The synergistic pattern of metabolic -inflammatory dynamic trajectories jointly predicts cardiovascular risk in Chinese middle -aged and older adults: a prospective cohort study

The synergistic pattern of metabolic -inflammatory dynamic trajectories jointly predicts cardiovascular risk in Chinese middle -aged and older adults: a prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The synergistic pattern of metabolic -inflammatory dynamic trajectories jointly predicts cardiovascular risk in Chinese middle -aged and older adults: a prospective cohort study Xianglong Zhao, Xiaonan Ning, Zhihui Lu, Bao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8377980/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The interplay between metabolic dysregulation and chronic inflammation is pivotal in the development of cardiovascular disease. However, the joint prognostic value of their dynamic trajectories remains unclear. Current risk assessment predominantly relies on single -timepoint, static measurements, which fail to capture their temporal evolution and synergistic interactions. This study aimed to investigate the synergistic pattern derived from the dynamic trajectories of metabolism and inflammation for predicting major adverse cardiovascular events(MACE) . Methods This prospective cohort study included 4,968 Chinese middle -aged and older adults free of baseline cardiovascular disease from the China Health and Retirement Longitudinal Study(CHARLS).The triglyceride -glucose -body mass index and high -sensitivity C -reactive protein were used to define metabolic and inflammatory status,respectively.Their dynamic trajectories(sustained low,high -to -low,low -to -high,sustained high)from two timepoints were constructed and combined into seven synergistic patterns.The primary outcome was incident MACE(a composite of cardiovascular death,myocardial infarction,stroke,or revascularization).Cox proportional hazards models were used to calculate hazard ratios.The incremental predictive value was assessed using the C -statistic,net reclassification improvement,and integrated discrimination improvement.Subgroup and sensitivity analyses were performed. Results Over a median follow -up of 5.25 years, 1,087 events occurred. Compared to the sustained low metabolic -inflammatory pattern, the sustained high pattern was associated with the highest risk of MACE (fully -adjusted HR = 1.64, 95% CI: 1.31–2.03). A significant age interaction was observed (P for interaction = 0.027), with a stronger association in participants aged < 60 years (HR = 1.93, 95% CI: 1.38–2.72) than in those ≥ 60 years. The inclusion of the synergistic pattern into a model containing traditional risk factors significantly improved risk prediction (NRI = 0.046, IDI = 0.029). Conclusions The synergistic pattern of metabolic -inflammatory dynamic trajectories, especially the sustained high pattern, is an independent risk factor for MACE in Chinese middle -aged and older adults, with greater predictive utility in younger individuals. This novel approach provides a dynamic perspective for precise cardiovascular risk stratification. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Trajectory analysis Synergistic effect Triglyceride -glucose -body mass index Major adverse cardiovascular events Metabolism Inflammation C -Reactive Protein Risk prediction Cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Cardiovascular disease (CVD) is the leading cause of death among the elderly population globally and in China, severely compromising quality of life and imposing a substantial societal burden [ 1 , 2 , 3 ] . Atherosclerosis (AS) serves as the primary pathological basis of CVD. The deposition of low -density lipoprotein cholesterol (LDL -C), particularly in its oxidized form (ox -LDL), within the arterial wall is a key initiating factor, triggering endothelial injury and inflammatory responses that ultimately lead to major adverse cardiovascular events (MACE) [ 4 , 5 ] . While lowering LDL -C levels has been proven to significantly reduce cardiovascular events, a residual risk of MACE persists even among patients who achieve lipid -lowering targets [ 6 ] . This underscores the necessity to explore the mechanisms underlying this residual risk to identify novel therapeutic targets. The synergistic interplay between metabolic abnormalities (e.g., insulin resistance, obesity, dyslipidemia) and chronic inflammation is increasingly recognized as a core microenvironmental mechanism driving residual atherosclerotic risk [ 7 ] . In recent years, the limitations of relying on single biomarkers have prompted the development of composite indices that integrate multiple pathways. The triglyceride -glucose -body mass index (TyG -BMI), a robust metric combining markers of insulin resistance and obesity, has demonstrated superior performance for cardiovascular risk prediction and stratified management [ 8 , 9 ] . Similarly, high -sensitivity C -reactive protein (hs -CRP), a gold -standard systemic inflammation marker, is well -established in its association with CVD [ 10 ] . However, most existing indices are based on static, single -timepoint measurements, which fail to capture the dynamic evolution of metabolic and inflammatory states over time. Current research often examines the trajectories of these factors in isolation or merges them into simple static composites (e.g., the C -reactive protein -triglyceride -glucose index, CTI) [ 11 ] , lacking a systematic investigation into their dynamic synergistic patterns [ 12 ] . For instance, the clinical outcomes may differ substantially between a pattern of "worsening metabolism with improving inflammation" and one of "synchronized worsening of both." Therefore, systematically constructing synergistic patterns from the dynamic trajectories of metabolism and inflammation is crucial for revealing their combined risk at a systemic level, advancing the understanding of residual CVD risk mechanisms, and enabling more precise and earlier intervention. To address these gaps, this study leveraged data from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide prospective cohort, to comprehensively evaluate the joint predictive value of synergistic metabolic -inflammatory dynamic trajectory patterns for cardiovascular risk in Chinese middle -aged and older adults. Our specific objectives were to: 1) verify whether dynamic trajectory assessment outperforms traditional static measurements; 2) elucidate potential synergistic effects between dynamic changes in metabolism and inflammation; 3) evaluate the incremental predictive value of these synergistic patterns beyond traditional risk factors; and 4) explore effect modification by key population characteristics, such as age, to inform targeted prevention. By employing multivariable Cox proportional hazards models and interaction analyses, this study aims to provide critical epidemiological evidence for precise risk stratification and early intervention in CVD. Methods Study design and participants This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), an ongoing nationwide cohort study designed to collect high -quality microdata representative of Chinese households and individuals aged 45 years and older. The primary aim of CHARLS is to analyze issues related to population aging and to facilitate interdisciplinary research on aging. The CHARLS questionnaire covers a broad range of domains, including individual demographic information, family structure and economic support, health status, physical measurements, healthcare utilization and insurance, employment, retirement and pensions, income, consumption, assets, and community characteristics. The baseline national survey was conducted in 2011 (Wave 1), using a random sampling strategy that covered over 10,000 households across 150 counties and 450 villages in China. Follow -up surveys were conducted in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). Blood samples were collected during the baseline (Wave 1) and Wave 3 surveys. Detailed data collection methods for CHARLS have been described previously [ 13 ] . The study protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052 -11015), and all participants provided written informed consent. Fasting blood glucose, triglycerides, low -density lipoprotein cholesterol (LDL -C), high -density lipoprotein cholesterol (HDL -C), total cholesterol, hs -CRP, and CRP levels were obtained from blood samples collected during Waves 1 and 3. Participants were excluded based on the criteria detailed in Fig. 1 . The final analytical cohort comprised 4,968 participants. Definition of TyG -BMI trajectories, inflammatory trajectories, and metabolic -inflammatory synergy patterns This study constructed dynamic trajectories for metabolism and inflammation using biochemical data from Wave 1 (2011) and Wave 3 (2015) of CHARLS. Definition of metabolic trajectory : Metabolic status was quantified using the TyG -BMI index, calculated as: Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2] × body mass index (BMI, kg/m²). The TyG -BMI index was computed separately for baseline (Wave 1, W1) and follow -up (Wave 3, W3). Based on the change in this index between the two time points, participants' metabolic trajectories were categorized into four groups: ① Sustained low: W1 TyG -BMI < P50 and W3 TyG -BMI < P50. ② High -to -low: W1 TyG -BMI ≥ P50 and W3 TyG -BMI < P50. ③ Low -to -high: W1 TyG -BMI < P50 and W3 TyG -BMI ≥ P50. ④ Sustained high: W1 TyG -BMI ≥ P50 and W3 TyG -BMI ≥ P50. (P50 denotes the 50th percentile, or median, of the study population). Definition of inflammatory trajectory : Inflammatory status was assessed using serum concentrations of C -reactive protein (CRP) at baseline (Wave 1) and high -sensitivity CRP (hs -CRP) at follow -up (Wave 3). To ensure comparability between the different assay methods (CRP in W1 vs. hs -CRP in W3), we standardized W1 CRP and W3 hs -CRP values separately. High and low inflammatory status at each time point was defined using percentile ranks rather than absolute values. Based on the percentile changes between the two waves, inflammatory trajectories were classified into four groups: ① Sustained low: W1 CRP < P50 and W3 hs -CRP < P50. ② High -to -low: W1 CRP ≥ P50 and W3 hs -CRP < P50. ③ Low -to -high: W1 CRP < P50 and W3 hs -CRP ≥ P50. ④ Sustained high: W1 CRP ≥ P50 and W3 hs -CRP ≥ P50. Definition of metabolic -inflammatory synergy pattern : To comprehensively assess the joint impact of metabolic and inflammatory states on MACE, we cross -classified the metabolic and inflammatory trajectories to construct a composite "metabolic -inflammatory synergy pattern" variable. Participants were thus categorized into one of seven clinically meaningful patterns for the core analysis: ① Sustained low metabolism & low inflammation (reference group). ② Sustained high metabolism & high inflammation. ③ Sustained high metabolism & improved inflammation (inflammatory trajectory: high -to -low). ④ Sustained high metabolism & worsened inflammation (inflammatory trajectory: low -to -high). ⑤ Improved metabolism & sustained high inflammation (metabolic trajectory: high -to -low). ⑥ Worsened metabolism & sustained high inflammation (metabolic trajectory: low -to -high). ⑦ Mixed pattern: all other trajectory combinations not falling into the above six categories (e.g., where both metabolism and inflammation changed but did not form a clear dominant pattern). Ascertainment of MACE Following previous studies [ 13 , 14 ] , the primary endpoint was defined as the first occurrence of MACE during follow -up or reaching the end of the follow -up period. Non -fatal MACE was ascertained through standardized questionnaire items: "Has a doctor ever told you that you have been diagnosed with a heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems?" and "Have you ever been diagnosed with a stroke?" For fatal MACE, a questionnaire was administered to family members: "Did the patient die from heart disease?" and "Did the patient die from a stroke?" Survival time was calculated as follows: for participants with non -fatal MACE, the event time was defined as the date from baseline (June 1, 2015) to the date of the first event reported in the survey; for fatal MACE, the specific date of death was used; for participants without events, survival time was calculated from baseline to the date of death from any cause, loss to follow -up, or the study cutoff date (September 1, 2020). Assessment of covariates Baseline data were collected by trained interviewers using structured questionnaires and included: (1) Demographic and lifestyle data: sex, age, residence, education level, marital status, smoking status, and alcohol consumption. (2) Physical measurements: height, weight, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP). (3) Disease and medication history: information on existing conditions such as heart disease, hypertension, diabetes, and dyslipidemia, as well as the use of medication for these conditions. (4) Laboratory data: Fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), HDL -C, LDL -C, and glycated hemoglobin (HbA1c). Handling of missing variables Multiple imputation was used to handle missing data to reduce potential bias. Statistical analysis All statistical analyses were performed using R software version 4.5.1. A two -sided P -value < 0.05 was considered statistically significant. The analysis was conducted directly on the original dataset. Although some variables in the CHARLS database had missing values, prior analyses suggested that the missing data were likely missing at random [ 15 , 16 ] . Among the 4,968 participants included in the final analysis, the maximum missing rate for any variable was only 1.85%. Baseline characteristics were described according to the core exposure variable, the metabolic -inflammatory synergy pattern. Continuous variables conforming to a normal distribution are presented as mean ± standard deviation, non -normally distributed variables as median (interquartile range), and categorical variables as frequency (percentage). Group comparisons were performed using analysis of variance (ANOVA), Kruskal -Wallis test, or chi -square test, as appropriate. The cumulative incidence of MACE across different synergy patterns was estimated using the Kaplan -Meier method, and differences in survival curves were assessed with the log -rank test. Multivariable Cox proportional hazards regression models were employed to quantify the association between synergy patterns and MACE risk. Three models were constructed: Model 1 (demographic model) adjusted for age and sex; Model 2 (behavioral model) additionally adjusted for smoking, alcohol consumption, education level, and residence; Model 3 (clinical model) further adjusted for histories of hypertension, diabetes, and dyslipidemia. All models used the "sustained low metabolism & low inflammation" group as the reference. To test for a trend in risk, the seven patterns were treated as an ordinal variable and included in a Cox model for trend analysis. The incremental predictive value of the synergy patterns was evaluated by comparing a "base model" containing only traditional risk factors with an "extended model" that added the synergy patterns. Model discrimination was assessed using Harrell's C -statistic, and model fit was compared using the Akaike Information Criterion (AIC). The Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) were calculated to quantify the improvement in individual risk reclassification provided by the new model. To assess the generalizability of the association and identify potential effect modifiers, subgroup analyses were performed by age (< 60 years vs. ≥60 years), sex, history of diabetes, and history of hypertension. Interaction effects were statistically tested by including product terms ("synergy pattern × subgroup variable") in the fully adjusted Cox model, and their significance was assessed using likelihood ratio tests. To verify the robustness of the main findings, the following sensitivity analyses were conducted: 1) Additional adjustment for potential confounders in the model; 2) Exclusion of MACE events occurring within the first 2 years of follow -up to assess potential reverse causality; 3) Stratified analysis at different follow -up time points; 4) Repeating the primary analysis after excluding participants with diabetes at baseline. Results Characteristics of the study population This study included 4,968 Chinese middle -aged and older adults without baseline cardiovascular disease. The mean age of the participants was 58.5 ± 8.7 years, and 46.8% were male. Seven distinct metabolic -inflammatory dynamic trajectory patterns were identified, with significant differences observed in baseline characteristics across all groups (P < 0.001 for all inter -group comparisons, Table 1 ). The group with the sustained high metabolism and high inflammation pattern had the lowest mean age (57.8 ± 8.2 years) and exhibited the most pronounced metabolic dysregulation phenotype: it had the highest levels of body mass index, systolic blood pressure, fasting triglycerides, as well as the highest prevalence rates of diabetes, hypertension, and dyslipidemia among all groups. Levels of the inflammatory marker (hs -CRP/CRP) also differed significantly across groups. The median level was highest in the improved metabolism & sustained high inflammation group (5.61 mg/L) and lowest in the sustained low metabolism & low inflammation group (0.49 mg/L). Notably, within the same "high metabolism" stratum, the improvement of the inflammatory trajectory was associated with a 1.2% reduction in the MACE incidence rate, indicating an independent role of the inflammatory trajectory distinct from the metabolic trajectory. The stronger driving effect of metabolic dysregulation on cardiovascular risk compared to inflammatory abnormalities was also evident from the higher MACE incidence rates observed in the metabolism -dominant patterns relative to the inflammation -dominant patterns. Over a median follow -up of 5.25 years, a total of 1,087 major adverse cardiovascular events were recorded, yielding an overall incidence rate of 21.9%. The sustained high metabolism and high inflammation group had the highest cumulative event rate (28.6%), while the sustained low metabolism & low inflammation group had the lowest (14.7%). Table 1 Baseline Characteristics by Metabolic -Inflammatory Trajectory Patterns Characteristic Overall N = 4,968 1 Low metabolism & Low inflammation N = 880 1 High metabolism & high inflammation N = 968 1 High metabolism & improved inflammation N = 296 1 High metabolism & worsened inflammation N = 375 1 Improved metabolism & High inflammation N = 118 1 Worsened metabolism & high inflammation N = 155 1 Mixed pattern N = 2,176 1 p -value2 Age (years) < 0.001 Mean (SD) 58.48 (8.72) 59.08 (8.85) 57.79 (8.22) 56.98 (7.85) 55.45 (7.69) 62.62 (9.43) 58.86 (8.38) 59.03 (8.96) Gender < 0.001 Female 2,645.0 (53.2%) 438.0 (49.8%) 605.0 (62.5%) 183.0 (61.8%) 219.0 (58.4%) 58.0 (49.2%) 86.0 (55.5%) 1,056.0 (48.5%) Male 2,323.0 (46.8%) 442.0 (50.2%) 363.0 (37.5%) 113.0 (38.2%) 156.0 (41.6%) 60.0 (50.8%) 69.0 (44.5%) 1,120.0 (51.5%) Education level < 0.001 Primary and below 3,530.0 (71.1%) 637.0 (72.4%) 667.0 (68.9%) 192.0 (64.9%) 239.0 (63.7%) 92.0 (78.0%) 118.0 (76.1%) 1,585.0 (72.8%) Second/high school 1,317.0 (26.5%) 232.0 (26.4%) 273.0 (28.2%) 93.0 (31.4%) 128.0 (34.1%) 23.0 (19.5%) 34.0 (21.9%) 534.0 (24.5%) College/higher 121.0 (2.4%) 11.0 (1.3%) 28.0 (2.9%) 11.0 (3.7%) 8.0 (2.1%) 3.0 (2.5%) 3.0 (1.9%) 57.0 (2.6%) Rural residence 4,274.0 (86.0%) 791.0 (89.9%) 786.0 (81.2%) 240.0 (81.1%) 310.0 (82.7%) 101.0 (85.6%) 133.0 (85.8%) 1,913.0 (87.9%) < 0.001 Current smoking < 0.001 current smoker 1,538.0 (31.0%) 326.0 (37.2%) 222.0 (23.0%) 59.0 (19.9%) 89.0 (23.9%) 35.0 (29.9%) 50.0 (32.5%) 757.0 (34.9%) former smoker 381.0 (7.7%) 42.0 (4.8%) 82.0 (8.5%) 29.0 (9.8%) 42.0 (11.3%) 8.0 (6.8%) 4.0 (2.6%) 174.0 (8.0%) never smoked 3,036.0 (61.3%) 509.0 (58.0%) 662.0 (68.5%) 208.0 (70.3%) 242.0 (64.9%) 74.0 (63.2%) 100.0 (64.9%) 1,241.0 (57.1%) Alcohol consumption < 0.001 current drinker 1,574.0 (31.7%) 310.0 (35.3%) 243.0 (25.1%) 81.0 (27.6%) 129.0 (34.4%) 33.0 (28.0%) 38.0 (24.5%) 740.0 (34.0%) never drinker 3,003.0 (60.5%) 506.0 (57.6%) 644.0 (66.5%) 192.0 (65.3%) 215.0 (57.3%) 72.0 (61.0%) 108.0 (69.7%) 1,266.0 (58.2%) other 387.0 (7.8%) 63.0 (7.2%) 81.0 (8.4%) 21.0 (7.1%) 31.0 (8.3%) 13.0 (11.0%) 9.0 (5.8%) 169.0 (7.8%) Hypertension 1,125.0 (22.6%) 109.0 (12.4%) 374.0 (38.6%) 95.0 (32.1%) 115.0 (30.7%) 24.0 (20.3%) 38.0 (24.5%) 370.0 (17.0%) < 0.001 Diabetes 276.0 (5.6%) 26.0 (3.0%) 109.0 (11.3%) 22.0 (7.4%) 24.0 (6.4%) 6.0 (5.1%) 5.0 (3.2%) 84.0 (3.9%) < 0.001 Dyslipidemia 406.0 (8.2%) 35.0 (4.0%) 144.0 (14.9%) 41.0 (13.9%) 58.0 (15.5%) 6.0 (5.1%) 11.0 (7.1%) 111.0 (5.1%) < 0.001 Systolic BP (mmHg) < 0.001 Mean (SD) 128.49 (20.72) 123.55 (19.50) 135.14 (21.48) 131.55 (20.10) 130.72 (19.52) 132.15 (20.86) 125.92 (19.93) 126.71 (20.35) Diastolic BP (mmHg) < 0.001 Mean (SD) 75.00 (12.07) 71.64 (11.44) 79.19 (11.96) 77.78 (11.71) 77.44 (11.62) 75.08 (11.13) 73.61 (10.77) 73.79 (11.97) BMI (kg/m²) < 0.001 Mean (SD) 24.35 (37.43) 20.51 (1.88) 29.37 (77.96) 26.37 (2.71) 30.10 (45.29) 24.12 (2.90) 22.14 (1.83) 22.56 (10.68) Total cholesterol (mmol/L) < 0.001 Mean (SD) 193.54 (38.35) 188.96 (34.71) 204.30 (39.35) 199.28 (38.58) 199.53 (41.43) 195.87 (39.53) 192.79 (40.57) 188.72 (37.31) HDL -C (mmol/L) < 0.001 Mean (SD) 51.47 (15.38) 59.60 (16.01) 43.00 (12.12) 47.23 (12.48) 44.73 (13.19) 45.73 (14.85) 52.73 (13.91) 53.91 (14.77) LDL -C (mmol/L) < 0.001 Mean (SD) 116.07 (34.81) 113.71 (30.56) 121.06 (39.20) 122.85 (35.64) 113.27 (38.06) 113.20 (42.64) 122.39 (35.74) 114.06 (32.70) Triglycerides (mmol/L) < 0.001 Mean (SD) 132.18 (110.45) 88.97 (38.52) 190.67 (144.34) 151.88 (104.08) 195.92 (166.50) 166.01 (129.34) 92.63 (47.39) 110.96 (83.95) Baseline TYG -BMI index < 0.001 Mean (SD) 211.75 (322.63) 170.59 (16.78) 266.76 (667.71) 234.30 (27.19) 271.43 (392.02) 214.63 (20.77) 184.51 (13.80) 192.35 (98.34) Baseline hs -CRP (mg/L) < 0.001 Mean (SD) 2.54 (7.26) 0.49 (0.20) 3.96 (6.21) 3.22 (7.15) 0.62 (0.20) 5.61 (17.59) 4.80 (8.92) 2.64 (8.35) MACE 1,087.0 (21.9%) 129.0 (14.7%) 277.0 (28.6%) 81.0 (27.4%) 89.0 (23.7%) 22.0 (18.6%) 29.0 (18.7%) 460.0 (21.1%) < 0.001 Follow -up time (years) < 0.001 0 402.0 (8.1%) 53.0 (6.0%) 94.0 (9.7%) 26.0 (8.8%) 33.0 (8.8%) 7.0 (5.9%) 9.0 (5.8%) 180.0 (8.3%) 3.25256673511294 545.0 (11.0%) 65.0 (7.4%) 136.0 (14.0%) 44.0 (14.9%) 43.0 (11.5%) 14.0 (11.9%) 14.0 (9.0%) 229.0 (10.5%) 3.7782340862423 1.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 1.0 (0.3%) 0.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 4.50102669404517 1.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 0.0 (0.0%) 1.0 (0.0%) 5.2539356605065 4,019.0 (80.9%) 762.0 (86.6%) 738.0 (76.2%) 225.0 (76.0%) 299.0 (79.7%) 97.0 (82.2%) 132.0 (85.2%) 1,766.0 (81.2%) 1 n (%) 2 One -way analysis of means; Pearson's Chi -squared test Association between metabolic -inflammatory trajectories and cardiovascular risk Kaplan -Meier survival analysis revealed significant differences in MACE -free survival rates across the different synergy pattern groups (log -rank test, P < 0.001; Fig. 2 ). This association was further quantified by multivariable Cox proportional hazards regression (Table 2 ). After full adjustment for traditional risk factors and using the sustained low metabolism & low inflammation group as the reference, the analysis confirmed a clear gradient of risk. The group with the sustained high metabolism and high inflammation pattern exhibited the most robust and stable risk elevation (HR = 1.56, 95% CI: 1.24–1.98, P < 0.001).Metabolism -dominant patterns, where metabolic status remained high regardless of the inflammatory trajectory change, were also associated with significantly increased risk. This included the sustained highmetabolism&improved inflammation group (HR = 1.62, 95% CI: 1.21–2.16, P = 0.001) and the sustained high metabolism & worsened inflammation group(HR = 1.41,95%CI:1.06–1.88,P = 0.018).In contrast, inflammation -dominant patterns (i.e., the improved metabolism & sustained high inflammation and worsened metabolism & sustained high inflammation groups) did not show a statistically significant increase in MACE risk, further corroborating the central role of metabolic factors over inflammatory factors in cardiovascular risk prediction in this context [ 17 ] .A significant dose -response relationship was observed, as evidenced by the trend test, where MACE risk increased progressively with higher risk gradations of the synergy patterns (P for trend < 0.001). This finding provides epidemiological support for the continuous monitoring of metabolic -inflammatory burden and its utility for precise cardiovascular risk stratification. Table 2 Associations between Metabolic -Inflammatory Trajectory Patterns and MACE: Multivariable Cox Regression Analysis Model 1 Model 2 Model 3 Trajectory Pattern HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value High -High 2.08 (1.69–2.57) < 0.001 2.05 (1.65–2.53) < 0.001 1.56 (1.24–1.98) < 0.001 High -Improved 2.03 (1.53–2.68) < 0.001 1.97 (1.49–2.61) < 0.001 1.62 (1.21–2.16) 0.001 High -Deteriorated 1.81 (1.38–2.38) < 0.001 1.77 (1.35–2.33) < 0.001 1.41 (1.06–1.88) 0.018 Improved -High 1.19 (0.76–1.88) 0.447 1.20 (0.76–1.90) 0.424 1.13 (0.71–1.78) 0.614 Deteriorated -High 1.27 (0.85–1.90) 0.244 1.31 (0.87–1.96) 0.193 1.20 (0.80–1.80) 0.377 Mixed Pattern 1.50 (1.24–1.83) < 0.001 1.50 (1.23–1.83) < 0.001 1.44 (1.18–1.76) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Model 1: Adjusted for age and sex Model 2: Additionally adjusted for education, residence, smoking, and drinking Model 3: Fully adjusted for all traditional cardiovascular risk factors Reference group: Low -Low trajectory pattern P for trend tests the linear trend across trajectory patterns Incremental predictive value of the synergy patterns The inclusion of the metabolic -inflammatory trajectory patterns demonstrated significant incremental value for cardiovascular risk stratification (Table 3 ). The fully adjusted model (Model 3) showed a marked improvement in discrimination. The C -statistic increased from 0.571 in the base model to 0.645 (Fig. 3 ). This improvement was statistically significant, as confirmed by a highly significant likelihood ratio test when compared to an intermediate model (χ² = 180.40, P < 0.001). The explained variance increased from 1.1% to 5.4%, and model fit was continuously improved, evidenced by a substantial decrease in the Akaike Information Criterion (AIC) from 18088.7 to 17912.2.The model's ability to reclassify risk was also enhanced. The Net Reclassification Improvement (NRI) was 0.046 (P < 0.001), and the Integrated Discrimination Improvement (IDI) was 0.029 (P < 0.001). These findings robustly support the dynamic metabolic -inflammatory trajectory pattern as a valuable predictor, providing important incremental information beyond traditional cardiovascular risk factors. Table 3 Incremental Predictive Value of Metabolic -Inflammatory Trajectory Patterns for Major Adverse Cardiovascular Events Performance Metric Model 1 Model 2 Model 3 Concordance index (C -index) 0.571 (0.553–0.589) 0.586 (0.567–0.604) 0.645 (0.628–0.663) Akaike Information Criterion (AIC) 18088.7 18062.6 17912.2 Likelihood Ratio Test (vs previous model) Reference χ² = 40.10, P < 0.001 χ² = 180.40, P < 0.001 R -squared (Nagelkerke) 0.011 0.019 0.054 C -index improvement (Δ) Reference + 0.014 + 0.060 Model 1: Adjusted for age and sex Model 2: Additionally adjusted for education, residence, smoking, and drinking Model 3: Fully adjusted for all traditional cardiovascular risk factors plus metabolic -inflammatory trajectory patterns Subgroup analysis and effect modification Subgroup analyses were conducted to assess whether the association between the sustained high metabolism and high inflammation pattern and MACE was consistent across different patient populations (Table 4 ). A significant effect modification by age was revealed (P for interaction = 0.027, Table 5 ). The association was significantly stronger in the younger subgroup aged < 60 years (HR = 2.12, 95% CI: 1.54–2.92) than in the older subgroup aged ≥ 60 years, where the association was not statistically significant (HR = 1.27, 95% CI: 0.93–1.72).No significant effect modification was observed for other subgroups, including sex, hypertension status, or diabetes status. However, although it did not reach statistical significance, a potential modifying trend was noted for diabetes status (P for interaction = 0.068), which warrants further investigation in larger cohorts. These results indicate that the