Investigation of Influencing Factors on the Prevalence of Retinopathy in Diabetic Patients Based on Medical Big Data

A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management

Objective

This study aimed to establish a risk prediction model for diabetic retinopathy (DR) in the Chinese type 2 diabetes mellitus (T2DM) population using few inspection indicators and to propose suggestions for chronic disease management.

Methods

This multi-centered retrospective cross-sectional study was conducted among 2,385 patients with T2DM. The predictors of the training set were, respectively, screened by extreme gradient boosting (XGBoost), a random forest recursive feature elimination (RF-RFE) algorithm, a backpropagation neural network (BPNN), and a least absolute shrinkage selection operator (LASSO) model. Model I, a prediction model, was established through multivariable logistic regression analysis based on the predictors repeated ≥3 times in the four screening methods. Logistic regression Model II built on the predictive factors in the previously released DR risk study was introduced into our current study to evaluate the model's effectiveness. Nine evaluation indicators were used to compare the performance of the two prediction models, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, balanced accuracy, calibration curve, Hosmer-Lemeshow test, and Net Reclassification Index (NRI).

Results

When including predictors, such as glycosylated hemoglobin A1c, disease course, postprandial blood glucose, age, systolic blood pressure, and albumin/urine creatinine ratio, multivariable logistic regression Model I demonstrated a better prediction ability than Model II. Model I revealed the highest AUROC (0.703), accuracy (0.796), precision (0.571), recall (0.035), F1 score (0.066), Hosmer-Lemeshow test (0.887), NRI (0.004), and balanced accuracy (0.514).

Conclusion

We have built an accurate DR risk prediction model with fewer indicators for patients with T2DM. It can be used to predict the individualized risk of DR in China effectively. In addition, the model can provide powerful auxiliary technical support for the clinical and health management of patients with diabetes comorbidities.

Retracted: Investigation of Influencing Factors on the Prevalence of Retinopathy in Diabetic Patients Based on Medical Big Data

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Inhibition of KCTD10 Affects Diabetic Retinopathy Progression by Reducing VEGF and Affecting Angiogenesis

Aim

We purposed to evaluate the KCTD10 effects of angiogenesis in diabetic retinopathy (DR).

Methods

We induced a DR cell model using high glucose (HG) treatment of HRECs and ARPE-19 cells. A DR rat was established by injecting streptozotocin. Small interference RNA targeted KCTD10 (si-KCTD10) was used to mediate KCTD10 inhibition in cell and animal models. The roles of KCTD10 on cell viability, apoptosis, angiogenesis, and related proteins (VEGF and HIF-1α) were observed by RT-qPCR, Western blot, CCK-8 assay, TUNEL staining, tube formation assay, ELISA, and immunohistochemistry assay.

Results

KCTD10 expression was upregulated in DR cells and retinal tissue of DR rats. Treatment of the cells with si-KCTD10 increased cell viability and decreased apoptosis and angiogenesis in DR cells. Inhibition of KCTD10 could reduce the expression of VEGF and HIF-1α in DR cells. Furthermore, KCTD10 inhibition reduced VEGF levels in the retinal tissue of DR rats.

Conclusion

This work showed that inhibition of KCTD10 relieved angiogenesis in DR.

Effects of Lycium barbarum L. Polysaccharides on Vascular Retinopathy: An Insight Review

Vascular retinopathy is a pathological change in the retina caused by ocular or systemic vascular diseases that can lead to blurred vision and the risk of blindness. Lycium barbarum polysaccharides (LBPs) are extracted from the fruit of traditional Chinese medicine, L. barbarum. They have strong biological activities, including immune regulation, antioxidation, and neuroprotection, and have been shown to improve vision in numerous studies. At present, there is no systematic literature review of LBPs on vascular retinal prevention and treatment. We review the structural characterization and extraction methods of LBPs, focus on the mechanism and pharmacokinetics of LBPs in improving vascular retinopathy, and discuss the future clinical application and lack of work. LBPs are involved in the regulation of VEGF, Rho/ROCK, PI3K/Akt/mTOR, Nrf2/HO-1, AGEs/RAGE signaling pathways, which can alleviate the occurrence and development of vascular retinal diseases in an inflammatory response, oxidative stress, apoptosis, autophagy, and neuroprotection. LBPs are mainly absorbed by the small intestine and stomach and excreted through urine and feces. Their low bioavailability in vivo has led to the development of novel dosage forms, including multicompartment delivery systems and scaffolds. Data from the literature confirm the medicinal potential of LBPs as a new direction for the prevention and complementary treatment of vascular retinopathy.

Physical Activity Status and Diabetic Retinopathy: A Review

Substantial evidence highlights the association between physical inactivity and diabetes onset and complications. Little is known regarding the link between physical inactivity and diabetic retinopathy in terms of onset, progression, and severity. This review aims to investigate these associations and understand the underlying mechanisms behind these associations. Decreased sedentary times and the inclusion of more physical activity have been linked to the delayed onset and progression of diabetic retinopathy and less severe forms of said condition. Physical activity provides both protective and anti-inflammatory effects on the retina. Further research is needed to understand and elucidate the exact mechanisms by which lack of physical activity affects retinal health and the onset, progression, and severity of diabetic retinopathy.