Hyperuricemia (HUA) (1) is a metabolic disorder characterized by an abnormal increase in serum uric acid (UA) levels. It can be triggered by various factors, including acquired factors (such as dietary habits, medication use, and bone marrow diseases) and genetic factors (such as enzyme deficiencies). In recent years, the incidence of HUA has been rising annually, with current data indicating that (2) it has become a prevalent metabolic disorder, second only to type 2 diabetes in prevalence, reaching 21.4% in the United States (2).

UA (1) is primarily synthesized in the liver, intestines and vascular endothelium. Due to its critical role in purine metabolism, UA is closely linked to various biological responses, including oxidative stress (3). Basic research has found that HUA exerts pathogenic effects on the occurrence and progression of cardiovascular diseases (4) through multiple pathways, including the renin-angiotensin system (1), nitric oxide synthase inhibition (5) and endothelial dysfunction (6). Additionally, HUA is considered an independent risk factor for cardiovascular and renal diseases (7), and is closely associated with hypertension (8), heart failure (9) and atherosclerotic heart disease (10). A U-shaped association has been identified between serum UA levels and all-cause mortality (3), indicating that both excessively high (3) and low (11) UA concentrations are linked to adverse outcomes. This presents challenges for the clinical management of hyperuricemia. These findings suggest that optimal regulation of serum UA may play an important role in the prevention and management of cardiovascular diseases. Numerous studies (12) have confirmed the clinical benefits of enhanced UA management; however, it remains unclear whether serum UA is merely a contributing factor to cardiovascular disease or a pathogenic risk factor and potential therapeutic target in clinical practice.

The anion gap (AG) is a commonly used biochemical marker in the clinical assessment of acid-base balance, and it is considered valuable for evaluating disease severity and predicting prognosis. In practice, the measurement of AG can be influenced by various factors. However, the novel biochemical marker known as the “albumin-corrected anion gap” (ACAG) can more effectively minimize these interferences.

Previous studies have shown that ACAG is closely associated with the prognosis of patients with acute myocardial infarction (13), including 30-day all-cause mortality (14). It has also been linked to poor in-hospital outcomes in cardiac arrest patients (15), as well as in-hospital and long-term mortality in patients undergoing coronary artery bypass grafting (16). Furthermore, ACAG has been associated with the prognosis of patients with aneurysmal subarachnoid hemorrhage (17).

However, there have been no studies examining the correlation between ACAG and HUA to date. Therefore, we collected baseline information from adult participants using the health examination system at Guangdong Provincial Hospital of Chinese Medicine. This cross-sectional study aimed to explore the relationship between HUA and ACAG, and through big data analysis, to further investigate the potential association between HUA and cardiovascular diseases.

In healthy adults, the liver produces approximately 0.2 g/kg of serum albumin (ALB) daily, based on body weight (19). ALB is the most abundant protein in human serum, accounting for about 60-65% of all proteins (19, 20). In addition to maintaining colloid osmotic pressure, ALB serves as a carrier in the serum through various post-translational modifications, such as glycation, oxidation, partial loss, and carbamylation (20).

Reversible modifications of proteins are crucial for preventing irreversible damage and regulating protein function (21). Moreover, cysteine-34 (Cys-34) (21) is the only cysteine residue in ALB that exists in a free form. Research suggests that (21) the antioxidant properties of ALB largely depend on Cys-34 and its role in maintaining vascular homeostasis, including the protection of vascular endothelium under conditions associated with oxidative stress. Therefore, ALB is regarded as one of the body’s important antioxidants, with its relative contribution reported to range from approximately 40% to 70% of total antioxidant capacity (22).

Reactive oxygen species (ROS) play a significant pathogenic role in the development of various systemic diseases. The inherent nature of oxidative stress arises from an imbalance between the production and degradation of ROS, which involves multiple pathological processes across different systems. Proteins are critical targets for ROS, as they can suffer irreversible damage that impairs their physiological functions, leading to issues such as cardiac cell injury (22).

