bims-glumda 2025-12-28 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2025–12–28
fourteen papers selected by
Mott Given

Important Continuous Glucose Monitoring Attributes for Patients With Type 2 Diabetes.
The Technological Transformation in Diabetes Care: From Continuous Glucose Monitoring to Artificial Intelligence and Beyond.
Accuracy of a continuous glucose monitoring system in people with diabetes on peritoneal dialysis.
Continuous glucose monitoring predicts glycemic status in postpartum women with a recent history of gestational diabetes.
Accuracy and reliability of a continuous glucose monitoring system with a focus on hypoglycaemia.
Time in Tight Range Versus Time in Range for Predicting Diabetic Retinopathy in Adults with Type 1 Diabetes Using Multiple Daily Insulin Injections: A Retrospective Longitudinal Study with isCGM Data.
Differences Between Glycemia Estimates from Hemoglobin A1c and Continuous Glucose Monitoring and Their Association with Complete Blood Counts.
Application of continuous glucose monitoring and automated insulin delivery technologies for pregnant women with type 1, type 2, or gestational diabetes: an international consensus statement.
Towards Standardization of CGM Performance Studies: The QUSS-CGM Questionnaire for Assessing User Satisfaction.
Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction.
Letter: Analysis of Adhesives and Allergens in Over-the-Counter Continuous Glucose Monitors.
The efficacy of CGM driven machine learning algorithms in predicting hypoglycemia in patients with T1DM: a systematic review and meta-analysis.
Reconsidering Intermittent CGM Use in Type 2 Diabetes: A View From Meta-Analytic Evidence.
Effect of a Diabetes-Specific Protein Supplement (DSPS) on Postprandial Glycemic Responses in Adults with Type 2 Diabetes: A Randomized Crossover Study in India.

Clin Diabetes. 2025 May;43(5): 670-680

Important Continuous Glucose Monitoring Attributes for Patients With Type 2 Diabetes.

Tim C Lai, Heather P Whitley, Mesfin Genie, Robert H Eckel, Surachat Ngorsuraches.

Although patient preferences are heterogeneous, the out-of-pocket cost and accuracy of continuous glucose monitoring (CGM) systems are the two most important attributes for patients with type 2 diabetes. Surprisingly, receiver screen information is not considered important when choosing a CGM system. Identifying important attributes could facilitate patient-provider communications in choosing a preferred CGM system and potentially increase adherence. Improving patient education on interpreting the information on the receiver screen could enhance the benefit of using CGM.

DOI: https://doi.org/10.2337/cd25-0047
Endocr Pract. 2025 Dec 18. pii: S1530-891X(25)01327-8. [Epub ahead of print]

The Technological Transformation in Diabetes Care: From Continuous Glucose Monitoring to Artificial Intelligence and Beyond.

Guillermo E Umpierrez, M Cecilia Lansang.



Keywords:  Continuous glucose monitoring; artificial intelligence; automatic insulin delivery; closed-loop; glucose monitoring

DOI:  https://doi.org/10.1016/j.eprac.2025.11.016
Perit Dial Int. 2025 Dec 23. 8968608251406514

Accuracy of a continuous glucose monitoring system in people with diabetes on peritoneal dialysis.

Jennifer K Williams, Mark Gilchrist, Angela C Shore.

  BackgroundLack of data on the accuracy of continuous glucose monitoring systems currently limits their usage in people with diabetes on peritoneal dialysis (PD).AimWe aimed to assess the analytical and clinical accuracy of the FreeStyle Libre (FSL) continuous glucose monitoring system in people with diabetes on PD.Methods12 participants using Icodextrin in their PD regime were recruited to a single-centre observational study. They wore a blinded research model of the FSL for 10 days. Results from the FSL were compared with venous glucose measured on a Yellow Springs Instrument (YSI) and self-monitored capillary blood glucose (SMBG) recorded five times per day over the 10-day study.ResultsThe mean absolute relative difference from 84 FSL-YSI matched pairs was 9.8% (95% CI 8.6-11.1) and from 416 FSL-SMBG matched pairs, it was 17.3% (95% CI 16.24-18.43). The systematic error for the FSL as determined by Bland-Altman analysis was -0.6 ± 1.0 mmol/l compared with YSI and -1.4 ± 1.9 mmol/l compared with SMBG. With regard clinical accuracy, compared with YSI and SMBG, respectively, 100% and 99.9% of sensor values were in clinically acceptable zones A and B of Parkes consensus error grid.ConclusionWe demonstrated satisfactory performance of the FSL monitoring system by both analytical and clinical metrics in this cohort of PD patients using treatment prescriptions including Icodextrin-based fluids. Larger studies are now needed to provide clinicians with appropriate reassurance if this technology is to be used with confidence in people on PD.

