Introduction

Recent International Diabetes Federation (IDF) estimates indicate that, around 589 million adults aged 20–79 were affected by diabetes globally in 2025, exceeding 10% of the world’s adult population. It is expected that this figure will climb to 853 million by 2050. China alone has an estimated 174 million patients, representing more than one-quarter of the global burden.1 Such a high prevalence suggests a substantial and growing impact on population health. The most serious consequences of diabetes arise from long-term chronic complications. Among them, diabetic retinopathy (DR) has received increasing attention due to its profound effect on visual function. Initially, DR typically presents without symptoms. With disease progression, microaneurysms, retinal hemorrhages, exudation, and macular edema may occur. If the condition worsens further, it can lead to pathological neovascularisation and fibrous vascular proliferation, which can develop into proliferative diabetic retinopathy (PDR), pulling retinal detachment, vitreous hemorrhage, and eventually leading to irreversible vision loss or blindness.2

Fundus examination is crucial in the clinical management of diabetic retinopathy (DR).3 However, in primary medical institutions, the early screening rate is low due to limited equipment, the shortage of trained professionals, and the lack of patient awareness and compliance. As a result, many patients missed the best time for intervention and suffered preventable visual impairment.4 Therefore, identifying simple and effective biomarkers is of great importance for enhancing early detection and management of DR.

The etiology of DR is complex. Oxidative stress is recognized as a key pathogenic driver.5 Obesity, a chronic inflammatory state, may exacerbate hyperglycemia and insulin resistance, thereby contributing to microvascular injury.6,7 In addition, dyslipidemia is another potential risk factor for DR. Damage to the retinal lipid clearance mechanism in diabetic patients may promote non-enzymatic oxidation, glycation and inflammatory activation, resulting in increased vascular permeability and damage to the blood retinal barrier.8

The cardiovascular metabolic index (CMI) comes from lipid accumulation products (LAP), which was first proposed by Japanese researcher Ichiro Wakabayashi. It is calculated as CMI = (triglycerides/HDL-C) × waist-to-height ratio (WHtR). By combining an anatomical indicator (WHtR) with a metabolic marker (TG/HDL-C), CMI provides a more comprehensive assessment of adiposity and metabolic status and can assist in the diagnosis of diabetes.9 Previous studies demonstrated strong associations between CMI and metabolic syndrome, insulin resistance, and cardiovascular diseases,10–12 suggesting potential value in predicting chronic diabetic complications. Although studies have confirmed that CMI is significantly related to diabetic nephropathy (DKD),13 research exploring its relationship with DR remains limited. Based on the composite nature of CMI reflecting both abdominal obesity and lipid metabolic disorders, and the established roles of these two factors in the development of microvascular complications, we hypothesized that higher CMI levels would be positively associated with an increased risk of DR in patients with T2DM, and that this association might exhibit a non-linear pattern with a potential threshold effect. Therefore, this study aimed to investigate the association between CMI and DR in patients with type 2 diabetes, and to explore its potential role as a comprehensive metabolic indicator for DR risk assessment.

Subjects and Methods Study Population and Design

This retrospective study analyzed more than 3000 patients with T2DM admitted to the Affiliated Lu’an Hospital of Anhui Medical University between June 1, 2024, and June 30, 2025. The criteria for patient selection are as follows:

Inclusion criteria: (1) Diagnosis according to the 2020 T2DM diagnosis and classification criteria; (2) Underwent complete fundus examination, with the presence or absence of DR confirmed by ophthalmologists. Exclusion criteria: (1) History of previously diagnosed DR; (2) Coexisting hepatobiliary, hematological, or systemic inflammatory diseases; (3) Fundus not assessable due to media opacity; (4) Age under 18 years.

Ultimately, 2571 eligible patients were enrolled. All underwent fundus examination and were classified into a non-DR (NDR) group and a DR group based on diagnostic findings (Figure 1).

Figure 1 Flow chart.

Notes: The step of “Data Cleaning” comprised two key procedures: 1. Deletion of records with missing data; 2. Standardization of measurement units.

