Twenty-Four-Month Evaluation of the Glycemia Risk Index in Adults With Type 1 Diabetes Using Advanced Hybrid Closed-Loop systems

Twenty-Four-Month Evaluation of the Glycemia Risk Index in Adults With Type 1 Diabetes Using Advanced Hybrid Closed-Loop systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Twenty-Four-Month Evaluation of the Glycemia Risk Index in Adults With Type 1 Diabetes Using Advanced Hybrid Closed-Loop systems Laura Nigi, Leonardo Distefano, Giuseppina EmanuelaGrieco, Dorica Cataldo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8918313/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Purpose The Glycemia Risk Index (GRI) is a recently introduced continuous glucose monitoring (CGM)–derived metric that has been evaluated to date in a limited number of studies involving type 1 diabetic adult users of Advanced Hybrid Closed-Loop (AHCL) systems. Methods To further characterize its clinical utility and to investigate its correlations with CGM-derived metrics, particularly Time in Tight Range (TITR), this single-center, observational, retrospective, real-world study assessed GRI in adults with type 1 diabetes using AHCL systems (MiniMed™ 780G, n = 45; Tandem t:slim X2 IQ technology, n = 20) in routine clinical practice, over a 24-month follow-up period. Results GRI showed progressive improvement throughout the observation period, consistent with sustained glycemic control. Baseline GRI was positively correlated with glycated hemoglobin and inversely correlated with Time in Tight Range (TITR) at 12 and 24 months. Conclusion These findings suggest that GRI may serve as a useful, readily interpretable metric for predicting long-term glycemic outcomes and support its complementary role alongside TITR in clinical assessment. Glycemia Risk Index continuous glucose monitoring Time in Tight Range automated insulin delivery type 1 diabetes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Achieving and maintaining optimal glycemic control is essential for reducing the risk of chronic diabetic complications. This goal is particularly challenging in type 1 diabetes (T1D), given the absolute reliance on exogenous insulin therapy and the intrinsic glucose variability that predisposes patients to both hypo- and hyperglycemia ( 1 – 2 ). In this clinical context, accurate and timely glycemic monitoring is therefore imperative ( 3 ). Over the past several decades, substantial technological advances have transformed the management of T1D, with continuous glucose monitoring (CGM) emerged as a critical tool for comprehensive glycemic assessment. Since the 2019 international consensus, CGM-derived metrics have been standardized and are now routinely used as complementary glycemic indicators, alongside glycated hemoglobin (HbA1c)( 4 ). Recently, new CGM parameters, such as the Time in Tight Range (TITR) and the Glycemia Risk Index (GRI), have been introduced to further refine and enhance the characterization of glycemic control ( 5 – 6 ). TITR represents the percentage of time spent within the 70–140 mg/dL range and is emerging as a valuable metric for assessing normoglycemia ( 7 – 9 ). Although an evidence-based therapeutic target for TITR in individuals with T1D has not yet been formally established, some researchers have suggested aiming for values between 45% and 50% as potential clinical goals ( 10 ). The Glycemia Risk Index (GRI) is a composite CGM-derived metric, designed to provide an overall assessment of glycemic quality and associated risk ( 5 – 6 ), with “quality” denoting the relative proportions of time spent in low and very low hypoglycemia, high and very high hyperglycemia ( 11 – 12 ). The GRI is calculated as a weighted sum of the time spent in four glucose ranges: low hypoglycemia (54 to < 70 mg/dL), very low hypoglycemia ( 250 mg/dL)( 11 ). It ranges from 0 to 100, and for improved interpretability, it has been stratified into five categories (zones A through E), each covering a range of 20. Zone A (GRI ≤ 20) denotes the highest glycemic quality, whereas Zone E (GRI 81–100) reflects the poorest glycemic quality. The GRI can be calculated easily, using data available in standard CGM reports; a 14-day CGM dataset has been identified as the optimal duration for reliable computation of this metric ( 11 , 13 – 16 ), now recognized as a promising tool for evaluating overall glycemic quality in clinical practice ( 17 – 20 ). A recent update to the international consensus on CGM metrics proposes a GRI ≤ 40 as a marker of acceptable glycemic quality in individuals with T1D ( 21 – 22 ). Picard et al. reported that a GRI ≥ 26 may identify T1D individuals needing additional clinical support, whereas values ≤ 20 generally meet established efficacy and safety targets ( 23 ). Furthermore, emerging evidence suggests that GRI may serve as a prognostic indicator for the development of T1D-associated chronic complications and for reduced quality of life ( 16 , 24 ). As a matter of fact, in a retrospective study examining the relationship between GRI and longitudinal HbA1c in youth, baseline GRI correlated positively with HbA1c at 3, 6, 9, and 12 months of follow-up ( 25 ). Because previous research has established HbA1c as a strong prognostic marker for the risk of T1D-associated complications, the observed positive correlations between an individual’s GRI and their longitudinal HbA1c measures suggest that the GRI may likewise possess predictive value, analogous to HbA1c, for identifying individuals at increased risk for suboptimal short- and long-term health outcomes. Moreover, in a large-scale study by Cichosz et al., involving 736 T1D individuals over four years, baseline GRI emerged as the strongest predictor of future poor glycemic control ( 24 ). The integration of CGM with continuous subcutaneous insulin infusion via automated, algorithm-driven devices known as advanced hybrid closed-loop (AHCL) systems ( 26 – 27 ), is considered the current standard of care in T1D management ( 28 – 29 ). Recent real-world evidence suggests that AHCL systems can substantially improve the GRI ( 21 – 22 , 30 – 33 ). However, to date, a limited number of studies have evaluated the GRI in adult T1D users of AHCL systems over extended observation periods, and no one, to our knowledge, has explored its relationship with the emerging glucose metric Time in Tight Range (TITR), which assess normoglycemia ( 7 , 8 , 9 , 34 ). Given the importance of thoroughly establishing the clinical utility of GRI ( 6 ), this study aimed to investigate this new parameter in T1D adults using AHCL systems in real-world practice over long-term follow-up, and to examine its associations with CGM-derived metrics, particularly TITR, and other clinical parameters. Materials and Methods A single-center, observational, retrospective, real-world study was conducted involving 65 T1D patients aged 18 years or older, treated with either the MiniMed™ 780G system (45 patients) integrated with the Guardian 4 or Simplera CGM sensor, or the Tandem t:slim X2 IQ technology (20 patients) integrated with the Dexcom G6 or G7 CGM sensor. Participants provided written informed consent for the collection and use of their data for research purposes prior to enrollment. The study protocol was approved by the local research ethics committee (Protocol number 24849. Comitato Etico Regionale per la Sperimentazione Clinica della regione Toscana, sezione area vasta sud-est) and conducted in accordance with the Declaration of Helsinki principles. Clinical data, along with CareLink and Glooko reports were collected and analyzed. Baseline parameters including age, diabetes duration, HbA1c, previous insulin therapy, total daily insulin dose (TDI), and body mass index (BMI) were recorded. During the 24-month follow-up period, BMI, TDI, HbA1c, and CGM metrics from the preceding 2-week interval were obtained at 14 days after AHCL activation (14d), at 3 month (3mo), 6 month (6mo), 12 month (12mo), and 24 month (24mo) follow-up visits. For subjects who initially used the MiniMed™ 780G in predictive low-glucose suspend (PLGS) mode (n = 35), CGM data were collected over the first 14 days of use in that mode (M). The recorded CGM metrics included: time in range (TIR%, 70–180 mg/dL), time above range (TAR% >180 mg/dL and TAR% >250 mg/dL -TAR250-), time below range (TBR% <70 mg/dL and TBR% <54 mg/dL -TBR54-), time in tight range (TITR%, 70–140 mg/dL), glucose management indicator (GMI%), coefficient of variation (CV%), mean sensor glucose (GM, mg/dL), glicemia risk index (GRI%). GRI was calculated according to the equation described by Klonoff et al ( 11 ) and ranged from 0 to 100, with values classified into five zones (A–E) of 20-point increments: Zone A (0–20), Zone B ( 21 – 40 ), Zone C (41–60), Zone D (61–80), and Zone E (81–100). Data collected at 3mo, 6mo, 12mo, and 24mo were compared with those obtained at 14d and at M. We examined correlations between GRI and CGM metrics, as well as baseline variables, along with time spent in automatic mode and sensor wear. For the MiniMed™ 780G active insulin time (AIT) and glycemic target (GT) were considered. Participants were also stratified according to baseline glycemic control (≥ 7.5% vs. <7.5%) and diabetes duration (≥ 20 years vs. <20 years). CGM metrics, HbA1c, TDI, and BMI were compared between the two AHCL