Predictive Value of C-Reactive Protein/Triglyceride-Glucose Index on the All-cause Mortality among Middle-Aged and Older Chinese Adults: A Prospective Cohort Study from CHARLS

Predictive Value of C-Reactive Protein/Triglyceride-Glucose Index on the All-cause Mortality among Middle-Aged and Older Chinese Adults: A Prospective Cohort Study from CHARLS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive Value of C-Reactive Protein/Triglyceride-Glucose Index on the All-cause Mortality among Middle-Aged and Older Chinese Adults: A Prospective Cohort Study from CHARLS Lili Zhang, Ruijie Tang, Yuyan Xiong, Xianpei Wang, Zhanying Han, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8358890/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The rising prevalence of metabolic syndrome in Chinese adults ≥ 45 years reflects rapid socioeconomic and lifestyle changes. C-reactive protein (CRP) and triglyceride-glucose (TyG) index, biomarkers of chronic inflammation and insulin resistance, jointly drive metabolic dysregulation. However, their combined index (CTI/CRP-TyG Index) remains understudied in mortality prediction. This prospective cohort included 9,055 participants from China Health and Retirement Longitudinal Study (CHARLS) database. CTI was categorized into quartiles (Q1-Q4). Kaplan-Meier curves and Cox regression (adjusting for sociodemographics, lifestyle, and clinical factors) were used in survival analysis. Restricted cubic splines (RCS), subgroup analysis and ROC/NRI/IDI evaluated CTI-mortality associations and predictive model performance. During follow-up, 221 deaths occurred, showing declining survival rates with higher CTI quartiles (98.50%→95.63%, p < 0.001). The highest CTI quartile had 3.48-fold mortality risk (HR = 3.48, 95%CI:2.25–5.40, p < 0.001). Subgroup analysis revealed stronger CTI-mortality associations in participants aged ≥ 55, primary education, or cardiovascular history, with overall HR = 2.76 (95%CI:2.20–3.47, p < 0.001). RCS and ROC analysis demonstrated that CTI quartiles linearly correlated with mortality (p < 0.001), and improved the efficiency of predictive models (AUC:0.849 vs 0.829, p = 0.008; NRI = 0.425, IDI = 0.029, p < 0.05). CTI quartiles increase elevated mortality of Chinese adults over 45, driven by CRP/triglyceride/glucose synergy. Targeting these biomarkers may lower mortality of metabolic-aging populations. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Metabolic syndrome C-reactive protein Triglyceride-glucose index All-cause mortality Aging population CHARLS database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The escalating global burden of age-related morbidity underscores the critical requirement for validated biomarkers to stratify health risks in aging populations. With China experiencing accelerated population aging, the rising prevalence of metabolic syndrome among adults aged ≥ 45 years presents a significant public health concern, necessitating urgent intervention strategies. Characterized clinically by hyperlipidemia, impaired glucose regulation, and elevated blood pressure, this pathophysiological condition demonstrates chronic inflammatory states and insulin resistance as its core pathogenic mechanisms during progression. Consequently, a critical need emerges for a composite biomarker that simultaneously incorporates inflammatory indices, lipid profiles and glycemic parameters. Such a multidimensional assessment tool holds significant potential as a robust prognostic predictor for both clinical outcomes and population-based mortality rates in metabolic syndrome management. C-reactive protein (CRP) refers to a sensitive marker of systemic inflammation commonly synthesized by the liver in response to IL-6 and other pro-inflammatory cytokines. Elevated CRP levels, especially when > 3 mg/L, could be strongly associated with chronic inflammation 1 . In the conditions of atherosclerosis, diabetes, and cardiovascular diseases, chronically abnormal CRP should reflect the persistent low-grade inflammation and consistently result in the remarkable all-cause mortality in Western cohorts 2 , 3 . Previously, the hyperinsulinemic-euglycemic clamp technique was introduced as the gold standard to evaluate the insulin sensitivity of peripheral tissues. In this test, researchers should control glucose perfusion and monitor the blood glucose to maintain it at a high level, during which the perfusion volumes of glucose must be detected on time, and the glucose metabolic rate can be evaluated eventually to represent the sensitivity of pancreatic β cells to glucose. Currently, the novel triglyceride-glucose(TyG) index, a surrogate for insulin resistance, correlates better with hyperinsulinemic-euglycemic clamp than individual parameters (triglycerides or glucose alone), particularly in obese and pre-diabetic groups 4 , 5 . And the TyG index, compared with the golden standard, is indeed more cost-effective and widely accessible to diagnose insulin resistance and identity high-risk groups for diabetes. Besides, a more representative cohort study from Korea demonstrated that an elevated TyG index over 8.5 could predict the incidence of type 2 diabetes 6 , independent of traditional risk factors. Generally, existing evidence predominantly examines CRP or TyG index in isolation. However, whether their combined effect confers additive or synergistic mortality risk remains understudied, particularly in non-Western aging populations with distinct lifestyles and genetic profiles. Currently, an increasing number of studies suggest that a higher TyG index should correlate with worse chronic systemic inflammation, evidenced by increased IL-6 and CRP levels, aggravating endothelial dysfunction and accelerating atherosclerosis 7 . This phenomenon suggests that chronic inflammation and insulin resistance could share pathophysiological crosstalk, such as NF-κB and oxidative stress pathways, yet few large-scale epidemiological studies have quantified their joint association with all-cause mortality in nationwide Chinese cohorts. This oversight impedes the development of integrated biomarker indexes for personalized risk assessment and mortality prediction of the Chinese aged over 45. Interestingly, the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort with longitudinal biomarker data, can provide a unique and available platform to fill in these gaps in the rapidly aging Chinese population. This study aims to evaluate the association of CRP/TyG index (CTI) with all-cause mortality in middle-aged and older Chinese adults, and explore heterogeneity across sex, age subgroups, and baseline cardio-metabolic status. By integrating dual biomarkers of systemic inflammation and metabolic disorder, this analysis can advance the understanding of their interplay in mortality prediction and inform specific interventions for subgroups at higher risk. These findings highlight translational value for precision prevention in aging societies facing dual metabolic-inflammatory burdens, particularly China's rapidly aging population undergoing complex health transitions. Methods Data sources and study population The datasets presented in this study can be found online as below: http://charls.pku.edu.cn . The CHARLS database refers to a nationally representative longitudinal survey especially for Chinese middle-aged and senior adults, initiated in 2011 by Peking University. Distinctively designed to mirror global aging studies (e.g., HRS, SHARE), CHARLS integrates rigorous methodological innovations, including high-frequency biometric measurements (e.g., grip strength, blood panels) and geospatial linkages to environmental and socioeconomic data. Its harmonized questionnaire enables cross-country comparisons while retaining culturally specific modules on inter-generational dynamics, rural-urban disparities, and informal care-giving. The CHARLS cohort undergoes systematic biennial monitoring through tablet-based Computer-Assisted Personal Interviews (CAPI), featuring real-time logic checks and GPS-validated household tracking to ensure spatial-temporal data fidelity. The Peking University Biomedical Ethics Review Committee (No. IRB00001052-11015) approves the data collection, and the study also complied with the ethical criteria of Helsinki Declaration 1975. All participants involved granted their consent after obtaining comprehensive written information. Out of the data quality and reliability in this study, 6,213 participants who lacked biomarker data, such as CRP, FBG and TG, were excluded. Furthermore, the study excluded 11 individuals died during the baseline assessment year (2011), 2,233 individuals missing follow-up and mortality information and 193 individuals failing to meet the age-eligibility criteria (< 45 years) or lacking valid age documentation. The final sample size, comprising individuals aged over 45, included 9,055 participants, as illustrated in the Fig. 1 . C- Reactive Protein/Triglyceride-Glucose Index (CTI) The CTI index, an innovative biomarker integrating inflammatory-metabolic pathways, quantifies the pathophysiological crosstalk between CRP-mediated systemic inflammation (measured in mg/L) and TyG-indexed metabolic dysregulation. This composite metric specifically operationalizes the vicious cycle linking insulin resistance (quantified through TyG components: fasting triglycerides [TG, mg/dL] and glucose [FPG, mg/dL]) with chronic low-grade inflammation, both constituting core pathophysiological mechanisms underlying metabolic syndrome progression. The CTI algorithm was computed using the standardized equation: CTI = 0.412 × Ln(CRP [mg/L]) + Ln (TG [mg/dl] × FPG [mg/dl])/2. Analysis of covariates The baseline data were systematically collected by trained interviewers using authorized standardized questionnaires, including (1) demographic and lifestyle data: gender, age, education, marital status, smoking status, and drinking status; (2) body measurements: waist circumference (WC), body mass index (BMI), systolic and diastolic blood pressure (SBP and DBP); (3) disease history: existing conditions such as cardiovascular diseases, depression, obesity, hypertension, diabetes and dyslipidemia; (4) laboratory tests: blood glucose, creatine, BUN, HbAlc, total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and CRP; (5) other synergistic index: Chinese Visceral Adiposity Index (CVAI) =-267.93 + 0.68*Age + 0.03*BMI + 4.00 *WC + 22.00*Log 10 (TG)-16.32*HDL-C(for female), CVAI = -187.32 + 1.71*Age + 4.23*BMI + 1.12*WC + 39.76*Log 10 (TG)-11.66*HDL-C(for male). Definitions Hypertension was confirmed through any of the following criteria: (1) SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg; (2) hypertension diagnosed by a physician before; (3) administration of anti-hypertensive medications. Diabetes mellitus was defined when any one of the following conditions occurred: (1) FPG ≥ 126mg/dL or HbA1c ≥ 6.5%; (2) current use of anti-diabetic drugs; (3) diabetes diagnosed before. Dyslipidemia was recognized in any one of following conditions: (1) TG ≥ 150mg/dL; (2) TC ≥ 240mg/dL; (3) HDL-C < 40mg/dL; (4) LDL-C ≥ 160mg/dL; (5) administration of lipid-lowering therapies; (6) dyslipidemia diagnosed by a physician before. Statistical analysis Multiple imputations were used to address the influence of missing values and minimize potential bias. Continuous variables demonstrating normal distribution were expressed as mean ± standard deviation (SD), with between-group comparisons analyzed using one-way analysis of variance (ANOVA) followed by Tukey's post hoc testing. Non-normally distributed quantitative variables were summarized as median (interquartile range, IQR), employing the Kruskal-Wallis test with Dunn-Bonferroni correction for multiple comparisons. Categorical parameters were presented as absolute frequencies (percentages), with intergroup differences assessed through χ² tests or Fisher's exact tests. All statistical premises including homogeneity of variance (Levene's test, α = 0.10) and normality (Kolmogorov-Smirnov test, p > 0.05) were systematically verified. Initially, we assessed the CTI as a continuous variable to increase the reliability of the conclusions, and a total of 9,055 participants enrolled were quartered into four groups by the quartiles of their CTI, namely 3.072651 ≤ Q1 ≤ 4.325255, 4.325255 < Q2 ≤ 4.691462, 4.691462 < Q3 ≤ 5.113818, 5.113818 < Q4 ≤ 7.528786. Next, all-cause mortality of Chinese participants aged over 45 was evaluated via the Kaplan–Meier curves and log-rank tests. Furthermore, to understand collinearity between the CTI and other covariates, we examined tolerance values and variance inflation factors (VIFs). The relationship between the CTI and all-cause mortality was studied in the four Cox regression models as follows: Model 1 was unadjusted; Model 2 was adjusted with age, residence, gender, education and marital status; Model 3 held additional adjustments for age, residence, gender, education, marital status, BMI, drinking status, smoking status, hypertension, dyslipidemia, diabetes; and Model 4 included adjustments for HbA1c, cholesterol, creatine, BUN, HDL, LDL, age, residence, gender, education level, marital status, BMI, drinking status, smoking status, hypertension, SBP, DBP, dyslipidemia, diabetes. Additionally, the restricted cubic splines (RCS) were used to explore the dose-response association between the CTI and all-cause mortality in different age groups and the total population. To describe the association between the CTI and all-cause mortality in Chinese participants aged over 45 across different demographic characteristics, subgroup and interaction analyses were conducted for age, gender, education level, smoking status, marital status, residence, CVD incidence, diabetes, drinking status, smoking status, obesity and dyslipidemia. Besides, the receiver operating characteristic (ROC) curves were also established to determine the predictive value of CTI on all-cause mortality in the corresponding age groups. The incremental effect of CTI was illustrated by the area under the ROC curve (AUC). All statistical analyses were performed in the R software (version 4.5.1), and a two-sided p-value < 0.05 was defined statistically significant. Results The Description of Baseline Characteristics In this study, total 9,055 participants in the CHARLS database were eventually enrolled after 8650 individuals were screened out. The baseline characteristics of the enrolled participants are presented in the Table 1 , including 54.7% female and 45.3% male, with the mean age of 58.3 years. After categorizing the chosen