bims-glumda 2026-08-16 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2026–08–16
24 papers selected by
Mott Given

Continuous Glucose Monitoring in Primary Care: A Practical, Multidisciplinary Review of Data Interpretation and Billing Updates.
Continuous Glucose Monitoring Systems in Europe: Regulatory Landscape and Certification Process Challenges Under MDR 2017/745.
Continuous glucose monitoring profiles during postpartum glucose tolerance test results among people with gestational diabetes.
Glucose monitoring in the management of diabetes in pregnancy: A systematic review and meta-analysis.
Factors Associated With Progression From Level 1 to Level 2 Sensor-Detected Hypoglycaemia in People With Type 1 and Insulin-Treated Type 2 Diabetes: A Post Hoc Analysis From the Hypo-METRICS Study.
CGM Metrics and the Risk of Pregnancy Complications in Type 1 Diabetes: A Secondary Exploratory Analysis of the CRISTAL Trial.
Community-Based Personalised Lifestyle Intervention Guided by Intermittent Continuous Glucose Monitoring in Adults With Type 2 Diabetes: A Multicentre Randomised Controlled Trial.
Accuracy, safety, and usability of a real-time continuous glucose monitoring system in pediatric diabetes management.
Youths with Type 1 Diabetes' and Their Parents' Experiences of Insulin Pumps and Continuous Glucose Monitoring.
Sociodemographic barriers to continuous glucose monitoring among pregnancies with type 1 diabetes.
Interpreting Continuous Glucose Monitoring Through Metabolic Physiology: A Framework for Precision Nutrition in Type 2 Diabetes.
Comparing efficacy in glycaemic control and pregnancy outcomes between continuous glucose monitoring and self-monitoring of blood glucose in pregnancies complicated by gestational diabetes mellitus: a systematic review and meta-analysis.
Predictive Modelling of 3-Month Glycemic Target Achievement in Type 2 Diabetes Mellitus Using Artificial Intelligence and Continuous Glucose Monitoring: A Retrospective Cohort Study.
The Impact of an Experience-Based Versus Traditional Continuous Glucose Monitoring Curriculum on Internal Medicine Resident Confidence and Knowledge.
Continuous Glucose Monitoring-Informed Basal Insulin Optimization System in Adults with Type 2 Diabetes: Initial Evaluation of Efficacy, Safety, and Impact on Patient-Reported Outcomes.
Association Between Short-Term Glycemic Variability Measured by Continuous Glucose Monitoring and Early Diabetic Kidney Disease in Type 2 Diabetes.
Intrapartum glucose metrics associated with adverse neonatal outcomes among people with gestational diabetes mellitus.
High blood glucose index stratifies glycaemic severity and identifies occult type 2 diabetes risk in prediabetes via single-day continuous glucose monitoring: A multi-cohort study.
Characterizing glucose in labor: Continuous glucose monitoring in parturients without diabetes.
Examining the Potential of Person-centered Temporal Network Models to Individualize Type 1 Diabetes Care.
Body composition-defined sarcopenia phenotypes, diabetes complications, and CGM-derived glycemic profiles in frail older adults with diabetes: a cross-sectional study.
The Emotional Burden and Behavioral Impacts of Alarms and Alerts Across the Spectrum of Diabetes Technology Use: a Cross-Sectional Study.
Protein-Containing Breakfasts Are Associated with Reduced Postprandial Glycaemic Excursions in Children and Young People with Type 1 Diabetes: A Pragmatic Study.
Contemporary automated insulin delivery systems in older adults with type 1 diabetes: A systematic review and meta-analysis.

South Med J. 2026 Aug 03. 119(8): 478-483

Continuous Glucose Monitoring in Primary Care: A Practical, Multidisciplinary Review of Data Interpretation and Billing Updates.

Ashley Higbea, Humza Siddiqi.

Continuous glucose monitoring (CGM) offers real-time and longitudinal insights into glycemic patterns, time in range, and hypoglycemia. Adopting CGMs into practice can improve clinical outcomes while strengthening patient engagement and enabling data-driven care across routine visits and population health programs. Despite strong evidence of benefit, CGM remains underused in primary care, where most patients with diabetes mellitus are managed. Barriers include limited familiarity with CGM technology, interpretation, workflow, documentation and billing, and patient access and education. The purpose of this clinical review was to help equip primary care clinicians with a concise, family medicine-focused framework for adopting CGM, including technology overview, patient selection and education, practical interpretation of standardized reports, team-based workflow, documentation, and reimbursement.

DOI: https://doi.org/10.14423/SMJ.0000000000001999
Diabetes Obes Metab. 2026 Aug 13.

Continuous Glucose Monitoring Systems in Europe: Regulatory Landscape and Certification Process Challenges Under MDR 2017/745.

Concetta Irace, Bassil Akra.

   AIMS: To analyze the certification process for continuous glucose monitoring (CGM) systems in Europe under the applicable Medical Device Regulation (MDR) 2017/745 and to highlight potential issues that may impact the reliability of CGM devices following introduction to the market.
METHODS: We undertook a detailed review of MDR 2017/745 using publicly available European Commission information portals to assess the current implementation status. Specifically, we examined where checks and balances intended to ensure the accuracy and safety of CGM devices were not in place and identify where delays in implementation of key functions may reduce transparency and efficacy of the Conformité Européenne certification process.
RESULTS: Key elements of the MDR have not been implemented, creating inertia for manufacturers and the Notified Bodies that undertake conformity assessments. Delays in implementing the European Databank on Medical Devices (EUDAMED), which was designed to harmonize pre- and post-marketing collection of medical device information, have limited access to critical evidence for Notified Bodies and delayed strengthening of post-market surveillance.
CONCLUSIONS: The current process of CGM Conformity Assessment under MDR 2017/745 limits access to regulatory submissions of clinical evidence reports, reference methodologies and other documentation necessary for CGM devices to be assessed as safe, accurate and effective in the populations of people with diabetes in whom they may be used. Additional checks and balances are required to increase transparency of the conformity assessment process prior to CE marking, and improve the traceability of CGM devices in the post-marketing period.

Keywords:  continuous glucose monitoring (CGM); effectiveness; medical device regulation; type 1 diabetes; type 2 diabetes

DOI:  https://doi.org/10.1111/dom.71202
Pregnancy (Hoboken). 2025 Nov;1(6): e70143

Continuous glucose monitoring profiles during postpartum glucose tolerance test results among people with gestational diabetes.

Alyssa R Hersh, Alexandra C Gallagher, Lucy Ward, Christian Huertas-Pagán, Monica Rincon, Amy M Valent.

   Objective: Current guidance recommends universal screening with a postpartum oral glucose tolerance testing (OGTT) 4-12 weeks following delivery among pregnancies complicated by gestational diabetes mellitus (GDM) to identify or assess risk for prediabetes and type 2 diabetes (T2D). Continuous glucose monitoring (CGM) is not currently a tool for screening for T2D among pregnant or postpartum people. Our objective was to assess CGM data during the postpartum OGTT.
Methods: This was a secondary analysis of a trial that randomized people with GDM to CGM versus capillary blood glucose (CBG). Participants were included if CGM data were available during their OGTT. Our primary outcome was mean glucose during the postpartum OGTT (75 g 2-h challenge), comparing those with dysglycemia on OGTT (fasting ≥100 mg/dL and/or 2-h ≥140 mg/dL) to normal OGTT results.
Results: A total of 51 patients met the inclusion criteria for this secondary analysis. Mean CGM glucoses were higher at all time points for those with dysglycemia compared with a normal OGTT result. Among those with dysglycemia, the mean fasting plasma glucose was 104.3 ± 10.5 and the mean 2-h plasma glucose was 129.9 ± 37.5. For those with normal OGTT results, the mean fasting plasma glucose was 89.8 ± 5.2 and the mean 2-h plasma glucose was 103.5 ± 17.9. When stratified by GDM type, mean CGM glucose was similar between groups.
Conclusion: In this study, we found that glucose measured by CGM follows the expected curve in response to the postpartum OGTT among individuals with pregnancies complicated by GDM. Those with dysglycemia had higher mean CGM glucose at all time points assessed. Future studies will need to assess the role of CGM in risk stratifying those with dysglycemia postpartum without an OGTT.

