bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–11–08
fifteen papers selected by
Mott Given
  1. Determinants of Discordance between Estimated and Measured HbA1c: The Role of Active Time in 14-Day Continuous Glucose Monitoring Data.
  2. Knowledge, attitude and practice on continuous glucose monitoring and self-monitoring of blood glucose among healthcare professionals across India: A cross-sectional study.
  3. Differences in the Glucose Management Indicator Between Continuous Glucose Monitoring Systems.
  4. Severe Hyperglycemia due to Calibration Misuse in an Eversense-365 Implantable Continuous Glucose Monitoring System.
  5. [Continuous glucose monitoring in diabetes: Clinical practice guidelines for primary care from SEMERGEN, RedGDPS, semFYC, SEMG and FAECAP].
  6. A systematic review and meta-analysis of the effects of continuous glucose monitoring compared to self-monitoring of blood glucose on glycemic control and maternal and infant outcomes in women with gestational diabetes mellitus.
  7. One less ouch: Implementation of Continuous Glucose Monitoring in the treatment of pediatric Diabetes Ketoacidosis.
  8. Translating CGM Regulatory Performance Metrics Into Clinical Interpretation: An Exploratory Expert Consensus Framework.
  9. The 2024 National Report on Continuous Glucose Monitoring Uptake in Pediatric Type 1 Diabetes Following Universal Funding in Aotearoa New Zealand: Early Findings from the KiwiDiab Pediatric Diabetes Network.
  10. Exploring Corresponding Cutpoints for Hypoglycaemia Awareness Questionnaire (HypoA-Q) Impaired Awareness Subscale in Relation to the Gold and Clarke Measures: A Post Hoc Analysis From the Hypo-METRICS Study.
  11. Beyond lung function: the changing landscape of cystic fibrosis-related diabetes.
  12. Sleep Architecture, Glucose Regulation and Subclinical Cardiovascular Risk in Adults With Type 1 Diabetes Mellitus: A Wearable Sleep Tracking and Continuous Glucose Monitoring Study.
  13. Alogliptin-Pioglitazone and Insulin Regimen for Inpatient Glycemic Control: A Randomized Continuous Glucose Monitoring Pilot Trial.
  14. Daily eating window duration and dietary intake shape 24-hour glycemic patterns in type 2 diabetes: a functional data analysis approach.
  15. Can a Single Maximally Fortified Food Improve Glycaemia in Type 2 Diabetes? Randomised Crossover Trial of High-Fibre High-Protein Bread.
  1. Diabetes Metab J. 2026 Nov 04.
    Determinants of Discordance between Estimated and Measured HbA1c: The Role of Active Time in 14-Day Continuous Glucose Monitoring Data.
    Jee Hee Yoo, Seohyun Kim, Yeonsoo Park, Sang-Man Jin, Choon Hee Chung, Soo Heon Kwak, Jae Hyeon Kim, Gyuri Kim.
       Background: We aimed to investigate factors contributing to discordance between estimated glycosylated hemoglobin (eHbA1c) and measured HbA1c (mHbA1c) from continuous glucose monitoring (CGM) using both 14- and 90-day data in a large Asian cohort.
    Methods: We retrospectively analyzed 3,724 14-day and 2,664 90-day glycemic profiles with ≥70% active CGM time (AT) from 1,028 individuals with type 1 diabetes mellitus (T1DM). eHbA1c was calculated using a regression model derived from our study population. Discordance was defined as eHbA1c-mHbA1c (overestimation [eHbA1c ≥ mHbA1c] or underestimation [eHbA1c < mHbA1c]). Similarity was defined as a difference within ±0.1%.
    Results: The 14-day eHbA1c model explained 74.7% of the variance in mHbA1c, compared with 88.4% for the 90-day eHbA1c model (conditional R2). Absolute discordance was significantly lower in real-time CGM (rtCGM) than in intermittently scanned CGM (isCGM) (P=0.004), and in those with higher time in range (TIR) and AT (all P<0.001). Overestimation was associated with rtCGM (P=0.016) and lower HbA1c (P<0.001). Underestimation was greater in isCGM users and in those with higher HbA1c and lower body mass index (all P≤0.001). In 14-day profiles, ≥78% AT was required for eHbA1c-mHbA1c similarity, whereas 90-day profiles maintained similarity regardless of AT. A prediction model incorporating AT, TIR, and sensor type improved accuracy (R2: 14-day, 0.774; 90-day, 0.888), particularly for 14-day data.
