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BackgroundAn association between Graves’ disease (GD) and the gut microbiome has been identified, but the causal effect between them remains unclear.MethodsBidirectional two-sample Mendelian randomization (MR) analysis was used to detect the causal effect between GD and the gut microbiome. Gut microbiome data were derived from samples from a range of different ethnicities (18,340 samples) and data on GD were obtained from samples of Asian ethnicity (212,453 samples). Single nucleotide polymorphisms (SNPs) were selected as instrumental variables according to different criteria. They were used to evaluate the causal effect between exposures and outcomes through inverse-variance weighting (IVW), weighted median, weighted mode, MR-Egger, and simple mode methods. F-statistics and sensitivity analyses were performed to evaluate bias and reliability.ResultsIn total, 1,560 instrumental variables were extracted from the gut microbiome data (p< 1 × 105). The classes Deltaproteobacteria [odds ratio (OR) = 3.603] and Mollicutes, as well as the genera Ruminococcus torques group, Oxalobacter, and Ruminococcaceae UCG 011 were identified as risk factors for GD. The family Peptococcaceae and the genus Anaerostipes (OR = 0.489) were protective factors for GD. In addition, 13 instrumental variables were extracted from GD (p< 1 × 10–8), causing one family and eight genera to be regulated. The genus Clostridium innocuum group (p = 0.024, OR = 0.918) and Anaerofilum (p = 0.049, OR = 1.584) had the greatest probability of being regulated. Significant bias, heterogeneity, and horizontal pleiotropy were not detected.ConclusionA causal effect relationship exists between GD and the gut microbiome, demonstrating regulatory activity and interactions, and thus providing evidence supporting the involvement of a thyroid–gut axis.

Mounting evidence indicates that tumor-derived exosomes (TDEs) play critical roles in tumor development and progression by regulating components in the tumor microenvironment (TME) in an autocrine or paracrine manner. Moreover, due to their delivery of critical molecules that react to chemotherapy and immunotherapy, TDEs also contribute to tumor drug resistance and impede the effective response of antitumor immunotherapy, thereby leading to poor clinical outcomes. There is a pressing need for the inhibition or removal of TDEs to facilitate the treatment and prognosis of cancer patients. Here, in the present review, we systematically overviewed the current strategies for TDE inhibition and clearance, providing novel insights for future tumor interventions in translational medicine. Moreover, existing challenges and potential prospects for TDE-targeted cancer therapy are also discussed to bridge the gaps between progress and promising applications.

Purpose This review aimed to provide an overview of current research into the risk factors for Graves' ophthalmopathy (GO). Methods To find information about the risk factors for GO, the research database PubMed was searched and relevant articles were obtained to extract information about risk factors. Results Smoking has been widely accepted as an important risk factor and cigarette smoking cessation has been shown to improve the outcome and decrease the onset of GO. Radioactive iodine on the thyroid may induce hyperthyroidism and increase the occurrence of GO. Selenium deficiency is a risk factor for GO and the supplementation of selenium has been an adjuvant therapy. Decreasing stressful life events (SLE) may help improve GO. Imbalance in intestinal flora is essential to GO, with Yersinia enterocolitica and Escherichia coli both increased in the digestive tract of the individual with GO. In addition, controlling serum cholesterol may help improve GO since adipogenesis is an important pathological change in its pathogenesis. Considering the correlation between Graves' disease and GO, maintaining normal thyroid function hormone level is the first-line therapeutic strategy to prevent progression of GO. An increase in antibodies such as TSHR and IGF-1R is the main predictor of GO. Besides, gender and gene polymorphism are also risk factors towards GO. Conclusions Risk factors for GO arise from five sources: physical and chemical environment, social-psychological environment, biological environment, the human organism, and genetic codes. Risk factors within these categories may interact with each other and their mechanisms in promoting the development of GO are complex. Research into risk factors for GO may promote emerging fields related to GO such as control of autoantibodies and intestinal microbiota.