Hyperuricemia (HUA) has emerged as one of the fastest-growing metabolic disorders worldwide in recent years, with a prevalence exceeding 14% among Chinese adults and showing a continuous upward trend (1, 2). As a direct manifestation of purine metabolism disorders, HUA not only serves as the major pathological basis of gout but is also closely associated with insulin resistance, metabolic syndrome, NAFLD, and cardiovascular diseases (3–5). Increasing evidence suggests that elevated serum uric acid (SUA) levels are not merely correlated with metabolic abnormalities but may also be involved in their progression, although the directionality of these associations remains incompletely understood (6). Therefore, identifying and understanding the key determinants of HUA are of great significance for the early prevention and control of metabolic diseases.

Obesity is recognized as a major risk factor for HUA; however, the metabolic consequences of obesity vary considerably among individuals (7, 8). Some individuals with obesity maintain normal metabolic profiles despite excessive body weight and are defined as having metabolically healthy obesity (MHO), whereas others exhibit dyslipidemia, insulin resistance and chronic low-grade inflammation, and are categorized as having metabolically unhealthy obesity phenotypes, such as metabolically unhealthy generalized obesity (MUGO) and metabolically unhealthy abdominal obesity (MUAO) (9). In recent years, researchers have increasingly acknowledged that metabolic phenotypes, rather than BMI alone, may better capture the heterogeneity of obesity-related disease risks (10, 11). Nevertheless, findings regarding the association between different metabolic obesity phenotypes and HUA remain inconsistent (12), and evidence from specific occupational populations is still limited.

The liver is the primary organ responsible for purine metabolism and uric acid production, and its functional state is closely associated with the dynamic balance between uric acid synthesis and excretion (13). Liver enzymes such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and γ-glutamyltransferase (GGT) are sensitive biochemical indicators reflecting hepatic metabolic activity and cellular injury (14, 15). Elevated liver enzyme levels not only indicate increased hepatic metabolic burden but are also closely associated with insulin resistance and lipid metabolism disorders (15, 16). Previous studies have suggested that hepatic dysfunction may be associated with altered purine metabolism and uric acid handling, potentially contributing to elevated SUA levels (17). Conversely, accumulating evidence also indicates that HUA itself may exacerbate hepatic oxidative stress and inflammation, suggesting a possible bidirectional relationship between liver enzyme elevation and HUA. However, studies investigating the modifying role of liver enzymes in the relationship between metabolic obesity phenotypes and HUA are still scarce.

Occupational and environmental factors may further influence these metabolic processes. Oilfield workers are often exposed to high physical workload, night-shift schedules, and irregular dietary and lifestyle patterns, all of which are associated with energy metabolism disturbances, fat accumulation, and liver dysfunction, thereby potentially increasing the risk of HUA (18, 19). Although previous studies have suggested a significantly higher prevalence of HUA among oilfield workers compared with the general population (20), the specific metabolic characteristics of this population and the underlying role of liver enzymes remain poorly understood.

Therefore, this study aimed to investigate the association between different metabolic obesity phenotypes and HUA among oilfield workers and to further evaluate the potential effect-modifying role of liver enzymes in this relationship. Given the cross-sectional design, this study focuses on identifying associations rather than inferring causality. By elucidating the interacting mechanisms linking hepatic metabolism, obesity phenotypes, and uric acid regulation, this study seeks to provide scientific evidence for risk stratification and early intervention of metabolic disorders in occupational populations.

In this study conducted among oilfield workers, we systematically examined the associations between different obesity–metabolic phenotypes and HUA, and further explored the modifying role of liver enzymes in these relationships. Our findings indicate that obesity, particularly metabolically unhealthy obesity phenotypes (MUGO and MUAO), is significantly associated with higher odds of HUA, and that elevated liver enzyme levels further strengthen these associations. It should be noted that, given the cross-sectional design of this study, causal direction cannot be determined, and the relationships among liver dysfunction, insulin resistance, and hyperuricemia are likely bidirectional.

