bims-glumda 2025-04-20 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2025–04–20
twenty-one papers selected by
Mott Given

Excessive Burden of Hyperglycemia Along With Hypoglycemia in Long-Term Care Facilities Identified by Continuous Glucose Monitoring.
Time at High Risk of Hypoglycemia: Validation of a Consensus-Based Continuous Glucose Monitoring-Metric for Hospitalized Patients.
Assessment of Glycemic Control Using Continuous Glucose Monitoring During Hospitalization and After Discharge for Older Adults.
[Current applications and challenges of artificial intelligence in diabetes management].
Narrative Review: Continuous Glucose Monitoring (CGM) in Older Adults with Diabetes.
Dynamic Interference Testing-Unexpected Results Obtained with the Abbott Libre 2 and Dexcom G6 Continuous Glucose Monitoring Devices.
Combined effect of continuous glucose monitoring and semaglutide: analysis of administrative claims.
Management and Medical Care for Individuals with Type 1 Diabetes Running a Marathon.
Predicting High Glycemia Risk Index Trajectory in Individuals With Type 1 Diabetes and Long-term Continuously Glucose Monitoring.
Real-World Performance of Personal Continuous Glucose Monitors During Hospitalization.
System accuracy evaluation of the new blood glucose monitoring meter "GLUCOCARD S onyx" beyond ISO 15197:2013/EN ISO 15197:2015 requirements and with new patient safety features.
Dysglycemic Events after Initiation of Intermittently Scanned Continuous Glucose Monitoring in Patients with Insulin-Treated Type 2 Diabetes.
A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.
Impact of real-time glucose monitoring using FreeStyle Libre 3 on glycaemia in type 2 diabetes managed with basal insulin plus SGLT2 inhibitor and/or GLP-1 agonist: the FreeDM2 randomised controlled trial protocol.
Semaglutide, central retinal thickness and continuous glucose monitoring in persons with type 2 diabetes: A post-hoc analysis from a randomised trial.
Education Practices of Dietitians Across Australia and New Zealand Around the Glycaemic Management of Dietary Fat and Protein in Type 1 Diabetes and the Use of Continuous Glucose Monitoring: A Survey Evaluation.
A Multidisciplinary Approach of Type 1 Diabetes: The Intersection of Technology, Immunotherapy, and Personalized Medicine.
Making Sense: Feasibility of an education program to Empower adults with type 1 diabetes to interpret CGM data.
Digital health technologies/interventions in smart ward development for elderly patients with diabetes: A perspective from China and beyond.
Seasonal, weekly, and individual variations in long-term use of wearable medical devices for diabetes management.
Noninvasive Glucose Measurements in Tissue Simulating Phantoms Using a Solid-State Near-Infrared Sensor.

J Am Med Dir Assoc. 2025 Apr 12. pii: S1525-8610(25)00107-0. [Epub ahead of print] 105590

Excessive Burden of Hyperglycemia Along With Hypoglycemia in Long-Term Care Facilities Identified by Continuous Glucose Monitoring.

Medha N Munshi, Christine Slyne, Atif Adam, Noa Krakoff, Haley Brabant, Molly Savory, Jennifer Maurer, Elena Toschi.

   OBJECTIVES: Recommendations for diabetes care in long-term care facilities (LTC) focus on the avoidance of hypoglycemia and symptomatic hyperglycemia. Using continuous glucose monitoring (CGM), we evaluated the current state of glycemia in LTC residents with multiple comorbidities.
DESIGN: Cross-sectional prospective observational study.
SETTINGS AND PARTICIPANTS: Participants with diabetes on 1 or more glucose-lowering medications residing in 1 of 8 LTC facilities in Ohio and Michigan.
METHODS: A masked Dexcom G6 pro CGM was placed for 10 days on LTC residents. Clinical and demographic information was collected from medical records.
RESULTS: Sixty-five residents (median age 68 years [range 44-84 years], 51% female, 100% with type 2 diabetes) completed the study. Overall, 68% of the cohort used insulin and 64% were on non-insulin agents (11% on sulfonylurea). The mean A1c of the cohort was 7.2% ± 1.5%. CGM data showed 26% of the cohort with ≥1% time spent in hypoglycemia (time <70 mg/dL). A larger burden of severe hyperglycemia (sensor glucose >250 mg/dL) was seen, with 52% of the cohort spending >10% time, 37% spending >25%, and 18% spending >50% time in severe hyperglycemia. The cohort had a median of 13 comorbid conditions, taking 19 medications daily, with 86% having functional disabilities and 63% reporting a recent fall. Fifty-four percent of the cohort had a body mass index (BMI) >30 kg/m2 and 22% had a BMI >40 kg/m2.
CONCLUSIONS AND IMPLICATIONS: In this multimorbid cohort of residents with diabetes living in LTC facilities, we identified a high burden of both hypoglycemia and severe hyperglycemia, despite optimal control of A1c. More consistent use of CGM may help to identify glycemic excursions and actionable glucose patterns to improve therapeutic decision-making by clinicians.

Keywords:  Continuous glucose monitoring; diabetes; hyperglycemia; hypoglycemia; long-term care; older adults

DOI:  https://doi.org/10.1016/j.jamda.2025.105590
J Diabetes Sci Technol. 2025 Apr 11. 19322968251331600

Time at High Risk of Hypoglycemia: Validation of a Consensus-Based Continuous Glucose Monitoring-Metric for Hospitalized Patients.

Mikkel Thor Olsen, Katrine Bagge Hansen, Ulrik Pedersen-Bjergaard, Peter Lommer Kristensen.

