Prevalence of hyperuricemia and its correlation with metabolic syndrome in young adults: a cross-sectional study in eastern China

Prevalence of hyperuricemia and its correlation with metabolic syndrome in young adults: a cross-sectional study in eastern China | 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 Prevalence of hyperuricemia and its correlation with metabolic syndrome in young adults: a cross-sectional study in eastern China Bohan Lu, Cuirong Hu, Jifang Lu, Jinkun Wang, Haijiao Jin, Ling Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal.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-3969671/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hyperuricemia (HUA) has emerged as a significant metabolic disease, particularly in young population with metabolic syndrome (MS). The purpose of this study was to study the prevalence of HUA and its correlation with metabolic syndrome among young adults in a coastal city of eastern China. Methods It was a cross-sectional study conducted in adults undergoing routine healthy checkup. Anthropometric data and serological parameters were collected and in related to serum uric acid (SUA) concentration and prevalence of HUA. Results A total of 9,196 adults with mean age of 34.3 ± 11.8 years old and 75.2% of males were recruited. Mean SUA level was 371.8 ± 95.6 µmol/l and overall HUA prevalence was 31.3%. SUA level was higher and HUA was more common in younger males, as well as in those with MS (OR: 3.07; 95% CI: 2.78–3.39) demonstrated by using the univariable binary logistic regression analysis model. The multivariable binary logistic regression analysis revealed that male (OR: 3.74; 95% CI: 3.12–4.48), young age (OR: 1.22; 95% CI: 1.19–1.26), low estimated-glomerular filtration rate (OR: 2.45; 95% CI: 2.11–2.84), high body mass index (OR: 2.31; 95%CI: 2.06–2.60), hypertension (OR: 1.18; 95% CI: 1.04–1.32), high serum triglyceride (OR: 2.08; 95%CI: 1.81–2.37), and low serum high-density lipoprotein cholesterol (OR: 1.33; 95% CI: 1.15–1.55) were independent risk factors associated with HUA prevalence. Conclusion SUA level increased and HUA was common in young adults. Male, young age, reduced kidney function, combined with MS and more MS components were associated with prevalence of HUA. Hyperuricemia serum uric acid metabolic syndrome kidney function Figures Figure 1 Figure 2 Figure 3 Introduction Hyperuricemia (HUA) is a purine metabolic disorder caused by excessive production and/or reduced excretion of uric acid [ 1 ]. Accompanied with economic development, the incidence of HUA increases significantly in the worldwide mainly due to elevated dietary purine intake. HUA is commonly prevalent with other metabolic disorders including diabetes, dyslipidemia, hypertension, etc [ 2 – 4 ]. It results in gout and gout arthritis, which leads to joint deformity and dysfunction [ 5 ]. Moreover, HUA has been confirmed as an independent risk factor for the development of chronic kidney disease [ 3 , 6 ]. Furthermore, increased serum uric acid is demonstrated to be associated with elevated cardiovascular-related and all-cause mortality [ 3 , 4 , 6 ]. The prevalence of HUA differs with variations in region, race, sex, and age [ 2 , 7 ]. HUA is more common in the western countries mainly because of the intake of high purine containing diet. Based on the results of the National Health and Nutrition Examination Survey (NHANES), the overall prevalence of HUA was up to 20.1% in America [ 7 ]. In a cross-sectional study of health insurance claims data, HUA was found to be present in 13.4% of the overall population in Japan [ 8 ]. The prevalence of HUA was reported to be lower in South Korea, India, and Thailand [ 9 – 11 ]. The overall prevalence of HUA in Chinese adults in 2018–2019 was reported to be 14.0% [ 12 ]. It showed a wide regional variation due to the large area of territory, differences in dietary patterns, and inconsistent economic status in different cities of China. The purpose of this study was to analyze the prevalence of HUA in a young adult population receiving routine health examination in a coastal city of eastern China. We investigated the MS and its components correlation with the prevalence of HUA and explored clinical strategies to lower SUA level. Methods Study design This was a cross-sectional study conducted in a young adult population undergoing routine healthy examination at Ningbo Hangzhou Bay Hospital, Zhejiang Province, China during 2020. The protocol was approved by the Hangzhou Bay Hospital Ethics Committee (approval number: WYLS2021-3) and all subjects participated in the study after written informed consent. Subject recruitment and clinical data retrieval Subjects were enrolled based on the following inclusion criteria: 1. age of over 18 years old; 2. long-term resident in mainland China; 3. no severe mental or psychiatric disorder. Anthropometric characteristics of height, body weight and blood pressure (BP) were measured according to standard protocols. Body mass index (BMI, kg/m 2 ) was calculated as body mass (kg) / height squared (m 2 ). Serological parameters including serum uric acid, serum creatinine (SCR), fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) were measured using an automated biochemistry analyzer Beckman Coulter AU5800 (Beckman Coulter Inc., Brea, CA, USA). Definitions of metabolic disorders HUA was defined as serum uric acid level ≥ 420.0 µmol/l (7 mg/dl) for males and ≥ 360 µmol/l (6 mg/dl) for females, according to the Integrated Diabetes & Endocrine Academy (IDEA) consensus statement [ 13 ]. According to the world-wide definition of metabolic syndrome (MS) by International Diabetes Federation (IDF) [ 14 ], MS was defined as having central obesity plus 2 or more of following 4 additional criteria: 1. Serum TG level of ≥ 1.7 mmol/l (150 mg/dl), 2. Serum HDL-cholesterol level of < 1.03 mmol/l (40 mg/dl) in males or < 1.29 mmol/l (50 mg/dl) in females, or undergoing specific treatment of dyslipidemia, 3. hypertension diagnosed as systolic BP ≥ 130 mmHg or diastolic BP ≥ 85mmHg, or undertaking anti-hypertensive medicine, 4. diagnosed type 2 diabetes or raised fasting blood glucose (FBG) of ≥ 5.6 mmol/l. Because waist circumference data was not recorded in this study, we used BMI ≥ 25 kg/m 2 to assess overweight instead of central obesity. Statistical analysis The data was presented as mean ± standard deviation for normally distributed continuous variables, median (25th to 75th percentile) for non-normally distributed variables, and percentages for categorical data. Differences between groups were analyzed using student’s t -test, Mann-Whitney U test, Kruskal-Wallis test, or χ 2 test depending on data type. Univariable and multivariable binary logistic regression analysis was used to investigate the association between clinical factors and prevalence of HUA. Forward likelihood ration (LR) was used to remove invalid variables in a stepwise fashion selected independent variables that significantly influence the dependent variable to enter the model, with odds ratios (OR) and 95% confidence intervals (95% CI) quantified. P values < 0.05 were considered to be statistically significant difference. SPSS version 24.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis, and graphs were created using GraphPad Prism 8.0 (GraphPad Software, Inc., San Diego, CA, USA). Results Subjects and prevalence of HUA A total of 9,196 adults were enrolled in the study, with mean age of 34.3 ± 11.8 years and 6,918 (75.2%) of them were male. The mean SUA level was 371.8 ± 95.6 µmol/l and HUA was present in 31.5% of the subjects. Patients with HUA demonstrated younger age, higher BMI and blood pressure, higher serum concentration of FBG, TC, TG and LDL, but reduced e-GFR and serum HDL level ( P < 0.01 for all, shown in Table. 1 ). There was significant higher prevalence of MS in patients with HUA versus those with normal SUA level (41.5% versus 21.0%, P < 0.01, shown in Table. 