Table_2_Causal effects of human serum metabolites on occurrence and progress indicators of chronic kidney disease: a two-sample Mendelian randomization study.xlsx

BackgroundChronic 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.
MethodsIn 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.
ResultsDuring 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.
ConclusionBased 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.

研究背景
慢性肾脏病（CKD）常伴随机体代谢谱的改变，但此类代谢变化在CKD发病中的致病作用仍存在持续争议。本研究借助486种血液代谢物的全基因组关联研究（GWAS）数据，采用大样本两样本孟德尔随机化（MR）分析方法，探究代谢物与CKD之间的因果关联。在筛选得到与CKD存在因果关联的代谢物后，本研究进一步通过富集分析，明确可能参与CKD发生与进展的代谢通路。

研究方法
本研究将486项代谢性状的GWAS数据作为暴露变量，以CKDGen联盟发布的基于血清肌酐的估算肾小球滤过率（eGFRcrea）、微量白蛋白尿以及尿白蛋白肌酐比（UACR）的GWAS数据作为结局变量。采用逆方差加权（IVW）分析筛选与结局存在因果关联的代谢物，并通过邦费罗尼校正进一步筛选因果关联更为稳健的代谢物。此外，针对IVW分析得到的阳性结果，补充采用加权中位数法、MR-Egger法、加权众数法以及简单众数法进行验证。同时，本研究通过科克伦Q检验、MR-Egger截距检验、MR-PRESSO法以及留一法（LOO）检验开展敏感性分析。对于符合筛选条件的代谢物，本研究分别采用京都基因与基因组百科全书（KEGG）与小分子通路数据库（SMPDB）进行通路富集分析。

研究结果
在完成批量孟德尔随机化（MR）分析、逆方差加权（IVW）法分析、敏感性分析以及方向一致性检验后，共筛选出78种符合标准的代谢物。其中4种代谢物通过邦费罗尼校正，分别为甘露糖、N-乙酰鸟氨酸、甘氨酸与(Z,Z)-胆红素，且甘露糖与所有CKD相关结局均存在因果关联。通过通路富集分析，本研究共鉴定出8条参与CKD发生与进展的代谢通路。

研究结论
基于本研究分析结果，甘露糖通过邦费罗尼校正，且与CKD、eGFRcrea、微量白蛋白尿以及UACR均存在因果关联。作为CKD诊断与治疗的潜在靶点，甘露糖在CKD的发生与发展过程中发挥重要作用。