detrimental impact of the metabolic -inflammatory high -risk phenotype is particularly pronounced in younger middle -aged adults, suggesting that this population could be a priority target for preventive measures. Table 4 Subgroup Analysis: Association of Metabolic -Inflammatory High -High Pattern with MACE Risk Subgroup Participants (n) Events (n) HR (95% CI) P -value Total population 4,968 1,087 1.56 (1.24–1.98) < 0.001 < 60 years 2,862 542 1.93 (1.38–2.72) < 0.001 ≥ 60 years 2,106 545 1.31 (0.93–1.84) 0.117 Male 2,323 446 1.32 (0.91–1.91) 0.141 Female 2,645 641 1.81 (1.33–2.47) < 0.001 Hypertension(No) 3,849 703 1.70 (1.28–2.25) < 0.001 Hypertension(Yes) 1,119 384 1.69 (1.03–2.78) 0.038 Diabetes(No) 4,697 1,005 1.64 (1.29–2.10) < 0.001 Diabetes(Yes) 271 82 0.59 (0.21–1.65) 0.317 Reference group: Low -Low metabolic -inflammatory trajectory pattern All models are fully adjusted for traditional cardiovascular risk factors HR: hazard ratio; CI: confidence interval Table 5 Interaction Analysis: Effect Modification of Metabolic -Inflammatory Trajectory Patterns on MACE Risk Characteristic Interaction P -value Age group (< 60 vs ≥ 60 years) 0.027 Sex 0.473 Hypertension 0.227 Diabetes 0.068 Interaction P -values test whether the association between metabolic -inflammatory High -High pattern and MACE differs across subgroups P -values < 0.05 indicate statistically significant effect modification All models are fully adjusted for traditional cardiovascular risk factors Sensitivity analysis A series of sensitivity analyses were conducted to verify the robustness of the primary findings (Table 6 ). The association between the sustained high metabolism and high inflammation pattern and MACE risk remained highly significant across different covariate adjustment models: fully adjusted model HR = 1.56 (95% CI: 1.24–1.98), minimally adjusted model HR = 2.08 (95% CI: 1.69–2.57), and model adjusted for socioeconomic factors HR = 2.05 (95% CI: 1.65–2.53; all P < 0.001).To assess potential reverse causality, MACE events occurring within the first 2 years of follow -up were excluded. The association was notably strengthened following this exclusion (HR = 1.89, 95% CI: 1.39–2.56; P < 0.001). Time -stratified analysis revealed a distinct temporal pattern: the association was not statistically significant at the 3 -year follow -up mark (HR = 1.19, 95% CI: 0.81–1.75; P = 0.364) but became significant by the 5 -year follow -up (HR = 1.38, 95% CI: 1.08–1.78; P = 0.011). Furthermore, the association persisted after excluding participants with diabetes at baseline (HR = 1.64, 95% CI: 1.29–2.10; P < 0.001).Collectively, these results indicate that the association between the metabolic -inflammatory high -risk phenotype and MACE is robust. Moreover, the harm appears to be cumulative, manifesting more clearly with longer follow -up duration, which supports the characterization of this phenotype as a cumulative cardiovascular risk factor. Table 6 Sensitivity Analyses of the Association Between Metabolic -Inflammatory High -High Pattern and MACE Risk Sensitivity Analysis HR (95% CI) P Value Events Main analysis (fully adjusted) 1.56 (1.24–1.98) < 0.001 1,081 Minimal adjustment (age + sex only) 2.08 (1.69–2.57) < 0.001 1,081 Medium adjustment (+ socioeconomic factors) 2.05 (1.65–2.53) < 0.001 1,081 Excluding early events (first 2 years) 1.89 (1.39–2.56) < 0.001 681 Time -limited analysis (3 years) 1.19 (0.81–1.75) 0.364 400 Time -limited analysis (5 years) 1.38 (1.08–1.78) 0.011 943 Excluding participants with baseline diabetes 1.64 (1.29–2.10) < 0.001 1,005 HR = hazard ratio; CI = confidence interval; MACE = major adverse cardiovascular events All models compare the metabolic -inflammatory High -High pattern to the Low -Low reference Discussion This study investigated the association between synergistic patterns derived from the dynamic trajectories of metabolism(TyG -BMI index)and inflammation(hs -CRP/CRP)and the risk of major adverse cardiovascular events(MACE)in Chinese middle -aged and older adults.Our findings demonstrate that these synergistic patterns are independent risk factors for MACE,with the sustained high metabolism and high inflammation pattern conferring the highest risk.The results underscore the dominant role of metabolic dysregulation in risk prediction,whereas alterations in inflammatory trajectory alone showed limited prognostic value.A significant age modification effect was observed,with the association being substantially stronger in individuals aged < 60 years compared to those aged ≥ 60 years.Incorporating this synergistic pattern into models based on traditional cardiovascular risk factors enhanced the predictive value for MACE,offering a novel perspective for precise risk stratification and early intervention. Both inflammation and insulin resistance have been established as independent risk factors for cardiovascular disease(CVD),jointly contributing to its pathogenesis [ 18 , 19 ] .From a pathophysiological perspective,C -reactive protein promotes atherogenesis through mechanisms including the induction of endothelial dysfunction,facilitation of oxidized lipoprotein accumulation,and enhancement of leukocyte infiltration [ 20 ] .The triglyceride -glucose index,reflecting the degree of insulin resistance,impairs glucose metabolism and vascular endothelial function via oxidative stress and lipotoxicity [ 21 ] .Previous studies have predominantly examined the relationship of inflammation or insulin resistance with CVD in isolation,consistently reporting positive associations [ 22 , 23 , 24 ] .In recent years,growing emphasis has been placed on the importance of a combined assessment of inflammation and metabolic abnormalities for the primary prevention of CVD [ 25 , 26 ] ,with recommendations to integrate the triglyceride -glucose index and inflammatory biomarkers to optimize risk stratification [ 11 , 27 ] . The"synergistic pattern of metabolic -inflammatory dynamic trajectories"proposed in our study provides significant incremental information to existing risk prediction models.Compared to recently proposed static composite indices like the C -reactive protein -triglyceride -glucose index(CTI) [ 28 ] ,our model enables the dynamic assessment of biomarker trajectories.This allows for the identification of"progressively high -risk individuals"whose biomarker levels may be borderline but exhibit a deteriorating trend.Furthermore,by constructing metabolic and inflammatory trajectories separately before combining them,our model can differentiate whether the primary source of risk stems from metabolic dysregulation or inflammatory activation,thereby offering more precise guidance for clinical intervention.Compared to cumulative exposure indices like the cumulative CTI(cuCTI),this trajectory -based model moves beyond the simple"dose -response"framework by focusing on the trend of change,which aligns with and advances the goals of precision medicine practice. Our results highlight the predominant driving role of metabolic abnormalities in cardiovascular risk.Both the sustained high metabolism&improved inflammation and sustained high metabolism&worsened inflammation patterns were associated with significantly elevated risk.In contrast,patterns characterized by high inflammation accompanied by improved or stable metabolism did not show a significant increase in risk.This suggests that in the formation of cardiovascular risk,metabolic dysregulation may represent a more fundamental,upstream driving event,while inflammation largely acts as a downstream effect that is initiated and amplified by metabolic abnormalities.Consequently,interventions targeting the metabolic core—such as improving insulin resistance and controlling body weight—may be more effective in fundamentally reducing risk than anti -inflammatory strategies alone. The significant age modification effect revealed in our study is another key finding.The predictive power of the metabolic -inflammatory synergy pattern for MACE was significantly stronger in the < 60 -year -old population,a result consistent with findings from research by Cui et al. [ 11 ] .Possible explanations include:(1)younger individuals typically have fewer non -modifiable risk factors(e.g.,advanced age,long -term target organ damage),allowing modifiable factors like metabolism and inflammation to constitute a larger proportion of their attributable risk [ 3 , 18 , 29 ] ;and(2)younger individuals presenting with a"dual -high"metabolic -inflammatory state early on may possess a stronger inherent genetic predisposition or be exposed to extremely unhealthy lifestyle behaviors over the long term,collectively leading to a markedly elevated future risk of MACE.In contrast,older adults(≥ 60 years)often present with multiple comorbidities and cumulative vascular damage,which may dilute or mask the relative contribution of metabolic -inflammatory factors amidst other,potentially stronger,risk factors(e.g.,severe arterial calcification,renal insufficiency).This finding carries significant public health implications,strongly suggesting that middle age(< 60 years)represents a"golden window period"for implementing active interventions targeting both metabolic and inflammatory risk factors to prevent future cardiovascular events. Strengths and limitations The key strengths of this study include the use of a large -scale, nationally representative cohort, a prospective design, a relatively long follow -up period, systematic dynamic trajectory analysis, and comprehensive statistical validation. This study also has several limitations. First, due to its observational design, causal inferences regarding the relationships between inflammatory trajectories, metabolic trajectories, and cardiovascular events cannot be made. Second, trajectory definitions relied on measurements from only two time points. While this captures general trends, it cannot delineate the detailed, continuous evolution of the biomarkers. Third, the assessment of inflammation was limited to percentile levels of CRP/hs-CRP and didi not encompass other important flammatory pathways (e.g., IL -6, TNF -α) [ 30 ] . Fourth, despite multivariable adjustments, the potential for residual confounding from unmeasured factors remains. Fifth, the findings are primarily based on a Chinese middle -aged and older adult population; therefore, the generalizability of the conclusions to other ethnicities or younger populations requires further validation. Conclusion In conclusion, this analysis of the CHARLS database demonstrates that the synergistic pattern of metabolic and inflammatory dynamic trajectories—particularly patterns centered on sustained metabolic dysregulation—serves as an independent and strong predictor of MACE in Chinese middle -aged and older adults. The predictive utility of this pattern is especially pronounced in the younger middle -aged population (< 60 years). This novel approach provides significant incremental predictive information beyond traditional risk factors. It advocates for a shift in clinical practice towards monitoring the long -term dynamic changes of risk factors and furnishes new scientific evidence for implementing age -specific, precision prevention strategies for cardiovascular disease. Abbreviations CHARLS China Health and Retirement Longitudinal Study TyG Triglyceride -glucose index CRP C -reactive protein hsCRP High -sensitivity C -reactive protein BMI Body mass index MACE major adverse cardiovascular events CVD cardiovascular disease CTI C -reactive protein -triglyceride -glucose index ANOVA Analysis of variance SBP systolic blood pressure DBP diastolic blood pressure FPG Fasting plasma glucose TC total cholesterol TG triglycerides HDL -C High density lipoprotein cholesterol LDL -C Low density lipoprotein cholesterol HbA1c glycated hemoglobin HR hazard rate CI confidence intervals CuCTI cumulative C -reactive protein -triglyceride -glucose index AIC Akaike Information Criterion NRI Net Reclassification Improvement IDI Integrated Discrimination Improvement C -index Concordance index Declarations Ethics approval and consent to participate CHARLS was approved by the Institutional Review Board of Peking University (IRB approval number for the household survey: IRB00001052 -11015; IRB approval number for blood sample collection: IRB00001052 -11014). Written informed consent was obtained from all participants. Consent for publication All data used in this study were sourced from the public China Health and Retirement Longitudinal Study (CHARLS) database. The data are fully anonymized aggregate data and contain no identifiable personal details, images, or videos from any individual. Therefore, consent for publication is Not applicable. Availability of data and materials The data sets used and/or analyzed during the current study are publicly avail - able or from the corresponding author upon reasonable request. All authors verify that all information and materials in the manuscript are original Competing interests The authors declare that they have no competing interests Funding None. Author contributions Literature search: Xianglong Zhao,Xiaonan Ning,Zhihui Lu; Study conception and design: Xianglong Zhao,Xiaonan Ning; Data collection: Xianglong Zhao; Data analysis and interpreta - tion: Xianglong Zhao,Zhihui Lu; Manuscript writing and reviewing: Xianglong Zhao;All authors read and approved the final manuscript. Acknowledgements This study used data from China Health and Retirement Longitudinal Study (CHARLS). We would like to thank the CHARLS research team for the time and effort into the CHARLS project. References https://www.who.int/news -room/fact -sheets/detail/cardiovascular -diseases -(cvds) GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990 -2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709 -733. doi:10.1016/S0140 -6736(20)30045 -3. HE Q. Promoting the development of research on cardiovascular health in the elderly. 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Triglyceride -glucose -body mass index and the incidence of cardiovascular diseases: a meta -analysis of cohort studies. Cardiovasc Diabetol. 2025;24(1):34. Published 2025 Jan 22. doi:10.1186/s12933 -025 -02584 -0 Arroyo -Espliguero R, Avanzas P, Quiles J, Kaski JC. Predictive value of coronary artery stenoses and C -reactive protein levels in patients with stable coronary artery disease. Atherosclerosis. 