bims-glumda 2026-06-07 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2026–06–07
eleven papers selected by
Mott Given

Continuous Glucose Monitoring Use in the Management of Type 2 Diabetes in Primary Care: Cross-Sectional Survey of Provider Comfort.
Impact of continuous glucose monitoring on patient-reported outcomes in adults with type 2 diabetes: a Systematic Review and meta-analysis.
Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.
Continuous Glucose Monitoring Intervention for Hispanic Adults With Type 1 Diabetes Receiving Care in a Federally Qualified Health Center: Protocol for a Mixed Methods, Pragmatic Pilot Randomized Controlled Trial.
Continuous Glucose Monitoring in Pregnant Women with Type 1 Diabetes: Outcomes of a Population-Based Real-World Study.
Continuous glucose monitoring reveals improved hyperglycemia and altered hypoglycemia target attainment after gastrectomy in patients with type 2 diabetes.
Hyperglycemia metrics from continuous glucose monitoring are independently associated with platelet morphology in adults with type 1 diabetes.
Continuous Glucose Monitoring and Mortality Risk Among U.S. Veterans Receiving Dialysis With Diabetes.
Corrigendum to "Clinical assessment and acceptance criteria for continuous glucose monitoring (CGM) system performance: A proposed guideline by the IFCC working group on CGM" [Clin. Chim. Acta 580 (2026) 120728].
Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting.
Trans-ethnic estimation and implications of genetic impact on continuous glycemic profiles.

JMIR Form Res. 2026 Jun 04. 10 e92196

Continuous Glucose Monitoring Use in the Management of Type 2 Diabetes in Primary Care: Cross-Sectional Survey of Provider Comfort.

Ryley Harris, Catherine Guariglia, Amy Cunningham.

   Unlabelled: This study assessed primary care providers' comfort with prescribing and using continuous glucose monitoring technology for type 2 diabetes management, identifying key barriers and educational needs.

Keywords:  CGM; PCP; continuous glucose monitor; diabetes; diabetes technology; patient education; primary care; primary care provider; type 2 diabetes

DOI:  https://doi.org/10.2196/92196
Front Endocrinol (Lausanne). 2026 ;17 1830980

Impact of continuous glucose monitoring on patient-reported outcomes in adults with type 2 diabetes: a Systematic Review and meta-analysis.

Haibo Zhang, Jiebin Li, Dawei Wang, Qingxia Du, Jing Zhang.

   Introduction: This research intended to systematically evaluate the impact of continuous glucose monitoring (CGM) on the emotional well-being of individuals with type 2 diabetes. It focused on the effects of CGM in four distinct patient-reported outcome domains: diabetes distress (measured by the diabetes distress scale [DDS]), treatment satisfaction (diabetes treatment satisfaction questionnaire [DTSQ]), psychological well-being (world health organization-5 well-being index [WHO-5]), and health-related quality of life (EuroQol five-dimensional questionnaire [EQ-5D]).
Methods: PubMed, Web of Science, Embase, and Cochrane Library were retrieved from inception until April 2026 to collect randomized controlled trials (RCTs) comparing CGM with self-monitoring of blood glucose (SMBG). After two researchers independently performed literature screening, data extraction, and quality assessment, meta-analysis was conducted using Stata 16.0.
Results: A total of 9 RCTs comprising 1,453 individuals were included. All five studies reporting treatment satisfaction (DTSQ) showed a direction of effect favoring CGM over SMBG, although the magnitude of improvement varied widely and heterogeneity was extremely high (I²=98.9%), precluding a meaningful single summary estimate. However, no statistically significant differences were observed between the two groups in terms of diabetes distress (DDS) [standardized mean difference (SMD)=-0.25, 95% confidence interval (CI) (-0.98, 0.17)], psychological well-being (WHO-5) [SMD = 0.08, 95% CI (-0.09, 0.25)], or health-related quality of life (EQ-5D) [SMD = 0.22, 95% CI (-0.20, 0.64)].
Conclusion: Current evidence indicates that CGM may improve treatment satisfaction in individuals with type 2 diabetes, although this finding is limited by very high heterogeneity. Its effects on alleviating diabetes distress, improving psychological well-being, and enhancing health-related quality of life remain unclear and should be further investigated.
Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42025634725.