participants by the quartiles of the CTI, we discovered that participants with the higher CTI witnessed the increased proportions of hypertension, dyslipidaemia, obesity, diabetes mellitus and cardiovascular disease (all p < 0.05), evidenced by significantly elevated SBP, DBP, cholesterol, LDL-C, TG, blood glucose and HbA1c, with lower HDL-C (all p < 0.05). Additionally, participants with higher CTI had bigger BMI, WC, CVAI, higher creatine and BUN (all p < 0.05). These participants always started smoking from a younger age and were inclined to consume more cigarettes and alcohol (all p 0.05). Figure 2 a illustrates the distribution of the CTI alongside the all-cause mortality, with a median value of 4.691462 and a p-value < 0.05 yielded by the Kolmogorov-Smirnov (K-S) test, showing that the CTI has a skewed distribution. With the increasing CTI, the all-cause mortality rate, in general, also shows an increasing trend, evidenced by the larger ratio of blue column/red column in Fig. 2 a. Corresponding to the results in Fig. 2 a, Fig. 2 b directly demonstrates that deceased participants exhibited a significantly higher average of CTI than their non-deceased counterparts from the comparison of CTI between deceased and non-deceased group. Table 1 Basic characteristics and patient demographics of participants Characteristic CTI quartiles p Overall Q1 Q2 Q3 Q4 p n 9055 2264 2264 2263 2264 Gender (%) 0.333 Male 4100 (45.3) 1053 (46.5) 1038 (45.8) 996 (44.0) 1013 (44.7) Female 4955 (54.7) 1211 (53.5) 1226 (54.2) 1267 (56.0) 1251 (55.3) Age (Mean ± SD) 58.3 ± 8.8 57.2 ± 8.9 58.2 ± 8.5 58.7 ± 8.8 59.1 ± 8.8 < 0.001 Age group (%) =65 2065 (22.8) 449 (19.8) 521 (23.0) 534 (23.6) 561 (24.8) Current married (%) 0.485 No 935 (10.3) 221 (9.8) 230 (10.2) 232 (10.3) 252 (11.1) Yes 8120 (89.7) 2043 (90.2) 2034 (89.8) 2031 (89.7) 2012 (88.9) Drinking (%) 0.001 No 6074 (67.1) 1458 (64.4) 1494 (66.0) 1565 (69.2) 1557 (68.8) Yes 2981 (32.9) 806 (35.6) 770 (34.0) 698 (30.8) 707 (31.2) Education (%) 0.754 College/University 128 (1.4) 28 (1.2) 31 (1.4) 30 (1.3) 39 (1.7) Second/High school 2717 (30.0) 688 (30.4) 656 (29.0) 683 (30.2) 690 (30.5) Primary 3663 (40.5) 911 (40.2) 929 (41.0) 935 (41.3) 888 (39.2) Illiterate 2547 (28.1) 637 (28.1) 648 (28.6) 615 (27.2) 647 (28.6) Depression (%) 0.604 No 5621 (62.1) 1429 (63.1) 1387 (61.3) 1396 (61.7) 1409 (62.2) Yes 3434 (37.9) 835 (36.9) 877 (38.7) 867 (38.3) 855 (37.8) Obesity (%) < 0.001 No 5469 (60.4) 1722 (76.1) 1498 (66.2) 1209 (53.4) 1040 (45.9) Yes 3586 (39.6) 542 (23.9) 766 (33.8) 1054 (46.6) 1224 (54.1) BMI (Mean ± SD) 23.6 ± 4.3 22.2 ± 3.8 23.2 ± 4.4 24.0 ± 4.0 24.9 ± 4.6 < 0.001 Diabetes (%) < 0.001 No 8367 (92.4) 2199 (97.1) 2169 (95.8) 2107 (93.1) 1892 (83.6) Yes 688 (7.6) 65 (2.9) 95 (4.2) 156 (6.9) 372 (16.4) Glucose (Median [IQR]) 102.2 [94.3, 113.0] 97.0 [90.5, 104.6] 100.6 [93.6, 108.4] 103.1 [95.9, 112.9] 112.0 [101.0, 133.6] < 0.001 HbAlc (Median [IQR]) 5.1 [4.9, 5.4] 5.0 [4.8, 5.3] 5.1 [4.9, 5.4] 5.1 [4.9, 5.4] 5.3 [5.0, 5.7] < 0.001 Hypertension (%) < 0.001 No 5269 (58.2) 1553 (68.6) 1418 (62.6) 1244 (55.0) 1054 (46.6) Yes 3786 (41.8) 711 (31.4) 846 (37.4) 1019 (45.0) 1210 (53.4) SBP (Median [IQR]) 126.7 [114.3, 141.3] 122.3 [111.0, 137.3] 124.7 [113.7, 138.7] 129.0 [115.7, 143.3] 131.0 [117.7, 145.7] < 0.001 DBP (Median [IQR]) 74.7 [67.3, 83.3] 72.7 [65.7, 81.0] 74.0 [67.0, 82.0] 75.7 [68.3, 84.0] 76.7 [69.0, 85.3] < 0.001 Dyslipidemia (%) < 0.001 No 8159 (90.1) 2146 (94.8) 2095 (92.5) 2024 (89.4) 1894 (83.7) Yes 896 (9.9) 118 (5.2) 169 (7.5) 239 (10.6) 370 (16.3) Cholesterol (Median[IQR]) 190.6 [167.4, 215.3] 182.5 [161.2, 204.1] 189.4 [167.0, 211.1] 193.3 [170.1, 219.0] 198.7 [173.2, 227.8] < 0.001 HDL (Median[IQR]) 49.1 [40.2, 59.9] 57.6 [49.1, 67.7] 51.6 [43.7, 61.1] 47.2 [39.4, 55.7] 40.6 [33.6, 50.3] < 0.001 LDL (Median[IQR]) 114.4 [93.6, 137.2] 110.6 [91.2, 129.5] 116.0 [96.3, 138.8] 119.1 [97.4, 141.9] 112.9 [88.9, 139.6] < 0.001 Creatine (Median [IQR]) 0.8[0.6, 0.9] 0.7[0.6, 0.8] 0.8[0.6, 0.9] 0.8[0.7, 0.9] 0.8[0.7, 0.9] < 0.001 BUN (Median [IQR]) 15.1 [12.5, 18.1] 15.2 [12.5, 18.5] 15.3 [12.7, 18.4] 15.1 [12.6, 18.0] 14.8 [12.3, 17.7] < 0.001 TyG (Median [IQR]) 8.6 [8.2, 9.1] 8.2 [7.9, 8.4] 8.5 [8.2, 8.8] 8.8 [8.5, 9.1] 9.2 [8.8, 9.8] < 0.001 Age Start Smoking (Mean ± SD)) 22.4 ± 14.4 24.5 ± 15.3 22.1 (14.0) 22.4 (14.5) 20.6 (13.4) < 0.001 Age Start Smoking Category (%) < 0.001 =25 2543 (28.1) 764 (33.7) 610 (26.9) 639 (28.2) 530 (23.4) Smoking Duration (Mean ± SD) 35.8 ± 18.6 32.8 ± 19.4 36.1 ± 18.0 36.2 ± 18.7 38.1 ± 17.8 < 0.001 Smoking Duration Category (%) < 0.001 =40 4516 (49.9) 985 (43.5) 1146 (50.6) 1140 (50.4) 1245 (55.0) Smoking Number (Mean ± SD) 19.2 ± 12.8 18.0 ± 11.8 18.5 ± 12.3 19.5 ± 13.4 20.6 ± 13.3 0.002 Smoking Number Category (%) 0.001 =40 3374 (37.3) 801 (35.4) 834 (36.8) 867 (38.3) 872 (38.5) Smoking Packs Per Month (Mean ± SD) 9.4 ± 16.9 8.5 ± 15.4 9.4 ± 16.9 9.6 ± 17.3 10.1 ± 17.9 0.019 Smoking Packs Per Month Category (%) 0.048 0 5675 (62.7) 1435 (63.4) 1414 (62.5) 1414 (62.5) 1412 (62.4) =30 1711 (18.9) 387 (17.1) 422 (18.6) 437 (19.3) 465 (20.5) All-cause Mortality(%) < 0.001 0 8834 (97.6) 2230 (98.5) 2225 (98.3) 2214 (97.8) 2165 (95.6) 1 221 (2.4) 34 (1.5) 39 (1.7) 49 (2.2) 99 (4.4) CVD (%) < 0.001 0 7883 (87.1) 2046 (90.4) 2002 (88.4) 1948 (86.1) 1887 (83.3) 1 1172 (12.9) 218 (9.6) 262 (11.6) 315 (13.9) 377 (16.7) TG (Median [IQR]) 106.2 [75.2, 156.6] 71.7 [57.5, 90.3] 99.1 [75.2, 130.1] 124.8 [92.9, 167.3] 175.2 [117.7, 260.2] < 0.001 CRP (Median [IQR]) 1.0 [0.5, 2.1] 0.4 [0.3, 0.6] 0.8 [0.6, 1.1] 1.4 [0.9, 2.1] 3.4 [1.9, 6.6] < 0.001 CTI (Median [IQR]) 4.7 [4.3, 5.1] 4.1 [3.9, 4.2] 4.5 [4.4, 4.6] 4.9 [4.8, 5.0] 5.4 [5.3, 5.7] < 0.001 WC (Median [IQR]) 84.0 [77.2, 91.5] 80.0 [74.5, 86.2] 82.8 [76.4, 89.6] 86.4 [79.2, 93.0] 88.8 [81.0, 96.4] < 0.001 CVAI (Median [IQR]) -486.6 [-666.2,-339.9] -625.0 [-800.8,-484.5] -526.5 [-691.6,-394.3] -447.4 [-600.8,-319.7] -353.7 [-498.4,-231.9] < 0.001 Data were summarized as mean (95% confidence intervals) or numbers (95% confidence intervals) according to their data type. CVD, cardiovascular disease; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; HDL, high-density lipoprotein cholesterol; CRP, C reactive protein; TyG, triglyceridesglucose index; CTI, C-reactive protein-triglyceride glucose index; BUN, blood urea nitrogen; CVAI, Chinese visceral adiposity index. Age-Stratified Survival Analysis and Progressive Mortality Risks in Cox Regression Models Initially, Kaplan-Meier survival curves were constructed to depict the change of all-cause mortality among groups in different CTI quartiles with the extension of follow-up time. In the overall population analysis (Fig. 3 a), the Q4 cohort demonstrated significantly reduced survival compared to the aggregated Q1-Q3 cohorts (Log-rank test p 0.05). Additionally, to study CTI’s contribution to the mortality of different age stratification, the survival curves were used to compare all-cause mortality between different CTI quartile zones in specific age groups, namely 45–54, 55–64 and 65+, respectively. For the Chinese participants aged from 45 to 54, the 10-year follow-up period failed witnessing statistical significance in the all-cause mortality between any two CTI groups (p = 0.269), as shown in the Fig. 3 b. However, Fig. 3 c demonstrated that participants aged 55–64 years in the Q4 CTI subgroup exhibited significantly lower survival rates compared to Q1-Q3 groups (p = 0.009). Notably, almost one out of eight seniors aged over 65 years in the Q4 subgroup experienced mortality, demonstrating the highest all-cause death risk compared to other groups (p 0.05). During the decade-long follow-up (2011–2020), 221 deaths occurred among 9,055 participants, corresponding to an overall all-cause mortality rate of 2.44%. Four Cox proportional hazards models were established to precisely clarify the tie between the CTI and all-cause mortality in patients aged over 45, shown in Table 2 . In Model 1, no covariate was adjusted; Model 2 included adjustments for residence, age, gender, education level, marital status; Model 3 was adjusted for residence, age, gender, education level, marital status, BMI, drinking, smoking, hypertension, dyslipidemia, diabetes; more indicators, namely, HbA1c, cholesterol, creatine, BUN, HDL, LDL, were added into adjustments of the novel Model 4 on the basis of those included in Model 3. To study the association between the CTI and all-cause mortality in the middle-aged and senior participants, the CTI range was quartered. In Model 4 (fully adjusted), each quartile increase in CTI was associated with a 175% higher mortality risk (HR = 2.75, 95% CI: 2.16–3.49, p < 0.001, Table 2 ). Additionally, participants with Q4 CTI demonstrated a HR of 3.48 (95% CI: 2.25–5.40, p < 0.001) compared to Q1, while Q3 participants showed a HR of 1.62 (95% CI: 1.03–2.55, p < 0.037). Similarly, Model 3 analysis revealed a 124% mortality increase per CTI quartile increment (HR:2.24, 95% CI:1.81–2.77, p < 0.001), with Q4 participants exhibiting a HR of 2.89 (95% CI: 1.93–4.34, p < 0.001), meaning a significant 189% increase in mortality for Q4 participants compared to Q1. Models 1 and 2 demonstrated 124% and 122% mortality increases per CTI quartile elevation (both p < 0.001). Q4 participants exhibited HRs of 2.97 (95% CI: 2.01–4.38, p < 0.001) and 2.82 (95% CI: 1.90–4.18, p 0.05). Table 2 The correlation between CTI and ten-year mortality risk. Variables Model 1 Model 2 Model 3 Model 4 HR(95%) P value HR(95%) P value HR(95%) P value HR(95%) P value CTI per IQR 2.24 (1.84, 2.73) < 0.001 2.22 (1.81, 2.72) < 0.001 2.24 (1.81, 2.77) < 0.001 2.75 (2.16, 3.49) < 0.001 CTI quartile Q1 ref ref ref ref Q2 1.15 (0.72, 1.82) 0.558 1.15 (0.73, 1.82) 0.552 1.19 (0.75, 1.89) 0.460 1.27 (0.80, 2.02) 0.314 Q3 1.45 (0.93, 2.24) 0.098 1.41 (0.91, 2.19) 0.125 1.48 (0.95, 2.30) 0.085 1.62 (1.03, 2.55) 0.037 Q4 2.97 (2.01, 4.38) < 0.001 2.82 (1.90, 4.18) < 0.001 2.89 (1.93, 4.34) < 0.001 3.48 (2.25, 5.40) < 0.001 Abbreviations: CI = Confidence Interval, HR = Hazard Ratio Model1: No covariates were adjusted. Model2: Residence + Age + Gender + Education Level + Marital Status. Model3: Residence + Age + Gender + Education Level + Marital Status + BMI + Drinking + Smoking + Hypertension + Dyslipidemia + Diabetes. Model4: Residence + Age + Gender + Education Level + Marital Status + BMI + Drinking + Smoking + Hypertension + Dyslipidemia + Diabetes + HbAlc + BUN + HDL + LDL + SBP + DBP + Cholesterol + Creatine. Nonlinear Exposure-Response Relationship of CTI with Mortality RCS regression demonstrated a nonlinear association between CTI and all-cause mortality (p = 0.005 for nonlinear) in the Chinese population aged ≥ 45 years, with overall significance confirmed by p < 0.001 for total (Fig. 4 a). A progressive elevation in mortality risk was observed particularly beyond the CTI threshold of 5.26. Next, RCS were also conducted for subgroups of different age stratification. In detail, for the participants aged between 45 to 54, the result of Fig. 4 b denied the meaningful relationship between the CTI and all-cause mortality (p = 0.814 for total, p = 0.677 for nonlinear). RCS analysis also identified a statistically significant overall association between CTI and all-cause mortality in the 55–64 year subgroup (p < 0.001 for total). Although the nonlinear relationship test showed marginal significance (p = 0.128 for nonlinear), Fig. 4 c demonstrated a characteristic threshold effect at CTI = 5.22 (HR = 1.00), with progressively elevated mortality risks observed at higher CTI levels. Finally, Fig. 4 d confirmed a nonlinear CTI-mortality association in adults ≥ 65 years (p < 0.001 for total, p = 0.048 for nonlinear), showing disproportionate risk escalation beyond the CTI = 4.72 threshold. Subgroup Heterogeneity in CTI-Associated Mortality Risk As shown in the Fig. 5 , subgroup analysis and interaction analysis were conducted for age, gender, education level, marital status, residence, CVD, diabetes, drinking, smoking, obesity and dyslipidemia. Most subgroups showed significant associations (all p < 0.05 except 45–54 age group and college-educated subgroup). Notably, participants aged ≥ 55 years exhibited significantly elevated risks (55–64 years: HR = 2.90, 95%CI: 1.86–4.54; ≥65 years: HR = 2.92, 95%CI: 2.18–3.91) compared to younger counterparts (45–54 years: HR = 1.24, 95%CI: 0.48–3.23). Lower education levels showed graded associations, with primary education (HR = 4.32, 95%CI: 2.37–4.95) and secondary education (HR = 3.68, 95%CI: 1.98–6.84) demonstrating particularly strong effects. Cardiovascular disease history marked the highest mortality risk (HR = 4.33, 95%CI: 2.58–7.27), followed by current smokers (HR = 2.60, 95%CI: 1.77–3.83) and rural residents (HR = 2.72, 95%CI: 2.04–3.62). Pooled analysis confirmed an overall significant association with all-cause mortality (HR = 2.76, 95%CI: 2.20–3.47, p < 0.001). The results also indicated that the significant interaction was only yielded between participants with different levels of education (interaction p = 0.021). Few interactions could be noticed in other subgroups (all interaction p > 0.05). Age-Modulated Prognostic Value of CTI in Mortality Prediction Then, ROC analysis was introduced to compare the full model to the base model in the efficacy for predicting all-cause mortality. On the basis of the base model as the reference, CTI was added into the adjustments of the full model. Supplementary Fig. 1a showed that, for the total participants, the area under the curve (AUC) of the full model was 0.849 (95%CI: 0.814–0.877, p = 0.008), with 0.829 for the base model (95%CI: 0.797–0.860), and that the full model had a positive net reclassification index (NRI, 0.425; 95%CI: 0.189–0.484; p < 0.0001; Table 3 ) and integrated discrimination improvement (IDI, 0.029; 95%CI: 0.0040–0.055; p = 0.017; Table 3 ). However, different age stratification had different results of ROC analysis. In the eldest age group, the full model was superior to the basic counterpart in AUC (0.815; 95%CI: 0.776–0.853 vs 0.782; 95%CI: 0.739–0.825, p < 0.006, Supplementary Fig. 1d), with a positive NRI (0.360; 95%CI: 0.152–0.451, p < 0.0001, Table 3 ) and IDI (0.0347; 95%CI: 0.0045–0.0670, p = 0.030, Table 3 ). For the other two age groups aged under 55, the full model cannot outweigh the base model in all indicators, namely AUC (p = 0.21, Supplementary Fig. 1b; p = 0.532, Supplementary Fig. 1c), NRI (p = 0.23 and p = 0.46, Table 3 ) and IDI (p = 0.479 and p = 0.064, Table 3 ). Table 3 ROC and reclassification analysis of CTI on mortality