systems. Continuous variables are presented as mean ± SD and categorical variables as percentages. Normality was assessed to guide use of paired t-tests or Wilcoxon signed-rank tests for longitudinal changes. Mann–Whitney U test was applied for group comparisons. Multiple linear regression with GRI as the independent variable and CGM metrics, HbA1c as dependent variables was constructed. Correlations were further assessed using the Spearman test. Statistical analyses were performed using GraphPad software, version 10.6.1. A p-value < 0.05 was considered statistically significant. Results The baseline characteristics of patients are summarized in Table 1 . All participants maintained a more than 90% of automated mode usage and CGM wear throughout the follow-up period. In the MiniMed™ 780G group, at 14d, subjects demonstrated glycemic metrics already reflecting the recommended target ranges for standardized CGM metrics: TIR 71.89 ± 10.42% (with 57.78% achieving TIR ≥ 70%), TAR 26.82 ± 10.84% (53.33% achieving TAR ≤ 25%), TAR 250 4.98 ± 4.58% (60% achieving TAR250 ≤ 5%), TBR 1.33 ± 1.55% (95.55% achieving TBR ≤ 4%), TBR54 0.27 ± 0.69% (97.83% achieving TBR54 ≤ 1%), GMI% 7.01 ± 0.38% (48.89% achieving GMI ≤ 7%), and CV 31.09 ± 4.13% (88.89% obtaining CV ≤ 36%). All CGM parameters and HbA1c remained stable or further improved over the 24-month follow-up compared with the initial 14 days of SmartGuard use (Fig. 1 ). At 24mo subjects showed the following glycemic outcomes: TIR 76.89 ± 10.81% (71.43% maintaining TIR ≥ 70%), TAR 22.64 ± 11.22% (60.71% achieving TAR ≤ 25%), TAR250 3.75 ± 3.58% (71.43% achieving TAR250 ≤ 5%), TBR 1.36 ± 1.03% (100% achieving TBR ≤ 4%), TBR54 0.18 ± 0.39% (100% achieving TBR54 ≤ 1%), GMI 6.88 ± 0.38% (71.43% achieving GMI ≤ 7%), and CV 30.1 ± 3.86% (96.43% achieving CV ≤ 36%). A significant increase in TIR% (p: 0.037) and a significant reduction in HbA1c% (p < 0.0001) as well as in TAR% (p: 0.0455) were observed at 24mo, compared to 14d (Fig. 1 ). TITR was 44.04 ± 11.29% during the first 14 days (14d) of MiniMed™ 780G system use in automatic mode. This percentage remained stable throughout the 24-month follow-up, with statistically significant increases observed at 3mo, 6mo, and 12mo compared with the 14d assessment (p: 0.0107, p: 0.0057, and p: 0.0237, respectively)(Fig. 1 ). The GRI was 28.8 ± 11.22% at 14d and showed a progressive, statistically significant improvement over the follow-up period. At 24mo, the GRI had decreased to 24.47 ± 10.74% (p: 0.0289 vs. 14d GRI%)(Fig. 2 ). At 24mo, approximately 40% of patients were classified in zone A, 54% in zone B, 7% in zone C; no patients in zones D or E (Fig. 2 ). Patients in zone A demonstrated the following characteristics: TIR 85.7 ± 3.62%, TITR 58.8 ± 5.09%, TAR 12.9 ± 4.22%, TBR 1.4 ± 1.07%, CV 27.94 ± 2.3%, GMI 6.56 ± 0.15%, GM 136.5 ± 6.32 mg/dL, HbA1c 6.53 ± 0.52%. The MiniMed™ 780G system was initiated in 35 patients in PLGS mode (also referred to as manual mode, M) during the initial phase of use. When comparing glycemic data from the first 14 days in SmartGuard mode (14d) with the first 14 days in manual mode (M), statistically significant improvements were observed across multiple CGM metrics. Specifically, TITR% (p < 0.0001) and TIR% (p < 0.0001) increased significantly. Moreover, significant reductions were noted in TAR% (p < 0.0001), TBR% (p: 0.0054), CV% (p: 0.0002), GMI% (p: 0.0003), GM (mg/dL; p < 0.0001), and GRI% (p < 0.0001). In the Tandem t:slim X2 group, glycemic metrics at 14d reflected, as well, the recommended CGM-target ranges: TIR 68.5 ± 15.59% (55% achieving TIR ≥ 70%), TAR 30.3 ± 16.35% (45% achieving TAR ≤ 25%), TAR250 9.05 ± 7.99% (95% achieving TAR25 ≤ 5%), TBR 1.25 ± 1.25% (100% achieving TBR ≤ 4%), TBR54 0.15 ± 0.37% (100% achieving TBR54 ≤ 1%), GMI 7.14 ± 0.6% (55% achieving GMI ≤ 7%), CV 33.09 ± 5.61% (65% achieving CV ≤ 36%), GM mg/dL 161.5 ± 24.93. These results remained stable throughout the follow-up (Fig. 3 ). At 24mo: TIR 74.3 ± 13.28% (70% of patients TIR ≥ 70%), TAR 23.4 ± 14.33% (50% of patients TAR ≤ 25%), TAR250 4.9 ± 5.69% (60% of patients TAR25 ≤ 5%), TBR 2.3 ± 3.27% (80% of patients TBR ≤ 4%), TBR54 0.5 ± 0.97% (90% of patients TBR54 ≤ 1%), GMI 6.9 ± 0.55% (60% of patients GMI ≤ 7%), CV 30.76 ± 5.16% (90% of patients CV ≤ 36%), and GM 150.2 ± 22.2 mg/dL. Additionally, HbA1c was 6.86 ± 1.05%. TITR was 42.1 ± 15.89% during the first 14 days of system use (14d), and this value remained stable over the 24-month follow-up (46.4 ± 14.28% at 24mo)(Fig. 3 ). The GRI was 34.57 ± 17.21% at 14d and decreased at the end of follow-up (even if not statistically significant), reaching 28.46 ± 14.28% at 24mo (Fig. 2 ). At the end of the observational period, approximately 30% of patients were classified in zone A, 50% in zone B, 20% in zone C; no patients in zones D or E (Fig. 2 ). Patients in zone A exhibited the following characteristics: TIR 88.33 ± 5.01%, TITR 58.33 ± 12.5%, TAR 11 ± 6.08%, TBR 0.67 ± 0.58%, CV 24.67 ± 1.03%, GMI 6.57 ± 0.31%, mean sensor glucose 136.33 ± 12.34 mg/dL, HbA1c 5.9 ± 0.6%. No changes in TDI or BMI were observed at 24mo compared with baseline for both AHCL systems. No differences were observed in CGM metrics or HbA1c values between the two AHCL systems. Analysis of potential correlations between GRI and other variables at time points M, 14d, 12mo, and 24mo, for the MiniMed™ 780G system showed the following results. At time M, GRI was positively correlated with HbA1c (p: 0.0064) and with TBR54 (p: 0.041). At 14d, no statistically significant correlations were detected. At 12mo, GRI showed positive correlations with HbA1c (p: 0.0119), GMI (p < 0.0001), and GM (p < 0.0001), as well as a negative correlation with TITR (p < 0.0001). At 24mo, no statistically significant correlations involving GRI were observed. For the other new parameter TITR, the following correlations were observed. At time M, TITR showed negative correlation with HbA1c (p: 0.0017) and positive correlation with TBR (p: 0.001). At 14d, TITR was positively correlated with TBR (p: 0.005) and negatively correlated with TAR250 (p < 0.0001). At 12mo, positive correlations were observed between TITR and TBR (p: 0.0007) and between TITR and TBR54 (p: 0.046), while negative correlations were found with HbA1c (p: 0.0001) and TAR250 (p < 0.0001). At 24mo, TITR remained positively correlated with TBR (p: 0.02) and negatively correlated with HbA1c (p < 0.0001) and CV (p: 0.05). Analysis of potential correlations between GRI and other variables at 14d, 12mo, and 24mo for the Tandem t:slim X2 system yielded the following results. At 14d, GRI was positively correlated with CV (p: 0.027) and HbA1c (p: 0.0104), and negatively correlated with TBR (p: 0.003). At 12mo, positive correlations were observed between GRI and HbA1c (p: 0.005), CV (p: 0.0008), GMI (p < 0.0001), and GM (p < 0.0001). Negative correlations were found between GRI and TIR (p < 0.0001) and between GRI and TITR (p < 0.0001). At 24mo, GRI showed positive correlations with TAR (p: 0.01) and TAR250 (p: 0.01), and a negative correlation with TIR (p: 0.0005). A negative correlation was also observed between GRI and TITR (p: 0.0306). For TITR, at 14d we observed positive correlations with TBR (p < 0.0001) and TBR54 (p: 0.02), as well as negative correlations with HbA1c (p: 0.0046) and TAR250 (p < 0.0001). At 12mo, TITR was positively correlated with TIR (p: 0.0003) and TBR (p: 0.017), and negatively correlated with HbA1c (p: 0.007), TAR250 (p < 0.0001), and GMI (p < 0.0001). At 24mo, TITR remained positively correlated with TIR (p: 0.001) and negatively correlated with HbA1c (p: 0.004), TAR% (p: 0.0001), TAR250 (p: 0.008), GMI (p < 0.0001), and GM (p < 0.0001). Analysis of correlations between GRI% at time M and other variables at 12mo and 24mo in the MiniMed™ 780G system showed the following results (Fig. 4 ): GRI M was positively correlated with HbA1c 12mo (p: 0.009), CV 12mo (p: 0.007), GMI 12mo (p < 0.0001), and GM 12mo (p < 0.0001), and negatively correlated with TITR 12mo (p < 0.0001). GRI M was positively correlated with HbA1c 24mo (p: 0.01), TAR 24mo (p < 0.0001), TAR250 24mo (p < 0.0001), CV 24mo (p: 0.002), GMI 24mo (p < 0.0001), and GM 24mo (p < 0.0001); negative correlations were observed with TIR 24mo (p < 0.0001) and TITR 24mo (p: 0.0006). Analysis of the correlations between GRI 14d and metabolic variables at 12mo and 24mo in users of the MiniMed™ 780G system showed the following results (Fig. 4 ): positive correlation with HbA1c 12mo (p: 0.004), TAR 12mo (p: 0.0006), TAR250 12mo (p < 0.0001), CV 12mo (p: 0.0001), GMI 12mo (p: 0.007), GM mg/dl 12mo (p:0.01); negative correlation with TIR 12mo (p: 0.0002), TITR 12mo (p: 0.015). Positive correlation with HbA1c 24mo (p: 0.0007), TAR 24mo (p < 0.0001), CV 24mo (p: 0.0001), GMI 24mo (p < 0.0001), GM 24mo (p < 0.0001); negative correlation with TIR 24mo (p < 0.0001), and TITR 24mo (p: 0.0002). From the analysis of the correlations between the GRI 14d and the other variables at 12mo and 24mo in users of the Tandem t:slim X2 system, we observed the following: positive correlation with HbA1c 12mo (p: 0.002), TAR 12mo (p: 0.0006), TAR250 12mo (p: 0.0004), CV 12mo (p: 0.02), GMI 12mo (p: 0.001), GM 12mo (p: 0.001); negative correlation with TIR 12mo (p: 0.019), TITR 12mo (p: 0.0009). Positive