bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–08–02
eleven papers selected by
Mott Given
  1. Skin Reactions With Continuous Glucose Monitoring Systems: Real-World Survey Data Highlight an Underappreciated Clinical Challenge.
  2. A large sensor foundation model pretrained on continuous glucose monitor data for diabetes management.
  3. INSIGHT: Ingest and Standardize Glucose Harmonization Tool.
  4. Managing Gestational Diabetes Complexity with Continuous Glucose Monitoring: A Narrative Review.
  5. Regulatory standardisation to improve the safety of continuous glucose monitoring systems in Australia and New Zealand: joint ADS/ADEA/ADIPS/ANZSPED/NZSSD statement.
  6. Ultra-processed foods and continuous glucose monitoring metrics in adults with type 2 diabetes.
  7. Time in Range and Adverse Outcomes in Type 2 Diabetes: A Quantitative Synthesis.
  8. Impact on Vulnerable Populations After Discontinuation of Continuous Glucose Monitoring in Patients With Type 2 Diabetes.
  9. Improvement in insulin injection timing and glucometrics using a connected insulin cap: the Insulclock v2.0® prospective study.
  10. Association of glycation gap with hypoglycemia CGM indices, diabetic complications, and quality of life in Japanese type 2 diabetes patients.
  11. Remote Glucose Monitoring Engagement and Glycemic Control in Type 2 Diabetes and Prediabetes: Retrospective Cohort Study.
  1. J Diabetes Sci Technol. 2026 Jul 31. 19322968261473290
    Skin Reactions With Continuous Glucose Monitoring Systems: Real-World Survey Data Highlight an Underappreciated Clinical Challenge.
    Birgit Olesen, Dominic Ehrmann, Bernhard Kulzer, Norbert Hermanns, Lutz Heinemann.
      
    DOI:  https://doi.org/10.1177/19322968261473290
  2. Npj Health Syst. 2025 Sep 16. pii: 35. [Epub ahead of print]2(1):
    A large sensor foundation model pretrained on continuous glucose monitor data for diabetes management.
    Junjie Luo, Abhimanyu Kumbara, Mansur Shomali, Rui Han, Anand Iyer, Grazia Aleppo, Ritu Agarwal, Gordon Gao.
      Continuous glucose monitoring (CGM) combined with AI offers new opportunities for proactive diabetes management through real-time glucose forecasting. However, most existing models are task-specific and lack generalization across patient populations. Inspired by the autoregressive paradigm of large language models, we introduce CGM-LSM, a Transformer decoder-based Large Sensor Model (LSM) pretrained on 1.6 million CGM records from patients with different diabetes types, ages, and genders. We model patients as sequences of glucose time steps to learn latent knowledge embedded in CGM data and apply it to the prediction of glucose readings for a 2-h horizon. Compared with prior methods, CGM-LSM significantly improves prediction accuracy and robustness: a 48.51% reduction in root mean square error in 1-h horizon forecasting and consistent zero-shot prediction performance across held-out patient groups. We analyze model performance variations across patient subgroups and prediction scenarios and outline key opportunities and challenges for advancing CGM foundation models.
    DOI:  https://doi.org/10.1038/s44401-025-00039-y
  3. J Diabetes Sci Technol. 2026 Jul 27. 19322968261467778
    INSIGHT: Ingest and Standardize Glucose Harmonization Tool.
    Ben Ehlert, Krish Shah, Dev Katarya, Yue Wu, Tao Wang, Michael P Snyder.
       BACKGROUND: Publicly available continuous glucose monitoring (CGM) datasets are fragmented across schemas, limiting reuse, pooling, and machine learning development. Although large biobanks provide harmonized resources, many smaller CGM studies remain difficult to access because ingestion and standardization require substantial manual effort.
