bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–05–03
seventeen papers selected by
Mott Given
  1. Reply to "Comment on 'Identifying high glucose variability using non-glycemic factors in low continuous glucose monitoring use settings'".
  2. Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition.
  3. Continuous Glucose Monitoring in Hospital Settings: Enhancing Patient Care and Outcomes.
  4. Integrating continuous glucose monitoring alarms in primary care: Evaluation of utilization rates and achieved glycemic control in people with type 2 diabetes treated with insulin.
  5. Continuous glucose monitoring versus self-monitoring of blood glucose in individuals with type 2 diabetes: a randomised, multicentre, open-label, superiority trial.
  6. Analysis of lifestyle factors related to improving blood glucose metabolism by continuous glucose monitoring: a multicenter study in the Chikugo region.
  7. Improving glycaemic control in primary care for Tongan adults with type 2 diabetes through the use of continuous glucose monitoring and holistic support: a pilot study.
  8. Imputation of Missing Continuous Glucose Monitor Data.
  9. Fatal Necrotizing Soft Tissue Infection Following Continuous Glucose Monitoring in a Patient with Type 1 Diabetes: A Case Report and Literature Review.
  10. Virtual Weight Management and Continuous Glucose Monitoring in Patients With Type 2 Diabetes: A Randomized Controlled Trial.
  11. Quality of Life and Humanistic Burden of Adults with Type 1 Diabetes with Recurrent Severe Hypoglycemic Events and Impaired Awareness of Hypoglycemia Using a Continuous Glucose Monitor.
  12. MetaboNet: The Largest Publicly Available Consolidated Data Set for Type 1 Diabetes Management.
  13. The Current Landscape of Remote Patient Monitoring Regarding Diabetes Mellitus and Hypoglycemia: Protocol for a Scoping Review.
  14. Device-measured baseline physical activity is associated with 24-h CGM glycemic responses and 12-week time-in-range after mixed exercise training in type 2 diabetes: a randomised crossover trial.
  15. Establishing Glycemic Targets: Epidemiologic Insights and Landmark Studies in Hospital Management.
  16. Time in range during caloric restriction in type 2 diabetes with obesity.
  17. Metaproteomics characterizes host-microbiota responses to the time in range derived from continuous glucose monitoring in type 1 diabetes.
  1. Prim Care Diabetes. 2026 Apr 25. pii: S1751-9918(26)00082-3. [Epub ahead of print]
    Reply to "Comment on 'Identifying high glucose variability using non-glycemic factors in low continuous glucose monitoring use settings'".
    Suresh Rama Chandran.
      
    Keywords:  Continuous glucose monitoring; Glucose variability; Type 2 diabetes
    DOI:  https://doi.org/10.1016/j.pcd.2026.04.011
  2. J Diabetes Sci Technol. 2026 Apr 26. 19322968261438523
    Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition.
    Timor Glatzer, Ajandek Peak, Eemeli Leppäaho, Patrick Lustenberger, Pau Herrero, Magí Andorrà, Ellen van Maren.
       BACKGROUND: Continuous glucose monitoring (CGM) provides real-time glucose data, aiding diabetes management. Identifying glucose patterns is difficult for patients due to data overload, hindering self-management. This study aimed to systematically identify glucose patterns using Accu-Chek SmartGuide and quantify their impact on glucose management.
    METHODS: This retrospective, observational analysis included real-world CGM data from 3379 individuals with type 1 diabetes (T1D; N = 2198) or type 2 diabetes (T2D; N = 1181), encompassing 23 486 valid user-weeks. An algorithm identified 29 predefined glucose patterns weekly. Pattern prevalence, demographic influence, persistence, their attribution to time above range/time below range (TAR/TBR) as well as their potential impact on time in range (TIR) in case of pattern resolution were analyzed.
    RESULTS: Resolving glucose patterns, defined as repeatedly occurring glucose events, showed varying potential for glycemic improvement. Cumulatively, actionable patterns contributed significantly to total TAR (T1D: 66.2 ± 14.7%, T2D: 58.0 ± 14.3%) and TBR (T1D: 56.3 ± 2.6%, T2D: 42.2 ± 1.4%). For instance, resolving the day-time hyperglycemia pattern could improve TIR by up to +10.72% (4.26, 16.9) in T1D and +5.16% (0.0, 12.92) in T2D, addressing an average of 9.33 (8.0, 10.75) events per week in T1D and 9.29 (8.0, 10.67) in T2D.