   BACKGROUND: Time at high risk of hypoglycemia (THRH), 3.9 to 5.6 mmol/L, is a continuous glucose monitoring (CGM)-based metric recommended for reporting in hospitalized patients. This study aims to validate THRH as a predictor of hypoglycemia.
METHODS: The CGM data from 166 non-intensive care unit (non-ICU) inpatients with type 2 diabetes from the DIATEC trial were analyzed. All participants received basal-bolus insulin therapy. Of these, 82 were monitored with point-of-care glucose testing and blinded CGM, while 84 had open CGM. Linear and negative binomial regression analyses assessed the relationship between THRH and time below range (TBR) (<3.0 mmol/L, 3.0-3.9 mmol/L, and <3.9 mmol/L) and hypoglycemic events. Analyses were conducted for day (07:00-23:00), night (23:01-06:59), and 24-hour periods.
RESULTS: For CGM-monitored patients, every 10%-point increase in THRH was associated with a 0.13%-point increase in TBR (<3.0 mmol/L) (95% confidence interval [CI] = 0.06-0.21), 0.66%-point increase in TBR (3.0-3.9 mmol/L) (95% CI = 0.47-0.86), and 0.74%-point increase in TBR (<3.9 mmol/L) (95% CI = 0.51-0.97), all P < .001. A THRH threshold below 50% was linked to a TBR <3.9 mmol/L of less than 4%, as recommended. Similar results were observed during both day and night analyses and for point-of-care monitored patients, also for hypoglycemic events.
CONCLUSIONS: The THRH is strongly associated with hypoglycemia in non-ICU hospitalized patients with type 2 diabetes on basal-bolus insulin. Aiming for THRH below 50% aligns with the recommended TBR target of <3.9 mmol/L below 4%, supporting THRH's role in guiding hypoglycemia prevention strategies.

Keywords:  consensus; continuous glucose monitoring; hypoglycemia; inpatient; time at high risk of hypoglycemia

DOI:  https://doi.org/10.1177/19322968251331600
J Diabetes Sci Technol. 2025 Apr 12. 19322968251332656

Assessment of Glycemic Control Using Continuous Glucose Monitoring During Hospitalization and After Discharge for Older Adults.

Sriya Kantipudi, Thaer Idrees, Iris Castro-Revoredo, Priya Vellanki, Eduard Castejon-Rosales, Kenneth Petty, Limin Peng, Guillermo E Umpierrez.



Keywords:  continuous glucose monitoring; diabetes mellitus; hospital

DOI:  https://doi.org/10.1177/19322968251332656
Zhonghua Yi Xue Za Zhi. 2025 Apr 15. 105(15): 1140-1144

[Current applications and challenges of artificial intelligence in diabetes management].

L Zhang, J H Cai, J Zhou.

In recent years, the rapid development of artificial intelligence (AI) has brought innovative opportunities to diabetes management, with significant application potential in various aspects such as prevention, screening, diagnosis, and treatment of diabetes. Through big data analysis, AI can achieve precise diabetes sub-typing and further promote personalized treatment. In terms of complication screening, AI can efficiently identify diabetic retinopathy, aiding in early diagnosis and treatment of complications. Combined with continuous glucose monitoring technology, AI can remotely assist in diabetes management through various novel glucose monitoring metrics and algorithms. The AI-based closed-loop insulin pump can automatically adjust insulin infusion, increasing glucose time in range and reducing the risk of hypoglycemia. However, this technological wave is also accompanied by multiple challenges: data privacy disputes, insufficient algorithm interpretability, and delays in clinical validation, which hinder its widespread clinical applications. While AI demonstrates high efficiency in supporting clinical decision-making, complex case management and psychological care should still be dominated by physicians. In the future, the "physician+AI" collaborative pattern will combine humanistic care with technology to optimize diabetes management together.

DOI: https://doi.org/10.3760/cma.j.cn112137-20250205-00253
Diabetes Ther. 2025 Apr 16.

Narrative Review: Continuous Glucose Monitoring (CGM) in Older Adults with Diabetes.

Abbie Wilson, Deborah Morrison, Christopher Sainsbury, Gregory Jones.

   INTRODUCTION: Continuous glucose monitoring (CGM) has revolutionised diabetes care, with proven effect on glycaemic control, adverse diabetic events (such as hypoglycaemia and diabetic ketoacidosis) and hospitalisations in the general population. However, the evidence for CGM in older people is less robust.
METHOD: We conducted a narrative review of trials reporting data comparing standard blood glucose monitoring (SBGM) and CGM in adults over 65 with type 1 or type 2 diabetes who were treated with insulin published between 1999 and 2024.
RESULTS: Seventeen studies were identified, including eight retrospective cohort studies and five randomised controlled trials (RCTs). Sixteen of the 17 papers were based in Europe or North America. The studies were highly heterogeneous; however, they provided clear evidence supporting the use of CGM in reducing hypoglycemia in older adults, with potential benefits for overall wellbeing and quality of life..
CONCLUSIONS: Current approaches to diabetes care in older adults may over-rely on HbA1c (haemoglobin A1c) as a measurement of control given accuracy may be reduced in older adults and propensity for hypoglycaemia. Although goals should be personalised, avoidance of hypoglycaemia is a key goal for many older people with diabetes. There is good evidence that CGM can improve time-in-range and reduce hypoglycaemia and glucose variability in older adults. CGM should be considered for older adults as a means of reducing hypoglycaemia and associated potential harm.

Keywords:  CGM; Diabetes; Libre; Older adults

DOI:  https://doi.org/10.1007/s13300-025-01720-z
Sensors (Basel). 2025 Mar 22. pii: 1985. [Epub ahead of print]25(7):

Dynamic Interference Testing-Unexpected Results Obtained with the Abbott Libre 2 and Dexcom G6 Continuous Glucose Monitoring Devices.

