Hyperuricaemia stands as the primary risk factor for gout, playing a central role in its pathogenesis. The primary routes for urate excretion involve the kidneys, intestine, and liver, with impaired renal excretion as the principal cause of hyperuricaemia. Saturation of SU results in the deposition of monocrystalline sodium urate (MSU) in the joints (1). Substantial MSU deposition leads to the formation of gout stones, causing additional damage to joint cartilage. Prolonged hyperuricaemia contributes to both renal function decline and kidney inflammation (2). Effective prevention and treatment are key to reducing hyperuricaemia incidence. Understanding the biological mechanisms of hyperuricaemia is crucial for preventing and treating gout. Despite significant contributions from genetic studies, the development of hyperuricaemia and gout remains a complex process (3). Recent evidence indicates a close relationship between SU levels, gout, and metabolic abnormalities. Widely recognized, hyperuricaemia is a common complication in individuals with metabolic syndrome (4). Individuals with insulin resistance face an elevated risk of developing hyperuricaemia, linked to the well-established, long-standing negative correlation between insulin resistance and renal clearance. Specifically, a reduction in renal clearance increases susceptibility to hyperuricaemia (5). Weight loss measures, particularly for those with visceral obesity, can decrease susceptibility to hyperuricaemia (6). Previous research has shown that hyperuricemia may increase the risk of cardiovascular disease by causing endothelial dysfunction through oxidative stress (7). Specifically, serum uric acid (SUA) production is closely linked with xanthine oxidoreductase (XOR). The oxidants produced by XOR activation can oxidize low-density lipoprotein (LDL), leading to the activation of inflammasomes and atherosclerosis, thus increasing the risk of cardiovascular disease (8). In this context, urate-lowering therapy appears to be a feasible treatment option for hyperuricemia with concomitant cardiovascular disease (9). Lowering uric acid levels is considered a method to optimize blood pressure regulation (10). Additionally, urate-lowering treatment with allopurinol can inhibit the renal reabsorption of uric acid, providing protection to the kidneys (11).

Gout stands as the most prevalent global form of inflammatory arthritis, primarily triggered by a sustained increase in uric acid levels, resulting in hyperuricemia and ultimately leading to the onset of gout (12). Clinical manifestations encompass intense pain in the joints of the lower limbs, primarily attributed to the deposition of MSU, intricately linked with both patient mortality and morbidity rates (13). As gout progresses, some individuals may experience advanced symptoms, characterized by persistent chronic pain and the formation of gout stones. The incidence of gout has steadily risen since the 20th century, possibly influenced by shifts in population age demographics, metabolic syndrome, and environmental factors. Reports from Europe and North America reveal an adult gout prevalence ranging from 0.68% to 3.9%, featuring gender disparities in both incidence and mortality, favoring males at a ratio of approximately 2:1 (14–17). Presently, treatment options for gout patients are severely limited, with less than one-third undergoing treatment and fewer than half adhering to prescribed regimens. This limitation has contributed to increased hospitalization and disability rates, resulting in diminished social productivity for both patients and their families, along with a heightened economic burden (18).

Researchers have diligently investigated the mechanisms underlying the inflammatory response triggered by MSU. However, persistent questions remain, such as the variability in patients developing either acute or chronic gout. The specific triggers for gout attacks and the factors preventing most hyperuricemia patients from transitioning to gout remain elusive. Consequently, there is an urgent need for exploring potential biomarkers indicating elevated SU levels and the initiation of gout. This represents a critical area that requires focused investigation.

Long-term metabolic disruptions lay the groundwork for the onset of hyperuricemia and gout, prompting an intensified examination of the roles played by metabolomics and immunomics in unraveling the intricate pathogenic mechanisms and physiological-pathological changes associated with this condition. Researchers have unearthed a correlation between hyperuricemia and gout not only with uric acid but also with other blood metabolites, prompting a novel avenue of exploration (19). Currently, blood metabolites exhibit substantial advantages in disease diagnosis and prognosis, offering fresh insights into predicting both hyperuricemia and gout (20). Thus, blood metabolites assume a pivotal role in anticipating the development of gout and uric acid level. However, the relationship between blood metabolites, SU, and gout remains unclear due to limitations in sample size and interference from confounding factors.

