Longitudinal association between the C-reactive protein-triglyceride-glucose index and incident cardiovascular disease in middle-aged and older adults with arthritis——A cohort study based on CHARLS

Longitudinal association between the C-reactive protein-triglyceride-glucose index and incident cardiovascular disease in middle-aged and older adults with arthritis——A cohort study based on 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 Longitudinal association between the C-reactive protein-triglyceride-glucose index and incident cardiovascular disease in middle-aged and older adults with arthritis——A cohort study based on CHARLS Qiang Yuan, Ruhui Fu, Ning Zhang, Jichao Li, Ruonan You, Chunyu Zou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9484339/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objectives To examine the longitudinal association between the C-reactive protein-triglyceride-glucose index (CTI) and incident cardiovascular disease (CVD) in middle-aged and older Chinese adults with arthritis, to assess the dose-response pattern of this association, and to explore the mediating role of body mass index (BMI). Methods This prospective cohort study was based on the China Health and Retirement Longitudinal Study, CHARLS. Baseline data were collected in 2011, and incident CVD was ascertained through the 2018 wave. Participants aged 45 years or older with physician-diagnosed arthritis, no history of CVD at baseline, and complete blood biomarker data were included. Multivariable logistic regression models were used to evaluate the association between CTI and incident CVD. Restricted cubic spline analysis was applied to assess the dose-response relationship, and causal mediation analysis was performed to evaluate the mediating role of BMI. Results During the 7-year follow-up, 266 participants developed incident CVD. In the fully adjusted model, each 1-unit increase in CTI was associated with a 22% higher risk of incident CVD (OR = 1.22, 95% CI: 1.01–1.48). Compared with the lowest quartile, participants in the highest quartile had a 54% higher risk (OR = 1.54, 95% CI: 1.12–2.11), with a significant trend across quartiles. Restricted cubic spline analysis revealed a significant positive dose-response association without nonlinearity. BMI partially mediated this association, with a mediation proportion of 36.5%. Conclusion CTI was independently associated with incident CVD in middle-aged and older Chinese adults with arthritis, showing a positive dose-response relationship. BMI appeared to be an important mediating pathway. Combined assessment of inflammation and metabolic dysfunction may be clinically useful for cardiovascular risk management in patients with arthritis. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Figures Figure 1 Figure 2 Key Points 1. CTI was independently associated with incident cardiovascular disease in adults with arthritis. 2. The association between CTI and cardiovascular disease showed a positive dose-response pattern. 3. BMI partially mediated the relationship between CTI and cardiovascular disease. 4. CTI may help improve cardiovascular risk stratification in patients with arthritis. Introduction Arthritis and cardiovascular disease are among the most common chronic disorders in middle-aged and older adults, and their coexistence substantially increases the risks of functional decline, disability, and mortality [ 1 , 2 ]. In particular, patients with rheumatoid arthritis have an approximately 1.5-fold higher risk of cardiovascular disease than the general population, and this excess risk cannot be fully explained by traditional cardiovascular risk factors alone [ 2 , 3 ]. Osteoarthritis was once regarded mainly as a localized degenerative disorder, but accumulating evidence indicates that it is also closely linked to systemic comorbidities. A meta-analysis showed that patients with osteoarthritis have an increased overall risk of cardiovascular disease. A population-based study from Japan further reported that older adults with osteoarthritis were at higher risk of ischemic heart disease, heart failure, and stroke [ 4 , 5 ]. Relevant reviews have suggested that chronic low-grade inflammation, oxidative stress, vascular dysfunction, and lifestyle-related factors may represent key links between arthritis and cardiovascular disease [ 6 , 7 ]. In 2022, the European Alliance of Associations for Rheumatology recommended that cardiovascular risk assessment and management should be incorporated into routine care for patients with rheumatic and musculoskeletal diseases [ 1 ]. In rheumatoid arthritis, systemic inflammation, insulin resistance, dyslipidemia, reduced physical activity, and treatment exposure may jointly contribute to excess cardiovascular risk. At the same time, traditional risk factors such as hypertension, diabetes, and obesity often interact with inflammatory pathways [ 3 , 8 ]. Therefore, cardiovascular risk assessment in patients with arthritis may require biomarkers that capture both inflammation and metabolic disturbance, rather than relying on a single indicator. The C-reactive protein-triglyceride-glucose index, CTI, is a novel composite indicator derived by incorporating C-reactive protein into the triglyceride-glucose index. It reflects both insulin resistance and systemic inflammatory burden [ 9 ]. Since its introduction, CTI has been increasingly linked to cardiovascular outcomes. For example, an analysis based on NHANES showed that CTI was positively associated with the prevalence of coronary heart disease [ 10 ]. Longitudinal studies have further shown that CTI is associated with stroke risk in patients with hypertension, stroke risk across different glycemic states in CHARLS, and both CVD incidence and mortality in the general population [ 11 – 13 ]. In addition, CTI has been used for cardiovascular risk stratification in individuals with cardiovascular-kidney-metabolic syndrome and has also been linked to the identification of new-onset coronary heart disease [ 14 , 15 ]. Recent studies based on CHARLS have shown that CTI is independently and linearly associated with incident CVD in the general middle-aged and older population, and that cumulative exposure or persistently elevated trajectories may further increase this risk [ 16 , 17 ]. However, existing evidence has focused mainly on the general population or metabolically high-risk groups. Longitudinal evidence remains limited in patients with arthritis, a group characterized by chronic inflammation, physical limitation, and metabolic abnormalities. A previous CHARLS-based study suggested that depressive status may increase subsequent CVD risk in patients with arthritis, indicating that more refined cardiovascular risk stratification is needed in this population [ 18 ]. However, the association between CTI and incident CVD in patients with arthritis, the dose-response pattern of this association, and the potential mediating pathway have not been systematically examined. Because obesity plays an important role in both arthritis and cardiovascular disease, whether BMI mediates the association between CTI and incident CVD also deserves attention. Therefore, using nationally representative longitudinal data from CHARLS, we included middle-aged and older adults with arthritis but without CVD at baseline to examine the longitudinal association between baseline CTI and incident CVD, to explore the dose-response relationship using restricted cubic spline analysis, and to assess the mediating role of BMI. Methods Study design and participants This study was based on the China Health and Retirement Longitudinal Study, CHARLS, and used a prospective cohort design. CHARLS is a nationally representative longitudinal survey of Chinese adults aged 45 years or older and uses a multistage stratified probability sampling design. Baseline data were collected in 2011, and follow-up surveys were conducted in 2013, 2015, 2018, and 2020 [ 19 ]. The protocols for venous blood collection, laboratory testing, and quality control in CHARLS have been described previously [ 20 ]. In the present study, 2011 was used as the baseline, and incident CVD was identified through the 2018 survey wave, yielding a 7-year follow-up period. The participant selection process is shown in Fig. 1 . Participants were eligible if they met the following criteria: 1, physician-diagnosed arthritis at baseline; 2, no history of CVD at baseline; 3, age 45 years or older; and 4, complete baseline blood biomarker data, including C-reactive protein, triglycerides, and fasting plasma glucose. Participants were excluded if they had 1, missing information on arthritis diagnosis or baseline CVD status; 2, missing data on key covariates; or 3, incomplete CVD outcome information during follow-up. Among the initial 17,705 participants, we excluded 404 individuals younger than 45 years, 160 with missing arthritis information, and 11,241 without arthritis at baseline, leaving 5,900 participants with baseline arthritis. We then excluded 2,239 participants with missing blood biomarker data required for CTI calculation, 635 with incomplete follow-up CVD information, 530 with baseline CVD, and 383 with missing covariates. Finally, 2,113 participants with arthritis but without CVD at baseline were included in the analysis. Among them, 1,847 remained free of CVD during follow-up, whereas 266 developed incident CVD. The CHARLS study was approved by the Ethical Review Committee of Peking University. All participants provided written informed consent. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology, STROBE, statement [ 21 ]. Assessment of CTI CTI was calculated as follows: $$\:CTI=0.412\times\:{ln}CRP+{ln}\left[\left(TG\times\:FPG\right)/2\right]$$ where CRP was expressed in mg/L, and both TG and FPG were expressed in mg/dL. This calculation was consistent with the original CTI study and subsequent CHARLS-based analyses [ 9 , 16 ]. Based on the quartile distribution of CTI in the present sample, CTI was categorized into four groups: Q1 < 4.34, Q2 4.34 to 4.68, Q3 4.68 to 5.08, and Q4 ≥ 5.08, with Q1 used as the reference group. Outcome assessment At baseline and during follow-up household interviews, participants were asked whether they had ever been diagnosed by a physician with heart disease or stroke. Participants who answered yes to either question were classified as having CVD. Incident CVD was defined as the absence of CVD at baseline and the first report of physician-diagnosed heart disease or stroke during follow-up through the 2018 survey. Assessment of covariates Based on previous CHARLS studies and related epidemiological investigations, the following variables were collected in this study: age, sex, residence, marital status, educational level, smoking status, drinking status, sleep duration, BMI, hypertension, diabetes, lung disease, liver disease, kidney disease, stomach disease, and asthma. Specifically, sociodemographic characteristics included age, sex, residence, marital status, and educational level. Residence was classified as rural or urban. Marital status was categorized as married or living with partner, and other marital status. Educational level was categorized as illiteracy, primary school, middle school, and high school or above. Health-related factors included smoking status, drinking status, sleep duration, and BMI. Smoking status was classified as current, former, or never. Drinking status was categorized as none, less than once per month, or at least once per month during the past year. Sleep duration was assessed as nighttime sleep duration in hours. BMI was calculated as weight in kilograms divided by height in meters squared and expressed as kg/m². Chronic conditions included hypertension, diabetes, lung disease, liver disease, kidney disease, stomach disease, and asthma. BMI categories were defined as follows: underweight, BMI < 18.5 kg/m²; normal weight, 18.5 kg/m² ≤ BMI < 24 kg/m²; overweight, 24 kg/m² ≤ BMI < 28 kg/m²; and obesity, BMI ≥ 28 kg/m². Hypertension was defined as self-reported physician-diagnosed hypertension or measured blood pressure ≥ 140/90 mmHg. Diabetes was defined as self-reported physician-diagnosed diabetes, fasting plasma glucose ≥ 126 mg/dL, random plasma glucose ≥ 200 mg/dL, or glycated hemoglobin ≥ 6.5%. For consistency with the regression results shown in Table 2 , the main multivariable models adjusted sequentially for age, location, educational level, marital status, BMI, smoking status, drinking status, sleep duration, hypertension, and