Association between glycemia risk index and diabetic retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study

Association between glycemia risk index and diabetic retinopathy in patients with type 2 diabetes mellitus: A 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 Research Article Association between glycemia risk index and diabetic retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study Nengguang Fan, Hongyan Xu, Liying Zhu, Ye Zhu, Lining Dong, Pei Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7932961/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Glycemia risk index (GRI), a composite metric derived from continuous glucose monitoring, has emerged as a novel indicator of glycemic burden. However, whether GRI is independently associated with diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM) after adjustment for conventional glycemic measures remains unclear. This study aimed to investigate the independent association between GRI and DR in T2DM patients. Methods This cross-sectional study enrolled 349 T2DM patients at the Endocrinology and Metabolism Department of Shanghai General Hospital. Continuous glucose monitoring (CGM) data were collected over 14 days using the FreeStyle Libre system. Patients were stratified into tertiles based on GRI values. DR was diagnosed through standardized ophthalmological examination. Multivariable logistic regression models were used to examine the association between GRI and DR. Results Higher GRI tertiles were associated with longer diabetes duration, higher HbA1c levels, increased glycemic variability, lower time in range (TIR), and more prevalent insulin use (all P < 0.05). The prevalence of DR increased significantly across GRI tertiles (19.4, 29.9, and 34.7% in the low, middle, and high tertiles, respectively; P for trend = 0.018). After adjusting for confounders including HbA1c and TIR, the highest GRI tertile was associated with a 3.41-fold increased risk of DR compared to the lowest tertile (OR: 3.41, 95% CI: 1.03–11.34, P = 0.045). Subgroup analyses confirmed the consistent association between GRI and DR across various clinical characteristics (all P for interaction > 0.05). Conclusions GRI is independently associated with DR in T2DM patients, even after adjusting for conventional glycemic measures. Glycaemia risk index Type 2 diabetes mellitus Diabetic retinopathy Figures Figure 1 Figure 2 1. Background Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by hyperglycemia, which can lead to various complications, including diabetic retinopathy (DR). DR remains a leading cause of vision impairment and blindness among working-age adults worldwide [ 1 ]. Although the pathogenesis of DR is multifactorial, chronic hyperglycemia plays a central role in its development and progression [ 2 ]. Traditionally, glycated hemoglobin (HbA1c) has been the gold standard for assessing long-term glycemic control and predicting diabetes complications [ 3 ]. However, HbA1c has limitations in capturing short-term glycemic variability and extreme glucose excursions, which are increasingly recognized as important contributors to the pathogenesis of diabetic complications, including DR [ 4 , 5 ]. Therefore, complementary tools are needed to provide a more comprehensive assessment of glycemic control and its associated risks. The advent of continuous glucose monitoring (CGM) technology has revolutionized glycemic assessment by providing detailed data on glucose fluctuations throughout the days [ 6 , 7 ]. This has led to the development of novel metrics for evaluating glycemic control, such as time in range (TIR), which has been associated with diabetes-related complications [ 8 , 9 ]. Recently, the glycemia risk index (GRI) has emerged as a promising composite indicator that quantifies both the magnitude and duration of glucose excursions beyond the target range [ 10 , 11 ]. GRI has been shown to correlate with glycemic control in adults with type 1 diabetes [ 12 , 13 ] and with arterial stiffness and albuminuria in patients with T2DM [ 14 , 15 ]. A recent cohort study suggested that higher GRI is associated with increased risk of DR in patients with T2DM [ 16 ]. However, whether this association remains significant after adjusting for conventional glycemic metrics such as HbA1c and TIR remains unclear. In this study, we aimed to examine the association between GRI, calculated from 14-day CGM data, and the presence of DR in patients with T2DM. By utilizing a longer CGM monitoring period than previous studies, we sought to provide a more comprehensive assessment of glycemic patterns and their relationship to DR. Specifically, we investigated whether the association between GRI and DR is independent of conventional glycemic metrics, evaluating GRI's potential added value in DR risk assessment. 2. Methods 2.1. Study participants This study enrolled individuals between May 2023 and July 2024 from the Department of Endocrinology and Metabolism at Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine. Eligible participants met the following criteria: (1) aged between 18 and 80 years; (2) confirmed T2DM diagnosis based on WHO 1999 diagnostic criteria; and (3) possessed complete CGM recordings spanning 14 days. Participants were deemed ineligible if they had: (1) type 1 diabetes or diabetes of other specific etiologies; (2) significant hepatic impairment (alanine/aspartate aminotransferase exceeding 3-fold the upper normal limit); (3) advanced renal dysfunction (estimated glomerular filtration rate below 30 mL/min/1.73 m²); (4) ongoing acute or chronic infectious conditions; (5) active malignant disease; (6) current pregnancy or breastfeeding status; and (7) diabetic ketoacidosis. After applying these criteria, 349 individuals qualified for inclusion in our analysis. DR classification followed the International Clinical Classification System established during the 2002 International Ophthalmology Conference [21]. The research received ethical approval from the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, and adhered strictly to the Declaration of Helsinki principles. All enrolled individuals provided written informed consent prior to participation. 2.2. Anthropometric and biochemical measurements Following an overnight fast (minimum 8 hours), all enrolled individuals underwent standardized anthropometric assessments. While wearing minimal clothing and no footwear, participants had their height and weight recorded. BMI was computed using the formula: weight in kilograms divided by the square of height in meters. Following a 5-minute rest period, blood pressure measurements were obtained with participants in a seated position. Venous blood collection occurred after overnight fasting. An automated analytical system (Beckman Coulter, Palo Alto, CA, USA) quantified serum levels of TG, TC, LDL-C, HDL-C, AST, and ALT. The hexokinase enzymatic method determined FPG concentrations. HbA1c levels were assessed using HPLC methodology standardized by the NGSP. 2.3. Metrics derived from CGM The FreeStyle Libre Flash Glucose Monitoring system (Abbott Diabetes Care, Alameda, CA, USA) was employed for 14-day continuous glucose monitoring. Sensors attached to the posterior upper arm region captured interstitial glucose concentrations at 5-minute intervals. Participants received instructions to perform sensor scans at minimum 8-hourly intervals. CGM recordings enabled calculation of multiple glycemic parameters: (1) TIR (glucose 3.9–10.0 mmol/L), (2) TAR (glucose > 10.0 mmol/L), (3) TBR (glucose < 3.9 mmol/L), (4) SD, (5) MAGE, (6) MODD, and (7) LAGE. GRI computation utilized the formula: GRI = (3.0 × percentage of time with glucose 13.9 mmol/L) + (0.8 × percentage of time with glucose 10.1–13.9 mmol/L). Participants were subsequently categorized into three groups based on GRI value distribution. 