Threshold effect of the METS-VF index on hyperuricemia in adults with hypertension: a cross-sectional study

Threshold effect of the METS-VF index on hyperuricemia in adults with hypertension: a cross-sectional 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 Threshold effect of the METS-VF index on hyperuricemia in adults with hypertension: a cross-sectional study Meihui Wu, Congcong Yan, Shiping Li, Lingjuan Zhu, Chao Yu, Tao Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9153773/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background : Hyperuricemia (HUA) is a significant public health issue closely associated with obesity. The novel visceral adiposity index (METS-VF) demonstrates predictive value for multiple chronic diseases, yet its association with HUA risk in hypertensive patients remains unclear. This study aimed to investigate this association in a Chinese hypertensive population. Methods: This study utilized data from the Chinese Hypertension Registry Study, including 13,341 hypertensive patients. Data collection was conducted through standardized questionnaires, physical examinations, and laboratory tests. The association between METS-VF and hyperuricemia (HUA) was evaluated using multivariate logistic regression. Robustness was assessed via multivariable-adjusted models and subgroup analyzes. Results: The prevalence of HUA was 44.32%. Each unit increase in METS-VF was associated with a 13.87 (95% CI: 12.29, 15.45) μmol/L increase in serum uric acid levels and a 30% higher risk of HUA (OR = 1.30; 95% CI: 1.25, 1.36). The risk of HUA rose significantly across increasing METS-VF quartiles (P for trend < 0.001). Subgroup analysis indicated a stronger association in women (P for interaction < 0.001). Conclusion: In patients with hypertension, the visceral adiposity index (METS-VF) was positively associated with the risk of hyperuricemia (HUA). hyperuricemia hypertension METS-VF Threshold Effect Chinese adults Figures Figure 1 Figure 2 Figure 3 1. Background Hyperuricemia (HUA) is a pathological state characterized by the accumulation of uric acid in the blood due to impaired purine metabolism. Its primary causes include excessive uric acid production and/or insufficient renal and intestinal excretion (1-2). Globally, the prevalence of HUA ranges from approximately 9.3% to 20.1%, closely linked to changes in dietary patterns, with a trend toward younger onset ages (3-5). The situation in China is particularly severe, with the prevalence rate rising rapidly from 11.1% in 2015–16 to 14.0% in 2018–19 (6), affecting approximately 170 million people (7). It has become a major metabolic health issue on par with hypertension. Extensive evidence indicates that HUA is not only a trigger for gout but also an independent risk factor for multiple diseases, including hypertension (8), diabetes (9), cardiovascular disease (10), stroke (11), chronic kidney disease (9), metabolic syndrome, and non-alcoholic fatty liver disease (12). Obesity is a public health challenge affecting over 100 million patients worldwide (13). Multiple studies (14-16) have confirmed that obesity (particularly abdominal obesity) is an independent predictor of hyperuricemia. Data from Chinese adults (17-18) also demonstrate a significant positive correlation between serum uric acid (SUA) levels and obesity severity. In recent years, Bello-Chavolla et al. (19) developed the Visceral Fat Metabolic Score (METS-VF). This metric integrates the Metabolic Syndrome Index for Insulin Resistance (METS-IR), waist-to-height ratio (WHtR), age, and sex, effectively predicting chronic diseases such as diabetes (20), cardiovascular disease (21), and hypertension (22). However, few studies have explored the association between METS-VF and HUA/SUA levels in hypertensive populations. Given the higher risk of adverse events in this group, this study aims to systematically evaluate the relationship between METS-VF, SUA levels, and HUA risk in Chinese patients with hypertension. This study sought to clarify the dose-response relationship and provide evidence for early identification of high-risk populations for HUA. 2. Methods 2.1 Participants The data for this study were derived from baseline information of the China Hypertension Registry Study, which was registered at the Chinese Clinical Trial Registry (Registration number: ChiCTR1800017274; Registration date: July 20, 2018). This registry study is a multicenter, real-world observational study designed to investigate the clinical characteristics, management status, and long-term prognosis of hypertensive patients in rural China.Wuyuan County, Jiangxi Province, was selected as the site for this study's field investigation. All participants included were hypertensive patients aged 18 years or older. Hypertension is defined as resting seated systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or a confirmed diagnosis of hypertension with ongoing antihypertensive medication. Participants with cognitive or neurological impairment, or those unable to cooperate with study follow-up, were excluded. This study strictly adheres to the principles of the Declaration of Helsinki. The research protocol was approved by the Ethics Committee of the Institute of Biomedical Sciences, Anhui Medical University (No.: CH1059) and the Ethics Committee of the Second Affiliated Hospital of Nanchang University (No.: 2018019). All participants signed written informed consent forms prior to enrollment. A total of 14,234 hypertensive patients were included in this study, exclusions were based on missing data for METS-VF (n = 878), missing data on BMI (n = 5), FPG (n = 7), smoking status (n = 2), and drinking status (n = 1), finally, an analysis was conducted on 13,341 subjects (Fig. 1). 2.2 Data collection All baseline data were collected by uniformly trained investigators following standardized procedures. Data on demographic characteristics (including age and sex), lifestyle factors (such as smoking and alcohol consumption history), medical history (e.g., diabetes, family history of coronary heart disease, and hypertension), and medication use (e.g., antihypertensive, antidiabetic, and lipid-lowering drugs) were gathered through structured questionnaires(23). Anthropometric measurements included height, body weight, and waist circumference, and body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Blood pressure was measured using an Omron electronic sphygmomanometer (Omron; Dalian, China). Participants rested quietly for at least 5 minutes, followed by three consecutive measurements at 1-minute intervals; the average value was recorded. All study subjects were required to fast for at least 8 hours prior to blood sample collection. Venous blood samples were centrifuged to separate serum, which was stored at -80°C. Subsequently, the samples were sent to Shenzhen Biaojia Bioengineering Co., Ltd. for analysis. Fasting Plasma Glucose, a four-item lipid panel (HDL-C, LDL-C, TG, TC), and homocysteine (Hcy) levels were measured using a fully automated biochemical analyzer (Beckman Coulter, USA). All testing procedures adhered to standardized operating protocols and quality control requirements. 