bims-glumda 2025-05-18 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2025–05–18
24 papers selected by
Mott Given

A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data.
Glucose interpretation meaning and action (GIMA): Insights to blood glucose user interface interpretation in type 1 diabetes.
Dexcom G7 Accuracy and Reproducibility in the Intensive Care Unit.
Use of an insulin titration protocol based on continuous glucose monitoring in postoperative cardiac surgery patients with type 2 diabetes and prediabetes: a randomized controlled trial.
Assessing the Accuracy of Continuous Glucose Monitoring Metrics: The Role of Missing Data and Imputation Strategies.
Improvement in Glycemic Metrics With the First and Second Continuous Glucose Monitoring (CGM) Sensor Use in Indian Patients With Type 2 Diabetes Mellitus.
Continuous glucose monitoring as equinox of nocturnal and daytime hypoglycaemia in type 1 diabetes: insights from the randomized controlled HypoDE trial.
Hybrid closed loop devices: The utility of continuous glucose monitoring metrics for retinal surveillance during transition from standard insulin pump.
Practical Approach to Continuous Glucose Monitoring (CGM) Interpretation and Automated Insulin Delivery (AID) Use in Pregnancy: Considerations for Obstetric Providers.
Effects of Individualized Nutrition Therapy and Continuous Glucose Monitoring on Dietary and Sleep Quality in Individuals with Prediabetes and Overweight or Obesity.
Association Between Plant-Based Diet and Continuous Glucose Monitoring-Derived Glycemic Dynamics Among Pregnant Women With Gestational Diabetes.
Cross-sectional evaluation of people with type 1 diabetes participating in the GoPump Structured Diabetes Education Program during "Insulin Pump Weeks".
The Effect of Omnipod 5 Automated Insulin Delivery on Glycemic Control in Adolescents and Adults with Cystic Fibrosis-Related Diabetes.
The efficacy of intermittent scanning continuous glucose monitoring in the elderly: A case-control study.
Diabetes technology use in young adults living with type 1 diabetes in Ireland: A qualitative interview study.
Cholesteric Contact Lenses for Diabetics-Related Noninvasive Glucose Monitoring and Eye Healthcare.
Effect of FSL-CGM on Maternal and Neonatal Outcomes in GDM: A Propensity Score Matching Study in Hangzhou, China.
Assessing the Financial Sustainability of a Virtual Clinic Providing Comprehensive Diabetes Care.
"Experience Is Simply the Name We Give to Our Mistakes": A New Opportunity to Improve Outcomes in Type 2 Diabetes.
Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review.
Cruciferous vegetables improve glycaemic control compared to root/squash vegetables in a randomized, controlled, crossover trial: The VEgetableS for vaScular hEaLth (VESSEL) study.
Model Design and Study of a U-Channel Photonic Crystal Fib Optic Sensor for Measuring Glucose Concentration in Blood.
Recent advances in glucose monitoring utilizing oxidase electrochemical biosensors integrating carbon-based nanomaterials and smart enzyme design.
Efficient Nonenzymatic Electrochemical Detection of Glucose Using CuO Nanoparticles@CNT-Wrapped Graphene Oxide Composite Electrode.

Sci Rep. 2025 May 10. 15(1): 16290

A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data.

Min Hyuk Lim, Hyocheol Chae, Jeongwon Yoon, Insik Shin.

While continuous glucose monitoring (CGM) has revolutionized metabolic health management, widespread adoption remains limited by cost constraints and usage burden, often resulting in interrupted monitoring periods. We propose a deep learning framework for glucose level inference that operates independently of prior glucose measurements, utilizing comprehensive life-log data. The model employs a bidirectional Long Short-Term Memory (LSTM) network with an encoder-decoder architecture, incorporating dual attention mechanisms for temporal and feature importance. The system was trained on data from 171 healthy adults, encompassing detailed records of dietary intake, physical activity metrics, and glucose measurements. The encoder's hidden state as latent representations were analyzed for distributions of patterns of glucose and life-log sequences. The model showed a 19.49 ± 5.42 (mg/dL) in Root Mean Squared Error, 0.43 ± 0.2 in correlation coefficient, and 12.34 ± 3.11 (%) in Mean Absolute Percentage Eror for current glucose level predictions without any information of glucose at the inference step. The distribution of latent representations from the encoder showed the potential differentiation for glucose patterns. The model's ability to maintain predictive accuracy during periods of CGM unavailability has the potential to support intermittent monitoring scenarios for users.

DOI: https://doi.org/10.1038/s41598-025-01367-7
Digit Health. 2025 Jan-Dec;11:11 20552076251332580

Glucose interpretation meaning and action (GIMA): Insights to blood glucose user interface interpretation in type 1 diabetes.

Rujiravee Kongdee, Bijan Parsia, Hood Thabit, Simon Harper.

   Background: Current glucose monitoring user interfaces (UIs) are problematic for people with Type 1 Diabetes Mellitus (T1DM) in maintaining recommended blood glucose levels effectively. However, there is a lack of in-depth investigation into this problem when these individuals interpret and make real-time decisions based on the glucose monitoring devices they use daily.
Objectives: We aim to investigate problems associated with glucose monitoring UIs by observing users' interpretation and decision-making while reading their Continuous Glucose Monitoring (CGM), Flash Glucose Monitoring (Flash) or Self-monitoring of Blood Glucose (SMBG).
Methods: A mixed-method study was conducted. The Think Aloud protocol was used to capture participants' decision-making process while reading various device UIs. Their responses were evaluated using standard clinical guidance to assess their accuracy. Additionally, a survey was distributed to gather their perceptions of self-management practices.
Results: Twenty-seven participants (17 patients and 10 carers) were recruited. Interpretation accuracy averaged 38.0% ± 11.1% for CGM, 39.5% ± 8.8% for Flash, and 33.3% ± 7.8% for SMBG group. Treatment action accuracy was 21.5% ± 15.6% for CGM, 21.2% ± 14.0% for Flash, and 18.0% ± 13.2% for SMBG group. Despite this, 75.0% of all participants expressed very high confidence in their self-management.
Conclusions: Interpreting and making decisions using glucose monitoring UIs remains significantly challenging for people with T1DM despite their self-perceived performance. Improving such UIs is crucial to reduce misinterpretation and help these individuals make better treatment decisions without relying on their potentially inaccurate interpretations.