1 ) Correlation of HUA with age and sex Level of SUA and the prevalence of HUA were higher in younger adults. Subject group of 18–24 years old demonstrated highest SUA level of 399.3 ± 94.3 µmol/l, with HUA prevalence of 37.7%. In addition, HUA was more common in male subjects compared to females (SUA level: 382.8 ± 84.6 versus 280.0 ± 62.9 µmol/l, HUA prevalence: 38.3% versus 10.0%, P < 0.01 for all). SUA level in male subjects were significantly higher than females in all the age groups ( P < 0.01 for all, shown in Fig. 1 a and b ). In male population, the SUA level (415.9 ± 87.9 µmol/l) and prevalence of HUA (41.8%) were highest in age group of 18–24 years old. SUA level and HUA prevalence decreased along with the increase in age and kept stable in subjects of over 45 years old ( P < 0.01, shown in Fig. 1 a and b ). Whilst SUA level (301.9 ± 68.2 µmol/l) and HUA prevalence (17.3%) were highest in age group of over 50 years in female population ( P < 0.01, shown in Fig. 1 a and b ). Correlation of HUA with MS SUA level and the prevalence of HUA were significantly higher in subjects with MS versus those without MS (SUA level: 412.6 ± 96.5 versus 350.4 ± 88.0 µmol/l; HUA prevalence: 47.4% versus 22.4%, P < 0.01 for all, shown in Fig. 2 a and b ). SUA level and the prevalence of HUA were also significantly higher in subjects with the presence of MS components including high BMI, dyslipidemia, hypertension, or diabetes, when compared to those without ( P < 0.01 for all, shown in Fig. 2 a and b ). Furthermore, SUA level and prevalence of HUA increased along with increase in the number of MS component that subjects exhibited ( P < 0.01 for all, shown in Fig. 3 a and b ). Patients with all of the 5 MS components demonstrated highest SUA level (417.7 ± 92.5 µmol/l) and HUA prevalence (50.3%). Risk factors associated with prevalence of HUA Univariable binary logistic regression analysis was performed to evaluate the association between HUA prevalence and clinical risk factors. It was demonstrated that male (OR: 5.58; 95% CI: 4.83–6.45), 5 years younger (OR: 1.10; 95% CI: 1.08–1.12), reduced kidney function (OR: 1.69; 95% CI: 1.52–1.88), and combination with MS (OR: 3.07; 95% CI: 2.78–3.39) were risk factors associated with the prevalence of HUA ( P < 0.01, shown in Table. 2 ). In the multivariable binary logistic regression analysis model, the results revealed that male (OR: 3.74; 95% CI: 3.12–4.48), younger age (OR: 1.22; 95% CI: 1.19–1.26), low e-GFR (OR: 2.45; 95% CI: 2.11–2.84), high BMI (OR: 2.31; 95% CI: 2.06–2.60), hypertension (OR: 1.18; 95% CI: 1.04–1.32), high serum TG (OR: 2.08; 95% CI: 1.81–2.37), and low serum HDL (OR: 1.33; 95% CI: 1.15–1.55) were independent risk factors for the presence of HUA ( P < 0.01 for all, shown in Table. 2 ). Discussion In the current cross-sectional study, we demonstrated that HUA was prevalent in 31.5% of a young adult population in a coastal city of eastern China. In addition, we found that SUA level was significantly higher and HUA was more common in younger males, as well as in those with MS. Furthermore, male, young age, reduced kidney function, and combined with MS were independent risk factors associated with HUA prevalence. The prevalence of HUA shown in the present study was relatively higher when compared with most of previous studies from China, and was even higher than selected studies conducted in developed countries. A nationwide representative cross-sectional survey conducted in mainland China from 2018 to 2019 found that the overall prevalence of HUA was 14.0%, with 24.4% in men and 3.6% in women [ 12 ]. Whilst the prevalence of HUA in USA was reported to be 17.7% in 2013 to 2018 [ 15 ]. The high prevalence of HUA found in our study was mainly explained by the coastal location of the area and high-purine containing seafood is a daily home-cooking meal for local residents. A direct association between increased seafood intake and elevated prevalence of HUA has been documented in a previous investigation [ 16 ]. In addition, the economic status of the city, where the present study conducted at, ranks in the top level of China. It may also contribute to the high HUA prevalence as HUA is more common in economically developed areas [ 12 , 17 ]. Indeed, we found that mean SUA level was significantly higher in local residents than non-locals, although the difference in HUA prevalence did not reach statistical significance. Younger individuals were more likely to develop HUA and young age was shown to be an independent risk factor associated with increases HUA prevalence in the current study. It may be related to elevated intake of high purine containing diet and high prevalence of obesity among young people, especially in male subjects [ 18 ]. In a large cross-sectional study performed in southwestern China, the researchers demonstrated younger subjects with overweight or obesity were more likely to develop HUA compared to older individuals [ 19 ]. Another large prospective cohort study conducted in northern China, also revealed HUA prevalence was the highest in younger male subjects [ 20 ]. In line with previous studies [ 12 , 15 , 17 , 21 ], our study demonstrated significant sex differences in SUA level and HUA prevalence. It may be explained by the differences in physiological hormone secretion, lifestyle such as smoking history and alcohol use, and diet habit between men and women. A logistic regression analysis showed that sex hormones had a dose-response relationship with HUA prevalence, such as estradiol and testosterone [ 21 ]. Furthermore, we found a U-shaped distribution of HUA prevalence along with the increase of age in female individuals, with highest HUA prevalence in the age group of over 50 years old. It may be related to postmenopausal estrogen depletion, as estrogen contributes to promote kidney clearance of uric acid [ 22 ]. A positive association between reduced e-GFR and increased SUA was shown in the current study. Impaired kidney function results in reduced excretion of uric acid through the urine. Thus HUA is a common clinical characteristic in patients with chronic kidney disease [ 23 ]. On the other hand, HUA could directly trigger kidney injury through inducing oxidative stress, endothelial injury, inflammatory and fibrotic responses in the kidney tissue [ 24 ]. It has been documented that HUA is a common reason for kidney impairment and it is an independent predictor for the incidence of end-stage kidney disease [ 25 ]. Consistent to previous studies [ 26 – 28 ], we found a close relationship between HUA prevalence and the presence of MS. Individuals with HUA demonstrated increased burden of MS and HUA prevalence was found to be significantly higher in subjects with MS or its components. A large cross-sectional study conducted using the NHANES datasets from 2013 to 2018 found that HUA were more likely to exhibit MS compared to those without HUA after controlling known risk factors of MS [ 15 ]. A meta-analysis on the basis of 11 studies, which included 54,970 participants and 8,719 MS cases, showed a linear dose-response relationship between SUA level and MS risk [ 29 ]. Mediated by insulin resistance, high fat diets and increased intake of fructose were shown to be positively correlated with the SUA level, which also contribute to the development of MS [ 4 , 30 ]. In addition, our results found MS components were independent risk factors for HUA prevalence, including overweight, hypertension, dyslipidemia. The positive relationship between overweight and the presence of HUA has been documented in previous study and HUA prevalence was reported to reach 66.9% in obese individuals [ 2 , 31 ]. The finding may be directly explained by the fact that increased amount of dietary purine intake in obese subjects. Similar to our results, hypertension and increased serum TG level were found to be two risk factors for the development of HUA in a prospective cohort study conducted in 2,017 Chinese subjects [ 32 ]. HUA could trigger or worsen hypertension through the potential mechanisms inducing inflammation, endothelial dysfunction, proliferation of vascular smooth muscle cells, and activation of the renin-angiotensin system [ 4 ]. Moreover, we found that patients with HUA had significantly higher level of serum TG but reduced serum HDL level. A positive association between TG/HDL ratio and HUA incidence was also indicated in a Chinese retrospective cohort study with 4 years’ follow-up [ 33 ]. It may be explained that HUA