2.4. Statistical analysis SPSS software (version 13.0, Chicago, IL, USA) facilitated all statistical computations. Data presentation included mean ± SD or median (IQR) for continuous parameters, while categorical data appeared as frequency counts (percentages). Between-tertile comparisons utilized ANOVA or Kruskal-Wallis testing for continuous parameters and chi-square analysis for categorical parameters. Multivariable logistic regression analysis with sequential confounder adjustment examined the GRI-DR relationship. The first model incorporated age, sex, and BMI adjustments; the second model added diabetes duration, blood pressure parameters, lipid measurements, and therapeutic agents; the third model further included HbA1c and TIR. A generalized additive modeling approach explored potential non-linear associations between GRI and DR. Effect modification was investigated through stratified subgroup analyses. Two-tailed P-values below 0.05 indicated statistical significance. 3. Results 3.1. Baseline characteristics stratified by GRI tertiles The 349 study participants were stratified into three groups based on GRI tertiles: T1 ( 25.9). Baseline clinical characteristics, CGM metrics, and medication use across GRI tertiles are shown in Table 1 . Significant differences were observed in several parameters. BMI was lower in the highest GRI tertile (P = 0.032), while diabetes duration varied significantly among tertiles (P = 0.001). HbA1c levels increased progressively from the lowest to highest GRI tertile (P < 0.01), indicating poorer glycemic control with increasing GRI. Table 1 Clinical characteristics and CGM metrics of the subjects according to the tertile of GRI GRI tertile T1 (0.0-9.9) T2 (10.0-25.8) T3 (25.9-173.2) P-value N 98 97 98 Age (years) 55.8 (13.0) 58.4 (13.1) 58.9 (15.3) 0.251 DBP (mmHg) 79.9 (9.9) 79.9 (12.6) 79.3 (10.8) 0.889 SBP (mmHg) 130.8 (19.5) 131.4 (17.2) 129.9 (16.9) 0.854 BMI (kg/m 2 ) 25.5 (3.7) 25.5 (4.1) 24.0 (3.4) 0.032 Duration (years) 6.0 (1.0–15.0) 14.0 (5.0–20.0) 10.0 (5.0–20.0) 0.001 FBG (mmol/L) 7.0 (2.4) 7.1 (2.2) 6.9 (2.6) 0.892 PBG (mmol/L) 10.9 (3.5) 11.7 (3.9) 11.8 (4.0) 0.227 HbA1c (%) 8.5 (2.5) 8.8 (2.0) 9.3 (2.2) 0.005 ALT (IU/L) 24.0 (15.9–35.0) 18.2 (14.1–24.9) 18.0 (14.5–28.3) 0.044 AST (IU/L) 20.4 (16.8–27.8) 18.7 (15.5–22.9) 19.6 (15.6–25.1) 0.143 TG (mmol/L) 1.4 (1.0-1.9) 1.3 (1.0-2.2) 1.7 (1.5) 1.3 (0.9–1.8) 0.344 TC (mmol/L) 4.4 (1.3) 4.2 (1.1) 4.3 (1.2) 0.443 HDL-C (mmol/L) 1.1 (0.5) 1.1 (0.4) 1.0 (0.3) 0.903 LDL-C (mmol/L) 2.6 (0.9) 2.4 (1.0) 2.6 (1.1) 0.276 SD (%) 1.5 (0.3) 2.0 (0.4) 2.5 (0.9) < 0.001 MAGE (mmol/L) 3.8 (0.8) 5.0 (1.2) 5.9 (2.1) < 0.001 MODD (mmol/L) 1.2 (0.2) 1.6 (0.4) 2.0 (1.0) < 0.001 LAGE (mmol/L) 9.3 (2.1) 11.3 (2.6) 12.4 (4.3) < 0.001 GMI (%) 6.2 (0.3) 6.4 (0.4) 6.5 (1.0) 0.002 TBR (%) 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.1 (0.0-0.2) < 0.001 TIR (%) 1.0 (0.0) 0.9 (0.1) 0.7 (0.1) < 0.001 TAR (%) 0.0 (0.0-0.1) 0.1 (0.0-0.2) 0.2 (0.0-0.3) < 0.001 Sex (%) 0.808 Female 41 (41.8%) 45 (46.4%) 44 (44.9%) Male 57 (58.2%) 52 (53.6%) 54 (55.1%) DR (%) 0.051 0 79 (80.6%) 68 (70.1%) 64 (65.3%) 1 19 (19.4%) 29 (29.9%) 34 (34.7%) Metformin (%) 0.052 0 40 (40.8%) 49 (50.5%) 57 (58.2%) 1 58 (59.2%) 48 (49.5%) 41 (41.8%) Acarbose (%) 0.753 0 61 (62.2%) 59 (60.8%) 56 (57.1%) 1 37 (37.8%) 38 (39.2%) 42 (42.9%) DPP-4i (%) 0.204 0 74 (75.5%) 78 (80.4%) 68 (69.4%) 1 24 (24.5%) 19 (19.6%) 30 (30.6%) SGLT-2i (%) 0.003 0 13 (13.3%) 14 (14.4%) 30 (30.6%) 1 85 (86.7%) 83 (85.6%) 68 (69.4%) Su (%) 0.551 0 98 (100.0%) 96 (99.0%) 96 (98.0%) 1 0 (0.0%) 1 (1.0%) 2 (2.0%) TZD (%) 0.824 0 81 (82.7%) 79 (81.4%) 81 (82.7%) 1 17 (17.3%) 18 (18.5%) 17 (17.3%) Insulin (%) < 0.001 0 58 (59.2%) 34 (35.1%) 27 (27.6%) 1 40 (40.8%) 63 (64.9%) 71 (72.4%) GLP-1RAs (%) 0.085 0 33 (33.7%) 27 (27.8%) 42 (42.9%) 1 65 (66.3%) 70 (72.2%) 56 (57.1%) Data is present: mean (SD) or median (IQR) or n (%) Moreover, all CGM-derived metrics differed significantly across GRI tertiles (all P < 0.001). Measures of glycemic variability, including SD, MAGE, MODD, and LAGE, increased progressively with higher GRI. Conversely, TIR decreased substantially from T1 to T3. Both TBR and TAR increased significantly in the higher GRI tertiles, consistent with the definition of GRI. Medication use patterns also varied significantly across GRI tertiles. Insulin therapy was more frequently prescribed in patients with higher GRI (40.8, 64.9, and 72.4% in T1, T2, and T3, respectively; P < 0.001). Interestingly, SGLT-2 inhibitor use was less common in the highest GRI tertile (69.4%) compared to the lower tertiles (86.7% and 85.6% in T1 and T2, respectively; P = 0.003). 3.2. Prevalence of DR across tertiles of GRI Next, the association between GRI and DR was examined. As shown in Fig. 1 A, the prevalence of DR increased across GRI tertiles (19.3, 29.9, and 34.69% in the low, middle, and high tertiles, respectively). A trend test revealed a statistically significant linear trend (P for trend = 0.0178). To further examine the relationship between GRI and DR, we employed a generalized additive model with adjustment for potential confounders. As illustrated in Fig. 1 B, GRI exhibited a positive, approximately linear relationship with the probability of DR. These findings indicate a significant and positive association between GRI and DR. 3.3. Independent association of GRI with DR risk To further investigate the independent association between GRI and the risk of DR, multiple logistic regression was performed (Table 2 ). When analyzed as a continuous variable, GRI showed a significant positive association with DR risk in the unadjusted model (OR: 1.01, 95% CI: 1.00–1.02, P = 0.0178). This association remained statistically significant after sequential adjustment for potential confounders. In Model I (adjusted for sex, age, blood pressure, and BMI), the OR was 1.01 (95% CI: 1.00–1.02, P = 0.0320). In Model II (further adjusted for diabetes duration, FBG, HbA1c, ALT, lipid profile, and diabetes medications), the OR was 1.02 (95% CI: 1.00–1.03, P = 0.0105). The association persisted in Model III with additional adjustment for TIR (OR: 1.03, 95% CI: 1.00–1.05, P = 0.0193). Table 2 Logistic regression analyses of the association between GRI and DR Exposure Non-adjusted Adjust I Adjust II Adjust III GRI 1.01 (1.00, 1.02) 0.0178 1.01 (1.00, 1.02) 0.0320 1.02 (1.00, 1.03) 0.0105 1.03 (1.00, 1.05) 0.0193 GRI T1 Ref Ref Ref Ref T2 1.77 (0.91, 3.44) 0.0905 1.80 (0.92, 3.52) 0.0880 1.67 (0.74, 3.78) 0.2182 1.81 (0.76, 4.34) 0.1816 T3 2.21 (1.15, 4.24) 0.0170 2.12 (1.09, 4.13) 0.0267 2.69 (1.20, 6.07) 0.0166 3.41 (1.03, 11.34) 0.0453 P for trend 0.0178 0.0282 0.0159 0.0454 Data is present: β (95%CI) P value / OR (95%CI) P value Non-adjusted model adjust for: None Adjust I model adjust for: Sex, Age, DBP, SBP, BMI Adjust II model adjust for: plus duration of diabetes, FBG, HbA1c, ALT, TG, TC, HDL-C, LDL-C and treatment of metformin, acarbose, DPP-4i, SGLT-2i, Su, insulin and GLP-1RAs. Adjust III model adjust for: plus TIR. When GRI was categorized into tertiles, participants in the highest tertile (T3) consistently showed a significantly higher risk of DR compared to those in the lowest tertile (T1) across all models. In the unadjusted model, T3 was associated with a 2.21-fold increased risk of DR (95% CI: 1.15–4.24, P = 0.0170) compared to T1. This significant association was maintained after adjustment for demographic and clinical factors in Model I (OR: 2.12, 95% CI: 1.09–4.13, P = 0.0267), and after further adjustments in Model II (OR: 2.69, 95% CI: 1.20–6.07, P = 0.0166) and Model III (OR: 3.41, 95% CI: 1.03–11.34, P = 0.0453). 