diabetes. The remaining chronic diseases were described at baseline. Statistical analysis Continuous variables were presented as mean ± standard deviation, and categorical variables were presented as number with percentage. Baseline characteristics across CTI quartiles were compared using one-way analysis of variance for continuous variables and the chi-square test for categorical variables. Multivariable logistic regression models were used to evaluate the prospective association between baseline CTI and incident CVD, and odds ratios, ORs, with 95% confidence intervals, CIs, were reported. CTI was analyzed both as a continuous variable and by quartiles. In addition, the interquartile range, IQR, increment in CTI was examined. Four models were constructed. The crude model was unadjusted. Model 1 was adjusted for age, location, educational level, and marital status. Model 2 was further adjusted for BMI, smoking status, drinking status, and sleep duration. Model 3 was additionally adjusted for hypertension and diabetes. Subgroup analyses were performed according to age, sex, sleep duration, residence, marital status, educational level, smoking status, drinking status, and obesity status. Interaction tests were conducted by adding cross-product terms to the regression models. Restricted cubic spline analysis was used to explore the dose-response relationship between CTI and incident CVD [ 22 ]. The figure position for the spline plot is provided in Fig. 2 . Causal mediation analysis was performed to assess the mediating role of BMI in the association between CTI and incident CVD. The average causal mediation effect, average direct effect, and proportion mediated were reported [ 23 , 24 ]. All analyses were conducted using R version 4.5.2, and a two-sided P < 0.05 was considered statistically significant. Results Baseline characteristics of the study population A total of 2,113 middle-aged and older adults with arthritis but without CVD at baseline were included in this study, including 1,257 women, 59.49%, and 856 men, 40.51%. According to baseline CTI quartiles, 528 participants were classified into Q1, 514 into Q2, 531 into Q3, and 540 into Q4. As shown in Table 1 , participants with higher CTI levels tended to be older and had higher BMI, CRP, TG, and fasting glucose levels, all with P values < 0.001. Compared with participants in Q1, those in Q4 were also more likely to live in urban areas, to have hypertension and diabetes, and to be overweight or obese. In addition, the proportion of participants with asthma increased across CTI quartiles, whereas the proportion with stomach disease decreased. By contrast, sex, marital status, educational level, smoking status, drinking status, sleep duration, lung disease, liver disease, and kidney disease did not differ significantly across CTI quartiles. Table 1 Baseline characteristics of participants according to CTI quartiles. Variable Total (n = 2113) Q1 (n = 528) Q2 (n = 514) Q3 (n = 531) Q4 (n = 540) P-value Age(year) 58.94 ± 8.44 57.57 ± 8.20 59.47 ± 8.42 59.34 ± 8.53 59.37 ± 8.50 < 0.001 Sex(n, %) 0.30 Female 1257(59.49) 315(59.66) 289(56.23) 329(61.96) 324(60.00) Male 856(40.51) 213(40.34) 225(43.77) 202(38.04) 216(40.00) BMI(Kg/m2) 23.60 ± 3.94 22.19 ± 3.13 22.92 ± 3.39 24.19 ± 3.92 25.05 ± 4.52 < 0.0001 CRP(mg/L) 2.53 ± 6.11 0.48 ± 0.22 0.89 ± 0.54 1.68 ± 1.19 6.95 ± 10.85 < 0.0001 TG(mg/dL) 133.67 ± 123.24 76.80 ± 27.69 105.57 ± 37.80 133.49 ± 58.57 216.21 ± 208.04 < 0.0001 Fast.glucose(mg/dL) 108.59 ± 31.17 98.99 ± 14.73 103.45 ± 18.02 105.99 ± 17.83 125.42 ± 50.66 < 0.0001 Sleep duration(h) 6.09 ± 1.96 5.98 ± 1.97 6.11 ± 2.04 6.05 ± 1.95 6.22 ± 1.90 0.23 Location(n, %) < 0.01 Rural 1484(70.23) 399(75.57) 366(71.21) 363(68.36) 356(65.93) Urban 629(29.77) 129(24.43) 148(28.79) 168(31.64) 184(34.07) Marital status(n, %) 0.14 Married/living with partner 1773(83.91) 428(81.06) 443(86.19) 450(84.75) 452(83.70) Other marital status 340(16.09) 100(18.94) 71(13.81) 81(15.25) 88(16.30) Educational level(n, %) 0.72 Illiteracy 1136(53.76) 292(55.30) 273(53.11) 280(52.73) 291(53.89) Middle school 344(16.28) 87(16.48) 73(14.20) 94(17.70) 90(16.67) Primary school 484(22.91) 120(22.73) 126(24.51) 117(22.03) 121(22.41) High school or above 149( 7.05) 29( 5.49) 42( 8.17) 40( 7.53) 38( 7.04) Smoking status(n, %) 0.37 Current smoke 572(27.07) 132(25.00) 156(30.35) 138(25.99) 146(27.04) Former 150( 7.10) 32( 6.06) 39( 7.59) 42( 7.91) 37( 6.85) Never smoke 1391(65.83) 364(68.94) 319(62.06) 351(66.10) 357(66.11) Drinking status(n, %) 0.14 1m 524(24.80) 144(27.27) 132(25.68) 128(24.11) 120(22.22) No 1433(67.82) 334(63.26) 349(67.90) 367(69.11) 383(70.93) Hypertension(n, %) < 0.0001 No 1341(63.46) 394(74.62) 350(68.09) 313(58.95) 284(52.59) Yes 772(36.54) 134(25.38) 164(31.91) 218(41.05) 256(47.41) Diabetes(n, %) < 0.0001 No 1814(85.85) 492(93.18) 461(89.69) 472(88.89) 389(72.04) Yes 299(14.15) 36( 6.82) 53(10.31) 59(11.11) 151(27.96) Obesity(n, %) < 0.0001 Low weight 148( 7.00) 57(10.80) 44( 8.56) 29( 5.46) 18( 3.33) Normal 1080(51.11) 335(63.45) 286(55.64) 242(45.57) 217(40.19) Obesity 261(12.35) 23( 4.36) 46( 8.95) 76(14.31) 116(21.48) Over weight 624(29.53) 113(21.40) 138(26.85) 184(34.65) 189(35.00) Lung disease(n, %) 0.11 No 1851(87.60) 478(90.53) 446(86.77) 463(87.19) 464(85.93) Yes 262(12.40) 50( 9.47) 68(13.23) 68(12.81) 76(14.07) Liver disease(n, %) 0.13 No 2033(96.21) 507(96.02) 503(97.86) 509(95.86) 514(95.19) Yes 80( 3.79) 21( 3.98) 11( 2.14) 22( 4.14) 26( 4.81) Kidney disease(n, %) 0.51 No 1959(92.71) 486(92.05) 484(94.16) 492(92.66) 497(92.04) Yes 154( 7.29) 42( 7.95) 30( 5.84) 39( 7.34) 43( 7.96) Stomach disease(n, %) < 0.01 No 1424(67.39) 332(62.88) 335(65.18) 368(69.30) 389(72.04) Yes 689(32.61) 196(37.12) 179(34.82) 163(30.70) 151(27.96) Asthma (n, %) < 0.01 No 2020(95.60) 512(96.97) 492(95.72) 513(96.61) 503(93.15) Yes 93( 4.40) 16( 3.03) 22( 4.28) 18( 3.39) 37( 6.85) Values are presented as mean ± standard deviation or n (%). Association between CTI and incident cardiovascular disease During the 7-year follow-up, 266 participants developed incident CVD, corresponding to a cumulative incidence of 12.59%. As a continuous variable, CTI was positively associated with incident CVD in all models. In the crude model, each 1-unit increase in CTI was associated with a higher risk of incident CVD, OR 1.36, 95% CI 1.14 to 1.63, P < 0.001. After adjustment for age, location, educational level, and marital status, the association remained significant, OR 1.32, 95% CI 1.10 to 1.59, P = 0.002. Further adjustment for BMI, smoking status, drinking status, and sleep duration did not materially change the estimate, OR 1.32, 95% CI 1.10 to 1.59, P = 0.002. In the fully adjusted model, which further included hypertension and diabetes, CTI remained independently associated with incident CVD, OR 1.22, 95% CI 1.01 to 1.48, P = 0.04. A similar pattern was observed when CTI was analyzed per IQR increase. In the fully adjusted model, each IQR increase in CTI was associated with a 16% higher risk of incident CVD, OR 1.16, 95% CI 1.01 to 1.34, P = 0.04. When CTI was analyzed by quartiles, a graded association was observed. Compared with Q1, the ORs for incident CVD in the fully adjusted model were 1.30, 95% CI 0.94 to 1.78, for Q2, 1.44, 95% CI 1.06 to 1.97, for Q3, and 1.54, 95% CI 1.12 to 2.11, for Q4. The trend across quartiles was statistically significant, P for trend = 0.01. These results suggest that higher CTI levels were associated with a progressively greater risk of incident CVD. Table 2 Association between baseline CTI and incident cardiovascular disease. Characteristic Crude model Model 1 Model 2 Model 3 Characteristic OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value CVD ~ CTI 1.36(1.14,1.63) < 0.001 1.32(1.10,1.59) 0.002 1.32(1.10,1.59) 0.002 1.22(1.01,1.48) 0.04 CVD~CTI_iqr 1.26(1.10,1.44) < 0.001 1.23(1.08,1.41) 0.002 1.23(1.08,1.41) 0.002 1.16(1.01,1.34) 0.04 CVD~CTIQ Q1 ref ref ref ref Q2 1.4(1.02,1.91) 0.04 1.32(0.96,1.81) 0.08 1.32(0.96,1.81) 0.09 1.3(0.94,1.78) 0.11 Q3 1.65(1.22,2.24) 0.001 1.55(1.14,2.11) 0.01 1.54(1.13,2.09) 0.01 1.44(1.06,1.97) 0.02 Q4 1.83(1.36,2.48) < 0.0001 1.74(1.28,2.35) < 0.001 1.74(1.28,2.35) < 0.001 1.54(1.12,2.11) 0.01 p for trend(character2integer) < 0.0001 < 0.001 < 0.001 0.01 p for trend(Median value) < 0.0001 < 0.001 < 0.001 0.01 Note: The crude model was unadjusted. Model 1 was adjusted for age, location, educational level, and marital status. Model 2 was further adjusted for BMI, smoking status, drinking status, and sleep duration. Model 3 was further adjusted for hypertension and diabetes. Dose-response relationship between CTI and incident cardiovascular disease Restricted cubic spline analysis showed that CTI was positively associated with incident CVD. In the crude model, a nonlinear pattern was observed. However, after adjustment for age, location, educational level, marital status, BMI, smoking status, drinking status, sleep duration, hypertension, and diabetes, the overall association remained significant, whereas evidence for nonlinearity was attenuated. Overall, the spline analysis suggested a positive dose-response relationship between CTI and incident CVD, with the adjusted association appearing closer to linear. The spline figure position is shown in Fig. 2 . Subgroup analyses are shown in Table 3 . The positive association between CTI and incident CVD appeared to be more pronounced in participants younger than 60 years. In this subgroup, the OR for Q4 versus Q1 was 2.224, 95% CI 1.415 to 3.537, whereas the corresponding OR in participants aged 60 years or older was 1.105, 95% CI 0.701 to 1.750. The interaction by age approached significance, P for interaction = 0.085. A significant interaction was observed for marital status, P for interaction = 0.028. Among participants with other marital status, the ORs for Q2 and Q3 versus Q1 were 3.480, 95% CI 1.455 to 8.682, and 3.006, 95% CI 1.296 to 7.293, respectively, whereas the estimate for Q4 was attenuated. Among participants who were married or living with a partner, the association was more gradual, and the OR for Q4 versus Q1 was 1.598, 95% CI 1.139 to 2.254. A significant interaction was also observed for educational level, P for interaction = 0.012. The association between CTI and incident CVD was more evident among participants with primary school education, in whom the ORs for Q3 and Q4 versus Q1 were 2.704, 95% CI 1.342 to 5.683, and 2.776, 95% CI 1.332 to 6.026, respectively. Although a stronger effect estimate was also observed in participants with high school education or above, the confidence intervals were wide, suggesting limited precision. No significant interactions were found for sex, sleep duration, residence, smoking status, drinking status, or obesity status. Subgroup analyses Table 3 Subgroup analyses of the association between CTI quartiles and incident cardiovascular disease. Subgroup Q1 Q2 Q3 Q4 p for interaction Age2 0.085 ≥ 60 ref 1.082(0.691, 1.702) 1.284(0.832, 1.996) 1.105(0.701, 1.750) < 60 ref 1.549(0.971,2.488) 1.687(1.068,2.688) 2.224(1.415,3.537) Sex 0.688 Male ref 1.513(0.915,2.529) 1.684(1.017,2.819) 2.127(1.287,3.565) Female ref 1.174(0.773,1.787) 1.282(0.860,1.921) 1.228(0.812,1.863) Sleep time 0.293 ≥ 7 ref 1.772(1.067,2.989) 1.772(1.067,2.987) 2.153(1.306,3.612) < 7 ref 1.060(0.699,1.609) 1.304(0.876,1.950) 1.250(0.826,1.895) Location 0.072 Rural ref 1.385(0.952,2.023) 1.727(1.202,2.497) 1.424(0.970,2.097) Urban ref 1.072(0.579,2.001) 0.908(0.496,1.680) 1.696(0.953,3.074) Marital status 0.028 Married/living with partner ref 1.175(0.831,1.666) 1.324(0.944,1.864) 1.598(1.139,2.254) Other marital status ref 3.480(1.455, 8.682) 3.006(1.296, 7.293) 1.263(0.493, 3.266) Educational level 0.012 Illiteracy ref 1.144(0.748,1.755) 1.245(0.819,1.901) 1.091(0.709,1.682) Middle school ref 1.632(0.735,3.664) 1.418(0.660,3.096) 1.694(0.772,3.795) Primary school ref 1.268(0.589,2.790) 2.704(1.342,5.683) 2.776(1.332,6.026) High school or above ref 4.880(1.023,36.797) 2.612(0.465,21.290) 13.451(2.797,104.334) Smoking status 0.584 Current smoke ref 1.950(1.025,3.840) 2.432(1.279,4.794) 2.733(1.428,5.429) Never smoke ref 1.046(0.704,1.553) 1.156(0.791,1.693) 1.196(0.813,1.762) Former ref 2.413(0.630,10.427) 1.884(0.503, 7.909) 3.213(0.769,15.327) Drinking status 0.916 1m ref 1.447(0.775,2.724) 1.544(0.833,2.887) 1.672(0.877,3.210) Obesity 0.671 Normal ref 1.091(0.717,1.660) 1.407(0.924,2.145) 1.288(0.821,2.017) Low weight ref 1.177(0.323,4.310) 1.827(0.428,7.570) 