correlation with TAR% 24mo (p: 0.0001), TAR250 24mo (p < 0.0001), GMI 24mo (p: 0.002), GM 24mo (p: 0.001); negative correlation with TIR 24mo (p: 0.0002) and TITR 24mo (p: 0.0008). No correlations were observed between GRI and either the time spent in automated mode, sensor usage, TDI, AIT, or GT in the MiniMed™ 780G cohort. When stratifying participants by baseline glycemic control, patients with HbA1c ≥ 7.5% at baseline exhibited higher GRI at 24mo in the MiniMed™ 780G cohort (p: 0.0058). In the Tandem t:slim X2 cohort, individuals with HbA1c ≥ 7.5% at baseline also showed higher GRI at 14d (p: 0.0055), 12mo (p: 0.0006), and 24mo (p: 0.033). No significant differences in GRI were detected based on diabetes duration. Table 1 Baseline characteristics of participants. MiniMed™ 780G Tandem t:slim X2 Subjects - M/F 45 (19/26) 20 (12/8) Age - years 44,42 ± 13,69 43,7 ± 15,15 Diabetes duration - years 28,73 ± 14,65 20,9 ± 12,13 HbA1c - % 7,8(62 mmol/mol) ± 1,38 7,7(61 mmol/mol) ± 1,61 BMI - kg/m 2 25,21 ± 4,33 24,53 ± 4,15 Mean daily insulin dose - UI 44,2 ± 21,81 44,87 ± 12,37 Previous insulin therapy 17 MDI, 9 CSII + CGM, 14 PLGS, 5 HCL 9 MDI, 3 PLGS, 8 HCL MDI: Multiple Daily Injections; CSII: Continuous Subcutaneous Insulin Infusion; CGM: Continuous Glucose Monitoring; PLGS: Predictive Low Glucose Suspend system; HCL: Hybrid Closed Loop system Discussion In the field of continuous glucose monitoring, new metrics for assessing glycemic control have emerged, including the TITR, the percentage of glucose values between 70 and 140 mg/dL, indicative of euglycemia, and, notably, the GRI, a composite metric that captures the quality of glycemic control by simultaneously accounting for the proportions of very high and very low glucose values. In this retrospective real-world study, including T1D adults using AHCL systems, we observed improvements in GRI values over an extended period of 24 months. At the end of follow-up GRI percentages were approximately 24% with the MiniMed™ 780G and 28% with the Tandem t:slim X2. Moreover, users of the MiniMed™ 780G experienced a statistically significant reduction in GRI compared with the initial 14 days of SmartGuard mode, consistent with previous findings ( 31 ). Importantly, these GRI percentages corresponded to a substantial proportion of patients falling within GRI zones A and B. In line with prior findings ( 30 ), the reduction in GRI was predominantly attributable to decreased hyperglycemia; to note, hypoglycemia rates were already low at baseline and remained stable throughout follow-up. The study confirmed that AHCL systems achieve and sustain optimal glycemic control in T1D adults over 24 months (independent of prior insulin therapy, diabetes duration, or baseline HbA1c), in line with international guidelines and consistent with previous observations ( 4 , 35 – 38 ). TITR percentages at 24 months reached approximately 48% with the MiniMed™ 780G and 46% with the Tandem t:slim X2, aligning with the 45–50% range proposed by several authors ( 39 – 41 ). Moreover, among patients classified in GRI zone A, indicative of excellent glycemic control, TITR reached approximately 59% with the MiniMed™ 780G and 58% with the Tandem t:slim X2 at 24 months. We observed positive correlation between GRI and TAR, CV, GMI, and mean sensor glucose levels, alongside a negative correlation with TIR. These findings indicate that GRI is highly informative for identifying subjects with suboptimal glycemic control. In particular, we confirmed previous findings ( 25 ) showing that GRI at 14d (and at M for the MiniMed™ 780G system) was positively correlated with HbA1c at 12 and 24 months (for the Tandem t:slim X2 IQ technology system, the correlation did not reach statistical significance at 24 months), supporting the potential predictive value of GRI for long-term glycemic outcomes. TITR correlated positively with TIR and strongly inversely with HbA1c and TAR, supporting its value as a complementary metric of glycemic control. Notably, this study is among the few to evaluate both GRI and TITR across two AHCL systems and, to our knowledge, the only one to examine their relationship in depth. A strong inverse correlation was observed between GRI and TITR, highlighting their complementary value in clinical assessment. Specifically, TITR values above 45–50% combined with GRI values below 20% identified individuals achieving optimal glycemic targets, whereas low TITR, together with GRI exceeding 60% indicated clearly inadequate glycemic control. This relationship highlights that maintaining glucose near the physiological range (70–140 mg/dL) with AHCL systems supports high-quality glycemic control, marked by few severe hypo- or hyperglycemic episodes and low GRI. Finally, correlation between GRI at M and 14 d and TITR at 12 and at 24 months in MiniMed™ 780G users, paralleling those with HbA1c, support GRI’s potential predictive value. A study limitation is the relatively small, though well-characterized, cohort, particularly in the Tandem group, reducing statistical power for some outcomes. In conclusion, GRI offers a practical and readily interpretable metric that adds qualitative and potentially predictive insight into glycemic control ( 5 , 11 – 13 , 18 – 19 , 42 ) providing a strong foundation for future research despite the study’s limitations. Declarations Conflict of interest The authors have no conflicts of interest to disclose. Funding Statement This manuscript was funded by the Italian Ministry of University and Research (MUR) PNRR ‘National Center for Gene Therapy and Drugs based on RNA Technology’ (Project No. CN00000041 CN3 Spoke #5 ‘Inflammatory and Infectious Diseases’) and by the Italian Ministry of Health 'Multidisciplinary and Interregional Hub for Research and Clinical Experimentation To Combat Pandemics and Antibiotic Resistance' (project T4-AN-07, PAN-HUB). Author Contribution All authors contributed to the manuscript. Laura Nigi: conceptualization, data curation, formal analysis, writing- original draft. Leonardo Distefano: data curation, writing- original draft. Giuseppina Emanuela Grieco: formal analysis. Dorica Cataldo: data curation. Francesco Dotta: writing- review and editing, supervision. All authors approved the final version of the manuscript. Acknowledgement The authors would like to thank all study participants. Data Availability The datasets generated and analyzed during the current study were available on CareLink™ System (https://carelinkhp.minimed.eu) and on Glooko System (https://eu.my.glooko.com) References R.D. Leslie, C. Evans-Molina, J. Freund-Brown et al., Adult-Onset Type 1 Diabetes: Current Understanding and Challenges. Diabetes Care. 44 (11), 2449–2456 (2021). 10.2337/dc21-0770 R.I.G. Holt, J.H. DeVries, A. Hess-Fischl et al., The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). 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Pyle, Optimal sampling duration for continuous glucose monitoring for the estimation of glycemia risk index. Diabetes Technol. Ther. 25 , 140–142 (2023). 10.1089/dia.2022.0401 P. Pérez-López, P. Férnandez-Velasco, P. Bahillo-Curieses et al., Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics. Endocrine. 82 (3), 560–568 (2023). 10.1007/s12020-023-03511-7 G. Díaz-Soto, P. Pérez-López, P. Férnandez-Velasco et al., Quality of life, diabetes-related stress and treatment satisfaction are correlated with glycemia risk index (GRI), time in range and hypoglycemia/hyperglycemia components in type 1 diabetes. Endocrine. 86 (1), 186–193 (2024). 10.1007/s12020-024-03846-9 W. Fan, C. Deng, R. Xu et al., Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis. Diabetes Metab. J. 49 (2), 235–251 (2025). 10.4093/dmj.2024.0130 K.E. Karakus, V.N. Shah, D. Klonoff et al., Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes. Diabetes Obes. Metab. 25 , 3144–3151 (2023). 10.1111/dom.15208 J.Y. Kim, J.H. Yoo, J.H. Kim, Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality. Diabetes Technol. Ther. 25 , 883–892 (2023). 10.1089/dia.2023.0264 C. Piona, M. Marigliano, C. Roncarà et al., Glycemia Risk Index as a Novel Metric to Evaluate the Safety of Glycemic Control in Children and Adolescents with Type 1 Diabetes: An Observational, Multicenter, Real-Life Cohort Study. Diabetes Technol. Ther. 25 , 507–512 (2023). 10.1089/dia.2023.0040 M.H. Lee, S. Vogrin, T.W. Jones et al., Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index. J. Diabetes Sci. Technol. 18 (4), 764–770 (2024). 10.1177/19322968241231307 L.E. Donaldson, S. Fourlanos, S. Vogrin et al., Automated insulin delivery among adults with type 1 diabetes for up to 2 years: a real-world, multicentre study. Intern. Med. J. 54 (1), 121–128 (2024). 10.1111/imj.16143 S. Picard, B. Courbebaisse, J. Dupont et al., Glycemia Risk Index (GRI) and international glucose targets before and 6 months after initiation of hybrid closed loop system in the CIRDIA, a French multisite out-of-hospital center. Diabetes Metab. 51 (2), 101617 (2025). 10.1016/j.diabet.2025.101617 S.L. Cichosz, Predicting High Glycemia Risk Index Trajectory in Individuals With Type 1 Diabetes and Long-term Continuously Glucose Monitoring. J. Diabetes Sci. Technol. 2025 :19322968251334365. 10.1177/19322968251334365 K. Panfil, J.M. Redel, C.A. Vandervelden et al., Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes. J. Diabetes Sci. Technol. 