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. 2025 Mar 21;104(12):e41911. doi: 10.1097/MD.0000000000041911

Identification of the relationship between 1400 blood metabolites and urolithiasis: A bidirectional Mendelian randomization study

Haoyang Zhang a, Haojie Mo a, Peng Li a, Qi Zhou b, Gang Shen a, Jiale Sun a,*
PMCID: PMC11936622  PMID: 40128055

Abstract

Relationships between blood metabolites and urolithiasis have been identified in few previous observational studies, and causality remains uncertain. We tried to examine whether blood metabolites were causally associated with upper and lower urinary stones in this bidirectional Mendelian randomization (MR) study. The causal relationship between 1400 blood metabolites and upper and lower urinary stones was investigated using genome-wide association study data. The primary analysis for causality analysis was the inverse variance weighted method, with 4 other methods used as complementary analyses. Intersection was then conducted to show the shared metabolites between upper and lower urinary tract stones, followed by the MR-Egger intercept test, Cochran Q test, leave-one-out analysis, MR-PRESSO and the linkage disequilibrium score regressions. The metabolic pathway analysis was conducted to identify potential metabolic pathways. Lastly, reverse MR analyses were also performed. We identified 15 metabolites as potential causal predictors of urinary stones in forward MR analyses. These metabolites consisted of 1 azole, 2 carbohydrates, 6 lipids, 1 nucleotide, 1 peptide, 1 urea, and 3 metabolites with unknown chemical properties. Additionally, urinary stones were found to be significantly associated with some of the above metabolites in reverse MR analyses. Metabolic pathway analysis identified several pathways that may be implicated in the development of urolithiasis. This MR study has established a causal relationship between 12 blood metabolites and the risk of upper and lower urinary tract stones. The identification of these blood metabolites provides valuable insights into early screening, prevention, and treatment of urolithiasis.

Keywords: blood metabolites, causality, Mendelian randomization, single-nucleotide polymorphisms, urolithiasis

1. Introduction

Urolithiasis is a common disease encountered in urology, with an overall prevalence of 7% to 13% in North America.[1] Over the last few decades, there has been an increasing incidence of urinary stones in both developed and developing countries.[2] The occurrence of urinary stones increases with age and the recurrence rate can reach 50% within 5 years after initial treatment,[3] imposing a huge public health and financial burden.[4] Although treatments for urolithiasis have improved in terms of efficacy and safety, it has not been cured. Therefore, it is crucial to identify potential risk factors for urinary stones. Various factors have been reported to be associated with the development of urinary stones, including gender, coffee consumption, body mass index (BMI), and diabetes.[5–7] Many studies indicated that urolithiasis is a chronic metabolic disorder.[8]

Nonetheless, limited research has been conducted on metabolic changes in urinary stones.

Metabolites serves as functional intermediates to understand the occurrence and development of several diseases.[9] Although the mechanism was not fully understood, urolithiasis was thought to be influenced by blood metabolites,[10] which could act as inhibitors or promoters for the development of urinary stones. Metabolomics is a systematic study of metabolites associated with metabolic processes in organisms, providing a novel approach to investigating the biological pathogenesis of urinary stones. This technique is able to elucidate the mechanisms of disease by identifying specific biomarkers related to environmental exposures and diseases.[11] Exploration of the metabolites connected with the development of urolithiasis can not only contribute to early screening and prevention of urinary stones, but also help to understand the mechanisms for treatment. The causal association between blood metabolites and urinary stones remains to be established.

Mendelian randomization (MR) is an epidemiological method of analysis that uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to simulate randomized controlled trials. MR analysis can be a reliable tool for assessing causality between exposure to risk factors and outcomes, with the advantage of reducing bias from reverse causality and confounding factors.[12] In this study, the causal relationship between 1400 blood metabolites and urolithiasis was investigated using genome-wide association studies (GWAS) data. The primary analysis for causality analysis was the inverse variance weighted (IVW) method, with 4 other methods used as complementary analyses. Intersection was then conducted to show the shared metabolites between upper and lower urinary tract stones, followed by the MR-Egger intercept test, Cochran Q test, leave-one-out analysis, MR-PRESSO and the linkage disequilibrium score (LDSC) regressions. The metabolic pathway analysis was conducted to identify potential metabolic pathways.

2. Methods

2.1. Study design

Figure 1 shows our two-sample MR design comparing 1400 human blood metabolites with the risk of urinary stones. We used summary statistics from a GWAS to investigate the causal relationship between blood metabolites and upper and lower urinary stones. The chosen IVs needed to comply with 3 assumptions: (1) IVs must be strongly associated with the exposure of interest; (2) IVs must be independent of unmeasured confounders; and (3) IVs must be related to outcomes solely through the exposure of interest rather than through confounders. In this MR study, the risk of urinary stones and human blood metabolites were respectively considered as the exposure and outcome.

Figure 1.

Figure 1.

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The flowchart of Mendelian Randomization analysis of 1400 metabolites and risk of urinary stones. Among the above metabolites, 309 which were defined as metabolite ratios were excluded in our study. IV = instrumental variables, SNPs = single nucleotide polymorphisms, LD = linkage disequilibrium, IVW = inverse variance weighted, LDSC = linkage disequilibrium score, MR = Mendelian randomization, MR-PRESSO = MR-Pleiotropy RESidual sum and outlier, KEGG = Kyoto Encyclopedia of Genes and Genomes, SMPDB = The Small Molecule Pathway Database.

