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Background: This study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout., Methods: Using the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium's summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release., Result: SMR analysis identified 14 gene probes in the eQTLGen blood summary-level data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, P SMR = 4.16 × 10 -13 ), ENSG00000231064 (labeled THBS3-AS1, P SMR = 1.88 × 10 -8 ), ENSG00000163463 (labeled KRTCAP2, P SMR = 3.88 × 10 -6 ), ENSG00000172977 (labeled KAT5, P SMR = 1.70 × 10 -5 ), and ENSG00000161395 (labeled PGAP3, P SMR = 3.24 × 10 -5 ). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue., Conclusion: Our findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Chen, Yao, Li, Xu and Zhang.)

Campanumoea javanica Bl. (CJ) traditionally used in Southwestern China, is now widely consumed as a health food across the nation. Due to its similar efficacy to Codonopsis Radix (CR) and their shared botanical family, CJ is often used as a substitute for CR. According to the Chinese Pharmacopoeia, Codonopsis pilosula var. modesta (Nannf.) L.T. Shen (CPM), Codonopsis pilosula (Franch.) Nannf. (CP), and Codonopsis tangshen Oliv. (CT) are the primary sources of CR. However, details on the differences in composition, effectiveness, and compositional between CJ and CR are still limited. Besides, there is little evidence to support the application of CJ as a drug. In this study, we employed widely targeted metabolomics, network pharmacology analysis, and molecular docking to explore the disparities in metabolite profiles between CJ and CR and to predict the pharmacological mechanisms of the dominant differential metabolites of CJ and their potential medicinal applications. The widely targeted metabolomics results indicated that 1,076, 1,102, 1,102, and 1,093 compounds, most phenolic acids, lipids, amino acids, and flavonoids, were characterized in CJ, CPM, CP, and CT, respectively. There were an average of 1061 shared compounds in CJ and CRs, with 95.07% similarity in metabolic profiles. Most of the metabolites in CJ were previously unreported. Twelve of the seventeen dominant metabolites found in CJ were directly associated with treating cancer and lactation, similar to the traditional medicinal efficacy. The molecular docking results showed that the dominant metabolites of CJ had good docking activity with the core targets PIK3R1, PIK3CA, ESR1, HSP90AA1, EGFR, and AKT1. This study provides a scientific basis for understanding the similarities and differences between CJ and CR at the metabolome level, offering a theoretical foundation for developing innovative medications from CJ. Additionally, it significantly enhances the metabolite databases for both CJ and CR., (© 2024. The Author(s).)