18 (4), 771–778 (2024). 10.1177/19322968241247219 S. Templer, Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions. Front. Endocrinol. (Lausanne). 13 , 919942 (2022). 10.3389/fendo.2022.919942 Z. Fang, M. Liu, J. Tao et al., Efficacy and safety of closed-loop insulin delivery versus sensor-augmented pump in the treatment of adults with type 1 diabetes: a systematic review and meta-analysis of randomized-controlled trials. J. Endocrinol. Invest. 45 (3), 471–481 (2022). 10.1007/s40618-021-01674-6 A. Janez, T. Battelino, T. Klupa et al., Hybrid Closed-Loop Systems for the Treatment of Type 1 Diabetes: A Collaborative, Expert Group Position Statement for Clinical Use in Central and Eastern Europe. Diabetes Ther. 12 (12), 3107–3135 (2021). 10.1007/s13300-021-01160-5 G. Grunberger, J. Sherr, M. Allende et al., American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus. Endocr. Pract. 27 (6), 505–537 (2021). 10.1016/j.eprac.2021.04.008 P.Y. Benhamou, A. Adenis, Y. Tourki et al., Efficacy of a Hybrid Closed-Loop Solution in Patients With Excessive Time in Hypoglycaemia: A Post Hoc Analysis of Trials With DBLG1 System. J. Diabetes Sci. Technol. 18 (2), 372–379 (2024). 10.1177/19322968221128565 F. Lombardo, S. Passanisi, A. Alibrandi et al., MiniMed 780G Six-Month Use in Children and Adolescents with Type 1 Diabetes: Clinical Targets and Predictors of Optimal Glucose Control. Diabetes Technol. Ther. 25 (6), 404–413 (2023). 10.1089/dia.2022.0491 M.H. Lee, J. Gooley, V. Obeyesekere et al., Hybrid Closed Loop in Adults With Type 1 Diabetes and Severely Impaired Hypoglycemia Awareness. J. Diabetes Sci. Technol. 19 (6), 1578–1590 (2025). 10.1177/19322968241245627 E. Resmini, E. Zarra, S. Dotti et al., Impact on Glycemia Risk Index and other metrics in type 1 adult patients switching to Advanced Hybrid Closed-Loop systems: a one-year real-life experience. Eur. J. Med. Res. 29 (1), 365 (2024). 10.1186/s40001-024-01946-w Z. Zhang, Y. Wang, J. Lu et al., Time in tight range: A key metric for optimal glucose control in the era of advanced diabetes technologies and therapeutics. Diabetes Obes. Metab. 27 (2), 450–456 (2025). 10.1111/dom.16033 B. Bombaci, S. Passanisi, M. Valenzise et al., Real-World Performance of First- Versus Second-Generation Automated Insulin Delivery Systems on a Pediatric Population With Type 1 Diabetes: A One-Year Observational Study. J. Diabetes Sci. Technol. 19 (1), 98–104 (2025). 10.1177/19322968231185115 L. Nigi, G. Iraci Sareri, D. Cataldo et al., 12-Month Efficacy of Advanced Hybrid Closed-Loop System in Adult Type 1 Diabetes Patients. Diabetes Technol. Ther. 26 (2), 130–135 (2024). 10.1089/dia.2023.0319 L. Nigi, M.L.A. Simon Batzibal, D. Cataldo et al., 12-Month Time in Tight Range Improvement with Advanced Hybrid-Closed Loop System in Adults with Type 1 Diabetes. Diabetes Ther. 15 (12), 2557–2568 (2024). 10.1007/s13300-024-01656-w S. Passanisi, G. Salzano, B. Bombaci et al., Sustained Effectiveness of an Advanced Hybrid Closed-Loop System in a Cohort of Children and Adolescents With Type 1 Diabetes: A 1-Year Real-World Study. Diabetes Care. 47 (6), 1084–1091 (2024). 10.2337/dc23-2311 J. Castañeda, A. Arrieta, van den T. Heuvel et al., Time in Tight Glucose Range in Type 1 Diabetes: Predictive Factors and Achievable Targets in Real-World Users of the MiniMed™ 780G System. Diabetes Care. 47 (5), 790–797 (2024). 10.2337/dc23-1581 S. Passanisi, C. Piona, G. Salzano et al., Aiming for the best glycemic control beyond time in range: time in tight range as a new CGM metric in children and adolescents with type 1 diabetes using different treatment modalities. Diabetes Technol. Ther. 26 (3), 161–166 (2024). 10.1089/dia.2023.0373 J. Petersson, K. Åkesson, F. Sundberg et al., Translating glycated hemoglobin A1c into time spent in glucose target range: A multicenter study. Pediatr. Diabetes. 20 (3), 339–344 (2019). 10.1111/pedi.12817 E. Eviz, G. Yesiltepe Mutlu, K.E. Karakus et al., The Advanced Hybrid Closed Loop Improves Glycemia Risk Index, Continuous Glucose Monitoring Index, and Time in Range in Children with Type 1 Diabetes: Real-World Data from a Single Center Study. Diabetes Technol. Ther. 25 (10), 689–696 (2023). 10.1089/dia.2023.0112 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 19 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8918313","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596886435,"identity":"13fa2349-2982-4824-b519-75b327061cd5","order_by":0,"name":"Laura Nigi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACPijNw8DMfICBwQDEIKCFDaGFLQGqhYAeNgSTxwDKIKSF/YzZhx8Md2Tk3Xm+SRcU1MkwsPMfwK+FJ8d4Zg/DMx7Dw7zbpGcYHCbGYTnGQPcf5jFsBmrhMThAhBb+N8aMf8BaeJ4BtdQRoUUix5gZZIs8Mw8bUAszMVqeFTPLAL1gwMxmbM0DZLAxMxvg1cLPn7yZ8U3FYXv5/sMPb/P8qbPn5z/4AL81YAA01uAAzF4i1EOAfAPRSkfBKBgFo2CkAQC0MiyXdlG+rwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Siena","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Nigi","suffix":""},{"id":596886436,"identity":"89f173d2-b4eb-49e1-9322-fe22c2326ba6","order_by":1,"name":"Leonardo Distefano","email":"","orcid":"","institution":"University of Siena","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"Distefano","suffix":""},{"id":596886437,"identity":"75798c56-e5e5-41c0-9617-5a46b0d72fe0","order_by":2,"name":"Giuseppina EmanuelaGrieco","email":"","orcid":"","institution":"University of Siena","correspondingAuthor":false,"prefix":"","firstName":"Giuseppina","middleName":"","lastName":"EmanuelaGrieco","suffix":""},{"id":596886438,"identity":"6f6576f0-38ef-49ff-95a1-492a9f6bae83","order_by":3,"name":"Dorica Cataldo","email":"","orcid":"","institution":"Azienda Ospedaliera Universitaria Senese","correspondingAuthor":false,"prefix":"","firstName":"Dorica","middleName":"","lastName":"Cataldo","suffix":""},{"id":596886439,"identity":"f1aa0b0b-f4fc-4a18-9da2-624ba984ef15","order_by":4,"name":"Francesco Dotta","email":"","orcid":"","institution":"University of Siena","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Dotta","suffix":""}],"badges":[],"createdAt":"2026-02-19 15:24:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8918313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8918313/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103538822,"identity":"2b50d0cf-1b18-43b7-beb8-0fde33233394","added_by":"auto","created_at":"2026-02-26 19:19:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19924,"visible":true,"origin":"","legend":"\u003cp\u003eResults at different time points with MiniMed™ 780G (T0: baseline; 14d: first 14 days auto-mode; 3mo: 14 days after 3 months auto-mode; 6mo: 14 days after 6 months auto-mode; 12mo: 14 days after 12 months auto-mode; 24mo: 14 days after 24 months auto-mode. a) HbA1c% T0, 14d, 3mo, 6mo, 12mo, 24mo; b) TIR (Time in Range)% 14d, 3mo, 6mo, 12mo, 24mo; c) Time in Tight Range (TITR)% 14d, 3mo, 6mo, 12mo, 24mo; d) TAR (Time above Range)% 14d, 3mo, 6mo, 12mo, 24mo; e) TBR (Time below Range)% 14d, 3mo, 6mo, 12mo, 24mo; f) CV (Coefficient of Variation)% 14d, 3mo, 6mo, 12mo, 24mo; g) GMI (Glucose Management Indicator)% 14d, 3mo, 6mo, 12mo, 24mo; h) mean sensor glucose (GM) mg/dL 14d, 3mo, 6mo, 12mo, 24mo. *p\u0026lt; 0.05; **p\u0026lt; 0.01; ***p\u0026lt; 0.005; ****p\u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8918313/v1/f1a6589fc758f9608c1acae4.png"},{"id":103538823,"identity":"b51db9e9-d562-48e6-a90e-f6fb2661440c","added_by":"auto","created_at":"2026-02-26 19:19:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14060,"visible":true,"origin":"","legend":"\u003cp\u003ea) GRI results at different time points with MiniMed™ 780G (left panel) and GRI zones at different time points with MiniMed™ 780G (right panel). M: first 14 days manual-mode; 14d: first 14 days auto-mode; 3mo: 14 days after 3 months auto-mode; 6mo: 14 days after 6 months auto-mode; 12mo: 14 days after 12 months auto-mode; 24mo: 14 days after 24 months auto-mode. b) GRI results at different time points with Tandem t:slim X2 IQ technology (left panel) and GRI zones at different time points with Tandem t:slim X2 IQ technology (right panel). 14d: first 14 days auto-mode; 3mo: 14 days after 3 months auto-mode; 6mo: 14 days after 6 months auto-mode; 12mo: 14 days after 12 months auto-mode; 24mo: 14 days after 24 months auto-mode.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8918313/v1/c21bda112bcf738d6978288b.png"},{"id":103538825,"identity":"77fd415f-b222-4c11-bcb8-d99bab9e4b29","added_by":"auto","created_at":"2026-02-26 19:19:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20557,"visible":true,"origin":"","legend":"\u003cp\u003eResults at different time points with Tandem t:slim X2 IQ technology (T0: baseline; 14d: first 14 days auto-mode; 3mo: 14 days after 3 months auto-mode; 6mo: 14 days after 6 months auto-mode; 12mo: 14 days after 12 months auto-mode; 24mo: 14 days after 24 months auto-mode. a) HbA1c% T0, 14d, 3mo, 6mo, 12mo, 24mo; b) TIR (Time in Range)% 14d, 3mo, 6mo, 12mo, 24mo; c) Time in Tight Range (TITR)% 14d, 3mo, 6mo, 12mo, 24mo; d) TAR (Time above Range)% 14d, 3mo, 6mo, 12mo, 24mo; e) TBR (Time below Range)% 14d, 3mo, 6mo, 12mo, 24mo; f) CV (Coefficient of Variation)% 14d, 3mo, 6mo, 12mo, 24mo; g) GMI (Glucose Management Indicator)% 14d, 3mo, 6mo, 12mo, 24mo; h) mean sensor glucose (GM) mg/dL 14d, 3mo, 6mo, 12mo, 24mo. *p\u0026lt; 0.05; **p\u0026lt; 0.01; ***p\u0026lt; 0.005; ****p\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8918313/v1/eb86d973a03c339691aaa44e.png"},{"id":103538826,"identity":"9ca493ce-b066-4e05-823d-a5eee6266791","added_by":"auto","created_at":"2026-02-26 19:19:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36463,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple linear regression MiniMed™ 780G with GRI as independent variable and other parameters as dependent variables (left panel); correlation between GRI and TITR (right panel). a) GRI% at 14d and other variables at 12mo (14d: first 14 days auto-mode; 12mo: 14 days after 12 months auto-mode); b) GRI% at 14d and other variables at 24mo (14d: first 14 days auto-mode; 24mo: 14 days after 24 months auto-mode); c) GRI% at M and other variables at 12mo (M: first 14 days manual-mode; 12mo: 14 days after 12 months auto-mode); d) GRI% at M and other variables at 24mo (M: first 14 days manual-mode; 24mo: 14 days after 24 months auto-mode). * Statistically significant.