2.2. Sources of GWAS data

The metabolite database was sourced from one of the most comprehensive metabolite studies by Chen et al.[13] By conducting studies on 1091 metabolites and 309 metabolite ratios in 8299 individuals of European descent from the Canadian longitudinal study on aging cohort, this report eventually identified almost 15.4 million SNPs for GWAS testing. The detailed information of 1400 metabolites is presented in Table S1, Supplemental Digital Content, http://links.lww.com/MD/O573. The urinary stones datasets were obtained from FinnGen R10,[14] with 10,566 cases and 400,681 controls in calculus of kidney and ureter, and 1514 cases and 400,681 controls in calculus of lower urinary tract.

2.3. Filtering of IVs for MR analyses and confounding analysis

We chose a lenient threshold for genome-wide significance using P < 5 × 10–5, because of the limited number of SNPs reaching GWAS. Then we conducted linkage disequilibrium analyses (r2 = 0.1 and with 1000kb window) among the included IVs to filter independent instrument SNPs. The F-statistic was evaluated to quantify the strength of genetic variation using the formula: F = (β/SE)2, where β denotes the estimated genetic effect of each SNP, and SE is the standard error of β. SNPs with an F-statistic of <10 were abandoned, indicativing weak association. Additionally, we conducted confounding analysis utilizing the Phenoscanner V2 website (http://www.phenoscanner.medschl.cam.ac.uk/) to investigate whether metabolite-associated SNPs were also connected to common risk factors that might influence MR estimates, including BMI[15], diabetes mellitus[16], serum calcium, and serum 1,25(OH)2D3.[17]

2.4. MR analysis

Several MR analyses including IVW, MR-Egger, Weighted median, Simple mode, and Weighted mode were employed to evaluate the causal association between human blood metabolites and urinary stones. Additionally, 2 reverse MR analyses were performed to investigate the causal association of upper and lower urinary stones on blood metabolites. A P-value < 0.05 was considered statistically significant.

2.5. Sensitivity analysis and LDSC analysis

We performed MR-Egger intercept test to detect potential horizontal or pleiotropic biases for significant estimates. Secondly, we employed Cochran Q test to identify heterogeneity among the selected SNPs. To assess the robustness of the causal estimates, we also presented leave-one-out plots to identify strong influences of SNPs. In addition, the LDSC regressions were performed to assess whether the genetic associations are influenced by common genetic factors.[18] The odds ratio (OR) and 95% confidence intervals (CIs) were used to estimate the degree of metabolic impact. All statistical analyses were conducted using the “TwoSampleMR,” “MRPRESSO” package and “ldscr” packages in R software (version 4.3.0, https://www.r-project.org/). A significant difference was considered when P < .05.

2.6. Metabolic pathway analysis

We used Web-based metaconflict 5.0 (https://www.Metaboanalyst.ca/) to estimate metabolic pathways. The pathway and enrichment analysis modules were utilized to identify clusters of metabolites or superpathways that may be associated with metabolic processes and urinary stone associations. We made use of the small molecule pathway database and the Kyoto Encyclopedia of Genes and Genomes database as reference.[19,20]

3. Results

3.1. Selected genetic IVs

After removing 309 metabolite ratios, a total of 1091 metabolites were retained for MR analysis in the instrument SNP selection procedure (Table S1, Supplemental Digital Content, http://links.lww.com/MD/O573). Fifteen metabolites were detected to be both significantly associated with upper and lower urinary stones. The number of SNPs for each metabolite ranged from 15 to 90. None of the F-values for SNP inclusion were <10, indicating no potentially weak instruments were employed (Tables S2 and S3, Supplemental Digital Content, http://links.lww.com/MD/O574). We also examined several common factors including BMI, diabetes mellitus, serum calcium, and serum 1,25(OH)2D3 to assess their potential impact as confounding variables, and several SNPs associated with any confounding factors were excluded from the instrument SNP before conducting MR analysis (Tables S4, Supplemental Digital Content, http://links.lww.com/MD/O575 and S5, Supplemental Digital Content, http://links.lww.com/MD/O576).

3.2. Primary analysis

IVW results identified 98 metabolites related to calculus of kidney and ureter, and 86 metabolites related to calculus of lower urinary tract. Intersection analysis showed 17 shared casual metabolites between upper and lower urinary tract stones (Fig. 2). Among them, 2 were removed due to lack of LDSC analysis results and 15 metabolites showed weak evidence of genetic correlation (Tables S6 and S7, Supplemental Digital Content, http://links.lww.com/MD/O577). Finally, these 15 metabolites were detected significantly associated to upper and lower urinary stones, of which 3 metabolites remained chemically unknown.

Figure 2.

Figure 2.

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Intersection analysis of significantly associated metabolites on upper and lower urinary stones. The Venn diagram showed the shared 17 metabolites screened by forward MR analyses. MR = Mendelian randomization.

3.3. Tightened MR analysis

Among all above metabolites, 10 metabolites increased the risk of upper urinary stones, including 1-methyl-4-imidazoleacetate (OR = 1.06, 95%CI = 0.95–1.16, P = .0174), 1-linoleoyl-2-arachidonoyl-GPC (OR = 1.05, 95%CI = 0.95–1.14, P = .0284), 1-stearoyl-2-docosahexaenoyl-GPE (OR = 1.06, 95%CI = 0.92–1.15, P = .0311), octadecenedioate (OR = 1.06, 95%CI = 0.99–1.13, P = .0030), 1-palmitoyl-2-oleoyl-GPE (OR = 1.04, 95%CI = 0.94–1.11, P = .0392), 1-oleoyl-2-arachidonoyl-GPE (OR = 1.05, 95%CI = 0.92–1.06, P = .0193), adenosine 5′-monophosphate (OR = 1.13, 95%CI = 0.84–1.42, P = .0250), urea (OR = 1.05, 95%CI = 1.00–1.09, P = .0095), X-17654 (OR = 1.06, 95%CI = 0.92–1.19, P = .0413), and X-19141 (OR = 1.05, 95%CI = 0.99–1.13, P = .0052). Five metabolites decreased the risk of upper urinary stones, including mannose (OR = 0.88, 95%CI = 0.69–0.97, P = .0020), N-acetylglucosamine/n-acetylgalactosamine (OR = 0.91, 95%CI = 0.81–1.05, P = .0037), eicosenedioate (OR = 0.90, 95%CI = 0.81–1.11, P = .0105), cysteinylglycine (OR = 0.94, 95%CI = 0.84–1.15, P = .0373), and X-26054 (OR = 0.96, 95%CI = 0.93–1.05, P = .0200). Detailed information was available in Fig. 3 and Table 1. MR-Egger regression analyses and MR-PRESSO tests were performed, and no incidence of potential pleiotropy was identified, validating the reliability of MR analyses. Cochran Q P-value indicated the absence of heterogeneity (Table 1). In Figs. S1–S4, Supplemental Digital Content, http://links.lww.com/MD/O578, forest plots, funnel plots, scatter plots, and leave-one-out plots were displayed.