Data Collection

Information on patient sex, age, diabetes duration, height, weight, waist circumference, systolic blood pressure (SBP), and diastolic blood pressure (DBP) was collected. Height and weight were measured using a calibrated stadiometer and electronic scale (Seca 213 stadiometer and Seca 877 scale, Germany), The height is measured by a height meter. The patient stands barefoot and the head is kept horizontal, which is accurate to 0.1 cm. Weight was recorded to 0.1 kg using a calibrated scale. Waist circumference was measured twice to 0.1 cm at the midpoint between the lowest rib and the iliac crest during gentle expiration, and the average was used.

Fasting blood samples were obtained the morning after admission to measure glycated hemoglobin (HbA1c), fasting blood glucose (FBG), albumin (ALB), fasting C-peptide, random C-peptide, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). All biochemical analyses were performed at the Department of Clinical Laboratory, Lu’an Hospital Affiliated to Anhui Medical University. For routine blood analysis, 2 mL peripheral blood samples were routinely collected from all hospitalized patients in EDTA tubes and analyzed within 30 minutes after collection using a hematology analyzer (Mindray BC5500, China). HbA1c levels were measured using a glycated hemoglobin analyzer (MQ6000, China) from 2 mL blood samples collected in EDTA tubes and processed within 2 hours. For biochemical parameters, 4 mL blood samples were collected in tubes containing coagulant and assessed within 2 hours after collection using a chemistry analyzer (Roche cobas, Switzerland). Body mass index (BMI) was calculated as weight/height2. The cardiometabolic index was calculated as (TG/HDL-C) × WHtR.

Sample Size Statement

As a single-center retrospective study, a formal sample size calculation was not performed beforehand. Instead, all eligible patients meeting the inclusion and exclusion criteria during the study period were enrolled, resulting in a total of 2571 cases. Although prospective sample size estimation was not conducted, the final sample size was comparable to previous studies investigating the association between cardiometabolic index or glycolipid metabolic parameters and diabetic retinopathy, providing sufficient statistical power for correlation analysis and supporting the reliability of the results.

Statistical Analysis

Data analysis was conducted using SPSS (Version 21.0), with graphs generated using R (Version 4.3.1). Patients were categorized into a DR group and a non-DR (NDR) group based on fundus examination findings. Normally distributed continuous data are expressed as mean ± standard deviation and compared via independent t-tests. Non-normally distributed data are presented as median (interquartile range) and compared using the Mann–Whitney U-test. Trend analysis employed the chi-square trend test. Binary logistic regression was used to identify independent DR risk factors. The diagnostic performance of CMI was evaluated using ROC curve analysis, including area under the curve (AUC) calculation. All thresholds for continuous variables were derived using data-driven approaches based on the distribution and predictive performance of the variables within our study cohort. Specifically, optimal cutoff values in the ROC analysis were determined by maximizing the Youden index (sensitivity + specificity − 1), and the turning point of the nonlinear relationship in the RCS analysis was identified using a likelihood ratio test. A two-sided P-value < 0.05 denoted statistical significance.

Results Univariate Comparison Based on Fundus Findings

A total of 2571 patients with T2DM were included, with 1606 in the NDR group and 965 in the DR group. Significant differences were observed between the two groups in sex (P = 0.006), age (P < 0.001), SBP (P < 0.001), DBP (P = 0.039), diabetes duration (P < 0.001), HbA1c (P = 0.042), random C-peptide (P = 0.010), lipid parameters (TC, TG, HDL-C, LDL-C; all P < 0.05), CMI (P < 0.001), urinary albumin indicators (24-hour urinary albumin excretion and UACR; both P < 0.001), and left-side ABI (P = 0.037). No significant differences were observed in BMI, fasting C-peptide, creatinine (Cr), uric acid (UA), blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), or visceral fat area (VFA) (Table 1). Notably, the DR group had higher TG and lower HDL-C levels, resulting in significantly elevated CMI compared with the NDR group (Figure 2).

Table 1 Comparison of Relevant Indicators Based on Grouping of Fundus Lesions

Figure 2 Comparison of CMI Index between NDR Group and DR Group.

Note: ****Denotes significance at a P value of <0.0001.

Multivariate Analysis of Factors Influencing Fundus Lesions

In this study, the samples were divided into 4 intervals based on the quartiles of CMI. The incidence of DR in group A was 12.8%, in group B it was 22.2%, in group C it was 49.4%, and in group D it was 65.8%. The Mantel-Haenszel test was used to test the trend of CMI grouping and DR incidence. The results showed that there was a significant linear trend between CMI and DR incidence (P < 0.001), and there was a significant positive correlation between CMI and DR incidence (r = 0.430, P < 0.001) (Table 2).