0.578(0.054,3.963) Obesity ref 1.012(0.280, 4.061) 1.501(0.475, 5.479) 1.470(0.467, 5.341) Over weight ref 1.981(1.012,4.031) 1.446(0.746,2.915) 1.554(0.795,3.155) Note: Odds ratios are presented for each CTI quartile compared with Q1. P for interaction was calculated from the fully adjusted model. Mediating effect of BMI Mediation analysis showed that BMI significantly and partially mediated the association between CTI and incident CVD. The average causal mediation effect was 0.007, 95% CI 0.002 to 0.017, P = 0.004, suggesting that CTI may indirectly increase the risk of incident CVD through higher BMI. The average direct effect was 0.012, 95% CI − 0.010 to 0.017, P = 0.252. The proportion mediated was 36.5%, 95% CI 6.1% to 248.4%, P = 0.04. Discussion In this nationwide prospective cohort of middle-aged and older Chinese adults with arthritis, we found that higher CTI levels were associated with a greater risk of incident CVD over 7 years of follow-up. This association remained after adjustment for sociodemographic characteristics, lifestyle-related factors, BMI, hypertension, and diabetes. When CTI was analyzed by quartiles, participants in the highest quartile had a significantly higher risk of incident CVD than those in the lowest quartile. The spline analysis further suggested a positive dose-response relationship, and mediation analysis indicated that BMI partly mediated the observed association. Taken together, these findings support CTI as a potentially useful indicator for cardiovascular risk stratification in patients with arthritis. Our findings are broadly consistent with previous studies showing that inflammation and metabolic disturbance jointly contribute to adverse cardiovascular outcomes [ 25 , 26 ]. In immune-mediated and degenerative joint disorders, cardiovascular risk cannot be fully explained by conventional risk factors alone. Persistent inflammation, altered lipid metabolism, insulin resistance, reduced physical activity, and treatment-related factors may all contribute to vascular injury and long-term cardiovascular burden [ 27 , 28 ]. In this context, CTI may offer some practical advantages because it integrates CRP, TG, and fasting glucose into a single marker and therefore captures both inflammatory and metabolic dimensions of risk. This may be particularly relevant in patients with arthritis, in whom chronic inflammation and metabolic abnormalities often coexist. The present study also adds to the growing literature on CTI. Previous analyses in NHANES and CHARLS have shown that CTI is associated with coronary heart disease, stroke, incident CVD, and cardiovascular mortality in different populations [ 10 – 17 ]. However, evidence in patients with arthritis has remained limited. This population warrants specific attention because arthritis is not simply a musculoskeletal condition. It is increasingly recognized as a systemic disorder accompanied by endothelial dysfunction, oxidative stress, a proatherogenic milieu, and multiple cardiometabolic comorbidities [ 28 – 32 ]. Against this background, our findings suggest that CTI may help identify patients with arthritis who are at particularly high cardiovascular risk. Another notable finding was the generally graded association across CTI quartiles. In the fully adjusted model, the risk estimate increased progressively from Q2 to Q4, and the trend test remained significant. Although the association for Q2 did not reach statistical significance, the direction of effect was consistent. This pattern supports the view that CTI behaves more like a continuous risk marker than a threshold-based indicator. The restricted cubic spline analysis pointed in the same direction. Although some curvature was seen in the crude analysis, the adjusted association appeared closer to linear. A similar pattern has been reported in recent CTI studies, where the association with adverse cardiovascular or metabolic outcomes was often positive and approximately linear after multivariable adjustment [ 16 , 17 , 33 , 34 ]. This is clinically relevant because it suggests that even moderate increases in CTI may carry information about future cardiovascular risk. The role of BMI in our analysis also deserves attention. Mediation analysis suggested that BMI accounted for part of the association between CTI and incident CVD. This finding is biologically plausible. Obesity is now widely regarded as a chronic low-grade inflammatory state characterized by adipose tissue dysfunction, altered adipokine secretion, insulin resistance, endothelial injury, and a prothrombotic tendency [ 35 – 39 ]. In patients with arthritis, this pathway may be even more important. Pain, stiffness, and physical limitation may reduce mobility and energy expenditure, leading to weight gain, worsening metabolic health, and a further increase in inflammatory burden. Evidence from umbrella reviews has shown that a higher number of daily steps is associated with better health outcomes, including cardiovascular outcomes [ 40 ]. In addition, a randomized controlled trial in older adults with rheumatoid arthritis and overweight or obesity showed that remotely supervised weight loss combined with exercise training improved cardiovascular risk and clinical outcomes [ 41 ]. This evidence is consistent with the BMI-mediated pathway observed in our study. The subgroup analyses provide some further clues, although they should be interpreted cautiously. The association between CTI and incident CVD appeared stronger in participants younger than 60 years, in those with other marital status, and in those with lower educational attainment, particularly participants with primary school education. These findings may reflect differences in social support, health literacy, healthcare access, or the ability to manage long-term cardiovascular risk factors. The interaction for marital status was especially notable, although some subgroup estimates were based on relatively small numbers and should therefore be interpreted with restraint. Likewise, the estimates in the highest educational subgroup were imprecise, as reflected by the wide confidence intervals. Even so, these results suggest that the clinical meaning of CTI may not be entirely uniform across population subgroups. Our findings also have potential clinical implications. Current guidelines increasingly emphasize the need for routine cardiovascular risk assessment in patients with rheumatic and musculoskeletal diseases [ 1 , 26 , 42 ]. However, implementation in daily practice remains suboptimal, and risk assessment still often relies on conventional factors alone. Because CTI is based on routine laboratory measures, it is simple to calculate and may be suitable for use in community and primary care settings. It is not intended to replace standard cardiovascular risk evaluation, but it may provide additional information in patients with arthritis, especially when inflammation and metabolic disturbance coexist. This is relevant because patients with arthritis often require long-term management, and cardiovascular prevention needs to be incorporated into that broader care framework. Several mechanisms may underlie the observed association. Chronic inflammation can impair endothelial function, enhance oxidative stress, alter lipoprotein composition and function, and accelerate atherosclerotic progression [ 28 , 29 , 31 , 43 , 44 ]. In parallel, hypertriglyceridemia and impaired glucose metabolism promote insulin resistance and vascular injury. These pathways do not operate in isolation. Rather, they interact and amplify one another over time. CTI may therefore reflect a broader state of immunometabolic dysfunction, which could help explain its association with incident CVD in our cohort. From this perspective, the observed relationship is consistent with current understanding of cardiovascular risk in arthritis and related inflammatory conditions. This study has several strengths. First, it was based on CHARLS, a nationally representative cohort with broad population coverage, which enhances the generalizability of the findings to middle-aged and older Chinese adults with arthritis. Second, the longitudinal design and exclusion of participants with baseline CVD improved the temporal interpretation of the observed association. Third, we evaluated CTI both as a continuous variable and by quartiles, and further explored its dose-response relationship and mediating pathway. Several limitations should also be acknowledged. First, arthritis and CVD were identified based on self-report of physician diagnosis, which may have introduced recall bias or misclassification. Second, CHARLS does not provide detailed information on arthritis subtype, disease duration, disease activity, or specific anti-rheumatic treatment, all of which may influence cardiovascular risk [ 30 , 45 – 47 ]. Third, some subgroup estimates were unstable, especially where the number of events was limited, and therefore those findings should be considered exploratory. Fourth, the confidence interval for the mediated proportion was relatively wide, suggesting limited precision in the mediation estimate. Finally, as an observational study, this work cannot establish causality despite multivariable adjustment and mediation analysis [ 23 , 24 ]. Conclusions Among middle-aged and older Chinese adults with arthritis, higher CTI was independently associated with an increased risk of incident CVD over 7 years of follow-up. This association showed a positive dose-response pattern, and BMI appeared to mediate part of the relationship. These findings suggest that CTI may be a useful marker for identifying patients with arthritis who are at elevated cardiovascular risk, and they support the integration of inflammatory and metabolic assessment into long-term cardiovascular risk management in this population. Abbreviations CTI： C-reactive protein-triglyceride-glucose index CVD： cardiovascular disease BMI： body mass index CHARLS： China Health and Retirement Longitudinal Study CRP： C-reactive protein TG： triglycerides FPG： fasting plasma glucose OR： odds ratio CI： confidence interval IQR： interquartile range STROBE： Strengthening the Reporting of Observational Studies in Epidemiology NHANES： National Health and Nutrition Examination Survey EULAR： European Alliance of Associations for Rheumatology Declarations Ethics approval and consent to participate Ethical approval was obtained from Peking University’s Ethics Review Committee, with written informed consent obtained from all participants. Consent for publication Written informed consent was obtained from all patients and participants prior to their involvement in the study. Availability of data and materials This study utilized the publicly accessible China Health and Retirement Longitudinal Study (CHARLS) dataset, available at: https://charls.pku.edu.cn/ Competing interests The authors declare no competing interests. Funding This work was supported by the Young and Middle-aged Discipline Leaders Funding Program of the Henan Provincial Health Commission (Grant No. HNSWJW-2020028); the General Program of the Natural Science Foundation of Henan Province (Grant No. 252300421383); and the Outstanding Talent Program in Traditional Chinese Medicine Discipline of Henan Province (Grant No. 2025021)；and the 2024 Graduate Student Research and Innovation Capacity Enhancement Program (Grant No. 2024KYCX084). Authors' contributions Qiang Yuan: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing—original draft, Writing—review & editing. Ruhui Fu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing—original draft, Writing—review & editing. Qiang Yuan and Ruhui Fu contributed equally to this work and share first authorship. Ning Zhang: Methodology, Validation, Resources, Writing—review & editing. Jichao Li: Investigation, Resources, Writing—review & editing. Ruonan You: Validation, Writing—review & editing. Chunyu Zou: Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review & editing. Ying Zhang: Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review & editing. Chunyu Zou and Ying Zhang are co-corresponding authors. Acknowledgements The authors acknowledge the CHARLS research team for providing high-quality data and thank all CHARLS participants for their essential contributions to this research. References Drosos GC, Vedder D, Houben E, Boekel L, Atzeni F, Badreh S, et al. 1EULAR recommendations for cardiovascular risk management in rheumatic and musculoskeletal diseases, including systemic lupus erythematosus and antiphospholipid syndrome. Ann Rheum Dis. 2022;81:768–79. https://doi.org/10.1136/annrheumdis-2021-221733 B D, R R, Mt N. 2Cardiovascular disease risk in rheumatoid arthritis anno 2022. J clin med [Internet]. J Clin Med; 2022 [cited 2026 Mar 10];11:1–11. https://doi.org/10.3390/jcm11102704 Atzeni F, Alciati A. 7Cardiovascular risk in systemic inflammatory arthritis. J Clin Med. 2023;12:2779. https://doi.org/10.3390/jcm12082779 Wang H, Bai J, He B, Hu X, Liu D. 3Osteoarthritis and the risk of cardiovascular disease: a meta-analysis of observational studies. 