2.3 Definitions According to the guidelines of the Chinese Society of Endocrinology, hyperuricemia is diagnosed when serum uric acid levels exceed 420 μmol/L (7.0 mg/dL) in males and 360 μmol/L (6.0 mg/dL) in females (24-26). 2.4 Statistical analyzes The presence or absence of HUA categorized baseline characteristics of the study population. Continuous variables were expressed as mean ± standard deviation (SD), while categorical variables were expressed as numbers and percentages. Descriptions were grouped according to METS-VF quartiles, with intergroup differences assessed using t-tests or chi-square tests. Multivariate logistic regression was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between METS-VF and serum uric acid (SUA) levels and hyperuricemia (HUA). The independent relationship between the METS-VF index and SUA and HUA was tested using smooth curve fitting and generalized additive models, with adjustments for major covariates in all three models. Model 1: Crude model (unadjusted); Model 2: Adjusted for age and sex; Model 3: Model 2 plus DBP, SBP, Hcy, TC, LDL-C, eGFR, current smoking, current drinking, diabetes, lipid-lowering drugs, antiplatelet drugs, and glucose-lowering drugs. Additionally, we conducted subgroup analyzes to explore potential factors influencing this association. All statistical analyzes were performed using R software (version 3.3.1; http://www.R-project.org) and EmpowerStats software (version 2.17.8; www.empowerstats.com). A two-tailed P value < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics of Study Participants The final data analysis included 13,341 hypertensive patients. The overall mean age of the participants was 63.95 ± 9.30 years and included 6,187 men (46.38%). The prevalence of hyperuricemia was 44.32%, with rates across the first to fourth METS-VF quartiles of 42.67%, 42.13%, 43.78%, and 48.71%, respectively. Participant characteristics grouped by METS-VF quartiles are presented in Table 1. Higher METS-VF was significantly associated with adverse body composition (higher WC and BMI) and a more adverse metabolic profile, characterized by elevated FPG, TG, and SUA, and reduced HDL-C. It was also associated with a higher prevalence of diabetes and use of related medications. Specifically, participants in higher METS-VF quartiles exhibited greater WC, BMI, DBP, FPG, SUA, and TG, and lower HDL-C. They were also more likely to be female, to have diabetes, and to use lipid-lowering and glucose-lowering drugs. Conversely, the lower proportions of current smokers and drinkers in the higher METS-VF groups may be related to their higher proportion of women. TC and LDL-C levels showed a trend of initial increase followed by a decline (Table 1). 3.2 Relationship between METS-VF Index and SUA and HUA Tables 2 and 3 shew the association of the METS-VF index with serum uric acid (SUA) levels and hyperuricemia (HUA) risk across different models. The multiple linear regression model indicated that each 1-unit increase in the METS-VF index was associated with a 13.87 (95% CI: 12.29, 15.45) μmol/L increase in SUA levels and a 30% higher risk of HUA (OR = 1.30; 95% CI: 1.25, 1.36). Multivariable linear regression models (Models 1–3) demonstrated a positive association between the METS-VF index and SUA (Table 2). When grouped by METS-VF quartiles, the adjusted β coefficients for SUA in Q2-Q4, compared to Q1, were 16.74 (95% CI: 11.99, 21.49), 36.60 (95% CI: 31.63, 41.56), and 50.22 (95% CI: 45.03, 55.40) in the fully adjusted model (Model 3) (Table 2). Consistently, the ORs of HUA for subjects in Q2-Q4 of the METS-VF index were 1.38 (95% CI: 1.23, 1.56), 1.98 (95% CI: 1.75, 2.24), and 2.57 (95% CI: 2.26, 2.93) , respectively, compared to those in the lowest quartile (Q1). A significant dose-response relationship was observed, with the strength of association increasing across METS-VF quartiles (P for trend < 0.001) (Table 3). 3.3 Dose-Response and Threshold Effect Analysis To characterize the nonlinear relationships and threshold effects of METS-VF on serum uric acid (SUA) levels and hyperuricemia (HUA) risk, we applied smoothing curve fitting. As visualized in Figure 2, a significant dose-response relationship was identified for both conditions. (Fig. 2). As shown in Table 4 and Table 5, the two-piecewise regression analysis identified a METS-VF inflection point of 8.5 in the fully adjusted Model 3. Below this threshold, each 1-unit increase in METS-VF was significantly associated with a 24.44 μmol/L rise in serum uric acid (SUA) levels (95% CI: 21.94, 26.93) and a higher risk of hyperuricemia (HUA) (OR = 1.58, 95% CI: 1.49, 1.68). Above this threshold, however, the associations were no longer significant for both SUA levels (β = 0.81, 95% CI: -2.05, 3.68) and HUA risk (OR = 1.01, 95% CI: 0.94, 1.08).(Table 4 and Table 5). 3.4 Subgroup analyzes We further conducted stratified analyzes to assess the impact of the METS-VF index (per 1-unit increase) on hyperuricemia across different subgroups. No significant interactions were observed in the subgroups of age (<65 vs. ≥65 years), BMI (<24 vs. ≥24 kg/m²), eGFR ( 0.05). However, a significant interaction was observed with sex. The association between the METS-VF index and hyperuricemia was stronger in women (OR = 1.43, 95% CI: 1.35, 1.52) than that in men (OR = 1.21, 95% CI: 1.14, 1.27; P for interaction < 0.001) (Fig. 3). 4. Discussion This large population-based study first demonstrated a positive association between the METS-VF index and hyperuricemia in hypertensive patients. After full adjustment, the METS-VF index was correlated with both HUA risk and SUA levels in a linear dose-response manner. A significantly stronger association was observed in females (P for interaction < 0.001). Although traditional indicators such as body mass index (BMI) and waist circumference (WC) have been widely demonstrated to correlate with hyperuricemia (HUA) (27-29), their clinical utility is limited. BMI cannot distinguish lean from fat mass and fails to reflect visceral fat accumulation, whereas WC and waist-to-height ratio (WHtR) are susceptible to influences from ethnicity, sex, and age (30-32). Therefore, more precise indicators are needed to assess metabolic risk.In recent years, the Metabolic Score for Visceral Fat (METS-VF) has garnered attention as a novel indicator for assessing HUA risk. The strength of METS-VF lies in its integration of multiple parameters, including age, sex, WHtR, and the Metabolic Score for Insulin Resistance (METS-IR) (21,33), enabling a more comprehensive assessment of visceral adipose tissue function. Multiple studies support its predictive value. For instance, Ji et al. (34) conducted a multicenter cross-sectional study involving 8,877 patients with hypertension and hyperuricemia, finding a significant positive correlation between METS-VF and gout