Table 2 CMI Quartile Interval DR Risk Probability and DR Risk Trend Test

multivariate Logistic regression model. The results are shown in Table 3. The results indicated that female (OR = 1.240, P = 0.020), longer duration of diabetes (OR = 1.060, P < 0.001), high CMI (OR = 1.167, P < 0.001), and high UACR (OR = 1.001, P < 0.001) were independent risk factors for fundus lesions. The regression forest diagram is shown in Figure 3.

Table 3 Multivariate Logistic Regression of Factors Affecting Fundus Lesions

Figure 3 Forest plot of multivariate Logistic regression results.

Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; Random C-P, Random C-Peptide; 24h UAE, 24-Hour Urinary Albumin Excretion; UACR, Urine Albumin-to-Creatinine Ratio; Left ABI, Left Ankle-Brachial Index.

This study employed RCS analysis to investigate the nonlinear relationship between DR and CMI, and corrects mixed factors such as gender, diabetes history and UACR. The turning point of the nonlinear relationship was identified using a likelihood ratio test. The results showed a significant nonlinear relationship between CMI and DR (P for non-linearity < 0.001, P for overall < 0.001). When CMI was greater than 1.01, the risk of DR significantly increased (Figure 4).

Figure 4 CMI and DR Risk RCS Chart.

An ROC curve analysis was conducted on the independent predictors of DR. The results are shown in Table 4. The optimal cutoff values for CMI and other continuous variables were determined using the Youden index (sensitivity + specificity − 1). The results indicated that the AUC corresponding to CMI was 0.760, the AUC corresponding to diabetes history was 0.640, the AUC corresponding to UACR was 0.624, and the AUC corresponding to Sex was 0.527. The ranking of the predictive performance for DR from high to low was: CMI > diabetes history > UACR > Sex. The ROC curve is shown in Figure 5.

Table 4 ROC Curve Analysis

Figure 5 ROC Curve.

Abbreviation: UACR, Urine Albumin-to-Creatinine Ratio.

Discussion

The global incidence of diabetes mellitus has risen dramatically in recent years, becoming an unprecedented public health challenge. IDF data from 2024 reported approximately 589 million affected adults worldwide, a number expected to reach about 853 million by 2050—a 46% increase; over 90% of cases are T2DM.1 Persistent chronic hyperglycemia and complex metabolic disturbances can cause continuous damage to the systemic microvascular network. DR, the most frequent and vision-impairing microvascular complication of T2DM, is a leading cause of blindness in working-age adults, severely affecting quality of life and mental health.14 Data from the Global Burden of Disease (GBD) study demonstrated that between 1990 and 2021, the number of blindness cases attributable to DR increased by 326.0%, with 97.3% occurring in patients with T2DM, a growth rate markedly exceeding the rise in overall diabetes prevalence. These findings suggest that relying solely on glycemic control or traditional risk stratification may be insufficient to fully explain the complex mechanisms underlying microvascular injury.15,16 Although fundus examination remains the diagnostic gold standard for DR, the global rate of early screening remains suboptimal, with many patients being diagnosed only after irreversible visual damage or progression to vision-threatening DR (VTDR).14,17 This is strongly associated with limited medical resources, poor patient compliance, and insufficient numbers of ophthalmologists. For example, studies in African regions have reported an ophthalmologist density of only approximately 0.91 per 1000 VTDR patients, reflecting pronounced regional healthcare shortages.18 Therefore, developing simple and clinically accessible biomarkers is essential to improve early diagnosis and intervention for DR.

