代谢物与痛风之间的关系:孟德尔随机研究。

IF 1.4 Q4 IMMUNOLOGY American journal of clinical and experimental immunology Pub Date : 2024-08-25 eCollection Date: 2024-01-01 DOI:10.62347/UTEW4812
Zhixiang Ding, Liting Wu, Ting Xu, Cui Zhang, Yi Liang, Jia Li, Wenfang Zhuang
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引用次数: 0

摘要

背景:痛风与新陈代谢密切相关:痛风与新陈代谢密切相关,但关于代谢物如何导致或预防痛风的证据却很有限:本研究采用双样本孟德尔随机分析法(MR)评估了 1400 种血清代谢物与痛风之间的因果关系。我们主要采用反方差加权法(IVW)来估计因果效应,并辅以MR-Egger回归法、加权中位数法、简单模式法和加权模式法进行综合评估。此外,我们还对多义性和异质性进行了检验:经过严格筛选,我们确定了与痛风相关的 8 种已知代谢物和 4 种未知代谢物。在这8种已知代谢物中,胡椒碱代谢物C17H21NO3的葡萄糖醛酸苷和磷酸与甘露糖的比率与痛风风险的增加呈正相关。相反,5 α-雄甾烷-3 β、17 α-二醇二硫酸盐、泛酸、N-氨基甲酰丙氨酸、鞘磷脂(d18:0/20:0、d16:0/22:0)、羟基棕榈酰鞘磷脂(d18:1/16:0(OH))和甘露糖的水平与痛风风险的降低有关:这项研究从1,400份血液样本中发现了与痛风风险密切相关的8种代谢物。基因组学和代谢组学的结合为痛风的筛查和预防提供了宝贵的见解,表明特定的血液代谢物有助于识别高风险人群。
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The relationship between metabolites and gout: a Mendelian randomization study.

Background: Gout is closely tied to metabolism, yet there is limited evidence on how metabolites may cause or prevent the condition.

Methods: This study utilized a two-sample Mendelian randomization (MR) analysis to evaluate the causal relationship between 1,400 serum metabolites and gout. We primarily employed the inverse variance-weighted (IVW) method to estimate causal effects, supplemented by MR-Egger regression, weighted median, simple mode, and weighted mode for comprehensive evaluations. Additionally, we conducted tests for pleiotropy and heterogeneity.

Results: After a rigorous selection process, we identified eight known metabolites and four unknown metabolites associated with gout. Among the eight known metabolites, Glucuronide of piperine metabolite C17H21NO3 and the Phosphate to mannose ratio were positively associated with an increased risk of gout. Conversely, levels of 5 alpha-androstan-3 beta, 17 alpha-diol disulfate, Pantoate, N-carbamoylalanine, Sphingomyelin (d18:0/20:0, d16:0/22:0), Hydroxypalmitoyl sphingomyelin (d18:1/16:0(OH)), and Mannose were linked to a decreased risk of gout.

Conclusion: This study identified eight metabolites from 1,400 blood samples significantly linked to gout risk. Integrating genomics and metabolomics offers valuable insights for gout screening and prevention, indicating that specific blood metabolites can help identify individuals at higher risk.

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