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8918313/v1/5788b97cfe531c6b57d96bf3.png"},{"id":104398489,"identity":"dac7d7fc-b172-43df-9c64-1c6c9f540aee","added_by":"auto","created_at":"2026-03-11 12:02:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":684221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8918313/v1/27bd597b-12a3-4ea6-961a-52e5df22a0f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Twenty-Four-Month Evaluation of the Glycemia Risk Index in Adults With Type 1 Diabetes Using Advanced Hybrid Closed-Loop systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAchieving and maintaining optimal glycemic control is essential for reducing the risk of chronic diabetic complications. This goal is particularly challenging in type 1 diabetes (T1D), given the absolute reliance on exogenous insulin therapy and the intrinsic glucose variability that predisposes patients to both hypo- and hyperglycemia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In this clinical context, accurate and timely glycemic monitoring is therefore imperative (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Over the past several decades, substantial technological advances have transformed the management of T1D, with continuous glucose monitoring (CGM) emerged as a critical tool for comprehensive glycemic assessment. Since the 2019 international consensus, CGM-derived metrics have been standardized and are now routinely used as complementary glycemic indicators, alongside glycated hemoglobin (HbA1c)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Recently, new CGM parameters, such as the Time in Tight Range (TITR) and the Glycemia Risk Index (GRI), have been introduced to further refine and enhance the characterization of glycemic control (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). TITR represents the percentage of time spent within the 70\u0026ndash;140 mg/dL range and is emerging as a valuable metric for assessing normoglycemia (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Although an evidence-based therapeutic target for TITR in individuals with T1D has not yet been formally established, some researchers have suggested aiming for values between 45% and 50% as potential clinical goals (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The Glycemia Risk Index (GRI) is a composite CGM-derived metric, designed to provide an overall assessment of glycemic quality and associated risk (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), with \u0026ldquo;quality\u0026rdquo; denoting the relative proportions of time spent in low and very low hypoglycemia, high and very high hyperglycemia (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The GRI is calculated as a weighted sum of the time spent in four glucose ranges: low hypoglycemia (54 to \u0026lt;\u0026thinsp;70 mg/dL), very low hypoglycemia (\u0026lt;\u0026thinsp;54 mg/dL), high hyperglycemia (180\u0026ndash;250 mg/dL), and very high hyperglycemia (\u0026gt;\u0026thinsp;250 mg/dL)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It ranges from 0 to 100, and for improved interpretability, it has been stratified into five categories (zones A through E), each covering a range of 20. Zone A (GRI\u0026thinsp;\u0026le;\u0026thinsp;20) denotes the highest glycemic quality, whereas Zone E (GRI 81\u0026ndash;100) reflects the poorest glycemic quality. The GRI can be calculated easily, using data available in standard CGM reports; a 14-day CGM dataset has been identified as the optimal duration for reliable computation of this metric (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), now recognized as a promising tool for evaluating overall glycemic quality in clinical practice (\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A recent update to the international consensus on CGM metrics proposes a GRI\u0026thinsp;\u0026le;\u0026thinsp;40 as a marker of acceptable glycemic quality in individuals with T1D (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Picard et al. reported that a GRI\u0026thinsp;\u0026ge;\u0026thinsp;26 may identify T1D individuals needing additional clinical support, whereas values\u0026thinsp;\u0026le;\u0026thinsp;20 generally meet established efficacy and safety targets (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Furthermore, emerging evidence suggests that GRI may serve as a prognostic indicator for the development of T1D-associated chronic complications and for reduced quality of life (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). As a matter of fact, in a retrospective study examining the relationship between GRI and longitudinal HbA1c in youth, baseline GRI correlated positively with HbA1c at 3, 6, 9, and 12 months of follow-up (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Because previous research has established HbA1c as a strong prognostic marker for the risk of T1D-associated complications, the observed positive correlations between an individual\u0026rsquo;s GRI and their longitudinal HbA1c measures suggest that the GRI may likewise possess predictive value, analogous to HbA1c, for identifying individuals at increased risk for suboptimal short- and long-term health outcomes. Moreover, in a large-scale study by Cichosz et al., involving 736 T1D individuals over four years, baseline GRI emerged as the strongest predictor of future poor glycemic control (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe integration of CGM with continuous subcutaneous insulin infusion via automated, algorithm-driven devices known as advanced hybrid closed-loop (AHCL) systems (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), is considered the current standard of care in T1D management (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Recent real-world evidence suggests that AHCL systems can substantially improve the GRI (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, to date, a limited number of studies have evaluated the GRI in adult T1D users of AHCL systems over extended observation periods, and no one, to our knowledge, has explored its relationship with the emerging glucose metric Time in Tight Range (TITR), which assess normoglycemia (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Given the importance of thoroughly establishing the clinical utility of GRI (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), this study aimed to investigate this new parameter in T1D adults using AHCL systems in real-world practice over long-term follow-up, and to examine its associations with CGM-derived metrics, particularly TITR, and other clinical parameters.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eA single-center, observational, retrospective, real-world study was conducted involving 65 T1D patients aged 18 years or older, treated with either the MiniMed\u0026trade; 780G system (45 patients) integrated with the Guardian 4 or Simplera CGM sensor, or the Tandem t:slim X2 IQ technology (20 patients) integrated with the Dexcom G6 or G7 CGM sensor. Participants provided written informed consent for the collection and use of their data for research purposes prior to enrollment. The study protocol was approved by the local research ethics committee (Protocol number 24849. Comitato Etico Regionale per la Sperimentazione Clinica della regione Toscana, sezione area vasta sud-est) and conducted in accordance with the Declaration of Helsinki principles. Clinical data, along with CareLink and Glooko reports were collected and analyzed. Baseline parameters including age, diabetes duration, HbA1c, previous insulin therapy, total daily insulin dose (TDI), and body mass index (BMI) were recorded. During the 24-month follow-up period, BMI, TDI, HbA1c, and CGM metrics from the preceding 2-week interval were obtained at 14 days after AHCL activation (14d), at 3 month (3mo), 6 month (6mo), 12 month (12mo), and 24 month (24mo) follow-up visits. For subjects who initially used the MiniMed\u0026trade; 780G in predictive low-glucose suspend (PLGS) mode (n\u0026thinsp;=\u0026thinsp;35), CGM data were collected over the first 14 days of use in that mode (M). The recorded CGM metrics included: time in range (TIR%, 70\u0026ndash;180 mg/dL), time above range (TAR% \u0026gt;180 mg/dL and TAR% \u0026gt;250 mg/dL -TAR250-), time below range (TBR% \u0026lt;70 mg/dL and TBR% \u0026lt;54 mg/dL -TBR54-), time in tight range (TITR%, 70\u0026ndash;140 mg/dL), glucose management indicator (GMI%), coefficient of variation (CV%), mean sensor glucose (GM, mg/dL), glicemia risk index (GRI%). GRI was calculated according to the equation described by Klonoff et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and ranged from 0 to 100, with values classified into five zones (A\u0026ndash;E) of 20-point increments: Zone A (0\u0026ndash;20), Zone B (\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), Zone C (41\u0026ndash;60), Zone D (61\u0026ndash;80), and Zone E (81\u0026ndash;100). Data collected at 3mo, 6mo, 12mo, and 24mo were compared with those obtained at 14d and at M. We examined correlations between GRI and CGM metrics, as well as baseline variables, along with time spent in automatic mode and sensor wear. For the MiniMed\u0026trade; 780G active insulin time (AIT) and glycemic target (GT) were considered. Participants were also stratified according to baseline glycemic control (\u0026ge;\u0026thinsp;7.5% vs. \u0026lt;7.5%) and diabetes duration (\u0026ge;\u0026thinsp;20 years vs. \u0026lt;20 years). CGM metrics, HbA1c, TDI, and BMI were compared between the two AHCL systems. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and categorical variables as percentages. Normality was assessed to guide use of paired t-tests or Wilcoxon signed-rank tests for longitudinal changes. Mann\u0026ndash;Whitney U test was applied for group comparisons. Multiple linear regression with GRI as the independent variable and CGM metrics, HbA1c as dependent variables was constructed. Correlations were further assessed using the Spearman test. Statistical analyses were performed using GraphPad software, version 10.6.1. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics of patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All participants maintained a more than 90% of automated mode usage and CGM wear throughout the follow-up period. In the MiniMed\u0026trade; 780G group, at 14d, subjects demonstrated glycemic metrics already reflecting the recommended target ranges for standardized CGM metrics: TIR 71.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42% (with 57.78% achieving TIR\u0026thinsp;\u0026ge;\u0026thinsp;70%), TAR 26.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84% (53.33% achieving TAR\u0026thinsp;\u0026le;\u0026thinsp;25%), TAR 250 4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58% (60% achieving TAR250\u0026thinsp;\u0026le;\u0026thinsp;5%), TBR 1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55% (95.55% achieving TBR\u0026thinsp;\u0026le;\u0026thinsp;4%), TBR54 0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69% (97.83% achieving TBR54\u0026thinsp;\u0026le;\u0026thinsp;1%), GMI% 7.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38% (48.89% achieving GMI\u0026thinsp;\u0026le;\u0026thinsp;7%), and CV 31.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13% (88.89% obtaining CV\u0026thinsp;\u0026le;\u0026thinsp;36%). All CGM parameters and HbA1c remained stable or further improved over the 24-month follow-up compared with the initial 14 days of SmartGuard use (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At 24mo subjects showed the following glycemic outcomes: TIR 76.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.81% (71.43% maintaining TIR\u0026thinsp;\u0026ge;\u0026thinsp;70%), TAR 22.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.22% (60.71% achieving TAR\u0026thinsp;\u0026le;\u0026thinsp;25%), TAR250 3.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58% (71.43% achieving TAR250\u0026thinsp;\u0026le;\u0026thinsp;5%), TBR 1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03% (100% achieving TBR\u0026thinsp;\u0026le;\u0026thinsp;4%), TBR54 0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39% (100% achieving TBR54\u0026thinsp;\u0026le;\u0026thinsp;1%), GMI 6.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38% (71.43% achieving GMI\u0026thinsp;\u0026le;\u0026thinsp;7%), and CV 30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86% (96.43% achieving CV\u0026thinsp;\u0026le;\u0026thinsp;36%). A significant increase in TIR% (p: 0.037) and a significant reduction in HbA1c% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) as well as in TAR% (p: 0.0455) were observed at 24mo, compared to 14d (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). TITR was 44.04\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29% during the first 14 days (14d) of MiniMed\u0026trade; 780G system use in automatic mode. This percentage remained stable throughout the 24-month follow-up, with statistically significant increases observed at 3mo, 6mo, and 12mo compared with the 14d assessment (p: 0.0107, p: 0.0057, and p: 0.0237, respectively)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The GRI was 28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.22% at 14d and showed a progressive, statistically significant improvement over the follow-up period. At 24mo, the GRI had decreased to 24.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74% (p: 0.0289 vs. 14d GRI%)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At 24mo, approximately 40% of patients were classified in zone A, 54% in zone B, 7% in zone C; no patients in zones D or E (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients in zone A demonstrated the following characteristics: TIR 85.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62%, TITR 58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09%, TAR 12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22%, TBR 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07%, CV 27.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3%, GMI 6.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15%, GM 136.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.32 mg/dL, HbA1c 6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52%. The MiniMed\u0026trade; 780G system was initiated in 35 patients in PLGS mode (also referred to as manual mode, M) during the initial phase of use. When comparing glycemic data from the first 14 days in SmartGuard mode (14d) with the first 14 days in manual mode (M), statistically significant improvements were observed across multiple CGM metrics. Specifically, TITR% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and TIR% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) increased significantly. Moreover, significant reductions were noted in TAR% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TBR% (p: 0.0054), CV% (p: 0.0002), GMI% (p: 0.0003), GM (mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and GRI% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In the Tandem t:slim X2 group, glycemic metrics at 14d reflected, as well, the recommended CGM-target ranges: TIR 68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.59% (55% achieving TIR\u0026thinsp;\u0026ge;\u0026thinsp;70%), TAR 30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.35% (45% achieving TAR\u0026thinsp;\u0026le;\u0026thinsp;25%), TAR250 9.05\u0026thinsp;\u0026plusmn;\u0026thinsp;7.99% (95% achieving TAR25\u0026thinsp;\u0026le;\u0026thinsp;5%), TBR 1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25% (100% achieving TBR\u0026thinsp;\u0026le;\u0026thinsp;4%), TBR54 0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37% (100% achieving TBR54\u0026thinsp;\u0026le;\u0026thinsp;1%), GMI 7.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6% (55% achieving GMI\u0026thinsp;\u0026le;\u0026thinsp;7%), CV 33.09\u0026thinsp;\u0026plusmn;\u0026thinsp;5.61% (65% achieving CV\u0026thinsp;\u0026le;\u0026thinsp;36%), GM mg/dL 161.5\u0026thinsp;\u0026plusmn;\u0026thinsp;24.93. These results remained stable throughout the follow-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At 24mo: TIR 74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.28% (70% of patients TIR\u0026thinsp;\u0026ge;\u0026thinsp;70%), TAR 23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33% (50% of patients TAR\u0026thinsp;\u0026le;\u0026thinsp;25%), TAR250 4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69% (60% of patients TAR25\u0026thinsp;\u0026le;\u0026thinsp;5%), TBR 2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27% (80% of patients TBR\u0026thinsp;\u0026le;\u0026thinsp;4%), TBR54 0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97% (90% of patients TBR54\u0026thinsp;\u0026le;\u0026thinsp;1%), GMI 6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55% (60% of patients GMI\u0026thinsp;\u0026le;\u0026thinsp;7%), CV 30.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16% (90% of patients CV\u0026thinsp;\u0026le;\u0026thinsp;36%), and GM 150.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2 mg/dL. Additionally, HbA1c was 6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05%. TITR was 42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.89% during the first 14 days of system use (14d), and this value remained stable over the 24-month follow-up (46.