Cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index versus the Cholesterol, high-density lipoprotein, and glucose index for incident hypertension prediction: a national cohort study

Cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index versus the Cholesterol, high-density lipoprotein, and glucose index for incident hypertension prediction: a national cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index versus the Cholesterol, high-density lipoprotein, and glucose index for incident hypertension prediction: a national cohort study Jiaqing Dou, Shuya Zhang, Chaofan Ding, Haoquan Li, Pengfei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8819257/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Apr, 2026 Read the published version in Cardiovascular Diabetology → Version 1 posted 10 You are reading this latest preprint version Abstract Background While the C-reactive protein-triglyceride-glucose (CTI) and cholesterol–high-density lipoprotein–glucose (CHG) indices have emerged as potent surrogates for inflammatory-metabolic status, the long-term effects of their sustained accumulation are not yet clearly understood. Specifically, the predictive divergence between cumulative CTI (cuCTI) and CHG (cuCHG) in determining incident hypertension remains a critical knowledge gap in aging Chinese cohorts. Methods Leveraging a nationwide longitudinal cohort from the China Health and Retirement Longitudinal Study (CHARLS), we modeled the cumulative burden of CTI and CHG by integrating temporal data from Wave 1 through Wave 3. We then used multivariable Cox proportional hazards models to assess associations and restricted cubic splines (RCS) for dose-response relationships. K-means clustering identified trajectory patterns. To gauge the predictive performance at 7 and 9 years, we analyzed time-dependent ROC curves, alongside the C-index, NRI, and IDI. Finally, our findings were further subjected to subgroup and sensitivity analyses to test their robustness. Results During follow-up, 437 (15.6%) participants developed hypertension. Both elevated cuCTI and cuCHG significantly increased hypertension risk. Multivariable Cox regression analysis unveiled a clear difference in risk magnitude: participants in the highest quartile of cuCTI faced a two-fold risk of hypertension (HR = 2.04; 95% CI: 1.55–2.68), surpassing the 58% increase seen with cuCHG (HR = 1.58; 95% CI: 1.20–2.06). Crucially, cuCTI demonstrated superior predictive accuracy in time-dependent ROC analysis (9-year DeLong P = 0.010). Adding cuCTI to the fully adjusted model significantly improved the C-index at both 7 and 9 years, whereas cuCHG did not. Furthermore, cuCTI showed stronger gains in NRI and IDI compared to cuCHG (all P < 0.05). Subgroup and sensitivity analyses also showed consistent results. Conclusion Although both indices serve as independent predictors, cuCTI offers superior predictive power, likely by capturing the synergistic detriment of systemic inflammation and insulin resistance. These findings substantiate the imperative of monitoring cumulative inflammatory-metabolic load for the early stratification of hypertension risk. Hypertension C-reactive protein-triglyceride-glucose index Cholesterol high-density lipoprotein glucose index Cumulative exposure CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Currently, hypertension is increasingly prevalent and remains a major threat to global cardiovascular health, with recent estimates exceeding 1.4 billion affected individuals [ 1 ]. In China, 44.7% of adults aged 35–75 had hypertension in 2017 [ 2 , 3 ]. Without adequate blood pressure control, permanent organ damage can occur and the risks of stroke, heart disease, and kidney disease increase markedly [ 4 – 7 ]. Thus, cost-effective biomarkers that capture metabolic or inflammatory status are valuable for early risk assessment. Prior studies have shown that traditional risk factors such as cigarette smoking and alcohol consumption are closely linked to hypertension risk. Yet these conventional exposures often provide limited insight into the hemodynamic dysregulation underlying the hypertensive state [ 8 – 9 ]. Hypertension is now often viewed as more than a simple consequence of altered hemodynamics; rather, it reflects an interaction between metabolic disruption and chronic inflammation [ 10 ], and it is closely connected with insulin resistance (IR) [ 11 ]. The cholesterol–high-density lipoprotein–glucose (CHG) index has been proposed as a novel surrogate for IR. Developed initially to assess diabetes and IR [ 12 ], CHG has later been associated with cardiovascular disease [ 13 – 15 ] and incident hypertension [ 16 ]. Notably, metabolic disturbance alone does not fully explain hypertension pathogenesis, and inflammation is a key