Analysis of Risk Factors for Hypertension Comorbidity in Patients with Gout: A  Retrospective Study

Analysis of Risk Factors for Hypertension Comorbidity in Patients with Gout: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Risk Factors for Hypertension Comorbidity in Patients with Gout: A Retrospective Study Jin-fu Hu, Pan Ge, Yin-hui Du, Xiang Zhang, Ji-wen Liu, Miao-miao Wen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9083729/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Although the comorbidity of gout and hypertension is well-established, the independent predictors for hypertension in gout patients remain insufficiently investigated. This study was designed to identify such factors for hypertension in patients with primary gout. Methods In this retrospective study, 253 patients were recruited from the Rheumatology and Immunology outpatient clinic of Xi'an No.5 Hospital between March 2024 and July 2025. Data concerning demographics, biochemical indicators, and comorbidities, were collected. The analysis involved descriptive statistics, chi-square tests, and univariate and multivariate logistic regression to estimate odds ratios (ORs) and confidence intervals (CIs), followed by model diagnostics and sensitivity analyses. Results The findings demonstrated that age of onset (OR = 1.064, 95% CI: 1.035–1.094), Fasting blood glucose (FPG) (OR = 1.277, 95% CI: 1.010–1.614), 24-hour urinary uric acid excretion (UUE) (OR = 1.002, 95% CI: 1.000–1.003), creatinine clearance rate (Ccr) (OR = 1.017, 95% CI: 1.005–1.030), and serum creatinine (SCr) (OR = 1.035, 95% CI: 1.012–1.058) were significantly associated with hypertension in gout patients (all P < 0.05). The model demonstrated good fit (the Hosmer-Lemeshow, P = 0.458). Conclusions The study highlight aging, hyperglycemia, and abnormal renal function indicators as independent risk factors for hypertension in individuals with gout, suggesting that closer monitoring and early intervention may improve clinical outcomes. Gout Hypertension Hyperuricemia Logistic regression Risk factors Figures Figure 1 Figure 2 Figure 3 1. Background Gout is a chronic metabolic disease caused by disturbance of purine metabolism or impaired uric acid excretion, it is characterized by the supersaturation and deposition of monosodium urate (MSU) crystals in joints and various organ tissues, potentially causing tophi, bone destruction, joint deformities and renal damage. As the disease progresses, it may also be accompanied by complications such as diabetes and cerebro-cardiovascular diseases. The incidence and prevalence of gouty arthritis have been increasing annually (Sun, 2019)Gout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of an increased risk for cardiovascular incidents (including fatal cardiac incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore, effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity. This retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies. Gout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension. It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li. (Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels. The association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research. Additionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients. This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension (Del Pinto R, 2021). Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017). The results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other. Although univariate analysis identified significant associations between multiple indicators (e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients. The model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias. In conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms. Conclusion Gout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of an increased risk for cardiovascular incidents (including fatal cardiac incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore, effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity. This retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies. Gout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension. It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li. (Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels. The association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research. Additionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients. This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension (Del Pinto R, 2021). Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017). The results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other. Although univariate analysis identified significant associations between multiple indicators (e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients. The model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias. In conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms. Conclusion Gout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of an increased risk for cardiovascular incidents (including fatal cardiac incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore, effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity. This retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies. Gout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension. It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li. (Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels. The association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research. Additionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients. This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension (Del Pinto R, 2021). Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017). The results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other. Although univariate analysis identified significant associations between multiple indicators (e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients. The model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias. In conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms. Conclusion Gout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of an increased risk for cardiovascular incidents (including fatal cardiac incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore, effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity. This retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies. Gout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension. It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li. (Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels. The association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research. Additionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients. This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension (Del Pinto R, 2021). Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017). The results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other. Although univariate analysis identified significant associations between multiple indicators (e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients. The model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias. In conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms. Conclusion Gout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of an increased risk for cardiovascular incidents (including fatal cardiac incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore, effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity. This retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies. Gout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension. It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li. (Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels. The association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research. Additionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients. This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension (Del Pinto R, 2021). Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017). The results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other. Although univariate analysis identified significant associations between multiple indicators (e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients. The model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias. In conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms. Conclusion . According to the 2017 Global Burden of Disease (GBD) study, the global prevalence of gout was 7.9‰ in males and 2.5‰ in females (FitzGerald, 2020). A cross-sectional survey from 31 provinces in China reported an incidence ranging from 0.03% to 15.3% among adults, with prevalence rates of 4.4% and 2.0% for males and females, respectively. In the United States, the NHANES 2007–2008 data indicated a high prevalence of comorbidities among individuals with gout, including hypertension, which was present in up to 74% of gout patients, underscoring the significant clinical overlap between these conditions (Zhu Y, 2012) . Uric acid is the end product of purine metabolism in the body, the uricase can oxidize uric acid into the easily soluble allantoin. However, during the evolutionary process of primates, the activity of uricase was lost due to gene mutations causing to the accumulation of uric acid in humans(Zhao, 2022).It is primarily produced by the liver, with two-thirds excreted by the kidneys and one-third through the intestines. Hyperuricemia (HUA) arises from either overproduction or underexcretion of uric acid, with underexcretion being the main cause. Hyperuricemia is the fundamental cause of gout development. In recent years, with the improvements in living standards and changes in lifestyle, the prevalence of hyperuricemia has been increasing annually. Statistics indicate that the total prevalence of hyperuricemia in China is approximately 14%(Liu, 2014), posing a significant threat to public health. Hypertension serves as both a risk factor for gout and one of the most common complications, often interacting with gout and hyperuricemia in a bidirectional manner.Potential mechanism may involve that uric acid, an extracellular antioxidant in hyperuricemia, can promote endothelial dysfunction through xanthine oxidase-related oxidative stress, as well as by increasing salt sensitivity and lipogenesis, thereby contributing to atherosclerosis and ischemic heart disease (De Becker, 2019; Furuhashi, 2020; Grayson, 2011;). Controlling serum uric acid levels may benefit hypertension management(Gibson, 2013; Lin KH, 2021).Conversely, hypertension can damage glomerular arteries, glomerulosclerosis, and a reduced filtration rate, which subsequently results in renal insufficiency and hyperuricemia (McAdams‐DeMarco, 2012). A prospective cohort study further confirmed a bidirectional association between hypertension and gout, with hypertensive patients having an 88% increased risk of developing gout, and gout patients having an 18% increased risk of developing hypertension (Pan, 2015) . However, the causal nature of the relationship between uric acid and hypertension remains a subject of debate. A Mendelian randomization study in a Chinese female population found no evidence supporting serum uric acid(SUA) as a causal risk factor for hypertension, suggesting that the observed epidemiological associations might be confounded by other shared metabolic abnormalities (L. Wang, 2020). Therefore, rather than establishing causality, this study primarily aims to analyze the prevalence of hypertension in gout patients and explore its risk factors to provide a basis for clinical prevention and treatment. 