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.28% at 24mo)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The GRI was 34.57\u0026thinsp;\u0026plusmn;\u0026thinsp;17.21% at 14d and decreased at the end of follow-up (even if not statistically significant), reaching 28.46\u0026thinsp;\u0026plusmn;\u0026thinsp;14.28% at 24mo (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At the end of the observational period, approximately 30% of patients were classified in zone A, 50% in zone B, 20% in zone C; no patients in zones D or E (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients in zone A exhibited the following characteristics: TIR 88.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01%, TITR 58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5%, TAR 11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08%, TBR 0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58%, CV 24.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03%, GMI 6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31%, mean sensor glucose 136.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.34 mg/dL, HbA1c 5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6%. No changes in TDI or BMI were observed at 24mo compared with baseline for both AHCL systems. No differences were observed in CGM metrics or HbA1c values between the two AHCL systems. Analysis of potential correlations between GRI and other variables at time points M, 14d, 12mo, and 24mo, for the MiniMed\u0026trade; 780G system showed the following results. At time M, GRI was positively correlated with HbA1c (p: 0.0064) and with TBR54 (p: 0.041). At 14d, no statistically significant correlations were detected. At 12mo, GRI showed positive correlations with HbA1c (p: 0.0119), GMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and GM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), as well as a negative correlation with TITR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At 24mo, no statistically significant correlations involving GRI were observed. For the other new parameter TITR, the following correlations were observed. At time M, TITR showed negative correlation with HbA1c (p: 0.0017) and positive correlation with TBR (p: 0.001). At 14d, TITR was positively correlated with TBR (p: 0.005) and negatively correlated with TAR250 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At 12mo, positive correlations were observed between TITR and TBR (p: 0.0007) and between TITR and TBR54 (p: 0.046), while negative correlations were found with HbA1c (p: 0.0001) and TAR250 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At 24mo, TITR remained positively correlated with TBR (p: 0.02) and negatively correlated with HbA1c (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and CV (p: 0.05). Analysis of potential correlations between GRI and other variables at 14d, 12mo, and 24mo for the Tandem t:slim X2 system yielded the following results. At 14d, GRI was positively correlated with CV (p: 0.027) and HbA1c (p: 0.0104), and negatively correlated with TBR (p: 0.003). At 12mo, positive correlations were observed between GRI and HbA1c (p: 0.005), CV (p: 0.0008), GMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and GM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Negative correlations were found between GRI and TIR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and between GRI and TITR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At 24mo, GRI showed positive correlations with TAR (p: 0.01) and TAR250 (p: 0.01), and a negative correlation with TIR (p: 0.0005). A negative correlation was also observed between GRI and TITR (p: 0.0306). For TITR, at 14d we observed positive correlations with TBR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and TBR54 (p: 0.02), as well as negative correlations with HbA1c (p: 0.0046) and TAR250 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At 12mo, TITR was positively correlated with TIR (p: 0.0003) and TBR (p: 0.017), and negatively correlated with HbA1c (p: 0.007), TAR250 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and GMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At 24mo, TITR remained positively correlated with TIR (p: 0.001) and negatively correlated with HbA1c (p: 0.004), TAR% (p: 0.0001), TAR250 (p: 0.008), GMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and GM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Analysis of correlations between GRI% at time M and other variables at 12mo and 24mo in the MiniMed\u0026trade; 780G system showed the following results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): GRI M was positively correlated with HbA1c 12mo (p: 0.009), CV 12mo (p: 0.007), GMI 12mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and GM 12mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and negatively correlated with TITR 12mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). GRI M was positively correlated with HbA1c 24mo (p: 0.01), TAR 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TAR250 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), CV 24mo (p: 0.002), GMI 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and GM 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); negative correlations were observed with TIR 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and TITR 24mo (p: 0.0006). Analysis of the correlations between GRI 14d and metabolic variables at 12mo and 24mo in users of the MiniMed\u0026trade; 780G system showed the following results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): positive correlation with HbA1c 12mo (p: 0.004), TAR 12mo (p: 0.0006), TAR250 12mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), CV 12mo (p: 0.0001), GMI 12mo (p: 0.007), GM mg/dl 12mo (p:0.01); negative correlation with TIR 12mo (p: 0.0002), TITR 12mo (p: 0.015). Positive correlation with HbA1c 24mo (p: 0.0007), TAR 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), CV 24mo (p: 0.0001), GMI 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), GM 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); negative correlation with TIR 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and TITR 24mo (p: 0.0002). From the analysis of the correlations between the GRI 14d and the other variables at 12mo and 24mo in users of the Tandem t:slim X2 system, we observed the following: positive correlation with HbA1c 12mo (p: 0.002), TAR 12mo (p: 0.0006), TAR250 12mo (p: 0.0004), CV 12mo (p: 0.02), GMI 12mo (p: 0.001), GM 12mo (p: 0.001); negative correlation with TIR 12mo (p: 0.019), TITR 12mo (p: 0.0009). Positive correlation with TAR% 24mo (p: 0.0001), TAR250 24mo (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), GMI 24mo (p: 0.002), GM 24mo (p: 0.001); negative correlation with TIR 24mo (p: 0.0002) and TITR 24mo (p: 0.0008). No correlations were observed between GRI and either the time spent in automated mode, sensor usage, TDI, AIT, or GT in the MiniMed\u0026trade; 780G cohort. When stratifying participants by baseline glycemic control, patients with HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7.5% at baseline exhibited higher GRI at 24mo in the MiniMed\u0026trade; 780G cohort (p: 0.0058). In the Tandem t:slim X2 cohort, individuals with HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7.5% at baseline also showed higher GRI at 14d (p: 0.0055), 12mo (p: 0.0006), and 24mo (p: 0.033). No significant differences in GRI were detected based on diabetes duration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiniMed\u0026trade; 780G\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTandem t:slim X2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubjects\u003c/b\u003e - M/F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (19/26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (12/8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge -\u003c/b\u003e years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,42\u0026thinsp;\u0026plusmn;\u0026thinsp;13,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43,7\u0026thinsp;\u0026plusmn;\u0026thinsp;15,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes duration -\u003c/b\u003e years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,73\u0026thinsp;\u0026plusmn;\u0026thinsp;14,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,9\u0026thinsp;\u0026plusmn;\u0026thinsp;12,13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c -\u003c/b\u003e %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,8(62 mmol/mol)\u0026thinsp;\u0026plusmn;\u0026thinsp;1,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,7(61 mmol/mol)\u0026thinsp;\u0026plusmn;\u0026thinsp;1,61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e - kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25,21\u0026thinsp;\u0026plusmn;\u0026thinsp;4,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,53\u0026thinsp;\u0026plusmn;\u0026thinsp;4,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean daily insulin dose\u003c/b\u003e - UI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,2\u0026thinsp;\u0026plusmn;\u0026thinsp;21,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44,87\u0026thinsp;\u0026plusmn;\u0026thinsp;12,37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious insulin therapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 MDI, 9 CSII\u0026thinsp;+\u0026thinsp;CGM, 14 PLGS, 5 HCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 MDI, 3 PLGS, 8 HCL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMDI: Multiple Daily