3.4. Subgroup analysis of the association between GRI and DR To investigate whether the association between GRI and DR was consistent across different clinical characteristics, a series of subgroup analyses were conducted. Participants were stratified based on sex, age (< 60 years vs. ≥ 60 years), BMI (< 24 kg/m² vs. ≥ 24 kg/m²), HbA1c (< 7.0% vs. ≥ 7.0%), TIR ( 0.05). The consistency of the association across these subgroups further supports the robustness of the association of GRI with DR. 3. Discussion In this cross-sectional study of patients with T2DM, we found that GRI was independently associated with the presence of DR, even after comprehensive adjustment for established risk factors and conventional glycemic parameters including HbA1c and TIR. This study provides novel insights into the utility of GRI as a potentially metric for assessing DR risk in T2DM patients. Our results demonstrated that higher GRI values were associated with poorer overall glycemic control and greater glycemic variability, as evidenced by higher HbA1c levels, lower TIR, and increased measures of glycemic fluctuation (SD, MAGE, MODD, LAGE). These observations align with previous studies that have reported similar relationships between GRI and various glycemic parameters in both type 1 and type 2 diabetes [ 10 , 11 , 17 ]. These results support the use of GRI as a comprehensive index reflective of glycemic control and variability. A key finding of our study was the progressive increase in DR prevalence across GRI tertiles, with a significant dose-response relationship. Multiple logistic regression analyses confirmed this association, revealing that patients in the highest GRI tertile had a 3.41-fold increased risk of DR compared to those in the lowest tertile, after adjusting for a wide range of demographic, clinical, and metabolic factors. Notably, this association remained robust after accounting for both HbA1c and TIR, suggesting that GRI captures aspects of glycemic dysregulation relevant to retinal pathology that are not fully reflected by conventional glycemic metrics. These results are consistent with a recent cohort study that also reported significant associations between GRI and DR [ 16 ], strengthening the evidence for GRI as a valuable marker of DR risk. The mechanisms underlying the association between GRI and DR are likely multifaceted. GRI captures both the magnitude and duration of glucose excursions beyond the target range[ 11 , 17 ], which may better reflect the overall glycemic burden on the retinal vasculature compared to average glucose levels alone. Prolonged exposure to hyperglycemia and acute glucose fluctuations can lead to oxidative stress, inflammation, and endothelial dysfunction, all of which contribute to the progression of DR [ 18 , 19 ]. A key strength of our study is the use of 14-day CGM data to calculate GRI, which provides a more comprehensive assessment of glycemic patterns compared to shorter monitoring periods. Additionally, our analysis accounted for a wide range of potential confounders, including HbA1C, TIR and diabetes medications, reinforcing the independent association between GRI and DR. However, this study has several limitations. First, the cross-sectional design precludes the establishment of causality. Secondly, though we adjusted for numerous confounders, residual confounding cannot be entirely ruled out. Third, the sample size of the present study was still relatively small. 4. Conclusions In conclusion, our study demonstrates a significant, independent association between GRI and DR in patients with T2DM. These findings suggest that GRI, derived from CGM data, may provide valuable information for assessing DR risk beyond traditional glycemic measures. Declarations Ethics approval and consent to participate The study was approved by the Institutional Review Board of Shanghai General Hospital, affiliated to the Shanghai Jiao Tong University School of Medicine. All procedures and protocols were meticulously crafted to align with the principles stipulated in the Helsinki Declaration. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This study was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0524202), the National Natural Science Foundation of China (81400785), and the Natural Science Foundation of Shanghai (21ZR1451200). Author Contribution Fang Liu, Yijiong Tan, Nengguang Fan, Hongyan Xu and Yanyun Hu handled the statistical analysis, study design, and manuscript writing. The tasks of Hongyan Xu, Ye Zhu, Lining Dong, Pei Wang, Shan Chen, Jiaying Yang, Xingdong Zheng, included recruiting participants, gathering and analyzing data, and following up. The manuscript was read and assessed by Fang Liu, Nengguang Fan. The study concept was contributed by Yanyun Hu and Nengguang Fan who also made significant manuscript edits. Each author accepted the submitted version of the paper and made contributions to it. Acknowledgements: The authors thank all the staff and participants of this study for their important contributions. Data Availability The datasets used and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. References Vision Loss Expert Group of the Global Burden of Disease Study. GBD 2019 Blindness and Vision Impairment Collaborators. Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020. Eye (Lond). 2024;38:2047–57. Antonetti DA, Silva PS, Stitt AW. Current understanding of the molecular and cellular pathology of diabetic retinopathy. Nat Rev Endocrinol. 2021;17:195–206. Zoungas S, Chalmers J, Ninomiya T, Li Q, Cooper ME, Colagiuri S, et al. Association of HbA1c levels with vascular complications and death in patients with type 2 diabetes: evidence of glycaemic thresholds. Diabetologia. 2012;55:636–43. Chen B, Shen C, Sun B. Current landscape and comprehensive management of glycemic variability in diabetic retinopathy. 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Diabetic retinopathy, oxidative stress, and sirtuins: an in depth look in enzymatic patterns and new therapeutic horizons. Surv Ophthalmol. 2022;67:168–83. Tang Q, Buonfiglio F, Böhm EW, Zhang L, Pfeiffer N, Korb CA, et al. Diabetic Retinopathy: New Treatment Approaches Targeting Redox and Immune Mechanisms. Antioxid Basel Switz. 