Prior research indicates DR development is influenced by various clinical and metabolic factors. In our univariate analysis, multiple indicators—including gender, age, blood pressure, duration of diabetes, HbA1c, C-peptide, lipid profiles, urinary albumin/creatinine ratio (UACR) and ankle-brachial index (ABI) are significantly related to the development of DR, which is reported in domestic and foreign literature. The result is consistent.19–30 Gender differences are recognized as an important demographic factor affecting the incidence of DR. An animal study by Chen et al revealed that female diabetic models are more susceptible to retinal microvascular damage compared to males, suggesting that sexual hormones and metabolic characteristics may play roles in the pathogenesis of DR19. Our observations of higher DR incidence in females align with this. Diabetes duration is a well-established DR risk factor; longer duration correlates with higher incidence and severity.23 Urinary albumin and UACR reflect the extent of renal microvascular damage and indirectly indicate systemic microvascular function. The research of Kim et al confirmed that elevated UACR is closely related to abnormal retinal microvascular structure, making it a key biochemical marker of DR risk.29 Our results are consistent, showing significantly higher UACR in the DR group. The ankle-brachial index (ABI) is an important indicator of peripheral artery disease. ADA 2024 guidelines note shared that, peripheral vascular disease shares common pathological pathways with microvascular complications such as DR and diabetic kidney disease.30

The Cardiometabolic Index (CMI), recently proposed as a comprehensive metabolic evaluation metric, is calculated as CMI = (TG/HDL-C) × WHtR.31,32 It integrates an anthropometric measure of central obesity (WHtR) with a lipid ratio (TG/HDL-C), offering a holistic view of the interplay between abdominal fat and dyslipidemia, thereby providing a comprehensive assessment of metabolic disturbance. WHtR, a simple marker of abdominal obesity, is linked to insulin resistance, chronic inflammation, and endothelial dysfunction—all relevant to DR pathogenesis. A meta-analysis by Fu et al demonstrated that abdominal obesity metrics, including WHtR, are positively correlated with DR incidence, suggesting central fat may exacerbate retinal damage via inflammatory and oxidative pathways.33 Zhou et al, in their meta-analysis, indicated that elevated triglyceride levels are positively correlated with DR incidence, whereas higher HDL-C levels may confer protective effects on retinal microvessels.26,27 Banach et al further noted in a review that disorders in cholesterol metabolism and elevated LDL levels can aggravate damage to retinal vessel walls.28 Our study found increased triglyceride levels and significantly reduced HDL-C in the DR group, corroborating these earlier reports. Furthermore, the TG/HDL-C ratio alone serves as an indicator of lipid metabolic dysregulation; an elevated ratio signifies the combined adverse effects of high triglycerides and low HDL-C. Research by Nakashima et al indicated that a high TG/HDL-C ratio is strongly associated with cardiorenal vascular events and microvascular injury in diabetic patients. This ratio correlates with insulin resistance, inflammation, and increased small dense LDL particles, exacerbating endothelial dysfunction and vascular permeability at the molecular level.34

By combining WHtR and TG/HDL-C, CMI provides a single parameter that can reflect systemic fat distribution and lipid metabolism disorders. Recent studies show CMI is closely linked to metabolic syndrome;31 Zhuo et al further confirmed that elevated CMI significantly increases the risk of chronic diabetes complications;32 Xu et al reported that CMI is positively related to insulin resistance and diabetes status.35 Moreover, multiple studies confirm a significant association between CMI and diabetic kidney disease (DKD), and is correlated with increased urinary albumin excretion and reduced glomerular filtration, suggesting that CMI may reflect systemic microvascular injury burden.13,36 In addition, other metabolic indices have been proposed for risk assessment of diabetic complications. The triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, has been associated with diabetic retinopathy (DR); however, the evidence remains inconsistent across studies.37 A meta-analysis of 12 studies involving 16,259 patients reported substantial heterogeneity (I2 = 72%) in the TyG-DR association.37 Moreover, one cross-sectional study of 1761 Chinese T2DM patients demonstrated that the TyG index exhibited poor discriminatory ability for DR (AUC = 0.5523), with an extremely low sensitivity of 28.2%, rendering it clinically inadequate for DR screening.38 TyG-derived indices (eg, TyG-BMI, TyG-WHtR) have also been explored for DR prediction, but their predictive performance requires further validation.39 The estimated glucose disposal rate (eGDR), another validated surrogate of insulin sensitivity, has shown promising predictive value for DR (AUC = 0.777–0.781),40 with each one-unit increase associated with a 21% reduction in DR risk.41 However, eGDR calculation requires waist circumference, hypertension status, and HbA1c,42 making it more resource-intensive and less feasible for rapid screening in primary-care settings compared with CMI, which is derived from routine lipid profiles and waist-to-height ratio. These practical considerations highlight the potential advantages of CMI as a simpler and more accessible screening tool in resource-limited clinical environments. However, research directly evaluating the association between CMI and DR remains limited, and its specific predictive value and mechanistic implications require further clarification.

