The HbA1c/GMI Ratio Does Not Independently Predict Diabetic Retinopathy in Adults with Type 1 Diabetes: A Multicenter Cross-Sectional Study

The HbA1c/GMI Ratio Does Not Independently Predict Diabetic Retinopathy in Adults with Type 1 Diabetes: A Multicenter Cross-Sectional Study | 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 Article The HbA1c/GMI Ratio Does Not Independently Predict Diabetic Retinopathy in Adults with Type 1 Diabetes: A Multicenter Cross-Sectional Study Carolina Sager-La Ganga, Jose Alfonso Arranz Martin, Jessica Jiménez-Díaz, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6792531/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The discordance between glycated haemoglobin (HbA1c) and the glucose management indicator (GMI) has been proposed as a marker of vascular risk in diabetes. This study evaluated whether the HbA1c/GMI ratio independently predicts diabetic retinopathy (DR) in adults with type 1 diabetes (T1D) using continuous glucose monitoring. We conducted a multicenter cross-sectional study involving 1,070 adults using flash glucose monitoring. Participants were stratified as high glycators (ratio > 0.9) or non-high glycators based on the HbA1c/GMI ratio. DR status was assessed by ophthalmologic evaluation. Multivariable logistic regression and 1:1 propensity score matching were used to assess independent associations with DR, adjusting for age, sex, diabetes duration, smoking, hypertension, LDL cholesterol, BMI, and insulin dose. While high glycators had a higher crude DR prevalence (31.3% vs. 23.1%, p = 0.020), the HbA1c/GMI ratio was not independently associated with DR in adjusted models (OR 1.19; 95% CI: 0.34–4.15; p = 0.785) or in the matched cohort (OR 1.23; 95% CI: 0.76–1.99; p = 0.391). Absolute HbA1c remained the strongest glycemic predictor. These findings suggest that the HbA1c/GMI ratio may aid in interpreting discordant glycemic profiles but lacks independent prognostic value for DR. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Diabetes complications Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes mellitus type 1 diabetes HbA1c glucose management indicator diabetic retinopathy glycation index continuous glucose monitoring Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Glycated haemoglobin (HbA1c) remains the primary biomarker for evaluating long-term glycaemic control and predicting vascular complication risk in individuals with diabetes mellitus (DM) 1 – 3 . Its role is firmly established both as a diagnostic criterion and a therapeutic target across international guidelines 4 , 5 . However, HbA1c reflects only a weighted average of glycemia over the previous 2–3 months and does not capture important aspects, such as glycemic variability, which has also been linked to increased cardiovascular risk and mortality 6 , 7 . To overcome these limitations, additional glycemic markers have been proposed to better characterize individual metabolic risk. One such marker is the hemoglobin glycation index (HGI), which is defined as the difference between measured HbA1c and HbA1c estimated from mean glucose via regression models. Elevated HGI levels have been associated with all-cause mortality and a higher incidence of cardiovascular events in people with diabetes 8 – 11 . The introduction of continuous glucose monitoring (CGM) has revolutionized the approach to glycemic control, allowing for dynamic analysis of daily glucose profiles. The glucose management indicator (GMI), derived from CGM data, estimates an HbA1c-equivalent value on the basis of average interstitial glucose levels 12 . However, a consistent discrepancy between measured HbA1c and estimated GMI has been reported in several studies 13 – 16 . Individuals with persistently higher HbA1c values than those predicted by the GMI are referred to as "high glycators." This finding is hypothesized to reflect interindividual differences in the glycation rate, erythrocyte lifespan, oxidative stress, or genetic factors 17 . In line with studies linking elevated HGI with macrovascular outcomes, some authors have proposed that an HbA1c-to-GMI ratio > 0.9% may represent a novel biomarker of vascular risk in persons with diabetes 18 , 19 . However, the prognostic relevance of this discrepancy in relation to microvascular complications—particularly diabetic retinopathy (DR)—has not been fully elucidated. This study aimed to assess whether the discrepancy between venous HbA1c and the GMI, expressed as the HbA1c/GMI ratio, is independently associated with the presence of diabetic retinopathy in individuals with type 1 diabetes via CGM. RESULTS Baseline characteristics and HbA1c/GMI ratio distribution A total of 1,070 individuals with type 1 diabetes (T1D) were included in the analysis. The mean age of the cohort was 47.6 ± 15.0 years, with 49.4% being women. The median diabetes duration was 21.4 years (IQR: 12–31), and the mean HbA1c was 7.4 ± 1.1%. Overall, 24.8% (n = 266) of the participants exhibited any degree of diabetic retinopathy (DR) on the basis of ophthalmologic assessment following the ETDRS criteria. The demographic characteristics are described in Table 1. The mean HbA1c/GMI ratio was 0.57 ± 0.42%. A total of 176 participants (16.4%) were classified as high glycators. Table 1 Clinical and metabolic characteristics of the cohort stratified by glycation phenotype. Distribution of sociodemographic, clinical, and glycemic control variables between high glycators (glycation index > 0.9%) and non-high glycators, prior to propensity score matching. Variable Obs n = 1070 High Glycators n = 179 Non-High Glycators n = 872 p-value Age (years) 47.6 ± 15.0 50.91 ± 15.62 47.08 ± 14.83 0.002 Sex women 528 (49.4%) 430 (49.3%) 93 (52.0%) 0.519 Duration of Diabetes (years) 21.43 ± 13.23 22.16 ± 13.01 21.28 ± 13.28 0.418 Diabetic retinopathy (DR) 257 (24.0%) 56 (31.3%) 201 (23.1%) 0.020 Mild 110 (10.3%) 23 (12.8%) 87 (9.9%) Moderate 49 (4.5%) 11 (6.1%) 38 (4.3%) Severe 12 (1.1%) 1 (0.5%) 11 (1.2%) Proliferative/macular edema 86 (8.0%) 21 (11.7%) 65 (7.4%) HbA1c (%) 7.37 ± 1.08 8.4 ± 0.9 7.13 ± 0.88 < 0.001 GMI 7.19 ± 0.80 7.05 ± 0.75 7.22 ± 0.81 0.010 BMI (kg/m²) 25.89 ± 5.63 26.0 ± 4.65 25.90 ± 5.85 0.833 Hypertension (%) 236 (22.1%) 37 (20.7%) 198 (22.7%) 0.552 Ischemic Heart Disease 41 (3.8%) 32 (3.7%) 8 (4.5%) 0.611 Stroke 20 (1.9%) 16 (1.8%) 4 (2.2%) 0.721 TIR 61.3 ± 17.60 63.8 ± 17.0 61.1 ± 17.3 0.058 TBR 4.62 ± 4.89 5.60 ± 5.28 4.4 ± 4.7 0.002 TAR > 180 mg/dL 34.1 ± 18.7 30.6 ± 17.9 34.5 ± 18.4 0.009 TAR > 250 mg/dL 11.4 ± 13.2 10.0 ± 11.4 11.3 ± 12.7 0.190 Coefficient of Variation 36.6 ± 7.1 36.4 ± 6.9 37.6 ± 7.4 0.036 [INSERT HERE Table 1] A modest but statistically significant difference in the HbA1c/GMI ratio was observed between individuals with and without DR (0.62 ± 0.44 vs. 0.55 ± 0.41, p = 0.006) ( Fig. 1 ). According to the crude analysis, the DR incidence was greater among high glycators than among nonhigh glycators (31.3% vs. 23.1%, p = 0.020). [INSERT HERE FIGURE 1] To assess the impact of the HbA1c/GMI ratio on diabetic retinopathy (DR) in our cohort and explore its relationship with the commonly used cut-off of < 0.9, we constructed a LOESS curve that revealed a nonlinear association between the HbA1c/GMI ratio and the presence of any form of DR (Fig. 2. ) . The curve revealed a greater prevalence of DR (approximately 30%) among participants with HbA1c/GMI values between 0.7% and 0.9, followed by a gradual decline to below 20% as the ratio approached 1. These findings suggest that the 0.9 threshold may be applicable to our sample. [INSERT HERE FIGURE 2] Multivariate analysis To explore the independent association between the HbA1c/GMI ratio and diabetic retinopathy (DR), a multivariable logistic regression model was constructed adjusting for age (categorized in quartiles), sex, diabetes duration, smoking status (Habitfum), hypertension, and LDL cholesterol ( Table 2 ) . An interaction term between HbA1c/GMI and age quartiles was included. Table 2 Multivariable logistic regression model for the presence of diabetic retinopathy. The HbA1c/GMI ratio > 0.9% was not significantly associated with retinopathy after adjustment. Significant predictors included female sex, higher HbA1c levels, hypertension, and longer diabetes duration. Variable OR (95% CI) p-value Glycation index (> 0.9) 1.18 (0.34–4.14) 0.785 Sex (women) 1.63 (1.17–2.28) 0.004 Age (years) 35–47 1.13 (0.65–1.97) 0.647 47–58 0.823 (0.45–1.47) 0.516 > 58 1.02 (0.54–0.93) 0.927 HbA1c (%) 1.35 (1.12–1.63) 0.002 Hypertension 1.57 (1.06–2.33) 0.023 Diabetes duration (years) 1.09 (1.07–1.10) < 0.001 LDL ( mg/dL) 0.99 (0.99–1.00) 0.318 [INSERT HERE Table 2] According to the adjusted model, the HbA1c/GMI ratio was not significantly associated with the presence of DR (OR 1.19; 95% CI: 0.34–4.15; p = 0.785). The age categories also showed no significant associations with DR: 35–47 years (OR 1.14; 95% CI: 0.66–1.97; p = 0.647), 47–58 years (OR 0.82; 95% CI: 0.46–1.48; p = 0.516), and > 58 years (OR 1.03; 95% CI: 0.55–1.93; p = 0.927), with ≤ 35 years used as the reference group. Male sex was significantly associated with increased odds of DR (OR 1.64; 95% CI: 1.18–2.29; p = 0.004), as was smoking (OR 2.22; 95% CI: 1.50–3.29; p < 0.001). Diabetes duration and hypertension were also significantly associated (OR 1.09; 95% CI: 1.07–1.11; p = 0.118) and (OR 1.58; 95% CI: 1.07–2.34; p = 0.021), respectively. LDL cholesterol was also significantly associated with LDL (OR 0.997; 95% CI: 0.994–1.000; p = 0.318). The interaction term between HbA1c/GMI and age quartile was not statistically significant (OR 1.07; 95% CI: 0.71–1.61; p = 0.736), indicating that there was no clear age-modifying effect on the relationship between the glycation phenotype and DR. These findings suggest that the initially observed association may be driven by confounding factors—particularly HbA1c itself ( Fig. 3B ). Although both HbA1c and GMI were associated with