2024;13:594. Additional Declarations No competing interests reported. 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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-7932961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554721379,"identity":"f04e1fc7-b9c5-466b-8ac9-b00f1fed17a5","order_by":0,"name":"Nengguang 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10:48:20","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78471,"visible":true,"origin":"","legend":"","description":"","filename":"f0422a4b27b04a88b0efc07f5b2efae21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7932961/v1/a9a6640dc6f7b351bfc2b3a7.xml"},{"id":97667541,"identity":"8e8116ec-5303-482e-a917-cbb678577453","added_by":"auto","created_at":"2025-12-08 09:23:45","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83343,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7932961/v1/79952893bea4c8e57daf1935.html"},{"id":97433394,"identity":"287f86a7-9fd6-49bb-a7f1-5791940810b9","added_by":"auto","created_at":"2025-12-04 10:48:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of GRI with DR.\u003c/strong\u003e(A) Prevalence of DR across the tertile of GRI (\u003cem\u003eP\u003c/em\u003e for trend ＜0.05); (B) Relationship between GRI and DR risk analyzed by a generalized additive model.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7932961/v1/a55c34a59a9d5858c4931f6b.png"},{"id":97433398,"identity":"f14aad8f-243c-45ea-b3ee-059e310e340d","added_by":"auto","created_at":"2025-12-04 10:48:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":830293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between GRI and DR across subgroups\u003c/strong\u003e. Forest plot showing odds ratios (ORs) and 95% confidence intervals (CIs) for the relationship between GRI standard deviation (SD) and DR risk, stratified by sex, age, BMI, HbA1c and TIR. Multivariable logistic regression models were adjusted for potential confounders.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7932961/v1/9d92c65b93474293af43a674.png"},{"id":97677485,"identity":"2d0ed445-a02a-42df-a504-43c1f952134b","added_by":"auto","created_at":"2025-12-08 09:53:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2057166,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7932961/v1/8ac64697-02f2-4298-a496-057dbc3b6b52.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between glycemia risk index and diabetic retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by hyperglycemia, which can lead to various complications, including diabetic retinopathy (DR). DR remains a leading cause of vision impairment and blindness among working-age adults worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the pathogenesis of DR is multifactorial, chronic hyperglycemia plays a central role in its development and progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTraditionally, glycated hemoglobin (HbA1c) has been the gold standard for assessing long-term glycemic control and predicting diabetes complications [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, HbA1c has limitations in capturing short-term glycemic variability and extreme glucose excursions, which are increasingly recognized as important contributors to the pathogenesis of diabetic complications, including DR [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, complementary tools are needed to provide a more comprehensive assessment of glycemic control and its associated risks.\u003c/p\u003e\u003cp\u003eThe advent of continuous glucose monitoring (CGM) technology has revolutionized glycemic assessment by providing detailed data on glucose fluctuations throughout the days [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This has led to the development of novel metrics for evaluating glycemic control, such as time in range (TIR), which has been associated with diabetes-related complications [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recently, the glycemia risk index (GRI) has emerged as a promising composite indicator that quantifies both the magnitude and duration of glucose excursions beyond the target range [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. GRI has been shown to correlate with glycemic control in adults with type 1 diabetes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and with arterial stiffness and albuminuria in patients with T2DM [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A recent cohort study suggested that higher GRI is associated with increased risk of DR in patients with T2DM [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, whether this association remains significant after adjusting for conventional glycemic metrics such as HbA1c and TIR remains unclear.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to examine the association between GRI, calculated from 14-day CGM data, and the presence of DR in patients with T2DM. By utilizing a longer CGM monitoring period than previous studies, we sought to provide a more comprehensive assessment of glycemic patterns and their relationship to DR. Specifically, we investigated whether the association between GRI and DR is independent of conventional glycemic metrics, evaluating GRI's potential added value in DR risk assessment.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study participants\u003c/h2\u003e\u003cp\u003eThis study enrolled individuals between May 2023 and July 2024 from the Department of Endocrinology and Metabolism at Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine. Eligible participants met the following criteria: (1) aged between 18 and 80 years; (2) confirmed T2DM diagnosis based on WHO 1999 diagnostic criteria; and (3) possessed complete CGM recordings spanning 14 days. Participants were deemed ineligible if they had: (1) type 1 diabetes or diabetes of other specific etiologies; (2) significant hepatic impairment (alanine/aspartate aminotransferase exceeding 3-fold the upper normal limit); (3) advanced renal dysfunction (estimated glomerular filtration rate below 30 mL/min/1.73 m\u0026sup2;); (4) ongoing acute or chronic infectious conditions; (5) active malignant disease; (6) current pregnancy or breastfeeding status; and (7) diabetic ketoacidosis. After applying these criteria, 349 individuals qualified for inclusion in our analysis.\u003c/p\u003e\u003cp\u003eDR classification followed the International Clinical Classification System established during the 2002 International Ophthalmology Conference [21]. The research received ethical approval from the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, and adhered strictly to the Declaration of Helsinki principles. All enrolled individuals provided written informed consent prior to participation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Anthropometric and biochemical measurements\u003c/h2\u003e\u003cp\u003eFollowing an overnight fast (minimum 8 hours), all enrolled individuals underwent standardized anthropometric assessments. While wearing minimal clothing and no footwear, participants had their height and weight recorded. BMI was computed using the formula: weight in kilograms divided by the square of height in meters. Following a 5-minute rest period, blood pressure measurements were obtained with participants in a seated position.