Serum uric acid as a biomarker for metabolic dysfunction-associated steatotic liver disease: insights from ultrasound elastography in a Chinese cohort

Abstract

Background

To evaluate the association between serum uric acid (SUA) levels and metabolic dysfunction-associated steatotic liver disease (MASLD), defined as excessive fat accumulation in the liver accompanied by at least one cardiometabolic risk factor, reflecting metabolic abnormalities associated with the condition, in a Chinese adult population.

Methods

This study included 3829 participants aged ≥ 18 years who underwent abdominal transient elastography and had complete SUA data. SUA was categorized into low, medium, and high tertiles. Hepatic steatosis was defined as a controlled attenuation parameter (CAP) ≥ 248 dB/m. MASLD diagnosis followed the latest definitions by relevant liver disease associations. Logistic regression analyzed the association between SUA and MASLD. Restricted cubic spline regression assessed non-linear relationships.

Results

A total of 1737 participants were diagnosed with MASLD. SUA levels were higher in the MASLD group (5.79 ± 1.50 mg/dL) than in the non-MASLD group (5.03 ± 1.35 mg/dL). SUA was linearly related to MASLD (P for nonlinearity = 0.8451). Both medium and high SUA groups had increased MASLD risk compared to the low SUA group (P < 0.05). Each unit increase in SUA was associated with a 14% higher risk of MASLD (odds ratio [OR] = 1.14, P = 0.0004).

Conclusions

This study highlights the association between SUA levels and MASLD, suggesting that SUA may serve as a potential biomarker for MASLD risk assessment. Monitoring SUA levels could inform preventive strategies and facilitate early intervention, contributing to improved MASLD management.

Background

Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a significant global health concern, representing a spectrum of liver pathologies closely linked to metabolic disturbances [1]. This condition, recently renamed from non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated fatty liver disease (MAFLD), encompasses a range of hepatic manifestations from simple steatosis to steatohepatitis, cirrhosis, and hepatocellular carcinoma [2, 3]. The evolution in nomenclature reflects a paradigm shift in our understanding of the disease, emphasizing the central role of metabolic dysfunction rather than merely the exclusion of alcohol consumption [4]. MASLD is characterized not only by hepatic fat accumulation but also by the presence of metabolic abnormalities such as insulin resistance, hypertension, and dyslipidemia [3].

In China, regional factors such as dietary patterns rich in carbohydrates and purines, genetic predispositions, and the rising prevalence of metabolic syndrome (MetS) may contribute to an increased burden of MASLD and highlight the relevance of serum uric acid (SUA) as a biomarker. Chinese dietary habits vary significantly across regions, influencing SUA levels and metabolic health. Research has identified distinct dietary patterns that correlate with SUA concentrations [5, 6]. Studies have also identified genetic polymorphisms, such as mutations in ABCG2 and URAT1, which are prevalent in the Chinese population and linked to hyperuricemia risk [7]. Furthermore, the prevalence of MetS in China is rising sharply, affecting nearly 34–45% of adults under various definitions [8, 9]. Components of MetS—such as obesity, hypertension, dyslipidemia, and insulin resistance—are closely associated with elevated SUA levels and metabolic disturbances characteristic of MASLD [10].

SUA has garnered attention as a potential key player in the pathogenesis of metabolic disorders, including fatty liver disease [11]. Elevated SUA levels have been associated with cardiovascular diseases, diabetes, and the development of fatty liver disease [12, 13]. However, the specific role of SUA in the context of MASLD, particularly across its entire spectrum, remains inadequately explored, especially in the Chinese population. Concurrently, advancements in diagnostic technologies, such as ultrasound elastography, have provided non-invasive means to assess hepatic steatosis and fibrosis, offering a valuable alternative to liver biopsy [14]. These techniques enable more comprehensive evaluations of liver health in large-scale population studies. Furthermore, the transition from NAFLD to MASLD conceptualization emphasizes a broader metabolic context, potentially altering our understanding of the relationships between various metabolic markers and liver health. Given the distinct diagnostic criteria of MASLD compared to NAFLD, the association between SUA and MASLD may differ from previously established relationships in NAFLD.