identification AUC (95%CI) p for difference in AUC NRI (95%CI) p for difference in NRI IDI (95%CI) p for difference in IDI Total population Base Model (reference) 0.829(0.797–0.861) Ref Ref Ref Ref Ref Full Model (ref + CTI) 0.849 (0.816–0.881) 0.008 0.425 (0.189–0.484) < 0.0001 0.029 (0.0040–0.055) 0.017 Population (aged 45–54) Base Model (reference) 0.829 (0.723–0.935) Ref Ref Ref Ref Ref Full Model (ref + CTI) 0.850 (0.764–0.937) 0.21 -0.270 (-0.480-0.406) 0.23 0.040 (-0.076-0.139) 0.479 Population (aged 55–64) Base Model (reference) 0.688 (0.608–0.767) Ref Ref Ref Ref Ref Full Model (ref + CTI) 0.705 (0.605–0.805) 0.532 0.092 (-0.100-0.389) 0.46 0.033 (-0.005-0.066) 0.064 Population (aged over 65) Base Model (reference) 0.782 (0.739–0.825) Ref Ref Ref Ref Ref Full Model (ref + CTI) 0.815 (0.776–0.853) 0.006 0.360 (0.152–0.451) < 0.0001 0.0347 (0.0045–0.0670) 0.030 Base model includes Residence, Age, Gender, Education Level, Marital Status, BMI, Drinking, Smoking, Hypertension, Dyslipidemia, Diabetes. Full model consists of the basic model and CTI. AUC, area under the ROC curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement. Discussion This prospective cohort study analyzed 9,055 Chinese adults ≥ 45 years to investigate the potential interplay between CTI and all-cause mortality. In general, from the baseline characteristic of the cohort, we knew that more elder individuals were inclined to have higher CTI. Additionally, CTI quartile elevation corresponded to the increased proportion of obesity, diabetes, hypertension. Meanwhile, participants with Q4 CTI quartile had larger WC and CVAI, and suffered higher Hb1Ac index, blood pressure and serum cholesterol with lower HDL-C. Then, participants in the highest CTI quartile exhibited earlier smoking initiation with prolonged duration (approximately 38 years on average) and higher daily cigarette consumption compared to lower quartiles. As a synergistic indicator, CTI can directly evaluate chronic systematic inflammation and metabolic syndrome. The formula of CTI determined that CTI must elevate when CRP, blood glucose and triglycerides leap to the higher level, which underlies the fact that seniors with higher CTIs are inclined to develop complications, like diabetes, obesity and hyperlipidemia. Besides, HbA1c, as a critical diagnostic criterion of diabetes, WC and CVAI, as important indexes for obesity should also positively correlated with CTI quartile, like the findings in Table 1 . As another type of hyperlipidemia, hypercholesteremia is always characterized by elevated TC, LDL-C and decreased HDL-C. This study found that CTI increase was indeed accompanied by higher TC, LDL-C and impaired HDL-C. Generally, HDL-C and TG exhibit a well-established inverse relationship in lipid metabolism, a phenomenon observed across epidemiological and clinical studies. This correlation stems from bidirectional metabolic interactions. High circulating TG levels promote cholesterol ester transfer protein (CETP)-mediated exchange, transferring TG from very-low-density lipoproteins (VLDL) to HDL particles while displacing cholesterol esters, which accelerates the production of TG-rich and cholesterol-depleted HDL particles and slash the level of normal cholesterol-enriched HDL 8 – 10 . Next, TG-enriched HDL becomes a preferential substrate for hepatic lipase, leading to rapid lipolysis and subsequent clearance of HDL fragments from plasma and reducing both HDL particle size and concentration eventually. Besides, hypertriglyceridemia often coexists with insulin resistance, suppressing lipoprotein lipase activity and further impairing HDL maturation 11 . On the contrary, elevated LDL-C frequently correlates with hypertriglyceridemia 12 . VLDL derived from the liver can carry endogenous TG, and generates LDL particles post to its lipolysis. Thus, elevated TG levels often coincide with accumulation of the small dense LDL (sdLDL) subfractions. Clinically, the complicated lipid metabolism among HDL, LDL and TG contributes to atherogenic dyslipidemia, characterized by the triad of high TG, high LDL and low HDL, and increasing cardiovascular risk 13 – 15 . This metabolic dysregulation plausibly underlies the atherogenic dyslipidemia profile (elevated TC/TG/LDL-C, depressed HDL-C) observed in Q4 CTI subjects, mechanistically linking to their increased cardiovascular morbidity and excess mortality risk in baseline-adjusted evaluations. Besides, CRP serves as a sensitive biomarker of systemic inflammation, which directly influences the CTI calculation. Originally, liver can synthesize excessive CRP in the acute-phase response to interleukin-6 (IL-6), bidirectionally related with hyperlipidemia and mechanistically intertwined with atherosclerosis progression 16 . Hyperlipidemia, particularly characterized by elevated LDL-C, promotes endothelial dysfunction and oxidative stress. Subendothelial retention and modification of LDL particles trigger innate immune responses 17 , in which macrophages are assembled under endothelium, transform into foam cells 18 and release proinflammatory cytokines, like IL-6, TNF-α after engulfing oxidized LDL deposits. This process also amplifies hepatic CRP production, establishing a positive feedback loop. Conversely, inflammation directly dysregulates lipid metabolism. In detail, IL-6 suppresses lipoprotein lipase activity, specifically manifested as HDL synthesis reduction and VLDL secretion explosion, exacerbating hypertriglyceridemia. Adipose tissue inflammation in obesity patients further fuels this cycle via adipokine release. Clinically, previous studies 19 suggested that elevated high-sensitivity CRP (hs-CRP) independently predicts cardiovascular events in hyperlipidemic patients, even when LDL-C concentrations are maintained within guideline-recommended ranges, highlighting the residual inflammatory risk beyond cholesterol management. Lipid-lowering treatments, represented by statin therapy, have exhibited pleiotropic anti-inflammatory effects, reducing both LDL-C and CRP, which can partially underlie their cardiovascular benefits 20 . Meanwhile, promising anti-inflammatory therapies, such as colchicine 21 and IL-6 inhibitors 22 , 23 , have also reduced the mortality risk in hyperlipidemic patients with elevated CRP. Thus, CRP and hyperlipidemia engage in a pathogenic synergy, accelerating systemic atherogenesis and increasing fatalities among the senior population. Combined assessment of lipids and inflammation optimizes risk stratification and therapeutic targeting. Additionally, CRP and insulin resistance (IR) are intertwined in a bidirectional pro-inflammatory metabolic cascade 24 . Their relationship is underpinned by immune-metabolic crosstalk 25 , and adipose tissue dysfunction acts as a critical nexus. IR-induced lipolysis increases free fatty acid flux, promoting ectopic lipid deposition and mitochondrial oxidative stress, activating innate immune pathways, especially the NF-κB and TLR4 signaling 26 , to trigger pro-inflammatory cytokine secretion from adipocytes and macrophages. These cytokines exacerbate IR by impairing insulin receptor substrate-1 (IRS-1) phosphorylation and promoting hepatic gluconeogenesis 27 . Concurrently, IL-6 stimulates hepatocytes to synthesize CRP, which further amplifies inflammation by activating complement pathways and promoting endothelial adhesion molecule expression. Hyperglycemia in IR also fosters advanced glycation end products (AGEs), which bind to surface receptors (RAGE) on immune cells to sustain cytokine production 28 . Clinically, elevated hs-CRP strongly correlates with IR severity 29 , even in normoglycemic individuals, and predicts potential type 2 diabetes and cardiovascular events 30 , 31 . Interestingly, lifestyle intervention targeting IR consistently reduced CRP levels, and administration of anti-inflammatory agents like IL-1 antagonists could improve insulin sensitivity 32 , 33 , underscoring their mechanistic linkage and confirming the causality. IR and CRP form a vicious cycle, where IR fuels inflammation, and inflammation perpetuates metabolic dysfunction. Therefore, IR, CRP elevation and hyperlipidemia interact as both cause and effect. This positive feedback loop accelerates CTI accumulation and leads its victims to mortality, just like the findings of this study. As shown in Table 1 , participants with elevated CTI usually have long-term worse lifestyles, especially a longer smoking history and a larger cigarette consumption. Smoking exacerbates systemic inflammation and metabolic dysfunction through shared mechanisms. Tobacco combustion generates reactive oxygen species (ROS) that induce NF-κB pathway activation, thereby promoting transcriptional upregulation of proinflammatory mediators (IL-6, TNF-α, CRP) to perpetuate chronic inflammation 34 . Then, smoking promotes oxidative modification of LDL particles, reduces HDL-C, and increases triglyceride via enhanced VLDL secretion and impaired lipoprotein lipase 35 . The following endothelial dysfunction further amplifies lipid abnormalities. Inflammatory cytokines interfere with insulin signaling, while nicotine induces catecholamine release and increases lipolysis and free fatty acid flux, resulting in ectopic lipid deposition in muscles and liver, followed by impaired glucose uptake and gluconeogenesis promotion 36 , 37 . The Kaplan-Meier curves demonstrated that participants in the Q4 CTI group had a significantly lower survival rate compared to those in the other three quartiles. This association was also observed in individuals aged over 55; however, no significant relationship between CTI quartile and all-cause mortality was evident among younger participants (aged 45–54). Further Cox proportional hazards modeling revealed a dose-dependent association between ascending CTI quartiles and mortality risk, with each quartile elevation corresponding to a 175% increased mortality risk in fully adjusted models (Model 4). The CTI-mortality linkage demonstrated amplified hazard magnitude at extreme exposure levels (Q4 vs Q1: 248% risk elevation). Thus, all-cause mortality generally has an increasingly stronger tie to CTI increase as participants grows older. The RCS curves revealed both nonlinear and linear associations between CTI levels and mortality risk, with a statistically significant positive correlation emerging above the CTI threshold of 5.26. Stratified analyses demonstrated downward threshold shifting to 4.72 in the elderly subgroup (≥ 65 years). Importantly, the ROC curve analysis was used to compare the Base Model with the Full Model adjusted by CTI in their efficacy of mortality prediction, suggesting that the analysis model can significantly enhanced discrimination, with a 2.9% improvement in predictive accuracy following CTI inclusion. This study is a prospective cohort study distinguished by its assessment of the association between CTI and all-cause mortality in senior patients aged over 45, and further tries to quantify the CTI dose response to mortality of the whole participants. These findings have fulfilled a research gap in this field, especially for the traditional East Asian population. Additionally, through the study on different age stratification, it has been found that CTI presents different all-cause mortality prediction capabilities for the social population in different ages, which is conducive to subsequently optimizing the potential mortality prediction models. Thirdly, CRP, triglycerides and blood glucose, as routine laboratory test indicators which can be precisely detected in basic medical institutions, have the advantages of accessibility and non-invasive, making primary prevention and chronic disease management easier, more efficient, and more economical. However, limitations should also be noticed. Firstly, our research could only describe the real world on the basis of the Chinese database and failed to collect data from different ethnic groups. Besides, our analysis was limited to assessing all-cause mortality outcomes without accounting for specific cause-of-death classifications. Furthermore, the potential longitudinal accumulation patterns of CTI warrant investigation, as dynamic indices quantifying temporal CTI progression may improve prognostic accuracy and require methodological validation in longitudinal studies. Conclusion This prospective cohort study identified a significant positive association between CTI and all-cause mortality, particularly pronounced in the geriatric population (aged ≥ 65 years). The dose-dependent relationship observed across CTI quartiles establishes these stratification criteria as a promising metric for cardiometabolic risk stratification in clinical practice. Moreover, this synergistic index has shown a significant potential in prognostic evaluation, with economic factors taken into account. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics statement The studies were approved by the Peking University Biomedical Ethics Review Committee. The studies were conducted legally. The participants provided their written informed consent when participating. Written informed consent was obtained from qualified investigators. Funding This study is supported by the National Natural Science Foundation of China Youth Program (Grant Number: 82300332) and the Fuwai Hospital Central China Support Fund (Grant Number: ZCK2025317). Author Contribution L.Z. and R.T. analyzed the data and drafted initial manuscript. Y.X. reviewed and edited the manuscript. X.W., Z.H. and C.G. designed study and analyzed data. All authors read and approved the final manuscript. Acknowledgement The authors acknowledge the contributions of the CHALRS faculty and the participants sharing their data. Data Availability The sources of all original data are listed in the "Data sources and study population" section of the "Methods" section. If the data cannot be accessed, please contact the corresponding author. References Ali, S., Zehra, A., Khalid, M. U., Hassan, M. & Shah, S. I. A. Role of C-Reactive Protein in Disease Progression, Diagnosis and Management. Discoveries (Craiova) . 11 , e179 (2023). Golabi, P. et al. Nonalcoholic Fatty Liver Disease (Nafld) and Associated Mortality in Individuals with Type 2 Diabetes, Pre-Diabetes, Metabolically Unhealthy, and Metabolically Healthy Individuals in the United States. Metabolism 146 , 155642 (2023). Sun, Y. et al. 