\u003c/p\u003e\u003cp\u003eVenous blood collection occurred after overnight fasting. An automated analytical system (Beckman Coulter, Palo Alto, CA, USA) quantified serum levels of TG, TC, LDL-C, HDL-C, AST, and ALT. The hexokinase enzymatic method determined FPG concentrations. HbA1c levels were assessed using HPLC methodology standardized by the NGSP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Metrics derived from CGM\u003c/h2\u003e\u003cp\u003eThe FreeStyle Libre Flash Glucose Monitoring system (Abbott Diabetes Care, Alameda, CA, USA) was employed for 14-day continuous glucose monitoring. Sensors attached to the posterior upper arm region captured interstitial glucose concentrations at 5-minute intervals. Participants received instructions to perform sensor scans at minimum 8-hourly intervals.\u003c/p\u003e\u003cp\u003eCGM recordings enabled calculation of multiple glycemic parameters: (1) TIR (glucose 3.9\u0026ndash;10.0 mmol/L), (2) TAR (glucose\u0026thinsp;\u0026gt;\u0026thinsp;10.0 mmol/L), (3) TBR (glucose\u0026thinsp;\u0026lt;\u0026thinsp;3.9 mmol/L), (4) SD, (5) MAGE, (6) MODD, and (7) LAGE. GRI computation utilized the formula: GRI = (3.0 \u0026times; percentage of time with glucose\u0026thinsp;\u0026lt;\u0026thinsp;3.0 mmol/L) + (2.4 \u0026times; percentage of time with glucose 3.0\u0026ndash;3.8 mmol/L) + (1.6 \u0026times; percentage of time with glucose\u0026thinsp;\u0026gt;\u0026thinsp;13.9 mmol/L) + (0.8 \u0026times; percentage of time with glucose 10.1\u0026ndash;13.9 mmol/L). Participants were subsequently categorized into three groups based on GRI value distribution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e\u003cp\u003eSPSS software (version 13.0, Chicago, IL, USA) facilitated all statistical computations. Data presentation included mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) for continuous parameters, while categorical data appeared as frequency counts (percentages). Between-tertile comparisons utilized ANOVA or Kruskal-Wallis testing for continuous parameters and chi-square analysis for categorical parameters.\u003c/p\u003e\u003cp\u003eMultivariable logistic regression analysis with sequential confounder adjustment examined the GRI-DR relationship. The first model incorporated age, sex, and BMI adjustments; the second model added diabetes duration, blood pressure parameters, lipid measurements, and therapeutic agents; the third model further included HbA1c and TIR. A generalized additive modeling approach explored potential non-linear associations between GRI and DR. Effect modification was investigated through stratified subgroup analyses. Two-tailed P-values below 0.05 indicated statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Baseline characteristics stratified by GRI tertiles\u003c/h2\u003e\u003cp\u003eThe 349 study participants were stratified into three groups based on GRI tertiles: T1 (\u0026lt;\u0026thinsp;9.9), T2 (10.0\u0026ndash;25.8), and T3 (\u0026gt;\u0026thinsp;25.9). Baseline clinical characteristics, CGM metrics, and medication use across GRI tertiles are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences were observed in several parameters. BMI was lower in the highest GRI tertile (P\u0026thinsp;=\u0026thinsp;0.032), while diabetes duration varied significantly among tertiles (P\u0026thinsp;=\u0026thinsp;0.001). HbA1c levels increased progressively from the lowest to highest GRI tertile (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating poorer glycemic control with increasing GRI.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical characteristics and CGM metrics of the subjects according to the tertile of GRI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI tertile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1 (0.0-9.9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT2 (10.0-25.8)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT3 (25.9-173.2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.8 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.4 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.9 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.9 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.9 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.3 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130.8 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.4 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129.9 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.5 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.5 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.0 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 (1.0\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.0 (5.0\u0026ndash;20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.0 (5.0\u0026ndash;20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.0 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.1 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePBG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.9 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.8 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.8 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.3 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.0 (15.9\u0026ndash;35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.2 (14.1\u0026ndash;24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.0 (14.5\u0026ndash;28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.4 (16.8\u0026ndash;27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.7 (15.5\u0026ndash;22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.6 (15.6\u0026ndash;25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4 (1.0-1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3 (1.0-2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.7 (1.5) 1.3 (0.9\u0026ndash;1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.2 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.6 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.6 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAGE (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.8 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMODD (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAGE (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.3 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.3 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.4 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGMI (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.5 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0 (0.0\u0026ndash;0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (0.0\u0026ndash;0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1 (0.0-0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0 (0.0-0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1 (0.0-0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.0-0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (41.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (44.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (58.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (53.