Keywords:  accuracy; continuous glucose monitoring; icodextrin; peritoneal dialysis

DOI:  https://doi.org/10.1177/08968608251406514
Expert Rev Med Devices. 2025 Dec 26. 1-7

Continuous glucose monitoring predicts glycemic status in postpartum women with a recent history of gestational diabetes.

Karen E Elkind-Hirsch, Melissa L Armatta, Kazanna C Hames, Edward W Veillon.

   BACKGROUND: Women with a history of gestational diabetes mellitus (GDM) face an elevated risk of developing diabetes, yet postpartum screening is often This study evaluated the diagnostic utility of a blinded continuous glucose monitoring (CGM) system (Dexcom G7) for assessing glycemic status.
METHODS: Of 40 enrolled participants, 39 completed testing at 6-24 weeks postpartum. Assessments included hemoglobin A1c (HbA1c,), fructosamine, a 75-g oral glucose tolerance test (OGTT), and 8-9 days of CGM wear. CGM metrics analyzed were mean glucose and time in range (TIR; 3.9-7.8 mmol/L).
RESULTS: Diabetes and prediabetes were identified in 1 and 9 participants by HbA1c, 3 and 10 by fructosamine, and 4 and 10 by OGTT. CGM metrics detected 4 cases of diabetes and 10 of prediabetes. Mean OGTT glucose correlated strongly with mean CGM glucose (r = 0.66, p < 0.001) and inversely with TIR (r = -0.57, p < 0.001). Paired OGTT and CGM values were highly correlated (r = 0.874; p < 0.001).
CONCLUSION: Postpartum dysglycemia is prevalent after GDM. HbA1c and fructosamine lacked sensitivity and specificity, while CGM performed comparably to OGTT in detecting abnormal glucose metabolism in this postpartum population.Clinical trial registration: www.clinicaltrials.gov identifier is NCT06057805.

Keywords:  Continuous glucose monitoring; HbA1c; fructosamine; gestational diabetes; oral glucose tolerance testing; postpartum

DOI:  https://doi.org/10.1080/17434440.2025.2607631
Diabetes Obes Metab. 2025 Dec 25.

Accuracy and reliability of a continuous glucose monitoring system with a focus on hypoglycaemia.

Lukas van Baal, Lorenz Raess, Annie Mathew, Harald Lahner, Nicole Unger, Dagmar Fuhrer.

   AIMS: Glucose monitoring is increasingly based on continuous glucose monitoring (CGM) systems. However, data on the accuracy of CGM within the hypoglycaemic range is sparse. This study investigated CGM accuracy within the hypoglycaemic range in three different clinical settings of hypoglycaemia.
MATERIALS AND METHODS: Ninety-two people with various causes of hypoglycaemia were analysed: (i) people during insulin tolerance testing (ITT) (n = 63); (ii) people with insulinoma (n = 16); and (iii) people with diabetes receiving subcutaneous insulin therapy (n = 13). CGM accuracy was evaluated for subgroups and different glucose rates of change (RoC) using mean absolute relative difference (MARD), percentage of glucose values within ±20 mg/dL of point-of-care glucose (%20/20), diabetes technology society error grid and Bland-Altman analysis (BAA).
RESULTS: Four hundred sixty-four CGM/POC glucose pairs were obtained (39.7% level 1, 60.3% level 2 hypoglycaemia). CGM accuracy decreased from people with diabetes receiving subcutaneous insulin therapy (MARD: 13.9%; %20/20: 81.5%) to those with ITT (MARD: 50.8%; p < 0.01, %20/20: 67.5%; p .02). CGM accuracy significantly decreased with higher RoC. Proportion of CGM values with moderate risk in failing to detect potentially dangerous hypoglycaemia increased from people with diabetes (1.5%) and insulinoma (1.2%) to people with ITT (14.2%, p < 0.01). BAA revealed a significantly increasing bias of -3.3 + 10.7 mg/dL in people with diabetes receiving insulin therapy to -15.2 ± 13.6 mg/dL in people with ITT (p < 0.01).
CONCLUSION: CGM accuracy can vary in different clinical hypoglycaemia scenarios. While it remains acceptable in people with diabetes receiving subcutaneous insulin therapy and people with insulinoma, it appears to be inaccurate for glucose monitoring during ITT.