Keywords:  Type 1 diabetes; blood glucose monitoring; interpretation; self-management; user interface

DOI:  https://doi.org/10.1177/20552076251332580
Diabetes Technol Ther. 2025 May 13.

Dexcom G7 Accuracy and Reproducibility in the Intensive Care Unit.

TIGHT RCT Study Group

  Objective: To evaluate the accuracy of Dexcom G7 continuous glucose monitor (CGM) in the intensive care unit (ICU) setting. Methods: We performed a prospective, single-center study in patients with known diagnosis of diabetes or stress hyperglycemia and treated with insulin. Two Dexcom G7 sensors were placed on the abdomen and/or upper arm. Blood glucose (BG) measurements obtained according to usual ICU care were paired with sensor glucose values, and accuracy metrics were analyzed. For further comparison, non-ICU patients were also studied. Results: The analyses included 30 participants with mean ± standard deviation age of 55 ± 12 years, with preexisting diabetes in 40% and stress hyperglycemia in 60%. A total of 1515 sensor-BG pairs were analyzed. The mean difference (bias) was -12 mg/dL (median: -6), and the mean relative absolute difference (RAD) was 16% (median: 12%). Mean RAD was 13% (median: 9%) using plasma glucose as the reference and 17% (median: 13%) using capillary glucose. For comparison, in 35 adults with type 2 diabetes in a non-ICU setting, the mean RAD was 15% (median: 13%). No meaningful differences were observed across the duration of time since sensor insertion. No correlation was found between mean RAD and severity of illness. Conclusions: Mean RAD of the Dexcom G7 sensor in the ICU setting was slightly higher than the outpatient use labeling, but was similar to a non-ICU hospital setting. Further studies are needed to determine whether CGM can be used nonadjunctively in an ICU setting for insulin management, including use of glucose trends and alarms for hypoglycemia or hyperglycemia.

Keywords:  accuracy; continuous glucose monitoring; hospital; intensive care unit; type 2 diabetes

DOI:  https://doi.org/10.1089/dia.2025.0184
Cardiovasc Diabetol. 2025 May 14. 24(1): 210

Use of an insulin titration protocol based on continuous glucose monitoring in postoperative cardiac surgery patients with type 2 diabetes and prediabetes: a randomized controlled trial.

Sun-Joon Moon, Min-Su Kim, Yun Tae Kim, Ha-Eun Lee, Young-Woo Lee, Su-Ji Lee, Euy-Suk Chung, Cheol-Young Park.

   BACKGROUND: Maintaining optimal glucose control is critical for postoperative care cardiac surgery patients. Continuous glucose monitoring (CGM) in this setting remains understudied. We evaluated the efficacy of CGM with a specialized titration protocol in cardiac surgery patients with type 2 diabetes (T2D) and prediabetes.
METHODS: In this randomized-controlled trial, 54 cardiac surgery patients were randomized one day post-surgery, with 27 CGM and 25 point-of-care (POC) patients completing the study. The CGM group used Dexcom G6 with a CGM-specialized titration protocol, while the POC group used standard monitoring with blinded CGM. The primary outcome was time-in-range (TIR) 100-180 mg/dL for 7 days post-surgery. Secondary outcomes included various glycemic metrics and surgical outcomes. Multiple comparison adjustments were performed using false-discovery-rate (FDR).
RESULTS: Thirty-one (59.6%) had diabetes and 21 (40.4%) had prediabetes. While TIR 100-180 mg/dL showed no difference (74.7% vs. 71.6%, FDR-adjusted p = 0.376), the CGM group demonstrated improvements in TIR 70-180 mg/dL (83.8% vs. 75.8%, FDR-adjusted p = 0.026), time-in-tight-range (TITR) 100-140 mg/dL (46.3% vs. 36.3%, FDR-adjusted p = 0.018), and TITR 70-140 mg/dL (55.3% vs. 40.5%, FDR-adjusted p = 0.003). Both groups maintained very low rates of time below range (< 70 mg/dL: 0.03% vs. 0.18%, FDR-adjusted p = 0.109). The CGM group showed lower postoperative atrial fibrillation (AF) (18.8% vs. 55.6%, FDR-adjusted p = 0.04999).
CONCLUSION: While the primary outcome was not achieved, CGM with a specialized titration protocol demonstrated safe glycemic control with improvements in TIR 70-180 mg/dL and TITRs in cardiac surgery patients with T2D and prediabetes. The observed reduction in postoperative AF warrants further investigation.
TRIAL REGISTRATION: ClinicalTrials.gov NCT06275971.

Keywords:  Atrial fibrillation; CGM; Cardiac surgery; Continuous glucose monitoring; Prediabetes; Time in range; Type 2 diabetes

DOI:  https://doi.org/10.1186/s12933-025-02747-z
Diabetes Technol Ther. 2025 May 14.

Assessing the Accuracy of Continuous Glucose Monitoring Metrics: The Role of Missing Data and Imputation Strategies.

Simon Lebech Cichosz, Thomas Kronborg, Stine Hangaard, Peter Vestergaard, Morten Hasselstrøm Jensen.

  Aim: This study aims to evaluate the accuracy of continuous glucose monitoring (CGM)-derived metrics, particularly those related to glycemic variability, in the presence of missing data. It systematically examines the effects of different missing data patterns and imputation strategies on both standard glycemic metrics and complex variability metrics. Methods: The analysis modeled and compared the effects of three types of missing data patterns-missing completely at random, segmental, and block-wise gaps-with proportions ranging from 5% to 50% on CGM metrics derived from 14-day profiles of individuals with type 1 and type 2 diabetes. Six imputation strategies were assessed: data removal, linear interpolation, mean imputation, piecewise cubic Hermite interpolation, temporal alignment imputation, and random forest-based imputation. Results: A total of 933 14-day CGM profiles from 468 individuals with diabetes were analyzed. Across all metrics, the coefficient of determination (R2) improved as the proportion of missing data decreased, regardless of the missing data pattern. The impact of missing data on the agreement between imputed and reference metrics varied depending on the missing data pattern. To achieve high accuracy (R2 > 0.95) in representing true metrics, at least 70% of the CGM data were required. While no imputation strategy fully compensated for high levels of missing data, simple removal outperformed others in most scenarios. Conclusion: This study examines the impact of missing data and imputation strategies on CGM-derived metrics. The findings suggest that while missing data may have varying effects depending on the metric and imputation method, removing periods without data is a general acceptable approach.