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (55.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (80.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (70.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (65.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (34.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetformin (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (40.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (50.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (58.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (59.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (49.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (41.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcarbose (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (62.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (60.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (37.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (39.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP-4i (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (75.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (80.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (69.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGLT-2i (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (86.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (85.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (69.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSu (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (99.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96 (98.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTZD (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (81.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (59.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (35.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (27.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (40.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (64.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71 (72.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLP-1RAs (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (33.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (72.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData is present: mean (SD) or median (IQR) or n (%)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMoreover, all CGM-derived metrics differed significantly across GRI tertiles (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Measures of glycemic variability, including SD, MAGE, MODD, and LAGE, increased progressively with higher GRI. Conversely, TIR decreased substantially from T1 to T3. Both TBR and TAR increased significantly in the higher GRI tertiles, consistent with the definition of GRI.\u003c/p\u003e\u003cp\u003eMedication use patterns also varied significantly across GRI tertiles. Insulin therapy was more frequently prescribed in patients with higher GRI (40.8, 64.9, and 72.4% in T1, T2, and T3, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, SGLT-2 inhibitor use was less common in the highest GRI tertile (69.4%) compared to the lower tertiles (86.7% and 85.6% in T1 and T2, respectively; P\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Prevalence of DR across tertiles of GRI\u003c/h2\u003e\u003cp\u003eNext, the association between GRI and DR was examined. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, the prevalence of DR increased across GRI tertiles (19.3, 29.9, and 34.69% in the low, middle, and high tertiles, respectively). A trend test revealed a statistically significant linear trend (P for trend\u0026thinsp;=\u0026thinsp;0.0178).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further examine the relationship between GRI and DR, we employed a generalized additive model with adjustment for potential confounders. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, GRI exhibited a positive, approximately linear relationship with the probability of DR. These findings indicate a significant and positive association between GRI and DR.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Independent association of GRI with DR risk\u003c/h2\u003e\u003cp\u003eTo further investigate the independent association between GRI and the risk of DR, multiple logistic regression was performed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When analyzed as a continuous variable, GRI showed a significant positive association with DR risk in the unadjusted model (OR: 1.01, 95% CI: 1.00\u0026ndash;1.02, P\u0026thinsp;=\u0026thinsp;0.0178). This association remained statistically significant after sequential adjustment for potential confounders. In Model I (adjusted for sex, age, blood pressure, and BMI), the OR was 1.01 (95% CI: 1.00\u0026ndash;1.02, P\u0026thinsp;=\u0026thinsp;0.0320). In Model II (further adjusted for diabetes duration, FBG, HbA1c, ALT, lipid profile, and diabetes medications), the OR was 1.02 (95% CI: 1.00\u0026ndash;1.03, P\u0026thinsp;=\u0026thinsp;0.0105). The association persisted in Model III with additional adjustment for TIR (OR: 1.03, 95% CI: 1.00\u0026ndash;1.05, P\u0026thinsp;=\u0026thinsp;0.0193).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analyses of the association between GRI and DR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-adjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjust I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdjust II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjust III\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01 (1.00, 1.02)\u003c/p\u003e\u003cp\u003e0.0178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01 (1.00, 1.02)\u003c/p\u003e\u003cp\u003e0.0320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02 (1.00, 1.03)\u003c/p\u003e\u003cp\u003e0.0105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03 (1.00, 1.05)\u003c/p\u003e\u003cp\u003e0.0193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI\u003c/p\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.77 (0.91, 3.44)\u003c/p\u003e\u003cp\u003e0.0905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.80 (0.92, 3.52)\u003c/p\u003e\u003cp\u003e0.0880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.67 (0.74, 3.78)\u003c/p\u003e\u003cp\u003e0.2182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.81 (0.76, 4.34)\u003c/p\u003e\u003cp\u003e0.1816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.21 (1.15, 4.24)\u003c/p\u003e\u003cp\u003e0.0170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12 (1.09, 4.13)\u003c/p\u003e\u003cp\u003e0.0267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.69 (1.20, 6.07)\u003c/p\u003e\u003cp\u003e0.0166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.41 (1.03, 11.34)\u003c/p\u003e\u003cp\u003e0.0453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0454\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData is present: β (95%CI) P value / OR (95%CI) P value\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNon-adjusted model adjust for: None\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdjust I model adjust for: Sex, Age, DBP, SBP, BMI\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdjust II model adjust for: plus duration of diabetes, FBG, HbA1c, ALT, TG, TC, HDL-C, LDL-C and treatment of metformin, acarbose, DPP-4i, SGLT-2i, Su, insulin and GLP-1RAs.