contributing component [ 17 ]. In this setting, the C-reactive protein–triglyceride–glucose (CTI) index extends IR related information by adding an inflammatory dimension. CTI has been reported to aid prognostic assessment in malignancies [ 18 ] and to predict cardiac event risk [ 19 ]. However, its association with new-onset hypertension has not been systematically examined. Therefore, this study assessed CTI in relation to incident hypertension and compared its predictive performance with the CHG index to improve identification of individuals at elevated risk. Many earlier studies have depended on a single baseline measurement, and this approach limits the ability to capture within-person changes over time or to reflect the cumulative harm associated with long-term exposure. Available evidence indicates that an accumulated burden often predicts chronic disease more effectively than a one-time measure [ 20 ]. However, longitudinal evidence on cumulative CTI (cuCTI) and cumulative CHG (cuCHG) in relation to incident hypertension among middle aged and older Chinese adults remains limited. This study aimed to evaluate the associations of cuCTI and cuCHG, together with their dynamic trajectory patterns, with the risk of incident hypertension. In addition, the predictive performance of cuCTI and cuCHG was further compared, with the goal of offering additional tools and perspectives for hypertension prevention. Methods Study population We used data from the China Health and Retirement Longitudinal Study (CHARLS). This is a nationwide cohort managed by Peking University to track aging trends in China. The 2011 baseline survey used multi-stage probability sampling. It covered 150 counties and 450 villages in 28 provinces, including 17708 participants from about 10,000 households. Follow-up surveys are done every two years, and data from five waves (2011、2013、2015、2018、2020) are currently available. The data includes sociodemographics, health status, biochemical markers, and 13 physical exam indicators. Trained staff collected the data through face-to-face interviews. The study was approved by the Institutional Review Board of Peking University (No. 00001005-11,015), and all participants gave written informed consent. More details are provided by Zhao et al. [21] and on the official website (http://charls.pku.edu.cn/en). We initially included 17708 participants from Wave 1 (2011) and subsequently excluded participants based on the following criteria: (1) missing lab data in Wave 1 or Wave 3 (n=10227); (2) missing baseline demographics (n=5); (3) non-fasting blood samples (n=97); (4) loss to follow-up in Waves 4 and 5 (n=434); (5) age under 45 at baseline (n=173); and (6) hypertension diagnosed before Wave 4 (n=3971). After this screening, the final sample included 2801 participants (Figure 1). Assessment of cuCHG and cuCTI The calculation of CHG and CTI indices involved five key biomarkers: total cholesterol (TC), fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C), C-reactive protein (CRP), and triglycerides (TG). These two indices were determined using the following formulas: CHG =ln[TC (mg/dL) × FPG (mg/dL) / 2 × HDL-C (mg/dL)] [13]. CTI =0.412×ln(CRP [mg/L])+ln(TG [mg/dL] × FPG [mg/dL])/2 [18]. We derived cumulative indices by averaging values from 2012 and 2015, multiplied by the time elapsed between assessments: cumulative Index = (Index2012 + Index2015) / 2 × time interval (2015−2012). This method follows the approach for cumulative metabolic indices used in previous CHARLS studies by Yang [22] and Ma [23]. Ascertainment of Outcomes Hypertension was defined as the presence of at least one of the following indicators: (1) a self-reported history of a physician diagnosis; (2) current use of blood pressure-lowering medication; or (3) physical examination findings showing systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg in two or more measurements. Participants who met any of these criteria during the 2018 or 2020 follow-up visits were classified as having new-onset hypertension [24，25]. Study Variables and Definitions Baseline covariates were gathered through structured interviews conducted by trained personnel. Sociodemographic and lifestyle variables included age, sex, urban or rural residence, education, marital status, and health behaviors (smoking/alcohol consumption). Anthropometric assessments followed standardized protocols to record height, weight, and waist circumference (WC). Medical history was ascertained via self-report, covering physician-diagnosed comorbidities such as diabetes and dyslipidemia, as well as concurrent medication regimens. Finally, venous blood samples were analyzed for key biomarkers, including FPG, HbA1c, CRP, and lipid profiles (TC, TG, HDL-C, LDL-C). Diabetes was defined as a self-reported