disrupts the balance of the ADMA/DDAH pathway, which further promotes lipid peroxidation and leads to lipid metabolic disorders [ 34 ]. There are several limitations in the study that need to be discussed. Firstly, the investigation was conducted at a single center located in a coastal city, thus the results may not be applicable to patient population living in non-coastal areas. Secondly, the cross-sectional design of the study resulted in a failure to elucidate a causal relationship between the development of HUA and the associated risk factors. Thus, prospective cohort studies are required to further confirm the results. Furthermore, there were some parameters which may influence SUA level were not collected in the study, such as diet purine containing, urine purine excretion, etc. Also, BMI was used to evaluate abdominal obesity instead of waist circumference. It was not an ideal index for the assessment of MS. Conclusion In summary, we demonstrated that the prevalence of HUA of young adults at the eastern coastal of China was high. Based on our findings, male, young age, reduced kidney function, combined with MS and more MS components were risk factors associated with HUA prevalence. It is reasonable to encourage young people to change their diet style, and to exercise more to reduce MS burden, and in doing so to decrease SUA level and thus lower the prevalence of HUA. Declarations Ethics approval and consent to participate Ethics approval statement: this study protocol was reviewed and approved by Hangzhou Bay Hospital Ethics Committee, approval number [WYLS2021-3]. Consent to participate statement: written informed consent was obtained from all participants in this study. Consent to participate Every human participant should provide their consent. Consent for publication Not applicable. Availability of data and materials The original data that support the findings of this study are not publicly available on ethical grounds. If editors, reviewers and readers want to access the data, please contact to the corresponding author upon reasonable request. Competing interests The authors have no conflicts of interest to declare. Funding This work was supported by grants from the National Natural Scientific Foundation of China (No. 81970574 and 82170685 to M.S.; No. 82370743 to J.N.), Shanghai Municipal Health Commission (NO.ZY(2021-2023)-0208 and NO.ZY(2021-2023)-0302 to M.S.), National Administration of Traditional Chinese Medicine High-level Key Disciplines Project: Translational integrated traditional Chinese and Western medicine awarded to M.S., Key discipline projects of Three-year Action Plan to strengthen public health system construction in Shanghai (2023-2025) awarded to M.S., Innovative research team of high-level local universities in Shanghai awarded to M.S. The funders had no role in study design, data collection, analysis, decision to publish or manuscript preparation. Authors' contributions L.B.H. recruited the subjects, collected and analyzed the data, and wrote the manuscript. J.N. and M.S. designed and supervised the study, interpreted the data, and revised the manuscript. H.C.R contributed to data analysis and interpretation. L.J.F., W.J.K., and J.H.J. participated in study recruitment. G.L.Y. and W.L. helped with the study design. N.Z.H. contributed to study design and data interpretation. Acknowledgements Not applicable. Authors' information 1 Department of Nephrology, Ningbo Hangzhou Bay Hospital, Ningbo, Zhejiang, China. 2 Department of Nephrology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. 3 Shanghai Jiaotong University School of Medicine, Shanghai, China. References Major TJ, Dalbeth N, Stahl EA, Merriman TR. An update on the genetics of hyperuricaemia and gout. Nat Rev Rheumatol. 2018;14(6):341–53. Kuwabara M, Kuwabara R, Niwa K, Hisatome I, Smits G, Roncal-Jimenez CA, MacLean PS, Yracheta JM, Ohno M, Lanaspa MA et al. Different Risk for Hypertension, Diabetes, Dyslipidemia, and Hyperuricemia According to Level of Body Mass Index in Japanese and American Subjects. Nutrients 2018, 10(8):1011. 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Hyperuricemia induces endothelial dysfunction and accelerates atherosclerosis by disturbing the asymmetric dimethylarginine/dimethylarginine dimethylaminotransferase 2 pathway. Redox Biol. 2021;46:102108. Tables Table 1. Comparisons of clinical characteristics between subjects with and without HUA Variable Total HUA (n=9196) With (n=2878) Without (n=6318) P value SUA, μmol/l 371.8 ± 95.6 479.5 ± 63.4 322.8 ± 61.5 <0.01 Male, n (%) 6918 (75.2%) 2650 (92.1%) 4268 (67.6%) <0.01 Local resident, n (%) 5334 (58.0%) 1653 (57.4%) 3681 (58.3%) 0.46 Age, years 34.3 ± 11.8 32.6 ± 11.4 35.1 ± 12.0 <0.01 BMI, kg/m 2 23.9 ± 3.7 25.6 ± 3.7 23.1 ± 3.4 <0.01 SBP, mmHg 124.9 ± 14.6 127.8 ± 14.1 123.6 ± 14.7 <0.01 DBP, mmHg 76.4 ± 10.7 78.4 ± 10.9 75.5 ± 10.5 <0.01 FBG, mmol/l 5.2 ± 0.9 5.2 ± 0.7 5.2 ± 1.0 <0.01 SCR, μmol/l 82.1 ± 13.0 88.3 ± 11.3 79.3 ± 12.8 <0.01 e-GFR, ml/min/1.73m 2 100.9 ± 13.6 98.1 ± 14.1 102.1 ± 13.1 <0.01 TC, mmol/l 4.9 ± 1.0 5.1 ± 1.0 4.8 ± 1.0 <0.01 TG, mmol/l 1.5 ± 1.1 1.8 ± 1.3 1.3 ± 0.9 <0.01 HDL, mmol/l 1.4 ± 0.4 1.2 ± 0.3 1.4 ± 0.4 <0.01 LDL, mmol/l 2.7 ± 0.7 2.9 ± 0.7 2.6 ± 0.7 <0.01 MS * , n (%) 2519 (27.4%) 1195 (41.5%) 1324 (21.0%) <0.01 Notes: HUA = Hyperuricemia; SUA = Serum uric acid; BMI = Body mass index; SBP = Systolic blood pressure; DBP = Diastolic blood pressure; FBG = Fasting blood glucose; SCR = Serum creatinine; e-GFR = estimated glomerular filtration rate; TC= Total cholesterol; TG = Triglyceride; HDL = high-density lipoprotein cholesterol; LDL = low-density lipoprotein cholesterol; MS = Metabolic syndrome. Normal distributed continuous data were expressed as mean ± standard deviation. Non-normally distributed continuous data were expressed as median (interquartile range). Categorical data were presented as number (percentage). * MS was assessed in 8,545 subjects, because selected data was incomplete or missing in 651 participants. Table 2. Binary logistic regression analysis of risk factors with prevelance of HUA Variable Univariable analysis Multivariable analysis OR (95%CI) P Value OR (95%CI) P Value Sex male 5.58 (4.83 - 6.45) 0.000 3.74 (3.12 - 4.48) 0.000 female Ref. Ref. Age, years each 5 years reduction 1.10 (1.08 - 1.12) 0.000 1.22(1.19 - 1.26) 0.000 Region local 0.97 (0.89 - 1.06) 0.457 - - nonlocal Ref. e-GFR, ml/min/1.73m 2 <90 1.69 (1.52 - 1.88) 0.000 2.45 (2.11 - 2.84) 0.000 ≥90 Ref. Ref. MS yes 3.07(2.78 - 3.39) 0.000 - - no Ref. MS component BMI, kg/m 2 ≥25 3.17 (2.88 - 3.47) 0.000 2.31 (2.06 - 2.60) 0.000 <25 Ref. Ref. BP, mmHg SBP ≥130 and/or DBP ≥85 1.57 (1.43 - 1.72) 0.000 1.18 (1.04 - 1.32) 0.008 SBP <130 and DBP <85 Ref. Ref. FBG, mmol/l ≥5.6 1.26 (1.11 - 1.42) 0.000 - 0.909 <5.6 Ref. TG, mmol/l ≥1.7 2.90 (2.61 - 3.23) 0.000 2.08 (1.81 - 2.37) 0.000 <1.7 Ref. Ref. HDL, mmol/l <1.03 (male) or <1.29 (female) 2.04 (1.81 - 2.31) 0.000 1.33 (1.15 - 1.55) 0.000 ≥1.03 (male) or ≥1.29 (female) Ref. Ref. Notes: Ref, reference; e-GFR = estimated glomerular filtration rate; MS = Metabolic syndrome; BMI = Body mass index; SBP = Systolic blood pressure; DBP = Diastolic blood pressure; FBG = Fasting blood glucose; TG = Triglyceride; HDL = high-density lipoprotein cholesterol. 