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Q2 2.8; 2025;17:109876. https://doi.org/10.4330/wjc.v17.i9.109876 England BR, Thiele GM, Anderson DR, Mikuls TR. 50Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ. Clinical research ed.: Q1 42.7; 2018;361:k1036. https://doi.org/10.1136/bmj.k1036 E R, La M, Am B, A C, C R. 51Ischemic Heart Disease and Rheumatoid Arthritis-Two Conditions, the Same Background. Life (Basel Switz) [Internet]. Q1 3.4: Life (Basel); 2021 [cited 2026 Mar 10];11. https://doi.org/10.3390/life11101042 Garmish O, Smiyan S, Hladkykh F, Koshak B, Komorovsky R. 37The effects of disease-modifying antirheumatic drugs on cardiovascular risk in inflammatory joint diseases: current evidence and uncertainties. Vasc Healt Risk Manage. Q2 2.8; 2025;21:593–605. https://doi.org/10.2147/VHRM.S523939 Avouac J, Ait-Oufella H, Habauzit C, Benkhalifa S, Combe B. 38The cardiovascular safety of tumour necrosis factor inhibitors in arthritic conditions: a structured review with recommendations. Rheumatol Ther. Q2 2.9; 2025;12:211–36. https://doi.org/10.1007/s40744-025-00753-x Ozdede A, Yazıcı H. 39Cardiovascular and cancer risk with tofacitinib in rheumatoid arthritis. N Engl J Med. Q1 78.5; 2022;386:1766. https://doi.org/10.1056/NEJMc2202778 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 May, 2026 Editor invited by journal 28 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 21 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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sample.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9484339/v1/538d75408557cc48a917ac96.png"},{"id":109337954,"identity":"ad2289d0-3027-4dac-9e43-a186dafa46a0","added_by":"auto","created_at":"2026-05-15 17:48:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56419,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analysis of the association between CTI and incident cardiovascular disease.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9484339/v1/d369b2999e7ad2e9038dc554.png"},{"id":109337956,"identity":"5a7921b1-b20b-4eea-a9ba-9b34a8a23ab8","added_by":"auto","created_at":"2026-05-15 17:48:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":623850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9484339/v1/988f03e5-9947-4a2c-9af2-c70dc27855c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal association between the C-reactive protein-triglyceride-glucose index and incident cardiovascular disease in middle-aged and older adults with arthritis——A cohort study based on CHARLS","fulltext":[{"header":"Key Points","content":"\u003cp\u003e1. CTI was independently associated with incident cardiovascular disease in adults with arthritis.\u003c/p\u003e\u003cp\u003e2. The association between CTI and cardiovascular disease showed a positive dose-response pattern.\u003c/p\u003e\u003cp\u003e3. BMI partially mediated the relationship between CTI and cardiovascular disease.\u003c/p\u003e\u003cp\u003e4. CTI may help improve cardiovascular risk stratification in patients with arthritis.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eArthritis and cardiovascular disease are among the most common chronic disorders in middle-aged and older adults, and their coexistence substantially increases the risks of functional decline, disability, and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In particular, patients with rheumatoid arthritis have an approximately 1.5-fold higher risk of cardiovascular disease than the general population, and this excess risk cannot be fully explained by traditional cardiovascular risk factors alone [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOsteoarthritis was once regarded mainly as a localized degenerative disorder, but accumulating evidence indicates that it is also closely linked to systemic comorbidities. A meta-analysis showed that patients with osteoarthritis have an increased overall risk of cardiovascular disease. A population-based study from Japan further reported that older adults with osteoarthritis were at higher risk of ischemic heart disease, heart failure, and stroke [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Relevant reviews have suggested that chronic low-grade inflammation, oxidative stress, vascular dysfunction, and lifestyle-related factors may represent key links between arthritis and cardiovascular disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2022, the European Alliance of Associations for Rheumatology recommended that cardiovascular risk assessment and management should be incorporated into routine care for patients with rheumatic and musculoskeletal diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In rheumatoid arthritis, systemic inflammation, insulin resistance, dyslipidemia, reduced physical activity, and treatment exposure may jointly contribute to excess cardiovascular risk. At the same time, traditional risk factors such as hypertension, diabetes, and obesity often interact with inflammatory pathways [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, cardiovascular risk assessment in patients with arthritis may require biomarkers that capture both inflammation and metabolic disturbance, rather than relying on a single indicator.\u003c/p\u003e \u003cp\u003eThe C-reactive protein-triglyceride-glucose index, CTI, is a novel composite indicator derived by incorporating C-reactive protein into the triglyceride-glucose index. It reflects both insulin resistance and systemic inflammatory burden [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Since its introduction, CTI has been increasingly linked to cardiovascular outcomes. For example, an analysis based on NHANES showed that CTI was positively associated with the prevalence of coronary heart disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Longitudinal studies have further shown that CTI is associated with stroke risk in patients with hypertension, stroke risk across different glycemic states in CHARLS, and both CVD incidence and mortality in the general population [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, CTI has been used for cardiovascular risk stratification in individuals with cardiovascular-kidney-metabolic syndrome and has also been linked to the identification of new-onset coronary heart disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies based on CHARLS have shown that CTI is independently and linearly associated with incident CVD in the general middle-aged and older population, and that cumulative exposure or persistently elevated trajectories may further increase this risk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, existing evidence has focused mainly on the general population or metabolically high-risk groups. Longitudinal evidence remains limited in patients with arthritis, a group characterized by chronic inflammation, physical limitation, and metabolic abnormalities.\u003c/p\u003e \u003cp\u003eA previous CHARLS-based study suggested that depressive status may increase subsequent CVD risk in patients with arthritis, indicating that more refined cardiovascular risk stratification is needed in this population [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the association between CTI and incident CVD in patients with arthritis, the dose-response pattern of this association, and the potential mediating pathway have not been systematically examined. Because obesity plays an important role in both arthritis and cardiovascular disease, whether BMI mediates the association between CTI and incident CVD also deserves attention. Therefore, using nationally representative longitudinal data from CHARLS, we included middle-aged and older adults with arthritis but without CVD at baseline to examine the longitudinal association between baseline CTI and incident CVD, to explore the dose-response relationship using restricted cubic spline analysis, and to assess the mediating role of BMI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study was based on the China Health and Retirement Longitudinal Study, CHARLS, and used a prospective cohort design. CHARLS is a nationally representative longitudinal survey of Chinese adults aged 45 years or older and uses a multistage stratified probability sampling design. Baseline data were collected in 2011, and follow-up surveys were conducted in 2013, 2015, 2018, and 2020 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The protocols for venous blood collection, laboratory testing, and quality control in CHARLS have been described previously [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the present study, 2011 was used as the baseline, and incident CVD was identified through the 2018 survey wave, yielding a 7-year follow-up period. The participant selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParticipants were eligible if they met the following criteria: 1, physician-diagnosed arthritis at baseline; 2, no history of CVD at baseline; 3, age 45 years or older; and 4, complete baseline blood biomarker data, including C-reactive protein, triglycerides, and fasting plasma glucose. Participants were excluded if they had 1, missing information on arthritis diagnosis or baseline CVD status; 2, missing data on key covariates; or 3, incomplete CVD outcome information during follow-up.\u003c/p\u003e \u003cp\u003eAmong the initial 17,705 participants, we excluded 404 individuals younger than 45 years, 160 with missing arthritis information, and 11,241 without arthritis at baseline, leaving 5,900 participants with baseline arthritis. We then excluded 2,239 participants with missing blood biomarker data required for CTI calculation, 635 with incomplete follow-up CVD information, 530 with baseline CVD, and 383 with missing covariates. Finally, 2,113 participants with arthritis but without CVD at baseline were included in the analysis. Among them, 1,847 remained free of CVD during follow-up, whereas 266 developed incident CVD.\u003c/p\u003e \u003cp\u003e The CHARLS study was approved by the Ethical Review Committee of Peking University. All participants provided written informed consent. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology, STROBE, statement [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of CTI\u003c/h3\u003e\n\u003cp\u003eCTI was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CTI=0.412\\times\\:{ln}CRP+{ln}\\left[\\left(TG\\times\\:FPG\\right)/2\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere CRP was expressed in mg/L, and both TG and FPG were expressed in mg/dL. This calculation was consistent with the original CTI study and subsequent CHARLS-based analyses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Based on the quartile distribution of CTI in the present sample, CTI was categorized into four groups: Q1\u0026thinsp;\u0026lt;\u0026thinsp;4.34, Q2 4.34 to 4.68, Q3 4.68 to 5.08, and Q4\u0026thinsp;\u0026ge;\u0026thinsp;5.08, with Q1 used as the reference group.\u003c/p\u003e\n\u003ch3\u003eOutcome assessment\u003c/h3\u003e\n\u003cp\u003eAt baseline and during follow-up household interviews, participants were asked whether they had ever been diagnosed by a physician with heart disease or stroke. Participants who answered yes to either question were classified as having CVD. Incident CVD was defined as the absence of CVD at baseline and the first report of physician-diagnosed heart disease or stroke during follow-up through the 2018 survey.\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eBased on previous CHARLS studies and related epidemiological investigations, the following variables were collected in this study: age, sex, residence, marital status, educational level, smoking status, drinking status, sleep duration, BMI, hypertension, diabetes, lung disease, liver disease, kidney disease, stomach disease, and asthma.