diagnosis, use of antidiabetic medication, or an FPG ≥ 7.0 mmol/L; other comorbidities relied on self-reported history or ongoing treatment. For subgroup analyses, the baseline cohort was stratified into a normal blood pressure group as well as an elevated blood pressure group. Elevated blood pressure was defined as SBP 120–139 mmHg or DBP 70–89 mmHg [26]. Statistical analysis Baseline characteristics were summarized using standard descriptive statistics. Continuous variables that followed an approximately normal distribution are presented as mean (SD), and group differences were assessed using Student’s t-test. Continuous variables showing a skewed distribution are presented as the median with interquartile range (IQR) and were examined using the Mann–Whitney U test. Categorical variables are reported as counts with percentages and were compared using Pearson’s chi-square test. All analyses were carried out in Python version 3.12 and R version 4.3.0, and two-sided p < 0.05 was considered statistically significant. Univariable and multivariable Cox proportional hazards models were used to assess how the cuCTI and cuCHG indices relate to hypertension risk, with findings expressed as hazard ratios (HRs) together with 95% confidence intervals (CIs). Both indices were entered into the models in two ways, namely as continuous measures and after being treated as categorical variables. To assess multicollinearity, variance inflation factors (VIFs) were computed. All VIFs remained below 5, indicating no evidence of multicollinearity (Table S1). Three nested models were fitted: Model 1 used no adjustment, Model 2 took age, sex, residence, education, marital status, smoking, alcohol consumption, and body mass index (BMI) into account, and Model 3 was fully adjusted by additionally including all comorbidities beyond Model 2. To keep the sample size as large as possible, missing values were handled by applying multiple imputation. For trajectory analysis, participants were assigned to groups by relying on measurements from Wave 1 and Wave 3. In practice, K-means clustering was paired with the elbow method to decide the number of groups, leading to three clusters: Cluster 1 (stable low level), Cluster 2 (moderate level), and Cluster 3 (stable high level). With Cluster 1 taken as the reference category, the associations between trajectory groups and incident hypertension were evaluated by applying the fully adjusted Cox model. To check whether the associations might deviate from linearity, restricted cubic splines (RCS) were fitted within the fully adjusted model. In light of the Bayesian information criterion, the preferred specification used four knots placed at the 5th, 35th, 65th, and 95th percentiles, and evidence for non-linearity was tested using a likelihood ratio test. To evaluate predictive performance, time-dependent receiver operating characteristic (ROC) curves were generated for 7-year and 9-year follow-up, and areas under the curve (AUCs) were compared by applying the DeLong test. To further contrast the predictive ability of cuCTI versus cuCHG, the restricted Harrell’s C-index, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also calculated. Differences were assessed via bootstrap resampling within each imputed dataset, and estimates were then pooled using Rubin’s rules. Finally, subgroup analyses were performed based on the fully adjusted model to examine potential heterogeneity, with stratification by sex, age, residence, BMI, lifestyle factors, history of diabetes and baseline blood pressure status. Sensitivity analysis To assess the robustness of the findings, five sensitivity analyses were conducted. First, participants with extreme cuCTI or cuCHG values, defined as outliers beyond mean ± 3 SD, were excluded and the analysis was repeated. Second, a complete-case analysis was performed by including only participants with full data. Third, the results were verified using a fully adjusted logistic regression model. Then, with all-cause death treated as a competing event, we performed a sensitivity analysis using the Fine–Gray subdistribution hazards model to assess the associations between cuCTI and cuCHG and incident hypertension. Finally, the E-value was calculated to estimate the potential impact of unmeasured confounding: E = RR + √[RR × (RR − 1)] [27]. Results Participant characteristics Table 1 presents the baseline characteristics of the study population stratified by incident hypertension. Out of 2801 participants, 437 (15.6%) developed hypertension during follow-up. The median age was 56 years, with an interquartile range (IQR) of 50–61 years, and females accounted for 55.8% of the sample. In comparison with the non-hypertension group, participants who developed hypertension were older (median: 57 vs. 56 years, P = 0.001), and they were more likely to have no formal education (54.2% vs. 46.3%) and to live in rural areas (72.8% vs. 66.5%; all P < 0.05). Participants in this group also exhibited higher baseline BMI, WC, and blood pressure levels (systolic and diastolic) (all P < 0.001). Similarly, higher TG, FPG, HbA1c, and CRP levels were observed (all P ≤ 0.005). The cuCTI index was significantly higher among those who developed hypertension, at 14.40 (IQR: 13.35–15.39), compared with 13.80 (12.96–14.76) in those without hypertension (P < 0.001), and a similar trend was observed for the cuCHG index (15.69 [15.10–16.35] vs. 15.50 [14.94–16.14], P < 0.001). However, no significant differences were observed for sex, marital status, lifestyle habits such as smoking and drinking, other lipid parameters including HDL-C, LDL-C, and TC, or comorbidities such as diabetes, heart disease, stroke, and kidney disease (all P > 0.05). Cox proportional hazards models evaluating the associations of cuCTI and cuCHG indices with the risk of incident hypertension. For the cuCTI index, a 1-unit increase was linked to a 21% higher risk of hypertension in Model 1 (HR=1.21, 95% CI 1.14–1.28, P<0.001). This association remained significant in the fully adjusted Model 3 (HR=1.22, 95% CI 1.14–1.29, P<0.001). Similarly, we found that a 1-unit increase in cuCHG corresponded to a 19% risk increase in the unadjusted model (HR=1.19, 95% CI 1.09–1.30, P<0.001). Models 2 and 3 supported this, both showing an HR of 1.20 (P<0.001). In categorical analyses, we observed a significant positive trend for both indices (P for trend < 0.001). Specifically, participants in the highest quartile (Q4) had a much higher risk compared to the lowest quartile for both cuCTI (HR=2.04, 95% CI 1.55–2.68, P<0.001) and cuCHG (HR=1.58, 95% CI 1.20–2.06, P<0.001) in the fully adjusted models (Table 2). Associations of CTI and CHG exposure trajectories with the risk of incident hypertension Three trajectory patterns were identified for both CTI and CHG, namely low-stable, moderate, and high-stable. Hypertension incidence rose across CTI trajectories (11.5%, 15.7%, 23.6%) and likewise across CHG trajectories (13.2%, 16.1%, 20.5%). After full adjustment in Cox models, the moderate and high-stable groups showed significantly higher hazards than the low-stable reference, with a larger magnitude of association for CTI (HR = 1.39 and 2.24) than for CHG (HR = 1.28 and 1.68; all P < 0.05). Overall, this graded pattern suggests a dose–response relationship, and the high-stable trajectory showed the highest hypertension risk (Figure 2). Dose-response associations of cuCTI and cuCHG with incident hypertension: An RCS analysis. Significant positive associations were observed for both cuCTI and cuCHG with hypertension risk (P for overall association < 0.001). For cuCTI, no evidence of nonlinearity was observed (P for nonlinearity = 0.452), indicating a continuous increase in risk without an evident threshold. cuCHG showed a similar linear trend, with P for nonlinearity = 0.382. Overall, each index is independently associated with hypertension risk across the continuous range, thus lowering cumulative exposure across that range could support primary prevention, as illustrated in Figure 3. Comparative predictive performance of cuCTI and cuCHG indices for incident hypertension. At 7 years, cuCTI yielded an AUC that was modestly higher than that of cuCHG (0.605 vs. 0.583), yet this difference did not reach statistical significance (P = 0.086). At 9 years, the DeLong test identified a significant difference, and cuCTI maintained a higher AUC (CTI: 0.622, 95% CI 0.593–0.651 vs. CHG: 0.595, 95% CI 0.565–0.625, P = 0.010). Overall, this pattern suggests that cuCTI has greater prognostic value for long-term risk assessment (Figure 4). In the fully adjusted model, adding cuCTI increased the C-index from 0.564 to 0.605 at 7 years (ΔC-index=0.041, P=0.006), whereas adding cuCHG increased it to 0.582 (ΔC-index=0.018, P=0.064). At the 9-year mark, cuCTI increased the C-index from 0.578 to 0.615 (ΔC-index=0.037, P=0.002), but the change for cuCHG was not significant (ΔC-index=0.012, P=0.129). Although both indices showed better reclassification and discrimination, cuCTI had larger NRI/IDI gains (7-year: 0.317/0.110 vs. 0.140/0.054; 9-year: 0.318/0.107 vs. 0.152/0.046) and significantly greater improvements in NRI and IDI at both time points (all P<0.05; Tables 3 and 4). Subgroup analysis Subgroup analyses continued to demonstrate consistent associations between higher cumulative indices and hypertension risk across groups defined by sex, age, BMI, and lifestyle, and no significant interactions were observed (P for interaction > 0.05). A similar pattern was observed in both the <60-year and ≥60-year cohorts. Upon