Predictive value of uric acid to high-density lipoprotein cholesterol ratio for cardiometabolic multimorbidity in middle-aged and older adults: a nationwide prospective cohort study

Predictive value of uric acid to high-density lipoprotein cholesterol ratio for cardiometabolic multimorbidity in middle-aged and older adults: a nationwide prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive value of uric acid to high-density lipoprotein cholesterol ratio for cardiometabolic multimorbidity in middle-aged and older adults: a nationwide prospective cohort study Shijing Jiang, Shuliang Wang, Zhiwei Miao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9133547/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Cardiometabolic multimorbidity (CMM) is an escalating public health challenge. The uric acid-to-high-density lipoprotein cholesterol ratio (UHR) is a composite biomarker reflecting metabolic disturbance, but prospective evidence regarding the association between UHR and CMM remains limited. Methods This prospective cohort study included 7,435 adults aged ≥ 45 years from CHARLS followed from 2011 to 2018. Cox proportional hazards models and restricted cubic spline analyses were used to examine the associations of UHR and cumulative UHR (CumUHR) with cardiometabolic multimorbidity (CMM). Receiver operating characteristic (ROC) curves compared the predictive performance of UHR and CumUHR with that of uric acid (UA) and high-density lipoprotein cholesterol (HDL-C) alone. Subgroup and sensitivity analyses were conducted to test the robustness of the findings. Result Among the 7,435 participants, 1,748 developed CMM. Kaplan–Meier analysis showed that the cumulative event rate of CMM increased progressively across UHR quartiles (log-rank P < 0.001). In the fully adjusted model, the highest UHR quartile (Q4) was associated with a significantly increased risk of CMM compared with the lowest quartile (Q1) (HR = 1.48, 95% CI 1.27–1.77). When cumulative exposure was considered, elevated CumUHR remained an independent predictor of CMM (HR = 1.26, 95% CI 1.15–1.39). Restricted cubic spline analyses further demonstrated significant nonlinear associations of both UHR and CumUHR with CMM risk, with inflection points observed around 8.5 for UHR and 35.7 for CumUHR. Furthermore, ROC analysis showed that the composite indicators UHR (AUC = 0.747) and CumUHR (AUC = 0.749) had better predictive performance for CMM than their individual components, UA (AUC = 0.745) and HDL-C (AUC = 0.744) alone. Conclusion Higher UHR and CumUHR levels were independently associated with an increased risk of CMM in middle-aged and older adults. As simple composite indicators integrating UA and HDL-C, UHR and CumUHR may provide additional value for identifying individuals at elevated cardiometabolic risk and improving early risk stratification for CMM. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Uric acid high-density lipoprotein cholesterol cardiometabolic multimorbidity CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Cardiometabolic multimorbidity (CMM) refers to the coexistence of at least two cardiometabolic diseases (CMDs) within the same individual, commonly including heart disease, stroke, diabetes, and hypertension [ 1 ] . With population aging and the rising prevalence of metabolic risk factors, CMM has become an increasingly important public health challenge. In a nationwide cohort of 512,723 Chinese adults, 15.8% of participants had multimorbidity overall, and 6.0% specifically exhibited CMM [ 2 ] . Beyond its high prevalence, CMM evolves through the progressive accumulation of cardiometabolic diseases over time. In a large prospective cohort of 461,047 adults followed for more than 11 years, participants transitioned sequentially from a healthy state to a first CMD and subsequently to CMM, with high-risk lifestyle factors accelerating these transitions [ 3 ] . Early identification of individuals at high risk of developing CMM is therefore essential. In this context, identifying simple and accessible biomarkers that reflect long-term cardiometabolic burden is of considerable clinical interest. Uric acid (UA) has been consistently associated with increased cardiometabolic risk. Epidemiological studies have shown that elevated UA levels are linked to a higher risk of hypertension, coronary heart disease, and stroke [ 4 ] . Meanwhile, high-density lipoprotein cholesterol (HDL-C) is widely recognized for its protective role in lipid metabolism and vascular health. Given their complementary yet opposing biological roles, integrating UA and HDL-C may better capture overall cardiometabolic imbalance. The UA to HDL-C ratio (UHR) has therefore been proposed as a composite biomarker capturing the joint information of UA–related metabolic burden and HDL-C–related lipid protection. In population-based studies, elevated UHR has been associated with a higher risk of adverse outcomes, such as increased all-cause and cardiovascular mortality and a greater likelihood of diabetic nephropathy [ 5 , 6 ] . These observations suggest that UHR may capture cardiometabolic vulnerability beyond single conditions; however, whether UHR predicts the development of CMM, characterized by the coexistence of multiple cardiometabolic diseases, remains unclear. Accordingly, we used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal cohort of Chinese adults aged 45 years and older, to examine the prospective associations of baseline and cumulative UHR levels with CMM. Elucidating the relationship between UHR and CMM may provide evidence for the use of a simple composite biomarker to characterize long-term cardiometabolic burden and improve risk stratification for CMM. 2. Method 2.1. Sample and study design This study used data from CHARLS, a nationally representative longitudinal cohort of Chinese adults aged 45 years and older. CHARLS employs a multistage, stratified probability sampling design and includes detailed information on sociodemographic characteristics, health behaviors, physician-diagnosed chronic diseases, physical measurements, and fasting blood biomarkers. Wave 1 (2011–2012) served as the baseline, with follow-up surveys conducted in Waves 2 (2013), 3 (2015), and 4 (2018). The CHARLS protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015), and all participants provided written informed consent [ 7 ] . To investigate the associations of baseline UHR and Cumulative UHR (CumUHR) with CMM, several exclusion criteria were sequentially applied. First, participants with missing UHR measurements required for the assessment of the exposure variables were excluded (n = 5,694). Second, participants with prevalent CMM at baseline or missing baseline CMM information were excluded (n = 1,721). Third, individuals without ascertainable CMM status during follow-up were excluded (n = 5,040). After these exclusions, 7,435 participants remained in the final analytic cohort. Baseline CMM status was determined using information collected in Wave 1 to exclude prevalent cases. CMM occurring during follow-up was identified in Waves 2, 3, or 4. Follow-up time was calculated from baseline to the first occurrence of CMM or the last available follow-up interview. The detailed participant selection process is illustrated in Fig. 1 . 2.2 Definition of UHR and cumulative UHR In this study, UHR was calculated as the ratio of UA (mg/dL) to HDL-C (mg/dL). Both biomarkers were measured from fasting venous blood samples collected during the CHARLS survey using standardized laboratory procedures. CumUHR was estimated based on repeated measurements obtained in 2011 and 2015 using a time-weighted average approach: CumUHR = [(UHR2011 + UHR2015) / 2] × time interval (2015 − 2011). The calculation methods were consistent with previous studies [ 8 , 9 ] . 2.3. Definition of CMM Similar to prior investigations [ 2 , 10 ] , cardiometabolic multimorbidity (CMM) was defined as the coexistence of two or more of the following cardiometabolic diseases (CMDs): heart disease, diabetes, stroke, and hypertension. Information on these conditions was obtained from self-reported physician diagnoses collected in the CHARLS survey. Heart disease included heart attack, coronary heart disease, angina, congestive heart failure, and other heart-related conditions. Participants who were free of CMM at baseline but developed at least two CMDs during follow-up were classified as having CMM. 2.4. Covariates Covariates were selected based on prior literature and the availability of variables in the CHARLS database [ 11 , 12 ] . Demographic factors included age, sex, education level, marital status, and residence (urban or rural). Lifestyle factors included smoking status and alcohol consumption. Body mass index (BMI) was included as an anthropometric indicator. Clinical and biochemical variables included estimated glomerular filtration rate (eGFR) and C-reactive protein (CRP). 2.5. Statistical analysis Baseline characteristics were summarized according to quartiles of UHR. The normality of continuous variables was assessed before analysis. As all continuous variables showed a skewed distribution, continuous variables are presented as medians with interquartile ranges (IQR), while categorical variables are presented as numbers and percentages. Differences across groups were compared using the Kruskal–Wallis test for continuous variables and the χ² test for categorical variables. Time-to-event analyses were performed using Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CMM. Follow-up time was calculated from baseline (2011) to the first occurrence of CMM or the last available follow-up interview. UHR was primarily analyzed in quartiles with the lowest quartile as the reference group, and linear trends were assessed by modeling quartile categories as an ordinal variable. UHR was also analyzed as a median-based dichotomous variable in sensitivity analyses. CumUHR was used to reflect long-term exposure burden. The optimal cut-point for CumUHR was determined using maximally selected rank statistics, and the association between CumUHR and CMM during follow-up was further evaluated using Cox models and Kaplan–Meier curves with log-rank tests. Three progressively adjusted Cox proportional hazards models were constructed. Model 1 was unadjusted. Model 2 adjusted for sociodemographic factors including age, sex, education level, marital status, and residence (urban/rural). Model 3 was further adjusted for smoking status, alcohol consumption, BMI, eGFR, and CRP. Restricted cubic spline (RCS) analyses were conducted to explore potential nonlinear associations between UHR and the risk of CMM. Receiver operating characteristic (ROC) curve analyses were performed to compare the predictive performance of UHR and CumUHR with that of their individual components (uric acid and HDL-C). All statistical analyses were performed using R software (version 4.3.1). A two-sided P value < 0.05 was considered statistically significant. 