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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Renal Endocrinology
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1429159

Causal effects of plasma metabolites on chronic kidney diseases and renal function: a bidirectional Mendelian randomization study

Provisionally accepted
  • 1 First Affiliated Hospital of Jilin University, Changchun, Jilin Province, China
  • 2 Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
  • 3 Jilin University, Changchun, Hebei Province, China

The final, formatted version of the article will be published soon.

    Despite the potential demonstrated by targeted plasma metabolite modulators in halting the progression of chronic kidney disease (CKD), a lingering uncertainty persists concerning the causal relationship between distinct plasma metabolites and the onset and progression of CKD.A genome-wide association study was conducted on 1,091 metabolites and 309 metabolite ratios derived from a cohort of 8,299 unrelated individuals of European descent. Employing a bidirectional two-sample Mendelian randomization (MR) analysis in conjunction with colocalization analysis, we systematically investigated the associations between these metabolites and three phenotypes: CKD, creatinine-estimated glomerular filtration rate (creatinine-eGFR), and urine albumin creatinine ratio (UACR). In the MR analysis, the primary analytical approach employed was inverse variance weighting (IVW), and sensitivity analysis was executed utilizing the MR-Egger method and MR-pleiotropy residual sum and outlier (MR-PRESSO). Heterogeneity was carefully evaluated through Cochrane's Q test. To ensure the robustness of our MR results, the leave-one-out method was implemented, and the strength of causal relationships was subjected to scrutiny via Bonferroni correction.Our thorough MR analysis involving 1,400 plasma metabolites and three clinical phenotypes yielded a discerning identification of 21 plasma metabolites significantly associated with diverse outcomes. Specifically, in the forward MR analysis, 6 plasma metabolites were determined to be causally associated with CKD, 16 with creatinine-eGFR, and 7 with UACR. Substantiated by robust evidence from colocalization analysis, 6 plasma metabolites shared causal variants with CKD, 16 with creatinine-eGFR, and 7 with UACR. In the reverse analysis, a diminished creatinine-eGFR was linked to elevated levels of nine plasma metabolites. Notably, no discernible associations were observed between other plasma metabolites and CKD, creatinine-eGFR, and UACR. Importantly, our analysis detected no evidence of horizontal pleiotropy.This study elucidates specific plasma metabolites causally associated with CKD and renal functions, providing potential targets for intervention. These findings contribute to an enriched understanding of the genetic underpinnings of CKD and renal functions, paving the way for precision medicine applications and therapeutic strategies aimed at impeding disease progression.

    Keywords: Chronic Kidney Disease, Plasma metabolites, Mendelian randomization, Renal function, genetic causal association

    Received: 14 May 2024; Accepted: 12 Jul 2024.

    Copyright: © 2024 Zhao, Kou, Zhou, Wang, Gao, Wang, Gao and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Kai Kou, First Affiliated Hospital of Jilin University, Changchun, 130012, Jilin Province, China
    Xi Wang, Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
    JiaLin Gao, First Affiliated Hospital of Jilin University, Changchun, 130012, Jilin Province, China
    Faping Li, First Affiliated Hospital of Jilin University, Changchun, 130012, Jilin Province, China

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