This study aims to investigate the association between serum uric acid levels and MASLD, as defined by ultrasound elastography parameters, in a Chinese population. By exploring this relationship, we seek to elucidate the potential role of SUA in the pathophysiology of MASLD and its utility as a biomarker for disease severity and progression. Our findings may provide new insights into the complex interplay between uric acid metabolism and liver health in the context of metabolic dysfunction, potentially informing future diagnostic and therapeutic approaches for MASLD management.

Methods

Study subjects

This retrospective study included all individuals who underwent liver ultrasound elastography at our institution between January 2018 and September 2023. The study protocol adhered to the ethical guidelines of the 1975 Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of Xi’an Fengcheng Hospital (approval number: 2024-001-01). The requirement for informed consent was waived due to the retrospective nature of the study. Participants were eligible if they were aged 18 years or older and had complete SUA data available. We excluded individuals based on the following criteria: (1) age below 18 years; (2) pregnant women; (3) missing SUA data; (4) poor quality ultrasound images that precluded accurate analysis; and (5) history of other liver diseases, including but not limited to viral hepatitis, drug-induced hepatitis, alcoholic hepatitis, and autoimmune hepatitis.

However, we recognize that this approach may have biased the sample toward healthier individuals, potentially underestimating the prevalence and severity of MASLD in the general population. SUA levels were measured at a single time point as part of this cross-sectional study. Although SUA may fluctuate due to dietary or pharmacological factors, it is considered a metabolic marker reflecting participants’ current physiological state. Baseline sociodemographic and clinical characteristics of the study participants, including age, BMI, blood pressure, lipid profiles, comorbidities, medication use, and lifestyle factors, are summarized in Table 1. These data provide a comprehensive overview of the study population and allow for subgroup comparisons based on SUA levels.

Hepatic ultrasound transient elastography

Hepatic ultrasound transient elastography is a precise and non-invasive method for evaluating the extent of steatosis and fibrosis in patients with MASLD. The Ultrasound-Guided Attenuation Parameter (UGAP) is a technique based on ultrasound attenuation coefficients that assesses tissue mechanical properties by analyzing the propagation characteristics of ultrasound waves in tissues [15]. Controlled attenuation parameter (CAP) is explained as a non-invasive ultrasound-based measure used to assess hepatic steatosis by quantifying liver fat content. The assessment of CAP and liver stiffness offers comprehensive insights into the status of patients with NAFLD [16].

Participants fasted for 8–10 h prior to examination, conducted by a single experienced senior sonographer using a LogiqE10s system (GE Healthcare, USA) with a probe frequency range of 1-6 MHz. Subjects were positioned supine with maximal right arm extension to optimize liver exposure. The examiner subsequently identified appropriate liver regions for measurement, activated UGAP mode on the ultrasound system, and performed intercostal scans of the right hepatic lobe, conducting measurements within the same hepatic area. Following confirmation of proper positioning, a minimum of 5 sequential measurements were obtained at the site to ensure result reliability. These assessments concurrently gathered CAP data (converted to dB/m), with CAP quantifying hepatic ultrasound attenuation at the GE system’s frequency (Fig. 1). This methodology guaranteed the precision and reproducibility of elastography measurements, yielding dependable data for clinical assessment.

Fig. 1

Ultrasound-guided attenuation parameter (UGAP) assessment of liver steatosis. (A) Demonstrates the quantification of hepatic stiffness, and (B) shows hepatic steatosis measurement (CAP). These pictures provide non-invasive evaluation of liver health, aiding MASLD diagnosis

Data collection

Data for this study were collected retrospectively from electronic medical records using a standardized data extraction form. Collected variables included demographics (e.g., age, sex), clinical characteristics (e.g., BMI, comorbidities), laboratory measurements (e.g., SUA, liver function tests, lipid profiles), and ultrasound elastography results (e.g., CAP values). Quality control measures were implemented to ensure the completeness and accuracy of data entry.

