{"paper_id":"2f96743a-52fb-4203-a9ba-d354c46bece9","body_text":"C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0–3 of cardiovascular–kidney–metabolic syndrome: 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 Research Article C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0–3 of cardiovascular–kidney–metabolic syndrome: a nationwide prospective cohort study Huiwen Ou, xiaoshuang xia, Xin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6726039/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Cardiovascular Diabetology → Version 1 posted 13 You are reading this latest preprint version Abstract Objective The American Heart Association (AHA) developed the notion of cardiovascular-kidney-metabolic (CKM) syndrome, which emphasizes the interconnection of heart, kidney, and metabolic illnesses. The C-reactive protein-triglyceride-glucose (CTI) represents a potential indicator to assess the resistance to insulin and an inflammatory response. However, the connection among CTI, cardiovascular disease (CVD) incidence, and overall mortality rates remains uncertain, particularly among individuals at CKM stages 0-3. Methods The China Health and Retirement Longitudinal Study (CHARLS) enrolled 17,705 middle-aged and elderly people. The primary outcome was the occurrence of CVD and overall mortality rates. The CTI was obtained by 0.412* Ln (CRP [mg/L]) + Ln (TG [mg/dl] × FPG [mg/dl])/2. The correlation among CTI and CVD incidence and overall mortality was assessed via Cox proportional hazard models, Kaplan-Meier curves and restricted cubic spline (RCS) analysis. To improve the study results, a stratified analysis evaluated the influence of varying socio-demographic characteristics. Results During a 9-years following-up, 5534 participants (9.2%) experienced a CVD event, while 121 participants (2.1%) experienced all-cause mortality. RCS analysis revealed a notable non-linear association between CTI and CVD occurrence, as well as a linear association between CTI and all-cause death. After comprehensive multivariate adjustment, the data showed a striking 97% increase in overall mortality risk for every 1-unit rise in continuous CTI measurements. Conclusions Findings show that higher CTI levels independently forecast CVD and death, highlighting its potential as a biomarker for individuals with CKM stages 0-3. C-reactive protein-triglyceride glucose index cardiovascular diseases all-cause mortality Cardiovascular–kidney–metabolic syndrome Figures Figure 1 Figure 2 What is currently known about this topic? Previous study has shown CTI as a measure of resistance to insulin resistance and inflammatory response. What is the key research question? How do CTI correlate with CVD occurrences and all-cause death in the individuals with stages 0-3 CKM syndrome? What is new? This research represents the first comprehensive large-scale analysis examining the link between CTI and both CVD occurrence and overall death in individuals with CKM syndrome (stages 0-3), while also evaluating the relationship based on age, gender and glucose condition. How might this study influence clinical practice? This study confirmed a notable curvilinear association between CTI and CVD occurrence, as well as a linear association between CTI and overall mortality. To reduce CVD risk and all-cause death, various strategies should be implemented for monitoring CTI levels, considering age, gender and glucose condition. Introduction Cardiovascular disease (CVD) ranks as the primary cause of mortality across the globe, accounting for 523 million instances in 2021, nearly twice the number in 1990 [ 1 ] . The overall mortality rate refers to the proportion of deaths from all causes within a specified period compared to the normal cohort [ 2 ] . For example, a total of 19.8 million deaths worldwide were ascribed to CVD, highlighting the critical need to address all-cause mortality in 2022 [ 3 ] . Several studies have shown the complicated and intimate link between CVD, chronic kidney disease (CKD), and metabolism disturbances [ 3 ] . The American Heart Association (AHA) has identified cardiovascular kidney-metabolic (CKM) syndrome as a complex, interconnected condition stemming from the detrimental interplay of heart disease, CKD, and metabolic imbalances. This systemic disorder arises when these conditions converge, creating a cascade of adverse health effects [ 4 ] . The interplay of these variables considerably increases CVD incidence and the occurrence of multiple organ dysfunction [ 4 ] . According to data from 2015 to 2020, over 25% of Americans may have CKM syndrome, and the expenditures of treating related conditions account for more than 75% of total medical spending [ 5 , 6 ] . As the interest in CKM has grown in academia, the staging system has been defined more precisely, ranging from Stage 0 to Stage 4 [ 4 ] . The AHA underscores the significance of preclinical prediction for people and advocates that studies of the CKM syndrome group in stages 0–3 should focus on avoiding cardiovascular outcomes [ 7 ] . Given the profound clinical consequences of CKM on CVD risk and mortality, the prevention and treatment of these three disorders collectively will aid in averting the fast advancement of CKM stages 0–3 [ 8 ] . Insulin resistance denotes the diminished physiological efficacy of insulin, a prevalent pathological mechanism underlying several metabolic disorders, and is intricately correlated with the onset and progression of atherosclerosis [ 9 , 10 ] . Insulin resistance may precipitate arterial rigidity as well as CVD due to increased inflammation, oxidative pressure, and impaired functional endothelial cells [ 11 ] . The triglyceride-glucose (TyG) ration effectively measures insulin sensitivity and is extensively utilized in clinical settings [ 12 ] . Increasing data suggests a substantial association between the TyG index and atherosclerosis, stroke, and worse CVD outcomes [ 13 – 16 ] . Moreover, inflammation is regarded as a key underlying cause of stroke [ 17 ] . Inflammation has been found to markedly elevate stroke risks through facilitating the development of arterial stiffness, impairing blood vessel endothelial integrity, and augmenting blood clots [ 18 ] . C-reactive protein (CRP) is markedly correlated with stroke risk and has proven as a practical marker for assessing stroke events [ 19 , 20 ] . Insulin resistance and vascular inflammation account for the main causes of atherosclerosis, which is the main risk factor for CVD [ 21 , 22 ] . The C-reactive protein-triglyceride glucose index (CTI), first proposed by Ruan et al. [ 23 ] , adeptly combines insulin resistance and inflammation, thereafter achieving widespread application in clinical investigations. CTI demonstrates considerable prognostic significance for cancer cachexia outcomes across the whole individuals, and heart attacks risk [ 24 – 26 ] . However, the connection between CTI and the CVD incidence and overall death, especially in persons who have CKM syndrome in the stage of 0–3, remains ambiguous. We analyzed the China Health and Retirement Longitudinal Study (CHARLS) data to evaluate the complicated connections among CTI, CVD and mortality within CKM syndrome in order to fill in these important research gaps and provide more proof that CTI can be used in real-world situations. Methods Study design and population Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), encompassing participants over the age of 45. Previous papers have provided detailed specifications of the research design and inclusion criteria [ 27 ] . The study data comprises baseline and following data obtained via standardized questionnaire and clinical assessments, which are based on a series of social, demographic, health status, and habitual behavior. The research complied with the Declaration of Helsinki and obtained approval from the Biomedical Ethics Review Board of Peking University (IRB 00001052–11015). All subjects provided written informed permission prior to being included in the study. More information on CHARLS is accessible on its official website. ( http://charls.pku.edu.cn/en ). The CHARLS nationwide baseline survey was performed from June 2011 to March 2012, with participants receiving face-to-face follow-up interviews every two years. The interviews were done by trained professionals using computer-assisted ways to make sure that all the data was collected in the same way [ 28 ] . In this study, individuals interviewed between 2011 and 2012 were classified as part of the baseline cohort, with follow-up data obtained in 2013, 2015, 2018, and 2020. The flowchart depicts the strict inclusion and exclusion criteria (Fig. 1 ). Among the 17,707 respondents in the 2011 baseline survey, 10,035 were excluded for the following reasons: (1) Age below 45 years at baseline; (2) presence of CVD, heart disease, or stroke at baseline; (3) absence of CKM stages 0–3 at baseline; (4) incomplete data on anthropometric, health-related, sociodemographic, or other biomarkers at baseline; (5) lack of death status information for 2018 and 2020. Consequently, a total of 7,669 participants were incorporated into the final analysis. The distribution of variables with missing data in study shows on Table S1. Calculation of CTI The CTI index is calculated according to this formula [ 23 ] : CTI = 0.412 × Ln (CRP [mg/L]) + Ln (TG [mg/ dl] × FPG [mg/dl])/2. Definition of CKM syndrome stages 0 to 3 The AHA Presidential Advisory Statement [ 4 ] lists the stages of CKM syndrome as follows: Stage 0: Absence of CKM risk factors. Stage 1: overweight or dysfunctional adiposity. Stage 2: Presence of metabolic disorders, including hypertension, diabetes and elevated triglycerides, or CKD. Stage 3: Subclinical CVD in the context of CKM syndrome [ 29 ] . Table S2 details the concrete stage criteria for CKM syndrome. Ascertainment of outcomes We chose the event of CVD and all-cause death as outcome indicators in people with 0–3 stages of CKM syndrome. The main endpoint CVD, encompassing heart disease and stroke, based on self-reported data. Individuals verified that they had obtained the accurate diagnosis of CVD through physicians, in accordance with established standards [ 30 , 31 ] . The CVD outcomes were defined as new instances occurring throughout the duration of observation, regardless of which happened early. The database team adopted stringent criteria to assure the precision and credibility of the data [ 27 ] . Deaths were determined from death certificates, medical records, or interviews with relatives in waves 2 to 5, but the exact time of death was only available in waves 2 and 5. The time-to-event was found by measuring the time between baseline and the last interview wave for participants. Data collection The CHARLS researchers collected variables based on previously established criteria. This study used the following sociodemographic and health data on baseline: Sociodemographic information included sex, age, educational attainment, marital condition, systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI). Lifestyle information included smoking and drinking habits. Physicians diagnosed diseases such as hypertension, glucose conditions (diabetes, prediabetes, and normal glucose regulation), dyslipidemia, and CVD and whether or not they were using medicine for hypertension, diabetes, and dyslipidemia. Furthermore, laboratory tests including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Cr), fasting blood glucose (Fglu), and HbA1c (Hb). Table S3 details the specific definitions of various diseases. Statistical analysis CTI tertiles were used to divide participants into three groups: Quartile (Q) 6.136 ≤ Q1 ≤ 8.271; 8.271 < Q2 ≤ 8.962; 8.962 < Q3 ≤ 12.696. To strengthen the study's credibility, CTI was evaluated both as a categorical and continuous measure. The characteristics of the study participants were outlined: continuous variables are expressed by mean ± SD, while categorical variables are represented as frequency and percentage. Multivariate Cox proportional hazards regression models were developed to explore the hazard ratios ( HR s) and 95% confidence intervals ( CI s) for the correlation among CTI and CVD incidence as well as all-cause mortality. Model 1 was unadjusted, Model 2 adjusted for age, sex, smoking status, drinking status, marital status and educational level, and Model 3 contained additional adjustments for hypertension, dyslipidemia, diabetes, Hypertension treatment, Diabetes treatment and dyslipidemia treatment. Restricted cubic spline (RCS) analysis was used as a method to examine the dose-response correlation with CTI and CVD incidence and all-cause death. To find potential threshold effects in nonlinear relationship scenarios, all potential inflection points were thoroughly tested, and the most likely values were chosen. A piecewise Cox proportional hazards regression model was applied to examine the correlation among the CTI index and CVD incidence as well as all-cause death, stratified according to the identified inflection point. Following identification of a nonlinear relationship through RCS analysis, we generated Kaplan–Meier curves and conducted log-rank tests to compare time-to-event outcomes between the resulting high and low exposure groups. Additionally, K–M curves and log-rank tests were also employed to assess CVD risk and all-cause mortality with CTI. To assess the potential impact of CTI, CRP and TyG index for CVD risk and all-cause mortality, receiver operating characteristic (ROC) curves were developed. The area under the ROC curve (AUC) was employed to assess the increased value of CTI. In addition, for a more in-depth analysis of these relationships, subgroup analyses and interaction assessments were performed. Above analyses included stratification by various items, including sex, age (45–60 years and ≥ 60 years), alcohol consumption, smoking condition, marital status, educational level, hypertension, dyslipidemia, glucose levels (NGR, Pre-DM, and DM) and CKM 0–3 stages. Results Baseline characteristics Table 1 displays baseline CVD risk factors stratified by CTI index tertiles. Finally, a total of 5723 participants were enrolled, averaging 57 years (IQR: 51.00–63.00). Males comprised 45.34% (n = 2595), females 54.66% (n = 3128). Individuals in the highest CTI tertile exhibit marked differences versus the lowest on various indicators. People in the top CTIs were the elderly and had high levels of BMI, SBP, DBP, fast blood glucose, HbA1c, LDL-C, TG, TC, serum creatinine, TyG, CRP, CTI. In