Association of Serum Uric Acid with Cardiovascular-Kidney-Metabolic Syndrome in Elderly Populations: Results from Chinese and US cohorts

Association of Serum Uric Acid with Cardiovascular-Kidney-Metabolic Syndrome in Elderly Populations: Results from Chinese and US cohorts | 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 Article Association of Serum Uric Acid with Cardiovascular-Kidney-Metabolic Syndrome in Elderly Populations: Results from Chinese and US cohorts Wencai Ke, Kangan Wang, Biying Wu, Bingbing Zha, Yong Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8851827/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Cardiovascular-kidney-metabolic (CKM) syndrome is highly prevalent among the elderly. Serum uric acid (SUA) may serve as a potential marker for CKM,but evidence on the association between SUA levels and the progression of CKM syndrome remains scarce and controversial. This study aims to investigate the relationship between SUA levels and CKM syndrome in elderly populations by utilizing cohort data from China and the United States. Methods We analyzed 3,299 participants from the Shanghai Friendship Community Elderly Cohort (SFCEC) and 2,372 from the US National Health and Nutrition Examination Survey (NHANES) 2011–2018.SUA was measured using standardized assays. Multivariate logistic regression models were performed. Restricted cubic spline regression was performed to visualize the dose-response relationship. Results The prevalence of CKM syndrome (stages 1–4) was strikingly high in both elderly cohorts(98.33% in the SFCEC cohort and 98.76% in the NHANES cohort). After comprehensive adjustment, multivariable analysis revealed that each 1 mg/dl increase in serum uric acid was associated with 24.9% (OR = 1.249, 95% CI: 1.186–1.315) and 27.8% (OR = 1.278, 95% CI:1.174–1.392) elevated risks of CKM syndrome in the Shanghai and US cohorts, respectively. Compared with the lowest tertile, participants in the highest serum uric acid tertile demonstrated significantly increased CKM risks (Shanghai cohort: OR = 1.966, 95% CI: 1.653–2.339; US cohort: OR = 1.975, 95% CI:1.472–2.650). Restricted cubic spline analysis indicated a positive linear dose-response relationship between serum uric acid levels and CKM syndrome staging. This association was more pronounced in female patients. Conclusions This study reveals that elevated SUA levels may be a potential independent risk factor for the progression of CKM syndrome in the elderly population.Clinical screening of SUA levels may be helpful for the early detection and prevention of CKM syndrome. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors Cardiovascular-kidney-metabolic (CKM) syndrome serum uric acid (SUA) Elderly population Figures Figure 1 Figure 2 1. Introduction Cardiovascular-kidney-metabolic (CKM) syndrome has recently been recognized by the American Heart Association (AHA) as a novel clinical construct, representing a complex multisystem condition characterized by interconnected pathophysiological processes.This syndrome manifests through the dynamic interplay among obesity,diabetes mellitus, chronic kidney disease, and cardiovascular disease. The AHA has established a five-stage classification system (Stage 0: no risk factors to Stage 4:established CVD) that provides a structured framework for comprehensive comorbidity management [1]. Epidemiological studies have demonstrated a considerable global health burden, with CKM-related health impairments showing elevated prevalence rates among adults in the United States [2,3] while revealing parallel concerns in China [4].Pathophysiological investigations suggest that sustained metabolic disturbances trigger sequential pathological events including sympathetic overactivity, persistent inflammatory activation, and progressive fibrotic processes, ultimately culminating in multi-organ dysfunction. However, the comprehensive delineation of contributing elements and underlying molecular mechanisms remains incomplete. As the final product of purine metabolism, serum uric acid (SUA) contributes to inflammation, oxidative stress, and endothelial dysfunction [5,6]. Elevated SUA levels have been associated with a higher risk of developing chronic kidney disease, diabetes, cardiovascular disease, and metabolic disorders[6–8].Several studies displayed that elevated SUA levels were associated with CKM, indicating that higher SUA levels may contribute to an increased risk of CKM[6,9].However, research teams from Italy, Brazil, and China investigating sarcopenia have proposed that SUA, acting as an antioxidant, may exert protective effects[10–12]. In plasma, SUA neutralizes key harmful oxidants such as peroxyl radicals, peroxynitrite, and hydroxyl radicals[13, 14]. These conflicting findings prompt us to examine whether SUA can serve as a risk factor or predictor for chronic kidney-metabolic (CKM) disease in the elderly population. To bridge this research void, we undertook a comprehensive analysis integrating the Shanghai Friendship Community Elderly Cohort (SFCEC) and National Health and Nutrition Examination Survey (NHANES 2011–2018) datasets. This investigation evaluates associations between serum uric acid concentrations and CKM staging while characterizing detailed dose-response relationships. 2. Materials and methods 2.1 Data source Study participants were derived from two independent cohort studies: the Shanghai Friendship Community Elderly Cohort (SFCEC) and the National Health and Nutrition Examination Survey (NHANES 2011–2018).The SFCEC specifically investigates health outcomes among community-dwelling elderly populations in Baoshan District,Shanghai between April and September 2020. NHANES constitutes a stratified, multistage probability sample designed to collect nationally representative data on health status and nutritional parameters of non-institutionalized civilian residents in the United States. Both databases provide comprehensive data on: Demographic characteristics,Socioeconomic status,Lifestyle factors,Physical examination measurements,Laboratory test results.The SFCEC study protocol was approved by the Institutional Review Board of Huashan Hospital Affiliated to Fudan University (Approval No. 2020-004). NHANES received ethical approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board. Written informed consent was obtained from all participants prior to study enrollment. All datasets were de-identified with no protected health information included. 2.2 Study population The study timeline and a flowchart illustrating the study design are presented in Fig. 1 . Initially, the study included 3299 subjects from the SFCEC and 2372 subjects from the NHANES. Exclusion criteria were: age < 60 years, missing laboratory data (including serum uric acid levels), unavailable CKM diagnosis data, or incomplete demographic data. 2.3 CKM Syndrome Ascertainment The staging of CKM syndrome was determined according to the established five-stage classification system (Stages 0–4) endorsed by the American Heart Association and Aggarwal et al, with detailed criteria adapted for NHANES data availability as summarized in eTable1 . Specifically, Stage 0 included individuals with normal body mass index (BMI) and waist circumference and no other metabolic abnormalities; Stage 1 comprised persons fulfilling criteria for excess adiposity or prediabetes; Stage 2 encompassed subjects presenting with multiple metabolic risk factors or moderate-to-high risk chronic kidney disease as classified by KDIGO guidelines;Stage 3 identified individuals with very-high-risk CKD per KDIGO classification or elevated 10-year cardiovascular disease risk based on PREVENT equations (eTable2) ; and Stage 4 represented those with established clinical cardiovascular disease. 2.4 Measurement of SUA Serum uric acid (SUA) levels were measured using the Roche Cobas e8000 automated biochemistry analyzer with original reagents on serum samples at the Department of Laboratory Medicine, Shanghai Fifth People's Hospital, Fudan University. Results are expressed in mg/dL. 2.5 Assessment of covariates This study adjusted for comprehensive covariates encompassing demographic characteristics(age, sex [male/female],marital status[married/cohabitating,divorced/separated, never married, widowed]), socioeconomic status (educational attainment [high school] and annual household income [≤ 14,000, 14,000–28,000, or ≥ 28,000 USD] converted from RMB at 7:1 exchange rate), and lifestyle factors (current smoking status and alcohol consumption, both dichotomized as yes/no). 2.6 Statistical analysis We employed a staged analytical approach: baseline characteristics were reported as mean ± standard deviation for continuous variables and frequencies (percentages) for categorical variables. Multivariable logistic regression models examined associations between serum uric acid (analyzed continuously and by tertiles [T1-T3]) and CKM syndrome using two models: Model 1 (unadjusted) and Model 2 (adjusted for age, sex, education, smoking status, alcohol consumption, marital status, and household income), with all analyses incorporating NHANES sampling weights, stratification, and primary sampling units to ensure representativeness. Stratified analyses were conducted across prespecified subgroups (age [≤ 70,>70 years], sex, education, smoking, alcohol use, marital status, and income). Baseline comparisons across uric acid tertiles presented continuous variables as mean ± standard error and categorical variables as percentages. Restricted cubic splines (RCS) with 3 knots assessed potential nonlinear dose-response relationships between uric acid and CKM syndrome, adjusted for Model 2 covariates (though technical constraints precluded inclusion of sampling weights in RCS analyses). Model fit was evaluated via Akaike Information Criterion (AIC), with lower values indicating better fit. All analyses used R version 4.4.2 (R Foundation for Statistical Computing). 3. Results 3.1 Baseline characteristics of study participants Table 1 summarizes the baseline characteristics of the study populations, comprising 3,299 participants from the SFCEC cohort (60.8% female; mean age 74.47 ± 3.35 years) and 2,372 participants from the NHANES cohort (54.3% female; mean age 68.97 ± 0.21 years). The distribution of CKM syndrome stages (0–4) in the SFCEC cohort was 1.7%, 14. 1%, 41.3%, 35.7%, and 7.2%, respectively, while the NHANES cohort showed corresponding proportions of 1.67%, 10.85%, 51.66%, 11.38%, and 24.44%.Mean serum uric acid levels were 5.60 ± 1.37 mg/dL in the SFCEC cohort and 5.68 ± 0.05 mg/dL in the NHANES cohort. Comparative analysis across serum uric acid tertiles revealed that in the SFCEC cohort, participants in higher tertiles (T2/T3) demonstrated significantly greater proportions of males, higher educational attainment,increased smoking and alcohol consumption prevalence, and elevated household income compared to the T1 group. In contrast, the NHANES cohort only showed a significantly higher male proportion in the upper SUA tertiles (T2/T3) relative to T1. Table 1 Baseline Characteristics of the Study Population Characteristic Shanghai Friendship Community Elderly Cohort NHANES Total n=3299 SUA levels,mg/dL P Total n=2372 SUA levels,mg/dL P T1(1.1-4.9) n=1097 T2(5.0-6.0) n=1107 T3(6.1-12.4) n=1095 T1(1.1-5.0) n=814 T2(5.1-6.2) n=774 T3(6.3-13.0) n=784 Age 74.47 (3.35) 74.41 (3.44) 74.36 (3.36) 74.64 (3.24) 0.120 68.97 (0.21) 68.81 (0.38) 68.88 (0.35) 69.25 (0.30) 0.608 Sex <0.001 <0.001 female 2007 (60.84) 898 (81.86) 705 (63.69) 404 (36.89) 1182 (54.29) 562 (73.73) 341 (47.94) 279 (39.35) male 1292 (39.16) 199 (18.14) 402 (36.31) 691 (63.11) 1190 (45.71) 252 (26.27) 433 (52.06) 505 (60.65) Education status <0.001 0.496 < high school 2329 (70.60) 821 (74.84) 764 (69.02) 744 (67.95) 306 (6.15) 121 (6.50) 98 (5.70) 87 (6.25) high school 586 (17.76) 177 (16.13) 216 (19.51) 193 (17.63) 867 (32.61) 274 (29.99) 285 (33.39) 308 (34.69) >high school 384 (11.64) 99 ( 9.03) 127 (11.49) 158 (14.42) 1199 (61.24) 419 (63.51) 391 (60.91) 389 (59.06) Marital status 0.004 0.360 divorced/separated 38 (1.15) 9 (0.82) 16 (1.45) 13 (1.19) 399 (14.08) 149 (16.29) 131 (13.65) 119 (12.08) married/cohabit 3029 (91.82) 986 (89.88) 1015 (91.69) 1028 (93.88) 1439 (66.48) 464 (62.62) 474 (68.58) 501 (68.57) never married 3 (0.09) 2 (0.18) 0 (0.00) 1 (0.09) 106 (3.51) 46 (4.25) 33 (3.18) 27 (3.01) widowed 229 (6.94) 100 (9.12) 76 (6.86) 53 (4.84) 428 (15.94) 155 (16.84) 136 (14.59) 137 (16.34) Smoking status <0.001 0.459 yes 260 (7.88) 43 (3.92) 82 (7.41) 135 (12.33) 307 (10.57) 103 (10.54) 100 (12.04) 104 (9.07) no 3039 (92.12) 1054 (96.08) 1025 (92.59) 960 (87.67) 2065 (89.43) 711 (89.46) 674 (87.96) 680 (90.93) Drinking status <0.001 0.532 yes 231 (7.00) 28 (2.55) 70 (6.32) 133 (12.15) 1459 (68.51) 473 (66.41) 486 (69.80) 500 (69.48) no 3068 (93.00) 1069 (97.45) 1037(93.68) 962 (87.85) 913 (31.49) 341 (33.59) 288 (30.20) 284 (30.52) Household income 0.013 0.685 <14000 2362 (71.60) 814 (74.20) 804 (72.63) 744 (67.95) 730 (17.77) 259 (17.83) 232 (18.24) 239 (17.22) 14000-28000 885 (26.83) 270 (24.61) 287 (25.93) 328 (29.95) 690 (25.11) 232 (24.22) 220 (23.93) 238 (27.31) >28000 52 (1.57) 13 (1.19) 16 (1.44) 23 (2.10) 952 (57.12) 323 (57.95) 322 (57.84) 307 (55.46) UA 5.60 (1.37) NA NA NA NA 5.68 (0.05) NA NA NA NA CKM syndrome <0.001 <0.001 Stage 0 55 (1.67) 38 (3.46) 11 (0.99) 6 (0.55) 26 (1.67) 18 (3.40) 7 (1.01) 1 (0.44) Stage 1 465 (14.10) 224 (20.42) 151 (13.64) 90 (8.22) 182 (10.85) 100 (17.95) 60 (10.25) 22 (3.63) Stage 2 1362 (41.29) 494 (45.03) 491 (44.35) 377 (34.43) 1196 (51.66) 418 (48.12) 407 (55.19) 371 (51.92) Stage 3 1178 (35.71) 265 (24.16) 383 (34.60) 530 (48.40) 376 (11.38) 100 (8.90) 120 (10.49) 156 (15.04) Stage 4 239 (7.23) 76 (6.93) 71 (6.42) 92 (8.40) 592 (24.44) 178 (21.63) 180 (23.07) 234 (28.97) National Health and Nutrition Examination Survey; SUA, serum uric acid; CKM syndrome, Cardiovascular-Kidney-Metabolic syndrome; T1,Tertile 1; T2, Tertile 2; T3, Tertile 3; Continuous variables were summarized as mean ± standard deviation (SD), while categorical variables were expressed as frequencies (percentages). 