As a novel and robust epidemiological research approach, MR analysis employs genetic variations as instrumental variables to investigate causal relationships between exposure and outcomes. The genetic type is determined at conception, eliminating interference from external confounding factors and allowing MR studies to effectively minimize bias. Recent extensions of MR studies into metabolomics necessitate (21–24) assessing whether causal relationships exist between blood metabolites, SU, and gout, shedding light on the roles of blood metabolites in the disease. The objectives of this study involve employing various MR analysis methods to identify blood metabolites influencing SU levels and gout, examining the reciprocal impact of SU levels and gout on blood metabolites, and elucidating potential metabolic pathways affecting SU levels and gout. These insights aim to contribute novel perspectives for early diagnosis and prevention.

The study design involved several crucial steps. Initially, we employed Linkage Disequilibrium Score Regression (LDSC) analysis to assess the genetic correlation using 486 blood metabolites as exposures, and SU and gout as outcomes. Subsequently, a comprehensive evaluation was conducted to determine the causal relationships between blood metabolites exhibiting potential genetic correlations and the risks of SU and gout. To ensure the rigor of the MR study, three conditions were met: (1) Instrumental Variables (IVs) were directly related to exposure; (2) IVs were unrelated to the outcome and remained unaffected by confounding factors; (3) IVs influenced outcomes solely through exposure. Importantly, data on blood metabolites, SU, and gout were sourced from independent Genome-Wide Association Study (GWAS) datasets to prevent sample overlap. The research methodology adhered to the STROBE-MR checklist (25), and a visual representation of the study design is provided in Figure 1 .

After LDSC analysis, we identified potential correlations between 102 blood metabolites and SU, as well as 32 blood metabolites potential correlated with gout ( Supplementary File, Table S1 ).

The number of SNPs in IV varies from 2 to 180. All SNPs showed F-value statistics exceeding 10, indicating the absence of weak instrumental variables ( Supplementary File, Table S2 ).

In recent decades, metabolomics has played a crucial role in researching various diseases, leading to significant progress in understanding the pathogenesis and risk factors of hyperuricemia and gout. However, most studies have been confined to animal models or small case-control studies, limiting their ability to establish causal relationships.

In this study, we identified a total of 16 blood metabolites associated with SU concentration, with three linked to gout. Among the blood metabolites related to SU, four known blood metabolites showed a significant correlation after FDR correction, and one gout-related metabolite exhibited a significant association. These findings were robust across various testing methods. Additionally, we detected 4 metabolic pathways associated with SU concentration. Simultaneously, in the reverse MR analysis, we identified 7 blood metabolites significantly associated with SU levels, and one associated with gout. To the best of our knowledge, this marks the first systematic evaluation of the MR study on human blood metabolites and SU levels. Our research contributes to unraveling the role of human blood metabolites in the early diagnosis and prevention of gout and hyperuricemia, offering novel insights for future metabolomics studies. With the growing emphasis on understanding the relationship between blood metabolites and diseases in clinical settings, there has been continuous exploration of blood metabolites associated with SU concentration and gout. Hyperuricemia and gout are no longer perceived as isolated pathologies; their underlying mechanisms may be interconnected with various comorbidities. Numerous studies have linked hyperuricemia and gout to conditions such as renal disorders, diabetes, metabolic syndrome, and aging (12, 37). Currently, blood stands out as the most crucial and commonly used sample in metabolomics. The versatility of blood samples lies in their ability to reveal a myriad of detectable blood metabolites. Moreover, in large-scale studies, obtaining blood samples is often more feasible than other sample types. This accessibility forms the foundation for early screening of hyperuricemia and gout. Metabolomics has unveiled alterations in the metabolic profiles related to uric acid levels, including changes in amino acids (38), energy blood metabolites (39), and lipids (40). Our research not only validates the presence of these metabolic shifts in SU and gout but also identifies key blood metabolites and pathways associated with SU levels and gout pathogenesis.

