Association of the serum uric acid-to-albumin ratio with all-cause, cancer specific, noncancer mortality in U.S. adults: a prospective study from the NHANES (1999--2018)

Association of the serum uric acid-to-albumin ratio with all-cause, cancer specific, noncancer mortality in U.S. adults: a prospective study from the NHANES (1999--2018) | 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 Association of the serum uric acid-to-albumin ratio with all-cause, cancer specific, noncancer mortality in U.S. adults: a prospective study from the NHANES (1999--2018) Yongzhi Ye, Meiqiong Chen, Fada Ji, Suicai Mi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5309667/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 The serum uric acid-to-albumin ratio (UAR) is closely correlated with mortality in some diseases, but its correlation with all-cause and cancer specific death in the general population requires further research. Methods This prospective cohort study included 52,534 participants from the NHANES database (1999–2018). The inclusion criteria were as follows: 18 years of age and older, complete serum uric acid and albumin examinations and mortality follow-up. We used Cox models to evaluate the correlation between UAR and all-cause, cancer specific, and noncancer mortality. The nonlinear relationship was evaluated via restricted cubic spline (RCS) analysis. Results Cox regression analysis revealed that an increased UAR was related to an increased risk of death after adjustment for confounding factors (HR (95% CI) for all-cause death = 1.49 (1.32, 1.68), HR (95% CI) for cancer = 1.69 (1.32, 2.17), HR (95% CI) for noncancer = 1.43 (1.24, 1.64)). Compared with those in UAR T1 individuals, the hazards of all-cause death and cancer-specific death were significantly greater in UAR T3 individuals, and the HRs (95% CIs) were 1.17 (1.03, 1.33) and 1.53 (1.13, 2.07), respectively. The trend test results were significant, and the P values were 0.0074 and 0.0027, respectively. The risk of noncancer death in UAR-T3 individuals was not statistically significant (HR (95% CI) = 1.10 (0.95, 1.26), p for trend = 0.1377). Tumor subgroup analysis suggested that the UAR was positively correlated with the risk of mortality for each tumor type. After adjusting for variables, it was found that the UAR had a more significant effect on the risk of death from urogenital and gynecological tumors, with an HR (95% CI) of 1.80 (1.28, 2.54). A restricted cubic spline analysis revealed a "U"-shaped nonlinear correlation between UAR and all-cause, cancer specific, noncancer death. Threshold effect analysis suggested that the turning points of all-cause, cancer-specific and noncancer death for UAR were 1.4, 0.8 and 1.5, respectively. Conclusion When the UAR was greater than the threshold effect, the hazard of mortality gradually increased, and cancer-specific death had the lowest threshold effect and a more significant impact, which may be used as a biomarker to predict survival prognosis. Serum uric acid-to-albumin ratio (UAR) Cancer Mortality NHANES Figures Figure 1 Figure 2 Figure 3 Introduction Tumours are still a disease with high morbidity and mortality worldwide [1]; thus, improving tumour prognosis has been a hot topic of research, and there is an urgent need for effective and convenient biomarkers to predict the risk of tumour death. Serum uric acid (UA) and albumin (ALB) can be routinely tested in all primary care facilities and hospitals and are simple and inexpensive. UA and ALB have been reported as endogenous antioxidants capable of countering excessive oxidative stress [2-4], thereby reducing DNA damage and mutation risk and helping prevent the development of malignant tumors [5]. A number of studies have suggested that UA and ALB levels are negatively associated with tumor risk [6-8], but some studies disagree that UA also plays a pro-oxidative role and promotes tumor progression [9, 10]. The factors influencing UA and ALB concentrations are complex and include nutritional status, insulin resistance, kidney disease, and metabolic syndrome [11], and the relationships between UA and ALB levels and cancer risk have not been fully studied. Currently, UAR is recognized as a marker of inflammation and oxidative stress [12], and its relationship with the risk of death from tumors needs further study. We aimed to evaluate the value of the UAR in predicting the risk of death. Research population and methods 1. Participant characteristics The data of this cohort study were from the NHANES database (1999--2018). This database is a cross-sectional study that collects health and nutritional information related to representative demographic statistics, physical examinations, disease questionnaires, and survival follow-up. This study included 52,534 participants aged 18 years and older with complete serum albumin, uric acid, and follow-up data on death ( Figure 1 ). 2. Variables and outcomes In this study, UAR was the ratio of uric acid (mg/dL) to albumin (g/dL), and the covariates included demographic factors, such as age, race and ethnicity, sex, the poverty income ratio (PIR) and education level; lifestyle surveys, such as body mass index (BMI, kg/m 2 ), smoking status (former or never), alcohol consumption (yes or no), and physical activity; and health questionnaires, such as hypertension, diabetes, heart disease, stroke, cancer, and death. Details of all the variables are publicly available on the NHANES website：https://www n.cdc.gov/nchs/nhanes/Default.aspx,https://wwwn.cdc.gov/nchs/data/nhanes/2017-2018/la bmethods/BIOPRO-J-MET-Uric-Acid-508.pdf,https://wwwn.cdc.gov/nchs/data/nhanes/201 7-2018/labmethods/BIOPRO-J-MET-Albumin-508.pdf). The primary outcome was all-cause mortality, and cancer-specific mortality was the secondary outcome. The follow-up for survival was from the day that the participants were interviewed to December 31, 2019. 3. Statistical analysis 1. The means ± standard deviations (SDs) and percentages were calculated for continuous variables and categorical variables, respectively. 2. The correlation between UAR levels and the risk of death was assessed via Cox proportional hazard models. 3. Subgroup analysis to explore the relationships between UAR levels and mortality in different cancer subgroups. 4. Stratified analyses were performed for age, sex, BMI, race and ethnicity, education level, PIR, physical activity, smoking status, and alcohol consumption to examine the correlation between UAR and all-cause mortality. The P value of the interaction was evaluated via the logarithm likelihood ratio test. 5. We used restricted cubic spline (RCS) regression to evaluate the nonlinear association between UAR and the hazard of all-cause, cancer specific, and noncancer death, and the threshold effect model was used to calculate the turning point. All the results were significant at P < 0.05 (two-tailed). The data were analysed via the R statistical package (version 4.2.0) and Empower stats. Results 1. Basic characteristics of the population This study included a total of 52534 participants, including 4429 cancer patients (9.06%). The overall age of the population was 47.50±19.23 years, 48.42% were male, the BMI was 28.74±6.80 kg/m 2 , and the UAR was 1.28±0.36. The UAR was divided into T1, T2, and T3 according to the tertile. There were 14,441 participants at T1 (0.1--1.0), 17,944 participants at T2 (1.1--1.3) and 20,149 participants at T3 (1.4--4.7). There were significant differences among the three groups according to the factors included in Table 1. 