bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–03–15
fifteen papers selected by
Mott Given



  1. Trends Endocrinol Metab. 2026 Mar 10. pii: S1043-2760(26)00042-1. [Epub ahead of print]
      Continuous glucose monitoring (CGM) has become standard care in type 1 diabetes, yet the key measures used to interpret CGM data, such as time spent within, below, or above the target glucose range, and glycemic variability, remain based on outdated thresholds. We discuss why updating these targets is essential for modern diabetes care.
    Keywords:  continuous glucose monitoring; diabetes technology; glycemic variability; time below range; time in range; type 1 diabetes
    DOI:  https://doi.org/10.1016/j.tem.2026.02.007
  2. Diabetes Obes Metab. 2026 Mar 09.
       AIMS: Real-time continuous glucose monitors (rt-CGM) have been found effective and economical for the treatment of diabetes in many countries. The objective of this study was to provide a cost-effectiveness analysis of rt-CGM versus self-monitoring of blood glucose (SMBG) from the perspective of a healthcare payer in New Zealand.
    MATERIALS AND METHODS: The cost-effectiveness of rt-CGM in patients with type 2 diabetes (T2D) who require intensive insulin therapy was analysed using the IQVIA Core Diabetes Model (CDM), providing outputs including life expectancy, quality-adjusted life years (QALYs), direct costs, incremental cost-effectiveness ratios (ICERs), and incidence rates of complications. A lifetime (50 years) time horizon was used with an annual discount rate of 3.5%.
    RESULTS: In the base case, rt-CGM was associated with a gain of 0.488 QALYs and incremental costs of NZD 5633 compared with SMBG, resulting in an ICER of NZD 11 533 per QALY. This corresponded to a gain of 87 QALYs per NZD 1 million invested. Scenario analyses suggested that CGM is potentially cost saving at earlier ages of rt-CGM initiation and among high-risk populations, such as Māori and Pacific Peoples, yielding higher gains in QALYs at lower total direct costs. Reductions were predicted in the risks of ophthalmic, renal, peripheral, and cardiovascular complications.
    CONCLUSIONS: This analysis provides insight into the cost-effectiveness of rt-CGM versus SMBG from the perspective of payers in New Zealand, demonstrating reductions in the risks of complications in addition to reductions in their associated costs.
    Keywords:  complications; continuous glucose monitoring; general diabetes; health economics
    DOI:  https://doi.org/10.1111/dom.70593
  3. Diabetes Technol Ther. 2026 Mar;28(3): 279-284
       INTRODUCTION: Gastroparesis (GP) is a well-recognized complication in patients with long-standing type 2 diabetes (T2D), impacting glycemic control and continuous glucose monitoring (CGM) metrics. We performed a prospective study to characterize real-time glucose metrics in patients with T2D with gastroparesis.
    METHODS: This pilot prospective study involved 11 adult patients with T2D and GP (GP group) and 20 patients with T2D without GP (non-GP group). Patients used real-time CGM (rtCGM) FreeStyle Libre 3 throughout the 4-week study. Fifteen glycemic metrics, including time in range (TIR), time above range (TAR), and time below range (TBR), were analyzed from the rtCGM profiles of study participants.
    RESULTS: Compared with the non-GP group, patients in the GP group had higher mean CGM glucose levels (GP group [172 ± 51 mg/dL] vs. non-GP group [157 ± 41 mg/dL]) and lower TIR, indicating inadequate glucose control. The GP group also had greater TAR, increased TBR, and higher standard deviation (SD) and coefficient of variation (CV), indicating greater glucose excursions and glucose variability.
    CONCLUSION: Patients with T2D and GP showed distinct and altered CGM glucose metrics compared with those with T2D without GP. These findings highlight the need for better glycemic control in this population. Whether CGM metrics could help identify a specific biomarker for gastroparesis remains to be determined. Further validation with a broader population, including patients with T1D and GLP-1RAs, is necessary.