Keywords:  accuracy; continuous glucose monitoring (CGM); hospital; hypoglycaemia; inpatient; reliability

DOI:  https://doi.org/10.1111/dom.70408
Diabetes Technol Ther. 2025 Dec 11.

Time in Tight Range Versus Time in Range for Predicting Diabetic Retinopathy in Adults with Type 1 Diabetes Using Multiple Daily Insulin Injections: A Retrospective Longitudinal Study with isCGM Data.

Fernando Sebastian-Valles, Elena García-Artacho, Alberto Santiago-Redondo, Sara González Castañar, Maria Sara Tapia-Sanchiz, Selma Amar, Victor Navas-Moreno, Eduardo Pedro López Palacios, Jose Alfonso Arranz Martín, Mónica Marazuela.

  Background: Time in tight range (TITR, 70-140 mg/dL) has emerged as a glycemic metric offering stricter assessment than conventional time in range (TIR, 70-180 mg/dL). Whether TITR provides additional prognostic value for diabetic retinopathy (DR) in adults with type 1 diabetes (T1D) remains unclear. Methods: We conducted a retrospective cohort study of 309 adults with T1D on multiple daily insulin injections using intermittently scanned continuous glucose monitoring (CGM) system. Ophthalmological assessments were performed at baseline and after 12 months (May 2024-May 2025). DR incidence (in those free of DR at baseline) and progression (in those with established DR) were defined according to Early Treatment Diabetic Retinopathy Study criteria. Longitudinal TITR and TIR were extracted every 14-28 days. Multivariable logistic regression adjusted for age, sex, diabetes duration, HbA1c, hypertension, Low-density lipoprotein (LDL) cholesterol, body mass index, and smoking was applied. Results: At baseline, 198 participants (64.1%) had no DR, 71 (23.0%) nonproliferative, and 40 (12.9%) proliferative DR. During follow-up, 10/198 (5.1%) developed DR and 26/111 (23.4%) with baseline DR progressed. Higher TITR was independently associated with lower risk of incident DR (adjusted OR per % increase: 0.965; 95% CI: 0.950-0.980), whereas TIR was not. Receiver operating characteristic analysis confirmed superior discrimination for TITR versus TIR (area under the curve 0.580 vs. 0.430; P < 0.001). In stratified analyses, TITR predicted incident DR only among participants with HbA1c below the cohort median (7.1%). Both TITR and TIR were associated with lower risk of DR progression in models including HbA1c, with similar discriminative performance. Diabetes duration, HbA1c, hypertension, and smoking were independently associated with DR outcomes alongside CGM metrics. Conclusions: TITR provides modestly superior predictive value over TIR for incident DR, particularly in individuals with near-target HbA1c, but both metrics perform similarly for predicting progression. CGM-derived metrics should be interpreted in the context of overall glycemic control and clinical risk factors.

Keywords:  diabetic retinopathy; time in range; time in tight range; type 1 diabetes

DOI:  https://doi.org/10.1177/15209156251403567
Diabetes Technol Ther. 2025 Dec 15.

Differences Between Glycemia Estimates from Hemoglobin A1c and Continuous Glucose Monitoring and Their Association with Complete Blood Counts.