Keywords:  continuous glucose monitoring; gestational diabetes; glucose tolerance test; postpartum; pregnancy

DOI:  https://doi.org/10.1002/pmf2.70143
Pregnancy (Hoboken). 2026 Jan;2(1): e70214

Glucose monitoring in the management of diabetes in pregnancy: A systematic review and meta-analysis.

Jessica Burk, Arianne Sweeting.

   Background: Diabetes in pregnancy (DIP: gestational, type 1 [T1D], and type 2 diabetes [T2D]) is increasing in global prevalence and was recently declared a public health priority by the World Health Organization (WHO).
Aim: This systematic review and meta-analysis evaluated different glucose monitoring management for DIP, including routine care at antenatal visits, self-monitoring of blood glucose (SMBG), and continuous glucose monitoring (CGM) to inform new WHO recommendations on care for pregnant women with DIP.
Methods: We searched Medline, Embase, CENTRAL, and LILACs from database inception to October 2024 for randomized controlled trials (RCTs), comparing SMBG with routine care at antenatal visits or CGM with SMBG. Data were extracted on maternal glycemia (HbA1c, SMBG fasting, 1-h postprandial glucose, CGM metrics [mean sensor glucose, pregnancy time-in-range, time-above-range, and time-below-range]) and perinatal outcomes (gestational weight gain, hypertensive disorders of pregnancy, preeclampsia, large-for-gestational age [LGA], macrosomia, small-for-gestational age, neonatal hypoglycemia, and neonatal intensive care unit [NICU] admissions). Measures of effect were summarized as mean differences (MDs) or risk ratios (RRs) with 95% confidence intervals (95% CI) for gestational diabetes, T1D and T2D. The GRADE framework was used to assess the certainty of effect estimates.
Results: In gestational diabetes, SMBG compared to routine care at antenatal visits reduced LGA (n = 2719; RR, 0.60 [0.50, 0.72]) and macrosomia (n = 2661; RR, 0.50 [0.39, 0.64]) (high-certainty evidence) and probably reduced gestational weight gain (n = 2616; MD, -1.58 kg [-2.22, -0.94]) (moderate-certainty evidence). CGM compared to SMBG probably reduced gestational weight gain (n = 221; MD, -0.61 kg [-0.92, -0.31]) and preeclampsia (n = 277; RR, 0.34 [0.12, 0.91]) (moderate-certainty evidence). In T1D, CGM compared to SMBG probably increased pregnancy time-in-range (n = 154; MD, 7.00% [2.57, 11.43]) and probably reduced neonatal hypoglycemia (n = 200; RR, 0.54 [0.31, 0.94]) and NICU admissions (n = 200; RR, 0.63 [0.42, 0.93]) (moderate-certainty evidence). There was insufficient RCT evidence to compare approaches to glucose monitoring in T2D.
Conclusions: SMBG improves important perinatal outcomes for pregnant women with gestational diabetes, T1D and T2D. CGM probably provides additional benefit for maternal glycemia in T1D, and for some perinatal outcomes in T1D and gestational diabetes. More evidence is needed to determine the potential benefits of CGM for women with T2D in pregnancy.

Keywords:  continuous glucose monitoring (CGM); large‐for‐gestational age (LGA); meta‐analysis; self‐management of blood glucose (SMBG); systematic review; type 1 diabetes (T1D) in pregnancy; type 2 diabetes (T2D) in pregnancy

DOI:  https://doi.org/10.1002/pmf2.70214
Diabetes Obes Metab. 2026 Aug 04.

Factors Associated With Progression From Level 1 to Level 2 Sensor-Detected Hypoglycaemia in People With Type 1 and Insulin-Treated Type 2 Diabetes: A Post Hoc Analysis From the Hypo-METRICS Study.

Vaios Koutroukas, Aikaterina Tziannou, Jonah J C Thomas, Gilberte Martine-Edith, Simon Heller, Julia K Mader, Julie Maria Bøggild Brøsen, Ulrik Pedersen-Bjergaard, Bastiaan E de Galan, Eric Renard, Mark L Evans, Rory J McCrimmon, Jane Speight, Frans Pouwer, Stephanie A Amiel, Pratik Choudhary.

   AIMS: We investigated the proportion of sensor-detected hypoglycaemic (SDH) events progressing to level 2, and associated variables.
MATERIALS AND METHODS: We used data from Hypo-METRICS, which recruited people with type 1 (pwT1D) and insulin-treated type 2 diabetes (pwT2D) with ≥ 1 hypoglycaemic event in preceding 3 months, wearing blinded continuous glucose monitoring devices (CGM), additional to usual monitoring modality, and FitBits for 10 weeks. We defined: L1: SDH < 3.9 mmol/L for ≥ 15 min but ≥ 3.0 mmol/L; L2: SDH < 3.9 mmol/L with ≥ 15 min of sensor glucose < 3.0 mmol/L; L2 ratio = L2/(L1 + L2), restricted to participants with both event types during the study period. Associations of selected variables on L2 ratio was assessed using beta regression and purposeful variable selection.
RESULTS: We analysed 23 768 SDH events from 212 pwT1D and 213 pwT2D. PwT1D were predominantly female (53% vs. 42% in T2D, p = 0.023) and younger (median age 49 vs. 62 years, p < 0.001), with a higher L2 ratio (16% vs. 14%, p = 0.03). Coefficient of variation (CV), OR = 1.07, 95% CI = 1.06-1.09 (p < 0.001), and mean sensor glucose, OR = 1.13, 95% CI = 1.08-1.18 (p < 0.001) were the principal predictors in T1D and T2D respectively; mean L1-SDH duration, weekly L1-SDH frequency, personal CGM usage, impaired awareness were not. Progression was higher during sleep than wakefulness (21% vs. 11%, p < 0.001); L2 ratios during sleep did not differ by awareness status.
CONCLUSIONS: Approximately one sixth of SDH events progressed to L2, with a higher proportion in T1D than T2D. The principal determinants were CV in T1D and mean glucose in T2D. Awareness status was not associated with progression risk during sleep.

Keywords:  Hypoglycaemia; continuous glucose monitoring (CGM); type 1 diabetes; type 2 diabetes

DOI:  https://doi.org/10.1111/dom.71172
Diabetes Technol Ther. 2026 Aug 12. 15209156261477154

CGM Metrics and the Risk of Pregnancy Complications in Type 1 Diabetes: A Secondary Exploratory Analysis of the CRISTAL Trial.

Ina Geerts, Kaat Beunen, Liese Berger, Nancy Van Wilder, Dominique Ballaux, Gerd Vanhaverbeke, Youri Taes, Xavier-Philippe Aers, Frank Nobels, Liesbeth Van Huffel, Joke Marlier, Dahae Lee, Joke Cuypers, Vanessa Preumont, Sarah E Siegelaar, Rebecca C Painter, Annouschka Laenen, Pieter Gillard, Chantal Mathieu, Katrien Benhalima.