Definition of MASLD

MASLD was defined according to the diagnostic criteria proposed by the American Association for the Study of Liver Diseases (AASLD) and European Association for the Study of the Liver (EASL) on June 24, 2023 [1, 17]. Patients were diagnosed with MASLD if they met two key criteria. First, the presence of significant hepatic steatosis, which in this study was diagnosed using a CAP threshold of > 248 dB/m [18,19,20]. This threshold was selected based on prior validation studies and expert consensus reports that demonstrate this value’s diagnostic accuracy for detecting moderate-to-severe hepatic steatosis. CAP values above 248 dB/m have been shown to correlate strongly with histological evidence of steatosis, providing a non-invasive and reliable means of assessment. Second, the presence of at least one of five cardiometabolic risk factors: (1) Body mass index (BMI) ≥ 23 kg/m² or waist circumference > 94 cm for men or > 80 cm for women; (2) Fasting plasma glucose (FPG) ≥ 5.6 mmol/L, or 2-hour postprandial glucose ≥ 7.8 mmol/L, or glycated hemoglobin (HbA1c) ≥ 5.7%, or presence of type 2 diabetes mellitus (T2DM); (3) Blood pressure ≥ 130/85 mmHg or use of antihypertensive medications; (4) Plasma triglycerides (TG) ≥ 1.7 mmol/L or use of lipid-lowering medications; (5) High-density lipoprotein cholesterol (HDL-C) ≤ 1.0 mmol/L for men or ≤ 1.3 mmol/L for women, or use of lipid-lowering medications. Patients meeting both the hepatic steatosis criterion and at least one of the cardiometabolic risk factors were classified as having MASLD.

Covariates

A comprehensive set of personal information was collected for this study to assess participants’ health status and potential risk factors associated with MASLD. This included data from medical history, physical examinations, and laboratory tests. The medical history gathered basic information such as age, sex, ethnicity, smoking status, and educational level, as well as medication history (including use of statins, antihypertensive drugs, and hypoglycemic agents) and medical history (including hypertension, diabetes, cardiovascular diseases). Physical examinations provided data on height, weight, and BMI. BMI was categorized according to the World Health Organization guidelines: Normal weight (18.5–24.9 kg/m²), Overweight (25–29.9 kg/m²), and Obesity (BMI of 30 kg/m² or greater). Laboratory tests yielded a range of biochemical parameters, including systolic blood pressure (SBP), diastolic blood pressure (DBP), HbA1c, alanine aminotransferase (ALT), alkaline phosphatase (ALP), total bilirubin, serum creatinine (SCr), aspartate transaminase (AST), total cholesterol (TC), SUA, blood urea nitrogen (BUN), and high-density lipoprotein cholesterol (HDL-C). This comprehensive data collection allowed for a thorough evaluation of the participants’ overall health status and potential MASLD-related risk factors.

Statistical analysis

In this study, continuous variables were presented as mean ± standard deviation, while categorical variables were expressed as percentages (%). Continuous variables were analyzed using T-tests for normally distributed data and Mann–Whitney U tests for skewed distributions. Categorical variables were compared using Chi-square (χ²) tests. Participants were categorized into low, medium, and high groups based on SUA tertiles. To explore the non-linear relationship between SUA and MASLD, we utilized Restricted Cubic Splines (RCS) models. To further investigate the relationship between MASLD prevalence and various factors, we performed multiple adjusted multivariate logistic regression analyses with SUA as a continuous variable, calculating odds ratios (ORs) and their 95% confidence intervals (CIs). In Model 1, no adjustments were made for confounding factors. Model 2 was adjusted for age, sex, BMI, and education level. Model 3 was further adjusted for SBP; DBP; HbA1C; ALT; AST; total bilirubin, creatinine, BUN, total cholesterol, HDL, cardiovascular diseases (CVD), smoking status, diabetes, hypertension, antihypertensive drugs, statins use, and antidiabetic drugs. To test for a linear trend between changes in SUA and MASLD across the three logistic regression models, we conducted trend tests to explore the statistical significance of this relationship. All statistical analyses were performed using R software version 4.1.2 (R Core Team, Vienna, Austria), with a P-value less than 0.05 considered statistically significant.