Figure 3.

Figure 3.

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Forest plot of the causal effects of 15 metabolites on the risk of urinary stones derived from the IVW method. (A) Causal effects of 15 metabolites on the risk of upper urinary stones. (B) Causal effects of 15 metabolites on the risk of lower urinary stones. CI = confidence interval, IVW = inverse variance weighted, OR = odds ratio.

Table 1.

MR analysis and sensitivity analyses for causality from 15 blood metabolites on upper urinary stones.

Name Exposure Metabolites Number of SNPs MR analysis Heterogeneity Pleiotropy MR-PRESSO
Method OR (95% CI) Lower CI Upper CI P IVW MR Egger Intercept P P
Q P Q P
1-methyl-4-imidazoleacetate GCST90199717 Azoles 39 IVW 1.06 0.95 1.16 0.0174 44.55 0.2154 44.46 0.1863 0.0024 0.7856 0.2448
Mannose GCST90200435 Carbohydrate 30 IVW 0.88 0.69 0.97 0.0020 49.00 0.0115 47.23 0.0130 0.0118 0.3141 0.0650
N-acetylglucosamine/n-acetylgalactosamine GCST90200021 Carbohydrate 23 IVW 0.91 0.81 1.05 0.0037 26.45 0.2330 26.32 0.1946 -0.0030 0.7502 0.2870
1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6) GCST90200073 Lipid 37 IVW 1.05 0.95 1.14 0.0284 43.84 0.1731 43.74 0.1477 0.0022 0.7715 0.1626
Eicosenedioate (C20:1-DC) GCST90200285 Lipid 20 IVW 0.90 0.81 1.11 0.0105 17.71 0.5416 17.24 0.5069 -0.0096 0.4984 0.6176
1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) GCST90200065 Lipid 42 IVW 1.06 0.92 1.15 0.0311 63.30 0.0184 62.75 0.0160 0.0051 0.5521 0.5110
Octadecenedioate (C18:1-DC) GCST90200165 Lipid 42 IVW 1.06 0.99 1.13 0.0030 37.63 0.6211 37.54 0.5816 0.0018 0.7607 0.6326
1-palmitoyl-2-oleoyl-GPE (16:0/18:1) GCST90200331 Lipid 45 IVW 1.04 0.94 1.11 0.0392 48.77 0.2870 48.25 0.2691 0.0047 0.4968 0.3034
1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) GCST90200079 Lipid 42 IVW 1.05 0.92 1.06 0.0193 46.72 0.2492 43.19 0.3367 0.0113 0.0781 0.2926
Adenosine 5’-monophosphate (AMP) GCST90200356 Nucleotide 15 IVW 1.13 0.84 1.42 0.0250 8.01 0.8888 7.94 0.8478 0.0037 0.7893 0.9012
Cysteinylglycine GCST90200364 Peptide 24 IVW 0.94 0.84 1.15 0.0373 28.82 0.1864 28.32 0.1653 -0.0067 0.5401 0.1962
Urea GCST90200417 Ureas 22 IVW 1.05 1.00 1.09 0.0095 12.09 0.9372 11.72 0.9252 0.0034 0.5535 0.9640
X-26054 GCST90200672 Unknown 55 IVW 0.96 0.93 1.05 0.0200 47.09 0.7358 45.77 0.7489 ‐0.0059 0.2560 0.7630
X-17654 GCST90200547 Unknown 35 IVW 1.06 0.92 1.19 0.0413 35.69 0.3890 35.64 0.3453 0.0019 0.8329 0.4124
X-19141 GCST90200711 Unknown 90 IVW 1.05 0.99 1.13 0.0052 143.00 0.0002 142.86 0.0002 -0.0018 0.7677 0.5020
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CI = confidence interval, IVW = inverse variance weighted, MR = Mendelian randomization, MR-PRESSO = MR-Pleiotropy RESidual sum and outlier, OR = odds ratio, SNPs = single nucleotide polymorphisms.

