Long-term cumulative and trajectory patterns of the C-reactive protein–triglyceride–glucose index and cardiometabolic multimorbidity: the mediating effects of adiposity indices

Long-term cumulative and trajectory patterns of the C-reactive protein–triglyceride–glucose index and cardiometabolic multimorbidity: the mediating effects of adiposity indices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term cumulative and trajectory patterns of the C-reactive protein–triglyceride–glucose index and cardiometabolic multimorbidity: the mediating effects of adiposity indices Yixiao Liang, Hu Ai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9291028/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Cardiometabolic multimorbidity (CMM) is increasingly prevalent in ageing populations and confers substantial clinical and societal burdens. The C-reactive protein–triglyceride–glucose index (CTI) integrates inflammatory and metabolic information, but evidence linking long-term CTI patterns to incident CMM and the role of adiposity as a potential mediator remains limited. Methods We included 5,778 participants aged ≥ 45 years who were free of CMM at baseline. Exposures were baseline CTI in 2012, cumulative CTI(cuCTI) from 2012 to 2015, and CTI change-pattern (trajectory) groups derived from repeated measurements. Incident CMM was assessed in 2018. Multivariable logistic regression models were pooled across multiple imputations; restricted cubic splines examined dose–response. Prediction performance was evaluated using C-statistics (DeLong test), net reclassification improvement, and integrated discrimination improvement. Mediation by adiposity indices was assessed. Results Incident CMM occurred in 333 participants (5.8%). In fully adjusted models, higher baseline CTI and greater cumulative CTI exposure were independently associated with higher odds of incident CMM (baseline CTI per IQR: OR 1.545 (95% CI 1.344–1.776); cuCTI per IQR: OR 1.651 (95% CI 1.429–1.907), both P < 0.001). Change-pattern (trajectory) analyses further indicated substantially higher risk among participants with persistently elevated CTI compared with those with persistently low CTI (OR 3.078 (95% CI 2.169–4.369), P < 0.001). Adding baseline CTI, cuCTI, or change-pattern grouping improved discrimination beyond the basic model and improved reclassification. Adiposity indices partially mediated the CTI–CMM associations. Conclusions Higher cumulative CTI and persistently elevated CTI change patterns were associated with incident cardiometabolic multimorbidity and improved risk prediction, with partial mediation by adiposity. C-reactive protein–triglyceride–glucose index trajectory cardiometabolic multimorbidity Body mass index CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Contributions to the Literature 1. To extend previous evidence on CTI by showing its association with cardiometabolic multimorbidity, a clinically important outcome that better reflects the clustering of cardiometabolic diseases in ageing populations. 2. To identify people with persistently high CTI or greater cumulative CTI burden as a high risk group, providing evidence for population risk stratification and targeted prevention. 3. To provide public health evidence that repeated CTI assessment and adiposity related pathways may be relevant for early identification and prevention of cardiometabolic multimorbidity. Background Cardiometabolic multimorbidity (CMM), commonly defined as the coexistence of two or more cardiometabolic conditions in the same individual, has become a dominant public health and clinical challenge in ageing populations[1, 2]. It is clinically important because the clustering of diabetes, cardiovascular disease and stroke reflects shared upstream mechanisms, increases treatment complexity, and amplifies risks of disability and mortality[3]. In China, the rapid demographic transition and the high prevalence of metabolic risk factors have made early identification of individuals at risk of progressing from a single condition to multimorbidity a priority for prevention and integrated care[4]. Beyond the burden on individuals, CMM challenges healthcare systems by requiring long-term management across specialties and by increasing polypharmacy and competing risk-factor targets[5, 6]. Systemic inflammation and insulin resistance(IR) are closely intertwined and are considered core biological processes underlying cardiometabolic disease accumulation[7–9]. Chronic low-grade inflammation can impair insulin signaling, while IR can amplify inflammatory pathways, creating a self-reinforcing cycle that promotes vascular injury, metabolic deterioration, and end-organ damage[10–12]. C-reactive protein (CRP) is a widely used marker of systemic inflammation and has been incorporated into cardiovascular risk assessment frameworks[13, 14]. The triglyceride–glucose (TyG) index is a practical surrogate of IR derived from fasting triglycerides and glucose[15–17]. Building on these two dimensions, the C-reactive protein–triglyceride–glucose index (CTI) was proposed as a composite marker integrating inflammatory and insulin resistance components into a single metric[18–21]. Most epidemiologic studies rely on a single baseline biomarker measurement, which may incompletely represent long-term exposure due to within-person variability and temporal changes[18, 22]. Repeated measures enable complementary views of risk: a time-integrated cumulative burden that captures sustained exposure and a trajectory-based grouping that summarizes heterogeneous patterns over time. In prior work, CTI has been associated with incident stroke and cardiovascular outcomes, and trajectory-based CTI patterns have been used to stratify future risk[23, 24]. More recently, evidence from cohort analyses has linked CTI to cardiometabolic multimorbidity, supporting its relevance for multi-condition cardiometabolic risk[24]. However, several gaps remain in the current evidence. Whether CTI measures based on repeated assessments, including cumulative exposure and trajectory patterns, provide additional information beyond a single baseline measurement for incident CMM remains unclear. It is also uncertain whether these CTI-derived measures improve prediction beyond established demographic, behavioural, and clinical risk factors. In addition, the potential mediating role of adiposity in the association between CTI and CMM has not been fully examined. Therefore, using CHARLS data with repeated biomarker assessment, we examined associations of baseline CTI (2012), cumulative CTI (cuCTI) exposure between 2012 and 2015, and data-driven CTI trajectories with incident CMM in 2018. We further evaluated dose–response relationships using restricted cubic splines, assessed incremental predictive performance beyond a conventional risk model using C-statistics, net reclassification improvement, and integrated discrimination improvement, and explored mediation by adiposity indices. Methods Study design and participants We conducted a prospective cohort analysis within the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal study of Chinese adults aged ≥ 45 years that collects questionnaire data and standardized physical and laboratory measurements across survey waves[25]. The present analysis used CTI measurements in 2012 and 2015 and adjudicated incident CMM status in 2018, thereby aligning exposure assessment with subsequent outcome ascertainment. CHARLS uses standardized interviews to collect sociodemographic characteristics, health behaviours, and physician-diagnosed conditions, and it incorporates physical examinations and laboratory measurements from fasting blood samples following study protocols. In the current analysis, we leveraged these harmonized data to define exposures, covariates, and outcomes consistently across waves. A total of 11,847 participants were initially screened. We excluded 4,375 individuals with missing CTI measurements in 2012 or 2015, and a further 221 participants aged < 45 years or with missing age. We additionally excluded 1,473 participants with prevalent CMM prior to 2018 or missing CMM status. The final analytic sample included 5,778 participants(Fig. 1 ). Exposure CTI was calculated using the formula: 0.412 × ln(CRP [mg/L]) + ln(TG [mg/dL] × FPG [mg/dL]/2) [17]. CTI 2012 and CTI 2015 represent CTI measured in 2012 (Wave 1) and 2015 (Wave 3), respectively, with a 3-year interval between assessments. To quantify long-term cumulative exposure, cumulative CTI was computed using the trapezoidal (area) approach: cuCTI = (CTI 2012 + CTI 2015 )/2 × (2015 − 2012). The average of the two time points was used to reflect the mean exposure level and was multiplied by the duration to estimate cumulative exposure over time, consistent with cumulative metabolic index calculations in CHARLS and prior studies. Based on CTI levels, participants were further classified into three CTI change-pattern (trajectory) groups using k-means clustering applied to z-standardized CTI 2012 and CTI 2015 values (K = 3). Clusters were labeled according to mean CTI level as Persistent Low, Moderate, and Stable High (Fig. 2 ), consistent with prior CHARLS-based CTI trajectory analyses. In line with prior literature that describes longitudinal CTI “changes” using repeated-measure patterning, we refer to these k-means clusters as CTI change-pattern (trajectory) groups rather than a simple two-point difference. Outcome CMM was defined as at least two of diabetes, heart disease, and stroke[26]. Diabetes was identified by self-report of physician diagnosis and objective evidence (use of anti-diabetic medication, fasting plasma glucose ≥ 126 mg/dL, or HbA1c ≥ 6.5%)[27]. Heart disease and stroke were ascertained by self-reported physician diagnosis. Covariates Covariates were selected a priori to reduce confounding and were organized into nested models to evaluate robustness of estimates to progressively broader adjustment. Model 1 included basic demographic characteristics (age and sex). Model 2 further adjusted for socio-demographic and lifestyle-related factors, including educational attainment, residential location, marital status, smoking status, and alcohol consumption. Model 3 additionally adjusted for clinical and physiological variables, including systolic blood pressure (SBP), diastolic blood pressure(DBP), hypertension, dyslipidaemia, sleep duration, estimated glomerular filtration rate(eGFR), and lipid-lowering medication use. Missing data and multiple imputation Missing covariate data were handled using multiple imputation by chained equations (MICE) to minimize potential bias and improve statistical efficiency ( Table S1 ). During data preprocessing, extreme values were detected in BMI, BRI and WHtR indices.To limit the influence of extreme values while retaining observations, prespecified continuous variables were winsorized at the 1st and 99th percentiles prior to model fitting. This procedure was applied