Serum Amino Acid Alterations in Hyperuricemia: Potential Targets for Renal Disease Prevention

doi:10.21203/rs.3.rs-5466288/v1

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Research Article

Serum Amino Acid Alterations in Hyperuricemia: Potential Targets for Renal Disease Prevention

https://doi.org/10.21203/rs.3.rs-5466288/v1

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published 18 Feb, 2025

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Background

Observational studies have linked uric acid (UA) levels and kidney disease to amino acid homeostasis, but the causal relationship is unclear. This study aims to determine if elevated UA affects amino acid levels and whether amino acids mediate this relationship, focusing on the causal links between UA, circulating amino acids, and kidney disease. Methods: This study utilized Uox-KO mice as a hyperuricemia model, assessed renal injury through blood biochemistry and pathology, analyzed serum amino acid changes via targeted amino acidomics, and employed Mendelian randomization to investigate the causal links between uric acid, amino acids, and renal disease. Results: Hyperuricemic Uox-KO mice have significantly higher serum UA and renal impairment markers, with histopathological analysis showing extensive renal tissue damage. Changes in amino acid balance were found in the mice's serum, with key metabolites like alanine, isoleucine, leucine, aspartic acid, cysteine, glutamate, and glycine potentially influencing UA pathophysiology.Genetically predicted UA was positively correlated with chronic renal failure (CRF) and blood urea nitrogen(BUN) levels and negatively with serum cystatin C (eGFRcys) and serum creatinine (eGFRcrea). Alanine (Ala) mediated the effect of UA on elevated CRF and BUN risk, accounting for 4.5% of the UA-CRF relationship and 14.4% of the UA-BUN association. Conclusion: In hyperuricemic mice, serum amino acids undergo metabolic changes. Genetically predicted UA levels are positively linked to CRF and BUN, but negatively linked to eGFRcys and eGFRcrea. Ala mediates UA's effect on CRF and BUN risk, indicating Ala could be a target for preventing renal diseases caused by hyperuricemia.

Uric acid

Hyperuricaemia

Amino acidomics

Mendelian randomisation

Mediating effectt

Introduction: Uric acid (UA) represents the terminal product of purine metabolism, primarily synthesized endogenously, with exogenous sources contributing minimally. Uric acid exerts several adverse physiological effects, such as promoting vascular smooth muscle cell proliferation, inhibiting nitric oxide production, inducing endothelial dysfunction, and increasing oxidative stress[1]. The bidirectional causal relationship between uric acid concentration and CRF has been the subject of extensive research for several decades[2]. Numerous observational studies have consistently identified elevated serum uric acid concentrations as a significant risk factor for the development of renal failure[3]. The bidirectional causal effects and potential mediating pathways between uric acid concentrations and kidney disease remain inadequately understood. Consequently, it is imperative to investigate the causal relationship between uric acid levels and kidney disease, as well as the potential mediating pathways involved.

Amino acids are fundamental metabolites in the human body, serving not only as the building blocks of proteins but also as substrates for energy metabolism and as precursors for biologically significant molecules, including neurotransmitters[4]. There is an established association between amino acid concentrations and renal disease, with individuals suffering from CRF often displaying an altered serum amino acid metabolic profile. One study identified significantly altered serum leucine levels in patients with CRF [5]. In a separate case-control study, arginine, methionine, and threonine were proposed as potential metabolic markers of residual renal function and prognostic biomarkers in individuals with renal disease[6]. Protective effect of D-alanine against acute kidney injury demonstrated by Yasunori Iwata [7].Several studies have shown that hyperuricemia causes changes in amino acids in the serum[8, 9].Through investigation, amino acids are crucial in the context of kidney disease, raising questions about whether amino acid metabolism has a causal or mediating role in the process by which uric acid induces or worsens kidney disease.

Mendelian randomization (MR) is a causal inference technique that utilizes genetic variants as proxies for exposure, analogous to performing a natural randomized controlled trial, thereby mitigating certain confounding biases and reverse causality inherent in observational studies[10]. Multivariate Mendelian Randomization (MVMR) extends this approach by enabling the examination of the independent effects of multiple exposures on outcomes through the incorporation of the genetic variance associated with each exposure within a unified model[11]. IFurthermore, the two-step MVMR approach can be employed in mediation analyses to disaggregate the effect of exposure on the outcome, thereby estimating the causal relationships among the three variables via the mediator variable[12].

