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.
Aims: Diabetes mellitus (types 1 and 2) prevalence in the Asia-Pacific (APAC) region has increased significantly. While real-world evidence demonstrates MiniMed™ 780G (MM780G) system effectiveness and safety in many countries, its use in the APAC region is not well reported. The present study explored MM780G system performance and glycemic outcomes in individuals with diabetes in the APAC region. Methods: CareLink™ personal data uploaded between January 2, 2020, and March 31, 2025, by consenting APAC MM780G system users (all ages) with ≥10 days of continuous glucose monitoring (CGM) data were de-identified, aggregated, and analyzed, including a subanalysis of those using recommended optimal settings (ROS; glucose target of 100 mg/dL with an active insulin time of 2 h, for ≥95% of time). Endpoints were compared between Australia/New Zealand (AUS/NZL) and APAC without AUS/NZL (APAC w/o AUS/NZL) and assessed across quartiles of user-announced total daily carbohydrate (TDCarb). Results: Compared to AUS/NZL ( N = 17,382), APAC w/o AUS/NZL ( N = 8,360) had clinically similar and favorable CGM-derived outcomes, despite a significantly greater mean TDCarb (210.5 g vs. 147.0 g, P < 0.001). Across increasing TDCarb quartiles, for both subregions, time in range improved ( P < 0.001 for both), and time below range <70mg/dL (%TBR) was low (≤2.8%). ROS users showed further glycemic improvement with unchanged %TBR. Conclusions: The MM780G system can effectively manage real-world APAC glycemia irrespective of different TDCarb.
Aims Diabetes mellitus (DM) is a common comorbidity of psoriatic arthritis (PsA); however, its global prevalence and the impact of treatment exposure remain unclear. This study aimed to assess the global prevalence of DM in patients with PsA using different subgroup analyses.Materials and Methods A systematic search of PubMed, Embase, the Cochrane Library and the Web of Science was performed from inception until February 2025 for observational studies reporting the prevalence or incidence of DM in PsA patients. Random-effects models were applied to pool estimates, and subgroup, meta-regression, and sensitivity analyses were conducted to explore heterogeneity. Publication bias was evaluated using the Egger's test and the trim-and-fill method.Results Sixty-three studies, including 293,582 patients with PsA, were analysed. Patients with PsA included in this study were divided into treatment-disclosed and non-treatment-disclosed groups. DM overall prevalence in patients with PsA was 13.84% in the non-treatment-disclosed population and 13.50% in the treatment-disclosed cohort. Geographically, the comorbidity prevalence rates were higher in South America (37.98%) and Asia (16.76%) than in Europe (12.81%) and North America (12.38%) in the non-treatment-disclosed population. In the treatment-disclosed population, prevalence was also highest in South America (28.13%), followed by Asia (18.30%). Meta-regression analysis identified age, type of DM, region, and study design as contributors to heterogeneity. Evidence of small-study effects was detected in the T2DM subgroup and cohorts receiving systemic therapy plus biologics.Conclusions Subgroup analysis shows that the prevalence of DM is associated with the type of study and the region in which the patients were located. These findings support routine metabolic screening for PsA and highlight the need for longitudinal studies to track disease activity, treatment patterns, and metabolic outcomes.
Objective: To evaluate the completeness of methodological reporting in continuous glucose monitoring (CGM)-based clinical trials, and based on our findings, to propose a standardized reporting framework to improve reproducibility and transparency. Research Design and Methods: We conducted a search to assemble a study sample of CGM-based clinical trials published between 2019 and 2024. Articles were identified via PubMed and screened based on predefined eligibility criteria. We measured the presence or absence of reporting elements across seven core methodological features essential for reproducibility, spanning CGM device characteristics, data platforms, data extraction processes, preprocessing and transformation methods, handling of missing data, CGM metrics, and analytic approaches. Data were manually extracted by trained reviewers, with quality assurance performed on a subset of records. Descriptive statistics were used to summarize reporting completeness. Results: A total of 248 studies met inclusion criteria. On average, studies reported 4.39 (SD 1.22) of the seven core methodological features. Reporting was highest for CGM metrics (100%), analytic methods (100%), and device make/model (96%). In contrast, key elements of the CGM data pipeline were frequently underreported, including data extraction processes (56%), preprocessing methods (43%), handling of missing data (32%), and CGM data platform (30%). Only 6% of studies reported all seven features. These findings demonstrate substantial heterogeneity and consistent gaps in reporting practices across CGM clinical research within the literature sampling frame. Conclusions: Incomplete reporting of CGM data pipelines limits reproducibility, comparability, and the clinical and computational utility of CGM-derived outcomes. We propose a preliminary, candidate CGM-specific reporting checklist to standardize documentation of data acquisition, processing, and analysis. Adoption of a refined, consensus-based version of such a framework may strengthen methodological rigor, support regulatory and clinical interpretation, and enhance the reliability of CGM data for emerging digital health and artificial intelligence applications.
Background: Automated insulin delivery (AID) systems are recommended for individuals with diabetes requiring insulin. Real-world effectiveness may differ from outcomes reported in prospective clinical trials. The twiist (TM) AID system recently received regulatory clearance, and real-world glycemic outcomes have not been previously described. Methods: This was an ongoing retrospective, observational real-world study with cross-sectional analyses of participant-level summary data. The analysis included individuals with type 1 diabetes (T1D) and type 2 diabetes (T2D) who initiated use of the twiist AID system and consented to data sharing. Deidentified continuous glucose monitoring (CGM) and insulin delivery data were analyzed. Inclusion in the analysis required >= 14 days of device use. Glycemic outcomes were assessed using standard CGM metrics, including percent time in range (%TIR; 70-180 mg/dL), time in tight range (%TITR; 70-140 mg/dL), time below range (%TBR; 54 to <70 and <54 mg/dL), and time above range (%TAR; >180-250 and >250 mg/dL). Subgroup analyses evaluated associations with baseline glycated hemoglobin (HbA1c), age, and bolus frequency. Results: Data from 4000 individuals were included. The duration of device use ranged from a minimum of 14 days to a maximum of 365 days. Mean overall %TIR was 73.5 +/- 14.5%, with overnight %TIR of 77.9 +/- 16.5%. Hypoglycemia exposure was low, with mean overall %TBR of 54 to <70 mg/dL of 1.9 +/- 2.0% and <54 mg/dL of 0.2 +/- 0.4%. Glycemic outcomes were generally within recommended targets across age categories except for %TIR among those <18 years of age. Among those with baseline HbA1c values and TIR values (n = 1131), %TIR was 81.5 +/- 10.2% and 71.1 +/- 11.4%, and 61.7 +/- 16.3% for those HbA1c <7%, 7%-8%, and >8%, respectively. More frequent boluses were associated with improved glycemic outcomes. Conclusions: In this large real-world cohort, use of the twiist AID system was associated with effective overall and overnight glycemic management and minimal hypoglycemia.