    CONCLUSION: The majority of glucose excursions in T1D and T2D can be explained by recurring glucose patterns. Detecting these actionable patterns provides an opportunity to improve TIR. Targeting therapy and behavior change toward resolving these patterns is a critical step toward more personalized diabetes management.
    Keywords:  artificial intelligence; continuous glucose monitoring (CGM); glucose patterns; glucose variability; hyperglycemia; hypoglycemia; pattern recognition
    DOI:  https://doi.org/10.1177/19322968261438523
  3. Med Clin North Am. 2026 May;pii: S0025-7125(25)00169-5. [Epub ahead of print]110(3): 397-414
    Continuous Glucose Monitoring in Hospital Settings: Enhancing Patient Care and Outcomes.
    Eileen R Faulds, Michael Hughes.
      Continuous glucose monitoring (CGM) is increasingly recognized as a valuable tool for inpatient diabetes management, offering continuous data streams that overcome the limitations of intermittent point-of-care testing. This review summarizes current evidence on CGM use in both critical care and non-ICU settings, highlighting improvements in time in range, hypoglycemia detection, and workflow efficiency. Implementation strategies, including hybrid protocols, calibration approaches, alarm management, and electronic health record integration, are discussed alongside regulatory considerations and guideline recommendations. CGM not only improves safety and patient care today but also lays the foundation for future AI-driven clinical decision support in hospitals.
    Keywords:  Continuous glucose monitoring (CGM); Critical care; Glucose monitoring; Hospital; Inpatient; Non-critical care
    DOI:  https://doi.org/10.1016/j.mcna.2025.11.008
  4. Prim Care Diabetes. 2026 Apr 27. pii: S1751-9918(26)00083-5. [Epub ahead of print]
    Integrating continuous glucose monitoring alarms in primary care: Evaluation of utilization rates and achieved glycemic control in people with type 2 diabetes treated with insulin.
    Diana Cristina Henao-Carrillo, Ana María Gómez, Julian Alberto Calvo-Hurtado, Carolina Concha-Vargas, Juan Diego Rodríguez-Martinez, María José Fuentes, Oscar Muñoz-Velandia.
       AIMS: This study aims to evaluate the impact of alarm activation on Continuous Glucose Monitoring (CGM) metrics in people with type 2 diabetes (PwT2D) using the FSL2 system in a primary care setting.
    METHODS: A cross-sectional study was conducted including PwT2D who were treated with insulin, used FreeStyle Libre 2 system and were managed at the Cafam FreeStyle Libre (FSL) Program, in Bogotá, Colombia. All patients received education upon enrolment and were followed up by a team of primary care physicians (PCP). Data were obtained from the LibreView and LibreLens platforms. CGM metrics based on alarm usage were analyzed. Additionally, glycemic control and adherence metrics were evaluated classifying patients into three groups: adequate control (TBR>70%,TBR<4%), high-risk of hypoglycemia (TBR≥4%), or high-risk of hyperglycemia (TIR≤70%,TBR<4% and TAR>25%).
    RESULTS: Analysis of 221 individuals (median age 61; IQR 51-71) revealed that only 14.5% had active glucose alarms. Those with active alarms showed a non-significant trend toward improved glycemic control (median TIR 65% [52.8-79.3%] vs 61% [42.5-75%], p = 0.107). Glycemic risk stratification showed that 53% of participants were at high risk for hyperglycemia and 19% at high risk for hypoglycemia, with only 28% achieving adequate control. No CGM adherence differences were found between groups.
    CONCLUSION: Patients with active alarms tend to have a higher TIR, suggesting that alarm activation may positively influence glycemic control in this population. However, given the low rate of alarm utilization, structured training to promote alarm use among PwT2D under PCPs follow-up is essential.