Besides, 11 metabolites increased the risk of lower urinary stones, including 1-methyl-4-imidazoleacetate (OR = 1.13, 95%CI = 1.00–1.26, P = .0410), N-acetylglucosamine/n-acetylgalactosamine (OR = 1.24, 95%CI = 1.06–1.45, P = .0068), 1-linoleoyl-2-arachidonoyl-GPC (OR = 1.14, 95%CI = 1.02–1.28, P = .0254), 1-stearoyl-2-docosahexaenoyl-GPE (OR = 1.21, 95%CI = 1.08–1.36, P = .0010), 1-palmitoyl-2-oleoyl-GPE (OR = 1.18, 95%CI = 1.01–1.50, P = .0015), 1-oleoyl-2-arachidonoyl-GPE (OR = 1.11, 95%CI = 1.01–1.23, P = .0255), Adenosine 5′-monophosphate (OR = 1.35, 95%CI = 1.03–1.78, P = .0309), urea (OR = 1.13, 95%CI = 0.97–1.27, P = .0299), X-26054 (OR = 1.10, 95%CI = 0.97–1.36, P = .0401), X-17654 (OR = 1.17, 95%CI = 1.00–1.38, P = .0491), and X-19141 (OR = 1.09, 95%CI = 0.91–1.17, P = .0160). On the other hand, 4 metabolites decreased the risk of lower urinary stones, including mannose (OR = 0.78, 95%CI = 0.51–1.03, P = .0015), eicosenedioate (OR = 0.77, 95%CI = 0.52–1.19, P = .0167), octadecenedioate (OR = 0.90, 95%CI = 0.81–1.00, P = .0454), and cysteinylglycine (OR = 0.85, 95%CI = 0.51–1.13, P = .0429) (Fig. 3 and Table 2). No risk of potential pleiotropy was found in the MR-Egger regression analyses or MR-PRESSO tests (Table 2). Cochran Q P-value indicated heterogeneity in some causal associations, but this was acceptable in the MR study (Table 2). Forest plots, funnel plots, scatter plots and leave-one-out plots were presented in Figs. S5–S8, Supplemental Digital Content, http://links.lww.com/MD/O578, demonstrating that the estimates were unaffected by individual SNPs and there were no violations of assumptions.

Table 2.

MR analysis and sensitivity analyses for causality from 15 blood metabolites on lower urinary stones.

Name Exposure Metabolites Number of SNPs MR analysis Heterogeneity Pleiotropy MR-PRESSO
Method OR (95% CI) Lower CI Upper CI P IVW MR Egger Intercept P P
Q P Q P
1-methyl-4-imidazoleacetate GCST90199717 Azoles 39 IVW 1.13 1.00 1.26 0.0410 32.54 0.7195 31.60 0.7197 0.0200 0.3392 0.7404
Mannose GCST90200435 Carbohydrate 30 IVW 0.78 0.51 1.03 0.0015 14.69 0.9873 14.53 0.9830 0.0094 0.6875 0.9916
N-acetylglucosamine/n-acetylgalactosamine GCST90200021 Carbohydrate 23 IVW 1.24 1.06 1.45 0.0068 20.20 0.5706 20.20 0.5087 0.0000 0.9986 0.6320
1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6) GCST90200073 Lipid 37 IVW 1.14 1.02 1.28 0.0254 38.61 0.3524 38.25 0.3240 ‐0.0103 0.5694 0.3674
Eicosenedioate (C20:1-DC) GCST90200285 Lipid 20 IVW 0.77 0.52 1.19 0.0167 15.21 0.7093 15.20 0.6483 ‐0.0035 0.9247 0.7106
1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) GCST90200065 Lipid 43 IVW 1.21 1.08 1.36 0.0010 28.05 0.9514 27.65 0.9449 0.0114 0.5329 0.9596
Octadecenedioate (C18:1-DC) GCST90200165 Lipid 42 IVW 0.90 0.81 1.00 0.0454 41.44 0.4514 41.36 0.4110 -0.0042 0.7820 0.4790
1-palmitoyl-2-oleoyl-GPE (16:0/18:1) GCST90200331 Lipid 45 IVW 1.18 1.01 1.50 0.0015 38.71 0.6970 38.49 0.6671 -0.0079 0.6374 0.7234
1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) GCST90200079 Lipid 42 IVW 1.11 1.01 1.23 0.0255 39.64 0.5311 39.59 0.4886 -0.0036 0.8217 0.5690
Adenosine 5’-monophosphate (AMP) GCST90200356 Nucleotide 15 IVW 1.35 1.03 1.78 0.0309 10.69 0.7102 9.74 0.7147 0.0343 0.3485 0.7212
Cysteinylglycine GCST90200364 Peptide 24 IVW 0.85 0.51 1.13 0.0429 26.67 0.2703 26.23 0.2419 0.0162 0.5510 0.3110
Urea GCST90200417 Ureas 22 IVW 1.13 0.97 1.27 0.0299 29.27 0.1077 28.79 0.0919 0.0101 0.5715 0.3722
X-26054 GCST90200672 Unknown 55 IVW 1.10 0.97 1.36 0.0401 60.64 0.2489 60.27 0.2296 -0.0082 0.5705 0.2650
X-17654 GCST90200547 Unknown 35 IVW 1.17 1.00 1.38 0.0491 45.49 0.0901 43.48 0.1048 0.0309 0.2258 0.0934
X-19141 GCST90200711 Unknown 90 IVW 1.09 0.91 1.17 0.0160 82.05 0.6860 81.11 0.6854 0.0122 0.3342 0.6796
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CI = confidence interval, IVW = inverse variance weighted, MR = Mendelian randomization, MR-PRESSO = MR-Pleiotropy RESidual sum and outlier, OR = odds ratio, SNPs = single nucleotide polymorphisms.

According to the forward MR analyses, 3 metabolites (N-acetylglucosamine/n-acetylgalactosamine, Octadecenedioate and X-26054) were removed before the reverse MR analyses due to their opposite effects on upper and lower tract urinary stones. The upper urinary tract stones increase mannose production in the blood (OR = 1.06, 95%CI = 1.00–1.13, P = .0449). Additionally, the upper urinary tract stones increase the generation of 1-oleoyl-2-arachidonoyl-GPE (OR = 1.03, 95%CI = 1.00–1.05, P = .0238) and 1-stearoyl-2-docosahexaenoyl-GPE (OR = 1.03, 95%CI = 1.01–1.05, P = .0097). Detailed information was available in Tables 3 and 4.

Table 3.