Hendrick Jensch, Steven Setford, Nicole Thomé, Geethan Srikanthamoorthy, Lea Weingärtner, Mike Grady, Elizabeth Holt, Andreas Pfützner.

   BACKGROUND: Sensors for continuous glucose monitoring (CGM) are now commonly used by people with type 1 and type 2 diabetes. However, the response of these devices to potentially interfering nutritional, pharmaceutical, or endogenous substances is barely explored. We previously developed an in vitro test method for continuous and dynamic CGM interference testing and herein explore the sensitivity of the Abbott Libre2 (L2) and Dexcom G6 (G6) sensors to a panel of 68 individual substances.
METHODS: In each interference experiment, L2 and G6 sensors were exposed in triplicate to substance gradients from zero to supraphysiological concentrations at a stable glucose concentration of 200 mg/dL. YSI Stat 2300 Plus was used as the glucose reference method. Interference was presumed if the CGM sensors showed a mean bias of at least ±10% from baseline with a tested substance at any given substance concentration.
RESULTS: Both L2 and G6 sensors showed interference with the following substances: dithiothreitol (maximal bias from baseline: L2/G6: +46%/-18%), galactose (>+100%/+17%), mannose (>+100%/+20%), and N-acetyl-cysteine (+11%/+18%). The following substances were found to interfere with L2 sensors only: ascorbic acid (+48%), ibuprofen (+14%), icodextrin (+10%), methyldopa (+16%), red wine (+12%), and xylose (>+100%). On the other hand, the following substances were found to interfere with G6 sensors only: acetaminophen (>+100%), ethyl alcohol (+12%), gentisic acid (+18%), hydroxyurea (>+100%), l-cysteine (-25%), l-Dopa (+11%), and uric acid (+33%). Additionally, G6 sensors could subsequently not be calibrated for use after exposure to dithiothreitol, gentisic acid, l-cysteine, and mesalazine (sensor fouling).
CONCLUSIONS: Our standardized dynamic interference testing protocol identified several nutritional, pharmaceutical and endogenous substances that substantially influenced L2 and G6 sensor signals. Clinical trials are now necessary to investigate whether our findings are of relevance during routine care.

Keywords:  Abbott Libre 2; Dexcom G6; continuous glucose monitoring; dynamic interference testing; interferents

DOI:  https://doi.org/10.3390/s25071985
Am J Manag Care. 2025 Apr;31(4): 183-188

Combined effect of continuous glucose monitoring and semaglutide: analysis of administrative claims.

Poorva M Nemlekar, Katia L Hannah, Courtney R Green, Thomas Grace, Peter M Lynch, Jessica R Castle, Gregory J Norman.

OBJECTIVES: This study evaluated whether the combined use of continuous glucose monitoring (CGM) and semaglutide, a glucagon-like peptide-1 receptor agonist, was associated with larger hemoglobin A1c (HbA1c) improvements compared with use of semaglutide alone.
STUDY DESIGN: Using US health care claims data from the Optum Clinformatics database, this retrospective analysis identified adults with type 2 diabetes (T2D) using semaglutide.
METHODS: The CGM cohort had at least 1 CGM-related claim between January 1, 2019, and September 30, 2022. Random index dates were used in the control (non-CGM) cohort. At least 1 laboratory HbA1c value was required during baseline and follow-up periods. Outcomes included change in HbA1c and the proportion of people who reached American Diabetes Association (ADA) or Healthcare Effectiveness Data and Information Set (HEDIS) HbA1c targets of less than 7.0% or less than 8.0%, respectively.
RESULTS: A total of 21,247 people with T2D were identified, with 18,488 in the control group and 2759 using CGM. Overall, a significantly greater reduction in HbA1c was observed in the CGM cohort compared with the control group (difference-in-differences, -0.55%; 95% CI, -0.64% to -0.47%; P < .0001). Among CGM users, the proportion meeting the ADA target of HbA1c less than 7.0% nearly doubled, and the proportion achieving the HEDIS target of HbA1c less than 8.0% increased by more than 50%.
CONCLUSIONS: The results suggest that CGM provides an additive benefit to semaglutide, leading to greater decreases in HbA1c. Expanded use of these complementary therapies in the primary care setting could enable more people with T2D to achieve their glycemic goals.

DOI: https://doi.org/10.37765/ajmc.2025.89719
J Clin Med. 2025 Apr 06. pii: 2493. [Epub ahead of print]14(7):

Management and Medical Care for Individuals with Type 1 Diabetes Running a Marathon.

Michał Kulecki, Marcin Daroszewski, Paulina Birula, Anita Bonikowska, Anna Kreczmer, Monika Pietrzak, Anna Adamska, Magdalena Michalak, Alicja Sroczyńska, Mateusz Michalski, Dorota Zozulińska-Ziółkiewicz, Andrzej Gawrecki.

