Exploring the Relationship Between Different Obesity Metabolism Indices and Hyperuricemia in Patients with Hypertension and Coronary Heart Disease

Introduction

Uric acid (UA), a purine metabolite, accumulates in peripheral blood when its production is upregulated or excretion is diminished, culminating in hyperuricemia.^1,2 The prevalence of this condition has been escalating in tandem with enhancements in living standards and shifts in lifestyle habits. Current estimates indicate that approximately 15% of the adult population in China is affected by hyperuricemia.^3,4 Hyperuricemia has emerged as a pivotal risk factor implicated in a spectrum of disorders, including gout, diabetes, chronic kidney disease, and cardiovascular diseases (CVD).^5–8 It has become a chronic disease that garners more public attention and poses a significant challenge to public health.⁹ Thus, early identification and management of hyperuricemia may reduce the disease burden and prevent adverse outcomes associated with it.

A substantial body of research has established the pertinence of UA to metabolic syndrome.^10–13 However, the specific contributions of various obesity-related metabolic indices to hyperuricemia are less well characterized. Emerging evidence underscores the potential significance of obesity and metabolic abnormalities in UA metabolism dysregulation, leading to hyperuricemia.^14–16 These conditions are also central to the pathophysiology of metabolic syndrome.^17,18 Traditionally, indicators for assessing obesity and metabolism have tended to be homogeneous and lacking in accuracy. Conversely, a suite of more accessible compound markers predicated on obesity and metabolic profiles has been proposed, encompassing the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), the triglyceride-glucose (TyG) index, the TyG to body mass index (TyG-BMI) ratio, TyG in conjunction with waist circumference (TyG-WC), the metabolic score for insulin resistance (METS-IR), and the metabolic score for visceral fat (METS-VF).^19–23

Individuals afflicted with hypertension and concomitant coronary heart disease (CHD) frequently exhibit obesity and a constellation of metabolic irregularities.^24–26 They are at an augmented risk for UA metabolism disturbances that may precipitate hyperuricemia, thereby exacerbating their susceptibility to CVD. While some studies have identified significant correlations between certain indicators, such as the TyG index or TG/HDL-C, and hyperuricemia, the clarity of these relationships within the high-risk group of patients with comorbid hypertension and CHD is yet to be established.^14,27,28 The identification of the most diagnostically valuable indicators for hyperuricemia in this population is an unresolved issue. To address this gap, we have conducted an extensive cross-sectional study to investigate the association between six obesity metabolism indices and hyperuricemia, as well as to evaluate their diagnostic accuracy.

Material and Methods

Study Population

This was a two-center study conducted at the Fifth Affiliated Hospital of Sun Yat-sen University and Suining City Central Hospital. The study population comprised individuals diagnosed with hypertension and CHD, enrolled from the Cardiovascular Center of the Fifth Affiliated Hospital of Sun Yat-sen University between January 2021 to September 2023, and from the Department of Cardiology of the Suining Central Hospital from March 2021 to July 2024. The initial enrollment yielded 7755 participants.

Exclusion criteria were applied to participants with incomplete basic measurement data, including BMI, triglycerides (TG), high-density lipoprotein (HDL), fasting plasma glucose (FPG), and WC, as well as those with severe renal dysfunction, hyperthyroidism, older than 75 years, excessive alcohol intake, or on chronic UA-lowering medication. Following these stringent exclusions, the final analysis included 6364 participants (Figure 1).

Figure 1 Flowchart for screening of study participants.

Ethical approval for this study was granted by the Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University (Approval No. 2022L047-1) and the Ethics Committee of Suining Central Hospital (Approval No. KYLLKS20240103). The study followed ethical guidelines in the Declaration of Helsinki, and all participants provided informed written consent.

Data Collection and Definitions

Demographic and clinical data were obtained from the electronic medical records and health insurance systems of the respective hospitals. These data included participants’ medical history, lifestyle habits, medication profiles, and laboratory results. Detailed methodology for various physical information is provided in the Supplementary Materials. Laboratory assessments included measurements of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (Cr), urea nitrogen (BUN), total cholesterol (TC), TG, HDL-C, low-density lipoprotein cholesterol (LDL-C), FPG, glycosylated hemoglobin (HbA1c), and UA, all of which were quantified using a fully automated biochemical analyzer. The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI formula. The Supplementary Materials provide detailed definitions and measurement procedures for the various diseases under investigation.

Calculation of Obesity Metabolism Indices

The following formulas were used to calculate various obesity metabolism indices, including TG/HDL-C ratio, TyG, TyG-BMI, TYG-WC, METS-IR, METS-VF:

TG/HDL-C = TG (mg/dL)/HDL-C (mg/dL).

TyG = ln[(TG (mg/dL) × FPG (mg/dL)/2)].