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-3969671","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274221339,"identity":"cd1c8140-1aa9-49a1-954e-ebc04cde612c","order_by":0,"name":"Bohan Lu","email":"","orcid":"","institution":"Shanghai Jiaotong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bohan","middleName":"","lastName":"Lu","suffix":""},{"id":274221340,"identity":"0551479d-6108-4957-bc41-a8ba1c45f6aa","order_by":1,"name":"Cuirong Hu","email":"","orcid":"","institution":"Ningbo Hangzhou Bay Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cuirong","middleName":"","lastName":"Hu","suffix":""},{"id":274221341,"identity":"fb2ad283-2f7b-41bb-a4ee-9e13426c6750","order_by":2,"name":"Jifang Lu","email":"","orcid":"","institution":"Ningbo Hangzhou Bay Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jifang","middleName":"","lastName":"Lu","suffix":""},{"id":274221342,"identity":"1bb57ff6-74a8-4eda-935e-6e8f607c1f1d","order_by":3,"name":"Jinkun Wang","email":"","orcid":"","institution":"Ningbo Hangzhou Bay Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinkun","middleName":"","lastName":"Wang","suffix":""},{"id":274221343,"identity":"39eb0791-a3f8-4ed9-aa88-df089e5d4603","order_by":4,"name":"Haijiao Jin","email":"","orcid":"","institution":"Renji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haijiao","middleName":"","lastName":"Jin","suffix":""},{"id":274221344,"identity":"2dfc2acf-ebeb-4d45-808a-3d4316885606","order_by":5,"name":"Ling Wang","email":"","orcid":"","institution":"Renji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Wang","suffix":""},{"id":274221345,"identity":"b96bbf38-2971-4539-b153-ebe3f428883b","order_by":6,"name":"Leyi Gu","email":"","orcid":"","institution":"Renji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Leyi","middleName":"","lastName":"Gu","suffix":""},{"id":274221347,"identity":"d6b50179-3da2-4021-92f2-2abce117bee1","order_by":7,"name":"Zhaohui Ni","email":"","orcid":"","institution":"Renji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Ni","suffix":""},{"id":274221351,"identity":"7e8191cc-d113-4061-b221-25521f4a292f","order_by":8,"name":"Shan Mou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDACCSjNz8x88AFpWiTb2ZINSNNicJ7HTIAoHfKzm589YMyxSdx8mMGMgaHGJpqgFoM7x8wNGLelJW47zJD2gOFYWm4DQS0SCWYSjNsOg7QcN2BsOExYi/yM9G9gLZubGdskiNLCcCMHYssGZmY24rQY3MgpA2pJM55xmI3ZIIEYvwAdtg2oxUa2v//8xwcfamyIcBgQMP+BsRKIUT4KRsEoGAWjgDAAAPYCO+JUoXTiAAAAAElFTkSuQmCC","orcid":"","institution":"Renji Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shan","middleName":"","lastName":"Mou","suffix":""},{"id":274221353,"identity":"dd3e0dd7-92f9-4bb3-9680-73633ffee36f","order_by":9,"name":"Na Jiang","email":"","orcid":"","institution":"Renji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-02-19 09:56:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3969671/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3969671/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51534851,"identity":"9989cfaa-8a6a-4e7b-b2b5-dabb58386ba4","added_by":"auto","created_at":"2024-02-23 09:14:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":445485,"visible":true,"origin":"","legend":"\u003cp\u003eSerum uric acid level and prevalence of hyperuricemia in different age groups and stratified by sex.\u003c/p\u003e\n\u003cp\u003eNotes: Panel a shows SUA level and panel b shows prevalence of HUA. HUA= Hyperuricemia; SUA = Serum uric acid.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01,\u003csup\u003e \u003c/sup\u003ens: no significance, comparison with subjects of age 18-24 years old.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ns: no significance, comparison between male and female subjects in the same age group.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3969671/v1/8cb9f58c0a6878ea2bc47d9e.jpg"},{"id":51534849,"identity":"8566dc7c-b157-4b94-a351-f938a68257e5","added_by":"auto","created_at":"2024-02-23 09:14:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486889,"visible":true,"origin":"","legend":"\u003cp\u003eSerum uric acid level and prevalence of hyperuricemia in subjects with and without metabolic syndrome and its components.\u003c/p\u003e\n\u003cp\u003eNotes: Panel a shows SUA level and panel b shows prevalence of HUA. HUA= Hyperuricemia; SUA = Serum uric acid; MS = Metabolic syndrome; BMI = Body mass index; TG = Triglyceride; HDL = high-density lipoprotein cholesterol; BP = Blood pressure; FBG = Fasting blood glucose.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ns: no significance, comparison between group with MS or MS component and those without.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3969671/v1/11ec44c06c82b448150881c3.jpg"},{"id":51534852,"identity":"a42d007c-c646-421f-a0b4-9e919e3e79ba","added_by":"auto","created_at":"2024-02-23 09:14:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199940,"visible":true,"origin":"","legend":"\u003cp\u003eSerum uric acid level and prevalence of hyperuricemia in subjects exhibited different number of MS components.\u003c/p\u003e\n\u003cp\u003eNotes: Panel A shows SUA level and panel B shows prevalence of HUA. HUA= Hyperuricemia; SUA = Serum uric acid; MS = Metabolic syndrome.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ns: no significance, comparison with group without MS component.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3969671/v1/e4904ca44c9b51bbbc2c5a80.jpg"},{"id":55290876,"identity":"1721f737-f1a9-4080-b53f-dfeede8ac8e7","added_by":"auto","created_at":"2024-04-25 09:08:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":826437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3969671/v1/05ecba25-cc7f-4ad8-9b6c-19bdab7b07e1.pdf"},{"id":51534850,"identity":"f5b99b30-3065-4288-8cd1-c176d37d320a","added_by":"auto","created_at":"2024-02-23 09:14:38","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":688001,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3969671/v1/087ff9a1433616a63e9063b9.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of hyperuricemia and its correlation with metabolic syndrome in young adults: a cross-sectional study in eastern China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHyperuricemia (HUA) is a purine metabolic disorder caused by excessive production and/or reduced excretion of uric acid [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Accompanied with economic development, the incidence of HUA increases significantly in the worldwide mainly due to elevated dietary purine intake. HUA is commonly prevalent with other metabolic disorders including diabetes, dyslipidemia, hypertension, etc [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It results in gout and gout arthritis, which leads to joint deformity and dysfunction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, HUA has been confirmed as an independent risk factor for the development of chronic kidney disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, increased serum uric acid is demonstrated to be associated with elevated cardiovascular-related and all-cause mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of HUA differs with variations in region, race, sex, and age [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. HUA is more common in the western countries mainly because of the intake of high purine containing diet. Based on the results of the National Health and Nutrition Examination Survey (NHANES), the overall prevalence of HUA was up to 20.1% in America [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a cross-sectional study of health insurance claims data, HUA was found to be present in 13.4% of the overall population in Japan [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The prevalence of HUA was reported to be lower in South Korea, India, and Thailand [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The overall prevalence of HUA in Chinese adults in 2018\u0026ndash;2019 was reported to be 14.0% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It showed a wide regional variation due to the large area of territory, differences in dietary patterns, and inconsistent economic status in different cities of China.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to analyze the prevalence of HUA in a young adult population receiving routine health examination in a coastal city of eastern China. We investigated the MS and its components correlation with the prevalence of HUA and explored clinical strategies to lower SUA level.