    Keywords:  Alarms; Alerts; Continuous glucose monitoring; Diabetes education; Time in range; Type 2 diabetes
    DOI:  https://doi.org/10.1016/j.pcd.2026.04.010
  5. Lancet Diabetes Endocrinol. 2026 Apr 23. pii: S2213-8587(26)00076-8. [Epub ahead of print]
    Continuous glucose monitoring versus self-monitoring of blood glucose in individuals with type 2 diabetes: a randomised, multicentre, open-label, superiority trial.
    FreeDM2 Study Group
       BACKGROUND: Type 2 diabetes is the most common metabolic disorder worldwide, accounting for about 90% of people living with diabetes. Glycated haemoglobin (HbA1c), a measure of chronic glycaemic exposure, correlates with the risk of long-term complications, which can result in substantial morbidity for people with diabetes and major costs to health-care systems. The value of continuous glucose monitoring (CGM) in people with type 2 diabetes managed with basal insulin and modern therapies remains unclear. FreeDM2 aimed to evaluate the effectiveness of real-time CGM in adults with type 2 diabetes.
    METHODS: This open-label, parallel-design, randomised controlled trial conducted across 24 primary and secondary care centres in the UK enrolled adults with type 2 diabetes managed with basal insulin and SGLT2 inhibitors or GLP-1 receptor agonists or dual GIP/GLP-1 receptor agonists with HbA1c 7·5-11·0%. Participants were assigned (2:1; using permuted block randomisation by study site, generated by Sealed Envelope) to CGM (intervention) or continuation of self-monitoring of blood glucose (SMBG; control), across two phases: weeks 1-16, self-management with basal insulin self-titration; and weeks 17-32, clinician-supported where additional therapies could be initiated in line with national guidance. Participants and study site staff were not masked to group allocation. The primary outcome was difference between groups in HbA1c concentrations at 16 weeks, and the key secondary outcome was the difference between groups at 32 weeks, both in the treatment policy estimand. Safety analysis included all randomly assigned participants. The FreeDM2 randomised controlled trial is registered at ClinicalTrials.gov (NCT05944432) and is complete.
    FINDINGS: Between July 26, 2023, and Jan 31, 2025, 469 individuals underwent screening for potential study inclusion, 140 were excluded due to not meeting inclusion criteria, and 329 were included in the baseline phase of the study. 26 individuals were then excluded due to insufficient data capture or withdrawal, and 303 participants were randomly assigned; 198 to the CGM intervention group and 105 to the SMBG control group. 204 (67%) participants were male and 99 (33%) were female, the mean age of the cohort was 60·7 years (SD 9·8), and mean diabetes duration was 16·7 years (6·9). Baseline HbA1c concentration was 8·8% (SD 1·0) in the CGM group and 8·8% (1·1) in the control group, decreasing to 8·0% (0·9) in the CGM group and to 8·7% (1·1) in the control group at week 16 (adjusted difference -0·6 [95% CI -0·8 to -0·3]; p<0·0001) and decreasing further to 7·8% (0·9) in the CGM group and to 8·3% (1·2) in the control group at week 32 (adjusted difference -0·5 [95% CI -0·7 to -0·2]; p<0·0001). There was a similar incidence of non-device-related adverse events in both groups, and two instances of severe hypoglycaemia in the control group.
    INTERPRETATION: In adults with type 2 diabetes on basal insulin plus modern therapies, real-time CGM improved glycaemic control versus SMBG during self-management and under clinician-supported management.
    FUNDING: Abbott Diabetes Care.
    DOI:  https://doi.org/10.1016/S2213-8587(26)00076-8
  6. Diabetol Int. 2026 Jul;17(3): 39
    Analysis of lifestyle factors related to improving blood glucose metabolism by continuous glucose monitoring: a multicenter study in the Chikugo region.
    Hitomi Nakayama, Toshihiko Hashinaga, Kentaro Yamada, Nobuhiko Wada, Chizuko Inada, Shimpei Iwata, Ichiro Tokubuchi, Satoko Yoshinobu, Yohei Tanaka, Ayako Nagayama, Yoshie Otsuka, Nao Hasuzawa, Hiroko Haruta, Itaru Morikawa, Miyu Kinjo, Masatoshi Nomura.
       Background: Although lifestyle-improving effects of continuous glucose monitoring (CGM) have long been suggested, it remains unclear which lifestyle changes contribute to improvements in blood glucose control. This multicenter study evaluated the effect of CGM on glycosylated hemoglobin (HbA1c) and identified lifestyle factors associated with its improvement.
