Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

Enhancing Ophthalmic Care: The Transformative Potential of Digital Twins in Healthcare

This literature review explores the emerging role of digital twin (DT) technology in ophthalmology, emphasizing its potential to revolutionize personalized medicine. DTs integrate diverse data sources, including genetic, environmental, and real-time patient data, to create dynamic, predictive models that enhance risk assessment, surgical planning, and postoperative care. The review highlights vital case studies demonstrating the application of DTs in improving the early detection and management of diseases such as glaucoma and age-related macular degeneration. While implementing DTs presents challenges, including data integration and privacy concerns, the potential benefits, such as improved patient outcomes and cost savings, position DTs as a valuable tool in the future of ophthalmic care. The review underscores the need for further research to address these challenges and fully realize the potential of DTs in clinical practice.

Cardiovascular Risk Factors as Independent Predictors of Diabetic Retinopathy in Type II Diabetes Mellitus: The Development of a Predictive Model

Background: Diabetic retinopathy (DR) is a leading cause of blindness in patients with type 2 diabetes mellitus (T2DM). Cardiovascular risk factors, such as hypertension, obesity, and dyslipidemia, may play a crucial role in the development and progression of DR, though the evidence remains mixed. This study aimed to assess cardiovascular risk factors as independent predictors of DR and to develop a predictive model for DR progression in T2DM patients. Methods: A retrospective cross-sectional study was conducted on 377 patients with T2DM who underwent a comprehensive eye exam. Clinical data, including blood pressure, lipid profile, BMI, and smoking status, were collected. DR staging was determined through fundus photography and classified as No DR, Non-Proliferative DR (NPDR), and Mild, Moderate, Severe, or Proliferative DR (PDR). A Multivariate Logistic Regression was used to evaluate the association between cardiovascular risk factors and DR presence. Several machine learning models, including Random Forest, XGBoost, and Support Vector Machines, were applied to assess the predictive value of cardiovascular risk factors and identify key predictors. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC. Results: The prevalence of DR in the cohort was 41.6%, with 34.5% having NPDR and 7.1% having PDR. A multivariate analysis identified systolic blood pressure (SBP), LDL cholesterol, and body mass index (BMI) as independent predictors of DR progression (p < 0.05). The Random Forest model showed a moderate predictive ability, with an AUC of 0.62 for distinguishing between the presence and absence of DR XGBoost showing a better performance, featuring a ROC-AUC of 0.68, while SBP, HDL cholesterol, and BMI were consistently identified as the most important predictors across models. After tuning, the XGBoost model showed a notable improvement, with an ROC-AUC of 0.72. Conclusions: Cardiovascular risk factors, particularly BP and BMI, play a significant role in the progression of DR in patients with T2DM. The predictive models, especially XGBoost, showed moderate accuracy in identifying DR stages, suggesting that integrating these risk factors into clinical practice may improve early detection and intervention strategies for DR.

Development and external validation of a predictive model for type 2 diabetic retinopathy

Diabetes retinopathy (DR) is a critical clinical disease with that causes irreversible visual damage in adults, and may even lead to permanent blindness in serious cases. Early identification and treatment of DR is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DR. 2381 patients with type 2 diabetes mellitus (T2DM) were retrospective study from the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China, hospitalised between Jan 1, 2019 and Jun 30, 2022. 962 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China hospitalised between Jul 1, 2020 to Jun 30, 2022 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of DR. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and decision curve analysis (DCA). Neutrophil, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, hemoglobin A1c (HbA1c), and Apolipoprotein A1 (ApoA1) were used to establish a nomogram model for predicting the risk of DR. In the development and external validation groups, the areas under the curve of the nomogram constructed from the above five factors were 0.834 (95%CI 0.820-0.849) and 0.851 (95%CI 0.829-0.874), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. This research has developed and externally verified that the nomograph model shows a good predictive ability in assessing DR risk in people with type 2 diabetes. The application of this model will help clinicians to intervene early, thus effectively reducing the incidence rate and mortality of DR in the future, and has far-reaching significance in improving the long-term health prognosis of diabetes patients.

The causal effect of hypertension, intraocular pressure, and diabetic retinopathy: a Mendelian randomization study

Background

Previous research has indicated a vital association between hypertension, intraocular pressure (IOP), and diabetic retinopathy (DR); however, the relationship has not been elucidated. In this study, we aim to investigate the causal association of hypertension, IOP, and DR.

Methods

The genome-wide association study (GWAS) IDs for DR, hypertension, and IOP were identified from the Integrative Epidemiology Unit (IEU) Open GWAS database. There were 33,519,037 single-nucleotide polymorphisms (SNPs) and a sample size of 1,030,836 for DR. There were 16,380,466 SNPs and 218,754 participants in the hypertension experiment. There were 9,851,867 SNPs and a sample size of 97,465 for IOP. Univariable, multivariable, and bidirectional Mendelian randomization (MR) studies were conducted to estimate the risk of hypertension and IOP in DR. Moreover, causality was examined using the inverse variance weighted method, and MR results were verified by numerous sensitivity analyses.

Results

A total of 62 SNPs at the genome-wide significance level were selected as instrumental variables (IVs) for hypertension-DR. The results of univariable MR analysis suggested a causal relationship between hypertension and DR and regarded hypertension as a risk factor for DR [p = 0.006, odds ratio (OR) = 1.080]. A total of 95 SNPs at the genome-wide significance level were selected as IVs for IOP-DR. Similarly, IOP was causally associated with DR and was a risk factor for DR (p = 0.029, OR = 1.090). The results of reverse MR analysis showed that DR was a risk factor for hypertension (p = 1.27×10-10, OR = 1.119), but there was no causal relationship between DR and IOP (p > 0.05). The results of multivariate MR analysis revealed that hypertension and IOP were risk factors for DR, which exhibited higher risk scores (p = 0.001, OR = 1.121 and p = 0.030, OR = 1.124, respectively) than those in univariable MR analysis. Therefore, hypertension remained a risk factor for DR after excluding the interference of IOP, and IOP was still a risk factor for DR after excluding the interference of hypertension.

Conclusion

This study validated the potential causal relationship between hypertension, IOP, and DR using MR analysis, providing a reference for the targeted prevention of DR.