3. Results 3.1. Baseline characteristics of participants A total of 7,435 participants were included in the present analysis. During a median follow-up of approximately 7 years, 1,748 participants (23.51%) developed CMM. Baseline characteristics stratified by CMM status are presented in Table 1 . Participants who developed CMM were older and had a less favorable cardiometabolic profile at baseline compared with those without CMM (all P 11.42). Participants in the highest UHR quartile were slightly older than those in the lower quartiles ( P = 0.034), and the proportion of men increased progressively across quartiles ( P < 0.001). Higher UHR quartiles were also associated with higher levels of body mass index, fasting blood glucose, systolic and diastolic blood pressure, triglycerides, serum uric acid, and C-reactive protein, as well as lower HDL-C concentrations (all P < 0.001). In addition, current smoking and alcohol consumption were more frequent in the higher UHR quartiles. Table 1 Baseline characteristics of participants by CMM status. Variables Total No-CMM CMM P value No. of participants 7435 5687 1748 Age, Years 57.00 [51.00, 64.00] 57.00 [50.00, 63.00] 60.00 [54.00, 66.00] < 0.001 Gender, n (%) 0.002 Male 3424 (46.1) 2676 (47.1) 748 (42.8) Female 4011 (53.9) 3011 (52.9) 1000 (57.2) Marital status, n (%) < 0.001 married 6677 (89.8) 5167 (90.9) 1510 (86.4) Other 758 (10.2) 520 ( 9.1) 238 (13.6) Education level, n (%) 0.013 Elementary school and below 5175 (69.6) 3916 (68.9) 1259 (72.0) Secondary school and above 2260 (30.4) 1771 (31.1) 489 (28.0) Residence, n (%) 0.018 Rural 4981 (67.0) 3851 (67.7) 1130 (64.6) Urban 2454 (33.0) 1836 (32.3) 618 (35.4) Smoking Status, n (%) 0.013 No 4574 (61.5) 3454 (60.7) 1120 (64.1) Yes 2861 (38.5) 2233 (39.3) 628 (35.9) Drinking Status, n (%) 0.25 No 4547 (61.2) 3457 (60.8) 1090 (62.4) Yes 2888 (38.8) 2230 (39.2) 658 (37.6) BMI, kg/m2 23.11 [20.89, 25.80] 22.73 [20.60, 25.15] 24.83 [22.16, 27.42] < 0.001 FBG, mg/dL 101.88 [94.14, 112.14] 100.98 [93.60, 109.80] 106.02 [96.48, 121.14] < 0.001 HbA1c, % 5.10 [4.90, 5.40] 5.10 [4.80, 5.40] 5.20 [4.90, 5.60] < 0.001 SBP, mmHg 125.00 [113.00, 139.50] 122.00 [111.00, 135.50] 135.00 [123.00, 149.12] < 0.001 DBP, mmHg 74.00 [66.50, 82.50] 72.50 [65.50, 81.00] 78.50 [71.00, 86.50] < 0.001 TC, mg/dL 190.59 [167.01, 215.34] 189.05 [166.62, 214.18] 193.69 [168.94, 219.98] < 0.001 TG, mg/dL 104.43 [74.34, 153.10] 100.00 [72.57, 145.14] 123.01 [84.07, 175.23] < 0.001 HDL-C, mg/dL 49.87 [40.59, 60.31] 50.64 [41.37, 61.08] 47.17 [37.89, 56.83] < 0.001 LDL-C, mg/dL 114.05 [93.56, 136.86] 113.66 [93.17, 135.70] 115.40 [93.94, 140.34] 0.021 UA, mg/dL 4.25 [3.54, 5.10] 4.23 [3.53, 5.06] 4.36 [3.59, 5.21] < 0.001 eGFR, ml/min/1.73m2 95.51 [85.17, 102.78] 96.24 [86.13, 103.40] 93.46 [82.82, 100.57] < 0.001 CRP, mg/L 0.97 [0.53, 2.02] 0.90 [0.50, 1.86] 1.26 [0.65, 2.61] < 0.001 Hypertension, n (%) < 0.001 No 5914 (79.5) 4885 (85.9) 1029 (58.9) Yes 1521 (20.5) 802 (14.1) 719 (41.1) Dyslipidaemia, n (%) < 0.001 No 6870 (92.4) 5371 (94.4) 1499 (85.8) Yes 565 ( 7.6) 316 ( 5.6) 249 (14.2) Diabetes, n (%) < 0.001 No 7221 (97.1) 5590 (98.3) 1631 (93.3) Yes 214 ( 2.9) 97 ( 1.7) 117 ( 6.7) Continuous variables are presented as median [interquartile range (IQR)] and categorical variables as n (%). P values were calculated using the Kruskal–Wallis test for continuous variables and the χ² test for categorical variables. Table 2 Baseline characteristics of study participants according to quartiles of UHR Variables Total Quartiles of UHR P value Q1 Q2 Q3 Q4 No. of participants 7435 1859 1858 1859 1859 Age, Years 57.00 [51.00, 64.00] 57.00 [51.00, 63.00] 57.00 [51.00, 64.00] 57.00 [52.00, 64.00] 58.00 [51.00, 64.00] 0.034 Gender, n (%) < 0.001 Male 3424 (46.1) 520 (28.0) 750 (40.4) 981 (52.8) 1173 (63.1) < 0.001 Female 4011 (53.9) 1339 (72.0) 1108 (59.6) 878 (47.2) 686 (36.9) Marital status, n (%) 0.01 married 6677 (89.8) 1638 (88.1) 1660 (89.3) 1695 (91.2) 1684 (90.6) Other 758 (10.2) 221 (11.9) 198 (10.7) 164 ( 8.8) 175 ( 9.4) Education level, n (%) < 0.001 Elementary school and below 5175 (69.6) 1391 (74.8) 1336 (71.9) 1261 (67.8) 1187 (63.9) Secondary school and above 2260 (30.4) 468 (25.2) 522 (28.1) 598 (32.2) 672 (36.1) Residence, n (%) < 0.001 Rural 4981 (67.0) 1388 (74.7) 1280 (68.9) 1226 (65.9) 1087 (58.5) Urban 2454 (33.0) 471 (25.3) 578 (31.1) 633 (34.1) 772 (41.5) Smoking Status, n (%) < 0.001 No 4574 (61.5) 1374 (73.9) 1205 (64.9) 1064 (57.2) 931 (50.1) < 0.001 Yes 2861 (38.5) 485 (26.1) 653 (35.1) 795 (42.8) 928 (49.9) Drinking Status, n (%) No 4547 (61.2) 1276 (68.6) 1200 (64.6) 1085 (58.4) 986 (53.0) < 0.001 Yes 2888 (38.8) 583 (31.4) 658 (35.4) 774 (41.6) 873 (47.0) BMI, kg/m 2 23.11 [20.89, 25.80] 22.03 [20.03, 24.13] 22.66 [20.49, 25.13] 23.40 [21.19, 26.05] 24.56 [22.28, 27.15] < 0.001 FBG, mg/dL 101.88 [94.14, 112.14] 100.08 [92.88, 108.99] 101.16 [93.96, 109.62] 102.06 [93.96, 112.95] 105.12 [96.48, 118.80] < 0.001 HbA1c, % 5.10 [4.90, 5.40] 5.10 [4.90, 5.40] 5.10 [4.90, 5.40] 5.10 [4.90, 5.40] 5.20 [4.90, 5.50] < 0.001 SBP, mmHg 125.00 [113.00, 139.50] 121.00 [110.50, 135.50] 123.00 [112.00, 137.50] 126.50 [113.50, 140.50] 128.50 [116.50, 143.00] < 0.001 DBP, mmHg 74.00 [66.50, 82.50] 72.00 [64.50, 80.00] 72.50 [66.00, 81.00] 75.00 [67.00, 83.50] 76.50 [68.50, 85.00] < 0.001 TC, mg/dL 190.59 [167.01, 215.34] 196.01 [172.04, 221.14] 190.59 [168.17, 213.40] 187.89 [164.69, 214.56] 186.73 [163.15, 212.24] < 0.001 TG, mg/dL 104.43 [74.34, 153.10] 79.65 [61.06, 107.08] 94.69 [69.92, 130.10] 111.51 [81.42, 158.41] 153.99 [105.76, 237.18] < 0.001 HDL-C, mg/dL 49.87 [40.59, 60.31] 64.18 [56.06, 74.23] 54.12 [47.55, 61.08] 46.39 [40.98, 52.58] 36.73 [31.70, 42.14] < 0.001 LDL-C, mg/dL 114.05 [93.56, 136.86] 114.05 [94.72, 137.63] 116.75 [95.97, 137.24] 116.37 [95.88, 139.18] 109.79 [85.83, 134.15] < 0.001 UA, mg/dL 4.25 [3.54, 5.10] 3.30 [2.86, 3.77] 3.98 [3.55, 4.51] 4.54 [4.02, 5.14] 5.42 [4.75, 6.20] < 0.001 eGFR, ml/min/1.73m 2 95.51 [85.17, 102.78] 98.98 [91.39, 105.30] 96.37 [87.23, 103.23] 94.23 [84.32, 102.02] 91.50 [78.34, 100.12] < 0.001 CRP, mg/L 0.97 [0.53, 2.02] 0.67 [0.41, 1.28] 0.89 [0.49, 1.86] 1.06 [0.58, 2.13] 1.43 [0.75, 2.84] < 0.001 Hypertension, n (%) < 0.001 No 5914 (79.5) 1596 (85.9) 1540 (82.9) 1441 (77.5) 1337 (71.9) Yes 1521 (20.5) 263 (14.1) 318 (17.1) 418 (22.5) 522 (28.1) Dyslipidaemia, n (%) < 0.001 No 6870 (92.4) 1771 (95.3) 1730 (93.1) 1711 (92.0) 1658 (89.2) Yes 565 ( 7.6) 88 ( 4.7) 128 ( 6.9) 148 ( 8.0) 201 (10.8) Diabetes, n (%) < 0.001 No 7221 (97.1) 1812 (97.5) 1806 (97.1) 1803 (97.0) 1800 (96.8) Yes 214 ( 2.9) 46 ( 2.5) 53 ( 2.9) 56 ( 3.0) 59 ( 3.2) Notes : Continuous variables are presented as median [interquartile range (IQR)] and categorical variables as n (%). P values were calculated using the Kruskal–Wallis test for continuous variables and the χ² test for categorical variables. 3.2. Association between UHR and CMM Associations between UHR and incident CMM across Cox proportional hazards models are presented in Table 3 . Model 1 was unadjusted, Model 2 adjusted for sociodemographic factors, and Model 3 further adjusted for lifestyle and metabolic factors. In the fully adjusted model, participants in the highest quartile of UHR had a significantly higher risk of CMM than those in the lowest quartile (HR 1.48, 95% CI 1.27–1.77; P for trend = 0.007). Similar associations were observed in the unadjusted and partially adjusted models. Kaplan–Meier curves showed progressively higher cumulative event rates of CMM across increasing quartiles of UHR (Fig. 2 ; log-rank P < 0.001). Table 3 Association between UHR and CMM UHR quartile Model 1 Model 2 Model 3 HR(95%CI) P HR(95%CI) P HR(95%CI) P Q1 Reference Reference Reference Q2 1.10 (0.95–1.27) 0.196 1.13 (0.97–1.30) 0.108 0.98 (0.84–1.13) 0.743 Q3 1.40 (1.22–1.60) < 0.001 1.50 (1.30–1.73) < 0.001 1.09 (0.94–1.25) 0.258 Q4 1.93 (1.68–2.22) < 0.001 1.76 (1.54–2.01) < 0.001 1.48 (1.27–1.77) 0.015 P for trend < 0.001 < 0.001 0.007 Notes : Model 1: unadjusted. Model 2: adjusted for age, sex, education level, marital status, and residence. Model 3: further adjusted for smoking status, alcohol consumption, body mass index (BMI), estimated glomerular filtration rate (eGFR), and C-reactive protein (CRP). 3.3. Association between CumUHR and CMM The optimal cut-off value of CumUHR for predicting CMM was 41.59 (Figure S1 ). Participants were categorized into low (< 41.59) and high (≥ 41.59) CumUHR groups. Kaplan–Meier curves showed a significantly higher cumulative event rates of CMM in the high CumUHR group than in the low CumUHR group (Fig. 3 ; log-rank P < 0.001). Higher CumUHR was associated with a greater risk of CMM (Table 4 ). In the fully adjusted model, participants with high CumUHR had a significantly higher risk of CMM than those with low CumUHR (HR 1.26, 95% CI 1.15–1.39; P = 0.004). Similar associations were observed in the unadjusted and partially adjusted models. Table 4 Association between CumUHR and CMM CumUHR Model 1 Model 2 Model 3 HR(95%CI) P HR(95%CI) P HR(95%CI) P Low Reference Reference Reference High 1.52 (1.38–1.67) < 0.001 1.63 (1.47–1.79) < 0.001 1.26 (1.15–1.39) 0.004 P value < 0.001 < 0.001 0.004 Notes : Model 1: unadjusted. Model 2: adjusted for age, sex, education level, marital status, and residence. Model 3: further adjusted for smoking status, alcohol consumption, body mass index (BMI), estimated glomerular filtration rate (eGFR), and C-reactive protein (CRP). 3.4. Dose–response associations of UHR and CumUHR with CMM Restricted cubic spline analyses were performed to evaluate the dose–response associations of UHR and CumUHR with CMM (Fig. 4 ). For UHR, significant overall associations with CMM were observed in both the crude and adjusted models (both P for overall < 0.01), and the adjusted model showed a significant nonlinear association ( P for nonlinearity = 0.047), with an inflection point around 8.5. For CumUHR, significant overall associations were also observed in both the crude and adjusted models (both P for overall < 0.01), with significant nonlinearity in both the crude model ( P for nonlinearity = 0.001) and the adjusted model ( P for nonlinearity = 0.033), and an inflection point around 35.7. In both analyses, the risk of CMM increased with increasing levels of UHR and CumUHR. 3.5. Predictive performance of UHR and CumUHR for CMM ROC curve results are presented in Table 5 and Fig. 5 . The base model yielded an AUC of 0.745 (95% CI 0.732–0.758). With UHR, which integrates uric acid and HDL-C, the AUC was 0.747 (95% CI 0.734–0.760; P = 0.041), whereas the model including CumUHR yielded an AUC of 0.749 (95% CI 0.735–0.761; P = 0.028). For the individual components of UHR, the corresponding AUCs were 0.745 (95% CI 0.732–0.758; P = 0.833) for UA and 0.744 (95% CI 0.734–0.759; P = 0.833) for HDL-C. Table 5 Predictive performance of UHR and CumUHR for CMM. Model AUC (95% CI) P value Threshold Sensitivity Specificity Base covariates 0.745 (0.732–0.758) Reference 0.195 0.660 0.712 Base + UHR 0.747 (0.734–0.760) 0.041 0.205 0.645 0.728 Base + CumUHR 0.749 (0.735–0.761) 0.028 0.237 0.619 0.752 Base + UA 0.745 (0.732–0.758) 0.833 0.195 0.662 0.710 Base + HDL-C 0.744 (0.734–0.759) 0.833 0.228 0.629 0.742 3.6. Subgroup and sensitivity analyses The associations of UHR and CumUHR with CMM were broadly consistent across most predefined subgroups, and no significant interactions were observed for either UHR or CumUHR (all P for interaction > 0.05; Fig. 6 ). Sensitivity analyses based on the median value of baseline UHR were conducted (Table S1 ). Participants with baseline UHR ≥ median had a significantly higher risk of CMM than those with baseline UHR < median (adjusted HR 1.16, 95% CI 1.05–1.29; P = 0.004). Kaplan–Meier curves also showed higher cumulative event rates of CMM among participants with higher baseline UHR levels (log-rank P < 0.001; Figure S2). Subgroup analyses showed no significant interactions between baseline UHR (≥ median vs 0.05; Figure S3). 