Keywords:  Self-monitoring of blood glucose; continuous glucose monitoring; health-related quality of life; systematic review and meta-analysis; type 2 diabetes

DOI:  https://doi.org/10.3389/fendo.2026.1830980
J Mater Sci Mater Med. 2026 Jun 01.

Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.

Roghieh Sodeify, Mir Amirhossein Seyednazari, Amir Mohammad Dorosti, Alireza Nourazarian.

This review examines the convergence of wearable biosensors and artificial intelligence (AI) in personalized diabetes care. It addresses the limitations of traditional glucose monitoring and underscores the need for continuous, multi-analyte physiological surveillance. The manuscript evaluates multi-biofluid sensing platforms, specifically those utilizing interstitial fluid (ISF), sweat, saliva, tears, and urine. ISF, an extracellular medium and a plasma ultrafiltrate, exhibits low protein content, a property that reduces sensor biofouling. ISF glucose demonstrates a strong correlation with blood glucose (R² > 0.95) and can achieve high analytical sensitivity and specificity in clinically validated systems; however, diffusion-based time lags of 5-10 min present a kinetic challenge. Consequently, AI correction is necessary to ensure real-time accuracy, which is often achieved through minimally invasive microneedle arrays. Sweat analysis allows for non-invasive, multi-parameter measurements. Nevertheless, challenges such as pH instability and analyte loss due to evaporation complicate this sensing approach. Therefore, microfluidic techniques are essential for maintaining sample stability. A primary finding indicates that clinically validated Continuous Glucose Monitoring (CGM) systems yield substantial improvements in glycemic control, increasing Time in Range (TIR) by 10-15% and reducing the incidence of hypoglycemic events by 30-40%. AI-based predictive algorithms can forecast glucose excursions 30-60 min in advance, exhibiting an accuracy exceeding 94%. Key barriers to implementation include sensor calibration challenges, algorithmic bias, and significant healthcare equity issues. Future research should prioritize the development of multi-analyte implantable devices, leverage federated learning frameworks, and incorporate additional biomarkers to deliver continuous, multi-analyte, skin-conformal monitoring.

DOI: https://doi.org/10.1007/s10856-026-07070-x
JMIR Res Protoc. 2026 Jun 02. 15 e60583

Continuous Glucose Monitoring Intervention for Hispanic Adults With Type 1 Diabetes Receiving Care in a Federally Qualified Health Center: Protocol for a Mixed Methods, Pragmatic Pilot Randomized Controlled Trial.

Kelley Newlin-Lew, MaryAnn Perez-Brescia, Veena Channamsetty, Mariana Salas-Vega, Maria Krol, Francky Jacque, Cathi Lippman, Nancy A Allen.