3.2 Association Between SUA Levels and CKM Syndrome Multivariate logistic regression analysis demonstrated a significant association between SUA levels and CKM syndrome stages in both cohorts ( Table 2 ) . In the SFCEC cohort, after full adjustment for covariates, each unit increase in SUA was associated with an elevated risk of higher CKM stage (OR = 1.249, 95% CI: 1.186 to 1.315). Participants in the highest SUA tertile (T3) exhibited a significantly increased risk compared to those in the lowest tertile (T1) (OR = 1.966, 95% CI: 1.653 to 2.339). A similar pattern was observed in the NHANES cohort: higher SUA levels (per unit increase: OR = 1.278, 95% CI: 1.174 to 1.392; T3 vs. T1: OR = 1.975, 95% CI: 1.472 to 2.650) were positively associated with advanced CKM stages. Table 2. Association Between UA Levels and CKM Syndrome Population Shanghai Friendship Community Elderly Cohort NHANES CKM Stage Distribution （n） Model 1 Model 2 CKM Stage Distribution（n） Model 1 Model 2 OR(95% CI) p OR(95% CI) p OR(95% CI) p OR(95% CI) p SUA(per 1 mg/dL) （55/465/1362/1178/239） 1.384 (1.320 - 1.451) <0.001 1.249 (1.186 - 1.315) <0.001 (26/182/1196/376/592) 1.330 (1.219 - 1.452) <0.001 1.278 (1.174 - 1.392) <0.001 T1 （38/224/494/265/76） Ref Ref (18/100/418100/178) Ref Ref T2 （11/151/491/383/71） 1.603 (1.370 - 1.875) <0.001 1.463 (1.245 - 1.720) <0.001 (7/60/407/120/180) 1.429 (1.064 - 1.918) 0.021 1.357 (1.001 - 1.841) 0.055 T3 （6/90/377/530/92） 2.801 (2.389 - 3.285) <0.001 1.966 (1.653 - 2.339) <0.001 (1/22/371/156/234) 2.236 (1.640 - 3.049) <0.001 1.975 (1.472 - 2.650) <0.001 Abbreviations:NHANES, National Health and Nutrition Examination Survey; SUA, serum uric acid; CKM syndrome, Cardiovascular-Kidney-Metabolic syndrome;OR, odds ratio;CI,confidence interval;Ref, reference; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3; 3.3 Subgroup Analyses Stratified analyses were performed across both cohorts by age group, sex, education level, household income, smoking status, alcohol consumption, and marital status ( Table 3 ). Elevated SUA levels were consistently associated with an increased risk of CKM across the majority of these subgroups. Notably, subgroup analyses stratified by SUA levels revealed a more pronounced association between SUA and CKM risk among female participants in both cohorts (all P < 0.05; P for interaction < 0.05). Table 3 Associations Between UA Levels and CKM Among Subgroups Subgroups Shanghai Friendship Community Elderly Cohort NHANES OR(95% CI) P Total_n P_interaction OR(95% CI) P Total_n P_interaction Age 0.158 0.038 > 70 1.39 (1.32–1.45) < 0.001 3244 1.25 (1.13–1.38) < 0.001 1087 <=70 1.08 (0.71–1.65) 0.708 55 1.44 (1.27–1.67) < 0.001 1285 Sex < 0.001 < 0.001 female 1.39 (1.30–1.49) < 0.001 2007 1.47 (1.32–1.64) < 0.001 1182 male 1.08 (1.00–1. 17) 0.057 1292 1. 11 (0.99–1.25) 0.087 1190 Education status 0.123 0.087 < high school 1.41 (1.33–1.49) < 0.001 2329 1.65 (1.36–1.99) < 0.001 306 high school 1.35 (1.20–1.52) < 0.001 586 1.34 (1.21–1.49) high school 1. 19 (1.03–1.37) 0.015 384 1.29 (1.16–1.45) < 0.001 1199 Marital status 0.529 0.459 divorced/separated 0.98 (0.60–1.61) 0.948 38 1.42 (1.19–1.70) < 0.001 399 married/cohabit 1.38 (1.32–1.45) < 0.001 3029 1.33 (1.20–1.47) < 0.001 1439 never married 1.22 (0.43–3.44) 0.708 3 1.01 (0.66–1.53) 0.978 106 widowed 1.41 (1.15–1.73) < 0.001 229 1.32 (1.12–1.55) < 0.001 428 Smoking status 0.013 0.901 yes 1.14 (0.92–1.41) 0.245 260 1.32 (1.07–1.63) 0.01 307 no 1.37 (1.31–1.44) < 0.001 3039 1.34 (1.22–1.47) < 0.001 2065 Drinking status 0.004 0.226 yes 1.05 (0.87–1.26) 0.6 231 1.30 (1.16–1.45) < 0.001 1459 no 1.40 (1.33–1.47) < 0.001 3068 1.41 (1.25–1.60) < 0.001 913 Household income 0.291 0.548 < 14000 1.41 (1.33–1.49) < 0.001 2362 1.26 (1.14–1.40) < 0.001 730 14000–28000 1.31 (1.20–1.43) < 0.001 885 1.28 (1.12–1.45) 28000 1.31 (0.93–1.84) 0.118 52 1.39 (1.22–1.59) < 0.001 952 Abbreviations:NHANES, National Health and Nutrition Examination Survey; SUA, serum uric acid; CKM syndrome, Cardiovascular-Kidney-Metabolic syndrome;OR, odds ratio;CI,confidence interval;Ref, reference; The Model is adjusted for age, sex, educational attainment, smoking status, alcohol consumption, marital status, and household income. 3.4 Dose–Response Relationships of SUA with CKM Syndrome Restricted cubic spline (RCS) analyses were employed to evaluate the dose -response relationship between SUA levels and CKM syndrome in the overall population and in gender-stratified subgroups. In the overall population, a statistically significant linear dose -response association was observed in both cohorts (SFCEC: P-overall < 0.001, P-nonlinear = 0.238; NHANES: P-overall < 0.001, P-nonlinear = 0.779), indicating a progressively elevated CKM risk with increasing SUA levels. Gender-stratified analyses revealed a similarly clear linear association among females in both cohorts (SFCEC: P-overall < 0.001, P-nonlinear = 0.789; NHANES: P-overall < 0.001, P-nonlinear = 0.528). In contrast, a modest linear relationship was observed in males, which remained statistically significant but was less pronounced than that in females (SFCEC: P-overall = 0.004, P-nonlinear = 0.443; NHANES: P-overall < 0.001, P-nonlinear = 0.051) ( Fig. 2 ) . 4. Discussion As far as we are aware, this is a large-scale study investigating the association between serum uric acid levels and cardiovascular-kidney-metabolic (CKM) syndrome stages utilizing data from elderly populations in China (SFCEC) and the United States (NHANES). The remarkably high prevalence of CKM abnormalities (approximately 98%) in both cohorts underscores the urgency of identifying modifiable risk factors in aging populations. The observation of a notably high prevalence of cardiometabolic-kidney (CKM) syndrome in the elderly cohorts may be attributed to the long-term cumulative exposure to metabolic and cardiovascular risk factors typically associated with advanced age. Furthermore, the CKM staging system is inherently designed to be inclusive and sensitive for early risk identification.Our key finding, confirmed by dose-response analysis that elevated SUA levels are significantly and positively associated with CKM risk, suggests that serum uric acid may serve as an independent risk predictor and potential biomarker for the progression of CKM syndrome. Multiple studies have pointed out that the relationship between SUA and cardiovascular risk varies between genders. Wakabayashi et al. [15] emphasized that SUA has a more significant predictive value for cardiovascular events in obese female patients, and the U-shaped association is more obvious. Sakata et al. [16] also found that low uric acid levels in women are closely related to an increased risk of stroke death, suggesting that uric acid may play a unique role in cardiovascular protection in women. These findings align with our research results: among female CKM patients, the correlation between serum uric acid levels and the progression of CKM syndrome is more pronounced. Previous research has established important connections between abnormal serum uric acid levels and key components of CKM syndrome, including obesity, diabetes mellitus (DM), chronic kidney disease (CKD), and cardiovascular disease (CVD). First, in our study, the proportion of obese patients was the highest (67.20% in the SFCEC cohort and 72.20% in the NHANES cohort). Previous observational studies have confirmed a significant positive correlation between serum uric acid (SUA) levels and obesity. A large-scale study involving 8,522 Chinese children and adolescents aged 2–18 years found that the detection rate of overweight or obesity significantly increased with ascending SUA quartiles (OR = 4.45, 95% CI: 3.33–5.93), indicating that elevated SUA is an independent risk factor for obesity[17]. Another retrospective study in adults (n = 19, 193) further demonstrated that non-interventional weight changes (e.g., a BMI increase > 5%) directly led to a rise in SUA levels (P < 0.001), while weight loss was associated with a corresponding decrease in SUA, revealing a bidirectional promoting relationship between obesity and SUA[18]. Additionally, studies on bariatric surgery have indicated that SUA levels significantly decreased in obese patients after surgery, conversely validating the correlation between obesity and SUA [19] .Second,Multiple observational studies have used uric acid levels as an indicator to predict the future development of type 2 diabetes in humans [20–22]. A study by Ryu et al. showed that serum uric acid levels are independently associated with an increased risk of type 2 diabetes and suggested that uric acid may be a component of metabolic syndrome [20]. Another study investigated the relationship between uric acid levels and the risk of type 2 diabetes in 4,536 participants with hyperuricemia and without diabetes at baseline. During a 10-year follow-up period, 462 participants developed diabetes, and the results indicated that approximately one-quarter of the diabetes cases could be attributed to high serum uric acid levels [21]. Niskanen et al. examined the predictive role of hyperuricemia on changes in glucose tolerance and the development of type 2 diabetes over a period of 4. 1 years. The results demonstrated that individuals with hyperuricemia at baseline had a doubled risk of developing type 2 diabetes [22].Third,The high prevalence of hyperuricemia in patients with chronic kidney disease (CKD) and its association with renal function deterioration have been well-documented. In a community-based cohort study of 39,039 elderly Chinese patients with diabetes, Zhou et al. demonstrated that elevated serum uric acid levels significantly increased the risk of new-onset CKD, with a notably higher risk among male patients (HR = 1.925, 95% CI: 1.724 -2. 