In our population, individuals with HUA were characterized by younger age, a higher proportion of males, lower educational attainment, and shorter job tenure. Moreover, they exhibited higher prevalence of both GO and AO, as well as less favorable metabolic profiles (e.g., BP, blood lipids) and liver enzyme levels compared with non-HUA individuals. These patterns are closely related to the specific occupational environment of oilfield workers: jobs such as oil extraction are characterized by high physical workload (27), shift work easily disrupts regular eating and sleeping patterns (28), the workforce is predominantly male (29, 30), and lifestyle factors shaped by traditional occupational culture (e.g., social drinking, insufficient physical activity) further increase the risks of obesity and metabolic disorders, thereby predisposing this group to a higher burden of HUA.

We found that both GO and AO were significantly correlated with higher odds of HUA. This is consistent with previous evidence that obesity can promote purine synthesis, reduce renal uric acid excretion, and induce insulin resistance, ultimately leading to elevated SUA levels (31). In addition, our results showed a sustained positive association between BMI and HUA, whereas the association between WC and HUA appeared nonlinear. This non-linear pattern may reflect several factors: a “tail effect” due to a smaller number of individuals with extremely high WC, potential behavioral or clinical interventions such as urate-lowering therapy in the most obese participants, or physiological phenomena sometimes referred to as the “obesity paradox,” in which very high abdominal adiposity does not linearly increase risk due to adaptive metabolic or pharmacological effects (32). This discrepancy likely reflects the distinct biological meanings of these anthropometric indicators (33): BMI primarily reflects overall adiposity and is more directly and linearly related to total metabolic load; WC reflects abdominal adiposity, and its relationship with HUA is influenced by visceral fat distribution, local inflammatory cytokine release, and other complex factors, with potential inter-individual variability (34). These findings suggest that, when assessing obesity-related HUA risk in occupational populations, it is important to integrate multiple anthropometric indices such as BMI and WC to more accurately identify high-risk individuals.

A substantial body of literature has confirmed that individuals with obesity may present with heterogeneous metabolic phenotypes (35–37). In our analysis based on obesity phenotypes defined jointly by BMI/WC and metabolic health status, we observed that metabolically unhealthy obese phenotypes were strongly associated with an elevated risk of HUA, whereas metabolically healthy obese phenotypes showed associations only in some models or even no significant increase in risk. These findings support the pathological heterogeneity between MHO and metabolically unhealthy obesity phenotypes (MUGO and MUAO): obesity per se is not the direct determinant of HUA; rather, metabolic dysregulation constitutes the core driving mechanism (38). Metabolic derangements may promote the development of HUA via multiple pathways, such as insulin resistance–induced reductions in renal tubular uric acid excretion (39), dyslipidemia–mediated increases in hepatic purine metabolic load (40), and chronic inflammation–driven enhancement of uric acid production (41). Overall, metabolic status is a key modifier of obesity-related disease risk.