    Conclusion: Factors influencing eHbA1c-mHbA1c concordance differ by CGM duration. While 14-day data require higher AT (≥78%) for accuracy, 90-day data provide more stable estimates. Clinical interpretation must account for mHbA1c, TIR, and sensor type, especially when using shorter-term CGM profiles in Asian individuals with T1DM.
    Keywords:  Asian people; Blood glucose; Continuous glucose monitoring; Diabetes mellitus, type 1; Glycated hemoglobin
    DOI:  https://doi.org/10.4093/dmj.2025.1170
  2. touchREV Endocrinol. 2026 Oct;22(2): 48-54
    Knowledge, attitude and practice on continuous glucose monitoring and self-monitoring of blood glucose among healthcare professionals across India: A cross-sectional study.
    Nitin Kapoor, Shehla Sheikh, Sambit Das, Navneet Agarwal, Mukut Roy, Sanjay Kalra.
       Background: Uncontrolled glycemia remains a major concern in India, underlining the need for wider adoption of effective glucose monitoring methods in diabetes care. Despite the benefits of continuous glucose monitoring (CGM), its adoption remains low in India and is unevenly distributed across healthcare settings and patient populations.
    Methods: A cross-sectional, questionnaire-based survey was conducted among healthcare professionals (HCPs) practicing across private and public healthcare settings in India to assess their knowledge, attitudes and practices regarding CGM metrics using an 18-item questionnaire. Responses were measured using a 5-point Likert scale.
    Results: A total of 126 HCPs from different specialties participated in the study. Most HCPs (81.7%) expressed confidence in the use of CGM in patients with gestational diabetes mellitus. The majority of HCPs were confident or highly confident in interpreting CGM reports (80.1%) and explaining them to patients (81.8%). More than 90% of HCPs agreed that CGM improves overall diabetes management and 88.1% were confident that CGM helps reduce hypoglycemic and hyperglycemic episodes compared with self-monitoring of blood glucose (SMBG) alone. Most HCPs (78.6%) agreed that the cost of CGM is a major barrier to its wider adoption in diabetes care. Despite recognizing the clinical value of CGM, fewer than half of HCPs believed it could completely replace SMBG.
    Conclusion: The study highlights the adoption-action gap of CGM use in diabetes care in Indian settings; however, the predominantly experienced, private-sector sample may limit generalizability to the broader HCP population. High costs, variable confidence in CGM interpretation and continued reliance on SMBG contributed to the gap despite HCPs' strong awareness of CGM's clinical value. Addressing these barriers and improving training may enhance CGM adoption nationwide.
    Keywords:  Clinical practice; continuous glucose monitoring; diabetes management; diabetes mellitus; glycemic variability; lifestyle; self-monitoring of blood glucose
    DOI:  https://doi.org/10.17925/EE.2026.22.2.7
  3. Diabetes Obes Metab. 2026 Nov 05.
    Differences in the Glucose Management Indicator Between Continuous Glucose Monitoring Systems.
    Katrine Bovién Gørlitz, Sara Heebøll, Morten Fenger-Grøn, Sanne Fisker, Søren Gregersen, Christian Trolle, Klavs Würgler Hansen.
       AIM: The glucose management indicator (GMI) estimates HbA1c from continuous glucose monitoring (CGM)-derived mean glucose. We assessed whether this relationship differs between contemporary CGM systems.
    MATERIALS AND METHODS: In this observational study, mean sensor glucose was derived from 2 months of CGM data preceding HbA1c measurement in adults with Type 1 diabetes using Dexcom (Dx) G6/G7 (n = 252) or Medtronic Guardian 4/Simplera (GSi) (n = 180). Device-specific GMI equations were derived using linear regression of measured HbA1c on mean sensor glucose and compared with the original GMI equation. For direct comparison of the Dx and GSi GMI equations, these GMI equations were combined, with 95% confidence intervals calculated using the delta method.