2. Relationship between UAR and mortality The median (Q1--Q3) follow-up time was 9.50 (5.17--14.33) years. Table 2 shows that there were 7623, 1559 and 6004 all-cause, cancer and noncancer deaths, respectively, and 60 deaths from uncertain factors were not counted. Cox proportional hazard regression analysis revealed that increased UAR levels were associated with an increased risk of death after adjustment for confounders (HR (95% CI) for all-cause death =1.49 (1.32, 1.68), HR (95% CI) for cancer =1.69 (1.32, 2.17), and noncancer HR (95% CI) =1.43 (1.24, 1.64)). Compared with those in UAR T1 individuals, the hazards of all-cause death and cancer-specific death were significantly greater in UAR T3 individuals, and the HRs (95% CIs) were 1.17 (1.03, 1.33) and 1.53 (1.13, 2.07), respectively. The trend test results were significant, and the P values were 0.0074 and 0.0027, respectively. The risk of noncancer death in UAR-T3 individuals was not statistically significant (HR (95% CI) =1.10 (0.95, 1.26), p value for trend=0.1377). 3. Relationships between UAR rates and tumor subgroup mortality As shown in Table 3 , tumor patients were divided into four subgroups: digestive system tumors, urogenital and gynecological tumors, skin soft tissue tumors, and other tumors. Tumor subgroup analysis revealed that the UAR was positively correlated with the risk of death for all tumor types. After adjusting for confounders, the UAR was more significantly associated with the risk of death from urogenital and gynecological tumors and other tumor types, with HRs (95% CIs) of 1.80 (1.28, 2.54) and 4.67 (1.91, 11.40), respectively. Compared with those of T1 individuals, the HRs (95% CIs) of UAR T3 individuals were 1.75 (1.12, 2.74) and 4.13 (1.44, 11.85). The p values for the trends were 0.0069 and 0.0051, respectively. 4. Stratified analysis and interaction test As shown in Figure 2 , the stratified analysis verified that the UAR level was positively correlated with all-cause death, and all P values for interaction>0.05, indicating that the association between the UAR level and all-cause death was not affected by confounding factors. 5. Dose‒response curves In Figure 3 (A-C), UAR presented a "U"-shaped nonlinear relationship with the risk of death according to the Cox model with restricted cubic spline analysis. Figure 3 (D-E) UAR also had a nonlinear relationship with overall survival, cancer survival and noncancer survival. In Table 4 , threshold effect analysis indicated that the turning points of the UAR level for all-cause, cancer specific, and noncancer death rates were 1.4, 0.8, and 1.5, respectively. When the UAR was >1.4, the HR (95% CI) was 1.93 (1.63, 2.28) for all-cause death; when the UAR was >0.8, the HR (95% CI) was 1.83 (1.43, 2.34) for cancer-specific death; and when the UAR was >1.5, the HR (95% CI) was 2.27 (1.55, 3.30) for noncancer death. Discussion This large cohort study examined the correlation between UAR levels and all-cause, cancer specific, and noncancer mortality rates. The results suggested that the UAR was positively correlated with mortality risk, the risk of cancer death increased significantly, and the hazard ratio increased by up to 1.17 times. Tumor subgroup analysis suggested that the UAR was positively associated with the death hazard of each tumor type. After adjusting for variables, it was found that the UAR had a more significant effect on the risk of death from urogenital and gynecological tumors, with an HR (95% CI) of 1.80 (1.28, 2.54). Threshold effect analysis suggested that the turning points for UAR were 0.8, 1.4 and 1.5. When the UAR was > 0.8, the risk of cancer death increased up to 1.34 times; when the UAR was > 1.4, the risk of all-cause death in the general population increased by 93%; when the UAR was > 1.5, the risk of noncancer death also increased. This suggests that the UAR threshold for cancer death risk is lower and more sensitive to the effects of UAR levels. Therefore, the UAR may become an original biomarker for predicting the risk of cancer death. Several studies have explored effective tumor prognostic markers, such as the fibrinogen-to-albumin ratio [ 13 ], the C-reactive protein-to-albumin ratio [ 14 ], and the serum urea nitrogen-to-albumin ratio [ 15 ]. The use of UAR as an inflammatory and oxidative stress biomarker has also been researched recently [ 16 ]. Research has suggested that UAR levels are positively correlated with the risk of death in STEMI patients and unstable angina pectoris patients after PCI [ 17 ], and UAR is also an indicator of poor prognosis in patients with coronary heart disease [ 18 ]. In addition, UAR is associated with the occurrence of kidney injury and death from acute renal failure [ 19 , 20 ]. There has been little research on whether there is a correlation between cancer death risk and UAR. After adjusting for confounding factors, we found that the UAR was positively associated with the risk of all-cause, cancer-specific and noncancer death. The effect on cancer death was even more significant. We believe that the effect of UAR levels on mortality risk may be related to oxidative stress and chronic inflammatory responses. A number of previous studies have confirmed the pro-oxidant properties of UA and the antioxidant properties of ALB [ 6 , 9 , 16 ], and disruption of this balance causes oxidative damage to cells and increases the risk of cancer progression. At the same time, hyperuricaemia is complementary to chronic inflammation and is closely correlated with several chronic diseases, such as diabetes, obesity, and hypertension [ 21 ], which antagonizes the anti-inflammatory effect of ALB [ 22 ] and is another risk factor for cancer. The ratio of UA to ALB eliminates the common confounding factors that affect both concentrations, and UAR may be more stable as a biomarker for predicting mortality risk than UA or ALB alone. Several advantages may be found in this research. The present prospective cohort study was based on a large sample, in which UAR levels were measured in a large number of individuals in the general population, with a long follow-up time, and is the first study on UAR and cancer death risk. As a predictor, it is easy to obtain, simple and convenient, and can be carried out at all primary medical institutions. We also note several limitations of the study. First, UAR is calculated by random one-time UA and ALB measurements, which makes it difficult to avoid potential measurement bias. Second, in the stratified analysis of tumors, due to the insufficient sample size of some tumors, the research results need to be further verified with a large number of samples. Finally, because the risk factors for death affecting the general population and cancer patients are complex, the confounders controlled for are not limited to the adjusted factors in this study. Conclusions The present cohort study revealed that when the UAR was greater than the threshold effect, all-cause, cancer-specific and noncancer mortality in the general population gradually increased, with cancer death having the lowest threshold effect and a more significant impact, and the UAR may be used as a biomarker to predict survival prognosis in the clinic. Declarations Ethics approval and consent The NHANES was approved by the National Institute of Health Research Ethics Review Board. All the participants signed and provided informed consent. Consent for publication Not applicable. Availability of data and materials Details of all variables and data are publicly available on the NHANES website, https://wwwn.cdc.gov/nchs /nhanes/Default.aspx Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions YZY, MQC, FDJ and SCM designed the research, conducted the literature search, and collected and organized the data. YZY and MQC performed the data analysis and interpretation and manuscript writing. Acknowledgements Not applicable. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA: a cancer journal for clinicians 2021, 71 (3):209-249. Ames BN, Cathcart R, Schwiers E, Hochstein P: Uric acid provides an antioxidant defense in humans against oxidant- and radical-caused aging and cancer: a hypothesis . Proceedings of the National Academy of Sciences of the United States of America 1981, 78 (11):6858-6862. Harris IS, DeNicola GM: The Complex Interplay between Antioxidants and ROS in Cancer . Trends in cell biology 2020, 30 (6):440-451. 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Quinlan GJ, Martin GS, Evans TW: Albumin: biochemical properties and therapeutic potential . Hepatology (Baltimore, Md) 2005, 41 (6):1211-1219. Tables Table 1. Baseline characteristics of the participants by UAR tertile Variables Total T1(0.1-1.0) T2(1.1-1.3) T3(1.4-4.7) P-value Number 52534 14441 17944 20149 Age, year 47.50(19.23) 42.92(18.22) 46.35(19.06) 51.82(19.17) <0.001 Sex (Male) 25437 (48.42%) 3256 (22.55%) 8918 (49.70%) 13263 (65.82%) <0.001 Race and ethnicity <0.001 Hispanic 14074 (26.79%) 4591 (31.79%) 5106 (28.46%) 4377 (21.72%) Non-Hispanic white 22818 (43.43%) 6030 (41.76%) 7764 (43.27%) 9024 (44.79%) Non-Hispanic black 10870 (20.69%) 2522 (17.46%) 3439 (19.17%) 4909 (24.36%) Others 4772 (9.08%) 1298 (8.99%) 1635 (9.11%) 1839 (9.13%) Poverty income ratio <0.001 <1.3 15409 (32.13%) 4468 (33.99%) 5246 (31.98%) 5695 (30.94%) (1.3,3.5) 18090 (37.72%) 4759 (36.21%) 6167 (37.59%) 7164 (38.92%) ≥3.5 14458 (30.15%) 3917 (29.80%) 4991 (30.43%) 5550 (30.15%) Education <0.001 <High school 13212 (27.04%) 3386 (25.84%) 4507 (27.24%) 5319 (27.70%) High school or equivalent 11285 (23.10%) 2776 (21.18%) 3789 (22.90%) 4720 (24.58%) College or above 24355 (49.85%) 6943 (52.98%) 8247 (49.85%) 9165 (47.72%) BMI, kg/m² 28.74(6.80) 25.83(5.40) 28.33(6.17) 31.23(7.32) <0.001 Alcohol consumption (Yes) 6165 (49.40%) 1875 (47.13%) 2094 (50.02%) 2196 (50.88%) 0.002 Smoking status (Ever) 22433 (45.19%) 5095 (38.04%) 7534 (44.76%) 9804 (50.48%) <0.001 Physical activity <0.001 Inactive 21826 (41.59%) 5539 (38.40%) 7050 (39.32%) 9237 (45.90%) Moderately active 15429 (29.40%) 4679 (32.44%) 5277 (29.43%) 5473 (27.20%) Active 15219 (29.00%) 4205 (29.15%) 5602 (31.25%) 5412 (26.90%) Hypertension 17074 (32.65%) 2960 (20.59%) 5136 (28.77%) 8978 (44.74%) <0.001 Diabetes 5919 (11.50%) 1222 (8.57%) 1743 (9.90%) 2954 (15.06%) <0.001 Heart disease 2145 (4.39%) 293 (2.24%) 581 (3.51%) 1271 (6.62%) <0.001 Stroke 1840 (3.77%) 310 (2.36%) 533 (3.22%) 997 (5.19%) <0.001 Cancer 4429 (9.06%) 960 (7.32%) 1434 (8.67%) 2035 (10.59%) <0.001 Uric acid, mg/dL 5.40(1.46) 3.82 ± 0.62 5.12 ± 0.57 6.79 ± 1.09 <0.001 Albumin, g/dL 4.24(0.37) 4.31 ± 0.35 4.27 ± 0.36 4.16 ± 0.38 <0.001 UAR 1.28(0.36) 0.88 (0.12) 1.20 (0.08) 1.64 (0.27) <0.001 Continuous and categorical variables were presented as mean ± SD or percentages. Abbreviations: UAR, serum uric acid to albumin ratio; BMI, body mass index. Table 2. Hazard ratios of mortality according to UAR and UAR tertile Mortality No. of events Model 1 Model 2 HR (95%) P-value HR (95%) P-value All-cause UAR 7623 3.65 (3.47, 3.85) <0.0001 1.49 (1.32, 1.68) <0.0001 T1 1395 Reference Reference T2 2193 1.35 (1.26, 1.44) <0.0001 1.02 (0.89, 1.16) 0.8098 T3 4035 2.46 (2.31, 2.61) <0.0001 1.17 (1.03, 1.33) 0.0176 p for trend <0.0001 0.0074 Cancer specific UAR 1559 2.51 (2.23, 2.83) <0.0001 1.69 (1.32, 2.17) <0.0001 T1 244 Reference Reference T2 464 1.38 (1.18, 1.61) <0.0001 1.16 (0.85, 1.58) 0.3581 T3 851 2.06 (1.79, 2.37) <0.0001 1.53 (1.13, 2.07) 0.0063 p for trend <0.0001 0.0027 Noncancer UAR 6004 3.50 (3.30, 3.72) <0.0001 1.43 (1.24, 1.64) <0.0001 T1 1131 Reference Reference T2 1713 1.30 (1.21, 1.40) <0.0001 0.99 (0.86, 1.14) 0.9167 T3 3160 2.32 (2.17, 2.48) <0.0001 1.10 (0.95, 1.26) 0.2003 p for trend <0.0001 0.1377 Abbreviations: UAR, serum uric acid to albumin ratio; HR, hazard ratio. Model 1 adjust for: None Model 2 adjusted for: Age; Sex; Race and ethnicity; Poverty income ratio; Education; BMI; Alcohol consumption; Smoking status; Physical activity; Hypertension; Diabetes; Heart disease; Stroke; Cancer. Table 3. Hazard ratios of cancer mortality according to UAR and UAR tertile Subgroup No. of events Model 1 Model 2 HR (95%) P-value HR (95%) P-value Digestive system tumors UAR 190 1.79 (1.27, 2.52) 0.0008 3.14 (0.87, 11.26) 0.0797 T1 31 Reference Reference T2 49 0.90 (0.57, 1.41) 0.6388 0.81 (0.27, 2.38) 0.6959 T3 110 1.22 (0.82, 1.82) 0.3317 0.83 (0.27, 2.56) 0.7476 P for trend 0.1540 0.8014 Urogenital and gynecological tumors UAR 709 2.48 (2.10, 2.93) <0.0001 1.80 (1.28, 2.54) 0.0007 T1 114 Reference Reference T2 206 1.51 (1.20, 1.90) 0.0004 1.20 (0.74, 1.94) 0.4660 T3 389 2.29 (1.86, 2.82) <0.0001 1.75 (1.12, 2.74) 0.0146 P for trend <0.0001 0.0069 Skin soft tissue tumors UAR 437 3.02 (2.36, 3.87) <0.0001 1.37 (0.78, 2.41) 0.2697 T1 54 Reference Reference T2 144 1.64 (1.20, 2.24) 0.0020 0.95 (0.49, 1.85) 0.8897 T3 239 2.32 (1.73, 3.12) <0.0001 1.06 (0.55, 2.02) 0.8649 P for trend <0.0001 0.7749 Other tumors UAR 222 2.22 (1.64, 3.01) <0.0001 4.67 (1.91, 11.40) 0.0007 T1 45 Reference Reference T2 65 1.06 (0.73, 1.55) 0.7542 1.68 (0.64, 4.38) 0.2917 T3 112 1.65 (1.17, 2.33) 0.0046 4.13 (1.44, 11.85) 0.0082 P for trend 0.0016 0.0051 Abbreviations: UAR, serum uric acid to albumin ratio; HR, hazard ratio. Model 1 adjust for: None Model 2 adjusted for: Age; Sex; Race and ethnicity; Poverty income ratio; Education; BMI; Alcohol consumption; Smoking status; Physical activity; Hypertension; Diabetes; Heart disease; Stroke. Table 4. Threshold effect analysis of UAR and mortality Outcomes Adjusted HR (95% CI) P-value P-value for LRT All-cause mortality UAR 1.49 (1.32, 1.68) <0.0001 Turning point(K) 1.4 <0.001 K1.4 1.93 (1.63, 2.28) <0.0001 Cancer mortality UAR 1.69 (1.32, 2.17) <0.0001 Turning point(K) 0.8 <0.001 K0.8 1.83 (1.43, 2.34) <0.0001 Noncancer mortality UAR 1.43 (1.24, 1.64) <0.0001 Turning point(K) 1.5 <0.001 K1.5 2.27 (1.55, 3.30) <0.0001 Abbreviations: UAR, serum uric acid to albumin ratio; HR, hazard ratio; LRT, log likelihood ratio test. Adjusted for: Age; Sex; Race and ethnicity; Poverty income ratio; Education; BMI; Alcohol consumption; Smoking status; Physical activity; Hypertension; Diabetes; Heart disease; Stroke; Cancer. 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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-5309667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370853518,"identity":"64692f9d-9e7e-4bfd-851d-aa1898ee1d68","order_by":0,"name":"Yongzhi Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACxmbG5gcf/0nIsbE3EKmFuZ25zXAGm4UxH88BIrWw97M3SPOwVSTOk0ggUgtvM2ODAQ+PRGKb5OONNxhqbKIJapEEankgISFh3CadVmzBcCwtt4GQFkOQLQYGErJt0jlmEowNhwlrsT/M2CCRkCDB2CZ5hkgtwEBukDhwQEKxTYKHeC1tho0NEsZsPEC/JBDjF8b+448f/22ok5NvP7zxxocaG8JakIEB0VGDpIVUHaNgFIyCUTAyAAC1sTriaa8cFgAAAABJRU5ErkJggg==","orcid":"","institution":"Xiamen Hospital Dongzhimen Hospital of Beijing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yongzhi","middleName":"","lastName":"Ye","suffix":""},{"id":370853519,"identity":"4f2c6c44-304c-48ca-a930-f778339d18db","order_by":1,"name":"Meiqiong Chen","email":"","orcid":"","institution":"Xiamen University Affiliated Women and Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meiqiong","middleName":"","lastName":"Chen","suffix":""},{"id":370853520,"identity":"9e79ca35-1ca7-48e5-804a-a83bf688fc95","order_by":2,"name":"Fada Ji","email":"","orcid":"","institution":"Xiamen Hospital Dongzhimen Hospital of Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fada","middleName":"","lastName":"Ji","suffix":""},{"id":370853521,"identity":"879707b1-4258-471a-8088-67c4b737b7b7","order_by":3,"name":"Suicai Mi","email":"","orcid":"","institution":"Xiamen Hospital Dongzhimen Hospital of Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Suicai","middleName":"","lastName":"Mi","suffix":""}],"badges":[],"createdAt":"2024-10-22 08:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5309667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5309667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68885217,"identity":"becfb817-1d95-4f02-8a5b-a3eded0ec06c","added_by":"auto","created_at":"2024-11-13 06:36:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134252,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5309667/v1/47265500ce2467175e659062.png"},{"id":68885218,"identity":"fb2907ec-c78e-49d6-a1b3-6578a40c1d2d","added_by":"auto","created_at":"2024-11-13 06:36:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":714000,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5309667/v1/5dcf64ba2f0ed5be3bdc8cbb.png"},{"id":68884706,"identity":"664fb25c-c9f8-4f38-a2f3-07b1da7fcadd","added_by":"auto","created_at":"2024-11-13 06:28:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433417,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5309667/v1/b23154fa909890566b6a0c2d.png"},{"id":85229785,"identity":"99e59373-bc34-4578-9ec0-d164522f3e9d","added_by":"auto","created_at":"2025-06-23 15:47:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3094279,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5309667/v1/8885a021-b024-4596-8c31-79c7b24b7734.