2. Materials and Methods 2.1 Study Population We conducted a retrospective study that enrolled 253 patients from the Rheumatology and Immunology outpatient clinic of Xi'an No.5 Hospital between March 2024 and July 2025.The participant screening process is illustrated in Fig. 1 . Inclusion criteria: Age ≥ 18 years; Definite diagnosis of gout according to the 2015 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria(Neogi, 2015). Exclusion criteria: Patients with severe diseases such as immunodeficiency, hematological diseases, or malignant tumors; Patients with incomplete clinical data; Patients who had taken medications affecting uric acid metabolism (e.g., aspirin, diuretics) within the three months preceding enrollment. Ultimately, a total of 253 patients with primary gout were included (see Fig. 1 ). All patients and their families were informed about the study and provided signed informed consent. All patient biodata were kept confidential. The study was reviewed and approved by the Medical Ethics Committee of Xi'an No.5 Hospital (Approval No. 2025 − 110). Hypertension diagnosis: Clinic systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg without antihypertensive medication use, or previous diagnosis of hypertension(J.-G. Wang, 2025 ). Dyslipidemia diagnosis: Based on the 2018 AHA / ACC / AACVPR / AAPA / ABC / ACPM / ADA / AGS / APhA / ASPC / NLA / PCNA Guideline on the Management of Blood Cholesterol: total cholesterol (TC) ≥ 5.17 mmol/L and/or triglycerides (TG) ≥ 1.70 mmol/L(Grundy SM, 2019). Tophus diagnosis: White or pale yellow abnormal protrusions or masses around joints, tendons, or subcutaneous soft tissues, with a stone-like, hard texture(Neogi, 2015). Drinking history was defined as: continuous or cumulative alcohol consumption for over 6 months, with a frequency of at least once per week. Smoking history was defined as: continuous or cumulative smoking for over 6 months, with a frequency of at least once per day. Obesity diagnosis: Based on Chinese Body Mass Index (BMI) criteria, BMI ≥ 28.0 kg/m² was diagnosed as obesity(Wang, J., 2018). Diagnosis of fatty liver, renal cysts, kidney stones: Based on imaging examinations such as X-ray, ultrasonography, or computed tomography (CT). 2.2 Collection of Demographic and Clinical Characteristics Data A power analysis was conducted with 80% power and a 0.05 significance level to calculate the required sample size. Data were collected from gout patients with comorbid hypertension to assess their risk factors including gender, age, height, weight, BMI, SBP, DBP, smoking history, alcohol history, disease duration, and history of comorbidities such as hypertension, renal insufficiency, and kidney stones, etc. Venous blood samples were collected from patients after an overnight fast (≥ 8 hours). Fasting blood glucose (FPG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), 24-hour urinary uric acid excretion (UUE), serum uric acid (SUA) and other relevant parameters were measured using an automated biochemical analyzer. 2.3 Calculation formulas The following parameters were calculated using standard formulas: BMI = weight (kg) / [height (m)] ² Creatinine Clearance Rate (Ccr) was estimated using the Cockcroft-Gault formula: (140 - age) × weight (kg) / [serum creatinine (µmol/L) × 0.818] × gender coefficient (1.0 for male, 0.85 for female). Uric Acid Clearance (CUA) = [24-hour urinary uric acid (µmol/L) × 24-hour urine volume (L)]/ serum uric acid (µmol/L) Fractional Excretion of Uric Acid (FEUA) = (24h-hour urinary uric acid excretion × serum creatinine) / (serum uric acid × 24-hour urinary creatinine excretion) ×100%. 2.4 Statistical Analysis Data were double-entered, checked, verified, and corrected using EpiData 3.1 software (EpiData Association, Østerbrogade 158, 1.sal, 2100 København Ø, Denmark). Statistical analysis was performed using SPSS 18.0 software (SPSS Inc., Chicago, IL, USA). Quantitative data conforming to normal or approximately normal distribution were described as mean ± standard deviation (X̄ ± s); skewed distributions were described using median (M) and interquartile range ( P₂₅, P₇₅ ). Qualitative data were described using counts and proportions. Independent samples t-test and Mann-Whitney U test were used for comparisons between two groups for quantitative data; ANOVA was used for comparisons among multiple groups for quantitative data; χ² test or Fisher's exact test (when the number of cells with expected frequency < 5 exceeded 20% or when any theoretical frequency was < 1) was used to compare differences between groups for qualitative data. Univariate and multivariate logistic regression analyses were used to explore risk factors for hypertension in gout patients, expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Model goodness-of-fit was assessed using the Hosmer-Lemeshow test ( P > 0.05). The significance level was set at α = 0.05, and P < 0.05 was considered statistically significant. 3. Results 3.1. Baseline Characteristics of Gout Patients This study included 253 gout patients. Their baseline characteristics were as follows (Table 1 ): mean age was 46.04 ± 15.679 years, with 1.2% (3 cases) being female. Systolic blood pressure (SBP) was 137.51 ± 26.257 mmHg, diastolic blood pressure (DBP) was 89.57 ± 16.914 mmHg, age of onset was 37.94 ± 15.111 years, and body mass index (BMI) was 26.80 ± 4.19 kg/m². Regarding inflammatory indicators, erythrocyte sedimentation rate (ESR) was 29.67 ± 24.213 mm/h, and C-reactive protein (CRP) was 23.99 ± 28.98 mg/L. Fasting blood glucose (FPG) was 5.40 ± 1.36 mmol/L, total cholesterol (TC) was 4.46 ± 1.08 mmol/L, triglycerides (TG) were 2.15 ± 1.36 mmol/L, low-density lipoprotein (LDL) was 2.69 ± 0.79 mmol/L, and high-density lipoprotein (HDL) was 0.91 ± 0.22 mmol/L. Among renal function-related indicators, mean estimated glomerular filtration rate (eGFR) was 95.40 ± 23.63 mL/min/1.73 m², 24-hour urinary uric acid excretion (UUE) was 504.59 ± 245.87 mmol/24h, fractional excretion of uric acid (FEUA) was 4.38 ± 1.77%, uric acid clearance (CUA) was 10.70 ± 8.12 ml/min, creatinine clearance rate (Ccr) was 115.23 ± 42.04 ml/min, serum uric acid (SUA) was 510.52 ± 135.74 µmol/L, serum creatinine (SCr) was 86.31 ± 28.83 µmol/L, and blood urea nitrogen (BUN) was 5.40 ± 4.04 mmol/L. Regarding lifestyle, 43.5% (110 cases) had a smoking history, and 52.6% (133 cases) had a drinking history. Regarding complications, tophi occurrence was 48.2% (122 cases), double contour sign was 18.2% (46 cases), obesity was 33.6% (85 cases), nephrolithiasis was 6.3% (16 cases), renal cyst was 19.0% (48 cases), and fatty liver was 51.0% (129 cases). Table 1 Baseline characteristics of gout patients. Variable \(\stackrel{\text{-}}{\text{X}}\text{±}\text{s}\) (n=253) Age, years 46.04 ± 15.679 Gender, Female 3(1.2%) SBP, mmHg 137.51 ± 26.257 DBP, mmHg 89.57 ± 16.914 Age of Onset, years 37.94 ± 15.111 BMI, kg/m² 26.80 ± 4.194 ESR, mm/h 29.67 ± 24.213 CRP, mg/L 23.99 ± 28.979 FBG, mmol/L 5.40 ± 1.360 TC, mmol/L 4.46 ± 1.076 TG, mmol/L 2.15 ± 1.360 LDL, mmol/L 2.69 ± 0.789 HDL, mmol/L 0.91 ± 0.216 eGFR, mL/min/1.73 m² 95.40 ± 23.63 UUE, mmol/24h 504.59 ± 245.869 FEUA, % 4.38 ± 1.769 CUA, ml/min 10.70 ± 8.122 Ccr, ml/min 115.23 ± 42.042 SUA, µmol/L 510.52 ± 135.738 SCr, µmol/L 86.31 ± 28.831 BUN, mmol/L 5.40 ± 4.044 Smoking, n (%) 110(43.5%) Drinking, n (%) 133(52.6%) Tophi, n (%) 122(48.2%) Double Contour Sign, n (%) 46(18.2%) Obesity, n (%) 85(33.6%) Nephrolithiasis, n (%) 16(6.3%) Renal Cyst, n (%) 48(19.0%) Fatty Liver, n (%) 129(51%) 3.2. Descriptive Statistics of General Data and Biochemical Indicators in the Hypertension Group vs. Normotensive Group among Gout Patients A total of 253 gout patients were included in this study, comprising 128 patients in the normotensive group and 125 in the hypertension group. Statistically significant differences ( P < 0.05) were found between the two groups in age, SBP, DBP, age of onset, BMI, FBG, glomerular filtration rate, FEUA, Ccr, BUN, abnormal FEUA, abnormal SCr, abnormal SUA, and the incidence of renal cyst, as detailed in Table 2 . Specifically, the hypertensive group had significantly higher values for age, SBP, DBP, age of onset, BMI, FBG, BUN, and FEUA compared to the normotensive group ( P < 0.05), while the eGFR and Ccr were significantly lower ( P < 0.05). The hypertensive group had significantly higher rates for abnormal FEUA, abnormal SCr, and renal cyst ( P < 0.05), but the rate of abnormal SUA was significantly lower ( P 0.05) were found between the two groups in gender, ESR, CRP, TC, TG, LDL, HDL, UUE, CUA, SUA, SCr, abnormal CRP, abnormal ESR, abnormal UUE, abnormal BUN, smoking history, drinking history, tophi, double contour sign, joint symptoms, obesity, dyslipidemia, Nephrolithiasis, fatty liver, and comorbidity status. Table 2 Comparison of baseline data between the hypertension group and normotensive group in gout patients. Variable Normotensive Group (n = 128) Hypertensive Group (n = 125) P Age, years 42.51 ± 15.118 49.66 ± 15.474 0.000*** Gender, Female 1(0.8%) 2(1.6%) 0.619 SBP, mmHg 119 ± 11.240 155.71 ± 24.735 0.000*** DBP, mmHg 78.28 ± 8.556 101.14 ± 15.493 0.000*** Age of Onset, years 34.31 ± 14.063 41.66 ± 15.294 0.000*** BMI, kg/m² 26.42 ± 4.634 27.19 ± 3.727 0.000*** ESR, mm/h 28.97 ± 24.276 30.31 ± 24.309 0.401 CRP, mg/L 23.63 ± 27.926 24.55 ± 30.396 0.852 FBG, mmol/L 5.167 ± 0.924 5.55 ± 1.437 0.007** TC, mmol/L 4.49 ± 0.943 4.40 ± 1.199 0.268 TG, mmol/L 2.04 ± 1.065 2.22 ± 1.494 0.475 LDL, mmol/L 2.72 ± 0.708 2.64 ± 0.864 0.271 HDL, mmol/L 0.92 ± 0.221 0.90 ± 0.215 0.562 eGFR, mL/min/1.73 m² 100.44 ± 22.080 90.32 ± 24.13 0.002** UUE, mmol/24h 473.16 ± 178.897 530.84 ± 298.987 0.194 FEUA, % 4.08 ± 1.534 4.69 ± 1.955 0.003** CUA, ml/min 9.99 ± 6.873 11.42 ± 9.199 0.475 Ccr, ml/min 120.44 ± 39.282 109.90 ± 44.214 0.015* SUA, µmol/L 509.13 ± 130.637 505.77 ± 138.317 0.897 SCr, µmol/L 82.55 ± 15.778 90.28 ± 37.841 0.134 BUN, mmol/L 4.85 ± 1.597 5.52 ± 2.132 0.000*** Abnormal CRP, n (%) 71(55.5%) 70(56.5%) 0.875 Abnormal ESR, n (%) 79(62.2%) 84(68.3%) 0.312 Abnormal UUE, n (%) 30(23.4%) 40(32.0%) 0.128 Abnormal FEUA, n (%) 10(7.8%) 20(16.0%) 0.044* Abnormal SCr, n (%) 22(17.2%) 36(28.8%) 0.028* Abnormal BUN, n (%) 13(10.2%) 21(16.8%) 0.121 Abnormal SUA, n (%) 101(78.9%) 85(68.0%) 0.049* Smoking, n (%) 50(39.1%) 60(48.0%) 0.152 Drinking, n (%) 66(51.6%) 67(53.6%) 0.746 Tophi, n (%) 60(46.9%) 62(49.6%) 0.665 Double Contour Sign, n (%) 24(18.8%) 22(17.6%) 0.813 Joint Symptoms, n (%) 100(78.7%) 93(74.4%) 0.416 Obesity, n (%) 37(28.9%) 48(38.4%) 0.110 Dyslipidemia, n (%) 76(59.4%) 79(63.2%) 0.532 Nephrolithiasis, n (%) 6(4.7%) 10(8.0%) 0.279 Renal Cyst, n (%) 13(10.2%) 35(28.0%) 0.000*** Fatty Liver, n (%) 61(47.7%) 68(54.4%) 0.665 Comorbidity Status, n (%) 96(75.0%) 103(82.4%) 0.151 *** P < 0.001, ** P < 0.01, * P < 0.05. 