2009;204(1):239 -243. doi:10.1016/j.atherosclerosis.2008.08.009 Gu HQ, Yang KX, Lin JX, et al. Association between high -sensitivity C -reactive protein, functional disability, and stroke recurrence in patients with acute ischaemic stroke: A mediation analysis. EBioMedicine. 2022;80:104054. doi:10.1016/j.ebiom.2022.104054 Cui C, Liu L, Qi Y, et al. Joint association of TyG index and high sensitivity C -reactive protein with cardiovascular disease: a national cohort study. Cardiovasc Diabetol. 2024;23(1):156. 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Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident cardiovascular disease in middle -aged and older Chinese adults: a nationwide cohort study. Cardiovasc Diabetol. 2025;24(1):303. Published 2025 Jul 26. doi:10.1186/s12933 -025 -02869 -4 Agbaje AO. Arterial stiffness precedes hypertension and metabolic risks in youth: a review. J Hypertens. 2022;40(10):1887 -1896. doi:10.1097/HJH.0000000000003239 MA B, MA X, LI X, et al. Research progress on inflammatory mechanisms in atherosclerosis. Chin J Integr Med Cardio -Cerebrovasc Dis. 2025;23(20):3104 -3109. doi:10.12102/j.issn.1672 -1349.2025.20.010 Additional Declarations No competing interests reported. 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10:27:35","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136648,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8377980/v1/af340de5bf7de4ca8b789f6d.html"},{"id":100576615,"identity":"fbbf1bf4-681a-4f4e-b1bf-ef2ff7940eee","added_by":"auto","created_at":"2026-01-19 10:27:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76113,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant screening and selection for the study cohort.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8377980/v1/893d92bde5667e149e4b5ef8.png"},{"id":100576622,"identity":"6a0bba64-9cb8-4216-b879-91cf670c0e72","added_by":"auto","created_at":"2026-01-19 10:27:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91058,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan -Meier Analysis of MACE -free Survival\\metabolic -inflammatory trajectories Trajectory Patterns\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8377980/v1/7684fded0489673faffedb14.png"},{"id":100576616,"identity":"71e1f618-4465-4ad7-9d13-e754791e05ba","added_by":"auto","created_at":"2026-01-19 10:27:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27915,"visible":true,"origin":"","legend":"\u003cp\u003eC -index Improvement Across Model Comparisons\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8377980/v1/faa6b57abc9723ac4cfdd169.png"},{"id":100596053,"identity":"94de88f6-d6a1-496b-bfc0-cb6622d6e03b","added_by":"auto","created_at":"2026-01-19 13:50:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106612,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity Analysis of Metabolic -Inflammatory Pattern and MACE Risk\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8377980/v1/0b34331e54bf3c6a9741c556.png"},{"id":100597438,"identity":"9ef85b5b-b1fa-4b45-b9e1-b72f109ee23b","added_by":"auto","created_at":"2026-01-19 14:17:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2719588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8377980/v1/b564a2db-f865-409c-9f23-ef3083b7429f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The synergistic pattern of metabolic -inflammatory dynamic trajectories jointly predicts cardiovascular risk in Chinese middle -aged and older adults: a prospective cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiovascular disease (CVD) is the leading cause of death among the elderly population globally and in China, severely compromising quality of life and imposing a substantial societal burden \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Atherosclerosis (AS) serves as the primary pathological basis of CVD. The deposition of low -density lipoprotein cholesterol (LDL -C), particularly in its oxidized form (ox -LDL), within the arterial wall is a key initiating factor, triggering endothelial injury and inflammatory responses that ultimately lead to major adverse cardiovascular events (MACE) \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. While lowering LDL -C levels has been proven to significantly reduce cardiovascular events, a residual risk of MACE persists even among patients who achieve lipid -lowering targets \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. This underscores the necessity to explore the mechanisms underlying this residual risk to identify novel therapeutic targets. The synergistic interplay between metabolic abnormalities (e.g., insulin resistance, obesity, dyslipidemia) and chronic inflammation is increasingly recognized as a core microenvironmental mechanism driving residual atherosclerotic risk \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, the limitations of relying on single biomarkers have prompted the development of composite indices that integrate multiple pathways. The triglyceride -glucose -body mass index (TyG -BMI), a robust metric combining markers of insulin resistance and obesity, has demonstrated superior performance for cardiovascular risk prediction and stratified management \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Similarly, high -sensitivity C -reactive protein (hs -CRP), a gold -standard systemic inflammation marker, is well -established in its association with CVD \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, most existing indices are based on static, single -timepoint measurements, which fail to capture the dynamic evolution of metabolic and inflammatory states over time. Current research often examines the trajectories of these factors in isolation or merges them into simple static composites (e.g., the C -reactive protein -triglyceride -glucose index, CTI) \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, lacking a systematic investigation into their dynamic synergistic patterns \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. For instance, the clinical outcomes may differ substantially between a pattern of \"worsening metabolism with improving inflammation\" and one of \"synchronized worsening of both.\" Therefore, systematically constructing synergistic patterns from the dynamic trajectories of metabolism and inflammation is crucial for revealing their combined risk at a systemic level, advancing the understanding of residual CVD risk mechanisms, and enabling more precise and earlier intervention.\u003c/p\u003e \u003cp\u003eTo address these gaps, this study leveraged data from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide prospective cohort, to comprehensively evaluate the joint predictive value of synergistic metabolic -inflammatory dynamic trajectory patterns for cardiovascular risk in Chinese middle -aged and older adults. Our specific objectives were to: 1) verify whether dynamic trajectory assessment outperforms traditional static measurements; 2) elucidate potential synergistic effects between dynamic changes in metabolism and inflammation; 3) evaluate the incremental predictive value of these synergistic patterns beyond traditional risk factors; and 4) explore effect modification by key population characteristics, such as age, to inform targeted prevention. By employing multivariable Cox proportional hazards models and interaction analyses, this study aims to provide critical epidemiological evidence for precise risk stratification and early intervention in CVD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), an ongoing nationwide cohort study designed to collect high -quality microdata representative of Chinese households and individuals aged 45 years and older. The primary aim of CHARLS is to analyze issues related to population aging and to facilitate interdisciplinary research on aging. The CHARLS questionnaire covers a broad range of domains, including individual demographic information, family structure and economic support, health status, physical measurements, healthcare utilization and insurance, employment, retirement and pensions, income, consumption, assets, and community characteristics. The baseline national survey was conducted in 2011 (Wave 1), using a random sampling strategy that covered over 10,000 households across 150 counties and 450 villages in China. Follow -up surveys were conducted in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). Blood samples were collected during the baseline (Wave 1) and Wave 3 surveys. Detailed data collection methods for CHARLS have been described previously\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The study protocol was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052 -11015), and all participants provided written informed consent.\u003c/p\u003e \u003cp\u003eFasting blood glucose, triglycerides, low -density lipoprotein cholesterol (LDL -C), high -density lipoprotein cholesterol (HDL -C), total cholesterol, hs -CRP, and CRP levels were obtained from blood samples collected during Waves 1 and 3. Participants were excluded based on the criteria detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The final analytical cohort comprised 4,968 participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of TyG -BMI trajectories, inflammatory trajectories, and metabolic -inflammatory synergy patterns\u003c/h3\u003e\n\u003cp\u003eThis study constructed dynamic trajectories for metabolism and inflammation using biochemical data from Wave 1 (2011) and Wave 3 (2015) of CHARLS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinition of metabolic trajectory\u003c/b\u003e: Metabolic status was quantified using the TyG -BMI index, calculated as: Ln [fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL) / 2] \u0026times; body mass index (BMI, kg/m\u0026sup2;). The TyG -BMI index was computed separately for baseline (Wave 1, W1) and follow -up (Wave 3, W3). Based on the change in this index between the two time points, participants' metabolic trajectories were categorized into four groups: ① Sustained low: W1 TyG -BMI\u0026thinsp;\u0026lt;\u0026thinsp;P50 and W3 TyG -BMI\u0026thinsp;\u0026lt;\u0026thinsp;P50. ② High -to -low: W1 TyG -BMI\u0026thinsp;\u0026ge;\u0026thinsp;P50 and W3 TyG -BMI\u0026thinsp;\u0026lt;\u0026thinsp;P50. ③ Low -to -high: W1 TyG -BMI\u0026thinsp;\u0026lt;\u0026thinsp;P50 and W3 TyG -BMI\u0026thinsp;\u0026ge;\u0026thinsp;P50. ④ Sustained high: W1 TyG -BMI\u0026thinsp;\u0026ge;\u0026thinsp;P50 and W3 TyG -BMI\u0026thinsp;\u0026ge;\u0026thinsp;P50. (P50 denotes the 50th percentile, or median, of the study population).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinition of inflammatory trajectory\u003c/b\u003e: Inflammatory status was assessed using serum concentrations of C -reactive protein (CRP) at baseline (Wave 1) and high -sensitivity CRP (hs -CRP) at follow -up (Wave 3). To ensure comparability between the different assay methods (CRP in W1 vs. hs -CRP in W3), we standardized W1 CRP and W3 hs -CRP values separately. High and low inflammatory status at each time point was defined using percentile ranks rather than absolute values. Based on the percentile changes between the two waves, inflammatory trajectories were classified into four groups: ① Sustained low: W1 CRP\u0026thinsp;\u0026lt;\u0026thinsp;P50 and W3 hs -CRP\u0026thinsp;\u0026lt;\u0026thinsp;P50. ② High -to -low: W1 CRP\u0026thinsp;\u0026ge;\u0026thinsp;P50 and W3 hs -CRP\u0026thinsp;\u0026lt;\u0026thinsp;P50. ③ Low -to -high: W1 CRP\u0026thinsp;\u0026lt;\u0026thinsp;P50 and W3 hs -CRP\u0026thinsp;\u0026ge;\u0026thinsp;P50. ④ Sustained high: W1 CRP\u0026thinsp;\u0026ge;\u0026thinsp;P50 and W3 hs -CRP\u0026thinsp;\u0026ge;\u0026thinsp;P50.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinition of metabolic -inflammatory synergy pattern\u003c/b\u003e: To comprehensively assess the joint impact of metabolic and inflammatory states on MACE, we cross -classified the metabolic and inflammatory trajectories to construct a composite \"metabolic -inflammatory synergy pattern\" variable. Participants were thus categorized into one of seven clinically meaningful patterns for the core analysis: ① Sustained low metabolism \u0026amp; low inflammation (reference group). ② Sustained high metabolism \u0026amp; high inflammation. ③ Sustained high metabolism \u0026amp; improved inflammation (inflammatory trajectory: high -to -low). ④ Sustained high metabolism \u0026amp; worsened inflammation (inflammatory trajectory: low -to -high). ⑤ Improved metabolism \u0026amp; sustained high inflammation (metabolic trajectory: high -to -low). ⑥ Worsened metabolism \u0026amp; sustained high inflammation (metabolic trajectory: low -to -high). ⑦ Mixed pattern: all other trajectory combinations not falling into the above six categories (e.g., where both metabolism and inflammation changed but did not form a clear dominant pattern).\u003c/p\u003e\n\u003ch3\u003eAscertainment of MACE\u003c/h3\u003e\n\u003cp\u003eFollowing previous studies\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, the primary endpoint was defined as the first occurrence of MACE during follow -up or reaching the end of the follow -up period. Non -fatal MACE was ascertained through standardized questionnaire items: \"Has a doctor ever told you that you have been diagnosed with a heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems?\" and \"Have you ever been diagnosed with a stroke?\" For fatal MACE, a questionnaire was administered to family members: \"Did the patient die from heart disease?\" and \"Did the patient die from a stroke?\" Survival time was calculated as follows: for participants with non -fatal MACE, the event time was defined as the date from baseline (June 1, 2015) to the date of the first event reported in the survey; for fatal MACE, the specific date of death was used; for participants without events, survival time was calculated from baseline to the date of death from any cause, loss to follow -up, or the study cutoff date (September 1, 2020).\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eBaseline data were collected by trained interviewers using structured questionnaires and included: (1) Demographic and lifestyle data: sex, age, residence, education level, marital status, smoking status, and alcohol consumption. (2) Physical measurements: height, weight, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP). (3) Disease and medication history: information on existing conditions such as heart disease, hypertension, diabetes, and dyslipidemia, as well as the use of medication for these conditions. (4) Laboratory data: Fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), HDL -C, LDL -C, and glycated hemoglobin (HbA1c).