TyG-BMI = ln[(TG (mg/dL) × FPG (mg/dL)/2] × BMI.

TyG-WC = ln[(TG (mg/dL) × FPG (mg/dL)/2] × WC.

METS-IR = ln[2×FPG (mg/dL) + TG (mg/dL)] × BMI (kg/m²) / ln[HDL-C (mg/dL)].

METS-VF = 4.466 + 0.011 × (LnMETS-IR)³ + 3.239 × [Ln(WC/height)³ + 0.319 (male sex) + 0.594 × Ln(age)].

Outcome

Hyperuricemia was diagnosed based on the criteria defined by the Chinese Endocrine Association. The condition is identified when serum UA levels surpass 420 μmol/L (equivalent to 7.0 mg/dL) in males and 360 μmol/L (6.0 mg/dL) in females.^29–31

Statistical Analysis

Participants were categorized into two distinct groups relative to the presence of hyperuricemia. The variance inflation factor (VIF) was used to assess multicollinearity between predictors, and a VIF value less than 5 indicating no multicollinearity (Table S1). To evaluate the correlation between the six obesity metabolism indices and hyperuricemia, these indices were stratified into quartiles. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were computed using multivariate logistic regression analysis. Furthermore, a restricted cubic spline (RCS) model was applied to scrutinize potential nonlinear dose-response relationships between the indices and hyperuricemia. Ultimately, the diagnostic efficacy of these indices for hyperuricemia was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). For a detailed exposition of the statistical methods employed, the reader is referred to the Supplementary Material.

Results

Basic Characteristics of Participants

A total of 6364 participants with hypertension combined with CHD were included in the study, with 1488 participants diagnosed with hyperuricemia. Baseline results showed that participants with hyperuricemia tended to be relatively younger, more often male, and had significantly higher BMI, WC, and diastolic blood pressure (DBP) than those without hyperuricemia. Additionally, the test results indicated that ALT, AST, Cr, TC, TG, LDL-C, FPG, and UA levels were significantly higher in participants with hyperuricemia, whereas LDL-C and eGFR were relatively lower. Furthermore, patients with hyperuricemia had a higher prevalence of diabetes and hyperlipidemia and were more likely to take diuretics. Notably, the six obesity metabolic indices were also significantly higher in hyperuricemia participants compared to non-hyperuricemia participants (Table 1).

Table 1 Characteristics of the Study Population

Association Between Obesity Metabolic Indices and Hyperuricemia

Participants were stratified into quartiles based on the six obesity metabolic indices. The prevalence of hyperuricemia increased with higher quartiles, and the trend test was statistically significant (P<0.001) (Figure 2). Multivariate logistic regression analysis further showed that the risk of hyperuricemia gradually increased with higher levels of obesity metabolic indices such as TG/HDL-C, TyG, TyG-BMI, TyG-WC, METS-IR, and METS-VF. Specifically, compared to the first quartile (Q1) group, the ORs of the highest quartile (Q4) group were 3.94 (95% CI, 3.29–4.74), 4.37 (95% CI, 3.64–2.27), 7.79 (95% CI, 6.40–9.56), 6.53 (95% CI, 5.40–7.93), 5.06 (95% CI, 6.40–9.56), and 6.31 (95% CI, 5.17–7.74), respectively. Moreover, this relationship remained stable even in the fully adjusted model 4, with the Q4 group showing a 2.44, 3.59, 8.09, 5.13, 2.36, and 6.94-fold increase in disease risk, respectively, compared to the Q1 group (Table 2). Finally, the RCS results also demonstrated the gradually increasing dose-response relationship between the metabolic indexes and hyperuricemia (Figure 3).

Table 2 Relationship Between Obesity-Related Metabolic Indices and Hyperuricemia

Figure 2 Prevalence of hyperuricaemia according to quartiles of six obesity metabolism indices. Notes: (A) TG/HDL-C; (B) TyG; (C) TyG-BMI; (D)TyG-WC; (E) METS-IR; (F) METS-VF.

Figure 3 Dose-response association between obesity metabolism indices and risk of hyperuricemia. Notes: (A) TG/HDL-C; (B) TyG; (C) TyG-BMI; (D) TyG-WC; (E) METS-IR; (F) METS-VF.

Comparative Analysis of Six Obesity Metabolic Indices in Diagnosis of Hyperuricemia

To compare the diagnostic value of these six indicators for hyperuricemia, we first evaluated their diagnostic performance by calculating the area under the curve (AUC) of the ROC. As shown in Figure 4 and Table 3, the AUC of METS-VF (AUC=0.780) was the largest among the six indicators, followed by METS-IR (AUC=0.761), TyG-BMI (AUC=0.712), TyG-WC (AUC=0.700), and TyG (AUC=0.662), while TG/HDL (AUC=0.653) was the smallest among all indicators. This result was consistent across genders (Figure 4B and C).