Keywords:  CGM; accuracy; continuous glucose monitoring; diabetes; imputation; metrics; missingness; temporal alignment imputation

DOI:  https://doi.org/10.1089/dia.2025.0102
Cureus. 2025 Apr;17(4): e82061

Improvement in Glycemic Metrics With the First and Second Continuous Glucose Monitoring (CGM) Sensor Use in Indian Patients With Type 2 Diabetes Mellitus.

Vipul Gupta.

   BACKGROUND: Continuous glucose monitoring (CGM) provides the real-time monitoring of glycemic fluctuations. Better control of hemoglobin A1C (HbA1c) levels in patients with type 2 diabetes mellitus (T2DM) using Abbott FreeStyle Libre (Chicago, Illinois, United States) was reported. This study evaluated the glycemic outcomes in T2DM patients using Abbott FreeStyle Libre in Indian settings.
METHODS: In this single-center retrospective study, data was collected from T2DM patients aged ≥18 years prescribed with Abbott FreeStyle Libre at Gupta Ultrasound and Heart Care Centre, New Delhi, India. The first application was considered the first use of FreeStyle Libre. The measurements were obtained from a sensor that operated continuously for as long as 14 days. The second application meant patients were using FreeStyle Libre for the second time continuously for 14 days following a break of two weeks after the first application. Time-in-range (TIR) is the percentage of time that a person spends with their blood glucose levels in a recommended target range. Time-below-range (TBR) is the time spent with blood sugar lower than the recommended range. Time-above-range (TAR) is the time spent above the recommended range. Relationship between TIR and various demographics/CGM metrics were analyzed.
RESULTS: Overall, 649 and 60 patients were included from the first application and the second application, respectively. The average duration of DM was 10-15 years in most patients, with hypertension being the predominant comorbidity. TIR negatively correlated with lower HbA1c (r=-0.547; p<0.001), lower average glucose (r=-0.790; p<0.001), and TAR (r=-0.951; p<0.001) and positively correlated with TBR (r=0.190; p<0.001). TAR, TBR, and HbA1c were identified as significant predictors of TIR.
CONCLUSION: TIR from FreeStyle Libre showed a meaningful association with glycemic control, which could aid in optimizing treatment plans and improving clinical outcomes of T2DM patients.

Keywords:  abbott freestyle libre; continuous glucose monitoring; diabetes control; standard blood glucose monitoring; time-in-range; type 2 diabetes mellitus

DOI:  https://doi.org/10.7759/cureus.82061
Diabetes Res Clin Pract. 2025 May 08. pii: S0168-8227(25)00242-6. [Epub ahead of print]224 112228

Continuous glucose monitoring as equinox of nocturnal and daytime hypoglycaemia in type 1 diabetes: insights from the randomized controlled HypoDE trial.

Norbert Hermanns, Lutz Heinemann, Bernhard Kulzer, Arne Schäfer, Malte Jacobsen, Dominic Ehrmann.

   AIMS: This study re-analysed data from the HypoDE trial to assess the prevalence of nocturnal hypoglycaemia, evaluate the impact of continuous glucose monitoring (CGM) on nocturnal and daytime hypoglycaemia, and explore their influence on severe hypoglycaemia (SH).
METHODS: The HypoDE trial was a randomized controlled trial involving 141 adults with type 1 diabetes, impaired hypoglycaemia awareness, or prior SH. Participants were randomized to CGM (Dexcom G5) or self-monitoring of blood glucose (control). Outcomes included the percentage of time spent in hypoglycaemia (<3.9 mmol/L, <3.0 mmol/L), episode duration, and SH incidence.
RESULTS: At baseline, nocturnal hypoglycaemia (<3.0 mmol/L) exposure exceeded daytime by 1.4 percentage points (95 % CI 0.6-2.2; p < 0.002), with episodes lasting 30.7 min longer (CI 21.5-39.9). Using CGM, these differences disappeared (<3.0 mmol/L: 0.3 percentage points, CI 0.7-1.3), while they persisted in the control group. Daytime hypoglycaemia significantly increased SH risk (IRR 1.10 per percentage point, CI 1.01-1.21; IRR 1.04 per minute, CI 1.01-1.07).
CONCLUSIONS: CGM effectively reduced nocturnal and daytime hypoglycaemia. Without CGM, nocturnal hypoglycaemia contributes to daytime risks, while daytime hypoglycaemia elevates SH risk. Expanding CGM access and addressing nocturnal hypoglycaemia in resource-limited settings are critical. TrialregistrationClinicaltrials.gov: NCT02671968.

Keywords:  Continuous glucose monitoring; Diabetestechnology; Nocturnal hypoglycaemia; Severe hypoglycemia

DOI:  https://doi.org/10.1016/j.diabres.2025.112228
Eye (Lond). 2025 May 13.

Hybrid closed loop devices: The utility of continuous glucose monitoring metrics for retinal surveillance during transition from standard insulin pump.

Maria S Varughese, Ananth U Nayak, Louise Payne, Lakshminarayanan Varadhan.

DOI: https://doi.org/10.1038/s41433-025-03834-x
J Diabetes Sci Technol. 2025 May 13. 19322968251330651

Practical Approach to Continuous Glucose Monitoring (CGM) Interpretation and Automated Insulin Delivery (AID) Use in Pregnancy: Considerations for Obstetric Providers.

Emily D Szmuilowicz, Celeste Durnwald, Denice S Feig.