   OBJECTIVE: To examine the associations between continuous glucose monitoring (CGM) metrics, including glucose management indicator (GMI) and overnight glucose levels, and pregnancy outcomes in women with type 1 diabetes.
RESEARCH DESIGN AND METHODS: Secondary exploratory analysis of the CRISTAL trial including 95 pregnant women with type 1 diabetes using CGM. Associations were assessed using logistic regression and Spearman correlations, presented as odds ratios (95% confidence intervals [CIs]) adjusted for baseline HbA1c. GMI validity was assessed using scatter and Bland-Altman plots.
RESULTS: Each 5% increase in overall pregnancy-specific time-in-range (TIRp) decreased the odds of gestational hypertension (odds ratio [OR] 0.63, 95% CI 0.41-0.97), birthweight >4.5 kg (OR 0.56, 95% CI 0.32-0.96), and neonatal hypoglycemia requiring hospital care (OR 0.09, 95% CI 0.01-0.57). Each 5% increase in overnight TIRp decreased the odds of gestational hypertension (OR 0.71, 95% CI 0.52-0.98) and neonatal hypoglycemia requiring hospital care (OR 0.15, 95% CI 0.03-0.79). Each 5% increase in overall time-above-range (TARp) increased the odds of birthweight >4.5 kg (OR 1.76, 95% CI 1.05-2.96), respiratory distress (OR 1.55, 95% CI 1.02-2.37), and neonatal hypoglycemia requiring hospital care (OR 5.10, 95% CI 1.14-22.78). Each 5% increase in TARp overnight increased the odds of hospital care for neonatal hypoglycemia (OR 2.55, 95% CI 1.15-5.66). Each 0.28 mmol/L increase in mean glucose and 0.5% increase in GMI were associated with increased respiratory distress (OR 1.54, 95% CI 1.07-2.23 and OR 6.15, 95% CI 1.33-28.40). Every 0.28 mmol/L increase in glycemic variability (SD) was associated with gestational hypertension (OR 1.69, 95% CI 1.02-2.80) and birthweight >4.5 kg (OR 2.31, 95% CI 1.20-4.43). Several combinations of CGM metrics (TIRp[-night], TARp[-night], SD, mean glucose) improved discriminative performance for pregnancy outcomes. GMI and HbA1c values were discordant.
CONCLUSIONS: Specific combinations of CGM metrics, including overnight TIRp/TARp, may be informative for predicting pregnancy outcomes. GMI and HbA1c should not be considered interchangeable for glycemic control during pregnancy.

Keywords:  continuous glucose monitoring metrics; diabetes and pregnancy; pregnancy complications; type 1 diabetes

DOI:  https://doi.org/10.1177/15209156261477154
Diabetes Obes Metab. 2026 Aug 12.

Community-Based Personalised Lifestyle Intervention Guided by Intermittent Continuous Glucose Monitoring in Adults With Type 2 Diabetes: A Multicentre Randomised Controlled Trial.

Yaqing Liu, Shan Zheng, Zifu Yu, Ran Zhao, Qian Li, Suyun Li, Liangxia Chen.

   AIMS: To evaluate whether a community-based personalised lifestyle intervention guided by intermittent continuous glucose monitoring (CGM) improves glycaemic and cardiometabolic outcomes in adults with type 2 diabetes.
MATERIALS AND METHODS: In this multicentre, open-label randomised trial, 180 adults with type 2 diabetes and pre-randomisation HbA1c > 7.0% were assigned 1:1 to the CGM-guided intervention or usual community diabetes management and followed for 12 months. The intervention combined FreeStyle Libre 2 use at baseline, after the Month 3 assessment and at Month 6 with personalised lifestyle guidance and supervised exercise concentrated in the first 3 months. The primary outcome was HbA1c at Month 3, analysed using multiple-imputation ANCOVA adjusted for baseline HbA1c and community site.
RESULTS: The primary analysis set included 171 participants. The adjusted intervention-minus-control difference in Month 3 HbA1c was -0.09 percentage points (95% CI, -0.38 to 0.21; p = 0.573). Supportive complete-data ANCOVA in 125 participants yielded a similar estimate of -0.12 percentage points (95% CI, -0.44 to 0.20; p = 0.472). No significant between-group HbA1c differences were observed at Months 6 or 12, and secondary outcomes showed no consistent benefit. Two Month 3 responder outcomes nominally favoured the intervention, but neither remained significant after multiplicity adjustment (both q = 0.104). Exploratory analyses identified no baseline subgroup with greater Month 3 benefit.
CONCLUSIONS: The intervention did not significantly improve glycaemic or cardiometabolic outcomes beyond usual community care. Whether more sustained feedback and engagement can improve effectiveness requires evaluation before routine implementation.
TRIAL REGISTRATION: ChiCTR2400085258.

Keywords:  continuous glucose monitoring (CGM); dietary intervention; randomised trial; type 2 diabetes

DOI:  https://doi.org/10.1111/dom.71221
J Diabetes Investig. 2026 Aug 13.

Accuracy, safety, and usability of a real-time continuous glucose monitoring system in pediatric diabetes management.

Bingyan Cao, Zhe Su, Yi Wang, Yanhui Chen, Zhigang Hu, Mingsong Han, Chunxiu Gong, Yifei Mo.

   BACKGROUND: The purpose of the study was to evaluate the accuracy, safety, and usability of a real-time Continuous Glucose Monitoring (CGM) system compared with venous blood glucose (vBG) in children and adolescents.
METHODS: The study enrolled 81 participants aged from 3 to 17 at three sites in China. Two sensors (GS1, SiBionics, Shenzhen) were inserted on the back of each upper arm for up to 14 days for each participant. A variety of analyses were conducted for evaluating accuracy by comparing the CGM readings and vBG values, including 20%/20 agreement rate, the mean absolute relative difference (MARD), the Clarke error grid, and consensus error grid analyses. Safety was measured by adverse event (AE) monitoring. AEs were documented, and the incidence rate was calculated. Usability was evaluated by self-reported questionnaires.
RESULTS: Data from 80 participants were analyzed. Our results demonstrated high accuracy of the real-time CGM system. The 20%/20 agreement rate across all glycemic ranges was 93.9%. The MARD value was 8.7%. The results of the Clarke and consensus error grid analyses in zone A + B were 99.6% and 100%, respectively. The average score of usability was 95.3 ± 7.6, reflecting high satisfaction. The CGM also showed high safety, given that only 3 device-related AEs in 2 participants were reported , and no serious adverse events (SAEs) were reported.
CONCLUSION: The real-time CGM system demonstrated high accuracy, safety, and usability in the glycemic monitoring of children and adolescents.

Keywords:  continuous glucose monitoring; diabetes mellitus; pediatrics

DOI:  https://doi.org/10.1111/jdi.70415
Compr Child Adolesc Nurs. 2026 Aug 10. 1-17

Youths with Type 1 Diabetes' and Their Parents' Experiences of Insulin Pumps and Continuous Glucose Monitoring.

Anna-Lena Fureman, Torbjörn Lind, Mikael Lilja, Stefan Särnblad, Ulf Samuelsson, Rita Sjöström.