diabetic retinopathy when included together in a multivariable model, when the HbA1c/GMI ratio (< 0.9) was analysed alongside HbA1c (< 7.0%), only HbA1c showed a statistically significant association, further supporting the idea that an elevated HbA1c/GMI ratio reflects the risk conferred by higher HbA1c rather than by itself ( Fig. 3 ). These findings suggest that the initially observed association between the HbA1c/GMI ratio and diabetic retinopathy (DR) may be confounded—particularly by HbA1c itself ( Fig. 3B ). In a multivariable model including both the continuous GMI value and the binary glycation ratio (< 0.9), both variables were significantly associated with DR: the glycation ratio (β coefficient 0.47; 95% CI: 0.12–0.83; p = 0.009) and GMI (%) (β coefficient 0.30; 95% CI: 0.13–0.47; p = 0.009), suggesting a potential additive effect (Fig. 3A). However, in a sensitivity analysis where the binary glycation ratio (< 0.9) was modelled together with a dichotomized HbA1c value (< 7%), only HbA1c remained significantly associated with DR (OR 0.72; 95% CI: 0.52–0.99; p = 0.042), whereas the glycation ratio was not (OR 1.34; 95% CI: 0.93–1.95; p = 0.117) ( Fig. 3B ). [INSERT HERE FIGURES 3a AND 3b] Propensity score matching analysis To further evaluate the independent contribution of the glycation phenotype, we performed a propensity score matching analysis (Table 3 ) . High and nonhigh glycators were matched 1:1 (n = 163 per group) on age, sex, diabetes duration, smoking, hypertension, LDL, BMI, and insulin dose. A total of 13 high-grade glycators were unmatched and excluded from the analysis. Table 3 Baseline variables between non-high glycators and high glycators. Variable Non-rapid glycators n = 163 Rapid glycators n = 163 P value Age (years) 51.66 ± 15.08 51.08 ± 15.12 0.728 Sex (male) 84 (51.5%) 81 (49.7%) 0.740 Diabetes duration (years) 21.87 ± 13.72 22.30 ± 12.72 0.768 Smoker 42 (25.8%) 43 (26.4%) HbA1c (%) 7.16 ± 0.83 8.42 ± 1.20 180 (TAR, %) 34.8 ± 18.6 30.47 ± 17.5 0.035 Time below range 250 mg/dL (%) 11.3 ± 13.3 9.8 ± 11.3 0.117 Coefficient variation (%) 35.6 ± 6.3 37.6 ± 7.1 0.009 Deprivation index (IP) -0.66 ± 0.86 -0.67 ± 0.83 0.885 [INSERT HERE Table 3] Within the matched cohort, the crude DR incidence was 30.7% in the high-glycator group and 26.4% in the nonhigh-glycator group (p = 0.391). Conditional logistic regression revealed no significant association between glycation phenotype and DR (OR = 1.23; 95% CI: 0.763–1.999; p = 0.391). These results indicate that once confounders are controlled for, the HbA1c/GMI ratio does not independently predict the presence of diabetic retinopathy. Notably, despite matching for clinical variables, there was a marked difference in the mean HbA1c between high and nonhigh glycators (8.4% vs. 7.2%, respectively), reinforcing the idea that the HbA1c/GMI ratio captures excess risk attributable to chronic hyperglycemia. DISCUSSION The primary aim of this study was to determine whether the HbA1c/GMI ratio—defined as the difference between venous HbA1c and the glucose management indicator derived from continuous glucose monitoring—is independently associated with the presence of diabetic retinopathy in individuals with type 1 diabetes. In this multicenter cohort of T1D patients treated with multiple daily insulin injections and regular CGM, we initially observed that an HbA1c/GMI ratio > 0.9%, reflecting a high glycation phenotype 18 , 19 , was associated with a higher prevalence of DR in the crude analysis. However, this association did not persist after multivariable adjustment and propensity score matching, suggesting that the HbA1c/GMI ratio does not independently predict diabetic retinopathy. Instead, absolute HbA1c was the only glycemic parameter consistently associated with DR across all models, indicating that the increased risk observed in high-glucose individuals may be attributable to higher HbA1c levels rather than to the discrepancy itself. Indeed, when comparing propensity score-matched groups with similar clinical profiles—including age, diabetes duration, hypertension, and lipid levels—no differences in DR incidence were found, despite significantly higher HbA1c in high-level glycators (8.4% vs. 7.2%). These findings suggest that the glycation index is a better interpretative marker than a prognostic marker. These findings are consistent with those of previous studies, including Shah et al. (2024) 20 , who reported no associations between the GMI/HbA1c ratio and microvascular outcomes in patients with type 2 diabetes and end-stage renal disease. [20] The clinical relevance of the high glycation phenotype—as evidenced by a persistent discrepancy between HbA1c and GMI—remains a topic of debate. Although this pattern has been linked to increased mortality and cardiovascular risk, particularly in individuals with type 2 diabetes 8 – 11 , 21 , our findings indicate that T1D does not independently predict DR when key clinical confounders are adjusted for. From a pathophysiological perspective, it has been hypothesized that high glycators may be more susceptible to microvascular damage due to accelerated nonenzymatic glycation, potentially influenced by inflammation, oxidative stress, endothelial dysfunction, or genetic predisposition 22 – 25 . However, our data do not support a direct effect of this discrepancy on DR. We also found no significant difference in DR incidence between high and nonhigh glycators after propensity matching, reinforcing the notion that microvascular risk is more closely related to total glycemic burden (i.e., absolute HbA1c) than to glycation efficiency per se. In addition to HbA1c, several well-established predictors of DR—including diabetes duration, hypertension, LDL levels, smoking, and age 26 – 28 —were confirmed in our multivariable analysis. The glycation index did not add prognostic value when these factors were accounted for, suggesting that the HbA1c/GMI ratio should not be considered an independent predictor of DR. From a clinical standpoint, these findings carry relevant implications. The HbA1c/GMI ratio may serve as a useful interpretive tool for contextualizing discordant HbA1c values when CGM data suggest adequate glycemic control, helping to avoid therapeutic overtreatment on the basis solely of elevated HbA1c levels in the absence of objective hyperglycemia 17 . However, its use as a microvascular risk stratification tool is not supported at present. This study has several strengths, including its large sample size, multicenter design, high-resolution CGM data, and the application of robust statistical techniques such as multivariable adjustment and propensity score matching. However, several limitations should be acknowledged. First, the cross-sectional design limits the ability to draw causal inferences. Second, the study did not assess diabetic retinopathy progression or long-term cardiovascular outcomes. Third, serum fructosamine was not measured, which could have provided an additional short-term glycemic marker independent of red blood cell turnover. Finally, important lifestyle factors such as dietary patterns and physical activity—which are known to influence glycemic control and diabetes-related complications—were not captured in this dataset. MATERIALS AND METHODS Study Design and Population A cross-sectional, observational, multicenter study was conducted in a cohort of 1,070 individuals with type 1 diabetes mellitus (T1D) who were recruited between December 2022 and January 2023 across three Spanish hospitals located in different geographic regions. This cohort has been previously described in a study published in Acta Diabetologica (Sebastian-Valles et al., 2025)​ 29 . For the present analysis, new statistical models were applied to specifically investigate the association between the HbA1c/GMI ratio and diabetic retinopathy (DR). All participants were regular users of intermittent continuous glucose monitoring (FGM) with the FreeStyle Libre 2® device (Abbott Laboratories), with a minimum usage duration of 3 months and sensor data coverage of ≥ 70% during the 14 days prior to database closure. Glucose metrics were acquired via cloud downloads from the Libreview platform over a 14-day period before December 1, 2022. Furthermore, the HbA1c value determined at the closest time (± 1 month) to that of the glucose metrics downloaded from the FGM platform was also obtained. Eligible participants were adults (aged ≥ 18 years) with a diagnosis of T1D for a minimum of six months who received intensive insulin therapy via either multiple daily injections (MDIs) or open-loop continuous subcutaneous insulin infusion (CSII). Individuals were excluded if they had any other form of diabetes (such as type 2 diabetes, maturity-onset diabetes of the young [MODY], or secondary diabetes), were hospitalized during the study period, or were using hybrid closed-loop systems, as these devices rely on different algorithms and CGM technologies that may introduce analytical bias. Additional exclusion criteria included insufficient CGM sensor usage (< 70% data capture per international recommendations) 30 ), absence of a complete sensor data download on the day prior to inclusion, less than three months of active use of the flash glucose monitoring system, and missing key clinical variables (such as HbA1c or metabolic parameters) within the three months prior to enrollment. This study followed the “Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)” guidelines 31 . The Research Ethics Committee of La Princesa University Hospital approved the study (Study number: 5084-01/2023). The research was conducted according to the Declaration of Helsinki. Variables and definitions of the HbA1c/GMI ratio Venous HbA1c levels were measured via high-performance liquid chromatography (ADAMS A1c HA8180 V®, Arkray®). The glucose management indicator (GMI) was automatically calculated from sensor data via the standard algorithms provided by the