Human blood metabolites and calculus of kidney: A bidirectional Mendelian randomized analysis

Abstract

Abstract Background Kidney stones affect people worldwide and place a burden on public healthcare systems. Understanding the underlying mechanism of its occurrence is helpful for its prevention. The analysis of serum metabolites may facilitate a more comprehensive comprehension of the underlying biological processes. Mendelian randomization (MR) can furnish valuable insights into the causality of associations. Methods We applied a two-sample MR analysis to evaluate relationships between 1,091 metabolites and 309 metabolite ratios and kidney calculus. The inverse-variance weighted (IVW) method was used to estimate the causal relationship of the exposure on the outcome, while The Cochran Q test statistic was utilized to quantify heterogeneity. The MR-PRESSO global test and MR-PRESSO outlier test were employed to calculate the horizontal pleiotropy and remove the outlying SNPs, respectively. Additionally, we conducted a "leave-one-out" sensitivity analysis to identify any potential impacts. We also performed reverse MR Analysis to determine the potential causal relationship between kidney stones and metabolites. Results We identified 2 known (1 risk and 1 protective) and 1 unknown serum metabolites associated with kidney calculus. The results of the IVW analysis (Figure 2) elucidated that causal effects of the genetically predicted increased abundance of blood sugar levels (OR: 1.002, 95% CI: 1.001–1.003) at the genus level were associated with the higher risk of calculus of kidney, while the increased abundance of maltotriose levels (OR: 0.998, 95% CI: 0.997–0.999) and X-24947 levels (OR: 0.999, 95% CI: 0.998–0.999) at genus level were associated with the lower risk of calculus of kidney. The reverse MR suggests a causal relationship between the occurrence of kidney stones and blood sugar levels (OR = 7.77e+08, 95% CI = 12.956 – 4.66e+16, p = 0.025, IVW). Conclusion Our study further validates the causal relationship between kidney stones and blood sugar levels, and identifies two other metabolites that act as protective factors for kidney stones, which may help prioritize metabolic features for kidney calculus mechanistic research and further evaluation of their potential role in risk assessment.