   BACKGROUND: To enhance the quality of life for individuals with type 1 diabetes and mitigate the risks of complications, new methods for insulin delivery and glucose monitoring are being developed. The ability to monitor a child's glucose levels via a mobile phone can provide parents with a feeling of security, potentially leading to improved sleep quality. However, constant monitoring could lead to increased stress both for youths and parents.
AIM: The aim of the study was to explore how youths with type 1 diabetes and their parents experienced using insulin pumps and continuous glucose monitoring devices (CGM) and the support from the diabetes team. Further, to explore their opinions about the glucose target HbA1c ≤ 48 mmol/mol (6.5%) and other glucose metabolic measurements.
METHOD: Sixteen individual interviews were performed with eight youths with type 1 diabetes and their parents. Qualitative content analysis was done according to Graneheim and Lundman.
RESULTS: The participants described increased satisfaction, security, strengthened independence and increased freedom. The parents described improved sleep at night leading to increased quality of life. The participants had not reflected so much over the change when the glucose target was lowered in 2017, but they found time in tight range to be more useful. They had a positive experience of healthcare with easy accessibility and continuity, and they described technical support as important.
CONCLUSION: Insulin pumps and CGM facilitate everyday life for both youths and their parents, and the opportunity to follow their youth's glucose values does not seem to be a problem to neither youths nor parents. The lowering of the glucose target is not something that parents and youth had much concern about, and they consider the gluco-metabolic measurement time in tight range to be more useful.

Keywords:  Type 1 diabetes; adolescents; continuous glucose monitoring; insulin pump; parents

DOI:  https://doi.org/10.1080/24694193.2026.2715377
Pregnancy (Hoboken). 2025 Nov;1(6): e70135

Sociodemographic barriers to continuous glucose monitoring among pregnancies with type 1 diabetes.

Frank B Will Williams, Kali Juracek, John A Morgan, James D Toppin, Joseph R Biggio, Shannon M McCloskey.

   Objective: Continuous glucose monitor (CGM) use is associated with improved glycemic management among pregnant patients with type 1 diabetes. Individual- and neighborhood-level social determinants of health (SDHs) are associated with low CGM use and adverse pregnancy outcomes. We hypothesized that SDHs are associated with decreased CGM use in pregnancy among patients with type 1 diabetes.
Methods: We evaluated a cohort of pregnancies receiving type 1 diabetes and delivery care at a single large health system from 2016 to 2023. Patients were evaluated by public payor status. The primary outcome was CGM use. Additional SDH characteristics evaluated included home Area Deprivation Index (ADI) percentile and rurality status. Regression analyses generating models predicting CGM use included age, baseline body mass index (BMI), diabetes duration, and delivery year.
Results: Among 288 pregnancies with type 1 diabetes, 144 (50.0%) had public insurance. Public payor was associated with younger age and shorter diabetes duration, and those patients were more commonly nulliparous and Black. Controlling for baseline characteristics, CGM uptake was lower among the public insured (38.9% vs. 61.8%; adjusted odds ratio [aOR], 0.39; 95% confidence interval [CI], 0.23-0.66). Over time, uptake increased, though publicly insured patients lagged by two years. Evaluating SDH characteristics and CGM usage over time, public insurance (aOR, 0.47; 95% CI, 0.24-0.91) and ADI (aOR, 0.96; 95% CI, 0.95-0.98) were associated with CGM use; rurality was not.
Conclusion: Public insurance and high neighborhood deprivation are risk factors for lower CGM uptake, potentially representing an indirect and targetable mechanism by which SDH impacts glycemic management for pregnancies with type 1 diabetes.

Keywords:  CGM; diabetes; disparities; pregnancy

DOI:  https://doi.org/10.1002/pmf2.70135
Nutrients. 2026 Aug 06. pii: 2578. [Epub ahead of print]18(15):

Interpreting Continuous Glucose Monitoring Through Metabolic Physiology: A Framework for Precision Nutrition in Type 2 Diabetes.

Jitendra D Belani.

  Background: Type 2 diabetes is characterized by substantial interindividual variability in postprandial glucose responses, yet continuous glucose monitoring (CGM)-derived glucose patterns are commonly described using quantitative metrics without sufficient consideration of the physiological mechanisms that generate them. Objective: To develop a physiology-based framework for interpreting CGM-derived glycemic phenotypes in relation to dietary exposures and individual metabolic characteristics, thereby supporting precision nutrition in adults with type 2 diabetes. Methods: A structured narrative review of the literature was conducted using PubMed to identify studies examining CGM, postprandial glucose regulation, dietary exposures, metabolic physiology, and precision nutrition. Evidence was synthesized within a predefined conceptual framework linking dietary exposures, underlying physiology, individual metabolic characteristics, CGM-derived glycemic phenotypes, and their clinical interpretation. Results: Postprandial glucose responses reflect coordinated interactions among nutrient digestion and absorption, hormonal regulation, hepatic glucose production, peripheral glucose disposal, and individual metabolic characteristics rather than dietary carbohydrate exposure alone. The proposed framework interprets recurring CGM-derived glucose patterns as physiologically meaningful glycemic phenotypes, including characteristic patterns of postprandial excursion, recovery, baseline glycemia, and glycemic variability. This framework provides a structured approach for physiology-informed interpretation of CGM observations and individualized nutritional assessment. Although direct evidence supporting phenotype-guided nutritional interventions and long-term clinical benefit remains limited, emerging computational approaches may further strengthen this interpretive process by integrating CGM with complementary biological and behavioral data. Conclusions: Rather than viewing CGM solely as a technology for measuring glucose, the proposed framework positions it as a physiological lens through which dietary exposures, metabolic regulation, and individual metabolic characteristics can be integrated to support precision nutrition in type 2 diabetes.

Keywords:  continuous glucose monitoring; glycemic phenotypes; glycemic variability; metabolic physiology; precision nutrition; type 2 diabetes

DOI:  https://doi.org/10.3390/nu18152578
Diabetes Res Clin Pract. 2026 Aug 13. pii: S0168-8227(26)00408-0. [Epub ahead of print] 113488

Comparing efficacy in glycaemic control and pregnancy outcomes between continuous glucose monitoring and self-monitoring of blood glucose in pregnancies complicated by gestational diabetes mellitus: a systematic review and meta-analysis.

Shirlyn Yang, Emelia Tze-Ting Boon, Qian Yang, Qing Yi Chen, Jiayi Shen, Jie Li, Yali Hu, Yufan Wang, Hongbo Qi, Mahesh Choolani, Katrien Benhalima, Ling-Jun Li.

  Continuous glucose monitoring (CGM) may offer advantages over self-monitored blood glucose (SMBG) in gestational diabetes mellitus (GDM)-complicated pregnancies, but evidence remains inconsistent. We conducted a systematic review and meta-analysis of randomised controlled trials and observational studies published between January 1980 and April 2026 in PubMed, Embase, Scopus, Cochrane Library, and Web of Science, comparing CGM with SMBG in GDM pregnancies. The review was preregistered with PROSPERO (CRD420251084931). Primary outcomes were glycaemic control measures; secondary outcomes included maternal and neonatal events. Risk of bias was assessed using the Jadad scale and Newcastle-Ottawa Scale. Pooled risk ratios (RRs) or mean differences (MDs) with 95% confidence intervals (CIs) were calculated. Twenty-one studies involving 5,650 pregnant women were included, with low-to-moderate bias risks. Compared with SMBG, CGM significantly lower mean blood glucose (MD -0.24 mmol/L; 95% CI: -0.41, -0.06), coefficient-of-variation (-0.78%; -1.50, -0.06), mean amplitude of glycaemic excursions (-0.22 mmol/L; -0.43, -0.02), and time-above-range > 7.8 mmol/L (-2.19%, -3.78, -0.60). CGM was also associated with an 21% increase in medication use and an 8-35% reduction in adverse maternal and neonatal outcomes, including caesarean delivery, macrosomia, neonatal hypoglycaemia, and hyperbilirubinemia. These findings suggest CGM improves glycaemic control in GDM and reduces maternal and neonatal complications.