  Background: Limited data exist on managing type 1 diabetes mellitus (T1DM) during long-distance endurance events such as marathons. This study aimed to assess glycemic control and participant safety during a marathon. Methods: Five men with T1DM, participating in the 22nd Poznan Marathon, were recruited. They completed health questionnaires and received training on glycemic management. Their physical capacity was assessed (including maximal oxygen uptake on a cycle ergometer). Participants reduced their insulin doses and consumed breakfast 2.5-3 h before the race. During the marathon, self-monitoring blood glucose (SMBG) and ketone levels were measured at five checkpoints (start, 10 km, 19 km, 30 km, and finish). The medical team followed a pre-approved protocol, providing carbohydrate and fluid supplementation as needed. Glycemia was monitored by two continuous glucose monitoring (CGM) systems (FreeStyle Libre 2 and Dexcom G6) and SMBG. Results: The participants' median age was 44 years (34-48), with a diabetes duration of 10 years (6-14), and a BMI of 22.5 kg/m2 (22.0-23.3). All finished the marathon in an average time of 4:02:56 (±00:43:11). Mean SMBG was 125.6 (±43.5) mg/dL, while CGM readings were 149.6 (±17.9) mg/dL (FreeStyle Libre 2) and 155.4 (±12.9) mg/dL (Dexcom G6). One participant experienced prolonged hypoglycemia undetected by CGM, whereas another developed symptomatic hypoglycemia between SMBG measurements. Conclusions: Safe marathon completion in people with T1DM requires individualized insulin dose adjustments, appropriate carbohydrate supplementation, and dedicated medical support at checkpoints. Combining CGM with periodic SMBG measurements further enhances safety and helps to detect potential glycemic excursions.

Keywords:  continuous glucose monitoring; hypoglycemia; marathon; physical activity; physical endurance; type 1 diabetes

DOI:  https://doi.org/10.3390/jcm14072493
J Diabetes Sci Technol. 2025 Apr 12. 19322968251334365

Predicting High Glycemia Risk Index Trajectory in Individuals With Type 1 Diabetes and Long-term Continuously Glucose Monitoring.

Simon Lebech Cichosz.

  The glycemia risk index (GRI) is an emerging metric designed to quantify the risk of both hypo- and hyperglycemia, providing a combined assessment of glycemic control quality. A high GRI is associated with an increased risk of diabetic complications. In this study, we leverage long-term continuous glucose monitoring (CGM) data to develop and validate predictive models for a high GRI (>60) in individuals with T1D. We assessed over 250 000 days of measurements collected over four years from 736 patients with type 1 diabetes. Our modeling approach shows promise for predicting glycemic control quality (area under the receiver operating characteristic curve [ROC-AUC] of 0.87) six to nine months from baseline. However, additional analysis and validation are imperative to determine its full clinical utility.

Keywords:  glycemia risk index; glycemic control; machine learning; prediction model; type 1 diabetes

DOI:  https://doi.org/10.1177/19322968251334365
Diabetes Technol Ther. 2025 Apr 14.

Real-World Performance of Personal Continuous Glucose Monitors During Hospitalization.

Adrian G Dumitrascu, Michelle Perry, Ana-Maria Chindris, Razvan M Chirila, Rebecca J Boone, Sophia G Blumenfeld, David O Hodge, Launia J White, Karthik Gnanapandithan, Diego A De La Cruz, Ricardo J Pagan, Michael Smerina, Tatjana Gavrancic, Melissa P Cortes, Aleksandra Murawska Baptista, Jessica R Wilson.

  Objective: To assess the accuracy of patients' personal continuous glucose monitors (CGMs) worn in the hospital and accuracy correlation with laboratory values and vital signs and to compare CGM glucometric data with data from published literature and guidelines. Method: We enrolled adult patients with diabetes mellitus wearing outpatient-inserted CGMs at the time of hospital admission to a noncritical setting. CGM readings were paired within 5 min with point of care (POC) glucometers and laboratory (Lab) blood glucose levels. CGM accuracy was expressed using mean absolute relative difference (MARD) and Clarke Error Grids. CGM accuracy variation with labs and vital signs was analyzed with Spearman's correlation method. Results: For 188 hospitalizations, we analyzed 3316 CGM-POC pairs from 101 patients (56 with Dexcom® sensors and 45 with Abbott's FreeStyle Libre® sensors) and 771 CGM-Lab pairs for 97 patients. For CGM-POC pairs, MARD was 13.7%, 14.4%, and 11.8% for all sensors, Dexcom, and Libre sensors, respectively. MARD was 22.6% for POC glucose <70 mg/dL. For CGM-Lab pairs, MARD was 13.6%, 12.7%, and 15.4% for all sensors, Dexcom, and Libre sensors, respectively. Of CGM-POC pairs, 98.7% and 98.8% of CGM-Lab pairs were in zones A and B of Error Grid Analysis. There was no correlation between ARD and daily mean arterial blood pressure, hemoglobin level, glomerular filtration rate, and pulse oximetry. CGMs' time below range (TBR), time in range (TIR), and time above range were 2.2% (standard deviation [SD]: 4.7%), 58.7% (SD: 22.5%), and 39.9% (SD: 23.4%), respectively. The coefficient of variation was 31.2%. Conclusion: Except for hypoglycemia ranges, patients' personal CGMs had adequate accuracy for glucose monitoring in the hospital. Vital signs and Lab values did not interfere with CGM accuracy. The TBR and glucose variability were low, better than outpatient recommendations. TIR was in line with inpatient consensus guidelines, and "glucometrics" were comparable with reports for hospital inserted sensors.

Keywords:  CGM; diabetes mellitus in hospital setting; glucometrics; personal CGM

DOI:  https://doi.org/10.1089/dia.2024.0639
Front Clin Diabetes Healthc. 2025 ;6 1465732

System accuracy evaluation of the new blood glucose monitoring meter "GLUCOCARD S onyx" beyond ISO 15197:2013/EN ISO 15197:2015 requirements and with new patient safety features.

Daisuke Azuma, Hisashi Okuda, Beate Saeger.