stratification by baseline blood pressure, the association was notably stronger among those starting with normal blood pressure: HRs were 1.30 with 95% CI 1.17–1.45 for cuCTI and 1.20 with 95% CI 1.02–1.42 for cuCHG. For participants with elevated baseline blood pressure, the association was smaller but still significant (cuCTI: HR 1.15, 95% CI 1.06–1.24; cuCHG: HR 1.15, 95% CI 1.03–1.30), suggesting a more important role during the early stages of hemodynamic disturbance, as shown in Figures 5 and 6. Sensitivity analysis Sensitivity analyses supported the robustness of the findings (Table 5). Results remained consistent with the primary analysis after removing outliers, restricting analyses to complete cases, and using a fully adjusted logistic regression model (HRs and ORs per 1-unit increase ranged from 1.20–1.24 for cuCTI and 1.17–1.22 for cuCHG). Competing-risk analyses treating all-cause death as a competing event showed similar associations in full adjusted Fine–Gray models (cuCTI: SHR = 1.19, 95% CI 1.12–1.27; cuCHG: SHR = 1.16, 95% CI 1.05–1.27). E-values of 1.73 for cuCTI and 1.69 for cuCHG indicated that relatively strong unmeasured confounding would be required to fully explain these associations. Discussion Our findings align with earlier work showing that metabolic factors as well as inflammatory factors are associated with hypertension risk [ 28 – 32 ]. In this analysis, the predictive ability of cuCTI and cuCHG was assessed in middle-aged and older Chinese adults. After adjustment for lifestyle variables and comorbid conditions, both cumulative indices remained approximately linearly associated with incident hypertension, indicating that higher long-term exposure was associated with a higher risk of developing hypertension. The estimated associations also remained robust across Models 1–3, with minimal HR fluctuation (< 0.02), suggesting that these cumulative measures retain predictive value that does not simply reflect traditional risk factors. The contributions of diabetes, heart disease, and stroke appeared limited, which is likely attributable to the low prevalence of these conditions in the cohort (1.0%–7.9%) and by the fact that they were not strongly associated with the indices. When the two indices were contrasted directly, cuCTI repeatedly provided better discrimination than cuCHG, as reflected by larger improvements in NRI and IDI at both follow-up points. The trajectory analysis further indicated that participants who maintained persistently high index levels over time carried the greatest risk [ 33 ]. In addition, subgroup analyses suggested that cumulative exposure exerted a stronger effect among participants with normal baseline blood pressure than among those who already had elevated blood pressure. Consistent with this pattern, cuCTI showed a stronger association than cuCHG (HR: 1.30 vs. 1.20), suggesting that inflammatory–metabolic burden may contribute to the early onset of hypertension rather than merely aggravating an already elevated state, which in turn underscores the value of early intervention. The connection between CTI and cardiovascular disease has been well documented [ 34 – 37 ], and single-time-point CHG has likewise been linked to hypertension [ 16 ]. K-means clustering was employed to characterize longer-term patterns, and several distinct trajectory groups were identified. However, the trajectories showed no clear temporal trend, suggesting that risk differences mainly reflect baseline levels rather than substantial changes over time. Future work would therefore benefit from examining populations in which these levels show greater fluctuation across follow-up. The biological basis linking CTI/CHG to incident hypertension has not been fully clarified, and it likely operates through multiple, partly overlapping pathways, including disrupted glucose and lipid metabolism, insulin resistance, inflammation, and oxidative stress [ 38 – 40 ]. The present results nevertheless illustrate how inflammatory status together with metabolic dysregulation can shape the likelihood of developing hypertension, supporting the view that hypertension onset may reflect the combined influence of inflammatory and metabolic abnormalities. Several limitations should also be recognized. First, many participants were excluded due to missing key laboratory measures at the first or third survey wave or because hypertension had already been diagnosed, and this exclusion process could have introduced selection bias. Second, although objective measurements (rather than self-reports) were used to improve ascertainment [ 41 ], some degree of underdiagnosis may still have occurred. Third, although the E-value analysis suggested that only a confounder of considerable strength could account for the observed associations, unmeasured