In the multivariate logistic regression analysis of this study, CMI remained an independent risk factor for DR (OR = 1.167, P < 0.001), suggesting that CMI exerts independent influence on DR risk. ROC analysis further showed CMI’s AUC for predicting DR was 0.760, exceeding that of diabetes duration (0.640) and UACR (0.624), with sensitivity and specificity of 74.8% and 68.8%, respectively, demonstrating stronger discriminatory ability. RCS analysis revealed a significant nonlinear relationship; DR risk increased sharply when CMI exceeded 1.01, indicating a clinically meaningful metabolic threshold. Collectively, by combining WHtR and TG/HDL-C—two indicators closely linked to DR pathophysiology—CMI provides a simple, economical, and effective metabolic risk-assessment tool that may assist in early DR screening and risk stratification.

Our findings are generally consistent with the limited available evidence supporting a positive association between metabolic indices and diabetic microvascular complications. Notably, the CMI, as a composite indicator integrating both lipid metabolism and central obesity, demonstrated superior predictive performance for DR (AUC = 0.760) compared to traditional single parameters such as diabetes duration (AUC = 0.640) and UACR (AUC = 0.624) in our cohort. In the multi-factor logistic regression analysis of this study, as a simple and accessible composite indicator, CMI may help clinicians rapidly identify individuals that might have high risk of DR. Our results expand its application to diabetic microvascular complications. Specifically, the RCS analysis revealed a non-linear association between CMI and DR, with DR risk rising substantially when CMI exceeds a threshold of 1.01. This threshold, derived from routine clinical parameters (waist-to-height ratio and lipid profile), provides clinicians with a clear, actionable cut-off for early risk stratification in T2DM patients. For T2DM patients with substantially elevated CMI, early fundus examination and intervention should be reinforced. Improvements in lipid control, weight reduction, and insulin sensitivity may not only reduce CMI but may also indirectly decrease the risk of DR. Unlike single laboratory indicators, the composite nature of CMI makes it more suitable for epidemiological risk screening and use in primary-care settings.

To our knowledge, direct evidence regarding the association between CMI and DR remains limited, and most published studies have focused on cardiovascular outcomes or metabolic syndrome rather than diabetic microvascular complications. Although the existing literature generally supports a positive relationship between CMI and metabolic risk, direct comparisons with our findings are constrained by the scarcity of methodologically comparable studies. We have therefore prioritized citing the most relevant supportive evidence to contextualize our results, while acknowledging that the field is still emerging.

However, this study has limitations. First, although its retrospective cross-sectional design can explore the relationship between CMI and DR, it cannot establish a causal relationship. Second, participants were drawn from a single tertiary hospital, and sample size was relatively limited, which may introduce selection bias and restrict generalizability. Third, CMI was measured only once, and longitudinal changes could not be evaluated. In addition, potential confounders such as lifestyle factors, medication profiles (eg, statins, fibrates), and smoking history were not included, although these variables may affect both CMI and DR. Finally, DR grading was primarily based on clinical fundus records, and we did not stratify patients into non-proliferative and proliferative stages, which may obscure stage-specific associations with CMI. We acknowledge that some degree of correlation may exist between CMI and other lipid parameters included in the model (TC and LDL-C), as they are biologically related. However, we deliberately excluded the direct components of CMI (TG and HDL-C) from the multivariate model, which effectively addressed the primary collinearity concern at the design stage.

Future research should therefore address these limitations through large-scale, multicenter prospective studies with longitudinal follow-up to validate the CMI-DR relationship and establish temporal causality. Specifically, further studies are warranted to: (1) explore the incremental value of combining CMI with other established biomarkers (eg, HbA1c, UACR) for a more comprehensive risk prediction model; (2) investigate whether CMI has differential predictive utility across DR subtypes (proliferative vs non-proliferative DR); and (3) evaluate the dynamic changes in CMI over time and their correlation with DR progression, which could inform personalized monitoring intervals and intervention strategies.

