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. 2024 Apr 24:11:1371995.
doi: 10.3389/fnut.2024.1371995. eCollection 2024.

Metabolome-wide Mendelian randomization reveals causal effects of betaine and N-acetylornithine on impairment of renal function

Yuqing Liu  1 , Lilu Ling  2 , Yue Shen  2 , Xiao Bi  2
Affiliations

Metabolome-wide Mendelian randomization reveals causal effects of betaine and N-acetylornithine on impairment of renal function

Yuqing Liu et al. Front Nutr. .

Abstract

Background: Chronic kidney disease (CKD) is a common public health problem, which is characterized as impairment of renal function. The associations between blood metabolites and renal function remained unclear. This study aimed to assess the causal effect of various circulation metabolites on renal function based on metabolomics.

Methods: We performed a two-sample Mendelian randomization (MR) analysis to estimate the causality of genetically determined metabolites on renal function. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, while summary-level data for creatinine-based estimated glomerular filtration rate (eGFR) or CKD occurrence were set the outcomes. Inverse variance weighted (IVW) was used for primary causality analysis and other methods including weight median, MR-egger, and MR-PRESSO were applied as complementary analysis. Cochran Q test, MR-Egger intercept test, MR-PRESSO global test and leave-one-out analysis were used for sensitivity analysis. For the identified metabolites, reverse MR analysis, linkage disequilibrium score (LDSC) regression and multivariable MR (MVMR) analysis were performed for further evaluation. The causality of the identified metabolites on renal function was further validated using GWAS data for cystatin-C-based eGFR. All statistical analyses were performed in R software.

Results: In this MR analysis, a total of 44 suggestive associations corresponding to 34 known metabolites were observed. After complementary analysis and sensitivity analysis, robust causative associations between two metabolites (betaine and N-acetylornithine) and renal function were identified. Reverse MR analysis showed no causal effects of renal function on betaine and N-acetylornithine. MVMR analysis revealed that genetically predicted betaine and N-acetylornithine could directly influence independently of each other. The causal effects of betaine and N-acetylornithine were also found on cystatin-C-based eGFR.

Conclusion: Our study provided evidence to support the causal effects of betaine and N-acetylornithine on renal function. These findings required further investigations to conduct mechanism exploration and drug target selection of these identified metabolites.

Keywords: Mendelian randomization; N-acetylornithine; betaine; genetically determined metabolites; renal function.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Flowchart of the study design.
Figure 2
Figure 2
Mendelian randomization associations of known metabolites with various renal function phenotype (derived from the fixed-effect or random-effect IVW analysis according to the Cochran’s Q test). IVW, inverse-variance weighted.
Figure 3
Figure 3
The causal estimates of the candidate metabolites on various renal function phenotype using IVW method. (A) Candidate metabolites with creatinine-based eGFR from CKDGen and UKB; (B) candidate metabolites with creatinine-based eGFR from CKDGen; (C) candidate metabolites with CKD from CKDGen; (D)Venn diagram showing the two metabolites (betaine and N-acetylornithine) were significantly correlated with renal function phenotype from all three datasets.
Figure 4
Figure 4
Scatter plots showing the genetic associations of the identified metabolites with various renal function phenotype. (A) Betaine on creatine-based eGFR from CKDGen and UKB; (B) betaine on creatine-based eGFR from CKDGen; (C) betaine on CKD phenotype; (D) N-acetylornithine on creatine-based eGFR from CKDGen and UKB; (E) N-acetylornithine on creatine-based eGFR from CKDGen; (F) N-acetylornithine on CKD phenotype.
Figure 5
Figure 5
Funnel plots representing IVs for each significant causal association between the identified metabolites and renal function phenotype. (A) Betaine on creatine-based eGFR from CKDGen and UKB; (B) betaine on creatine-based eGFR from CKDGen; (C) betaine on CKD phenotype; (D) N-acetylornithine on creatine-based eGFR from CKDGen and UKB; (E) N-acetylornithine on creatine-based eGFR from CKDGen; (F) N-acetylornithine on CKD phenotype.
Figure 6
Figure 6
Leave-one-out sensitivity analysis showing the causal effect of the identified metabolites on renal function phenotype excluding that certain variant from the analysis. (A) Betaine on creatine-based eGFR from CKDGen and UKB; (B) betaine on creatine-based eGFR from CKDGen; (C) betaine on CKD phenotype; (D) N-acetylornithine on creatine-based eGFR from CKDGen and UKB; (E) N-acetylornithine on creatine-based eGFR from CKDGen; (F) N-acetylornithine on CKD phenotype.
Figure 7
Figure 7
The funnel plot, scatter plot and leave-one-out analysis of the causal interactions of the identified metabolites with cystatin-C-based eGFR from CKDGen and UKB. (A) The funnel plot of betaine; (B) the scatter plot of betaine; (C) leave-one-out analysis of betaine; (D) the funnel plot of N-acetylornithine; (E) the scatter plot of N-acetylornithine; (F) leave-one-out analysis of N-acetylornithine.
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