confounding, such as salt intake and genetic predisposition, may still have influenced the magnitude of the observed risk effects linking the two cumulative indices to incident hypertension [ 42 – 46 ]. Finally, because the sample was restricted to Chinese adults aged 45 years and older, the generalizability of these findings to other populations remains uncertain. Conclusion Both cuCTI and cuCHG indices showed a clear linear link to new-onset hypertension risk in middle-aged and older Chinese adults. Specifically, cuCTI includes an inflammatory marker, and it performed significantly better than cuCHG in predicting long-term risk. Trajectory analysis also indicated that the highest risk was seen in individuals who maintained high index levels. These results suggest that monitoring cumulative inflammatory and metabolic burden is valuable for early identification and prevention of hypertension. Abbreviations AUC Area under the curve BMI Body mass index CHARLS China health and retirement longitudinal study CHG Cholesterol–high-density lipoprotein–glucose CI Confidence interval CRP C-reactive protein CTI C-reactive protein-triglyceride-glucose IR insulin resistance cuCHG Cumulative cholesterol–high-density lipoprotein–glucose cuCTI Cumulative C-reactive protein-triglyceride-glucose DBP Diastolic blood pressure FPG Fasting plasma glucose HbA1c Hemoglobin A1c HDL-C High-density lipoprotein cholesterol HR Hazard ratio HTN Hypertension IQR Interquartile range LDL-C Low-density lipoprotein cholesterol NRI Net reclassification improvement OR Odds ratio RCS Restricted cubic spline ROC Receiver operating characteristic C-index concordance index SBP Systolic blood pressure SD Standard deviation TC Total cholesterol TG Triglyceride RR Relative risk VIF variance inflation factor WC waist circumference Declarations Acknowledgements We thank the CHARLS team for their hard work and every participant who gave their time and data to this research. Author Contributions JD: Writing – original draft, Formal analysis, Investigation, Methodology, Visualization. SZ: Formal analysis. HL: Data curation. DC: HL: Data curation. PZ: Writing – review & editing, Conceptualization, Funding acquisition, Project administration. All authors read and approved the final manuscript. Funding This work was supported by the National Key R&D Program of China (Grant No. 2023YFC2506502) and the Shenzhen Science and Technology Program (Nos. JCYJ20220818103407015, GJHZ20240218113401003). Data availability The data analyzed in this study are available in the CHARLS database (http://charls.pku.edu.cn/). Ethics approval and consent to participate The CHARLS study was approved by the Institutional Review Board of Peking University (IRB00001052-11015 for the household survey and IRB00001052-11014 for blood samples). All participants provided written informed consent. Conflicts of interest The authors declare no conflicts of interest. 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Zhang, Zenglei et al. “Role of inflammation, immunity, and oxidative stress in hypertension: New insights and potential therapeutic targets.” Frontiers in immunology vol. 13 1098725. 10 Jan. 2023, doi:10.3389/fimmu.2022.1098725. Kaur, Sukhchain et al. “A cross-sectional study to correlate antioxidant enzymes, oxidative stress and inflammation with prevalence of hypertension.” Life sciences vol. 313 (2023): 121134. doi:10.1016/j.lfs.2022.121134. Hu, Xinying et al. “Metabolic Status and Hypertension: The Impact of Insulin Resistance-Related Indices on Blood Pressure Regulation and Hypertension Risk.” Journal of the American Nutrition Association vol. 44,6 (2025): 487-497. doi:10.1080/27697061.2025.2450711. 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Cui, Cancan et al. “Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study.” Cardiovascular diabetology vol. 23,1 156. 7 May. 2024, doi:10.1186/s12933-024-02244-9. Feng, Guijuan 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.” Nutrition, metabolism, and cardiovascular diseases : NMCD vol. 33,6 (2023): 1245-1253. doi:10.1016/j.numecd.2023.04.001. García-Sánchez, Andrés et al. “Prevalence of Hypertension and Obesity: Profile of Mitochondrial Function and Markers of Inflammation and Oxidative Stress.” Antioxidants (Basel, Switzerland) vol. 12,1 165. 10 Jan. 2023, doi:10.3390/antiox12010165. Hall, John E et al. “Obesity, kidney dysfunction, and inflammation: interactions in hypertension.” Cardiovascular research vol. 117,8 (2021): 1859-1876. doi:10.1093/cvr/cvaa336. Liu, Weifang et al. “Major lipids and lipoprotein levels and risk of blood pressure elevation: a Mendelian Randomisation study.” EBioMedicine vol. 100 (2024): 104964. doi:10.1016/j.ebiom.2023.104964. Mo, Degang et al. “Association between the atherogenic index of plasma and incident hypertension across different blood pressure states: a national cohort study.” Cardiovascular diabetology vol. 24,1 219. 