4. Disscussion In this prospective cohort study of middle-aged and older Chinese adults, higher UHR levels were independently associated with an increased risk of CMM during follow-up. Similar findings were observed for CumUHR, indicating that both baseline UHR and cumulative exposure burden were relevant to CMM development, with a progressive increase in risk at higher levels. ROC analyses further showed that UHR and CumUHR, which integrate information from UA and HDL-C, improved the prediction of CMM, underscoring the potential value of this composite biomarker in cardiometabolic risk assessment. Several considerations may help explain why UHR is associated with the risk of CMM during follow-up. UHR integrates UA and HDL-C, two biomarkers with complementary cardiometabolic implications [ 13 ] . Elevated UA has long been linked to a higher risk of CMDs such as hypertension and heart disease [ 14 – 16 ] . A meta-analysis of eight observational studies including 21,832 participants further reported that higher UA levels were associated with increased risk of prehypertension [ 17 ] . Mendelian randomization analyses have also supported a causal relationship between genetically predicted UA levels and CMDs [ 18 ] . In contrast, HDL-C is generally regarded as protective for lipid metabolism and vascular health [ 19 ] . As a composite indicator integrating adverse metabolic burden reflected by UA and vascular protection reflected by HDL-C, UHR may provide a more comprehensive assessment of cardiometabolic risk than either marker alone. Consistent with this concept, several studies have reported associations between UHR and cardiometabolic outcomes, such as CVD and diabetes [ 20 , 21 ] . Furthermore, an NHANES-based analysis showed that UHR achieved superior overall discriminatory ability for hypertension compared with its individual components, supporting the value of combining uric acid and HDL information into a composite indicator [ 22 ] . In addition, our focus on CMM rather than individual CMDs is clinically meaningful, because cardiometabolic diseases rarely occur in isolation and tend to accumulate with age [ 23 , 24 ] . Evidence from the China Kadoorie Biobank suggests that cardiometabolic multimorbidity commonly clusters as diabetes, coronary heart disease, stroke, and hypertension [ 2 ] . Thus, compared with a single CMD outcome, CMM may better reflect the real-world clustering and accumulation of cardiometabolic abnormalities. Consistent with this concept, a recent prospective study reported that higher baseline UHR levels were associated with an increased risk of CMM [ 25 ] . Our findings further extend these observations by additionally evaluating cumulative UHR based on repeated measurements. In the present study, ROC analyses showed that UHR provided better predictive performance for CMM than either UA or HDL-C alone, whereas CumUHR showed the highest predictive performance among the evaluated biomarkers. Several prospective analyses have also suggested that persistent or worsening adverse metabolic exposure over time is associated with a higher risk of CMM progression [ 26 , 27 ] . Given that CMM develops through the progressive accumulation of CMDs over time [ 28 ] , cumulative UHR may therefore provide additional prognostic information beyond a single baseline assessment by better capturing sustained metabolic burden. Elevated UHR may reflect a biological milieu characterized by the coexistence of increased UA–related metabolic and inflammatory stress and reduced HDL-mediated vascular protection [ 29 – 31 ] . Experimental and clinical evidence indicates that elevated UA promotes oxidative stress and inflammatory activation and contributes to endothelial dysfunction [ 32 , 33 ] . It has also been linked to activation of the renin–angiotensin–aldosterone system, insulin resistance and other features of metabolic syndrome [ 34 ] . At the same time, insulin resistance is increasingly recognized as a central pathophysiological hub in CMM [ 35 ] , linking metabolic dysregulation with endothelial injury and β-cell failure [ 36 ] . In this context, elevated UA may promote the progression of interconnected cardiometabolic abnormalities through shared pathways of oxidative stress, vascular dysfunction, and impaired glucose metabolism. In contrast, HDL exerts multiple protective effects beyond cholesterol transport [ 37 ] . HDL promotes endothelial nitric oxide production, helps preserve endothelial integrity, and possesses antioxidative and anti-inflammatory properties that may counteract vascular injury and atherogenesis [ 38 ] . These protective functions are also relevant in diabetes and atherosclerotic cardiovascular disease, where impaired HDL functionality has been linked to reduced antioxidant capacity, diminished anti-inflammatory effects, and altered nitric oxide signaling [ 39 ] . Therefore, a higher UHR may indicate that uric acid–related adverse metabolic signals outweigh HDL-associated vascular protection, thereby favoring the clustering and co-development of multiple cardiometabolic disorders rather than a single isolated disease. This study has several strengths. First, this study used a large, nationally representative prospective cohort of middle-aged and older Chinese adults, supporting clear temporality between UHR and CMM and improving the generalizability of our findings within this population. Second, we evaluated both baseline UHR and cumulative UHR based on repeated measurements, allowing assessment of long-term exposure burden. Third, we assessed the clinical utility of UHR by comparing its discriminatory performance with UA and HDL-C using ROC analyses, providing interpretable evidence for the added value of this composite indicator in predicting CMM. Notably, CumUHR showed the highest discriminative performance among the evaluated biomarkers, highlighting the potential value of cumulative exposure assessment. Fourth, focusing on CMM rather than individual CMDs enhances clinical relevance by capturing the real-world clustering and accumulation of cardiometabolic conditions over time. Several limitations should be noted. First, residual confounding cannot be fully excluded. CHARLS lacks detailed information on diet such as purine/fructose intake, urate-lowering therapy, and some cardiometabolic medications, which may influence UA/HDL-C levels and CMM risk [ 40 , 41 ] . Second, CMM components (heart disease and stroke in particular) are largely based on self-reported physician diagnoses, which may introduce recall bias and underdiagnosis, especially for subclinical disease. Third, exposure measurement is limited. UHR and cumulative UHR were derived from measurements at only two time points (2011 and 2015), which may not fully capture short-term fluctuations or longer-term trajectories between and after visits. Future studies with more detailed covariate information, more frequent repeated biomarker assessments, and validated outcome ascertainment are warranted to confirm and extend these findings. 5. Conclusion In this prospective cohort study of middle-aged and older Chinese adults, both baseline UHR and cumulative UHR were independently associated with a higher risk of CMM. These findings suggest that UHR, as a simple composite indicator integrating uric acid and HDL-C, may be useful for identifying individuals at elevated cardiometabolic risk. The additional predictive value of cumulative exposure further highlights the importance of considering long-term metabolic burden in CMM risk assessment. Early incorporation of UHR-related indicators into routine evaluation may help improve risk stratification and support timely prevention of CMM. Declarations Acknowledgements The researchers express their gratitude to the CHARLS research team and all participants involved in this study. Author contributions Shijing Jiang drafted the manuscript and contributed to the study concept. Shuliang Wang performed the data analysis and prepared the figures. Zhiwei Miao contributed to the study concept and revised the manuscript. Funding This research received no external funding. Ethics approval and consent to participate The CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent prior to participation. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Competing interests The authors declare that they have no competing interests. References Langenberg, C., Hingorani, A. D. & Whitty, C. J. M. Biological and functional multimorbidity-from mechanisms to management [J]. Nat. Med. 29 (7), 1649–1657 (2023). Fan, J. et al. Multimorbidity patterns and association with mortality in 0.5 million Chinese adults [J]. Chin. Med. J. (Engl) . 135 (6), 648–657 (2022). Han, Y. et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study [J]. Eur. Heart J. 42 (34), 3374–3384 (2021). Li, B. et al. Association of Serum Uric Acid With All-Cause and Cardiovascular Mortality in Diabetes [J]. Diabetes Care . 46 (2), 425–433 (2023). Li, Z., Liu, Q. & Yao, Z. The serum uric acid-to-high-density lipoprotein cholesterol ratio is a predictor for all-cause and cardiovascular disease mortality: a cross-sectional study [J]. Front. Endocrinol. (Lausanne) . 15 , 1417485 (2024). Che, Y. et al. Serum uric acid to high–density lipoprotein cholesterol ratio as a novel predictor of diabetic nephropathy: a cross–sectional analysis of NHANES 2011–2018 [J]. Clin. Exp. Med. 25 (1), 359 (2025). Zhao, Y. et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS) [J]. Int. J. Epidemiol. 43 (1), 61–68 (2014). Liu, Q. et al. Association between uric acid to high-density lipoprotein cholesterol ratio and chronic kidney disease among Chinese middle-aged and older adults with abnormal glucose metabolism: a nationwide cohort study [J]. Int. Urol. Nephrol. 57 (4), 1297–1309 (2025). Yang, Y. & Liu, A. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident stroke in middle-aged and older Chinese adults: a longitudinal analysis based on CHARLS [J]. Cardiovasc. Diabetol. 24 (1), 386 (2025). He, L. et al. Correlation of cardiometabolic index and sarcopenia with cardiometabolic multimorbidity in middle-aged and older adult: a prospective study [J]. Front. Endocrinol. (Lausanne) . 15 , 1387374 (2024). Dong, J. et al. Association of HbA1c/HDL-C ratio and depression with cardiometabolic multimorbidity in middle-aged and older adults: a nationwide prospective cohort study [J]. Front. Nutr. 12 , 1642243 (2025). Lv, L. et al. The association between triglyceride glucose-waist height ratio index and cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national prospective cohort study [J]. Cardiovasc. Diabetol. 24 (1), 358 (2025). Tang, Y. et al. Association of serum uric acid-to-high-density lipoprotein cholesterol ratio with obstructive sleep apnea: a cross-sectional study [J]. Lipids Health Dis. 24 (1), 188 (2025). Kuwabara, M. et al. Relationship between serum uric acid levels and hypertension among Japanese individuals not treated for hyperuricemia and hypertension [J]. Hypertens. Res. 37 (8), 785–789 (2014). Qin, L. et al. Association between serum uric acid levels and cardiovascular disease in middle-aged and elderly Chinese individuals [J]. BMC Cardiovasc. Disord . 14 , 26 (2014). Liu, L. et al. Serum uric acid and risk of prehypertension: a dose-response meta-analysis of 17 observational studies of approximately 79 thousand participants [J]. Acta Cardiol. 77 (2), 136–145 (2022). Jiang, M., Gong, D. & Fan, Y. Serum uric acid levels and risk of prehypertension: a meta-analysis [J]. Clin. Chem. Lab. Med. 55 (3), 314–321 (2017). Xu, J. et al. Serum uric acid levels as a causal factor in hypertension: Insights from Mendelian randomization analysis [J]. Clin. Exp. Hypertens. 47 (1), 2496514 (2025). Beazer, J. D. et al. High-density lipoprotein's vascular protective functions in metabolic and cardiovascular disease - could extracellular vesicles be at play? [J]. Clin. Sci. (Lond) . 134 (22), 2977–2986 (2020). Lai, X. & Chen, T. Association of serum uric acid to high-density lipoprotein cholesterol ratio with all-cause and cardiovascular mortality in patients with diabetes or prediabetes: a prospective cohort study [J]. Front. Endocrinol. (Lausanne) . 