3.3. Correlation Analysis Univariate and multivariate logistic regression analyses were used to explore risk factors associated with hypertension in gout patients, with results summarized in Table 3 and visually represented in a forest plot (Fig. 2 ). Univariate logistic regression analysis showed that 12 factors were significantly associated with hypertension in gout patients (all P < 0.05): age, age of onset, FBG, eGFR, UUE, FEUA, Ccr, SCr, BUN, abnormal FEUA, abnormal creatinine, and renal cyst. Variables with P < 0.1 in the univariate analysis were included in the multivariate logistic regression model for adjustment. The results showed that age of onset (OR = 1.064, 95% CI: 1.035–1.094, P < 0.001), FBG (OR = 1.277, 95% CI: 1.010–1.614, P = 0.041), UUE (OR = 1.002, 95% CI: 1.000-1.003, P = 0.017), Ccr (OR = 1.017, 95% CI: 1.005–1.030, P = 0.007), and SCr (OR = 1.035, 95% CI: 1.012–1.058, P = 0.003) were independent risk factors for hypertension in gout patients. Table 3 Logistic regression analysis of risk factors for hypertension in gout patients. Variable Univariate Analysis Multivariate Analysis OR 95%CI P OR 95%CI P Age 1.031 1.014 ~ 1.048 0.000 Age of onset 1.035 1.017 ~ 1.053 0.000 1.064 1.035 ~ 1.094 0.000 FBG 1.363 1.075 ~ 1.729 0.011 1.277 1.010 ~ 1.614 0.041 eGFR 0.981 0.970 ~ 0.993 0.001 UUE 1.001 1.000 ~ 1.002 0.066 1.002 1.000 ~ 1.003 0.017 FEUA 1.241 1.058 ~ 1.455 0.008 Ccr 0.994 0.988 ~ 1.000 0.047 1.017 1.005 ~ 1.030 0.007 SCr 1.013 1.000 ~ 1.026 0.042 1.035 1.012 ~ 1.058 0.003 BUN 1.241 1.061 ~ 1.450 0.007 Abnormal FEUA 2.248 1.007 ~ 5.019 0.048 Abnormal Creatinine 1.949 1.069 ~ 3.553 0.029 Renal Cyst 3.440 1.719 ~ 6.885 0.000 3.4. Model Diagnostics and Sensitivity Analysis Model diagnostics and sensitivity analyses were conducted to evaluate the robustness of the multivariate logistic regression model. The Hosmer-Lemeshow goodness-of-fit test was not statistically significant (χ² (8) = 7.752, P = 0.458), indicating no significant difference between the predicted probabilities and observed outcomes, suggesting that the logistic regression model fits well. To evaluate the robustness of the multivariate logistic regression model, a sensitivity analysis was performed using a leave-one-out sensitivity analysis on 14 influential observations. The results for key variables under different analysis scenarios are shown in Table 4 and Fig. 3 . The association strength and statistical significance of age of onset, UUE, Ccr, and SCr demonstrated high stability across all analysis scenarios (OR values fluctuated minimally, all P values < 0.05), indicating these variables are robust independent factors for hypertension in gout patients. However, the statistical significance of the glucose was context-dependent. In the full (original) model, glucose was significantly associated with the risk of hypertension (OR = 1.277, P = 0.041). Nevertheless, during the sequential removal of the 14 influential points, the P -value exhibited variability. After removing specific cases, the P -value for glucose increased to a borderline level ( P range: 0.055–0.070), and the confidence interval included 1 (data not shown), indicating that the association lost its statistical significance; in the remaining 10 analyses (71.4%), the association remained significant ( P < 0.05), and the OR was always greater than 1 (range: 1.249–1.331). The result indicates that the overall trend of glucose as a risk factor holds true in the study model, but its statistical significance is less robust than that of other factors. It may be influenced by a limited number of specific individuals in the dataset or it could suggest that the effect is confined to a specific subgroup. Table 4 Comparison of multivariate logistic regression results for key variables under different analysis scenarios. No. Analysis Scenario Age of Onset (OR, P ) FBG (OR, P ) UUE (OR, P ) Ccr (OR, P ) SCr(OR, P ) 1 Full Model (Original) 1.064,0.000 1.277,0.041 1.002,0.017 1.017,0.007 1.035,0.003 2 Excluding Hosp. No.=2025014637 1.065,0.000 1.256, 0.070 1.002,0.017 1.017,0.007 1.035,0.003 3 Excluding Hosp. No.=2025014251 1.065,0.000 1.249,0.070 1.002,0.018 1.017,0.006 1.035,0.003 4 Excluding Hosp. No.=2025017147 1.064,0.000 1.255, 0.067 1.002,0.017 1.017,0.007 1.035,0.003 5 Excluding Hosp. No.=2025011055 1.063,0.000 1.269,0.045 1.002,0.018 1.017,0.007 1.037,0.002 6 Excluding Hosp. No.=2025018053 1.064,0.000 1.287,0.037 1.002,0.027 1.017,0.006 1.035,0.003 7 Excluding Hosp. No.=2025007699 1.064,0.000 1.276,0.040 1.002,0.026 1.017,0.008 1.035,0.003 8 Excluding Hosp. No.=2025016878 1.063,0.000 1.331,0.028 1.002,0.020 1.017,0.007 1.034,0.003 9 Excluding Hosp. No.=2025013237 1.064,0.000 1.278,0.040 1.002,0.025 1.017,0.007 1.035,0.003 10 Excluding Hosp. No.=2025004897 1.065,0.000 1.286,0.037 1.002,0.015 1.017,0.008 1.032,0.006 11 Excluding Hosp. No.=202501020613 1.066, 0.000 1.278,0.040 1.002,0.20 1.019,0.004 1.037,0.002 12 Excluding Hosp. No.=2025016479 1.063,0.000 1.272,0.043 1.002,0.018 1.016,0.011 1.034,0.004 13 Excluding Hosp. No.=2025001636 1.063,0.000 1.266,0.048 1.002,0.013 1.017,0.009 1.035,0.003 14 Excluding Hosp. No.=2025006549 1.064,0.000 1.268,0.047 1.002,0.010 1.017,0.007 1.035,0.003 15 Excluding Hosp. No.=2024007690 1.064,0.000 1.260,0.055 1.002,0.017 1.017,0.007 1.035,0.003 4. Discussion Gout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of an increased risk for cardiovascular incidents (including fatal cardiac incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore, effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity. This retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies. Gout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension. It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li. (Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels. The association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research. Additionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients. This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension (Del Pinto R, 2021). Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017). The results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other. Although univariate analysis identified significant associations between multiple indicators (e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients. The model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias. In conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms. Conclusion This cross-sectional study identified several independent risk factors for hypertension in patients with primary gout, including age of onset, fasting blood glucose (FPG), 24-hour urinary uric acid excretion (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr). These findings underscore that aging, hyperglycemia, and impaired renal function are significant contributors to the development of hypertension in this population. The results highlight the need for integrated clinical management that addresses not only urate control but also glycemic status and renal health in gout patients. Early identification and targeted intervention in high-risk individuals - particularly those with earlier gout onset, elevated blood glucose, or renal dysfunction indicators - may help mitigate the risk of hypertension and improve overall patient outcomes. Future prospective studies are warranted to confirm these associations and explore underlying mechanisms. Abbreviations ORs odds ratios CIs confidence intervals FPG Fasting blood glucose UUE 24-hour urinary uric acid excretion Ccr creatinine clearance rate SCr serum creatinine MSU monosodium urate GBD Global Burden of Disease HUA Hyperuricemia SUA serum uric acid ACR American College of Rheumatology EULAR European League Against Rheumatism SBP systolic blood pressure DBP diastolic blood pressure TC total cholesterol TG triglycerides BMI body Mass Index CT computed tomography LDL low-density lipoprotein HDL high-density lipoprotein Ccr Creatinine Clearance Rate CUA Uric Acid Clearance FEUA Fractional Excretion of Uric Acid ANOVA Analysis of Variance ESR erythrocyte sedimentation rate CRP C-reactive protein eGFR estimated glomerular filtration rate BUN blood urea nitrogen Declarations Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All procedures were performed in compliance with relevant laws and institutional guidelines. The study was reviewed and approved by the Medical Ethics Committee of Xi'an No.5 Hospital (Approval No. 2025-110). All patients and their families were informed about the study and provided signed informed consent. Consent for publication Not applicable Availability of data and materials The datasets used and / or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors have declared that they have no known competing economic interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors` contributions Jin-fu Hu: Conceptualization, Methodology, Writing-original draft. Pan Ge: Writing - review & editing. Yin-hui Du: Project administration，Resources. Xiang Zhang: Investigation, Data curation. Ji-wen Liu: Formal analysis. Miao-miao Wen: Data Curation. Meng Bai: Project administration, Supervision. Corresponding authors Correspondence to Meng Bai. Acknowledgements Not applicable. Authors` information Department of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China Jin-fu Hu, Ji-wen Liu Department of Pathology, School of Basic Medical Sciences, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China Pan Ge Department of Ultrasound Medicine, Xi'an No.5 Hospital, Xi'an, Shaanxi, 710082, China Yin-hui Du, Meng Bai Department of Clinical Laboratory, Xi'an No.5 Hospital, Xi'an, Shaanxi, 710082, China Miao-miao Wen Department of Electrocardiographic Diagnosis, Xi’an Children’s Hospital, Xi’an, Shaanxi, 710003, China Xiang Zhang References Belovol, A. N., Knyazkova, I. I., & Miroshnykova, I. A. (2015). GOUT AND HYPERTENSION. Likars’ka sprava , 1 ～ 2 , 32～38. De Becker, B., Borghi, C., Burnier, M., & Van De Borne, P. (2019). Uric acid and hypertension: A focused review and practical recommendations. Journal of Hypertension , 37 (5), 878～883. https://doi.org/10.1097/HJH.0000000000001980 Del Pinto R, Viazzi F, Pontremoli R, Ferri C, & Russo E. (2021). The URRAH study. Panminerva Medica , 63 (4), 416～423. https://doi.org/10.23736/S0031-0808.21.04357-3 FitzGerald, J. D., Dalbeth, N., Mikuls, T., Brignardello‐Petersen, R., Guyatt, G., Abeles, A. M., Gelber, A. C., Harrold, L. R., Khanna, D., King, C., Levy, G., Libbey, C., Mount, D., Pillinger, M. H., Rosenthal, A., Singh, J. A., Sims, J. E., Smith, B. J., Wenger, N. S., … Neogi, T. (2020). 2020 American College of Rheumatology Guideline for the Management of Gout. Arthritis Care & Research , 72 (6), 744～760. https://doi.org/10.1002/acr.24180 Furuhashi, M. (2020). New insights into purine metabolism in metabolic diseases: Role of xanthine oxidoreductase activity. American Journal of Physiology-Endocrinology and Metabolism , 319 (5), E827～E834. https://doi.org/10.1152/ajpendo.00378.2020 Gibson, T. J. (2013). Hypertension, its treatment, hyperuricaemia and gout. Current Opinion in Rheumatology , 25 (2), 217～222. https://doi.org/10.1097/BOR.0b013e32835cedd4 Grayson, P. C., Kim, S. Y., LaValley, M., & Choi, H. K. (2011). Hyperuricemia and incident hypertension: A systematic review and meta‐analysis. Arthritis Care & Research , 63 (1), 102～110. https://doi.org/10.1002/acr.20344 Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, Braun LT, de Ferranti S, Faiella-Tommasino J, Forman DE, Goldberg R, Heidenreich PA, Hlatky MA, Jones DW, Lloyd-Jones D, Lopez-Pajares N, Ndumele CE, Orringer CE, Peralta CA, … Yeboah J. (2019). 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Journal of the American College of Cardiology , 73 (24), e285～e350. https://doi.org/10.1016/j.jacc.2018.11.003 Huang, C.-F., Liu, J.-C., Huang, H.-C., Chuang, S.-Y., Chen, C.-I., & Lin, K.-C. (2017). Longitudinal transition trajectory of gouty arthritis and its comorbidities: A population-based study. Rheumatology International , 37 (2), 313～322. https://doi.org/10.1007/s00296-016-3634-9 Lai, B., Yu, H.-P., Chang, Y.-J., Wang, L.-C., Chen, C.-K., Zhang, W., Doherty, M., Chang, S.-H., Hsu, J.-T., Yu, K.-H., & Kuo, C.-F. (2022). Assessing the causal relationships between gout and hypertension: A bidirectional Mendelian randomisation study with coarsened exposures. Arthritis Research & Therapy , 24 (1), 243. https://doi.org/10.1186/s13075-022-02933-4 Li, M., Nie, Q., Lv, K., Liu, J., & Jiang, Z. (2025). Exploring causal pathways between hypertension, lipid levels, and gout: Insights from Mendelian randomization and NHANES observations. Medicine , 104 (31), e43638. https://doi.org/10.1097/MD.0000000000043638 Lin KH, Yen FS, Li HL, Wei JC, Hsu CC, Yang CC, & Hwu CM. (2021). Urate-lowering therapy exerts protective effects against hypertension development in patients with gout. Journal of Human Hypertension , 35 (4), 351～359. https://doi.org/10.1038/s41371-020-0342-4 Liu, H., Zhang, X.-M., Wang, Y.-L., & Liu, B.-C. (2014). Prevalence of hyperuricemia among Chinese adults: A national cross-sectional survey using multistage, stratified sampling. Journal of Nephrology , 27 (6), 653～658. https://doi.org/10.1007/s40620-014-0082-z McAdams‐DeMarco, M. A., Maynard, J. W., Baer, A. N., & Coresh, J. (2012). Hypertension and the Risk of Incident Gout in a Population‐Based Study: The Atherosclerosis Risk in Communities Cohort. The Journal of Clinical Hypertension , 14 (10), 675～679. https://doi.org/10.1111/j.1751-7176.2012.00674.x Neogi, T., Jansen, T. L. Th. A., Dalbeth, N., Fransen, J., Schumacher, H. R., Berendsen, D., Brown, M., Choi, H., Edwards, N. L., Janssens, H. J. E. M., Lioté, F., Naden, R. P., Nuki, G., Ogdie, A., Perez‐Ruiz, F., Saag, K., Singh, J. A., Sundy, J. S., Tausche, A., … Taylor, W. J. (2015). 