In this study, we investigated the possible renal diseases caused by hyperuricaemia and the changes in the levels of 20 amino acids. In this study, we examined the potential renal diseases associated with hyperuricemia and the alterations in the levels of 20 circulating amino acids. Utilizing univariable and multivariable Mendelian randomization analyses of large-sample genome-wide association study (GWAS) data, we explored the independent causal relationship between uric acid levels and renal disease. Furthermore, we assessed the mediating role of AAS in this pathogenesis, thereby offering a research direction for the clinical prevention of chronic renal disease in affected patients.

2.1 Experimental animal

Twelve-week-old male mice were utilized in this study. Uox gene-deficient (Uox-KO) mice, bred on a C57BL/6J genetic background, were procured from Changzhou Cavens Laboratory Animal Limited Company. All procedures related to animal care and handling adhered to the guidelines established by the National Institutes of Health and received approval from the Animal Policy and Welfare Committee of the Naval Medical University. The mice were maintained in an environmentally controlled room set at a temperature of 22 ± 2.0°C and a relative humidity of 50% ± 5%, with a 12-hour light/dark cycle. They were provided with standard rodent chow and tap water.

2.2 Blood biochemistry tests

The serum was isolated from the collected blood samples by centrifugation at 3,000 × g for 15 minutes at 4°C for subsequent analysis. The concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (Crea), blood urea nitrogen (BUN), urinary creatinine (BU), total cholesterol (TC), and triglycerides (TG) were quantified using an automated biochemical analyzer.

2.3 Histological analysis

The right kidney was excised and subsequently fixed in 4% paraformaldehyde (pH 7.4) for a minimum duration of 24 hours. Following fixation, the tissue underwent dehydration and was then embedded in paraffin wax. Sections were cut at a thickness of 4 micrometers and subjected to hematoxylin and eosin (H&E) staining as well as Masson's staining. The stained sections were examined under a light microscope to evaluate histopathological alterations.

2.4 Amino acidomics

2.4.1 2.4.1 Sample processing

Take 50μL of serum and mix it with 300μL of acetonitrile (L-2-chlorophenylalanine, 0.3 mg/mL), after vigorous shaking, centrifuge the sample for 10 min (12,000 rpm, 4°C), collect the supernatant and filter it through a needle filter (0.22 μm pore size) for LC-MS analysis.

2.4.2 LC-MS analysis

The analysis was performed using a Waters Xevo TQ-XS mass spectrometer (Waters Corporation, Manchester, UK). The sample solution was injected onto an ACQUITY UPLC™ AMID column (inner diameter 100 mm × 2.1 mm, film thickness 1.7 μm) at a flow rate of 300 μL/min. A gradient elution was performed using solution A (10 mM ammonium formate and 0.3% formic acid in water) and solution B (0.3% formic acid in acetonitrile). MS analysis was performed in positive ion mode using ESI and multiple reaction monitoring scans. The electrospray cone voltage was set at 3.2 kV, and the source temperature was maintained at 150 °C. The peak area of the internal standard was monitored for mass spectrometry. Internal standard peak areas were monitored for quality control and individual samples whose peak areas differed from the group mean by more than two standard deviations were reanalyzed. Automated peak integration was performed using the MarkerLynx Application Manager software (version 4.1; Waters Corporation, Milford, MA, USA). Finally, metabolites were identified by analyzing the metabolite peaks and comparing them against known standards.

2.4.3 Statistical analysis

The univariate analyses conducted comprised the Student's t-test and fold change (FC) analysis. For multivariate analyses, unsupervised principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed utilizing SIMCA-P version 14.0 software. The significance of differential metabolites (DMs) was determined by integrating predictor variable importance (VIP) values greater than 1 from the OPLS-DA model with P-values obtained from the Student's t-test. DMs were deemed significant when the VIP score exceeded 1 and the p-value was less than 0.05. Pathway enrichment analysis, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG), was conducted utilizing the MetaboAnalyst online platform[13].

2.5 Mendelian Randomization

2.5.1 Study design

This study is structured into two primary phases. In the initial phase, univariate Mendelian randomization (UVMR) was employed to evaluate the causal relationship between uric acid levels and various renal diseases, utilizing single nucleotide polymorphisms (SNPs) as instrumental variables for each exposure. In the subsequent phase, amino acid levels were selected as potential mediators in the causal pathway between uric acid levels and kidney disease. Ultimately, two-step multivariate Mendelian randomization (MVMR) was applied to quantify the mediating effects and proportions.