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study conducted in a young adult population undergoing routine healthy examination at Ningbo Hangzhou Bay Hospital, Zhejiang Province, China during 2020. The protocol was approved by the Hangzhou Bay Hospital Ethics Committee (approval number: WYLS2021-3) and all subjects participated in the study after written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSubject recruitment and clinical data retrieval\u003c/h2\u003e \u003cp\u003eSubjects were enrolled based on the following inclusion criteria: 1. age of over 18 years old; 2. long-term resident in mainland China; 3. no severe mental or psychiatric disorder. Anthropometric characteristics of height, body weight and blood pressure (BP) were measured according to standard protocols. Body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated as body mass (kg) / height squared (m\u003csup\u003e2\u003c/sup\u003e). Serological parameters including serum uric acid, serum creatinine (SCR), fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) were measured using an automated biochemistry analyzer Beckman Coulter AU5800 (Beckman Coulter Inc., Brea, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions of metabolic disorders\u003c/h2\u003e \u003cp\u003eHUA was defined as serum uric acid level\u0026thinsp;\u0026ge;\u0026thinsp;420.0 \u0026micro;mol/l (7 mg/dl) for males and \u0026ge;\u0026thinsp;360 \u0026micro;mol/l (6 mg/dl) for females, according to the Integrated Diabetes \u0026amp; Endocrine Academy (IDEA) consensus statement [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to the world-wide definition of metabolic syndrome (MS) by International Diabetes Federation (IDF) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], MS was defined as having central obesity plus 2 or more of following 4 additional criteria: 1. Serum TG level of \u0026ge;\u0026thinsp;1.7 mmol/l (150 mg/dl), 2. Serum HDL-cholesterol level of \u0026lt;\u0026thinsp;1.03 mmol/l (40 mg/dl) in males or \u0026lt;\u0026thinsp;1.29 mmol/l (50 mg/dl) in females, or undergoing specific treatment of dyslipidemia, 3. hypertension diagnosed as systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;85mmHg, or undertaking anti-hypertensive medicine, 4. diagnosed type 2 diabetes or raised fasting blood glucose (FBG) of \u0026ge;\u0026thinsp;5.6 mmol/l. Because waist circumference data was not recorded in this study, we used BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e to assess overweight instead of central obesity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data was presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed continuous variables, median (25th to 75th percentile) for non-normally distributed variables, and percentages for categorical data. Differences between groups were analyzed using student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, Mann-Whitney U test, Kruskal-Wallis test, or χ\u003csup\u003e2\u003c/sup\u003e test depending on data type. Univariable and multivariable binary logistic regression analysis was used to investigate the association between clinical factors and prevalence of HUA. Forward likelihood ration (LR) was used to remove invalid variables in a stepwise fashion selected independent variables that significantly influence the dependent variable to enter the model, with odds ratios (OR) and 95% confidence intervals (95% CI) quantified.\u003c/p\u003e \u003cp\u003eP values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be statistically significant difference. SPSS version 24.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis, and graphs were created using GraphPad Prism 8.0 (GraphPad Software, Inc., San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and prevalence of HUA\u003c/h2\u003e \u003cp\u003eA total of 9,196 adults were enrolled in the study, with mean age of 34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 years and 6,918 (75.2%) of them were male. The mean SUA level was 371.8\u0026thinsp;\u0026plusmn;\u0026thinsp;95.6 \u0026micro;mol/l and HUA was present in 31.5% of the subjects. Patients with HUA demonstrated younger age, higher BMI and blood pressure, higher serum concentration of FBG, TC, TG and LDL, but reduced e-GFR and serum HDL level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all, \u003cb\u003eshown in Table. 1\u003c/b\u003e). There was significant higher prevalence of MS in patients with HUA versus those with normal SUA level (41.5% versus 21.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cb\u003eshown in Table. 1\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of HUA with age and sex\u003c/h2\u003e \u003cp\u003eLevel of SUA and the prevalence of HUA were higher in younger adults. Subject group of 18\u0026ndash;24 years old demonstrated highest SUA level of 399.3\u0026thinsp;\u0026plusmn;\u0026thinsp;94.3 \u0026micro;mol/l, with HUA prevalence of 37.7%. In addition, HUA was more common in male subjects compared to females (SUA level: 382.8\u0026thinsp;\u0026plusmn;\u0026thinsp;84.6 versus 280.0\u0026thinsp;\u0026plusmn;\u0026thinsp;62.9 \u0026micro;mol/l, HUA prevalence: 38.3% versus 10.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all). SUA level in male subjects were significantly higher than females in all the age groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all, \u003cb\u003eshown in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn male population, the SUA level (415.9\u0026thinsp;\u0026plusmn;\u0026thinsp;87.9 \u0026micro;mol/l) and prevalence of HUA (41.8%) were highest in age group of 18\u0026ndash;24 years old. SUA level and HUA prevalence decreased along with the increase in age and kept stable in subjects of over 45 years old (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cb\u003eshown in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e). Whilst SUA level (301.9\u0026thinsp;\u0026plusmn;\u0026thinsp;68.2 \u0026micro;mol/l) and HUA prevalence (17.3%) were highest in age group of over 50 years in female population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cb\u003eshown in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of HUA with MS\u003c/h2\u003e \u003cp\u003eSUA level and the prevalence of HUA were significantly higher in subjects with MS versus those without MS (SUA level: 412.6\u0026thinsp;\u0026plusmn;\u0026thinsp;96.5 versus 350.4\u0026thinsp;\u0026plusmn;\u0026thinsp;88.0 \u0026micro;mol/l; HUA prevalence: 47.4% versus 22.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all, \u003cb\u003eshown in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e). SUA level and the prevalence of HUA were also significantly higher in subjects with the presence of MS components including high BMI, dyslipidemia, hypertension, or diabetes, when compared to those without (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all, \u003cb\u003eshown in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e). Furthermore, SUA level and prevalence of HUA increased along with increase in the number of MS component that subjects exhibited (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all, \u003cb\u003eshown in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u003cb\u003eand b\u003c/b\u003e). Patients with all of the 5 MS components demonstrated highest SUA level (417.7\u0026thinsp;\u0026plusmn;\u0026thinsp;92.5 \u0026micro;mol/l) and HUA prevalence (50.3%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors associated with prevalence of HUA\u003c/h2\u003e \u003cp\u003eUnivariable binary logistic regression analysis was performed to evaluate the association between HUA prevalence and clinical risk factors. It was demonstrated that male (OR: 5.58; 95% CI: 4.83\u0026ndash;6.45), 5 years younger (OR: 1.10; 95% CI: 1.08\u0026ndash;1.12), reduced kidney function (OR: 1.69; 95% CI: 1.52\u0026ndash;1.88), and combination with MS (OR: 3.07; 95% CI: 2.78\u0026ndash;3.39) were risk factors associated with the prevalence of HUA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cb\u003eshown in Table. 2\u003c/b\u003e). In the multivariable binary logistic regression analysis model, the results revealed that male (OR: 3.74; 95% CI: 3.12\u0026ndash;4.48), younger age (OR: 1.22; 95% CI: 1.19\u0026ndash;1.26), low e-GFR (OR: 2.45; 95% CI: 2.11\u0026ndash;2.84), high BMI (OR: 2.31; 95% CI: 2.06\u0026ndash;2.60), hypertension (OR: 1.18; 95% CI: 1.04\u0026ndash;1.32), high serum TG (OR: 2.08; 95% CI: 1.81\u0026ndash;2.37), and low serum HDL (OR: 1.33; 95% CI: 1.15\u0026ndash;1.55) were independent risk factors for the presence of HUA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all, \u003cb\u003eshown in Table. 2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current cross-sectional study, we demonstrated that HUA was prevalent in 31.5% of a young adult population in a coastal city of eastern China. In addition, we found that SUA level was significantly higher and HUA was more common in younger males, as well as in those with MS. Furthermore, male, young age, reduced kidney function, and combined with MS were independent risk factors associated with HUA prevalence.