Injections; CSII: Continuous Subcutaneous Insulin Infusion; CGM: Continuous Glucose Monitoring; PLGS: Predictive Low Glucose Suspend system; HCL: Hybrid Closed Loop system\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the field of continuous glucose monitoring, new metrics for assessing glycemic control have emerged, including the TITR, the percentage of glucose values between 70 and 140 mg/dL, indicative of euglycemia, and, notably, the GRI, a composite metric that captures the quality of glycemic control by simultaneously accounting for the proportions of very high and very low glucose values. In this retrospective real-world study, including T1D adults using AHCL systems, we observed improvements in GRI values over an extended period of 24 months. At the end of follow-up GRI percentages were approximately 24% with the MiniMed\u0026trade; 780G and 28% with the Tandem t:slim X2. Moreover, users of the MiniMed\u0026trade; 780G experienced a statistically significant reduction in GRI compared with the initial 14 days of SmartGuard mode, consistent with previous findings (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Importantly, these GRI percentages corresponded to a substantial proportion of patients falling within GRI zones A and B. In line with prior findings (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), the reduction in GRI was predominantly attributable to decreased hyperglycemia; to note, hypoglycemia rates were already low at baseline and remained stable throughout follow-up. The study confirmed that AHCL systems achieve and sustain optimal glycemic control in T1D adults over 24 months (independent of prior insulin therapy, diabetes duration, or baseline HbA1c), in line with international guidelines and consistent with previous observations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). TITR percentages at 24 months reached approximately 48% with the MiniMed\u0026trade; 780G and 46% with the Tandem t:slim X2, aligning with the 45\u0026ndash;50% range proposed by several authors (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Moreover, among patients classified in GRI zone A, indicative of excellent glycemic control, TITR reached approximately 59% with the MiniMed\u0026trade; 780G and 58% with the Tandem t:slim X2 at 24 months. We observed positive correlation between GRI and TAR, CV, GMI, and mean sensor glucose levels, alongside a negative correlation with TIR. These findings indicate that GRI is highly informative for identifying subjects with suboptimal glycemic control. In particular, we confirmed previous findings (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) showing that GRI at 14d (and at M for the MiniMed\u0026trade; 780G system) was positively correlated with HbA1c at 12 and 24 months (for the Tandem t:slim X2 IQ technology system, the correlation did not reach statistical significance at 24 months), supporting the potential predictive value of GRI for long-term glycemic outcomes. TITR correlated positively with TIR and strongly inversely with HbA1c and TAR, supporting its value as a complementary metric of glycemic control. Notably, this study is among the few to evaluate both GRI and TITR across two AHCL systems and, to our knowledge, the only one to examine their relationship in depth. A strong inverse correlation was observed between GRI and TITR, highlighting their complementary value in clinical assessment. Specifically, TITR values above 45\u0026ndash;50% combined with GRI values below 20% identified individuals achieving optimal glycemic targets, whereas low TITR, together with GRI exceeding 60% indicated clearly inadequate glycemic control. This relationship highlights that maintaining glucose near the physiological range (70\u0026ndash;140 mg/dL) with AHCL systems supports high-quality glycemic control, marked by few severe hypo- or hyperglycemic episodes and low GRI. Finally, correlation between GRI at M and 14 d and TITR at 12 and at 24 months in MiniMed\u0026trade; 780G users, paralleling those with HbA1c, support GRI\u0026rsquo;s potential predictive value. A study limitation is the relatively small, though well-characterized, cohort, particularly in the Tandem group, reducing statistical power for some outcomes. In conclusion, GRI offers a practical and readily interpretable metric that adds qualitative and potentially predictive insight into glycemic control (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) providing a strong foundation for future research despite the study\u0026rsquo;s limitations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eThis manuscript was funded by the Italian Ministry of University and Research (MUR) PNRR \u0026lsquo;National Center for Gene Therapy and Drugs based on RNA Technology\u0026rsquo; (Project No. CN00000041 CN3 Spoke #5 \u0026lsquo;Inflammatory and Infectious Diseases\u0026rsquo;) and by the Italian Ministry of Health 'Multidisciplinary and Interregional Hub for Research and Clinical Experimentation To Combat Pandemics and Antibiotic Resistance' (project T4-AN-07, PAN-HUB).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the manuscript. Laura Nigi: conceptualization, data curation, formal analysis, writing- original draft. Leonardo Distefano: data curation, writing- original draft. Giuseppina Emanuela Grieco: formal analysis. Dorica Cataldo: data curation. Francesco Dotta: writing- review and editing, supervision. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank all study participants.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study were available on CareLink\u0026trade; System (https://carelinkhp.minimed.eu) and on Glooko System (https://eu.my.glooko.com)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eR.D. Leslie, C. Evans-Molina, J. Freund-Brown et al., Adult-Onset Type 1 Diabetes: Current Understanding and Challenges. 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Karakus et al., The Advanced Hybrid Closed Loop Improves Glycemia Risk Index, Continuous Glucose Monitoring Index, and Time in Range in Children with Type 1 Diabetes: Real-World Data from a Single Center Study. Diabetes Technol. Ther. \u003cb\u003e25\u003c/b\u003e(10), 689\u0026ndash;696 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/dia.2023.0112\u003c/span\u003e\u003cspan address=\"10.1089/dia.2023.0112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Glycemia Risk Index, continuous glucose monitoring, Time in Tight Range, automated insulin delivery, type 1 diabetes","lastPublishedDoi":"10.21203/rs.3.rs-8918313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8918313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe Glycemia Risk Index (GRI) is a recently introduced continuous glucose monitoring (CGM)\u0026ndash;derived metric that has been evaluated to date in a limited number of studies involving type 1 diabetic adult users of Advanced Hybrid Closed-Loop (AHCL) systems.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo further characterize its clinical utility and to investigate its correlations with CGM-derived metrics, particularly Time in Tight Range (TITR), this single-center, observational, retrospective, real-world study assessed GRI in adults with type 1 diabetes using AHCL systems (MiniMed\u0026trade; 780G, n\u0026thinsp;=\u0026thinsp;45; Tandem t:slim X2 IQ technology, n\u0026thinsp;=\u0026thinsp;20) in routine clinical practice, over a 24-month follow-up period.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGRI showed progressive improvement throughout the observation period, consistent with sustained glycemic control. Baseline GRI was positively correlated with glycated hemoglobin and inversely correlated with Time in Tight Range (TITR) at 12 and 24 months.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings suggest that GRI may serve as a useful, readily interpretable metric for predicting long-term glycemic outcomes and support its complementary role alongside TITR in clinical assessment.\u003c/p\u003e","manuscriptTitle":"Twenty-Four-Month Evaluation of the Glycemia Risk Index in Adults With Type 1 Diabetes Using Advanced Hybrid Closed-Loop systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 19:19:33","doi":"10.21203/rs.3.rs-8918313/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-17T15:40:27+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"290262318059566228714527519430311216770","date":"2026-03-16T17:47:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T18:37:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78658360656106603092430246813868402961","date":"2026-02-24T16:34:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T15:55:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T10:05:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T10:04:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Endocrine","date":"2026-02-19T13:13:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7d8e2db3-58af-43f3-96bb-a3173fa2896b","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-17T15:40:27+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T15:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 19:19:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8918313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8918313","identity":"rs-8918313","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}