Conclusion

Our findings demonstrate that when CMI exceeds a threshold of 1.01, the risk of DR rises substantially. This threshold, derived from routine clinical parameters (waist-to-height ratio and lipid profile), provides clinicians with a clear, actionable cut-off for early risk stratification in T2DM patients. As a simple composite indicator, CMI may serve as a practical, low-cost screening tool for primary care physicians to identify high-risk individuals and prioritize ophthalmic referrals. Future research should explore (1) the incremental value of combining CMI with other biomarkers (eg, HbA1c, UACR) for improved risk prediction, (2) its differential performance across DR subtypes, and (3) the prognostic significance of longitudinal CMI changes in relation to DR progression.

Data Sharing Statement

The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy considerations but are available from the corresponding author on reasonable request and with permission from the Ethics Committee of the Affiliated Lu’an Hospital of Anhui Medical University.

Ethical Statement

This study strictly complied with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Lu’an Hospital of Anhui Medical University (Approval Number: 2025LL-008). The requirement for informed consent was waived by the ethics committee due to the retrospective nature of the study and the use of fully anonymized data.

Acknowledgment

The authors gratefully acknowledge the staff of the Department of Information and Medical Records at the Affiliated Lu’an Hospital of Anhui Medical University, for their valuable technical support in data collection and management.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. The individual author contributions, defined using the CRediT taxonomy, are as follows:

[Jiajia Liu]: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization.

[Hanxiao Li]: Methodology, Validation, Formal Analysis, Writing – Review & Editing.

[Lei Liu]: Investigation, Validation, Resources, Writing – Review & Editing.

[Qi Wang]: Conceptualization, Resources, Writing – Review & Editing, Supervision, Project Administration, Funding Acquisition.

Funding

This study was funded by the Domestic Scientific and Technological Collaboration Project of the 2025 Shanghai Innovation Ecosystem Construction Program (Grant No. 25010701700).

Disclosure

The authors declare that there are no conflicts of interest in this study.

References

1. International Diabetes Federation. Diabetes facts & figures. International Diabetes Federation; 2025. Available from: https://idf.org/about-diabetes/diabetes-facts-figures/. Accessed July14, 2026.

2. Lin KY, Hsih WH, Lin YB, Wen CY, Chang TJ. Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy. J Diabetes Invest. 2021;12(8):1322–13. doi:10.1111/jdi.13480

3. Le HG, Shakoor A. Diabetic and retinal vascular eye disease. Med Clin North Am. 2021;105(3):455–472. doi:10.1016/j.mcna.2021.02.004

4. Vujosevic S, Aldington SJ, Silva P, et al. Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol. 2020;8(4):337–347. doi:10.1016/S2213-8587(19)30411-5

5. Madsen-Bouterse SA, Zhong Q, Mohammad G, Ho YS, Kowluru RA. Oxidative damage of mitochondrial DNA in diabetes and its protection by manganese superoxide dismutase. Free Radical Res. 2010;44(3):313–321. doi:10.3109/10715760903494168

6. Xie Q, Gu X, Chen J, et al. Soyasaponins reduce inflammation and improve serum lipid profiles and glucose homeostasis in high fat diet-induced obese mice. Mol Nutr Food Res. 2018;62(19):e1800205. doi:10.1002/mnfr.201800205

7. Keller MP, O’Connor C, Bitzer M, et al. Genetic analysis of obesity-induced diabetic nephropathy in BTBR mice. Diabetes. 2024;73(2):312–317. doi:10.2337/db23-0444

8. Modjtahedi BS, Bose N, Papakostas TD, Morse L, Vavvas DG, Kishan AU. Lipids and diabetic retinopathy. Seminars Ophthalmol. 2016;31(1–2):10–18. doi:10.3109/08820538.2015.1114869

9. Wakabayashi I, Daimon T. The “cardiometabolic index” as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Int J Clin Chem. 2015;438:274–278. doi:10.1016/j.cca.2014.08.042

10. Sun Y, Bai C, Yin S, Liu J. Assessment of the association between cardiac metabolic markers and carotid atherosclerosis, and the role of insulin resistance. Diab Metab Syndr Obes. 2025;18:2899–2911. doi:10.2147/DMSO.S539166