addition, the prevalence of comorbidities such as hypertension, dyslipidemia, and diabetes was notably higher in people with elevated CTI levels. Baseline characteristics were compared between participants with and without CVD and all-cause death in Table S4-S5, while the detailed baseline characteristics comparison, including both included and excluded participants, is detailed in Supplemental Table S6. Table 1 Baseline characteristics of the study individuals variable Total (n = 5723) Q1 (n = 1908) Q2 (n = 1908) Q3 (n = 1907) p .value Age, year 57.00(51.00,63.00) 56.00(49.00,62.00) 57.00(52.00,64.00) 58.00(52.00,63.00) < 0.0001 Sex, n (%) < 0.0001 female 3128(54.66) 967(50.68) 1063(55.71) 1098(57.58) male 2595(45.34) 941(49.32) 845(44.29) 809(42.42) Smoke status, n (%) < 0.01 current 1729(30.21) 632(33.12) 567(29.72) 530(27.79) ever 424( 7.41) 123( 6.45) 146( 7.65) 155( 8.13) never 3570(62.38) 1153(60.43) 1195(62.63) 1222(64.08) Drink status, n (%) < 0.0001 no 3757(65.65) 1183(62.00) 1259(65.99) 1315(68.96) yes 1966(34.35) 725(38.00) 649(34.01) 592(31.04) Marital status, n (%) 0.48 married 5174(90.41) 1723(90.30) 1715(89.88) 1736(91.03) others 549( 9.59) 185( 9.70) 193(10.12) 171( 8.97) Educational level, n (%) 0.12 Above junior high school 575(10.05) 201(10.53) 186( 9.75) 188( 9.86) illiterate 1609(28.11) 517(27.10) 578(30.29) 514(26.95) Junior high school and below 3539(61.84) 1190(62.37) 1144(59.96) 1205(63.19) BMI, kg/m 2 23.53 ± 3.75 22.22 ± 3.13 23.44 ± 3.73 24.92 ± 3.86 < 0.0001 SBP, mmHg 128.81 ± 20.43 124.63 ± 19.56 128.86 ± 20.08 132.95 ± 20.79 < 0.0001 DBP, mmHg 75.42 ± 11.87 73.24 ± 11.57 75.44 ± 11.77 77.59 ± 11.89 < 0.0001 Fglu, mg/dL 102.06(94.41,111.60) 97.02(90.72,104.40) 100.98(94.50,108.54) 109.26(100.62,125.19) < 0.0001 Hb, mg/dL 5.26 ± 0.76 5.08 ± 0.46 5.18 ± 0.53 5.51 ± 1.06 < 0.0001 LDL, mg/dL 117.86 ± 34.43 113.50 ± 29.79 122.18 ± 33.59 117.90 ± 38.79 < 0.0001 TG, mg/dL 101.78(73.46,148.68) 68.14(55.76,83.19) 106.20(84.96,132.75) 173.46(126.56,233.64) < 0.0001 TC, mg/dL 194.70 ± 37.52 185.53 ± 33.58 195.00 ± 36.28 203.58 ± 40.23 < 0.0001 Cr, mg/dL 0.77 ± 0.18 0.76 ± 0.18 0.76 ± 0.17 0.78 ± 0.19 < 0.001 TyG 8.64 ± 0.64 8.10 ± 0.32 8.59 ± 0.33 9.24 ± 0.61 < 0.0001 CRP 0.96(0.53,1.96) 0.51(0.35,0.76) 0.99(0.62,1.73) 2.10(1.14,4.14) < 0.0001 Hypertension, n (%) < 0.0001 no 4530(79.15) 1661(87.05) 1528(80.08) 1341(70.32) yes 1193(20.85) 247(12.95) 380(19.92) 566(29.68) Dyslipidemia, n (%) < 0.0001 no 5307(92.73) 1846(96.75) 1789(93.76) 1672(87.68) yes 416( 7.27) 62( 3.25) 119( 6.24) 235(12.32) Diabetes, n (%) < 0.0001 no 5472(95.61) 1878(98.43) 1847(96.80) 1747(91.61) yes 251( 4.39) 30( 1.57) 61( 3.20) 160( 8.39) CVD, n (%) < 0.0001 no 4302(75.17) 1540(80.71) 1408(73.79) 1354(71.00) yes 1421(24.83) 368(19.29) 500(26.21) 553(29.00) Hypertension treatment, n (%) < 0.0001 no 4846(84.68) 1750(91.72) 1633(85.59) 1463(76.72) yes 877(15.32) 158( 8.28) 275(14.41) 444(23.28) Diabetes treatment, n (%) < 0.0001 no 5568(97.29) 1887(98.90) 1873(98.17) 1808(94.81) yes 155( 2.71) 21( 1.10) 35( 1.83) 99( 5.19) Dyslipidemia treatment, n (%) < 0.0001 no 5493(95.98) 1875(98.27) 1845(96.70) 1773(92.97) yes 230( 4.02) 33( 1.73) 63( 3.30) 134( 7.03) CKM < 0.0001 0 503( 8.79) 351(18.40) 132( 6.92) 20( 1.05) 1 1106(19.33) 603(31.60) 394(20.65) 109( 5.72) 2 2229(38.95) 571(29.93) 828(43.40) 830(43.52) 3 1885(32.94) 383(20.07) 554(29.04) 948(49.71) NGR, mg/dL < 0.0001 no 631(11.03) 85( 4.45) 162( 8.49) 384(20.14) yes 5092(88.97) 1823(95.55) 1746(91.51) 1523(79.86) PRE-DM, mg/dL < 0.0001 no 5323(93.01) 1838(96.33) 1779(93.24) 1706(89.46) yes 400( 6.99) 70( 3.67) 129( 6.76) 201(10.54) Association of CTI index with CVD and total mortality in CKM syndrome patients As shown in Table 2 , multivariable-adjusted analysis revealed a graded cardiovascular risk profile associated with CTI. Among CKM stage 0–3 individuals, the highest CTIQ quartile (Q3) showed 54% increased CVD risk versus Q1 in the crude model ( HR = 1.54, 95% CI 1.44–1.65, P < 0.0001), which remained significant after adjusting for demographic (age, gender, smoking, drinking status, marital status) and clinical confounders (hypertension, diabetes, dyslipidemia, Hypertension treatment, Diabetes treatment, dyslipidemia treatment,), with 27% excess risk in Model 2 ( HR = 1.27, 1.19–1.36, P < 0.0001; P -trend < 0.001). Meanwhile, the highest quartile (Q3) showed adjusted hazard ratios of 1.35 for TyG (95% CI 1.26–1.44; P < 0.001) and 1.43 for standardized CRP values (CRP-SD) (95% CI 1.34–1.52; P < 0.001). Both were lower than the CTI's highest quartile HR of 1.54 (95% CI 1.44–1.65; P < 0.001) (Table S7). For all-cause mortality, each unit increase in continuous CTI corresponded to 60% higher risk (Model 2: HR = 1.60, 1.29–1.99, P < 0.0001). Stratified analysis demonstrated a 97% mortality increase in Q3 versus Q1 (Model 2: HR = 1.97, 1.23–3.15, P = 0.005), whereas Q2 showed non-significant association ( P = 0.65), indicating a threshold effect of CTI. Table 2 Multivariate cox regression for the correlation between CTI, CVD and overall mortality risk CTI Crude model Model 1 Model 2 95%CI P 95%CI P 95%CI P CVD incidence categories Q1 ref ref ref Q2 1.38(1.29,1.48) < 0.0001 1.33(1.24,1.42) < 0.0001 1.26(1.17,1.35) < 0.0001 Q3 1.54(1.44,1.65) < 0.0001 1.47(1.38,1.57) < 0.0001 1.27(1.19,1.36) < 0.0001 All-cause mortality continuous 1.56(1.28,1.89) < 0.0001 1.67(1.36, 2.04) < 0.0001 1.6(1.29, 1.99) < 0.0001 categories Q1 ref ref ref Q2 1.22(0.74,2.01) 0.43 1.17(0.71, 1.93) 0.54 1.12(0.68, 1.86) 0.65 Q3 2.14(1.36,3.35) < 0.001 2.16(1.37, 3.41) 0.001 1.97(1.23, 3.15) 0.005 p for trend < 0.001 < 0.001 0.003 crude model: unadjusted for covariates; model 1: age, gender, smoke status, drink status, marital status, education; model 2: age, gender, smoke status, drink status, marital status, educational level, hypertension, dyslipidemia, diabetes, Hypertension treatment, Diabetes treatment, dyslipidemia treatment. RCS and threshold effect analysis We performed RCS analysis and threshold analysis to verify the association of CTI and CVD prevalence and mortality rates. For CVD incidence, standard Cox regression revealed non-linear association between CTI and CVD incidence ( HR = 1.103–1.219, P < 0.0001) (Fig. 2 ). Two-piecewise linear regression revealed a critical inflection point at CTI = 9.285: below this value, CTI exhibited a strong positive association with CVD risk ( HR = 1.240–1.382, P < 0.0001), with no notable association was found above the inflection point ( HR = 0.920–1.010, P ≥ 0.115). The log-likelihood ratio test ( P < 0.0001) confirmed the superiority in segmented model, highlighting a nonlinear dose-response pattern between CTI and CVD risk (Table S8). For all-cause mortality, standard Cox regression showed a notable linear trend between CTI and overall mortality ( HR = 1.556–1.666, P < 0.0001) (Fig. 2 ). While two-piecewise linear regression suggested a potential risk transition at CTI = 8.608 ( HR = 1.522–1.806, P ≤ 0.007 above the threshold; no significant association below the threshold, HR = 1.241–1.315, P ≥ 0.500), the log-likelihood ratio tests ( P ≥ 0.797 for all models) indicated no statistically significant improvement in model fit with segmentation. Thus, despite localized risk differences near the inflection point, the overall relationship predominantly aligns with a linear pattern (Table S9). Kaplan–Meier (K–M) survival curves The Kaplan–Meier (K–M) survival curves showed an elevated CVD incidence or overall mortality in the high CTI group. The log-rank test's p-values for the Q2 and Q3 groups, all below 0.05, confirm a higher risk compared to the Q1 group (Figure S1). Furthermore, using the inflection point (CTI = 8.602), participants were stratified into high- and low-exposure groups, with Kaplan-Meier analysis confirming significant differences in survival rates (log-rank P < 0.001) (Figure S2). Subgroup analyses To delve deeper into the connection between CTI and the likelihood of CVD or all-cause mortality, researchers conducted subgroup and interaction analyses on various variables, which include age, gender, tobacco use, alcohol consumption, marital status, education attainment, diabetes statuse, hypertension, dyslipidemia, glucose levels (including NGR, Pre-DM, and DM), and CKM syndrome (0–3 stages) (Figure S4 ,Table 12). For CVD incidence, sex stratification revealed markedly higher CVD risk in males ( HR = 1.627, 95% CI: 1.451–1.824; P < 0.0001) compared to females ( HR = 1.359, 95% CI: 1.223–1.510; P < 0.0001), with a pronounced interaction effect ( P = 0.02). A dose-response relationship was evident, as higher CTIQ quartiles (Q3 vs. Q1) consistently correlated with elevated CVD risk across all strata ( P for trend < 0.0001). Notably, smoking status ( P = 0.029) and marital status ( P < 0.0001) exhibited significant interactions, where current smokers ( HR = 1.722, Q2 vs. Q1) and married individuals ( HR = 1.809, Q3 vs. Q1) showed heightened vulnerability. Conversely, no interaction was observed for age, hypertension, or dyslipidemia ( P > 0.05). Education level further modulated risk, with individuals above junior high school education displaying the steepest CTIQ-associated risk gradient ( HR = 2.935, Q3 vs. Q1; P < 0.0001). For overall mortality, lower education (e.g., \"Junior high school and below\": HR = 1.656, P < 0.001) and non-drinkers ( HR = 1.774, P < 0.0001) showed elevated mortality. CTI consistently predicted mortality across most subgroups (e.g., males: HR = 1.538; females: HR = 1.628, both P < 0.01), despite nonsignificant interactions for sex ( P = 0.783) and age ( P = 0.658). Notably, pre-diabetic individuals exhibited a non-significant trend toward heightened risk ( HR = 1.497, P = 0.254), warranting further investigation. The most pronounced interaction emerged in CKM strata ( P < 0.001), which revealed CKM = 0 individuals have an exceptionally high risk ( HR = 8.225, 95% CI 2.558–27.964, P < 0.001). Therefore, the future study assessed the connection between CTIQ and overall death in the 0–3 stage of CKM group: CKM Stage 0 exhibited an extreme hazard ratio (Q3 vs Q1: HR = 19.611, 95% CI = 2.251–171.300, P = 0.004), but with wide confidence intervals (Figure S5, Table 13). AUC and ROC The research aims to assess the forecasting capabilities of TyG, CRP and CTI in predicting overall mortality and CVD occurrence via ROC analysis. For CVD risk, CRP and CTI retained comparable performance (AUC = 0.66 and 0.61, respectively), whereas TyG again performed at chance level (AUC = 0.5). Similarly, for all-cause mortality, CRP demonstrated moderate predictive utility (AUC = 0.66, 95% CI : 0.61–0.66), just ahead of CTI (AUC = 0.61, 95% CI : 0.56–0.61), while TyG showed no discriminative capacity (AUC = 0.5, 95% CI : 0.45–0.5). The findings indicate that CTI could outperformed CRP and the TyG index in assessing overall mortality risk stratification (Figure S6) Sensitivity analyses To check the stability of our findings, we conducted multiple sensitivity analyses. Firstly, in order to tackle the issue of missing data and to limit the possibility of bias, we employed multiple imputations. Subsequent analysis revealed that the correlation among CTI and CVD incidence and overall mortality was in accordance with the basic results (Table S10). Secondly, the application of logistic regression models to investigate the connection between CTI and CVD incidence, as well as all-cause mortality, yields consistent results. (Table S11). Thirdly, the analysis of the piecewise Cox regression model confirmed result stability (Table S12-13). Fourthly, we also assessed the correlation among CTI and CVD incidence and overall mortality stratified by sex, age, and glucose level (grouped into NGR, Pre-DM, and DM). (Table S14). Furthermore, we explored additional analyses to analyze the associations of TyG and CRP standardized values (CRP-SD) with both CVD incidence and all-cause mortality, thereby validating the robustness of our primary findings (Table S7). Finally, analyses stratified by sex and CKM stage (0–3) revealed consistent associations between CTI and adverse outcomes, as evidenced by Kaplan-Meier curves (all log-rank P < 0.05) (Figure S3). Discussion We enrolled the subjects diagnosed with CKM syndrome across stages 0 to 3, categorizing them according to initial CTI evaluations. Using Cox proportional hazards models, we then analyzed how CTI levels correlated with both CVD occurrence and overall mortality rates. The main points of the study can be described in the following way: The dual role of CTI: a threshold-driven nonlinear association with CVD incidence and a continuous linear predictor of all-cause mortality. Specifically, below an inflection point at 9.28, CTI was strongly associated with CVD risk, whereas the association attenuated above the inflection point. Notably, each 1-unit increment of continuous CTI was linked to a 97% excess overall mortality in completely adjusted analyses. These results not only support the therapeutic usefulness of CTI evaluation in older patients with CKM syndrome but also furnish critical data for precise risk classification in this demographic. This index integrates CRP, an established marker of inflammatory reactions, with the TyG, an indication that signifies insulin resistance. Prior research has demonstrated the correlation between increased TyG and a heightened CVD incidence or overall mortality. Zhang et al. identified significant links between TyG and total mortality as well as CVD incidence [ 32 ] . Using the CHARLS database, Huo and colleagues demonstrate the association between increased TyG at baseline and an augmented risk of stroke [ 33 ] . A UK Biobank study of 410,515 participants explored a strong correlation between TyG and mortality across the entire cohort [ 34 ] . The TyG index functions as a reliable indictors of CVD incidence and overall mortality across the whole people, as well as a relevant measure for specific subgroups. Specifically, Li et al., using the NHANSE database, discovered that TyG was an independent indicator of both overall and cardiovascular death in hypertension persons [ 35 ] . Additionally, Li et al. conducted a prospective cohort study with 7,376 participants, demonstrating that TyG is a valuable tool for predicting the development of CVD risk in CKM stages 0–3 [ 36 ] . Furthermore, one study revealed a substantial correlation between an increased TyG ratio and a heightened likelihood of CVD and overall mortality in diabetes patients [ 37 ] . Nevertheless, inflammation