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Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 20 Dec, 2025 Submission checks completed at journal 20 Dec, 2025 First submitted to journal 14 Dec, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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07:52:41","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163060,"visible":true,"origin":"","legend":"","description":"","filename":"9d0fbcb65ce84da68fdacab4a853eff31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/eae070d3e8e318c300961f15.xml"},{"id":100068022,"identity":"c6d067b0-4e54-45b1-ae83-c9575eb8d44d","added_by":"auto","created_at":"2026-01-12 15:57:08","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176411,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/343f3b5e9a4b4c11071f3496.html"},{"id":100068016,"identity":"342fc4d3-c32b-458e-a56a-7d657e2a909f","added_by":"auto","created_at":"2026-01-12 15:57:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165826,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for participants’ selection. The 2011 baseline survey accumulated 17,705 respondents, and subjects missing biological data (n = 6213), died in 2011 (n = 11), lost in follow-up (n = 2233), and aged below 45 years (n = 193), were excluded.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/6864379e575cbbd8381c75d8.png"},{"id":100365328,"identity":"a7da2cd9-53db-46d3-ad2f-6c54b4ac0702","added_by":"auto","created_at":"2026-01-16 07:55:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":281267,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of CTI distribution by mortality status. (a) Distribution of CTI by mortality. Non-mortality=0, Mortality=1. (b) Comparison of average CTI value between mortality and non-mortality. ****, p\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/44fc0d8731db9b62166ce918.png"},{"id":100364354,"identity":"36899b3d-3cd6-49cb-8052-a2890d9720c7","added_by":"auto","created_at":"2026-01-16 07:53:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":793796,"visible":true,"origin":"","legend":"\u003cp\u003eThe Survival curves by CTI levels in middle-aged and older people from 2011 to 2020. (a) Survival curves by CTI levels in all samples. (b) Survival curves by CTI levels in aged 45–54 years group. (c) Survival curves by CTI levels in aged 55–64 years group. (d) Survival curves by CTI levels in aged 65 years and above group. These models were adjusted for residence, age, gender, education level, marital status, BMI, drinking, smoking, hypertension, dyslipidemia, diabetes, HbAlc, BUN, HDL, LDL, SBP, DBP, cholesterol, creatine.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/8548356af44c8e9423ce20fa.jpeg"},{"id":100364494,"identity":"75abf86a-4164-426c-bc89-ebe3fd8176c9","added_by":"auto","created_at":"2026-01-16 07:53:48","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":682514,"visible":true,"origin":"","legend":"\u003cp\u003eThe Limited cubic splines (RCS) evaluate the linearity of the association between CTI and the all-cause mortality. (a) The relationship between CTI and all-cause mortality in all samples. (b) The relationship between CTI and all-cause mortality in aged 45–54 years group. (c) The relationship between CTI and all-cause mortality in aged 55–64 years group. (d) The relationship between CTI and all-cause mortality in aged 65 years and above group. These models were adjusted for residence, age, gender, education level, marital status, BMI, drinking, smoking, hypertension, dyslipidemia, diabetes, HbAlc, BUN, HDL, LDL, SBP, DBP, cholesterol, creatine.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/2745752a15aebfade7e1695b.jpeg"},{"id":100068025,"identity":"1f13a06d-6ebf-400d-8f52-15e6ad7ede06","added_by":"auto","created_at":"2026-01-12 15:57:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":134413,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of socioeconomic characteristics, living habits and clinical factors in the association of CTI with all-cause mortality.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/1e5ac1bd2793548c0f7749f5.png"},{"id":100382096,"identity":"440d0e02-0eaa-4cc2-b844-f8fd62d5cdca","added_by":"auto","created_at":"2026-01-16 10:40:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4228766,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/95666c97-7def-4de7-b69a-127c6b31d925.pdf"},{"id":100365310,"identity":"b52500f9-a950-411f-830f-d9a7d1c1bcfa","added_by":"auto","created_at":"2026-01-16 07:55:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":114884,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8358890/v1/be48bf92aefb4848337ec5b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of C-Reactive Protein/Triglyceride-Glucose Index on the All-cause Mortality among Middle-Aged and Older Chinese Adults: A Prospective Cohort Study from CHARLS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe escalating global burden of age-related morbidity underscores the critical requirement for validated biomarkers to stratify health risks in aging populations. With China experiencing accelerated population aging, the rising prevalence of metabolic syndrome among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years presents a significant public health concern, necessitating urgent intervention strategies. Characterized clinically by hyperlipidemia, impaired glucose regulation, and elevated blood pressure, this pathophysiological condition demonstrates chronic inflammatory states and insulin resistance as its core pathogenic mechanisms during progression. Consequently, a critical need emerges for a composite biomarker that simultaneously incorporates inflammatory indices, lipid profiles and glycemic parameters. Such a multidimensional assessment tool holds significant potential as a robust prognostic predictor for both clinical outcomes and population-based mortality rates in metabolic syndrome management. C-reactive protein (CRP) refers to a sensitive marker of systemic inflammation commonly synthesized by the liver in response to IL-6 and other pro-inflammatory cytokines. Elevated CRP levels, especially when \u0026gt;\u0026thinsp;3 mg/L, could be strongly associated with chronic inflammation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In the conditions of atherosclerosis, diabetes, and cardiovascular diseases, chronically abnormal CRP should reflect the persistent low-grade inflammation and consistently result in the remarkable all-cause mortality in Western cohorts\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Previously, the hyperinsulinemic-euglycemic clamp technique was introduced as the gold standard to evaluate the insulin sensitivity of peripheral tissues. In this test, researchers should control glucose perfusion and monitor the blood glucose to maintain it at a high level, during which the perfusion volumes of glucose must be detected on time, and the glucose metabolic rate can be evaluated eventually to represent the sensitivity of pancreatic β cells to glucose. Currently, the novel triglyceride-glucose(TyG) index, a surrogate for insulin resistance, correlates better with hyperinsulinemic-euglycemic clamp than individual parameters (triglycerides or glucose alone), particularly in obese and pre-diabetic groups\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. And the TyG index, compared with the golden standard, is indeed more cost-effective and widely accessible to diagnose insulin resistance and identity high-risk groups for diabetes. Besides, a more representative cohort study from Korea demonstrated that an elevated TyG index over 8.5 could predict the incidence of type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, independent of traditional risk factors.\u003c/p\u003e \u003cp\u003eGenerally, existing evidence predominantly examines CRP or TyG index in isolation. However, whether their combined effect confers additive or synergistic mortality risk remains understudied, particularly in non-Western aging populations with distinct lifestyles and genetic profiles. Currently, an increasing number of studies suggest that a higher TyG index should correlate with worse chronic systemic inflammation, evidenced by increased IL-6 and CRP levels, aggravating endothelial dysfunction and accelerating atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This phenomenon suggests that chronic inflammation and insulin resistance could share pathophysiological crosstalk, such as NF-κB and oxidative stress pathways, yet few large-scale epidemiological studies have quantified their joint association with all-cause mortality in nationwide Chinese cohorts. This oversight impedes the development of integrated biomarker indexes for personalized risk assessment and mortality prediction of the Chinese aged over 45. Interestingly, the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort with longitudinal biomarker data, can provide a unique and available platform to fill in these gaps in the rapidly aging Chinese population.\u003c/p\u003e \u003cp\u003eThis study aims to evaluate the association of CRP/TyG index (CTI) with all-cause mortality in middle-aged and older Chinese adults, and explore heterogeneity across sex, age subgroups, and baseline cardio-metabolic status. By integrating dual biomarkers of systemic inflammation and metabolic disorder, this analysis can advance the understanding of their interplay in mortality prediction and inform specific interventions for subgroups at higher risk. These findings highlight translational value for precision prevention in aging societies facing dual metabolic-inflammatory burdens, particularly China's rapidly aging population undergoing complex health transitions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources and study population\u003c/h2\u003e \u003cp\u003eThe datasets presented in this study can be found online as below: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The CHARLS database refers to a nationally representative longitudinal survey especially for Chinese middle-aged and senior adults, initiated in 2011 by Peking University. Distinctively designed to mirror global aging studies (e.g., HRS, SHARE), CHARLS integrates rigorous methodological innovations, including high-frequency biometric measurements (e.g., grip strength, blood panels) and geospatial linkages to environmental and socioeconomic data. Its harmonized questionnaire enables cross-country comparisons while retaining culturally specific modules on inter-generational dynamics, rural-urban disparities, and informal care-giving.\u003c/p\u003e \u003cp\u003eThe CHARLS cohort undergoes systematic biennial monitoring through tablet-based Computer-Assisted Personal Interviews (CAPI), featuring real-time logic checks and GPS-validated household tracking to ensure spatial-temporal data fidelity. The Peking University Biomedical Ethics Review Committee (No. IRB00001052-11015) approves the data collection, and the study also complied with the ethical criteria of Helsinki Declaration 1975. All participants involved granted their consent after obtaining comprehensive written information. Out of the data quality and reliability in this study, 6,213 participants who lacked biomarker data, such as CRP, FBG and TG, were excluded. Furthermore, the study excluded 11 individuals died during the baseline assessment year (2011), 2,233 individuals missing follow-up and mortality information and 193 individuals failing to meet the age-eligibility criteria (\u0026lt;\u0026thinsp;45 years) or lacking valid age documentation. The final sample size, comprising individuals aged over 45, included 9,055 participants, as illustrated in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eC- Reactive Protein/Triglyceride-Glucose Index (CTI)\u003c/h3\u003e\n\u003cp\u003eThe CTI index, an innovative biomarker integrating inflammatory-metabolic pathways, quantifies the pathophysiological crosstalk between CRP-mediated systemic inflammation (measured in mg/L) and TyG-indexed metabolic dysregulation. This composite metric specifically operationalizes the vicious cycle linking insulin resistance (quantified through TyG components: fasting triglycerides [TG, mg/dL] and glucose [FPG, mg/dL]) with chronic low-grade inflammation, both constituting core pathophysiological mechanisms underlying metabolic syndrome progression. The CTI algorithm was computed using the standardized equation: 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\u003eAnalysis of covariates\u003c/h3\u003e\n\u003cp\u003eThe baseline data were systematically collected by trained interviewers using authorized standardized questionnaires, including (1) demographic and lifestyle data: gender, age, education, marital status, smoking status, and drinking status; (2) body measurements: waist circumference (WC), body mass index (BMI), systolic and diastolic blood pressure (SBP and DBP); (3) disease history: existing conditions such as cardiovascular diseases, depression, obesity, hypertension, diabetes and dyslipidemia; (4) laboratory tests: blood glucose, creatine, BUN, HbAlc, total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and CRP; (5) other synergistic index: Chinese Visceral Adiposity Index (CVAI) =-267.93\u0026thinsp;+\u0026thinsp;0.68*Age\u0026thinsp;+\u0026thinsp;0.03*BMI\u0026thinsp;+\u0026thinsp;4.00 *WC\u0026thinsp;+\u0026thinsp;22.00*Log\u003csub\u003e10\u003c/sub\u003e(TG)-16.32*HDL-C(for female),\u003c/p\u003e \u003cp\u003eCVAI = -187.32\u0026thinsp;+\u0026thinsp;1.71*Age\u0026thinsp;+\u0026thinsp;4.23*BMI\u0026thinsp;+\u0026thinsp;1.12*WC\u0026thinsp;+\u0026thinsp;39.76*Log\u003csub\u003e10\u003c/sub\u003e(TG)-11.66*HDL-C(for male).\u003c/p\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eHypertension was confirmed through any of the following criteria: (1) SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg; (2) hypertension diagnosed by a physician before; (3) administration of anti-hypertensive medications. Diabetes mellitus was defined when any one of the following conditions occurred: (1) FPG\u0026thinsp;\u0026ge;\u0026thinsp;126mg/dL or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; (2) current use of anti-diabetic drugs; (3) diabetes diagnosed before. Dyslipidemia was recognized in any one of following conditions: (1) TG\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL; (2) TC\u0026thinsp;\u0026ge;\u0026thinsp;240mg/dL; (3) HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;40mg/dL; (4) LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;160mg/dL; (5) administration of lipid-lowering therapies; (6) dyslipidemia diagnosed by a physician before.