MR analysis and sensitivity analyses for causality from upper urinary stones on 12 blood metabolites

Name Outcome Metabolites Number of SNPs MR analysis Heterogeneity Pleiotropy MR-PRESSO
Method OR (95% CI) Lower CI Upper CI P IVW MR Egger Intercept P P
Q P Q P
1-methyl-4-imidazoleacetate GCST90199717 Azoles 55 IVW 1.03 0.98 1.09 0.2144 53.45 0.4954 53.27 0.4638 ‐0.0031 0.6695 0.2175
Mannose GCST90200435 Carbohydrate 55 IVW 1.06 1.00 1.13 0.0449 66.38 0.1202 66.33 0.1033 0.0018 0.8325 0.1154
1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6) GCST90200073 Lipid 55 IVW 0.96 0.91 1.01 0.1403 53.69 0.4864 53.69 0.4478 0.0002 0.9787 0.1449
Eicosenedioate (C20:1-DC) GCST90200285 Lipid 55 IVW 1.02 0.96 1.08 0.5097 53.53 0.4923 51.73 0.5237 0.0101 0.1850 0.5107
1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) GCST90200065 Lipid 55 IVW 1.04 0.99 1.10 0.1236 51.11 0.5867 51.07 0.5495 0.0013 0.8596 0.1193
1-palmitoyl-2-oleoyl-GPE (16:0/18:1) GCST90200331 Lipid 55 IVW 1.05 0.99 1.11 0.1123 56.21 0.3921 56.07 0.3604 0.0028 0.7197 0.1182
1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) GCST90200079 Lipid 55 IVW 1.00 0.95 1.05 0.9470 49.11 0.6629 49.11 0.6263 -0.0004 0.9565 0.9447
Adenosine 5’-monophosphate (AMP) GCST90200356 Nucleotide 55 IVW 0.98 0.93 1.03 0.4519 55.80 0.4070 55.79 0.3704 -0.0007 0.9217 0.4552
Cysteinylglycine GCST90200364 Peptide 55 IVW 0.98 0.93 1.03 0.4040 48.16 0.6981 47.92 0.6716 -0.0036 0.6306 0.3808
Urea GCST90200417 Ureas 55 IVW 1.05 0.99 1.11 0.1282 63.49 0.1768 63.34 0.1563 0.0027 0.7306 0.1341
X-17654 GCST90200547 Unknown 55 IVW 0.97 0.92 1.02 0.2403 40.55 0.9123 40.54 0.8951 -0.0004 0.9547 0.1811
X-19141 GCST90200711 Unknown 55 IVW 1.00 0.94 1.05 0.8769 48.62 0.6811 48.47 0.6511 0.0029 0.6936 0.2175
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CI = confidence interval, IVW = inverse variance weighted, MR = Mendelian randomization, MR-PRESSO = MR-Pleiotropy RESidual sum and outlier, OR = odds ratio, SNPs = single nucleotide polymorphisms.

Table 4.

MR analysis and sensitivity analyses for causality from lower urinary stones on 12 blood metabolites.

Name Outcome Metabolites Number of SNPs MR analysis Heterogeneity Pleiotropy MR-PRESSO
Method OR (95% CI) Lower CI Upper CI P IVW MR Egger Intercept P P
Q P Q P
1-methyl-4-imidazoleacetate GCST90199717 Azoles 11 IVW 1.01 0.98 1.03 0.5667 12.01 0.2847 8.45 0.4898 -0.0183 0.0919 0.5793
Mannose GCST90200435 Carbohydrate 11 IVW 1.01 0.98 1.04 0.6419 20.15 0.0279 19.80 0.0192 -0.0059 0.7012 0.6519
1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) GCST90200079 Lipid 11 IVW 1.03 1.00 1.05 0.0238 6.98 0.7274 4.91 0.8423 -0.0148 0.1840 0.5178
1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6) GCST90200073 Lipid 11 IVW 0.99 0.97 1.02 0.5510 12.72 0.2398 12.49 0.1870 0.0048 0.6941 0.5643
1-palmitoyl-2-oleoyl-GPE (16:0/18:1) GCST90200331 Lipid 11 IVW 1.01 0.98 1.04 0.5139 18.03 0.0544 10.29 0.3279 ‐0.0284 0.0286 0.5286
1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) GCST90200065 Lipid 11 IVW 1.03 1.01 1.05 0.0097 6.91 0.7343 6.53 0.6863 ‐0.0061 0.5534 0.5392
Eicosenedioate (C20:1-DC) GCST90200285 Lipid 11 IVW 0.99 0.97 1.01 0.2391 7.92 0.6368 7.90 0.5443 0.0014 0.8926 0.2153
Adenosine 5’-monophosphate (AMP) GCST90200356 Nucleotide 11 IVW 1.00 0.98 1.02 0.8203 3.32 0.9728 3.32 0.9503 0.0004 0.9704 0.7017
Cysteinylglycine GCST90200364 Peptide 11 IVW 0.99 0.97 1.01 0.2056 5.91 0.8229 4.82 0.8499 ‐0.0105 0.3236 0.1307
Urea GCST90200417 Ureas 11 IVW 1.00 0.98 1.02 0.7455 10.01 0.4399 9.75 0.3712 0.0050 0.6372 0.7522
X-17654 GCST90200547 Unknown 11 IVW 1.00 0.98 1.02 0.8169 9.83 0.4553 9.81 0.3662 0.0016 0.8859 0.8201
X-19141 GCST90200711 Unknown 11 IVW 1.01 0.99 1.03 0.5551 8.31 0.5983 8.25 0.5096 0.0026 0.8007 0.5320
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CI = confidence interval, IVW = inverse variance weighted, MR = Mendelian randomization, MR-PRESSO = MR-Pleiotropy RESidual sum and outlier, OR = odds ratio, SNPs = single nucleotide polymorphisms.