GRADE Research Group

  Objective: Continuous glucose monitoring (CGM) and hemoglobin A1c (HbA1c) provide estimates of mean glycemia that may differ, in part, due to the effects of variation in red blood cell (RBC) age and turnover on HbA1c. Measurements derived from the complete blood count (CBC) may vary with RBC age and might be used to reduce the difference between glycemia estimates derived from CGM and HbA1c. Methods: We analyzed CBC measurements from 1,325 individuals with type 2 diabetes who participated in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) CGM substudy. Mean glycemia was estimated from HbA1c (eAGA1c) using the A1c-Derived Average Glucose (ADAG) formula and from CGM by averaging 10 days of measurements (eAGCGM). We evaluated the association between CBC-derived data and the difference (eAGA1c - eAGCGM) using linear models, both unadjusted and adjusted for age and self-identified sex. Results: In adjusted analyses, several CBC-derived measurements were significantly associated with the difference between eAGA1c and eAGCGM. Platelet count and RBC distribution width (RDW) were positively associated, while hemoglobin concentration (HGB), reticulocyte fraction, mean corpuscular volume (MCV), mean corpuscular hemoglobin content (MCH), mean corpuscular hemoglobin concentration (MCHC), and reticulocyte MCHC were negatively associated. A linear model from HbA1c to eAGCGM adjusted with all significantly associated CBC measurements (CBCall-AGA1c) provided modestly improved estimates of eAGCGM compared with ADAG, with R2 (SD) for ADAG of 0.68 (0.07) and for CBCall-AGA1c 0.72 (0.06). Conclusions: CBC measurements are associated with differences between estimates of glycemia derived from HbA1c and CGM. Further studies with longer periods of CGM are needed to determine whether CBCs can complement HbA1c and CGM and can help reconcile differences in estimates of mean glycemia provided by HbA1c and CGM.

Keywords:  complete blood count; continuous glucose monitoring; hemoglobin A1c; hemoglobin A1c-glucose discordance; type 2 diabetes

DOI:  https://doi.org/10.1177/15209156251395036
Lancet Diabetes Endocrinol. 2025 Dec 17. pii: S2213-8587(25)00335-3. [Epub ahead of print]

Application of continuous glucose monitoring and automated insulin delivery technologies for pregnant women with type 1, type 2, or gestational diabetes: an international consensus statement.

Katrien Benhalima, Celeste Durnwald, Arianne Sweeting, Dawn Adams, Ananta Addala, Tadej Battelino, Richard M Bergenstal, Anders Carlson, Lois E Donovan, Denise Reis Franco, Julie Heverly, Diana Isaacs, Kamlesh Khunti, Anne-Beatrice Kihara, Naomi Levitt, Carol J Levy, Mareda Lewer, Elisabeth Mathiesen, Helen R Murphy, Rimei Nishimura, Sarit Polsky, Uma Ram, David Simmons, Jennifer M Yamamoto, Ádám G Tabák, Denice Feig, Eleanor M Scott.

Insulin resistance increases after the first trimester of pregnancy, leading to glycaemic challenges for women with pregestational type 1 diabetes or type 2 diabetes. Additionally, insulin resistance can promote hyperglycaemia in pregnant women without type 1 diabetes or type 2 diabetes, who develop gestational diabetes. Although most (>95%) women with diabetes deliver healthy babies, maternal dysglycaemia can have consequences for the mother and child, including prenatal, perinatal, immediate, and long-term postnatal complications. Diabetes technologies, such as continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems can aid in optimising glycaemia outside of pregnancy. These novel technologies have not been extensively tested in large randomised controlled trials before and during pregnancy. However, compelling data report the benefits of CGM in type 1 diabetes, and increasing data report on AID systems in pregnancies complicated by type 1 diabetes. Appropriate CGM glucose thresholds for the diagnosis of gestational diabetes and the recommended time in range treatment targets for the routine management of gestational diabetes and type 2 diabetes still need to be determined. The recommendations in this Consensus Statement emphasise the value of CGM during preconception and pregnancy for women with pregestational type 1 diabetes in reducing pregnancy complications. Recommendations also include the use of AID systems in women with pregestational type 1 diabetes to improve glycaemic management during preconception, during pregnancy and delivery, and in the postpartum period. This Consensus Statement has been endorsed by 24 societies and groups.

DOI: https://doi.org/10.1016/S2213-8587(25)00335-3
Patient Relat Outcome Meas. 2025 ;16 203-214

Towards Standardization of CGM Performance Studies: The QUSS-CGM Questionnaire for Assessing User Satisfaction.

Anne Beltzer, Marta Gil Miró, Manuel Eichenlaub, Delia Waldenmaier, Cornelia Haug, Dominic Ehrmann, Guido Freckmann.