Keywords:  CGM; Continuous glucose monitoring; Diabetes; Epidemiology; GDM; Gestational diabetes mellitus; Glycaemic control; Maternal outcomes; Meta-analysis; Neonatal outcomes; Pregnancy; SMBG; Self monitoring blood glucose

DOI:  https://doi.org/10.1016/j.diabres.2026.113488
Diabetes Obes Metab. 2026 Aug 11.

Predictive Modelling of 3-Month Glycemic Target Achievement in Type 2 Diabetes Mellitus Using Artificial Intelligence and Continuous Glucose Monitoring: A Retrospective Cohort Study.

Qinyan Huang, Jing Kang, Rui Wang, Ming Zhang, Yan Chen.

   AIMS: To develop and validate a machine learning model incorporating continuous glucose monitoring (CGM) metrics to predict 3-month glycemic target achievement in type 2 diabetes mellitus (T2DM) after short-term intensive insulin pump therapy followed by physician-selected maintenance treatment, and to build a web-based prediction tool.
MATERIALS AND METHODS: We retrospectively included 1079 patients with T2DM and divided them into training, validation, and test sets (6:2:2). Eleven models were evaluated for discrimination, calibration, net benefit, and classification performance. Candidate models were compared for sensitivity to compression of correlated CGM predictors, interpretability, and suitability for web implementation. Nested logistic regression assessed the incremental value of CGM, and SHapley Additive exPlanations (SHAP) assessed feature contributions.
RESULTS: SVM_RBF achieved the highest test-set area under the curve (AUC; 0.926), compared with 0.917 for XGBoost (paired DeLong p = 0.521). The models had similar test-set discrimination and probabilistic accuracy, with no consistent difference in calibration. XGBoost was selected for implementation because it was less sensitive to compression of correlated CGM predictors and supported TreeSHAP. Leading predictors included diabetes duration, age, fasting C-peptide (FCP), mean amplitude of glucose excursions (MAGE), body mass index (BMI), and time in range (TIR). Adding CGM improved continuous net reclassification improvement (0.308, p = 0.013) and integrated discrimination improvement (0.0062, p = 0.029), but not AUC or categorical net reclassification improvement.
CONCLUSION: The XGBoost model estimates 3-month glycemic target achievement within this treatment pathway and was implemented as a web-based clinical decision-support tool.

Keywords:  3‐month glycemic target achievement; artificial intelligence; continuous glucose monitoring; machine learning

DOI:  https://doi.org/10.1111/dom.71224
J Gen Intern Med. 2026 Aug 14.

The Impact of an Experience-Based Versus Traditional Continuous Glucose Monitoring Curriculum on Internal Medicine Resident Confidence and Knowledge.

Eric B Ohlendorf, Kenneth M Fifer, Emily J Gallagher, Cary J Blum.

   BACKGROUND: Continuous glucose monitoring (CGM) is an important tool in diabetes care, yet providers often have limited familiarity with its use. Personal CGM use may enhance provider knowledge and confidence.
OBJECTIVE: We evaluated whether an experience-based CGM curriculum (EC) improves confidence and knowledge among internal medicine (IM) residents compared with a traditional curriculum (TC).
DESIGN: IRB-exempt, prospective, randomized educational intervention.
PARTICIPANTS: Thirty-six IM residents.
INTERVENTIONS: Residents were assigned to either EC (didactic, clinic, 10 days of personal CGM wear) or TC (didactic and clinic only).
MAIN MEASURES: Primary outcome: change in CGM confidence scores as measured by pre- and post-curriculum surveys.
SECONDARY OUTCOME: percentage of post-curriculum knowledge questions answered correctly on the knowledge assessment. Paired pre- vs post-survey responses were compared using McNemar testing; EC vs TC were compared using Fisher's exact test. A qualitative outcome analyzed free-text responses regarding participant experience using CGM.
RESULTS: Thirty-six residents completed the pre-curriculum survey and 27 the post-curriculum survey (n = 13 EC, n = 14 TC), with 23 paired responses. Overall confidence improved across multiple CGM domains. Confidence in helping a patient apply a CGM sensor increased significantly overall (p = 0.0001), with greater improvement in EC (p = 0.0003). Smartphone integration showed a similar pattern (EC vs TC p = 0.012) as did confidence addressing patient questions (EC vs TC p = 0.012). Confidence explaining CGM function (p = 0.004) and interpreting reports (p = 0.004) increased overall but without between-group differences. Knowledge scores were similar (EC 67 ± 12% vs TC 62 ± 16%). Qualitative themes included insight into the patient experience, improved procedural confidence, greater lifestyle counseling ability, and endorsement of experiential learning.
CONCLUSIONS: Providing an opportunity to personally use CGMs among IM residents produced greater gains in applied and patient-facing skills than a traditional curriculum. These findings support incorporating hands-on CGM experience into residency training to better prepare trainees for modern diabetes care.

Keywords:  CGM; curriculum; experiential

DOI:  https://doi.org/10.1007/s11606-026-10687-x
Diabetes Ther. 2026 Aug 10.

Continuous Glucose Monitoring-Informed Basal Insulin Optimization System in Adults with Type 2 Diabetes: Initial Evaluation of Efficacy, Safety, and Impact on Patient-Reported Outcomes.

Sean M Oser, Chad Crystal, Kazanna C Hames, Xuan Li, Peggy Gao, Margaret A Crawford, Christian Hicks, Matthew Gerber, Thibault Gautier, Patricio Colmegna, Lei Yang, Harsimran Singh, Matthew L Johnson, Stayce E Beck.

   INTRODUCTION: Clinical inertia and the complexity of basal insulin titration are major barriers to optimal glycemic control in adults with type 2 diabetes (T2D). A prototype of the Dexcom Smart Basal system known as basal therapy optimization (BTO), a continuous glucose monitoring (CGM)-informed basal insulin optimization system, was evaluated for its efficacy, safety, and impact on patient-reported outcomes (PRO).
METHODS: In this prospective, single-arm study, 14 adults with T2D (three initiating and 11 optimizing basal insulin) used the BTO system. The study consisted of three phases: a ten-day baseline, up to 35 days of active titration, and a 20-day post-titration follow-up. Outcomes included change in CGM-derived glycemic metrics (mean glucose, time in range [TIR], time above range [TAR], time below range [TBR]), fasting blood glucose (FBG), and PROs (diabetes treatment satisfaction, glucose monitoring satisfaction, and patient-doctor depth of relationship) assessed using validated questionnaires. Safety was also assessed by the number of adverse events.
RESULTS: All participants completed the study. The BTO system generated 401 insulin dose recommendations, with 96.5% accepted by the healthcare provider. After BTO-guided titration, mean glucose decreased by median (IQR) 26.3 (- 64.6, - 6.0) mg/dl (p = 0.0031), TIR 70-180 mg/dl increased by 16.0 (2.5, 43.7) percentage points (p = 0.0017), TAR > 180 mg/dl decreased by 15.9 (- 43.7, - 2.5) percentage points (p = 0.0017), and TBR < 70 mg/dl remained low (0.0-0.1%), with no clinically significant change in TBR. Median FBG decreased by 17.8 (- 54.4, 2.3) mg/dl (p = 0.052). No BTO- or study-related- adverse or severe adverse events were reported. Treatment satisfaction was significantly higher; median post-titration Diabetes Treatment Satisfaction Questionnaire (DTSQ) Change score was 17 (14, 18) on a scale ranging from - 18 to 18 and was significantly different from baseline DTSQ status score (p = 0.001). High satisfaction with glucose management and strong patient-provider relationships were preserved. Three illustrative cases highlighted successful insulin dose optimization through dose increases or decreases.
CONCLUSIONS: CGM-informed basal insulin optimization with a prototype of Dexcom Smart Basal was associated with improvements in glycemic control and treatment satisfaction in adults with T2D, with a favorable safety profile. The system may help overcome clinical inertia and support individualized insulin titration in real-world settings.
TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT07681375 (Submitted for retrospective registration on 1/28/2026 and registered on 7/1/2026).