  While automated insulin delivery (AID) systems have multiple well-established benefits outside of pregnancy and are widely used in non-pregnant individuals with type 1 diabetes (T1D), none of the commercially available AID systems in North America are approved for use during pregnancy. Use of commercially available AID systems off-label in pregnancy is currently limited by: (1) glucose targets higher than the fasting glucose target range recommended during pregnancy and (2) algorithms which were not designed for the dynamic changes in insulin resistance which occur across gestation. However, as AID use in the general population expands, many individuals will opt to continue using these systems off-label during pregnancy, and thus, guidance for providers regarding AID use and optimization during pregnancy is of the utmost importance. A cornerstone to the effective use of AID systems is the systematic and accurate interpretation of continuous glucose monitoring (CGM) data. One obstacle to the use of both CGM and AID systems by obstetric providers is the lack of comfort with CGM interpretation. We therefore present here: (1) a systematic approach to CGM interpretation during pregnancy and (2) practical guidance regarding AID use during pregnancy for individuals who opt to use commercially available AID systems off-label during pregnancy after consideration of individualized risks and benefits.

Keywords:  automated insulin delivery; continuous glucose monitoring; diabetes technology; hybrid closed-loop; pregnancy; type 1 diabetes

DOI:  https://doi.org/10.1177/19322968251330651
Nutrients. 2025 Apr 30. pii: 1507. [Epub ahead of print]17(9):

Effects of Individualized Nutrition Therapy and Continuous Glucose Monitoring on Dietary and Sleep Quality in Individuals with Prediabetes and Overweight or Obesity.

Raedeh Basiri, Yatisha Rajanala.

  Background/Objectives: Despite advances in public health and medical treatment, the number of patients with type 2 diabetes is increasing and it remains among the top 10 causes of death and a leading cause of disability in the United States. Early interventions with innovative approaches are essential to improving dietary intake and blood glucose control, potentially preventing or delaying type 2 diabetes and related complications. This study examined the effects of integrating real-time feedback from continuous glucose monitoring (CGM) into individualized nutrition therapy (INT) on diet and sleep quality in individuals with prediabetes and overweight or obesity. Methods: Thirty participants were randomized to either the treatment (n = 15) or the control group (n = 15). Both groups received individualized nutrition recommendations tailored to energy needs for weight maintenance and blood glucose control. The treatment group had real-time access to CGM data, while the control group remained blinded. Dietary intake and sleep quality were assessed using ASA24 recall and analyzed via general linear model repeated measures. Results: Incorporating CGM feedback into nutrition therapy significantly increased whole-grain (p = 0.02) and plant-based protein intake (p = 0.02) in the treatment group, with trends toward increased fruit intake (p = 0.07) and a reduced percentage of calories from carbohydrates (p = 0.08). Sleep efficiency also improved significantly by 5% (p = 0.02) following the intervention. Conclusions: These findings support the effectiveness of CGM-enhanced nutrition therapy in improving diet and sleep quality in individuals with prediabetes and overweight or obesity. Further research is needed to assess the sustainability and long-term impact of this approach.

Keywords:  alternative healthy eating index; continuous glucose monitoring; dietary intake; individualized nutrition therapy; prediabetes; sleep quality; type 2 diabetes

DOI:  https://doi.org/10.3390/nu17091507
Mol Nutr Food Res. 2025 May 13. e70085

Association Between Plant-Based Diet and Continuous Glucose Monitoring-Derived Glycemic Dynamics Among Pregnant Women With Gestational Diabetes.

Meng Ye, Congmei Xiao, Meiqi Shi, Sha Lu, Xinxiu Liang, Zelei Miao, Ke Zhang, Wanglong Gou, Jingnan Chen, Jiali Wang, Xuhong Wang, Sirithon Siriamornpun, Wensheng Hu, Ju-Sheng Zheng, Yuanqing Fu.

  We aim to investigate the association of plant-based diets with the continuous glucose monitoring (CGM)-derived glycemic metrics among gestational diabetes mellitus (GDM) patients. We included 1756 GDM patients in the present analyses and assessed plant-based dietary patterns through constructing a plant-based diet index (PDI), healthy PDI (hPDI), and unhealthy PDI (uPDI). CGM-glycemic metrics, such as time in range (TIR), mean blood glucose (MBG), time below range (TBR), low blood glucose index (LBGI), mean of daily differences (MODD), and glycemic risk assessment in diabetes equation (GRADE), were constructed. We found that individuals in the highest quartile of PDI were more likely to have greater TIR (β: 0.28, 95% CI: 0.14 to 0.41) and MBG (β: 0.23, 95% CI: 0.09 to 0.36), while lower TBR (β: -0.26, 95% CI: -0.39 to -0.12), LBGI (β: -0.18, 95% CI: -0.32 to -0.05), and GRADE (β: -0.25, 95% CI: -0.39 to -0.11), compared to those in the lowest quartile. Moreover, most of these associations demonstrated a dose-response relationship, and hPDI and uPDI showed distinct associations with MODD, with higher hPDI favoring a healthier MODD pattern (FDR < 0.05). This study suggests potential benefits of increasing intake of plant-based food for glycemic management among GDM patients.

Keywords:  cohort; continuous glucose monitoring; gestational diabetes mellitus; glycemic control; plant‐based diet

DOI:  https://doi.org/10.1002/mnfr.70085
Pediatr Endocrinol Diabetes Metab. 2025 ;pii: 55734. [Epub ahead of print]31(1): 1-8

Cross-sectional evaluation of people with type 1 diabetes participating in the GoPump Structured Diabetes Education Program during "Insulin Pump Weeks".

Marta Najmanowicz, Weronika Gajda, Aleksandra Nowatkowska, Mikołaj Kamiński, Aleksandra Cieluch, Alicja Sroczyńska, Anna Kreczmer, Magdalena Michalak, Anna Adamska, Urszula Frąckowiak, Mateusz Michalski, Aleksandra Araszkiewicz, Dorota Zozulińska-Ziółkiewicz, Andrzej Gawrecki.