   Introduction: Blood glucose monitoring meters (BGM) have not become redundant yet. The accuracy and precision of "GLUCOCARD S onyx," a new BGM with Bluetooth function, has been evaluated and proven to exceed the actual ISO 15197:2013/EN ISO 15197:2015 guidelines besides offering features for better patient safety and telemedicine.
Methods: 100 finger-prick whole blood samples from subjects with diabetes and 32 without diabetes were collected and measured with GLUCOCARD S onyx. Plasma blood glucose levels were measured using YSI2300 STAT PLUS as reference analyzer for comparison. The evaluation followed ISO 15197:2013, section 6.3 accuracy criteria. Furthermore, the MARD factor was calculated for the overall clinical important range (with n=132 samples).
Results: The performance of GLUCOCARD S onyx was evaluated according to ISO 15197:2013, revealing that 99.7% (598/600) of the results fell within ±15% or ±0.8 mmol/L (± 15 mg/dL) of difference over the total clinically relevant glucose range compared to the YSI2300 STAT PLUS. 100% (600/600) of the measurement results over the total range fell within Clark Error Grid Zone A. An overall mean absolute relative difference (MARD) factor of 4.15% was obtained; 5.05% for glucose <5.6 mmol/L (<100 mg/dL), and 3.65% for glucose ≥5.6 mmol/L (≥100 mg/dL).
Discussion: GLUCOCARD S onyx shows clinically satisfactory accuracy and reliability, even exceeding the ISO 15197:2013 criteria, for hypoglycemic cases with glucose critically low as <3.9 mmol/L (<70 mg/dL) and hyperglycemic cases with glucose ≥10.0 mmol/L (≥180 mg/dL). Healthcare organizations as well as manufacturers are aiming to offer new BGM systems that go beyond the ISO criteria and offer systems that can be consulted instead or besides CGM (Continuous Glucose Monitoring) in case of e.g. severe hypo- and/or hyperglycemic episodes. A MARD factor of 4.15% revealed an excellent system accuracy over the total clinically relevant glucose range. With additional user-friendly features, this BGM can be seen as a useful tool for efficient diabetes therapy, especially in the event of severe blood glucose fluctuations.

Keywords:  Clark error grid; EN ISO 15197:2015; GLUCOCARD S onyx; ISO 15197:2013; MARD; YSI 2300; accuracy evaluation; blood glucose meter

DOI:  https://doi.org/10.3389/fcdhc.2025.1465732
Diabetes Technol Ther. 2025 Apr 16.

Dysglycemic Events after Initiation of Intermittently Scanned Continuous Glucose Monitoring in Patients with Insulin-Treated Type 2 Diabetes.

Lisa K Gilliam, Melissa M Parker, Andrew J Karter.

  Initiation of intermittently scanned continuous glucose monitors (isCGM) has been shown to reduce hemoglobin A1c (A1c) in patients with insulin-treated type 2 diabetes (T2D), but its effect on acute dysglycemic events (hypoglycemia and hyperglycemia) merits additional study. We conducted an observational, comparative effectiveness analysis of patients with insulin-treated T2D, comparing the efficacy of isCGM versus self-monitoring of blood glucose to improve glycemia and reduce acute dysglycemic events. We utilized a difference-in-differences framework to estimate pre-post changes in these outcomes, addressing confounding using overlap weighting based on propensity scores using rigorous causal analysis and machine learning. Initiating isCGM was associated with improved glycemia (reduced A1c, more patients with A1c <8% and <9%), but not the incidence of acute dysglycemic (hypoglycemic or hyperglycemic) events. This study on isCGM use is one of the largest to date and provides important information about the benefits of this technology in a population of patients with insulin-treated T2D.

Keywords:  continuous glucose monitor; emergency room; hospital admissions; hyperglycemia; hypoglycemia

DOI:  https://doi.org/10.1089/dia.2025.0021
Sci Rep. 2025 Apr 15. 15(1): 13032

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Ji Seung Ryu, Jang Hoon Ru, Yunkoo Kang, Sejung Yang.

Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. With the advent of continuous glucose monitoring systems, development of algorithms to analyze and predict glucose levels has gained considerable attention, with the aim of preemptively managing fluctuations before they become problematic. However, there is a lack of research focusing specifically on patients with GSD. Therefore, this study aimed to forecast glucose levels in patients with GSD using state-of-the-art deep-learning (DL) algorithms. This retrospective study utilized blood glucose data from patients with GSD who were either hospitalized or managed at Yonsei University Wonju Severance Christian Hospital, Korea, between August 2020 and February 2024. In this study, three state-of-the-art DL models for time-series forecasting were employed: PatchTST, LTSF N-Linear, and TS Mixer. First, the models were used to predict the patients' Glucose levels for the next hour. Second, a binary classification task was performed to assess whether hypoglycemia could be predicted alongside direct glucose levels. Consequently, this is the first study to demonstrate the capability of forecasting glucose levels in patients with GSD using continuous glucose-monitoring data and DL models. Our model provides patients with GSD with a more accessible tool for managing glucose levels. This study has a broader effect, potentially serving as a foundation for improving the care of patients with rare diseases using DL-based solutions.

DOI: https://doi.org/10.1038/s41598-025-97391-8
BMJ Open. 2025 Apr 15. 15(4): e090154

Impact of real-time glucose monitoring using FreeStyle Libre 3 on glycaemia in type 2 diabetes managed with basal insulin plus SGLT2 inhibitor and/or GLP-1 agonist: the FreeDM2 randomised controlled trial protocol.

Emma G Wilmot, Ramzi A Ajjan, Yee S Cheah, Pratik Choudhary, Iain Cranston, Rachel Ann Elliott, Mark Evans, Ahmed Iqbal, Shafie Kamaruddin, Katharine Barnard-Kelly, Alistair Lumb, Thinzar Min, Patrick Moore, Parth Narendran, Sankalpa Neupane, Gerry Rayman, Thozhukat Sathyapalan, Hood Thabit, Thomas Yates, Lalantha Leelarathna.