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMultiple imputations were used to address the influence of missing values and minimize potential bias. Continuous variables demonstrating normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), with between-group comparisons analyzed using one-way analysis of variance (ANOVA) followed by Tukey's post hoc testing. Non-normally distributed quantitative variables were summarized as median (interquartile range, IQR), employing the Kruskal-Wallis test with Dunn-Bonferroni correction for multiple comparisons. Categorical parameters were presented as absolute frequencies (percentages), with intergroup differences assessed through χ\u0026sup2; tests or Fisher's exact tests. All statistical premises including homogeneity of variance (Levene's test, α\u0026thinsp;=\u0026thinsp;0.10) and normality (Kolmogorov-Smirnov test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were systematically verified.\u003c/p\u003e \u003cp\u003eInitially, we assessed the CTI as a continuous variable to increase the reliability of the conclusions, and a total of 9,055 participants enrolled were quartered into four groups by the quartiles of their CTI, namely 3.072651\u0026thinsp;\u0026le;\u0026thinsp;Q1\u0026thinsp;\u0026le;\u0026thinsp;4.325255, 4.325255\u0026thinsp;\u0026lt;\u0026thinsp;Q2\u0026thinsp;\u0026le;\u0026thinsp;4.691462, 4.691462\u0026thinsp;\u0026lt;\u0026thinsp;Q3\u0026thinsp;\u0026le;\u0026thinsp;5.113818, 5.113818\u0026thinsp;\u0026lt;\u0026thinsp;Q4\u0026thinsp;\u0026le;\u0026thinsp;7.528786. Next, all-cause mortality of Chinese participants aged over 45 was evaluated via the Kaplan\u0026ndash;Meier curves and log-rank tests. Furthermore, to understand collinearity between the CTI and other covariates, we examined tolerance values and variance inflation factors (VIFs). The relationship between the CTI and all-cause mortality was studied in the four Cox regression models as follows: Model 1 was unadjusted; Model 2 was adjusted with age, residence, gender, education and marital status; Model 3 held additional adjustments for age, residence, gender, education, marital status, BMI, drinking status, smoking status, hypertension, dyslipidemia, diabetes; and Model 4 included adjustments for HbA1c, cholesterol, creatine, BUN, HDL, LDL, age, residence, gender, education level, marital status, BMI, drinking status, smoking status, hypertension, SBP, DBP, dyslipidemia, diabetes. Additionally, the restricted cubic splines (RCS) were used to explore the dose-response association between the CTI and all-cause mortality in different age groups and the total population. To describe the association between the CTI and all-cause mortality in Chinese participants aged over 45 across different demographic characteristics, subgroup and interaction analyses were conducted for age, gender, education level, smoking status, marital status, residence, CVD incidence, diabetes, drinking status, smoking status, obesity and dyslipidemia. Besides, the receiver operating characteristic (ROC) curves were also established to determine the predictive value of CTI on all-cause mortality in the corresponding age groups. The incremental effect of CTI was illustrated by the area under the ROC curve (AUC). All statistical analyses were performed in the R software (version 4.5.1), and a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe Description of Baseline Characteristics\u003c/h2\u003e \u003cp\u003eIn this study, total 9,055 participants in the CHARLS database were eventually enrolled after 8650 individuals were screened out. The baseline characteristics of the enrolled participants are presented in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including 54.7% female and 45.3% male, with the mean age of 58.3 years. After categorizing the chosen participants by the quartiles of the CTI, we discovered that participants with the higher CTI witnessed the increased proportions of hypertension, dyslipidaemia, obesity, diabetes mellitus and cardiovascular disease (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), evidenced by significantly elevated SBP, DBP, cholesterol, LDL-C, TG, blood glucose and HbA1c, with lower HDL-C (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, participants with higher CTI had bigger BMI, WC, CVAI, higher creatine and BUN (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These participants always started smoking from a younger age and were inclined to consume more cigarettes and alcohol (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences in gender, marital status, education level, or depression status were detected among the four comparison groups (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea illustrates the distribution of the CTI alongside the all-cause mortality, with a median value of 4.691462 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 yielded by the Kolmogorov-Smirnov (K-S) test, showing that the CTI has a skewed distribution. With the increasing CTI, the all-cause mortality rate, in general, also shows an increasing trend, evidenced by the larger ratio of blue column/red column in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. Corresponding to the results in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eb directly demonstrates that deceased participants exhibited a significantly higher average of CTI than their non-deceased counterparts from the comparison of CTI between deceased and non-deceased group.\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\u003eBasic characteristics and patient demographics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTI quartiles p Overall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \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\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2264\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\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4100 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1053 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1038 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e996 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1013 (44.7)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4955 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1211 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1226 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1267 (56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1251 (55.3)\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\u003eAge (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3314 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e974 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e813 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e780 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e747 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3676 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e841 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e930 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e949 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e956 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2065 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e449 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e521 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e534 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e561 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent married (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e935 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e232 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e252 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8120 (89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2043 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2034 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2031 (89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2012 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6074 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1458 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1494 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1565 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1557 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2981 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e806 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e770 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e698 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e707 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege/University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (1.7)\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\u003eSecond/High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2717 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e688 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e656 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e683 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e690 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3663 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e911 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e929 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e935 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e888 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2547 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e637 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e648 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e615 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e647 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5621 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1429 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1387 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1396 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1409 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3434 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e835 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e877 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e867 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e855 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5469 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1722 (76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1498 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1209 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1040 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3586 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e542 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e766 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1054 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1224 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8367 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2199 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2169 (95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2107 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1892 (83.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e372 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.2\u003c/p\u003e \u003cp\u003e[94.3, 113.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003cp\u003e[90.5, 104.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.6\u003c/p\u003e \u003cp\u003e[93.6, 108.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.1\u003c/p\u003e \u003cp\u003e[95.9, 112.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112.0\u003c/p\u003e \u003cp\u003e[101.0, 133.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbAlc\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003cp\u003e[4.9, 5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003cp\u003e[4.8, 5.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003cp\u003e[4.9, 5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003cp\u003e[4.9, 5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003cp\u003e[5.0, 5.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5269 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1553 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1418 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1244 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1054 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3786 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e711 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e846 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1019 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1210 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.7\u003c/p\u003e \u003cp\u003e[114.3, 141.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.3\u003c/p\u003e \u003cp\u003e[111.0, 137.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.7\u003c/p\u003e \u003cp\u003e[113.7, 138.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129.0\u003c/p\u003e \u003cp\u003e[115.7, 143.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131.0\u003c/p\u003e \u003cp\u003e[117.7, 145.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.7\u003c/p\u003e \u003cp\u003e[67.3, 83.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003cp\u003e[65.7, 81.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003cp\u003e[67.0, 82.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003cp\u003e[68.3, 84.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003cp\u003e[69.0, 85.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8159 (90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2146 (94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2095 (92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2024 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1894 (83.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e896 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e239 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e370 (16.3)\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\u003eCholesterol (Median[IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.6\u003c/p\u003e \u003cp\u003e[167.4, 215.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182.5\u003c/p\u003e \u003cp\u003e[161.2, 204.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189.4\u003c/p\u003e \u003cp\u003e[167.0, 211.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193.3\u003c/p\u003e \u003cp\u003e[170.1, 219.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198.7\u003c/p\u003e \u003cp\u003e[173.2, 227.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003cp\u003e(Median[IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003cp\u003e[40.2, 59.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003cp\u003e[49.1, 67.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003cp\u003e[43.7, 61.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003cp\u003e[39.4, 55.