3.4. Metabolic pathway analysis

Metabolites significantly associated with upper and lower urinary stones were entered into the Metabolic Analyzer 5.0 platform to determine potential metabolic pathways involved in the pathogenesis of urinary stones. The network, enrichment, and pathway analyses of interactions among the metabolic pathways involved in this study were exhibited in Fig. 4. Detailed information was displayed in Tables S8 and S9, Supplemental Digital Content, http://links.lww.com/MD/O577.

Figure 4.

Figure 4.

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(A) Network analysis of significantly associated metabolites on upper and lower urinary stones. (B) Enrichment analysis of significantly associated metabolites on upper and lower urinary stones. (C) Pathway analysis of significantly associated metabolites on upper and lower urinary stones. The different colors indicated the level of significance, and the size of the circle reflected the level of the ratio.

4. Discussion

In the current study, bidirectional MR analysis provided clues that could contribute to the search for causal effects between blood metabolites and urolithiasis. We identified 15 metabolites, of which 3 remained chemically unknown, as potential causal predictors of urinary stones in forward MR analyses. Additionally, urinary stones were found to be significantly associated with 12 metabolites. Limited research has focused on metabolic changes in urinary stones. Our research is notable for incorporating the most comprehensive blood metabolite GWAS data for bidirectional MR analysis, allowing us to explore the causal connection between blood metabolites and both upper and lower tract urinary stones.

Metabolomics is a powerful analytical technique that allows for the simultaneous quantification of a wide range of small molecule metabolites within biological systems.[21] It is high-throughput and unbiased, making it extremely valuable for studying urolithiasis and uncovering the complex metabolic changes associated with this condition.[22] In the forward MR analyses, we identified 15 blood metabolites that are associated with the risk of both upper and lower tract urinary stones. These metabolites consist of 1 azole, 2 carbohydrates, 6 lipids, 1 nucleotide, 1 peptide, 1 urea, and 3 metabolites with unknown chemical properties. However, 3 metabolites were excluded from the reverse MR analyses due to their contradictory effects on upper and lower tract urinary stones. The results of the reverse MR analyses revealed that urolithiasis can indeed impact the expression of some metabolites in the blood, suggesting a potential interaction between blood metabolites and urinary stones. These findings contribute to a more comprehensive understanding of the role of these blood metabolites in urinary stones and their associated health outcomes.

We observed that lipids accounted for nearly half of the metabolites in our study. It has been established that disruptions in lipid metabolism are linked to the development and progression of various diseases that impact multiple bodily systems.[23–25] In recent years, there has been a growing interest in the connection between metabolic disorders and the occurrence of urinary stones, suggesting a link between circulating lipids and urolithiasis.[26–28] In a study conducted by Tan et al,[29] a causal relationship was discovered between an elevated risk of urolithiasis and increased serum triglyceride levels. Membrane lipids play a critical role in the development of kidney stones, particularly those composed of calcium oxalate (CaOx) and calcium phosphate. Individuals who form CaOx and uric acid stones show considerably elevated levels of cholesterol, cholesterol ester, and triglycerides in their urine compared to healthy individuals, and the urine of CaOx stone formers contains higher levels of acidic phospholipids.[30] Our results indicated that 4 acidic phospholipids in the blood, including 1-linoleoyl-2-arachidonoyl-GPC, 1-stearoyl-2-docosahexaenoyl-GPE, 1-palmitoyl-2-oleoyl-GPE, and 1-oleoyl-2-arachidonoyl-GPE increases the likelihood of developing upper and lower urinary tract stones, which is in line with previous studies. In reverse MR analysis, we also observed an increase in the expression of 1-oleoyl-2-arachidonoyl-GPE and 1-stearoyl-2-docosahexaenoyl-GPE in blood with lower urinary tract stones. This finding suggests a mutual relationship between metabolites and urinary stones.

Octadecenedioate, a long-chain fatty acid, has been potentially associated with a decreased risk of coronary heart disease.[31] In our study, we found that octadecenedioate also reduced the risk of stones in both upper and lower urinary tract. Research on the relationship between lipid metabolites and urolithiasis is currently limited, and further discussion is still required.

Several other metabolites were also identified as causal risk factors in urolithiasis in our study. 1-methyl-4-imidazole acetate is the primary metabolite of histamine and can be utilized to estimate the production and release of histamine.[32] Mannose is a carbohydrate that plays a role in the formation of Tamm-Horsfall glycoprotein. It has the ability to prevent the aggregation of calcium oxalate and calcium phosphate crystals, ultimately helping to prevent the formation of kidney stones.[33] Upper urinary tract stones seem to cause an increase in the level of Mannose in the bloodstream, as indicated by our reverse MR analysis. N-acetylglucosamine/N-acetylgalactosamine is a sugar residue commonly found in living organisms. It plays a vital role as a component of various essential polysaccharides and sugar complexes.[34,35] Metformin contributes to the alleviation of urolithiasis by indirectly activating 5′ adenosine monophosphate-activated protein kinase.[36] Cysteinylglycine is a prooxidant that has been shown to cause oxidative damage to DNA bases.[37] Higher levels of cysteinylglycine may indicate an increased risk of developing breast cancer in women who are exposed to environments that are prone to peroxidation.[38] Urea is the final product of protein metabolism in the body. Previous studies have not revealed any evidence to support the claim that a long-term high protein intake leads to kidney stones.[39] However, we do have data that indicates that elevated levels of urea in the blood may potentially contribute to the formation of urolithiasis. Although few studies have previously explored the relationship between the above metabolites and urinary stones, our findings present a novel approach to investigating the causes of urolithiasis.