21 May. 2025, doi:10.1186/s12933-025-02775-9. Payne Riches, Sarah et al. “A Mobile Health Salt Reduction Intervention for People With Hypertension: Results of a Feasibility Randomized Controlled Trial.” JMIR mHealth and uHealth vol. 9,10 e26233. 21 Oct. 2021, doi:10.2196/26233. Mani, Arya. “Update in genetic and epigenetic causes of hypertension.” Cellular and molecular life sciences : CMLS vol. 81,1 201. 30 Apr. 2024, doi:10.1007/s00018-024-05220-4. Zheng, Zhihao et al. “Sleep quality and incident hypertension.” Revista espanola de cardiologia (English ed.) vol. 78,7 (2025): 600-608. doi:10.1016/j.rec.2024.12.003. Xiao, Zhihao et al. “Night Shift Work, Genetic Risk, and Hypertension.” Mayo Clinic proceedings vol. 97,11 (2022): 2016-2027. doi:10.1016/j.mayocp.2022.04.007. Weng, Zhenkun et al. “Associations of genetic risk factors and air pollution with incident hypertension among participants in the UK Biobank study.” Chemosphere vol. 299 (2022): 134398. doi:10.1016/j.chemosphere.2022.134398. Tables Table. 1 Baseline characteristics of the study population. HTN, hypertension; WC, waist circumference; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; CRP, C-reactive protein; CTI, C-reactive protein-triglyceride-glucose; cuCTI, cumulative C-reactive protein-triglyceride-glucose; CHG, cholesterol–high-density lipoprotein–glucose; cuCHG, cumulative cholesterol–high-density lipoprotein–glucose. Characteristics Total (N=2801) Non-HTN (n=2364) HTN (n=437) P-value Age, years 56.00 (50.00-61.00) 56.00 (50.00-61.00) 57.00 (51.00-62.00) 0.001 Gender, n(%) 0.356 Female 1562 (55.8%) 1309 (55.4%) 253 (57.9%) Male 1239 (44.2%) 1055 (44.6%) 184 (42.1%) Education, n(%) 0.013 No formal education 1332 (47.6%) 1095 (46.3%) 237 (54.2%) Primary school 577 (20.6%) 490 (20.7%) 87 (19.9%) Middle school 583 (20.8%) 509 (21.5%) 74 (16.9%) High school 309 (11.0%) 270 (11.4%) 39 (8.9%) Marital status, n(%) 0.771 Other 331 (11.8%) 277 (11.7%) 54 (12.4%) Married 2467 (88.1%) 2084 (88.2%) 383 (87.6%) Residence, n(%) 0.011 Urban 912 (32.6%) 793 (33.5%) 119 (27.2%) Rural 1889 (67.4%) 1571 (66.5%) 318 (72.8%) WC, cm 82.00 (76.00-89.00) 81.60 (75.80-88.40) 84.40 (78.50-91.00) <0.001 BMI, kg/m² 22.50 (20.56-24.76) 22.38 (20.44-24.65) 23.18 (21.25-25.63) <0.001 SBP, mmHg 114.00 (106.62-122.50) 113.50 (106.00-121.50) 119.00 (111.50-127.50) <0.001 DBP, mmHg 68.50 (63.00-75.00) 68.50 (62.50-74.50) 71.00 (65.00-77.50) <0.001 Smoking status, n(%) 0.852 No 1941 (69.3%) 1635 (69.2%) 306 (70.0%) Yes 812 (29.0%) 687 (29.1%) 125 (28.6%) Drinking status, n(%) 0.997 No 1867 (66.7%) 1575 (66.6%) 292 (66.8%) Yes 922 (32.9%) 777 (32.9%) 145 (33.2%) HDL-C, mg/dL 50.26 (41.37-60.31) 50.64 (41.75-60.70) 49.10 (40.21-59.15) 0.068 LDL-C, mg/dL 112.50 (93.17-134.15) 112.11 (92.78-133.76) 114.43 (93.56-137.24) 0.13 TC, mg/dL 186.73 (165.08-209.92) 185.57 (165.46-209.15) 192.14 (164.69-214.18) 0.095 TG, mg/dL 97.35 (70.80-140.71) 96.02 (69.92-138.95) 107.97 (76.11-153.10) 0.001 FPG, mg/dL 100.80 (93.42-109.44) 100.44 (93.24-109.08) 102.42 (94.68-111.24) 0.005 HbA1c, % 5.10 (4.90-5.40) 5.10 (4.90-5.40) 5.20 (4.90-5.50) <0.001 CRP, mg/L 0.81 (0.48-1.63) 0.78 (0.47-1.53) 0.96 (0.54-2.15) <0.001 Diabetes, n(%) 97 (3.5%) 76 (3.2%) 21 (4.8%) 0.131 Heart disease, n(%) 219 (7.8%) 174 (7.4%) 45 (10.3%) 0.051 Stroke, n(%) 29 (1.0%) 23 (1.0%) 6 (1.4%) 0.631 Kidney disease, n(%) 158 (5.6%) 128 (5.4%) 30 (6.9%) 0.285 CTI (Wave 1) 4.55 (4.24-4.92) 4.52 (4.22-4.89) 4.68 (4.35-5.07) <0.001 CTI (Wave 3) 4.70 (4.35-5.10) 4.68 (4.32-5.07) 4.81 (4.48-5.27) <0.001 cuCTI 13.90 (13.02-14.89) 13.80 (12.96-14.76) 14.40 (13.35-15.39) <0.001 CHG (Wave 1) 5.22 (5.00-5.48) 5.21 (4.99-5.47) 5.27 (5.04-5.55) 0.001 CHG (Wave 3) 5.12 (4.93-5.34) 5.11 (4.92-5.33) 5.17 (4.98-5.39) <0.001 cuCHG 15.53 (14.96-16.18) 15.50 (14.94-16.14) 15.69 (15.10-16.35) <0.001 Table. 2 Associations of cuCTI and cuCHG indices with incident hypertension. Model 1: Unadjusted for any covariates. Model 2: Adjusted for sociodemographic factors, including age, gender, residence, education level, marital status, smoking status, drinking status, and BMI. Model 3: Fully adjusted model, adjusting for age, gender, residence, education level, marital status, smoking status, drinking status, BMI, and comorbidities. Events（n） Model 1 Model 2 Model 3 HR(95%CI) P value HR(95%CI) P value HR(95%CI) P value Cumulative CTI Per 1-unit increase 1.21 (1.14-1.28) <0.001 1.22 (1.15-1.29) <0.001 1.22 (1.14-1.29) <0.001 Q1 80 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Q2 82 1.03 (0.75-1.40) 0.866 1.02 (0.75-1.38) 0.916 1.02 (0.75-1.38) 0.921 Q3 125 1.63 (1.23-2.16) <0.001 1.63 (1.23-2.17) <0.001 1.62 (1.22-2.15) <0.001 Q4 150 1.99 (1.52-2.61) <0.001 2.07 (1.58-2.73) <0.001 2.04 (1.55-2.68) <0.001 P for trend <0.001 <0.001 <0.001 Cumulative CHG Per 1-unit increase 1.19 (1.09-1.30) <0.001 1.20 (1.10-1.31) <0.001 1.20 (1.09-1.31) <0.001 Q1 94 1.00 (Ref) 1.00 (Ref) 1.00 (Ref) Q2 97 1.05 (0.79-1.39) 0.755 1.06 (0.80-1.41) 0.667 1.07 (0.80-1.42) 0.646 Q3 110 1.20 (0.91-1.58) 0.190 1.22 (0.92-1.61) 0.161 1.22 (0.92-1.61) 0.162 Q4 136 1.51 (1.16-1.97) 0.002 1.59 (1.22-2.08) <0.001 1.58 (1.20-2.06) <0.001 P for trend <0.001 <0.001 <0.001 Table. 