150), suggesting that uric acid serves as an independent predictor of CKD[23]. Furthermore, in a long-term follow-up analysis of 9,891 CKD patients, Liu et al. reported that a serum uric acid level ≥ 5.9 mg/dL was significantly associated with an increased risk of all-cause mortality (HR = 1.102, 95% CI: 1.043–1.165), further underscoring the prognostic significance of uric acid levels in this population[24].Moreover, Although epidemiological studies generally support the correlation between uric acid and the occurrence and development of CKD[23–25], there is still considerable controversy over whether uric acid is an independent pathogenic factor for CKD[26–27].Furthermore, a large number of cohort studies and cross-sectional studies have revealed the correlation between SUA and cardiovascular diseases. A multicenter prospective cohort study in Japan found that the SUA level was a significant predictor of cardiovascular events (coronary heart disease, stroke, arteriosclerotic occlusive disease) in obese women, and it showed a U-shaped association, indicating that both too low or too high uric acid levels increase the risk[28].In a retrospective study of the Punjabi population in Pakistan[29], it was found that hyperuricemia was significantly associated with coronary artery syndrome, myocardial infarction, and heart failure and other cardiovascular diseases, and this association remained significant after adjusting for confounding factors such as age, gender, diabetes, and hypertension. It is believed that SUA is an independent cardiovascular risk factor. A Mendelian randomization study systematically evaluated the causal relationship between genetic SUA levels and various cardiovascular diseases (coronary heart disease, hypertension,myocardial infarction, heart failure, angina pectoris). The results showed that genetic hyperuricemia significantly increased the risk of the above cardiovascular diseases, and the sensitivity analysis verified the robustness of the results, strongly supporting that an increase in SUA has a causal promoting effect on cardiovascular diseases[30]. In conclusion, although existing evidence has established serum uric acid levels as a contributing factor to individual components of CKM syndrome such as obesity, DM, and CVD, the comprehensive relationship between serum uric acid levels and integrated CKM staging remained poorly characterized prior to this study. Emerging evidence clarifies the multifaceted mechanisms by which elevated serum uric acid contributes to the development and progression of cardiorenal metabolic (CKM) syndrome. In obesity, elevated serum uric acid promotes the infiltration of macrophages into adipose tissue and activates the NLRP3 inflammasome, exacerbating chronic inflammation and insulin resistance [31]. In vitro studies confirm that uric acid directly stimulates preadipocyte differentiation through specific oxidative stress pathways.Building on these findings, in diabetes, uric acid induces hepatic steatosis and insulin resistance via mitochondrial oxidative stress.It also promotes β -cell dysfunction through AMPK- and mTOR-mediated autophagy and apoptosis, and triggers NLRP3-driven inflammation, resulting in IL-1 β release [31–32]. In chronic kidney disease, uric acid activates the tubular NLRP3 inflammasome, leading to fibrosis, and causes podocyte injury through endoplasmic reticulum stress, and autophagic disruption [33]. In cardiovascular disease, it accelerates vascular calcification via osteogenic differentiation of smooth muscle cells and amplifies atherosclerotic plaque instability through macrophage polarization toward a pro-inflammatory M1 phenotype and endothelial dysfunction [34]. Although not fully understood, these interconnected pathways—centered on inflammation, oxidative stress, and insulin resistance — collectively position uric acid as a systemic mediator and a shared underlying factor driving the concurrent progression of cardiovascular, metabolic, and renal disorders. Strengths and Limitations This study has several notable strengths.First,the analysis incorporated data from two large, well-characterized cohorts — the Shanghai Friendship Community Elderly Cohort (SFCEC) and the nationally representative US NHANES population—which improves the generalizability of the findings across different ethnic groups. Second, the analytical methods rigorously accounted for the complex survey design of NHANES by applying appropriate weighting, stratification, and clustering adjustments, thereby ensuring robust population-level estimates.However, several limitations should be considered. As an observational study, the identified association between serum uric acid and cardiovascular-kidney-metabolic (CKM) syndrome indicates correlation rather than causation. Although extensive adjustments were made for demographic and lifestyle confounders,residual confounding from unmeasured variables cannot be excluded. Additionally, the generalizability of the findings to younger adult populations may be limited, given that the study population primarily comprised older adult. Our research group will pursue the following directions: 1) further elucidating the role of serum uric acid (SUA) across different stages of CKM syndrome; 2) conducting large-scale randomized controlled trials to evaluate whether reducing SUA levels can delay or reverse the progression of CKM syndrome; and 3) exploring targeted therapeutic strategies against downstream pathways of SUA-induced pathology. Conclusions In summary, this study reveals that elevated serum uric acid levels represent a potential independent risk factor for cardiovascular-kidney-metabolic (CKM) syndrome in older adult populations across both Chinese and American cohorts, furthermore, among female CKM patients, the correlation between serum uric acid levels and the progression of CKM syndrome is more pronounced. The consistency of these associations observed across diverse groups suggests that serum uric acid may serve as a potential biomarker for monitoring CKM progression in adults. Consequently, routine screening of serum uric acid may facilitate early detection and prevention of CKM syndrome. Further research is warranted to validate these associations and elucidate the specific mechanistic role of uric acid in the pathogenesis of CKM syndrome. Abbreviations NHANES,National Health and Nutrition Examination Survey;SUA,serum uric acid;CKM syndrome,Cardiovascular-Kidney-Metabolic syndrome;DM,diabetes mellitus;CKD,chronic kidney disease;CVD,cardiovascular disease;OR, odds ratio;CI, confidence interval;Ref, reference; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3; Declarations Acknowledgements The data used in this research were obtained from the Shanghai Friendship Community Elderly Cohort (SFCEC) and US National Health and Nutrition Examination Survey(NHANES). We would like to thank the workers, researchers, and participants involved in the SFCEC and NHANES. Author contributions W.C.K and Y.L were responsible for the design and conceptualization of the study, as well as drafting and revising the manuscript. K.A.W and B.B.Z contributed to data collecting, statistical analysis, and result interpretation.B.Y.W was responsible for the data results visualization. Y.L read and revised the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the Shanghai Municipal Key Clinical Specialty (Grant No. 2024ZDXK0016) and the Medical Specialty Construction Project of Minhang District,Shanghai (No.2025MWTZB01). Data availability The data are publicly available from the NHANES database (https://wwwn.cdc.gov/nchs/nhanes/ ). Ethics approval and consent to participate All studies involved in this research adhered to the ethical principles of the Declaration of Helsinki (1975) and were approved by the respective ethics committees. Specifically, the SFCEC study protocol was approved by the Institutional Review Board of Huashan Hospital Affiliated to Fudan University (Approval No. 2020-004). The NHANES study received ethical approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board, and its procedures complied with the Declaration of Helsinki. Further details are available at: https://www.cdc.gov/nchs/nhanes/ .All participants executed a written informed consent form before their involvement. Consent for publication The authors give consent for publication of this paper in Archives of Public Health Competing interests The authors declare no competing interests References Ndumele CE, Neeland IJ, Tuttle KR, et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome:A Scientific Statement From the American Heart Association. Circulation. 2023;148(20):1636-1664. doi:10. 1161/CIR.0000000000001186 Aggarwal R,Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020. JAMA.2024;331(21):1858-1860. doi:10. 1001/jama.2024.6892 Zhu R, Wang R, He J, et al. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages by Social Determinants of Health. JAMA Netw Open.2024;7(11):e2445309.Published 2024 Nov 4. doi:10. 1001/jamanetworkopen.2024.45309 Chen A, He Q, Wu Y, et al. Incidence of cardiovascular-kidney metabolic syndrome and its risk factors for progression in China. medRxiv 2024, doi:http://dx.doi.org/10.1101/2024.08.07.24311650 2024.08.07.24311650. Ouyang R, Zhao X, Zhang R, Yang J, Li S, Deng D. FGF21 attenuates high uric acid‑induced endoplasmic reticulum stress, inflammation and vascular endothelial cell dysfunction by activating Sirt1. Mol Med Rep. 2022;25(1):35. doi:10.3892/mmr.2021.12551 Wang F, Wen L, Guo X, et al. Association of Serum Uric Acid With Relative Muscle Loss: A US Population-Based Cross-Sectional Study. J Cachexia Sarcopenia Muscle.2025;16(3):e13867. doi:10. 1002/jcsm.13867 Vareldzis R, Perez A, Reisin E. Hyperuricemia: An Intriguing Connection to Metabolic Syndrome, Diabetes, Kidney Disease, and Hypertension. Curr Hypertens Rep.2024;26(6):237-245. doi:10. 1007/s11906-024-01295-3 Ma F, Shao X, Zhang Y, et al. An arterial spin labeling-based radiomics signature and machine learning for the prediction and detection of various stages of kidney damage due to diabetes. Front Endocrinol (Lausanne). 2024;15:1333881. Published 2024 Nov 18. doi:10.3389/fendo.2024.1333881 Boruah P, Ruram A, Baruah AJ, et al. Association of Serum Uric Acid Level and Thyroid Function in Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study From Northeast India. Cureus. 2024;16(9):e69392. Published 2024 Sep 14. doi:10.7759/cureus.69392 Molino-Lova R, Sofi F, Pasquini G, et al. Higher uric acid serum levels are associated with better muscle function in the oldest old: Results from the Mugello Study. Eur J Intern Med. 2017;41:39-43. doi:10. 1016/j.ejim.2017.03.014 Floriano JP, Nahas PC, de Branco FMS, et al. Serum Uric Acid Is Positively Associated with Muscle Mass and Strength, but Not with Functional Capacity, in Kidney Transplant Patients. Nutrients. 2020;12(8):2390. Published 2020 Aug 10. doi:10.3390/nu12082390 Xu L, Jing Y, Zhao C, et al. Cross-sectional analysis of the association between serum uric acid levels and handgrip strength among Chinese adults over 45 years of age.Ann Transl Med. 2020;8(23):1562. doi:10.21037/atm-20-2813a Roumeliotis S, Roumeliotis A, Dounousi E, Eleftheriadis T, Liakopoulos V. Dietary Antioxidant Supplements and Uric Acid in Chronic Kidney Disease: A Review.Nutrients. 2019;11(8):1911. Published 2019 Aug 15. doi:10.3390/nu11081911 Wang M, Wu J, Jiao H, et al. Enterocyte synthesizes and secrets uric acid as antioxidant to protect against oxidative stress via the involvement of Nrf pathway. Free Radic Biol Med. 2022;179:95-108. doi:10. 1016/j.freeradbiomed.2021. 12.307 Wakabayashi D, Kato S, Tanaka M, Yamakage H, Kato H, Kusakabe T, Ozu N, Kasama S, Kasahara M, Satoh-Asahara N; Japan Obesity Metabolic Syndrome Study (JOMS) Group. Novel pathological implications of serum uric acid with cardiovascular disease risk in obesity. Diabetes Res Clin Pract. 2023 Nov;205:110919. doi:10. 1016/j.diabres.2023.110919. Epub 2023 Sep 22. PMID: 37742802. Li Y, Yang H, Tian Y, Duan L. Factors Influencing the Serum Uric Acid in Gout with Cerebral Infarction. Mediators Inflamm. 2021 Jul 12;2021:5523490. doi:10. 1155/2021/5523490. PMID: 34335087; PMCID: PMC8289599. Ye W, Zhou X, Xu Y, Zheng C, Liu P. Serum Uric Acid Levels among Chinese Children: Reference Values and Association With Overweight/Obesity. Clin Pediatr (Phila). 2024 Dec;63(12):1684-1690. doi: 10. 1177/00099228241238510. Epub 2024 Mar 21. PMID: 38515070. Weinstein S, Maor E, Bleier J, Kaplan A, Hod T, Leibowitz A, Grossman E, Shlomai G. Non-Interventional Weight Changes Are Associated with Alterations in Serum Uric Acid Levels. J Clin Med. 2024 Apr 17;13(8):2314. doi: 10.3390/jcm13082314. PMID: 38673586; PMCID: PMC11051435. Bashyal S, Qu S, Karki M. Bariatric Surgery and Its Metabolic Echo Effect on Serum Uric Acid Levels. Cureus. 2024 Apr 12;16(4):e58103. doi: 10.7759/cureus.58103.PMID: 38616980; PMCID: PMC11013573. Ryu S, Song J, Choi BY, Lee SJ, Kim WS, Chang Y, Kim DI, Suh BS, Sung KC. Incidence and risk factors for metabolic syndrome in Korean male workers, ages 30 to 39. Ann Epidemiol. 