stands as a pivotal risk element contributing to the development of CVD or to mortality from any cause [ 38 ] . Inflammation is becoming more well recognized as a critical role on CVD and mortality risk, based on the study by Cho et al. [ 39 ] . Furthermore, a thorough research of 8420 individuals from ten prospective studies found a link between higher CRP levels and elevated stroke recurrence [ 19 ] . Additionally, a large-scale study involving 1,555 participants pinpointed that elevated CRP levels correlated with a heightened likelihood of CVD in diabetics [ 40 ] . Moreover, Cui et al. demonstrated that TyG and CRP had a co-exposure impact and mutual mediation on CVD [ 41 ] . CTI, created by Ruan et al. [ 23 ] , serves as a key tool for diagnosing and predicting outcomes in cancer patients. One study of 10,443 individuals found a strong positive linear connection between CTI and stroke risk [ 42 ] . Xu et al. have shown the prevalence of CHD in the total American population is positively, linearly, and robustly associated with CTI [ 25 ] . Prior studies indicate that CTI is significantly related to increased cardiovascular events in the whole population. However, given the intricate interplay between CVD, CKD, and metabolic diseases, it is critical to examine how CTI influences cardiovascular events and overall mortality—especially within the framework of CKM syndrome. Although the specific mechanism of CTI and CVD and overall mortality during the 0–3 CKM phase is still obscure, it can be elucidated by the following factors. First of all, insulin resistance and chronic inflammation impair the integrity of the endothelium, reduce the body's ability to utilize nitric oxide effectively, disrupt healthy blood clotting mechanisms, and speed up the occurrence of atherosclerosis. All of these significantly increase CVD risk [ 43 , 44 ] . Furthermore, inflammation may exacerbate insulin resistance, triggering tissue-derived inflammatory mediators and amplifying systemic inflammation. Inflammation and insulin resistance exhibit collaborative consequences that mutually promote and exacerbate each other, thereby increasing the risk of CVD [ 45 ] . Furthermore, Atherosclerotic plaque stability undermined by inflammation and insulin resistance. This instability heightens rupture risk, potentially resulting in thrombosis and consequently contributing to the incidence of CVD and overall mortality [ 46 ] . Insulin resistant and inflamed patients often suffer from co-morbidities such as hypertension, diabetes, obesity, and metabolic syndrome. These conditions collectively serve as major contributors to CVD and increased mortality rates [ 47 – 49 ] . For metabolic syndrome, the processes of CVD risk and overall mortality essentially include lipid metabolism, Oxidative Stress, and inflammatory response [ 50 ] . Lipid abnormalities associated with CKM are defined by increased TC, TG and LDL-C, coupled with reduced HDL-C. Such dyslipidemia doesn't just heighten CVD risk—it actively fuels disease progression by triggering thrombotic mechanisms [ 51 ] . Research indicates that overweight and having insulin resistance may elevate the production of Reactive Oxygen Species (ROS), potentially damaging Vascular Endothelial Cells (VECs) and worsening lipid metabolism dysfunction. This process contributes to the formation of oxidized Low-Density Lipoprotein (ox-LDL), a key driver of atherosclerotic plaque development [ 52 ] . Inflammation is vital in the context of these disorders. Macrophages in adipose tissue secrete factors like Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), playing a key role in driving the persistent, low-level inflammation characteristic of metabolic syndrome [ 53 ] . These inflammatory mediators not only promote atherosclerotic plaque formation but also increase the risk of thrombosis through the activation of Endothelial Cells (ECs) and platelets, which is closely correlate with CVD and overall death events [ 54 ] . Therefore, it follows that people with higher CTI may have more severe vascular damage, a higher incidence of CVD, and a higher death rate from all causes. The analysis of RCS demonstrated a complex, non-linear relationship correlation between CTI and CVD incidence, indicating that varying levels of CTI within the population experiencing 0–3 stages of CKM syndrome may exert dynamic effects: Below the threshold (CTI < 9.28), subclinical inflammation indicated by elevated CRP, along with developing insulin resistance reflected by an increased TyG index, may work together to activate pro-inflammatory pathways in vascular endothelial cells, such as NF-κB signaling. This activation can enhance monocyte adhesion and foam cell formation through the regulation by modulating adhesion factors like VCAM-1 and ICAM-1, thus fostering the progression of early atherogenesis [ 55 , 56 ] . Interventions aimed at suppressing inflammation, such as statins, or enhancing insulin sensitivity, such as GLP-1 receptor agonists, during this phase may produce optimal preventive results. Beyond the inflection (CTI ≥ 9.28), chronic inflammation drives macrophage M1 polarization, releasing IL-6/TNF-α to activate MMP-9/MMP-2, degrading fibrous caps and increasing intraplaque neovascularization [ 57 ] , while TyG-induced AGEs-RAGE signaling perpetuates mitochondrial dysfunction and endothelial apoptosis, accelerating fibrosis even post-CTI reduction [ 58 ] . Simultaneously, persistent elevation of CTI interferes with intrinsic protective mechanisms via dual metabolic and inflammatory stress. This nonlinearity indicates a shift from reversible endothelial damage to irreversible vascular remodeling, underscoring the necessity for early dual-pathway targeting. Our analysis reveals a paradoxical association between CKM stage 0 (no overt cardiometabolic disease) and heightened all-cause mortality ( P -interaction < 0.001). While counterintuitive, this finding may reflect limitations in current CKM stratification. First, classification misalignment is plausible: undiagnosed subclinical conditions (e.g., IR, vascular dysfunction) or non-traditional risk factors (chronic inflammation, epigenetic alterations) could drive mortality without meeting conventional diagnostic thresholds. Second, residual confounding occurs even after multivariable adjustments: socioeconomic inequalities, lifestyle heterogeneity (e.g., psychological stress, food habits), and competing hazards (e.g., cancer/trauma-related deaths) may disproportionately impact \"apparently healthy\" groups. Third, selection bias merits scrutiny: CKM stage 0 cohorts often exclude individuals with incomplete biomarker data, potentially inflating mortality estimates. Methodologically, we propose three validations: 1) Reclassification using extended biomarkers (e.g., coronary calcium scoring, urinary albumin-to-creatinine ratio) to detect occult disease; 2) Competing risk analysis differentiating between cardiovascular and non-cardiovascular causes; 3) Sensitivity analyses that include social determinants, such as neighborhood deprivation indices and healthcare access, are essential. If validated, this indicates a significant shift in understanding: \"metabolically healthy\" phenotypes may conceal underlying multiorgan dysregulation that necessitates the identification of new biomarkers, including mitochondrial DNA integrity and senescent cell burden. Meanwhile, this study also underscores the urgency of expanding multicenter cohorts to validate extreme risk estimates in CKM Stage 0 and resolve contradictions in intermediate phases. These results cast doubt on the CKM framework's capacity to identify dangers in their early stages. To improve preventative measures, we suggest using dynamic risk trajectories instead of static baseline staging. These results give fresh perspectives for clinical treatment, implying that including CTI into routine examinations is a convenient, easily available technique for assessing individuals with CKM phases 0–3. Physicians can more accurately assess patients' metabolic health and create customized treatment plans by using dynamic monitoring of CTI changes, which increases the accuracy and efficacy of illness management. Periodic CTI monitoring reduces the risk of negative outcomes and improves long-term survival rates by facilitating early diagnosis of disease development, supporting clinical decision-making, and enabling prompt interventions to avert deterioration. Strength and limitation There are some noteworthy advantages to the current investigation. First, it concentrates on the clinically significant but frequently disregarded person in CKM stages 0–3. This article is the earliest research to investigate the use of the CTI index in evaluating CVD incidence and overall mortality in people with 0–3 stages of CKM, offering considerable clinical significance and novelty. Secondly, the data included in this study employed a complicated method to finally choose 5,723 qualifying individuals from a nationwide survey. The extensive dataset guarantees robust statistical significance. Furthermore, this research examines the connection among CTI, CVD incidence and overall mortality in people throughout 0–3 stages of CKM, which advances research in this area. Finally, we carried out thorough sensitivity analyses and strictly adjusted for potential contributors to guarantee the validity and dependability in our findings. However, certain study constraints must be noted. Initially, the subjects consist exclusively of middle and elderly Chinese, thereby constraining the scope of the results' applicability. Secondly, Secondly, the Framingham ten-year score for the risk of CVD was used for CKM syndrome staging instead of the most recent PREVENT equation, which may have an impact on staging accuracy. Thirdly, a restricted number of individuals were included in this research due to stringent exclusion criteria, which may have resulted in attrition bias for the participants. Fourthly, the accuracy of results is impacted by the reliance on self-reports for illness diagnosis, which may result in an underestimation of prevalence and an inability to differentiate between different forms of CVD or all-cause death. Fifthly, the possible impact of these alterations on mortality risk is unknown since the CTI index was merely evaluated at the initial assessment, and no analysis of fluctuations during the follow-up phase was conducted. Future research should explore how changes in CTI across time affect CVD incidence and all-cause mortality. Finally, we were unable to do a Mendelian randomization study because there was insufficient genetic data for the CKM stages 0–3 cohort. Future research should concentrate on capturing related evidence to enhance the precision of causal inferences. In order to confirm the results and offer stronger support for research in related fields, more thorough investigation in additional substantial cohort studies is required. Conclusions In summary, this study employed the CHARLS database and an innovative integrated predictor of insulin resistance and inflammation, termed CTI, to forecast CVD incidence and all-cause death in the stage 0–3 CKM cohort. The results indicated the substantial connection among elevated CTI levels and a heightened CVD risk and all-cause mortality, implying that CTI may function as a predictive indicator in stages 0–3 of CKM subjects. Declarations Author contributions Huiwen Ou developed the study framework and drafted the manuscript. Huiwen Ou participated in the statistical analysis. Xiaoshuang xia performed the literature review and compiled the illustrations. Xin Li conducted manuscript editing and review. All contributors endorsed the final draft. Funding Projects of Tianjin Municipal Health Commission (TJWJ2024XK008), the Tianjin Key Tianjin Municipal Science and Technology Bureau Project (21JCZDJC01230), the Key Center for Health and Meteorology Multidisciplinary Innovation, the National Natural Science Foundation of China (42275197) Data availability CHARLS data repository: http://charls.pku.edu.cn/en. Ethics approval and consent to participate The CHARLS has been cleared by Peking University Biomedical Ethics Review Board, with all individuals offering informed agreement. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details Department of Neurology, The second Hospital of Tianjin Medical University, No23, PingJiang Road, Tianjin 300211, China References ROTH G A, MENSAH G A, JOHNSON C O, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study [J]. J Am Coll Cardiol. 2020;76(25):2982–3021. KNOTT C S, COOMBS N. All cause mortality and the case for age specific alcohol consumption guidelines: pooled analyses of up to 10 population based cohorts [J]. BMJ. 2015;350:h384. MATSUSHITA K, BALLEW S H, WANG A Y, et al. Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease [J]. Nat Rev Nephrol. 2022;18(11):696–707. NDUMELE C E, RANGASWAMI J, CHOW SL, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association [J]. Circulation. 2023;148(20):1606–35. OSTROMINSKI J W, ARNOLD S V BUTLERJ, et al. 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Supplementary Files GA.jpg Cite Share Download PDF Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 15 Jun, 2025 Reviews received at journal 10 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 29 May, 2025 Reviews received at journal 26 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers agreed at journal 24 May, 2025 Reviewers agreed at journal 24 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers invited by journal 23 May, 2025 Editor assigned by journal 23 May, 2025 Submission checks completed at journal 23 May, 2025 First submitted to journal 22 May, 2025 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. 