   BACKGROUND: Hispanic adults with type 1 diabetes (T1D) have suboptimal access to continuous glucose monitoring (CGM). Widening access to and increasing uptake of CGM for Hispanic adults with T1D are warranted.
OBJECTIVE: This randomized controlled trial (RCT) will evaluate the feasibility of a federally qualified health center (FQHC) CGM intervention and assess for an intervention signal in patient outcomes.
METHODS: A mixed methods, pragmatic pilot RCT will be used. A total of 30 adult Hispanic patients with T1D will be recruited from 4 FQHC sites allocated to provide the intervention (n=2) or control (n=2) conditions. At intervention sites, participants must be willing to use CGM for 3 months and have a willing adult family member participate in the study. Guided by the socioecological model, our intervention has three levels: (1) individual (culturally sensitive CGM information, motivation, and skills acquisition), (2) family or social networks (integration of the core Hispanic values of familismo and collectivismo to leverage family and peer support for CGM uptake), and (3) health care provider levels with CGM training using Project ECHO (Extension for Community Healthcare Outcomes). Intervention participants (n=15) will receive a culturally sensitive CGM intervention with 4 weekly intervention sessions (coattended by a family member), followed by 7 peer support group sessions over 6 months. Control participants will receive a self-monitoring of blood glucose control condition over a 6-month period. Study feasibility will be assessed in terms of recruitment, enrollment, retention, adherence, study procedures and implementation, and acceptability with mixed methods. We will collect physiological (eg, glycated hemoglobin and CGM metrics) and psychosocial (eg, depression, quality of life, social support, and interpersonal processes of care) outcome data. Feasibility data will be analyzed using content analysis and univariate or bivariate statistics. Linear and generalized linear mixed modeling will assess intervention signals and clinically meaningful differences from baseline to 3 and 6 months.
RESULTS: Funding for this project was secured in September 2022. As of May 2024, recruitment commenced following formative qualitative data collection on the social determinants of health and CGM uptake in Hispanic adults with T1D (N=32). Our community advisory board informed protocol modifications by reviewing qualitative findings, collaborating on related intervention refinement, and advising on cultural sensitivity methods.
CONCLUSIONS: Guided by the socioecological model, our novel FQHC CGM intervention will provide feasibility and outcome data to guide a full-scale RCT. Our intervention model has unique potential to widen CGM access and increase CGM uptake in low-income Hispanic adults with T1D while improving outcomes for this vulnerable population.
TRIAL REGISTRATION: ClinicalTrials.gov NCT06487962; https://clinicaltrials.gov/study/NCT06487962.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60583.

Keywords:  Hispanic adults; community-based participatory research; continuous glucose monitoring; diabetes; familismo; feasibility; federally qualified health center; mixed methods; participatory research; peer support; protocol; type 1 diabetes

DOI:  https://doi.org/10.2196/60583
Diabetes Technol Ther. 2026 Jun 01. 15209156261455567

Continuous Glucose Monitoring in Pregnant Women with Type 1 Diabetes: Outcomes of a Population-Based Real-World Study.

ENDIA Study Group

   BACKGROUND: Continuous glucose monitoring (CGM) is increasingly used by women with type 1 diabetes (T1D) during pregnancy, but there are limited data on glycemic and pregnancy outcomes in large real-world cohorts.
METHODS: We collected glycemic and pregnancy outcome data from women with T1D across Australian centers between 2015 and 2025. The primary outcome was change in HbA1c from the first to the third trimester of pregnancy. Prespecified secondary outcomes included trimester-specific mean HbA1c, the proportion of women achieving HbA1c ≤6.5% (≤48 mmol/L) and ≤7.0% (≤53 mmol/L) in the third trimester, and a range of maternal and neonatal health outcomes.
RESULTS: Among 951 women, 456 (48%) were CGM users. Demographics and baseline characteristics were similar between CGM users and nonusers. For the primary outcome, adjusted linear regression models accounting for first-trimester HbA1c and insulin regimen demonstrated that CGM use was independently associated with a greater reduction in HbA1c over pregnancy (adjusted mean difference -0.20%, 95% confidence interval [CI] -0.40 to 0.00, P = 0.037). First-trimester HbA1c was the strongest predictor of HbA1c change (P < 0.001).After adjustment for first-trimester HbA1c, first-trimester body mass index, insulin regimen, parity and year of delivery, CGM use was not associated with large- or small-for-gestational-age offspring and there was no significant difference between the groups in mean birthweight. Among primiparous women, CGM use was associated with lower odds of cesarean section after adjustment for confounders (adjusted odds ratio 0.22, 95% CI 0.08-0.58, P = 0.002).
CONCLUSIONS: CGM use in pregnancy is associated with improved glycemia. The treatment effect of CGM in this real-world study is similar to that observed in the CONCEPTT study, thus supporting CGM use in pregnant women with T1D.

Keywords:  continuous glucose monitoring; pregnancy; type 1 diabetes

DOI:  https://doi.org/10.1177/15209156261455567
Sci Rep. 2026 May 30.

Continuous glucose monitoring reveals improved hyperglycemia and altered hypoglycemia target attainment after gastrectomy in patients with type 2 diabetes.