Amino acids serve diverse functions within the human body, with a particular emphasis on their pivotal role in protein synthesis (41). Leucine, isoleucine, and valine collectively constitute the branched-chain amino acids (BCAAs). Previous studies have demonstrated that leucine is significantly altered in the course of several metabolic diseases (42). Additionally, in overweight and obese (OB) children, leucine concentrations were significantly elevated (43), showing a positive correlation with inflammatory markers (CRP, IL-6), SU, visceral adiposity, type 2 diabetes (T2D), insulin resistance, and CVD (38, 39). Simultaneously, levels of inflammation-related fibrinogen and hs-CRP displayed an increasing trend with elevated leucine levels (44). Given the limited sample size in previous studies, we cannot infer the precise mechanism underlying the correlation between leucine and IL-6. While IL-6, a standard inflammation marker (45), does not directly contribute to SU elevation, its levels increase upon activation of MSU (46). Nevertheless, the role of leucine in predicting SU and gout remains a topic of debate. A study discovered elevated valine levels in patients with acute gout and hyperuricemia, while the increases in leucine and phenylalanine were not statistically significant (47). However, the correlation between leucine and UA is likely due to abnormalities in glucose metabolism leading to disruptions in amino acid metabolism (48). Additionally, it may also be related to the dietary habits of the patients themselves (49). Hyperuricemia patients tend to consume more meat, eggs, and dairy products, and have higher intakes of fructose-containing beverages and alcohol compared to healthy individuals (50). These unhealthy lifestyle habits can lead to the development of metabolic syndrome and insulin resistance in the patients (51). In our study, we identified a robust correlation between the biosynthesis and degradation pathways of valine, leucine, and isoleucine and SU levels. In general, our research findings are in partial agreement with previous studies. Hence, conducting further in-depth research with larger sample sizes is imperative to elucidate the intricate relationship between leucine and SU.

The typical absence of N-acetylornithine expression in the human body stems from the non-expression of specific enzymes responsible for its processing in mammals. In microorganisms, N-acetylornithine primarily contributes to arginine synthesis (52), while in plants, its principal role lies in ornithine synthesis (53). The current literature on the correlation between N-acetylornithine and SU levels is limited, with a handful of studies suggesting a potential link to impaired kidney function. Uric acid, the final product of purine metabolism in the human body, is usually excreted through urine. Disruptions in uric acid metabolism occur as renal function declines, leading to abnormal SU levels. A GWAS assessing concentrations of 308 non-targeted blood metabolites in African Americans found an association between elevated N-acetylornithine levels and kidney function, as indicated by creatinine levels (54). Daily protein intake, particularly from plant sources, correlates with the progression of Chronic Kidney Disease (CKD) (55, 56). Metabolite sample testing in CKD patients post-kidney transplantation revealed a significant decrease in concentrations of key renal function indicators (creatinine, eGFR, cystatin C) with elevated N-acetylornithine levels (57). Our MR study found a significant bidirectional relationship between N-acetylornithine and SU, with both being risk factors for the other, so we speculate that the relationship between N-acetylornithine and SU may be mediated bi-directionally through renal function, and when one of these factors leads to impaired renal function, it affects the other. Given the close and pronounced relationship between N-acetylornithine and SU levels, it emerges as a promising biomarker. However, a more comprehensive understanding of its mechanistic implications requires further dedicated clinical investigations into the nuanced relationship between N-acetylornithine and SU levels.

Succinyl carnitine, a form of acylcarnitine, plays a pivotal role in mitochondria by facilitating the uptake of fatty acids, a crucial step in the β-oxidation process (58). Within the tricarboxylic acid cycle, succinyl coenzyme A undergoes conversion into succinyl carnitine, actively participating in various metabolic pathways. Recent studies have unveiled a correlation between succinyl carnitine levels and systemic inflammatory response as well as heart failure (59, 60). Our MR study unequivocally demonstrates a significant causal relationship between succinyl carnitine and elevated SU levels. Despite shedding light on their connection, the precise mechanism linking succinyl carnitine to SU elevation remains elusive. Thus, a pressing need exists for further experimental and clinical research to delve deeper into understanding the intricate relationship between these two factors.