2007;17(4):245-252. https://www.clinicalkey.es/playcontent/1-s2.0-S1047279706002547.https://doi.org/10.1016/j.annepidem.2006.10.001. Dehghan A, van Hoek M, Sijbrands EJG, Hofman B, Witteman J. High serum uric acid as a novel risk factor for type 2 diabetes. Diabetes Care. 2008;31(2):361-362.http://care. diabetesjournals.org/content/31/2/361.abstract. https://doi.org/ 10.2337/dc07-1276. Niskanen L, Laaksanen DE, Lindstrom J, et al. Serum uric acid as a harbinger of metabolic outcome in subjects with impaired glucose tolerance: the Finnish diabetes prevention study. Diabetes Care. 2006;29(3):709-711. https://www.ncbi.nlm.nih.gov/pubmed/16505534. https://doi.org/10.2337/diacare.29.03.06. dc05-1465. Zhou Q, Ke S, Yan Y, Guo Y, Liu Q. Serum uric acid is associated with chronic kidney disease in elderly Chinese patients with diabetes. Ren Fail. 2023Dec;45(1):2238825. Liu YF, Han L, Geng YH, Wang HH, Yan JH, Tu SH. Nonlinearity association between hyperuricemia and all-cause mortality in patients with chronic kidney disease.Sci Rep. 2024 Jan 5;14(1):673. Johnson RJ, Sanchez Lozada LG, Lanaspa MA, Piani F, Borghi C. Uric Acid and Chronic Kidney Disease: Still More to Do. Kidney Int Rep. 2022 Dec 5;8(2):229-239. Goldberg A, Garcia-Arroyo F, Sasai F, Rodriguez-Iturbe B, Sanchez-Lozada LG, Lanaspa MA, Johnson RJ. Mini Review: Reappraisal of Uric Acid in Chronic Kidney Disease. Am J Nephrol. 2021;52(10-11):837-844 Piani F, Sasai F, Bjornstad P, Borghi C, Yoshimura A, Sanchez-Lozada LG, Roncal-Jimenez C, Garcia GE, Hernando AA, Fuentes GC, Rodriguez-Iturbe B, Lanaspa MA,Johnson RJ. Hyperuricemia and chronic kidney disease: to treat or not to treat. J Bras Nefrol. 2021 Oct-Dec;43(4):572-579. Wakabayashi D, Kato S, Tanaka M, Yamakage H, Kato H, Kusakabe T, Ozu N, Kasama S, Kasahara M, Satoh-Asahara N; Japan Obesity Metabolic Syndrome Study (JOMS) Group. Novel pathological implications of serum uric acid with cardiovascular disease risk in obesity. Diabetes Res Clin Pract. 2023 Nov;205:110919. doi:10. 1016/j.diabres.2023.110919. Epub 2023 Sep 22. PMID: 37742802. Hussain M, Ghori MU, Aslam MN, Abbas S, Shafique M, Awan FR. Serum uric acid: an independent risk factor for cardiovascular disease in Pakistani Punjabi patients.BMC Cardiovasc Disord. 2024 Oct 10;24(1):546. doi: 10. 1186/s12872-024-04055-y. PMID: 39385070; PMCID: PMC11465846. Zhang Y, Lian Q, Nie Y, Zhao W. Causal relationship between serum uric acid and cardiovascular disease: A Mendelian randomization study. Int J Cardiol Heart Vasc.2024 Jun 29;54:101453. doi: 10. 1016/j.ijcha.2024.101453. PMID: 39411145; PMCID: PMC11473680. Johnson RJ, Bakris GL, Borghi C, et al. Hyperuricemia, Acute and Chronic Kidney Disease, Hypertension, and Cardiovascular Disease: Report of a Scientific Workshop Organized by the National Kidney Foundation. Am J Kidney Dis. 2018;71(6):851-865. Baldwin W, McRae S, Marek G, et al. Hyperuricemia as a mediator of the proinflammatory endocrine imbalance in the adipose tissue in a murine model of the metabolic syndrome. Diabetes. 2011;60(4):1258-1269. Kang DH, Chen W. Uric acid and chronic kidney disease: new understanding of an old problem. Semin Nephrol. 2011;31(5):447-452. Krishnan E. Hyperuricemia and incident heart failure. Circ Heart Fail. 2009;2(6):556-562. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 27 Mar, 2026 Editor invited by journal 16 Feb, 2026 Editor assigned by journal 13 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 11 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8851827","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614882964,"identity":"3fc4c5e9-4fa1-4106-913b-7d127c5b214f","order_by":0,"name":"Wencai Ke","email":"","orcid":"","institution":"Fifth People's Hospital of Shanghai Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Wencai","middleName":"","lastName":"Ke","suffix":""},{"id":614882965,"identity":"90b3a7cc-dcb6-4c77-b259-95856215969f","order_by":1,"name":"Kangan Wang","email":"","orcid":"","institution":"Fifth People's Hospital of Shanghai Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Kangan","middleName":"","lastName":"Wang","suffix":""},{"id":614882966,"identity":"692dd345-7ebf-41f0-be74-78614ae0942d","order_by":2,"name":"Biying Wu","email":"","orcid":"","institution":"Fifth People's Hospital of Shanghai Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Biying","middleName":"","lastName":"Wu","suffix":""},{"id":614882967,"identity":"513d8043-9f10-4360-9e24-b0e0f9168a4e","order_by":3,"name":"Bingbing Zha","email":"","orcid":"","institution":"Fifth People's Hospital of Shanghai Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Bingbing","middleName":"","lastName":"Zha","suffix":""},{"id":614882968,"identity":"05c55a1a-7596-4a05-84d5-7de88ec68994","order_by":4,"name":"Yong Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3PvQrCMBDA8SuRdKnNmqL4DBGh4NskCG6CY4f6MUg6WHHuWzg6CkJc4p5R38BujlYUR5NRMP/lCNwPLgA+3w8Wo9dcSETqC89yO8FvEmzD5YBdtHIg7xlU5SFNrivkQMK2ovV+hpjh40wsMZBizS2HxeOk0ifcEGXEvgtUn3cWEqWdtlQRM0IaoTEwOnEjlJkRngqJnEnOklJhcCWDYSUPnIQlolyryPoXQnTf1HLOGxvU9yzvkWLznTS1KMDx84ps68/QDWDusujz+Xz/2gO1L0MT5zXp1QAAAABJRU5ErkJggg==","orcid":"","institution":"Fifth People's Hospital of Shanghai Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2026-02-11 12:40:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8851827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8851827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105905390,"identity":"556c4406-6516-47ba-9898-fee0ef1950a4","added_by":"auto","created_at":"2026-04-01 10:11:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of participants included in this study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations:NHANES,National Health and Nutrition Examination Survey; CKM syndrome, Cardiovascular-Kidney-Metabolic syndrome;BMI, body mass index; eGFR, Estimated Glomerular Filtration Rate;\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-8851827/v1/4fc7dc5fb12185cfe266faf7.png"},{"id":105904624,"identity":"cb2737f9-35a7-40ee-972d-1faa500c9cd5","added_by":"auto","created_at":"2026-04-01 10:09:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":400549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline plots ofthe association between SUA levels and CKM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: The association between SUA levels and CKM in SFCEC. B: The association between SUA levels and CKM in NHANES. C:The association between SUA levels and CKM in subgroups classified by female in SFCEC. D:The association between SUA levels and CKM in subgroups classified by female in NHANES.E:The association between SUA levels and CKM in subgroups classified by male in SFCEC.F:The association between SUA levels and CKM in subgroups classified by male in NHANES\u003c/p\u003e\n\u003cp\u003eAbbreviations:NHANES, National Health and Nutrition Examination Survey; SFCEC,Shanghai Friendship Community Elderly Cohort;SUA, serum uric acid; CKM syndrome, Cardiovascular-Kidney-Metabolic syndrome;\u003c/p\u003e\n\u003cp\u003eThe Model is adjusted for age, sex(except in the gender subgroup analyses), educational attainment, smoking status, alcohol consumption, marital status, and household income.\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-8851827/v1/7278d1090003e00c06784464.png"},{"id":105906680,"identity":"0f9f394a-83f2-47e9-bd73-20a38d563875","added_by":"auto","created_at":"2026-04-01 10:24:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1459684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8851827/v1/d5c772f2-dcc9-4524-b1de-4b9393d89bef.pdf"},{"id":105878500,"identity":"9c9596a7-ac85-4932-9a4c-ca2fc7ee7753","added_by":"auto","created_at":"2026-04-01 06:14:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":29714,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8851827/v1/e5e80c876d789196770cd1a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation of Serum Uric Acid with Cardiovascular-Kidney-Metabolic Syndrome in Elderly Populations: Results from Chinese and US cohorts\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular-kidney-metabolic (CKM) syndrome has recently been recognized by the American Heart Association (AHA) as a novel clinical construct, representing a complex multisystem condition characterized by interconnected pathophysiological processes.This syndrome manifests through the dynamic interplay among obesity,diabetes mellitus, chronic kidney disease, and cardiovascular disease. The AHA has established a five-stage classification system (Stage 0: no risk factors to Stage 4:established CVD) that provides a structured framework for comprehensive comorbidity management [1]. Epidemiological studies have demonstrated a considerable global health burden, with CKM-related health impairments showing elevated prevalence rates among adults in the United States [2,3] while revealing parallel concerns in China [4].Pathophysiological investigations suggest that sustained metabolic disturbances trigger sequential pathological events including sympathetic overactivity, persistent inflammatory activation, and progressive fibrotic processes, ultimately culminating in multi-organ dysfunction. However, the comprehensive delineation of contributing elements and underlying molecular mechanisms remains incomplete.\u003c/p\u003e \u003cp\u003eAs the final product of purine metabolism, serum uric acid (SUA) contributes to inflammation, oxidative stress, and endothelial dysfunction [5,6]. Elevated SUA levels have been associated with a higher risk of developing chronic kidney disease, diabetes, cardiovascular disease, and metabolic disorders[6\u0026ndash;8].Several studies displayed that elevated SUA levels were associated with CKM, indicating that higher SUA levels may contribute to an increased risk of CKM[6,9].However, research teams from Italy, Brazil, and China investigating sarcopenia have proposed that SUA, acting as an antioxidant, may exert protective effects[10\u0026ndash;12]. In plasma, SUA neutralizes key harmful oxidants such as peroxyl radicals, peroxynitrite, and hydroxyl radicals[13, 14]. These conflicting findings prompt us to examine whether SUA can serve as a risk factor or predictor for chronic kidney-metabolic (CKM) disease in the elderly population.\u003c/p\u003e \u003cp\u003eTo bridge this research void, we undertook a comprehensive analysis integrating the Shanghai Friendship Community Elderly Cohort (SFCEC) and National Health and Nutrition Examination Survey (NHANES 2011\u0026ndash;2018) datasets. This investigation evaluates associations between serum uric acid concentrations and CKM staging while characterizing detailed dose-response relationships.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eStudy participants were derived from two independent cohort studies: the Shanghai Friendship Community Elderly Cohort (SFCEC) and the National Health and Nutrition Examination Survey (NHANES 2011\u0026ndash;2018).The SFCEC specifically investigates health outcomes among community-dwelling elderly populations in Baoshan District,Shanghai between April and September 2020. NHANES constitutes a stratified, multistage probability sample designed to collect nationally representative data on health status and nutritional parameters of non-institutionalized civilian residents in the United States. Both databases provide comprehensive data on: Demographic characteristics,Socioeconomic status,Lifestyle factors,Physical examination measurements,Laboratory test results.The SFCEC study protocol was approved by the Institutional Review Board of Huashan Hospital Affiliated to Fudan University (Approval No. 2020-004). NHANES received ethical approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board. Written informed consent was obtained from all participants prior to study enrollment. All datasets were de-identified with no protected health information included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study population\u003c/h2\u003e \u003cp\u003eThe study timeline and a flowchart illustrating the study design are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, the study included 3299 subjects from the SFCEC and 2372 subjects from the NHANES. Exclusion criteria were: age\u0026thinsp;\u0026lt;\u0026thinsp;60 years, missing laboratory data (including serum uric acid levels), unavailable CKM diagnosis data, or incomplete demographic data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 CKM Syndrome Ascertainment\u003c/h2\u003e \u003cp\u003eThe staging of CKM syndrome was determined according to the established five-stage classification system (Stages 0\u0026ndash;4) endorsed by the American Heart Association and Aggarwal et al, with detailed criteria adapted for NHANES data availability as summarized in \u003cb\u003eeTable1\u003c/b\u003e. Specifically, Stage 0 included individuals with normal body mass index (BMI) and waist circumference and no other metabolic abnormalities; Stage 1 comprised persons fulfilling criteria for excess adiposity or prediabetes; Stage 2 encompassed subjects presenting with multiple metabolic risk factors or moderate-to-high risk chronic kidney disease as classified by KDIGO guidelines;Stage 3 identified individuals with very-high-risk CKD per KDIGO classification or elevated 10-year cardiovascular disease risk based on PREVENT equations \u003cb\u003e(eTable2)\u003c/b\u003e; and Stage 4 represented those with established clinical cardiovascular disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Measurement of SUA\u003c/h2\u003e \u003cp\u003eSerum uric acid (SUA) levels were measured using the Roche Cobas e8000 automated biochemistry analyzer with original reagents on serum samples at the Department of Laboratory Medicine, Shanghai Fifth People's Hospital, Fudan University. Results are expressed in mg/dL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Assessment of covariates\u003c/h2\u003e \u003cp\u003eThis study adjusted for comprehensive covariates encompassing demographic characteristics(age, sex [male/female],marital status[married/cohabitating,divorced/separated, never married, widowed]), socioeconomic status (educational attainment [\u0026lt;\u0026thinsp;high school, high school, \u0026gt;high school] and annual household income [\u0026le;\u0026thinsp;14,000, 14,000\u0026ndash;28,000, or \u0026ge;\u0026thinsp;28,000 USD] converted from RMB at 7:1 exchange rate), and lifestyle factors (current smoking status and alcohol consumption, both dichotomized as yes/no).