risk. Each one-standard-deviation increase in METS-VF was associated with an 82% higher risk of gout (OR=1.82, 95% CI: 1.62, 2.03). This finding aligns with the role of METS-VF in broader uric acid metabolism abnormalities. A large-scale retrospective cohort study by Liu et al. (35) confirmed that in non-obese adults, METS-VF was significantly and positively associated with HUA incidence, an association particularly pronounced in women. Xie et al. (36), using NHANES data, further validated that the highest METS-VF quartile carried a 6.07-fold higher HUA risk compared to the lowest quartile, with stronger associations observed in women and certain ethnic groups. Our findings in a hypertensive population are consistent, showing a significant association between METS-VF and HUA risk. Our findings of a positive correlation between METS-VF and HUA can be explained by its representation of visceral adipose tissue dysfunction. Excess visceral fat leads to an increased influx of free fatty acids to the liver via the portal vein, which not only promotes triglyceride synthesis but also enhances uric acid production by altering purine metabolism (37). Concurrently, adipose tissue dysfunction induces insulin resistance, which impairs renal function and reduces uric acid excretion (38-39), in hypertensive patients, hyperuricemia (HUA) commonly coexists with hypertension, and the two conditions often mutually exacerbate each other (40). Basic research has shown that elevated serum uric acid (SUA) contributes to the progression of hypertension through mechanisms such as renal impairment, chronic inflammation, and endothelial dysfunction (41-42). Observational studies have consistently identified SUA as an independent risk factor for both hypertension and cardiovascular events (43-46). This underscores the importance of investigating HUA in this population. Our study further identified a potentially stronger association between METS-VF and HUA in women. As women age, particularly post-menopause, declining estrogen levels prompt a redistribution of body fat from subcutaneous to visceral depots, leading to a substantial accumulation of visceral adipose tissue that can reach twice the premenopausal levels (47). This physiological shift aligns with the epidemiological trend of HUA prevalence in women exceeding that in men after the age of 65 (48). Furthermore, evidence suggests that visceral fat accumulation poses a greater metabolic hazard for women (49). By incorporating age and sex, METS-VF effectively captures this sex-specific pattern of visceral fat accumulation and the consequent HUA risk. In summary, in our hypertensive cohort, visceral obesity as captured by the METS-VF index and its related metabolic disturbances may form a shared pathophysiological link among obesity, HUA, and hypertension. For instance, obesity-related dyslipidemia can reduce uric acid excretion (50-51), while SUA-associated inflammatory responses may further raise blood pressure (52-53), thereby creating a vicious cycle. Given that visceral fat poses a greater threat to uric acid metabolism than subcutaneous fat (54), METS-VF offers a superior assessment capability. Therefore, the early use of METS-VF to identify hypertensive patients at high risk of HUA holds important clinical value, enabling targeted interventions and potentially improving cardiovascular outcomes. The primary strength of this study lies in its pioneering investigation of the association between the Metabolic Score for Visceral Fat (METS-VF) and hyperuricemia (HUA) in a Chinese hypertensive population. Additionally, the large, population-based sample provided substantial statistical power, and the robustness of the findings was supported by subgroup analyzes. However, several limitations should be considered. First, the cross-sectional design precludes causal inference, and the exact role of METS-VF in the progression of HUA warrants verification in prospective studies. Second, although multiple potential confounders were adjusted for, residual confounding cannot be fully excluded. Third, as all participants were of Chinese origin, the generalizability of the results to other ethnic groups requires further validation. Finally, due to data limitations, we were unable to account for certain potential influences on serum uric acid levels, such as detailed dietary patterns and specific medication use. 5. Conclusions In this hypertensive population, we observed a significant positive association between the visceral adiposity index (METS-VF) and the risk of hyperuricemia (HUA). This association was particularly pronounced in female patients. The METS-VF index, which integrates age, sex, anthropometric measures, and metabolic parameters, shows promise as a clinical tool for HUA risk assessment. Its application could potentially improve hyperuricemia screening in Southern China and offer new avenues for developing urate-lowering strategies in obese hypertensive patients. Declarations Data availability statement The original contributions presented in this study are included in the article/supplementary materials. Further inquiries may be directed to the corresponding author. Consent for Publication Not applicable. Ethics approval and consent to participate The Ethics Committee of the Anhui Medical University's Institute of Biomedicine (No. CH1059) approved this study. All procedures adhered to the Declaration of Helsinki, and written informed consent was acquired from all participants. Author Contributions MW: Data curation, writing-review editing, writing-manuscript. CCY: Review and editing, data oversight. SL: Review and editing, supervision.CY: Review and editing, data oversight, supervision. LZ: Data curation, drafting initial manuscript. TW: Writing-review and editing. WZ: Writing-review editing, final approval. WFZ: Data curation, methodology, writing-review and editing. HB: Manuscript review and editing, data organization, formal analysis. XC: Data curation, writing-review and editing. Funding This work was supported by Jiangxi Science and Technology Innovation Base Plan - Jiangxi Clinical Medical Research Center(20223BCG74012), Key Research and Development Program of Jiangxi (20243BBI91021),Fund project of the Second Affiliated Hospital of Nanchang University（2021efyA01,2023efyA05). Acknowledgments We are grateful to all the staff of the China Hypertension Registry Study and to the research participants for their valuable contributions and time dedicated to this work. Competing interests The authors declare that no potential conflicts of interest, whether commercial or financial, exist in relation to this research. Generative AI statement The authors provide the following declaration: No generative AI was used during the drafting of this work. Should any concerns arise, we welcome communication. Publisher's note The views and opinions presented in this article belong solely to the authors and are not representative of their affiliated institutions, the publisher, or the article's editors and reviewers. Furthermore, the publisher does not endorse or assume responsibility for any product evaluation or manufacturer's claims mentioned in the text. References Wen S, Arakawa H, Tamai I. Uric acid in health and disease: From physiological functions to pathogenic mechanisms. Pharmacol Ther 2024;256:108615. doi: 10.1016/j.pharmthera.2024.108615. Major TJ, Dalbeth N, Stahl EA, Merriman TR. An update on the genetics of hyperuricaemia and gout. Nat Rev Rheumatol 2018;14:341-353. doi: 10.1038/s41584-018-0004-x. Ali N, Perveen R, Rahman S, Mahmood S, Rahman S, Islam S, et al. Prevalence of hyperuricemia and the relationship between serum uric acid and obesity: A study on Bangladeshi adults. 