\u003c/p\u003e \u003cp\u003eSpecifically, sociodemographic characteristics included age, sex, residence, marital status, and educational level. Residence was classified as rural or urban. Marital status was categorized as married or living with partner, and other marital status. Educational level was categorized as illiteracy, primary school, middle school, and high school or above. Health-related factors included smoking status, drinking status, sleep duration, and BMI. Smoking status was classified as current, former, or never. Drinking status was categorized as none, less than once per month, or at least once per month during the past year. Sleep duration was assessed as nighttime sleep duration in hours. BMI was calculated as weight in kilograms divided by height in meters squared and expressed as kg/m\u0026sup2;. Chronic conditions included hypertension, diabetes, lung disease, liver disease, kidney disease, stomach disease, and asthma.\u003c/p\u003e \u003cp\u003eBMI categories were defined as follows: underweight, BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;; normal weight, 18.5 kg/m\u0026sup2; \u0026le; BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;; overweight, 24 kg/m\u0026sup2; \u0026le; BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 kg/m\u0026sup2;; and obesity, BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;. Hypertension was defined as self-reported physician-diagnosed hypertension or measured blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg. Diabetes was defined as self-reported physician-diagnosed diabetes, fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, random plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, or glycated hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;6.5%.\u003c/p\u003e \u003cp\u003eFor consistency with the regression results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the main multivariable models adjusted sequentially for age, location, educational level, marital status, BMI, smoking status, drinking status, sleep duration, hypertension, and diabetes. The remaining chronic diseases were described at baseline.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables were presented as number with percentage. Baseline characteristics across CTI quartiles were compared using one-way analysis of variance for continuous variables and the chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression models were used to evaluate the prospective association between baseline CTI and incident CVD, and odds ratios, ORs, with 95% confidence intervals, CIs, were reported. CTI was analyzed both as a continuous variable and by quartiles. In addition, the interquartile range, IQR, increment in CTI was examined. Four models were constructed. The crude model was unadjusted. Model 1 was adjusted for age, location, educational level, and marital status. Model 2 was further adjusted for BMI, smoking status, drinking status, and sleep duration. Model 3 was additionally adjusted for hypertension and diabetes.\u003c/p\u003e \u003cp\u003eSubgroup analyses were performed according to age, sex, sleep duration, residence, marital status, educational level, smoking status, drinking status, and obesity status. Interaction tests were conducted by adding cross-product terms to the regression models. Restricted cubic spline analysis was used to explore the dose-response relationship between CTI and incident CVD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The figure position for the spline plot is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCausal mediation analysis was performed to assess the mediating role of BMI in the association between CTI and incident CVD. The average causal mediation effect, average direct effect, and proportion mediated were reported [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All analyses were conducted using R version 4.5.2, and a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 2,113 middle-aged and older adults with arthritis but without CVD at baseline were included in this study, including 1,257 women, 59.49%, and 856 men, 40.51%. According to baseline CTI quartiles, 528 participants were classified into Q1, 514 into Q2, 531 into Q3, and 540 into Q4.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, participants with higher CTI levels tended to be older and had higher BMI, CRP, TG, and fasting glucose levels, all with P values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Compared with participants in Q1, those in Q4 were also more likely to live in urban areas, to have hypertension and diabetes, and to be overweight or obese. In addition, the proportion of participants with asthma increased across CTI quartiles, whereas the proportion with stomach disease decreased. By contrast, sex, marital status, educational level, smoking status, drinking status, sleep duration, lung disease, liver disease, and kidney disease did not differ significantly across CTI quartiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBaseline characteristics of participants according to CTI quartiles.\u003c/b\u003e\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;2113)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;528)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;514)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;531)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;540)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1257(59.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315(59.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289(56.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e329(61.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e324(60.00)\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e856(40.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213(40.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225(43.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202(38.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216(40.00)\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(Kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.95\u0026thinsp;\u0026plusmn;\u0026thinsp;10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.67\u0026thinsp;\u0026plusmn;\u0026thinsp;123.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.80\u0026thinsp;\u0026plusmn;\u0026thinsp;27.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.57\u0026thinsp;\u0026plusmn;\u0026thinsp;37.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133.49\u0026thinsp;\u0026plusmn;\u0026thinsp;58.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216.21\u0026thinsp;\u0026plusmn;\u0026thinsp;208.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFast.glucose(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.59\u0026thinsp;\u0026plusmn;\u0026thinsp;31.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.99\u0026thinsp;\u0026plusmn;\u0026thinsp;14.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.45\u0026thinsp;\u0026plusmn;\u0026thinsp;18.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.99\u0026thinsp;\u0026plusmn;\u0026thinsp;17.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125.42\u0026thinsp;\u0026plusmn;\u0026thinsp;50.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration(h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1484(70.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399(75.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366(71.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e363(68.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e356(65.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e629(29.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129(24.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148(28.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168(31.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e184(34.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1773(83.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428(81.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443(86.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e450(84.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e452(83.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e340(16.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(18.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(13.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81(15.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88(16.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1136(53.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292(55.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273(53.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e280(52.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e291(53.89)\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\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e344(16.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(16.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(14.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94(17.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90(16.67)\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 school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e484(22.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(22.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126(24.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117(22.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121(22.41)\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\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149( 7.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29( 5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42( 8.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40( 7.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38( 7.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e572(27.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156(30.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138(25.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e146(27.04)\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\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150( 7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32( 6.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39( 7.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42( 7.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37( 6.85)\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\u003eNever smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1391(65.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364(68.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319(62.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e351(66.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357(66.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156( 7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50( 9.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33( 6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36( 6.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37( 6.85)\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;\u0026thinsp;1m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e524(24.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144(27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132(25.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128(24.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120(22.22)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1433(67.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334(63.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349(67.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e367(69.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e383(70.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1341(63.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394(74.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350(68.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313(58.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e284(52.59)\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\u003e772(36.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134(25.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164(31.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218(41.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e256(47.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1814(85.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492(93.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e461(89.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e472(88.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e389(72.04)\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\u003e299(14.