Differentially expressed inflammatory proteins in acute gouty arthritis based on protein chip / 浙江大学学报·医学版

OBJECTIVE@#To detect the differentially expressed inflammatory proteins in acute gouty arthritis (AGA) with protein chip.@*METHODS@#The Raybiotech cytokine antibody chip was used to screen the proteomic expression in serum samples of 10 AGA patients and 10 healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were applied to determine the biological function annotation of differentially expressed proteins and the enrichment of signal pathways. ELISA method was used to verify the differential protein expression in 60 AGA patients and 60 healthy subjects. The ROC curve was employed to evaluate the diagnostic value of differential proteins in AGA patients.@*RESULTS@#According to|log@*CONCLUSIONS@#Proteomics can be applied to identify the biomarkers of AGA, which may be used for risk prediction and diagnosis of AGA patients.

Correlations of echocardiographic parameters in Gout patients: a retrospective analysis / 南方医科大学学报

OBJECTIVE@#To explore the correlations of echocardiographic parameters in patients with gout.@*METHODS@#The hospitalization data and medical records of patients with gout between January, 2012 and June, 2019 were retrieved from the database of Anhui Provincial Hospital of Traditional Chinese Medicine, and the echocardiographic parameters and clinical laboratory test results of the inflammatory, immunological and metabolic indicators were analyzed. SPSS 22.0, SPSS Clementine 11.1 Aprior and other statistical software were used to determine the association rules and carry out correlation analysis, heat map analysis and multi-factor logistic regression analysis of the indicators.@*RESULTS@#Heat map analysis showed that the expressions of EF and SV were the most significant, followed by AODd, LADs, LVDd and FS. Cluster analysis showed that AODd, EF, FS, LADs, LVDd, and SV were all in cluster 1, and IVSTd, LVPWTd, MPAD, Pmax, and RVDd were in cluster 2. Correlation analysis showed that in the 383 patients, EF was negatively correlated with LVDd ( < 0.05) and positively correlated with FS and SV ( < 0.05); AODd was positively correlated with IVSTd, LADs, LVDd, LVPWTd, RVDd, SV, and ESR ( < 0.05); FS was positively correlated with EF and SV ( < 0.05) and negatively correlated with LVDd ( < 0.05);IVSTd was positively correlated with AODd, LADs, LVPWTd, and complement C4 ( < 0.05); LADs were positively correlated with AODd, IVSTd, MPAD, RVDd, and SV ( < 0.05); LVDd was positively correlated with AODd, IVSTd ( < 0.05), and negatively correlated with LVDd and complement C3 ( < 0.05); MPAD and LADs, HDLC and TC were positively correlated ( < 0.05)and negatively correlated with Pmax ( < 0.05); Pmax was positively correlated with LVDd, RVDd and SV ( < 0.05)and negatively correlated with FS and MPAD ( < 0.05); RVDd was positively correlated with AODd, LADs, LVDd, Pmax, SV ( < 0.05); SV was positively correlated with AODd, EF, LADs, LVDd, Pmax, and RVDd ( < 0.05); complement C3 was positively correlated with complement C4 and CRP ( < 0.05), and negatively correlated with LVPWTd ( < 0.05); complement C4 was positively correlated with IVSTd, complement C3, CRP, and ESR ( < 0.05); CRP was positively correlated with complement C3, complement C4, IgA, IgG ( < 0.05), and negatively correlated with TC, HDLC, and TG ( < 0.05); TG was positively correlated with HDLC, IgM, and TC ( < 0.05), and negatively correlated with CRP ( < 0.05); HDLC was positively correlated with MPAD, HDLC and TC ( < 0.05) and negatively correlated with CRP ( < 0.05); IgA was positively correlated with CRP, IgG and IgM ( < 0.05); IgG was positively correlated with CRP, IgA and IgM ( < 0.05); IgM is positively correlated with TG, IgA, IgG, UA ( < 0.05) and negatively correlated with CRP ( < 0.05); UA was positively correlated with IgM ( < 0.05); ESR was positively correlated with AODd and complement C4 ( < 0.05); HCY was negatively correlated with RVDd ( < 0.05); TC was positively correlated with MPAD and TG ( < 0.05), and negatively correlated with CRP ( < 0.05). The increase of Pmax was significantly associated with the increase of LDL-C, UA, complement C4, TG, HCY, HDL-C, IgG, ESR, CRP, and complement C3; the increase of SV was associated with the elevations of UA, LDL-C, complement C4, HDL-C, CRP, IgG, HCY, TC, ESR, TG, and complement C3. Multivariate logistic regression analysis indicated that FS was positively correlated with LDL-C ( < 0.05), Pmax was negatively correlated with IgM ( < 0.05), and SV was negatively correlated with ESR ( < 0.05).@*CONCLUSIONS@#The changes of echocardiographic parameters in patients with gout are correlated with the increase in inflammation, immunity, and metabolic indexes. Patients with a history of smoking and drinking do not show obvious changes in cardiac function. The changes in metabolic indexes are risk factors for changes in echocardiographic parameters.