consistently within each imputed dataset where relevant to enhance the robustness of regression estimates in the presence of measurement error and outliers. Statistical analysis Baseline characteristics were summarized for the overall sample and across CTI trajectory groups. Normally distributed continuous variables are presented as mean ± standard deviation (SD), non-normally distributed variables as median (interquartile range, IQR), and categorical variables as number (percentage). Between-group comparisons were performed using one-way analysis of variance (ANOVA) for normally distributed continuous variables, the Kruskal–Wallis test for non-normally distributed variables, and the Pearson chi-square test for categorical variables. Missing covariates were imputed under the missing-at-random assumption using multiple imputation by chained equations. Associations between CTI measures and incident CMM were estimated using logistic regression within each imputed dataset and pooled. Effect estimates are reported as odds ratios (ORs) with 95% confidence intervals (CIs) and two-sided P values. CTI 2012 and cuCTI were analyzed per interquartile range (IQR) and by quartiles (Q1 as reference). Linear trends across quartiles and ordered trajectory groups were evaluated by entering category ranks as continuous terms (P for trend). Three models with increasing adjustment were fitted. Dose–response relationships were examined using restricted cubic splines with four knots, with the median exposure as the reference. We report Poverall for the overall exposure–outcome association and Pnonlinear for departure from linearity and interpret spline curves alongside exposure distributions. Incremental predictive value beyond conventional risk factors was assessed by comparing a basic model corresponding to Model 3 covariates with models additionally including CRP, TyG, baseline CTI, cuCTI, or CTI trajectories. Discrimination was quantified using the C-statistic and compared using DeLong’s test for correlated ROC curves. Reclassification was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). ROC curves are provide, with summary prediction metrics in Table 3 . Table 3 Discrimination and reclassification improvement for incident cardiometabolic multimorbidity (CMM) Model C-statistics (95% CI) P (DeLong vs Base) NRI (95% CI) P (NRI) IDI (95% CI) P (IDI) Basic model 0.708(0.678–0.739) Ref Ref Basic model + CRP 0.710(0.679–0.740) 0.285 0.1414(0.0422–0.2406) 0.005 0.0003(-0.0006–0.0012) 0.474 Basic model + TyG 0.726(0.698–0.754) 0.002 0.1890(0.0781–0.3000) < 0.001 0.0072(0.0031–0.0113) < 0.001 Basic model + CTI 0.732(0.704–0.760) < 0.001 0.2785(0.1676–0.3893) < 0.001 0.0090(0.0044–0.0135) < 0.001 Basic model + cuCTI 0.737(0.709–0.764) < 0.001 0.3378(0.2273–0.4482) < 0.001 0.0116(0.0063–0.0168) < 0.001 Basic model + CTI trajectory 0.739(0.712–0.766) < 0.001 0.3461(0.2488–0.4434) < 0.001 0.0084(0.0051–0.0117) < 0.001 Abbreviations: CTI, C-reactive protein–triglyceride–glucose index; cuCTI, cumulative CTI; CMM, cardiometabolic multimorbidity; OR, odds ratio; CI, confidence interval; IQR, interquartile range. One-way mediation analyses assessed whether adiposity indices mediated CTI–CMM associations. Mediators included body mass index (BMI), body roundness index (BRI), and waist-to-height ratio (WHtR). Mediator models were specified using linear regression and outcome models using logistic regression including the exposure, mediator, and Model 3 covariates. Prespecified subgroup analyses were conducted by age group, sex, smoking, drinking, marital status, and hypertension status; effect modification was examined using multiplicative interaction terms. Sensitivity analyses included: (1) complete-case analyses without imputation and (2) analyses excluding participants with baseline diabetes, heart disease, or stroke. All analyses were conducted using R version 4.4.2, and statistical significance was defined as a two-sided P < 0.05. Results Baseline characteristics Baseline characteristics by CTI trajectory are shown in Table 1 . The mean age was 58.50 (8.31) years, and 50.7% were women. At baseline, 32.1% had at least one cardiometabolic condition. CTI trajectory clustering identified three groups: Persistent Low (n = 2,206), Moderate (n = 2,474), and Stable High (n = 1,098). Participants in the Stable High trajectory group tended to have a more adverse cardiometabolic profile compared with the Persistent Low group, including higher blood pressure, more atherogenic lipid and glycaemic profiles, higher inflammatory burden, and higher adiposity-related indices. These differences are consistent with CTI integrating inflammatory and metabolic components and with a higher burden of established risk factors among individuals with persistently higher CTI. Table 1 Baseline characteristics by CTI trajectory group Variables Overall Persistent Low Group Moderate Group Stable High Group p n 5778 2206 2474 1098 Age (mean (SD)) 58.3 (8.5) 58.1 (8.6) 58.5 (8.5) 58.3 (8.2) 0.274 sex (%) Female 3158 (54.7) 1105 ( 50.1) 1401 ( 56.6) 652 ( 59.4) < 0.001 Male 2620 (45.3) 1101 ( 49.9) 1073 ( 43.4) 446 ( 40.6) Education (%) Elementary school 1334 (23.1) 500 ( 22.7) 572 ( 23.1) 262 ( 23.9) 0.662 High school and above 543 ( 9.4) 219 ( 9.9) 217 ( 8.8) 107 ( 9.7) Junior high school 1180 (20.4) 434 ( 19.7) 516 ( 20.9) 230 ( 20.9) No formal education 2721 (47.1) 1053 ( 47.7) 1169 ( 47.3) 499 ( 45.4) Location (%) Rural 4919 (85.1) 1917 ( 86.9) 2105 ( 85.1) 897 ( 81.7) < 0.001 Urban 859 (14.9) 289 ( 13.1) 369 ( 14.9) 201 ( 18.3) Marital (%) Married 5213 (90.2) 1998 ( 90.6) 2225 ( 89.9) 990 ( 90.2) 0.764 Other 565 ( 9.8) 208 ( 9.4) 249 ( 10.1) 108 ( 9.8) Smoking (%) No 4034 (69.8) 1473 ( 66.8) 1756 ( 71.0) 805 ( 73.3) < 0.001 Yes 1744 (30.2) 733 ( 33.2) 718 ( 29.0) 293 ( 26.7) Drinking (%) No 4019 (69.6) 1470 ( 66.6) 1766 ( 71.4) 783 ( 71.3) 0.001 Yes 1759 (30.4) 736 ( 33.4) 708 ( 28.6) 315 ( 28.7) Sleep-time (mean (SD)) 6.4 (1.9) 6.4 (1.9) 6.3 (1.9) 6.4 (1.8) 0.276 SBP (mean (SD)) 128.7 (20.7) 124.3 (19.4) 130.1 (20.9) 134.2 (21.2) < 0.001 DBP (mean (SD)) 75.1 (11.8) 72.7 (11.5) 76.0 (11.5) 78.2 (11.7) < 0.001 TC (mean (SD)) 193.0 (36.6) 184.2 (33.0) 194.8 (36.4) 206.4 (39.2) < 0.001 TG (median [IQR]) 104.4 [74.3, 152.2] 74.3 [59.3, 94.7] 115.1 [88.5, 152.2] 199.1 [143.4, 284.7] < 0.001 HDL (mean (SD)) 51.2 (14.9) 58.0 (14.6) 50.1 (13.0) 40.1 (11.7) < 0.001 LDL (mean (SD)) 116.2 (33.3) 111.9 (29.1) 120.2 (33.3) 116.1 (39.6) < 0.001 Glu (median [IQR]) 101.9 [94.3, 111.4] 97.8 [91.3, 105.1] 102.1 [95.2, 110.7] 112.4 [102.2, 133.3] < 0.001 HbA1c (median [IQR]) 5.1 [4.9, 5.4] 5.1 [4.8, 5.3] 5.1 [4.9, 5.4] 5.3 [5.0, 5.7] < 0.001 CRP (median [IQR]) 1.0 [0.5, 2.0] 0.6 [0.4, 1.0] 1.2 [0.7, 2.2] 2.1 [1.1, 4.6] < 0.001 Tyg (mean (SD)) 8.7 (0.7) 8.2 (0.4) 8.7 (0.4) 9.5 (0.7) < 0.001 eGFR (mean (SD)) 93.3 (13.5) 95.2 (12.3) 92.4 (13.7) 91.4 (14.5) < 0.001 BMI (mean (SD)) 23.6 (3.6) 22.2 (3.1) 23.8 (3.4) 25.6 (3.6) < 0.001 BRI (mean (SD)) 4.2 (1.4) 3.7 (1.2) 4.3 (1.4) 4.9 (1.5) < 0.001 WHtR (mean (SD)) 0.5 (0.1) 0.5 (0.1) 0.5 (0.1) 0.6 (0.1) < 0.001 CTI 2012 (mean (SD)) 8.7 (0.8) 8.0 (0.4) 8.8 (0.5) 9.8 (0.7) < 0.001 CTI 2015 (mean (SD)) 8.9 (0.9) 8.1 (0.5) 9.0 (0.5) 10.0 (0.7) < 0.001 cuCTI (median [IQR]) 26.1 [24.8, 27.7] 24.4 [23.6, 25.0] 26.7 [26.1, 27.4] 29.4 [28.8, 30.4] < 0.001 DM (%) 662 (11.5) 107 ( 4.9) 232 ( 9.4) 323 ( 29.4) < 0.001 Heart (%) 529 ( 9.2) 167 ( 7.6) 264 ( 10.7) 98 ( 8.9) 0.001 Stroke (%) 71 ( 1.2) 23 ( 1.0) 32 ( 1.3) 16 ( 1.5) 0.552 CMM2018 (%) 333 ( 5.8) 53 ( 2.4) 165 ( 6.7) 115 ( 10.5) < 0.001 Baseline characteristics are shown overall and by CTI trajectory group (Persistent Low, Moderate, Stable High) derived from k-means clustering of CTI 2012 and CTI 2015 . Continuous variables are presented as mean (SD) or median [IQR]; categorical variables as n (%). P values compare characteristics across trajectory groups. During follow-up, 333 participants developed CMM (5.8%). The proportion of incident CMM increased across the three trajectory groups and was highest in the Stable High group. Differences were also observed in biomarkers related to CTI components, including triglycerides, fasting glucose, and CRP, as well as in the related index TyG. Associations of CTI measures with incident CMM Main association results are summarized in Table 2 . In the fully adjusted model (Model 3), baseline CTI per IQR was associated with higher odds of incident CMM (OR 1.545, 95% CI 1.344–1.776; P < 0.001). Similarly, cuCTI per IQR was associated with incident CMM (OR 1.651, 95% CI 1.429–1.907; P < 0.001). Table 2 Logistic regression for incident cardiometabolic multimorbidity (CMM) Model 1 Model 2 Model 3 OR (95% CI) P OR (95% CI) P OR (95% CI) P Baseline CTI 1.824 (1.602, 2.076) < 0.001 1.819 (1.597, 2.072) < 0.001 1.545 (1.344, 1.776) < 0.001 cuCTI 1.982 (1.733, 2.267) < 0.001 1.974 (1.725, 2.258) < 0.001 1.651 (1.429, 1.907) < 0.001 Baseline CTI Q1 Ref Ref Ref Q2 1.464 (0.958, 2.237) 0.078 1.463 (0.957, 2.237) 0.079 1.315 (0.857, 2.020) 0.210 Q3 2.709 (1.842, 3.983) < 0.001 2.692 (1.830, 3.959) < 0.001 2.119 (1.429, 3.141) < 0.001 Q4 3.931 (2.713, 5.696) < 0.001 3.903 (2.693, 5.658) < 0.001 2.637 (1.793, 3.878) < 0.001 P for trend < 0.001 < 0.001 < 0.001 cuCTI Q1 Ref Ref Ref Q2 1.687 (1.076, 2.645) 0.023 1.683 (1.073, 2.640) 0.023 1.501 (0.953, 2.365) 0.080 Q3 3.319 (2.203, 4.998) < 0.001 3.308 (2.196, 4.983) < 0.001 2.576 (1.696, 3.912) < 0.001 Q4 4.897 (3.296, 7.276) < 0.001 4.856 (3.266, 7.220) < 0.001 3.226 (2.139, 4.868) < 0.001 P for trend < 0.001 < 0.001 < 0.001 CTI trajectory Low Ref Ref Ref Middle 2.804 (2.045, 3.845) < 0.001 2.793 (2.036, 3.830) < 0.001 2.255 (1.633, 3.116) < 0.001 High 4.550 (3.254, 6.361) < 0.001 4.509 (3.223, 6.308) < 0.001 3.078 (2.169, 4.369) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Odds ratios (OR) and 95% confidence intervals (CI) for incident cardiometabolic multimorbidity in 2018 associated with baseline CTI (2012), cumulative cuCTI (2012–2015), and CTI trajectory groups. Continuous exposure estimates are per IQR increase: IQR(CTI 2012 ) = 1.06938 and IQR(cuCTI) = 2.957853. Quartile cutpoints: CTI 2012 min 5.98, Q1 8.12, median 8.63, Q3 9.19, max 13.15; cuCTI min 19.44, Q1 24.78, median 26.13, Q3 27.74, max 37.54. Quartile analyses provided additional insight into risk gradients and potential thresholds. In general, associations were modest in the second quartile and became more pronounced in higher quartiles, with significant trends across quartiles for both baseline CTI and cuCTI (P for trend < 0.001). The strongest contrasts were observed comparing the highest quartile with the lowest quartile for both exposures (Table 2). Trajectory analyses were consistent with continuous and quartile results. Compared with the Persistent Low group, both the Moderate and Stable High trajectory groups had higher odds of incident CMM in Model 3 (Moderate: OR 2.255, 95% CI 1.633–3.116; Stable High: OR 3.078, 95% CI 2.169–4.369; both P < 