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe employed a staged analytical approach: baseline characteristics were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and frequencies (percentages) for categorical variables. Multivariable logistic regression models examined associations between serum uric acid (analyzed continuously and by tertiles [T1-T3]) and CKM syndrome using two models: Model 1 (unadjusted) and Model 2 (adjusted for age, sex, education, smoking status, alcohol consumption, marital status, and household income), with all analyses incorporating NHANES sampling weights, stratification, and primary sampling units to ensure representativeness. Stratified analyses were conducted across prespecified subgroups (age [\u0026le;\u0026thinsp;70,\u0026gt;70 years], sex, education, smoking, alcohol use, marital status, and income). Baseline comparisons across uric acid tertiles presented continuous variables as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error and categorical variables as percentages. Restricted cubic splines (RCS) with 3 knots assessed potential nonlinear dose-response relationships between uric acid and CKM syndrome, adjusted for Model 2 covariates (though technical constraints precluded inclusion of sampling weights in RCS analyses). Model fit was evaluated via Akaike Information Criterion (AIC), with lower values indicating better fit. All analyses used R version 4.4.2 (R Foundation for Statistical Computing).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline characteristics of study participants\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of the study populations, comprising 3,299 participants from the SFCEC cohort (60.8% female; mean age 74.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35 years) and 2,372 participants from the NHANES cohort (54.3% female; mean age 68.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 years). The distribution of CKM syndrome stages (0\u0026ndash;4) in the SFCEC cohort was 1.7%, 14. 1%, 41.3%, 35.7%, and 7.2%, respectively, while the NHANES cohort showed corresponding proportions of 1.67%, 10.85%, 51.66%, 11.38%, and 24.44%.Mean serum uric acid levels were 5.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37 mg/dL in the SFCEC cohort and 5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/dL in the NHANES cohort. Comparative analysis across serum uric acid tertiles revealed that in the SFCEC cohort, participants in higher tertiles (T2/T3) demonstrated significantly greater proportions of males, higher educational attainment,increased smoking and alcohol consumption prevalence, and elevated household income compared to the T1 group. In contrast, the NHANES cohort only showed a significantly higher male proportion in the upper SUA tertiles (T2/T3) relative to T1.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of the Study Population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 111px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 375px;\"\u003e\n \u003cp\u003eShanghai Friendship Community Elderly Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 370px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003en=3299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 243px;\"\u003e\n \u003cp\u003eSUA levels,mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003en=2372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 224px;\"\u003e\n \u003cp\u003eSUA levels,mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eT1(1.1-4.9)\u003c/p\u003e\n \u003cp\u003en=1097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eT2(5.0-6.0)\u003c/p\u003e\n \u003cp\u003en=1107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eT3(6.1-12.4)\u003c/p\u003e\n \u003cp\u003en=1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eT1(1.1-5.0)\u003c/p\u003e\n \u003cp\u003en=814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eT2(5.1-6.2)\u003c/p\u003e\n \u003cp\u003en=774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eT3(6.3-13.0)\u003c/p\u003e\n \u003cp\u003en=784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e74.47 (3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e74.41 (3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e74.36 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e74.64 (3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e68.97 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e68.81 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e68.88 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e69.25 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\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: 111px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2007 (60.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e898 (81.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e705 (63.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e404 (36.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1182 (54.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e562 (73.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e341 (47.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e279 (39.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1292 (39.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e199 (18.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e402 (36.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e691 (63.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1190 (45.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e252 (26.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e433 (52.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e505 (60.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eEducation status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt; high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2329 (70.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e821 (74.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e764 (69.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e744 (67.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e306 (6.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e121 (6.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e98 (5.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e87 (6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e586 (17.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e177 (16.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e216 (19.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e193 (17.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e867 (32.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e274 (29.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e285 (33.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e308 (34.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026gt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e384 (11.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e99 ( 9.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e127 (11.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e158 (14.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1199 (61.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e419 (63.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e391 (60.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e389 (59.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003edivorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e38 (1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e9 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e16 (1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e13 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e399 (14.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e149 (16.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e131 (13.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e119 (12.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003emarried/cohabit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3029 (91.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e986 (89.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1015 (91.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1028 (93.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1439 (66.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e464 (62.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e474 (68.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e501 (68.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003enever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e106 (3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e46 (4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e33 (3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e27 (3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003ewidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e229 (6.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e100 (9.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e76 (6.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e53 (4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e428 (15.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e155 (16.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e136 (14.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e137 (16.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e260 (7.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e43 (3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e82 (7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e135 (12.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e307 (10.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e103 (10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e100 (12.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e104 (9.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3039 (92.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1054 (96.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1025 (92.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e960 (87.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2065 (89.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e711 (89.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e674 (87.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e680 (90.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e231 (7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e28 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e70 (6.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e133 (12.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1459 (68.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e473 (66.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e486 (69.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e500 (69.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e3068 (93.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1069 (97.