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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-6726039\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":462165778,\"identity\":\"1a976b14-db01-46bb-b0ca-761e4c00e342\",\"order_by\":0,\"name\":\"Huiwen Ou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Huiwen\",\"middleName\":\"\",\"lastName\":\"Ou\",\"suffix\":\"\"},{\"id\":462165780,\"identity\":\"c57d47bd-4234-4b72-9559-31fbd64fb1b0\",\"order_by\":1,\"name\":\"xiaoshuang xia\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACZiBOADEkwFwbHn72BtK0pMlI9hwg1jqIlsM2Bjcc8Cs0OM78TOJBjQ2D/Owew88Fv87zMNxgYPzwMQe3FslmNmODhGNpDAZ3zhhLz+y7zcM4u4FZcuY23Fr4mRkMHySwHWYwkMgxkObtuc3DLHOAjZkXjxY2ZvYPBxL+HWaQn5Fj/Ju35xwPm0QCfi38zDyGDxLbDjMw3Mgxk+b5cYCHh5AWyWaeYoPEPqBfbqSVWfM2JPNI8BxsxusXg/PHt0n++AYMsRnJm2/z/LGztz/efPDDRzxaYKC+AUQytoHJBsLqEeAPKYpHwSgYBaNgpAAAhjxLGsL44EcAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"xiaoshuang\",\"middleName\":\"\",\"lastName\":\"xia\",\"suffix\":\"\"},{\"id\":462165782,\"identity\":\"5e2a6fce-ddc4-4a9b-b561-5cdf0fb93fc8\",\"order_by\":2,\"name\":\"Xin Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-22 14:38:22\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6726039/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6726039/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12933-025-02848-9\",\"type\":\"published\",\"date\":\"2025-07-22T15:57:31+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":83603638,\"identity\":\"38742fb0-9406-4ef3-839d-1d8f216da7bf\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:58:13\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":38744,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow chart of study subjects\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6726039/v1/b99eddc859d6c6624f01ec4d.jpg\"},{\"id\":83602845,\"identity\":\"8d272e55-b828-41b4-a049-5a09a4515213\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:50:13\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":49601,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRSC showing the connection between CTI, CVD incidence and all-cause death events. (A-C) cardiovascular disease. (D-F) All-cause mortality. Crude model: unadjusted for covariates; Model 1: Adjust for: age, gender, smoke, drink, marital status, education; Model 2: Adjusted for: age, gender, smoke, drink, marital status, education, hypertension, dyslipidemia, diabetes, Hypertension treatment, Diabetes treatment, dyslipidemia treatment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6726039/v1/ee4e4a1f7c084c4bfcde3e55.jpg\"},{\"id\":87756695,\"identity\":\"9911b434-6405-4c8d-b9ca-ed2906e8f8c3\",\"added_by\":\"auto\",\"created_at\":\"2025-07-28 16:07:49\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1261202,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6726039/v1/802e29f2-037a-4158-a6bd-c1bc2aa531a0.pdf\"},{\"id\":83602848,\"identity\":\"29762d92-4320-4ef0-8646-eeb1a560c3d1\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:50:13\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":102584,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"GA.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6726039/v1/2d7a0fb338d9c0347a477379.jpg\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0–3 of cardiovascular–kidney–metabolic syndrome: a nationwide prospective cohort study\",\"fulltext\":[{\"header\":\"What is currently known about this topic? \",\"content\":\"\\u003cp\\u003ePrevious study has shown CTI as a measure of resistance to insulin resistance and inflammatory response.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWhat is the key research question?\\u0026nbsp;\\u003c/strong\\u003eHow do CTI correlate with CVD occurrences and all-cause death in the individuals with stages 0-3 CKM syndrome?\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWhat is new?\\u0026nbsp;\\u003c/strong\\u003eThis research represents the first comprehensive large-scale analysis examining the link between CTI and both CVD occurrence and overall death in individuals with CKM syndrome (stages 0-3), while also evaluating the relationship based on age, gender and glucose condition.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHow might this study influence clinical practice?\\u0026nbsp;\\u003c/strong\\u003eThis study confirmed a notable curvilinear association between CTI and CVD occurrence, as well as a linear association between CTI and overall mortality. To reduce CVD risk and all-cause death, various strategies should be implemented for monitoring CTI levels, considering age, gender and glucose condition.\\u003c/p\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCardiovascular disease (CVD) ranks as the primary cause of mortality across the globe, accounting for 523\\u0026nbsp;million instances in 2021, nearly twice the number in 1990 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e. The overall mortality rate refers to the proportion of deaths from all causes within a specified period compared to the normal cohort \\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e. For example, a total of 19.8\\u0026nbsp;million deaths worldwide were ascribed to CVD, highlighting the critical need to address all-cause mortality in 2022\\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSeveral studies have shown the complicated and intimate link between CVD, chronic kidney disease (CKD), and metabolism disturbances \\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. The American Heart Association (AHA) has identified cardiovascular kidney-metabolic (CKM) syndrome as a complex, interconnected condition stemming from the detrimental interplay of heart disease, CKD, and metabolic imbalances. This systemic disorder arises when these conditions converge, creating a cascade of adverse health effects \\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. The interplay of these variables considerably increases CVD incidence and the occurrence of multiple organ dysfunction \\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eAccording to data from 2015 to 2020, over 25% of Americans may have CKM syndrome, and the expenditures of treating related conditions account for more than 75% of total medical spending \\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. As the interest in CKM has grown in academia, the staging system has been defined more precisely, ranging from Stage 0 to Stage 4 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. The AHA underscores the significance of preclinical prediction for people and advocates that studies of the CKM syndrome group in stages 0\\u0026ndash;3 should focus on avoiding cardiovascular outcomes\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. Given the profound clinical consequences of CKM on CVD risk and mortality, the prevention and treatment of these three disorders collectively will aid in averting the fast advancement of CKM stages 0\\u0026ndash;3 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eInsulin resistance denotes the diminished physiological efficacy of insulin, a prevalent pathological mechanism underlying several metabolic disorders, and is intricately correlated with the onset and progression of atherosclerosis \\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. Insulin resistance may precipitate arterial rigidity as well as CVD due to increased inflammation, oxidative pressure, and impaired functional endothelial cells \\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e. The triglyceride-glucose (TyG) ration effectively measures insulin sensitivity and is extensively utilized in clinical settings \\u003csup\\u003e[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. Increasing data suggests a substantial association between the TyG index and atherosclerosis, stroke, and worse CVD outcomes \\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR14 CR15\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e. Moreover, inflammation is regarded as a key underlying cause of stroke \\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e. Inflammation has been found to markedly elevate stroke risks through facilitating the development of arterial stiffness, impairing blood vessel endothelial integrity, and augmenting blood clots \\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. C-reactive protein (CRP) is markedly correlated with stroke risk and has proven as a practical marker for assessing stroke events \\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eInsulin resistance and vascular inflammation account for the main causes of atherosclerosis, which is the main risk factor for CVD \\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e. The C-reactive protein-triglyceride glucose index (CTI), first proposed by Ruan et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e, adeptly combines insulin resistance and inflammation, thereafter achieving widespread application in clinical investigations. CTI demonstrates considerable prognostic significance for cancer cachexia outcomes across the whole individuals, and heart attacks risk \\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR25\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e. However, the connection between CTI and the CVD incidence and overall death, especially in persons who have CKM syndrome in the stage of 0\\u0026ndash;3, remains ambiguous.\\u003c/p\\u003e \\u003cp\\u003eWe analyzed the China Health and Retirement Longitudinal Study (CHARLS) data to evaluate the complicated connections among CTI, CVD and mortality within CKM syndrome in order to fill in these important research gaps and provide more proof that CTI can be used in real-world situations.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and population\\u003c/h2\\u003e \\u003cp\\u003eData were obtained from the China Health and Retirement Longitudinal Study (CHARLS), encompassing participants over the age of 45. Previous papers have provided detailed specifications of the research design and inclusion criteria \\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. The study data comprises baseline and following data obtained via standardized questionnaire and clinical assessments, which are based on a series of social, demographic, health status, and habitual behavior. The research complied with the Declaration of Helsinki and obtained approval from the Biomedical Ethics Review Board of Peking University (IRB 00001052\\u0026ndash;11015). All subjects provided written informed permission prior to being included in the study. More information on CHARLS is accessible on its official website. (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://charls.pku.edu.cn/en\\u003c/span\\u003e\\u003cspan address=\\\"http://charls.pku.edu.cn/en\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe CHARLS nationwide baseline survey was performed from June 2011 to March 2012, with participants receiving face-to-face follow-up interviews every two years. The interviews were done by trained professionals using computer-assisted ways to make sure that all the data was collected in the same way \\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e. In this study, individuals interviewed between 2011 and 2012 were classified as part of the baseline cohort, with follow-up data obtained in 2013, 2015, 2018, and 2020. The flowchart depicts the strict inclusion and exclusion criteria (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Among the 17,707 respondents in the 2011 baseline survey, 10,035 were excluded for the following reasons: (1) Age below 45 years at baseline; (2) presence of CVD, heart disease, or stroke at baseline; (3) absence of CKM stages 0\\u0026ndash;3 at baseline; (4) incomplete data on anthropometric, health-related, sociodemographic, or other biomarkers at baseline; (5) lack of death status information for 2018 and 2020. Consequently, a total of 7,669 participants were incorporated into the final analysis. The distribution of variables with missing data in study shows on Table S1.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eCalculation of CTI\\u003c/h3\\u003e\\n\\u003cp\\u003eThe CTI index is calculated according to this formula \\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e: CTI\\u0026thinsp;=\\u0026thinsp;0.412 \\u0026times; Ln (CRP [mg/L])\\u0026thinsp;+\\u0026thinsp;Ln (TG [mg/ dl] \\u0026times; FPG [mg/dl])/2.\\u003c/p\\u003e\\n\\u003ch3\\u003eDefinition of CKM syndrome stages 0 to 3\\u003c/h3\\u003e\\n\\u003cp\\u003eThe AHA Presidential Advisory Statement \\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e lists the stages of CKM syndrome as follows: Stage 0: Absence of CKM risk factors. Stage 1: overweight or dysfunctional adiposity. Stage 2: Presence of metabolic disorders, including hypertension, diabetes and elevated triglycerides, or CKD. Stage 3: Subclinical CVD in the context of CKM syndrome\\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e. Table S2 details the concrete stage criteria for CKM syndrome.\\u003c/p\\u003e\\n\\u003ch3\\u003eAscertainment of outcomes\\u003c/h3\\u003e\\n\\u003cp\\u003eWe chose the event of CVD and all-cause death as outcome indicators in people with 0\\u0026ndash;3 stages of CKM syndrome. The main endpoint CVD, encompassing heart disease and stroke, based on self-reported data. Individuals verified that they had obtained the accurate diagnosis of CVD through physicians, in accordance with established standards \\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e. The CVD outcomes were defined as new instances occurring throughout the duration of observation, regardless of which happened early. The database team adopted stringent criteria to assure the precision and credibility of the data \\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. Deaths were determined from death certificates, medical records, or interviews with relatives in waves 2 to 5, but the exact time of death was only available in waves 2 and 5. The time-to-event was found by measuring the time between baseline and the last interview wave for participants.\\u003c/p\\u003e\\n\\u003ch3\\u003eData collection\\u003c/h3\\u003e\\n\\u003cp\\u003eThe CHARLS researchers collected variables based on previously established criteria. This study used the following sociodemographic and health data on baseline: Sociodemographic information included sex, age, educational attainment, marital condition, systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI). Lifestyle information included smoking and drinking habits. Physicians diagnosed diseases such as hypertension, glucose conditions (diabetes, prediabetes, and normal glucose regulation), dyslipidemia, and CVD and whether or not they were using medicine for hypertension, diabetes, and dyslipidemia. Furthermore, laboratory tests including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Cr), fasting blood glucose (Fglu), and HbA1c (Hb). Table S3 details the specific definitions of various diseases.