   INTRODUCTION: Effective management of type 2 diabetes mellitus (T2DM) consists of lifestyle modification and therapy optimisation. While glycaemic monitoring can be used as a tool to guide these changes, this can be challenging with self-monitoring of blood glucose (SMBG). The FreeStyle Libre 3 (FSL3) is a real-time continuous glucose monitoring (CGM) system designed to replace SMBG. The evidence for the benefit of CGM in people with T2DM on non-intensive insulin regimens is limited. This study aims primarily to assess the glycaemic impact of FSL3 in people with suboptimally controlled T2DM treated with basal-only insulin regimens plus sodium-glucose cotransporter-2 (SGLT-2) inhibitor and/or glucagon-like peptide (GLP)-1 agonist.
METHODS AND ANALYSIS: This is an open-label, multicentre, parallel design, randomised (2:1) controlled trial. Recruitment has been offered across 24 clinical centres in the UK and nationally through self-referral. Adults with T2DM treated with basal-only insulin regimens plus SGLT-2 inhibitor and/or GLP-1 agonist and with screening HbA1c from ≥59 mmol/mol to ≤97 mmol/mol are included. Eligible participants will be randomised to either FSL3 (intervention) for 32 weeks or continuation of SMBG (control). The study is split into two phases, each of 16 weeks duration: phase 1 consisting of self-management with basal-insulin self-titration and phase 2 where additional therapies may be initiated. Control group participants may subsequently enter an optional extension phase to receive FSL3. The primary endpoint is the difference between treatment groups in mean change from baseline in HbA1c at 16 weeks. Secondary outcomes include HbA1c at 32 weeks, CGM-based metrics, therapy changes, physical activity levels and psychosocial measures. An economic evaluation for costs and patient outcomes will be undertaken.
ETHICS AND DISSEMINATION: The study was approved by the Health Research Authority, Health and Care Research Wales and the West Midlands-Edgbaston Research Ethics Committee (reference: 23/WM/0092). Study results will be disseminated in peer-reviewed journals.
TRIAL REGISTRATION NUMBER: NCT05944432.
SECONDARY IDENTIFYING NUMBER: Identifier assigned by the sponsor: ADC-UK-PMS-22057.
PROTOCOL VERSION: Revision D. Dated, 13 December 2024.

Keywords:  DIABETES & ENDOCRINOLOGY; Diabetes Mellitus, Type 2; Randomized Controlled Trial

DOI:  https://doi.org/10.1136/bmjopen-2024-090154
J Diabetes Complications. 2025 Apr 10. pii: S1056-8727(25)00092-3. [Epub ahead of print]39(6): 109039

Semaglutide, central retinal thickness and continuous glucose monitoring in persons with type 2 diabetes: A post-hoc analysis from a randomised trial.

Søren Gullaksen, Liv Vernstrøm, Steffen Skovgaard Sørensen, Kristian Løkke Funck, Line Petersen, Toke Bek, Per Løgstrup Poulsen, Esben Laugesen.

   AIMS: Glycemic control is important for preventing diabetic retinopathy (DR), but rapid improvements could deteriorate the disease. In some, but not all studies, semaglutide is speculated to worsen DR, but the mechanism is unknown. Central retinal thickness (CRT) is an early marker of DR. Therefore, the objective was to investigate whether increased Time in Range (TIR (3.9-10.0 mmol/L)), was associated with reduced CRT in persons treated with semaglutide.
METHODS: Forty participants with type 2 diabetes were included in this post-hoc analysis of a 32-week randomised, placebo-controlled, partly open-label trial investigating the separate and combined effects of semaglutide and empagliflozin on target organ damage in 120 participants with type 2 diabetes. Individuals were randomised into four groups: i) semaglutide, ii) empagliflozin, iii) the combination or iv) placebo, n = 30 for each group). In the present study, 10 participants from each of the 4 arms participated. TIR was assessed using Continuous Glucose Measurement for 7-8 days and CRT was assessed using ocular coherence tomography.
RESULTS: In the 10 individuals treated with semaglutide, CRT increased ~1 % (3.76 μm, 95%CI [-0.32; 7.85], p = 0.065) compared to placebo. This was attenuated with adjustment for TIR (p = 0.21). Independently of the four interventions, increased TIR remained associated with increased CRT (0.07 μm, 95%CI[0.03; 0.12]μm, p = 0.002).
CONCLUSION: Semaglutide treatment did not impact CRT beyond what could be explained by changes in glycaemia. Across all interventions, increased TIR was associated with increases in CRT, thus supporting the link between rapid improved glycemia and DR.

Keywords:  Continuous glucose monitoring; Diabetic retinopathy; Glucagon-like peptide 1 receptor agonist; Type 2 diabetes

DOI:  https://doi.org/10.1016/j.jdiacomp.2025.109039
Nutrients. 2025 Mar 22. pii: 1109. [Epub ahead of print]17(7):

Education Practices of Dietitians Across Australia and New Zealand Around the Glycaemic Management of Dietary Fat and Protein in Type 1 Diabetes and the Use of Continuous Glucose Monitoring: A Survey Evaluation.

Evangeline Laurence, Carmel E Smart, Kirrilly M Pursey, Tenele A Smith.