   INTRODUCTION: Despite advances in therapy, most persons with type 1 diabetes (PwT1Ds) do not achieve treatment goals. Education is fundamental to the care of PwT1Ds treated with continuous subcutaneous insulin infusion (CSII).
AIM OF THE STUDY: To evaluate PwT1Ds treated with CSII and receiving in-hospital education and to identify factors associated with treatment effectiveness.
MATERIAL AND METHODS: This cross-sectional study included adults with type 1 diabetes (T1D), who received diabetes education using the proprietary Structured Diabetes Education Program, GoPump, during "Insulin Pump Weeks" in 2022-2023. Metabolic control of diabetes was evaluated. Reports from personal insulin pumps, blood glucose meters, and continuous glucose monitoring (CGM) systems were assessed.
RESULTS: Data from 107 individuals with a median age of 26.7 years (Q1-Q3: 19.0-30.8) were analysed, including 65 women (60.7%). The median duration of T1D was 13 years (Q1-Q3: 10.0-18.0), and the median duration of personal insulin pump use was 8 years (Q1-Q3: 5.0-12.0). The median body mass index was 23.9 kg/m². CGM was used by 52.3% of individuals. The median time in range (TIR) was 57.0% (Q1-Q3: 45.0-69.5%), and the median glycated haemoglobin (HbA1c) level was 7.9% (Q1-Q3: 6.8-8.5%). A positive correlation was found between age and TIR (rs = 0.42, p = 0.001). The use of temporary basal rate and dual-wave and square bolus features was positively correlated with TIR (rs = 0.34, p = 0.012 and rs = 0.31, p = 0.021, respectively) and inversely with time above range > 250 mg/dl (rs = -0.37, p = 0.007 and rs = -0.27, p = 0.045, respectively). Lower HbA1c levels were observed in individuals with a higher number of daily boluses (rs = -0.33, p = 0.001).
CONCLUSIONS: In the study cohort, older age, more frequent use of advanced insulin pump features, and a higher number of daily boluses were associated with better glycaemic control in adults with T1D.

Keywords:   continuous glucose monitoring; continuous subcutaneous insulin infusion; type 1 diabetes mellitus.; diabetes education

DOI:  https://doi.org/10.5114/pedm.2025.148400
Diabetes Technol Ther. 2025 May 09.

The Effect of Omnipod 5 Automated Insulin Delivery on Glycemic Control in Adolescents and Adults with Cystic Fibrosis-Related Diabetes.

Kevin J Scully, Michelle Reed, Julia Douvas, Melissa S Putman, Brynn E Marks.

  Background: Studies investigating the safety and efficacy of automated insulin delivery (AID) systems in people with cystic fibrosis-related diabetes are limited. There are no published studies investigating the tubeless Omnipod 5 (OP5) AID system. Methods: This dual-center retrospective cohort study compared 14 days of baseline continuous glucose monitoring (CGM) data with days 1-90 and 91-180 post-OP5 initiation. Multivariable mixed-effects linear regression models were used to assess changes in glycemic metrics. Results: Among the 26 individuals with sufficient data initiating OP5, 65% were female, with a median age of 27.3 years and median diabetes duration of 10.9 years. Six (23%) had a history of solid organ transplant, and 2 (8%) were receiving enteral tube feeds. Participants transitioned to OP5 from multiple daily injections (54%), prior Omnipod generation (31%), or another AID system (15%). CGM time in range (70-180 mg/dL) increased from 54% (95% confidence interval [CI]: 45.0, 63.0) to 64% (95% CI: 57, 71.8, P < 0.001) during the first 90 days and to 62.7% (95% CI: 54.9, 70.5, P < 0.001) during 91-180 days. Time above range (TAR) 181-250 mg/dL and TAR >250 mg/dL improved at 1-90 days and 91-180 days compared with baseline (P = 0.001 and P = 0.002, respectively). There were no significant changes in time below range (54-69 mg/dL, <54 mg/dL) or coefficient of variation. Two individuals discontinued OP5 within 14 days due to persistent hypoglycemia. One adult experienced a hypoglycemic seizure after 3 months of use. Conclusions: Use of the OP5 system in youth and adults with CFRD led to significant improvements in multiples measures of hyperglycemia without a change in CGM-measured hypoglycemia over a 6-month period, although patient experience with hypoglycemia may limit sustained use. Given the unique comorbidities and pathophysiology of CFRD, these results emphasize the need for future studies to investigate the safety and efficacy of AID devices in this patient population.

Keywords:  Omnipod 5; automated insulin delivery; continuous glucose monitoring; cystic fibrosis; cystic fibrosis-related diabetes

DOI:  https://doi.org/10.1089/dia.2025.0075
Diabetes Obes Metab. 2025 May 13.

The efficacy of intermittent scanning continuous glucose monitoring in the elderly: A case-control study.

Riemer A Been, Max R X van Helsdingen, Darren Zammit, Marco Grzegorczyk, Wim P Krijnen, Katalin Tamasi, Rijk O B Gans, André P van Beek, Peter R van Dijk.

   AIMS: The efficacy of intermittently scanning continuous glucose monitoring (is-CGM) technology among elderly people with diabetes mellitus (DM) has been understudied. We investigated if the initiation of is-CGM results in improved glycemic control among this population.
MATERIALS AND METHODS: Retrospective, observational case-control study. Cases were selected randomly from the DM outpatient clinic of the University Medical Center Groningen between 2015 and 2022 if they had type 1 or type 2 DM, were registered users of is-CGM devices and were aged 60 or older at initiation of those devices. They were matched to randomly selected controls with no CGM usage based on age, gender, DM type and treatment type (insulin-pen or pump). Data were collected at baseline, 6 months prior and 3, 6, 9, 12 and 24 months after initiation. Linear mixed-effects regression was performed to assess the effects of is-CGM usage on HbA1c.
RESULTS: Three hundred and fifty-three participants were analysed (142 cases, 211 controls). Median HbA1c at baseline for is-CGM users was 8.00% [7.20%-9.10%] and for controls 7.90% [7.10%-8.70%]. Unadjusted analysis showed significant reductions of HbA1c 3 months after initialization (cases vs. controls, -0.50% vs. -0.02%, p = 0.016), persisting throughout the study. Adjusting for confounders, is-CGM initiation resulted in significant HbA1c reductions after 6 months (-0.29%, p = 0.006) up until 24 months (-0.39%, p = 0.033). The rate of sensor discontinuation was 2.8%.
CONCLUSIONS: The use of is-CGM improves glycemic control in elderly (≥60 years old) persons with DM after 3 months, and this persists for at least 24 months. The number of discontinuations is low. This data emphasizes the positive impact of is-CGM on the elderly population with DM.