LibreView® platform. The HbA1c/GMI ratio was computed as the division between venous HbA1c and the corresponding GMI value during the same monitoring period. Participants were classified as high glycators if their ratio exceeded 0.9%, as in previous studies ( 18 , 19 ). Primary outcome and covariates The primary outcome of the study was the presence of diabetic retinopathy (DR), which was defined according to the criteria established in the Early Treatment Diabetic Retinopathy Study (ETDRS) Report No. 10 32 , and was ascertained through electronic medical records reviewed by ophthalmologists. For analytical purposes, clinical presentations were categorized as follows: no retinopathy and any degree of retinopathy (including mild, moderate, severe, or proliferative stages/diabetic macular edema). In addition, the following demographic, clinical, and biochemical variables were collected on the basis of census data from the Spanish National Statistics Institute (INE): age, sex, diabetes duration, active smoking status, presence of arterial hypertension, body mass index (BMI), daily insulin dose adjusted for body weight, low-density lipoprotein (LDL) cholesterol, and average annual income per person (as a proxy for individual socioeconomic status). Statistical analysis Continuous variables are presented as the means ± standard deviations (SDs) or medians with interquartile ranges (p25–p75), depending on their distribution, as assessed via Kolmogorov–Smirnov tests and visual inspection (Q–Q plots). Categorical variables are summarized as frequencies and percentages. Comparisons between groups were performed via Student’s t test or the Mann–Whitney U test for continuous variables and via the chi-square test or Fisher’s exact test for categorical variables, as appropriate. The primary analysis aimed to evaluate the association between the HbA1c/GMI ratio and the presence of DR, as well as the relevance of the < 0.9 threshold previously used in other studies to classify participants as “high glycators” ( 18 , 19 ). The crude prevalence of DR across glycation strata was initially compared. A locally estimated scatterplot smoothing (LOESS) curve was used as a sensitivity analysis to visually assess the relationship between the HbA1c/GMI ratio and the presence of any type of DR in our cohort, in comparison with the previously validated cut-off of < 0.9. A multivariable logistic regression model was subsequently fitted to examine the independent association between the HbA1c/GMI ratio and DR. This model was adjusted for clinically relevant confounders previously associated with DR in this cohort (age, sex, diabetes duration, smoking status, hypertension, LDL cholesterol, BMI, and insulin dose). Sensor-derived variables and HbA1c were intentionally excluded to avoid collinearity, as both contribute to the ratio itself. An interaction term between age and the HbA1c/GMI ratio was also introduced, following prior recommendations (Puig-Jové et al., Cardiovasc Diabetol. 2025) 19 )​. To further strengthen causal inference and control for confounding factors, a propensity score matching analysis was performed. Propensity scores were estimated via logistic regression incorporating the aforementioned covariates. A 1:1 nearest-neighbor matching without replacement was applied. This resulted in a matched cohort of 163 high glycators and 163 nonhigh glycators after 13 unmatched cases were excluded. Conditional logistic regression was then conducted within the matched sample to assess the association between the high glycation phenotype and DR. All analyses were performed via R (v4.0.3) and Stata SE (v17.0). A two-tailed p value < 0.05 was considered statistically significant 33 . CONCLUSION In conclusion, a rapid glycation phenotype, reflected by a greater discrepancy between HbA1c and GMI, is associated with higher HbA1c levels and a greater crude prevalence of DR. However, this association is not independent when adjusted for clinical risk factors. The HbA1c/GMI ratio should be interpreted as a contextual aid rather than a prognostic biomarker for retinopathy in people with T1D. Declarations Conflict of interest of declarations Carolina Sager-La Ganga, Jose Alfonso Arranz Martin, Jessica Jiménez-Díaz, Iñigo Hernando Alday, Victor Navas Moreno, Maria del Mar Fandiño García, Gisela Liz Román Gómez, Jon Garai Hierro, Luis Eduardo Lander Lobariñas, Purificación Martinez de Icaya, Miguel Antonio Sampedro-Nuñez, Mónica Marazuela, Fernando Sebastian-Valles have no conflicts of interest or financial ties to disclose. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethics approval and consent to participate The Research Ethics Committee of the Hospital de La Princesa, Madrid (Study number: 5084-01/2023), approved this study and waived informed consent from patients. The research was conducted in accordance with the Declaration of Helsinki. AUTHOR CONTRIBUTIONS C.S.-L.G. and F.S.-V. conceptualized the study. Data curation and formal analysis were performed by F.S-V. Manuscript was drafted by C.S-L.G. and reviewed by all authors. DATA AVAILABILITY The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Gubitosi-Klug, R. A. et al. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up. Diabetes Care 39 , 686–693 (2016). Turner, R. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352 , 837–853 (1998). DM, N. et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329 , 977–986 (1993). Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes-2025. Diabetes Care 48 , (2025). Type 1 diabetes in adults: diagnosis and management. (2022). Hsu, J. C. et al. Long-Term Visit-to-Visit Glycemic Variability as a Predictor of Major Adverse Limb and Cardiovascular Events in Patients With Diabetes. J Am Heart Assoc 12 , 25438 (2023). Belli, M. et al. Glucose variability: a new risk factor for cardiovascular disease. Acta Diabetol 60 , 1291–1299 (2023). Shangguan, Q., Yang, J., Li, B., Chen, H. & Yang, L. Association of the hemoglobin glycation index with cardiovascular and all-cause mortality in individuals with hypertension: findings from NHANES 1999-2018. Front Endocrinol (Lausanne) 15 , 1401317 (2024). Zhang, L. et al. Hemoglobin glycation index in relationship to the risk of cardiovascular complication in patients with type 2 diabetes: A systematic review and meta-analysis. J Diabetes Complications 34 , (2020). Huang, Y., Huang, X., Zhong, L. & Yang, J. Glycated haemoglobin index is a new predictor for all-cause mortality and cardiovascular mortality in the adults. Sci Rep 14 , (2024). Wei, X. et al. Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database. Cardiovasc Diabetol 23 , (2024). Bergenstal, R. M. et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 41 , 2275–2280 (2018). Perlman, J. E., Gooley, T. A., McNulty, B., Meyers, J. & Hirsch, I. B. HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis. Diabetes Technol Ther 23 , 253–258 (2021). Sterner Isaksson, S. et al. Discordance between mean glucose and time in range in relation to HbA1c in individuals with type 1 diabetes: results from the GOLD and SILVER trials. Diabetologia 67 , 1517 (2024). Piona, C. et al. Evaluation of HbA1c and glucose management indicator discordance in a population of children and adolescents with type 1 diabetes. Pediatr Diabetes 23 , 84–89 (2022). Foti Randazzese, S. et al. Discordance between Glucose Management Indicator and Glycated Hemoglobin in a Pediatric Cohort with Type 1 Diabetes: A Real-World Study. Children 2024, Vol. 11, Page 210 11 , 210 (2024). Lenters-Westra, E. et al. Managing discordance between HbA1c and glucose management indicator. Diabet Med (2025) doi:10.1111/DME.70023. Maran, A., Morieri, M. L., Falaguasta, D., Avogaro, A. & Fadini, G. P. The Fast-Glycator Phenotype, Skin Advanced Glycation End Products, and Complication Burden Among People With Type 1 Diabetes. Diabetes Care 45 , 2439–2444 (2022). Puig-Jové, C. et al. Association between the GMI/HbA1c ratio and preclinical carotid atherosclerosis in type 1 diabetes: impact of the fast-glycator phenotype across age groups. Cardiovasc Diabetol 24 , (2025). Shah, V. N., Kanapka, L. G., Karakus, K. E., Kollman, C. & Beck, R. W. The Association of High and Low Glycation With Incident Diabetic Retinopathy in Adults With Type 1 Diabetes. J Diabetes Sci Technol (2024) doi:10.1177/19322968241254811. Yang, J., Shangguan, Q., Xie, G., Yang, M. & Sheng, G. Sex-specific associations between haemoglobin glycation index and the risk of cardiovascular and all-cause mortality in individuals with pre-diabetes and diabetes: A large prospective cohort study. Diabetes Obes Metab 26 , 2275–2283 (2024). De La Cruz-Ares, S. et al. Endothelial Dysfunction and Advanced Glycation End Products in Patients with Newly Diagnosed Versus Established Diabetes: From the CORDIOPREV Study. Nutrients 12 , (2020). Goh, S. Y. & Cooper, M. E. Clinical review: The role of advanced glycation end products in progression and complications of diabetes. J Clin Endocrinol Metab 93 , 1143–1152 (2008). Liu, J., Pan, S., Wang, X., Liu, Z. & Zhang, Y. Role of advanced glycation end products in diabetic vascular injury: molecular mechanisms and therapeutic perspectives. Eur J Med Res 28 , 1–7 (2023). Ward, M. S., Fortheringham, A. K., Cooper, M. E. & Forbes, J. M. Targeting advanced glycation endproducts and mitochondrial dysfunction in cardiovascular disease. Curr Opin Pharmacol 13 , 654–661 (2013). Pedro, R. A., Ramon, S. A., Marc, B. B., Juan, F. B. & Isabel, M. M. Prevalence and relationship between diabetic retinopathy and nephropathy, and its risk factors in the North-East of Spain, a population-based study. Ophthalmic Epidemiol 17 , 251–265 (2010). R, K., J, F., V, S., P, G. & A, A. Predictors of diabetic retinopathy in patients with type 2 diabetes who have normoalbuminuria. Ann Med Health Sci Res 3 , 536 (2013). Cardoso, C. R. L., Leite, N. C., DIb, E. & Salles, G. F. Predictors of Development and Progression of Retinopathy in Patients with Type 2 Diabetes: Importance of Blood Pressure Parameters. Sci Rep 7 , (2017). Sebastian-Valles, F. et al. Time above range and no coefficient of variation is associated with diabetic retinopathy in individuals with type 1 diabetes and glycated hemoglobin within target. Acta Diabetol 62 , 205–214 (2024). Battelino, T. et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol 11 , 42–57 (2023). Von Elm, E. et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Ann Intern Med 147 , 573–577 (2007). Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs — An Extension of the Modified Airlie House Classification: ETDRS Report Number 10. Ophthalmology 127 , S99–S119 (2020). Team RC. A language and environment for statistical computing. (2013). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviewers invited by journal 03 Jul, 2025 Editor assigned by journal 10 Jun, 2025 Editor invited by journal 10 Jun, 2025 Submission checks completed at journal 10 Jun, 2025 First submitted to journal 31 May, 2025 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. 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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-6792531","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":481154122,"identity":"4834ef2a-6f41-4757-9487-ef7d231c752b","order_by":0,"name":"Carolina Sager-La Ganga","email":"data:image/png;base64,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","orcid":"","institution":"Universidad Autónoma de Madrid","correspondingAuthor":true,"prefix":"","firstName":"Carolina","middleName":"Sager-La","lastName":"Ganga","suffix":""},{"id":481154123,"identity":"8f265aba-cdfc-46a9-b30c-11fdb3691dbe","order_by":1,"name":"Jose Alfonso Arranz Martin","email":"","orcid":"","institution":"Hospital Universitario de La Princesa","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Alfonso Arranz","lastName":"Martin","suffix":""},{"id":481154124,"identity":"08772f6d-e790-4171-9eca-2b02c9927b96","order_by":2,"name":"Jessica Jiménez-Díaz","email":"","orcid":"","institution":"Hospital Universitario Severo Ochoa","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Jiménez-Díaz","suffix":""},{"id":481154126,"identity":"f243d762-7381-4be9-a0bb-64e6df1d6225","order_by":3,"name":"Iñigo Hernando-Alday","email":"","orcid":"","institution":"Hospital Universitario Basurto","correspondingAuthor":false,"prefix":"","firstName":"Iñigo","middleName":"","lastName":"Hernando-Alday","suffix":""},{"id":481154128,"identity":"a4196c83-9e38-4263-bfaa-b0490b00e5ea","order_by":4,"name":"Victor Navas Moreno","email":"","orcid":"","institution":"Hospital Universitario de La Princesa","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"Navas","lastName":"Moreno","suffix":""},{"id":481154129,"identity":"0f8e842e-99b9-48e1-85ca-8295bfeb6932","order_by":5,"name":"Maria del Mar Fandiño García","email":"","orcid":"","institution":"Hospital Universitario Severo Ochoa","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"del Mar Fandiño","lastName":"García","suffix":""},{"id":481154131,"identity":"f45a5b84-7997-4a13-978e-0d1034fc18a4","order_by":6,"name":"Gisela Liz Román Gómez","email":"","orcid":"","institution":"Hospital Universitario Severo Ochoa","correspondingAuthor":false,"prefix":"","firstName":"Gisela","middleName":"Liz Román","lastName":"Gómez","suffix":""},{"id":481154132,"identity":"659d5143-2d52-499f-9dcc-0a1b7b10cb13","order_by":7,"name":"Jon Garai Hierro","email":"","orcid":"","institution":"Hospital Universitario Basurto","correspondingAuthor":false,"prefix":"","firstName":"Jon","middleName":"Garai","lastName":"Hierro","suffix":""},{"id":481154135,"identity":"20114a50-c9c6-493c-b48b-68d78b0722cd","order_by":8,"name":"Luis Eduardo Lander Lobariñas","email":"","orcid":"","institution":"Hospital Universitario Severo Ochoa","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Eduardo Lander","lastName":"Lobariñas","suffix":""},{"id":481154136,"identity":"138c9a1b-e6b5-44ac-9a59-5d30bf568a91","order_by":9,"name":"Purificación Martinez de Icaya","email":"","orcid":"","institution":"Hospital Universitario Severo Ochoa","correspondingAuthor":false,"prefix":"","firstName":"Purificación","middleName":"Martinez","lastName":"de Icaya","suffix":""},{"id":481154138,"identity":"146bed0e-8882-49a5-b42b-4215c98c66d8","order_by":10,"name":"Miguel Antonio Sampedro-Nuñez","email":"","orcid":"","institution":"Hospital Universitario de La Princesa","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"Antonio","lastName":"Sampedro-Nuñez","suffix":""},{"id":481154139,"identity":"ee9d316d-4632-44e7-8bcb-aeef3187197e","order_by":11,"name":"Mónica Marazuela Azpiroz","email":"","orcid":"","institution":"Hospital Universitario de La Princesa","correspondingAuthor":false,"prefix":"","firstName":"Mónica","middleName":"Marazuela","lastName":"Azpiroz","suffix":""},{"id":481154140,"identity":"af3b1670-9846-4c86-b6fa-fe529a70b9db","order_by":12,"name":"Fernando Sebastian-Valles","email":"","orcid":"","institution":"Hospital Universitario de La Princesa","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Sebastian-Valles","suffix":""}],"badges":[],"createdAt":"2025-05-31 18:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6792531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6792531/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-19705-0","type":"published","date":"2025-10-14T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86193988,"identity":"1de05c0b-8e8c-46da-9fc7-92b68919c72a","added_by":"auto","created_at":"2025-07-07 20:33:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of the Hba1c/GMI ratio by diabetic retinopathy status. \u003c/strong\u003eThe HbA1c/GMI ratio was significantly higher in those with DR (p = 0.006), although considerable overlap in distributions was observed.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6792531/v1/ba2b170cd893ffc4c677e02a.png"},{"id":86193989,"identity":"7eeda21b-1514-40a0-8d03-982c30858e3b","added_by":"auto","created_at":"2025-07-07 20:33:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of diabetic retinopathy across HbA1c/GMI ratio values (LOESS curve). \u003c/strong\u003eThe curve supports a threshold of 0.9% as a potential cut-off to identify “high glycators,” beyond which DR prevalence increases. The shaded area represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6792531/v1/7f3987fea6a9b6f4b2b0edff.png"},{"id":86193987,"identity":"8eaa3c39-fe29-4282-b455-4316a4614231","added_by":"auto","created_at":"2025-07-07 20:33:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between the HbA1c/GMI ratio and diabetic retinopathy, adjusted for GMI (3a) and HbA1c (3b). \u003c/strong\u003ePanel 3a shows that both the HbA1c/GMI ratio and GMI are significantly associated with diabetic retinopathy (DR). However, in panel 3b, when adjusting for HbA1c, the glycation index loses its statistical significance, and only HbA1c remains a significant predictor. These findings suggest that the apparent effect of the HbA1c/GMI ratio may be largely mediated by elevated HbA1c itself.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6792531/v1/347c1eac827ed6867ccd302e.png"},{"id":93956056,"identity":"db99be99-baa1-4dc9-b501-ef62a1d416e1","added_by":"auto","created_at":"2025-10-20 16:09:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1255749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6792531/v1/a601a370-6514-4d1f-a664-c57c0a0a5151.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe HbA1c/GMI Ratio Does Not Independently Predict Diabetic Retinopathy in Adults with Type 1 Diabetes: A Multicenter Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlycated haemoglobin (HbA1c) remains the primary biomarker for evaluating long-term glycaemic control and predicting vascular complication risk in individuals with diabetes mellitus (DM)\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Its role is firmly established both as a diagnostic criterion and a therapeutic target across international guidelines\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, HbA1c reflects only a weighted average of glycemia over the previous 2\u0026ndash;3 months and does not capture important aspects, such as glycemic variability, which has also been linked to increased cardiovascular risk and mortality \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. To overcome these limitations, additional glycemic markers have been proposed to better characterize individual metabolic risk. One such marker is the hemoglobin glycation index (HGI), which is defined as the difference between measured HbA1c and HbA1c estimated from mean glucose via regression models. Elevated HGI levels have been associated with all-cause mortality and a higher incidence of cardiovascular events in people with diabetes \u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe introduction of continuous glucose monitoring (CGM) has revolutionized the approach to glycemic control, allowing for dynamic analysis of daily glucose profiles. The glucose management indicator (GMI), derived from CGM data, estimates an HbA1c-equivalent value on the basis of average interstitial glucose levels\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, a consistent discrepancy between measured HbA1c and estimated GMI has been reported in several studies \u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Individuals with persistently higher HbA1c values than those predicted by the GMI are referred to as \"high glycators.\" This finding is hypothesized to reflect interindividual differences in the glycation rate, erythrocyte lifespan, oxidative stress, or genetic factors\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn line with studies linking elevated HGI with macrovascular outcomes, some authors have proposed that an HbA1c-to-GMI ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.9% may represent a novel biomarker of vascular risk in persons with diabetes \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, the prognostic relevance of this discrepancy in relation to microvascular complications\u0026mdash;particularly diabetic retinopathy (DR)\u0026mdash;has not been fully elucidated.