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36( 6.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151(27.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObesity(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148( 7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(10.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44( 8.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29( 5.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18( 3.33)\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\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1080(51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335(63.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e286(55.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e242(45.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e217(40.19)\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 \u003cp\u003e261(12.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23( 4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46( 8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76(14.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116(21.48)\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\u003eOver weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e624(29.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(21.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138(26.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184(34.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189(35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung disease(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\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\u003e1851(87.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478(90.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446(86.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e463(87.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e464(85.93)\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\u003e262(12.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50( 9.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(13.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68(12.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76(14.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver disease(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\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\u003e2033(96.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e507(96.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e503(97.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e509(95.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e514(95.19)\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\u003e80( 3.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21( 3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11( 2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22( 4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26( 4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKidney disease(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\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\u003e1959(92.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e486(92.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484(94.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e492(92.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e497(92.04)\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\u003e154( 7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42( 7.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30( 5.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39( 7.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43( 7.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStomach disease(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e1424(67.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332(62.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e335(65.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e368(69.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e389(72.04)\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\u003e689(32.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196(37.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179(34.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163(30.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151(27.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsthma (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e2020(95.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512(96.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e492(95.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e513(96.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e503(93.15)\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\u003e93( 4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16( 3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22( 4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18( 3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37( 6.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or n (%).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between CTI and incident cardiovascular disease\u003c/h3\u003e\n\u003cp\u003eDuring the 7-year follow-up, 266 participants developed incident CVD, corresponding to a cumulative incidence of 12.59%.\u003c/p\u003e \u003cp\u003eAs a continuous variable, CTI was positively associated with incident CVD in all models. In the crude model, each 1-unit increase in CTI was associated with a higher risk of incident CVD, OR 1.36, 95% CI 1.14 to 1.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. After adjustment for age, location, educational level, and marital status, the association remained significant, OR 1.32, 95% CI 1.10 to 1.59, P\u0026thinsp;=\u0026thinsp;0.002. Further adjustment for BMI, smoking status, drinking status, and sleep duration did not materially change the estimate, OR 1.32, 95% CI 1.10 to 1.59, P\u0026thinsp;=\u0026thinsp;0.002. In the fully adjusted model, which further included hypertension and diabetes, CTI remained independently associated with incident CVD, OR 1.22, 95% CI 1.01 to 1.48, P\u0026thinsp;=\u0026thinsp;0.04.\u003c/p\u003e \u003cp\u003eA similar pattern was observed when CTI was analyzed per IQR increase. In the fully adjusted model, each IQR increase in CTI was associated with a 16% higher risk of incident CVD, OR 1.16, 95% CI 1.01 to 1.34, P\u0026thinsp;=\u0026thinsp;0.04.\u003c/p\u003e \u003cp\u003eWhen CTI was analyzed by quartiles, a graded association was observed. Compared with Q1, the ORs for incident CVD in the fully adjusted model were 1.30, 95% CI 0.94 to 1.78, for Q2, 1.44, 95% CI 1.06 to 1.97, for Q3, and 1.54, 95% CI 1.12 to 2.11, for Q4. The trend across quartiles was statistically significant, P for trend\u0026thinsp;=\u0026thinsp;0.01. These results suggest that higher CTI levels were associated with a progressively greater risk of incident CVD.\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\u003e\u003cb\u003eAssociation between baseline CTI and incident cardiovascular disease.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\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\u003eOR (95% CI)\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\u003eOR (95% CI)\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\u003eOR (95% CI)\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\u003eCVD\u0026thinsp;~\u0026thinsp;CTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36(1.14,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32(1.10,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32(1.10,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22(1.01,1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD~CTI_iqr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26(1.10,1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23(1.08,1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23(1.08,1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16(1.01,1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD~CTIQ\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4(1.02,1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32(0.96,1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32(0.96,1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3(0.94,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\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\u003e1.65(1.22,2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55(1.14,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54(1.13,2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.44(1.06,1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\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\u003e1.83(1.36,2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74(1.28,2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74(1.28,2.35)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54(1.12,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend(character2integer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend(Median value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote: The crude model was unadjusted. Model 1 was adjusted for age, location, educational level, and marital status. Model 2 was further adjusted for BMI, smoking status, drinking status, and sleep duration. Model 3 was further adjusted for hypertension and diabetes.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDose-response relationship between CTI and incident cardiovascular disease\u003c/h2\u003e \u003cp\u003eRestricted cubic spline analysis showed that CTI was positively associated with incident CVD. In the crude model, a nonlinear pattern was observed. However, after adjustment for age, location, educational level, marital status, BMI, smoking status, drinking status, sleep duration, hypertension, and diabetes, the overall association remained significant, whereas evidence for nonlinearity was attenuated. Overall, the spline analysis suggested a positive dose-response relationship between CTI and incident CVD, with the adjusted association appearing closer to linear. The spline figure position is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSubgroup analyses are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The positive association between CTI and incident CVD appeared to be more pronounced in participants younger than 60 years. In this subgroup, the OR for Q4 versus Q1 was 2.224, 95% CI 1.415 to 3.537, whereas the corresponding OR in participants aged 60 years or older was 1.105, 95% CI 0.701 to 1.750. The interaction by age approached significance, P for interaction\u0026thinsp;=\u0026thinsp;0.085.\u003c/p\u003e \u003cp\u003eA significant interaction was observed for marital status, P for interaction\u0026thinsp;=\u0026thinsp;0.028. Among participants with other marital status, the ORs for Q2 and Q3 versus Q1 were 3.480, 95% CI 1.455 to 8.682, and 3.006, 95% CI 1.296 to 7.293, respectively, whereas the estimate for Q4 was attenuated. Among participants who were married or living with a partner, the association was more gradual, and the OR for Q4 versus Q1 was 1.598, 95% CI 1.139 to 2.254.\u003c/p\u003e \u003cp\u003eA significant interaction was also observed for educational level, P for interaction\u0026thinsp;=\u0026thinsp;0.012. The association between CTI and incident CVD was more evident among participants with primary school education, in whom the ORs for Q3 and Q4 versus Q1 were 2.704, 95% CI 1.342 to 5.683, and 2.776, 95% CI 1.332 to 6.026, respectively. Although a stronger effect estimate was also observed in participants with high school education or above, the confidence intervals were wide, suggesting limited precision.\u003c/p\u003e \u003cp\u003eNo significant interactions were found for sex, sleep duration, residence, smoking status, drinking status, or obesity status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\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\u003eSubgroup analyses of the association between CTI quartiles and incident cardiovascular disease.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep for interaction\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\u003eAge2\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.082(0.691, 1.702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.284(0.832, 1.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.105(0.701, 1.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.549(0.971,2.