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1037(93.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e962 (87.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e913 (31.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e341 (33.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e288 (30.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e284 (30.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;14000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2362 (71.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e814 (74.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e804 (72.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e744 (67.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e730 (17.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e259 (17.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e232 (18.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e239 (17.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e14000-28000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e885 (26.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e270 (24.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e287 (25.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e328 (29.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e690 (25.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e232 (24.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e220 (23.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e238 (27.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026gt;28000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e52 (1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e13 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e16 (1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e23 (2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e952 (57.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e323 (57.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e322 (57.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e307 (55.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5.60 (1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5.68 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eCKM syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\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: 111px;\"\u003e\n \u003cp\u003eStage 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e55 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e38 (3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e11 (0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e6 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e26 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e18 (3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e465 (14.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e224 (20.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e151 (13.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e90 (8.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e182 (10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e100 (17.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e60 (10.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e22 (3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1362 (41.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e494 (45.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e491 (44.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e377 (34.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1196 (51.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e418 (48.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e407 (55.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e371 (51.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1178 (35.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e265 (24.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e383 (34.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e530 (48.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e376 (11.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e100 (8.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e120 (10.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e156 (15.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eStage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e239 (7.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e76 (6.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e71 (6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e92 (8.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e592 (24.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e178 (21.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e180 (23.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e234 (28.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\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 \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003eNational Health and Nutrition Examination Survey; SUA, serum uric acid; CKM \u0026nbsp;syndrome, Cardiovascular-Kidney-Metabolic \u0026nbsp; syndrome;\u003c/p\u003e\n \u003cp\u003eT1,Tertile 1; T2, Tertile 2; T3, Tertile 3;\u003c/p\u003e\n \u003cp\u003eContinuous variables were summarized as mean \u0026plusmn; standard deviation (SD), while categorical variables were expressed as frequencies (percentages).\u003c/p\u003e\n \u003ch2\u003e3.2 Association Between SUA Levels and CKM Syndrome\u003c/h2\u003e\n \u003cp\u003eMultivariate logistic regression analysis demonstrated a significant association between SUA levels and CKM syndrome stages in both cohorts \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. In the SFCEC cohort, after full adjustment for covariates, each unit increase in SUA was associated with an elevated risk of higher CKM stage (OR\u0026thinsp;=\u0026thinsp;1.249, 95% CI: 1.186 to 1.315). Participants in the highest SUA tertile (T3) exhibited a significantly increased risk compared to those in the lowest tertile (T1) (OR\u0026thinsp;=\u0026thinsp;1.966, 95% CI: 1.653 to 2.339). A similar pattern was observed in the NHANES cohort: higher SUA levels (per unit increase: OR\u0026thinsp;=\u0026thinsp;1.278, 95% CI: 1.174 to 1.392; T3 vs. T1: OR\u0026thinsp;=\u0026thinsp;1.975, 95% CI: 1.472 to 2.650) were positively associated with advanced CKM stages.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Association Between UA Levels and CKM Syndrome\u003c/strong\u003e\u003c/p\u003e\n \u003ctable width=\"950\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 65px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 428px;\"\u003e\n \u003cp\u003eShanghai Friendship Community Elderly Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCKM Stage Distribution （n）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 172px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCKM Stage Distribution（n）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 171px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 178px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eSUA(per\u003c/p\u003e\n \u003cp\u003e1 mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e（55/465/1362/1178/239）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.384 (1.320 - 1.451)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.249 (1.186 - 1.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e(26/182/1196/376/592)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.330 (1.219 - 1.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1.278 (1.174 - 1.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\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: 65px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e（38/224/494/265/76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e(18/100/418100/178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e（11/151/491/383/71）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.603 (1.370 - 1.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.463 (1.245 - 1.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e(7/60/407/120/180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.429 (1.064 - 1.918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1.357 (1.001 - 1.841)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e（6/90/377/530/92）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.801 (2.389 - 3.285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.966 (1.653 - 2.339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e(1/22/371/156/234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2.236 (1.640 - 3.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1.975 (1.472 - 2.650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\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\u003eAbbreviations:NHANES, National Health and Nutrition Examination Survey; SUA, serum uric acid; CKM syndrome, Cardiovascular-Kidney-Metabolic syndrome;OR, odds ratio;CI,confidence interval;Ref, reference; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Subgroup Analyses\u003c/h2\u003e\n \u003cp\u003eStratified analyses were performed across both cohorts by age group, sex, education level, household income, smoking status, alcohol consumption, and marital status \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e Elevated SUA levels were consistently associated with an increased risk of CKM across the majority of these subgroups. Notably, subgroup analyses stratified by SUA levels revealed a more pronounced association between SUA and CKM risk among female participants in both cohorts (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations Between UA Levels and CKM Among Subgroups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSubgroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eShanghai Friendship Community Elderly Cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal_n\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP_interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal_n\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP_interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.39 (1.32\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.25 (1.13\u0026ndash;1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.08 (0.71\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.44 (1.27\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.39 (1.30\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.47 (1.32\u0026ndash;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.08 (1.00\u0026ndash;1. 17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1. 11 (0.99\u0026ndash;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.41 (1.33\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.65 (1.36\u0026ndash;1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.35 (1.20\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.34 (1.21\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1. 19 (1.03\u0026ndash;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.29 (1.16\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edivorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.98 (0.60\u0026ndash;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.42 (1.19\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emarried/cohabit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.38 (1.32\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.33 (1.20\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.22 (0.43\u0026ndash;3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.01 (0.66\u0026ndash;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.41 (1.15\u0026ndash;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.32 (1.12\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.14 (0.92\u0026ndash;1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.32 (1.07\u0026ndash;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.37 (1.31\u0026ndash;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.34 (1.22\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.05 (0.87\u0026ndash;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.30 (1.16\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.40 (1.33\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.41 (1.25\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;14000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.41 (1.33\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.26 (1.14\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14000\u0026ndash;28000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.31 (1.20\u0026ndash;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.28 (1.12\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;28000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.31 (0.93\u0026ndash;1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.39 (1.22\u0026ndash;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eAbbreviations:NHANES, National Health and Nutrition Examination Survey; SUA, serum uric acid; CKM syndrome, Cardiovascular-Kidney-Metabolic syndrome;OR, odds ratio;CI,confidence interval;Ref, reference;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eThe Model is adjusted for age, sex, educational attainment, smoking status, alcohol consumption, marital status, and household income.