    RESULTS: The Dx GMI equation yielded statistically significantly lower GMI values compared with the original GMI equation at mean sensor glucose levels < 13.7 mmol/L, with increasing differences at lower glucose levels. Conversely, at mean sensor glucose levels above 8.3 mmol/L, the GSi GMI equation yielded statistically significantly higher GMI values than the original GMI equation, with increasing differences at higher glucose levels. The Dx and GSi device-specific GMI equations yielded statistically significantly different GMI values > 6.7 mmol/L, with increasing differences at higher glucose levels.
    CONCLUSIONS: The relationship between mean sensor glucose and HbA1c differed between contemporary CGM systems. This finding suggests that device-specific GMI equations may improve interpretation of glycaemic control in the absence of standardisation across contemporary CGM systems.
    Keywords:  Type 1 diabetes; continuous glucose monitoring (CGM); database research; glycaemic control
    DOI:  https://doi.org/10.1111/dom.71486
  4. AACE Endocrinol Diabetes. 2026 Sep-Oct;13(5):13(5): 732-734
    Severe Hyperglycemia due to Calibration Misuse in an Eversense-365 Implantable Continuous Glucose Monitoring System.
    Jennifer Moeller.
       Background: Continuous glucose monitoring (CGM) systems have transformed diabetes management by improving glycemic outcomes and providing real-time glucose data. Accuracy is commonly assessed by using metrics such as mean absolute relative difference. Implantable CGM systems, including Eversense-365, have demonstrated high accuracy and safety when used in accordance with recommended protocols.
    Objective: To describe a case of clinically significant CGM inaccuracy resulting from improper user calibration and to highlight associated safety implications.
    Case Report: An adult with a 24-year history of type 1 diabetes complicated by microvascular and macrovascular disease, hypoglycemia unawareness, and mild cognitive impairment was using an implantable CGM system. Initially, sensor readings closely matched capillary glucose values when appropriate fingerstick calibration techniques were followed. However, the patient subsequently used CGM-derived glucose values in place of capillary measurements for calibration. This practice led to progressively inaccurate sensor readings, yielding falsely reassuring glucose values and delaying recognition of clinically significant hyperglycemia.
    Discussion: This case highlights a critical safety consideration in implantable CGM systems that rely on user-dependent calibration. Even with otherwise high-performing technology, improper calibration practices can significantly compromise accuracy. As CGM systems become increasingly integrated into automated insulin delivery platforms, user-related factors may introduce risk if proper technique is not consistently followed.
    Conclusion: Improper calibration of implantable CGM systems can lead to clinically significant inaccuracies and delayed recognition of hyperglycemia. Reinforcing patient education, adhering to recommended calibration protocols, and selecting patients thoughtfully are essential to ensure the safe and effective use of these devices.
    Keywords:  calibration error; continuous glucose monitoring; diabetes technology; hyperglycemia; implantable CGM
    DOI:  https://doi.org/10.1016/j.aed.2026.07.007
  5. Semergen. 2026 Nov 02. pii: S1138-3593(26)00189-9. [Epub ahead of print] 102872
    [Continuous glucose monitoring in diabetes: Clinical practice guidelines for primary care from SEMERGEN, RedGDPS, semFYC, SEMG and FAECAP].
    Grupo de Consenso
      Continuous glucose monitoring (CGM) is a key tool for diabetes management in primary care (PC), as it provides dynamic and detailed information on glycemic control. This document summarizes scientific evidence and formulates clinical practice recommendations to optimize the use of CGM in people with diabetes (PwD) managed in PC. The recommendations are based on a Delphi involving 58 PC professionals representing SEMERGEN, redGDPS, semFYC, SEMG and FAECAP; and address the management of GCM within a framework of shared decision-making with PwD, including its indication, prescription, initiation, monitoring, and discontinuation. The integration of CGM into clinical practice in PC, along with adequate training for healthcare professionals and structured PwD education, can improve clinical, PwD-centered outcomes and the efficiency of the healthcare system.
    Keywords:  Atención primaria; Clinical practice; Continuous glucose monitoring; Diabetes mellitus tipo 2; Monitorización continua de glucosa; Primary care; Práctica clínica; Recomendaciones; Recommendations; Type 2 diabetes mellitus
    DOI:  https://doi.org/10.1016/j.semerg.2026.102872
  6. BMC Pregnancy Childbirth. 2026 Aug 08. pii: 1272. [Epub ahead of print]26(1):
    A systematic review and meta-analysis of the effects of continuous glucose monitoring compared to self-monitoring of blood glucose on glycemic control and maternal and infant outcomes in women with gestational diabetes mellitus.