2015 Gout Classification Criteria: An American College of Rheumatology/European League Against Rheumatism Collaborative Initiative. Arthritis & Rheumatology , 67 (10), 2557～2568. https://doi.org/10.1002/art.39254 Pan, A., Teng, G. G., Yuan, J.-M., & Koh, W.-P. (2015). Bidirectional Association between Self-Reported Hypertension and Gout: The Singapore Chinese Health Study. PLOS ONE , 10 (10), e0141749. https://doi.org/10.1371/journal.pone.0141749 Sun, C., Qi, X., Tian, Y., Gao, L., Jin, H., & Guo, H. (2019). Risk factors for the formation of double-contour sign and tophi in gout. Journal of Orthopaedic Surgery and Research , 14 (1), 239. https://doi.org/10.1186/s13018-019-1280-0 Wang, J., Taylor, A. W., Zhang, T., Appleton, S., & Shi, Z. (2018). Association between Body Mass Index and All-Cause Mortality among Oldest Old Chinese. The Journal of Nutrition, Health & Aging , 22 (2), 262～268. https://doi.org/10.1007/s12603-017-0907-2 Wang, J.-G. (2025). Chinese Guidelines for the Prevention and Treatment of Hypertension (2024 revision). Journal of Geriatric Cardiology , 22 (1), 1～149. https://doi.org/10.26599/1671-5411.2025.01.008 Wang, L., Zhang, T., Liu, Y., Tang, F., & Xue, F. (2020). Association of Serum Uric Acid with Metabolic Syndrome and Its Components: A Mendelian Randomization Analysis. BioMed Research International , 2020 (1), 6238693. https://doi.org/10.1155/2020/6238693 Zhao, R., Li, Z., Sun, Y., Ge, W., Wang, M., Liu, H., Xun, L., & Xia, Y. (2022). Engineered Escherichia coli Nissle 1917 with urate oxidase and an oxygen-recycling system for hyperuricemia treatment. Gut Microbes , 14 (1), 2070391. https://doi.org/10.1080/19490976.2022.2070391 Zhu Y, Pandya BJ, & Choi HK. (2012). Comorbidities of gout and hyperuricemia in the US general population: NHANES 2007-2008. The American Journal of Medicine , 125 (7), 679-687.e1. https://doi.org/10.1016/j.amjmed.2011.09.033 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 15 Apr, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 10 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9083729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633584712,"identity":"3510bd25-d5c7-4b0d-893c-3237f5e44f0d","order_by":0,"name":"Jin-fu Hu","email":"","orcid":"","institution":"Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jin-fu","middleName":"","lastName":"Hu","suffix":""},{"id":633584713,"identity":"1b4ae828-4b16-43d2-906a-0a5d9ae864db","order_by":1,"name":"Pan Ge","email":"","orcid":"","institution":"Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Ge","suffix":""},{"id":633584714,"identity":"3a3eb19f-f2ac-485d-bb4d-89babeee47f7","order_by":2,"name":"Yin-hui Du","email":"","orcid":"","institution":"Xi'an No.5 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yin-hui","middleName":"","lastName":"Du","suffix":""},{"id":633584715,"identity":"4c530c9d-1679-4247-a41d-99d6746a67a4","order_by":3,"name":"Xiang Zhang","email":"","orcid":"","institution":"Xi’an Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Zhang","suffix":""},{"id":633584716,"identity":"7b0ca093-14bf-4380-9e10-cec8d53c9703","order_by":4,"name":"Ji-wen Liu","email":"","orcid":"","institution":"Xi’an Jiaotong 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Enrollment.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1FlowchartofStudySubjectEnrollment..jpg","url":"https://assets-eu.researchsquare.com/files/rs-9083729/v1/0a20875f41072aba9c794234.jpg"},{"id":108806152,"identity":"4cba17f2-b495-41eb-afab-c8ac051093ff","added_by":"auto","created_at":"2026-05-08 15:27:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of risk factors for hypertension in gout patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2Forestplotofriskfactorsforhypertensioningoutpatients..jpg","url":"https://assets-eu.researchsquare.com/files/rs-9083729/v1/8115c9577ae26be1096c8cb5.jpg"},{"id":108735385,"identity":"1f9cac70-3fc3-474f-aaae-8615074a3496","added_by":"auto","created_at":"2026-05-07 20:06:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity analysis of FBG: Variation in OR and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e after removing influential cases.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3SensitivityanalysisofFBGVariationinORandPafterremovinginfluentialcases..jpg","url":"https://assets-eu.researchsquare.com/files/rs-9083729/v1/d42bd959bc8586aa3521e358.jpg"},{"id":108809841,"identity":"d7d51e4b-a112-47ef-9e53-84d1cb9b8e98","added_by":"auto","created_at":"2026-05-08 15:55:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":851675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9083729/v1/c3c37ccc-75cc-4075-a1e8-71b0ae99d043.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Risk Factors for Hypertension Comorbidity in Patients with Gout: A Retrospective Study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eGout is a chronic metabolic disease caused by disturbance of purine metabolism or impaired uric acid excretion, it is characterized by the supersaturation and deposition of monosodium urate (MSU) crystals in joints and various organ tissues, potentially causing tophi, bone destruction, joint deformities and renal damage. As the disease progresses, it may also be accompanied by complications such as diabetes and cerebro-cardiovascular diseases. The incidence and prevalence of gouty arthritis have been increasing annually (Sun, 2019)Gout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of\u0026nbsp;an increased risk for\u0026nbsp;cardiovascular\u0026nbsp;incidents\u0026nbsp;(including fatal cardiac\u0026nbsp;incidents) and all-cause mortality\u0026nbsp;(Belovol, A. N., 2015). Therefore,\u0026nbsp;effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity.\u003c/p\u003e\n\u003cp\u003eThis retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003eGout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension.\u0026nbsp;It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li.\u0026nbsp;(Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels.\u003c/p\u003e\n\u003cp\u003eThe association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients.\u0026nbsp;This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension\u0026nbsp;(Del Pinto R, 2021).\u0026nbsp;Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017).\u003c/p\u003e\n\u003cp\u003eThe results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other.\u003c/p\u003e\n\u003cp\u003eAlthough univariate analysis identified significant associations between\u0026nbsp;multiple indicators\u0026nbsp;(e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients.\u003c/p\u003e\n\u003cp\u003eThe model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of\u0026nbsp;an increased risk for\u0026nbsp;cardiovascular\u0026nbsp;incidents\u0026nbsp;(including fatal cardiac\u0026nbsp;incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore,\u0026nbsp;effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity.\u003c/p\u003e\n\u003cp\u003eThis retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003eGout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension.\u0026nbsp;It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li.\u0026nbsp;(Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels.\u003c/p\u003e\n\u003cp\u003eThe association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients.\u0026nbsp;This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension\u0026nbsp;(Del Pinto R, 2021).\u0026nbsp;Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017).\u003c/p\u003e\n\u003cp\u003eThe results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other.\u003c/p\u003e\n\u003cp\u003eAlthough univariate analysis identified significant associations between\u0026nbsp;multiple indicators\u0026nbsp;(e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients.\u003c/p\u003e\n\u003cp\u003eThe model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of\u0026nbsp;an increased risk for\u0026nbsp;cardiovascular\u0026nbsp;incidents\u0026nbsp;(including fatal cardiac\u0026nbsp;incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore,\u0026nbsp;effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity.\u003c/p\u003e\n\u003cp\u003eThis retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003eGout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension.\u0026nbsp;It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li.\u0026nbsp;(Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels.\u003c/p\u003e\n\u003cp\u003eThe association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients.\u0026nbsp;This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension\u0026nbsp;(Del Pinto R, 2021).\u0026nbsp;Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017).\u003c/p\u003e\n\u003cp\u003eThe results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other.\u003c/p\u003e\n\u003cp\u003eAlthough univariate analysis identified significant associations between\u0026nbsp;multiple indicators\u0026nbsp;(e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients.\u003c/p\u003e\n\u003cp\u003eThe model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of\u0026nbsp;an increased risk for\u0026nbsp;cardiovascular\u0026nbsp;incidents\u0026nbsp;(including fatal cardiac\u0026nbsp;incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore,\u0026nbsp;effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity.\u003c/p\u003e\n\u003cp\u003eThis retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003eGout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension.\u0026nbsp;It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li.\u0026nbsp;(Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels.\u003c/p\u003e\n\u003cp\u003eThe association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients.\u0026nbsp;This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension\u0026nbsp;(Del Pinto R, 2021).\u0026nbsp;Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017).\u003c/p\u003e\n\u003cp\u003eThe results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other.\u003c/p\u003e\n\u003cp\u003eAlthough univariate analysis identified significant associations between\u0026nbsp;multiple indicators\u0026nbsp;(e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients.\u003c/p\u003e\n\u003cp\u003eThe model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of\u0026nbsp;an increased risk for\u0026nbsp;cardiovascular\u0026nbsp;incidents\u0026nbsp;(including fatal cardiac\u0026nbsp;incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore,\u0026nbsp;effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity.\u003c/p\u003e\n\u003cp\u003eThis retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003eGout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension.\u0026nbsp;It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li.\u0026nbsp;(Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels.\u003c/p\u003e\n\u003cp\u003eThe association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients.\u0026nbsp;This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension\u0026nbsp;(Del Pinto R, 2021).\u0026nbsp;Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017).\u003c/p\u003e\n\u003cp\u003eThe results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other.\u003c/p\u003e\n\u003cp\u003eAlthough univariate analysis identified significant associations between\u0026nbsp;multiple indicators\u0026nbsp;(e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients.\u003c/p\u003e\n\u003cp\u003eThe model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e. According to the 2017 Global Burden of Disease (GBD) study, the global prevalence of gout was 7.9‰ in males and 2.5‰ in females (FitzGerald, 2020). A cross-sectional survey from 31 provinces in China reported an incidence ranging from 0.03% to 15.3% among adults, with prevalence rates of 4.4% and 2.0% for males and females, respectively.