    METHODS: We developed INSIGHT (Ingest and Standardize Glucose Harmonization Tool), an open-source large language model (LLM)-assisted harmonization pipeline that standardizes public CGM datasets into a minimal common schema: Subject_ID, Timestamp, and Glucose. INSIGHT combines deterministic rules with structured LLM assistance to identify CGM files, classify file roles, generate schema-specific read plans, normalize timestamps and glucose units, resolve subject identifiers, detect overlapping exports, merge outputs, and retain auditable JSON artifacts. We evaluated INSIGHT on 14 public CGM datasets with curated reference outputs using pre-specified training and held-out testing partitions. Performance was assessed using file-level precision/recall, subject recovery, temporal alignment, glucose fidelity, scalar CGM feature accuracy, and composite INSIGHT scores.
    RESULTS: INSIGHT is open-source at https://github.com/benehlert/CGMHarmonization. Across 90 model-dataset evaluations spanning 9 model variants and 10 datasets, top configurations achieved near-perfect held-out harmonization fidelity. GPT-5.4 and Gemini 3.1 Pro Preview achieved mean INSIGHT scores of 0.990, with glucose mean absolute error (MAE) below 0.03 mg/dL and within 5 mg/dL agreement of 0.9996. Errors primarily reflected complex subject-timestamp recovery rather than glucose conversion, with failures concentrated in deeply nested datasets containing overlapping or co-located non-CGM exports.
    CONCLUSION: INSIGHT demonstrates that LLM-assisted code generation can generalize CGM harmonization across heterogeneous datasets while producing auditable loaders. By reducing reuse costs, INSIGHT complements curated repositories and supports large-scale pooled analyses and foundation model development for glycemic physiology.
    Keywords:  continuous glucose monitoring; data harmonization; diabetes; health sensors; large language model
    DOI:  https://doi.org/10.1177/19322968261467778
  4. Diagnostics (Basel). 2026 Jul 08. pii: 2145. [Epub ahead of print]16(14):
    Managing Gestational Diabetes Complexity with Continuous Glucose Monitoring: A Narrative Review.
    Anca-Elena Crăciun, Dana Mihaela Ciobanu, Georgeta Inceu, Camelia Larisa Vonica, Denisa Herman, Cristian-Ioan Crăciun, Adriana Fodor, Cornelia Bala, Adriana Rusu.
      Gestational diabetes (GDM) is a frequent health problem associated with both short- and long-term adverse outcomes for mother and child. Standard management includes lifestyle interventions and, when necessary, pharmacologic therapy. However, the effectiveness and timely initiation of pharmacological therapy depend on accurate glucose monitoring. Continuous glucose monitoring (CGM) systems have emerged as valuable tools in diabetes care, providing real-time information on glycemic variability and enabling more individualized therapeutic interventions. In this narrative review, we explore the role of CGM in the early detection of dysglycemia, its diagnostic and prognostic value, and its ability to identify specific glycemic patterns during pregnancies complicated by GDM. We also assess its role in optimizing lifestyle interventions and guiding pharmacotherapeutic strategies. Current evidence suggests that CGM supports clinical decision-making and patient engaging by providing real-time glucose data. This facilitates earlier identification of hyperglycemic patterns, more precise treatment changes and improved glucose control. Furthermore, CGM use has been associated with improved neonatal and maternal outcomes. Despite these promising findings, barriers such as cost and limited access persist. Although the existing evidence remains relatively limited, it supports the integration of CGM into routine care of women with GDM as part of a comprehensive and personalized treatment strategy. Larger clinical trials are needed to fully understand the benefits and optimal use of CGM in GDM, as well as its impact on pregnancy outcomes, glycemic control and psychological well-being.
    Keywords:  continuous glucose monitoring; gestational diabetes; narrative review; pregnancy outcome
    DOI:  https://doi.org/10.3390/diagnostics16142145
  5. Intern Med J. 2026 Jul 29.
    Regulatory standardisation to improve the safety of continuous glucose monitoring systems in Australia and New Zealand: joint ADS/ADEA/ADIPS/ANZSPED/NZSSD statement.
    Sofianos Andrikopoulos, Lisa M Raven, Ramzi Ajjan, Kate Lomax, Tara Whelan, Brett Fenton, Amanda Bartlett, Shantha Joseph, Matthew Hare, Mary B Abraham, Hamish R Crocket, Tom M Wilkinson, Joanna McClintock, Benjamin J Wheeler, Glynis P Ross.