\u003c/p\u003e \u003cp\u003eThe prevalence of HUA shown in the present study was relatively higher when compared with most of previous studies from China, and was even higher than selected studies conducted in developed countries. A nationwide representative cross-sectional survey conducted in mainland China from 2018 to 2019 found that the overall prevalence of HUA was 14.0%, with 24.4% in men and 3.6% in women [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Whilst the prevalence of HUA in USA was reported to be 17.7% in 2013 to 2018 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The high prevalence of HUA found in our study was mainly explained by the coastal location of the area and high-purine containing seafood is a daily home-cooking meal for local residents. A direct association between increased seafood intake and elevated prevalence of HUA has been documented in a previous investigation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, the economic status of the city, where the present study conducted at, ranks in the top level of China. It may also contribute to the high HUA prevalence as HUA is more common in economically developed areas [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Indeed, we found that mean SUA level was significantly higher in local residents than non-locals, although the difference in HUA prevalence did not reach statistical significance.\u003c/p\u003e \u003cp\u003eYounger individuals were more likely to develop HUA and young age was shown to be an independent risk factor associated with increases HUA prevalence in the current study. It may be related to elevated intake of high purine containing diet and high prevalence of obesity among young people, especially in male subjects [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In a large cross-sectional study performed in southwestern China, the researchers demonstrated younger subjects with overweight or obesity were more likely to develop HUA compared to older individuals [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another large prospective cohort study conducted in northern China, also revealed HUA prevalence was the highest in younger male subjects [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn line with previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], our study demonstrated significant sex differences in SUA level and HUA prevalence. It may be explained by the differences in physiological hormone secretion, lifestyle such as smoking history and alcohol use, and diet habit between men and women. A logistic regression analysis showed that sex hormones had a dose-response relationship with HUA prevalence, such as estradiol and testosterone [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, we found a U-shaped distribution of HUA prevalence along with the increase of age in female individuals, with highest HUA prevalence in the age group of over 50 years old. It may be related to postmenopausal estrogen depletion, as estrogen contributes to promote kidney clearance of uric acid [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA positive association between reduced e-GFR and increased SUA was shown in the current study. Impaired kidney function results in reduced excretion of uric acid through the urine. Thus HUA is a common clinical characteristic in patients with chronic kidney disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. On the other hand, HUA could directly trigger kidney injury through inducing oxidative stress, endothelial injury, inflammatory and fibrotic responses in the kidney tissue [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It has been documented that HUA is a common reason for kidney impairment and it is an independent predictor for the incidence of end-stage kidney disease [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsistent to previous studies [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we found a close relationship between HUA prevalence and the presence of MS. Individuals with HUA demonstrated increased burden of MS and HUA prevalence was found to be significantly higher in subjects with MS or its components. A large cross-sectional study conducted using the NHANES datasets from 2013 to 2018 found that HUA were more likely to exhibit MS compared to those without HUA after controlling known risk factors of MS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A meta-analysis on the basis of 11 studies, which included 54,970 participants and 8,719 MS cases, showed a linear dose-response relationship between SUA level and MS risk [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Mediated by insulin resistance, high fat diets and increased intake of fructose were shown to be positively correlated with the SUA level, which also contribute to the development of MS [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, our results found MS components were independent risk factors for HUA prevalence, including overweight, hypertension, dyslipidemia. The positive relationship between overweight and the presence of HUA has been documented in previous study and HUA prevalence was reported to reach 66.9% in obese individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The finding may be directly explained by the fact that increased amount of dietary purine intake in obese subjects. Similar to our results, hypertension and increased serum TG level were found to be two risk factors for the development of HUA in a prospective cohort study conducted in 2,017 Chinese subjects [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. HUA could trigger or worsen hypertension through the potential mechanisms inducing inflammation, endothelial dysfunction, proliferation of vascular smooth muscle cells, and activation of the renin-angiotensin system [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, we found that patients with HUA had significantly higher level of serum TG but reduced serum HDL level. A positive association between TG/HDL ratio and HUA incidence was also indicated in a Chinese retrospective cohort study with 4 years\u0026rsquo; follow-up [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It may be explained that HUA disrupts the balance of the ADMA/DDAH pathway, which further promotes lipid peroxidation and leads to lipid metabolic disorders [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several limitations in the study that need to be discussed. Firstly, the investigation was conducted at a single center located in a coastal city, thus the results may not be applicable to patient population living in non-coastal areas. Secondly, the cross-sectional design of the study resulted in a failure to elucidate a causal relationship between the development of HUA and the associated risk factors. Thus, prospective cohort studies are required to further confirm the results. Furthermore, there were some parameters which may influence SUA level were not collected in the study, such as diet purine containing, urine purine excretion, etc. Also, BMI was used to evaluate abdominal obesity instead of waist circumference. It was not an ideal index for the assessment of MS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we demonstrated that the prevalence of HUA of young adults at the eastern coastal of China was high. Based on our findings, male, young age, reduced kidney function, combined with MS and more MS components were risk factors associated with HUA prevalence. It is reasonable to encourage young people to change their diet style, and to exercise more to reduce MS burden, and in doing so to decrease SUA level and thus lower the prevalence of HUA.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthics approval statement: this study protocol was reviewed and approved by Hangzhou Bay Hospital Ethics Committee, approval number [WYLS2021-3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to participate statement: written informed consent was obtained from all participants in this study.