Keywords:  Basal insulin; Clinical inertia; Clinical trial; Continuous glucose monitoring (CGM); Insulin calculator; Long-acting insulin; Smart Basal; Type 2 diabetes

DOI:  https://doi.org/10.1007/s13300-026-01901-4
Diabetes Metab Syndr Obes. 2026 ;19 615113

Association Between Short-Term Glycemic Variability Measured by Continuous Glucose Monitoring and Early Diabetic Kidney Disease in Type 2 Diabetes.

Nan Chen, Nuojin Wang, Hao Duan, Tianrong Pan, Limin Zhai.

   Background: The role of short‑term glycemic variability (GV) in early diabetic kidney disease (DKD) among patients with type 2 diabetes (T2D) remains controversial. This study aims to investigate the independent association between continuous glucose monitoring (CGM)-derived GV indices and early DKD in T2D patients.
Methods: This cross-sectional study included 315 patients with T2D. Early DKD was identified as persistent microalbuminuria (urine albumin-to-creatinine ratio 30-300 mg/g) with preserved renal function (eGFR≥60 mL/min/1.73 m2). GV metrics, including mean amplitude of glycemic excursions (MAGE) and glucose standard deviation (SDBG), were derived from CGM data. Multivariate logistic regression and generalized additive models (GAMs) were used to assess associations, with subgroup analyses and interaction tests.
Results: Participants with early DKD had significantly higher MAGE and SDBG (both P<0.001). After full adjustment for HbA1c, medication use, and other covariates, both MAGE (OR =1.58, 95% CI: 1.31-1.89, P<0.001) and SDBG (OR =2.14, 95% CI: 1.39-3.30, P = 0.001) were independently associated with early DKD. Compared with the lowest tertile, the highest tertiles of MAGE and SDBG were associated with a 5.78-fold (95% CI: 2.83-11.81) and 3.56-fold (95% CI: 1.77-7.18) higher risk, respectively (all P< 0.001). BMI was a significant effect modifier of the MAGE-DKD association (P for interaction = 0.014), which was present only in overweight/obese individuals. GAM analysis demonstrated a linear positive relationship for MAGE and a non-linear U-shaped association for SDBG.
Conclusion: CGM-derived short-term GV indices MAGE and SDBG are independently associated with early DKD in T2D patients, with distinct dose-response patterns: MAGE shows a linear positive relationship, whereas SDBG exhibits a U-shaped association. Moreover, the MAGE-DKD association is confined to overweight individuals. Prospective studies are needed to establish causality and to verify whether personalized targeting of short‑term GV might be associated with lower DKD risk.

Keywords:  CGM; continuous glucose monitoring; early diabetic kidney disease; microalbuminuria; short-term glycemic variability; type 2 diabetes mellitus

DOI:  https://doi.org/10.2147/DMSO.S615113
Pregnancy (Hoboken). 2026 May;2(3): e70308

Intrapartum glucose metrics associated with adverse neonatal outcomes among people with gestational diabetes mellitus.

Alexandra C Gallagher, Alyssa R Hersh, Lucy Ward, Amy M Valent.

   Introduction: Hyperglycemia is a recognized risk factor for adverse neonatal outcomes. However, the impact of achieving glucose goals during labor on the risk of such outcomes remains poorly understood for individuals with gestational diabetes mellitus (GDM). We sought to compare continuous glucose monitoring (CGM) metrics between laboring GDM pregnancies complicated by adverse neonatal outcomes to those without complications.
Methods: Secondary analysis of pregnant people with GDM randomized to using CGM versus capillary blood glucose (CBG) for management of GDM. Among all participants wearing a CGM during labor, we compared pregnancies that were complicated by a significant neonatal outcome as defined by a composite including one or more of the following: hypoglycemia, hyperbilirubinemia, respiratory distress syndrome, or neonatal intensive care unit admission to those without this composite adverse outcome. Continuous variables were compared with two-sample t-tests, and categorical variables were tested with Fisher's Exact tests. We correlated antepartum to intrapartum glucose management by measuring mean glucose and glucose percent time-in-range (TIR; primary intrapartum range 70-120 mg/dL, with candidate ranges 70-110 and 70-140 mg/dL), stratified by composite adverse neonatal outcome.
Results: Of the 111 in the primary trial, 59 were included in this secondary analysis. A total of 52 of 111 patients in the primary trial were excluded, as 23 were missing intrapartum data and 29 had no trial of labor prior to cesarean delivery. The cohort with the composite adverse neonatal outcome spent significantly less intrapartum TIR between 70 and 120 mg/dL (62.7% vs. 80.7%, p = 0.012) and more time-above-range (TAR) ≥ 120 mg/dL (26.7% vs. 10.6%, p = 0.007) compared to those with no composite adverse neonatal outcome. The mean glucose for those with adverse neonatal outcomes was non-significantly higher than those without (103.9 vs. 94.6, p = 0.059). There was a weak-to-moderate positive correlation between antenatal and intrapartum glucose management.
Conclusion: Intrapartum participants with GDM and a composite adverse neonatal outcome spent less TIR, more TAR, with a higher mean glucose in labor than those without a composite adverse outcome. A weak-to-moderate positive correlation between antenatal and intrapartum glucose management was observed.

Keywords:  continuous glucose monitoring; gestational diabetes; intrapartum; neonatal hypoglycemia

DOI:  https://doi.org/10.1002/pmf2.70308
Diabet Med. 2026 Aug 13. e70437

High blood glucose index stratifies glycaemic severity and identifies occult type 2 diabetes risk in prediabetes via single-day continuous glucose monitoring: A multi-cohort study.

Weiming Wu, Shihan Wang, Junxiang Gao, Shuoning Song, Yating Zhang, Yong Fu, Mengmeng Zhang, Tao Yuan, Weigang Zhao.

   AIMS: To evaluate the platform-independent robustness of the continuous glucose monitoring (CGM)-derived High Blood Glucose Index (HBGI) and the predictive utility of a single day for progression to type 2 diabetes.
METHODS: We analysed 365 valid CGM records from three devices across four independent cohorts. Linear mixed-effects modelling partitioned HBGI variance by device, dataset and metabolic status. Cross-sectional analyses, utilizing full monitoring periods to ensure biological robustness, evaluated HBGI gradients across glycaemic stages. Longitudinal analyses, utilizing single-day recordings, evaluated its predictive accuracy for progression to overt diabetes.
RESULTS: Device type accounted for only 3.4% of total HBGI variance, compared with 31.8% for metabolic status. Cross-sectionally, HBGI demonstrated a robust stepwise increase from healthy through prediabetes to diabetes (p < 0.001). Longitudinally, baseline HBGI significantly differentiated metabolic progressors from non-progressors (p = 0.005) and showed moderate predictive accuracy (area under the curve = 0.74; 95% confidence interval: 0.61-0.85). Discriminative performance was comparable to mean glucose and superior to the M-value, time in range (70-180 mg/dL) and coefficient of variation. Notably, among prediabetic individuals with established glycaemic indices within recommended ranges, elevated HBGI (>0.174) identified an occult high-risk subgroup with a 5.39- to 6.13-fold higher progression risk.
CONCLUSIONS: HBGI is a device-agnostic metric that effectively stratifies glycaemic severity. A single-day HBGI assessment provides complementary risk stratification by unmasking occult risk in prediabetes with established CGM indices within recommended ranges. However, given the limited progression events, its predictive threshold requires validation in larger, independent cohorts.