15 , 1476336 (2024). Rubino, M. et al. Serum uric acid to HDL-Chol ratio (UHR) is associated with insulin resistance/sensitivity in individuals without diabetes [J]. Acta Diabetol. 63 (1), 87–95 (2026). Zhou, R. et al. Association between the uric acid to high-density lipoprotein ratio (UHR) and hypertension in US adults: evidence from NHANES 2005–2020 [J]. Acta Cardiol. 80 (8), 929–937 (2025). Freilich, M. et al. Elevated Serum Uric Acid and Cardiovascular Disease: A Review and Potential Therapeutic Interventions [J]. Cureus 14 (3), e23582 (2022). Jin, Y. et al. Serum/plasma biomarkers and the progression of cardiometabolic multimorbidity: a systematic review and meta-analysis [J]. Front. Public. Health . 11 , 1280185 (2023). Li, S. et al. Correlation between serum uric acid to high-density lipoprotein cholesterol ratio and cardiometabolic multimorbidity in China: A nationwide longitudinal cohort study [J]. Nutr. Metab. Cardiovasc. Dis. 35 (7), 103865 (2025). Li, D. et al. Change of serum uric acid and progression of cardiometabolic multimorbidity among middle aged and older adults: A prospective cohort study [J]. Front. Public. Health . 10 , 1012223 (2022). Nwabuo, C. C. et al. Long-term cumulative blood pressure in young adults and incident heart failure, coronary heart disease, stroke, and cardiovascular disease: The CARDIA study [J]. Eur. J. Prev. Cardiol. 28 (13), 1445–1451 (2021). He, Y. et al. Accelerated biological aging: unveiling the path to cardiometabolic multimorbidity, dementia, and mortality [J]. Front. Public. Health . 12 , 1423016 (2024). Prabhakar, A. P. & Lopez-Candales, A. Uric acid and cardiovascular diseases: a reappraisal [J]. Postgrad. Med. 136 (6), 615–623 (2024). Tian, Y. et al. Mendelian randomization studies of lifestyle-related risk factors for stroke: a systematic review and meta-analysis [J]. Front. Endocrinol. (Lausanne) . 15 , 1379516 (2024). Guo, K. et al. ApoA1/HDL and sepsis-associated vascular endothelial injury: a narrative review [J]. Crit. Care . 29 (1), 426 (2025). Sánchez-Lozada, L. G. et al. Uric acid-induced endothelial dysfunction is associated with mitochondrial alterations and decreased intracellular ATP concentrations [J]. Nephron Exp. Nephrol. 121 (3–4), e71–78 (2012). Wei, X. et al. Hyperuricemia: A key contributor to endothelial dysfunction in cardiovascular diseases [J]. Faseb j. 37 (7), e23012 (2023). Asma Sakalli, A., Küçükerdem, H. S. & Aygün, O. What is the relationship between serum uric acid level and insulin resistance? A case-control study [J]. Med. (Baltim). 102 (52), e36732 (2023). Gusev, E., Sarapultsev, A. & Zhuravleva, Y. Insulin Resistance and Inflammation [J]. Int. J. Mol. Sci. , 27 (3). (2026). Chen, J. et al. Inflammatory signaling pathways in pancreatic β-cell: New insights into type 2 diabetes pathogenesis [J]. Pharmacol. Res. 216 , 107776 (2025). Ouyang, F. W. et al. Dysfunctional high-density lipoprotein: an updated review [J]. Front. Cardiovasc. Med. 12 , 1713387 (2025). Riwanto, M. & Landmesser, U. High density lipoproteins and endothelial functions: mechanistic insights and alterations in cardiovascular disease [J]. J. Lipid Res. 54 (12), 3227–3243 (2013). Podrez, E. A. Anti-oxidant properties of high-density lipoprotein and atherosclerosis [J]. Clin. Exp. Pharmacol. Physiol. 37 (7), 719–725 (2010). Jamil, Y. et al. Cardiovascular Outcomes of Uric Acid Lowering Medications: A Meta-Analysis [J]. Curr. Cardiol. Rep. 26 (12), 1427–1437 (2024). Chen, J. et al. Effects of Uric Acid-Lowering Treatment on Glycemia: A Systematic Review and Meta-Analysis [J]. Front. Endocrinol. (Lausanne) . 11 , 577 (2020). Additional Declarations No competing interests reported. Supplementary Files 0311.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9133547","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":613996706,"identity":"df090b1d-f9df-48f6-bd15-63832401c96b","order_by":0,"name":"Shijing Jiang","email":"","orcid":"","institution":"Taixing Second People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shijing","middleName":"","lastName":"Jiang","suffix":""},{"id":613996707,"identity":"332eb590-18e4-43fa-8a8d-d2cb3eca7635","order_by":1,"name":"Shuliang Wang","email":"","orcid":"","institution":"Taixing Second People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuliang","middleName":"","lastName":"Wang","suffix":""},{"id":613996708,"identity":"e5777aa1-6429-4a68-bdda-189f93834131","order_by":2,"name":"Zhiwei Miao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3QsQrCMBCA4ZRCXYpZU5D6CgGhUx8mh5CpiuCSQTBQaQcRV32Ljo4RIS5xd2zfQDdH66yYujnkm/NzuUPIcf5Q0MvPNybSJcb5sWZiYU/6oeaoNpxFOz2mtdH2JCZZ4jWrE6MqS6Jm5Xf4WDulBsmnkVSJABkgXK6ZdRcKh3SOfcmvcBggYi6VbQojYLi3l0pfwQSIkoklIRklUJy8SkExg8LvlCSvBCo1DlC3JGxvywwftUf2CTM6tO4yLPNj8xBpjPH2fn+IRYzLzffkTfjbc8dxHOejJ0MQUs+w5pkWAAAAAElFTkSuQmCC","orcid":"","institution":"Zhangjiagang TCM Hospital, Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Miao","suffix":""}],"badges":[],"createdAt":"2026-03-16 06:08:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9133547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9133547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105787923,"identity":"a268d2bc-410b-4cbb-9c20-9d72f7df61c9","added_by":"auto","created_at":"2026-03-31 06:57:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study population\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/d36c72fd09ee69a20f46b330.png"},{"id":105787887,"identity":"5d0c2758-cbe8-4555-8eb6-96114d64a1a0","added_by":"auto","created_at":"2026-03-31 06:57:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for cumulative events of CMM across UHR quartiles.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/bbad848a1dfab79bcae1f26a.png"},{"id":105787888,"identity":"e22e4073-4a71-4969-af4e-1c8b1805410f","added_by":"auto","created_at":"2026-03-31 06:57:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46548,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for cumulative events of CMM according to CumUHR groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/a7454ec0579966b69ce61842.png"},{"id":105787900,"identity":"f2c4e1e5-850d-4b70-813d-c362275002f6","added_by":"auto","created_at":"2026-03-31 06:57:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":173629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline analyses of the associations of UHR and CumUHR with the risk of CMM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanels (A) and (B) show the associations between baseline UHR and CMM in the crude and multivariable-adjusted models, respectively. Panels (C) and (D) show the associations between CumUHR and CMM in the crude and multivariable-adjusted models, respectively. Solid lines represent hazard ratios (HRs), and shaded areas represent 95% confidence intervals (CIs). The horizontal dashed line indicates HR = 1.0, and the vertical dashed line indicates the reference value. P values for overall association and nonlinearity were obtained from the restricted cubic spline models.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/f225c42ea57f50dfb67f084b.png"},{"id":105787896,"identity":"91e41dd4-32b6-44f4-9a56-212286f09ff7","added_by":"auto","created_at":"2026-03-31 06:57:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive performance of UHR and CumUHR for CMM\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/e5a93c555449bc009e5bb106.png"},{"id":105787927,"identity":"0f3532a4-5535-4ae4-8ac8-97bce9b5a5ec","added_by":"auto","created_at":"2026-03-31 06:57:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":193649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup and interaction analyses of baseline UHR and cumulative UHR in relation to CMM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanel (A) shows hazard ratios (HRs) for incident CMM comparing the highest versus lowest quartile of baseline UHR (Q4 vs Q1) across predefined subgroups. Panel (B) shows HRs comparing high versus low cumulative UHR. HRs were estimated using multivariable Cox proportional hazards models adjusted for the same covariates as in the primary analysis. Squares indicate point estimates and horizontal lines indicate 95% confidence intervals. The vertical dashed line denotes HR = 1.0. P values represent tests for interaction between exposure and subgroup variable.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/585f2a92d8f45d0cf10c983f.png"},{"id":105788039,"identity":"b9ccdb5c-3b95-4ad1-af18-cb9cde508380","added_by":"auto","created_at":"2026-03-31 06:58:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2122494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/91bc6d14-8683-4300-ab76-14c2bb01263e.pdf"},{"id":105787901,"identity":"aea4829c-ed10-40bf-a930-9e88f9ef8088","added_by":"auto","created_at":"2026-03-31 06:57:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":356521,"visible":true,"origin":"","legend":"","description":"","filename":"0311.docx","url":"https://assets-eu.researchsquare.com/files/rs-9133547/v1/d10a594e8e876afb8ab6ca5f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePredictive value of uric acid to high-density lipoprotein cholesterol ratio for cardiometabolic multimorbidity in middle-aged and older adults: a nationwide prospective cohort study\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiometabolic multimorbidity (CMM) refers to the coexistence of at least two cardiometabolic diseases (CMDs) within the same individual, commonly including heart disease, stroke, diabetes, and hypertension\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. With population aging and the rising prevalence of metabolic risk factors, CMM has become an increasingly important public health challenge. In a nationwide cohort of 512,723 Chinese adults, 15.8% of participants had multimorbidity overall, and 6.0% specifically exhibited CMM\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Beyond its high prevalence, CMM evolves through the progressive accumulation of cardiometabolic diseases over time. In a large prospective cohort of 461,047 adults followed for more than 11 years, participants transitioned sequentially from a healthy state to a first CMD and subsequently to CMM, with high-risk lifestyle factors accelerating these transitions\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Early identification of individuals at high risk of developing CMM is therefore essential. In this context, identifying simple and accessible biomarkers that reflect long-term cardiometabolic burden is of considerable clinical interest.\u003c/p\u003e \u003cp\u003eUric acid (UA) has been consistently associated with increased cardiometabolic risk. Epidemiological studies have shown that elevated UA levels are linked to a higher risk of hypertension, coronary heart disease, and stroke\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, high-density lipoprotein cholesterol (HDL-C) is widely recognized for its protective role in lipid metabolism and vascular health. Given their complementary yet opposing biological roles, integrating UA and HDL-C may better capture overall cardiometabolic imbalance. The UA to HDL-C ratio (UHR) has therefore been proposed as a composite biomarker capturing the joint information of UA\u0026ndash;related metabolic burden and HDL-C\u0026ndash;related lipid protection. In population-based studies, elevated UHR has been associated with a higher risk of adverse outcomes, such as increased all-cause and cardiovascular mortality and a greater likelihood of diabetic nephropathy\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. These observations suggest that UHR may capture cardiometabolic vulnerability beyond single conditions; however, whether UHR predicts the development of CMM, characterized by the coexistence of multiple cardiometabolic diseases, remains unclear.