Keywords:  blood glucose self‐monitoring; diabetes mellitus; elderly; insulin therapy; intermittently scanned continuous glucose monitoring

DOI:  https://doi.org/10.1111/dom.16417
Br J Health Psychol. 2025 May;30(2): e12803

Diabetes technology use in young adults living with type 1 diabetes in Ireland: A qualitative interview study.

Ann-Marie Creaven, Fay M O'Donoghue, Eimear C Morrissey, Aisling T O'Donnell, Tomás P Griffin.

   BACKGROUND: The challenges of living with and managing type 1 diabetes during youth and emerging adulthood are well documented. The management burden may be alleviated in part using diabetes technologies including continuous glucose monitoring and hybrid closed-loop insulin pumps. However, young people's experiences of diabetes technology during this life stage are not well understood. This study aims to address that gap.
METHODS: This study will recruit 30-40 young people living with T1D, aged 16-21 years, from paediatric, transition and adult T1D clinics. Semi-structured qualitative interviews will be conducted. The data will be analysed using framework analysis.
RESULTS: TBC (registered report format).

Keywords:  adolescence; framework analysis; qualitative; technology; transition; type 1 diabetes

DOI:  https://doi.org/10.1111/bjhp.12803
ACS Macro Lett. 2025 May 15. 743-749

Cholesteric Contact Lenses for Diabetics-Related Noninvasive Glucose Monitoring and Eye Healthcare.

Xinxin Yan, Yungeng Qi, Weiqing Liu, Shihao Wang, Afroza Akter Liza, Xuemei Ge, Junlong Song, Long Bai, Haiming Li, Orlando J Rojas, Jiaqi Guo.

Blood sugar monitoring has crucial significance for diabetes mellitus diagnosis, and noninvasive continuous detection methods are the future development trend. Among various noninvasive detection methods, glucose detection in tears has the advantages of a high level of subject compliance, minimal pollution, and accuracy. However, sensors used for detecting glucose concentration in tears usually embed noble microelectrical components into contact lenses, making the process complicated and costly, and easily cause environmental pollution and resource wastage. Here, we propose a construction strategy for contact lenses based on the cellulose nanocrystal (CNC) cholesteric structure, preparing products that change color according to the concentration of glucose. In addition, the surface of the contact lenses can be loaded with drugs for adjuvant treatment of diabetic eye complications. Contact lenses offer advantages such as a fast response speed (<240 s), high sensitivity with distinct colors at specific glucose concentrations (green at 0 mM, yellow at 5 mM, and red at 10 mM), and a reversible response process. Furthermore, they exhibit good biocompatibility (90% cell viability by CCK-8 assay) and biodegradability (complete biodegradation in soil within 120 days). CNC cholesteric contact lenses realize noninvasive, wearable continuous glucose detection, providing a new strategy for health monitoring of diabetics.

DOI: https://doi.org/10.1021/acsmacrolett.5c00200
Diabetes Ther. 2025 May 12.

Effect of FSL-CGM on Maternal and Neonatal Outcomes in GDM: A Propensity Score Matching Study in Hangzhou, China.

Mengkai Du, Songjia Yi, Yili Wei, Yijiong Jiang, Shuting Bao, Junjun Lu, Danqing Chen.

   INTRODUCTION: This study aims to evaluate the impact of using FreeStyle Libre continuous glucose monitoring (FSL-CGM) on maternal glucose control and obstetric and neonatal outcomes among women with gestational diabetes mellitus (GDM).
METHODS: A total of 3062 women with GDM in gestational weeks 24-28 were enrolled in this study and divided into FSL-CGM and self-monitoring of blood glucose (SMBG) groups according to the method of monitoring blood glucose. Nearest-neighbor matching propensity score matching (PSM) was used to balance covariates at a ratio of 1:2.
RESULTS: Compared with the first 6 days during the study period, the index of glycemic variability, such as the mean largest amplitude of glycemic excursions (LAGE), average daily risk range (ADRR) and glucose management indicators (GMI) during the last 6 days were improved (all p < 0.05). The fasting blood glucose before delivery in the FSL-CGM group was lower than that in the SMBG group (p < 0.05). In the normal weight subgroup, the FSL-CGM group had a lower gestational weight gain (GWG) than the SMBG group (p < 0.05). The incidence of neonatal hypoglycemia was higher in the SMBG group than in the FSL-CGM group (p < 0.05).
CONCLUSIONS: This study demonstrated that FSL-CGM helps reduce maternal glycemic variability and the incidence of neonatal hypoglycemia. Additionally, FSL-CGM may contribute to appropriate gestational weight gain during pregnancy.
TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT05003154.

Keywords:  Gestational diabetes mellitus; Glucose monitoring; Neonatal hypoglycemia; Neonatal outcomes; Weight gain

DOI:  https://doi.org/10.1007/s13300-025-01749-0
J Diabetes Sci Technol. 2025 May 13. 19322968251340664

Assessing the Financial Sustainability of a Virtual Clinic Providing Comprehensive Diabetes Care.

Paul Dupenloup, Grace Guan, Grazia Aleppo, Richard M Bergenstal, Korey Hood, Davida Kruger, Teresa McArthur, Beth Olson, Sean Oser, Tamara Oser, Ruth S Weinstock, Robin L Gal, Craig Kollman, David Scheinker.