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdjust III model adjust for: plus TIR.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen GRI was categorized into tertiles, participants in the highest tertile (T3) consistently showed a significantly higher risk of DR compared to those in the lowest tertile (T1) across all models. In the unadjusted model, T3 was associated with a 2.21-fold increased risk of DR (95% CI: 1.15\u0026ndash;4.24, P\u0026thinsp;=\u0026thinsp;0.0170) compared to T1. This significant association was maintained after adjustment for demographic and clinical factors in Model I (OR: 2.12, 95% CI: 1.09\u0026ndash;4.13, P\u0026thinsp;=\u0026thinsp;0.0267), and after further adjustments in Model II (OR: 2.69, 95% CI: 1.20\u0026ndash;6.07, P\u0026thinsp;=\u0026thinsp;0.0166) and Model III (OR: 3.41, 95% CI: 1.03\u0026ndash;11.34, P\u0026thinsp;=\u0026thinsp;0.0453).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Subgroup analysis of the association between GRI and DR\u003c/h2\u003e\u003cp\u003eTo investigate whether the association between GRI and DR was consistent across different clinical characteristics, a series of subgroup analyses were conducted. Participants were stratified based on sex, age (\u0026lt;\u0026thinsp;60 years vs. \u0026ge; 60 years), BMI (\u0026lt;\u0026thinsp;24 kg/m\u0026sup2; vs. \u0026ge; 24 kg/m\u0026sup2;), HbA1c (\u0026lt;\u0026thinsp;7.0% vs. \u0026ge; 7.0%), TIR (\u0026lt;\u0026thinsp;70% vs. \u0026ge; 70%). The forest plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the positive association between GRI and DR remained consistent across all subgroups examined (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The consistency of the association across these subgroups further supports the robustness of the association of GRI with DR.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eIn this cross-sectional study of patients with T2DM, we found that GRI was independently associated with the presence of DR, even after comprehensive adjustment for established risk factors and conventional glycemic parameters including HbA1c and TIR. This study provides novel insights into the utility of GRI as a potentially metric for assessing DR risk in T2DM patients.\u003c/p\u003e\u003cp\u003eOur results demonstrated that higher GRI values were associated with poorer overall glycemic control and greater glycemic variability, as evidenced by higher HbA1c levels, lower TIR, and increased measures of glycemic fluctuation (SD, MAGE, MODD, LAGE). These observations align with previous studies that have reported similar relationships between GRI and various glycemic parameters in both type 1 and type 2 diabetes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These results support the use of GRI as a comprehensive index reflective of glycemic control and variability.\u003c/p\u003e\u003cp\u003eA key finding of our study was the progressive increase in DR prevalence across GRI tertiles, with a significant dose-response relationship. Multiple logistic regression analyses confirmed this association, revealing that patients in the highest GRI tertile had a 3.41-fold increased risk of DR compared to those in the lowest tertile, after adjusting for a wide range of demographic, clinical, and metabolic factors. Notably, this association remained robust after accounting for both HbA1c and TIR, suggesting that GRI captures aspects of glycemic dysregulation relevant to retinal pathology that are not fully reflected by conventional glycemic metrics. These results are consistent with a recent cohort study that also reported significant associations between GRI and DR [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], strengthening the evidence for GRI as a valuable marker of DR risk.\u003c/p\u003e\u003cp\u003eThe mechanisms underlying the association between GRI and DR are likely multifaceted. GRI captures both the magnitude and duration of glucose excursions beyond the target range[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which may better reflect the overall glycemic burden on the retinal vasculature compared to average glucose levels alone. Prolonged exposure to hyperglycemia and acute glucose fluctuations can lead to oxidative stress, inflammation, and endothelial dysfunction, all of which contribute to the progression of DR [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA key strength of our study is the use of 14-day CGM data to calculate GRI, which provides a more comprehensive assessment of glycemic patterns compared to shorter monitoring periods. Additionally, our analysis accounted for a wide range of potential confounders, including HbA1C, TIR and diabetes medications, reinforcing the independent association between GRI and DR. However, this study has several limitations. First, the cross-sectional design precludes the establishment of causality. Secondly, though we adjusted for numerous confounders, residual confounding cannot be entirely ruled out. Third, the sample size of the present study was still relatively small.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn conclusion, our study demonstrates a significant, independent association between GRI and DR in patients with T2DM. These findings suggest that GRI, derived from CGM data, may provide valuable information for assessing DR risk beyond traditional glycemic measures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study was approved by the Institutional Review Board of Shanghai General Hospital, affiliated to the Shanghai Jiao Tong University School of Medicine. All procedures and protocols were meticulously crafted to align with the principles stipulated in the Helsinki Declaration.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0524202), the National Natural Science Foundation of China (81400785), and the Natural Science Foundation of Shanghai (21ZR1451200).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFang Liu, Yijiong Tan, Nengguang Fan, Hongyan Xu and Yanyun Hu handled the statistical analysis, study design, and manuscript writing. The tasks of Hongyan Xu, Ye Zhu, Lining Dong, Pei Wang, Shan Chen, Jiaying Yang, Xingdong Zheng, included recruiting participants, gathering and analyzing data, and following up. The manuscript was read and assessed by Fang Liu, Nengguang Fan. The study concept was contributed by Yanyun Hu and Nengguang Fan who also made significant manuscript edits. Each author accepted the submitted version of the paper and made contributions to it.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eThe authors thank all the staff and participants of this study for their important contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVision Loss Expert Group of the Global Burden of Disease Study. GBD 2019 Blindness and Vision Impairment Collaborators. Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020. Eye (Lond). 2024;38:2047\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntonetti DA, Silva PS, Stitt AW. Current understanding of the molecular and cellular pathology of diabetic retinopathy. Nat Rev Endocrinol. 