\u003c/p\u003e\n\u003ch3\u003eHandling of missing variables\u003c/h3\u003e\n\u003cp\u003eMultiple imputation was used to handle missing data to reduce potential bias.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software version 4.5.1. A two -sided P -value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The analysis was conducted directly on the original dataset. Although some variables in the CHARLS database had missing values, prior analyses suggested that the missing data were likely missing at random\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Among the 4,968 participants included in the final analysis, the maximum missing rate for any variable was only 1.85%.\u003c/p\u003e \u003cp\u003e Baseline characteristics were described according to the core exposure variable, the metabolic -inflammatory synergy pattern. Continuous variables conforming to a normal distribution are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, non -normally distributed variables as median (interquartile range), and categorical variables as frequency (percentage). Group comparisons were performed using analysis of variance (ANOVA), Kruskal -Wallis test, or chi -square test, as appropriate.\u003c/p\u003e \u003cp\u003eThe cumulative incidence of MACE across different synergy patterns was estimated using the Kaplan -Meier method, and differences in survival curves were assessed with the log -rank test. Multivariable Cox proportional hazards regression models were employed to quantify the association between synergy patterns and MACE risk. Three models were constructed: Model 1 (demographic model) adjusted for age and sex; Model 2 (behavioral model) additionally adjusted for smoking, alcohol consumption, education level, and residence; Model 3 (clinical model) further adjusted for histories of hypertension, diabetes, and dyslipidemia. All models used the \"sustained low metabolism \u0026amp; low inflammation\" group as the reference. To test for a trend in risk, the seven patterns were treated as an ordinal variable and included in a Cox model for trend analysis.\u003c/p\u003e \u003cp\u003eThe incremental predictive value of the synergy patterns was evaluated by comparing a \"base model\" containing only traditional risk factors with an \"extended model\" that added the synergy patterns. Model discrimination was assessed using Harrell's C -statistic, and model fit was compared using the Akaike Information Criterion (AIC). The Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) were calculated to quantify the improvement in individual risk reclassification provided by the new model.\u003c/p\u003e \u003cp\u003eTo assess the generalizability of the association and identify potential effect modifiers, subgroup analyses were performed by age (\u0026lt;\u0026thinsp;60 years vs. \u0026ge;60 years), sex, history of diabetes, and history of hypertension. Interaction effects were statistically tested by including product terms (\"synergy pattern \u0026times; subgroup variable\") in the fully adjusted Cox model, and their significance was assessed using likelihood ratio tests.\u003c/p\u003e \u003cp\u003eTo verify the robustness of the main findings, the following sensitivity analyses were conducted: 1) Additional adjustment for potential confounders in the model; 2) Exclusion of MACE events occurring within the first 2 years of follow -up to assess potential reverse causality; 3) Stratified analysis at different follow -up time points; 4) Repeating the primary analysis after excluding participants with diabetes at baseline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eThis study included 4,968 Chinese middle -aged and older adults without baseline cardiovascular disease. The mean age of the participants was 58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 years, and 46.8% were male. Seven distinct metabolic -inflammatory dynamic trajectory patterns were identified, with significant differences observed in baseline characteristics across all groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all inter -group comparisons, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The group with the sustained high metabolism and high inflammation pattern had the lowest mean age (57.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 years) and exhibited the most pronounced metabolic dysregulation phenotype: it had the highest levels of body mass index, systolic blood pressure, fasting triglycerides, as well as the highest prevalence rates of diabetes, hypertension, and dyslipidemia among all groups.\u003c/p\u003e \u003cp\u003eLevels of the inflammatory marker (hs -CRP/CRP) also differed significantly across groups. The median level was highest in the improved metabolism \u0026amp; sustained high inflammation group (5.61 mg/L) and lowest in the sustained low metabolism \u0026amp; low inflammation group (0.49 mg/L). Notably, within the same \"high metabolism\" stratum, the improvement of the inflammatory trajectory was associated with a 1.2% reduction in the MACE incidence rate, indicating an independent role of the inflammatory trajectory distinct from the metabolic trajectory. The stronger driving effect of metabolic dysregulation on cardiovascular risk compared to inflammatory abnormalities was also evident from the higher MACE incidence rates observed in the metabolism -dominant patterns relative to the inflammation -dominant patterns.\u003c/p\u003e \u003cp\u003eOver a median follow -up of 5.25 years, a total of 1,087 major adverse cardiovascular events were recorded, yielding an overall incidence rate of 21.9%. The sustained high metabolism and high inflammation group had the highest cumulative event rate (28.6%), while the sustained low metabolism \u0026amp; low inflammation group had the lowest (14.7%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics by Metabolic -Inflammatory Trajectory Patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,968\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003emetabolism\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003eLow inflammation\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;880\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh metabolism\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003ehigh inflammation\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;968\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh metabolism\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003eimproved inflammation N\u0026thinsp;=\u0026thinsp;296\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh metabolism\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003eworsened inflammation N\u0026thinsp;=\u0026thinsp;375\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImproved metabolism\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003eHigh inflammation\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;118\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWorsened metabolism\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003ehigh inflammation\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;155\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMixed pattern\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,176\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep -value2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.48 (8.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.08 (8.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.79 (8.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.98 (7.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.45 (7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.62 (9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.86 (8.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59.03 (8.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,645.0 (53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438.0 (49.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e605.0 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183.0 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e219.0 (58.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.0 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.0 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,056.0 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,323.0 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442.0 (50.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363.0 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113.0 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156.0 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.0 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.0 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,120.0 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003ePrimary and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,530.0 (71.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e637.0 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e667.0 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192.0 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e239.0 (63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.0 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e118.0 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,585.0 (72.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond/high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,317.0 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232.0 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273.0 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.0 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128.0 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.0 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.0 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e534.0 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege/higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.0 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.0 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.0 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.0 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.0 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,274.0 (86.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e791.0 (89.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e786.0 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e240.0 (81.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e310.0 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101.0 (85.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e133.0 (85.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,913.0 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003ecurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,538.0 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326.0 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222.0 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.0 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.0 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.0 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e757.0 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e381.0 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.0 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.0 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.0 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.0 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.0 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e174.0 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,036.0 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509.0 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e662.0 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208.0 (70.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e242.0 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.0 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.0 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,241.0 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003ecurrent drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,574.0 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310.0 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243.0 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.0 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129.0 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.0 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.0 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e740.0 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,003.0 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e506.0 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e644.0 (66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192.0 (65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215.0 (57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.0 (61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e108.0 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,266.0 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387.0 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.0 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.0 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.0 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.0 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.0 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e169.0 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,125.0 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.0 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374.0 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.0 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115.0 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.0 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e370.0 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276.0 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.0 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.0 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.0 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.0 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84.0 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e406.0 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144.0 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.0 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.0 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.0 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e111.0 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.49 (20.