Ji Won Seo, Ki Bum Park, Kyong-Hwa Jun.

  This study aimed to investigate glycemic variability and biochemical profiles before and after gastrectomy using continuous glucose monitoring (CGM) and to evaluate whether postoperative improvements occur in blood glucose levels and biochemical parameters. We analyzed data from patients who underwent surgery for gastric cancer. Glucose profiles were assessed using a CGM device before gastrectomy and after discharge. All patients discontinued antidiabetic medications one day before surgery and resumed them only after reassessment at outpatient follow-up. CGM-derived metrics were evaluated according to international consensus recommendations, including time above range (TAR), time below range (TBR), time in range (TIR), CV (coefficient of variation), and MAGE (mean amplitude of glycemic excursions). A total of 33 patients were included. The mean duration of diabetes was 16.9 ± 10.4 years, and 12.1% were receiving insulin therapy. Mean glucose levels and TAR significantly decreased after gastrectomy while TIR significantly increased. Although TBR did not increase significantly, adherence to hypoglycemia targets worsened postoperatively. Glucose variability, assessed by CV, remained stable. Also, most patients maintained CV values below the threshold for glycemic instability. MAGE significantly decreased after surgery, indicating a reduction in large-amplitude glucose excursions. Among 16 patients whose postoperative medication changes were evaluated, seven (43.8%) reduced their antidiabetic medication after gastrectomy. In conclusion, despite the temporary discontinuation of antidiabetic medications, hyperglycemia improved without aggravation of glycemic variability, as reflected by increased TIR, stable CV and reduced MAGE. However, the achievement of hypoglycemia targets was reduced. Therefore, instead of routinely resuming preoperative antidiabetic regimens, postoperative glucose management should be individualized based on CGM-derived glycemic profiles.

Keywords:  Continuous glucose monitoring; Diabetes mellitus; Gastrectomy; Gastric cancer; Glycemic variability

DOI:  https://doi.org/10.1038/s41598-026-55271-9
Pol Arch Intern Med. 2026 Jun 01. pii: 17312. [Epub ahead of print]

Hyperglycemia metrics from continuous glucose monitoring are independently associated with platelet morphology in adults with type 1 diabetes.

Michal Kulecki, Dariusz Naskret, Adrian Kubicki, Anna Lemanska, Sandra Moska, Paulina Haras, Agata Grzelka-Woźniak, Aleksandra Uruska, Aleksandra Araszkiewicz, Dorota Zozulinska-Ziolkiewicz.

INTRODUCTION: Platelet morphology indices are indirectly related to platelet reactivity and may link glycemic exposure to cardiovascular risk.
OBJECTIVES: We aimed to investigate associations between continuous glucose monitoring (CGM)-derived metrics and platelet morphology in adults with T1DM.
PATIENTS AND METHODS: In this cross-sectional study, we enrolled adults with T1DM without established cardiovascular disease. Platelet morphology indices were measured from fasting blood samples using the Sysmex XN-1000 analyzer within 2 hours of blood collection. Glucose profiles were assessed using CGM over 7-, 14-, and 30-day windows and calculated with Glyculator 3.0. We used Spearman correlation and multivariable linear regression models adjusted for age, sex, BMI, diabetes duration, C-reactive protein, glomerular filtration rate, HbA1c, smoking, and platelet count.
RESULTS: We included 301 adults with T1DM [median age 33.1 (24.1-41.0), 44.5% men, diabetes duration 13 (7-19) years]. Platelet large cell ratio (P-LCR), mean platelet volume (MPV), and platelet distribution width (PDW)correlated positively with mean glucose (R = 0.27-0.30), time above range (TAR) level 2 (P = 0.25-0.29), glycemic risk index (R = 0.28-0.30), and mean amplitude of glucose excursion (MAGE) (R = 0.20-0.22), and inversely with time in range (R = -0.25 to -0.30; all P < 0.001), but not with hypoglycemia indices. In multivariable models, hyperglycemia-related metrics remained independently associated with P-LCR (standardized β 1.37-1.59; ΔR² 0.014-0.024; P = 0.002); MAGE lost significance in the multivariable model after accounting for TAR level 2.
CONCLUSION: In adults with T1DM, platelet morphology independently relates to cumulative hyperglycemic exposure rather than glycemic variability or hypoglycemia.