Results

Baseline characteristics of study subjects

A total of 3,829 participants were included in this study and categorized into three groups based on SUA levels: low (n = 1,205), middle (n = 1,305), and high (n = 1,319). Significant differences were observed across these groups for most baseline characteristics (P < 0.001, Table 1). Mean age, BMI, and CAP values increased progressively across the SUA tertiles. Blood pressure, HbA1c, liver enzymes, and renal function markers also showed an increasing trend with higher SUA levels. The proportion of males increased from 21.16% in the low SUA group to 72.71% in the high group. The prevalence of cardiovascular disease, smoking, diabetes, and hypertension, as well as the use of related medications, was higher in groups with elevated SUA. Conversely, HDL cholesterol levels decreased across the SUA tertiles. Education level was the only characteristic without significant differences across groups (P = 0.129). Older participants and males were more likely to exhibit higher metabolic risk factors, including elevated BMI, triglycerides, and blood pressure. These findings may be attributed to age-related insulin resistance, loss of lean muscle mass, and sex-specific hormonal effects, such as reduced estrogen levels in postmenopausal women, which are known to increase visceral fat accumulation and hepatic lipid deposition.

Table 1 Baseline characteristics of study participants stratified by serum uric acid tertiles

Associations between serum uric acid and MASLD

The relationship between serum uric acid levels and MASLD was examined using both continuous and categorical analyses. Figure 2 illustrates a linear association between SUA and the odds of MASLD (P for nonlinearity = 0.8451), suggesting that higher SUA levels are associated with increased odds of MASLD. In the fully adjusted model (Model 3), each 1 mg/dL increase in SUA was associated with 14% higher odds of MASLD (OR: 1.14, 95% CI: 1.06–1.23, P = 0.0004). When SUA was categorized into tertiles, participants in the highest tertile had 57% higher odds of MASLD compared to those in the lowest tertile (OR: 1.57, 95% CI: 1.21–2.03, P = 0.0006). A significant trend was observed across SUA tertiles (P for trend = 0.0006), indicating a dose-response relationship between SUA levels and MASLD risk (Table 2). These associations remained consistent across different adjustment models, with the relationship attenuated but still significant after adjusting for potential confounders. The results suggest that elevated SUA levels are independently associated with higher odds of MASLD, even after accounting for various demographic, clinical, and biochemical factors.

Fig. 2

Restricted cubic spline analysis of the association between serum uric acid levels and the odds of metabolic dysfunction-associated steatotic liver disease (MASLD). The solid line represents the odds ratio, and the dashed lines represent the 95% confidence intervals. Restricted cubic spline analysis shows a linear association between SUA levels and MASLD risk (P for nonlinearity = 0.8451). The analysis highlights a dose-response relationship supporting SUA as a potential biomarker

Table 2 Associations between serum uric acid and the presence of metabolic dysfunction-associated steatotic liver disease as defined by Ultrasound elastography

Subgroup analyses

We conducted subgroup analyses to assess the consistency of the association between serum uric acid and MASLD across various demographic and clinical characteristics (Fig. 3). The positive association between SUA and MASLD was generally consistent across most subgroups, with some notable variations.

Fig. 3

Subgroup analyses of the association between serum uric acid levels and metabolic dysfunction-associated steatotic liver disease (MASLD). Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each subgroup

Subgroup analysis reveals consistent associations between SUA levels and MASLD across demographic and clinical groups. Stronger associations were noted in females, younger participants, and those without diabetes.