2.5.2 GWAS data sources

The dataset utilized in this study was sourced from the IEU GWAS database at the University of Bristol (https://gwasmrcieu.ac.uk). Regarding the exposure variables, data on serum uric acid levels were derived from a study conducted by Sakaue S, published in 2021, encompassing 343,836 samples and 19,041,286 SNPs[14].

For the mediator variables,data of alanine(Ala), glutamine(Gln), glycine(Gly), histidine(His), isoleucine(Ile), leucine(Leu), phenylalanine(Phe), tyrosine(Tyr), and valine(Val) were sourced from a study conducted by Richardson TG in 2021[15], data of arginine(Arg), asparagine(Asn), asparticacid(Asp), cysteine(Cys), glutamine(Glu), lysine(Lys), methionine(Met), proline(Pro), serine(Ser), threonine(Tyr), and tryptophan(Trp) from a study conducted by Shin in 2014[16].We selected the mediators between UA and kidney disease based on the following criteria:(I) There exists a causal association between UA and the mediator[17];The causal relationship between the mediator and the outcome must remain consistent regardless of whether UA is adjusted for; (III) The causal relationship between UA and the mediator should correspond with the causal relationship between the mediator and the outcome[12]. In the two-step Mendelian Randomization analysis, SNPs demonstrating genome-wide significance in the genome-wide association study (GWAS) of the mediator were identified following a linkage disequilibrium analysis, which applied a threshold of R² < 0.001 and a distance criterion of greater than 10,000 kilobases.

For the outcome variables, including the date of CRF and nephrotic syndrome, blood urea nitrogen levels were obtained from a study conducted by Sakaue in 2021[14], which analyzed 482,858 samples and 24,185,976 SNPs. The study on glomerulonephritis (GN) included 16,380,466 SNPs. The estimated glomerular filtration rate based on eGFRcr and eGFRcys, as well as the urinary albumin-to-creatinine ratio (UACR), were derived from the CKDGen Consortium. The fundamental characteristics of the genome-wide association studies (GWAS) from CKDGen were based on summary data reported by Pattaro C[18] and Teumer A et al[19].

Details of all data sources are provided in Table 1. Ethical approval from the relevant institutional review boards, informed consent from participants, and rigorous quality control measures were secured for all genome-wide association studies (GWASs). For this study, ethical approval was not necessary as the data utilized were at an aggregated level.

2.5.3 Statistical analysis

2.5.3.1 MR Analysis and Mediation Analysis

This study concentrated on employing MR analysis to elucidate the causal relationship between UA and kidney disease, with a specific focus on the pathways mediating this relationship. Initially, a bidirectional two-sample MR analysis was conducted, designating UA as the exposure variable and kidney disease as the outcome variable, to ascertain the causal linkage between these entities. Upon identifying a unidirectional causal relationship from UA to kidney disease, we proceeded to further investigate this association through mediated Mendelian analysis.

We employed IVW as the primary methodology for both UVMR and MVMR analyses. All Mendelian randomization analyses adhered to three essential assumptions: (1) Genetic variants must exhibit a strong association with the exposure in UVMR analyses and with at least one of the multiple exposures in MVMR analyses; (2) Genetic variants must not be associated with confounding factors that could influence the relationship between each exposure instrument and the outcome; (3) The impact of genetic variants on outcomes is mediated through each exposure.

In the context of mediation analysis, to evaluate whether amino acid mediators serve as intermediaries between uric acid and kidney disease, the initial step involved estimating the causal effect of genetically determined uric acid on the mediator (β1) using UVMR. The subsequent phase involved estimating the causal impact of the mediators on kidney disease through MVMR, with adjustments made for UA (β2), and the computation of F-values exceeding 10 to ensure the robustness of the findings. Following this, the indirect effect (β1 × β2) was divided by the overall causal effect of UA on kidney disease, as determined by UVMR (β), to ascertain the proportion of the total effect of UA attributable to AAS - mediated kidney disease (Fig. 1). Finally, we employ the Delta method to derive the standard error of the mediated proportion, utilizing the effect estimates obtained from the two-sample Mendelian Randomization analysis, and subsequently calculate the corresponding confidence intervals.