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.687(1.068,2.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.224(1.415,3.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.688\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\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.513(0.915,2.529)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.684(1.017,2.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.127(1.287,3.565)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.174(0.773,1.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.282(0.860,1.921)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.228(0.812,1.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep time\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.772(1.067,2.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.772(1.067,2.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.153(1.306,3.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.060(0.699,1.609)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.304(0.876,1.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.250(0.826,1.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.385(0.952,2.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.727(1.202,2.497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.424(0.970,2.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.072(0.579,2.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.908(0.496,1.680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.696(0.953,3.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.175(0.831,1.666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.324(0.944,1.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.598(1.139,2.254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.480(1.455, 8.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.006(1.296, 7.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.263(0.493, 3.266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.144(0.748,1.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.245(0.819,1.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.091(0.709,1.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.632(0.735,3.664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.418(0.660,3.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.694(0.772,3.795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.268(0.589,2.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.704(1.342,5.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.776(1.332,6.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.880(1.023,36.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.612(0.465,21.290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.451(2.797,104.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.950(1.025,3.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.432(1.279,4.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.733(1.428,5.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.046(0.704,1.553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.156(0.791,1.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.196(0.813,1.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.413(0.630,10.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.884(0.503, 7.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.213(0.769,15.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.130(0.291, 4.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.926(0.907, 9.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.175(0.285,4.706)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.230(0.830,1.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.326(0.901,1.962)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.480(1.007,2.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.447(0.775,2.724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.544(0.833,2.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.672(0.877,3.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObesity\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.091(0.717,1.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.407(0.924,2.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.288(0.821,2.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.177(0.323,4.310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.827(0.428,7.570)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.578(0.054,3.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.012(0.280, 4.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.501(0.475, 5.479)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.470(0.467, 5.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOver weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.981(1.012,4.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.446(0.746,2.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.554(0.795,3.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: Odds ratios are presented for each CTI quartile compared with Q1. P for interaction was calculated from the fully adjusted model.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMediating effect of BMI\u003c/h2\u003e \u003cp\u003eMediation analysis showed that BMI significantly and partially mediated the association between CTI and incident CVD. The average causal mediation effect was 0.007, 95% CI 0.002 to 0.017, P\u0026thinsp;=\u0026thinsp;0.004, suggesting that CTI may indirectly increase the risk of incident CVD through higher BMI. The average direct effect was 0.012, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.010 to 0.017, P\u0026thinsp;=\u0026thinsp;0.252. The proportion mediated was 36.5%, 95% CI 6.1% to 248.4%, P\u0026thinsp;=\u0026thinsp;0.04.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide prospective cohort of middle-aged and older Chinese adults with arthritis, we found that higher CTI levels were associated with a greater risk of incident CVD over 7 years of follow-up. This association remained after adjustment for sociodemographic characteristics, lifestyle-related factors, BMI, hypertension, and diabetes. When CTI was analyzed by quartiles, participants in the highest quartile had a significantly higher risk of incident CVD than those in the lowest quartile. The spline analysis further suggested a positive dose-response relationship, and mediation analysis indicated that BMI partly mediated the observed association. Taken together, these findings support CTI as a potentially useful indicator for cardiovascular risk stratification in patients with arthritis.\u003c/p\u003e \u003cp\u003eOur findings are broadly consistent with previous studies showing that inflammation and metabolic disturbance jointly contribute to adverse cardiovascular outcomes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In immune-mediated and degenerative joint disorders, cardiovascular risk cannot be fully explained by conventional risk factors alone. Persistent inflammation, altered lipid metabolism, insulin resistance, reduced physical activity, and treatment-related factors may all contribute to vascular injury and long-term cardiovascular burden [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this context, CTI may offer some practical advantages because it integrates CRP, TG, and fasting glucose into a single marker and therefore captures both inflammatory and metabolic dimensions of risk. This may be particularly relevant in patients with arthritis, in whom chronic inflammation and metabolic abnormalities often coexist.\u003c/p\u003e \u003cp\u003eThe present study also adds to the growing literature on CTI. Previous analyses in NHANES and CHARLS have shown that CTI is associated with coronary heart disease, stroke, incident CVD, and cardiovascular mortality in different populations [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, evidence in patients with arthritis has remained limited. This population warrants specific attention because arthritis is not simply a musculoskeletal condition. It is increasingly recognized as a systemic disorder accompanied by endothelial dysfunction, oxidative stress, a proatherogenic milieu, and multiple cardiometabolic comorbidities [\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Against this background, our findings suggest that CTI may help identify patients with arthritis who are at particularly high cardiovascular risk.\u003c/p\u003e \u003cp\u003eAnother notable finding was the generally graded association across CTI quartiles. In the fully adjusted model, the risk estimate increased progressively from Q2 to Q4, and the trend test remained significant. Although the association for Q2 did not reach statistical significance, the direction of effect was consistent. This pattern supports the view that CTI behaves more like a continuous risk marker than a threshold-based indicator. The restricted cubic spline analysis pointed in the same direction. Although some curvature was seen in the crude analysis, the adjusted association appeared closer to linear. A similar pattern has been reported in recent CTI studies, where the association with adverse cardiovascular or metabolic outcomes was often positive and approximately linear after multivariable adjustment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This is clinically relevant because it suggests that even moderate increases in CTI may carry information about future cardiovascular risk.\u003c/p\u003e \u003cp\u003eThe role of BMI in our analysis also deserves attention. Mediation analysis suggested that BMI accounted for part of the association between CTI and incident CVD. This finding is biologically plausible. Obesity is now widely regarded as a chronic low-grade inflammatory state characterized by adipose tissue dysfunction, altered adipokine secretion, insulin resistance, endothelial injury, and a prothrombotic tendency [\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In patients with arthritis, this pathway may be even more important. Pain, stiffness, and physical limitation may reduce mobility and energy expenditure, leading to weight gain, worsening metabolic health, and a further increase in inflammatory burden. Evidence from umbrella reviews has shown that a higher number of daily steps is associated with better health outcomes, including cardiovascular outcomes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In addition, a randomized controlled trial in older adults with rheumatoid arthritis and overweight or obesity showed that remotely supervised weight loss combined with exercise training improved cardiovascular risk and clinical outcomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This evidence is consistent with the BMI-mediated pathway observed in our study.\u003c/p\u003e \u003cp\u003eThe subgroup analyses provide some further clues, although they should be interpreted cautiously. The association between CTI and incident CVD appeared stronger in participants younger than 60 years, in those with other marital status, and in those with lower educational attainment, particularly participants with primary school education. These findings may reflect differences in social support, health literacy, healthcare access, or the ability to manage long-term cardiovascular risk factors. The interaction for marital status was especially notable, although some subgroup estimates were based on relatively small numbers and should therefore be interpreted with restraint. Likewise, the estimates in the highest educational subgroup were imprecise, as reflected by the wide confidence intervals. Even so, these results suggest that the clinical meaning of CTI may not be entirely uniform across population subgroups.\u003c/p\u003e \u003cp\u003eOur findings also have potential clinical implications. Current guidelines increasingly emphasize the need for routine cardiovascular risk assessment in patients with rheumatic and musculoskeletal diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, implementation in daily practice remains suboptimal, and risk assessment still often relies on conventional factors alone. Because CTI is based on routine laboratory measures, it is simple to calculate and may be suitable for use in community and primary care settings. It is not intended to replace standard cardiovascular risk evaluation, but it may provide additional information in patients with arthritis, especially when inflammation and metabolic disturbance coexist. This is relevant because patients with arthritis often require long-term management, and cardiovascular prevention needs to be incorporated into that broader care framework.