Studies (23–25) have shown that UA plays a role in promoting the elimination of reactive ROS, acting as an antioxidant capable of scavenging up to 55% of extracellular free radicals. However, the process of UA formation is also accompanied by the generation of ROS. When extracellular uric acid reacts with myeloperoxidase, it produces a compound with pro-oxidative properties known as hydrogen peroxide urate. In cases of HUA, the increased concentration of extracellular uric acid enhances this reaction. Consequently, HUA may induce oxidative stress, which can, in turn, promote inflammation.

Our study found that the ALB levels in the HUA group were significantly higher than those in the Non-HUA group (P<0.001). This observation, when combined with previous research, suggests that the elevated ALB levels in HUA patients may be closely linked to a heightened inflammatory state and the body’s response to oxidative stress. Higher ALB levels could reflect a pathological compensation for a stronger inflammatory burden in the body; however, this hypothesis requires further experimental validation.

The AG represents the difference between the concentrations of negatively charged anions (13) and positively charged cations in plasma, primarily consisting of negatively charged ALB (26). It is primarily used to assess acid-base balance and electrolyte disturbances. Our study indicates that the AG levels in the HUA group were significantly higher than those in the Non-HUA group (P<0.001). High AG metabolic acidosis is a subclass of metabolic acidosis (27), consistent with the traditional understanding that the pH in HUA patients tends to be acidic.

Acid retention (28) can reduce the pH levels in renal interstitium and intracellular compartments, leading to increased levels of angiotensin II, aldosterone, endothelin, and pro-inflammatory cytokines in the kidneys—all of which are associated with renal fibrosis and damage. Research has shown (26, 29) that the excretion of UA increases with rising pH levels in the renal tubule. Clinically, physicians often use sodium bicarbonate to alkalize urine and adjust dietary acid-base balance to influence the pH-dependent transport systems in the kidneys, thereby affecting UA excretion. Additionally, individuals who consume animal proteins are more prone to HUA compared to vegetarians (29), which may also be a significant factor contributing to the higher ALB levels observed in the HUA group compared to the Non-HUA group.

Compared to using ALB and AG individually, the ACAG provides a more precise assessment of hidden tissue anions, allows for a better assessment of acid-base homeostasis, and partially reflects nutritional status. Gao et al. (26) conducted a retrospective analysis of clinical data from 9,625 critically ill patients with acute kidney injury (AKI), revealing that elevated ACAG levels (greater than 20 mmol/L) upon ICU admission significantly increased the 30-day and 360-day all-cause mortality rates. Similarly, Zhao et al. (28) identified a correlation between higher ACAG levels and the risk of AKI, suggesting that ACAG may serve as an early indicator of adverse outcomes in ICU patients.

A key advantage of this approach lies in its ability to not only highlight abnormalities in acid-base balance but also potentially reflect underlying malnutrition, providing a more comprehensive assessment tool for clinicians. In our study, we used RCS analysis to reveal a nonlinear relationship between ACAG, UA and HUA (P for nonlinear <0.001). The increase in ACAG showed a significant positive correlation with UA levels (β=3.10, 95% CI, 2.15-4.06), and this significance remained after adjusting for sex and age. When ACAG exceeded 12.64, each additional unit of ACAG was associated with a 12% increase in the odds ratio for HUA.

These findings suggest that monitoring ACAG levels may aid in predicting and managing HUA and its related complications. This evidence provides important clinical insights and emphasizes the critical role of acid-base balance monitoring in evaluating patients’ metabolic status.

In clinical practice, it is frequently observed that HUA is associated with an increased risk of cardiovascular and cerebrovascular diseases. Previous studies have indicated a close relationship between the ACAG and the occurrence and progression of cardiovascular diseases, and our data also suggest its role in HUA. However, there is currently a lack of in-depth exploration regarding the mediating relationship between HUA and ACAG. Therefore, we conducted a more detailed study that integrates clinical realities.