\u0026nbsp;\u003cstrong\u003eIn the United States, the NHANES 2007–2008 data indicated a high prevalence of comorbidities among individuals with gout, including hypertension, which was present in up to 74% of gout patients, underscoring the significant clinical overlap between these conditions\u0026nbsp;\u003c/strong\u003e(Zhu Y, 2012)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUric acid is the end product of purine metabolism in the body, the uricase can oxidize uric acid into the easily soluble allantoin. However, during the evolutionary process of primates, the activity of uricase was lost due to gene mutations causing to the accumulation of uric acid in humans(Zhao, 2022).It is primarily produced by the liver, with two-thirds excreted by the kidneys and one-third through the intestines. Hyperuricemia (HUA) arises from either overproduction or underexcretion of uric acid, with underexcretion being the main cause. Hyperuricemia is the fundamental cause of gout development. In recent years, with the improvements in living standards and changes in lifestyle, the prevalence of hyperuricemia has been increasing annually. Statistics indicate that the total prevalence of hyperuricemia in China is approximately\u0026nbsp;14%(Liu, 2014), posing a significant threat to public health.\u003c/p\u003e\n\u003cp\u003eHypertension serves as both a risk factor for gout and one of the most common complications, often interacting with gout and hyperuricemia in a bidirectional manner.Potential mechanism may involve that\u0026nbsp;uric acid, an extracellular antioxidant in hyperuricemia, can promote endothelial dysfunction through xanthine oxidase-related oxidative stress, as well as by increasing salt sensitivity and lipogenesis, thereby contributing to atherosclerosis and ischemic heart disease\u0026nbsp;(De Becker, 2019; Furuhashi, 2020; Grayson, 2011;).\u0026nbsp;Controlling serum uric acid levels may benefit hypertension management(Gibson, 2013; Lin KH, 2021).Conversely, hypertension can damage glomerular arteries, glomerulosclerosis, and a reduced filtration rate, which subsequently results in renal insufficiency and hyperuricemia\u0026nbsp;(McAdams‐DeMarco, 2012).\u0026nbsp;\u003cstrong\u003eA prospective cohort study further confirmed a bidirectional association between hypertension and gout, with hypertensive patients having an 88% increased risk of developing gout, and gout patients having an 18% increased risk of developing hypertension\u0026nbsp;\u003c/strong\u003e(Pan, 2015)\u003cstrong\u003e.\u003c/strong\u003eHowever, the causal nature of the relationship between uric acid and hypertension remains a subject of debate. A Mendelian randomization study in a Chinese female population found no evidence supporting serum uric acid(SUA) as a causal risk factor for hypertension, suggesting that the observed epidemiological associations might be confounded by other shared metabolic abnormalities (L. Wang, 2020). Therefore, rather than establishing causality, this study primarily aims to analyze the prevalence of hypertension in gout patients and explore its risk factors to provide a basis for clinical prevention and treatment.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cb\u003eStudy Population\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e We conducted a retrospective study that enrolled 253 patients from the Rheumatology and Immunology outpatient clinic of Xi'an No.5 Hospital between March 2024 and July 2025.The participant screening process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Inclusion criteria: Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; Definite diagnosis of gout according to the 2015 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria(Neogi, 2015). Exclusion criteria: Patients with severe diseases such as immunodeficiency, hematological diseases, or malignant tumors; Patients with incomplete clinical data; Patients who had taken medications affecting uric acid metabolism (e.g., aspirin, diuretics) within the three months preceding enrollment. Ultimately, a total of 253 patients with primary gout were included (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All patients and their families were informed about the study and provided signed informed consent. All patient biodata were kept confidential. The study was reviewed and approved by the Medical Ethics Committee of Xi'an No.5 Hospital (Approval No. 2025\u0026thinsp;\u0026minus;\u0026thinsp;110).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHypertension diagnosis: Clinic systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg without antihypertensive medication use, or previous diagnosis of hypertension(J.-G. Wang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Dyslipidemia diagnosis: Based on the 2018 AHA / ACC / AACVPR / AAPA / ABC / ACPM / ADA / AGS / APhA / ASPC / NLA / PCNA Guideline on the Management of Blood Cholesterol: total cholesterol (TC)\u0026thinsp;\u0026ge;\u0026thinsp;5.17 mmol/L and/or triglycerides (TG)\u0026thinsp;\u0026ge;\u0026thinsp;1.70 mmol/L(Grundy SM, 2019). Tophus diagnosis: White or pale yellow abnormal protrusions or masses around joints, tendons, or subcutaneous soft tissues, with a stone-like, hard texture(Neogi, 2015). Drinking history was defined as: continuous or cumulative alcohol consumption for over 6 months, with a frequency of at least once per week. Smoking history was defined as: continuous or cumulative smoking for over 6 months, with a frequency of at least once per day. Obesity diagnosis: Based on Chinese Body Mass Index (BMI) criteria, BMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0 kg/m\u0026sup2; was diagnosed as obesity(Wang, J., 2018). Diagnosis of fatty liver, renal cysts, kidney stones: Based on imaging examinations such as X-ray, ultrasonography, or computed tomography (CT).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cb\u003eCollection of Demographic and Clinical Characteristics Data\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eA power analysis was conducted with 80% power and a 0.05 significance level to calculate the required sample size. Data were collected from gout patients with comorbid hypertension to assess their risk factors including gender, age, height, weight, BMI, SBP, DBP, smoking history, alcohol history, disease duration, and history of comorbidities such as hypertension, renal insufficiency, and kidney stones, etc.\u003c/p\u003e \u003cp\u003eVenous blood samples were collected from patients after an overnight fast (\u0026ge;\u0026thinsp;8 hours). Fasting blood glucose (FPG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), 24-hour urinary uric acid excretion (UUE), serum uric acid (SUA) and other relevant parameters were measured using an automated biochemical analyzer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 \u003cb\u003eCalculation formulas\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe following parameters were calculated using standard formulas:\u003c/p\u003e \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;weight (kg) / [height (m)] \u0026sup2;\u003c/p\u003e \u003cp\u003eCreatinine Clearance Rate (Ccr) was estimated using the Cockcroft-Gault formula: (140 - age) \u0026times; weight (kg) / [serum creatinine (\u0026micro;mol/L) \u0026times; 0.818] \u0026times; gender coefficient (1.0 for male, 0.85 for female).\u003c/p\u003e \u003cp\u003eUric Acid Clearance (CUA) = [24-hour urinary uric acid (\u0026micro;mol/L) \u0026times; 24-hour urine volume (L)]/ serum uric acid (\u0026micro;mol/L)\u003c/p\u003e \u003cp\u003eFractional Excretion of Uric Acid (FEUA) = (24h-hour urinary uric acid excretion \u0026times; serum creatinine) / (serum uric acid \u0026times; 24-hour urinary creatinine excretion) \u0026times;100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 \u003cb\u003eStatistical Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eData were double-entered, checked, verified, and corrected using EpiData 3.1 software (EpiData Association, \u0026Oslash;sterbrogade 158, 1.sal, 2100 K\u0026oslash;benhavn \u0026Oslash;, Denmark). Statistical analysis was performed using SPSS 18.0 software (SPSS Inc., Chicago, IL, USA). Quantitative data conforming to normal or approximately normal distribution were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (X̄ \u0026plusmn; s); skewed distributions were described using median (M) and interquartile range (\u003cem\u003eP₂₅, P₇₅\u003c/em\u003e). Qualitative data were described using counts and proportions.\u003c/p\u003e \u003cp\u003eIndependent samples t-test and Mann-Whitney U test were used for comparisons between two groups for quantitative data; ANOVA was used for comparisons among multiple groups for quantitative data; χ\u0026sup2; test or Fisher's exact test (when the number of cells with expected frequency\u0026thinsp;\u0026lt;\u0026thinsp;5 exceeded 20% or when any theoretical frequency was \u0026lt;\u0026thinsp;1) was used to compare differences between groups for qualitative data.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses were used to explore risk factors for hypertension in gout patients, expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Model goodness-of-fit was assessed using the Hosmer-Lemeshow test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The significance level was set at α\u0026thinsp;=\u0026thinsp;0.05, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":" \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. \u003cb\u003eBaseline Characteristics of Gout Patients\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThis study included 253 gout patients. Their baseline characteristics were as follows (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): mean age was 46.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.679 years, with 1.2% (3 cases) being female. Systolic blood pressure (SBP) was 137.51\u0026thinsp;\u0026plusmn;\u0026thinsp;26.257 mmHg, diastolic blood pressure (DBP) was 89.57\u0026thinsp;\u0026plusmn;\u0026thinsp;16.914 mmHg, age of onset was 37.94\u0026thinsp;\u0026plusmn;\u0026thinsp;15.111 years, and body mass index (BMI) was 26.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19 kg/m\u0026sup2;. Regarding inflammatory indicators, erythrocyte sedimentation rate (ESR) was 29.67\u0026thinsp;\u0026plusmn;\u0026thinsp;24.213 mm/h, and C-reactive protein (CRP) was 23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;28.98 mg/L. Fasting blood glucose (FPG) was 5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 mmol/L, total cholesterol (TC) was 4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08 mmol/L, triglycerides (TG) were 2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 mmol/L, low-density lipoprotein (LDL) was 2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 mmol/L, and high-density lipoprotein (HDL) was 0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 mmol/L.\u003c/p\u003e \u003cp\u003eAmong renal function-related indicators, mean estimated glomerular filtration rate (eGFR) was 95.40\u0026thinsp;\u0026plusmn;\u0026thinsp;23.63 mL/min/1.73 m\u0026sup2;, 24-hour urinary uric acid excretion (UUE) was 504.59\u0026thinsp;\u0026plusmn;\u0026thinsp;245.87 mmol/24h, fractional excretion of uric acid (FEUA) was 4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77%, uric acid clearance (CUA) was 10.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12 ml/min, creatinine clearance rate (Ccr) was 115.23\u0026thinsp;\u0026plusmn;\u0026thinsp;42.04 ml/min, serum uric acid (SUA) was 510.52\u0026thinsp;\u0026plusmn;\u0026thinsp;135.74 \u0026micro;mol/L, serum creatinine (SCr) was 86.31\u0026thinsp;\u0026plusmn;\u0026thinsp;28.83 \u0026micro;mol/L, and blood urea nitrogen (BUN) was 5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04 mmol/L.\u003c/p\u003e \u003cp\u003eRegarding lifestyle, 43.5% (110 cases) had a smoking history, and 52.6% (133 cases) had a drinking history. Regarding complications, tophi occurrence was 48.2% (122 cases), double contour sign was 18.2% (46 cases), obesity was 33.6% (85 cases), nephrolithiasis was 6.3% (16 cases), renal cyst was 19.0% (48 cases), and fatty liver was 51.0% (129 cases).