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Nutr Metab Cardiovasc Dis 2018;28:243-251. doi: 10.1016/j.numecd.2017.12.003. Facchini F, Chen YD, Hollenbeck CB, Reaven GM. Relationship between resistance to insulin-mediated glucose uptake, urinary uric acid clearance, and plasma uric acid concentration. JAMA 1991;266:3008-11. Li L, Zhang Y, Zeng C. Update on the epidemiology, genetics, and therapeutic options of hyperuricemia. Am J Transl Res 2020 ;12:3167-3181. Hwu CM, Lin KH. Uric acid and the development of hypertension. Med Sci Monit 2010;16:RA224-30. Mazzali M, Kanbay M, Segal MS, Shafiu M, Jalal D, Feig DI, et al. Uric acid and hypertension: cause or effect? Curr Rheumatol Rep 2010;12:108-17. doi: 10.1007/s11926-010-0094-1. Wang J, Qin T, Chen J, Li Y, Wang L, Huang H, et al. Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies. PLoS One 2014;9:e114259. doi: 10.1371/journal.pone.0114259. Yang T, Chu CH, Bai CH, You SL, Chou YC, Hwang LC, et al. Uric acid concentration as a risk marker for blood pressure progression and incident hypertension: a Chinese cohort study. Metabolism 2012;61:1747-55. doi: 10.1016/j.metabol.2012.05.006. Nagahama K, Inoue T, Kohagura K, Kinjo K, Ohya Y. Associations between serum uric acid levels and the incidence of hypertension and metabolic syndrome: a 4-year follow-up study of a large screened cohort in Okinawa, Japan. Hypertens Res 2015;38:213-8. doi: 10.1038/hr.2014.161. Wei F, Sun N, Cai C, Feng S, Tian J, Shi W, et al. Associations between serum uric acid and the incidence of hypertension: a Chinese senior dynamic cohort study. J Transl Med 2016;14:110. doi: 10.1186/s12967-016-0866-0. Enzi G, Gasparo M, Biondetti PR, Fiore D, Semisa M, Zurlo F. Subcutaneous and visceral fat distribution according to sex, age, and overweight, evaluated by computed tomography. Am J Clin Nutr 1986;44:739-46. doi: 10.1093/ajcn/44.6.739. Cui L, Meng L, Wang G, Yuan X, Li Z, Mu R, et al. Prevalence and risk factors of hyperuricemia: results of the Kailuan cohort study. Mod Rheumatol 2017;27:1066-1071. doi: 10.1080/14397595.2017.1300117. de Mutsert R, Gast K, Widya R, de Koning E, Jazet I, Lamb H, et al. Associations of Abdominal Subcutaneous and Visceral Fat with Insulin Resistance and Secretion Differ Between Men and Women: The Netherlands Epidemiology of Obesity Study. Metab Syndr Relat Disord 2018;16:54-63. doi: 10.1089/met.2017.0128. Cui LF, Shi HJ, Wu SL, Shu R, Liu N, Wang GY, et al. Association of serum uric acid and risk of hypertension in adults: a prospective study of Kailuan Corporation cohort. Clin Rheumatol 2017;36:1103-1110. doi: 10.1007/s10067-017-3548-2. Zhang H, Chen R, Xu X, Yang M, Xu W, Xiang S, et al. Metabolically healthy obesity is associated with higher risk of both hyperfiltration and mildly reduced estimated glomerular filtration rate: the role of serum uric acid in a cross-sectional study. J Transl Med 2023;21:216. doi: 10.1186/s12967-023-04003-y. Tsushima Y, Nishizawa H, Tochino Y, Nakatsuji H, Sekimoto R, Nagao H, et al. Uric acid secretion from adipose tissue and its increase in obesity. J Biol Chem 2013;288:27138-27149. doi: 10.1074/jbc.M113.485094. Joosten LAB, Crişan TO, Bjornstad P, Johnson RJ. Asymptomatic hyperuricaemia: a silent activator of the innate immune system. Nat Rev Rheumatol 2020;16:75-86. doi: 10.1038/s41584-019-0334-3. Takahashi S, Yamamoto T, Tsutsumi Z, Moriwaki Y, Yamakita J, Higashino K. Close correlation between visceral fat accumulation and uric acid metabolism in healthy men. Metabolism 1997;46:1162-5. doi: 10.1016/s0026-0495(97)90210-9. Tables Tables 1 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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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-9153773","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626286259,"identity":"4341e37d-a1a1-4e2b-b476-31708ef21174","order_by":0,"name":"Meihui Wu","email":"","orcid":"","institution":"the Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Meihui","middleName":"","lastName":"Wu","suffix":""},{"id":626286260,"identity":"e11c6edf-fa23-49d6-b091-378509f48524","order_by":1,"name":"Congcong Yan","email":"","orcid":"","institution":"the Second Affiliated Hospital, Jiangxi Medical College","correspondingAuthor":false,"prefix":"","firstName":"Congcong","middleName":"","lastName":"Yan","suffix":""},{"id":626286261,"identity":"d853d720-110e-4868-8667-67871be4163d","order_by":2,"name":"Shiping Li","email":"","orcid":"","institution":"the Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Shiping","middleName":"","lastName":"Li","suffix":""},{"id":626286262,"identity":"10cbc14a-26fa-47d7-b740-649d0c3bbf4a","order_by":3,"name":"Lingjuan Zhu","email":"","orcid":"","institution":"the Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Lingjuan","middleName":"","lastName":"Zhu","suffix":""},{"id":626286263,"identity":"5fd61236-4591-43ba-b622-57e96b002417","order_by":4,"name":"Chao Yu","email":"","orcid":"","institution":"the Second Affiliated Hospital of Nanchang 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University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoshu","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2026-03-18 02:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9153773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9153773/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107619007,"identity":"016f91ac-6117-463e-9039-f5a771f5c6a4","added_by":"auto","created_at":"2026-04-23 09:27:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":886618,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study participants.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9153773/v1/0b66f3ffa03d9a490e6ad736.png"},{"id":107618985,"identity":"ba0a7390-e15d-49ba-ae80-f5dfd1414852","added_by":"auto","created_at":"2026-04-23 09:27:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":350534,"visible":true,"origin":"","legend":"\u003cp\u003eThe dose-response relationship between the METS-VF index and serum uric acid levels and hyperuricemia risk.\u003c/p\u003e\n\u003cp\u003e(a) METS-VF index and SUA; (b) METS-VF index and HUA.