2.5.3.2 Sensitivity analyses

In order to assess the robustness of the IVW results in UVMR analysis, up to four MR methods—namely MR-Egger, weighted median, simple mode, and weighted mode—were employed, each of which assumes different models of pleiotropy. Similarly, in MVMR analysis, the MR-Egger, MR-Lasso, and median methods were utilized to validate the robustness of the IVW results. The findings indicated that at least one method consistently demonstrated significance in alignment with the direction of the IVW results. Notably, the MR-Egger method accounted for horizontal pleiotropy by incorporating the MR-Egger intercept[20]. The intercept signifies the mean pleiotropic effect across the genetic variants, representing the average direct effect of a variant on the outcome. A significant deviation of the intercept from zero indicated by an MR-Egger intercept P-value < 0.05) suggests the presence of horizontal pleiotropy. The validity of the instrument was assessed using the conditional F-statistic, with values exceeding 10 indicating a reduced likelihood of distortion in MR estimates due to weak instruments[21]. The Q' heterogeneity test was employed to statistically evaluate the heterogeneity among the instruments. This MR analysis was conducted using the TwoSampleMR package, version 0.5.8, within the R 4.3.1 framework.

3.1 Hyperuricemia leads to kidney damage

In comparison to the WT group, the Uox-KO group of mice exhibited a significant upregulation of UA, BUN, and CREA(Fig. 2A-C). However, the differences in ALT and AST levels were not statistically significant (Fig. 2D-E).Compared with male WT mice, Uox-KO mice demonstrated significant renal morphological impairments, characterized by a less smooth surface and structural damage, as well as renal swelling accompanied by polycystic changes and stone formation. H&E staining of renal tissues revealed the presence of atrophic glomeruli and tubular dilation, while Masson's staining indicated extensive fibrosis within the renal tissues of Uox-KO mice (Fig. 2F-I).

3.2 Targeted metabolomics analysis of amino acids

3.2.1 Multivariate statistical analysis

To investigate the alterations in amino acid homeostasis associated with hyperuricemia, we conducted a targeted amino acid profiling of mouse serum to gain deeper insights into metabolic changes. The PCA results revealed a significant separation between the WT group and the Uox-KO group(Fig. 2A). Furthermore, the OPLS-DA analysis demonstrated distinctly differentiated profiles between the WT group and the Uox-KO group(Fig. 3B). Permutation tests, comprising 200 permutations, indicated that the model was not overfitted, with R2Y values of (0.0, 0.417) and Q2 values of (0.0, -1.22) (Fig. 3C). These findings suggest that variations in amino acid profiles can effectively discriminate between WT and Uox-KO groups.

3.2.2 Differential amino acid metabolites between WT and Uox-KO

To investigate the differential metabolite profiles between WT and Uox-KO groups, a total of 20 amino acid metabolites were identified, as detailed in Table. 2 and visualized in the volcano plot (Fig. 4A). Subsequently, hierarchical clustering analysis was employed to generate a heatmap for all identified metabolites (Fig. 4B). Among these, seven amino acids—Glu, Asp, Cys, Gly, Ala, Leu, and Ile—exhibited significant differential expression in the WT group compared to the Uox-KO group, with statistical significance defined as P < 0.05 and VIP scores exceeding 1, as shown in Table. 2. To elucidate the mutual regulatory interactions among metabolites, a correlation analysis was conducted to uncover the synergistic alterations between them (Fig. 4C).The analysis revealed significant differences in the metabolites between the WT group and the Uox-KO group.

3.2.3 Metabolic pathway analysis and enrichment analysis

All seven notable differential amino acid metabolites were enriched to facilitate a more in-depth exploration of the metabolomic pathways implicated in Uox-KO. As illustrated in Figure 4A, these metabolites were enriched across 25 metabolic pathways (Table S1). Among these, eight metabolic pathways demonstrated statistical significance, with p < 0.05 and impact >0.1, when comparing WT group and the Uox-KO group.