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eCTI tertiles were used to divide participants into three groups: Quartile (Q) 6.136\\u0026thinsp;\\u0026le;\\u0026thinsp;Q1\\u0026thinsp;\\u0026le;\\u0026thinsp;8.271; 8.271\\u0026thinsp;\\u0026lt;\\u0026thinsp;Q2\\u0026thinsp;\\u0026le;\\u0026thinsp;8.962; 8.962\\u0026thinsp;\\u0026lt;\\u0026thinsp;Q3\\u0026thinsp;\\u0026le;\\u0026thinsp;12.696. To strengthen the study's credibility, CTI was evaluated both as a categorical and continuous measure. The characteristics of the study participants were outlined: continuous variables are expressed by mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD, while categorical variables are represented as frequency and percentage. Multivariate Cox proportional hazards regression models were developed to explore the hazard ratios (\\u003cem\\u003eHR\\u003c/em\\u003es) and 95% confidence intervals (\\u003cem\\u003eCI\\u003c/em\\u003es) for the correlation among CTI and CVD incidence as well as all-cause mortality. Model 1 was unadjusted, Model 2 adjusted for age, sex, smoking status, drinking status, marital status and educational level, and Model 3 contained additional adjustments for hypertension, dyslipidemia, diabetes, Hypertension treatment, Diabetes treatment and dyslipidemia treatment. Restricted cubic spline (RCS) analysis was used as a method to examine the dose-response correlation with CTI and CVD incidence and all-cause death. To find potential threshold effects in nonlinear relationship scenarios, all potential inflection points were thoroughly tested, and the most likely values were chosen. A piecewise Cox proportional hazards regression model was applied to examine the correlation among the CTI index and CVD incidence as well as all-cause death, stratified according to the identified inflection point. Following identification of a nonlinear relationship through RCS analysis, we generated Kaplan\\u0026ndash;Meier curves and conducted log-rank tests to compare time-to-event outcomes between the resulting high and low exposure groups. Additionally, K\\u0026ndash;M curves and log-rank tests were also employed to assess CVD risk and all-cause mortality with CTI. To assess the potential impact of CTI, CRP and TyG index for CVD risk and all-cause mortality, receiver operating characteristic (ROC) curves were developed. The area under the ROC curve (AUC) was employed to assess the increased value of CTI. In addition, for a more in-depth analysis of these relationships, subgroup analyses and interaction assessments were performed. Above analyses included stratification by various items, including sex, age (45\\u0026ndash;60 years and \\u0026ge;\\u0026thinsp;60 years), alcohol consumption, smoking condition, marital status, educational level, hypertension, dyslipidemia, glucose levels (NGR, Pre-DM, and DM) and CKM 0\\u0026ndash;3 stages.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline characteristics\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e displays baseline CVD risk factors stratified by CTI index tertiles. Finally, a total of 5723 participants were enrolled, averaging 57 years (IQR: 51.00\\u0026ndash;63.00). Males comprised 45.34% (n\\u0026thinsp;=\\u0026thinsp;2595), females 54.66% (n\\u0026thinsp;=\\u0026thinsp;3128). Individuals in the highest CTI tertile exhibit marked differences versus the lowest on various indicators. People in the top CTIs were the elderly and had high levels of BMI, SBP, DBP, fast blood glucose, HbA1c, LDL-C, TG, TC, serum creatinine, TyG, CRP, CTI. In addition, the prevalence of comorbidities such as hypertension, dyslipidemia, and diabetes was notably higher in people with elevated CTI levels. Baseline characteristics were compared between participants with and without CVD and all-cause death in Table S4-S5, while the detailed baseline characteristics comparison, including both included and excluded participants, is detailed in Supplemental Table S6.\\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 the study individuals\\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=\\\"char\\\" char=\\\".\\\" 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\\u003evariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;5723)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;1908)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;1908)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;1907)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e.value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e57.00(51.00,63.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56.00(49.00,62.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57.00(52.00,64.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e58.00(52.00,63.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3128(54.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e967(50.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1063(55.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1098(57.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2595(45.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e941(49.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e845(44.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e809(42.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmoke status, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ecurrent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1729(30.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e632(33.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e567(29.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e530(27.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e424( 7.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e123( 6.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e146( 7.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e155( 8.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003enever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3570(62.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1153(60.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1195(62.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1222(64.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDrink status, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3757(65.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1183(62.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1259(65.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1315(68.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1966(34.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e725(38.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e649(34.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e592(31.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarital status, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.48\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5174(90.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1723(90.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1715(89.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1736(91.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eothers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e549( 9.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e185( 9.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e193(10.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e171( 8.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEducational level, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAbove junior high school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e575(10.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e201(10.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e186( 9.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e188( 9.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eilliterate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1609(28.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e517(27.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e578(30.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e514(26.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJunior high school and below\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3539(61.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1190(62.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1144(59.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1205(63.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e128.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e124.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;19.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e128.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e132.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;20.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e75.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e73.24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e75.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e77.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFglu, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102.06(94.41,111.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e97.02(90.72,104.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100.98(94.50,108.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e109.26(100.62,125.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHb, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.51\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e117.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e113.50\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e122.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;33.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e117.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;38.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTG, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e101.78(73.46,148.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e68.14(55.76,83.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e106.20(84.96,132.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e173.46(126.56,233.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTC, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e194.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;37.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e185.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;33.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e195.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;36.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e203.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;40.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCr, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.78\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.19\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTyG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9.24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCRP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.96(0.53,1.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.51(0.35,0.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.99(0.62,1.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.10(1.14,4.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4530(79.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1661(87.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1528(80.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1341(70.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1193(20.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e247(12.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e380(19.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e566(29.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDyslipidemia, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5307(92.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1846(96.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1789(93.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1672(87.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e416( 7.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e62( 3.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e119( 6.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e235(12.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5472(95.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1878(98.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1847(96.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1747(91.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e251( 4.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30( 1.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e61( 3.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e160( 8.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCVD, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4302(75.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1540(80.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1408(73.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1354(71.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1421(24.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e368(19.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500(26.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e553(29.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension treatment, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4846(84.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1750(91.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1633(85.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1463(76.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e877(15.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e158( 8.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e275(14.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e444(23.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes treatment, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5568(97.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1887(98.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1873(98.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1808(94.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e155( 2.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21( 1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35( 1.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e99( 5.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDyslipidemia treatment, n (%)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5493(95.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1875(98.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1845(96.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1773(92.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e230( 4.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33( 1.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e63( 3.