    Methods: We retrospectively investigated changes in insulin injection methods and lifestyle habits after starting CGM via a questionnaire and analyzed the associations between these changes and the degree of reduction in HbA1c over a 6-month period.
    Results: Wearing a CGM device reduced HbA1c by a mean of 0.83% (median 0.5%), regardless of sex or age. This reduction was smaller in individuals with type 1 diabetes and in those receiving insulin injections 3 or more times per day. Changes in insulin injection doses were not the primary cause of the reduction in HbA1c. Increased insulin doses and more frequent adjustments were not associated with lower HbA1c. After adjusting for HbA1c levels at the start of CGM use, dietary changes associated with HbA1c improvement included reduced intake of carbohydrates and foods that increase blood glucose levels and decreased meal skipping. Increases in resistance exercise and postprandial exercise were also associated with this improvement.
    Conclusions: The decrease in HbA1c levels after starting CGM was driven primarily by participants becoming aware of fluctuations in blood glucose levels due to diet and exercise, and making lifestyle changes to improve glycemic control.
    Keywords:  Continuous glucose monitoring; Dietary habit; Exercise habit; Insulin injection; Life-style; Questionnaire
    DOI:  https://doi.org/10.1007/s13340-026-00897-3
  7. J Prim Health Care. 2026 Apr 27.
    Improving glycaemic control in primary care for Tongan adults with type 2 diabetes through the use of continuous glucose monitoring and holistic support: a pilot study.
    Janina'ofa Galewski-Tangataevaha, Hamish Crocket, S 'Apo' Aporosa, Sione Vaka, Seong Hoon Yoon, Lynne Chepulis.
       INTRODUCTION: In Aotearoa New Zealand, Pacific peoples, including Tongans, experience disproportionately higher rates of type 2 diabetes and related complications. There is an urgent need for innovative, culturally appropriate interventions to improve outcomes.
    AIM: This study aimed to determine the impact of continuous glucose monitoring devices with cultural wrap-around support on medium-term glycaemic control and other type 2 diabetes biomarkers in Tongan adults with high-risk type 2 diabetes.
    METHODS: Twenty-two Tongan adults with HbA1c ≥60 mmol/mol were invited to participate in a 6-month pilot intervention study involving 4 weeks of continuous glucose monitoring wear at baseline and 2 weeks at 3-months, alongside wrap-around care delivered by a Tongan kaiāwhina (support health worker). The primary endpoint was 3-month HbA1c. Clinical (glycated haemoglobin, lipids, estimated glomerular filtration rate, urinary albumin to creatinine ratio) and psychosocial (Diabetes Self-Management Questionnaire, measured at baseline and 3 months) outcomes were measured at baseline, 3, and 6 months.
    RESULTS: Nineteen participants completed the study through to 6 months. Mean HbA1c significantly decreased from 80.2 ± 19.4 mmol/mol at baseline to 68.6 ± 14.2 mmol/mol at 3 months, with reductions maintained at 6 months. No significant changes in lipids or renal function were observed. Diabetes Self-Management Questionnaire scores increased from 4.9 ± 0.8 to 6.0 ± 1.0 (P < 0.001).
    DISCUSSION: Culturally tailored continuous glucose monitoring-based interventions have the potential to support Tongan adults with understanding, optimising, and managing type 2 diabetes.
    Keywords:  Pacific peoples; Tongan health; continuous glucose monitoring; culturally responsive care; diabetes intervention; primary care; self-management; type 2 diabetes
    DOI:  https://doi.org/10.1071/HC25177
  8. J Diabetes Sci Technol. 2026 May;20(3): 815-824
    Imputation of Missing Continuous Glucose Monitor Data.
    Alan Kuang, Yuanzhi Yu, Juned Siddique, Denise Scholtens.
       BACKGROUND: Continuous glucose monitors (CGMs) in research and clinical settings characterize glycemic profiles through repeated measurement of interstitial glucose levels on the order of minutes. Missing values from devices are unavoidable. Data from the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs) study were used to investigate the impact of missing data on CGM summary metrics. Several imputation techniques were evaluated by comparing mean relative bias (MRB) between true and imputed CGM data for the summary metrics.