Table 3 ROC Curve Analysis of Six Obesity-Related Metabolic Indices

Figure 4 Receiver operating characteristic (ROC) curves assessing the diagnostic utility of six obesity metabolic indices for hyperuricemia. Notes: (A) Total population; (B) Male population; (C) Female population.

We then further used DCA to compare the incremental benefits of the six obesity metabolic indices. The results remained consistent, with METS-VF showing the largest overall net benefit compared to the other five measures, while TG/HDL had the smallest benefit. This pattern held true both overall and when analysed independently by gender, further supporting the optimal diagnostic performance of METS-VF for hyperuricaemia (Figure 5A–C).

Figure 5 Decision curve analysis (DCA) for the diagnostic evaluation of hyperuricemia using obesity metabolic indices. Notes: (A) Total population; (B) Male population; (C) Female population.

Subgroup and Sensitivity Analysis

Considering the possible effects of different conditions on UA metabolism, we conducted a stratified analysis based on gender, age, hyperlipidemia, and diabetes. The results showed that even under different stratification conditions, the increase in obesity metabolic index was still independently associated with the occurrence of hyperuricemia (Figure 6). This indicates that the association is not affected by stratification conditions.

Figure 6 Association between obesity metabolism indices and hyperuricemia in various subgroups.

Furthermore, we performed a series of sensitivity analyses to assess the robustness of our findings. First, we excluded participants taking diuretics, considering the potential effect of diuretics on UA metabolism, and the results were consistent with the overall trend (Table S2). Next, since the UA metabolism of obese patients might be higher, we excluded obese patients with BMI > 30 kg/m², and the results remained stable (Table S3). Moreover, we excluded patients with cancer to account for the potential influence of anti-tumor drugs, and the results remained unchanged (Table S4). Finally, to address the possibility of unmeasured confounders, we conducted an E-value analysis, which showed that unmeasured confounders had little effect on our results (Table S5). Collectively, these analyses reinforce the conclusion that an escalation in obesity metabolism indices is concomitantly associated with an increased risk of hyperuricemia.

Discussion

In this large cross-sectional study, we found that six obesity metabolic indices were associated with hyperuricemia in hypertensive patients with CHD. Our analysis revealed a robust association, indicating that the risk of hyperuricemia escalates in tandem with elevated levels of obesity metabolism indices. Notably, the stratified analysis substantiated these findings, demonstrating that the upsurge in obesity metabolic markers is independently linked to the onset of hyperuricemia, irrespective of various stratified factors In a comparative analysis of the diagnostic efficacy of these metabolic indicators, the METS-VF indice, which integrates both visceral obesity and metabolic dysfunction, emerged as the most efficacious predictor of hyperuricemia. This underscores the potential of METS-VF as a reliable clinical tool for risk assessment in this patient population.

The implications of these findings are profound. Hyperuricemia, arising from a metabolic disorder of UA, is not merely a benign condition but is intricately linked to a spectrum of health complications.^5,6,32 Research indicates that hyperuricemia elevates the risk of cardiovascular disease (CVD) and all-cause mortality.³³ In adolescents, it is associated with the onset of early hypertension, and reducing UA levels might prevent the development and progression of hypertension.^34,35 Moreover, prolonged hyperuricemia can lead to phosphate deposition in bones and joints, resulting in bone destruction and promoting gout.⁵ Given the multifaceted ramifications of hyperuricemia, the emphasis on early detection and prevention cannot be overstated.

Previous studies have identified obesity as a major risk factor for insulin resistance (IR) and metabolic dysfunction associated with type 2 diabetes.³⁶ At the same time, one study has identified IR in obesity as a possible pathway to macrovascular disease.³⁷ Individuals afflicted with hypertension and CHD frequently exhibit comorbidities such as obesity and metabolic dysregulations, which are known to amplify the risk of cardiovascular events.^38–41 When combined with hyperuricemia, this group may face an even higher risk of future cardiovascular events and mortality. However, the relationship between obesity, metabolism, and hyperuricemia has not been thoroughly studied in this population, nor have studies comprehensively compared the predictive performance of various indicators. Therefore, we selected this high-risk population to further explore the relationship between obesity, metabolism, and hyperuricemia and to evaluate the best diagnostic indicators. This aims to provide a more in-depth basis for the future management of hyperuricemia and the prevention of worsening cardiovascular events and mortality.