    Keywords:  coefficient of variation and standard deviation; gastroparesis; glucose metrics; glycemic variability; real-time continuous glucose monitor; type 2 diabetes
    DOI:  https://doi.org/10.1177/15209156251408393
  4. Diabetologia. 2026 Mar 09.
      Continuous glucose monitoring (CGM) has transformed diabetes management by providing continuous, real-time insights into glucose dynamics, while enhancing the lived experience of individuals with type 1 diabetes. In established type 1 diabetes, CGM-derived measures of glucose management, such as time in range, time above range, time below range and glycaemic variability, have become integral tools to optimise therapy, reduce episodes of hypoglycaemia and guide clinical decision-making. More recently, CGM has emerged as a promising tool to detect early hyperglycaemia and other glucose abnormalities in individuals with early-stage type 1 diabetes, for whom current screening and staging methods, including fasting glucose, HbA1c and the OGTT, remain limited by episodic sampling, participant burden and variable reproducibility. This review examines the rationale, evidence and practical considerations for integrating CGM into early-stage type 1 diabetes research and clinical frameworks. We discuss its potential to complement existing metabolic and immunological markers, as well as the technical, analytical and regulatory challenges that must be addressed for CGM to serve as a reliable tool for screening, staging and monitoring and as a clinical endpoint in early-stage type 1 diabetes.
    Keywords:  Continuous glucose monitoring; Progression; Review; Stage 1 diabetes; Stage 2 diabetes; Type 1 diabetes
    DOI:  https://doi.org/10.1007/s00125-026-06707-4
  5. Acta Anaesthesiol Scand. 2026 Apr;70(4): e70219
       BACKGROUND: Perioperative glucose monitoring traditionally relies on intermittent point-of-care (POC) testing, whereas continuous glucose monitoring (CGM) enables real-time glucose assessment with automated alerts for dysglycaemia. CGM remains understudied in hospitalised surgical patients with diabetes. This protocol outlines a clinical trial designed to evaluate the effect of CGM on achieving normoglycaemia in surgical patients with diabetes.
    METHODS: A multicentre, two-group, randomised controlled trial (NCT06314061). Eligible patients are adults with Type 1 or Type 2 diabetes undergoing surgery lasting more than 45 min with an expected hospital stay of at least one night. Patients in the intervention group will be monitored using CGM (Dexcom G7, Dexcom Inc., CA, USA) with active alerts for hyperglycaemia and hypoglycaemia for up to 10 days after surgery during hospitalisation. Patients in the control group will wear a CGM device with glucose values and alerts concealed from the patient and clinical staff. All patients will receive routine diabetes care, including intermittent POC glucose testing, in addition to CGM. The primary outcome is CGM time-in-range between 6.0 and 10.0 mmol/L. Secondary outcomes include the frequency and cumulative duration of hypoglycaemia and hyperglycaemia as well as postoperative complications. A sample size of 200 patients will allow 90% power to detect a 15% relative difference in the primary outcome between groups, with an expected 10% dropout. To ensure standardised use of CGM and to support clinical decision-making during the trial, a trial-specific guideline has been developed, integrating CGM with insulin treatment and POC tests. The guideline recommends intervention for glucose levels < 5.0 mmol/L when trending downward. For CGM glucose levels > 10.0 mmol/L, rapid-acting insulin may be administered according to a sliding scale regimen rather than delaying treatment until the next scheduled POC test.
    CONCLUSION: This randomised controlled trial will provide clinical evidence for CGM use by clinical staff to enhance perioperative glycaemic control in surgical patients with diabetes.
    TRIAL REGISTRATION: NCT06314061.
    DOI:  https://doi.org/10.1111/aas.70219
  6. J Diabetes Sci Technol. 2026 Mar 07. 19322968261432498
      A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale-lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.
    Keywords:  composite; continuous glucose monitoring; data platforms; data visualization; glycemia risk index; time in range
    DOI:  https://doi.org/10.1177/19322968261432498
  7. Diabet Med. 2026 Mar 09. e70284
       AIMS: Continuous glucose monitoring (CGM) remains underutilized in low- and middle-income countries (LMICs). We extend earlier observations on the feasibility and impact of CGM among people living with type 1 diabetes (T1D) in Rwanda in a real-world continuation phase study.
    METHODS: This was a 1-year continuation phase of a single-arm, mixed-methods, prospective observational study conducted in Kigali, Rwanda, from August 2022 to September 2024. Completers of the 12-month Phase I were transitioned to a current-generation CGM device and, in months 19-24, reduced frequency of clinic visits reflecting routine care. The primary outcomes were change in haemoglobin A1c (HbA1c) and the CGM-based metrics time in range (TIR, 3.9-10 mmol/L), and time below range (TBR, <3.9 mmol/L). Secondary outcomes included self-reported hospitalizations, incidences of severe hypoglycaemia and diabetic ketoacidosis, as well as diabetes-related questionnaire results.