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Dose\u0026ndash;Response Relationships of SUA with CKM Syndrome\u003c/h2\u003e\n \u003cp\u003eRestricted cubic spline (RCS) analyses were employed to evaluate the dose -response relationship between SUA levels and CKM syndrome in the overall population and in gender-stratified subgroups. In the overall population, a statistically significant linear dose -response association was observed in both cohorts (SFCEC: P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P-nonlinear\u0026thinsp;=\u0026thinsp;0.238; NHANES: P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P-nonlinear\u0026thinsp;=\u0026thinsp;0.779), indicating a progressively elevated CKM risk with increasing SUA levels.\u003c/p\u003e\n \u003cp\u003eGender-stratified analyses revealed a similarly clear linear association among females in both cohorts (SFCEC: P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P-nonlinear\u0026thinsp;=\u0026thinsp;0.789; NHANES: P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P-nonlinear\u0026thinsp;=\u0026thinsp;0.528). In contrast, a modest linear relationship was observed in males, which remained statistically significant but was less pronounced than that in females (SFCEC: P-overall\u0026thinsp;=\u0026thinsp;0.004, P-nonlinear\u0026thinsp;=\u0026thinsp;0.443; NHANES: P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P-nonlinear\u0026thinsp;=\u0026thinsp;0.051) \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs far as we are aware, this is a large-scale study investigating the association between serum uric acid levels and cardiovascular-kidney-metabolic (CKM) syndrome stages utilizing data from elderly populations in China (SFCEC) and the United States (NHANES). The remarkably high prevalence of CKM abnormalities (approximately 98%) in both cohorts underscores the urgency of identifying modifiable risk factors in aging populations. The observation of a notably high prevalence of cardiometabolic-kidney (CKM) syndrome in the elderly cohorts may be attributed to the long-term cumulative exposure to metabolic and cardiovascular risk factors typically associated with advanced age. Furthermore, the CKM staging system is inherently designed to be inclusive and sensitive for early risk identification.Our key finding, confirmed by dose-response analysis that elevated SUA levels are significantly and positively associated with CKM risk, suggests that serum uric acid may serve as an independent risk predictor and potential biomarker for the progression of CKM syndrome.\u003c/p\u003e \u003cp\u003eMultiple studies have pointed out that the relationship between SUA and cardiovascular risk varies between genders. Wakabayashi et al. [15] emphasized that SUA has a more significant predictive value for cardiovascular events in obese female patients, and the U-shaped association is more obvious. Sakata et al. [16] also found that low uric acid levels in women are closely related to an increased risk of stroke death, suggesting that uric acid may play a unique role in cardiovascular protection in women. These findings align with our research results: among female CKM patients, the correlation between serum uric acid levels and the progression of CKM syndrome is more pronounced.\u003c/p\u003e \u003cp\u003ePrevious research has established important connections between abnormal serum uric acid levels and key components of CKM syndrome, including obesity, diabetes mellitus (DM), chronic kidney disease (CKD), and cardiovascular disease (CVD). First, in our study, the proportion of obese patients was the highest (67.20% in the SFCEC cohort and 72.20% in the NHANES cohort). Previous observational studies have confirmed a significant positive correlation between serum uric acid (SUA) levels and obesity. A large-scale study involving 8,522 Chinese children and adolescents aged 2\u0026ndash;18 years found that the detection rate of overweight or obesity significantly increased with ascending SUA quartiles (OR\u0026thinsp;=\u0026thinsp;4.45, 95% CI: 3.33\u0026ndash;5.93), indicating that elevated SUA is an independent risk factor for obesity[17]. Another retrospective study in adults (n\u0026thinsp;=\u0026thinsp;19, 193) further demonstrated that non-interventional weight changes (e.g., a BMI increase\u0026thinsp;\u0026gt;\u0026thinsp;5%) directly led to a rise in SUA levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while weight loss was associated with a corresponding decrease in SUA, revealing a bidirectional promoting relationship between obesity and SUA[18]. Additionally, studies on bariatric surgery have indicated that SUA levels significantly decreased in obese patients after surgery, conversely validating the correlation between obesity and SUA [19] .Second,Multiple observational studies have used uric acid levels as an indicator to predict the future development of type 2 diabetes in humans [20\u0026ndash;22]. A study by Ryu et al. showed that serum uric acid levels are independently associated with an increased risk of type 2 diabetes and suggested that uric acid may be a component of metabolic syndrome [20]. Another study investigated the relationship between uric acid levels and the risk of type 2 diabetes in 4,536 participants with hyperuricemia and without diabetes at baseline. During a 10-year follow-up period, 462 participants developed diabetes, and the results indicated that approximately one-quarter of the diabetes cases could be attributed to high serum uric acid levels [21]. Niskanen et al. examined the predictive role of hyperuricemia on changes in glucose tolerance and the development of type 2 diabetes over a period of 4. 1 years. The results demonstrated that individuals with hyperuricemia at baseline had a doubled risk of developing type 2 diabetes [22].Third,The high prevalence of hyperuricemia in patients with chronic kidney disease (CKD) and its association with renal function deterioration have been well-documented. In a community-based cohort study of 39,039 elderly Chinese patients with diabetes, Zhou et al. demonstrated that elevated serum uric acid levels significantly increased the risk of new-onset CKD, with a notably higher risk among male patients (HR\u0026thinsp;=\u0026thinsp;1.925, 95% CI: 1.724 -2. 150), suggesting that uric acid serves as an independent predictor of CKD[23]. Furthermore, in a long-term follow-up analysis of 9,891 CKD patients, Liu et al. reported that a serum uric acid level\u0026thinsp;\u0026ge;\u0026thinsp;5.9 mg/dL was significantly associated with an increased risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.102, 95% CI: 1.043\u0026ndash;1.165), further underscoring the prognostic significance of uric acid levels in this population[24].Moreover, Although epidemiological studies generally support the correlation between uric acid and the occurrence and development of CKD[23\u0026ndash;25], there is still considerable controversy over whether uric acid is an independent pathogenic factor for CKD[26\u0026ndash;27].Furthermore, a large number of cohort studies and cross-sectional studies have revealed the correlation between SUA and cardiovascular diseases. A multicenter prospective cohort study in Japan found that the SUA level was a significant predictor of cardiovascular events (coronary heart disease, stroke, arteriosclerotic occlusive disease) in obese women, and it showed a U-shaped association, indicating that both too low or too high uric acid levels increase the risk[28].In a retrospective study of the Punjabi population in Pakistan[29], it was found that hyperuricemia was significantly associated with coronary artery syndrome, myocardial infarction, and heart failure and other cardiovascular diseases, and this association remained significant after adjusting for confounding factors such as age, gender, diabetes, and hypertension. It is believed that SUA is an independent cardiovascular risk factor. A Mendelian randomization study systematically evaluated the causal relationship between genetic SUA levels and various cardiovascular diseases (coronary heart disease, hypertension,myocardial infarction, heart failure, angina pectoris). The results showed that genetic hyperuricemia significantly increased the risk of the above cardiovascular diseases, and the sensitivity analysis verified the robustness of the results, strongly supporting that an increase in SUA has a causal promoting effect on cardiovascular diseases[30]. In conclusion, although existing evidence has established serum uric acid levels as a contributing factor to individual components of CKM syndrome such as obesity, DM, and CVD, the comprehensive relationship between serum uric acid levels and integrated CKM staging remained poorly characterized prior to this study.\u003c/p\u003e \u003cp\u003eEmerging evidence clarifies the multifaceted mechanisms by which elevated serum uric acid contributes to the development and progression of cardiorenal metabolic (CKM) syndrome. In obesity, elevated serum uric acid promotes the infiltration of macrophages into adipose tissue and activates the NLRP3 inflammasome, exacerbating chronic inflammation and insulin resistance [31]. In vitro studies confirm that uric acid directly stimulates preadipocyte differentiation through specific oxidative stress pathways.Building on these findings, in diabetes, uric acid induces hepatic steatosis and insulin resistance via mitochondrial oxidative stress.It also promotes β -cell dysfunction through AMPK- and mTOR-mediated autophagy and apoptosis, and triggers NLRP3-driven inflammation, resulting in IL-1 β release [31\u0026ndash;32]. In chronic kidney disease, uric acid activates the tubular NLRP3 inflammasome, leading to fibrosis, and causes podocyte injury through endoplasmic reticulum stress, and autophagic disruption [33]. In cardiovascular disease, it accelerates vascular calcification via osteogenic differentiation of smooth muscle cells and amplifies atherosclerotic plaque instability through macrophage polarization toward a pro-inflammatory M1 phenotype and endothelial dysfunction [34]. Although not fully understood, these interconnected pathways\u0026mdash;centered on inflammation, oxidative stress, and insulin resistance \u0026mdash; collectively position uric acid as a systemic mediator and a shared underlying factor driving the concurrent progression of cardiovascular, metabolic, and renal disorders.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths and Limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study has several notable strengths.First,the analysis incorporated data from two large, well-characterized cohorts \u0026mdash; the Shanghai Friendship Community Elderly Cohort (SFCEC) and the nationally representative US NHANES population\u0026mdash;which improves the generalizability of the findings across different ethnic groups. Second, the analytical methods rigorously accounted for the complex survey design of NHANES by applying appropriate weighting, stratification, and clustering adjustments, thereby ensuring robust population-level estimates.However, several limitations should be considered. As an observational study, the identified association between serum uric acid and cardiovascular-kidney-metabolic (CKM) syndrome indicates correlation rather than causation. Although extensive adjustments were made for demographic and lifestyle confounders,residual confounding from unmeasured variables cannot be excluded. Additionally, the generalizability of the findings to younger adult populations may be limited, given that the study population primarily comprised older adult.