoring.
    Yawen Pan, Yuanyuan Cheng, Jing Wu, Hongbin Qi.
      Wearable biosensors have emerged as a pivotal technology for diabetes management, enabling noninvasive glucose monitoring through sweat analysis. While conventional enzyme-based systems face limitations in stability and manufacturing scalability, this study pioneers an enzyme-free flexible sensor utilizing a nickel nanoparticle Modified conductive hydrogel composite. By integrating polyvinyl alcohol (PVA) crosslinked with ethylene glycol (EG) into a PEDOT:PSS matrix, we engineered adhesive hydrogel films with dual-phase conductivity enhancement via EG-mediated chain alignment and fatigue-resistant Ni nanoparticle dispersion. The optimized sensor demonstrated robust mechanical-electrical coupling under dynamic strain, achieving clinically relevant sensitivity and detection limits for sweat glucose quantification. Validation against commercial glucometers confirmed measurement reliability, addressing the challenge in interfacial delamination. This work establishes a scalable paradigm for enzyme-free wearable biosensors, leveraging synergistic material design to advance continuous glucose monitoring strategies critical for diabetes care.
    Keywords:  Conductive hydrogels; Electrochemical sensors; Enzyme-free; Flexible and wearable; Sweat glucose
    DOI:  https://doi.org/10.1016/j.talanta.2025.128732
  14. JMIR Diabetes. 2025 Aug 22. 10 e68694
    A Novel Mobile Health App to Educate and Empower Young Adults With Type 1 Diabetes to Exercise Safely: Prospective Single-Arm Pre-Post Noninferiority Clinical Trial.
    Vinutha Beliyurguthu Shetty, Rachel Lim, Shaun Teo, Wayne H K Soon, Heather C Roby, Alison G Roberts, Grant J Smith, Paul A Fournier, Timothy W Jones, Elizabeth A Davis.
       Background: A novel mobile health (mHealth) app "acT1ve," developed using a co-design model, provides real-time support during exercise for young people with type 1 diabetes (T1D).
    Objective: This study aimed to demonstrate the noninferiority of acT1ve compared with "treatment as usual" with regard to hypoglycemic events.
    Methods: Thirty-nine participants living with T1D (age: 17.2, SD 3.3 years; HbA1c: 64, SD 6.0 mmol/mol) completed a 12-week single-arm, pre-post noninferiority study with a follow-up qualitative component. During the intervention, continuous glucose monitoring (CGM) and physical activity were monitored while participants used acT1ve to manage exercise. CGM data were used to assess the number of hypoglycemic events (<3.9 mmol/L for ≥15 minutes) in each phase. Using a mixed effects negative binomial regression, the difference in the rates of hypoglycemia between the preapp and app-use phases was analyzed. Participants completed both a semistructured interview and the user Mobile Application Rating Scale (uMARS) questionnaire postintervention. All interviews were audio-recorded for transcription, and a deductive content analysis approach was used to analyze the participant interviews. The uMARS Likert scores for each subscale (engagement, functionality, esthetics, and information) were calculated and reported as medians with IQRs.
    Results: The rates of hypoglycemia were similar for both the preapp and app-use phases (0.79 and 0.83 hypoglycemia events per day, respectively). The upper bound of the CI of the hypoglycemia rate ratio met the prespecified criteria for noninferiority (rate ratio=1.06; 95% CI 0.91-1.22). The uMARS analysis showed a high rating (≥4 out of 5) of acT1ve by 80% of participants for both functionality and information, 72% for esthetics, and 63% for overall uMARS rating. Content analysis of the interview transcripts identified 3 main themes: "Provision of information," "Exercising with the App," and "Targeted Population."
    Conclusions: The mHealth app "acT1ve," which was developed in collaboration with young people with T1D, is functional, acceptable, and safe for diabetes management around exercise. The study supports the noninferiority of acT1ve compared with "treatment as usual" with regards to hypoglycemic events.
    Keywords:  acT1ve; blood glucose level; exercise; mobile health app; type 1 diabetes; young people
    DOI:  https://doi.org/10.2196/68694
  15. IEEE J Biomed Health Inform. 2025 Aug 27. PP
    Enhanced Post-Prandial Glycemic Response Prediction in Type 2 Diabetes with Microbiome Data and Deep Learning.