\u003c/p\u003e\n\u003cp\u003eRelationship between METS-VF and SUA, HUA. All adjusted for Age, Sex, DBP, SBP, Hcy, TC, LDL-C, eGFR, Current smoking, Current drinking, Diabetes, Lipid-lowering drugs, Antiplatelet drugs, and Glucose-lowering drugs\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9153773/v1/ebf94775f7d1f75030f56729.png"},{"id":107619021,"identity":"12e28e4f-7f23-43bb-9d7d-07a812ad4994","added_by":"auto","created_at":"2026-04-23 09:27:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":789822,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyzes of the effect of METS-VF ratio on hyperuricemia.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analysis of the association between METS-VF and hyperuricemia.\u003c/p\u003e\n\u003cp\u003eEach subgroup analysis is adjusted if not stratified for Age, Sex, DBP, SBP, Hcy, TC, LDL-C, eGFR, Current smoking, Current drinking, Diabetes, Lipid-lowering drugs, Antiplatelet drugs, and Glucose-lowering drug\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9153773/v1/c02d4549c17f6e4977f97281.png"},{"id":107619301,"identity":"0f201698-20ff-404d-94b9-d336411f2ad7","added_by":"auto","created_at":"2026-04-23 09:27:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2040101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9153773/v1/824029c8-b271-48cd-a3a3-09bf0a524d0f.pdf"},{"id":107619175,"identity":"2c114f64-9ad6-434b-9e21-5e51addfb554","added_by":"auto","created_at":"2026-04-23 09:27:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":462159,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileQuestionnaireEnglishversion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9153773/v1/781e59dbe2f91920c6e81be3.pdf"},{"id":107619030,"identity":"681ac893-ec22-4975-9c32-a259cd65a87f","added_by":"auto","created_at":"2026-04-23 09:27:14","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":130048,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.doc","url":"https://assets-eu.researchsquare.com/files/rs-9153773/v1/6ab3496190e54ee3310b1e69.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Threshold effect of the METS-VF index on hyperuricemia in adults with hypertension: a cross-sectional study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eHyperuricemia (HUA) is a pathological state characterized by the accumulation of uric acid in the blood due to impaired purine metabolism. Its primary causes include excessive uric acid production and/or insufficient renal and intestinal excretion (1-2). Globally, the prevalence of HUA ranges from approximately 9.3% to 20.1%, closely linked to changes in dietary patterns, with a trend toward younger onset ages (3-5). The situation in China is particularly severe, with the prevalence rate rising rapidly from 11.1% in 2015\u0026ndash;16 to 14.0% in 2018\u0026ndash;19 (6), affecting approximately 170 million people (7). It has become a major metabolic health issue on par with hypertension. Extensive evidence indicates that HUA is not only a trigger for gout but also an independent risk factor for multiple diseases, including hypertension (8), diabetes (9), cardiovascular disease (10), stroke (11), chronic kidney disease (9), metabolic syndrome, and non-alcoholic fatty liver disease (12).\u003c/p\u003e\n\u003cp\u003eObesity is a public health challenge affecting over 100 million patients worldwide (13). Multiple studies (14-16) have confirmed that obesity (particularly abdominal obesity) is an independent predictor of hyperuricemia. Data from Chinese adults (17-18) also demonstrate a significant positive correlation between serum uric acid (SUA) levels and obesity severity. In recent years, Bello-Chavolla et al. (19) developed the Visceral Fat Metabolic Score (METS-VF). This metric integrates the Metabolic Syndrome Index for Insulin Resistance (METS-IR), waist-to-height ratio (WHtR), age, and sex, effectively predicting chronic diseases such as diabetes (20), cardiovascular disease (21), and hypertension (22).\u003c/p\u003e\n\u003cp\u003eHowever, few studies have explored the association between METS-VF and HUA/SUA levels in hypertensive populations. Given the higher risk of adverse events in this group, this study aims to systematically evaluate the relationship between METS-VF, SUA levels, and HUA risk in Chinese patients with hypertension. This study sought to clarify the dose-response relationship and provide evidence for early identification of high-risk populations for HUA.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Participants\u003c/p\u003e\n\u003cp\u003eThe data for this study were derived from baseline information of the China Hypertension Registry Study, which was registered at the Chinese Clinical Trial Registry (Registration number: ChiCTR1800017274; Registration date: July 20, 2018). This registry study is a multicenter, real-world observational study designed to investigate the clinical characteristics, management status, and long-term prognosis of hypertensive patients in rural China.Wuyuan County, Jiangxi Province, was selected as the site for this study\u0026apos;s field investigation. All participants included were hypertensive patients aged 18 years or older. Hypertension is defined as resting seated systolic blood pressure\u0026nbsp;\u0026ge;\u0026nbsp;140 mmHg and/or diastolic blood pressure\u0026nbsp;\u0026ge;\u0026nbsp;90 mmHg, or a confirmed diagnosis of hypertension with ongoing antihypertensive medication. Participants with cognitive or neurological impairment, or those unable to cooperate with study follow-up, were excluded. This study strictly adheres to the principles of the Declaration of Helsinki. The research protocol was approved by the Ethics Committee of the Institute of Biomedical Sciences, Anhui Medical University (No.: CH1059) and the Ethics Committee of the Second Affiliated Hospital of Nanchang University (No.: 2018019). All participants signed written informed consent forms prior to enrollment.\u003c/p\u003e\n\u003cp\u003eA total of 14,234 hypertensive patients were included in this study, exclusions were based on missing data for METS-VF (n = 878), missing data on BMI (n = 5), FPG (n = 7), smoking status (n = 2), and drinking status (n = 1), finally, an analysis was conducted on 13,341 subjects (Fig. 1).\u003c/p\u003e\n\u003cp\u003e2.2 Data collection\u003c/p\u003e\n\u003cp\u003eAll baseline data were collected by uniformly trained investigators following standardized procedures. Data on demographic characteristics (including age and sex), lifestyle factors (such as smoking and alcohol consumption history), medical history (e.g., diabetes, family history of coronary heart disease, and hypertension), and medication use (e.g., antihypertensive, antidiabetic, and lipid-lowering drugs) were gathered through structured questionnaires(23). Anthropometric measurements included height, body weight, and waist circumference, and body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Blood pressure was measured using an Omron electronic sphygmomanometer (Omron; Dalian, China). Participants rested quietly for at least 5 minutes, followed by three consecutive measurements at 1-minute intervals; the average value was recorded.