      Self-monitoring of glycaemia is a cornerstone of diabetes management, particularly for people using insulin. Traditionally, glucose monitoring involved finger-pricking and measuring capillary blood glucose concentration. Continuous glucose monitors (CGMs) have revolutionised self-monitoring of glucose levels. These devices provide continuous interstitial fluid glucose measurements, including trend arrows and alarms to alert the person if glucose levels are outside predetermined ranges. The increased reliance on CGM metrics for clinical decisions to manage diabetes necessitates the consideration and implementation of minimum standards that device manufacturers should meet to demonstrate safety, precision and accuracy. This statement provides recommendations to ensure that CGM devices that are approved for the Australian and New Zealand markets meet/exceed these minimum standards of accuracy, to provide health professionals and people living with diabetes the confidence to manage their glucose levels effectively.
    Keywords:  CGM; continuous glucose monitor; diabetes; type 1 diabetes
    DOI:  https://doi.org/10.1111/imj.70586
  6. Diabetes Res Clin Pract. 2026 Jul 28. pii: S0168-8227(26)00385-2. [Epub ahead of print]239 113465
    Ultra-processed foods and continuous glucose monitoring metrics in adults with type 2 diabetes.
    Soohyun Nam, Minjung Lee, Filippa Juul, Niyati Parekh, Sangchoon Jeon.
       AIM: We examined associations between ultra-processed foods (UPFs) consumption and continuous glucose monitoring (CGM)-derived metrics using individual- and food-level measures of UPFs in adults with T2D.
    METHODS: Adults with T2D (n = 190) completed two 24-hour dietary recalls and wore blinded CGM devices for 14 days. Foods were classified by the NOVA system. Individual-level UPF intake was defined as the proportion of total energy or grams derived from UPFs (energy-weighted). Food-level intake was assessed as Mean UPF Energy (%) and Mean UPF Gram (%) across all reported food items (item-weighted). CGM outcomes included time in range (TIR), time above range (TAR), glucose management indicator (GMI), and coefficient of variation. Multivariable logistic regression models were adjusted for demographic, lifestyle, and clinical factors.
    RESULTS: After adjustment, higher Mean UPF Energy (%) was associated with lower odds of achieving TIR > 70% (OR = 0.59, 95% CI: 0.38-0.92) and higher odds of TAR ≥ 25% (OR = 1.62, 95% CI: 1.06-2.47) and GMI ≥ 7% (OR = 1.56, 95% CI: 1.03-2.35). Individual-level UPF intake was not significantly associated with CGM-derived outcomes.
    CONCLUSIONS: Higher UPF energy content at the food level was associated with poorer CGM-derived glycemic outcomes. Food-level UPF measures may more closely reflect CGM-derived glycemic metrics than individual-level measures.
    Keywords:  Continuous glucose monitoring; Time above range; Time in Range; Type 2 diabetes; Ultra-processed foods
    DOI:  https://doi.org/10.1016/j.diabres.2026.113465
  7. J Clin Med. 2026 Jul 21. pii: 5713. [Epub ahead of print]15(14):
    Time in Range and Adverse Outcomes in Type 2 Diabetes: A Quantitative Synthesis.
    Furong Qu, Qinbo Yang, Qingyue Zeng, Zhipeng Li, Jing Li.