    Kawon Jeong, Sun-Joon Moon, Vega Pradana Rachim, YoonJu Song, Young-Min Cho, Sung-Min Park.
      Nutritional intervention can improve glycemic control for type 2 diabetes mellitus (T2DM), and thus accurately predicting post-prandial glycemic responses (PPGRs) to each meal is essential. PPGRs can vary significantly between individuals, even when consuming the same foods, due to the diverse and complex nature of individual characteristics. However, to date, system-scale studies investigating the variability of PPGRs in people living with T2DM are scarce. This research collected meal logs, continuous glucose monitoring records, clinicodemographic profiles, and gut microbiota data comprising over 2,000 real-life meals across 88 individuals with T2DM, revealing causal relationships in the diet-microbiome-PPGR interplay. Furthermore, we developed a multimodal deep learning predictive PPGR model that integrates heterogeneous input data. The proposed model achieves R of 0.62 and 0.66 for 2- and 4-h PPGR prediction, respectively, significantly surpassing the perfor-mance of the carbohydrate single predictor and state-of-the-art machine learning algorithms. This model substantially improved the prediction in the subgroup of low responders to carbohydrates, a traditionally challenging population for accurate prediction using carbohydrate-based methods. This advancement empowers personalized PPGR prediction, laying the foundation for precision nutrition and better glycemic management for individuals with T2DM.
    DOI:  https://doi.org/10.1109/JBHI.2025.3602827
  16. JAMA Netw Open. 2025 Aug 01. 8(8): e2528933
    Global Inequities in Diabetes Technology and Insulin Access and Glycemic Outcomes.
    SWEET Study Group
       Importance: Advanced diabetes technologies such as continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (insulin pumps [CSII]), and glucometers alongside insulin access represent the criterion standard for managing type 1 diabetes (T1D) in children. Global disparities in their access and reimbursement may be associated with glycemic outcomes.
    Objective: To describe how accessibility and reimbursement of advanced diabetes technologies and insulin are associated with glycated hemoglobin (HbA1c) levels in centers participating in the SWEET initiative, an international pediatric diabetes registry.
    Design, Setting, and Participants: This global multicenter cross-sectional study collected data from 81 centers in 56 countries. Web-based questionnaires were distributed to representatives of all 121 pediatric diabetes centers participating in the SWEET initiative from March 1 to May 31, 2024, and used to map accessibility of and reimbursement for CGM, CSII, glucometers, and insulin. Reimbursement data were compared with HbA1c levels using the SWEET Study dataset. Participants included 42 349 children with T1D.
    Exposures: Responses were categorized into 4 groups based on the extent of reimbursement for diabetes technologies and insulin.
    Main Outcomes and Measures: Mean HbA1c levels across centers calculated from measurements current as of December 31, 2023, analyzed by categories of accessibility of and reimbursement for diabetes technologies and insulin.
    Results: Data collected from 81 of 121 SWEET centers (67%) across 56 countries included HbA1c levels from 42 349 children with T1D (22 021 male [52%]; mean [SD] age, 14.3 [4.4] years; mean [SD] diabetes duration, 6.0 [4.2] years). Universal access with complete reimbursement for all technologies and insulin was reported by 32 centers from 19 countries, while 8 countries reported no reimbursement for any technologies or insulin. Centers with full reimbursement for CSII, CGM, glucometers, and insulin showed mean HbA1c levels of 7.62% (95% CI, 7.59%-7.64%) to 7.75% (95% CI, 7.73%-7.77%) compared with 9.65% (95% CI, 9.55%-9.71%) to 10.49% (95% CI, 10.40%-10.58%) in centers with no reimbursement and/or no availability (P < .001 for all items).
    Conclusions and Relevance: This cross-sectional study found that HbA1c levels were associated with the accessibility of modern diabetes technologies and insulin. Efforts to ensure universal accessibility are required to reduce global inequities and glycemic outcomes for children with T1D.
    DOI:  https://doi.org/10.1001/jamanetworkopen.2025.28933
  17. Sleep Med. 2025 Aug 18. pii: S1389-9457(25)00429-0. [Epub ahead of print]134 106754
    Association between sleep health and glycemic variability in people with type 2 diabetes: An ecological momentary assessment study.