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e134( 7.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCKM\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e503( 8.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e351(18.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e132( 6.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20( 1.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1106(19.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e603(31.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e394(20.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e109( 5.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2229(38.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e571(29.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e828(43.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e830(43.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1885(32.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e383(20.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e554(29.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e948(49.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNGR, mg/dL\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e631(11.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e85( 4.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e162( 8.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e384(20.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5092(88.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1823(95.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1746(91.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1523(79.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePRE-DM, mg/dL\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5323(93.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1838(96.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1779(93.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1706(89.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e400( 6.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e70( 3.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e129( 6.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e201(10.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation of CTI index with CVD and total mortality in CKM syndrome patients\\u003c/h2\\u003e \\u003cp\\u003eAs shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, multivariable-adjusted analysis revealed a graded cardiovascular risk profile associated with CTI. Among CKM stage 0\\u0026ndash;3 individuals, the highest CTIQ quartile (Q3) showed 54% increased CVD risk versus Q1 in the crude model (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.54, 95%\\u003cem\\u003eCI\\u003c/em\\u003e 1.44\\u0026ndash;1.65, \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001), which remained significant after adjusting for demographic (age, gender, smoking, drinking status, marital status) and clinical confounders (hypertension, diabetes, dyslipidemia, Hypertension treatment, Diabetes treatment, dyslipidemia treatment,), with 27% excess risk in Model 2 (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.27, 1.19\\u0026ndash;1.36, \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001; \\u003cem\\u003eP\\u003c/em\\u003e-trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Meanwhile, the highest quartile (Q3) showed adjusted hazard ratios of 1.35 for TyG (95% \\u003cem\\u003eCI\\u003c/em\\u003e 1.26\\u0026ndash;1.44; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 1.43 for standardized CRP values (CRP-SD) (95% \\u003cem\\u003eCI\\u003c/em\\u003e 1.34\\u0026ndash;1.52; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Both were lower than the CTI's highest quartile HR of 1.54 (95% \\u003cem\\u003eCI\\u003c/em\\u003e 1.44\\u0026ndash;1.65; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Table S7). For all-cause mortality, each unit increase in continuous CTI corresponded to 60% higher risk (Model 2: \\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.60, 1.29\\u0026ndash;1.99, \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001). Stratified analysis demonstrated a 97% mortality increase in Q3 versus Q1 (Model 2: \\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.97, 1.23\\u0026ndash;3.15, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.005), whereas Q2 showed non-significant association (\\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.65), indicating a threshold effect of CTI.\\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\\u003eMultivariate cox regression for the correlation between CTI, CVD and overall mortality risk\\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=\\\"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=\\\"char\\\" char=\\\".\\\" 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\\u003eCTI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eCrude model\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e95%CI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95%CI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e95%CI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCVD incidence\\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\\u003ecategories\\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\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eref\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eref\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eref\\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\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.38(1.29,1.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.33(1.24,1.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.26(1.17,1.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.54(1.44,1.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.47(1.38,1.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.27(1.19,1.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAll-cause mortality\\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\\u003econtinuous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.56(1.28,1.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.67(1.36, 2.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.6(1.29, 1.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ecategories\\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\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eref\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eref\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eref\\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\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.22(0.74,2.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.17(0.71, 1.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.12(0.68, 1.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.14(1.36,3.35)\\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 \\u003cp\\u003e2.16(1.37, 3.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.97(1.23, 3.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ep for trend\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.003\\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\\u003ecrude model: unadjusted for covariates; model 1: age, gender, smoke status, drink status, marital status, education; model 2: age, gender, smoke status, drink status, marital status, educational level, hypertension, dyslipidemia, diabetes, Hypertension treatment, Diabetes treatment, dyslipidemia treatment.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRCS and threshold effect analysis\\u003c/h2\\u003e \\u003cp\\u003eWe performed RCS analysis and threshold analysis to verify the association of CTI and CVD prevalence and mortality rates. For CVD incidence, standard Cox regression revealed non-linear association between CTI and CVD incidence (\\u003cem\\u003eHR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;1.103\\u0026ndash;1.219, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Two-piecewise linear regression revealed a critical inflection point at CTI\\u0026thinsp;=\\u0026thinsp;9.285: below this value, CTI exhibited a strong positive association with CVD risk (\\u003cem\\u003eHR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;1.240\\u0026ndash;1.382, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001), with no notable association was found above the inflection point (\\u003cem\\u003eHR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.920\\u0026ndash;1.010, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.115). The log-likelihood ratio test (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) confirmed the superiority in segmented model, highlighting a nonlinear dose-response pattern between CTI and CVD risk (Table S8). For all-cause mortality, standard Cox regression showed a notable linear trend between CTI and overall mortality (\\u003cem\\u003eHR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;1.556\\u0026ndash;1.666, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). While two-piecewise linear regression suggested a potential risk transition at CTI\\u0026thinsp;=\\u0026thinsp;8.608 (\\u003cem\\u003eHR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;1.522\\u0026ndash;1.806, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;0.007 above the threshold; no significant association below the threshold, \\u003cem\\u003eHR\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;1.241\\u0026ndash;1.315, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.500), the log-likelihood ratio tests (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.797 for all models) indicated no statistically significant improvement in model fit with segmentation. Thus, despite localized risk differences near the inflection point, the overall relationship predominantly aligns with a linear pattern (Table S9).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eKaplan\\u0026ndash;Meier (K\\u0026ndash;M) survival curves\\u003c/h2\\u003e \\u003cp\\u003eThe Kaplan\\u0026ndash;Meier (K\\u0026ndash;M) survival curves showed an elevated CVD incidence or overall mortality in the high CTI group. The log-rank test's p-values for the Q2 and Q3 groups, all below 0.05, confirm a higher risk compared to the Q1 group (Figure S1). Furthermore, using the inflection point (CTI\\u0026thinsp;=\\u0026thinsp;8.602), participants were stratified into high- and low-exposure groups, with Kaplan-Meier analysis confirming significant differences in survival rates (log-rank \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Figure S2).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSubgroup analyses\\u003c/h2\\u003e \\u003cp\\u003eTo delve deeper into the connection between CTI and the likelihood of CVD or all-cause mortality, researchers conducted subgroup and interaction analyses on various variables, which include age, gender, tobacco use, alcohol consumption, marital status, education attainment, diabetes statuse, hypertension, dyslipidemia, glucose levels (including NGR, Pre-DM, and DM), and CKM syndrome (0\\u0026ndash;3 stages) (Figure S4 ,Table\\u0026nbsp;12). For CVD incidence, sex stratification revealed markedly higher CVD risk in males (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.627, 95% CI: 1.451\\u0026ndash;1.824; \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001) compared to females (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.359, 95% CI: 1.223\\u0026ndash;1.510; \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001), with a pronounced interaction effect (\\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.02). A dose-response relationship was evident, as higher CTIQ quartiles (Q3 vs. Q1) consistently correlated with elevated CVD risk across all strata (\\u003cem\\u003eP\\u003c/em\\u003e for trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Notably, smoking status (\\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.029) and marital status (\\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001) exhibited significant interactions, where current smokers (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.722, Q2 vs. Q1) and married individuals (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.809, Q3 vs. Q1) showed heightened vulnerability. Conversely, no interaction was observed for age, hypertension, or dyslipidemia (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Education level further modulated risk, with individuals above junior high school education displaying the steepest CTIQ-associated risk gradient (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;2.935, Q3 vs. Q1; \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001). For overall mortality, lower education (e.g., \\\"Junior high school and below\\\": \\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.656, \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001) and non-drinkers (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.774, \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.0001) showed elevated mortality. CTI consistently predicted mortality across most subgroups (e.g., males: \\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.538; females: \\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.628, both \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.01), despite nonsignificant interactions for sex (\\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.783) and age (\\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.658). Notably, pre-diabetic individuals exhibited a non-significant trend toward heightened risk (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;1.497, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.254), warranting further investigation. The most pronounced interaction emerged in CKM strata (\\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001), which revealed CKM\\u0026thinsp;=\\u0026thinsp;0 individuals have an exceptionally high risk (\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;8.225, 95% CI 2.558\\u0026ndash;27.964, \\u003cem\\u003eP\\u0026thinsp;\\u0026lt;\\u003c/em\\u003e\\u0026thinsp;0.001). Therefore, the future study assessed the connection between CTIQ and overall death in the 0\\u0026ndash;3 stage of CKM group: CKM Stage 0 exhibited an extreme hazard ratio (Q3 vs Q1:\\u003cem\\u003eHR\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;19.611, 95%\\u003cem\\u003eCI\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;2.251\\u0026ndash;171.300, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), but with wide confidence intervals (Figure S5, Table\\u0026nbsp;13).