\u003c/p\u003e \u003cp\u003eAccordingly, we used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal cohort of Chinese adults aged 45 years and older, to examine the prospective associations of baseline and cumulative UHR levels with CMM. Elucidating the relationship between UHR and CMM may provide evidence for the use of a simple composite biomarker to characterize long-term cardiometabolic burden and improve risk stratification for CMM.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sample and study design\u003c/h2\u003e \u003cp\u003eThis study used data from CHARLS, a nationally representative longitudinal cohort of Chinese adults aged 45 years and older. CHARLS employs a multistage, stratified probability sampling design and includes detailed information on sociodemographic characteristics, health behaviors, physician-diagnosed chronic diseases, physical measurements, and fasting blood biomarkers. Wave 1 (2011\u0026ndash;2012) served as the baseline, with follow-up surveys conducted in Waves 2 (2013), 3 (2015), and 4 (2018). The CHARLS protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052\u0026ndash;11015), and all participants provided written informed consent\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo investigate the associations of baseline UHR and Cumulative UHR (CumUHR) with CMM, several exclusion criteria were sequentially applied. First, participants with missing UHR measurements required for the assessment of the exposure variables were excluded (n\u0026thinsp;=\u0026thinsp;5,694). Second, participants with prevalent CMM at baseline or missing baseline CMM information were excluded (n\u0026thinsp;=\u0026thinsp;1,721). Third, individuals without ascertainable CMM status during follow-up were excluded (n\u0026thinsp;=\u0026thinsp;5,040). After these exclusions, 7,435 participants remained in the final analytic cohort. Baseline CMM status was determined using information collected in Wave 1 to exclude prevalent cases. CMM occurring during follow-up was identified in Waves 2, 3, or 4. Follow-up time was calculated from baseline to the first occurrence of CMM or the last available follow-up interview. The detailed participant selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of UHR and cumulative UHR\u003c/h2\u003e \u003cp\u003eIn this study, UHR was calculated as the ratio of UA (mg/dL) to HDL-C (mg/dL). Both biomarkers were measured from fasting venous blood samples collected during the CHARLS survey using standardized laboratory procedures. CumUHR was estimated based on repeated measurements obtained in 2011 and 2015 using a time-weighted average approach: CumUHR = [(UHR2011\u0026thinsp;+\u0026thinsp;UHR2015) / 2] \u0026times; time interval (2015\u0026thinsp;\u0026minus;\u0026thinsp;2011). The calculation methods were consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Definition of CMM\u003c/h2\u003e \u003cp\u003eSimilar to prior investigations\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, cardiometabolic multimorbidity (CMM) was defined as the coexistence of two or more of the following cardiometabolic diseases (CMDs): heart disease, diabetes, stroke, and hypertension. Information on these conditions was obtained from self-reported physician diagnoses collected in the CHARLS survey. Heart disease included heart attack, coronary heart disease, angina, congestive heart failure, and other heart-related conditions. Participants who were free of CMM at baseline but developed at least two CMDs during follow-up were classified as having CMM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Covariates\u003c/h2\u003e \u003cp\u003eCovariates were selected based on prior literature and the availability of variables in the CHARLS database\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Demographic factors included age, sex, education level, marital status, and residence (urban or rural). Lifestyle factors included smoking status and alcohol consumption. Body mass index (BMI) was included as an anthropometric indicator. Clinical and biochemical variables included estimated glomerular filtration rate (eGFR) and C-reactive protein (CRP).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were summarized according to quartiles of UHR. The normality of continuous variables was assessed before analysis. As all continuous variables showed a skewed distribution, continuous variables are presented as medians with interquartile ranges (IQR), while categorical variables are presented as numbers and percentages. Differences across groups were compared using the Kruskal\u0026ndash;Wallis test for continuous variables and the χ\u0026sup2; test for categorical variables.\u003c/p\u003e \u003cp\u003eTime-to-event analyses were performed using Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CMM. Follow-up time was calculated from baseline (2011) to the first occurrence of CMM or the last available follow-up interview. UHR was primarily analyzed in quartiles with the lowest quartile as the reference group, and linear trends were assessed by modeling quartile categories as an ordinal variable. UHR was also analyzed as a median-based dichotomous variable in sensitivity analyses. CumUHR was used to reflect long-term exposure burden. The optimal cut-point for CumUHR was determined using maximally selected rank statistics, and the association between CumUHR and CMM during follow-up was further evaluated using Cox models and Kaplan\u0026ndash;Meier curves with log-rank tests.\u003c/p\u003e \u003cp\u003eThree progressively adjusted Cox proportional hazards models were constructed. Model 1 was unadjusted. Model 2 adjusted for sociodemographic factors including age, sex, education level, marital status, and residence (urban/rural). Model 3 was further adjusted for smoking status, alcohol consumption, BMI, eGFR, and CRP. Restricted cubic spline (RCS) analyses were conducted to explore potential nonlinear associations between UHR and the risk of CMM. Receiver operating characteristic (ROC) curve analyses were performed to compare the predictive performance of UHR and CumUHR with that of their individual components (uric acid and HDL-C).\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.3.1). A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristics of participants\u003c/h2\u003e \u003cp\u003eA total of 7,435 participants were included in the present analysis. During a median follow-up of approximately 7 years, 1,748 participants (23.51%) developed CMM. Baseline characteristics stratified by CMM status are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants who developed CMM were older and had a less favorable cardiometabolic profile at baseline compared with those without CMM (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The baseline characteristics of the study population according to quartiles of UHR are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(Q1\u0026thinsp;\u0026le;\u0026thinsp;6.39; Q2 6.39\u0026ndash;8.49; Q3 8.49\u0026ndash;11.42; Q4\u0026thinsp;\u0026gt;\u0026thinsp;11.42). Participants in the highest UHR quartile were slightly older than those in the lower quartiles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), and the proportion of men increased progressively across quartiles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher UHR quartiles were also associated with higher levels of body mass index, fasting blood glucose, systolic and diastolic blood pressure, triglycerides, serum uric acid, and C-reactive protein, as well as lower HDL-C concentrations (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, current smoking and alcohol consumption were more frequent in the higher UHR quartiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants by CMM status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo-CMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo. of participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.00 [51.00, 64.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.00 [50.00, 63.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.00 [54.00, 66.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3424 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2676 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e748 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4011 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3011 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6677 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5167 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1510 (86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520 ( 9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5175 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3916 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1259 (72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2260 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1771 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e489 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4981 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3851 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1130 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2454 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1836 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e618 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4574 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3454 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1120 (64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2861 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2233 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e628 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking Status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4547 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3457 (60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1090 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2888 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2230 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e658 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.11 [20.89, 25.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.73 [20.60, 25.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.83 [22.16, 27.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBG, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.88 [94.14, 112.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.98 [93.60, 109.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.02 [96.48, 121.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.10 [4.90, 5.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10 [4.80, 5.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.20 [4.90, 5.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.00 [113.00, 139.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.00 [111.00, 135.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.00 [123.00, 149.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.00 [66.50, 82.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.50 [65.50, 81.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.50 [71.00, 86.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.59 [167.01, 215.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189.05 [166.62, 214.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193.69 [168.94, 219.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.43 [74.34, 153.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00 [72.57, 145.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.01 [84.07, 175.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.87 [40.59, 60.