ObjectiveDiabetic retinopathy (DR) is one of the most common complications of type 2 diabetes mellitus. The current study investigates the composition, structure, and function of gut microbiota in DR patients and explores the correlation between gut microbiota and clinical characteristics of DR.MethodsA total of 50 stool samples were collected from 50 participants, including 25 DR patients and 25 healthy controls (HCs). 16S ribosomal RNA gene sequencing was used to analyze the gut microbial composition in these two groups. DNA was extracted from the fecal samples using the MiSeq platform.ResultsThe microbial structure and composition of DR patients were different from that of HCs. The microbial richness of gut microbiota in DR was higher than that of normal individuals. The alterations of microbiome of DR patients were associated with disrupted Firmicutes, Bacteroidetes, Synergistota, and Desulfobacterota phyla. In addition, increased levels of Bacteroides, Megamonas, Ruminococcus_torques_group, Lachnoclostridium, and Alistipes, and decreased levels of Blautia, Eubacterium_ hallii_group, Collinsella, Dorea, Romboutsia, Anaerostipes, and Fusicatenibacter genera were observed in the DR groups. Additionally, a stochastic forest model was developed to identify a set of biomarkers with seven bacterial genera that can differentiate patients with DR from those HC. The microbial communities exhibited varied functions in these two groups because of the alterations of the above-mentioned bacterial genera.ConclusionThe altered composition and function of gut microbiota in DR patients indicated that gut microbiome could be used as non-invasive biomarkers, improve clinical diagnostic methods, and identify putative therapeutic targets for DR.

Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening.Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed.Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors’ presented relatively large AUC (0.891–0.997), high sensitivity (87.65–100%), and high specificity (80.12–99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors’ compared to senior and junior ophthalmologists (p < 0.05).Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening.

PurposeAge-related macular degeneration (AMD) is the leading cause of vision loss in those over the age of 50. Recently, intestinal microbiota has been reported to be involved in the pathogenesis of ocular diseases. The purpose of this study was to discover more about the involvement of the intestinal microbiota in AMD patients.MethodsFecal samples from 30 patients with AMD (AMD group) and 17 age- and sex-matched healthy controls (control group) without any fundus disease were collected. DNA extraction, PCR amplification, and 16S rRNA gene sequencing of the samples were performed to identify intestinal microbial alterations. Further, we used BugBase for phenotypic prediction and PICRUSt2 for KEGG Orthology (KO) as well as metabolic feature prediction.ResultsThe intestinal microbiota was found to be significantly altered in the AMD group. The AMD group had a significantly lower level of Firmicutes and relatively higher levels of Proteobacteria and Bacteroidota compared to those in the control group. At the genus level, the AMD patient group showed a considerably higher proportion of Escherichia-Shigella and lower proportions of Blautia and Anaerostipes compared with those in the control group. Phenotypic prediction revealed obvious differences in the four phenotypes between the two groups. PICRUSt2 analysis revealed KOs and pathways associated with altered intestinal microbiota. The abundance of the top eight KOs in the AMD group was higher than that in the control group. These KOs were mainly involved in lipopolysaccharide biosynthesis.ConclusionThe findings of this study indicated that AMD patients had different gut microbiota compared with healthy controls, and that AMD pathophysiology might be linked to changes in gut-related metabolic pathways. Therefore, intestinal microbiota might serve as non-invasive indicators for AMD clinical diagnosis and possibly also as AMD treatment targets.