   Purpose: User satisfaction and ease of use of continuous glucose monitoring (CGM) systems are key factors in patients' device acceptance. CGM user satisfaction is often assessed through questionnaires, but item selection varies widely across studies. The aim of this study was to design, develop and validate a Questionnaire for User Satisfaction Standardized for CGM performance studies (QUSS-CGM).
Methods: Selection of attributes and design of questionnaire items was based on a systematic literature search of publications on CGM performance evaluation studies. Content and response process validation of a draft-questionnaire was performed by experts (n=9) and people with diabetes (n=10), respectively. The resulting German pre-QUSS-CGM questionnaire underwent validation in two CGM performance studies ("pilot" studies) performed between June and August of 2024, via a pooled psychometric evaluation (exploratory factor analysis (EFA) and reliability) of n=126 questionnaires from these studies, followed by bidirectional translation to English.
Results: Two hundred and five items on user satisfaction in CGM performance studies were identified by systematic literature search and classified into six attributes according to their content. Items were summarized in a 25-item draft-questionnaire on a 5-point Likert scale. Content and face validity were considered acceptable with a scale-level content validity index (S-CVI/Ave) of 0.90 and a scale-level face validity index (S-FVI/Ave) of 0.93, both based on the average method. EFA revealed a two-factor structure for the final QUSS-CGM questionnaire summarized to 11 items, demonstrating high internal consistency (Cronbach's α of 0.84).
Conclusion: The QUSS-CGM was designed, developed, and validated as a reliable and standardized tool to measure user satisfaction in CGM performance evaluation studies.

Keywords:  content validation; continuous glucose monitoring; explorative factor analysis; performance; questionnaire; user satisfaction

DOI:  https://doi.org/10.2147/PROM.S554524
PLoS One. 2025 ;20(12): e0339360

Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction.

Jian Xue, Huaimin Cao, Lu Yang, Jiajia Liu, Ying Zhang, Chunlei Zhang, Ke Bai, Peng Jiang, Fanchang Hao, LiNa Xu.

The management of blood glucose in hospitalized patients is confined to retrospective interventions, preventing healthcare professionals from predicting patients' blood glucose levels and potential adverse events in advance. This study employs a deep learning model, specifically a Stacked Attention-Gated Recurrent Unit (SA-GRU) network, to forecast short-term blood glucose (BG) levels and predict adverse events in hospitalized patients, assisting clinicians in making clinical decisions. We collect continuous glucose monitoring(CGM) data from 196 hospitalized patients with type 2 diabetes, and by constructing and training this deep learning model, we predict blood glucose levels and adverse events.The model's predictions are then compared with the actual CGM data, and different evaluation metrics are used to assess the predictions of blood glucose levels and adverse events. Additionally, experiments were conducted on another publicly available type 2 diabetes dataset. On our collected data, for the 30-minute prediction, the root mean square error (RMSE) and mean absolute relative difference (MARD) of blood glucose are 4.27 ± 0.31 mg/dL and 1.77% ± 0.08%, respectively, with an adverse event classification accuracy of 98.57% ± 0.11%. For the 60-minute prediction, the RMSE and MARD of blood glucose are 10.46 ± 0.55 mg/dL and 4.59% ± 0.22%, respectively, with an adverse event classification accuracy of 95.74% ± 0.33%. Similar positive results were obtained on another publicly available dataset. The proposed model demonstrates accurate predictions for blood glucose values and adverse events in the next 30 and 60 minutes.

DOI: https://doi.org/10.1371/journal.pone.0339360
Dermatitis. 2025 Dec 15.

Letter: Analysis of Adhesives and Allergens in Over-the-Counter Continuous Glucose Monitors.

Sarah Kamsiah Zemlok, Adam Griechen, Jon Kibbie, JiaDe Yu.

DOI: https://doi.org/10.1177/17103568251406663
J Diabetes Metab Disord. 2026 Jun;25(1): 2

The efficacy of CGM driven machine learning algorithms in predicting hypoglycemia in patients with T1DM: a systematic review and meta-analysis.

Tameem Quader, Siddharth Pai, Yong Mong Tan, Harshal Deshmukh, Usman Malabu, Theophilus I Emeto.