0.001), with evidence of a monotonic trend across trajectory levels. Dose–response analyses Restricted cubic spline models further characterized the shape of associations between CTI measures and incident CMM (Fig. 3). The overall association was statistically significant for both baseline CTI and cuCTI (Poverall < 0.001 for both). There was no strong evidence of departure from linearity for either exposure (baseline CTI: Pnonlinear = 0.152; cuCTI: Pnonlinear = 0.161), indicating approximately monotonic associations across the observed exposure ranges. Prediction performance Prediction performance is summarized in Fig. 4. The basic model had C-statistics 0.708 (0.678–0.739). Adding CRP yielded a small increase to 0.710 (0.679–0.740; DeLong P = 0.285), whereas adding TyG increased C-statistics to 0.726 (0.698–0.754; DeLong P = 0.002). Adding baseline CTI increased C-statistics to 0.732 (0.704–0.760; DeLong P < 0.001), adding cuCTI increased C-statistics to 0.737 (0.709–0.764; DeLong P < 0.001), and adding CTI trajectory increased C-statistics to 0.739 (0.712–0.766; DeLong P < 0.001). These comparisons indicate that CTI-derived features improved discrimination more than CRP alone and at least comparably to TyG, consistent with CTI integrating inflammatory and metabolic information in a single index. Reclassification metrics supported these discrimination improvements: models incorporating CTI measures yielded positive NRI and IDI estimates with statistical significance (Table 3). Mediation analyses Mediation analyses suggested partial mediation by adiposity indices (Fig. 5). For baseline CTI, the proportion mediated by BMI was 18.5%; for cuCTI, the proportion mediated by BMI was 16.9%. BRI and WHtR also mediated smaller proportions of the association. Mediation results were broadly consistent across exposures, and direct effects remained after accounting for adiposity indices. Sensitivity and subgroup analyses Subgroup analyses ( Table S2-S4) showed that associations were broadly consistent across strata defined by age group, sex, smoking, drinking, marital status, and hypertension status, with no strong evidence of effect modification across most subgroups. Sensitivity analyses ( Table S5-S6 ) supported the robustness of the associations for cuCTI and CTI trajectories under alternative analytic assumptions. Estimates remained directionally consistent in non-imputed datasets and in analyses excluding participants with baseline diabetes, heart disease, or stroke. Across sensitivity and subgroup analyses, the direction of associations was generally consistent. Discussion In this prospective CHARLS cohort analysis, baseline CTI, cumulative CTI, and CTI trajectories were consistently associated with higher odds of incident CMM by 2018. The pattern was coherent across different analytic representations, including per IQR, quartiles, and trajectories, and remained robust after adjustment for sociodemographic, behavioural, and clinical covariates. Dose response analyses supported significant overall associations and suggested largely monotonic relationships, with no strong evidence of nonlinearity. Beyond these etiologic associations, CTI related measures improved prediction beyond a conventional risk model. These findings support the translational relevance of CTI as a feasible composite index derived from routinely available biomarkers. Mediation analyses further suggested that adiposity indices partially mediated the association between CTI and CMM, highlighting potentially modifiable pathways while also indicating that additional mechanisms may contribute. Our findings extend and complement prior CTI research. In studies of stroke, cumulative CTI exposure and dynamic CTI trajectories were associated with incident stroke, suggesting that CTI captures longitudinal inflammatory and metabolic burden relevant to vascular outcomes [16, 19–21]. In addition, another study found that different dimensions of CTI were associated with incident frailty, supporting its role as a marker of systemic vulnerability and ageing related phenotypes[23]. The present study extends these observations to cardiometabolic multimorbidity, a composite outcome that may better reflect the accumulation and clustering of cardiometabolic disease than a single event alone. The emphasis on cumulative exposure and trajectories is particularly relevant to multimorbidity. CMM often develops progressively, so a measure that better reflects sustained exposure could plausibly show stronger or more consistent associations[7]. Cumulative exposure metrics also mitigate regression dilution bias that can occur when biomarkers have within-person variability[22]. In our analysis, both cuCTI and CTI trajectory groups were strongly associated with incident CMM, supporting the relevance of longer-term inflammatory-metabolic burden in this context. The RCS analyses demonstrated statistically significant overall associations between CTI measures and incident CMM, with no strong evidence of nonlinearity. These findings suggest that the associations were broadly monotonic across the observed exposure ranges and that per-IQR estimates provide a reasonable summary of association strength. CTI integrates inflammatory burden via CRP and metabolic dysregulation via the triglyceride–glucose component. Chronic low-grade inflammation may promote IR and dyslipidaemia through cytokine-mediated effects on adipose tissue, liver and muscle. In parallel, IR contributes to higher circulating triglycerides and glucose levels and may further stimulate inflammatory pathways via oxidative stress and endothelial activation[28, 29]. These interrelated processes may contribute to cardiometabolic disease development across multiple organ systems, which is consistent with the concept of CMM as a shared-pathway phenotype rather than simply the coexistence of independent diseases[28]. Among the adiposity related mediators, BMI accounted for the largest proportion of the association between CTI and CMM[30, 31]. This pattern is plausible because BMI captures overall adiposity, which is closely linked to the systemic inflammatory–metabolic milieu that CTI is designed to reflect: expansion of adipose tissue can promote chronic low-grade inflammation and worsen IR [28, 32–34]. In this context, BMI may be more closely linked than WHtR or BRI to both the inflammatory and metabolic components of CTI, and therefore may account for a larger proportion of the observed association[35]. By contrast, WHtR and BRI emphasize body shape and central adiposity[3, 36]; although clinically relevant, these indices can share substantial variance with BMI and may be more sensitive to waist-measurement variability, particularly in older adults, which could attenuate estimated indirect effects in fully adjusted models[37–40]. Similar patterns have been reported in CHARLS-based studies using other metabolic or lipid burden measures[41–43]. Overall, these findings suggest that adiposity-related pathways contribute to the association between CTI and CMM, although they do not fully explain it. A key contribution of this study is the evaluation of incremental predictive performance. Adding baseline CTI, cumulative CTI, or CTI trajectory group to the basic model improved the C statistic, whereas the improvement observed with CRP alone was small and not statistically significant. CTI related measures also showed positive and statistically significant NRI and IDI values, indicating improved risk reclassification beyond conventional risk factors. Notably, the improvements were slightly greater for long term CTI related measures. Both cumulative CTI and CTI trajectory group showed higher C statistic and NRI values than baseline CTI, and cumulative CTI showed the largest IDI. These findings suggest that repeated assessment of CTI may provide additional predictive information for incident CMM. Several limitations should be considered. CMM and component diseases were derived from questionnaire items indicating self-reported physician diagnosis; misclassification is possible and could attenuate associations. CTI was assessed at two time points (2012 and 2015), limiting trajectory granularity and potentially missing shorter-term variability. Although we adjusted for a broad set of confounders, residual confounding may remain, including unmeasured diet, detailed physical activity, and medication classes beyond dyslipidaemia medication use. Future work should validate CTI-based risk prediction in external cohorts and evaluate whether repeated CTI assessment over longer follow-up improves performance further. Mechanistic studies could assess whether interventions that reduce inflammatory–metabolic burden, improve adiposity, or address insulin resistance can modify CTI trajectories and reduce progression to multimorbidity. Finally, integrating CTI with additional multimorbidity-relevant markers and applying modern prediction approaches may help translate these findings into actionable risk stratification tools in both clinical and public health settings. Conclusions In this nationally representative CHARLS cohort, greater cumulative CTI exposure between 2012 and 2015, and persistently elevated CTI trajectories were associated with higher odds of incident CMM. Adding long-term CTI measures to a conventional risk model improved discrimination and reclassification, indicating potential utility for risk stratification. Mediation analyses suggested that adiposity contributes partially to these associations. CTI may help identify high-risk adults for integrated prevention of cardiometabolic disease clustering. Abbreviations BMI body mass index BRI body roundness index WHtR waist-to-height ratio CHARLS China Health and Retirement Longitudinal Study CI confidence interval CMM cardiometabolic multimorbidity CRP C-reactive protein CTI C-reactive protein–triglyceride–glucose index cuCTI cumulative CTI DBP diastolic blood pressure eGFR estimated glomerular filtration rate FPG fasting plasma glucose IDI integrated discrimination improvement IQR interquartile range MI multiple imputation NRI net reclassification improvement OR odds ratio RCS restricted cubic spline ROC receiver operating characteristic SBP systolic blood pressure TG triglycerides TyG triglyceride–glucose index. Declarations Ethics approval and consent to participate CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent at each survey wave. All study procedures were conducted in accordance with the Declaration of Helsinki. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The data used in this study are from the China Health and Retirement Longitudinal Study (CHARLS). CHARLS datasets are available for download at the CHARLS home website (http://charls.pku.edu.cn/en). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National High Level Hospital Clinical Research Funding (BJ-2025-159). Authors’ contributions YL and HA conceived and designed the study. YL performed the statistical analyses and drafted the manuscript. HA supervised the study and critically revised the manuscript for important intellectual content. Both authors read and approved the final manuscript. Acknowledgements We thank the China Health and Retirement Longitudinal Study (CHARLS) research team for their efforts and all the participants for their valuable contributions. 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International journal of obesity (2005) 2011, 35 (2):300-308. Neeland IJ, Ross R, Després JP, Matsuzawa Y, Yamashita S, Shai I, Seidell J, Magni P, Santos RD, Arsenault B et al : Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement . The lancet Diabetes & endocrinology 2019, 7 (9):715-725. Stefan N: Causes, consequences, and treatment of metabolically unhealthy fat distribution . The lancet Diabetes & endocrinology 2020, 8 (7):616-627. Després JP: Body fat distribution and risk of cardiovascular disease: an update . Circulation 2012, 126 (10):1301-1313. Xu J, Zhang L, Wu Q, Zhou Y, Jin Z, Li Z, Zhu Y: Body roundness index is a superior indicator to associate with the cardio-metabolic risk: evidence from a cross-sectional study with 17,000 Eastern-China adults . BMC cardiovascular disorders 2021, 21 (1):97. Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, Maeda Y, McDougall A, Peterson CM, Ravussin E et al : Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model . Obesity (Silver Spring, Md) 2013, 21 (11):2264-2271. Ashwell M, Gunn P, Gibson S: Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis . Obesity reviews : an official journal of the International Association for the Study of Obesity 2012, 13 (3):275-286. Mason C, Katzmarzyk PT: Variability in waist circumference measurements according to anatomic measurement site . Obesity (Silver Spring, Md) 2009, 17 (9):1789-1795. Guerra RS, Amaral TF, Marques EA, Mota J, Restivo MT: Anatomical location for waist circumference measurement in older adults: a preliminary study . Nutricion hospitalaria 2012, 27 (5):1554-1561. Lai H, Tu Y, Liao C, Zhang S, He L, Li J: Joint assessment of abdominal obesity and non-traditional lipid parameters for primary prevention of cardiometabolic multimorbidity: insights from the China health and retirement longitudinal study 2011-2018 . Cardiovascular diabetology 2025, 24 (1):109. Chen ZT, Wang XM, Zhong YS, Zhong WF, Song WQ, Wu XB: Association of changes in waist circumference, waist-to-height ratio and weight-adjusted-waist index with multimorbidity among older Chinese adults: results from the Chinese longitudinal healthy longevity survey (CLHLS) . BMC public health 2024, 24 (1):318. Cai X, Liao Y, Yang X, Liang Y, Ma J, Liu R, Wen X, Yin W, Chen S, Wang G et al : Body Roundness Index Associated With Cardiometabolic Multimorbidity and Mortality: A Multistate Model . Obesity (Silver Spring, Md) 2025, 33 (12):2377-2386. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 01 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9291028","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635687878,"identity":"bd71c535-c5f9-42db-a151-637b20b4f5dc","order_by":0,"name":"Yixiao Liang","email":"","orcid":"","institution":"Beijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yixiao","middleName":"","lastName":"Liang","suffix":""},{"id":635687879,"identity":"24564a7a-2d06-43f2-8cfe-4c0478767d2d","order_by":1,"name":"Hu Ai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYDACZh4gwWbDwMdMopY0BjbitTCAtRxmYCNaA3877zGpG2Xn5dnYecykCxjs5HQbCGiROMyXJp1z7rZhGzNQywyGZGOzA4SsOQxUmdt2mxGshYfhQOI2QlrkIVrO2ROvxQCi5UAi8VoMD/MYW+ecS05uY2YrtuYxIMIvcufPGN7OKbOz7ec/vPE2T4WdHGHvIwCHAdCdxCsHAfYHpKkfBaNgFIyCEQMA6woy8rEVS9YAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hu","middleName":"","lastName":"Ai","suffix":""}],"badges":[],"createdAt":"2026-04-01 11:11:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9291028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9291028/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108972515,"identity":"554f2581-8a7c-4abc-a24d-eeae98970d2e","added_by":"auto","created_at":"2026-05-11 10:36:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":371726,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participant selection. Abbreviations: CTI, C-reactive protein–triglyceride–glucose index; CMM, cardiometabolic multimorbidity.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9291028/v1/815a7ea5f8423c9708d56b08.png"},{"id":108972512,"identity":"ada36499-d3c8-49c1-96e3-c72cd0830ffb","added_by":"auto","created_at":"2026-05-11 10:36:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2463024,"visible":true,"origin":"","legend":"\u003cp\u003eCTI change-pattern groups identified from repeated CTI measurements in 2012 and 2015. Abbreviations: CTI, C-reactive protein–triglyceride–glucose index; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9291028/v1/cbec6aaa797a3159fc89b834.png"},{"id":108972520,"identity":"62690799-d1a0-43bb-97c1-9d1a82b9944d","added_by":"auto","created_at":"2026-05-11 10:36:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":679880,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses of the associations of baseline CTI and cumulative CTI with incident cardiometabolic multimorbidity. Abbreviations: CTI, C-reactive protein–triglyceride–glucose index; cuCTI, cumulative CTI; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9291028/v1/78a5ca75c31b6199102a63bb.png"},{"id":108972519,"identity":"5545a339-03f3-4f8a-97bd-5857d7deb8a1","added_by":"auto","created_at":"2026-05-11 10:36:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":587308,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves for the prediction of incident cardiometabolic multimorbidity by different models. Abbreviations: ROC, receiver operating characteristic; CRP, C-reactive protein; TyG, triglyceride–glucose index; CTI, C-reactive protein–triglyceride–glucose index; cuCTI, cumulative CTI.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9291028/v1/8c52c90ad1ca0cd1e8e775fa.png"},{"id":108972517,"identity":"144a561a-4aec-4395-84fb-e4615bc3706a","added_by":"auto","created_at":"2026-05-11 10:36:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":315969,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analyses of the associations of baseline CTI and cumulative CTI with incident cardiometabolic multimorbidity through adiposity indices. Abbreviations: CTI, C-reactive protein–triglyceride–glucose index; cuCTI, cumulative CTI; BMI, body mass index; BRI, body roundness index; WHtR, waist-to-height ratio.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9291028/v1/3412fc28ca8b0ecb4efd45b7.png"},{"id":108978147,"identity":"11ae5546-18da-4802-ace3-f2f7b2fc2cac","added_by":"auto","created_at":"2026-05-11 11:34:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4879466,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9291028/v1/fbe5a7ab-cabd-43b3-9b6d-c4e47c007d13.pdf"},{"id":108972514,"identity":"271fa637-4fc0-467c-9d8d-c32db2324143","added_by":"auto","created_at":"2026-05-11 10:36:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23878,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9291028/v1/bfeffb1720ffa70040bd9005.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLong-term cumulative and trajectory patterns of the C-reactive protein–triglyceride–glucose index and cardiometabolic multimorbidity: the mediating effects of adiposity indices\u003c/p\u003e","fulltext":[{"header":"Contributions to the Literature","content":"\u003cp\u003e1. To extend previous evidence on CTI by showing its association with cardiometabolic multimorbidity, a clinically important outcome that better reflects the clustering of cardiometabolic diseases in ageing populations.\u003c/p\u003e\n\n\u003cp\u003e2. To identify people with persistently high CTI or greater cumulative CTI burden as a high risk group, providing evidence for population risk stratification and targeted prevention.\u003c/p\u003e\n\n\u003cp\u003e3. To provide public health evidence that repeated CTI assessment and adiposity related pathways may be relevant for early identification and prevention of cardiometabolic multimorbidity.\u003c/p\u003e\n\n"},{"header":"Background","content":"\u003cp\u003eCardiometabolic multimorbidity (CMM), commonly defined as the coexistence of two or more cardiometabolic conditions in the same individual, has become a dominant public health and clinical challenge in ageing populations[1, 2]. It is clinically important because the clustering of diabetes, cardiovascular disease and stroke reflects shared upstream mechanisms, increases treatment complexity, and amplifies risks of disability and mortality[3]. In China, the rapid demographic transition and the high prevalence of metabolic risk factors have made early identification of individuals at risk of progressing from a single condition to multimorbidity a priority for prevention and integrated care[4]. Beyond the burden on individuals, CMM challenges healthcare systems by requiring long-term management across specialties and by increasing polypharmacy and competing risk-factor targets[5, 6].\u003c/p\u003e \u003cp\u003eSystemic inflammation and insulin resistance(IR) are closely intertwined and are considered core biological processes underlying cardiometabolic disease accumulation[7\u0026ndash;9]. Chronic low-grade inflammation can impair insulin signaling, while IR can amplify inflammatory pathways, creating a self-reinforcing cycle that promotes vascular injury, metabolic deterioration, and end-organ damage[10\u0026ndash;12]. C-reactive protein (CRP) is a widely used marker of systemic inflammation and has been incorporated into cardiovascular risk assessment frameworks[13, 14]. The triglyceride\u0026ndash;glucose (TyG) index is a practical surrogate of IR derived from fasting triglycerides and glucose[15\u0026ndash;17]. Building on these two dimensions, the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index (CTI) was proposed as a composite marker integrating inflammatory and insulin resistance components into a single metric[18\u0026ndash;21].\u003c/p\u003e \u003cp\u003eMost epidemiologic studies rely on a single baseline biomarker measurement, which may incompletely represent long-term exposure due to within-person variability and temporal changes[18, 22]. Repeated measures enable complementary views of risk: a time-integrated cumulative burden that captures sustained exposure and a trajectory-based grouping that summarizes heterogeneous patterns over time. In prior work, CTI has been associated with incident stroke and cardiovascular outcomes, and trajectory-based CTI patterns have been used to stratify future risk[23, 24]. More recently, evidence from cohort analyses has linked CTI to cardiometabolic multimorbidity, supporting its relevance for multi-condition cardiometabolic risk[24].