    Lin Zheng, Run Tian, Lulu Zhang, Annuo Liu, Peng Wang.
       BACKGROUND: Continuous glucose monitoring (CGM) compared to self-blood glucose monitoring (SMBG) has been a controversial topic regarding glycemic control in pregnant women with gestational diabetes mellitus (GDM), as well as maternal and infant outcomes. The aim of this study was to assess and compare the effectiveness of CGM and SMBG in managing glycemic control and perinatal outcomes among homebound pregnant women with GDM.
    METHODS: A comprehensive search was conducted across five databases-PubMed, Web of Science, Medline, Embase, and the Cochrane Library-commencing on July 15, 2024. We included four randomized controlled trials (RCTs) that compared CGM with SMBG in patients with GDM. The primary outcomes assessed were gestational weight gain (GWG), glycated hemoglobin(HbA1c), and the number of people treated with hypoglycemic drugs. Participants were excluded if they had received any glucose-lowering medication prior to enrollment. A meta-analysis of interventions on the basis of at least two studies with 95% confidence intervals and an assessment of heterogeneity via the I² statistic were performed.
    RESULTS: The results of the study showed that CGM was significantly associated with a decrease in the number of women with excessive GWG and an increase in the number of GWG-appropriate (RR = 0.57; 95% CI: 0.41, 0.80; I² = 0.0%; P = 0.001), (RR = 1.55; 95% CI: 1.19, 2.03; I² = 0.0%; P = 0.001). However, GWG-insufficiency was not significantly different (RR = 1.03; 95% CI: 0.47, 2.26; I² = 0.0%; P = 0.948). Real-time continuous glucose monitoring (rt-CGM) may offer superior effectiveness in glycemic control management compared with SMBG (SMD=-0.70; 95%CI=-1.07, -0.34; P = 0.000; I2 = 0.0%), whereas retrospective continuous glucose monitoring (r-CGM) demonstrates greater effectiveness in reducing infant birth weight (SMD=-0.30; 95%CI=-0.54, -0.06; P = 0.018; I2 = 8.6%). No significant differences were observed in the other perinatal outcomes.
    CONCLUSION: CGM may be more effective than SMBG in preventing excessive weight gain and maintaining appropriate gestational weight in women with GDM. Moreover, rt-CGM appears to offer superior glycemic control compared with SMBG. r-CGM may be more effective in reducing neonatal birth weight.
    TRIAL REGISTRATION: The PROSPERO registration number is CRD42024571538.
    Keywords:  Continuous glucose monitoring; Gestational diabetes mellitus(GDM); Glucose control; Intervention; Pregnant outcome; Self-monitoring of blood glucose
    DOI:  https://doi.org/10.1186/s12884-026-09769-9
  7. J Pediatr Nurs. 2026 Nov 05. pii: S0882-5963(26)00483-5. [Epub ahead of print]
    One less ouch: Implementation of Continuous Glucose Monitoring in the treatment of pediatric Diabetes Ketoacidosis.
    Ana Bandin, Amada Rico.
      
    DOI:  https://doi.org/10.1016/j.pedn.2026.09.032
  8. Clin Med Insights Endocrinol Diabetes. 2026 ;19 11795514261490268
    Translating CGM Regulatory Performance Metrics Into Clinical Interpretation: An Exploratory Expert Consensus Framework.
    Alex Ramírez-Rincón, Ana M Gómez, Carlos A Yepes-Cortés, Karen Feriz-Bonelo, Alejandro Marín-Sánchez, Carlos E Builes-Montaño.
       Background: Continuous glucose monitoring (CGM) performance is characterized by technical metrics that are inconsistently standardized across regulatory frameworks and variably connected to clinically meaningful outcomes, complicating interpretation and cross-device comparison.
    Objectives: To develop an exploratory framework translating CGM performance characteristics and derived metrics into clinically interpretable domains while distinguishing empirical evidence from expert judgment.
    Methods: Candidate metrics were extracted from contemporary clinical, technical, and regulatory sources and organized into six functional domains. Targeted PubMed searches in ambulatory adults with diabetes evaluated domain-specific technical-to-clinical questions. Evidence was classified as direct device-performance, direct between-device comparative, or indirect/contextual. Five experts participated in two private voting rounds using a virtual Nominal Group Technique and prespecified RAND/UCLA agreement criteria. The study was conducted from November 2025 to January 2026.