The association was stronger in females (OR: 1.29, 95% CI: 1.15–1.45) compared to males (OR: 1.06, 95% CI: 0.96–1.17), although the interaction was not statistically significant (P for interaction = 0.0998). A stronger association was observed in participants aged < 60 years (OR: 1.19, 95% CI: 1.07–1.33) compared to those ≥ 60 years (OR: 1.11, 95% CI: 1.00-1.23). Among BMI categories, the association was strongest in the normal weight group (OR: 1.35, 95% CI: 1.12–1.63) and remained significant in the obesity group (OR: 1.26, 95% CI: 1.12–1.42). The association was similar between smokers and non-smokers. Notably, a significant interaction was observed for diabetes history (P for interaction = 0.0026). The association between SUA and MASLD was stronger in participants without a history of diabetes (OR: 1.21, 95% CI: 1.11–1.31) compared to those with diabetes (OR: 0.95, 95% CI: 0.81–1.13). The association remained consistent across other subgroups, including history of cardiovascular disease, hypertension, and statin use, with no significant interactions observed. These results suggest that the association between SUA and MASLD is largely consistent across various subgroups, with potential variations by gender, age, BMI, and notably, diabetes status.

Discussion

This study demonstrates a linear relationship between SUA and MASLD risk, with each 1 mg/dL increase in SUA associated with 14% higher odds of MASLD (OR: 1.14, 95% CI: 1.06–1.23) in the fully adjusted model. When categorized into tertiles, participants in the highest SUA tertile had 57% higher odds of MASLD compared to those in the lowest tertile (OR: 1.57, 95% CI: 1.21–2.03). This dose-response relationship suggests a potential causal link between elevated SUA and MASLD development. Notably, this is the first study to utilize ultrasound elastography for MASLD diagnosis in a Chinese population while investigating its relationship with SUA levels.

Our findings align with previous studies on the relationship between SUA and NAFLD. Sirota et al. reported that elevated SUA levels were independently associated with NAFLD in the US population, even after adjusting for metabolic syndrome features [21]. Similarly, Zheng et al. observed a positive correlation between SUA and NAFLD in non-obese Chinese adults [13]. Our study extends these findings, confirming that this association persists in MASLD across different BMI categories, with the strongest association found in normal-weight individuals. Notably, our study employed the latest MASLD diagnostic criteria and ultrasound elastography technology, representing a significant methodological advancement over previous research. Ultrasound elastography provides a more precise assessment of hepatic steatosis compared to conventional ultrasound examinations. Additionally, the MASLD diagnostic criteria potentially encompass a broader spectrum of metabolic liver disease than traditional NAFLD definitions. These methodological innovations enhance the reliability and clinical relevance of our findings.

Several non-invasive biomarkers and indices, including the Fibrosis-4 Index (FIB-4) and the neutrophil-to-lymphocyte ratio (NLR), have been widely used for assessing fibrosis severity and systemic inflammation in patients with MASLD [22,23,24]. Compared to these indices, SUA offers distinct advantages as a metabolic marker due to its simplicity, cost-effectiveness, and availability in routine clinical practice. SUA levels can reflect metabolic dysfunction, a central component of MASLD, which is not directly captured by fibrosis- or inflammation-focused biomarkers. However, SUA also has limitations. Unlike the FIB-4 index, which provides insights into liver fibrosis severity, or NLR, which highlights systemic inflammation, SUA does not directly assess hepatic structural damage or inflammatory processes. This suggests the potential utility of integrating SUA with these established markers to improve risk stratification and diagnostic precision, although such approaches require further validation.

