人血清代谢物对慢性肾脏病发生及进展指标的因果效应:一项两样本孟德尔随机化研究

Causal effects of human serum metabolites on occurrence and progress indicators of chronic kidney disease: a two-sample Mendelian randomization study.

作者信息

Yin Yu, Shan Conghui, Han Qianguang, Chen Congcong, Wang Zijie, Huang Zhengkai, Chen Hao, Sun Li, Fei Shuang, Tao Jun, Han Zhijian, Tan Ruoyun, Gu Min, Ju Xiaobing

机构信息

Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Department of Clinical Laboratory, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

出版信息

Front Nutr. 2024 Jan 8;10:1274078. doi: 10.3389/fnut.2023.1274078. eCollection 2023.

Abstract

BACKGROUND

Chronic kidney disease (CKD) is often accompanied by alterations in the metabolic profile of the body, yet the causative role of these metabolic changes in the onset of CKD remains a subject of ongoing debate. This study investigates the causative links between metabolites and CKD by leveraging the results of genomewide association study (GWAS) from 486 blood metabolites, employing bulk two-sample Mendelian randomization (MR) analyses. Building on the metabolites that exhibit a causal relationship with CKD, we delve deeper using enrichment analysis to identify the metabolic pathways that may contribute to the development and progression of CKD.

METHODS

In conducting the Mendelian randomization analysis, we treated the GWAS data for 486 metabolic traits as exposure variables while using GWAS data for estimated glomerular filtration rate based on serum creatinine (eGFRcrea), microalbuminuria, and the urinary albumin-to-creatinine ratio (UACR) sourced from the CKDGen consortium as the outcome variables. Inverse-variance weighting (IVW) analysis was used to identify metabolites with a causal relationship to outcome. Using Bonferroni correction, metabolites with more robust causal relationships are screened. Additionally, the IVW-positive results were supplemented with the weighted median, MR-Egger, weighted mode, and simple mode. Furthermore, we performed sensitivity analyses using the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out (LOO) test. Pathway enrichment analysis was conducted using two databases, KEGG and SMPDB, for eligible metabolites.

RESULTS

During the batch Mendelian randomization (MR) analyses, upon completion of the inverse-variance weighted (IVW) approach, sensitivity analysis, and directional consistency checks, 78 metabolites were found to meet the criteria. The following four metabolites satisfy Bonferroni correction: mannose, N-acetylornithine, glycine, and bilirubin (Z, Z), and mannose is causally related to all outcomes of CKD. By pathway enrichment analysis, we identified eight metabolic pathways that contribute to CKD occurrence and progression.

CONCLUSION

Based on the present analysis, mannose met Bonferroni correction and had causal associations with CKD, eGFRcrea, microalbuminuria, and UACR. As a potential target for CKD diagnosis and treatment, mannose is believed to play an important role in the occurrence and development of CKD.

摘要

背景

慢性肾脏病(CKD)常伴有身体代谢谱的改变,然而这些代谢变化在CKD发病中的因果作用仍是一个持续争论的话题。本研究通过利用486种血液代谢物的全基因组关联研究(GWAS)结果,采用两组分孟德尔随机化(MR)分析,来探究代谢物与CKD之间的因果联系。基于与CKD存在因果关系的代谢物,我们进一步通过富集分析来确定可能促成CKD发生和发展的代谢途径。

方法

在进行孟德尔随机化分析时,我们将486种代谢性状的GWAS数据作为暴露变量,同时将来自CKDGen联盟的基于血清肌酐的估计肾小球滤过率(eGFRcrea)、微量白蛋白尿和尿白蛋白与肌酐比值(UACR)的GWAS数据作为结果变量。采用逆方差加权(IVW)分析来确定与结果存在因果关系的代谢物。使用Bonferroni校正筛选出因果关系更强的代谢物。此外,IVW阳性结果还补充了加权中位数、MR-Egger、加权模式和简单模式。此外,我们使用Cochran Q检验、MR-Egger截距检验、MR-PRESSO和留一法(LOO)检验进行敏感性分析。使用KEGG和SMPDB两个数据库对符合条件的代谢物进行通路富集分析。

结果

在批量孟德尔随机化(MR)分析中,在完成逆方差加权(IVW)方法、敏感性分析和方向一致性检查后,发现有78种代谢物符合标准。以下四种代谢物满足Bonferroni校正:甘露糖、N-乙酰鸟氨酸、甘氨酸和胆红素(Z,Z),且甘露糖与CKD的所有结果均存在因果关系。通过通路富集分析,我们确定了八条促成CKD发生和发展的代谢途径。

结论

基于目前的分析,甘露糖满足Bonferroni校正,且与CKD、eGFRcrea、微量白蛋白尿和UACR存在因果关联。作为CKD诊断和治疗的潜在靶点,甘露糖被认为在CKD的发生和发展中起重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e368/10800733/278b954b65ba/fnut-10-1274078-g001.jpg

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