    Results: More than 30 metrics and attributes were mapped across analytical accuracy and performance, glycemic exposure, glycemic variability, clinical safety, adherence and device use, and device reliability. Direct evidence was most clearly identified for device-level analytical performance and between-device differences in analytical accuracy and glycemic exposure; evidence in other domains was predominantly indirect, contextual, or device-specific. Twenty-four of 25 statements achieved consensus after round 1 and all 25 after round 2. The panel considered dynamic accuracy, alert functionality when available, and monitoring continuity potentially clinically meaningful, but these ratings did not establish category-wide superiority of FDA-designated integrated CGM systems.
    Conclusion: The framework provides an exploratory structure for interpreting CGM technical performance and derived metrics. Regulatory designation and individual device characteristics should be considered separately, and purpose-designed comparative studies are required to determine whether specific attributes produce reproducible clinical benefits.
    Keywords:  CGM performance metrics; analytical accuracy; continuous glucose monitoring; diabetes technology; eCGM; iCGM; nominal group technique; regulatory standards
    DOI:  https://doi.org/10.1177/11795514261490268
  9. Diabetes Technol Ther. 2026 Nov 02. 15209156261500903
    The 2024 National Report on Continuous Glucose Monitoring Uptake in Pediatric Type 1 Diabetes Following Universal Funding in Aotearoa New Zealand: Early Findings from the KiwiDiab Pediatric Diabetes Network.
    KiwiDiab Diabetes Data Network
       AIM: To describe national patterns in continuous glucose monitoring (CGM) uptake and glycemic patterns among children with type 1 diabetes (T1D) in Aotearoa New Zealand following universal CGM funding on October 1, 2024.
    METHODS: Data from the KiwiDiab pediatric diabetes network for 2024 included children <16 years with T1D who were attending 14 regional pediatric diabetes centers. CGM uptake was evaluated using the weekly proportion using CGM and the weekly proportion initiating CGM. Overall and ethnicity-specific initiation was analyzed using negative binomial regression of weekly initiation counts, with the number not using CGM at the start of each week as an offset, comparing prefunding, rollout, and postfunding periods. HbA1c measurements were summarized as cross-sectional monthly distributions by prioritized ethnicity.
    RESULTS: In total, 1681 children and adolescents with T1D were included. Mean weekly proportions initiating CGM increased from 1.4% prefunding to 12.9% during rollout (incidence rate ratio [IRR] = 9.05, 95% confidence interval [CI]: 5.03-17.5) and remained elevated postfunding at 6.6% (IRR = 4.64, 95% CI: 3.27-6.61). During rollout, mean weekly proportions initiating CGM increased from 1.0% to 14.0% among Māori, from 0.8% to 11.6% among Pacific, from 1.5% to 15.9% among Asian, and from 1.8% to 11.0% among European children. There was no statistically significant evidence that the magnitude of change across funding periods differed by ethnicity (period-by-ethnicity interaction, P = 0.72). Differences in the proportion using CGM between ethnic groups narrowed during 2024 but remained at year-end. Cross-sectional HbA1c distributions remained relatively stable throughout 2024, with no clear early lowering observed.
    CONCLUSION: Universal CGM funding was associated with a rapid increase in CGM uptake across Aotearoa New Zealand. CGM initiation increased markedly across ethnic groups, and ethnic disparities in CGM use narrowed during 2024, although they persisted at year-end.
    Keywords:  Aotearoa New Zealand; HbA1c; continuous glucose monitoring; diabetes technology; ethnicity; health equity; health policy; pediatric diabetes; type 1 diabetes; universal funding
    DOI:  https://doi.org/10.1177/15209156261500903
  10. Diabetes Obes Metab. 2026 Nov 03.
    Exploring Corresponding Cutpoints for Hypoglycaemia Awareness Questionnaire (HypoA-Q) Impaired Awareness Subscale in Relation to the Gold and Clarke Measures: A Post Hoc Analysis From the Hypo-METRICS Study.
