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Diabetic retinopathy is one of the most common complications of diabetes mellitus and the leading cause of low vision and blindness worldwide. Mounting evidence demonstrates that inflammation is a key mechanism driving diabetes-associated retinal disturbance, yet the pathophysiological process and molecular mechanisms of inflammation underlying diabetic retinopathy are not fully understood. Cytokines, chemokines, and adhesion molecules interact with each other to form a complex molecular network that propagates the inflammatory and pathological cascade of diabetic retinopathy. Therefore, it is important to understand and elucidate inflammation-related mechanisms behind diabetic retinopathy progression. Here, we review the current understanding of the pathology and pathogenesis of inflammation in diabetic retinopathy. In addition, we also summarize the relevant clinical trials to further suggest inflammation-targeted therapeutics for prevention and management of diabetic retinopathy.

BackgroundThe profile of gut microbiota, serum metabolites, and lipids of type 1 diabetes (T1D) patients with different human leukocyte antigen (HLA) genotypes remains unknown. We aimed to explore gut microbiota, serum metabolites, and lipids signatures in individuals with T1D typed by HLA genotypes.MethodsWe did a cross-sectional study that included 73 T1D adult patients. Patients were categorized into two groups according to the HLA haplotypes they carried: those with any two of three susceptibility haplotypes (DR3, DR4, DR9) and without any of the protective haplotypes (DR8, DR11, DR12, DR15, DR16) were defined as high-risk HLA genotypes group (HR, n=30); those with just one or without susceptibility haplotypes as the non-high-risk HLA genotypes group (NHR, n=43). We characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites with liquid chromatography-mass spectrometry.ResultsStudy individuals were 32.5 (8.18) years old, and 60.3% were female. Compared to NHR, the gut microbiota of HR patients were characterized by elevated abundances of Prevotella copri and lowered abundances of Parabacteroides distasonis. Differential serum metabolites (hypoxanthine, inosine, and guanine) which increased in HR were involved in purine metabolism. Different lipids, phosphatidylcholines and phosphatidylethanolamines, decreased in HR group. Notably, Parabacteroides distasonis was negatively associated (p ≤ 0.01) with hypoxanthine involved in purine metabolic pathways.ConclusionsThe present findings enabled a better understanding of the changes in gut microbiome and serum metabolome in T1D patients with HLA risk genotypes. Alterations of the gut microbiota and serum metabolites may provide some information for distinguishing T1D patients with different HLA risk genotypes.

Background and objectivesMyasthenia gravis (MG) is a T cell-driven, autoantibody-mediated disorder affecting transmission in neuromuscular junctions. The associations between the peripheral T cells and MG have been extensively studied. However, they are mainly of observational nature, thus limiting our understanding of the effect of inflammatory biomarkers on MG risk. With large data sets now available, we used Mendelian randomization (MR) analysis to investigate whether the biomarkers on T cells are causally associated with MG and further validate the relationships.MethodsWe performed a two-sample MR analysis using genetic data from one genome-wide association study (GWAS) for 210 extensive T-cell traits in 3,757 general population individuals and the largest GWAS for MG currently available (1,873 patients versus 36,370 age/gender-matched controls) from US and Italy. Then the biomarkers of interest were validated separately in two GWASs for MG in FIN biobank (232 patients versus 217,056 controls) and UK biobank (152 patients versus 386,631 controls).ResultsIn the first analysis, three T-cell traits were identified to be causally protective for MG risk: 1) CD8 on terminally differentiated CD8+ T cells (OR [95% CI] = 0.71 [0.59, 0.86], P = 5.62e-04, adjusted P =2.81e-02); 2) CD4+ regulatory T proportion in T cells (OR [95% CI] = 0.44 [0.26, 0.72], P = 1.30e-03, adjusted P =2.81e-02); 3) HVEM expression on total T cells (OR [95% CI] = 0.67 [0.52, 0.86], P = 1.61e-03, adjusted P =2.81e-02) and other eight T-cell subtypes (e.g., naïve CD4+ T cells). In particular, HVEM is a novel immune checkpoint on T cells that has never been linked to MG before. The SNPs on the TNFRSF14 per se further support a more direct link between the HVEM and MG. The validation analysis replicated these results in both FIN and UK biobanks. Both datasets showed a concordant protective trend supporting the findings, albeit not significant.ConclusionThis study highlighted the role of HVEM on T cells as a novel molecular-modified factor for MG risk and validated the causality between T cells and MG. These findings may advance our understanding of MG’s immunopathology and facilitate the future development of predictive disease-relevant biomarkers.