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of gout patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{\\text{-}}{\\text{X}}\\text{\u0026plusmn;}\\text{s}\\)\u003c/span\u003e\u003c/span\u003e(n=253)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.51\u0026thinsp;\u0026plusmn;\u0026thinsp;26.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.57\u0026thinsp;\u0026plusmn;\u0026thinsp;16.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of Onset, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.94\u0026thinsp;\u0026plusmn;\u0026thinsp;15.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR, mm/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.67\u0026thinsp;\u0026plusmn;\u0026thinsp;24.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;28.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73 m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.40\u0026thinsp;\u0026plusmn;\u0026thinsp;23.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUUE, mmol/24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e504.59\u0026thinsp;\u0026plusmn;\u0026thinsp;245.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEUA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUA, ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCcr, ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.23\u0026thinsp;\u0026plusmn;\u0026thinsp;42.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUA, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510.52\u0026thinsp;\u0026plusmn;\u0026thinsp;135.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.31\u0026thinsp;\u0026plusmn;\u0026thinsp;28.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110(43.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133(52.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTophi, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122(48.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble Contour Sign, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(18.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(33.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNephrolithiasis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Cyst, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(19.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty Liver, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129(51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.2. \u003cb\u003eDescriptive Statistics of General Data and Biochemical Indicators in the Hypertension Group vs. Normotensive Group among Gout Patients\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 253 gout patients were included in this study, comprising 128 patients in the normotensive group and 125 in the hypertension group. Statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were found between the two groups in age, SBP, DBP, age of onset, BMI, FBG, glomerular filtration rate, FEUA, Ccr, BUN, abnormal FEUA, abnormal SCr, abnormal SUA, and the incidence of renal cyst, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSpecifically, the hypertensive group had significantly higher values for age, SBP, DBP, age of onset, BMI, FBG, BUN, and FEUA compared to the normotensive group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the eGFR and Ccr were significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The hypertensive group had significantly higher rates for abnormal FEUA, abnormal SCr, and renal cyst (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but the rate of abnormal SUA was significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eNo statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were found between the two groups in gender, ESR, CRP, TC, TG, LDL, HDL, UUE, CUA, SUA, SCr, abnormal CRP, abnormal ESR, abnormal UUE, abnormal BUN, smoking history, drinking history, tophi, double contour sign, joint symptoms, obesity, dyslipidemia, Nephrolithiasis, fatty liver, and comorbidity status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline data between the hypertension group and normotensive group in gout patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormotensive Group (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypertensive Group (n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.51\u0026thinsp;\u0026plusmn;\u0026thinsp;15.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.66\u0026thinsp;\u0026plusmn;\u0026thinsp;15.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119\u0026thinsp;\u0026plusmn;\u0026thinsp;11.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.71\u0026thinsp;\u0026plusmn;\u0026thinsp;24.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.14\u0026thinsp;\u0026plusmn;\u0026thinsp;15.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of Onset, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.66\u0026thinsp;\u0026plusmn;\u0026thinsp;15.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR, mm/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.97\u0026thinsp;\u0026plusmn;\u0026thinsp;24.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.31\u0026thinsp;\u0026plusmn;\u0026thinsp;24.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.63\u0026thinsp;\u0026plusmn;\u0026thinsp;27.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.55\u0026thinsp;\u0026plusmn;\u0026thinsp;30.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73 m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.44\u0026thinsp;\u0026plusmn;\u0026thinsp;22.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.32\u0026thinsp;\u0026plusmn;\u0026thinsp;24.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUUE, mmol/24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473.16\u0026thinsp;\u0026plusmn;\u0026thinsp;178.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e530.84\u0026thinsp;\u0026plusmn;\u0026thinsp;298.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEUA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUA, ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;9.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCcr, ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.44\u0026thinsp;\u0026plusmn;\u0026thinsp;39.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.90\u0026thinsp;\u0026plusmn;\u0026thinsp;44.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUA, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e509.13\u0026thinsp;\u0026plusmn;\u0026thinsp;130.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e505.77\u0026thinsp;\u0026plusmn;\u0026thinsp;138.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.55\u0026thinsp;\u0026plusmn;\u0026thinsp;15.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.28\u0026thinsp;\u0026plusmn;\u0026thinsp;37.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal CRP, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal ESR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79(62.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal UUE, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal FEUA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal SCr, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal BUN, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal SUA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(68.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTophi, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble Contour Sign, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint Symptoms, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100(78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93(74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(59.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNephrolithiasis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Cyst, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty Liver, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity Status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. \u003cb\u003eCorrelation Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses were used to explore risk factors associated with hypertension in gout patients, with results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and visually represented in a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnivariate logistic regression analysis showed that 12 factors were significantly associated with hypertension in gout patients (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05): age, age of onset, FBG, eGFR, UUE, FEUA, Ccr, SCr, BUN, abnormal FEUA, abnormal creatinine, and renal cyst.\u003c/p\u003e \u003cp\u003eVariables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the univariate analysis were included in the multivariate logistic regression model for adjustment. The results showed that age of onset (OR\u0026thinsp;=\u0026thinsp;1.064, 95% CI: 1.035\u0026ndash;1.094, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FBG (OR\u0026thinsp;=\u0026thinsp;1.277, 95% CI: 1.010\u0026ndash;1.614, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), UUE (OR\u0026thinsp;=\u0026thinsp;1.002, 95% CI: 1.000-1.003, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), Ccr (OR\u0026thinsp;=\u0026thinsp;1.017, 95% CI: 1.005\u0026ndash;1.030, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), and SCr (OR\u0026thinsp;=\u0026thinsp;1.035, 95% CI: 1.012\u0026ndash;1.058, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) were independent risk factors for hypertension in gout patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of risk factors for hypertension in gout patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.014\u0026thinsp;~\u0026thinsp;1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.017\u0026thinsp;~\u0026thinsp;1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035\u0026thinsp;~\u0026thinsp;1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.075\u0026thinsp;~\u0026thinsp;1.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.010\u0026thinsp;~\u0026thinsp;1.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.970\u0026thinsp;~\u0026thinsp;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u0026thinsp;~\u0026thinsp;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u0026thinsp;~\u0026thinsp;1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.058\u0026thinsp;~\u0026thinsp;1.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCcr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u0026thinsp;~\u0026thinsp;1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.005\u0026thinsp;~\u0026thinsp;1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u0026thinsp;~\u0026thinsp;1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.012\u0026thinsp;~\u0026thinsp;1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.061\u0026thinsp;~\u0026thinsp;1.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal FEUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.007\u0026thinsp;~\u0026thinsp;5.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal Creatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.069\u0026thinsp;~\u0026thinsp;3.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Cyst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.719\u0026thinsp;~\u0026thinsp;6.