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may underlie the observed association. Chronic inflammation can impair endothelial function, enhance oxidative stress, alter lipoprotein composition and function, and accelerate atherosclerotic progression [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In parallel, hypertriglyceridemia and impaired glucose metabolism promote insulin resistance and vascular injury. These pathways do not operate in isolation. Rather, they interact and amplify one another over time. CTI may therefore reflect a broader state of immunometabolic dysfunction, which could help explain its association with incident CVD in our cohort. From this perspective, the observed relationship is consistent with current understanding of cardiovascular risk in arthritis and related inflammatory conditions.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, it was based on CHARLS, a nationally representative cohort with broad population coverage, which enhances the generalizability of the findings to middle-aged and older Chinese adults with arthritis. Second, the longitudinal design and exclusion of participants with baseline CVD improved the temporal interpretation of the observed association. Third, we evaluated CTI both as a continuous variable and by quartiles, and further explored its dose-response relationship and mediating pathway.\u003c/p\u003e \u003cp\u003eSeveral limitations should also be acknowledged. First, arthritis and CVD were identified based on self-report of physician diagnosis, which may have introduced recall bias or misclassification. Second, CHARLS does not provide detailed information on arthritis subtype, disease duration, disease activity, or specific anti-rheumatic treatment, all of which may influence cardiovascular risk [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Third, some subgroup estimates were unstable, especially where the number of events was limited, and therefore those findings should be considered exploratory. Fourth, the confidence interval for the mediated proportion was relatively wide, suggesting limited precision in the mediation estimate. Finally, as an observational study, this work cannot establish causality despite multivariable adjustment and mediation analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAmong middle-aged and older Chinese adults with arthritis, higher CTI was independently associated with an increased risk of incident CVD over 7 years of follow-up. This association showed a positive dose-response pattern, and BMI appeared to mediate part of the relationship. These findings suggest that CTI may be a useful marker for identifying patients with arthritis who are at elevated cardiovascular risk, and they support the integration of inflammatory and metabolic assessment into long-term cardiovascular risk management in this population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCTI：\u003c/em\u003e\u003c/strong\u003eC-reactive protein-triglyceride-glucose index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCVD：\u003c/em\u003e\u003c/strong\u003ecardiovascular disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI：\u003c/em\u003e\u003c/strong\u003ebody mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCHARLS：\u003c/em\u003e\u003c/strong\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCRP：\u003c/em\u003e\u003c/strong\u003eC-reactive protein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTG：\u003c/em\u003e\u003c/strong\u003etriglycerides\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFPG：\u003c/em\u003e\u003c/strong\u003efasting plasma glucose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR：\u003c/em\u003e\u003c/strong\u003eodds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCI：\u003c/em\u003e\u003c/strong\u003econfidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIQR：\u003c/em\u003e\u003c/strong\u003einterquartile range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSTROBE：\u003c/em\u003e\u003c/strong\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNHANES：\u003c/em\u003e\u003c/strong\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEULAR：\u003c/em\u003e\u003c/strong\u003eEuropean Alliance of Associations for Rheumatology\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from Peking University’s Ethics Review Committee, with written informed consent obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients and participants prior to their involvement in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized the publicly accessible China Health and Retirement Longitudinal Study (CHARLS) dataset, available at: https://charls.pku.edu.cn/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Young and Middle-aged Discipline Leaders Funding Program of the Henan Provincial Health Commission (Grant No. HNSWJW-2020028); the General Program of the Natural Science Foundation of Henan Province (Grant No. 252300421383); and the Outstanding Talent Program in Traditional Chinese Medicine Discipline of Henan Province (Grant No. 2025021)；and the 2024 Graduate Student Research and Innovation Capacity Enhancement Program (Grant No. 2024KYCX084).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQiang Yuan:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing—original draft, Writing—review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eRuhui Fu:\u003c/strong\u003e Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing—original draft, Writing—review \u0026amp; editing. Qiang Yuan and Ruhui Fu contributed equally to this work and share first authorship.\u0026nbsp;\u003cstrong\u003eNing Zhang:\u0026nbsp;\u003c/strong\u003eMethodology, Validation, Resources, Writing—review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eJichao Li:\u003c/strong\u003e Investigation, Resources, Writing—review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eRuonan You:\u003c/strong\u003e Validation, Writing—review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eChunyu Zou:\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Funding acquisition, Project administration, Writing—review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eYing Zhang:\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Funding acquisition, Project administration, Writing—review \u0026amp; editing. Chunyu Zou and Ying Zhang are co-corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the CHARLS research team for providing high-quality data and thank all CHARLS participants for their essential contributions to this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDrosos GC, Vedder D, Houben E, Boekel L, Atzeni F, Badreh S, et al. 1EULAR recommendations for cardiovascular risk management in rheumatic and musculoskeletal diseases, including systemic lupus erythematosus and antiphospholipid syndrome. Ann Rheum Dis. 2022;81:768\u0026ndash;79. https://doi.org/10.1136/annrheumdis-2021-221733\u003c/li\u003e\n\u003cli\u003eB D, R R, Mt N. 2Cardiovascular disease risk in rheumatoid arthritis anno 2022. J clin med [Internet]. 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Q1 30,6; 2024;149:1621\u0026ndash;3. https://doi.org/10.1161/CIRCULATIONAHA.123.065485\u003c/li\u003e\n\u003cli\u003eBianchettin RG, Lavie CJ, Lopez-Jimenez F. 31Challenges in cardiovascular evaluation and management of obese patients: JACC state-of-the-art review. J Am Coll Cardiol. 2023;81:491\u0026ndash;504. https://doi.org/10.1016/j.jacc.2022.11.031\u003c/li\u003e\n\u003cli\u003eXu C, Jia J, Zhao B, Yuan M, Luo N, Zhang F, et al. 33Objectively measured daily steps and health outcomes: an umbrella review of the systematic review and meta-analysis of observational studies. BMJ open. 2024;14:e088524. https://doi.org/10.1136/bmjopen-2024-088524\u003c/li\u003e\n\u003cli\u003eAndonian BJ, Ross LM, Sudnick AM, Johnson JL, Pieper CF, Belski KB, et al. 32Effect of remotely supervised weight loss and exercise training versus lifestyle counseling on cardiovascular risk and clinical outcomes in older adults with rheumatoid arthritis: a randomized controlled trial. ACR open rheumatol. Q2 2.8; 2024;6:124\u0026ndash;36. https://doi.org/10.1002/acr2.11639\u003c/li\u003e\n\u003cli\u003eZotova LA, Enenkov NV. 40arthritis therapy alters the fate of the heart. World J Cardiol. Q2 2.8; 2025;17:109876. https://doi.org/10.4330/wjc.v17.i9.109876\u003c/li\u003e\n\u003cli\u003eEngland BR, Thiele GM, Anderson DR, Mikuls TR. 50Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ. Clinical research ed.: Q1 42.7; 2018;361:k1036. https://doi.org/10.1136/bmj.k1036\u003c/li\u003e\n\u003cli\u003eE R, La M, Am B, A C, C R. 51Ischemic Heart Disease and Rheumatoid Arthritis-Two Conditions, the Same Background. Life (Basel Switz) [Internet]. 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Q1 78.5; 2022;386:1766. https://doi.org/10.1056/NEJMc2202778\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9484339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9484339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo examine the longitudinal association between the C-reactive protein-triglyceride-glucose index (CTI) and incident cardiovascular disease (CVD) in middle-aged and older Chinese adults with arthritis, to assess the dose-response pattern of this association, and to explore the mediating role of body mass index (BMI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective cohort study was based on the China Health and Retirement Longitudinal Study, CHARLS. Baseline data were collected in 2011, and incident CVD was ascertained through the 2018 wave. Participants aged 45 years or older with physician-diagnosed arthritis, no history of CVD at baseline, and complete blood biomarker data were included. Multivariable logistic regression models were used to evaluate the association between CTI and incident CVD. Restricted cubic spline analysis was applied to assess the dose-response relationship, and causal mediation analysis was performed to evaluate the mediating role of BMI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring the 7-year follow-up, 266 participants developed incident CVD. In the fully adjusted model, each 1-unit increase in CTI was associated with a 22% higher risk of incident CVD (OR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.01\u0026ndash;1.48). Compared with the lowest quartile, participants in the highest quartile had a 54% higher risk (OR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.12\u0026ndash;2.11), with a significant trend across quartiles. Restricted cubic spline analysis revealed a significant positive dose-response association without nonlinearity. BMI partially mediated this association, with a mediation proportion of 36.5%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCTI was independently associated with incident CVD in middle-aged and older Chinese adults with arthritis, showing a positive dose-response relationship. BMI appeared to be an important mediating pathway. Combined assessment of inflammation and metabolic dysfunction may be clinically useful for cardiovascular risk management in patients with arthritis.\u003c/p\u003e","manuscriptTitle":"Longitudinal association between the C-reactive protein-triglyceride-glucose index and incident cardiovascular disease in middle-aged and older adults with arthritis——A cohort study based on CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 17:48:05","doi":"10.21203/rs.3.rs-9484339/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-07T07:16:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-28T11:14:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T09:52:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-22T09:52:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-21T12:31:15+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":"6cb1dde3-459f-4820-867a-889a0a09af56","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"12","date":"2026-05-07T07:16:34+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67686624,"name":"Health sciences/Biomarkers"},{"id":67686625,"name":"Health sciences/Cardiology"},{"id":67686626,"name":"Health sciences/Diseases"},{"id":67686627,"name":"Health sciences/Medical research"},{"id":67686628,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-15T17:48:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 17:48:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9484339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9484339","identity":"rs-9484339","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}