      Objective: We aimed to quantify the prognostic value of glucose monitoring-derived time in range (TIR), including continuous glucose monitoring (CGM), flash glucose monitoring (FGM), and fingertip capillary glucose monitoring (FCGM), for predicting adverse clinical outcomes in patients with type 2 diabetes mellitus (T2DM). Research Design and Methods: PubMed, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL, via OVID) were systematically searched from 2017 to November 2025 for studies evaluating the risk of all clinically relevant outcomes associated with different TIRs in T2DM. Extracted data were standardized to evaluate the effect of a 10% increment in TIR. Pooled estimates were calculated using inverse-variance random-effects models incorporating dose-response analysis. The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. Results: Twenty-four observational studies involving 20 distinct associations and 35,916 participants were included. Dose-response meta-analyses were conducted for nine associations. The results showed that each 10% increment in TIR was significantly associated with a reduced risk of multiple adverse complications, including all-cause mortality (odd ratio [OR] = 0.88, 95% confidence interval [CI]: 0.82-0.93), vision-threatening diabetic retinopathy (OR = 0.93, 95% CI: 0.87-0.98), diabetic retinopathy (OR = 0.92, 95% CI: 0.89-0.95), lower extremity atherosclerotic disease (OR = 0.86, 95% CI: 0.82-0.91), diabetic peripheral neuropathy (OR = 0.77, 95% CI: 0.71-0.84), and cardiovascular autonomic neuropathy (OR = 0.81, 95% CI: 0.68-0.97). In contrast, the associations for albuminuria (KDIGO [Kidney Disease: Improving Global Outcomes] A2 and A3) and amputation did not reach statistical significance in the primary meta-analysis. Conclusions: In conclusion, each 10% increment in TIR is consistently associated with a reduced risk of mortality and various micro- and macrovascular complications in T2DM. These findings suggest TIR as a robust prognostic indicator and actionable therapeutic target in diabetes management.
    Keywords:  continuous glucose monitoring; diabetes complications; systematic review; time in range; type 2 diabetes mellitus
    DOI:  https://doi.org/10.3390/jcm15145713
  8. Ann Pharmacother. 2026 Jul 28. 10600280261470410
    Impact on Vulnerable Populations After Discontinuation of Continuous Glucose Monitoring in Patients With Type 2 Diabetes.
    Bernadette Asias-Dinh, Natalie Rosario, Kevin Garey, Aya Hussein, Minoo Madi, Jodie Gee.
       BACKGROUND: Continuous glucose monitoring (CGM) reinforces positive nonpharmacologic behaviors and optimizes pharmacotherapy. Long-term CGM may be financially unsustainable in vulnerable populations, but data on durability of benefits after short-term use are limited.
    OBJECTIVE: Building upon a prior study of a 3-month pharmacist-led CGM program, this study compared 1-year changes in glycemic management (glycated hemoglobin [A1c]) between patients who continued vs discontinued CGM.
    METHODS: This single-center pilot study, approved by University of Houston Institutional Review Board, was conducted at a federally qualified health center (FQHC). Patients in the initial program continued CGM if they were able to pay for CGM supplies via insurance or self-pay. All patients were followed for 12 months to assess changes in mean A1c and the proportion meeting A1c goals after 3 to 6 (early) and 9 to 12 (late) months.
    RESULTS: Fifty-two patients were analyzed (continued CGM: n = 15; discontinued CGM: n = 37). Baseline A1c was similar between the groups. At early follow-up, mean A1c decreased slightly in the continued CGM group (-0.18 ± 1.0; P = .523) but increased significantly in the discontinued group (1.3 ± 1.6; P = .002). At late follow-up, A1c increased nonsignificantly with continued CGM (0.7 ± 1.4; P < .12) but significantly worsened with discontinuation (2.6 ± 1.9; P < .001). The proportion meeting A1c goals also significantly worsened in the discontinuation group.
    CONCLUSION AND RELEVANCE: Although differences existed between groups, this study supports uninterrupted access to CGM for patients with diabetes. Despite initial 3-month use, discontinuation was associated with significant A1c worsening within 1 year. Large-scale, randomized studies are needed to identify whether an optimal timing of personal CGM use exists to sustain glycemic management when consistent use is not feasible.
    Keywords:  ambulatory care; continuous glucose monitoring; diabetes; medically underserved; pharmacist collaborative drug therapy management
    DOI:  https://doi.org/10.1177/10600280261470410
  9. Front Endocrinol (Lausanne). 2026 ;17 1883791
    Improvement in insulin injection timing and glucometrics using a connected insulin cap: the Insulclock v2.0® prospective study.