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003cp\u003e[33.6, 50.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003cp\u003e(Median[IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.4\u003c/p\u003e \u003cp\u003e[93.6, 137.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.6\u003c/p\u003e \u003cp\u003e[91.2, 129.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116.0\u003c/p\u003e \u003cp\u003e[96.3, 138.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119.1\u003c/p\u003e \u003cp\u003e[97.4, 141.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112.9\u003c/p\u003e \u003cp\u003e[88.9, 139.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine (Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8[0.6, 0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7[0.6, 0.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8[0.6, 0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8[0.7, 0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8[0.7, 0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003cp\u003e[12.5, 18.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003cp\u003e[12.5, 18.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003cp\u003e[12.7, 18.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003cp\u003e[12.6, 18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003cp\u003e[12.3, 17.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6 [8.2, 9.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2 [7.9, 8.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5 [8.2, 8.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8 [8.5, 9.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.2 [8.8, 9.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Start Smoking\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.1 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.4 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.6 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Start Smoking\u003c/p\u003e \u003cp\u003eCategory (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4063 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e905 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1045 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1011 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1102 (48.7)\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\u003e13\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e628 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1821 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e474 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e458 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e446 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2543 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e764 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e610 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e639 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e530 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Duration\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Duration Category (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2687 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e806 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e659 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e664 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e558 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1852 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e459 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e459 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e461 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4516 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e985 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1146 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1140 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1245 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Number\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Number\u003c/p\u003e \u003cp\u003eCategory (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3672 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e993 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e932 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e902 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e845 (37.3)\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\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2009 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e498 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e494 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e547 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3374 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e801 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e834 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e867 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e872 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Packs Per Month\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Packs Per Month Category (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e5675 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1435 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1414 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1414 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1412 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e964 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e209 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e705 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e178 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1711 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e422 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e437 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e465 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8834 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2230 (98.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2225 (98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2214 (97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2165 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7883 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2046 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2002 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1948 (86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1887 (83.3)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1172 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e315 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e377 (16.7)\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\u003eTG\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.2\u003c/p\u003e \u003cp\u003e[75.2, 156.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003cp\u003e[57.5, 90.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.1\u003c/p\u003e \u003cp\u003e[75.2, 130.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.8\u003c/p\u003e \u003cp\u003e[92.9, 167.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175.2\u003c/p\u003e \u003cp\u003e[117.7, 260.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003cp\u003e[0.5, 2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003cp\u003e[0.3, 0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003cp\u003e[0.6, 1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003cp\u003e[0.9, 2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003cp\u003e[1.9, 6.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003cp\u003e[4.3, 5.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003cp\u003e[3.9, 4.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003cp\u003e[4.4, 4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003cp\u003e[4.8, 5.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003cp\u003e[5.3, 5.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003cp\u003e[77.2, 91.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003cp\u003e[74.5, 86.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003cp\u003e[76.4, 89.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003cp\u003e[79.2, 93.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003cp\u003e[81.0, 96.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVAI\u003c/p\u003e \u003cp\u003e(Median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-486.6\u003c/p\u003e \u003cp\u003e[-666.2,-339.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-625.0\u003c/p\u003e \u003cp\u003e[-800.8,-484.5]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-526.5\u003c/p\u003e \u003cp\u003e[-691.6,-394.3]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-447.4\u003c/p\u003e \u003cp\u003e[-600.8,-319.7]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-353.7\u003c/p\u003e \u003cp\u003e[-498.4,-231.9]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eData were summarized as mean (95% confidence intervals) or numbers (95% confidence intervals) according to their data type. CVD, cardiovascular disease; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; HDL, high-density lipoprotein cholesterol; CRP, C reactive protein; TyG, triglyceridesglucose index; CTI, C-reactive protein-triglyceride glucose index; BUN, blood urea nitrogen; CVAI, Chinese visceral adiposity index.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAge-Stratified Survival Analysis and Progressive Mortality Risks in Cox Regression Models\u003c/h3\u003e\n\u003cp\u003eInitially, Kaplan-Meier survival curves were constructed to depict the change of all-cause mortality among groups in different CTI quartiles with the extension of follow-up time. In the overall population analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), the Q4 cohort demonstrated significantly reduced survival compared to the aggregated Q1-Q3 cohorts (Log-rank test p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with no significant intergroup differences observed among Q1-Q3 when analyzed collectively (all pairwise p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, to study CTI\u0026rsquo;s contribution to the mortality of different age stratification, the survival curves were used to compare all-cause mortality between different CTI quartile zones in specific age groups, namely 45\u0026ndash;54, 55\u0026ndash;64 and 65+, respectively. For the Chinese participants aged from 45 to 54, the 10-year follow-up period failed witnessing statistical significance in the all-cause mortality between any two CTI groups (p\u0026thinsp;=\u0026thinsp;0.269), as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. However, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec demonstrated that participants aged 55\u0026ndash;64 years in the Q4 CTI subgroup exhibited significantly lower survival rates compared to Q1-Q3 groups (p\u0026thinsp;=\u0026thinsp;0.009). Notably, almost one out of eight seniors aged over 65 years in the Q4 subgroup experienced mortality, demonstrating the highest all-cause death risk compared to other groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while no significant mortality difference was observed between Q1-Q3 subgroups across all age stratifications (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the decade-long follow-up (2011\u0026ndash;2020), 221 deaths occurred among 9,055 participants, corresponding to an overall all-cause mortality rate of 2.44%. Four Cox proportional hazards models were established to precisely clarify the tie between the CTI and all-cause mortality in patients aged over 45, shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In Model 1, no covariate was adjusted; Model 2 included adjustments for residence, age, gender, education level, marital status; Model 3 was adjusted for residence, age, gender, education level, marital status, BMI, drinking, smoking, hypertension, dyslipidemia, diabetes; more indicators, namely, HbA1c, cholesterol, creatine, BUN, HDL, LDL, were added into adjustments of the novel Model 4 on the basis of those included in Model 3. To study the association between the CTI and all-cause mortality in the middle-aged and senior participants, the CTI range was quartered. In Model 4 (fully adjusted), each quartile increase in CTI was associated with a 175% higher mortality risk (HR\u0026thinsp;=\u0026thinsp;2.75, 95% CI: 2.16\u0026ndash;3.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, participants with Q4 CTI demonstrated a HR of 3.48 (95% CI: 2.25\u0026ndash;5.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to Q1, while Q3 participants showed a HR of 1.62 (95% CI: 1.03\u0026ndash;2.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.037). Similarly, Model 3 analysis revealed a 124% mortality increase per CTI quartile increment (HR:2.24, 95% CI:1.81\u0026ndash;2.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Q4 participants exhibiting a HR of 2.89 (95% CI: 1.93\u0026ndash;4.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning a significant 189% increase in mortality for Q4 participants compared to Q1. Models 1 and 2 demonstrated 124% and 122% mortality increases per CTI quartile elevation (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Q4 participants exhibited HRs of 2.97 (95% CI: 2.01\u0026ndash;4.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 2.82 (95% CI: 1.90\u0026ndash;4.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to Q1 in Models 1 and 2, respectively. Nonetheless, across Models 1\u0026ndash;3, no significant mortality differences were observed between Q2, Q3 and Q1 participants (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eThe correlation between CTI and ten-year mortality risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR(95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR(95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI per IQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003cp\u003e(1.84, 2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003cp\u003e(1.81, 2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003cp\u003e(1.81, 2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003cp\u003e(2.16, 3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003eCTI quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \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\u003e1.15\u003c/p\u003e \u003cp\u003e(0.72, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003cp\u003e(0.73, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(0.75, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003cp\u003e(0.80, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.314\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\u003e1.45\u003c/p\u003e \u003cp\u003e(0.93, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003cp\u003e(0.91, 2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003cp\u003e(0.95, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003cp\u003e(1.03, 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003cp\u003e(2.01, 4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003cp\u003e(1.90, 4.