DYSREGULATION OF THE PLASMA METABOLOME PROFILE IN KIDNEY STONE FORMERS

Introduction:

Kidney stones (KS) have been linked to cardiometabolic disorders, but limited research has evaluated the effect of metabolic disorders on KS risk. Thus, we investigated this association in a large dataset using an available panel of serum metabolites and genotyping data.

Methods:

Participants in the United Kingdom Biobank (UKBB) with available metabolomics data(n=186,362) were used to examine the relationship between KS prevalence. Linear regression accounted for comorbidities, eGFR, sex, age, smoking activity, physical activity, alcohol use, and approximate lipid intake. Genome wide association studies (GWAS) were conducted on the phenotypes of KS, BMI, serum HDL, TG, and AIP (log10(TG/HDL)), with or without BMI adjustment. Mendelian randomization (MR) analysis was then utilized to search for potential causal relationships. Linkage disequilibrium score regression (LDSC) was performed in advanced analyses to examine the genetic correlation between phenotypes.

Results:

A total of 63 metabolites in very large, large, and medium high-density lipoproteins (HDL) and very low-density lipoproteins (VLDL) were related to KS. Multivariate analysis produced 31 significant metabolites including HDL cholesterol (β=-0.04, 95% CI: [-0.06, -0.03], p<0.001) and VLDL triglycerides (TG) (β=0.07, 95% CI: [0.03, 0.11, p=0.02]). GWAS identified several SNP locations associated with KS, BMI, and HDL. MR analysis was significant for BMI and HDL (β=-0.02, 95% CI: [-0.04, -0.01], p<0.001), but not between KS and BMI adjusted HDL. Genetic correlation results revealed a significant correlation between HDL and KS (rg = -0.13, p = 0.003), which was no longer significant when adjusted for BMI (rg = -0.05, p = 0.26). AIP ratio and BMI-adjusted KS were also significantly associated (rg = 0.13, p = 0.001).

Result 1

Univariate and multivariate analyses were conducted to find dysregulated metabolites in stone formers.

FIGURE 1

Figure legend: Univariate results produced 63 metabolites significantly associated with KS.

FIGURE 2

Figure legend: Multivariate results produced 31 metabolites after comprehensive adjustment.

Result 2

Advanced analysis included genome wide association studies (GWAS), Mendelian randomization (MR) analysis, and linkage disequilibrium score regression (LDSC) correlative analysis evaluated risk implications.

FIGURE 3

Figure legend: A) KS GWAS from UKBB shows several peaks indicating significantly associated genetic loci with kidney stones.

FIGURE 4

Figure legend: Genetic correlation analysis shows a significant correlation between the TG/HDL ratio and BMI-adjusted KS, as well as between TG and KSD. The relationship between KSD and HDL is mediated by BMI.

FIGURE 5

Figure legend: MR analysis results show a significant relationship between HDL, AIP, and TG with KS.

Conclusions:

Kidney stones are characterized by lipid dysregulation, with higher large VLDL and lower HDL components.

This study highlights the role of obesity-related genetic changes in mediating the comorbid relationship of plasma metabolome profile and kidney stones.

Weaknesses of this study include difficult parameters with more potential confounders.

Future research should investigate further in clinical studies the effect of analyzing TG/HDL ratio to use as a biomarker and find mechanistic proof of this in the formation of kidney stones.

I have no potential conflict of interest to disclose.

I did not use generative AI and AI-assisted technologies in the writing process.