\u003c/p\u003e \u003cp\u003eHowever, several gaps remain in the current evidence. Whether CTI measures based on repeated assessments, including cumulative exposure and trajectory patterns, provide additional information beyond a single baseline measurement for incident CMM remains unclear. It is also uncertain whether these CTI-derived measures improve prediction beyond established demographic, behavioural, and clinical risk factors. In addition, the potential mediating role of adiposity in the association between CTI and CMM has not been fully examined. Therefore, using CHARLS data with repeated biomarker assessment, we examined associations of baseline CTI (2012), cumulative CTI (cuCTI) exposure between 2012 and 2015, and data-driven CTI trajectories with incident CMM in 2018. We further evaluated dose\u0026ndash;response relationships using restricted cubic splines, assessed incremental predictive performance beyond a conventional risk model using C-statistics, net reclassification improvement, and integrated discrimination improvement, and explored mediation by adiposity indices.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe conducted a prospective cohort analysis within the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal study of Chinese adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years that collects questionnaire data and standardized physical and laboratory measurements across survey waves[25]. The present analysis used CTI measurements in 2012 and 2015 and adjudicated incident CMM status in 2018, thereby aligning exposure assessment with subsequent outcome ascertainment.\u003c/p\u003e \u003cp\u003eCHARLS uses standardized interviews to collect sociodemographic characteristics, health behaviours, and physician-diagnosed conditions, and it incorporates physical examinations and laboratory measurements from fasting blood samples following study protocols. In the current analysis, we leveraged these harmonized data to define exposures, covariates, and outcomes consistently across waves.\u003c/p\u003e \u003cp\u003eA total of 11,847 participants were initially screened. We excluded 4,375 individuals with missing CTI measurements in 2012 or 2015, and a further 221 participants aged\u0026thinsp;\u0026lt;\u0026thinsp;45 years or with missing age. We additionally excluded 1,473 participants with prevalent CMM prior to 2018 or missing CMM status. The final analytic sample included 5,778 participants(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure\u003c/h3\u003e\n\u003cp\u003eCTI was calculated using the formula: 0.412 \u0026times; ln(CRP [mg/L])\u0026thinsp;+\u0026thinsp;ln(TG [mg/dL] \u0026times; FPG [mg/dL]/2) [17]. CTI\u003csub\u003e2012\u003c/sub\u003e and CTI\u003csub\u003e2015\u003c/sub\u003e represent CTI measured in 2012 (Wave 1) and 2015 (Wave 3), respectively, with a 3-year interval between assessments.\u003c/p\u003e \u003cp\u003eTo quantify long-term cumulative exposure, cumulative CTI was computed using the trapezoidal (area) approach: cuCTI = (CTI\u003csub\u003e2012\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CTI\u003csub\u003e2015\u003c/sub\u003e)/2 \u0026times; (2015\u0026thinsp;\u0026minus;\u0026thinsp;2012). The average of the two time points was used to reflect the mean exposure level and was multiplied by the duration to estimate cumulative exposure over time, consistent with cumulative metabolic index calculations in CHARLS and prior studies. Based on CTI levels, participants were further classified into three CTI change-pattern (trajectory) groups using k-means clustering applied to z-standardized CTI\u003csub\u003e2012\u003c/sub\u003e and CTI\u003csub\u003e2015\u003c/sub\u003e values (K\u0026thinsp;=\u0026thinsp;3). Clusters were labeled according to mean CTI level as Persistent Low, Moderate, and Stable High (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), consistent with prior CHARLS-based CTI trajectory analyses. In line with prior literature that describes longitudinal CTI \u0026ldquo;changes\u0026rdquo; using repeated-measure patterning, we refer to these k-means clusters as CTI change-pattern (trajectory) groups rather than a simple two-point difference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003eCMM was defined as at least two of diabetes, heart disease, and stroke[26]. Diabetes was identified by self-report of physician diagnosis and objective evidence (use of anti-diabetic medication, fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%)[27]. Heart disease and stroke were ascertained by self-reported physician diagnosis.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates were selected a priori to reduce confounding and were organized into nested models to evaluate robustness of estimates to progressively broader adjustment. Model 1 included basic demographic characteristics (age and sex). Model 2 further adjusted for socio-demographic and lifestyle-related factors, including educational attainment, residential location, marital status, smoking status, and alcohol consumption. Model 3 additionally adjusted for clinical and physiological variables, including systolic blood pressure (SBP), diastolic blood pressure(DBP), hypertension, dyslipidaemia, sleep duration, estimated glomerular filtration rate(eGFR), and lipid-lowering medication use.\u003c/p\u003e\n\u003ch3\u003eMissing data and multiple imputation\u003c/h3\u003e\n\u003cp\u003eMissing covariate data were handled using multiple imputation by chained equations (MICE) to minimize potential bias and improve statistical efficiency (\u003cb\u003eTable S1\u003c/b\u003e). During data preprocessing, extreme values were detected in BMI, BRI and WHtR indices.To limit the influence of extreme values while retaining observations, prespecified continuous variables were winsorized at the 1st and 99th percentiles prior to model fitting. This procedure was applied consistently within each imputed dataset where relevant to enhance the robustness of regression estimates in the presence of measurement error and outliers.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were summarized for the overall sample and across CTI trajectory groups. Normally distributed continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), non-normally distributed variables as median (interquartile range, IQR), and categorical variables as number (percentage). Between-group comparisons were performed using one-way analysis of variance (ANOVA) for normally distributed continuous variables, the Kruskal\u0026ndash;Wallis test for non-normally distributed variables, and the Pearson chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eMissing covariates were imputed under the missing-at-random assumption using multiple imputation by chained equations. Associations between CTI measures and incident CMM were estimated using logistic regression within each imputed dataset and pooled. Effect estimates are reported as odds ratios (ORs) with 95% confidence intervals (CIs) and two-sided P values. CTI\u003csub\u003e2012\u003c/sub\u003e and cuCTI were analyzed per interquartile range (IQR) and by quartiles (Q1 as reference). Linear trends across quartiles and ordered trajectory groups were evaluated by entering category ranks as continuous terms (P for trend). Three models with increasing adjustment were fitted.\u003c/p\u003e \u003cp\u003eDose\u0026ndash;response relationships were examined using restricted cubic splines with four knots, with the median exposure as the reference. We report Poverall for the overall exposure\u0026ndash;outcome association and Pnonlinear for departure from linearity and interpret spline curves alongside exposure distributions.\u003c/p\u003e \u003cp\u003eIncremental predictive value beyond conventional risk factors was assessed by comparing a basic model corresponding to Model 3 covariates with models additionally including CRP, TyG, baseline CTI, cuCTI, or CTI trajectories. Discrimination was quantified using the C-statistic and compared using DeLong\u0026rsquo;s test for correlated ROC curves. Reclassification was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). ROC curves are provide, with summary prediction metrics in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscrimination and reclassification improvement for incident cardiometabolic multimorbidity (CMM)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-statistics\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e (DeLong\u003c/p\u003e \u003cp\u003evs Base)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNRI (95%\u003c/p\u003e \u003cp\u003eCI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e (NRI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIDI (95%\u003c/p\u003e \u003cp\u003eCI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e (IDI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.708(0.678\u0026ndash;0.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic model\u0026thinsp;+\u0026thinsp;CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.710(0.679\u0026ndash;0.740)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1414(0.0422\u0026ndash;0.2406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003(-0.0006\u0026ndash;0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic model\u0026thinsp;+\u0026thinsp;TyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.726(0.698\u0026ndash;0.754)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1890(0.0781\u0026ndash;0.3000)\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\u003e0.0072(0.0031\u0026ndash;0.0113)\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\u003eBasic model\u0026thinsp;+\u0026thinsp;CTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.732(0.704\u0026ndash;0.760)\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\u003e0.2785(0.1676\u0026ndash;0.3893)\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\u003e0.0090(0.0044\u0026ndash;0.0135)\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\u003eBasic model\u0026thinsp;+\u0026thinsp;cuCTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737(0.709\u0026ndash;0.764)\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\u003e0.3378(0.2273\u0026ndash;0.4482)\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\u003e0.0116(0.0063\u0026ndash;0.0168)\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\u003eBasic model\u0026thinsp;+\u0026thinsp;CTI trajectory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.739(0.712\u0026ndash;0.766)\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\u003e0.3461(0.2488\u0026ndash;0.4434)\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\u003e0.0084(0.0051\u0026ndash;0.0117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: CTI, C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index; cuCTI, cumulative CTI; CMM, cardiometabolic multimorbidity; OR, odds ratio; CI, confidence interval; IQR, interquartile range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOne-way mediation analyses assessed whether adiposity indices mediated CTI\u0026ndash;CMM associations. Mediators included body mass index (BMI), body roundness index (BRI), and waist-to-height ratio (WHtR). Mediator models were specified using linear regression and outcome models using logistic regression including the exposure, mediator, and Model 3 covariates. Prespecified subgroup analyses were conducted by age group, sex, smoking, drinking, marital status, and hypertension status; effect modification was examined using multiplicative interaction terms. Sensitivity analyses included: (1) complete-case analyses without imputation and (2) analyses excluding participants with baseline diabetes, heart disease, or stroke. All analyses were conducted using R version 4.4.2, and statistical significance was defined as a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eBaseline characteristics by CTI trajectory are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age was 58.50 (8.31) years, and 50.7% were women. At baseline, 32.1% had at least one cardiometabolic condition. CTI trajectory clustering identified three groups: Persistent Low (n\u0026thinsp;=\u0026thinsp;2,206), Moderate (n\u0026thinsp;=\u0026thinsp;2,474), and Stable High (n\u0026thinsp;=\u0026thinsp;1,098). Participants in the Stable High trajectory group tended to have a more adverse cardiometabolic profile compared with the Persistent Low group, including higher blood pressure, more atherogenic lipid and glycaemic profiles, higher inflammatory burden, and higher adiposity-related indices. These differences are consistent with CTI integrating inflammatory and metabolic components and with a higher burden of established risk factors among individuals with persistently higher CTI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics by CTI trajectory group\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePersistent Low Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStable High Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1098\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\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.1 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.5 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.3 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex (%)\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 \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\u003e3158 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1105 ( 50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1401 ( 56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e652 ( 59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2620 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1101 ( 49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1073 ( 43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e446 ( 40.6)\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\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1334 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 ( 22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e572 ( 23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e262 ( 23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e543 ( 9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 ( 9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e217 ( 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107 ( 9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1180 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434 ( 19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e516 ( 20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230 ( 20.9)\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 formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2721 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1053 ( 47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1169 ( 47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e499 ( 45.4)\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\u003eLocation (%)\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 \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\u003e4919 (85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1917 ( 86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2105 ( 85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e897 ( 81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e859 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289 ( 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e369 ( 14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201 ( 18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5213 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1998 ( 90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2225 ( 89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e990 ( 90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e565 ( 9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208 ( 9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249 ( 10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 ( 9.8)\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\u003eSmoking (%)\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 \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\u003e4034 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1473 ( 66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1756 ( 71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e805 ( 73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1744 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e733 ( 33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e718 ( 29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e293 ( 26.7)\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\u003eDrinking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e4019 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1470 ( 66.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1766 ( 71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e783 ( 71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1759 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e736 ( 33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e708 ( 28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e315 ( 28.7)\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\u003eSleep-time (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.7 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.3 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.1 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134.2 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.1 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.7 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.0 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.2 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.0 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184.2 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194.8 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206.4 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.4 [74.3, 152.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.3 [59.3, 94.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.1 [88.5, 152.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199.1 [143.4, 284.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.2 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.1 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.1 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.2 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.9 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.2 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.1 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.9 [94.3, 111.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.8 [91.3, 105.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.1 [95.2, 110.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.4 [102.2, 133.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1 [4.9, 5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1 [4.8, 5.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1 [4.9, 5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.3 [5.0, 5.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 [0.5, 2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 [0.4, 1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 [0.7, 2.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1 [1.1, 4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyg (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.3 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.2 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.4 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.4 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.6 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.6 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI\u003csub\u003e2012\u003c/sub\u003e (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI\u003csub\u003e2015\u003c/sub\u003e (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.0 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecuCTI (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1 [24.8, 27.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4 [23.6, 25.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.7 [26.1, 27.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4 [28.8, 30.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e662 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 ( 4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 ( 9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e323 ( 29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529 ( 9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 ( 7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264 ( 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 ( 8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 ( 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 ( 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 ( 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 ( 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMM2018 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333 ( 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 ( 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 ( 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 ( 10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBaseline characteristics are shown overall and by CTI trajectory group (Persistent Low, Moderate, Stable High) derived from k-means clustering of CTI\u003csub\u003e2012\u003c/sub\u003e and CTI\u003csub\u003e2015\u003c/sub\u003e. Continuous variables are presented as mean (SD) or median [IQR]; categorical variables as n (%). P values compare characteristics across trajectory groups.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring follow-up, 333 participants developed CMM (5.8%). The proportion of incident CMM increased across the three trajectory groups and was highest in the Stable High group. Differences were also observed in biomarkers related to CTI components, including triglycerides, fasting glucose, and CRP, as well as in the related index TyG.