    Hypo‐RESOLVE Consortium
       AIMS: The Hypoglycaemia Awareness Questionnaire-Impaired Awareness subscale (HypoA-Q/IA) was developed to enhance impaired awareness of hypoglycaemia (IAH) assessment, addressing methodological deficiencies of the established Gold and Clarke measures. An IAH cutpoint (HypoA-Q/IA ≥ 12) was identified from a clamp study in Type 1 diabetes (T1D). We explored cutpoints in relation to Gold and Clarke measures, and their co-occurrence, in the larger, real-world observational Hypo-METRICS study, in people with T1D (pwT1D) and insulin-treated Type 2 diabetes (pwT2D).
    MATERIAL AND METHODS: Hypo-METRICS recruited pwT1D or pwT2D with ≥ 1 hypoglycaemic event in the previous 3 months. IAH was defined as Gold ≥ 4, Clarke ≥ 4 and/or HypoA-Q/IA ≥ 12. Bidirectional ROC analysis was performed between HypoA-Q/IA and Gold/Clarke cutpoints.
    RESULTS: Questionnaires were completed by 232 pwT1D and 285 pwT2D (male 47% vs. 62%, p < 0.001; median age 49 vs. 63 years, p < 0.001; HbA1c 7.3% vs. 7.4%, p = 0.03). IAH prevalence was 24% by Gold ≥ 4, 16% by Clarke ≥ 4 and 7% by HypoA-Q/IA ≥ 12. ROC analysis yielded HypoA-Q/IA cutpoints of ≥ 7 against Gold ≥ 4 (AUC 0.87) and ≥ 9 against Clarke ≥ 4 (AUC 0.89), and cutpoints of Gold ≥ 4 (AUC 0.87) and Clarke ≥ 3 (AUC 0.93) against HypoA-Q/IA ≥ 12. Of those with IAH by either measure, 24% were identified by both HypoA-Q/IA ≥ 12 and Gold ≥ 4, and 32% by both HypoA-Q/IA ≥ 12 and Clarke ≥ 4, with lower overlap in T2D than T1D.
    CONCLUSIONS: HypoA-Q/IA cutpoints corresponding to legacy IAH cutpoints (≥ 7-9) were lower than the clamp-derived threshold (≥ 12), suggesting ≥ 12 may identify a subgroup with more severe IAH. These cutpoints enable cross-study comparison; prospective validation against severe hypoglycaemia risk is needed.
    Keywords:  Type 1 diabetes; Type 2 diabetes; continuous glucose monitoring (CGM); hypoglycaemia
    DOI:  https://doi.org/10.1111/dom.71504
  11. J Cyst Fibros. 2026 Nov 01. pii: S1569-1993(26)03777-X. [Epub ahead of print]
    Beyond lung function: the changing landscape of cystic fibrosis-related diabetes.
    Christine L Chan, Scott M Blackman.
      
    Keywords:  Body composition; Body mass index; CFRD; CGM; Continuous glucose monitor; Insulin; Modulator; Sleep
    DOI:  https://doi.org/10.1016/j.jcf.2026.10.008
  12. Diabetes Obes Metab. 2026 Nov 03.
    Sleep Architecture, Glucose Regulation and Subclinical Cardiovascular Risk in Adults With Type 1 Diabetes Mellitus: A Wearable Sleep Tracking and Continuous Glucose Monitoring Study.
    Natália Marhefková, Viktória Mikušová, Robert Roland, Michal Pazderník, Peter Wohlfahrt, Peter Novodvorský, Martin Sládek, Alena Sumová, Martin Haluzík, Michal Dubský.
       OBJECTIVE: To investigate the associations of wearable-derived sleep architecture with CGM-derived glucose metrics, HbA1c and markers of subclinical atherosclerosis in adults with Type 1 diabetes mellitus (T1DM) in the CORDIA study.
    STUDY DESIGN AND METHODS: One hundred and seventy-nine adults (79 women) with long-standing T1DM wore an Apple Watch for 14 nights; glycaemia was assessed from routine 14-day CGM reports (FreeStyle Libre 2, Dexcom G6/G7 or Guardian 4) and HbA1c. Carotid plaques were assessed by ultrasound and coronary artery calcium (CAC) by computed tomography. Participants also completed the Munich Chronotype Questionnaire (MCTQ). Spearman correlations with false discovery rate (FDR) correction and multivariable linear regression were used.