    RESULTS: At the end of Phase I, 40 of the original 50 participants entered Phase II with mean HbA1c, TIR and TBR of 44 mmol/mol (6.2%), 44.9% and 5.6%, respectively, all metrics being significantly different from study start. These did not change significantly by the end of the study (48 mmol/mol [6.6%], 46.5% and 7.9%, respectively). No hospitalizations, three episodes of severe hypoglycaemia and two episodes of diabetic ketoacidosis were reported. Most respondents were satisfied with the device, used it consistently and trusted the values provided.
    CONCLUSIONS: CGM-related improvements in HbA1c and TIR among Rwandans living with T1D were maintained for at least 24 months. CGM should be considered as an important tool to improve diabetes self-management in LMICs.
    Keywords:   Rwanda ; continuous glucose monitoring; feasibility studies; glycaemic management; low‐ and middle‐income countries; time in range (TIR); type 1 diabetes
    DOI:  https://doi.org/10.1111/dme.70284
  8. JMIR Res Protoc. 2026 Mar 04. 15 e83218
       Background: Type 1 diabetes (T1D) requires repeated self-management behaviors and ongoing problem-solving to maintain optimal glucose levels and prevent complications. Despite increasing adoption of continuous glucose monitoring (CGM), which can alleviate some of the constant self-management burden, adolescents struggle to achieve glycemic recommendations and report low engagement with diabetes device data. Previous studies have used retrospective or quantitative approaches to describe adolescent self-management; however, it is unclear how psychosocial influences (eg, mood and distress) and contexts impact adolescent self-management behaviors and engagement with their diabetes devices in everyday life. Exploration of real-time experiences will help to identify potential targets and strategies for future interventions to improve glycemic outcomes in adolescents with T1D using advanced diabetes technologies.
    Objective: This study has two aims: (1) to develop a grounded theory of self-management decision-making using diabetes devices among adolescents with T1D and (2) to assess the acceptability and feasibility of longitudinal and real-time qualitative data collection methods in this population.
    Methods: We will conduct a mixed methods study informed by the capability, opportunities, and motivation of behavior model. Adolescents (aged 12-18 y) with T1D who regularly use CGMs will be recruited from a Midwest pediatric diabetes clinic. Purposive sampling strategy will ensure participants with varied glycemic levels (hemoglobin A1c [HbA1c] ≤9% and HbA1c >9%) and diabetes experiences (eg, diabetes duration, devices used) are included. Using a longitudinal convergent mixed methods design, enrolled participants (n=30-40) will complete data collection over 6 weeks including: (1) a baseline survey to capture demographic, clinical, and behavioral characteristics; (2) 30 days of SMS text messaging surveys to describe real-time self-management behaviors, technology use, and decision-making; (3) 30 days of CGM data; and (4) an interview focused on self-management behaviors and technology use. Recruitment will continue until appropriate data completeness and/or theoretical saturation is achieved. Analysis of text responses and interview transcripts will follow a grounded theory approach. Summarized glycemic metrics (eg, time in range) and visuals (ie, ambulatory glucose profile) will be integrated with qualitative findings through participant profiles and joint displays. Integrated findings will be used to refine a grounded theory of daily self-management decision-making using diabetes devices among adolescents with T1D.
    Results: As of December 2025, 25 participants have enrolled in this study. We expect SMS text messaging survey completion rates and CGM use near 70% throughout the study period. We anticipate findings to become available in the following several years through conference presentations and peer-reviewed publications.
    Conclusions: While routine diabetes self-management behaviors and use of diabetes technologies are important for achieving glycemic goals, adolescents report low adherence to diabetes devices. This real-time mixed methods study will improve our understanding of daily decision-making and influences on diabetes self-management. Findings from this study will identify facilitators and barriers to optimal T1D self-management. In addition, results will inform future studies using real-time qualitative and mixed methods approaches.
