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Macular edema (ME) is the main cause of visual impairment in patients with retinal vein occlusion (RVO). The degree of ME affects the prognosis of RVO patients, while it lacks objective laboratory biomarkers. We aimed to compare aqueous humor samples from 28 patients with retinal vein occlusion macular edema (RVO-ME) to 27 age- and sex-matched controls by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry, so as to identify the key biomarkers and to increase the understanding of the mechanism of RVO-ME at the molecular level. Through univariate and multivariate statistical analyses, we identified 60 metabolites between RVO-ME patients and controls and 40 differential metabolites in mild RVO-ME [300 μm ≤ central retinal thickness (CRT) < 400 μm] patients compared with severe RVO-ME (CRT ≥ 400 μm). Pathway enrichment analysis showed that valine, leucine, and isoleucine biosynthesis; ascorbate and aldarate metabolism; and pantothenate and coenzyme A biosynthesis were significantly altered in RVO-ME in comparison with controls. Compared with mild RVO-ME, degradation and biosynthesis of valine, leucine, and isoleucine; histidine metabolism; beta-alanine metabolism; and pantothenate and coenzyme A biosynthesis were significantly changed in severe RVO-ME. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that adenosine, threonic acid, pyruvic acid, and pyro-L-glutaminyl-l-glutamine could differentiate RVO-ME from controls with an area under the curve (AUC) of >0.813. Urocanic acid, diethanolamine, 8-butanoylneosolaniol, niacinamide, paraldehyde, phytosphingosine, 4-aminobutyraldehyde, dihydrolipoate, and 1-(beta-D-ribofuranosyl)-1,4-dihydronicotinamide had an AUC of >0.848 for distinguishing mild RVO-ME from severe RVO-ME. Our study expanded the understanding of metabolomic changes in RVO-ME, which could help us to have a good understanding of the pathogenesis of RVO-ME.

BackgroundAntioxidant micronutrients have a therapeutic potential for clinical treatment of obesity. NO research, however, has examined the connection between the complex level of dietary antioxidants and obesity.Materials and methodsWe mainly aimed to investigate the relationship between a combination of antioxidants and obesity using the database of the national health and nutrition examination survey (NHANES). This cross-sectional study contains a survey of 41,021 people (≥18 years) in total ranging from 2005 to 2018. Multivariate logistic and weighted quantile sum (WQS) regression were performed to investigate the associations between these antioxidants, both individually and collectively, and the prevalence of obesity. The restricted cubic spline (RCS) regression was also utilized to analyze the linearity of these associations.ResultsAccording to multivariate logistic models, we found that the levels of most antioxidants in the highest quartile were independently related to a lower prevalence of obesity, while a reverse result was observed in selenium (p for trend <0.05). The WQS index revealed that a total of the 11 antioxidants is negatively related to the prevalence of obesity and abdominal obesity (all p<0.001), and iron/vitamin C have the greatest weight in the negative associations between antioxidant complex and obesity, as well as abdominal obesity. In addition, the RCS regression showed that retinol, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, iron, and copper all had a non-linear association with obesity. Threshold effect analysis demonstrated that the inflection points of retinol, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, iron, and cooper were 235.57, 374.81, 58.89, 891.44, 30.70, 43,410.00, 11,240.00, and 990.00 μg/day, respectively.ConclusionOur study found that a high level of a complex of 11 dietary antioxidants is related to a lower prevalence of obesity and abdominal obesity, among this inverse associations iron and vitamin C have the greatest weight.

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IntroductionPolycystic ovary syndrome (PCOS) is a common metabolic and endocrine disorder prevalent among women of reproductive age. Recent studies show that autophagy participated in the pathogenesis of PCOS, including anovulation, hyperandrogenism, and metabolic disturbances. This study was designed to screen autophagy-related genes (ATGs) that may play a pivotal role in PCOS, providing potential biomarkers and identifying new molecular subgroups for therapeutic intervention.MethodsGene expression profiles of the PCOS and control samples were obtained from the publicly available Gene Expression Omnibus database. The gene lists of ATGs from databases were integrated. Then, the weighted gene co-expression network analysis was conducted to obtain functional modules and construct a multifactorial co-expression network. Gene Ontology and KEGG pathway enrichment analyses were performed for further exploration of ATG's function in the key modules. Differentially expressed ATGs were identified and validated in external datasets with the Limma R package. To provide guidance on PCOS phenotyping, the dysfunction module consists of a co-expression network mapped to PCOS patients. A PCOS-Autophagy-related co-expression network was established using Cytoscape, followed by identifying molecular subgroups using the Limma R package. ps. RNA-sequencing analysis was used to confirm the differential expression of hub ATGs, and the diagnostic value of hub ATGs was assessed by receiver operating characteristic curve analysis.ResultsThree modules (Brown, Turquoise, and Green) in GSE8157, three modules (Blue, Red, and Green) in GSE43264, and four modules (Blue, Green, Black, and Yellow) in GSE106724 were identified to be PCOS-related by WGCNA analysis. 29 ATGs were found to be the hub genes that strongly correlated with PCOS. These hub ATGs were mainly enriched in autophagy-related functions and pathways such as autophagy, endocytosis, apoptosis, and mTOR signaling pathways. The mRNA-miRNA-lncRNA multifactorial network was successfully constructed. And three new molecular subgroups were identified via the K-means algorithm.DiscussionWe provide a novel insight into the mechanisms behind autophagy in PCOS. BRCA1, LDLR, MAP1B, hsa-miR-92b-3p, hsa-miR-20b-5p, and NEAT1 might play a considerably important role in PCOS dysfunction. As a result, new potential biomarkers can be evaluated for use in PCOS diagnosis and treatment in the future.