    METHODS: We used 105 CGM profiles with nine days of complete glucose measurements and introduced missing data strings using a zero-inflated negative binomial hurdle model. Overall missingness was introduced at 2% consistent with GO MOMs data and increased to 5%, 10%, and 20%. Imputation approaches included single, multiple, machine learning techniques, and hot-deck imputation, where missing values are replaced with the participant's observed values. Removing missing values prior to analysis (complete case analysis) was also evaluated.
    RESULTS: The MRB is minimal across most metrics and imputation methods at overall 2% missing data and increases with higher missing data frequency, with trends depending on metric and imputation method. Hot-deck imputation and complete case analysis show consistently low MRB.
    CONCLUSIONS: Missing CGM data are to be expected. For periods of wear with up to 20% missing data, hot-deck imputation and complete case analysis may be acceptable if data are missing completely at random. Explored imputation techniques are robust, but each has their own limitations, which should be considered if these techniques are implemented.
    Keywords:  continuous glucose monitoring; hot-deck imputation; machine learning; missing data; multiple imputation
    DOI:  https://doi.org/10.1177/19322968241308217
  9. Diseases. 2026 Mar 31. pii: 124. [Epub ahead of print]14(4):
    Fatal Necrotizing Soft Tissue Infection Following Continuous Glucose Monitoring in a Patient with Type 1 Diabetes: A Case Report and Literature Review.
    Constantin Popazu, Cristiana Voineag, Ionica Grigore, Cristina Șerban, Mădălin Guliciuc, Dragoș Voicu, Alexandra Toma.
       BACKGROUND: Continuous glucose monitoring (CGM) systems have significantly improved glycemic management in patients with type 1 diabetes mellitus and are generally considered safe. However, transcutaneous sensor insertion disrupts the skin barrier and, in susceptible individuals, may contribute to infectious complications. Severe soft tissue infections occurring in temporal association with CGM use are exceedingly rare.
    CASE PRESENTATION: We report a fatal case of necrotizing soft tissue infection in a 54-year-old male with long-standing type 1 diabetes mellitus occurring in temporal association with CGM use. The patient initially developed localized inflammation at a prior sensor insertion site that failed to fully resolve. Over subsequent weeks, he experienced progressive systemic symptoms and worsening local findings, culminating in advanced necrotizing infection. Despite emergency surgical debridement, broad-spectrum antimicrobial therapy, and intensive care support, the clinical course was complicated by septic shock and multiorgan failure, resulting in death.
    DISCUSSION: This case highlights the role of patient-specific vulnerability, persistent insertion-site inflammation, and delayed clinical recognition in the progression from localized skin changes to life-threatening infection. Importantly, this report does not establish a direct causal relationship between CGM use and necrotizing soft tissue infection but underscores the need for heightened vigilance in high-risk individuals.
    CONCLUSIONS: Although CGM systems have a favorable safety profile, careful inspection of insertion sites, avoidance of sensor reapplication over incompletely healed tissue, and early evaluation of persistent or progressive symptoms are essential to minimize the risk of severe outcomes in susceptible patients.
    Keywords:  case report; continuous glucose monitoring; device-associated infection; necrotizing soft tissue infection; sepsis; skin barrier disruption; type 1 diabetes mellitus
    DOI:  https://doi.org/10.3390/diseases14040124
  10. Obesity (Silver Spring). 2026 Apr 28.
    Virtual Weight Management and Continuous Glucose Monitoring in Patients With Type 2 Diabetes: A Randomized Controlled Trial.
    Peter T Katzmarzyk, Kimberly Drews, Jamy D Ard, Tracey L McLaughlin, Melissa N Harris, Alexandra Lee, Gary D Foster, Hyun Jung Lee, Timothy C Dunn, Corby K Martin, Frank L Greenway, Michelle I Cardel.
       OBJECTIVE: This study examined the effectiveness of behavioral weight management integrated with continuous glucose monitoring (CGM) among adults with overweight or obesity and type 2 diabetes.