The pathways identified included 'glutathione metabolism', 'alanine, aspartate and glutamate metabolism', 'arginine biosynthesis, glyoxylate and dicarboxylate metabolism', and 'glycine, serine and threonine metabolism(Fig. 5A)(Table. 3). Among these, 'glutathione metabolism', 'alanine, aspartate and glutamate metabolism', and 'arginine biosynthesis' were regarded as key targets for investigating the pathological mechanisms underlying hyperuricemia serum metabolism.Metabolite set enrichment analysis identified the top three pathways associated with hyperuricemia serum metabolism as 'alanine, aspartate and glutamate metabolism','glutathione metabolism',and 'valine , leucine and iosleucine biosynthesis'(Fig. 5B).

3.3 Mendelian randomization analysis

3.3.1Genetically predicting the overall relationship between uric acid levels, amino acid mediators, and kidney disease outcomes

Utilizing the IVW method, a genetically predicted decreased in UA was significantly associated with four out of the seven outcomes. Furthermore, there was suggestive evidence indicating positive associations between genetically predicted UA and CRF (OR, 1.36; 95% CI , 1.22-1.52; P=2.25e-08) , as well as BUN (OR, 1.16; 95% CI, 1.10-1.21; P=6.82e-09). Conversely, negative associations were observed eGFRcr (OR, 0.99; 95% CI, 0.97-1.00; P=0.04) and eGFRcys (OR, 0.97; 95% CI, 0.96-0.99; P=9.72e-4) (Fig. 6). According to the IVW assessment, genetically predicted UA were not significantly associated with the risks of nephrolithiasis NC, GN and UACR (Table S2). Sensitivity analyses indicated heterogeneity across all Mendelian randomization (MR) analyses. Furthermore, Sensitivity analysis showed heterogeneity in all MR analyses. CRF results were reliable without pleiotropy, but BUN, eGFRcr, eGFRcys showed pleiotropy and the possibility of false positives in subsequent results (Table S5).

Analyses using the IVW method on UA levels and 20 circulating amino acids revealed that seven amino acids exhibited a causal relationship with UA. The genetically predicted UA was positive associated with Ala (OR, 1.06; 95% CI, 1.02-1.10; P=3.41e-03), Ile (OR, 1.06; 95% CI, 1.03-1.09; P=3.16e-04), Leu (OR, 1.06; 95% CI, 1.03-1.10; P=1.49e-04), and Val (OR, 1.07; 95% CI, 1.03-1.11; P=3.59e-04). Conversely, UA was negatively associated with Asn (OR, 0.99; 95% CI, 0.97-1.00; P=0.04), Gln (OR, 0.92; 95% CI,0.86-0.99; P=0.02), and Met (OR, 0.99; 95% CI, 0.98-1.00; P=0.02) (Fig. 7 ). The IVW estimate showed that genetically predicted UA were not significantly associated with the other 13 amino acids (Tables S3). Pleiotropy and heterogeneity were absent (Tables S5).

3.3.2 Effect of Mediator on outcomes

The seven amino acid mediators were subsequently evaluated for their causal relationships with four selected outcomes.In the results of the UVMR analysis, genetically predicted Ala demonstrated a positive association with BUN (OR, 1.16; 95% CI, 1.08-1.24; P=4.27e-05) and CRF(OR, 1.36; 95% CI, 1.16-1.59; P=1.98e-04)). Genetically predicted Gln exhibited a negative association with BUN (OR, 0.94; 95% CI, 0.89-0.99, P=2.95e-02) (Fig. 8). All other UVMR showed no causal relationship (Tables S4).Instrumental variables for Ala and Gln showed persistent heterogeneity with respect to outcomes, and neither exhibited horizontal pleiotropy (Tables S5).

3.3.3 Effect of each mediator on outcome and mediating effect

In subsequent MVMR analyses, genetically predicted Ala demonstrated a positive association with BUN (OR, 1.16; 95% CI, 1.06-1.27; P=0.001) and CRF( OR, 1.29; 95%CI, 1.06-1.57; P=0.01) after adjusting for UA. However, after adjustment for UA, the results of Gln (OR, 0.95; 95% CI, 0.89-1.01; P=0.07) with BUN were no causal relationship (Fig. 9).Therefore, Glnwas excluded as it did not fulfill the necessary criteria. Ultimately, Ala was selected as the mediating factors of UA on CRF and BUN.

The instrument strength test indicates that SNPs possess adequate instrument strength for all variables within the MVMR model, with F-statistics exceeding 10 for all cases (Table S6). In the final calculation of the proportion mediated, Ala exhibited mediation effects of 1.4% between UA and CRF, with mediation proportions of 4.54%. Additionally, Ala demonstrated mediation effects of 2.1% between UA and BUN, with mediation proportions of 14.43% (Fig. 10).