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAUC and ROC\\u003c/h2\\u003e \\u003cp\\u003eThe research aims to assess the forecasting capabilities of TyG, CRP and CTI in predicting overall mortality and CVD occurrence via ROC analysis. For CVD risk, CRP and CTI retained comparable performance (AUC\\u0026thinsp;=\\u0026thinsp;0.66 and 0.61, respectively), whereas TyG again performed at chance level (AUC\\u0026thinsp;=\\u0026thinsp;0.5). Similarly, for all-cause mortality, CRP demonstrated moderate predictive utility (AUC\\u0026thinsp;=\\u0026thinsp;0.66, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.61\\u0026ndash;0.66), just ahead of CTI (AUC\\u0026thinsp;=\\u0026thinsp;0.61, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.56\\u0026ndash;0.61), while TyG showed no discriminative capacity (AUC\\u0026thinsp;=\\u0026thinsp;0.5, 95% \\u003cem\\u003eCI\\u003c/em\\u003e: 0.45\\u0026ndash;0.5). The findings indicate that CTI could outperformed CRP and the TyG index in assessing overall mortality risk stratification (Figure S6)\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSensitivity analyses\\u003c/h2\\u003e \\u003cp\\u003eTo check the stability of our findings, we conducted multiple sensitivity analyses. Firstly, in order to tackle the issue of missing data and to limit the possibility of bias, we employed multiple imputations. Subsequent analysis revealed that the correlation among CTI and CVD incidence and overall mortality was in accordance with the basic results (Table S10). Secondly, the application of logistic regression models to investigate the connection between CTI and CVD incidence, as well as all-cause mortality, yields consistent results. (Table S11). Thirdly, the analysis of the piecewise Cox regression model confirmed result stability (Table S12-13). Fourthly, we also assessed the correlation among CTI and CVD incidence and overall mortality stratified by sex, age, and glucose level (grouped into NGR, Pre-DM, and DM). (Table S14). Furthermore, we explored additional analyses to analyze the associations of TyG and CRP standardized values (CRP-SD) with both CVD incidence and all-cause mortality, thereby validating the robustness of our primary findings (Table S7). Finally, analyses stratified by sex and CKM stage (0\\u0026ndash;3) revealed consistent associations between CTI and adverse outcomes, as evidenced by Kaplan-Meier curves (all log-rank \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Figure S3).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eWe enrolled the subjects diagnosed with CKM syndrome across stages 0 to 3, categorizing them according to initial CTI evaluations. Using Cox proportional hazards models, we then analyzed how CTI levels correlated with both CVD occurrence and overall mortality rates. The main points of the study can be described in the following way: The dual role of CTI: a threshold-driven nonlinear association with CVD incidence and a continuous linear predictor of all-cause mortality. Specifically, below an inflection point at 9.28, CTI was strongly associated with CVD risk, whereas the association attenuated above the inflection point. Notably, each 1-unit increment of continuous CTI was linked to a 97% excess overall mortality in completely adjusted analyses. These results not only support the therapeutic usefulness of CTI evaluation in older patients with CKM syndrome but also furnish critical data for precise risk classification in this demographic.\\u003c/p\\u003e \\u003cp\\u003eThis index integrates CRP, an established marker of inflammatory reactions, with the TyG, an indication that signifies insulin resistance. Prior research has demonstrated the correlation between increased TyG and a heightened CVD incidence or overall mortality. Zhang et al. identified significant links between TyG and total mortality as well as CVD incidence \\u003csup\\u003e[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e. Using the CHARLS database, Huo and colleagues demonstrate the association between increased TyG at baseline and an augmented risk of stroke \\u003csup\\u003e[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/sup\\u003e. A UK Biobank study of 410,515 participants explored a strong correlation between TyG and mortality across the entire cohort \\u003csup\\u003e[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e. The TyG index functions as a reliable indictors of CVD incidence and overall mortality across the whole people, as well as a relevant measure for specific subgroups. Specifically, Li et al., using the NHANSE database, discovered that TyG was an independent indicator of both overall and cardiovascular death in hypertension persons \\u003csup\\u003e[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/sup\\u003e. Additionally, Li et al. conducted a prospective cohort study with 7,376 participants, demonstrating that TyG is a valuable tool for predicting the development of CVD risk in CKM stages 0\\u0026ndash;3\\u003csup\\u003e[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/sup\\u003e. Furthermore, one study revealed a substantial correlation between an increased TyG ratio and a heightened likelihood of CVD and overall mortality in diabetes patients \\u003csup\\u003e[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. Nevertheless, inflammation stands as a pivotal risk element contributing to the development of CVD or to mortality from any cause \\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e. Inflammation is becoming more well recognized as a critical role on CVD and mortality risk, based on the study by Cho et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e. Furthermore, a thorough research of 8420 individuals from ten prospective studies found a link between higher CRP levels and elevated stroke recurrence \\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e. Additionally, a large-scale study involving 1,555 participants pinpointed that elevated CRP levels correlated with a heightened likelihood of CVD in diabetics \\u003csup\\u003e[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e. Moreover, Cui et al. demonstrated that TyG and CRP had a co-exposure impact and mutual mediation on CVD \\u003csup\\u003e[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eCTI, created by Ruan et al. \\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e, serves as a key tool for diagnosing and predicting outcomes in cancer patients. One study of 10,443 individuals found a strong positive linear connection between CTI and stroke risk \\u003csup\\u003e[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e. Xu et al. have shown the prevalence of CHD in the total American population is positively, linearly, and robustly associated with CTI \\u003csup\\u003e[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. Prior studies indicate that CTI is significantly related to increased cardiovascular events in the whole population. However, given the intricate interplay between CVD, CKD, and metabolic diseases, it is critical to examine how CTI influences cardiovascular events and overall mortality\\u0026mdash;especially within the framework of CKM syndrome.\\u003c/p\\u003e \\u003cp\\u003eAlthough the specific mechanism of CTI and CVD and overall mortality during the 0\\u0026ndash;3 CKM phase is still obscure, it can be elucidated by the following factors. First of all, insulin resistance and chronic inflammation impair the integrity of the endothelium, reduce the body's ability to utilize nitric oxide effectively, disrupt healthy blood clotting mechanisms, and speed up the occurrence of atherosclerosis. All of these significantly increase CVD risk \\u003csup\\u003e[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]\\u003c/sup\\u003e. Furthermore, inflammation may exacerbate insulin resistance, triggering tissue-derived inflammatory mediators and amplifying systemic inflammation. Inflammation and insulin resistance exhibit collaborative consequences that mutually promote and exacerbate each other, thereby increasing the risk of CVD \\u003csup\\u003e[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]\\u003c/sup\\u003e. Furthermore, Atherosclerotic plaque stability undermined by inflammation and insulin resistance. This instability heightens rupture risk, potentially resulting in thrombosis and consequently contributing to the incidence of CVD and overall mortality \\u003csup\\u003e[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]\\u003c/sup\\u003e. Insulin resistant and inflamed patients often suffer from co-morbidities such as hypertension, diabetes, obesity, and metabolic syndrome. These conditions collectively serve as major contributors to CVD and increased mortality rates \\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR48\\\" citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]\\u003c/sup\\u003e. For metabolic syndrome, the processes of CVD risk and overall mortality essentially include lipid metabolism, Oxidative Stress, and inflammatory response \\u003csup\\u003e[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]\\u003c/sup\\u003e. Lipid abnormalities associated with CKM are defined by increased TC, TG and LDL-C, coupled with reduced HDL-C. Such dyslipidemia doesn't just heighten CVD risk\\u0026mdash;it actively fuels disease progression by triggering thrombotic mechanisms \\u003csup\\u003e[\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]\\u003c/sup\\u003e. Research indicates that overweight and having insulin resistance may elevate the production of Reactive Oxygen Species (ROS), potentially damaging Vascular Endothelial Cells (VECs) and worsening lipid metabolism dysfunction. This process contributes to the formation of oxidized Low-Density Lipoprotein (ox-LDL), a key driver of atherosclerotic plaque development \\u003csup\\u003e[\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]\\u003c/sup\\u003e. Inflammation is vital in the context of these disorders. Macrophages in adipose tissue secrete factors like Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), playing a key role in driving the persistent, low-level inflammation characteristic of metabolic syndrome \\u003csup\\u003e[\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]\\u003c/sup\\u003e. These inflammatory mediators not only promote atherosclerotic plaque formation but also increase the risk of thrombosis through the activation of Endothelial Cells (ECs) and platelets, which is closely correlate with CVD and overall death events \\u003csup\\u003e[\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eTherefore, it follows that people with higher CTI may have more severe vascular damage, a higher incidence of CVD, and a higher death rate from all causes. The analysis of RCS demonstrated a complex, non-linear relationship correlation between CTI and CVD incidence, indicating that varying levels of CTI within the population experiencing 0\\u0026ndash;3 stages of CKM syndrome may exert dynamic effects: Below the threshold (CTI\\u0026thinsp;\\u0026lt;\\u0026thinsp;9.28), subclinical inflammation indicated by elevated CRP, along with developing insulin resistance reflected by an increased TyG index, may work together to activate pro-inflammatory pathways in vascular endothelial cells, such as NF-κB signaling. This activation can enhance monocyte adhesion and foam cell formation through the regulation by modulating adhesion factors like VCAM-1 and ICAM-1, thus fostering the progression of early atherogenesis \\u003csup\\u003e[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]\\u003c/sup\\u003e. Interventions aimed at suppressing inflammation, such as statins, or enhancing insulin sensitivity, such as GLP-1 receptor agonists, during this phase may produce optimal preventive results. Beyond the inflection (CTI\\u0026thinsp;\\u0026ge;\\u0026thinsp;9.28), chronic inflammation drives macrophage M1 polarization, releasing IL-6/TNF-α to activate MMP-9/MMP-2, degrading fibrous caps and increasing intraplaque neovascularization \\u003csup\\u003e[\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]\\u003c/sup\\u003e, while TyG-induced AGEs-RAGE signaling perpetuates mitochondrial dysfunction and endothelial apoptosis, accelerating fibrosis even post-CTI reduction \\u003csup\\u003e[\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]\\u003c/sup\\u003e. Simultaneously, persistent elevation of CTI interferes with intrinsic protective mechanisms via dual metabolic and inflammatory stress. This nonlinearity indicates a shift from reversible endothelial damage to irreversible vascular remodeling, underscoring the necessity for early dual-pathway targeting.\\u003c/p\\u003e \\u003cp\\u003eOur analysis reveals a paradoxical association between CKM stage 0 (no overt cardiometabolic disease) and heightened all-cause mortality (\\u003cem\\u003eP\\u003c/em\\u003e-interaction\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). While counterintuitive, this finding may reflect limitations in current CKM stratification. First, classification misalignment is plausible: undiagnosed subclinical conditions (e.g., IR, vascular dysfunction) or non-traditional risk factors (chronic inflammation, epigenetic alterations) could drive mortality without meeting conventional diagnostic thresholds. Second, residual confounding occurs even after multivariable adjustments: socioeconomic inequalities, lifestyle heterogeneity (e.g., psychological stress, food habits), and competing hazards (e.g., cancer/trauma-related deaths) may disproportionately impact \\\"apparently healthy\\\" groups. Third, selection bias merits scrutiny: CKM stage 0 cohorts often exclude individuals with incomplete biomarker data, potentially inflating mortality estimates. Methodologically, we propose three validations: 1) Reclassification using extended biomarkers (e.g., coronary calcium scoring, urinary albumin-to-creatinine ratio) to detect occult disease; 2) Competing risk analysis differentiating between cardiovascular and non-cardiovascular causes; 3) Sensitivity analyses that include social determinants, such as neighborhood deprivation indices and healthcare access, are essential. If validated, this indicates a significant shift in understanding: \\\"metabolically healthy\\\" phenotypes may conceal underlying multiorgan dysregulation that necessitates the identification of new biomarkers, including mitochondrial DNA integrity and senescent cell burden. Meanwhile, this study also underscores the urgency of expanding multicenter cohorts to validate extreme risk estimates in CKM Stage 0 and resolve contradictions in intermediate phases. These results cast doubt on the CKM framework's capacity to identify dangers in their early stages. To improve preventative measures, we suggest using dynamic risk trajectories instead of static baseline staging.