\u003c/p\u003e\u003cp\u003eThis study aimed to assess whether the discrepancy between venous HbA1c and the GMI, expressed as the HbA1c/GMI ratio, is independently associated with the presence of diabetic retinopathy in individuals with type 1 diabetes via CGM.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eBaseline characteristics and HbA1c/GMI ratio distribution\u003c/h2\u003e\n \u003cp\u003eA total of 1,070 individuals with type 1 diabetes (T1D) were included in the analysis. The mean age of the cohort was 47.6 ± 15.0 years, with 49.4% being women. The median diabetes duration was 21.4 years (IQR: 12–31), and the mean HbA1c was 7.4 ± 1.1%. Overall, 24.8% (n = 266) of the participants exhibited any degree of diabetic retinopathy (DR) on the basis of ophthalmologic assessment following the ETDRS criteria. The demographic characteristics are described in Table 1. The mean HbA1c/GMI ratio was 0.57 ± 0.42%. A total of 176 participants (16.4%) were classified as high glycators.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eClinical and metabolic characteristics of the cohort stratified by glycation phenotype.\u003c/strong\u003e Distribution of sociodemographic, clinical, and glycemic control variables between high glycators (glycation index \u0026gt; 0.9%) and non-high glycators, prior to propensity score matching.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003cp\u003en = 1070\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh Glycators\u003c/p\u003e\n \u003cp\u003en = 179\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-High Glycators\u003c/p\u003e\n \u003cp\u003en = 872\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.6 ± 15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.91 ± 15.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.08 ± 14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex women\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e528 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of Diabetes\u003c/strong\u003e (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.43 ± 13.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.16 ± 13.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.28 ± 13.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetic retinopathy (DR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProliferative/macular edema\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37 ± 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4 ± 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13 ± 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.19 ± 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.05 ± 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.22 ± 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e (kg/m²)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.89 ± 5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.0 ± 4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.90 ± 5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIschemic Heart Disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.3 ± 17.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.8 ± 17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.1 ± 17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62 ± 4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.60 ± 5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4 ± 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAR \u0026gt; 180 mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1 ± 18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.6 ± 17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.5 ± 18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAR \u0026gt; 250 mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4 ± 13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 ± 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3 ± 12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient of Variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.6 ± 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.4 ± 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6 ± 7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e[INSERT HERE Table 1]\u003c/h3\u003e\n\u003cp\u003eA modest but statistically significant difference in the HbA1c/GMI ratio was observed between individuals with and without DR (0.62 ± 0.44 vs. 0.55 ± 0.41, p = 0.006) (\u003cstrong\u003eFig. 1\u003c/strong\u003e). According to the crude analysis, the DR incidence was greater among high glycators than among nonhigh glycators (31.3% vs. 23.1%, p = 0.020).\u003c/p\u003e\n\u003ch3\u003e[INSERT HERE FIGURE 1]\u003c/h3\u003e\n\u003cp\u003eTo assess the impact of the HbA1c/GMI ratio on diabetic retinopathy (DR) in our cohort and explore its relationship with the commonly used cut-off of \u0026lt; 0.9, we constructed a LOESS curve that revealed a nonlinear association between the HbA1c/GMI ratio and the presence of any form of DR (Fig. 2.\u003cstrong\u003e)\u003c/strong\u003e. The curve revealed a greater prevalence of DR (approximately 30%) among participants with HbA1c/GMI values between 0.7% and 0.9, followed by a gradual decline to below 20% as the ratio approached 1. These findings suggest that the 0.9 threshold may be applicable to our sample.\u003c/p\u003e\n\u003ch3\u003e[INSERT HERE FIGURE 2]\u003c/h3\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eMultivariate analysis\u003c/h2\u003e\n \u003cp\u003eTo explore the independent association between the HbA1c/GMI ratio and diabetic retinopathy (DR), a multivariable logistic regression model was constructed adjusting for age (categorized in quartiles), sex, diabetes duration, smoking status (Habitfum), hypertension, and LDL cholesterol \u003cstrong\u003e(\u003c/strong\u003eTable 2\u003cstrong\u003e)\u003c/strong\u003e. An interaction term between HbA1c/GMI and age quartiles was included.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable logistic regression model for the presence of diabetic retinopathy.\u003c/strong\u003e The HbA1c/GMI ratio \u0026gt; 0.9% was not significantly associated with retinopathy after adjustment. Significant predictors included female sex, higher HbA1c levels, hypertension, and longer diabetes duration.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlycation index\u003c/strong\u003e (\u0026gt; 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18 (0.34–4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e (women)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63 (1.17–2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e35–47\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13 (0.65–1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e47–58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823 (0.45–1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt; 58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (0.54–0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.35 (1.12–1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57 (1.06–2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes duration\u003c/strong\u003e (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09 (1.07–1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL (\u003c/strong\u003emg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99 (0.99–1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e[INSERT HERE Table 2]\u003c/h2\u003e\n \u003cp\u003eAccording to the adjusted model, the HbA1c/GMI ratio was not significantly associated with the presence of DR (OR 1.19; 95% CI: 0.34–4.15; p = 0.785). The age categories also showed no significant associations with DR: 35–47 years (OR 1.14; 95% CI: 0.66–1.97; p = 0.647), 47–58 years (OR 0.82; 95% CI: 0.46–1.48; p = 0.516), and \u0026gt; 58 years (OR 1.03; 95% CI: 0.55–1.93; p = 0.927), with ≤ 35 years used as the reference group. Male sex was significantly associated with increased odds of DR (OR 1.64; 95% CI: 1.18–2.29; p = 0.004), as was smoking (OR 2.22; 95% CI: 1.50–3.29; p \u0026lt; 0.001). Diabetes duration and hypertension were also significantly associated (OR 1.09; 95% CI: 1.07–1.11; p = 0.118) and (OR 1.58; 95% CI: 1.07–2.34; p = 0.021), respectively. LDL cholesterol was also significantly associated with LDL (OR 0.997; 95% CI: 0.994–1.000; p = 0.318). The interaction term between HbA1c/GMI and age quartile was not statistically significant (OR 1.07; 95% CI: 0.71–1.61; p = 0.736), indicating that there was no clear age-modifying effect on the relationship between the glycation phenotype and DR.\u003c/p\u003e\n \u003cp\u003eThese findings suggest that the initially observed association may be driven by confounding factors—particularly HbA1c itself \u003cstrong\u003e(\u003c/strong\u003eFig. 3B\u003cstrong\u003e).\u003c/strong\u003e Although both HbA1c and GMI were associated with diabetic retinopathy when included together in a multivariable model, when the HbA1c/GMI ratio (\u0026lt; 0.9) was analysed alongside HbA1c (\u0026lt; 7.0%), only HbA1c showed a statistically significant association, further supporting the idea that an elevated HbA1c/GMI ratio reflects the risk conferred by higher HbA1c rather than by itself \u003cstrong\u003e(\u003c/strong\u003eFig. 3\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThese findings suggest that the initially observed association between the HbA1c/GMI ratio and diabetic retinopathy (DR) may be confounded—particularly by HbA1c itself \u003cstrong\u003e(\u003c/strong\u003eFig. 3B\u003cstrong\u003e).