\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eEvery human participant should provide their consent.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe original data that support the findings of this study are not publicly available on ethical grounds. If editors, reviewers and readers want to access the data, please contact to the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Scientific Foundation of China (No. 81970574 and 82170685 to M.S.; No. 82370743 to J.N.), Shanghai Municipal Health Commission (NO.ZY(2021-2023)-0208 and NO.ZY(2021-2023)-0302 to M.S.), National Administration of Traditional Chinese Medicine High-level Key Disciplines Project: Translational integrated traditional Chinese and Western medicine awarded to M.S., Key discipline projects of Three-year Action Plan to strengthen public health system construction in Shanghai (2023-2025) awarded to M.S., Innovative research team of high-level local universities in Shanghai awarded to M.S. The funders had no role in study design, data collection, analysis, decision to publish or manuscript preparation.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eL.B.H. recruited the subjects, collected and analyzed the data, and wrote the manuscript. J.N. and M.S. designed and supervised the study, interpreted the data, and revised the manuscript. H.C.R contributed to data analysis and interpretation. L.J.F., W.J.K., and J.H.J. participated in study recruitment. G.L.Y. and W.L. helped with the study design. N.Z.H. contributed to study design and data interpretation.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; information\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eDepartment of Nephrology, Ningbo Hangzhou Bay Hospital, Ningbo, Zhejiang, China. \u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Nephrology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. \u003csup\u003e3\u0026nbsp;\u003c/sup\u003eShanghai Jiaotong University School of Medicine, Shanghai, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMajor TJ, Dalbeth N, Stahl EA, Merriman TR. An update on the genetics of hyperuricaemia and gout. 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Front Endocrinol. 2022;13:1035114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoannou GN, Boyko EJ. Effects of menopause and hormone replacement therapy on the associations of hyperuricemia with mortality. Atherosclerosis. 2013;226(1):220\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKielstein JT, Pontremoli R, Burnier M. Management of Hyperuricemia in Patients with Chronic Kidney Disease: a Focus on Renal Protection. Curr Hypertens Rep. 2020;22(12):102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu HY, Yang C, Liang D, Liu HF. Research Advances in the Mechanisms of Hyperuricemia-Induced Renal Injury. \u003cem\u003eBioMed research international\u003c/em\u003e 2020, 2020:5817348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi CH, Lee CL, Hsieh YC, Chen CH, Wu MJ, Tsai SF. Hyperuricemia and diabetes mellitus when occurred together have higher risks than alone on all-cause mortality and end-stage renal disease in patients with chronic kidney disease. BMC Nephrol. 2022;23(1):157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen WY, Fu YP, Zhou M. The bidirectional relationship between metabolic syndrome and hyperuricemia in China: A longitudinal study from CHARLS. Endocrine. 2022;76(1):62\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Hsieh MC, Chang SJ. Metabolic syndrome, diabetes, and hyperuricemia. Curr Opin Rheumatol. 2013;25(2):210\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou L, Liu A, Wuyun G, Wu H, Wang P. Prevalence of hyperuricemia and the relationship between serum uric acid and metabolic syndrome in the Asian Mongolian area. J Atheroscler Thromb. 2014;21(4):355\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan H, Yu C, Li X, Sun L, Zhu X, Zhao C, Zhang Z, Yang Z. Serum Uric Acid Levels and Risk of Metabolic Syndrome: A Dose-Response Meta-Analysis of Prospective Studies. J Clin Endocrinol Metab. 2015;100(11):4198\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanbay M, Jensen T, Solak Y, Le M, Roncal-Jimenez C, Rivard C, Lanaspa MA, Nakagawa T, Johnson RJ. Uric acid in metabolic syndrome: From an innocent bystander to a central player. Eur J Intern Med. 2016;29:3\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Tan M, Liu B, Zeng M, Zhou Y, Zhang M, Wang Y, Wu J, Wang M. Relationship between bone mineral density and hyperuricemia in obesity: A cross-sectional study. Front Endocrinol. 2023;14:1108475.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhang M, Yu X, Wei F, Chen C, Zhang K, Feng S, Wang Y, Li WD. Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study. J translational Med. 2020;18(1):409.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu XY, Wu QY, Chen ZH, Yan GY, Lu Y, Dai HJ, Li Y, Yang PT, Yuan H. Elevated triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio increased risk of hyperuricemia: a 4-year cohort study in China. Endocrine. 2020;68(1):71\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee TS, Lu TM, Chen CH, Guo BC, Hsu CP. Hyperuricemia induces endothelial dysfunction and accelerates atherosclerosis by disturbing the asymmetric dimethylarginine/dimethylarginine dimethylaminotransferase 2 pathway. Redox Biol. 2021;46:102108.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Comparisons of clinical characteristics between subjects with and without HUA\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.282828282828284%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.535353535353536%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHUA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.714285714285715%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=9196)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.714285714285715%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith (n=2878)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout (n=6318)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eSUA, \u0026mu;mol/l\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e371.8 \u0026plusmn; 95.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e479.5 \u0026plusmn; 63.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e322.8 \u0026plusmn; 61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e6918 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2650 (92.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e4268 (67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eLocal resident, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e5334 (58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e1653 (57.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e3681 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e34.3 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e32.6 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e35.1 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e23.9 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e25.6 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e23.1 \u0026plusmn; 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e124.9 \u0026plusmn; 14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e127.8 \u0026plusmn; 14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e123.6 \u0026plusmn; 14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e76.4 \u0026plusmn; 10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e78.4 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e75.5 \u0026plusmn; 10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eFBG, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e5.2 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e5.2 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e5.2 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eSCR, \u0026mu;mol/l\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e82.1 \u0026plusmn; 13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e88.3 \u0026plusmn; 11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e79.3 \u0026plusmn; 12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003ee-GFR, ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e100.9 \u0026plusmn; 13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e98.1 \u0026plusmn; 14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e102.1 \u0026plusmn; 13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eTC, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e5.1 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eTG, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e1.5 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e1.8 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e1.3 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eHDL, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e1.4 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e1.2 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e1.4 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eLDL, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eMS\u003csup\u003e*\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2519 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e1195 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e1324 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: HUA = Hyperuricemia; SUA = Serum uric acid; BMI = Body mass index; SBP = Systolic blood pressure; DBP = Diastolic blood pressure; FBG = Fasting blood glucose; SCR = Serum creatinine; e-GFR = estimated glomerular filtration rate; TC= Total cholesterol; TG = Triglyceride; HDL = high-density lipoprotein cholesterol; LDL = low-density lipoprotein cholesterol; MS = Metabolic syndrome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNormal distributed continuous data were expressed as mean \u0026plusmn; standard deviation. Non-normally distributed continuous data were expressed as median (interquartile range). Categorical data were presented as number (percentage).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e MS was assessed in 8,545 subjects, because selected data was incomplete or missing in 651 participants.\u003c/p\u003e\n\u003cp\u003eTable 2. Binary logistic regression analysis of risk factors with prevelance of HUA\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.74193548387097%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.483870967741936%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.064516129032258%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.70967741935484%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.567307692307693%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.865384615384615%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.5673076923076925%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.93269230769231%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.067307692307693%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e5.58 (4.83 - 6.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e3.74 (3.12 - 4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; each 5 years reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e1.10 (1.08 - 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e1.22(1.19 - 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; local\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e0.97 (0.89 - 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; nonlocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003ee-GFR, ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e1.69 (1.52 - 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e2.45 (2.11 - 2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e3.07(2.78 - 3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eMS\u0026nbsp;component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e3.17 (2.88 - 3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e2.31 (2.06 - 2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; SBP\u0026nbsp;\u0026ge;130 and/or DBP\u0026nbsp;\u0026ge;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e1.57 (1.43 - 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e1.18 (1.04 - 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; SBP \u0026lt;130 and DBP \u0026lt;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eFBG, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e1.26 (1.11 - 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eTG, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e2.90 (2.61 - 3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e2.08 (1.81 - 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003eHDL, mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;1.03 (male) or \u0026lt;1.29 (female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003e2.04 (1.81 - 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003e1.33 (1.15 - 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.794830371567045%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;1.03\u0026nbsp;(male) or \u0026ge;1.29\u0026nbsp;(female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.87075928917609%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.662358642972537%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.0694668820678515%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.132471728594506%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470113085621971%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: Ref, reference; e-GFR = estimated glomerular filtration rate; MS = Metabolic syndrome; BMI = Body mass index; SBP = Systolic blood pressure; DBP = Diastolic blood pressure; FBG = Fasting blood glucose; TG = Triglyceride; HDL = high-density lipoprotein cholesterol.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hyperuricemia, serum uric acid, metabolic syndrome, kidney function","lastPublishedDoi":"10.21203/rs.3.rs-3969671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3969671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHyperuricemia (HUA) has emerged as a significant metabolic disease, particularly in young population with metabolic syndrome (MS). The purpose of this study was to study the prevalence of HUA and its correlation with metabolic syndrome among young adults in a coastal city of eastern China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIt was a cross-sectional study conducted in adults undergoing routine healthy checkup. Anthropometric data and serological parameters were collected and in related to serum uric acid (SUA) concentration and prevalence of HUA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 9,196 adults with mean age of 34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 years old and 75.2% of males were recruited. Mean SUA level was 371.8\u0026thinsp;\u0026plusmn;\u0026thinsp;95.6 \u0026micro;mol/l and overall HUA prevalence was 31.3%. SUA level was higher and HUA was more common in younger males, as well as in those with MS (OR: 3.07; 95% CI: 2.78\u0026ndash;3.39) demonstrated by using the univariable binary logistic regression analysis model. The multivariable binary logistic regression analysis revealed that male (OR: 3.74; 95% CI: 3.12\u0026ndash;4.48), young age (OR: 1.22; 95% CI: 1.19\u0026ndash;1.26), low estimated-glomerular filtration rate (OR: 2.45; 95% CI: 2.11\u0026ndash;2.84), high body mass index (OR: 2.31; 95%CI: 2.06\u0026ndash;2.60), hypertension (OR: 1.18; 95% CI: 1.04\u0026ndash;1.32), high serum triglyceride (OR: 2.08; 95%CI: 1.81\u0026ndash;2.37), and low serum high-density lipoprotein cholesterol (OR: 1.33; 95% CI: 1.15\u0026ndash;1.55) were independent risk factors associated with HUA prevalence.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSUA level increased and HUA was common in young adults. Male, young age, reduced kidney function, combined with MS and more MS components were associated with prevalence of HUA.\u003c/p\u003e","manuscriptTitle":"Prevalence of hyperuricemia and its correlation with metabolic syndrome in young adults: a cross-sectional study in eastern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 09:14:33","doi":"10.21203/rs.3.rs-3969671/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3318512-b25b-4c17-8682-446170277011","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-24T17:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 09:14:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3969671","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3969671","identity":"rs-3969671","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}