Our research also revealed a dose-response relationship between SUA levels and MASLD risk. This observation is consistent with the meta-analysis conducted by Sun et al., which similarly found a dose-response relationship between SUA levels and NAFLD risk [25]. This consistency further highlights the potential importance of SUA as a biomarker for MASLD. Nonetheless, our results regarding gender differences differ to some extent from earlier studies. We observed a stronger association between SUA and MASLD in females, although this difference did not reach statistical significance. In contrast, Sirota et al. reported similar association strengths in both genders [21], while Zheng et al. found higher SUA levels in males with NAFLD compared to females [13]. These disparities highlight the need for further investigation into potential gender-specific effects. While most observational studies, including ours, demonstrate an association between SUA and NAFLD/MASLD, the Mendelian randomization study by Li et al. presents an intriguing perspective. Their research suggests a potential causal effect of NAFLD on SUA levels, but not vice versa [26]. This finding emphasizes the complex relationship between SUA and fatty liver disease and underscores the need for further research to establish causality.

Our subgroup analyses revealed several notable variations in the association between SUA and MASLD. The stronger association observed in females (OR: 1.29, 95% CI: 1.15–1.45) compared to males (OR: 1.06, 95% CI: 0.96–1.17) warrants further investigation, although the interaction was not statistically significant (P for interaction = 0.0998). This gender difference may be related to hormonal influences on uric acid metabolism or gender-specific fat distribution patterns. The stronger association in participants aged < 60 years (OR: 1.19, 95% CI: 1.07–1.33) compared to those ≥ 60 years (OR: 1.11, 95% CI: 1.00-1.23) suggests that SUA may be a more sensitive marker for MASLD risk in younger adults. Interestingly, the association was strongest in the normal weight group (OR: 1.35, 95% CI: 1.12–1.63), highlighting the importance of considering SUA levels even in individuals without obesity. The significant interaction observed for diabetes history (P for interaction = 0.0026) is particularly noteworthy. The stronger association between SUA and MASLD in participants without a history of diabetes (OR: 1.21, 95% CI: 1.11–1.31) compared to those with diabetes (OR: 0.95, 95% CI: 0.81–1.13) suggests that SUA may be a more relevant risk factor in the early stages of metabolic dysfunction, before the onset of overt diabetes.

Several mechanisms may explain this association. Hyperuricemia is often associated with insulin resistance, a key feature of metabolic syndrome. Elevated uric acid levels can exacerbate insulin resistance, promoting hepatic fat accumulation and steatosis [27, 28]. The elevation of SUA levels can lead to insulin resistance through multiple mechanisms, including IRS2/AKT and Nrf2/HO-1 pathways [29, 30]. Additionally, high SUA levels can trigger inflammatory responses through NLRP3 inflammasome activation, leading to the release of pro-inflammatory cytokines that induce hepatocyte damage [28, 31]. Furthermore, hyperuricemia might induce the expression of hepatic inflammatory molecules by activating the proinflammatory NF-κB signaling cascade [32]. Uric acid also increases oxidative stress within the liver, damaging hepatocytes and activating hepatic stellate cells crucial in fibrogenesis [28, 33]. Specifically, hyperuricemia stimulates the renin-angiotensin system in blood vessels, leading to excessive production of reactive oxygen species (ROS) [34]. These ROS play a central role in the pathogenesis of MASLD [35]. Furthermore, elevated SUA can decrease nitric oxide availability, impairing hepatic blood flow and oxygen delivery, contributing to steatosis and fibrosis [27, 28].

These findings have important clinical implications. SUA monitoring offers a cost-effective and accessible approach to complement ultrasound elastography in MASLD screening, particularly in resource-limited settings. Unlike ultrasound elastography, SUA testing requires minimal equipment and can be easily implemented in primary care. SUA monitoring is a simple and affordable test that could be used as a first step to find people at higher risk of MASLD. Those with high SUA levels could then be referred for more detailed tests, like ultrasound elastography, to confirm the diagnosis, especially in places with limited medical equipment. The consistent association between SUA and MASLD across various subgroups supports the potential use of SUA as a biomarker for MASLD risk stratification. Clinicians should consider incorporating SUA measurements into routine health screenings, particularly for younger adults and those without overt metabolic disorders. For individuals with elevated SUA levels, more intensive monitoring for MASLD using ultrasound elastography may be warranted. Evidence highlights the importance of lifestyle interventions, such as dietary modifications and increased physical activity, as primary strategies to manage MASLD [36]. Lifestyle modification, including weight loss through hypocaloric diets and aerobic or resistance training, has been shown to significantly reduce hepatic steatosis and improve insulin sensitivity. Recent guidelines recommend achieving weight loss of ≥ 5% to reduce hepatic fat content and ≥ 10% to improve fibrosis [36]. In addition, restricting alcohol intake and managing coexisting metabolic disorders, such as diabetes and hypertension, are critical for MASLD management [37]. Furthermore, interventions aimed at lowering SUA levels, such as dietary modifications or pharmacological treatments, could potentially reduce MASLD risk. However, the efficacy of such interventions needs to be evaluated in prospective clinical trials.