2021;17:195\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZoungas S, Chalmers J, Ninomiya T, Li Q, Cooper ME, Colagiuri S, et al. Association of HbA1c levels with vascular complications and death in patients with type 2 diabetes: evidence of glycaemic thresholds. Diabetologia. 2012;55:636\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen B, Shen C, Sun B. Current landscape and comprehensive management of glycemic variability in diabetic retinopathy. J Transl Med. 2024;22:700.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSterner Isaksson S, Imberg H, Hirsch IB, Schwarcz E, Hellman J, Wijkman M, 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. 2024;67:1517\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodbard D. Continuous glucose monitoring metrics (Mean Glucose, time above range and time in range) are superior to glycated haemoglobin for assessment of therapeutic efficacy. Diabetes Obes Metab. 2023;25:596\u0026ndash;601.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCappon G, Vettoretti M, Sparacino G, Facchinetti A. Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications. Diabetes Metab J. 2019;43:383\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeck RW, Bergenstal RM, Riddlesworth TD, Kollman C, Li Z, Brown AS, et al. Time in Range Is Associated with Incident Diabetic Retinopathy in Adults with Type 1 Diabetes: A Longitudinal Study. Diabetes Care. 2022;45:2723\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu J, Ma X, Zhou J, Zhang L, Mo Y, Ying L, et al. Association of Time in Range, as Assessed by Continuous Glucose Monitoring, With Diabetic Retinopathy in Type 2 Diabetes. Diabetes Care. 2018;41:2370\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim JY, Yoo JH, Kim JH. Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality. Diabetes Technol Ther. 2023;25:883\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlonoff DC, Wang J, Rodbard D, Kohn MA, Li C, Liepmann D, et al. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol. 2023;17:1226\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee MH, Vogrin S, Jones TW, O'Neal DN. Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index. J Diabetes Sci Technol. 2024;18:764\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanfil K, Redel JM, Vandervelden CA, Lockee B, Kahkoska AR, Tallon EM, et al. Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes. J Diabetes Sci Technol. 2024;18:771\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai L, Shen W, Li J, Wang B, Sun Y, Chen Y, et al. Association between glycemia risk index and arterial stiffness in type 2 diabetes. J Diabetes Investig. 2024;15:614\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoo JH, Kim JY, Kim JH. Association Between Continuous Glucose Monitoring-Derived Glycemia Risk Index and Albuminuria in Type 2 Diabetes. Diabetes Technol Ther. 2023;25:726\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Lu J, Ni J, Wang M, Shen Y, Lu W, et al. Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study. Diabetes Obes Metab. 2023;25:2457\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarakus KE, Shah VN, Klonoff D, Akturk HK. Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes. Diabetes Obes Metab. 2023;25:3144\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNebbioso M, Lambiase A, Armentano M, Tucciarone G, Sacchetti M, Greco A, et al. Diabetic retinopathy, oxidative stress, and sirtuins: an in depth look in enzymatic patterns and new therapeutic horizons. Surv Ophthalmol. 2022;67:168\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang Q, Buonfiglio F, B\u0026ouml;hm EW, Zhang L, Pfeiffer N, Korb CA, et al. Diabetic Retinopathy: New Treatment Approaches Targeting Redox and Immune Mechanisms. Antioxid Basel Switz. 2024;13:594.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glycaemia risk index, Type 2 diabetes mellitus, Diabetic retinopathy","lastPublishedDoi":"10.21203/rs.3.rs-7932961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7932961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGlycemia risk index (GRI), a composite metric derived from continuous glucose monitoring, has emerged as a novel indicator of glycemic burden. However, whether GRI is independently associated with diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM) after adjustment for conventional glycemic measures remains unclear. This study aimed to investigate the independent association between GRI and DR in T2DM patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study enrolled 349 T2DM patients at the Endocrinology and Metabolism Department of Shanghai General Hospital. Continuous glucose monitoring (CGM) data were collected over 14 days using the FreeStyle Libre system. Patients were stratified into tertiles based on GRI values. DR was diagnosed through standardized ophthalmological examination. Multivariable logistic regression models were used to examine the association between GRI and DR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigher GRI tertiles were associated with longer diabetes duration, higher HbA1c levels, increased glycemic variability, lower time in range (TIR), and more prevalent insulin use (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The prevalence of DR increased significantly across GRI tertiles (19.4, 29.9, and 34.7% in the low, middle, and high tertiles, respectively; P for trend\u0026thinsp;=\u0026thinsp;0.018). After adjusting for confounders including HbA1c and TIR, the highest GRI tertile was associated with a 3.41-fold increased risk of DR compared to the lowest tertile (OR: 3.41, 95% CI: 1.03\u0026ndash;11.34, P\u0026thinsp;=\u0026thinsp;0.045). Subgroup analyses confirmed the consistent association between GRI and DR across various clinical characteristics (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eGRI is independently associated with DR in T2DM patients, even after adjusting for conventional glycemic measures.\u003c/p\u003e","manuscriptTitle":"Association between glycemia risk index and diabetic retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 10:48:15","doi":"10.21203/rs.3.rs-7932961/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-11T20:01:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T03:59:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110629383573220257074576229612003487645","date":"2025-12-09T11:52:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201742463356371358055322722166632297634","date":"2025-12-03T13:51:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-29T08:24:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-26T09:56:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-06T08:07:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-05T16:34:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-11-05T16:25:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8475b26-b2a5-4a2f-8cf8-bb02160b7566","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-04T10:48:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 10:48:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7932961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7932961","identity":"rs-7932961","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}