\u003c/p\u003e\n\u003cp\u003eAll study subjects were required to fast for at least 8 hours prior to blood sample collection. Venous blood samples were centrifuged to separate serum, which was stored at -80\u0026deg;C. Subsequently, the samples were sent to Shenzhen Biaojia Bioengineering Co., Ltd. for analysis. Fasting Plasma Glucose, a four-item lipid panel (HDL-C, LDL-C, TG, TC), and homocysteine (Hcy) levels were measured using a fully automated biochemical analyzer (Beckman Coulter, USA). All testing procedures adhered to standardized operating protocols and quality control requirements.\u003c/p\u003e\n\u003cp\u003e2.3 Definitions\u003c/p\u003e\n\u003cp\u003eAccording to the guidelines of the Chinese Society of Endocrinology, hyperuricemia is diagnosed when serum uric acid levels exceed 420 \u0026mu;mol/L (7.0 mg/dL) in males and 360 \u0026mu;mol/L (6.0 mg/dL) in females (24-26).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1776932458.png\" width=\"625\" height=\"183\"\u003e\u003c/p\u003e\n\u003cp\u003e2.4 Statistical analyzes\u003c/p\u003e\n\u003cp\u003eThe presence or absence of HUA categorized baseline characteristics of the study population. Continuous variables were expressed as mean \u0026plusmn; standard deviation (SD), while categorical variables were expressed as numbers and percentages. Descriptions were grouped according to METS-VF quartiles, with intergroup differences assessed using t-tests or chi-square tests.\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between METS-VF and serum uric acid (SUA) levels and hyperuricemia (HUA). The independent relationship between the METS-VF index and SUA and HUA was tested using smooth curve fitting and generalized additive models, with adjustments for major covariates in all three models. Model 1: Crude model (unadjusted); Model 2: Adjusted for age and sex; Model 3: Model 2 plus DBP, SBP, Hcy, TC, LDL-C, eGFR, current smoking, current drinking, diabetes, lipid-lowering drugs, antiplatelet drugs, and glucose-lowering drugs. Additionally, we conducted subgroup analyzes to explore potential factors influencing this association.\u003c/p\u003e\n\u003cp\u003eAll statistical analyzes were performed using R software (version 3.3.1; http://www.R-project.org) and EmpowerStats software (version 2.17.8; www.empowerstats.com). A two-tailed P value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Baseline Characteristics of Study Participants\u003c/p\u003e\n\u003cp\u003eThe final data analysis included 13,341 hypertensive patients. The overall mean age of the participants was 63.95\u0026nbsp;\u0026plusmn;\u0026nbsp;9.30 years and included 6,187 men (46.38%). The prevalence of hyperuricemia was 44.32%, with rates across the first to fourth METS-VF quartiles of 42.67%, 42.13%, 43.78%, and 48.71%, respectively. Participant characteristics grouped by METS-VF quartiles are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eHigher METS-VF was significantly associated with adverse body composition (higher WC and BMI) and a more adverse metabolic profile, characterized by elevated FPG, TG, and SUA, and reduced HDL-C. It was also associated with a higher prevalence of diabetes and use of related medications. Specifically, participants in higher METS-VF quartiles exhibited greater WC, BMI, DBP, FPG, SUA, and TG, and lower HDL-C. They were also more likely to be female, to have diabetes, and to use lipid-lowering and glucose-lowering drugs. Conversely, the lower proportions of current smokers and drinkers in the higher METS-VF groups may be related to their higher proportion of women. TC and LDL-C levels showed a trend of initial increase followed by a decline (Table 1).\u003c/p\u003e\n\u003cp\u003e3.2 Relationship between METS-VF Index and SUA and HUA\u003c/p\u003e\n\u003cp\u003eTables 2 and 3 shew the association of the METS-VF index with serum uric acid (SUA) levels and hyperuricemia (HUA) risk across different models. The multiple linear regression model indicated that each 1-unit increase in the METS-VF index was associated with a 13.87 \u0026nbsp;(95% CI: 12.29, 15.45) \u0026mu;mol/L increase in SUA levels and a 30% higher risk of HUA (OR = 1.30; 95% CI: 1.25, 1.36).\u003c/p\u003e\n\u003cp\u003eMultivariable linear regression models (Models 1\u0026ndash;3) demonstrated a positive association between the METS-VF index and SUA (Table 2). When grouped by METS-VF quartiles, the adjusted \u0026beta; coefficients for SUA in Q2-Q4, compared to Q1, were 16.74 (95% CI: 11.99, 21.49), 36.60 (95% CI: 31.63, 41.56), and 50.22 (95% CI: 45.03, 55.40) in the fully adjusted model (Model 3) (Table 2).\u003c/p\u003e\n\u003cp\u003eConsistently, the ORs of HUA for subjects in Q2-Q4 of the METS-VF index were 1.38 (95% CI: 1.23, 1.56), 1.98 (95% CI: 1.75, 2.24), and 2.57 (95% CI: 2.26, 2.93) , respectively, compared to those in the lowest quartile (Q1). A significant dose-response relationship was observed, with the strength of association increasing across METS-VF quartiles (P for trend \u0026lt; 0.001) (Table 3).\u003c/p\u003e\n\u003cp\u003e3.3 Dose-Response and Threshold Effect Analysis\u003c/p\u003e\n\u003cp\u003eTo characterize the nonlinear relationships and threshold effects of METS-VF on serum uric acid (SUA) levels and hyperuricemia (HUA) risk, we applied smoothing curve fitting. As visualized in Figure 2, a significant dose-response relationship was identified for both conditions. (Fig. 2).\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4 and Table 5, the two-piecewise regression analysis identified a METS-VF inflection point of 8.5 in the fully adjusted Model 3. Below this threshold, each 1-unit increase in METS-VF was significantly associated with a 24.44 \u0026mu;mol/L rise in serum uric acid (SUA) levels (95% CI: 21.94, 26.93) and a higher risk of hyperuricemia (HUA) (OR = 1.58, 95% CI: 1.49, 1.68). Above this threshold, however, the associations were no longer significant for both SUA levels (\u0026beta; = 0.81, 95% CI: -2.05, 3.68) and HUA risk (OR = 1.01, 95% CI: 0.94, 1.08).(Table 4 and Table 5).\u003c/p\u003e\n\u003cp\u003e3.4 Subgroup analyzes\u003c/p\u003e\n\u003cp\u003eWe further conducted stratified analyzes to assess the impact of the METS-VF index (per 1-unit increase) on hyperuricemia across different subgroups. No significant interactions were observed in the subgroups of age (\u0026lt;65 vs. \u0026ge;65 years), BMI (\u0026lt;24 vs. \u0026ge;24 kg/m\u0026sup2;), eGFR (\u0026lt;60 vs. \u0026ge;60 mL/min per 1.73 m\u0026sup2;), current smoking \u0026nbsp;(no vs. yes),current drinking (no vs. yes), or diabetes (no vs. yes) (all P for interaction \u0026gt; 0.05). However, a significant interaction was observed with sex. The association between the METS-VF index and hyperuricemia was stronger in women (OR = 1.43, 95% CI: 1.35, 1.52) than that in men (OR = 1.21, 95% CI: 1.14, 1.27; P for interaction \u0026lt; 0.001) (Fig. 3).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis large population-based study\u0026nbsp;first demonstrated\u0026nbsp;a positive association between the METS-VF index and hyperuricemia in hypertensive patients. After full adjustment, the METS-VF index was correlated with both HUA risk and SUA levels in a linear dose-response manner.