Our study offers several novel contributions to understanding the SUA-MASLD relationship. First, we uniquely employed ultrasound elastography with CAP measurements in a Chinese population, providing more precise hepatic steatosis assessment than conventional ultrasound methods. Second, we specifically addressed MASLD using the latest 2023 diagnostic criteria, highlighting the recent shift in the classification of fatty liver disease from NAFLD to MASLD. Additionally, our comprehensive subgroup analyses revealed new insights into demographic and clinical variations in the SUA-MASLD association, particularly among females and individuals without diabetes, contributing valuable perspectives for risk stratification.

Several important limitations of this study should be acknowledged. First, this study is cross-sectional in nature, and SUA levels were measured at a single time point. While this approach captures the metabolic profile at a specific moment, fluctuations in SUA due to dietary intake, medications, or other variables may affect its long-term reliability as a biomarker for MASLD. Future longitudinal studies are needed to validate the temporal relationship between SUA and MASLD. Second, while we adjusted for multiple confounders, we were unable to account for certain dietary factors such as purine-rich food intake, sugar-sweetened beverage consumption, and specific dietary patterns that could influence both SUA levels and MASLD risk. Third, some potential unmeasured comorbidities, such as sleep apnea, polycystic ovary syndrome, and thyroid dysfunction, which may affect both SUA metabolism and hepatic steatosis, were not captured in our analysis. Fourth, genetic factors that could influence both SUA metabolism and MASLD susceptibility were not assessed in this study. Fifth, our study population was limited to Chinese adults from a single center, potentially limiting the generalizability of our findings to other ethnic groups and geographic regions. Furthermore, interventions targeting SUA reduction may offer promising strategies for MASLD prevention and management. Lifestyle modifications, including dietary adjustments to reduce purine, fructose, and alcohol intake, as well as increased physical activity, have been shown to effectively lower SUA levels.

The findings of this study highlight the potential utility of SUA monitoring as a cost-effective and readily available biomarker for MASLD. With the increasing prevalence of obesity and metabolic syndrome worldwide, particularly in developing countries experiencing rapid urbanization and dietary transitions, early identification of MASLD risk factors is critical for effective intervention. Integrating SUA assessments into routine health screenings could facilitate early detection and enable targeted interventions, such as lifestyle modifications and pharmacological treatments, to reduce MASLD incidence and progression. Furthermore, SUA monitoring may complement emerging non-invasive imaging techniques, such as transient elastography, providing scalable options for public health programs aiming to combat the rising burden of MASLD. Future longitudinal studies should evaluate the effectiveness of SUA monitoring combined with lifestyle interventions and SUA-lowering therapies to validate these approaches as preventive strategies for MASLD in high-risk populations.

In summary, our study provides robust evidence for a positive association between serum uric acid levels and MASLD risk in a Chinese adult population, as assessed by ultrasound elastography. The observed dose-response relationship and consistency across various subgroups underscore the potential utility of SUA as a biomarker for MASLD risk stratification. These findings highlight the importance of considering SUA levels in the clinical assessment and management of MASLD, particularly in younger adults and those without overt metabolic disorders. Future research should focus on elucidating the causal nature of this relationship and evaluating the efficacy of SUA-lowering interventions in MASLD prevention and management.