    METHODS: This was a two-arm, parallel, randomized clinical trial. Participants (n = 151) were randomly assigned to intervention (INT; n = 75) or usual care (UC; N = 76). INT participants received a behavioral weight management program tailored for type 2 diabetes and CGM. UC participants received a session with a dietitian and educational materials. The primary outcome was 6-month change in HbA1c%. Additional outcomes included changes in weight, waist circumference, blood pressure, CGM metrics, diabetes stress, and treatment satisfaction.
    RESULTS: INT reduced HbA1c% (-0.87%; 95% CI: -1.17%, -0.57%) more than UC (-0.41%; -0.72%, -0.10%) (difference: -0.46%; -0.89%, -0.03%; p = 0.037). INT had greater reductions in percent body weight compared to UC (difference: -3.2%; -4.9%, -1.5%; p < 0.001). Several CGM metrics improved significantly more in INT than UC, and INT had significantly greater improvements in diabetes treatment satisfaction and regimen-related diabetes stress relative to UC.
    CONCLUSIONS: Integrating CGM with a digital weight management program produced significantly greater improvements in HbA1c%, body weight, and other glucose metrics compared to UC among adults with overweight or obesity and type 2 diabetes.
    TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05935514.
    Keywords:  behavioral; digital; glucose; hemoglobin A1c; weight loss
    DOI:  https://doi.org/10.1002/oby.70204
  11. Diabetes Ther. 2026 Apr 30.
    Quality of Life and Humanistic Burden of Adults with Type 1 Diabetes with Recurrent Severe Hypoglycemic Events and Impaired Awareness of Hypoglycemia Using a Continuous Glucose Monitor.
    Adriana Boateng-Kuffour, Caitlin S Kelly, Huyen T Nguyen, Nanxin Li, Keval Chandarana, Katherine S Chapman, Liang Chen, Emilee M Cornelius, Wendy A Wolf, Beth Barber, William H Polonsky.
       INTRODUCTION: This study aimed to examine psychosocial well-being, quality of life (QoL), and productivity in people with type 1 diabetes (pwT1D) who were experiencing recurrent severe hypoglycemic events (SHEs) and impaired awareness of hypoglycemia (IAH), despite using continuous glucose monitors (CGMs).
    METHODS: The study utilized a cross-sectional, observational design which incorporated an online survey about SHE experiences, diabetes-related complications, psychosocial burden, QoL, and productivity in a sample of adult pwT1D who use CGM in the United States. Participants completed measures of IAH status (modified Gold score ≥ 4 = IAH), diabetes distress (DDS-17), fear of hypoglycemia (HFS-II), QoL (DIDP and EQ-5D-5L), and productivity (DPM). Participants were categorized into two cohorts based on self-reported history of SHEs and IAH: the cohort of recurrent SHE [≥ 2 SHEs in the past 12 months] with IAH, and the cohort of No SHE and No IAH to provide context. Unadjusted comparisons (Welch's t test, Pearson's chi-squared test) were conducted to describe differences across cohorts.
    RESULTS: In this US study population of adult CGM users, the recurrent SHE (≥ 2) with IAH cohort included 174 participants, and the No SHE and No IAH cohort included 689 participants. On average, participants with recurrent SHEs and IAH reported 8.6 SHEs in the past year. Compared to those with No SHE and No IAH, those with recurrent SHEs and IAH had a higher psychosocial burden (fear of hypoglycemia and diabetes distress), lower QoL, worse overall health status, and reduced productivity (all p < 0.001).
    CONCLUSIONS: Despite using CGM, adults with T1D with recurrent SHEs and IAH experienced lower psychosocial well-being, QoL, and reduced productivity compared to adults with T1D with no SHEs and no IAH, highlighting the unmet need for novel therapies for this group.
    Keywords:  Patient-reported outcomes; Psychosocial burden; Quality of life; Recurrent hypoglycemic events; Type 1 diabetes
    DOI:  https://doi.org/10.1007/s13300-026-01869-1
  12. J Diabetes Sci Technol. 2026 Apr 26. 19322968261441637
    MetaboNet: The Largest Publicly Available Consolidated Data Set for Type 1 Diabetes Management.
    Miriam Kopperstad Wolff, Peter Calhoun, Eleonora Maria Aiello, Yao Qin, Sam Fox Royston.
       BACKGROUND: Progress in type 1 diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management data sets. Current data sets differ substantially in structure and are time-consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D algorithm development.