   INTRODUCTION: The Virtual Diabetes Specialty Clinic (VDiSC) study demonstrated the feasibility of providing comprehensive diabetes care entirely virtually by combining virtual visits with continuous glucose monitoring support and remote patient monitoring (RPM). However, the financial sustainability of this model remains uncertain.
METHODS: We developed a financial model to estimate the variable costs and revenues of virtual diabetes care, using visit data from the 234 VDiSC participants with type 1 or type 2 diabetes. Data included virtual visits with certified diabetes care and education specialists (CDCES), endocrinologists, and behavioral health services (BHS). The model estimated care utilization, variable costs, reimbursement revenue, gross profit, and gross profit margin per member, per month (PMPM) for privately insured, publicly insured, and overall clinic populations (75% privately insured). We performed two-way sensitivity analyses on key parameters.
RESULTS: Gross profit and gross profit margin PMPM (95% confidence interval) were estimated at $-4 ($-14.00 to $5.68) and -4% (-3% to -6%) for publicly insured patients; $267.26 ($256.59-$277.93) and 73% (58%-88%) for privately insured patients; and $199.41 ($58.43-$340.39) and 67% (32%-102%) for the overall clinic. Profits were primarily driven by CDCES visits and RPM. Results were sensitive to insurance mix, cost-to-charge ratio, and commercial-to-Medicare price ratio.
CONCLUSIONS: Virtual diabetes care can be financially viable, although profitability relies on privately insured patients. The analysis excluded fixed costs of clinic infrastructure, and securing reimbursement may be challenging in practice. The financial model is adaptable to various care settings and can serve as a planning tool for virtual diabetes clinics.

Keywords:  continuous glucose monitoring; diabetes; health economics; remote patient monitoring; telemedicine

DOI:  https://doi.org/10.1177/19322968251340664
J Diabetes Sci Technol. 2025 May 13. 19322968251342254

"Experience Is Simply the Name We Give to Our Mistakes": A New Opportunity to Improve Outcomes in Type 2 Diabetes.

Theocharis Koufakis, Djordje S Popovic, Giuseppe Maltese.



Keywords:  continuous glucose monitoring; education; elderly; type 2 diabetes

DOI:  https://doi.org/10.1177/19322968251342254
Int J Mol Sci. 2025 Apr 22. pii: 3935. [Epub ahead of print]26(9):

Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review.

Rahul Mittal, Matthew B Weiss, Alexa Rendon, Shirin Shafazand, Joana R N Lemos, Khemraj Hirani.

  Type 1 diabetes (T1D) is an autoimmune condition characterized by the destruction of insulin-producing pancreatic beta cells, leading to lifelong insulin dependence and significant complications. Early detection of T1D is essential to delay disease onset and improve outcomes. Recent advancements in artificial intelligence (AI) and machine learning (ML) have provided powerful tools for predicting and diagnosing T1D. This systematic review evaluates the current landscape of AI/ML-based approaches for early T1D detection. A comprehensive search across PubMed, EMBASE, Science Direct, and Scopus identified 1447 studies, of which 10 met the inclusion criteria for narrative synthesis after screening and full-text review. The studies utilized diverse ML models, including logistic regression, support vector machines, random forests, and artificial neural networks. The datasets encompassed clinical parameters, genetic risk markers, continuous glucose monitoring (CGM) data, and proteomic and metabolomic biomarkers. The included studies involved a total of 49,172 participants and employed case-control, retrospective cohort, and prospective cohort designs. Models integrating multimodal data achieved the highest predictive accuracy, with area under the curve (AUC) values reaching up to 0.993 in sex-specific models. CGM data and plasma biomarkers, such as CXCL10 and IL-1RA, also emerged as valuable tools for identifying at-risk individuals. While the results highlight the potential of AI/ML in revolutionizing T1D risk stratification and diagnosis, challenges remain. Data heterogeneity and limited model generalizability present barriers to widespread implementation. Future research should prioritize the development of universal frameworks and real-world validation to enhance the reliability and clinical integration of these tools. Ultimately, AI/ML technologies hold transformative potential for clinical practice by enabling earlier diagnosis, guiding targeted interventions, and improving long-term patient outcomes. These advancements could support clinicians in making more informed, timely decisions, thus reducing diagnostic delays and paving the way for personalized prevention strategies in both pediatric and adult populations.

Keywords:  early detection; evidence synthesis; machine learning; predictive modeling; type 1 diabetes

DOI:  https://doi.org/10.3390/ijms26093935
Diabetes Obes Metab. 2025 May 15.

Cruciferous vegetables improve glycaemic control compared to root/squash vegetables in a randomized, controlled, crossover trial: The VEgetableS for vaScular hEaLth (VESSEL) study.

Emma L Connolly, Alex H Liu, Richard J Woodman, Armaghan Shafaei, Lisa G Wood, Richard Mithen, Anthony P James, Carl J Schultz, Seng Khee Gan, Catherine P Bondonno, Joshua R Lewis, Jonathan M Hodgson, Lauren C Blekkenhorst.

   AIMS: Higher cruciferous vegetable (e.g., broccoli) intake is associated with lower risk of type 2 diabetes and cardiovascular disease, but limited causal evidence exists. We investigated if cruciferous vegetable intake improved glycaemic control compared to root/squash vegetables in non-diabetic adults with elevated blood pressure.
MATERIALS AND METHODS: This randomized, controlled, crossover trial consisted of two 2-week dietary interventions (300 g/day cruciferous [active] and root/squash [control] soups with standardized lunch/dinner meals) separated by a 2-week washout. Participants were blinded to the intervention allocation. Glycaemic measures were a pre-specified secondary outcome. Flash glucose monitoring measured interstitial glucose every 15-min throughout both interventions. Mealtimes and consumption were recorded in food diaries. Differences in continuous glucose, glycaemic variability (coefficient of variation [CV]), and overall, lunch, and dinner postprandial glucose response (PPGR; 2-h mean glucose [PPGR 2-h] and area under the curve [AUC]) were assessed using linear mixed-effects regression.
RESULTS: Eighteen participants (female = 89%) completed the study (median [IQR] age: 68 [66-70 years]). Glycaemic variability was lower in the active versus control (mean difference: -2.0%, 95% CI -2.8, -1.1, p < 0.001). Overall PPGR 2-h and AUC were lower in the active versus control (mean difference: -0.14 mmol/L, 95% CI -0.24, -0.04, p = 0.005 and -20.1 mmol/L × min, 95% CI -34.1, -6.1, p = 0.005, respectively), driven by the dinner PPGR (p = 0.004 and p = 0.003, respectively). There was no difference in mean continuous glucose for active versus control (p = 0.411).
CONCLUSIONS: Cruciferous vegetable consumption improved postprandial glycaemic control compared with root/squash vegetables. The clinical impact remains uncertain and warrants further investigation, particularly in individuals with impaired glycaemic control.
CLINICAL TRIAL REGISTRY: This trial was registered at www.anzctr.org.au (ACTRN12619001294145).

