ABSTRACT
Diabetic retinopathy (DR) is a chronic complication of diabetes. Given that adiponectin plays a key role in DR progression, this study aims to elucidate the molecular mechanisms of sDR progression related to adiponectin. First, we extracted the microarray dataset GSE60436 from the Gene Expression Omnibus (GEO) database to identify hub genes associated with DR. Pathway enrichment analysis revealed a focus on inflammation, oxidative stress, and metabolic disease pathways. Gene Set Enrichment Analysis (GSEA) identified nine significant pathways related to DR. Immune infiltration analysis indicated increased infiltration of fibroblasts and endothelial cells in DR patients. Second, at the gene level, single-cell RNA sequencing (scRNA-seq) results showed a decrease in ADIPOQ gene expression as the disease progressed in our mouse models. At the protein level, ELISA results from sera of 31 patients and 11 control subjects demonstrated significantly lower adiponectin expression in the proliferative diabetic retinopathy (PDR) group compared to controls. Our findings reveal that adiponectin is involved in the advanced glycation end products (AGEs) and receptor of advanced glycation end products (RAGE) axis, as evidenced by hub gene analysis, scRNA-seq, and ELISA. In conclusion, adiponectin acts as a central molecule in the AGEs-RAGE axis, regulated by ADIPOQ, to influence DR progression.
ABSTRACT
ObjectiveTo construct and validate a clinical prediction model for diabetic kidney disease （DKD） based on optical coherence tomography angiography （OCTA）. MethodsThis study enrolled 567 diabetes patients. The random forest algorithm as well as logistic regression analysis were applied to construct the prediction model. The model discrimination and clinical usefulness were evaluated by receiver operating characteristic curve （ROC） and decision curve analysis （DCA）， respectively. ResultsThe clinical prediction model for DKD based on OCTA was constructed with area under the curve （AUC） of 0.878 and Brier score of 0.11. ConclusionsThrough multidimensional verification， the clinical prediction nomogram model based on OCTA allowed for early warning and advanced intervention of DKD.