With shifts in lifestyle and dietary habits, hyperuricemia has emerged as the second most prevalent metabolic disorder[22]. It contributes to an elevated risk of developing diabetes, metabolic syndrome, atherosclerosis, chronic kidney disease, and cardiovascular disease, thereby exacerbating the public health burden and, in severe instances, posing a potential threat to life[23].

The bidirectional causal relationship between the two necessitates further investigation. Several potential mechanisms by which uric acid may contribute to impaired renal function have been identified, including adverse physiological effects such as vascular smooth muscle cell proliferation, endothelial dysfunction, and oxidative stress[24-26]. Early and precise testing, followed by timely intervention, is crucial for patients diagnosed with CRF[27]. It is crucial to halt the progression of kidney disease, as failure to do so can ultimately result in end-stage renal failure[28]. In the context of this study, although elevated UA levels have been recognized as a potential factor in CRF, there remains debate as to whether hyperuricemia is a consequence of impaired renal excretion of SUA or a causative factor in renal disease[29, 30].The bidirectional causal relationships and potential mediating pathways between uric acid concentrations and kidney disease are not yet fully elucidated. Amino acids, as fundamental constituents of proteins, play a crucial role in tissue function. Recent research indicates that amino acids, along with their related upstream and downstream metabolites, are implicated in the pathophysiological processes of kidney injury by influencing oxidative stress, inflammation, and immune responses[5]. Several studies have demonstrated that UA influences amino acid levels[31]. Concurrently, amino acids play a vital role in the pathophysiology of kidney disease, prompting inquiries into whether amino acid metabolism exerts a causal or mediating influence on the mechanism by which uric acid initiates or exacerbates kidney disease. Consequently, examining the altered amino acid metabolism in hyperuricemia and the potential mediating role of amino acids in the relationship between UA and kidney disease is of significant interest. In this context, a MR analysis was conducted to investigate the association between UA and 20 amino acids.

The kidney is integral to the regulation of homeostasis, metabolism, and plasma amino acid concentrations[32]. To examine alterations in amino acid profiles associated with hyperuricemia, we conducted targeted amino acid profiling of serum from hyperuricemic Uox-KO mice. The findings revealed a substantial disruption in amino acid homeostasis, characterized by a significant up-regulation of Ala, Ile, and Leu, alongside a marked down-regulation of Glu, Asp, Cys, Gly. These results are consistent with those reported in previous studies[31, 33]. For instance, leucine, an essential amino acid, plays a critical role in providing energy and regulating energy homeostasis during prolonged energy expenditure. Elevated levels of leucine suggest that the genus Uox-KO expends substantial amounts of energy as a result of renal pathology and inflammation[34]. Given that glycine is an essential amino acid necessary for de novo purine synthesis, alterations in amino acid levels can influence uric acid production[35]. The imbalance between Cys and its oxidized form represents a primary thiol/disulfide redox pair, potentially reflecting or exacerbating oxidative stress, which may be linked to elevated uric acid levels as part of a defensive response[36]. The atherogenic characteristics of cysteine could also compromise endothelial function, leading to an upregulation of vascular adenosine and an increase in circulating uric acid[37]. KEGG pathway enrichment analysis identified key amino acids involved in 'glutathione metabolism', 'alanine, aspartate and glutamate metabolism', and 'arginine biosynthesis'. Collectively, these findings indicate that amino acid metabolism plays a role in the regulation of UA metabolism.

CRF denotes a condition characterized by the progressive decline in renal function, rendering the kidneys incapable of sustaining the body's normal internal milieu. This condition arises as a consequence of the advancement of various CKD. The findings from the UVMR segment of this study indicated a positive causal relationship between UA and CRF, as well as BUN, while demonstrating a negative causal relationship with estimated glomerular filtration rate based on eGFRcr and eGFRcys, corroborating results from previous research[38]. Nevertheless, another MR analysis did not substantiate a causal relationship between serum uric acid levels and eGFR[39]. This discrepancy may be attributed to the variation in genetic variants associated with uric acid levels and kidney function across different populations, resulting in divergent outcomes.In addition to genetic factors, environmental and lifestyle influences may also contribute to the observed differences. This study employed UVMR to demonstrate a positive causal relationship between UA levels and the amino acids Ala, Ile, Leu, and Val, as well as a negative causal relationship with Asn, Gln, and Met. Notably, the causal directions for Ala, Ile, Leu, Asn, and Met were validated using targeted amino acid data, showing consistency and statistical significance. However, the results for Gln and Val were not statistically significant in the context of targeted amino acids, which may be attributed to genetic variation differences among species.