DOI: https://doi.org/10.20452/pamw.17312
Diabetes Care. 2026 Jun 01. pii: dc260004. [Epub ahead of print]

Continuous Glucose Monitoring and Mortality Risk Among U.S. Veterans Receiving Dialysis With Diabetes.

Yoko Narasaki, Kamyar Kalantar-Zadeh, Yaara Zisman-Ilani, David C Klonoff, Csaba P Kovesdy, Zhaoping Li, Anna Jin, Susan Crowley, Justin M Belcher, Patrick Gee, Greg E Gin, Mark Jordan, Amy S You, Andrea C Daza, Ramy Hanna, Danh V Nguyen, Connie M Rhee.

OBJECTIVE: Patients receiving dialysis who have diabetes have a high burden of dysglycemia. Whereas traditional glycemic markers have limited accuracy and convenience in dialysis, continuous glucose monitoring (CGM) provides automated, less invasive, and more comprehensive glycemic data than do conventional measures. However, it remains unclear whether CGM is associated with improved clinical outcomes in patients undergoing dialysis.
RESEARCH DESIGN AND METHODS: We examined the association between CGM use versus non-CGM use and survival among U.S. veterans receiving dialysis who also have diabetes, using linked national Veterans Affairs, U.S. Renal Data System, and Medicare data. We examined data from veterans undergoing dialysis and with diabetes with incident CGM use (newly prescribed CGM) versus non-CGM use over the period January 2012 to December 2023 matched by propensity score (PS) to address confounding by indication and who were followed for all-cause mortality events through February 2025. Associations of CGM use versus non-CGM use with death were evaluated using complete case analysis and multiple imputation.
RESULTS: Among 2,008 patients in the complete case analysis cohort, CGM use was associated with a lower mortality risk in PS-matched unadjusted and doubly adjusted Cox models (reference: non-CGM use; hazard ratio [HR] 0.86 [95% CI 0.76, 0.98]; and 0.83 [95% CI 0.75, 0.92], respectively). Among 3,088 patients in the multiple imputation cohort, CGM use was also associated with greater survival in PS-matched unadjusted and doubly adjusted Cox models (HR 0.88 [95% CI 0.77, 0.99]; and 0.84 [95% CI 0.73, 0.96], respectively).
CONCLUSIONS: In veterans receiving dialysis who also had diabetes, incident CGM use was associated with better survival versus non-CGM use. Further studies are needed to determine underlying mechanisms and the impact of CGM on other dialysis outcomes.

DOI: https://doi.org/10.2337/dc26-0004
Clin Chim Acta. 2026 Jun 04. pii: S0009-8981(26)00306-2. [Epub ahead of print] 121124

Corrigendum to "Clinical assessment and acceptance criteria for continuous glucose monitoring (CGM) system performance: A proposed guideline by the IFCC working group on CGM" [Clin. Chim. Acta 580 (2026) 120728].

Stefan Pleus, Manuel Eichenlaub, Pradeep Kumar Dabla, Peter Diem, Elisabet Eriksson Boija, Marion Fokkert, Rolf Hinzmann, Johan Jendle, David C Klonoff, Jingyi Lu, Konstantinos Makris, Viswanathan Mohan, James H Nichols, John S Pemberton, Elizabeth Selvin, Robbert J Slingerland, Andreas Thomas, Nam K Tran, Lilian Witthauer, Guido Freckmann.

DOI: https://doi.org/10.1016/j.cca.2026.121124
Diabetes Obes Metab. 2026 Jun 03.

Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting.

Pallavi M Sawant, Rajesh B Ghongade.