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003cp\u003e(1.93, 4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003cp\u003e(2.25, 5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, HR\u0026thinsp;=\u0026thinsp;Hazard Ratio\u003c/p\u003e \u003cp\u003eModel1: No covariates were adjusted.\u003c/p\u003e \u003cp\u003eModel2: Residence\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Gender\u0026thinsp;+\u0026thinsp;Education Level\u0026thinsp;+\u0026thinsp;Marital Status.\u003c/p\u003e \u003cp\u003eModel3: Residence\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Gender\u0026thinsp;+\u0026thinsp;Education Level\u0026thinsp;+\u0026thinsp;Marital Status\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;Drinking\u0026thinsp;+\u0026thinsp;Smoking\u0026thinsp;+\u0026thinsp;Hypertension\u0026thinsp;+\u0026thinsp;Dyslipidemia\u0026thinsp;+\u0026thinsp;Diabetes.\u003c/p\u003e \u003cp\u003eModel4: Residence\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Gender\u0026thinsp;+\u0026thinsp;Education Level\u0026thinsp;+\u0026thinsp;Marital Status\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;Drinking\u0026thinsp;+\u0026thinsp;Smoking\u0026thinsp;+\u0026thinsp;Hypertension\u0026thinsp;+\u0026thinsp;Dyslipidemia\u0026thinsp;+\u0026thinsp;Diabetes\u0026thinsp;+\u0026thinsp;HbAlc\u0026thinsp;+\u0026thinsp;BUN\u0026thinsp;+\u0026thinsp;HDL\u0026thinsp;+\u0026thinsp;LDL\u0026thinsp;+\u0026thinsp;SBP\u0026thinsp;+\u0026thinsp;DBP\u0026thinsp;+\u0026thinsp;Cholesterol\u0026thinsp;+\u0026thinsp;Creatine.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNonlinear Exposure-Response Relationship of CTI with Mortality\u003c/h2\u003e \u003cp\u003eRCS regression demonstrated a nonlinear association between CTI and all-cause mortality (p\u0026thinsp;=\u0026thinsp;0.005 for nonlinear) in the Chinese population aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years, with overall significance confirmed by p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for total (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). A progressive elevation in mortality risk was observed particularly beyond the CTI threshold of 5.26. Next, RCS were also conducted for subgroups of different age stratification. In detail, for the participants aged between 45 to 54, the result of Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eb denied the meaningful relationship between the CTI and all-cause mortality (p\u0026thinsp;=\u0026thinsp;0.814 for total, p\u0026thinsp;=\u0026thinsp;0.677 for nonlinear). RCS analysis also identified a statistically significant overall association between CTI and all-cause mortality in the 55\u0026ndash;64 year subgroup (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for total). Although the nonlinear relationship test showed marginal significance (p\u0026thinsp;=\u0026thinsp;0.128 for nonlinear), Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ec demonstrated a characteristic threshold effect at CTI\u0026thinsp;=\u0026thinsp;5.22 (HR\u0026thinsp;=\u0026thinsp;1.00), with progressively elevated mortality risks observed at higher CTI levels. Finally, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ed confirmed a nonlinear CTI-mortality association in adults\u0026thinsp;\u0026ge;\u0026thinsp;65 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for total, p\u0026thinsp;=\u0026thinsp;0.048 for nonlinear), showing disproportionate risk escalation beyond the CTI\u0026thinsp;=\u0026thinsp;4.72 threshold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Heterogeneity in CTI-Associated Mortality Risk\u003c/h2\u003e \u003cp\u003eAs shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e, subgroup analysis and interaction analysis were conducted for age, gender, education level, marital status, residence, CVD, diabetes, drinking, smoking, obesity and dyslipidemia. Most subgroups showed significant associations (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 except 45\u0026ndash;54 age group and college-educated subgroup). Notably, participants aged\u0026thinsp;\u0026ge;\u0026thinsp;55 years exhibited significantly elevated risks (55\u0026ndash;64 years: HR\u0026thinsp;=\u0026thinsp;2.90, 95%CI: 1.86\u0026ndash;4.54; \u0026ge;65 years: HR\u0026thinsp;=\u0026thinsp;2.92, 95%CI: 2.18\u0026ndash;3.91) compared to younger counterparts (45\u0026ndash;54 years: HR\u0026thinsp;=\u0026thinsp;1.24, 95%CI: 0.48\u0026ndash;3.23). Lower education levels showed graded associations, with primary education (HR\u0026thinsp;=\u0026thinsp;4.32, 95%CI: 2.37\u0026ndash;4.95) and secondary education (HR\u0026thinsp;=\u0026thinsp;3.68, 95%CI: 1.98\u0026ndash;6.84) demonstrating particularly strong effects. Cardiovascular disease history marked the highest mortality risk (HR\u0026thinsp;=\u0026thinsp;4.33, 95%CI: 2.58\u0026ndash;7.27), followed by current smokers (HR\u0026thinsp;=\u0026thinsp;2.60, 95%CI: 1.77\u0026ndash;3.83) and rural residents (HR\u0026thinsp;=\u0026thinsp;2.72, 95%CI: 2.04\u0026ndash;3.62). Pooled analysis confirmed an overall significant association with all-cause mortality (HR\u0026thinsp;=\u0026thinsp;2.76, 95%CI: 2.20\u0026ndash;3.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results also indicated that the significant interaction was only yielded between participants with different levels of education (interaction p\u0026thinsp;=\u0026thinsp;0.021). Few interactions could be noticed in other subgroups (all interaction p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAge-Modulated Prognostic Value of CTI in Mortality Prediction\u003c/h2\u003e \u003cp\u003eThen, ROC analysis was introduced to compare the full model to the base model in the efficacy for predicting all-cause mortality. On the basis of the base model as the reference, CTI was added into the adjustments of the full model. Supplementary Fig.\u0026nbsp;1a showed that, for the total participants, the area under the curve (AUC) of the full model was 0.849 (95%CI: 0.814\u0026ndash;0.877, p\u0026thinsp;=\u0026thinsp;0.008), with 0.829 for the base model (95%CI: 0.797\u0026ndash;0.860), and that the full model had a positive net reclassification index (NRI, 0.425; 95%CI: 0.189\u0026ndash;0.484; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and integrated discrimination improvement (IDI, 0.029; 95%CI: 0.0040\u0026ndash;0.055; p\u0026thinsp;=\u0026thinsp;0.017; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, different age stratification had different results of ROC analysis. In the eldest age group, the full model was superior to the basic counterpart in AUC (0.815; 95%CI: 0.776\u0026ndash;0.853 vs 0.782; 95%CI: 0.739\u0026ndash;0.825, p\u0026thinsp;\u0026lt;\u0026thinsp;0.006, Supplementary Fig.\u0026nbsp;1d), with a positive NRI (0.360; 95%CI: 0.152\u0026ndash;0.451, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and IDI (0.0347; 95%CI: 0.0045\u0026ndash;0.0670, p\u0026thinsp;=\u0026thinsp;0.030, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the other two age groups aged under 55, the full model cannot outweigh the base model in all indicators, namely AUC (p\u0026thinsp;=\u0026thinsp;0.21, Supplementary Fig.\u0026nbsp;1b; p\u0026thinsp;=\u0026thinsp;0.532, Supplementary Fig.\u0026nbsp;1c), NRI (p\u0026thinsp;=\u0026thinsp;0.23 and p\u0026thinsp;=\u0026thinsp;0.46, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and IDI (p\u0026thinsp;=\u0026thinsp;0.479 and p\u0026thinsp;=\u0026thinsp;0.064, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC and reclassification analysis of CTI on mortality identification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep for difference in AUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNRI\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep for difference in NRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIDI\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep for difference in IDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal population\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase Model (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.829(0.797\u0026ndash;0.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Model (ref\u0026thinsp;+\u0026thinsp;CTI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003cp\u003e(0.816\u0026ndash;0.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003cp\u003e(0.189\u0026ndash;0.484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003cp\u003e(0.0040\u0026ndash;0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation (aged 45\u0026ndash;54)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase Model (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003cp\u003e(0.723\u0026ndash;0.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Model (ref\u0026thinsp;+\u0026thinsp;CTI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003cp\u003e(0.764\u0026ndash;0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.270\u003c/p\u003e \u003cp\u003e(-0.480-0.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003cp\u003e(-0.076-0.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation (aged 55\u0026ndash;64)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase Model (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003cp\u003e(0.608\u0026ndash;0.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Model (ref\u0026thinsp;+\u0026thinsp;CTI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003cp\u003e(0.605\u0026ndash;0.805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003cp\u003e(-0.100-0.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003cp\u003e(-0.005-0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation (aged over 65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase Model (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003cp\u003e(0.739\u0026ndash;0.825)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Model (ref\u0026thinsp;+\u0026thinsp;CTI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003cp\u003e(0.776\u0026ndash;0.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003cp\u003e(0.152\u0026ndash;0.451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0347\u003c/p\u003e \u003cp\u003e(0.0045\u0026ndash;0.0670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eBase model includes Residence, Age, Gender, Education Level, Marital Status, BMI, Drinking, Smoking, Hypertension, Dyslipidemia, Diabetes. Full model consists of the basic model and CTI. AUC, area under the ROC curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prospective cohort study analyzed 9,055 Chinese adults\u0026thinsp;\u0026ge;\u0026thinsp;45 years to investigate the potential interplay between CTI and all-cause mortality. In general, from the baseline characteristic of the cohort, we knew that more elder individuals were inclined to have higher CTI. Additionally, CTI quartile elevation corresponded to the increased proportion of obesity, diabetes, hypertension. Meanwhile, participants with Q4 CTI quartile had larger WC and CVAI, and suffered higher Hb1Ac index, blood pressure and serum cholesterol with lower HDL-C. Then, participants in the highest CTI quartile exhibited earlier smoking initiation with prolonged duration (approximately 38 years on average) and higher daily cigarette consumption compared to lower quartiles. As a synergistic indicator, CTI can directly evaluate chronic systematic inflammation and metabolic syndrome. The formula of CTI determined that CTI must elevate when CRP, blood glucose and triglycerides leap to the higher level, which underlies the fact that seniors with higher CTIs are inclined to develop complications, like diabetes, obesity and hyperlipidemia. Besides, HbA1c, as a critical diagnostic criterion of diabetes, WC and CVAI, as important indexes for obesity should also positively correlated with CTI quartile, like the findings in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As another type of hyperlipidemia, hypercholesteremia is always characterized by elevated TC, LDL-C and decreased HDL-C. This study found that CTI increase was indeed accompanied by higher TC, LDL-C and impaired HDL-C. Generally, HDL-C and TG exhibit a well-established inverse relationship in lipid metabolism, a phenomenon observed across epidemiological and clinical studies. This correlation stems from bidirectional metabolic interactions. High circulating TG levels promote cholesterol ester transfer protein (CETP)-mediated exchange, transferring TG from very-low-density lipoproteins (VLDL) to HDL particles while displacing cholesterol esters, which accelerates the production of TG-rich and cholesterol-depleted HDL particles and slash the level of normal cholesterol-enriched HDL \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Next, TG-enriched HDL becomes a preferential substrate for hepatic lipase, leading to rapid lipolysis and subsequent clearance of HDL fragments from plasma and reducing both HDL particle size and concentration eventually. Besides, hypertriglyceridemia often coexists with insulin resistance, suppressing lipoprotein lipase activity and further impairing HDL maturation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. On the contrary, elevated LDL-C frequently correlates with hypertriglyceridemia\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. VLDL derived from the liver can carry endogenous TG, and generates LDL particles post to its lipolysis. Thus, elevated TG levels often coincide with accumulation of the small dense LDL (sdLDL) subfractions. Clinically, the complicated lipid metabolism among HDL, LDL and TG contributes to atherogenic dyslipidemia, characterized by the triad of high TG, high LDL and low HDL, and increasing cardiovascular risk\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This metabolic dysregulation plausibly underlies the atherogenic dyslipidemia profile (elevated TC/TG/LDL-C, depressed HDL-C) observed in Q4 CTI subjects, mechanistically linking to their increased cardiovascular morbidity and excess mortality risk in baseline-adjusted evaluations.