  Background/Objectives: International guidelines recommend that all children and adolescents with type 1 diabetes (T1D) receive education on the glycaemic impact of fat and protein from diagnosis. In addition, the insulin strategy should be adjusted to compensate for fat and protein excursions. Data from continuous glucose monitoring (CGM) can guide insulin adjustment. This study sought to determine whether the current practices of dietitians in Australia and New Zealand align with guidelines. Methods: An anonymous, online survey of paediatric T1D dietitians working in tertiary centres (n = 20; Australia, n = 14, New Zealand, n = 6) was undertaken from February to March 2023. The Australian and New Zealand Society for Paediatric Endocrinology and Diabetes (ANZSPED) disseminated the survey link. The questionnaire covered three content domains: demographic information about the clinic and practitioner, the health professionals' education practices regarding fat and protein, and the use of CGM. Results: This pilot study had a 100% response rate, with a dietitian representative from all eligible centres responding on behalf of the diabetes team. Only 10% (n = 2) of respondents both (i) provided education on the glycaemic impact of fat and protein to all families at diagnosis and (ii) always provided insulin strategies to manage fat and protein where it impacted glycemia, as per guidelines. Barriers to education included a lack of procedure (47%, n = 7), consumer resources (40%, n = 6), and time (33%, n = 5). Reasons for not recommending strategies to manage fat and protein were perceptions that the family was overwhelmed (100%, n = 10) or not interested (60%, n = 6), and uncertainty of the best strategy (40%, n = 4). CGM was used by "almost all" respondents to educate and adjust the insulin strategy (90%, n = 18). Conclusions: Most dietitians surveyed were not consistently providing fat and protein education and management strategies to children with T1D in line with guidelines. CGM is a key tool routinely used by dietitians in nutrition education to help guide insulin adjustment. Dietitians need greater support through educational resources for families and training in evidence-based strategies to manage deglycation from dietary fat and protein to align with guidelines.

Keywords:  child; continuous glucose monitoring; diabetes mellitus; dietary fats; insulin; type 1

DOI:  https://doi.org/10.3390/nu17071109
J Clin Med. 2025 Mar 21. pii: 2144. [Epub ahead of print]14(7):

A Multidisciplinary Approach of Type 1 Diabetes: The Intersection of Technology, Immunotherapy, and Personalized Medicine.

Denisa Batir-Marin, Claudia Simona Ștefan, Monica Boev, Gabriela Gurău, Gabriel Valeriu Popa, Mădălina Nicoleta Matei, Maria Ursu, Aurel Nechita, Nicoleta-Maricica Maftei.

  Background: Type 1 diabetes (T1D) is a chronic autoimmune disorder characterized by the destruction of pancreatic β-cells, leading to absolute insulin deficiency. Despite advancements in insulin therapy and glucose monitoring, achieving optimal glycemic control remains a challenge. Emerging technologies and novel therapeutic strategies are transforming the landscape of T1D management, offering new opportunities for improved outcomes. Methods: This review synthesizes recent advancements in T1D treatment, focusing on innovations in continuous glucose monitoring (CGM), automated insulin delivery systems, smart insulin formulations, telemedicine, and artificial intelligence (AI). Additionally, we explore biomedical approaches such as stem cell therapy, gene editing, immunotherapy, gut microbiota modulation, nanomedicine-based interventions, and trace element-based therapies. Results: Advances in digital health, including CGM integration with hybrid closed-loop insulin pumps and AI-driven predictive analytics, have significantly improved real-time glucose management. AI and telemedicine have enhanced personalized diabetes care and patient engagement. Furthermore, regenerative medicine strategies, including β-cell replacement, CRISPR-based gene editing, and immunomodulatory therapies, hold potential for disease modification. Probiotics and microbiome-targeted therapies have demonstrated promising effects in maintaining metabolic homeostasis, while nanomedicine-based trace elements provide additional strategies to regulate insulin sensitivity and oxidative stress. Conclusions: The future of T1D management is shifting toward precision medicine and integrated technological solutions. While these advancements present promising therapeutic avenues, challenges such as long-term efficacy, safety, accessibility, and clinical validation must be addressed. A multidisciplinary approach, combining biomedical research, artificial intelligence, and nanotechnology, will be essential to translate these innovations into clinical practice, ultimately improving the quality of life for individuals with T1D.

Keywords:  continuous glucose monitoring; future solutions in T1D therapy; insulin therapy; type 1 diabetes

DOI:  https://doi.org/10.3390/jcm14072144
Diabetes Res Clin Pract. 2025 Apr 16. pii: S0168-8227(25)00185-8. [Epub ahead of print] 112171

Making Sense: Feasibility of an education program to Empower adults with type 1 diabetes to interpret CGM data.

Georgina Manos, John Apostolopoulos, Meaghan Read, Michelle Cox, Frank Gao, Kim Henshaw, Carolyn Hines, Alicia Jenkins, Katerina Kiburg, Cecilia Pham, Catriona Sims, Jane Speight, Spiros Fourlanos, Elif Ekinci, David O'Neal.

AIMS: To assess the feasibility and acceptability of a structured education program focused on continuous glucose monitoring (CGM) data interpretation for adults living with type 1 diabetes.
METHODS: This was a multi-centre before and after feasibility study conducted in Australia. Adults with type 1 diabetes were enrolled in the Making Sense program. This two-week program, designed in consultation with adults living with diabetes, was delivered in a hybrid format involving group sessions and self-directed online modules. Participants were followed for 6-months post-education completion. The primary outcome was feasibility, pre-determined at 75% completion rate following education module enrolment. Secondary outcomes included participant acceptability, psychosocial measures and glycemic outcomes.
RESULTS: Between June 2023 and October 2023, 67 participants (median age 54, 70 % female, mean HbA1c 7.2 % (55 mmol/mol)) were enrolled. All used CGM continuously. Sixty-six percent used insulin pumps. Eighty-one percent completed the study and 83 % would recommend the program to other adults with diabetes. Participation was associated with improved well-being, diabetes management satisfaction and reduction in diabetes distress (p < 0.05). HbA1c fell 0.5 % (4.5 mmol/mol) for those > 7.5 % (58 mmol/mol) at baseline (p = 0.006).
CONCLUSIONS: Our CGM-related education program was feasible and acceptable. Participation may be associated with improvement psychosocial and glycemic outcomes.