    RESULTS: Shorter REM sleep correlated with higher HbA1c (ρ = -0.24; padj = 0.011) and, with borderline significance after FDR correction, with higher GRI, TAR2, mean sensor glucose and glucose SD and lower TIR (|ρ| 0.17-0.21; padj = 0.052). REM sleep expressed as a proportion of total sleep correlated with GRI, TIR, mean glucose and glucose SD (|ρ| 0.18-0.20; padj = 0.044). In adjusted models, REM duration was associated with all glycaemic metrics; the association with glucose SD persisted after additional adjustment for HbA1c and after exclusion of nights with less than 4 h of recorded sleep. After adjustment for age and sex, participants with two or more carotid plaques had 9.4 min shorter REM sleep (p = 0.034); associations with CAC and deep sleep were attenuated. Older age was associated with lower social jetlag (ρ = -0.20; p = 0.010).
    CONCLUSIONS: In adults with T1DM, shorter REM sleep was modestly associated with worse glycaemia, most consistently with glucose variability and HbA1c, and with carotid plaque burden. These cross-sectional findings generate hypotheses for longitudinal studies combining sleep tracking with CGM.
    Keywords:  CGM; REM sleep; Type 1 diabetes mellitus; cardiovascular risk; glucose control; sleep monitoring
    DOI:  https://doi.org/10.1111/dom.71509
  13. AACE Endocrinol Diabetes. 2026 Sep-Oct;13(5):13(5): 762-769
    Alogliptin-Pioglitazone and Insulin Regimen for Inpatient Glycemic Control: A Randomized Continuous Glucose Monitoring Pilot Trial.
    Parson Camuto, Dioze Guadagnini, Heloisa B Assalin, Daniela O Magro, Andrey Santos, Mario Ja Saad.
       Objectives: Hospitalized patients with type 2 diabetes (T2D) frequently develop hyperglycemia, hypoglycemia, and glycemic variability, which are associated with adverse outcomes outside the intensive care unit. This pilot randomized controlled trial evaluated whether adding a fixed-dose combination (FDC) of alogliptin/pioglitazone to standard basal-bolus insulin improved time in range (TIR), reduced hypoglycemia and/or bolus insulin requirements versus placebo, while generating effect size estimates for a future multicenter trial.
    Methods: In this randomized, double-blind, placebo-controlled pilot study, the placebo group (n = 12) received basal-bolus insulin (neutral protamine Hagedorn plus regular insulin) with placebo, whereas the intervention group (IG) (n = 18) received the same insulin regimen plus daily alogliptin/pioglitazone FDC (25/15 mg). Continuous glucose monitoring (FreeStyle Libre 2) assessed TIR (70-180 mg/dL) as the primary endpoint, together with time above range and time below range, during a prespecified 4-d postrandomisation analysis period.
    Results: The IG achieved significantly higher TIR than placebo (72.44% vs 38.58%; P < 0.001) and lower time above range >250 mg/dL (2.94% vs 28.16%; P < 0.001). Sensor-detected glucose exposure between 54 and 69 mg/dL was numerically higher with the FDC (4.16% vs 0.91%), although not statistically significant (P > 0.05). By day 4, the IG required significantly lower daily bolus insulin doses (P = 0.0003).
    Conclusions: In this highly selected inpatient population, adjunctive alogliptin/pioglitazone added to basal-bolus insulin improved continuous glucose monitoring-derived glycaemic stability. Larger adequately powered multicentre trials are needed to confirm safety and efficacy.
    Keywords:  alogliptin; basal-bolus insulin; continuous glucose monitoring; inpatient hyperglycaemia; pioglitazone; type 2 diabetes
    DOI:  https://doi.org/10.1016/j.aed.2026.06.010
  14. BMC Med. 2026 Aug 01. pii: 602. [Epub ahead of print]24(1):
    Daily eating window duration and dietary intake shape 24-hour glycemic patterns in type 2 diabetes: a functional data analysis approach.
    Wenxin Xu, Collin Sakal, Wei Zhang, Tong Chen, Congrong Wang, Qinpei Zhao, Xinyue Li.
       BACKGROUND: The increasing prevalence of type 2 diabetes (T2D) highlights the need for effective glycemic control strategies. Time-restricted eating (TRE) shows promise for metabolic health, but its effectiveness in glucose management among individuals with type 2 diabetes, and its role in modulating glucose responses to dietary intake remain poorly understood. We aimed to identify temporal associations of TRE and dietary intake with 24-hour glucose dynamics. We further examined the interactions between eating windows and carbohydrate intake.