    METHOD: Multiple publicly available T1D data sets were harmonized into a unified resource, termed the MetaboNet data set. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. In addition, auxiliary information such as reported carbohydrate intake and physical activity was retained when present.
    RESULTS: The MetaboNet data set comprises 3135 subjects and 1228 patient-years of overlapping CGM and insulin data, making it substantially larger than existing standalone benchmark data sets. The resource is distributed as a fully public subset available for immediate download at https://metabo-net.org/, and with a data use agreement (DUA)-restricted subset accessible through their respective application processes. For the data sets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format.
    CONCLUSIONS: A harmonized public data set for T1D research is presented, and the access pathways for both its unrestricted and DUA-governed components are described. The resulting data set covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual data sets.
    Keywords:  data set; glucose prediction; machine learning; type 1 diabetes
    DOI:  https://doi.org/10.1177/19322968261441637
  13. JMIR Res Protoc. 2026 Apr 29. 15 e88197
    The Current Landscape of Remote Patient Monitoring Regarding Diabetes Mellitus and Hypoglycemia: Protocol for a Scoping Review.
    Mathew J Stephen, Baylor Akhavan, Melinda B Chu, Kavita Batra, Roberto Sagaribay.
       BACKGROUND: Diabetes mellitus is characterized by impaired glucose regulation, predisposing patients to both hyperglycemia and hypoglycemia. Hypoglycemia, particularly frequent in insulin-treated individuals, remains a serious but underrecognized complication. Remote patient monitoring (RPM) technologies, such as continuous glucose monitors, smartphone apps, and hybrid closed-loop systems with remote monitoring capabilities, have emerged as promising tools to improve glycemic control and prevent hypoglycemia in nonclinical settings. Evidence examining the use of RPM technologies has expanded rapidly; however, the scope, characteristics, and reported outcomes of these interventions remain fragmented across modalities and settings.
    OBJECTIVE: This scoping review aims to systematically map and describe the extent, range, and characteristics of published evidence on RPM interventions for glycemic management among adults with type 1 and type 2 diabetes in nonclinical settings.
    METHODS: The review will follow the Joanna Briggs Institute scoping review methodology and adhere to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines. The population, concept, and context framework defines the population as adults with type 1 or type 2 diabetes who experience or are at risk for hypoglycemia; the concept as RPM techniques (continuous glucose monitors, hybrid closed-loop systems with remote monitoring capabilities, telemedicine, and smartphone apps); and the context as nonclinical environments. The PubMed, Embase, and Scopus databases will be searched, supplemented by gray literature. Eligible studies will include clinical trials, observational studies, and cohort studies; reviews, case studies, and non-English articles will be excluded. Two independent reviewers will conduct screening, data extraction, and summarization. Findings will be synthesized descriptively in tabular and narrative formats.
    RESULTS: At the time of submission, the protocol has been registered, and the formal search strategy is being finalized. As of March 2026, the formal database search has been completed, and screening of studies is scheduled to begin in April 2026.
    CONCLUSIONS: This protocol outlines a structured approach to mapping the current landscape of RPM interventions for glycemic management in nonclinical settings. The completed review will synthesize reported intervention characteristics and outcomes to clarify existing evidence and identify areas for future investigation.
    TRIAL REGISTRATION: OSF Registries 10.17605/OSF.IO/XNBWF; https://osf.io/xnbwf/overview.
    INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/88197.
    Keywords:  continuous glucose monitoring; diabetes management; digital health technologies; hypoglycemia prevention; remote patient monitoring
    DOI:  https://doi.org/10.2196/88197
  14. BMC Sports Sci Med Rehabil. 2026 Apr 27.
    Device-measured baseline physical activity is associated with 24-h CGM glycemic responses and 12-week time-in-range after mixed exercise training in type 2 diabetes: a randomised crossover trial.
    Bin Gai, Min Yan, Guangxin Yang, Hao Shi, Yongliang Zhu, Xuewen Tian.