11. Yang D, Li W, Luo W, et al. Association between cardiometabolic index and mortality among patients with atherosclerotic cardiovascular disease: evidence from NHANES 1999-2018. Medicina. 2025;61(6). doi:10.3390/medicina61061064

12. Liu C, Zhang Z, Meng T, et al. The impact of cardiometabolic index on cardiovascular disease risk among diabetic patients: evidence from two national cohorts. Diabetes/Metab Res Rev. 2025;41(4):e70044. doi:10.1002/dmrr.70044

13. Kong J, Tao W, Sun Y, Xu Y, Li H, Li J. The relationship between cardiometabolic index and diabetic kidney disease in people with diabetes. Front Endocrinol. 2024;15:1376813. doi:10.3389/fendo.2024.1376813

14. Wong TY, Tan TE. The diabetic retinopathy “pandemic” and evolving global strategies: the 2023 friedenwald lecture. Invest Ophthalmol Visual Sci. 2023;64(15):47. doi:10.1167/iovs.64.15.47

15. Meng Y, Liu Y, Ma Y, et al. Global, regional, and national burden of blindness due to diabetic retinopathy, 1990-2021. Ophthalmol Ther. 2025;14(10):2599–2615. doi:10.1007/s40123-025-01230-y

16. Pan Y, Li Y, Cui M, He G, Wang G. Global, regional and national burden of blindness and vision loss attributable to diabetic retinopathy, 1990-2021: a systematic analysis for the global burden of disease study 2021. Diabetes Obesity Metab. 2025;27(10):5464–5477. doi:10.1111/dom.16588

17. Donaghue KC, Liew G. Measuring outcomes of diabetic retinopathy screening: what is important? Diabetes Care. 2024;47(6):930–932. doi:10.2337/dci24-0021

18. Teo ZL, Tham YC, Yu M, Cheng CY, Wong TY, Sabanayagam C. Do we have enough ophthalmologists to manage vision-threatening diabetic retinopathy? A global perspective. Eye. 2020;34(7):1255–1261. doi:10.1038/s41433-020-0776-5

19. Chen Y, Schlotterer A, Lin J, et al. Sex differences in the development of experimental diabetic retinopathy. Sci Rep. 2024;14(1):22812. doi:10.1038/s41598-024-73279-x

20. Prevention CfDCa. VEHSS modeled estimates: prevalence of diabetic retinopathy (DR). US Centers for Disease Control and Prevention; 2024. Available from: https://www.cdc.gov/vision-health-data/prevalence-estimates/dr-prevalence.html. Accessed July14, 2026.

21. Tomić M, Vrabec R, Ljubić S, Prkačin I, Bulum T. Patients with type 2 diabetes, higher blood pressure, and infrequent fundus examinations have a higher risk of sight-threatening retinopathy. J Clin Med. 2024;13(9):2496. doi:10.3390/jcm13092496

22. Li YT, Wang Y, Hu XJ, et al. Association between systolic blood pressure and diabetic retinopathy in both hypertensive and normotensive patients with type 2 diabetes: risk factors and healthcare implications. Healthcare. 2021;9(5). doi:10.3390/healthcare9050580

23. Wang Z, Bai X, Wang M, et al. Risk factors for diabetic retinopathy in young and middle-aged patients: a retrospective study. BMC Ophthalmol. 2024;24(1):544. doi:10.1186/s12886-024-03821-y

24. Alswaina N. Association between HbA1c levels and the severity of diabetic retinopathy. Cureus. 2024;16(12):e76395. doi:10.7759/cureus.76395

25. Latres E, Greenbaum CJ, Oyaski ML, et al. Evidence for C-peptide as a validated surrogate to predict clinical benefits in trials of disease-modifying therapies for type 1 diabetes. Diabetes. 2024;73(6):823–833. doi:10.2337/dbi23-0012

26. Zhou J, Zhu L, Li Y. Association between the triglyceride glucose index and diabetic retinopathy in type 2 diabetes: a meta-analysis. Front Endocrinol. 2023;14:1302127. doi:10.3389/fendo.2023.1302127

27. Xu W, Xu X, Zhang M, Sun C. Association between HDL cholesterol with diabetic retinopathy in diabetic patients: a cross-sectional retrospective study. BMC Endocr Disord. 2024;24(1):65. doi:10.1186/s12902-024-01599-0