This experiment conducted an in-depth investigation into the potential mediators within the BUN pathway from UA to chronic CRF. Amino acids were evaluated as potential mediators in this study, and Ala was ultimately identified as a mediator between UA and CRF within the BUN pathway. Alanine is one of the 20 common amino acids constituting the human body, primarily involved in protein synthesis and energy metabolism, particularly gluconeogenesis. It is recognized as the primary glucogenic amino acid[40]. Research has demonstrated a positive causal relationship between UA levels and blood glucose levels, indicating that elevated UA contributes to an increase in blood glucose levels[41]. Elevated blood glucose levels can result in glomerulosclerosis and impaired kidney function, ultimately leading to kidney damage[42]. BUN is recognized as one of the indicators of kidney damage[43]. Consequently, we hypothesize that Ala may mediate the relationship between UA and CRF as well as BUN through this pathway. Therefore, further investigation and validation of this process are warranted in future studies.

The study demonstrates superiority over others due to several aspects: (1) The measurement of serum amino acid levels in mice employed a targeted metabolomics approach, thereby enhancing precision. (2) The summary statistics for MR exposure and outcomes were derived from the largest and most recent GWAS. (3) Rigorous criteria were implemented to select instrumental variables that could enhance statistical power. (4)Multiple sensitivity analyses were conducted to ensure that at least one statistical method, apart from the IVW approach, demonstrated significance and consistency in direction, thereby bolstering confidence in the analytical results.

This study is subject to several limitations: (1) The primary focus was on the kidney, suggesting that further investigation into amino acid alterations within renal tissue could enhance the validation of the experimental outcomes. (2) The hyperuricemia samples were obtained from murine models, while the data utilized in the MR analysis were exclusively human, raising the possibility of interspecies variability. (3) The MR analysis was conducted solely on individuals of European descent, leaving the applicability of the findings to other populations uncertain. Additionally, there may be some overlap between the samples used for exposures and outcomes, which could potentially influence the results. (4)The analysis exhibited a degree of heterogeneity and pleiotropy. Due to the utilization of GWAS data, it was not feasible to examine the potential for nonlinear linkage or stratification effects based on age, health status, or sex. (5)the study evaluated only a limited selection of amino acid metabolites. Future analyses should include a broader range of metabolite species to identify additional potential mediators.

In conclusion, the findings indicate that hyperuricemia contributes to renal damage and is associated with disruptions in amino acid metabolism. Key differential amino acid metabolites, such as Ala, Ile, Leu, Asp, Cys, Glu, and Gly, may play significant roles in the pathophysiology of UA. This MR study identifies hyperuricemia as a risk factor for increased susceptibility to CRF, BUN, eGFRcr, and eGFRcys. It further elucidates alanine as a mediator in the effect of hyperuricemia on CRF and BUN. The study investigates the reprogramming of amino acid metabolism in the context of hyperuricemia and proposes preventive and interventional strategies to manage the progression of hyperuricemia.

This study results demonstrate that hyperuricemia leads to kidney disease as well as disturbances in amino acid metabolism. Based on MR predictions, Ala may be a target for intervention to treat the elevated risk of CRF and BUN due to UA.

CRediT authorship contribution statement

Qinglin Sheng: Investigation, Methodology, Validation, Writing – original draft. Yuqing Ma: Conceptualization. Bingjie Geng: Data curation. Jiahui Chen: Formal analysis. Junfei Cheng:Investigation. Su liu: Methodology. Rui Li, Xiangtong Li, and Jing Wang: Software. Hongtao Lu: Supervision, Writing – review and editing. Fangyuan Gao: Supervision, Writing – review and editing. Fu Gao:Project administration,Supervision, Writing – review and editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The datasets applied in this study are readily accessible from previously published studies and online repositories.

Acknowledgements

We gratefully acknowledge the authors and participants of all GWASs from which we used summary statistics data.

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Serum Amino Acid Alterations in Hyperuricemia: Potential Targets for Renal Disease Prevention

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