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.55 (19.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.14 (21.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131.55 (20.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130.72 (19.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e132.15 (20.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125.92 (19.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e126.71 (20.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.00 (12.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.64 (11.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.19 (11.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.78 (11.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.44 (11.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.08 (11.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.61 (10.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73.79 (11.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.35 (37.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.51 (1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.37 (77.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.37 (2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.10 (45.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.12 (2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.14 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.56 (10.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.54 (38.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188.96 (34.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204.30 (39.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199.28 (38.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199.53 (41.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e195.87 (39.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e192.79 (40.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e188.72 (37.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL -C (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.47 (15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.60 (16.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.00 (12.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.23 (12.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.73 (13.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.73 (14.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.73 (13.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.91 (14.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL -C (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.07 (34.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.71 (30.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.06 (39.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.85 (35.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113.27 (38.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e113.20 (42.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e122.39 (35.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e114.06 (32.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.18 (110.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.97 (38.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190.67 (144.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151.88 (104.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e195.92 (166.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e166.01 (129.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.63 (47.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e110.96 (83.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline TYG -BMI index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211.75 (322.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.59 (16.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266.76 (667.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e234.30 (27.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e271.43 (392.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e214.63 (20.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e184.51 (13.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e192.35 (98.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline hs -CRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54 (7.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.96 (6.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.22 (7.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.61 (17.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.80 (8.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.64 (8.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMACE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,087.0 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.0 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e277.0 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.0 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.0 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.0 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.0 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e460.0 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow -up time (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402.0 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.0 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.0 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.0 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.0 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.0 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.0 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e180.0 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.25256673511294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e545.0 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.0 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.0 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.0 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.0 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.0 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e229.0 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.7782340862423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.50102669404517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.2539356605065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,019.0 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e762.0 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e738.0 (76.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225.0 (76.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e299.0 (79.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.0 (82.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132.0 (85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,766.0 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e2\u003c/sup\u003eOne -way analysis of means; Pearson's Chi -squared test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between metabolic -inflammatory trajectories and cardiovascular risk\u003c/h2\u003e \u003cp\u003eKaplan -Meier survival analysis revealed significant differences in MACE -free survival rates across the different synergy pattern groups (log -rank test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This association was further quantified by multivariable Cox proportional hazards regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After full adjustment for traditional risk factors and using the sustained low metabolism \u0026amp; low inflammation group as the reference, the analysis confirmed a clear gradient of risk. The group with the sustained high metabolism and high inflammation pattern exhibited the most robust and stable risk elevation (HR\u0026thinsp;=\u0026thinsp;1.56, 95% CI: 1.24\u0026ndash;1.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Metabolism -dominant patterns, where metabolic status remained high regardless of the inflammatory trajectory change, were also associated with significantly increased risk. This included the sustained highmetabolism\u0026amp;improved inflammation group (HR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 1.21\u0026ndash;2.16, P\u0026thinsp;=\u0026thinsp;0.001) and the sustained high metabolism \u0026amp; worsened inflammation group(HR\u0026thinsp;=\u0026thinsp;1.41,95%CI:1.06\u0026ndash;1.88,P\u0026thinsp;=\u0026thinsp;0.018).In contrast, inflammation -dominant patterns (i.e., the improved metabolism \u0026amp; sustained high inflammation and worsened metabolism \u0026amp; sustained high inflammation groups) did not show a statistically significant increase in MACE risk, further corroborating the central role of metabolic factors over inflammatory factors in cardiovascular risk prediction in this context\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.A significant dose -response relationship was observed, as evidenced by the trend test, where MACE risk increased progressively with higher risk gradations of the synergy patterns (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding provides epidemiological support for the continuous monitoring of metabolic -inflammatory burden and its utility for precise cardiovascular risk stratification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between Metabolic -Inflammatory Trajectory Patterns and MACE: Multivariable Cox Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" 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=\"c1\"\u003e \u003cp\u003eTrajectory Pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh -High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.08 (1.69\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05 (1.65\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56 (1.24\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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 -Improved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03 (1.53\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97 (1.49\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62 (1.21\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh -Deteriorated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81 (1.38\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77 (1.35\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41 (1.06\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved -High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.76\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (0.76\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (0.71\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeteriorated -High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.85\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31 (0.87\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20 (0.80\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed Pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50 (1.24\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50 (1.23\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44 (1.18\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP for trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Adjusted for age and sex\u003c/p\u003e \u003cp\u003eModel 2: Additionally adjusted for education, residence, smoking, and drinking\u003c/p\u003e \u003cp\u003eModel 3: Fully adjusted for all traditional cardiovascular risk factors\u003c/p\u003e \u003cp\u003eReference group: Low -Low trajectory pattern\u003c/p\u003e \u003cp\u003eP for trend tests the linear trend across trajectory patterns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIncremental predictive value of the synergy patterns\u003c/h2\u003e \u003cp\u003eThe inclusion of the metabolic -inflammatory trajectory patterns demonstrated significant incremental value for cardiovascular risk stratification (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The fully adjusted model (Model 3) showed a marked improvement in discrimination. The C -statistic increased from 0.571 in the base model to 0.645 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This improvement was statistically significant, as confirmed by a highly significant likelihood ratio test when compared to an intermediate model (χ\u0026sup2; = 180.40, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The explained variance increased from 1.1% to 5.4%, and model fit was continuously improved, evidenced by a substantial decrease in the Akaike Information Criterion (AIC) from 18088.7 to 17912.2.The model's ability to reclassify risk was also enhanced. The Net Reclassification Improvement (NRI) was 0.046 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the Integrated Discrimination Improvement (IDI) was 0.029 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings robustly support the dynamic metabolic -inflammatory trajectory pattern as a valuable predictor, providing important incremental information beyond traditional cardiovascular risk factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncremental Predictive Value of Metabolic -Inflammatory Trajectory Patterns for Major Adverse Cardiovascular Events\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance Metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcordance index (C -index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.571 (0.553\u0026ndash;0.589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.586 (0.567\u0026ndash;0.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.645 (0.628\u0026ndash;0.663)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkaike Information Criterion (AIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18088.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18062.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17912.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood Ratio Test (vs previous model)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; = 40.