\u0026nbsp;A significantly stronger association was observed\u0026nbsp;in females (P for interaction \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eAlthough traditional indicators such as body mass index (BMI) and waist circumference (WC) have been widely demonstrated to correlate with hyperuricemia (HUA) (27-29), their clinical utility is limited. BMI cannot distinguish lean from fat mass and fails to reflect visceral fat accumulation, whereas WC and waist-to-height ratio (WHtR) are susceptible to influences from ethnicity, sex, and age (30-32). Therefore, more precise indicators are needed to assess metabolic risk.In recent years, the Metabolic Score for Visceral Fat (METS-VF) has garnered attention as a novel indicator for assessing HUA risk. The strength of METS-VF lies in its integration of multiple parameters, including age, sex, WHtR, and the Metabolic Score for Insulin Resistance (METS-IR) (21,33), enabling a more comprehensive assessment of visceral adipose tissue function.\u003c/p\u003e\n\u003cp\u003eMultiple studies support its predictive value. For instance, Ji et al. (34) conducted a multicenter cross-sectional study involving 8,877 patients with hypertension and hyperuricemia, finding a significant positive correlation between METS-VF and gout risk. Each one-standard-deviation increase in METS-VF was associated with an 82% higher risk of gout (OR=1.82, 95% CI: 1.62, 2.03). This finding aligns with the role of METS-VF in broader uric acid metabolism abnormalities. A large-scale retrospective cohort study by Liu et al. (35) confirmed that in non-obese adults, METS-VF was significantly and positively associated with HUA incidence, an association particularly pronounced in women. Xie et al. (36), using NHANES data, further validated that the highest METS-VF quartile carried a 6.07-fold higher HUA risk compared to the lowest quartile, with stronger associations observed in women and certain ethnic groups. Our findings in a hypertensive population are consistent, showing a significant association between METS-VF and HUA risk.\u003c/p\u003e\n\u003cp\u003eOur findings of a positive correlation between METS-VF and HUA can be explained by its representation of visceral adipose tissue dysfunction. Excess visceral fat leads to an increased influx of free fatty acids to the liver via the portal vein, which not only promotes triglyceride synthesis but also enhances uric acid production by altering purine metabolism (37). Concurrently, adipose tissue dysfunction induces insulin resistance, which impairs renal function and reduces uric acid excretion (38-39), in hypertensive patients, hyperuricemia (HUA) commonly coexists with hypertension, and the two conditions often mutually exacerbate each other (40). Basic research has shown that elevated serum uric acid (SUA) contributes to the progression of hypertension through mechanisms such as renal impairment, chronic inflammation, and endothelial dysfunction (41-42). Observational studies have consistently identified SUA as an independent risk factor for both hypertension and cardiovascular events (43-46). This underscores the importance of investigating HUA in this population.\u003c/p\u003e\n\u003cp\u003eOur study further identified a potentially stronger association between METS-VF and HUA in women. As women age, particularly post-menopause, declining estrogen levels prompt a redistribution of body fat from subcutaneous to visceral depots, leading to a substantial accumulation of visceral adipose tissue that can reach twice the premenopausal levels (47). This physiological shift aligns with the epidemiological trend of HUA prevalence in women exceeding that in men after the age of 65 (48). Furthermore, evidence suggests that visceral fat accumulation poses a greater metabolic hazard for women (49). By incorporating age and sex, METS-VF effectively captures this sex-specific pattern of visceral fat accumulation and\u0026nbsp;the consequent HUA risk.\u003c/p\u003e\n\u003cp\u003eIn summary, in our hypertensive cohort, visceral obesity as captured by the METS-VF index and its related metabolic disturbances may form a shared pathophysiological link among obesity, HUA, and hypertension. For instance, obesity-related dyslipidemia can reduce uric acid excretion (50-51), while SUA-associated inflammatory responses may further raise blood pressure (52-53), thereby creating a vicious cycle. Given that visceral fat poses a greater threat to uric acid metabolism than subcutaneous fat (54), METS-VF offers a superior assessment capability. Therefore, the early use of METS-VF to identify hypertensive patients at high risk of HUA holds important clinical value, enabling targeted interventions and potentially improving cardiovascular outcomes.\u003c/p\u003e\n\u003cp\u003eThe primary strength of this study lies in its pioneering investigation of the association between the Metabolic Score for Visceral Fat (METS-VF) and hyperuricemia (HUA) in a Chinese hypertensive population. Additionally, the large, population-based sample provided substantial statistical power, and the robustness of the findings was supported by subgroup analyzes.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations should be considered. First, the cross-sectional design precludes causal inference, and the exact role of METS-VF in the progression of HUA warrants verification in prospective studies. Second, although multiple potential confounders were adjusted for, residual confounding cannot be fully excluded. Third, as all participants were of Chinese origin, the generalizability of the results to other ethnic groups requires further validation. Finally, due to data limitations, we were unable to account for certain potential influences on serum uric acid levels, such as detailed dietary patterns and specific medication use.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this hypertensive population, we observed a significant positive association between the visceral adiposity index (METS-VF) and the risk of hyperuricemia (HUA). This association was particularly pronounced in female patients. The METS-VF index, which integrates age, sex, anthropometric measures, and metabolic parameters, shows promise as a clinical tool for HUA risk assessment. Its application could potentially improve hyperuricemia screening in Southern China and offer new avenues for developing urate-lowering strategies in obese hypertensive patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in the article/supplementary materials. Further inquiries may be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of the Anhui Medical University\u0026apos;s Institute of Biomedicine (No. CH1059) approved this study. All procedures adhered to the Declaration of Helsinki, and written informed consent was acquired from all participants.