\u003c/strong\u003e In a multivariable model including both the continuous GMI value and the binary glycation ratio (\u0026lt; 0.9), both variables were significantly associated with DR: the glycation ratio (β coefficient 0.47; 95% CI: 0.12–0.83; p = 0.009) and GMI (%) (β coefficient 0.30; 95% CI: 0.13–0.47; p = 0.009), suggesting a potential additive effect (Fig. 3A). However, in a sensitivity analysis where the binary glycation ratio (\u0026lt; 0.9) was modelled together with a dichotomized HbA1c value (\u0026lt; 7%), only HbA1c remained significantly associated with DR (OR 0.72; 95% CI: 0.52–0.99; p = 0.042), whereas the glycation ratio was not (OR 1.34; 95% CI: 0.93–1.95; p = 0.117) \u003cstrong\u003e(\u003c/strong\u003eFig. 3B\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e[INSERT HERE FIGURES 3a AND 3b]\u003c/h3\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003ePropensity score matching analysis\u003c/h2\u003e\n \u003cp\u003eTo further evaluate the independent contribution of the glycation phenotype, we performed a propensity score matching analysis (Table 3\u003cstrong\u003e)\u003c/strong\u003e. High and nonhigh glycators were matched 1:1 (n = 163 per group) on age, sex, diabetes duration, smoking, hypertension, LDL, BMI, and insulin dose. A total of 13 high-grade glycators were unmatched and excluded from the analysis.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline variables between non-high glycators and high glycators.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-rapid glycators n = 163\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRapid glycators n = 163\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.66 ± 15.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.08 ± 15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (49.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.87 ± 13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.30 ± 12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.16 ± 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.42 ± 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin dose (U/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 ± 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 ± 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime in Range (70–180 mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.2 ± 17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.8 ± 16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime above range \u0026gt; 180 (TAR, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.8 ± 18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.47 ± 17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime below range \u0026lt; 70 (TBR, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00 ± 4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7 ± 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime \u0026gt; 250 mg/dL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3 ± 13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.8 ± 11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.6 ± 6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6 ± 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeprivation index (IP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.66 ± 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.67 ± 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e[INSERT HERE Table 3]\u003c/h2\u003e\n \u003cp\u003eWithin the matched cohort, the crude DR incidence was 30.7% in the high-glycator group and 26.4% in the nonhigh-glycator group (p = 0.391). Conditional logistic regression revealed no significant association between glycation phenotype and DR (OR = 1.23; 95% CI: 0.763–1.999; p = 0.391). These results indicate that once confounders are controlled for, the HbA1c/GMI ratio does not independently predict the presence of diabetic retinopathy.\u003c/p\u003e\n \u003cp\u003eNotably, despite matching for clinical variables, there was a marked difference in the mean HbA1c between high and nonhigh glycators (8.4% vs. 7.2%, respectively), reinforcing the idea that the HbA1c/GMI ratio captures excess risk attributable to chronic hyperglycemia.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe primary aim of this study was to determine whether the HbA1c/GMI ratio\u0026mdash;defined as the difference between venous HbA1c and the glucose management indicator derived from continuous glucose monitoring\u0026mdash;is independently associated with the presence of diabetic retinopathy in individuals with type 1 diabetes.\u003c/p\u003e\u003cp\u003eIn this multicenter cohort of T1D patients treated with multiple daily insulin injections and regular CGM, we initially observed that an HbA1c/GMI ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.9%, reflecting a high glycation phenotype \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, was associated with a higher prevalence of DR in the crude analysis. However, this association did not persist after multivariable adjustment and propensity score matching, suggesting that the HbA1c/GMI ratio does not independently predict diabetic retinopathy.\u003c/p\u003e\u003cp\u003eInstead, absolute HbA1c was the only glycemic parameter consistently associated with DR across all models, indicating that the increased risk observed in high-glucose individuals may be attributable to higher HbA1c levels rather than to the discrepancy itself. Indeed, when comparing propensity score-matched groups with similar clinical profiles\u0026mdash;including age, diabetes duration, hypertension, and lipid levels\u0026mdash;no differences in DR incidence were found, despite significantly higher HbA1c in high-level glycators (8.4% vs. 7.2%). These findings suggest that the glycation index is a better interpretative marker than a prognostic marker. These findings are consistent with those of previous studies, including Shah et al. (2024) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, who reported no associations between the GMI/HbA1c ratio and microvascular outcomes in patients with type 2 diabetes and end-stage renal disease. [20]\u003c/p\u003e\u003cp\u003eThe clinical relevance of the high glycation phenotype\u0026mdash;as evidenced by a persistent discrepancy between HbA1c and GMI\u0026mdash;remains a topic of debate. Although this pattern has been linked to increased mortality and cardiovascular risk, particularly in individuals with type 2 diabetes \u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, our findings indicate that T1D does not independently predict DR when key clinical confounders are adjusted for.\u003c/p\u003e\u003cp\u003eFrom a pathophysiological perspective, it has been hypothesized that high glycators may be more susceptible to microvascular damage due to accelerated nonenzymatic glycation, potentially influenced by inflammation, oxidative stress, endothelial dysfunction, or genetic predisposition \u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, our data do not support a direct effect of this discrepancy on DR. We also found no significant difference in DR incidence between high and nonhigh glycators after propensity matching, reinforcing the notion that microvascular risk is more closely related to total glycemic burden (i.e., absolute HbA1c) than to glycation efficiency per se.\u003c/p\u003e\u003cp\u003eIn addition to HbA1c, several well-established predictors of DR\u0026mdash;including diabetes duration, hypertension, LDL levels, smoking, and age \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u0026mdash;were confirmed in our multivariable analysis. The glycation index did not add prognostic value when these factors were accounted for, suggesting that the HbA1c/GMI ratio should not be considered an independent predictor of DR.\u003c/p\u003e\u003cp\u003eFrom a clinical standpoint, these findings carry relevant implications. The HbA1c/GMI ratio may serve as a useful interpretive tool for contextualizing discordant HbA1c values when CGM data suggest adequate glycemic control, helping to avoid therapeutic overtreatment on the basis solely of elevated HbA1c levels in the absence of objective hyperglycemia \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, its use as a microvascular risk stratification tool is not supported at present.\u003c/p\u003e\u003cp\u003eThis study has several strengths, including its large sample size, multicenter design, high-resolution CGM data, and the application of robust statistical techniques such as multivariable adjustment and propensity score matching. However, several limitations should be acknowledged. First, the cross-sectional design limits the ability to draw causal inferences. Second, the study did not assess diabetic retinopathy progression or long-term cardiovascular outcomes. Third, serum fructosamine was not measured, which could have provided an additional short-term glycemic marker independent of red blood cell turnover. Finally, important lifestyle factors such as dietary patterns and physical activity\u0026mdash;which are known to influence glycemic control and diabetes-related complications\u0026mdash;were not captured in this dataset.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003eA cross-sectional, observational, multicenter study was conducted in a cohort of 1,070 individuals with type 1 diabetes mellitus (T1D) who were recruited between December 2022 and January 2023 across three Spanish hospitals located in different geographic regions. This cohort has been previously described in a study published in \u003cem\u003eActa Diabetologica\u003c/em\u003e (Sebastian-Valles et al., 2025)​ \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. For the present analysis, new statistical models were applied to specifically investigate the association between the HbA1c/GMI ratio and diabetic retinopathy (DR).\u003c/p\u003e\u003cp\u003eAll participants were regular users of intermittent continuous glucose monitoring (FGM) with the FreeStyle Libre 2\u0026reg; device (Abbott Laboratories), with a minimum usage duration of 3 months and sensor data coverage of \u0026ge;\u0026thinsp;70% during the 14 days prior to database closure. Glucose metrics were acquired via cloud downloads from the Libreview platform over a 14-day period before December 1, 2022. Furthermore, the HbA1c value determined at the closest time (\u0026plusmn;\u0026thinsp;1 month) to that of the glucose metrics downloaded from the FGM platform was also obtained.