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of the serum uric acid-to-albumin ratio with all-cause, cancer specific, noncancer mortality in U.S. adults: a prospective study from the NHANES (1999--2018)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTumours are still a disease with high morbidity and mortality worldwide [1]; thus, improving tumour prognosis has been a hot topic of research, and there is an urgent need for effective and convenient biomarkers to predict the risk of tumour death. Serum uric acid (UA) and albumin (ALB) can be routinely tested in all primary care facilities and hospitals and are simple and inexpensive. UA and ALB have been reported as endogenous antioxidants capable of countering excessive oxidative stress [2-4], thereby reducing DNA damage and mutation risk and helping prevent the development of malignant tumors [5]. A number of studies have suggested that UA and ALB levels are negatively associated with tumor risk [6-8], but some studies disagree that UA also plays a pro-oxidative role and promotes tumor progression [9, 10]. The factors influencing UA and ALB concentrations are complex and include nutritional status, insulin resistance, kidney disease, and metabolic syndrome [11], and the relationships between UA and ALB levels and cancer risk have not been fully studied. Currently, UAR is recognized as a marker of inflammation and oxidative stress [12], and its relationship with the risk of death from tumors needs further study. We aimed to evaluate the value of the UAR in predicting the risk of death.\u003c/p\u003e"},{"header":"Research population and methods","content":"\u003cp\u003e\u003cstrong\u003e1. Participant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this cohort study were from the NHANES database (1999--2018). This database is a cross-sectional study that collects health and nutritional information related to representative demographic statistics, physical examinations, disease questionnaires, and survival follow-up. This study included 52,534 participants aged 18 years and older with complete serum albumin, uric acid, and follow-up data on death (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Variables and outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, UAR was the ratio of uric acid (mg/dL) to albumin (g/dL), and the covariates included demographic factors, such as age, race and ethnicity, sex, the poverty income ratio (PIR) and education level; lifestyle surveys, such as body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e), smoking status (former or never), alcohol consumption (yes or no), and physical activity; and health questionnaires, such as hypertension, diabetes, heart disease, stroke, cancer, and death. Details of all the variables are publicly available on the NHANES website：https://www \u0026nbsp; \u0026nbsp; n.cdc.gov/nchs/nhanes/Default.aspx,https://wwwn.cdc.gov/nchs/data/nhanes/2017-2018/la bmethods/BIOPRO-J-MET-Uric-Acid-508.pdf,https://wwwn.cdc.gov/nchs/data/nhanes/201 7-2018/labmethods/BIOPRO-J-MET-Albumin-508.pdf). The primary outcome was all-cause mortality, and cancer-specific mortality was the secondary outcome. The follow-up for survival was from the day that the participants were interviewed to December 31, 2019.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. The means \u0026plusmn; standard deviations (SDs) and percentages were calculated for continuous variables and categorical variables, respectively. 2. The correlation between UAR levels and the risk of death was assessed via Cox proportional hazard models. 3. Subgroup analysis to explore the relationships between UAR levels and mortality in different cancer subgroups. 4. Stratified analyses were performed for age, sex, BMI, race and ethnicity, education level, PIR, physical activity, smoking status, and alcohol consumption to examine the correlation between UAR and all-cause mortality. The P value of the interaction was evaluated via the logarithm likelihood ratio test. 5. We used restricted cubic spline (RCS) regression to evaluate the nonlinear association between UAR and the hazard of all-cause, cancer specific, and noncancer death, and the threshold effect model was used to calculate the turning point. All the results were significant at P \u0026lt; 0.05 (two-tailed). The data were analysed via the R statistical package (version 4.2.0) and Empower stats.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Basic characteristics of the population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included a total of 52534 participants, including 4429 cancer patients (9.06%). The overall age of the population was 47.50\u0026plusmn;19.23 years, 48.42% were male, the BMI was 28.74\u0026plusmn;6.80 kg/m\u003csup\u003e2\u003c/sup\u003e, and the UAR was 1.28\u0026plusmn;0.36. The UAR was divided into T1, T2, and T3 according to the tertile. There were 14,441 participants at T1 (0.1--1.0), 17,944 participants at T2 (1.1--1.3) and 20,149 participants at T3 (1.4--4.7). There were significant differences among the three groups according to the factors included in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Relationship between UAR and mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median (Q1--Q3) follow-up time was 9.50 (5.17--14.33) years. \u003cstrong\u003eTable 2\u003c/strong\u003e shows that there were 7623, 1559 and 6004 all-cause, cancer and noncancer deaths, respectively, and 60 deaths from uncertain factors were not counted. Cox proportional hazard regression analysis revealed that increased UAR levels were associated with an increased risk of death after adjustment for confounders (HR (95% CI) for all-cause death =1.49 (1.32, 1.68), HR (95% CI) for cancer =1.69 (1.32, 2.17), and noncancer HR (95% CI) =1.43 (1.24, 1.64)). Compared with those in UAR T1 individuals, the hazards of all-cause death and cancer-specific death were significantly greater in UAR T3 individuals, and the HRs (95% CIs) were 1.17 (1.03, 1.33) and 1.53 (1.13, 2.07), respectively. The trend test results were significant, and the P values were 0.0074 and 0.0027, respectively. The risk of noncancer death in UAR-T3 individuals was not statistically significant (HR (95% CI) =1.10 (0.95, 1.26), p value for trend=0.1377).