Keywords:  continuous glucose monitoring; disease progression; high blood glucose index; risk stratification; single‐day assessment

DOI:  https://doi.org/10.1111/dme.70437
Pregnancy (Hoboken). 2025 Sep;1(5): e70108

Characterizing glucose in labor: Continuous glucose monitoring in parturients without diabetes.

Kelsey Pinson, Jane J Kim, Navyaa Sharma, Emily Kobayashi, Amit R Majithia, Valentina Stanley, Gladys A Ramos.

   Introduction: Current recommendations for intrapartum glycemic control in those with diabetes mellitus are based upon expert opinion. Our objective was to characterize intrapartum interstitial glucose values using continuous glucose monitors (CGMs) in term gravidas without diabetes.
Methods: A prospective observational cohort of 72 parturients without pre-gestational or gestational diabetes. CGMs were placed in early labor and the following were obtained: glucose at delivery, mean glucose in active and second stage of labor, time in range defined as 70-120 mg/dL, time above range, and time below range. Glucose parameters were compared by body mass index (BMI) and by adequacy of gestational weight gain. Neonatal outcomes were assessed.
Results: The mean glucose value at delivery was 109 ± 27 mg/dL and over the 2 h prior to delivery was 99 ± 23 mg/dL. A total of 26% of parturients had a glucose value > 120 mg/dL at the time of delivery. Mean glucose in active labor was 101 ± 25 mg/dL and in the second stage was 99.5 ± 23 mg/dL. In labor, average time-in-range was 69.8% (10th-90th percentile 33.8%-95.8%), time-above-range was 19.9% (10th-90th percentile 0%-66.2%), and time-below-range was 10.3% (10th-90th percentile 0%-66%). Neither BMI nor gestational weight gain had a significant clinical impact on these parameters. A total of 28 infants had glucose checked for clinical indications and 5 required treatment for neonatal hypoglycemia; this was unrelated to mean glucose during labor or at delivery, time-above-range in labor and second stage, and time-above-range in the 4 h prior to delivery.
Conclusion: Interstitial glucose greater than 120 mg/dL was common, and neither BMI nor gestational weight gain appears to have a significant clinical impact on glucose in labor. These data challenge contemporary definitions of glucose goals in labor, and further study is needed to better define glucose in labor and identify risk factors for adverse outcomes.

Keywords:  continuous glucose monitor (CGM); gestational diabetes; glucose

DOI:  https://doi.org/10.1002/pmf2.70108
Int J Behav Med. 2026 Aug 13.

Examining the Potential of Person-centered Temporal Network Models to Individualize Type 1 Diabetes Care.

Elizabeth Pyatak, Raymond Hernandez, Jeffrey S Gonzalez, Loree Pham, Anya Khurana, Stefan Schneider.

   PURPOSE: We aimed to characterize dynamic, individualized relationships among glucose, physical symptoms, emotions, and functioning (e.g., experiential variables) in adults with type 1 diabetes (T1D) using person-centered temporal network models, and assess whether network features relate to retrospective well-being and functioning.
METHODS: We analyzed data from 158 adults with T1D who completed 14 days of blinded continuous glucose monitoring (CGM) and ecological momentary assessments of experiential variables. For each participant, we estimated a temporal network using the Group Iterative Multiple Model Estimation (GIMME) algorithm, which quantifies dynamic interconnections of variables within individuals. We assessed the variability of models across participants, and examined whether interconnections among variables (network density) were associated with retrospective measures of well-being and functioning.
RESULTS: GIMME models converged for 96.3% of participants, and showed excellent model fit for 77.2% of participants, establishing feasibility. Individual networks had a median of 20.5 (IQR 16-32.75) connections, of which 5 (IQR 3-8) were between glucose and experiential variables. Networks showed marked heterogeneity in the presence, direction, and strength of associations between variables. Individuals whose glucose was strongly predicted by experiential variables (i.e., those whose networks had higher "glucose-in" density), experienced greater depressive symptoms (β = 0.17, p = 0.03), perceived stress (β = 0.24, p < 0.001), anxiety symptoms (β = 0.19, p = 0.02), and negative affect (β = 0.16, p = 0.05) and poorer lifestyle/occupational balance (β = -0.16, p = 0.04).
CONCLUSIONS: GIMME models identify individualized patterns linking glucose with daily experiences, which may help generate hypotheses relevant to tailoring care. Network features are associated with retrospective measures of well-being and functioning, supporting the potential of network-informed approaches to T1D care.

Keywords:  Continuous glucose monitoring; Digital health; Ecological momentary assessment; Network analysis; Psychosocial functioning; Type 1 Diabetes

DOI:  https://doi.org/10.1007/s12529-026-10483-1
J Nutr Health Aging. 2026 Aug 08. pii: S1279-7707(26)00180-6. [Epub ahead of print]30(10): 100947

Body composition-defined sarcopenia phenotypes, diabetes complications, and CGM-derived glycemic profiles in frail older adults with diabetes: a cross-sectional study.

Ernesto Guevara, Andreu Simó-Servat, Verónica Perea, Carmen Quirós, Carlos Puig-Jové, Francesc Formiga, María-José Barahona.

   BACKGROUND AND AIMS: Sarcopenia in older adults with diabetes may encompass clinically distinct phenotypes. We examined whether body composition-defined phenotypes differed in diabetes complications, insulin resistance-related metabolic features, and continuous glucose monitoring (CGM)-derived glycemic profiles.
METHODS: This cross-sectional study included 109 adults aged 70 years or older with diabetes and frailty. Sarcopenia was defined according to the European Working Group on Sarcopenia in Older People 2 criteria; adiposity was defined using body mass index, body fat percentage, and central obesity. Analyses were mainly descriptive; exploratory logistic regression assessed poor CGM time in range (TIR), defined as TIR below 70%.
RESULTS: Sarcopenia was identified in 86 participants (78.9%). After exclusion of 2 participants in a small non-sarcopenic with adiposity subgroup, 107 participants formed three phenotypes. Sarcopenic obesity showed an insulin resistance-related cluster comprising greater central adiposity, higher triglycerides and TyG index, metabolic syndrome in all participants, and greater insulin requirements. Sarcopenia without adiposity showed more neuropathy, albuminuria, cerebrovascular disease, and lower TIR despite similar glycated hemoglobin. Glycated hemoglobin was the only independent correlate of poor TIR; phenotype was not significant overall.
CONCLUSIONS: Phenotypes showed distinct clinical, insulin resistance-related, and CGM profiles. These preliminary findings support phenotype-aware assessment but require confirmation in larger longitudinal studies before informing management.

Keywords:  Body composition; Continuous glucose monitoring; Diabetes mellitus; Frailty; Sarcopenia

DOI:  https://doi.org/10.1016/j.jnha.2026.100947
JMIR Diabetes. 2026 Aug 13. 11 e97122

The Emotional Burden and Behavioral Impacts of Alarms and Alerts Across the Spectrum of Diabetes Technology Use: a Cross-Sectional Study.