\\u003c/p\\u003e \\u003cp\\u003eThese results give fresh perspectives for clinical treatment, implying that including CTI into routine examinations is a convenient, easily available technique for assessing individuals with CKM phases 0\\u0026ndash;3. Physicians can more accurately assess patients' metabolic health and create customized treatment plans by using dynamic monitoring of CTI changes, which increases the accuracy and efficacy of illness management. Periodic CTI monitoring reduces the risk of negative outcomes and improves long-term survival rates by facilitating early diagnosis of disease development, supporting clinical decision-making, and enabling prompt interventions to avert deterioration.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrength and limitation\\u003c/h2\\u003e \\u003cp\\u003eThere are some noteworthy advantages to the current investigation. First, it concentrates on the clinically significant but frequently disregarded person in CKM stages 0\\u0026ndash;3. This article is the earliest research to investigate the use of the CTI index in evaluating CVD incidence and overall mortality in people with 0\\u0026ndash;3 stages of CKM, offering considerable clinical significance and novelty. Secondly, the data included in this study employed a complicated method to finally choose 5,723 qualifying individuals from a nationwide survey. The extensive dataset guarantees robust statistical significance. Furthermore, this research examines the connection among CTI, CVD incidence and overall mortality in people throughout 0\\u0026ndash;3 stages of CKM, which advances research in this area. Finally, we carried out thorough sensitivity analyses and strictly adjusted for potential contributors to guarantee the validity and dependability in our findings.\\u003c/p\\u003e \\u003cp\\u003eHowever, certain study constraints must be noted. Initially, the subjects consist exclusively of middle and elderly Chinese, thereby constraining the scope of the results' applicability. Secondly, Secondly, the Framingham ten-year score for the risk of CVD was used for CKM syndrome staging instead of the most recent PREVENT equation, which may have an impact on staging accuracy. Thirdly, a restricted number of individuals were included in this research due to stringent exclusion criteria, which may have resulted in attrition bias for the participants. Fourthly, the accuracy of results is impacted by the reliance on self-reports for illness diagnosis, which may result in an underestimation of prevalence and an inability to differentiate between different forms of CVD or all-cause death. Fifthly, the possible impact of these alterations on mortality risk is unknown since the CTI index was merely evaluated at the initial assessment, and no analysis of fluctuations during the follow-up phase was conducted. Future research should explore how changes in CTI across time affect CVD incidence and all-cause mortality. Finally, we were unable to do a Mendelian randomization study because there was insufficient genetic data for the CKM stages 0\\u0026ndash;3 cohort. Future research should concentrate on capturing related evidence to enhance the precision of causal inferences. In order to confirm the results and offer stronger support for research in related fields, more thorough investigation in additional substantial cohort studies is required.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn summary, this study employed the CHARLS database and an innovative integrated predictor of insulin resistance and inflammation, termed CTI, to forecast CVD incidence and all-cause death in the stage 0\\u0026ndash;3 CKM cohort. The results indicated the substantial connection among elevated CTI levels and a heightened CVD risk and all-cause mortality, implying that CTI may function as a predictive indicator in stages 0\\u0026ndash;3 of CKM subjects.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHuiwen Ou developed the study framework and drafted the manuscript. Huiwen Ou participated in the statistical analysis. Xiaoshuang xia performed the literature review and compiled the illustrations. Xin Li conducted manuscript editing and review. All contributors endorsed the final draft.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eProjects of Tianjin Municipal Health Commission (TJWJ2024XK008), the Tianjin Key\\u003c/p\\u003e\\n\\u003cp\\u003eTianjin Municipal Science and Technology Bureau Project (21JCZDJC01230), the Key\\u003c/p\\u003e\\n\\u003cp\\u003eCenter for Health and Meteorology Multidisciplinary Innovation, the National Natural\\u003c/p\\u003e\\n\\u003cp\\u003eScience Foundation of China (42275197)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCHARLS data repository: http://charls.pku.edu.cn/en.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe CHARLS has been cleared by Peking University Biomedical Ethics Review Board, with all individuals offering informed agreement.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor details\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDepartment of Neurology, The second Hospital of Tianjin Medical University, No23, PingJiang\\u003c/p\\u003e\\n\\u003cp\\u003eRoad, Tianjin 300211, China\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eROTH G A, MENSAH G A, JOHNSON C O, et al. 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Lipids Health Dis. 2025;24(1):126.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLI C, ZHANG Z, LUO X, et al. The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis [J]. Cardiovasc Diabetol. 2025;24(1):47.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLI W, SHEN C, KONG W, et al. Association between the triglyceride glucose-body mass index and future cardiovascular disease risk in a population with Cardiovascular-Kidney-Metabolic syndrome stage 0\\u0026ndash;3: a nationwide prospective cohort study [J]. Cardiovasc Diabetol. 2024;23(1):292.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOH R, KIM S, PARK S H, et al. Elevated triglyceride-glucose index is a risk factor for cardiovascular events in adults with type 1 diabetes: a cohort study [J]. 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Curr Diab Rep. 2013;13(3):435\\u0026ndash;44.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJANANI K V SABERIANP, PATEL H B, et al. Prevalence of metabolic syndrome in patients with inflammatory bowel disease: a meta-analysis on a global scale [J]. J Health Popul Nutr. 2025;44(1):112.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXING D, XU J, WENG X, et al. Correlation between estimated glucose disposal rate, insulin resistance, and cardiovascular mortality among individuals with metabolic syndrome: a population-based analysis, evidence from NHANES 1999\\u0026ndash;2018 [J]. Diabetol Metab Syndr. 2025;17(1):11.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHU Y, LIANG Y, LI J, et al. Correlation between atherogenic index of plasma and cardiovascular disease risk across Cardiovascular-kidney-metabolic syndrome stages 0\\u0026ndash;3: a nationwide prospective cohort study [J]. Cardiovasc Diabetol. 2025;24(1):40.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTAGOE E A, DWAMENA-AKOTO E, NSAFUL J, et al. High atherogenic index of plasma and cardiovascular risk factors among Ghanaian breast cancer patients [J]. Exp Biol Med (Maywood). 2020;245(18):1648\\u0026ndash;55.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYEN C H, CHANG P S, CHIU C J et al. β-Carotene Status Is Associated with Inflammation and Two Components of Metabolic Syndrome in Patients with and without Osteoarthritis [J]. Nutrients, 2021, 13(7).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKIR S, EKIZ K, ALACAM H, THE ASSOCIATION BETWEEN PRO AND ANTI-INFLAMMATORY MARKERS WITH THE COMPONENTS OF METABOLIC SYNDROME [J], et al. Acta Endocrinol (Buchar). 2019;15(4):430\\u0026ndash;5.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSYAUQY A, HSU C Y, RAU H H et al. Association of Sleep Duration and Insomnia Symptoms with Components of Metabolic Syndrome and Inflammation in Middle-Aged and Older Adults with Metabolic Syndrome in Taiwan [J]. Nutrients, 2019, 11(8).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSHI H, KOKOEVA M V, INOUYE K, et al. TLR4 links innate immunity and fatty acid-induced insulin resistance [J]. J Clin Invest. 2006;116(11):3015\\u0026ndash;25.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHUO Y, LEY K. Adhesion molecules and atherogenesis [J]. Acta Physiol Scand. 2001;173(1):35\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHENEIN M Y, VANCHERI S, LONGO G et al. The Role of Inflammation in Cardiovascular Disease [J]. Int J Mol Sci, 2022, 23(21).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTWARDA-CLAPA A, OLCZAK A, BIAŁKOWSKA A M et al. 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Cells, 2022, 11(8).\\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\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cardiovascular-diabetology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"cvdb\",\"sideBox\":\"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)\",\"snPcode\":\"12933\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12933/3\",\"title\":\"Cardiovascular Diabetology\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"C-reactive protein-triglyceride glucose index, cardiovascular diseases, all-cause mortality, Cardiovascular–kidney–metabolic syndrome\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6726039/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6726039/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eObjective\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe American Heart Association (AHA) developed the notion of cardiovascular-kidney-metabolic (CKM) syndrome, which emphasizes the interconnection of heart, kidney, and metabolic illnesses. The C-reactive protein-triglyceride-glucose (CTI) represents a potential indicator to assess the resistance to insulin and an inflammatory response. However, the connection among CTI, cardiovascular disease (CVD) incidence, and overall mortality rates remains uncertain, particularly among individuals at CKM stages 0-3.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe China Health and Retirement Longitudinal Study (CHARLS) enrolled 17,705 middle-aged and elderly people. The primary outcome was the occurrence of CVD and overall mortality rates. The CTI was obtained by 0.412* Ln (CRP [mg/L]) + Ln (TG [mg/dl] × FPG [mg/dl])/2. The correlation among CTI and CVD incidence and overall mortality was assessed via Cox proportional hazard models, Kaplan-Meier curves and restricted cubic spline (RCS) analysis. To improve the study results, a stratified analysis evaluated the influence of varying socio-demographic characteristics.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDuring a 9-years following-up, 5534 participants (9.2%) experienced a CVD event, while 121 participants (2.1%) experienced all-cause mortality. RCS analysis revealed a notable non-linear association between CTI and CVD occurrence, as well as a linear association between CTI and all-cause death. After comprehensive multivariate adjustment, the data showed a striking 97% increase in overall mortality risk for every 1-unit rise in continuous CTI measurements.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFindings show that higher CTI levels independently forecast CVD and death, highlighting its potential as a biomarker for individuals with CKM stages 0-3.\\u003c/p\\u003e\",\"manuscriptTitle\":\"C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0–3 of cardiovascular–kidney–metabolic syndrome: a nationwide prospective cohort study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-29 09:50:08\",\"doi\":\"10.21203/rs.3.rs-6726039/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-06-15T05:01:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-06-10T15:16:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-06-02T11:33:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"70019106433135231182109853601699471635\",\"date\":\"2025-05-29T17:02:09+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-05-27T00:20:01+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"157568652637336619422359985311556505341\",\"date\":\"2025-05-26T22:56:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"238607926384767564496928468941069740746\",\"date\":\"2025-05-24T14:30:07+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"118266613189526083728479651270435041919\",\"date\":\"2025-05-24T06:05:30+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"316694415700529624737472446854475836676\",\"date\":\"2025-05-23T12:32:15+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-05-23T12:25:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-05-23T12:17:42+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-05-23T07:48:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Cardiovascular Diabetology\",\"date\":\"2025-05-22T14:34:58+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cardiovascular-diabetology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"cvdb\",\"sideBox\":\"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)\",\"snPcode\":\"12933\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12933/3\",\"title\":\"Cardiovascular Diabetology\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"58d9bfe8-f372-47e4-b7f1-5184359dda62\",\"owner\":[],\"postedDate\":\"May 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-28T16:01:08+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6726039\",\"link\":\"https://doi.org/10.1186/s12933-025-02848-9\",\"journal\":{\"identity\":\"cardiovascular-diabetology\",\"isVorOnly\":false,\"title\":\"Cardiovascular Diabetology\"},\"publishedOn\":\"2025-07-22 15:57:31\",\"publishedOnDateReadable\":\"July 22nd, 2025\"},\"versionCreatedAt\":\"2025-05-29 09:50:08\",\"video\":\"\",\"vorDoi\":\"10.1186/s12933-025-02848-9\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12933-025-02848-9\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6726039\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6726039\",\"identity\":\"rs-6726039\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}