    Jinjin Yuan, Jinle Wang, Yueying Wang, Pei Chen, Shui Wu, Ying Shen, Chunyan Zhao, Sirimon Reutrakul, Chang G Park, Xiangfang Chen, Bingqian Zhu.
       BACKGROUND: Glycemic variability (GV) is an important indicator for glycemic control. Identifying factors contributing to GV may support development of targeted interventions. Besides non-modifiable factors, sleep plays a role in glucose regulation. This study aimed to examine the association between multidimensional sleep health with GV in people with type 2 diabetes (T2D).
    METHODS: This study used a correlational design. Participants were recruited from a community hospital. They completed data collection for seven consecutive days in real-life setting. They were instructed to fill out sleep diaries and wear ActiGraphs for sleep health assessment, including sleep duration, efficiency, quality, timing, regularity, and daytime sleepiness. Continuous glucose monitors were used to measure GV, including standard deviation (SD), coefficient of variation (CV), and mean amplitude of glycemic excursion (MAGE). 300 days of sleep-GV matched nocturnal data were used for analyses. Linear mixed-effect models (LMMs) and linear regression models were performed.
    RESULTS: Participants aged 60.0 years and 29 were females (total N = 52). Compared to objective total sleep time (TST) of 6-8h, those with TST≥8h had significantly higher SD, CV, and MAGE. Similarly, those with subjective TST≥8h had significantly higher CV and MAGE. Both longer objective and subjective wake after sleep onset was associated with higher SD, CV, and MAGE. Longer objective SD of TST was associated with higher SD, CV, and MAGE.
    CONCLUSIONS: This study highlights the importance of maintaining consolidated and regular sleep, appropriate sleep duration, and regular sleep in achieving optimal glucose control.
    Keywords:  Diabetes; Disease management; Glycemic control; Sleep
    DOI:  https://doi.org/10.1016/j.sleep.2025.106754
  18. Diabetes Obes Metab. 2025 Aug 26.
    CGM profiling in Roux-en-Y gastric bypass, type 1 diabetes and healthy adults: Unmasking deviations from normoglycaemia.
    Luca Cossu, Andrea Facchinetti, Lia Bally.
      
    DOI:  https://doi.org/10.1111/dom.70059
  19. Sci Rep. 2025 Aug 20. 15(1): 30636
    Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model.
    Kihoon Moon, Jaehong Kim, Seohyun Yoo, Jaehyuk Cho.
      Personalized blood glucose (BG) prediction in Type 1 Diabetes (T1D) is challenged by significant inter-patient heterogeneity. To address this, we propose BiT-MAML, a hybrid model combining a Bidirectional LSTM-Transformer with Model-Agnostic Meta-Learning. We evaluated our model using a rigorous Leave-One-Patient-Out Cross-Validation (LOPO-CV) on the OhioT1DM dataset, ensuring a fair comparison against re-implemented LSTM and Edge-LSTM baselines. The results show our model achieved a mean RMSE of 24.89 mg/dL for the 30 min prediction horizon, marking a substantial improvement of 19.3% over the standard LSTM and 14.2% over the Edge-LSTM. Notably, our model also achieved the lowest standard deviation (±4.60 mg/dL), indicating more consistent and generalizable performance across the patient cohort. A key finding of our study is the confirmation of significant performance variability across individuals, a known clinical challenge. This was evident as our model's 30 min RMSE ranged from an excellent 19.64 mg/dL to a more challenging 30.57 mg/dL, reflecting the inherent difficulty of personalizing predictions rather than model instability. From a clinical safety perspective, Clarke Error Grid Analysis confirmed the model's robustness, with over 92% of predictions falling within the clinically acceptable Zones A and B. This study concludes that the development of effective personalized BG prediction requires not only advanced model architectures but also robust evaluation methods that transparently report the full spectrum of performance, providing a realistic pathway toward reliable clinical tools.
    Keywords:  Bidirectional long short term memory; Blood glucose prediction; Deep learning; Model-agnostic meta-learning; Transformer; Type 1 diabetes
    DOI:  https://doi.org/10.1038/s41598-025-13491-5
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