   BACKGROUND: Accurate real-time prediction of blood glucose (BG) levels is essential for improving insulin-dosing decision support systems, including closed-loop insulin delivery and bolus calculators. However, existing deep learning models often suffer from high computational complexity, limited utilization of physiological factors, and inadequate handling of temporal glucose dependencies.
METHODS: This study proposes Glucose Dynamics Analysis Network (GlucoDiaNet), a hybrid framework for BG prediction integrating spline interpolation for missing value handling, a Dilated Convolutional Residual Network (DilaConv-ResNet) for spatial-temporal feature extraction, Adamax optimization for feature selection and hyperparameter tuning, and a Bidirectional Long Short-Term Memory network for bidirectional sequence learning. The model was evaluated using the OhioT1DM dataset across multiple prediction horizons ranging from 30 to 60 min.
RESULTS: At the 30-min prediction horizon, GlucoDiaNet achieved a Root Mean Squared Error (RMSE) of 5.2435 mg/dL, Mean Absolute Error (MAE) of 4.3622 mg/dL, R2 value of 0.9948, and Mean Squared Error (MSE) of 29.3056. The proposed model consistently outperformed baseline models including LSTM, GRU, and TCN across both short- and long-term forecasting tasks while maintaining robust predictive performance at extended prediction intervals.
CONCLUSION: GlucoDiaNet effectively enhances blood glucose prediction by integrating efficient preprocessing, deep temporal modeling, and optimization strategies. The proposed framework demonstrates strong potential for future deployment in real-time and wearable diabetes monitoring systems, subject to further hardware-level validation and computational efficiency analysis.

Keywords:  BiLSTM; blood glucose prediction; continuous glucose monitoring; deep learning models; diabetes management

DOI:  https://doi.org/10.1111/dom.70930
Cell Discov. 2026 Jun 04. pii: 40. [Epub ahead of print]12(1):

Trans-ethnic estimation and implications of genetic impact on continuous glycemic profiles.

Evan Yi-Wen Yu, Hui-Ying Ren, Xinxiu Liang, Yue Xi, Menglei Shuai, Zelei Miao, Fengzhe Xu, Ke Zhang, Luqi Shen, Hui Xia, Miranda T Schram, Marleen van Greevenbroek, Bastiaan E de Galan, Carla J H van der Kallen, David E J Linden, Gabriëlla A M Blokland, Ilja C W Arts, Tos T J M Berendschot, Yan Yan, Yuanqing Fu, Anke Wesselius, Yuming Chen, Ju-Sheng Zheng.

The genetic architecture of glycemic dynamic metrics derived from continuous-glucose monitoring (CGM) across different populations remains poorly understood. Here, we conducted a trans-ethnic genome-wide association study (GWAS) meta-analysis of 20 CGM-derived glycemic traits, building upon a previously established European-ancestry CGM dataset and extending it through the inclusion of additional cohorts, in up to 9677 individuals originating from 2051 Chinese, 901 Dutch, and 6725 Israelis. Across 20 glycemic traits, we identified 18 genome-wide significant associations, of which 9 met study-wide significance, and three variants were novel. These variants indicated a shared genetic basis for continuous glycemic regulation and exhibited consistent patterns with those of sequential fingerstick glucose tests. Our findings further demonstrated that the identified genetic variants were enriched in pathways related to the nervous system. These findings were further supported by observed associations with brain magnetic resonance imaging (MRI) metrics, high CGM-related gene expression and co-regulation of quantitative trait loci in brain tissues. Additionally, we observed a positive relationship between genetic liability for the coefficient of variation (CV) and total cholesterol and a bi-directional putative causal relationship between hyperglycemia and type 1 diabetes across trans-ethnic populations. Moreover, we established a polygenic risk score (PRS) for additional participants and reported that certain glycemic traits were significantly associated with the risk of diabetes or pre-diabetes. These variants constituting the PRS demonstrated high transferability across general populations and pregnant women. Overall, our study yields unique insights into the high trans-ethnic and generalizable genetic architecture of CGM-derived glycemic profiles, supporting improved characterization of interindividual differences in glycemic dynamics and underscoring the potential for more personalized glucose management.

DOI: https://doi.org/10.1038/s41421-026-00897-2