    Fernando Gomez-Peralta, José Miguel Borrachero, Estefanía Santos, Cristina Abreu, Xoán Valledor, Luis Ruiz-Valdepeñas, Rosa Corcoy.
       Objectives: Matching insulin injection timing with meals to optimize postprandial glucose excursions is a daily challenge for individuals with diabetes on a multiple daily injection (MDI) regimen. We aimed to analyze the impact of using a connected insulin pen cap (CIPC) on insulin injection timing and glycemic control.
    Research design and methods: Pragmatic, real-life, multicenter, prospective, open-label, observational study, including one week of run-in and a 6-week follow-up, split into a two-week masked mode phase and a four-week active phase. Continuous glucose monitoring (CGM) and automatically tracked insulin injection data in individuals with insulin-treated diabetes (ITD) who started using the CIPC Insulclock. The baseline and five hours of paired CGM and rapid-acting insulin data collected from Insulclock v2.0® users were analyzed using the ROC detection methodology to identify meal events and the timing of insulin doses.
    Results: Of 82 recruited patients, 52 completed the study (54.4 y, 56.6% women, 60.4% with type 1 diabetes [T1D]) across three hospitals and one primary care center in Spain. The CGM glucometrics comparison between the consecutive masked and active phases showed: Glucose Management Indicator (GMI) 8.1 + 1.7 vs 7.8 + 1.4% (-0.3%, p 0.034); Time in Range 70-180 (TIR): 56.9 + 24.5 vs 61.9 + 21.6% (+5.0%, p 0.0054); Time below range <70 (TBR70): 2.5 + 3.3 vs 1.76 + 2.6% (-0.74%, p 0.0015); Time above range >180 (TAR180): 40.9 + 25.3 vs 36.6 + 22.4% (-5.3%, p 0.016). The on-time insulin injections increased: 45.5 + 15.52 to 54.4 + 16.6% (p 0.0017). The timing of insulin injection relative to the post-meal glycemic excursions shifted from +5.6 min (IQR -19.8 to +34.0) in the masked phase (n = 231 events) to -5.9 min (IQR -27.6 to +33.9) in the active phase (n = 467 events) (p = 0.023). An earlier injection was associated with a reduction in TAR180 (p = 0.042). Questionnaires measuring patients' reported outcomes (PROs) indicated a reduction in perceived treatment burden with the use of the Insulclock v2.0® CIPC.
    Conclusions: The use of Insulclock v2.0® connected insulin pen cap is associated with improved insulin injection timing and glucometrics.
    Keywords:  Connected insulin pen cap; Continuous glucose monitoring; Glycemic control; Insulin adherence; Insulin injection timing
    DOI:  https://doi.org/10.3389/fendo.2026.1883791
  10. J Diabetes Investig. 2026 Jul 29.
    Association of glycation gap with hypoglycemia CGM indices, diabetic complications, and quality of life in Japanese type 2 diabetes patients.
    Satomi Sonoda, Yosuke Okada, Tomoya Mita, Keiichi Torimoto, Kenichi Tanaka, Fumiya Sato, Naoto Katakami, Hidenori Yoshii, Keiko Nishida, Shingo Nakayamada, Ryota Ishii, Masahiko Gosho, Iichiro Shimomura, Hirotaka Watada.
       BACKGROUND: The glycation gap (GGap), defined as the discrepancy between glycated hemoglobin (HbA1c) and the value estimated from actual blood glucose level, is associated with diabetic complications, but its association with hypoglycemia remains unclear.
    METHODS: We evaluated the association between GGap and continuous glucose monitoring (CGM)-based hypoglycemic indices in patients with type 2 diabetes mellitus (T2DM). Baseline data from a multicenter cohort of 999 T2DM patients without cardiovascular disease were analyzed. The difference between HbA1c and estimated A1c (eA1c) was defined as the GGap, and various CGM indices were compared among low (≤0.16), medium (<0.16 to ≤0.60), and high (>0.60) GGap tertile groups.