However, it is important to acknowledge several limitations in this investigation. Firstly, all participants we obtained were exclusively from European populations, which may restrict the generalization of our conclusions to other ethnic backgrounds. Secondly, although we included 1091 metabolites in our MR study through rigorous selection, we still have 220 metabolites that remain chemically unknown. We still include 3 unknown metabolites in our findings. Thirdly, our analysis did not differentiate between the composition of stones, which is an important factor that could potentially influence the associations between blood metabolites and urolithiasis. Therefore, further investigation is necessary to explore this aspect.

5. Conclusion

This MR study has established a causal relationship between 15 blood metabolites and the risk of upper and lower urinary tract stones. Furthermore, it has been found that urolithiasis affects the generation of 12 of these metabolites. The identification of these blood metabolites provides valuable insights into early screening, prevention, and treatment of urolithiasis.

Author contributions

Conceptualization: Haoyang Zhang, Haojie Mo, Jiale Sun.

Data curation: Haoyang Zhang, Haojie Mo, Peng Li, Jiale Sun.

Formal analysis: Haoyang Zhang, Haojie Mo, Peng Li.

Funding acquisition: Gang Shen.

Investigation: Haoyang Zhang, Jiale Sun.

Methodology: Haoyang Zhang, Jiale Sun.

Project administration: Haoyang Zhang, Haojie Mo, Jiale Sun.

Resources: Haoyang Zhang, Haojie Mo.

Software: Haoyang Zhang, Haojie Mo, Peng Li, Qi Zhou.

Supervision: Haoyang Zhang, Haojie Mo, Peng Li, Qi Zhou.

Validation: Haoyang Zhang, Haojie Mo, Qi Zhou.

Visualization: Haoyang Zhang, Haojie Mo, Qi Zhou.

Writing – original draft: Haoyang Zhang, Haojie Mo, Jiale Sun.

Writing – review & editing: Haoyang Zhang, Haojie Mo, Jiale Sun.

Supplementary Material

SUPPLEMENTARY MATERIAL
medi-104-e41911-s001.xlsx (43.1KB, xlsx)
medi-104-e41911-s002.xlsx (29.8KB, xlsx)
medi-104-e41911-s003.xlsx (376.6KB, xlsx)
medi-104-e41911-s004.xlsx (376.5KB, xlsx)
medi-104-e41911-s005.xlsx (20.6KB, xlsx)

Abbreviations:

BMI
body mass index
CaOx
calcium oxalate
CI
confidence interval
GWAS
genome-wide association study
IVs
instrumental variables
IVW
inverse variance weighted
LDSC
linkage disequilibrium score
MR
Mendelian randomization
OR
odds ratio
SNPs
single nucleotide polymorphisms

This work was supported by grants from the Suzhou Industrial Park clinical medical expert team introduction project (0202140004), Jiangsu Industry University Research Cooperation Project (BY2022855), Collaborative innovation research on the combination of medicine and engineering (SZM2023016) and Open project of the State Key Laboratory of Radiation Medicine and Protection (GZK1202304).

The present Mendelian randomization analysis was based on summary data from previous studies that had gained written informed consent and ethics approval. No ethical permit is required for the secondary analysis of summary data.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Zhang H, Mo H, Li P, Zhou Q, Shen G, Sun J. Identification of the relationship between 1400 blood metabolites and urolithiasis: A bidirectional Mendelian randomization study. Medicine 2025;104:12(e41911).

The authors have nothing to disclose.

Contributor Information

Haoyang Zhang, Email: drzhanghaoyang@sina.com.

Haojie Mo, Email: tcmohaojie@163.com.

Peng Li, Email: jmarlipeng@163.com.

Qi Zhou, Email: zq10101678@163.com.

Gang Shen, Email: gshen119@163.com.