The human body utilizes niacin (nicotinic acid, vitamin B3) or nicotinamide (NAM) along with dietary tryptophan as substrates to synthesize nicotinamide adenine dinucleotide (NAD). N-methyl-2-pyridone-5-carboxamide (2PY) and N1-methyl-4-pyridone-3-carboxamide (4py) are degradation products of NAD, recognized as uremic retention molecules eventually excreted through urine (61). Studies indicate that the excessive accumulation of 4py may inhibit the biological activity of PARP-1, adversely affecting uric acid production (62). Current research on 4py primarily focuses on kidney diseases, inflammatory responses, and diabetes. In a study on children with chronic renal failure (CRF), a positive correlation was found between 4py and creatinine concentration, while a negative correlation was observed with creatinine clearance. This suggests that the accumulation of 4py parallels renal dysfunction, possibly due to the decline in renal function in CRF patients (63). Research on systemic inflammatory response syndrome, measuring the metabolic profile of hemodialysis patients and its association with renal function, revealed a significant negative correlation between 4py and renal function as renal function declined (64). The established correlation between SUA concentration and renal function is supported by a retrospective cohort study assessing the association between SU levels and glomerular filtration rate (eGFR) in the Chinese population. Elevated SU was also significantly correlated with the risk of CKD (65). Another clinical study measuring the content of uracil and purines in the urine of patients at different stages of CKD found a positive correlation between GFR and the ratio of uric acid, xanthine, and hypoxanthine in urine (66). Although existing research cannot conclusively prove a direct impact of 4py on SU levels, there is reason to believe that there may be a genetic causal relationship between them. This connection may be mediated through the impact of 4py on renal function.

Findings from our study indicate that an elevated genetic susceptibility to increased levels of 1-oleoylglycerol is associated with a higher risk of gout. Previous research has established a close connection between disruption in lipid metabolism and gout risk (67). Currently, there is limited research on 1-oleoylglycerol. In a recent large-scale clinical study, the metabolic profiling of gut microbiota-related blood metabolites and their correlation with T2D was analyzed in 5,000 middle-aged Finnish men. After correcting for various factors such as smoking, age, BMI, and physical activity, it was found that 1-oleoylglycerol, controlled by the gut microbiota, increased the incidence of T2D by 29%, showing a significant association with the onset of T2D (68). Existing research has already confirmed that changes in the gut microbiota contribute to an increased risk of gout (69, 70). Given the gut microbiota’s potential to increase the risk of gout, 1-oleoylglycerol may induce gout through alterations in the gut microbiota. However, further exploration is required under specific experimental conditions to substantiate this hypothesis.

Our study boasts several strengths. Firstly, we systematically investigated 486 blood metabolites, providing the most comprehensive analysis to date of the metabolic characteristics associated with hyperuricemia and gout. Secondly, prior to conducting the MR study, we performed LDSC analysis to ensure a potential genetic correlation between the studied blood metabolites and both SU levels and the occurrence of gout. This precautionary step minimizes biases arising from polygenicity and confounding factors, thereby enhancing the robustness of our results. Lastly, adopting a bidirectional MR study approach, which considers both causal directions, helps mitigate potential issues related to reverse causation and residual confounding factors.

However, this study also has some limitations. Firstly, we utilized publicly available GWAS data, and potential sample overlap might introduce confounding bias. Secondly, our study’s exposures and outcomes are derived from the European population, warranting further investigation to determine whether similar causal relationships exist in other populations. Thirdly, the limited number of participants in our exposure dataset may result in missing relationships between blood metabolites and SU levels and gout. Additionally, some blood metabolites and pathways in this study lack a comprehensive understanding of their mechanisms and pathological relationships with SU and gout, limiting the interpretation of our MR analysis results. While MR studies contribute to identifying potential blood metabolites associated with SU and gout, further clinical and experimental research is essential to explore the correlations between them.

Two-sample MR studies unveiled the significant roles of five serum blood metabolites associated with SU levels and gout. Nineteen blood metabolites were identified with potential causal relationships with SU levels and gout, elucidating four crucial metabolic pathways in SU levels. The discoveries from this study aid in comprehending the biological mechanisms of hyperuricemia and gout, paving the way for future targeted therapeutic interventions.

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding authors.

YZ: Writing – original draft, Conceptualization, Data curation, Investigation, Methodology, Visualization. CAY: Writing – original draft, Investigation. BHZ: Data curation, Writing – original draft, Investigation. YWC: Investigation, Visualization, Writing – original draft. WJC: Writing – original draft. GCW: Writing – original draft. CWZ: Writing – review & editing. WHZ: Writing – review & editing.