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of CTI measures with incident CMM\u003c/h2\u003e \u003cp\u003eMain association results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the fully adjusted model (Model 3), baseline CTI per IQR was associated with higher odds of incident CMM (OR 1.545, 95% CI 1.344\u0026ndash;1.776; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, cuCTI per IQR was associated with incident CMM (OR 1.651, 95% CI 1.429\u0026ndash;1.907; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression for incident cardiometabolic multimorbidity (CMM)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline CTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.824 (1.602, 2.076)\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\u003e1.819 (1.597, 2.072)\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\u003e1.545 (1.344, 1.776)\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\u003ecuCTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.982 (1.733, 2.267)\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\u003e1.974 (1.725, 2.258)\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\u003e1.651 (1.429, 1.907)\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\u003eBaseline CTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.464 (0.958, 2.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.463 (0.957, 2.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.315 (0.857, 2.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.709 (1.842, 3.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.692 (1.830, 3.959)\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\u003e2.119 (1.429, 3.141)\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\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.931 (2.713, 5.696)\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\u003e3.903 (2.693, 5.658)\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\u003e2.637 (1.793, 3.878)\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\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecuCTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.687 (1.076, 2.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.683 (1.073, 2.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.501 (0.953, 2.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.080\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\u003e3.319 (2.203, 4.998)\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\u003e3.308 (2.196, 4.983)\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\u003e2.576 (1.696, 3.912)\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\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.897 (3.296, 7.276)\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\u003e4.856 (3.266, 7.220)\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\u003e3.226 (2.139, 4.868)\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\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTI trajectory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.804 (2.045, 3.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.793 (2.036, 3.830)\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\u003e2.255 (1.633, 3.116)\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\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.550 (3.254, 6.361)\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\u003e4.509 (3.223, 6.308)\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\u003e3.078 (2.169, 4.369)\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\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOdds ratios (OR) and 95% confidence intervals (CI) for incident cardiometabolic multimorbidity in 2018 associated with baseline CTI (2012), cumulative cuCTI (2012\u0026ndash;2015), and CTI trajectory groups. Continuous exposure estimates are per IQR increase: IQR(CTI\u003csub\u003e2012\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;1.06938 and IQR(cuCTI)\u0026thinsp;=\u0026thinsp;2.957853. Quartile cutpoints: CTI\u003csub\u003e2012\u003c/sub\u003e min 5.98, Q1 8.12, median 8.63, Q3 9.19, max 13.15; cuCTI min 19.44, Q1 24.78, median 26.13, Q3 27.74, max 37.54.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eQuartile analyses provided additional insight into risk gradients and potential thresholds. In general, associations were modest in the second quartile and became more pronounced in higher quartiles, with significant trends across quartiles for both baseline CTI and cuCTI (P for trend \u0026lt; 0.001). The strongest contrasts were observed comparing the highest quartile with the lowest quartile for both exposures (Table 2).\u003c/p\u003e\n\u003cp\u003eTrajectory analyses were consistent with continuous and quartile results. Compared with the Persistent Low group, both the Moderate and Stable High trajectory groups had higher odds of incident CMM in Model 3 (Moderate: OR 2.255, 95% CI 1.633–3.116; Stable High: OR 3.078, 95% CI 2.169–4.369; both P \u0026lt; 0.001), with evidence of a monotonic trend across trajectory levels.\u003c/p\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eDose–response analyses\u003c/h2\u003e\n \u003cp\u003eRestricted cubic spline models further characterized the shape of associations between CTI measures and incident CMM (Fig. 3). The overall association was statistically significant for both baseline CTI and cuCTI (Poverall \u0026lt; 0.001 for both). There was no strong evidence of departure from linearity for either exposure (baseline CTI: Pnonlinear = 0.152; cuCTI: Pnonlinear = 0.161), indicating approximately monotonic associations across the observed exposure ranges.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003ePrediction performance\u003c/h2\u003e\n \u003cp\u003ePrediction performance is summarized in Fig. 4. The basic model had C-statistics 0.708 (0.678–0.739). Adding CRP yielded a small increase to 0.710 (0.679–0.740; DeLong P = 0.285), whereas adding TyG increased C-statistics to 0.726 (0.698–0.754; DeLong P = 0.002). Adding baseline CTI increased C-statistics to 0.732 (0.704–0.760; DeLong P \u0026lt; 0.001), adding cuCTI increased C-statistics to 0.737 (0.709–0.764; DeLong P \u0026lt; 0.001), and adding CTI trajectory increased C-statistics to 0.739 (0.712–0.766; DeLong P \u0026lt; 0.001).\u003c/p\u003e\n \u003cp\u003eThese comparisons indicate that CTI-derived features improved discrimination more than CRP alone and at least comparably to TyG, consistent with CTI integrating inflammatory and metabolic information in a single index. Reclassification metrics supported these discrimination improvements: models incorporating CTI measures yielded positive NRI and IDI estimates with statistical significance (Table 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eMediation analyses\u003c/h2\u003e\n \u003cp\u003eMediation analyses suggested partial mediation by adiposity indices (Fig. 5). For baseline CTI, the proportion mediated by BMI was 18.5%; for cuCTI, the proportion mediated by BMI was 16.9%. BRI and WHtR also mediated smaller proportions of the association. Mediation results were broadly consistent across exposures, and direct effects remained after accounting for adiposity indices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eSensitivity and subgroup analyses\u003c/h2\u003e\n \u003cp\u003eSubgroup analyses (\u003cstrong\u003eTable S2-S4)\u003c/strong\u003e showed that associations were broadly consistent across strata defined by age group, sex, smoking, drinking, marital status, and hypertension status, with no strong evidence of effect modification across most subgroups.\u003c/p\u003e\n \u003cp\u003eSensitivity analyses (\u003cstrong\u003eTable S5-S6\u003c/strong\u003e) supported the robustness of the associations for cuCTI and CTI trajectories under alternative analytic assumptions. Estimates remained directionally consistent in non-imputed datasets and in analyses excluding participants with baseline diabetes, heart disease, or stroke. Across sensitivity and subgroup analyses, the direction of associations was generally consistent.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective CHARLS cohort analysis, baseline CTI, cumulative CTI, and CTI trajectories were consistently associated with higher odds of incident CMM by 2018. The pattern was coherent across different analytic representations, including per IQR, quartiles, and trajectories, and remained robust after adjustment for sociodemographic, behavioural, and clinical covariates. Dose response analyses supported significant overall associations and suggested largely monotonic relationships, with no strong evidence of nonlinearity. Beyond these etiologic associations, CTI related measures improved prediction beyond a conventional risk model. These findings support the translational relevance of CTI as a feasible composite index derived from routinely available biomarkers. Mediation analyses further suggested that adiposity indices partially mediated the association between CTI and CMM, highlighting potentially modifiable pathways while also indicating that additional mechanisms may contribute.\u003c/p\u003e \u003cp\u003eOur findings extend and complement prior CTI research. In studies of stroke, cumulative CTI exposure and dynamic CTI trajectories were associated with incident stroke, suggesting that CTI captures longitudinal inflammatory and metabolic burden relevant to vascular outcomes [16, 19\u0026ndash;21]. In addition, another study found that different dimensions of CTI were associated with incident frailty, supporting its role as a marker of systemic vulnerability and ageing related phenotypes[23]. The present study extends these observations to cardiometabolic multimorbidity, a composite outcome that may better reflect the accumulation and clustering of cardiometabolic disease than a single event alone.\u003c/p\u003e \u003cp\u003eThe emphasis on cumulative exposure and trajectories is particularly relevant to multimorbidity. CMM often develops progressively, so a measure that better reflects sustained exposure could plausibly show stronger or more consistent associations[7]. Cumulative exposure metrics also mitigate regression dilution bias that can occur when biomarkers have within-person variability[22]. In our analysis, both cuCTI and CTI trajectory groups were strongly associated with incident CMM, supporting the relevance of longer-term inflammatory-metabolic burden in this context. The RCS analyses demonstrated statistically significant overall associations between CTI measures and incident CMM, with no strong evidence of nonlinearity. These findings suggest that the associations were broadly monotonic across the observed exposure ranges and that per-IQR estimates provide a reasonable summary of association strength.