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. \u003cb\u003eModel Diagnostics and Sensitivity Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eModel diagnostics and sensitivity analyses were conducted to evaluate the robustness of the multivariate logistic regression model. The Hosmer-Lemeshow goodness-of-fit test was not statistically significant (χ\u0026sup2; (8)\u0026thinsp;=\u0026thinsp;7.752, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.458), indicating no significant difference between the predicted probabilities and observed outcomes, suggesting that the logistic regression model fits well. To evaluate the robustness of the multivariate logistic regression model, a sensitivity analysis was performed using a leave-one-out sensitivity analysis on 14 influential observations. The results for key variables under different analysis scenarios are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe association strength and statistical significance of age of onset, UUE, Ccr, and SCr demonstrated high stability across all analysis scenarios (OR values fluctuated minimally, all \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating these variables are robust independent factors for hypertension in gout patients. However, the statistical significance of the glucose was context-dependent. In the full (original) model, glucose was significantly associated with the risk of hypertension (OR\u0026thinsp;=\u0026thinsp;1.277, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041). Nevertheless, during the sequential removal of the 14 influential points, the \u003cem\u003eP\u003c/em\u003e -value exhibited variability. After removing specific cases, the \u003cem\u003eP\u003c/em\u003e -value for glucose increased to a borderline level (\u003cem\u003eP\u003c/em\u003e range: 0.055\u0026ndash;0.070), and the confidence interval included 1 (data not shown), indicating that the association lost its statistical significance; in the remaining 10 analyses (71.4%), the association remained significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the OR was always greater than 1 (range: 1.249\u0026ndash;1.331). The result indicates that the overall trend of glucose as a risk factor holds true in the study model, but its statistical significance is less robust than that of other factors. It may be influenced by a limited number of specific individuals in the dataset or it could suggest that the effect is confined to a specific subgroup.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of multivariate logistic regression results for key variables under different analysis scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalysis Scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge of Onset (OR, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFBG (OR, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUUE (OR, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCcr (OR, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSCr(OR, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull Model (Original)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.277,0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025014637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.065,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.256, 0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025014251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.065,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.249,0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025017147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.255, 0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025011055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.063,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.269,0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.037,0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025018053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.287,0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025007699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.276,0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025016878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.063,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.331,0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.034,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025013237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.278,0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025004897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.065,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.286,0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.032,0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=202501020613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.066, 0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.278,0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.019,0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.037,0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025016479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.063,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.272,0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.016,0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.034,0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025001636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.063,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.266,0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2025006549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.268,0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluding Hosp. No.=2024007690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064,0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.260,0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002,0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.017,0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035,0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGout is the most common inflammatory arthritis in adults. It is driven by elevated serum uric acid, which leads to the deposition of monosodium urate crystals in and around the joints, inducing acute inflammation. Moreover, these crystals can also deposit in the blood vessel walls, activating inflammatory pathways and significantly contributing to the risk of cardiovascular diseases such as hypertension and coronary heart disease. Studies have shown that hypertension is not only a risk factor for gout and one of its most common comorbidities. Among hypertensive patients, hyperuricemia is an independent predictor of an increased risk for cardiovascular incidents (including fatal cardiac incidents) and all-cause mortality (Belovol, A. N., 2015). Therefore, effective management of shared risk factors in patients with both conditions is crucial for reducing the incidence and adverse outcomes of this comorbidity.\u003c/p\u003e \u003cp\u003eThis retrospective study investigated the risk factors for hypertension in gout patients. Multivariate logistic regression analysis identified age of onset, glucose, 24-hour urinary uric acid (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr) as independent risk factors. This finding provides an important basis for clinically identifying high-risk patients and developing targeted intervention strategies.\u003c/p\u003e \u003cp\u003eGout patients with an earlier age of onset had a significantly increased risk of hypertension, consistent with previous research. As a chronic metabolic disease, a longer disease duration may promote the development of atherosclerosis and abnormalities in blood pressure regulation mechanisms through multiple pathways, including persistent inflammatory responses, vascular endothelial dysfunction, and progressive renal impairment (Huang, 2017). Furthermore, longer disease duration is often accompanied by progressive renal function damage, further exacerbating the risk of hypertension. It is noteworthy that while hyperuricemia is the pathophysiological hallmark of gout, serum uric acid (SUA) itself was not retained as an independent predictor in our multivariate model. This finding corresponds with results from a Mendelian randomization analysis by Li. (Li, 2025), which indicated that despite the strong observational association between hyperuricemia and hypertension, their causal relationship may be mediated by other metabolic factors such as triglycerides. Collectively, these observations suggest that the strong association between gout and hypertension is likely not driven solely by circulating uric acid levels.\u003c/p\u003e \u003cp\u003eThe association between abnormal glucose metabolism and hypertension reflects the intrinsic links between components of metabolic syndrome. Insulin resistance may be the common pathophysiological basis connecting hyperuricemia, abnormal glucose metabolism, and hypertension (Lai, 2022). Insulin resistance contributes to hypertension through various mechanisms, such as promoting renal tubular sodium reabsorption, activating the sympathetic nervous system, and impairing vascular dilation function. The sensitivity analysis revealed that the significance of the glucose indicator was influenced by individual data points, which may reflect population heterogeneity: the effect of blood glucose on blood pressure is more pronounced in specific subgroups (e.g., patients with potential diabetes, severe insulin resistance, or specific genetic backgrounds). This finding suggests the need for more refined stratified analysis strategies in future research.\u003c/p\u003e \u003cp\u003eAdditionally, the study found that there existed a complex association between renal uric acid excretion function and the risk of hypertension. Higher UUE levels indicated an increased renal uric acid load, potentially it is related to renal tubular dysfunction and an increase in sodium reabsorption, indirectly affecting blood pressure regulation. Ccr and SCr, as core indicators of renal function, showed a positive correlation with hypertension, emphasizing the key role of renal insufficiency in the development of hypertension in gout patients. This aligns with the findings of the URRAH study, a large-scale investigation that also established a close pathophysiological relationship between uric acid, renal impairment, and hypertension (Del Pinto R, 2021). Decreased renal function can lead to water and sodium retention, activation of the RAAS system, and further obstruction of uric acid excretion, making a vicious cycle of hyperuricemia-hypertension-renal impairment(Huang, 2017).\u003c/p\u003e \u003cp\u003eThe results have clear clinical translational value. It is recommended to strengthen blood pressure monitoring and early intervention for gout patients with the following characteristics: Early age of onset; Accompanied by elevated blood glucose or pre-diabetes; signs of renal dysfunction (e.g., elevated UUE, abnormal Ccr, or elevated SCr). This approach aligns with the holistic management strategy advocated by the 2020 American College of Rheumatology (ACR) guideline for gout, which emphasizes the importance of screening for and managing comorbidities, including hypertension and chronic kidney disease, as an integral part of gout care (FitzGerald, 2020). Comprehensive interventions targeting these risk factors, including optimized urate-lowering therapy, blood glucose control, renal protection, and lifestyle modifications, may help break the vicious cycle in which gout and hypertension exacerbate each other.\u003c/p\u003e \u003cp\u003eAlthough univariate analysis identified significant associations between multiple indicators (e.g., abnormal FEUA, renal cysts) and hypertension in gout patients, these were not retained as independent predictors in the multivariate model. This may occur because their effects are mediated or confounded by stronger predictors, such as renal function indicators or age of onset. For instance, renal cysts may influence blood pressure indirectly through renal structural changes, while FEUA may share physiological pathways with glomerular filtration function. Thus, while these factors may have clinical relevance, the multivariate analysis underscores the predominant role of age at onset, blood glucose, and core renal function parameters in hypertension risk stratification among gout patients.\u003c/p\u003e \u003cp\u003eThe model indicated a good fit according to the Hosmer-Lemeshow test. the sensitivity analysis confirmed the high robustness of the associations for age of onset, UUE, Ccr, and SCr, enhancing the reliability of the core conclusions. However, this study has several limitations: Firstly, the cross-sectional design cannot establish causality; Secondly, although multiple confounding factors were adjusted for, residual confounding (e.g., dietary details, medication adherence, socioeconomic status) may still exist; Finally, the study subjects were from a single center, potentially introducing selection bias.