References

  • [1].Raja A, Wood F, Joshi HB. The impact of urinary stone disease and their treatment on patients’ quality of life: a qualitative study. Urolithiasis. 2020;48:227–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Ziemba JB, Matlaga BR. Epidemiology and economics of nephrolithiasis. Investig Clin Urol. 2017;58:299–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [3].Skolarikos A, Straub M, Knoll T, et al. Metabolic evaluation and recurrence prevention for urinary stone patients: EAU guidelines. Eur Urol. 2015;67:750–63. [DOI] [PubMed] [Google Scholar]
  • [4].Ruhayel Y, Tepeler A, Dabestani S, et al. Tract sizes in miniaturized percutaneous nephrolithotomy: a systematic review from the european association of urology urolithiasis guidelines panel. Eur Urol. 2017;72:220–35. [DOI] [PubMed] [Google Scholar]
  • [5].Antonelli JA, Maalouf NM, Pearle MS, Lotan Y. Use of the National Health and Nutrition Examination Survey to calculate the impact of obesity and diabetes on cost and prevalence of urolithiasis in 2030. Eur Urol. 2014;66:724–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Ferraro PM, Taylor EN, Curhan GC. Factors associated with sex differences in the risk of kidney stones. Nephrol Dial Transplant. 2023;38:177–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Geng J, Qiu Y, Kang Z, et al. The association between caffeine intake and risk of kidney stones: a population-based study. Front Nutr. 2022;9:935820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [8].Zhang XZ, Lei XX, Jiang YL, et al. Application of metabolomics in urolithiasis: the discovery and usage of succinate. Signal Transduct Target Ther. 2023;8:41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Tremblay BL, Guénard F, Lamarche B, Pérusse L, Vohl M-C. Familial resemblances in human plasma metabolites are attributable to both genetic and common environmental effects. Nutr Res. 2019;61:22–30. [DOI] [PubMed] [Google Scholar]
  • [10].Khamaysi A, Anbtawee-Jomaa S, Fremder M, et al. Systemic succinate homeostasis and local succinate signaling affect blood pressure and modify risks for calcium oxalate lithogenesis. J Am Soc Nephrol. 2019;30:381–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Wang Y, Liu F, Sun L, et al. Association between human blood metabolome and the risk of breast cancer. Breast Cancer Res. 2023;25:9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Chen Y, Lu T, Pettersson-Kymmer U, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet. 2023;55:44–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Wang K, Ge J, Han W, et al. Risk factors for kidney stone disease recurrence: a comprehensive meta-analysis. BMC Urol. 2022;22:62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Lin W, Ye Q, Lin ME. Relationship between the weight-adjusted-waist index and kidney stone: a population-based study. World J Urol. 2023;41:3141–7. [DOI] [PubMed] [Google Scholar]
  • [17].Jian Z, Huang Y, He Y, et al. Genetically predicted lifelong circulating 25(OH)D levels are associated with serum calcium levels and kidney stone risk. J Clin Endocrinol Metab. 2022;107:e1159–66. [DOI] [PubMed] [Google Scholar]
  • [18].Bulik-Sullivan BK, Loh PR, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Jewison T, Su Y, Disfany FM, et al. SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res. 2014;42:D478–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40:D109–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Wishart DS. Metabolomics for investigating physiological and pathophysiological processes. Physiol Rev. 2019;99:1819–75. [DOI] [PubMed] [Google Scholar]
  • [22].Newgard CB. Metabolomics and metabolic diseases: where do we stand? Cell Metab. 2017;25:43–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Yu Z, Zhang L, Zhang G, et al. Lipids, apolipoproteins, statins, and intracerebral hemorrhage: a Mendelian randomization study. Ann Neurol. 2022;92:390–9. [DOI] [PubMed] [Google Scholar]
  • [24].Chen L, Qiu W, Sun X, et al. Novel insights into causal effects of serum lipids and lipid-modifying targets on cholelithiasis. Gut. 2024;73:521–32. [DOI] [PubMed] [Google Scholar]
  • [25].Zeng Q, Gong Y, Zhu N, Shi Y, Zhang C, Qin L. Lipids and lipid metabolism in cellular senescence: emerging targets for age-related diseases. Ageing Res Rev. 2024;97:102294. [DOI] [PubMed] [Google Scholar]
  • [26].Wen J, Cao Y, Li Y, et al. Metabolomics analysis of the serum from children with urolithiasis using UPLC-MS. Clin Transl Sci. 2021;14:1327–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Hung JA, Li CH, Geng JH, Wu D-W, Chen S-C. Dyslipidemia increases the risk of incident kidney stone disease in a large Taiwanese population follow-up study. Nutrients. 2022;14:1339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Taguchi K, Chen L, Usawachintachit M, et al. Fatty acid-binding protein 4 downregulation drives calcification in the development of kidney stone disease. Kidney Int. 2020;97:1042–56. [DOI] [PubMed] [Google Scholar]
  • [29].Tan Z, Hong J, Sun A, Ding M, Shen J. Causal effects of circulating lipids and lipid-lowering drugs on the risk of urinary stones: a Mendelian randomization study. Front Endocrinol (Lausanne). 2023;14:1301163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Khan SR, Glenton PA, Backov R, Talham DR. Presence of lipids in urine, crystals and stones: implications for the formation of kidney stones. Kidney Int. 2002;62:2062–72. [DOI] [PubMed] [Google Scholar]
  • [31].Chen H, Huang Y, Wan G, Zou X. Circulating metabolites and coronary heart disease: a bidirectional Mendelian randomization. Front Cardiovasc Med. 2024;11:1371805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Granerus G. Urinary excretion of histamine, methylhistamine and methylimidazoleacetic acids in man under standardized dietary conditions. Scand J Clin Lab Invest Suppl. 1968;104:59–68. [PubMed] [Google Scholar]
  • [33].Serafini-Cessi F, Monti A, Cavallone D. N-Glycans carried by Tamm-Horsfall glycoprotein have a crucial role in the defense against urinary tract diseases. Glycoconj J. 2005;22:383–94. [DOI] [PubMed] [Google Scholar]
  • [34].Hart GW, Housley MP, Slawson C. Cycling of O-linked beta-N-acetylglucosamine on nucleocytoplasmic proteins. Nature. 2007;446:1017–22. [DOI] [PubMed] [Google Scholar]
  • [35].Vocadlo DJ, Hang HC, Kim EJ, Hanover JA, Bertozzi CR. A chemical approach for identifying O-GlcNAc-modified proteins in cells. Proc Natl Acad Sci U S A. 2003;100:9116–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Song A, Zhang C, Meng X. Mechanism and application of metformin in kidney diseases: an update. Biomed Pharmacother. 2021;138:111454. [DOI] [PubMed] [Google Scholar]
  • [37].Dominici S, Paolicchi A, Lorenzini E, et al. Gamma-glutamyltransferase-dependent prooxidant reactions: a factor in multiple processes. Biofactors. 2003;17:187–98. [DOI] [PubMed] [Google Scholar]
  • [38].Lin J, Manson JE, Selhub J, Buring JE, Zhang SM. Plasma cysteinylglycine levels and breast cancer risk in women. Cancer Res. 2007;67:11123–7. [DOI] [PubMed] [Google Scholar]
  • [39].Remer T, Kalotai N, Amini AM, et al. Protein intake and risk of urolithiasis and kidney diseases: an umbrella review of systematic reviews for the evidence-based guideline of the German Nutrition Society. Eur J Nutr. 2023;62:1957–75. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials

SUPPLEMENTARY MATERIAL
medi-104-e41911-s001.xlsx (43.1KB, xlsx)
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medi-104-e41911-s003.xlsx (376.6KB, xlsx)
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