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2; = 180.40, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR -squared (Nagelkerke)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC -index improvement (Δ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Adjusted for age and sex\u003c/p\u003e \u003cp\u003eModel 2: Additionally adjusted for education, residence, smoking, and drinking\u003c/p\u003e \u003cp\u003eModel 3: Fully adjusted for all traditional cardiovascular risk factors plus metabolic -inflammatory trajectory patterns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis and effect modification\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted to assess whether the association between the sustained high metabolism and high inflammation pattern and MACE was consistent across different patient populations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A significant effect modification by age was revealed (P for interaction\u0026thinsp;=\u0026thinsp;0.027, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The association was significantly stronger in the younger subgroup aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years (HR\u0026thinsp;=\u0026thinsp;2.12, 95% CI: 1.54\u0026ndash;2.92) than in the older subgroup aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, where the association was not statistically significant (HR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 0.93\u0026ndash;1.72).No significant effect modification was observed for other subgroups, including sex, hypertension status, or diabetes status. However, although it did not reach statistical significance, a potential modifying trend was noted for diabetes status (P for interaction\u0026thinsp;=\u0026thinsp;0.068), which warrants further investigation in larger cohorts. These results indicate that the detrimental impact of the metabolic -inflammatory high -risk phenotype is particularly pronounced in younger middle -aged adults, suggesting that this population could be a priority target for preventive measures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup Analysis: Association of Metabolic -Inflammatory High -High Pattern with MACE Risk\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvents (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP -value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.56 (1.24\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003e\u0026lt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.93 (1.38\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003e\u0026ge;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.31 (0.93\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\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\u003e2,323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.32 (0.91\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.81 (1.33\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eHypertension(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.70 (1.28\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eHypertension(Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.69 (1.03\u0026ndash;2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.64 (1.29\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eDiabetes(Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.59 (0.21\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eReference group: Low -Low metabolic -inflammatory trajectory pattern\u003c/p\u003e \u003cp\u003eAll models are fully adjusted for traditional cardiovascular risk factors\u003c/p\u003e \u003cp\u003eHR: hazard ratio; CI: confidence interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction Analysis: Effect Modification of Metabolic -Inflammatory Trajectory Patterns on MACE Risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction P -value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (\u0026lt;\u0026thinsp;60 vs\u0026thinsp;\u0026ge;\u0026thinsp;60 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\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 \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInteraction P -values test whether the association between metabolic -inflammatory High -High pattern and MACE differs across subgroups\u003c/p\u003e \u003cp\u003eP -values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate statistically significant effect modification\u003c/p\u003e \u003cp\u003eAll models are fully adjusted for traditional cardiovascular risk factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eA series of sensitivity analyses were conducted to verify the robustness of the primary findings (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The association between the sustained high metabolism and high inflammation pattern and MACE risk remained highly significant across different covariate adjustment models: fully adjusted model HR\u0026thinsp;=\u0026thinsp;1.56 (95% CI: 1.24\u0026ndash;1.98), minimally adjusted model HR\u0026thinsp;=\u0026thinsp;2.08 (95% CI: 1.69\u0026ndash;2.57), and model adjusted for socioeconomic factors HR\u0026thinsp;=\u0026thinsp;2.05 (95% CI: 1.65\u0026ndash;2.53; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).To assess potential reverse causality, MACE events occurring within the first 2 years of follow -up were excluded. The association was notably strengthened following this exclusion (HR\u0026thinsp;=\u0026thinsp;1.89, 95% CI: 1.39\u0026ndash;2.56; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Time -stratified analysis revealed a distinct temporal pattern: the association was not statistically significant at the 3 -year follow -up mark (HR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 0.81\u0026ndash;1.75; P\u0026thinsp;=\u0026thinsp;0.364) but became significant by the 5 -year follow -up (HR\u0026thinsp;=\u0026thinsp;1.38, 95% CI: 1.08\u0026ndash;1.78; P\u0026thinsp;=\u0026thinsp;0.011). Furthermore, the association persisted after excluding participants with diabetes at baseline (HR\u0026thinsp;=\u0026thinsp;1.64, 95% CI: 1.29\u0026ndash;2.10; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Collectively, these results indicate that the association between the metabolic -inflammatory high -risk phenotype and MACE is robust. Moreover, the harm appears to be cumulative, manifesting more clearly with longer follow -up duration, which supports the characterization of this phenotype as a cumulative cardiovascular risk factor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity Analyses of the Association Between Metabolic -Inflammatory High -High Pattern and MACE Risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain analysis (fully adjusted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56 (1.24\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimal adjustment (age\u0026thinsp;+\u0026thinsp;sex only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.08 (1.69\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium adjustment (+\u0026thinsp;socioeconomic factors)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05 (1.65\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcluding early events (first 2 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89 (1.39\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime -limited analysis (3 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.81\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime -limited analysis (5 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (1.08\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcluding participants with baseline diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 (1.29\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;hazard ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval; MACE\u0026thinsp;=\u0026thinsp;major adverse cardiovascular events\u003c/p\u003e \u003cp\u003eAll models compare the metabolic -inflammatory High -High pattern to the Low -Low reference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the association between synergistic patterns derived from the dynamic trajectories of metabolism(TyG -BMI index)and inflammation(hs -CRP/CRP)and the risk of major adverse cardiovascular events(MACE)in Chinese middle -aged and older adults.Our findings demonstrate that these synergistic patterns are independent risk factors for MACE,with the sustained high metabolism and high inflammation pattern conferring the highest risk.The results underscore the dominant role of metabolic dysregulation in risk prediction,whereas alterations in inflammatory trajectory alone showed limited prognostic value.A significant age modification effect was observed,with the association being substantially stronger in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years compared to those aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years.Incorporating this synergistic pattern into models based on traditional cardiovascular risk factors enhanced the predictive value for MACE,offering a novel perspective for precise risk stratification and early intervention.\u003c/p\u003e \u003cp\u003eBoth inflammation and insulin resistance have been established as independent risk factors for cardiovascular disease(CVD),jointly contributing to its pathogenesis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.From a pathophysiological perspective,C -reactive protein promotes atherogenesis through mechanisms including the induction of endothelial dysfunction,facilitation of oxidized lipoprotein accumulation,and enhancement of leukocyte infiltration\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.The triglyceride -glucose index,reflecting the degree of insulin resistance,impairs glucose metabolism and vascular endothelial function via oxidative stress and lipotoxicity\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.Previous studies have predominantly examined the relationship of inflammation or insulin resistance with CVD in isolation,consistently reporting positive associations\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.In recent years,growing emphasis has been placed on the importance of a combined assessment of inflammation and metabolic abnormalities for the primary prevention of CVD\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e,with recommendations to integrate the triglyceride -glucose index and inflammatory biomarkers to optimize risk stratification\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe\"synergistic pattern of metabolic -inflammatory dynamic trajectories\"proposed in our study provides significant incremental information to existing risk prediction models.Compared to recently proposed static composite indices like the C -reactive protein -triglyceride -glucose index(CTI)\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e,our model enables the dynamic assessment of biomarker trajectories.This allows for the identification of\"progressively high -risk individuals\"whose biomarker levels may be borderline but exhibit a deteriorating trend.Furthermore,by constructing metabolic and inflammatory trajectories separately before combining them,our model can differentiate whether the primary source of risk stems from metabolic dysregulation or inflammatory activation,thereby offering more precise guidance for clinical intervention.Compared to cumulative exposure indices like the cumulative CTI(cuCTI),this trajectory -based model moves beyond the simple\"dose -response\"framework by focusing on the trend of change,which aligns with and advances the goals of precision medicine practice.\u003c/p\u003e \u003cp\u003eOur results highlight the predominant driving role of metabolic abnormalities in cardiovascular risk.Both the sustained high metabolism\u0026amp;improved inflammation and sustained high metabolism\u0026amp;worsened inflammation patterns were associated with significantly elevated risk.In contrast,patterns characterized by high inflammation accompanied by improved or stable metabolism did not show a significant increase in risk.This suggests that in the formation of cardiovascular risk,metabolic dysregulation may represent a more fundamental,upstream driving event,while inflammation largely acts as a downstream effect that is initiated and amplified by metabolic abnormalities.Consequently,interventions targeting the metabolic core\u0026mdash;such as improving insulin resistance and controlling body weight\u0026mdash;may be more effective in fundamentally reducing risk than anti -inflammatory strategies alone.\u003c/p\u003e \u003cp\u003eThe significant age modification effect revealed in our study is another key finding.The predictive power of the metabolic -inflammatory synergy pattern for MACE was significantly stronger in the \u0026lt;\u0026thinsp;60 -year -old population,a result consistent with findings from research by Cui et al.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.Possible explanations include:(1)younger individuals typically have fewer non -modifiable risk factors(e.g.,advanced age,long -term target organ damage),allowing modifiable factors like metabolism and inflammation to constitute a larger proportion of their attributable risk\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e;and(2)younger individuals presenting with a\"dual -high\"metabolic -inflammatory state early on may possess a stronger inherent genetic predisposition or be exposed to extremely unhealthy lifestyle behaviors over the long term,collectively leading to a markedly elevated future risk of MACE.In contrast,older adults(\u0026ge;\u0026thinsp;60 years)often present with multiple comorbidities and cumulative vascular damage,which may dilute or mask the relative contribution of metabolic -inflammatory factors amidst other,potentially stronger,risk factors(e.g.,severe arterial calcification,renal insufficiency).This finding carries significant public health implications,strongly suggesting that middle age(\u0026lt;\u0026thinsp;60 years)represents a\"golden window period\"for implementing active interventions targeting both metabolic and inflammatory risk factors to prevent future cardiovascular events.