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Relationships between UAR rates and tumor subgroup mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 3\u003c/strong\u003e, tumor patients were divided into four subgroups: digestive system tumors, urogenital and gynecological tumors, skin soft tissue tumors, and other tumors. Tumor subgroup analysis revealed that the UAR was positively correlated with the risk of death for all tumor types. After adjusting for confounders, the UAR was more significantly associated with the risk of death from urogenital and gynecological tumors and other tumor types, with HRs (95% CIs) of 1.80 (1.28, 2.54) and 4.67 (1.91, 11.40), respectively. Compared with those of T1 individuals, the HRs (95% CIs) of UAR T3 individuals were 1.75 (1.12, 2.74) and 4.13 (1.44, 11.85). The p values for the trends were 0.0069 and 0.0051, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Stratified analysis and interaction test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 2\u003c/strong\u003e, the stratified analysis verified that the UAR level was positively correlated with all-cause death, and all P values for interaction\u0026gt;0.05, indicating that the association between the UAR level and all-cause death was not affected by confounding factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Dose‒response curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn \u003cstrong\u003eFigure 3\u003c/strong\u003e (A-C), UAR presented a \u0026quot;U\u0026quot;-shaped nonlinear relationship with the risk of death according to the Cox model with restricted cubic spline analysis. \u003cstrong\u003eFigure 3\u003c/strong\u003e (D-E) UAR also had a nonlinear relationship with overall survival, cancer survival and noncancer survival. In \u003cstrong\u003eTable 4\u003c/strong\u003e, threshold effect analysis indicated that the turning points of the UAR level for all-cause, cancer specific, and noncancer death rates were 1.4, 0.8, and 1.5, respectively. When the UAR was \u0026gt;1.4, the HR (95% CI) was 1.93 (1.63, 2.28) for all-cause death; when the UAR was \u0026gt;0.8, the HR (95% CI) was 1.83 (1.43, 2.34) for cancer-specific death; and when the UAR was \u0026gt;1.5, the HR (95% CI) was 2.27 (1.55, 3.30) for noncancer death.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large cohort study examined the correlation between UAR levels and all-cause, cancer specific, and noncancer mortality rates. The results suggested that the UAR was positively correlated with mortality risk, the risk of cancer death increased significantly, and the hazard ratio increased by up to 1.17 times. Tumor subgroup analysis suggested that the UAR was positively associated with the death hazard of each tumor type. After adjusting for variables, it was found that the UAR had a more significant effect on the risk of death from urogenital and gynecological tumors, with an HR (95% CI) of 1.80 (1.28, 2.54). Threshold effect analysis suggested that the turning points for UAR were 0.8, 1.4 and 1.5. When the UAR was \u0026gt;\u0026thinsp;0.8, the risk of cancer death increased up to 1.34 times; when the UAR was \u0026gt;\u0026thinsp;1.4, the risk of all-cause death in the general population increased by 93%; when the UAR was \u0026gt;\u0026thinsp;1.5, the risk of noncancer death also increased. This suggests that the UAR threshold for cancer death risk is lower and more sensitive to the effects of UAR levels. Therefore, the UAR may become an original biomarker for predicting the risk of cancer death.\u003c/p\u003e \u003cp\u003eSeveral studies have explored effective tumor prognostic markers, such as the fibrinogen-to-albumin ratio [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the C-reactive protein-to-albumin ratio [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and the serum urea nitrogen-to-albumin ratio [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The use of UAR as an inflammatory and oxidative stress biomarker has also been researched recently [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Research has suggested that UAR levels are positively correlated with the risk of death in STEMI patients and unstable angina pectoris patients after PCI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and UAR is also an indicator of poor prognosis in patients with coronary heart disease [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, UAR is associated with the occurrence of kidney injury and death from acute renal failure [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. There has been little research on whether there is a correlation between cancer death risk and UAR. After adjusting for confounding factors, we found that the UAR was positively associated with the risk of all-cause, cancer-specific and noncancer death. The effect on cancer death was even more significant.\u003c/p\u003e \u003cp\u003eWe believe that the effect of UAR levels on mortality risk may be related to oxidative stress and chronic inflammatory responses. A number of previous studies have confirmed the pro-oxidant properties of UA and the antioxidant properties of ALB [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and disruption of this balance causes oxidative damage to cells and increases the risk of cancer progression. At the same time, hyperuricaemia is complementary to chronic inflammation and is closely correlated with several chronic diseases, such as diabetes, obesity, and hypertension [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which antagonizes the anti-inflammatory effect of ALB [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and is another risk factor for cancer. The ratio of UA to ALB eliminates the common confounding factors that affect both concentrations, and UAR may be more stable as a biomarker for predicting mortality risk than UA or ALB alone.\u003c/p\u003e \u003cp\u003eSeveral advantages may be found in this research. The present prospective cohort study was based on a large sample, in which UAR levels were measured in a large number of individuals in the general population, with a long follow-up time, and is the first study on UAR and cancer death risk. As a predictor, it is easy to obtain, simple and convenient, and can be carried out at all primary medical institutions. We also note several limitations of the study. First, UAR is calculated by random one-time UA and ALB measurements, which makes it difficult to avoid potential measurement bias. Second, in the stratified analysis of tumors, due to the insufficient sample size of some tumors, the research results need to be further verified with a large number of samples. Finally, because the risk factors for death affecting the general population and cancer patients are complex, the confounders controlled for are not limited to the adjusted factors in this study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present cohort study revealed that when the UAR was greater than the threshold effect, all-cause, cancer-specific and noncancer mortality in the general population gradually increased, with cancer death having the lowest threshold effect and a more significant impact, and the UAR may be used as a biomarker to predict survival prognosis in the clinic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe\u0026nbsp;\u003c/strong\u003eNHANES was approved by the National Institute of Health Research Ethics Review Board. All the participants signed and provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetails of all variables and data are publicly available on the NHANES website, https://wwwn.cdc.gov/nchs /nhanes/Default.aspx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZY, MQC, FDJ and SCM designed the research, conducted the literature search, and collected and organized the data. YZY and MQC performed the data analysis and interpretation and manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA: a cancer journal for clinicians \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n\u003cli\u003eAmes BN, Cathcart R, Schwiers E, Hochstein P: \u003cstrong\u003eUric acid provides an antioxidant defense in humans against oxidant- and radical-caused aging and cancer: a hypothesis\u003c/strong\u003e. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America \u003c/em\u003e1981, \u003cstrong\u003e78\u003c/strong\u003e(11):6858-6862.\u003c/li\u003e\n\u003cli\u003eHarris IS, DeNicola GM: \u003cstrong\u003eThe Complex Interplay between Antioxidants and ROS in Cancer\u003c/strong\u003e. \u003cem\u003eTrends in cell biology \u003c/em\u003e2020, \u003cstrong\u003e30\u003c/strong\u003e(6):440-451.\u003c/li\u003e\n\u003cli\u003eRoche M, Rondeau P, Singh NR, Tarnus E, Bourdon E: \u003cstrong\u003eThe antioxidant properties of serum albumin\u003c/strong\u003e. \u003cem\u003eFEBS letters \u003c/em\u003e2008, \u003cstrong\u003e582\u003c/strong\u003e(13):1783-1787.\u003c/li\u003e\n\u003cli\u003eReuter S, Gupta SC, Chaturvedi MM, Aggarwal BB: \u003cstrong\u003eOxidative stress, inflammation, and cancer: how are they linked?\u003c/strong\u003e \u003cem\u003eFree radical biology \u0026amp; 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organo del Hospital de Enfermedades de la Nutricion \u003c/em\u003e2022, \u003cstrong\u003e74\u003c/strong\u003e(3):156-164.\u003c/li\u003e\n\u003cli\u003eAn Q, Liu W, Yang Y, Yang B: \u003cstrong\u003ePreoperative fibrinogen-to-albumin ratio, a potential prognostic factor for patients with stage IB-IIA cervical cancer\u003c/strong\u003e. \u003cem\u003eBMC cancer \u003c/em\u003e2020, \u003cstrong\u003e20\u003c/strong\u003e(1):691.\u003c/li\u003e\n\u003cli\u003eYu J, Liu H, Zeng X, Zhao Y, Jiang D, Lu H, Qian J: \u003cstrong\u003ePrognostic and clinicopathological significance of C-reactive protein/albumin ratio (CAR) in patients with gastric cancer: A meta-analysis\u003c/strong\u003e. \u003cem\u003ePloS one \u003c/em\u003e2021, \u003cstrong\u003e16\u003c/strong\u003e(4):e0250295.\u003c/li\u003e\n\u003cli\u003eDumitriu Carcoana AO, Labib KM, Fiedler CR, Marek JC, Ladehoff LC, West WJ, 3rd, Malavet JA, Doyle WN, Jr., Moodie CC, Garrett JR\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA High Preoperative Blood Urea Nitrogen to Serum Albumin Ratio Does Not Predict Worse Outcomes Following the Robotic-Assisted Pulmonary Lobectomy for Lung Cancer\u003c/strong\u003e. \u003cem\u003eCureus \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(12):e50468.\u003c/li\u003e\n\u003cli\u003eZhang XJ, Hou AJ, Luan B, Wang CF, Li JJ: \u003cstrong\u003eUric acid to albumin ratio as a novel predictor for coronary slow flow phenomenon in patients with chronic coronary syndrome and nonobstructive coronary arteries\u003c/strong\u003e. \u003cem\u003eBMC cardiovascular disorders \u003c/em\u003e2024, \u003cstrong\u003e24\u003c/strong\u003e(1):358.\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;ınar T, Şaylık F, Hayıroğlu M, Asal S, Sel\u0026ccedil;uk M, \u0026Ccedil;i\u0026ccedil;ek V, Tanboğa İ H: \u003cstrong\u003eThe Association of Serum Uric Acid/Albumin Ratio with No-Reflow in Patients with ST Elevation Myocardial Infarction\u003c/strong\u003e. \u003cem\u003eAngiology \u003c/em\u003e2023, \u003cstrong\u003e74\u003c/strong\u003e(4):381-386.\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;akmak E, Bayam E, \u0026Ccedil;elik M, Kahyaoğlu M, Eren K, Imanov E, Karag\u0026ouml;z A, İzgi İ A: \u003cstrong\u003eUric Acid-to-Albumin Ratio: A Novel Marker for the Extent of Coronary Artery Disease in Patients with Non-ST-Elevated Myocardial Infarction\u003c/strong\u003e. \u003cem\u003ePulse (Basel, Switzerland) \u003c/em\u003e2021, \u003cstrong\u003e8\u003c/strong\u003e(3-4):99-107.\u003c/li\u003e\n\u003cli\u003eMohamed RA, Ali IA: \u003cstrong\u003eRole of neutrophil/lymphocyte ratio, uric acid/albumin ratio and uric acid/creatinine ratio as predictors to severity of preeclampsia\u003c/strong\u003e. \u003cem\u003eBMC pregnancy and childbirth \u003c/em\u003e2023, \u003cstrong\u003e23\u003c/strong\u003e(1):763.\u003c/li\u003e\n\u003cli\u003eZhang Y, Xu Z, He W, Lin Z, Liu Y, Dai Y, Chen W, Chen W, He W, Duan C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eElevated Serum Uric Acid/Albumin Ratio as a Predictor of Post-Contrast Acute Kidney Injury After Percutaneous Coronary Intervention in Patients with ST-Segment Elevation Myocardial Infarction\u003c/strong\u003e. \u003cem\u003eJournal of inflammation research \u003c/em\u003e2022, \u003cstrong\u003e15\u003c/strong\u003e:5361-5371.\u003c/li\u003e\n\u003cli\u003eFini MA, Elias A, Johnson RJ, Wright RM: \u003cstrong\u003eContribution of uric acid to cancer risk, recurrence, and mortality\u003c/strong\u003e. \u003cem\u003eClinical and translational medicine \u003c/em\u003e2012, \u003cstrong\u003e1\u003c/strong\u003e(1):16.\u003c/li\u003e\n\u003cli\u003eQuinlan GJ, Martin GS, Evans TW: \u003cstrong\u003eAlbumin: biochemical properties and therapeutic potential\u003c/strong\u003e. \u003cem\u003eHepatology (Baltimore, Md) \u003c/em\u003e2005, \u003cstrong\u003e41\u003c/strong\u003e(6):1211-1219.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of the participants by UAR tertile\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" align=\"\" width=\"510\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1(0.1-1.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2(1.1-1.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3(1.4-4.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\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 style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e52534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e14441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e17944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e20149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eAge, year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e47.50(19.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e42.92(18.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e46.35(19.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e51.82(19.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eSex (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e25437 (48.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e3256 (22.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e8918 (49.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e13263 (65.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eRace and ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e14074 (26.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4591 (31.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5106 (28.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4377 (21.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eNon-Hispanic white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e22818 (43.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e6030 (41.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e7764 (43.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e9024 (44.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eNon-Hispanic black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e10870 (20.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e2522 (17.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e3439 (19.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4909 (24.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e4772 (9.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1298 (8.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1635 (9.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1839 (9.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003ePoverty income ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003e\u0026lt;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e15409 (32.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4468 (33.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5246 (31.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5695 (30.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003e(1.3,3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e18090 (37.