    Keywords:  CGM; SMS text messaging; adolescent; continuous glucose monitoring; diabetes; mixed methods; qualitative research; self-management; type 1 diabetes; youth
    DOI:  https://doi.org/10.2196/83218
  9. Diabetes Technol Ther. 2026 Mar 12. 15209156261432144
       BACKGROUND: Glucose predictions aim to empower continuous glucose monitoring (CGM) users by enabling preventive actions to reduce adverse glycemic events. The Accu-Chek® SmartGuide Predict app offers several AI-enabled predictive features, driven by machine learning algorithms. These include notifications for a low glucose predict within 30 min (LGP) and for nighttime low glucose risk, as well as a 2-h continuous glucose forecast.
    AIMS: This study aimed to quantify the potential glycemic benefits of using the Predict app's predictive features in an adult population with type 1 diabetes (T1D).
    METHODS: A comparative in silico study was conducted using the clinically backed University of Virginia Replay digital twin simulator. A control arm, simulating standard hypoglycemia and hyperglycemia mitigation strategies in line with international guidelines, was compared against intervention arms that incorporated probabilistic user behavior models responding to the app's predictive features. The evaluation was performed on 204 digital twins, representing 29,929 days of data, generated from the REPLACE-BG clinical trial dataset.
    RESULTS: Results demonstrated that using the app's predictive features has the potential to improve glycemic control in adults with T1D. The simulated intervention led to an average 2.9 percentage point reduction in time below range (<70 mg/dL), and a clinically significant increase of more than 3.6 percentage points in time in range (70-180 mg/dL). Furthermore, the daily number of CGM hypoglycemia alarms (<70 mg/dL) was reduced by 67%. The findings also suggest that consuming 10 g of fast-acting carbohydrates in response to LGP notifications provides an optimal balance, effectively preventing hypoglycemia while limiting rebound hyperglycemia.
    CONCLUSIONS: This in silico evaluation provides strong evidence supporting the potential clinical utility of the Accu-Chek SmartGuide Predict app for improving glycemic management in adults with T1D.
    Keywords:  artificial intelligence; continuous glucose monitoring; digital twin; glucose prediction; mHealth; machine learning; type 1 diabetes
    DOI:  https://doi.org/10.1177/15209156261432144
  10. Diabetes Technol Ther. 2026 Mar;28(3): 193-200
       OBJECTIVE: A majority of adults with type 1 diabetes (T1D) are overweight (OW) or obese (OB) and often struggle to reach glycemic targets. Tirzepatide, a dual-incretin approved for type 2 diabetes (T2D) and OW/OB, has been shown to improve glucose control, reduce body weight, and insulin requirements in off-label adjunctive use for patients with T1D. This study evaluated changes in continuous glucose monitoring (CGM) data over 12-months of tirzepatide use in OW/OB adults with T1D.
    MATERIALS AND METHODS: This a single-center, retrospective, longitudinal case-control study included 61 OW/OB adults with T1D using tirzepatide and 54 computer-matched (for age, HbA1c, and weight) controls. CGM data were analyzed at baseline and every 3 months over a 15-month period (-3, 3, 6, 9, and 12 months). We assessed both within- and between-groups changes in CGM metrics from baseline at each time point.
    RESULTS: Baseline characteristics were similar between tirzepatide-treated and control groups for age, HbA1c, and body weight. Compared with controls, tirzepatide-treated group significantly improved CGM metrics over 12 months. Time in range (TIR) was higher at 3 months (+4.6%, P = 0.04) and remained greater at 6 (+9.0%, P < 0.001), 9 (+6.9%, P < 0.001), and 12 months (+7.4%, P < 0.001). Time in tight range (TITR) was also higher at 6-12 months (P ≤ 0.02). Mean glucose, time above range (TAR), time >250 mg/dL, and coefficient of variation were all lower in the tirzepatide group compared with controls. Time below range <70 mg/dL (TBR) and TBR2 (<54 mg/dL) remained similar between groups throughout the study. At 12 months, a greater proportion of tirzepatide-treated participants achieved composite CGM targets (TIR ≥70% and TBR <4%) compared with controls (50.8% vs. 25.9%; P < 0.01). No severe hypoglycemia or diabetic ketoacidosis occurred in either group.
    CONCLUSIONS: We conclude that adjunctive tirzepatide treatment in OW/OB adults with T1D was associated with sustained improvements in CGM metrics over 12 months, without increased hypoglycemia risk in this real-world study. Proper long-term randomized control trials are needed to confirm our findings.