    METHODS: Clinical information, dietary records, and continuous glucose monitoring data from 90 Chinese adults with type 2 diabetes were analyzed. Two digital biomarkers were developed to characterize duration and regularity of eating patterns: a binary indicator for eating windows < 10 h (TRE10) and a continuous measure of deviation from an individual's median eating window (TWD). Linear mixed-effects models and functional data analysis were used to examine independent and temporal associations of glucose levels with TRE and dietary intakes.
    RESULTS: TRE10 was associated with reduced mean amplitude of glycemic excursions (MAGE) (β = -7.14, P = 0.031) and glucose standard deviation (SD) (β = -2.16, P = 0.035), with the strongest associations around 09:00. TWD was positively associated with glycemic coefficient of variation (CV) (β = 0.36, P = 0.050) and higher glucose levels between 07:00 and 08:00. Carbohydrate intake was significantly associated with time in range (TIR) and glycemic variability, with notable glucose changes at 10:00 and 21:00. Dietary vitamin D intake was linked to reduced glycemic area under the curve (AUC) (β = -0.29, P = 0.040), with pronounced effects at 12:00 and 20:00. Additionally, eating windows < 10 h were associated with lower carbohydrate-induced glucose spikes in the morning but higher glucose responses in the early afternoon.
    CONCLUSIONS: In adults with type 2 diabetes, eating windows < 10 h and consistent eating windows were associated with better glycemic control, with distinct time-of-day effects. These findings support integrating timing-based nutritional strategies into personalized diabetes management.
    Keywords:  dietary pattern; functional data analysis; time-restricted eating; type 2 diabetes
    DOI:  https://doi.org/10.1186/s12916-026-05105-2
  15. Diabetes Obes Metab. 2026 Nov 04.
    Can a Single Maximally Fortified Food Improve Glycaemia in Type 2 Diabetes? Randomised Crossover Trial of High-Fibre High-Protein Bread.
    Md Altaf Hossain, Dominic Agyei, Charles Brennan, Biniam Kebede, Andrew N Reynolds.
       OBJECTIVE: To examine the effects of a maximally fortified high-fibre high-protein bread on glycaemia in free-living adults with Type 2 diabetes.
    RESEARCH DESIGN AND METHODS: Participants were provided commercially produced high-fibre high-protein bread (fibre 8.1 g and protein 19.5 g/100 g) fortified with lupin flour, pea protein and wheat fibre or refined grain bread control (fibre 2.9 g and protein 10.4 g/100 g) for 2 weeks in random order, separated by a 2-week washout. Continuous glucose monitoring (CGM), fasted blood samples, anthropometry and three-day dietary records were collected during each intervention period. The primary outcome was postprandial glycaemia measured by all-meal incremental area under the curve (iAUC) with CGM during both interventions.
    RESULTS: Thirty-three adults (mean age 57.9 ± 12.6 years, BMI 33.1 ± 7.5 kg/m2, HbA1c 60.1 ± 15.1 mmol/mol) were randomised, with 31 completing both interventions. Dietary energy intake was comparable between interventions, while fibre and protein intakes were higher during the high-fibre high-protein bread intervention. Compared with control bread, the high-fibre high-protein bread improved glycaemic metrics, including all-meal iAUC (-21 mmol L-1 min-1 [95% CI: -39 to -3]), time in range (4.1% [95% CI: 2.2-6.1]), time above range (-4.3% [95% CI: -6.3 to -2.4]) and glycaemic variability (CONGA -0.4 [95% CI: -0.5 to -0.2]). Systolic (-7.2 mmHg [95% CI: -11.2 to -3.2]) and diastolic (-7.2 mmHg [95% CI: -9.9 to -4.6]) blood pressure also improved. No differences were observed between interventions for anthropometry, lipid profile or C-reactive protein.
    CONCLUSIONS: Use of a single food, maximally fortified with fibre and protein, improved glycaemia in type 2 diabetes management compared with an unfortified equivalent over 2 weeks. Reformulation of common staples with these two nutrients presents a practical and scalable strategy for manufacturers to support diabetes management.
    Keywords:  continuous glucose monitoring (CGM); dietary intervention; randomised trial; type 2 diabetes
    DOI:  https://doi.org/10.1111/dom.71523
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