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4759 (36.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e6167 (37.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e7164 (38.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003e\u0026ge;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e14458 (30.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e3917 (29.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4991 (30.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5550 (30.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003e\u0026lt;High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e13212 (27.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e3386 (25.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4507 (27.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5319 (27.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eHigh school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e11285 (23.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e2776 (21.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e3789 (22.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4720 (24.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e24355 (49.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e6943 (52.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e8247 (49.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e9165 (47.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e28.74(6.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e25.83(5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e28.33(6.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e31.23(7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eAlcohol consumption (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e6165 (49.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1875 (47.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e2094 (50.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e2196 (50.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eSmoking status (Ever)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e22433 (45.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5095 (38.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e7534 (44.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e9804 (50.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e21826 (41.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5539 (38.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e7050 (39.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e9237 (45.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eModerately active\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e15429 (29.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4679 (32.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5277 (29.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5473 (27.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eActive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e15219 (29.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4205 (29.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5602 (31.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5412 (26.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e17074 (32.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e2960 (20.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5136 (28.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e8978 (44.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5919 (11.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1222 (8.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1743 (9.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e2954 (15.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2145 (4.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e293 (2.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e581 (3.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1271 (6.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1840 (3.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e310 (2.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e533 (3.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e997 (5.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e4429 (9.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e960 (7.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1434 (8.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e2035 (10.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eUric acid, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5.40(1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e3.82 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e5.12 \u0026plusmn; 0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e6.79 \u0026plusmn; 1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e4.24(0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4.31 \u0026plusmn; 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4.27 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e4.16 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9116%;\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1.28(0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e0.88 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1.20 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8594%;\"\u003e\n \u003cp\u003e1.64 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8434%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous and categorical variables were presented as mean \u0026plusmn; SD or percentages.\u003c/p\u003e\n\u003cp\u003eAbbreviations: UAR, serum uric acid to albumin ratio; BMI, body mass index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Hazard ratios of mortality according to UAR and UAR tertile\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHR (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHR (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAll-cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.65 (3.47, 3.85)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49 (1.32, 1.68)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (1.26, 1.44)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02 (0.89, 1.16)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.46 (2.31, 2.61)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17 (1.03, 1.33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCancer specific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.51 (2.23, 2.83)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.69 (1.32, 2.17)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.38 (1.18, 1.61)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16 (0.85, 1.58)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.06 (1.79, 2.37)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.53 (1.13, 2.07)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNoncancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.50 (3.30, 3.72)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43 (1.24, 1.64)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30 (1.21, 1.40)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.86, 1.14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32 (2.17, 2.48)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.95, 1.26)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: UAR, serum uric acid to albumin ratio; HR, hazard ratio.\u003c/p\u003e\n\u003cp\u003eModel 1 adjust for: None\u003c/p\u003e\n\u003cp\u003eModel 2 adjusted for: Age; Sex; Race and ethnicity; Poverty income ratio; Education; BMI; Alcohol consumption; Smoking status; Physical activity; Hypertension; Diabetes; Heart disease; Stroke; Cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Hazard ratios of cancer mortality according to UAR and UAR tertile\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eevents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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\u003e\n \u003cp\u003eDigestive system tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.79 (1.27, 2.52)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.14 (0.87, 11.26)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90 (0.57, 1.