\u003c/p\u003e \u003cp\u003eIn conclusion, this study identified multiple independent risk factors for hypertension in gout patients, particularly emphasizing the importance of age of onset, blood glucose levels, and renal function indicators. In clinical practice, gout patients with early onset, elevated blood glucose, or signs of renal dysfunction should receive enhanced blood pressure monitoring and management. Comprehensive measures to intervene on modifiable risk factors are expected to break the vicious cycle of mutual exacerbation between gout and hypertension and improve long-term patient prognosis. Future large-scale prospective cohort studies are needed to further validate these risk factors and explore the underlying molecular mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis cross-sectional study identified several independent risk factors for hypertension in patients with primary gout, including age of onset, fasting blood glucose (FPG), 24-hour urinary uric acid excretion (UUE), creatinine clearance rate (Ccr), and serum creatinine (SCr). These findings underscore that aging, hyperglycemia, and impaired renal function are significant contributors to the development of hypertension in this population. The results highlight the need for integrated clinical management that addresses not only urate control but also glycemic status and renal health in gout patients. Early identification and targeted intervention in high-risk individuals - particularly those with earlier gout onset, elevated blood glucose, or renal dysfunction indicators - may help mitigate the risk of hypertension and improve overall patient outcomes. Future prospective studies are warranted to confirm these associations and explore underlying mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eORs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCIs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFPG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting blood glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUUE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e24-hour urinary uric acid excretion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCcr\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatinine clearance rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSCr\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eserum creatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMSU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonosodium urate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGBD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Burden of Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHUA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHyperuricemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSUA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eserum uric acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eACR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican College of Rheumatology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEULAR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean League Against Rheumatism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCcr\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatinine Clearance Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCUA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUric Acid Clearance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFEUA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFractional Excretion of Uric Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eESR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eerythrocyte sedimentation rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBUN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eblood urea nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were performed in compliance with relevant laws and institutional guidelines. The study was reviewed and approved by the Medical Ethics Committee of Xi\u0026apos;an No.5 Hospital (Approval No. 2025-110). All patients and their families were informed about the study and provided signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and / or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that they have no known competing economic interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors` contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJin-fu Hu: Conceptualization, Methodology, Writing-original draft. Pan Ge: Writing - review \u0026amp; editing. Yin-hui Du: Project administration，Resources. Xiang Zhang: Investigation,\u0026nbsp;Data curation. Ji-wen Liu: Formal analysis. Miao-miao Wen: Data Curation.\u0026nbsp;Meng Bai: Project administration,\u0026nbsp;Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Meng Bai.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors` information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Medicine, Xi\u0026rsquo;an Jiaotong University, Xi\u0026rsquo;an, Shaanxi, 710061, China\u003c/p\u003e\n\u003cp\u003eJin-fu Hu, Ji-wen Liu\u003c/p\u003e\n\u003cp\u003eDepartment of Pathology, School of Basic Medical Sciences, Health Science Center, Xi\u0026rsquo;an Jiaotong University, Xi\u0026rsquo;an, Shaanxi, 710061, China\u003c/p\u003e\n\u003cp\u003ePan Ge\u003c/p\u003e\n\u003cp\u003eDepartment of Ultrasound Medicine, Xi\u0026apos;an No.5 Hospital, Xi\u0026apos;an, Shaanxi, 710082, China\u003c/p\u003e\n\u003cp\u003eYin-hui Du, Meng Bai\u003c/p\u003e\n\u003cp\u003eDepartment of Clinical Laboratory, Xi\u0026apos;an No.5 Hospital, Xi\u0026apos;an, Shaanxi, 710082, China\u003c/p\u003e\n\u003cp\u003eMiao-miao Wen\u003c/p\u003e\n\u003cp\u003eDepartment of Electrocardiographic Diagnosis, Xi\u0026rsquo;an Children\u0026rsquo;s Hospital, Xi\u0026rsquo;an, Shaanxi, 710003, China\u003c/p\u003e\n\u003cp\u003eXiang Zhang\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBelovol, A. 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Hypertension and the Risk of Incident Gout in a Population‐Based Study: The Atherosclerosis Risk in Communities Cohort. \u003cem\u003eThe Journal of Clinical Hypertension\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(10), 675～679. https://doi.org/10.1111/j.1751-7176.2012.00674.x\u003c/li\u003e\n\u003cli\u003eNeogi, T., Jansen, T. L. Th. A., Dalbeth, N., Fransen, J., Schumacher, H. R., Berendsen, D., Brown, M., Choi, H., Edwards, N. L., Janssens, H. J. E. M., Liot\u0026eacute;, F., Naden, R. P., Nuki, G., Ogdie, A., Perez‐Ruiz, F., Saag, K., Singh, J. A., Sundy, J. S., Tausche, A., \u0026hellip; Taylor, W. J. (2015). 2015 Gout Classification Criteria: An American College of Rheumatology/European League Against Rheumatism Collaborative Initiative. \u003cem\u003eArthritis \u0026amp; Rheumatology\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(10), 2557～2568. https://doi.org/10.1002/art.39254\u003c/li\u003e\n\u003cli\u003ePan, A., Teng, G. G., Yuan, J.-M., \u0026amp; Koh, W.-P. (2015). Bidirectional Association between Self-Reported Hypertension and Gout: The Singapore Chinese Health Study. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(10), e0141749. https://doi.org/10.1371/journal.pone.0141749\u003c/li\u003e\n\u003cli\u003eSun, C., Qi, X., Tian, Y., Gao, L., Jin, H., \u0026amp; Guo, H. (2019). Risk factors for the formation of double-contour sign and tophi in gout. \u003cem\u003eJournal of Orthopaedic Surgery and Research\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 239. https://doi.org/10.1186/s13018-019-1280-0\u003c/li\u003e\n\u003cli\u003eWang, J., Taylor, A. W., Zhang, T., Appleton, S., \u0026amp; Shi, Z. (2018). Association between Body Mass Index and All-Cause Mortality among Oldest Old Chinese. \u003cem\u003eThe Journal of Nutrition, Health \u0026amp; Aging\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 262～268. https://doi.org/10.1007/s12603-017-0907-2\u003c/li\u003e\n\u003cli\u003eWang, J.-G. (2025). Chinese Guidelines for the Prevention and Treatment of Hypertension (2024 revision). \u003cem\u003eJournal of Geriatric Cardiology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 1～149. https://doi.org/10.26599/1671-5411.2025.01.008\u003c/li\u003e\n\u003cli\u003eWang, L., Zhang, T., Liu, Y., Tang, F., \u0026amp; Xue, F. (2020). Association of Serum Uric Acid with Metabolic Syndrome and Its Components: A Mendelian Randomization Analysis. \u003cem\u003eBioMed Research International\u003c/em\u003e, \u003cem\u003e2020\u003c/em\u003e(1), 6238693. https://doi.org/10.1155/2020/6238693\u003c/li\u003e\n\u003cli\u003eZhao, R., Li, Z., Sun, Y., Ge, W., Wang, M., Liu, H., Xun, L., \u0026amp; Xia, Y. (2022). Engineered \u003cem\u003eEscherichia coli\u003c/em\u003e Nissle 1917 with urate oxidase and an oxygen-recycling system for hyperuricemia treatment. \u003cem\u003eGut Microbes\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 2070391. https://doi.org/10.1080/19490976.2022.2070391\u003c/li\u003e\n\u003cli\u003eZhu Y, Pandya BJ, \u0026amp; Choi HK. (2012). Comorbidities of gout and hyperuricemia in the US general population: NHANES 2007-2008. \u003cem\u003eThe American Journal of Medicine\u003c/em\u003e, \u003cem\u003e125\u003c/em\u003e(7), 679-687.e1. https://doi.org/10.1016/j.amjmed.2011.09.033\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gout, Hypertension, Hyperuricemia, Logistic regression, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-9083729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9083729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough the comorbidity of gout and hypertension is well-established, the independent predictors for hypertension in gout patients remain insufficiently investigated. This study was designed to identify such factors for hypertension in patients with primary gout.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective study, 253 patients were recruited from the Rheumatology and Immunology outpatient clinic of Xi'an No.5 Hospital between March 2024 and July 2025. Data concerning demographics, biochemical indicators, and comorbidities, were collected. The analysis involved descriptive statistics, chi-square tests, and univariate and multivariate logistic regression to estimate odds ratios (ORs) and confidence intervals (CIs), followed by model diagnostics and sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings demonstrated that age of onset (OR\u0026thinsp;=\u0026thinsp;1.064, 95% CI: 1.035\u0026ndash;1.094), Fasting blood glucose (FPG) (OR\u0026thinsp;=\u0026thinsp;1.277, 95% CI: 1.010\u0026ndash;1.614), 24-hour urinary uric acid excretion (UUE) (OR\u0026thinsp;=\u0026thinsp;1.002, 95% CI: 1.000\u0026ndash;1.003), creatinine clearance rate (Ccr) (OR\u0026thinsp;=\u0026thinsp;1.017, 95% CI: 1.005\u0026ndash;1.030), and serum creatinine (SCr) (OR\u0026thinsp;=\u0026thinsp;1.035, 95% CI: 1.012\u0026ndash;1.058) were significantly associated with hypertension in gout patients (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The model demonstrated good fit (the Hosmer-Lemeshow, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.458).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study highlight aging, hyperglycemia, and abnormal renal function indicators as independent risk factors for hypertension in individuals with gout, suggesting that closer monitoring and early intervention may improve clinical outcomes.\u003c/p\u003e","manuscriptTitle":"Analysis of Risk Factors for Hypertension Comorbidity in Patients with Gout: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 20:06:01","doi":"10.21203/rs.3.rs-9083729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-23T10:34:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T06:32:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T14:19:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T14:18:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Medicine","date":"2026-03-10T11:59:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24b7685a-5253-4555-9b63-34dc77be8978","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T20:06:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 20:06:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9083729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9083729","identity":"rs-9083729","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}