DOI: https://doi.org/10.1016/j.diabres.2025.112171
World J Diabetes. 2025 Apr 15. 16(4): 103002

Digital health technologies/interventions in smart ward development for elderly patients with diabetes: A perspective from China and beyond.

Wei Yang, Juan Lu, Si-Cong Si, Wei-Hua Wang, Jing Li, Yi-Xin Ma, Huan Zhao, Jia Liu.

  Diabetes is highly prevalent among the elderly worldwide, with the highest number of diabetes cases in China. Yet, the management of diabetes remains unsatisfactory. Recent advances in digital health technologies have facilitated the establishment of smart wards for diabetes patients. There is a lack of smart wards tailored specifically for older diabetes patients who encounter unique challenges in glycemic control and diabetes management, including an increased vulnerability to hypoglycemia, the presence of multiple chronic diseases, and cognitive decline. In this review, studies on digital health technologies for diabetes in China and beyond were summarized to elucidate how the adoption of digital health technologies, such as real-time continuous glucose monitoring, sensor-augmented pump technology, and their integration with 5th generation networks, big data cloud storage, and hospital information systems, can address issues specifically related to elderly diabetes patients in hospital wards. Furthermore, the challenges and future directions for establishing and implementing smart wards for elderly diabetes patients are discussed, and these challenges may also be applicable to other countries worldwide, not just in China. Taken together, the smart wards may enhance clinical outcomes, address specific issues, and eventually improve patient-centered hospital care for elderly patients with diabetes.

Keywords:  Continuous glucose monitoring; Diabetes; Digital health technology; Elderly care; Sensor-augmented pump; Smart ward; Wearable devices

DOI:  https://doi.org/10.4239/wjd.v16.i4.103002
Sci Rep. 2025 Apr 18. 15(1): 13386

Seasonal, weekly, and individual variations in long-term use of wearable medical devices for diabetes management.

Yanjun Cui, Catherine Stanger, Temiloluwa Prioleau.

  Wearable medical-grade devices are transforming the standard of care for prevalent chronic conditions like diabetes. Yet, adoption and long-term use remain a challenge for many people. In this study, we investigate patterns of consistent versus disrupted use of continuous glucose monitors (CGMs) through analysis of more than 118,000 days of data, with over 22 million blood glucose samples, from 108 young adults with type 1 diabetes (average: 3 years of CGM data per person). In this population, we found more consistent CGM use at the start and end of the year (e.g., January, December), and more disrupted CGM use in the middle of the year/warmer months (i.e., May to July). We also found more consistent CGM use on weekdays (Monday to Thursday) and during waking hours (6AM - 6PM), but more disrupted CGM use on weekends (Friday to Sunday) and during evening/night hours (7PM - 5AM). Only 52.7% of participants (57 out of 108) had consistent and sustained CGM use over the years (i.e., over 70% daily wear time for more than 70% of their data duration). From semi-structured interviews, we unpack factors contributing to sustained CGM use (e.g., easier and better blood glucose management) and factors contributing to disrupted CGM use (e.g., changes in insurance coverage, issues with sensor adhesiveness/lifespan, and college/life transitions). We leverage insights from this study to elicit implications for next-generation technology and interventions that can circumvent seasonal and other factors that disrupt sustained use of wearable medical devices for the goal of improving health outcomes.

Keywords:  Continuous glucose monitors; Seasonal variations; Type 1 diabetes

DOI:  https://doi.org/10.1038/s41598-025-98276-6
Sensors (Basel). 2025 Apr 02. pii: 2238. [Epub ahead of print]25(7):

Noninvasive Glucose Measurements in Tissue Simulating Phantoms Using a Solid-State Near-Infrared Sensor.

Ariel B Kauffman, Ruben Shakya, Shuai Yu, Mark A Arnold.

  Benchmark data are reported for a solid-state laser-based near-infrared spectrometer designed for noninvasive measurements in human skin. These data were obtained using a set of aqueous phantoms composed of polystyrene beads, triton X-100, saline, and glucose. The performance of this prototype solid-state laser platform was compared to parallel results obtained with a Fourier-transform (FT) spectrometer. The fundamental spectroscopic performances of the two spectrometer systems were quantified by an analysis of 100% lines determined by ratioing back-to-back spectra collected over time for each phantom. Root mean square (RMS) noise levels were computed for each dataset and the median RMS noise levels were 327.8 µAU and 667.2 µAU for the FT spectrometer and prototype laser platform, respectively. The analytical utility of the solid-state laser platform was assessed through a series of leave-one-phantom-out partial least squares analyses. Results for the laser prototype data included a standard error of cross validation (SECV) of 7.82 mg/dL for an optimized PLS model with 10 factors over a spectral range of 1401-2238 nm. This compares favorably with the results from the FT spectrometer of an SECV of 6.62 mg/dL with 8 factors and a spectral range of 1551-2378 nm. The additional two PLS factors for the laser prototype were shown to be a consequence of its higher spectral noise. Selectivity of these PLS models was demonstrated by comparing models associated with correct and random glucose assignments to each spectrum. Overall, these findings benchmark the analytical utility of this solid-state laser prototype.

Keywords:  RMS spectral noise benchmarking; near-infrared spectroscopy; noninvasive glucose monitor; noninvasive glucose sensing; photonic integrated chips; skin glucose phantoms; solid-state laser spectroscopy

DOI:  https://doi.org/10.3390/s25072238