Estelle Everett, Erin Shaw, Bryan Escobar Barrios, Beatrice Brumley, Kyrstin Lane, Tannaz Moin, Lauren E Wisk.

   Background: Continuous glucose monitors (CGMs), sensor-augmented pumps (SAPs), and automated insulin delivery (AID) systems have substantially improved glycemic outcomes for people with type 1 diabetes (T1D). However, these technologies also generate frequent alarms and alerts that may contribute to emotional burden, alarm fatigue, and maladaptive behavioral responses. Despite increasing recognition of alarm-related distress, little is known about how alarm burden differs across contemporary diabetes technologies.
Objective: This study evaluated differences in alarm frequency, emotional burden, and behavioral responses to alarms and alerts among adults using CGMs alone, SAP therapy, and AID systems.
Methods: We conducted a cross-sectional survey of adults (≥18 years) with T1D receiving care within a large academic health system between August 2024 and March 2025. Eligible participants completed an online survey assessing demographics, diabetes technology use, perceptions of alarm frequency and burden, and responses to alarms and alerts. Participants were categorized as CGM-only, SAP, or AID users. Differences between groups were evaluated using χ2 tests and ANOVA. Ordinal logistic regression models adjusted for age, gender, and diabetes duration were used to examine associations between diabetes technology type and alarm-related outcomes.
Results: Among 838 respondents (mean age 46.0, SD 16.6 years; mean diabetes duration 23.0, SD 14.0 years; n=457, 55% women), AID users reported the highest alarm frequency, with 49% (n=252) experiencing alarms several times daily, compared with 35% (n=28) of SAP users and 32% (n=79) of CGM-only users (P<.001). AID users also reported greater alarm disruptiveness, annoyance, and unwanted attention than CGM-only users. Overcorrection of glucose levels in response to alerts was common across all technologies: 44% (n=371) and 50% (n=419) of participants reported sometimes overcorrecting low and high glucose levels, respectively, while 32% (n=265) and 18% (n=147) reported often or always overcorrecting low and high glucose levels, respectively. After adjustment, AID users had greater odds of frequent alarms (odds ratio [OR] 1.98, 95% CI 1.48-2.65), alarm disruptiveness (OR 1.67, 95% CI 1.24-2.25), annoyance (OR 1.65, 95% CI 1.23-2.19), unwanted attention (OR 1.85, 95% CI 1.38-2.47), ignoring alarms (OR 1.86, 95% CI 1.40-2.47), and overcorrecting low glucose levels (OR 1.61, 95% CI 1.20-2.15) compared with CGM-only users. Perceived effectiveness of alarms did not differ by technology type.
Conclusions: Although AID systems provide important clinical benefits, they are associated with the greatest alarm and alert burden. Alarm-driven behaviors, including ignoring alerts and overcorrecting glucose levels, represent previously underrecognized consequences of diabetes technology that may diminish user experience and potentially affect glycemic management. These findings identify opportunities to improve alert algorithms while highlighting the need for individualized patient education, clinician engagement in setting appropriate alert thresholds, and routine assessment of alarm burden to optimize both glycemic outcomes and patient-centered experiences with diabetes technology.

Keywords:  CGM; alarm fatigue; automated insulin delivery; continuous glucose monitoring; diabetes distress; insulin pump; quality of life; type 1 diabetes

DOI:  https://doi.org/10.2196/97122
Nutrients. 2026 Aug 04. pii: 2536. [Epub ahead of print]18(15):

Protein-Containing Breakfasts Are Associated with Reduced Postprandial Glycaemic Excursions in Children and Young People with Type 1 Diabetes: A Pragmatic Study.

Julie Johnson, Victoria Franklin, Ashley Shepherd, Sonja Allen, Debbie Blissitt, Kate Keen, Kirsty Maclean, Anne-Marie McKillup, Elizabeth Procter, Susan Roach, Adele Swart, Stuart D R Galloway.

  Background/Objectives: This study examined whether adding 10 g of protein to a high glycaemic index (GI) breakfast could attenuate postprandial glycaemic responses in children and young people (CYP) with type 1 diabetes (T1D). Postprandial glucose responses following this modified breakfast were compared with those after a high GI breakfast alone, a low GI breakfast, and participants' usual breakfast. Methods: A pragmatic randomised crossover study was conducted in n = 25 CYP aged 5-17 years. Participants consumed three standardised test breakfasts on two study occasions and their usual breakfast as a control. Test meals varied by GI, glycaemic load (GL), and protein content: high GI/high GL (HGL), high GI/high GL with an additional 10 g protein (HGLP), and low GI/medium GL (MGL). Continuous glucose monitoring data were collected for three hours following each meal. Linear mixed model analyses were used, and a subgroup analysis included participants using hybrid closed-loop (HCL) systems. Results: Participants' (mean age 12.1 ± 3.6 years) mean postprandial glucose over a three-hour follow-up was significantly lower following the HGLP meal compared with the HGL meal (8.0 ± 2.2 vs. 9.5 ± 2.5 mmol/L; p < 0.01). Time in Range (TIR) over 180 min was significantly shorter after the HGL meal compared with the control, HGLP, and MGL meals (p ≤ 0.03). Glucose excursions at 30 and 60 min were significantly higher following the HGL meal compared with all other meals, with differences persisting up to 120 min when compared with the HGLP meal. Results were not different for participants using HCL systems. Conclusions: Adding 10 g of protein to a high GI breakfast or choosing a lower GI option improves postprandial glycaemic control in CYP with type 1 diabetes.

Keywords:  carbohydrate; continuous glucose monitoring; diabetes mellitus; glycaemic control; meal; nutrition; protein

DOI:  https://doi.org/10.3390/nu18152536
Diabetes Res Clin Pract. 2026 Aug 11. pii: S0168-8227(26)00412-2. [Epub ahead of print]239 113492

Contemporary automated insulin delivery systems in older adults with type 1 diabetes: A systematic review and meta-analysis.

Piyush Ratan, Marconi Abreu, Luisa Saldarriaga, Hilária Saugo Faria, Talita Trevisan.

  Older adults with type 1 diabetes are clinically heterogeneous, with variable cognition, dexterity, comorbidity burden, caregiver support, and prior technology experience. Evidence on automated insulin delivery (AID) systems in this population remains limited and largely observational. We performed a systematic review and meta-analysis of contemporary AID systems in adults aged ≥60 years with type 1 diabetes. We searched PubMed, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov through February 2026. Nine studies (7 observational studies and 2 randomized crossover trials; 8,765 participants) were included. We pooled outcomes during AID use using single-arm random-effects meta-analyses. When baseline data were available, we performed change-from-baseline analyses. Pooled mean time in range (TIR) was 77.95% (95% CI 75.31-80.59), HbA1c was 6.95% (52 mmol/mol) (95% CI 6.67-7.24), and time below range (TBR) < 70 mg/dL was 1.35% (95% CI 1.12-1.57). From baseline, AID use was associated with higher TIR, lower hyperglycemia, lower HbA1c (MD - 0.36%; 95% CI - 0.63 to - 0.09), and lower diabetes distress, without increased hypoglycemia. Findings were consistent in direction with broader AID literature. Older age alone should not preclude individualized AID use.

Keywords:  Automated insulin delivery; Continuous glucose monitoring; Hybrid closed-loop; Older adults; Patient-reported outcomes; Type 1 diabetes

DOI:  https://doi.org/10.1016/j.diabres.2026.113492