28. Banach M, Surma S, Dzida G, et al. The prevention opportunities of retinopathy in diabetic patients – position paper endorsed by the Polish Lipid Association. Arch Med Sci. 2024;20(6):1754–1769. doi:10.5114/aoms/197331

29. Kim JS, Kim ES, Hwang HS, Jeong KH, Yu SY, Kim K. Association between albuminuria and retinal microvascular parameters measured with swept-source optical coherence tomography angiography in patients with diabetic retinopathy. PLoS One. 2024;19(3):e0295768. doi:10.1371/journal.pone.0295768

30. ElSayed NA, Aleppo G, Bannuru RR. 12. Retinopathy, neuropathy, and foot care: standards of care in Diabetes—2024. Diabetes Care. 2024;47(Suppl 1):S231–s243. doi:10.2337/dc24-S012

31. Duan S, Yang D, Xia H, Ren Z, Chen J, Yao S. Cardiometabolic index: a new predictor for metabolic associated fatty liver disease in Chinese adults. Front Endocrinol. 2022;13:1004855. doi:10.3389/fendo.2022.1004855

32. Zhuo L, Lai M, Wan L, Zhang X, Chen R. Cardiometabolic index and the risk of new-onset chronic diseases: results of a national prospective longitudinal study. Front Endocrinol. 2024;15:1446276. doi:10.3389/fendo.2024.1446276

33. Fu S, Zhang L, Xu J, Liu X, Zhu X. Association between abdominal obesity and diabetic retinopathy in patients with diabetes mellitus: a systematic review and meta-analysis. PLoS One. 2023;18(1):e0279734. doi:10.1371/journal.pone.0279734

34. Nakashima R, Ikeda S, Shinohara K, et al. Triglyceride/high density lipoprotein cholesterol index and future cardiovascular events in diabetic patients without known cardiovascular disease. Sci Rep. 2025;15(1):9217. doi:10.1038/s41598-025-92933-6

35. Xu B, Wu Q, Yin G, et al. Associations of cardiometabolic index with diabetic statuses and insulin resistance: the mediating role of inflammation-related indicators. BMC Public Health. 2024;24(1):2736. doi:10.1186/s12889-024-20048-0

36. Zhang L, Liang C, Yan Z, Li Q. Association between cardiac metabolic index and diabetic kidney disease: a cross-sectional study of NHANES 1999-2018. J Health Popul Nutr. 2025;44(1):105. doi:10.1186/s41043-025-00826-1

37. Yu L, Li B. Association between triglyceride-glucose index and diabetic retinopathy: a meta-analysis. Hormone Metab Res. 2024;56(11):785–794. doi:10.1055/a-2279-7112

38. Qiu X, Wen S, Lin R, et al. The triglyceride-glucose index for diabetic retinopathy prediction in Chinese patients with type 2 diabetes: a preliminary cross-sectional study. Front Endocrinol. 2026;17:1784073. doi:10.3389/fendo.2026.1784073

39. Wang Y, Zang B, Zhao J, et al. Triglyceride-glucose indexes are predictive of early and severe diabetic retinal microvascular abnormalities: a prospective cohort study. Ophthalmol Sci. 2026;6(7):101187. doi:10.1016/j.xops.2026.101187

40. Gao J, Wu K, Wang X, et al. Study on the prevalence of diabetic retinopathy in Pudong New Area and the predictive value of estimated glucose disposal rate: a cross-sectional study. Front Endocrinol. 2025;16:1571488. doi:10.3389/fendo.2025.1571488

41. Bahreiny SS, Khaksar MA, Kazemzadeh R, Mohammadpour Fard R, Radmehr V, Zahedian M. Decoding diabetic retinopathy risk through hybrid modeling: integrative assessment of machine learning algorithms and advanced regression frameworks leveraging estimated glucose disposal rate (eGDR). J Diabetes Metab Disord. 2025;24(2):260. doi:10.1007/s40200-025-01765-8

42. Guo L, Zhang J, An R, et al. The role of estimated glucose disposal rate in predicting cardiovascular risk among general and diabetes mellitus population: a systematic review and meta-analysis. BMC Med. 2025;23(1):234. doi:10.1186/s12916-025-04064-4