    Keywords:  TAR and DKA risk with GIP/GLP-1RA use in T1D; TIR and TITR with GIP/GLP-1RA in T1D; achieving international consensus target with GIP/GLP-1RA in T1D; changes in CGM metrics with GIP/GLP-1RA in T1D; glucose control with GIP/GLP-1RA in T1D; hypoglycemia with GIP/GLP-1RA use in T1D
    DOI:  https://doi.org/10.1177/15209156251398060
  11. J Diabetes Sci Technol. 2026 Mar 09. 19322968261426305
       BACKGROUND: Discrepancies between HbA1c and glucose management indicator (GMI) may reflect individual variations in glycation rate, independent of mean glycemia, and could influence complication risk stratification in type 1 diabetes (T1D). We evaluated the phenotype of individuals with T1D using continuous glucose monitoring (CGM), identified as high glycators based on HbA1c/updatedGMI ratio, and assessed retrospectively their risk of diabetic retinopathy (DR) and the time to DR diagnosis. The secondary aim was to identify clinical correlates of high glycation.
    PRIMARY OUTCOME: time to first diagnosis of DR.
    SECONDARY OUTCOMES: clinical factors associated with high glycation.
    METHODS: A retrospective study of 411 individuals with T1D using CGM and concurrent HbA1c values. Patients with conditions affecting red blood cell (RBC) lifespan were excluded. Participants were divided into 3 subgroups based on current HbA1c/updatedGMI ratio ≤0.95 (low glycators), >0.95 and <1.05 (concordant glycators), and ≥1.05 (high glycators). Time to diagnosis of DR was retrieved.
    RESULTS: High glycation is associated with shorter time to first diagnosis of DR (adjusted hazard ratio 1.60). Non-HDL-C, RBC indices, and metformin were associated with high glycation.
    CONCLUSION: Among individuals with T1D, an HbA1c/updatedGMI ratio ≥1.05 is associated with higher odds of DR. Non-HDL-C and RBC indices are correlates of high glycation. These results underscore the relevance of HbA1c and updatedGMI discrepancy in cardiometabolic risk assessment, but cutoffs remain to be set.
    Keywords:  HbA1c and GMI discordance; HbA1c/uGMI ratio; glycation gap; retinopathy; type 1 diabetes
    DOI:  https://doi.org/10.1177/19322968261426305
  12. Diabetologia. 2026 Mar 09.
       AIMS/HYPOTHESIS: Gestational diabetes (GDM) results in adverse outcomes for the pregnant individual and neonate. Lifestyle modifications are first-line interventions used to achieve pregnancy-specific glucose targets. We investigated how temporal eating patterns influence glucose concentrations in individuals with GDM. We hypothesise that eating the first meal early in the morning may lower overall 24 h interstitial glucose, which could be an intervention to improve 24 h glucose metrics among people with GDM.
    METHODS: This is a secondary analysis of pregnant people with GDM randomised to self-capillary blood glucose (SCBG) with or without additional real-time continuous glucose monitoring (CGM) for management of GDM. Participants measured SCBG and were included in the analysis if postprandial SCBG were available to infer meal timing (n=71). The cohort was split by the median time of first meal into early (first meal before 09:56 hours) and late eating (first meal after 09:56 hours) groups. The 24 h CGM glucose profiles were compared between groups by cosinor and linear analyses, adjusted for maternal and gestational age, medication usage, and primary study group assignment.
    RESULTS: Over 24 h, glucose increased during the day and decreased during the night. This rhythm was shifted earlier for the early eating group (time-of-day: 24 h component: -0.32 mmol l-1 min-1, t102,232=-188.9, p<0.001; 12 h component: -0.11 mmol l-1 min-1, t102,232=-65.2, p<0.001; and group × time-of-day: 24 h component: 0.09 mmol l-1 min-1, t102,232=37.9, p<0.001; 12 h component: 0.04 mmol l-1 min-1, t102,232=15.3, p<0.001). During the daytime, there was a significant time-of-day (7.0 × 10-4 mmol l-1 min-1, t72,418=150.8, p<0.001) and group × time-of-day effect (7.0 × 10-5 mmol l-1 min-1, t72,418=10.0, p<0.001), but no group effect (0.01 mmol/l, t65=0.06, p=0.950). Overnight, glucose decreased in both groups by approximately 0.67 ± 0.39 mmol/l. The late eating group, however, had significantly higher nocturnal glucose compared with the early eating group (group: 0.26 mmol/l, t65=2.3, p=0.023, time-of-day: -0.09 mmol l-1 min-1, t29,818=-119.0, p<0.001; and group × time-of-day effect: -0.01 mmol l-1 min-1, t29,818=-11.8, p<001).