   Purpose: An important function of continuous glucose monitoring (CGM) is to alert individuals with type one diabetes mellitus (T1DM) to impending hypoglycemia, however, it lacks the ability to predict episodes beyond 30 min. Machine learning (ML) algorithms incorporating other contextual data can be used to overcome this deficiency. This study aims to quantitatively evaluate the diagnostic accuracy of these algorithms.
Methods: A systematic search of databases following PRISMA guidelines identified relevant studies that trained and assessed ML algorithms (PROSPERO CRD42024588619). The set of 2 × 2 data (i.e., number of true positives, false positives, true negatives, and false negatives) was extracted and meta-analyzed using a generalized linear mixed model to calculate pooled estimates of sensitivity and specificity and construct a summary receiver operating characteristic curve. A two-sided p-value of < 0.05 was deemed significant.
Results: Of 611 studies screened, 20 met the inclusion criteria. The pooled point estimates (95% CI) were 80% (71-87%), 89% (78-95%), 7.27 (3.96-14.20) and 0.25 (0.14-0.37) for sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR), respectively.
Conclusions: Current ML algorithms have a substantial ability to predict hypoglycemia in patients with T1DM according to the Users' Guide to Medical Literature on diagnostic tests where PLR should be ≥ 5 and NLR should be ≤ 0.2 for moderate reliability. The incorporation of other inputs such as insulin, carbohydrates and physical activity have enhanced prediction accuracy. The clinical utility of these algorithms, however, should be evaluated as per the patient's daily hypoglycemic risk profile due to the moderate risk of false positives.
Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-025-01820-4.

Keywords:  Continuous glucose monitoring; Hypoglycemia: machine learning; Type 1 diabetes mellitus

DOI:  https://doi.org/10.1007/s40200-025-01820-4
Sci Diabetes Self Manag Care. 2025 Dec 25. 26350106251397922

Reconsidering Intermittent CGM Use in Type 2 Diabetes: A View From Meta-Analytic Evidence.

Abhijit Pratap, Pradnya Phalak, Anjali Garg.

DOI: https://doi.org/10.1177/26350106251397922
Diabetes Ther. 2025 Dec 24.

Effect of a Diabetes-Specific Protein Supplement (DSPS) on Postprandial Glycemic Responses in Adults with Type 2 Diabetes: A Randomized Crossover Study in India.

Sheryl S Salis, Ami Sanghvi, Natasha Vora, Shefa Syed, Priya Karkera, Pushkala Padmanabh, Mirian Lansink, Mia Isabelle, Samrin Khan, Riya Gaglani, Sharvari R Desai, Apurva A Halbe, S A Udipi.

   INTRODUCTION: Traditional Indian cereal-based breakfast items have high glycemic index (GI) contributing to postprandial (PP) glucose spikes. Use of diabetes-specific protein supplement (DSPS) may reduce glycemic excursions. The study aimed to evaluate the effect of partially replacing breakfast with DSPS (Protinex Diabetes Care) on PP glycemic response.
METHODS: Forty-two persons with type 2 diabetes mellitus (T2DM) participated in this randomized, controlled, open-label, crossover study. Participants consumed a test breakfast (DSPS in 200 ml milk + reduced portion of popular Indian savory breakfast (upma/poha)) or isocaloric control breakfast (upma/poha) for 5 days, with 3-day washout. PP glucose and insulin were assessed on the first day of intervention at baseline, 30, 60, 90, 120, 150, and 180 min after breakfast to calculate incremental area under the curve (iAUC0-3h) and delta peak (ΔCmax). During in-home use, macronutrient intake was assessed using dietary recalls, and glycemic variability (GV) was assessed using continuous glucose monitoring (CGM).
RESULTS: Glucose iAUC0-3h and ΔCmax were 59% and 46% lower in test vs control, respectively. Insulin iAUC0-3h and ΔCmax did not differ significantly. During the test period, protein intake was significantly higher by 8.8 g; mean amplitude of glycemic excursions (MAGE)-a GV metric-was significantly lower. There were no gastrointestinal or adverse events. DSPS was well accepted by participants.
CONCLUSIONS: DSPS as a partial breakfast replacement improves blood glucose control without significantly impacting insulin response. In a real-world setting, DSPS enhances protein intake and reduces GV. These findings support DSPS as a practical, well-tolerated strategy for improving glycemic control and macronutrient intake balance in people with T2DM.
TRIAL REGISTRATION: The trial was registered with Clinical Trials Registry India (CTRI) CTRI/2024/08/072006 and has been registered in the International Clinical Trials Registry Platform (ICTRP).

Keywords:  Continuous glucose monitoring; Diabetes-specific protein supplement (DSPS); Glycemic variability; Indian diets; Partial meal replacement; Postprandial glucose; Protein intake; Type 2 diabetes

DOI:  https://doi.org/10.1007/s13300-025-01834-4