High metabolism and obesity are the strongest indicators of metabolic syndrome.^42–44 Previous studies have highlighted that metabolic syndrome increases the risk of several diseases, including hyperuricemia.^45–47 Research on the relationship between dietary patterns, obesity, and hyperuricemia in the United States showed that while the direct effect of diet on hyperuricemia was weak, obesity played a key mediating role in this relationship.⁴⁸ Additionally, two national surveys in China identified a high BMI as a major risk factor for hyperuricemia, directly pointing to the role of obesity in its development.⁴⁹ At the same time, metabolic factors are also indispensable.^16,50,51 For example, a Mendelian randomized study demonstrated a strong association between hyperuricemia and IR syndrome, where hyperinsulinemia leads to hyperuricemia but not vice versa.¹⁶ Similarly, research on the relationship between hyperuricemia, IR, and the risk of hypertension emphasized that IR is associated with the development of hyperuricemia and may also partially mediate the effects of uric acid on hypertension.⁵⁰ Together, these studies support the important role of metabolic functions, represented by obesity and insulin resistance, in the development of hyperuricemia.

In the realm of metabolic health, numerous indices have been developed to evaluate obesity metabolism. The advent of novel surrogates, based on straightforward anthropometric and biochemical indicators such as TG/HDL-C, the TyG index, TyG-BMI, TyG-WC, METS-IR, and METS-VF, has garnered widespread clinical application.^23,52–55 Prior research has consistently demonstrated that these six alternative markers are profoundly correlated with metabolism and obesity, and their utility as reliable indicators of metabolic syndrome has been affirmed.^{1–19,20,56,57} Our study corroborates and extends these findings, positing that these metabolic signifiers of obesity are independently correlated with hyperuricemia.^58–61 In a national cohort study of middle-aged and older adults in China, elevated baseline values of several IR alternatives, including TyG, TG/HDL, METS-IR, and TyG-BMI, were significantly associated with an increased risk of hyperuricemia. Compared to individuals with consistently low IR surrogate levels, those who transitioned from low to high IR levels and those who consistently maintained high IR levels had a significantly higher risk of developing hyperuricemia.^58,59 This relationship has also been demonstrated in other populations. For instance, a study among non-diabetic adults in the United States established a positive correlation between the risk of hyperuricemia and heightened levels of TyG, TyG-BMI, TG/HDL-C, and METS-IR.⁶⁰ Similarly, this relationship between obesity metabolism indice and hyperuricemia has been observed in Iranian populations.⁶¹

The specific relationship between obesity metabolism and hyperuricemia is complex, involving various metabolic pathways and mechanisms that may promote hyperuricemia (Figure S1). First, obesity is frequently comorbid with lipid metabolism disorders, such as hypertriglyceridemia, which can impair the renal excretory capacity for UA, leading to its accumulation within the body.^62–64 Additionally, IR, a condition often observed in obesity, influences UA metabolism by augmenting its renal reabsorption, thus reducing UA excretion and contributing to the development of hyperuricemia.^16,58,65,66 The role of visceral fat accumulation is also significant, as it not only increases the de novo synthesis of UA but also enhances its production through the secretion of free fatty acids and pro-inflammatory cytokines by adipocytes in visceral fat depots.^67–69 Finally, the chronic inflammatory state associated with obesity metabolism is a major etiological factor. This inflammation can stimulate UA synthesis and concurrently attenuate its renal clearance, further propagating the hyperuricemic state.^70–73

Our study is the first to explore the relationship between six kinds of obesity metabolic indices and hyperuricemia in a high-risk population with hypertension combined with CHD. Notably, our analysis has identified the METS-VF indice, indicative of visceral obesity, as possessing the most robust diagnostic utility. This significant finding is underpinned by rigorously applied inclusion and exclusion criteria, coupled with a comprehensive suite of statistical methodologies. Despite these strengths, it is imperative to acknowledge the limitations inherent to our study. First, the cross-sectional nature of our study can only establish correlations and cannot capture longitudinal effects. Second, our research did not incorporate data pertaining to dietary patterns and levels of physical activity, both of which are recognized to exert influence on UA metabolism, and thus, these factors warrant consideration in forthcoming studies. Third, although our sample was drawn from two distinct centers in southern China, the geographic and demographic homogeneity of our participants may limit the generalizability of our results to other populations. Therefore, caution must be exercised when attempting to extrapolate these findings to different ethnic, geographic, or demographic groups. Lastly, despite our comprehensive approach to adjusting for potential confounding factors, there remains the possibility that unidentified confounders could influence the observed associations. The strength of our study is somewhat mitigated by these limitations; however, the results of our E-value analysis suggest that the likelihood of our conclusions being significantly overturned is minimal. Future research should aim to replicate our findings in more diverse populations and over longer periods to better understand the causal relationships between obesity metabolism and hyperuricemia.

Conclusion

This study was the first to explore the relationship between various obesity metabolic indices and hyperuricemia in patients with hypertension and CHD. The results showed that the risk of hyperuricemia increased with higher obesity metabolic indices. The METS-VF index, integrating visceral obesity and metabolism, offers optimal diagnostic potential. These findings are instrumental for early clinical intervention, underscoring the need to address abdominal obesity and metabolic health. Further research is essential to confirm these insights.