    RESULTS: In the high GGap group, the average blood glucose was lower, while the Time Below Range <3.9 mmol/L (TBR<3.9) and <3.0 mmol/L (TBR<3.0), and low blood glucose index (LBGI) were higher than in the low and middle GGap groups. Patients with minimum blood glucose levels of <3.9, <3.0 mmol/L, and TBR<3.9 ≥ 4%, and TBR<3.0 ≥ 1% had significantly higher GGap values.
    CONCLUSION: This is the first study to show the strong association of high GGap with CGM-based hypoglycemic indices in T2DM. To achieve diabetes treatment that effectively prevents the progression of diabetic complications, it is essential to assess the GGap of the individual patient before intensifying diabetes management.
    Keywords:  continuous glucose monitoring; glycation gap; hypoglycemia
    DOI:  https://doi.org/10.1111/jdi.70403
  11. JMIR Form Res. 2026 Jul 23.
    Remote Glucose Monitoring Engagement and Glycemic Control in Type 2 Diabetes and Prediabetes: Retrospective Cohort Study.
    Yong Huang, Nitish Nagesh, Ajan Subramanian, Li Wang, Zhiyu Liu, Yichen Sun, Weiyi Hou, Iman Azimi, Amir M Rahmani, Randall Stafford.
       BACKGROUND: Suboptimal glycemic control remains a significant public health challenge among adults with type 2 diabetes and prediabetes, with 47.4% of US adults with diagnosed diabetes having HbA1c ≥7.0%. Remote glucose monitoring programs have shown promise for supporting self-management, but real-world evidence on the causal impact of varying patient engagement levels on glycemic outcomes remains limited.
    OBJECTIVE: To estimate the causal dose-response relationship between patient engagement, operationalized as weekly glucose monitoring frequency, and glycemic control measured by hemoglobin A1c (HbA1c), among adults enrolled in a comprehensive primary care-integrated remote monitoring program.
    METHODS: We conducted a retrospective cohort study of 1,436 adults with type 2 diabetes or prediabetes enrolled in the iHealth Unified Care program between 2019 and 2024. The program integrated Bluetooth-connected glucose meters, a mobile application, structured lifestyle coaching, and primary care coordination across 74 physician practices. Engagement was defined as mean weekly glucose monitoring frequency during the first 6 months. The causal effect of monitoring frequency on 6-month HbA1c was estimated using marginal structural models (MSMs) with inverse probability weighting (IPW) to address time-varying confounding. Covariates included age, sex, BMI, baseline HbA1c, comorbidities, medication status, and physical activity level.
    RESULTS: The cohort was predominantly older (95.4% aged ≥46 years), with 82.6% having hypertension and 45.9% classified as obese. Overall, HbA1c decreased by 0.54 percentage points (95% CI 0.47-0.62; P < .001) over 6 months. Cluster analysis identified three engagement tiers: low (n=835; mean 2.55 measurements/week), medium (n=493; 6.19/week), and high (n=108; 12.59/week). A monotonic dose-response was observed, with HbA1c reductions of 0.38 (95% CI 0.29-0.48), 0.71 (95% CI 0.58-0.85), and 1.01 (95% CI 0.69-1.32) percentage points for the low, medium, and high tiers, respectively (all P < .001 by paired t-test). In weighted MSMs, each additional weekly measurement was associated with a 0.05 percentage point greater HbA1c reduction (95% CI 0.03-0.07; P < .001). Among patients with high baseline HbA1c (≥9.0%), the high-engagement group achieved a mean reduction of 3.12 percentage points (95% CI 2.29-3.94). Findings were consistent in sensitivity analyses at 3 months (β = -0.04; P < .01) and 12 months (β = -0.03; P < .01) and across alternative weighting specifications.
    CONCLUSIONS: Higher engagement with a digitally enabled, primary care-integrated remote glucose monitoring program was causally associated with significantly greater HbA1c reductions in adults with type 2 diabetes and prediabetes. These findings support scalable remote patient monitoring strategies that actively foster sustained patient engagement as an effective approach to improving glycemic control and reducing the burden of diabetes-related complications at a population level.
    CLINICALTRIAL:
    DOI:  https://doi.org/10.2196/95679
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