\u003c/p\u003e\u003cp\u003eEligible participants were adults (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years) with a diagnosis of T1D for a minimum of six months who received intensive insulin therapy via either multiple daily injections (MDIs) or open-loop continuous subcutaneous insulin infusion (CSII).\u003c/p\u003e\u003cp\u003eIndividuals were excluded if they had any other form of diabetes (such as type 2 diabetes, maturity-onset diabetes of the young [MODY], or secondary diabetes), were hospitalized during the study period, or were using hybrid closed-loop systems, as these devices rely on different algorithms and CGM technologies that may introduce analytical bias. Additional exclusion criteria included insufficient CGM sensor usage (\u0026lt;\u0026thinsp;70% data capture per international recommendations)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e), absence of a complete sensor data download on the day prior to inclusion, less than three months of active use of the flash glucose monitoring system, and missing key clinical variables (such as HbA1c or metabolic parameters) within the three months prior to enrollment.\u003c/p\u003e\u003cp\u003eThis study followed the \u0026ldquo;Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)\u0026rdquo; guidelines \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The Research Ethics Committee of La Princesa University Hospital approved the study (Study number: 5084-01/2023). The research was conducted according to the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eVariables and definitions of the HbA1c/GMI ratio\u003c/h2\u003e\u003cp\u003eVenous HbA1c levels were measured via high-performance liquid chromatography (ADAMS A1c HA8180 V\u0026reg;, Arkray\u0026reg;). The glucose management indicator (GMI) was automatically calculated from sensor data via the standard algorithms provided by the LibreView\u0026reg; platform.\u003c/p\u003e\u003cp\u003eThe HbA1c/GMI ratio was computed as the division between venous HbA1c and the corresponding GMI value during the same monitoring period. Participants were classified as high glycators if their ratio exceeded 0.9%, as in previous studies (\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePrimary outcome and covariates\u003c/h2\u003e\u003cp\u003eThe primary outcome of the study was the presence of diabetic retinopathy (DR), which was defined according to the criteria established in the Early Treatment Diabetic Retinopathy Study (ETDRS) Report No. 10 \u003csup\u003e32\u003c/sup\u003e, and was ascertained through electronic medical records reviewed by ophthalmologists. For analytical purposes, clinical presentations were categorized as follows: no retinopathy and any degree of retinopathy (including mild, moderate, severe, or proliferative stages/diabetic macular edema).\u003c/p\u003e\u003cp\u003eIn addition, the following demographic, clinical, and biochemical variables were collected on the basis of census data from the Spanish National Statistics Institute (INE): age, sex, diabetes duration, active smoking status, presence of arterial hypertension, body mass index (BMI), daily insulin dose adjusted for body weight, low-density lipoprotein (LDL) cholesterol, and average annual income per person (as a proxy for individual socioeconomic status).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) or medians with interquartile ranges (p25\u0026ndash;p75), depending on their distribution, as assessed via Kolmogorov\u0026ndash;Smirnov tests and visual inspection (Q\u0026ndash;Q plots). Categorical variables are summarized as frequencies and percentages. Comparisons between groups were performed via Student\u0026rsquo;s t test or the Mann\u0026ndash;Whitney U test for continuous variables and via the chi-square test or Fisher\u0026rsquo;s exact test for categorical variables, as appropriate.\u003c/p\u003e\u003cp\u003eThe primary analysis aimed to evaluate the association between the HbA1c/GMI ratio and the presence of DR, as well as the relevance of the \u0026lt;\u0026thinsp;0.9 threshold previously used in other studies to classify participants as \u0026ldquo;high glycators\u0026rdquo; (\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e). The crude prevalence of DR across glycation strata was initially compared. A locally estimated scatterplot smoothing (LOESS) curve was used as a sensitivity analysis to visually assess the relationship between the HbA1c/GMI ratio and the presence of any type of DR in our cohort, in comparison with the previously validated cut-off of \u0026lt;\u0026thinsp;0.9.\u003c/p\u003e\u003cp\u003eA multivariable logistic regression model was subsequently fitted to examine the independent association between the HbA1c/GMI ratio and DR. This model was adjusted for clinically relevant confounders previously associated with DR in this cohort (age, sex, diabetes duration, smoking status, hypertension, LDL cholesterol, BMI, and insulin dose). Sensor-derived variables and HbA1c were intentionally excluded to avoid collinearity, as both contribute to the ratio itself. An interaction term between age and the HbA1c/GMI ratio was also introduced, following prior recommendations (Puig-Jov\u0026eacute; et al., Cardiovasc Diabetol. 2025)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e)​.\u003c/p\u003e\u003cp\u003eTo further strengthen causal inference and control for confounding factors, a propensity score matching analysis was performed. Propensity scores were estimated via logistic regression incorporating the aforementioned covariates. A 1:1 nearest-neighbor matching without replacement was applied. This resulted in a matched cohort of 163 high glycators and 163 nonhigh glycators after 13 unmatched cases were excluded. Conditional logistic regression was then conducted within the matched sample to assess the association between the high glycation phenotype and DR.\u003c/p\u003e\u003cp\u003eAll analyses were performed via R (v4.0.3) and Stata SE (v17.0). A two-tailed p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, a rapid glycation phenotype, reflected by a greater discrepancy between HbA1c and GMI, is associated with higher HbA1c levels and a greater crude prevalence of DR. However, this association is not independent when adjusted for clinical risk factors. The HbA1c/GMI ratio should be interpreted as a contextual aid rather than a prognostic biomarker for retinopathy in people with T1D.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of interest\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarolina Sager-La Ganga, Jose Alfonso Arranz Martin, Jessica Jim\u0026eacute;nez-D\u0026iacute;az, I\u0026ntilde;igo Hernando Alday, Victor Navas Moreno, Maria del Mar Fandi\u0026ntilde;o Garc\u0026iacute;a, Gisela Liz Rom\u0026aacute;n G\u0026oacute;mez, Jon Garai Hierro, Luis Eduardo Lander Lobari\u0026ntilde;as, Purificaci\u0026oacute;n Martinez de Icaya, Miguel Antonio Sampedro-Nu\u0026ntilde;ez, M\u0026oacute;nica Marazuela, Fernando Sebastian-Valles have no conflicts of interest or financial ties to disclose.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Research Ethics Committee of the Hospital de La Princesa, Madrid (Study number: 5084-01/2023), approved this study and waived informed consent from patients. The research was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.S.-L.G. and F.S.-V. conceptualized the study. Data curation and formal analysis were performed by F.S-V. Manuscript was drafted by C.S-L.G. and reviewed by all authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGubitosi-Klug, R. A. \u003cem\u003eet al.\u003c/em\u003e Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 686\u0026ndash;693 (2016).\u003c/li\u003e\n\u003cli\u003eTurner, R. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e352\u003c/strong\u003e, 837\u0026ndash;853 (1998).\u003c/li\u003e\n\u003cli\u003eDM, N. \u003cem\u003eet al.\u003c/em\u003e The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e329\u003c/strong\u003e, 977\u0026ndash;986 (1993).\u003c/li\u003e\n\u003cli\u003eGlycemic Goals and Hypoglycemia: Standards of Care in Diabetes-2025. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, (2025).\u003c/li\u003e\n\u003cli\u003eType 1 diabetes in adults: diagnosis and management. (2022).\u003c/li\u003e\n\u003cli\u003eHsu, J. C. \u003cem\u003eet al.\u003c/em\u003e Long-Term Visit-to-Visit Glycemic Variability as a Predictor of Major Adverse Limb and Cardiovascular Events in Patients With Diabetes. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 25438 (2023).\u003c/li\u003e\n\u003cli\u003eBelli, M. \u003cem\u003eet al.\u003c/em\u003e Glucose variability: a new risk factor for cardiovascular disease. \u003cem\u003eActa Diabetol\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 1291\u0026ndash;1299 (2023).\u003c/li\u003e\n\u003cli\u003eShangguan, Q., Yang, J., Li, B., Chen, H. \u0026amp; Yang, L. Association of the hemoglobin glycation index with cardiovascular and all-cause mortality in individuals with hypertension: findings from NHANES 1999-2018. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1401317 (2024).\u003c/li\u003e\n\u003cli\u003eZhang, L. \u003cem\u003eet al.\u003c/em\u003e Hemoglobin glycation index in relationship to the risk of cardiovascular complication in patients with type 2 diabetes: A systematic review and meta-analysis. \u003cem\u003eJ Diabetes Complications\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eHuang, Y., Huang, X., Zhong, L. \u0026amp; Yang, J. Glycated haemoglobin index is a new predictor for all-cause mortality and cardiovascular mortality in the adults. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, (2024).\u003c/li\u003e\n\u003cli\u003eWei, X. \u003cem\u003eet al.