\u003c/p\u003e \u003cp\u003eBesides, CRP serves as a sensitive biomarker of systemic inflammation, which directly influences the CTI calculation. Originally, liver can synthesize excessive CRP in the acute-phase response to interleukin-6 (IL-6), bidirectionally related with hyperlipidemia and mechanistically intertwined with atherosclerosis progression\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Hyperlipidemia, particularly characterized by elevated LDL-C, promotes endothelial dysfunction and oxidative stress. Subendothelial retention and modification of LDL particles trigger innate immune responses\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, in which macrophages are assembled under endothelium, transform into foam cells\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and release proinflammatory cytokines, like IL-6, TNF-α after engulfing oxidized LDL deposits. This process also amplifies hepatic CRP production, establishing a positive feedback loop. Conversely, inflammation directly dysregulates lipid metabolism. In detail, IL-6 suppresses lipoprotein lipase activity, specifically manifested as HDL synthesis reduction and VLDL secretion explosion, exacerbating hypertriglyceridemia. Adipose tissue inflammation in obesity patients further fuels this cycle via adipokine release. Clinically, previous studies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e suggested that elevated high-sensitivity CRP (hs-CRP) independently predicts cardiovascular events in hyperlipidemic patients, even when LDL-C concentrations are maintained within guideline-recommended ranges, highlighting the residual inflammatory risk beyond cholesterol management. Lipid-lowering treatments, represented by statin therapy, have exhibited pleiotropic anti-inflammatory effects, reducing both LDL-C and CRP, which can partially underlie their cardiovascular benefits\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Meanwhile, promising anti-inflammatory therapies, such as colchicine\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and IL-6 inhibitors\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, have also reduced the mortality risk in hyperlipidemic patients with elevated CRP. Thus, CRP and hyperlipidemia engage in a pathogenic synergy, accelerating systemic atherogenesis and increasing fatalities among the senior population. Combined assessment of lipids and inflammation optimizes risk stratification and therapeutic targeting. Additionally, CRP and insulin resistance (IR) are intertwined in a bidirectional pro-inflammatory metabolic cascade\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Their relationship is underpinned by immune-metabolic crosstalk\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and adipose tissue dysfunction acts as a critical nexus. IR-induced lipolysis increases free fatty acid flux, promoting ectopic lipid deposition and mitochondrial oxidative stress, activating innate immune pathways, especially the NF-κB and TLR4 signaling\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, to trigger pro-inflammatory cytokine secretion from adipocytes and macrophages. These cytokines exacerbate IR by impairing insulin receptor substrate-1 (IRS-1) phosphorylation and promoting hepatic gluconeogenesis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Concurrently, IL-6 stimulates hepatocytes to synthesize CRP, which further amplifies inflammation by activating complement pathways and promoting endothelial adhesion molecule expression. Hyperglycemia in IR also fosters advanced glycation end products (AGEs), which bind to surface receptors (RAGE) on immune cells to sustain cytokine production\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Clinically, elevated hs-CRP strongly correlates with IR severity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, even in normoglycemic individuals, and predicts potential type 2 diabetes and cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Interestingly, lifestyle intervention targeting IR consistently reduced CRP levels, and administration of anti-inflammatory agents like IL-1 antagonists could improve insulin sensitivity\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, underscoring their mechanistic linkage and confirming the causality. IR and CRP form a vicious cycle, where IR fuels inflammation, and inflammation perpetuates metabolic dysfunction. Therefore, IR, CRP elevation and hyperlipidemia interact as both cause and effect. This positive feedback loop accelerates CTI accumulation and leads its victims to mortality, just like the findings of this study.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, participants with elevated CTI usually have long-term worse lifestyles, especially a longer smoking history and a larger cigarette consumption. Smoking exacerbates systemic inflammation and metabolic dysfunction through shared mechanisms. Tobacco combustion generates reactive oxygen species (ROS) that induce NF-κB pathway activation, thereby promoting transcriptional upregulation of proinflammatory mediators (IL-6, TNF-α, CRP) to perpetuate chronic inflammation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Then, smoking promotes oxidative modification of LDL particles, reduces HDL-C, and increases triglyceride via enhanced VLDL secretion and impaired lipoprotein lipase\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The following endothelial dysfunction further amplifies lipid abnormalities. Inflammatory cytokines interfere with insulin signaling, while nicotine induces catecholamine release and increases lipolysis and free fatty acid flux, resulting in ectopic lipid deposition in muscles and liver, followed by impaired glucose uptake and gluconeogenesis promotion\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier curves demonstrated that participants in the Q4 CTI group had a significantly lower survival rate compared to those in the other three quartiles. This association was also observed in individuals aged over 55; however, no significant relationship between CTI quartile and all-cause mortality was evident among younger participants (aged 45\u0026ndash;54). Further Cox proportional hazards modeling revealed a dose-dependent association between ascending CTI quartiles and mortality risk, with each quartile elevation corresponding to a 175% increased mortality risk in fully adjusted models (Model 4). The CTI-mortality linkage demonstrated amplified hazard magnitude at extreme exposure levels (Q4 vs Q1: 248% risk elevation). Thus, all-cause mortality generally has an increasingly stronger tie to CTI increase as participants grows older. The RCS curves revealed both nonlinear and linear associations between CTI levels and mortality risk, with a statistically significant positive correlation emerging above the CTI threshold of 5.26. Stratified analyses demonstrated downward threshold shifting to 4.72 in the elderly subgroup (\u0026ge;\u0026thinsp;65 years). Importantly, the ROC curve analysis was used to compare the Base Model with the Full Model adjusted by CTI in their efficacy of mortality prediction, suggesting that the analysis model can significantly enhanced discrimination, with a 2.9% improvement in predictive accuracy following CTI inclusion.\u003c/p\u003e \u003cp\u003eThis study is a prospective cohort study distinguished by its assessment of the association between CTI and all-cause mortality in senior patients aged over 45, and further tries to quantify the CTI dose response to mortality of the whole participants. These findings have fulfilled a research gap in this field, especially for the traditional East Asian population. Additionally, through the study on different age stratification, it has been found that CTI presents different all-cause mortality prediction capabilities for the social population in different ages, which is conducive to subsequently optimizing the potential mortality prediction models. Thirdly, CRP, triglycerides and blood glucose, as routine laboratory test indicators which can be precisely detected in basic medical institutions, have the advantages of accessibility and non-invasive, making primary prevention and chronic disease management easier, more efficient, and more economical.\u003c/p\u003e \u003cp\u003eHowever, limitations should also be noticed. Firstly, our research could only describe the real world on the basis of the Chinese database and failed to collect data from different ethnic groups. Besides, our analysis was limited to assessing all-cause mortality outcomes without accounting for specific cause-of-death classifications. Furthermore, the potential longitudinal accumulation patterns of CTI warrant investigation, as dynamic indices quantifying temporal CTI progression may improve prognostic accuracy and require methodological validation in longitudinal studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis prospective cohort study identified a significant positive association between CTI and all-cause mortality, particularly pronounced in the geriatric population (aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years). The dose-dependent relationship observed across CTI quartiles establishes these stratification criteria as a promising metric for cardiometabolic risk stratification in clinical practice. Moreover, this synergistic index has shown a significant potential in prognostic evaluation, with economic factors taken into account.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The studies were approved by the Peking University Biomedical Ethics Review Committee. The studies were conducted legally. The participants provided their written informed consent when participating. Written informed consent was obtained from qualified investigators.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study is supported by the National Natural Science Foundation of China Youth Program (Grant Number: 82300332) and the Fuwai Hospital Central China Support Fund (Grant Number: ZCK2025317).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.Z. and R.T. analyzed the data and drafted initial manuscript. Y.X. reviewed and edited the manuscript. X.W., Z.H. and C.G. designed study and analyzed data. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the contributions of the CHALRS faculty and the participants sharing their data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe sources of all original data are listed in the \"Data sources and study population\" section of the \"Methods\" section. 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Disord\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 1225\u0026ndash;1232 (2001).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, C-reactive protein, Triglyceride-glucose index, All-cause mortality, Aging population, CHARLS database","lastPublishedDoi":"10.21203/rs.3.rs-8358890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8358890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rising prevalence of metabolic syndrome in Chinese adults\u0026thinsp;\u0026ge;\u0026thinsp;45 years reflects rapid socioeconomic and lifestyle changes. C-reactive protein (CRP) and triglyceride-glucose (TyG) index, biomarkers of chronic inflammation and insulin resistance, jointly drive metabolic dysregulation. However, their combined index (CTI/CRP-TyG Index) remains understudied in mortality prediction. This prospective cohort included 9,055 participants from China Health and Retirement Longitudinal Study (CHARLS) database. CTI was categorized into quartiles (Q1-Q4). Kaplan-Meier curves and Cox regression (adjusting for sociodemographics, lifestyle, and clinical factors) were used in survival analysis. Restricted cubic splines (RCS), subgroup analysis and ROC/NRI/IDI evaluated CTI-mortality associations and predictive model performance. During follow-up, 221 deaths occurred, showing declining survival rates with higher CTI quartiles (98.50%\u0026rarr;95.63%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest CTI quartile had 3.48-fold mortality risk (HR\u0026thinsp;=\u0026thinsp;3.48, 95%CI:2.25\u0026ndash;5.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis revealed stronger CTI-mortality associations in participants aged\u0026thinsp;\u0026ge;\u0026thinsp;55, primary education, or cardiovascular history, with overall HR\u0026thinsp;=\u0026thinsp;2.76 (95%CI:2.20\u0026ndash;3.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RCS and ROC analysis demonstrated that CTI quartiles linearly correlated with mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and improved the efficiency of predictive models (AUC:0.849 vs 0.829, p\u0026thinsp;=\u0026thinsp;0.008; NRI\u0026thinsp;=\u0026thinsp;0.425, IDI\u0026thinsp;=\u0026thinsp;0.029, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CTI quartiles increase elevated mortality of Chinese adults over 45, driven by CRP/triglyceride/glucose synergy. Targeting these biomarkers may lower mortality of metabolic-aging populations.\u003c/p\u003e","manuscriptTitle":"Predictive Value of C-Reactive Protein/Triglyceride-Glucose Index on the All-cause Mortality among Middle-Aged and Older Chinese Adults: A Prospective Cohort Study from CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 15:57:03","doi":"10.21203/rs.3.rs-8358890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"25351924115969001772756562518056834161","date":"2026-05-11T22:54:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T12:47:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156832324575345640458305725440122598297","date":"2026-04-07T01:53:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T05:24:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-20T09:07:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-20T09:07:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-14T15:00:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92122e9f-fd0b-4bae-941d-188bf09a7897","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"25351924115969001772756562518056834161","date":"2026-05-11T22:54:45+00:00","index":39,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T12:47:05+00:00","index":33,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60791126,"name":"Health sciences/Biomarkers"},{"id":60791127,"name":"Health sciences/Cardiology"},{"id":60791128,"name":"Health sciences/Diseases"},{"id":60791129,"name":"Health sciences/Endocrinology"},{"id":60791130,"name":"Health sciences/Medical research"},{"id":60791131,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-12T15:57:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 15:57:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8358890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8358890","identity":"rs-8358890","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}