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eMW: Data curation, writing-review editing, writing-manuscript. CCY: Review and editing, data oversight. SL: Review and editing, supervision.CY: Review and editing, data oversight, supervision. LZ: Data curation, drafting initial manuscript. TW: Writing-review and editing. WZ: Writing-review editing, final approval. WFZ: Data curation, methodology, writing-review and editing. HB: Manuscript review and editing, data organization, formal analysis. XC: Data curation, writing-review and editing.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Jiangxi Science and Technology Innovation Base Plan - Jiangxi Clinical Medical Research Center(20223BCG74012), Key Research and Development Program of Jiangxi (20243BBI91021),Fund project of the Second Affiliated Hospital of Nanchang University（2021efyA01,2023efyA05).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the staff of the China Hypertension Registry Study and to the research participants for their valuable contributions and time dedicated to this work.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that no potential conflicts of interest, whether commercial or financial, exist in relation to this research.\u003c/p\u003e\n\u003cp\u003eGenerative AI statement\u003c/p\u003e\n\u003cp\u003eThe authors provide the following declaration: No generative AI was used during the drafting of this work. Should any concerns arise, we welcome communication.\u003c/p\u003e\n\u003cp\u003ePublisher\u0026apos;s note\u003c/p\u003e\n\u003cp\u003eThe views and opinions presented in this article belong solely to the authors and are not representative of their affiliated institutions, the publisher, or the article\u0026apos;s editors and reviewers. Furthermore, the publisher does not endorse or assume responsibility for any product evaluation or manufacturer\u0026apos;s claims mentioned in the text.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWen S, Arakawa H, Tamai I. Uric acid in health and disease: From physiological functions to pathogenic mechanisms. Pharmacol Ther 2024;256:108615. doi: 10.1016/j.pharmthera.2024.108615. \u003c/li\u003e\n\u003cli\u003eMajor TJ, Dalbeth N, Stahl EA, Merriman TR. An update on the genetics of hyperuricaemia and gout. 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Clin Rheumatol 2017;36:1103-1110. doi: 10.1007/s10067-017-3548-2. \u003c/li\u003e\n\u003cli\u003eZhang H, Chen R, Xu X, Yang M, Xu W, Xiang S, et al. Metabolically healthy obesity is associated with higher risk of both hyperfiltration and mildly reduced estimated glomerular filtration rate: the role of serum uric acid in a cross-sectional study. J Transl Med 2023;21:216. doi: 10.1186/s12967-023-04003-y. \u003c/li\u003e\n\u003cli\u003eTsushima Y, Nishizawa H, Tochino Y, Nakatsuji H, Sekimoto R, Nagao H, et al. Uric acid secretion from adipose tissue and its increase in obesity. J Biol Chem 2013;288:27138-27149. doi: 10.1074/jbc.M113.485094. \u003c/li\u003e\n\u003cli\u003eJoosten LAB, Crişan TO, Bjornstad P, Johnson RJ. Asymptomatic hyperuricaemia: a silent activator of the innate immune system. Nat Rev Rheumatol 2020;16:75-86. doi: 10.1038/s41584-019-0334-3. \u003c/li\u003e\n\u003cli\u003eTakahashi S, Yamamoto T, Tsutsumi Z, Moriwaki Y, Yamakita J, Higashino K. Close correlation between visceral fat accumulation and uric acid metabolism in healthy men. Metabolism 1997;46:1162-5. doi: 10.1016/s0026-0495(97)90210-9. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section.\u003c/p\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":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hyperuricemia, hypertension, METS-VF, Threshold Effect, Chinese adults","lastPublishedDoi":"10.21203/rs.3.rs-9153773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9153773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Hyperuricemia (HUA) is a significant public health issue closely associated with obesity. The novel visceral adiposity index (METS-VF) demonstrates predictive value for multiple chronic diseases, yet its association with HUA risk in hypertensive patients remains unclear. This study aimed to investigate this association in a Chinese hypertensive population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis study utilized data from the Chinese Hypertension Registry Study, including 13,341 hypertensive patients. Data collection was conducted through standardized questionnaires, physical examinations, and laboratory tests. The association between METS-VF and hyperuricemia (HUA) was evaluated using multivariate logistic regression. Robustness was assessed via multivariable-adjusted models and subgroup analyzes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThe prevalence of HUA was 44.32%. Each unit increase in METS-VF was associated with a 13.87 (95% CI: 12.29, 15.45) μmol/L increase in serum uric acid levels and a 30% higher risk of HUA (OR = 1.30; 95% CI: 1.25, 1.36). The risk of HUA rose significantly across increasing METS-VF quartiles (P for trend \u0026lt; 0.001). Subgroup analysis indicated a stronger association in women (P for interaction \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn patients with hypertension, the visceral adiposity index (METS-VF) was positively associated with the risk of hyperuricemia (HUA).\u003c/p\u003e","manuscriptTitle":"Threshold effect of the METS-VF index on hyperuricemia in adults with hypertension: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:26:52","doi":"10.21203/rs.3.rs-9153773/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"174925536816231695803885403239796105477","date":"2026-04-28T08:19:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T01:53:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T01:09:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313278534447798323670880850029556124436","date":"2026-04-27T00:38:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T16:52:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35745431862890659685606008456570158351","date":"2026-04-24T15:05:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T15:27:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255902712281041249895303373456837442764","date":"2026-04-22T13:39:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140134970944031867773718190428688305294","date":"2026-04-22T09:42:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T09:50:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T07:10:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T08:58:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T08:00:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2026-03-23T07:29:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64d6578a-2e0e-44ea-91fe-2d5ab9a4bc7b","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:26:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:26:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9153773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9153773","identity":"rs-9153773","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}