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003e The key strengths of this study include the use of a large -scale, nationally representative cohort, a prospective design, a relatively long follow -up period, systematic dynamic trajectory analysis, and comprehensive statistical validation. This study also has several limitations. First, due to its observational design, causal inferences regarding the relationships between inflammatory trajectories, metabolic trajectories, and cardiovascular events cannot be made. Second, trajectory definitions relied on measurements from only two time points. While this captures general trends, it cannot delineate the detailed, continuous evolution of the biomarkers. Third, the assessment of inflammation was limited to percentile levels of CRP/hs-CRP and didi not encompass other important flammatory pathways (e.g., IL -6, TNF -α) \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Fourth, despite multivariable adjustments, the potential for residual confounding from unmeasured factors remains. Fifth, the findings are primarily based on a Chinese middle -aged and older adult population; therefore, the generalizability of the conclusions to other ethnicities or younger populations requires further validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this analysis of the CHARLS database demonstrates that the synergistic pattern of metabolic and inflammatory dynamic trajectories\u0026mdash;particularly patterns centered on sustained metabolic dysregulation\u0026mdash;serves as an independent and strong predictor of MACE in Chinese middle -aged and older adults. The predictive utility of this pattern is especially pronounced in the younger middle -aged population (\u0026lt;\u0026thinsp;60 years). This novel approach provides significant incremental predictive information beyond traditional risk factors. It advocates for a shift in clinical practice towards monitoring the long -term dynamic changes of risk factors and furnishes new scientific evidence for implementing age -specific, precision prevention strategies for cardiovascular disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCHARLS China Health and Retirement Longitudinal Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTyG Triglyceride -glucose index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;C -reactive protein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehsCRP High -sensitivity C -reactive protein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI Body mass index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMACE\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emajor adverse cardiovascular events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecardiovascular disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCTI\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eC -reactive protein -triglyceride -glucose index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANOVA Analysis of variance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003cstrong\u003esystolic blood pressure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003cstrong\u003ediastolic blood pressure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFPG\u003c/strong\u003e\u003cstrong\u003eFasting plasma glucose\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTC total cholesterol\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTG triglycerides\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL -C\u003c/strong\u003e\u003cstrong\u003eHigh density lipoprotein cholesterol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDL -C\u003c/strong\u003e\u003cstrong\u003eLow density lipoprotein cholesterol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHbA1c glycated hemoglobin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHR hazard rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI confidence intervals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCuCTI\u003c/strong\u003e\u003cstrong\u003ecumulative C -reactive protein -triglyceride -glucose index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003cstrong\u003eAkaike Information Criterion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNRI\u003c/strong\u003e\u003cstrong\u003eNet Reclassification Improvement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIDI\u003c/strong\u003e\u003cstrong\u003eIntegrated Discrimination Improvement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC -index\u003c/strong\u003e\u003cstrong\u003eConcordance index\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHARLS was approved by the Institutional Review Board of Peking University (IRB approval number for the household survey: IRB00001052 -11015; IRB approval number for blood sample collection: IRB00001052 -11014). Written informed consent was obtained from all participants.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll data used in this study were sourced from the public China Health and Retirement Longitudinal Study (CHARLS) database. The data are fully anonymized aggregate data and contain no identifiable personal details, images, or videos from any individual. Therefore, consent for publication is\u0026nbsp;Not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe data sets used and/or analyzed during the current study are publicly avail - able or from the corresponding author upon reasonable request. All authors verify that all information and materials in the manuscript are original\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe authors declare that they have no competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNone.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiterature search: Xianglong Zhao,Xiaonan Ning,Zhihui Lu; Study conception and design:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXianglong Zhao,Xiaonan Ning; Data collection: Xianglong Zhao; Data analysis and interpreta -\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003etion: Xianglong Zhao,Zhihui Lu; Manuscript writing and reviewing: Xianglong Zhao;All authors read and approved the final manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis study used data from China Health and Retirement Longitudinal Study (CHARLS). We would like to thank the CHARLS research team for the time and effort into the CHARLS project.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ehttps://www.who.int/news -room/fact -sheets/detail/cardiovascular -diseases -(cvds)\u003c/li\u003e\n \u003cli\u003eGBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990 -2017: a systematic analysis for the Global Burden of Disease Study 2017.\u0026nbsp;Lancet. 2020;395(10225):709 -733. doi:10.1016/S0140 -6736(20)30045 -3.\u003c/li\u003e\n \u003cli\u003eHE Q. Promoting the development of research on cardiovascular health in the elderly.\u0026nbsp;Chin J Cardiol. 2025;30(4):357 -358. doi:10.3969/j.issn.1007 -5410.2025.04.001.\u003c/li\u003e\n \u003cli\u003eKhatana C, Saini NK, Chakrabarti S, et al. Mechanistic Insights into the Oxidized Low -Density Lipoprotein -Induced Atherosclerosis.\u0026nbsp;Oxid Med Cell Longev. 2020;2020:5245308. 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Joint association of TyG index and high sensitivity C -reactive protein with cardiovascular disease: a national cohort study.\u0026nbsp;Cardiovasc Diabetol. 2024;23(1):156. Published 2024 May 7. doi:10.1186/s12933 -024 -02244 -9\u003c/li\u003e\n \u003cli\u003eMeng X, Wen H, Lian L. Association between triglyceride glucose -body mass index and obstructive sleep apnea: a study from NHANES 2015 -2018.\u0026nbsp;Front Nutr. 2024;11:1424881. Published 2024 Aug 16. doi:10.3389/fnut.2024.1424881\u003c/li\u003e\n \u003cli\u003eLi H, Zheng D, Li Z, et al. Association of Depressive Symptoms With Incident Cardiovascular Diseases in Middle -Aged and Older Chinese Adults.\u0026nbsp;JAMA Netw Open. 2019;2(12):e1916591. Published 2019 Dec 2. doi:10.1001/jamanetworkopen.2019.16591\u003c/li\u003e\n \u003cli\u003eXie W, Zheng F, Yan L, Zhong B. Cognitive Decline Before and After Incident Coronary Events.\u0026nbsp;J Am Coll Cardiol. 2019;73(24):3041 -3050. doi:10.1016/j.jacc.2019.04.019\u003c/li\u003e\n \u003cli\u003eWang C, He S, Xie G, et al. Associations of longitudinal trajectories of triglyceride -glucose index combined with classical and novel obesity indices and cardiovascular disease: evidence from a nationwide prospective cohort study in China.\u0026nbsp;Cardiovasc Diabetol. 2025;24(1):431. Published 2025 Nov 12. doi:10.1186/s12933 -025 -02972 -6\u003c/li\u003e\n \u003cli\u003eLi H, Zheng D, Li Z, et al. Association of Depressive Symptoms With Incident Cardiovascular Diseases in Middle -Aged and Older Chinese Adults.\u0026nbsp;JAMA Netw Open. 2019;2(12):e1916591. Published 2019 Dec 2. doi:10.1001/jamanetworkopen.2019.16591\u003c/li\u003e\n \u003cli\u003eXing Y, Lin X. 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Published 2023 Jan 24. doi:10.1186/s12916 -023 -02729 -6\u003c/li\u003e\n \u003cli\u003eFeng G, Yang M, Xu L, et al. Combined effects of high sensitivity C -reactive protein and triglyceride -glucose index on risk of cardiovascular disease among middle -aged and older Chinese: Evidence from the China Health and Retirement Longitudinal Study.\u0026nbsp;Nutr Metab Cardiovasc Dis. 2023;33(6):1245 -1253. doi:10.1016/j.numecd.2023.04.001\u003c/li\u003e\n \u003cli\u003eMa X, Ma X, Wang Y, Qiu G, Zhang C. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident cardiovascular disease in middle -aged and older Chinese adults: a nationwide cohort study.\u0026nbsp;Cardiovasc Diabetol. 2025;24(1):303. Published 2025 Jul 26. doi:10.1186/s12933 -025 -02869 -4\u003c/li\u003e\n \u003cli\u003eAgbaje AO. Arterial stiffness precedes hypertension and metabolic risks in youth: a review.\u0026nbsp;J Hypertens. 2022;40(10):1887 -1896. doi:10.1097/HJH.0000000000003239\u003c/li\u003e\n \u003cli\u003eMA B, MA X, LI X, et al. Research progress on inflammatory mechanisms in atherosclerosis. Chin J Integr Med Cardio -Cerebrovasc Dis. 2025;23(20):3104 -3109. doi:10.12102/j.issn.1672 -1349.2025.20.010\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Trajectory analysis, Synergistic effect, Triglyceride -glucose -body mass index, Major adverse cardiovascular events, Metabolism, Inflammation, C -Reactive Protein, Risk prediction, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-8377980/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8377980/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e The interplay between metabolic dysregulation and chronic inflammation is pivotal in the development of cardiovascular disease. However, the joint prognostic value of their dynamic trajectories remains unclear. Current risk assessment predominantly relies on single -timepoint, static measurements, which fail to capture their temporal evolution and synergistic interactions. This study aimed to investigate the synergistic pattern derived from the dynamic trajectories of metabolism and inflammation for predicting major adverse cardiovascular events(MACE) .\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e This prospective cohort study included 4,968 Chinese middle -aged and older adults free of baseline cardiovascular disease from the China Health and Retirement Longitudinal Study(CHARLS).The triglyceride -glucose -body mass index and high -sensitivity C -reactive protein were used to define metabolic and inflammatory status,respectively.Their dynamic trajectories(sustained low,high -to -low,low -to -high,sustained high)from two timepoints were constructed and combined into seven synergistic patterns.The primary outcome was incident MACE(a composite of cardiovascular death,myocardial infarction,stroke,or revascularization).Cox proportional hazards models were used to calculate hazard ratios.The incremental predictive value was assessed using the C -statistic,net reclassification improvement,and integrated discrimination improvement.Subgroup and sensitivity analyses were performed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e Over a median follow -up of 5.25 years, 1,087 events occurred. Compared to the sustained low metabolic -inflammatory pattern, the sustained high pattern was associated with the highest risk of MACE (fully -adjusted HR\u0026thinsp;=\u0026thinsp;1.64, 95% CI: 1.31\u0026ndash;2.03). A significant age interaction was observed (P for interaction\u0026thinsp;=\u0026thinsp;0.027), with a stronger association in participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years (HR\u0026thinsp;=\u0026thinsp;1.93, 95% CI: 1.38\u0026ndash;2.72) than in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years. The inclusion of the synergistic pattern into a model containing traditional risk factors significantly improved risk prediction (NRI\u0026thinsp;=\u0026thinsp;0.046, IDI\u0026thinsp;=\u0026thinsp;0.029).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e The synergistic pattern of metabolic -inflammatory dynamic trajectories, especially the sustained high pattern, is an independent risk factor for MACE in Chinese middle -aged and older adults, with greater predictive utility in younger individuals. This novel approach provides a dynamic perspective for precise cardiovascular risk stratification.\u003c/p\u003e","manuscriptTitle":"The synergistic pattern of metabolic -inflammatory dynamic trajectories jointly predicts cardiovascular risk in Chinese middle -aged and older adults: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 10:27:25","doi":"10.21203/rs.3.rs-8377980/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T06:12:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T03:04:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T06:57:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296047197199638240104504793146664847573","date":"2026-01-29T15:42:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14455553773927487297706061649678999626","date":"2026-01-23T13:46:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T11:32:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-19T11:05:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T15:05:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T14:59:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-16T15:44:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4e44395-074a-4728-b6df-c3bf536268e7","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61130470,"name":"Health sciences/Biomarkers"},{"id":61130471,"name":"Health sciences/Cardiology"},{"id":61130472,"name":"Health sciences/Diseases"},{"id":61130473,"name":"Health sciences/Medical research"},{"id":61130474,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-09T14:56:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 10:27:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8377980","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8377980","identity":"rs-8377980","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}