Keywords:  cardiovascular disease; cruciferous vegetables; diabetes; glucosinolates; glycaemic control; postprandial glucose response; randomized controlled trial

DOI:  https://doi.org/10.1111/dom.16467
Sensors (Basel). 2025 Apr 22. pii: 2647. [Epub ahead of print]25(9):

Model Design and Study of a U-Channel Photonic Crystal Fib Optic Sensor for Measuring Glucose Concentration in Blood.

Lei Zhao, Hua Yang, Tangyou Sun, Qianju Song, Zao Yi, Yougen Yi.

  This research introduces a biosensor utilizing surface plasmon resonance in a photonic crystal fiber (PCF) configuration. PCF uses fused silica as the base material, with a layer of gold placed over the U-channels in the cross-section of the fiber to create a surface plasmon resonance. There are three different sizes of internal fiber optic air hole diameters, with a larger channel circle below the u-channel for the formation of an energy leakage window. COMSOL software 6.0 assisted us in tuning the fiber optic structure and performance for the study, and the structural parameters analyzed mainly include the channel circle diameter, the channel circle spacing, the profundity measurement of the polished layer, and the nanoscale size variation of metal films. The results of the simulation study show that the optical fiber sensor achieves refractive index (RI) responsiveness across the 1.30 to 1.41 range, and in the RI interval of 1.40 to 1.41, the sensor exhibits the largest resonance peak shift, and its highest sensitivity reaches 10,200 nm/RIU, and the smallest full width at half peak (FWHM) corresponds to the RI of 1.34 with a value of 4.8 nm, and the highest figure of merit (FOM) corresponds to the RI of 1.34 with a value of 895.83 (1/RIU). COMSOL 6.0 simulation software, was used to simulate the changes in blood refractive index corresponding to different glucose concentrations, and the detection performance of the sensor for different concentrations of glucose was tested. Then, the results show that the glucose concentration in 75 mg/dL-175 mg/dL with RI detection sensitivity is 3750 nm/RIU, where the maximum refractive index sensitivity is 5455 nm/RIU. It shows that the sensor can be applied in the field of biomedical applications, with its convenience, fast response, and high sensitivity, it has great potential and development prospect in the market.

Keywords:  biosensing; glucose concentration detection in blood; surface plasmon resonance; u-channel photonic crystal fiber

DOI:  https://doi.org/10.3390/s25092647
Front Chem. 2025 ;13 1591302

Recent advances in glucose monitoring utilizing oxidase electrochemical biosensors integrating carbon-based nanomaterials and smart enzyme design.

Guan Guoqiang, Qu Liang, Zhao Yani, Wang Pengyun, Kong Fanzhuo, Zhang Yuyang, Lin Zhiyuan, Ni Xing, Zhang Xue, Lu Qiongya, Zou Bin.

  Glucose oxidase (GOx), as a molecular recognition element of glucose biosensors, has high sensitivity and selectivity advantages. As a type of biosensor, the glucose oxidase electrode exhibits advantages such as ease of operation, high sensitivity, and strong specificity, promising broad application prospects in biomedical science, the food industry, and other fields. In recent years, with the advancement of nanotechnology, research efforts to enhance the performance of GOx biosensors have primarily focused on improving the conductive properties and specific surface area of nanomaterials, while neglecting the potential to modify the structure of the core component, GOx itself, to improve biosensor performance. Rapid modification of the GOx surface through chemical modification techniques yields a new modified enzyme (mGOx). Meanwhile, composite techniques involving carbon nanomaterials can be employed to further enhance sensor performance. This article reviews the construction methods and optimization strategies of glucose oxidase electrodes in recent years, along with research progress in their application in electrochemical sensing for glucose detection, and provides an outlook for future developments.

Keywords:  carbon nanomaterials; chemical modification; electrochemical sensing; glucose detection; glucose oxidase

DOI:  https://doi.org/10.3389/fchem.2025.1591302
Anal Sci Adv. 2025 Jun;6(1): e70019

Efficient Nonenzymatic Electrochemical Detection of Glucose Using CuO Nanoparticles@CNT-Wrapped Graphene Oxide Composite Electrode.

Amina Khalid, Rizwan Shoukat, Abid Ali, Salih Akyürekli, Arfaa Sajid, Muhammad Adeel Asghar, Qaisar Manzoor, Arif Nazir, Norah Alsadun, Amel Y Ahmed.

  A highly sensitive and stable nonenzymatic glucose biosensor has been developed via composite materials composed of CuO and graphene oxide (GO)/carbon nanotube (CNT) nanohybrid (CuO/GO/CNTs). Copper oxide nanoparticle(NP)-modified CNTs were stacked via graphene sheets and synthesized through hydrothermal method, providing a larger surface area with boosted catalytic activity for efficient mass and electron passage, respectively. Scanning electron microscopy (SEM) and energy-dispersive x-ray (EDX) spectroscopy have been used to investigate the morphology and composition of as-prepared nanohybrids, whereas x-ray diffraction (XRD) patterns provide information about the crystal structure and lattice parameters. Fabricated nanohybrid was used as electrode material to develop the nonenzymatic glucose biosensor, which exhibited better performance with a linear dynamic range from 0.06 to 0.74 mM, a high sensitivity of 328 mA mM-1 cm-2 and a low detection limit of up to 0.033 mM with a fast response time of 2 s. Although the stability and reusability of the fabricated electrode have been tested. The limit of detection was determined by using the traditional formula LOD = (SNR × σ)/Slope. The outcomes recommend the synthesized novel structured nanohybrid as a promising material possessing significant impact for flexible and wearable biosensing applications.

Keywords:  carbon; copper oxide; electrode; glucose Sensing; nanohybrid

DOI:  https://doi.org/10.1002/ansa.70019