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.64 [41.37, 61.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.17 [37.89, 56.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.05 [93.56, 136.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.66 [93.17, 135.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.40 [93.94, 140.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUA, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.25 [3.54, 5.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.23 [3.53, 5.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.36 [3.59, 5.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR, ml/min/1.73m2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.51 [85.17, 102.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.24 [86.13, 103.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.46 [82.82, 100.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP, mg/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 [0.53, 2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 [0.50, 1.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 [0.65, 2.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5914 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4885 (85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1029 (58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1521 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e802 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e719 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDyslipidaemia, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6870 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5371 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1499 (85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e565 ( 7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316 ( 5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7221 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5590 (98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1631 (93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 ( 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 ( 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 ( 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables are presented as median [interquartile range (IQR)] and categorical variables as n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eP\u003c/em\u003e values were calculated using the Kruskal\u0026ndash;Wallis test for continuous variables and the χ\u0026sup2; test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study participants according to quartiles of UHR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eQuartiles of UHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo. of participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.00 [51.00, 64.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.00 [51.00, 63.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.00 [51.00, 64.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.00 [52.00, 64.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.00 [51.00, 64.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3424 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e750 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e981 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1173 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4011 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1339 (72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1108 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e878 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e686 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6677 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1638 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1660 (89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1695 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1684 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164 ( 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175 ( 9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5175 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1391 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1336 (71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1261 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1187 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2260 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e522 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e598 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e672 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4981 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1388 (74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1280 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1226 (65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1087 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2454 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e578 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e633 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e772 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4574 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1374 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1205 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1064 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e931 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2861 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e485 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e653 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e795 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e928 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking Status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4547 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1276 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1200 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1085 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e986 (53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2888 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e583 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e658 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e774 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e873 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.11 [20.89, 25.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.03 [20.03, 24.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.66 [20.49, 25.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.40 [21.19, 26.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.56 [22.28, 27.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBG, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.88 [94.14, 112.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.08 [92.88, 108.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.16 [93.96, 109.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102.06 [93.96, 112.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e105.12 [96.48, 118.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.10 [4.90, 5.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10 [4.90, 5.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.10 [4.90, 5.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.10 [4.90, 5.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.20 [4.90, 5.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.00 [113.00, 139.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.00 [110.50, 135.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.00 [112.00, 137.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.50 [113.50, 140.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128.50 [116.50, 143.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.00 [66.50, 82.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.00 [64.50, 80.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.50 [66.00, 81.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.00 [67.00, 83.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.50 [68.50, 85.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.59 [167.01, 215.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196.01 [172.04, 221.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190.59 [168.17, 213.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187.89 [164.69, 214.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186.73 [163.15, 212.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.43 [74.34, 153.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.65 [61.06, 107.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.69 [69.92, 130.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111.51 [81.42, 158.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153.99 [105.76, 237.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.87 [40.59, 60.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.18 [56.06, 74.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.12 [47.55, 61.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.39 [40.98, 52.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.73 [31.70, 42.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.05 [93.56, 136.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.05 [94.72, 137.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116.75 [95.97, 137.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.37 [95.88, 139.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.79 [85.83, 134.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUA, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.25 [3.54, 5.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.30 [2.86, 3.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.98 [3.55, 4.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.54 [4.02, 5.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.42 [4.75, 6.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR, ml/min/1.73m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.51 [85.17, 102.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.98 [91.39, 105.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.37 [87.23, 103.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.23 [84.32, 102.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.50 [78.34, 100.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP, mg/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 [0.53, 2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 [0.41, 1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 [0.49, 1.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 [0.58, 2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43 [0.75, 2.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5914 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1596 (85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1540 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1441 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1337 (71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1521 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e418 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e522 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDyslipidaemia, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6870 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1771 (95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1730 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1711 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1658 (89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e565 ( 7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 ( 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 ( 6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148 ( 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e201 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7221 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1812 (97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1806 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1803 (97.