\u003c/em\u003e Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, (2024).\u003c/li\u003e\n\u003cli\u003eBergenstal, R. M. \u003cem\u003eet al.\u003c/em\u003e Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 2275\u0026ndash;2280 (2018).\u003c/li\u003e\n\u003cli\u003ePerlman, J. E., Gooley, T. A., McNulty, B., Meyers, J. \u0026amp; Hirsch, I. B. HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis. \u003cem\u003eDiabetes Technol Ther\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 253\u0026ndash;258 (2021).\u003c/li\u003e\n\u003cli\u003eSterner Isaksson, S. \u003cem\u003eet al.\u003c/em\u003e Discordance between mean glucose and time in range in relation to HbA1c in individuals with type 1 diabetes: results from the GOLD and SILVER trials. \u003cem\u003eDiabetologia\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e, 1517 (2024).\u003c/li\u003e\n\u003cli\u003ePiona, C. \u003cem\u003eet al.\u003c/em\u003e Evaluation of HbA1c and glucose management indicator discordance in a population of children and adolescents with type 1 diabetes. \u003cem\u003ePediatr Diabetes\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 84\u0026ndash;89 (2022).\u003c/li\u003e\n\u003cli\u003eFoti Randazzese, S. \u003cem\u003eet al.\u003c/em\u003e Discordance between Glucose Management Indicator and Glycated Hemoglobin in a Pediatric Cohort with Type 1 Diabetes: A Real-World Study. \u003cem\u003eChildren 2024, Vol. 11, Page 210\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 210 (2024).\u003c/li\u003e\n\u003cli\u003eLenters-Westra, E. \u003cem\u003eet al.\u003c/em\u003e Managing discordance between HbA1c and glucose management indicator. \u003cem\u003eDiabet Med\u003c/em\u003e (2025) doi:10.1111/DME.70023.\u003c/li\u003e\n\u003cli\u003eMaran, A., Morieri, M. L., Falaguasta, D., Avogaro, A. \u0026amp; Fadini, G. P. The Fast-Glycator Phenotype, Skin Advanced Glycation End Products, and Complication Burden Among People With Type 1 Diabetes. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 2439\u0026ndash;2444 (2022).\u003c/li\u003e\n\u003cli\u003ePuig-Jov\u0026eacute;, C. \u003cem\u003eet al.\u003c/em\u003e Association between the GMI/HbA1c ratio and preclinical carotid atherosclerosis in type 1 diabetes: impact of the fast-glycator phenotype across age groups. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, (2025).\u003c/li\u003e\n\u003cli\u003eShah, V. N., Kanapka, L. G., Karakus, K. E., Kollman, C. \u0026amp; Beck, R. W. The Association of High and Low Glycation With Incident Diabetic Retinopathy in Adults With Type 1 Diabetes. \u003cem\u003eJ Diabetes Sci Technol\u003c/em\u003e (2024) doi:10.1177/19322968241254811.\u003c/li\u003e\n\u003cli\u003eYang, J., Shangguan, Q., Xie, G., Yang, M. \u0026amp; Sheng, G. Sex-specific associations between haemoglobin glycation index and the risk of cardiovascular and all-cause mortality in individuals with pre-diabetes and diabetes: A large prospective cohort study. \u003cem\u003eDiabetes Obes Metab\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 2275\u0026ndash;2283 (2024).\u003c/li\u003e\n\u003cli\u003eDe La Cruz-Ares, S. \u003cem\u003eet al.\u003c/em\u003e Endothelial Dysfunction and Advanced Glycation End Products in Patients with Newly Diagnosed Versus Established Diabetes: From the CORDIOPREV Study. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eGoh, S. Y. \u0026amp; Cooper, M. E. Clinical review: The role of advanced glycation end products in progression and complications of diabetes. \u003cem\u003eJ Clin Endocrinol Metab\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, 1143\u0026ndash;1152 (2008).\u003c/li\u003e\n\u003cli\u003eLiu, J., Pan, S., Wang, X., Liu, Z. \u0026amp; Zhang, Y. Role of advanced glycation end products in diabetic vascular injury: molecular mechanisms and therapeutic perspectives. \u003cem\u003eEur J Med Res\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1\u0026ndash;7 (2023).\u003c/li\u003e\n\u003cli\u003eWard, M. S., Fortheringham, A. K., Cooper, M. E. \u0026amp; Forbes, J. M. Targeting advanced glycation endproducts and mitochondrial dysfunction in cardiovascular disease. \u003cem\u003eCurr Opin Pharmacol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 654\u0026ndash;661 (2013).\u003c/li\u003e\n\u003cli\u003ePedro, R. A., Ramon, S. A., Marc, B. B., Juan, F. B. \u0026amp; Isabel, M. M. Prevalence and relationship between diabetic retinopathy and nephropathy, and its risk factors in the North-East of Spain, a population-based study. \u003cem\u003eOphthalmic Epidemiol\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 251\u0026ndash;265 (2010).\u003c/li\u003e\n\u003cli\u003eR, K., J, F., V, S., P, G. \u0026amp; A, A. Predictors of diabetic retinopathy in patients with type 2 diabetes who have normoalbuminuria. \u003cem\u003eAnn Med Health Sci Res\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 536 (2013).\u003c/li\u003e\n\u003cli\u003eCardoso, C. R. L., Leite, N. C., DIb, E. \u0026amp; Salles, G. F. Predictors of Development and Progression of Retinopathy in Patients with Type 2 Diabetes: Importance of Blood Pressure Parameters. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, (2017).\u003c/li\u003e\n\u003cli\u003eSebastian-Valles, F. \u003cem\u003eet al.\u003c/em\u003e Time above range and no coefficient of variation is associated with diabetic retinopathy in individuals with type 1 diabetes and glycated hemoglobin within target. \u003cem\u003eActa Diabetol\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 205\u0026ndash;214 (2024).\u003c/li\u003e\n\u003cli\u003eBattelino, T. \u003cem\u003eet al.\u003c/em\u003e Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 42\u0026ndash;57 (2023).\u003c/li\u003e\n\u003cli\u003eVon Elm, E. \u003cem\u003eet al.\u003c/em\u003e The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. \u003cem\u003eAnn Intern Med\u003c/em\u003e \u003cstrong\u003e147\u003c/strong\u003e, 573\u0026ndash;577 (2007).\u003c/li\u003e\n\u003cli\u003eGrading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs \u0026mdash; An Extension of the Modified Airlie House Classification: ETDRS Report Number 10. \u003cem\u003eOphthalmology\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, S99\u0026ndash;S119 (2020).\u003c/li\u003e\n\u003cli\u003eTeam RC. A language and environment for statistical computing. (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"type 1 diabetes, HbA1c, glucose management indicator, diabetic retinopathy, glycation index, continuous glucose monitoring","lastPublishedDoi":"10.21203/rs.3.rs-6792531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6792531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe discordance between glycated haemoglobin (HbA1c) and the glucose management indicator (GMI) has been proposed as a marker of vascular risk in diabetes. This study evaluated whether the HbA1c/GMI ratio independently predicts diabetic retinopathy (DR) in adults with type 1 diabetes (T1D) using continuous glucose monitoring. We conducted a multicenter cross-sectional study involving 1,070 adults using flash glucose monitoring. Participants were stratified as high glycators (ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.9) or non-high glycators based on the HbA1c/GMI ratio. DR status was assessed by ophthalmologic evaluation. Multivariable logistic regression and 1:1 propensity score matching were used to assess independent associations with DR, adjusting for age, sex, diabetes duration, smoking, hypertension, LDL cholesterol, BMI, and insulin dose. While high glycators had a higher crude DR prevalence (31.3% vs. 23.1%, p\u0026thinsp;=\u0026thinsp;0.020), the HbA1c/GMI ratio was not independently associated with DR in adjusted models (OR 1.19; 95% CI: 0.34\u0026ndash;4.15; p\u0026thinsp;=\u0026thinsp;0.785) or in the matched cohort (OR 1.23; 95% CI: 0.76\u0026ndash;1.99; p\u0026thinsp;=\u0026thinsp;0.391). Absolute HbA1c remained the strongest glycemic predictor. These findings suggest that the HbA1c/GMI ratio may aid in interpreting discordant glycemic profiles but lacks independent prognostic value for DR.\u003c/p\u003e","manuscriptTitle":"The HbA1c/GMI Ratio Does Not Independently Predict Diabetic Retinopathy in Adults with Type 1 Diabetes: A Multicenter Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 20:33:23","doi":"10.21203/rs.3.rs-6792531/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-17T14:19:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T15:10:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T12:15:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241273211767409877212290101375012657622","date":"2025-07-07T19:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156448709633947701115251032120848971490","date":"2025-07-05T22:17:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-03T12:19:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T17:07:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-10T15:47:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T04:18:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-31T18:26:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50504af1-d85f-4ebe-9866-4a68c07b8005","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51134068,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Diabetes complications"},{"id":51134069,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes mellitus"}],"tags":[],"updatedAt":"2025-10-20T16:03:21+00:00","versionOfRecord":{"articleIdentity":"rs-6792531","link":"https://doi.org/10.1038/s41598-025-19705-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-14 15:58:11","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-07-07 20:33:23","video":"","vorDoi":"10.1038/s41598-025-19705-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-19705-0","workflowStages":[]},"version":"v1","identity":"rs-6792531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6792531","identity":"rs-6792531","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}