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81 (0.27, 2.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.82, 1.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.83 (0.27, 2.56)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrogenital and gynecological tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.48 (2.10, 2.93)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.80 (1.28, 2.54)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51 (1.20, 1.90)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20 (0.74, 1.94)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.29 (1.86, 2.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.75 (1.12, 2.74)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSkin soft tissue tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.02 (2.36, 3.87)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37 (0.78, 2.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.64 (1.20, 2.24)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.49, 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32 (1.73, 3.12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06 (0.55, 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.22 (1.64, 3.01)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.67 (1.91, 11.40)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06 (0.73, 1.55)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.68 (0.64, 4.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65 (1.17, 2.33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.13 (1.44, 11.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: UAR, serum uric acid to albumin ratio; HR, hazard ratio.\u003c/p\u003e\n\u003cp\u003eModel 1 adjust for: None\u003c/p\u003e\n\u003cp\u003eModel 2 adjusted for: Age; Sex; Race and ethnicity; Poverty income ratio; Education; BMI; Alcohol consumption; Smoking status; Physical activity; Hypertension; Diabetes; Heart disease; Stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Threshold effect analysis of UAR and mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"73%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted HR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value for LRT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.49 (1.32, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eTurning point(K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; K\u0026lt;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.93 (0.72, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.5603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; K\u0026gt;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.93 (1.63, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eCancer mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.69 (1.32, 2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eTurning point(K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e\u0026nbsp; K\u0026lt;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.02 (0.00, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eK\u0026gt;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.83 (1.43, 2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eNoncancer mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eUAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.43 (1.24, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eTurning point(K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eK\u0026lt;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.92 (0.72, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.5143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003eK\u0026gt;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e2.27 (1.55, 3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: UAR, serum uric acid to albumin ratio; HR, hazard ratio; LRT, log likelihood ratio test.\u003c/p\u003e\n\u003cp\u003eAdjusted for: Age; Sex; Race and ethnicity; Poverty income ratio; Education; BMI; Alcohol consumption; Smoking status; Physical activity; Hypertension; Diabetes; Heart disease; Stroke; Cancer.\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":"Serum uric acid-to-albumin ratio (UAR), Cancer, Mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5309667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5309667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe serum uric acid-to-albumin ratio (UAR) is closely correlated with mortality in some diseases, but its correlation with all-cause and cancer specific death in the general population requires further research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective cohort study included 52,534 participants from the NHANES database (1999\u0026ndash;2018). The inclusion criteria were as follows: 18 years of age and older, complete serum uric acid and albumin examinations and mortality follow-up. We used Cox models to evaluate the correlation between UAR and all-cause, cancer specific, and noncancer mortality. The nonlinear relationship was evaluated via restricted cubic spline (RCS) analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCox regression analysis revealed that an increased UAR was related to an increased risk of death after adjustment for confounding factors (HR (95% CI) for all-cause death\u0026thinsp;=\u0026thinsp;1.49 (1.32, 1.68), HR (95% CI) for cancer\u0026thinsp;=\u0026thinsp;1.69 (1.32, 2.17), HR (95% CI) for noncancer\u0026thinsp;=\u0026thinsp;1.43 (1.24, 1.64)). Compared with those in UAR T1 individuals, the hazards of all-cause death and cancer-specific death were significantly greater in UAR T3 individuals, and the HRs (95% CIs) were 1.17 (1.03, 1.33) and 1.53 (1.13, 2.07), respectively. The trend test results were significant, and the P values were 0.0074 and 0.0027, respectively. The risk of noncancer death in UAR-T3 individuals was not statistically significant (HR (95% CI)\u0026thinsp;=\u0026thinsp;1.10 (0.95, 1.26), p for trend\u0026thinsp;=\u0026thinsp;0.1377). Tumor subgroup analysis suggested that the UAR was positively correlated with the risk of mortality for each tumor type. After adjusting for variables, it was found that the UAR had a more significant effect on the risk of death from urogenital and gynecological tumors, with an HR (95% CI) of 1.80 (1.28, 2.54). A restricted cubic spline analysis revealed a \"U\"-shaped nonlinear correlation between UAR and all-cause, cancer specific, noncancer death. Threshold effect analysis suggested that the turning points of all-cause, cancer-specific and noncancer death for UAR were 1.4, 0.8 and 1.5, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWhen the UAR was greater than the threshold effect, the hazard of mortality gradually increased, and cancer-specific death had the lowest threshold effect and a more significant impact, which may be used as a biomarker to predict survival prognosis.\u003c/p\u003e","manuscriptTitle":"Association of the serum uric acid-to-albumin ratio with all-cause, cancer specific, noncancer mortality in U.S. adults: a prospective study from the NHANES (1999--2018)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 06:28:01","doi":"10.21203/rs.3.rs-5309667/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":"b22be0de-a0bb-4c40-ba58-65b7cafc22bb","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T15:38:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-13 06:28:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5309667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5309667","identity":"rs-5309667","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}