      
    Keywords:  Continuous glucose monitoring; Exercise adherence; Glycemic control; Mixed exercise training; Physical activity phenotype; Time-in-range; Type 2 diabetes
    DOI:  https://doi.org/10.1186/s13102-026-01683-z
  15. Med Clin North Am. 2026 May;pii: S0025-7125(25)00167-1. [Epub ahead of print]110(3): 329-347
    Establishing Glycemic Targets: Epidemiologic Insights and Landmark Studies in Hospital Management.
    Diana Soliman, Ron T Varghese, Guillermo E Umpierrez, Rodolfo J Galindo.
      Dysglycemia in hospitalized patients, including hyperglycemia, hypoglycemia, and glycemic variability, is associated with adverse clinical outcomes and increased health care utilization. Evidence from landmark trials in critically ill patients supports moderate glycemic targets (140-180 mg/dL) over intensive control. Basal-bolus regimens improve outcomes compared to sliding scale insulin alone in noncritically ill settings. However, limited studies have compared glycemic targets in the noncritically ill hospitalized population. Emerging data support the use of continuous glucose monitoring and diabetes technology in the inpatient setting. Current guidelines emphasize individualized and protocol-driven management to optimize inpatient care.
    Keywords:  Basal-bolus insulin; Continuous glucose monitoring; Diabetes technology; Hyperglycemia; Hypoglycemia; Inpatient glycemic management
    DOI:  https://doi.org/10.1016/j.mcna.2025.11.006
  16. J Diabetes Investig. 2026 Apr 28.
    Time in range during caloric restriction in type 2 diabetes with obesity.
    Jinyoung Kim, Joonyub Lee, Mee Kyoung Kim, Oakkee Hong, Young Na, Su-Jeong Park, Yoon-Ju Song, Ki-Hyun Baek, Ki-Ho Song, Hyuk-Sang Kwon.
       INTRODUCTION: To evaluate glycemic changes during caloric restriction with continuous glucose monitoring (CGM)-derived time in range (TIR) in individuals with type 2 diabetes and obesity.
    MATERIALS AND METHODS: This 12-week single-arm intervention consisted of 6 weeks of home-delivered meals (800-1,200 kcal/day), followed by 6 weeks of self-managed diet (1,500-1,800 kcal/day for men; 1,200-1,500 kcal/day for women). CGM (14-day sensor) was performed at baseline, weeks 5-6, and weeks 11-12. A 75-g oral glucose tolerance test was conducted at baseline, week 6, and week 12 to calculate the C-peptide index (CPI) and Matsuda index.
    RESULTS: Participants had a median age of 46.0 years [38.0, 53.5], body mass index (BMI) of 29.2 kg/m2 [26.8, 31.2], HbA1c 6.6% [6.0, 7.13], and diabetes duration 2.01 years [0.91, 3.65]. Over 12 weeks, TIR improved from 84.3% to 90.3% (P = 0.041), and BMI decreased from 29.3 to 26.7 kg/m2 (P < 0.001). Weight reduction was associated with improved insulin sensitivity, whereas changes in CPI were not significant. CPI showed a stronger association with TIR than the Matsuda index, underscoring the importance of insulin secretion capacity in glycemic control. The association between CPI and TIR was more pronounced participants with higher insulin sensitivity (P = 0.011), suggesting that adequate peripheral sensitivity is required to influence glycemic outcomes.
    CONCLUSIONS: In individuals with type 2 diabetes and obesity, caloric restriction was associated with improved glycemic profiles and reduced body weight. Enhanced insulin sensitivity appears to be the predominant contributor to improved TIR, while preserved β-cell function remains essential for achieving optimal glycemic outcomes.
    Keywords:  Diabetes mellitus, type 2; Diet therapy; Insulin resistance
    DOI:  https://doi.org/10.1111/jdi.70308
  17. Chin Med J (Engl). 2026 Apr 24.
    Metaproteomics characterizes host-microbiota responses to the time in range derived from continuous glucose monitoring in type 1 diabetes.
    Liyuan Zhou, Jiyu Xu, Shunhua Li, Mingqun Deng, Xiaolian Xiao, Liyuan Ma, Haidan Sun, Xiaoyan Liu, Zhengguang Guo, Lu Ding, Ruiqi Yu, Jieying Liu, Qian Zhang, Xinhua Xiao, Wei Sun.
      
    DOI:  https://doi.org/10.1097/CM9.0000000000004079
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