    CONCLUSIONS/INTERPRETATION: These results suggest that meal timing, with an emphasis on earlier eating patterns, is a potential lifestyle intervention that can improve nocturnal interstitial glucose.
    Keywords:  Circadian rhythm; Food timing; Gestational diabetes; Hyperglycaemia; Hypoglycaemia; Pregnancy diet
    DOI:  https://doi.org/10.1007/s00125-026-06701-w
  13. Proc Natl Acad Sci U S A. 2026 Mar 17. 123(11): e2532127123
      In situ monitoring of sweat glucose during exercise can provide a real-time and continuous assessment of blood glucose dynamics. However, the relatively poor correlation between sweat and blood glucose concentrations during exercise makes it challenging for blood glucose management (BGM) during exercise therapy for diabetes, along with training for athletes and fitness enthusiasts. This work presents a flexible wireless sweat glucose and pH sensing platform integrated with a pH-based correlation model to accurately predict the continuous changes in blood glucose. The pH-based correlation model calibrates enzyme activity changes in glucose oxidase and accounts for the effects of sweat dilution and filtering during paracellular transport of glucose from interstitial fluid and plasma to sweat during exercise. The correlation model has been validated in both healthy individuals and diabetic patients, revealing distinct blood glucose dynamic patterns between the two cohorts. The observed different glucose fluctuations after the intake of various nutritive foods further facilitate the management of diabetes and allow for the identification of hypo-/hyperglycemic risks during training or fitness exercise. The exercise-based device platform combines continuous blood glucose monitoring with diabetes management through effective treatment evaluation and can also provide early prevention for the at-risk population and reduce or even reverse diabetes.
    Keywords:  flexible sweat-sensing platform; glucose and pH monitoring; glucose management during exercise; noninvasive CGM; pH-based sweat–blood glucose correlation model
    DOI:  https://doi.org/10.1073/pnas.2532127123
  14. Curr Diab Rep. 2026 Mar 10. pii: 4. [Epub ahead of print]26(1):
      
    Keywords:  Continuous glucose monitoring; Ecological momentary assessment; Mental health; Person-reported outcomes; Precision medicine
    DOI:  https://doi.org/10.1007/s11892-026-01618-5
  15. Medicine (Baltimore). 2026 Mar 13. 105(11): e48074
      Time in range (TIR) derived from continuous glucose monitoring and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) are independent predictors of maternal and neonatal outcomes in gestational diabetes mellitus (GDM). However, their combined effect on neonatal outcomes remains unclear. This study aimed to investigate the joint influence of TIR and HOMA-IR on neonatal outcomes in GDM patients. In this retrospective cohort study, 166 women with GDM were categorized into 4 groups based on TIR levels (cutoff: 90%) and HOMA-IR (cutoff: cohort median): Group A (high-TIR/low-IR), Group B (high-TIR/high-IR), Group C (low-TIR/low-IR), and Group D (low-TIR/high-IR). HOMA-IR was calculated at "24 to 28 weeks' gestation" gestation using fasting plasma glucose and insulin levels obtained during the oral glucose tolerance test. Neonatal complications, including hypoglycemia, macrosomia, and hyperbilirubinemia, were compared across groups. Compared with Group A (high-TIR/low-IR), Group D (low-TIR/high-IR) demonstrated the highest risk for adverse neonatal outcomes, with significantly higher incidence of neonatal hypoglycemia (OR: 4.52, 95% CI: 1.89-10.78) and macrosomia (OR: 3.45, 95% CI: 1.45-8.19), along with higher mean neonatal bilirubin levels (10.97 ± 1.72 mg/dL vs 8.99 ± 2.09 mg/dL, P < .001). Poor glycemic control (low TIR) combined with significant insulin resistance (high HOMA-IR) identifies a subgroup of GDM patients at the highest risk for adverse neonatal outcomes. The joint assessment of TIR and HOMA-IR may facilitate precise risk stratification and personalized management in clinical practice.
    Keywords:  HOMA-IR; gestational diabetes mellitus; insulin resistance; neonatal outcomes; time in range
    DOI:  https://doi.org/10.1097/MD.0000000000048074