\u003c/p\u003e \u003cp\u003eOur research group will pursue the following directions: 1) further elucidating the role of serum uric acid (SUA) across different stages of CKM syndrome; 2) conducting large-scale randomized controlled trials to evaluate whether reducing SUA levels can delay or reverse the progression of CKM syndrome; and 3) exploring targeted therapeutic strategies against downstream pathways of SUA-induced pathology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study reveals that elevated serum uric acid levels represent a potential independent risk factor for cardiovascular-kidney-metabolic (CKM) syndrome in older adult populations across both Chinese and American cohorts, furthermore, among female CKM patients, the correlation between serum uric acid levels and the progression of CKM syndrome is more pronounced. The consistency of these associations observed across diverse groups suggests that serum uric acid may serve as a potential biomarker for monitoring CKM progression in adults. Consequently, routine screening of serum uric acid may facilitate early detection and prevention of CKM syndrome. Further research is warranted to validate these associations and elucidate the specific mechanistic role of uric acid in the pathogenesis of CKM syndrome.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHANES,National Health and Nutrition Examination Survey;SUA,serum uric acid;CKM syndrome,Cardiovascular-Kidney-Metabolic syndrome;DM,diabetes mellitus;CKD,chronic kidney disease;CVD,cardiovascular disease;OR, odds ratio;CI, confidence interval;Ref, reference; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this research were obtained from the Shanghai Friendship Community Elderly Cohort (SFCEC) and US National Health and Nutrition Examination Survey(NHANES). We would like to thank the workers, researchers, and participants involved in the SFCEC and NHANES.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.C.K and Y.L were responsible for the design and conceptualization of the study, as well as drafting and revising the manuscript. K.A.W and B.B.Z contributed to data collecting, statistical analysis, and result interpretation.B.Y.W was responsible for the data results visualization. Y.L read and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Shanghai Municipal Key Clinical Specialty (Grant No. 2024ZDXK0016) and the Medical Specialty Construction Project of Minhang District,Shanghai (No.2025MWTZB01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are publicly available from the NHANES database (https://wwwn.cdc.gov/nchs/nhanes/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll studies involved in this research adhered to the ethical principles of the Declaration of Helsinki (1975) and were approved by the respective ethics committees. Specifically, the SFCEC study protocol was approved by the Institutional Review Board of Huashan Hospital Affiliated to Fudan University (Approval No. 2020-004). The NHANES study received ethical approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board, and its procedures complied with the Declaration of Helsinki. Further details are available at: https://www.cdc.gov/nchs/nhanes/\u003c/a\u003e.All participants executed a written informed consent form before their involvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors give consent for publication of this paper in Archives of Public Health\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNdumele CE, Neeland IJ, Tuttle KR, et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome:A Scientific Statement From the American Heart Association. Circulation. 2023;148(20):1636-1664. doi:10. 1161/CIR.0000000000001186\u003c/li\u003e\n\u003cli\u003eAggarwal R,Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic\u0026nbsp;\u0026nbsp; Syndrome Stages in US Adults, 2011-2020. JAMA.2024;331(21):1858-1860. doi:10. 1001/jama.2024.6892\u003c/li\u003e\n\u003cli\u003eZhu R, Wang R, He J, et al. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages by Social\u0026nbsp; Determinants of Health. JAMA Netw Open.2024;7(11):e2445309.Published 2024 Nov 4. doi:10. 1001/jamanetworkopen.2024.45309\u003c/li\u003e\n\u003cli\u003eChen A, He Q, Wu Y, et al. Incidence of cardiovascular-kidney metabolic syndrome and its risk factors for\u0026nbsp; progression in China. medRxiv 2024, doi:http://dx.doi.org/10.1101/2024.08.07.24311650 2024.08.07.24311650.\u003c/li\u003e\n\u003cli\u003eOuyang R, Zhao X, Zhang R, Yang J, Li S, Deng D. FGF21 attenuates high uric acid‑induced endoplasmic reticulum stress, inflammation and vascular endothelial cell dysfunction by activating Sirt1. 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Association of Serum Uric Acid Level and Thyroid Function in Chronic Kidney Disease: A Hospital-Based Cross-Sectional Study From Northeast India. Cureus. 2024;16(9):e69392. Published 2024 Sep 14. doi:10.7759/cureus.69392\u003c/li\u003e\n\u003cli\u003eMolino-Lova R, Sofi F, Pasquini G, et al. Higher uric acid serum levels are associated with better muscle function in the oldest old: Results from the Mugello Study. Eur J Intern Med. 2017;41:39-43. doi:10. 1016/j.ejim.2017.03.014\u003c/li\u003e\n\u003cli\u003eFloriano JP, Nahas PC, de Branco FMS, et al. Serum Uric Acid Is Positively Associated with Muscle Mass and Strength, but Not with Functional Capacity, in Kidney Transplant Patients. Nutrients. 2020;12(8):2390. Published 2020 Aug 10. doi:10.3390/nu12082390\u003c/li\u003e\n\u003cli\u003eXu L, Jing Y, Zhao C, et al. 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Novel pathological\u0026nbsp; implications\u0026nbsp; of serum\u0026nbsp; uric\u0026nbsp; acid with\u0026nbsp; cardiovascular\u0026nbsp; disease\u0026nbsp; risk\u0026nbsp; in\u0026nbsp; obesity. Diabetes Res\u0026nbsp; Clin\u0026nbsp; Pract. 2023 Nov;205:110919.\u0026nbsp; doi:10. 1016/j.diabres.2023.110919. Epub 2023 Sep 22. PMID: 37742802.\u003c/li\u003e\n\u003cli\u003eLi Y, Yang H, Tian Y, Duan L. Factors Influencing the\u0026nbsp; Serum Uric Acid in\u0026nbsp; Gout with\u0026nbsp; Cerebral Infarction. Mediators Inflamm. 2021 Jul\u0026nbsp; 12;2021:5523490.\u0026nbsp; doi:10. 1155/2021/5523490. PMID: 34335087; PMCID: PMC8289599.\u003c/li\u003e\n\u003cli\u003eYe W, Zhou X, Xu Y, Zheng C, Liu P. Serum Uric Acid Levels among Chinese Children: Reference Values and Association With Overweight/Obesity. Clin Pediatr (Phila). 2024 Dec;63(12):1684-1690. doi: 10. 1177/00099228241238510. Epub 2024 Mar 21. 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J Bras Nefrol. 2021 Oct-Dec;43(4):572-579.\u003c/li\u003e\n\u003cli\u003eWakabayashi D, Kato S, Tanaka M, Yamakage H, Kato H, Kusakabe T, Ozu N, Kasama S, Kasahara M, Satoh-Asahara N; Japan Obesity Metabolic Syndrome Study (JOMS)\u0026nbsp; Group. Novel pathological\u0026nbsp; implications\u0026nbsp; of serum\u0026nbsp; uric\u0026nbsp; acid with\u0026nbsp; cardiovascular\u0026nbsp; disease\u0026nbsp; risk\u0026nbsp; in\u0026nbsp; obesity. Diabetes Res\u0026nbsp; Clin\u0026nbsp; Pract. 2023 Nov;205:110919. doi:10. 1016/j.diabres.2023.110919. Epub 2023 Sep 22. PMID: 37742802.\u003c/li\u003e\n\u003cli\u003eHussain M, Ghori MU, Aslam MN, Abbas S, Shafique M, Awan FR. Serum uric acid: an independent risk factor for cardiovascular disease in Pakistani Punjabi patients.BMC Cardiovasc Disord. 2024 Oct 10;24(1):546. doi: 10. 1186/s12872-024-04055-y. PMID: 39385070; PMCID: PMC11465846.\u003c/li\u003e\n\u003cli\u003eZhang Y, Lian Q, Nie Y, Zhao W. Causal relationship between serum uric acid and cardiovascular disease: A Mendelian randomization study. Int J Cardiol Heart Vasc.2024 Jun 29;54:101453. doi: 10. 1016/j.ijcha.2024.101453. PMID: 39411145; PMCID: PMC11473680.\u003c/li\u003e\n\u003cli\u003eJohnson RJ, Bakris GL, Borghi C, et al. Hyperuricemia, Acute and Chronic Kidney Disease, Hypertension, and Cardiovascular Disease: Report of a Scientific Workshop Organized by the National Kidney Foundation. Am J Kidney Dis. 2018;71(6):851-865.\u003c/li\u003e\n\u003cli\u003eBaldwin W, McRae S, Marek G, et al. Hyperuricemia as a mediator of the proinflammatory endocrine imbalance in the adipose tissue in a murine model of the metabolic syndrome. Diabetes. 2011;60(4):1258-1269.\u003c/li\u003e\n\u003cli\u003eKang DH, Chen W. Uric acid and chronic kidney disease: new understanding of an old problem. Semin Nephrol. 2011;31(5):447-452.\u003c/li\u003e\n\u003cli\u003eKrishnan E. Hyperuricemia and incident heart failure. Circ Heart Fail. 2009;2(6):556-562.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular-kidney-metabolic (CKM) syndrome, serum uric acid (SUA), Elderly population","lastPublishedDoi":"10.21203/rs.3.rs-8851827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8851827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiovascular-kidney-metabolic (CKM) syndrome is highly prevalent among the elderly. Serum uric acid (SUA) may serve as a potential marker for CKM,but evidence on the association between SUA levels and the progression of CKM syndrome remains scarce and controversial. This study aims to investigate the relationship between SUA levels and CKM syndrome in elderly populations by utilizing cohort data from China and the United States.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed 3,299 participants from the Shanghai Friendship Community Elderly Cohort (SFCEC) and 2,372 from the US National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2018.SUA was measured using standardized assays. Multivariate logistic regression models were performed. Restricted cubic spline regression was performed to visualize the dose-response relationship.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of CKM syndrome (stages 1\u0026ndash;4) was strikingly high in both elderly cohorts(98.33% in the SFCEC cohort and 98.76% in the NHANES cohort). After comprehensive adjustment, multivariable analysis revealed that each 1 mg/dl increase in serum uric acid was associated with 24.9% (OR\u0026thinsp;=\u0026thinsp;1.249, 95% CI: 1.186\u0026ndash;1.315) and 27.8% (OR\u0026thinsp;=\u0026thinsp;1.278, 95% CI:1.174\u0026ndash;1.392) elevated risks of CKM syndrome in the Shanghai and US cohorts, respectively. Compared with the lowest tertile, participants in the highest serum uric acid tertile demonstrated significantly increased CKM risks (Shanghai cohort: OR\u0026thinsp;=\u0026thinsp;1.966, 95% CI: 1.653\u0026ndash;2.339; US cohort: OR\u0026thinsp;=\u0026thinsp;1.975, 95% CI:1.472\u0026ndash;2.650). Restricted cubic spline analysis indicated a positive linear dose-response relationship between serum uric acid levels and CKM syndrome staging. This association was more pronounced in female patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study reveals that elevated SUA levels may be a potential independent risk factor for the progression of CKM syndrome in the elderly population.Clinical screening of SUA levels may be helpful for the early detection and prevention of CKM syndrome.\u003c/p\u003e","manuscriptTitle":"Association of Serum Uric Acid with Cardiovascular-Kidney-Metabolic Syndrome in Elderly Populations: Results from Chinese and US cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 06:14:07","doi":"10.21203/rs.3.rs-8851827/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T16:57:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-02T06:22:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T04:02:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198084718078264943309200202219110702624","date":"2026-04-01T03:32:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302872298966078960049964133523894149009","date":"2026-04-01T00:54:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-27T12:11:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T10:40:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T13:46:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T13:42:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-11T12:20:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5057296-e6cb-4904-96b3-319d601bb229","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65510433,"name":"Health sciences/Biomarkers"},{"id":65510434,"name":"Health sciences/Diseases"},{"id":65510435,"name":"Health sciences/Medical research"},{"id":65510436,"name":"Health sciences/Nephrology"},{"id":65510437,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-12T09:23:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 06:14:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8851827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8851827","identity":"rs-8851827","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}