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1800 (96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 ( 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 ( 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 ( 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 ( 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 ( 3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNotes\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eContinuous variables are presented as median [interquartile range (IQR)] and categorical variables as n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eP\u003c/em\u003e values were calculated using the Kruskal\u0026ndash;Wallis test for continuous variables and the χ\u0026sup2; test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Association between UHR and CMM\u003c/h2\u003e \u003cp\u003eAssociations between UHR and incident CMM across Cox proportional hazards models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Model 1 was unadjusted, Model 2 adjusted for sociodemographic factors, and Model 3 further adjusted for lifestyle and metabolic factors. In the fully adjusted model, participants in the highest quartile of UHR had a significantly higher risk of CMM than those in the lowest quartile (HR 1.48, 95% CI 1.27\u0026ndash;1.77; \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.007). Similar associations were observed in the unadjusted and partially adjusted models. Kaplan\u0026ndash;Meier curves showed progressively higher cumulative event rates of CMM across increasing quartiles of UHR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between UHR and CMM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUHR quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.95\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 (0.97\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98 (0.84\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.22\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (1.30\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09 (0.94\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93 (1.68\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76 (1.54\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.48 (1.27\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNotes\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1: unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2: adjusted for age, sex, education level, marital status, and residence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 3: further adjusted for smoking status, alcohol consumption, body mass index (BMI), estimated glomerular filtration rate (eGFR), and C-reactive protein (CRP).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Association between CumUHR and CMM\u003c/h2\u003e \u003cp\u003eThe optimal cut-off value of CumUHR for predicting CMM was 41.59 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Participants were categorized into low (\u0026lt;\u0026thinsp;41.59) and high (\u0026ge;\u0026thinsp;41.59) CumUHR groups. Kaplan\u0026ndash;Meier curves showed a significantly higher cumulative event rates of CMM in the high CumUHR group than in the low CumUHR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher CumUHR was associated with a greater risk of CMM (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the fully adjusted model, participants with high CumUHR had a significantly higher risk of CMM than those with low CumUHR (HR 1.26, 95% CI 1.15\u0026ndash;1.39; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Similar associations were observed in the unadjusted and partially adjusted models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between CumUHR and CMM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCumUHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.38\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63 (1.47\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.26 (1.15\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNotes\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1: unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2: adjusted for age, sex, education level, marital status, and residence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 3: further adjusted for smoking status, alcohol consumption, body mass index (BMI), estimated glomerular filtration rate (eGFR), and C-reactive protein (CRP).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Dose\u0026ndash;response associations of UHR and CumUHR with CMM\u003c/h2\u003e \u003cp\u003eRestricted cubic spline analyses were performed to evaluate the dose\u0026ndash;response associations of UHR and CumUHR with CMM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For UHR, significant overall associations with CMM were observed in both the crude and adjusted models (both \u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the adjusted model showed a significant nonlinear association (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.047), with an inflection point around 8.5. For CumUHR, significant overall associations were also observed in both the crude and adjusted models (both \u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with significant nonlinearity in both the crude model (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.001) and the adjusted model (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.033), and an inflection point around 35.7. In both analyses, the risk of CMM increased with increasing levels of UHR and CumUHR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Predictive performance of UHR and CumUHR for CMM\u003c/h2\u003e \u003cp\u003eROC curve results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The base model yielded an AUC of 0.745 (95% CI 0.732\u0026ndash;0.758). With UHR, which integrates uric acid and HDL-C, the AUC was 0.747 (95% CI 0.734\u0026ndash;0.760; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), whereas the model including CumUHR yielded an AUC of 0.749 (95% CI 0.735\u0026ndash;0.761; P\u0026thinsp;=\u0026thinsp;0.028). For the individual components of UHR, the corresponding AUCs were 0.745 (95% CI 0.732\u0026ndash;0.758; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.833) for UA and 0.744 (95% CI 0.734\u0026ndash;0.759; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.833) for HDL-C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive performance of UHR and CumUHR for CMM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBase covariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.745 (0.732\u0026ndash;0.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBase\u0026thinsp;+\u0026thinsp;UHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.747 (0.734\u0026ndash;0.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBase\u0026thinsp;+\u0026thinsp;CumUHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.749 (0.735\u0026ndash;0.761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBase\u0026thinsp;+\u0026thinsp;UA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.745 (0.732\u0026ndash;0.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBase\u0026thinsp;+\u0026thinsp;HDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.744 (0.734\u0026ndash;0.759)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Subgroup and sensitivity analyses\u003c/h2\u003e \u003cp\u003eThe associations of UHR and CumUHR with CMM were broadly consistent across most predefined subgroups, and no significant interactions were observed for either UHR or CumUHR (all \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Sensitivity analyses based on the median value of baseline UHR were conducted (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Participants with baseline UHR\u0026thinsp;\u0026ge;\u0026thinsp;median had a significantly higher risk of CMM than those with baseline UHR\u0026thinsp;\u0026lt;\u0026thinsp;median (adjusted HR 1.16, 95% CI 1.05\u0026ndash;1.29; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Kaplan\u0026ndash;Meier curves also showed higher cumulative event rates of CMM among participants with higher baseline UHR levels (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S2). Subgroup analyses showed no significant interactions between baseline UHR (\u0026ge;\u0026thinsp;median vs\u0026thinsp;\u0026lt;\u0026thinsp;median) and the examined variables (all \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Figure S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Disscussion","content":"\u003cp\u003eIn this prospective cohort study of middle-aged and older Chinese adults, higher UHR levels were independently associated with an increased risk of CMM during follow-up. Similar findings were observed for CumUHR, indicating that both baseline UHR and cumulative exposure burden were relevant to CMM development, with a progressive increase in risk at higher levels. ROC analyses further showed that UHR and CumUHR, which integrate information from UA and HDL-C, improved the prediction of CMM, underscoring the potential value of this composite biomarker in cardiometabolic risk assessment.\u003c/p\u003e \u003cp\u003eSeveral considerations may help explain why UHR is associated with the risk of CMM during follow-up. UHR integrates UA and HDL-C, two biomarkers with complementary cardiometabolic implications\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Elevated UA has long been linked to a higher risk of CMDs such as hypertension and heart disease\u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis of eight observational studies including 21,832 participants further reported that higher UA levels were associated with increased risk of prehypertension \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Mendelian randomization analyses have also supported a causal relationship between genetically predicted UA levels and CMDs\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In contrast, HDL-C is generally regarded as protective for lipid metabolism and vascular health\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. As a composite indicator integrating adverse metabolic burden reflected by UA and vascular protection reflected by HDL-C, UHR may provide a more comprehensive assessment of cardiometabolic risk than either marker alone. Consistent with this concept, several studies have reported associations between UHR and cardiometabolic outcomes, such as CVD and diabetes\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Furthermore, an NHANES-based analysis showed that UHR achieved superior overall discriminatory ability for hypertension compared with its individual components, supporting the value of combining uric acid and HDL information into a composite indicator\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In addition, our focus on CMM rather than individual CMDs is clinically meaningful, because cardiometabolic diseases rarely occur in isolation and tend to accumulate with age\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Evidence from the China Kadoorie Biobank suggests that cardiometabolic multimorbidity commonly clusters as diabetes, coronary heart disease, stroke, and hypertension\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Thus, compared with a single CMD outcome, CMM may better reflect the real-world clustering and accumulation of cardiometabolic abnormalities. Consistent with this concept, a recent prospective study reported that higher baseline UHR levels were associated with an increased risk of CMM\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Our findings further extend these observations by additionally evaluating cumulative UHR based on repeated measurements. In the present study, ROC analyses showed that UHR provided better predictive performance for CMM than either UA or HDL-C alone, whereas CumUHR showed the highest predictive performance among the evaluated biomarkers. Several prospective analyses have also suggested that persistent or worsening adverse metabolic exposure over time is associated with a higher risk of CMM progression\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Given that CMM develops through the progressive accumulation of CMDs over time\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, cumulative UHR may therefore provide additional prognostic information beyond a single baseline assessment by better capturing sustained metabolic burden.