Of particular note, we observed a pronounced association between abnormal liver enzymes and metabolically unhealthy obesity phenotypes (MUGO and MUAO) in relation to HUA, rather than implying direct causation. Among individuals with elevated liver enzyme levels (defined as having at least one of ALT, AST, ALP, or GGT above the reference range, to capture overall hepatic dysfunction while avoiding sparse subgroup data), the risks of HUA in those with MUGO and MUAO were further increased. A plausible mechanism is that overweight and obesity contribute to fatty liver and hepatic steatosis, accompanied by oxidative stress and inflammation in adipose tissue. As the central organ of uric acid metabolism (42), the steatotic liver is prone to hepatocellular injury, with elevated serum levels of ALT, AST and GGT representing potential manifestations of hepatic metabolic stress (43–45). Hepatocellular injury may contribute to insulin resistance, and conversely, HUA itself can induce oxidative stress and inflammatory responses that further impair liver function, suggesting a bidirectional interaction rather than a strictly unidirectional causal pathway (46, 47). Thus, the observed “vicious cycle” likely reflects complex, reciprocal interactions between liver dysfunction, insulin resistance, and hyperuricemia, rather than a simple linear sequence of events. Liver-injury–induced insulin resistance may exert a dual impact on uric acid metabolism: on the one hand, it reduces renal uric acid excretion (48), leading to increased SUA levels, consistent with the findings of Molla and colleagues (26, 49); on the other hand, it activates the hexose monophosphate shunt (pentose phosphate pathway) (50), thereby promoting purine synthesis and further increasing SUA levels, which in turn aggravates hepatic fat accumulation (51). Simultaneously, insulin resistance disrupts glucose and lipid metabolism, exacerbating global metabolic disturbances (52). Against this background of metabolic dysregulation, HUA can further progress; in addition, HUA itself induces oxidative stress in adipose tissue and triggers adipose inflammation, thereby aggravating insulin resistance (53). Persistent metabolic disturbances continuously impair liver function and worsen hepatic injury, which is difficult to reverse due to the ongoing presence of obesity and metabolic abnormalities. Ultimately, this may lead to a vicious cycle in which “obesity aggravates hepatic injury, hepatic injury promotes insulin resistance, insulin resistance worsens metabolic dysfunction, and metabolic dysfunction is associated with HUA while reinforcing each upstream step.” These results suggest that abnormal liver enzymes may constitute a key pathological hub linking MUGO, MUAO and HUA.

The present findings have important implications for metabolic health management in oilfield workers. First, routine monitoring of liver function should be incorporated into occupational health examinations to facilitate early identification of individuals at elevated metabolic risk. Second, for workers with metabolically unhealthy obesity phenotypes (MUGO and MUAO), interventions should not be limited to weight control but should also target metabolic improvement and liver function, including optimization of dietary patterns, reduction of night-shift exposure and promotion of regular physical activity, in order to reduce the risk of HUA and related metabolic diseases. Finally, future occupational health strategies should aim to develop comprehensive risk assessment models that integrate body weight, metabolic status and liver function indices, thereby providing a more accurate basis for precision prevention in this population.

Several limitations of this study should be acknowledged. First, the cross-sectional design precludes causal inference regarding the relationships among obesity–metabolic phenotypes, liver enzymes, and HUA, and reverse causality cannot be excluded. Second, although a proportion of participants were excluded because of missing anthropometric data, supplementary analyses indicated that the included participants were broadly representative of the eligible population; nevertheless, some degree of residual selection bias cannot be ruled out. Third, lifestyle factors were assessed in a relatively crude manner. In particular, alcohol consumption was collected only as a binary variable, without quantification of intake volume or differentiation by beverage type (e.g., beer vs. spirits). Similarly, other lifestyle factors, including diet composition and physical activity intensity, were not quantitatively assessed. Given that hyperuricemia is strongly influenced by purine-rich foods, fructose intake, and the quantity and type of alcohol consumed, these limitations may affect the interpretation of the observed associations between liver enzymes, metabolic status, and HUA. In addition, information on the use of urate-lowering therapy or medications that may influence liver enzyme levels was unavailable, which could have resulted in residual confounding. Finally, as the study population consisted of actively employed oilfield workers from a single enterprise, a healthy worker effect may exist, potentially leading to an underestimation of the prevalence of HUA and related metabolic abnormalities and limiting the generalizability of the findings. Future prospective, multi-center studies with more detailed exposure assessment and comprehensive lifestyle characterization are warranted.

In this occupational population, obesity—particularly metabolically unhealthy general and abdominal obesity—was significantly associated with an increased risk of hyperuricemia, while elevated liver enzyme levels were independently related to HUA. BMI showed a linear association with HUA, whereas waist circumference exhibited a nonlinear relationship, suggesting distinct roles of general and abdominal adiposity. These findings underscore the importance of hepatic metabolic dysfunction in the obesity–HUA link. Integrated monitoring of adiposity, metabolic phenotypes, and liver function may facilitate early identification and prevention of HUA in high-risk working populations.