\u003c/p\u003e \u003cp\u003eCTI integrates inflammatory burden via CRP and metabolic dysregulation via the triglyceride\u0026ndash;glucose component. Chronic low-grade inflammation may promote IR and dyslipidaemia through cytokine-mediated effects on adipose tissue, liver and muscle. In parallel, IR contributes to higher circulating triglycerides and glucose levels and may further stimulate inflammatory pathways via oxidative stress and endothelial activation[28, 29]. These interrelated processes may contribute to cardiometabolic disease development across multiple organ systems, which is consistent with the concept of CMM as a shared-pathway phenotype rather than simply the coexistence of independent diseases[28].\u003c/p\u003e \u003cp\u003eAmong the adiposity related mediators, BMI accounted for the largest proportion of the association between CTI and CMM[30, 31]. This pattern is plausible because BMI captures overall adiposity, which is closely linked to the systemic inflammatory\u0026ndash;metabolic milieu that CTI is designed to reflect: expansion of adipose tissue can promote chronic low-grade inflammation and worsen IR [28, 32\u0026ndash;34]. In this context, BMI may be more closely linked than WHtR or BRI to both the inflammatory and metabolic components of CTI, and therefore may account for a larger proportion of the observed association[35]. By contrast, WHtR and BRI emphasize body shape and central adiposity[3, 36]; although clinically relevant, these indices can share substantial variance with BMI and may be more sensitive to waist-measurement variability, particularly in older adults, which could attenuate estimated indirect effects in fully adjusted models[37\u0026ndash;40]. Similar patterns have been reported in CHARLS-based studies using other metabolic or lipid burden measures[41\u0026ndash;43]. Overall, these findings suggest that adiposity-related pathways contribute to the association between CTI and CMM, although they do not fully explain it.\u003c/p\u003e \u003cp\u003eA key contribution of this study is the evaluation of incremental predictive performance. Adding baseline CTI, cumulative CTI, or CTI trajectory group to the basic model improved the C statistic, whereas the improvement observed with CRP alone was small and not statistically significant. CTI related measures also showed positive and statistically significant NRI and IDI values, indicating improved risk reclassification beyond conventional risk factors. Notably, the improvements were slightly greater for long term CTI related measures. Both cumulative CTI and CTI trajectory group showed higher C statistic and NRI values than baseline CTI, and cumulative CTI showed the largest IDI. These findings suggest that repeated assessment of CTI may provide additional predictive information for incident CMM.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. CMM and component diseases were derived from questionnaire items indicating self-reported physician diagnosis; misclassification is possible and could attenuate associations. CTI was assessed at two time points (2012 and 2015), limiting trajectory granularity and potentially missing shorter-term variability. Although we adjusted for a broad set of confounders, residual confounding may remain, including unmeasured diet, detailed physical activity, and medication classes beyond dyslipidaemia medication use.\u003c/p\u003e \u003cp\u003eFuture work should validate CTI-based risk prediction in external cohorts and evaluate whether repeated CTI assessment over longer follow-up improves performance further. Mechanistic studies could assess whether interventions that reduce inflammatory\u0026ndash;metabolic burden, improve adiposity, or address insulin resistance can modify CTI trajectories and reduce progression to multimorbidity. Finally, integrating CTI with additional multimorbidity-relevant markers and applying modern prediction approaches may help translate these findings into actionable risk stratification tools in both clinical and public health settings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this nationally representative CHARLS cohort, greater cumulative CTI exposure between 2012 and 2015, and persistently elevated CTI trajectories were associated with higher odds of incident CMM. Adding long-term CTI measures to a conventional risk model improved discrimination and reclassification, indicating potential utility for risk stratification. Mediation analyses suggested that adiposity contributes partially to these associations. CTI may help identify high-risk adults for integrated prevention of cardiometabolic disease clustering.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody roundness index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHtR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewaist-to-height ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiometabolic multimorbidity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecuCTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecumulative CTI\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efasting plasma glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintegrated discrimination improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultiple imputation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enet reclassification improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erestricted cubic spline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTyG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglyceride\u0026ndash;glucose index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eCHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent at each survey wave. All study procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are from the China Health and Retirement Longitudinal Study (CHARLS). CHARLS datasets are available for download at the CHARLS home website (http://charls.pku.edu.cn/en).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National High Level Hospital Clinical Research Funding (BJ-2025-159).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL and HA conceived and designed the study. YL performed the statistical analyses and drafted the manuscript. HA supervised the study and critically revised the manuscript for important intellectual content. Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the China Health and Retirement Longitudinal Study (CHARLS) research team for their efforts and all the participants for their valuable contributions.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B: \u003cstrong\u003eEpidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study\u003c/strong\u003e. \u003cem\u003eLancet (London, England) \u003c/em\u003e2012, \u003cstrong\u003e380\u003c/strong\u003e(9836):37-43.\u003c/li\u003e\n\u003cli\u003eDi Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N, O\u0026apos;Keeffe LM, Gao P, Wood AM, Burgess S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAssociation of Cardiometabolic Multimorbidity With 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\u003cstrong\u003e33\u003c/strong\u003e(12):2377-2386.\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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein–triglyceride–glucose index, trajectory, cardiometabolic multimorbidity, Body mass index, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-9291028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9291028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiometabolic multimorbidity (CMM) is increasingly prevalent in ageing populations and confers substantial clinical and societal burdens. The C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index (CTI) integrates inflammatory and metabolic information, but evidence linking long-term CTI patterns to incident CMM and the role of adiposity as a potential mediator remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We included 5,778 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years who were free of CMM at baseline. Exposures were baseline CTI in 2012, cumulative CTI(cuCTI) from 2012 to 2015, and CTI change-pattern (trajectory) groups derived from repeated measurements. Incident CMM was assessed in 2018. Multivariable logistic regression models were pooled across multiple imputations; restricted cubic splines examined dose\u0026ndash;response. Prediction performance was evaluated using C-statistics (DeLong test), net reclassification improvement, and integrated discrimination improvement. Mediation by adiposity indices was assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIncident CMM occurred in 333 participants (5.8%). In fully adjusted models, higher baseline CTI and greater cumulative CTI exposure were independently associated with higher odds of incident CMM (baseline CTI per IQR: OR 1.545 (95% CI 1.344\u0026ndash;1.776); cuCTI per IQR: OR 1.651 (95% CI 1.429\u0026ndash;1.907), both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Change-pattern (trajectory) analyses further indicated substantially higher risk among participants with persistently elevated CTI compared with those with persistently low CTI (OR 3.078 (95% CI 2.169\u0026ndash;4.369), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Adding baseline CTI, cuCTI, or change-pattern grouping improved discrimination beyond the basic model and improved reclassification. Adiposity indices partially mediated the CTI\u0026ndash;CMM associations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigher cumulative CTI and persistently elevated CTI change patterns were associated with incident cardiometabolic multimorbidity and improved risk prediction, with partial mediation by adiposity.\u003c/p\u003e","manuscriptTitle":"Long-term cumulative and trajectory patterns of the C-reactive protein–triglyceride–glucose index and cardiometabolic multimorbidity: the mediating effects of adiposity indices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:36:17","doi":"10.21203/rs.3.rs-9291028/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-03T07:59:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68257096808465378305696400014051365280","date":"2026-05-01T11:55:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T18:08:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T13:20:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T04:29:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T04:29:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-04-01T11:05:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e0ba350-43a4-4fae-b576-25ccb1c717ab","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-03T07:59:11+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"68257096808465378305696400014051365280","date":"2026-05-01T11:55:34+00:00","index":39,"fulltext":""},{"type":"reviewersInvited","content":"32","date":"2026-04-30T18:08:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:36:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:36:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9291028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9291028","identity":"rs-9291028","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}