\u003c/p\u003e \u003cp\u003eElevated UHR may reflect a biological milieu characterized by the coexistence of increased UA\u0026ndash;related metabolic and inflammatory stress and reduced HDL-mediated vascular protection\u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Experimental and clinical evidence indicates that elevated UA promotes oxidative stress and inflammatory activation and contributes to endothelial dysfunction\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. It has also been linked to activation of the renin\u0026ndash;angiotensin\u0026ndash;aldosterone system, insulin resistance and other features of metabolic syndrome\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. At the same time, insulin resistance is increasingly recognized as a central pathophysiological hub in CMM\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, linking metabolic dysregulation with endothelial injury and β-cell failure\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In this context, elevated UA may promote the progression of interconnected cardiometabolic abnormalities through shared pathways of oxidative stress, vascular dysfunction, and impaired glucose metabolism. In contrast, HDL exerts multiple protective effects beyond cholesterol transport\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. HDL promotes endothelial nitric oxide production, helps preserve endothelial integrity, and possesses antioxidative and anti-inflammatory properties that may counteract vascular injury and atherogenesis\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. These protective functions are also relevant in diabetes and atherosclerotic cardiovascular disease, where impaired HDL functionality has been linked to reduced antioxidant capacity, diminished anti-inflammatory effects, and altered nitric oxide signaling\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Therefore, a higher UHR may indicate that uric acid\u0026ndash;related adverse metabolic signals outweigh HDL-associated vascular protection, thereby favoring the clustering and co-development of multiple cardiometabolic disorders rather than a single isolated disease.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, this study used a large, nationally representative prospective cohort of middle-aged and older Chinese adults, supporting clear temporality between UHR and CMM and improving the generalizability of our findings within this population. Second, we evaluated both baseline UHR and cumulative UHR based on repeated measurements, allowing assessment of long-term exposure burden. Third, we assessed the clinical utility of UHR by comparing its discriminatory performance with UA and HDL-C using ROC analyses, providing interpretable evidence for the added value of this composite indicator in predicting CMM. Notably, CumUHR showed the highest discriminative performance among the evaluated biomarkers, highlighting the potential value of cumulative exposure assessment. Fourth, focusing on CMM rather than individual CMDs enhances clinical relevance by capturing the real-world clustering and accumulation of cardiometabolic conditions over time.\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted. First, residual confounding cannot be fully excluded. CHARLS lacks detailed information on diet such as purine/fructose intake, urate-lowering therapy, and some cardiometabolic medications, which may influence UA/HDL-C levels and CMM risk\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Second, CMM components (heart disease and stroke in particular) are largely based on self-reported physician diagnoses, which may introduce recall bias and underdiagnosis, especially for subclinical disease. Third, exposure measurement is limited. UHR and cumulative UHR were derived from measurements at only two time points (2011 and 2015), which may not fully capture short-term fluctuations or longer-term trajectories between and after visits. Future studies with more detailed covariate information, more frequent repeated biomarker assessments, and validated outcome ascertainment are warranted to confirm and extend these findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this prospective cohort study of middle-aged and older Chinese adults, both baseline UHR and cumulative UHR were independently associated with a higher risk of CMM. These findings suggest that UHR, as a simple composite indicator integrating uric acid and HDL-C, may be useful for identifying individuals at elevated cardiometabolic risk. The additional predictive value of cumulative exposure further highlights the importance of considering long-term metabolic burden in CMM risk assessment. Early incorporation of UHR-related indicators into routine evaluation may help improve risk stratification and support timely prevention of CMM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers express their gratitude to the CHARLS research team and all participants involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShijing Jiang drafted the manuscript and contributed to the study concept. Shuliang Wang performed the data analysis and prepared the figures. Zhiwei Miao contributed to the study concept and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent prior to participation. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLangenberg, C., Hingorani, A. D. \u0026amp; Whitty, C. J. M. 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Cardiovascular Outcomes of Uric Acid Lowering Medications: A Meta-Analysis [J]. \u003cem\u003eCurr. Cardiol. Rep.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (12), 1427\u0026ndash;1437 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J. et al. Effects of Uric Acid-Lowering Treatment on Glycemia: A Systematic Review and Meta-Analysis [J]. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 577 (2020).\u003c/span\u003e\u003c/li\u003e\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":"Uric acid, high-density lipoprotein cholesterol, cardiometabolic multimorbidity, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-9133547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9133547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiometabolic multimorbidity (CMM) is an escalating public health challenge. The uric acid-to-high-density lipoprotein cholesterol ratio (UHR) is a composite biomarker reflecting metabolic disturbance, but prospective evidence regarding the association between UHR and CMM remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective cohort study included 7,435 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years from CHARLS followed from 2011 to 2018. Cox proportional hazards models and restricted cubic spline analyses were used to examine the associations of UHR and cumulative UHR (CumUHR) with cardiometabolic multimorbidity (CMM). Receiver operating characteristic (ROC) curves compared the predictive performance of UHR and CumUHR with that of uric acid (UA) and high-density lipoprotein cholesterol (HDL-C) alone. Subgroup and sensitivity analyses were conducted to test the robustness of the findings.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eAmong the 7,435 participants, 1,748 developed CMM. Kaplan\u0026ndash;Meier analysis showed that the cumulative event rate of CMM increased progressively across UHR quartiles (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted model, the highest UHR quartile (Q4) was associated with a significantly increased risk of CMM compared with the lowest quartile (Q1) (HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI 1.27\u0026ndash;1.77). When cumulative exposure was considered, elevated CumUHR remained an independent predictor of CMM (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI 1.15\u0026ndash;1.39). Restricted cubic spline analyses further demonstrated significant nonlinear associations of both UHR and CumUHR with CMM risk, with inflection points observed around 8.5 for UHR and 35.7 for CumUHR. Furthermore, ROC analysis showed that the composite indicators UHR (AUC\u0026thinsp;=\u0026thinsp;0.747) and CumUHR (AUC\u0026thinsp;=\u0026thinsp;0.749) had better predictive performance for CMM than their individual components, UA (AUC\u0026thinsp;=\u0026thinsp;0.745) and HDL-C (AUC\u0026thinsp;=\u0026thinsp;0.744) alone.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigher UHR and CumUHR levels were independently associated with an increased risk of CMM in middle-aged and older adults. As simple composite indicators integrating UA and HDL-C, UHR and CumUHR may provide additional value for identifying individuals at elevated cardiometabolic risk and improving early risk stratification for CMM.\u003c/p\u003e","manuscriptTitle":"Predictive value of uric acid to high-density lipoprotein cholesterol ratio for cardiometabolic multimorbidity in middle-aged and older adults: a nationwide prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 06:55:03","doi":"10.21203/rs.3.rs-9133547/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-21T12:16:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T02:24:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40873430589952989391675643477548706475","date":"2026-04-21T02:16:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T09:55:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131790894755647447015771866837480258130","date":"2026-04-01T02:14:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155965768989151540241333037545799057147","date":"2026-03-29T07:23:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286349929983494132481053163884737777091","date":"2026-03-28T05:33:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317491370446013659628807420596453252002","date":"2026-03-27T02:46:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-27T02:11:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T14:58:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-18T02:51:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T02:50:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-16T06:04:22+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":"7e2b848f-5f88-401a-9433-22f7eaf4a6d1","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65415366,"name":"Health sciences/Biomarkers"},{"id":65415367,"name":"Health sciences/Cardiology"},{"id":65415368,"name":"Health sciences/Diseases"},{"id":65415369,"name":"Health sciences/Endocrinology"},{"id":65415370,"name":"Health sciences/Medical research"},{"id":65415371,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-02T04:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 06:55:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9133547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9133547","identity":"rs-9133547","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}