Hypertriglyceridemia (30) is a disorder of lipoprotein metabolism and is recognized as a significant risk factor for cardiovascular diseases. A retrospective analysis by Hou et al. (30) involving 3,884 subjects found that TG are an independent risk factor for HUA, with increasing levels of TG correlated with a heightened risk of HUA. Notably, there are significant differences in glycerolipid metabolism (31) between HUA patients and the normal population; the conversion of certain metabolites, such as saturated fatty acids, is associated with the production of inflammatory factors, which may contribute to the increased inflammatory burden in HUA patients.

While LDL-C has traditionally been the primary focus in the treatment of dyslipidemia, non-HDL-C (32) has increasingly been recognized as an important risk factor for cardiovascular diseases and is gradually becoming a key target in the management of lipid abnormalities. A retrospective analysis by Xu et al. (33)involving 9,580 subjects demonstrated a significant association between non-HDL-C and HUA, suggesting the need for future development of therapies that can simultaneously lower both triglyceride and UA levels to manage the combined syndrome of hyperuricemia and hyperlipidemia.

Through data analysis, we found a significant direct association between ACAG and HUA (β = 0.027, 95% CI: 0.011, 0.042). Additionally, TG partially accounted for the relationship between ACAG and HUA (β = 0.011, 95% CI: 0.008, 0.014), while non-HDL-C also explained part of this association (β = 0.005, 95% CI: 0.003, 0.008). Approximately 28.3% and 13.5% of the relationship between ACAG and HUA was explained by TG and non-HDL-C levels, respectively, which is consistent with previous research findings.

In this study, although we achieved several important findings, several limitations should be acknowledged. The foremost limitation is the cross-sectional nature of our design, which fundamentally prevents the establishment of causality or temporality between the observed variables. This inherent constraint of the cross-sectional approach means that our findings can only be interpreted as associations rather than evidence of cause and effect. Furthermore, the relatively small sample size and single-center setting may limit the generalizability of our results, as regional variations in diet, metabolism, and lifestyle could influence HUA. Second, although we analyzed various biochemical markers and adjusted for key variables, other potential confounding factors—such as genetic background, dietary patterns, medication use, renal function, inflammatory status, and environmental exposures—were not fully accounted for, which might introduce bias into the observed associations. Additionally, the use of static laboratory values may not fully capture the dynamic metabolic state of the patients. Therefore, future research should prioritize multicenter, large-scale prospective cohort studies to overcome these limitations. Such longitudinal designs are essential to establish temporality, control for unmeasured confounders, and validate the associations found here. The implementation of dynamic monitoring methods could also provide a more comprehensive metabolic perspective. Furthermore, while our research preliminarily explored the relationships among ACAG, TG, and non-HDL-C, more in-depth mechanistic studies are needed to validate and elucidate these associations. Finally, before any potential clinical applications of our findings can be implemented, their safety and effectiveness must be rigorously validated through interventional studies.

The findings of this present study demonstrate that patients with HUA exhibit significantly higher levels of ALB and AG than those without HUA, suggesting a heightened inflammatory state and possible compensatory mechanisms in this population. Furthermore, a positive correlation was identified between ACAG and UA levels. Further analysis revealed that TG and non-HDL-C partially mediate the association between ACAG and HUA, underscoring the pivotal role of lipid metabolism disturbances in the development of HUA.

These results provide valuable clinical implications and generate important hypotheses, supporting the potential utility of monitoring acid-base balance and related metabolic biomarkers in the prediction and management of HUA and its associated complications. Future investigations should prioritize longitudinal or interventional designs aimed at elucidating the underlying mechanisms of these biomarkers and validating their applications in risk stratification and tailored interventions. Such research is essential to contribute to more effective strategies for the prevention and treatment of hyperuricemia.