3 Incremental predictive value of cuCTI and cuCHG at 7- and 9-year follow-up. cuCTI, cumulative C-reactive protein-triglyceride-glucose; cuCHG, cumulative cholesterol–high-density lipoprotein–glucose; NRI, net reclassification improvement; C-index, concordance index; IDI, integrated discrimination improvement; CI, confidence interval. Fully adjusted models included: age, gender, rural residency, education level, marital status, smoking status, drinking status, BMI, diabetes, heart disease, stroke, and kidney disease. C-index NRI IDI Estimate (95% CI) P value Estimate (95% CI) P value Estimate (95% CI) P value 7-year follow-up Fully adjusted model 0.564 (0.528–0.600) Reference Reference Reference Fully adjusted model + cuCHG 0.582 (0.545–0.620) 0.064 0.1400 (0.0137–0.2663) 0.030 0.0541 (0.0241–0.0840) <0.001 Fully adjusted model + cuCTI 0.605 (0.568–0.641) 0.006 0.3174 (0.1943–0.4405) <0.001 0.1097 (0.0623–0.1572) <0.001 9-year follow-up Fully adjusted model 0.578 (0.550–0.606) Reference Reference Reference Fully adjusted model + cuCHG 0.590 (0.562–0.619) 0.129 0.1517 (0.0496–0.2537) 0.004 0.0459 (0.0212–0.0705) <0.001 Fully adjusted model + cuCTI 0.615 (0.588–0.643) 0.002 0.3180 (0.2172–0.4188) <0.001 0.1069 (0.0680–0.1458) <0.001 Table. 4 Comparisons of incremental predictive performance between cuCTI and cuCHG at 7- and 9-years. cuCTI, cumulative C-reactive protein-triglyceride-glucose; cuCHG, cumulative cholesterol–high-density lipoprotein–glucose; NRI, net reclassification improvement; C-index, concordance index; IDI, integrated discrimination improvement; CI, confidence interval. Δ, the difference between cuCTI and cuCHG in their incremental improvement over the fully adjusted model. cuCTI vs cuCHG Δ (95% CI) P value 7-year follow-up C-index 0.023 (-0.002–0.047) 0.073 NRI 0.1774 (0.0340–0.3208) 0.015 IDI 0.0556 (0.0195–0.0919) 0.003 9-year follow-up C-index 0.025 (0.006–0.045) 0.012 NRI 0.1663 (0.0552–0.2775) 0.003 IDI 0.0610 (0.0296–0.0926) <0.001 Additional Declarations No competing interests reported. Supplementary Files file.docx Cite Share Download PDF Status: Published Journal Publication published 12 Apr, 2026 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 07 Mar, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 08 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8819257","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589882891,"identity":"742bdc75-04c4-470c-83b8-0f4a53231074","order_by":0,"name":"Jiaqing Dou","email":"","orcid":"","institution":"Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqing","middleName":"","lastName":"Dou","suffix":""},{"id":589882893,"identity":"54b8ec48-ae41-4f07-8189-5de44111146e","order_by":1,"name":"Shuya Zhang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shuya","middleName":"","lastName":"Zhang","suffix":""},{"id":589882896,"identity":"15cf5f67-5f41-4c96-99a2-53c00b1ccf30","order_by":2,"name":"Chaofan Ding","email":"","orcid":"","institution":"Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Chaofan","middleName":"","lastName":"Ding","suffix":""},{"id":589882903,"identity":"9f99edc2-60de-478f-b307-f7819652426c","order_by":3,"name":"Haoquan Li","email":"","orcid":"","institution":"Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Haoquan","middleName":"","lastName":"Li","suffix":""},{"id":589882914,"identity":"f52e6b14-029c-49b8-959a-9958e2741da4","order_by":4,"name":"Pengfei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYLCCCgaGBCDF+CChooZILWcgWpgNHpw5RpoWNsmHLcyEVZuz9x5+cYChLo9/dvu1isQGNgb+9u4EvFose86lWRxgYCuWuHOm7EbiDhkGiTNnN+DVYnAjx8z44z+exIYbOWk3Es+wMRhI5BLQcv+NmcEBBonE+UAtBYltzERoucFj/OAAg0HihhvpxxiI0mLZk2PGcIAhIXHjjRxmiYQzx3gI+sWc/YzxB2CIJc67kf7w44+KGjn+9l4CDgNGhwSEyWMAJvEqh2ph/gBhsj8gqHoUjIJRMApGJgAA4zdO6RVZrJAAAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen Research Institute of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-08 05:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8819257/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8819257/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-026-03175-3","type":"published","date":"2026-04-12T15:59:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102597669,"identity":"250ba416-0f46-4b00-aac2-116345d3b987","added_by":"auto","created_at":"2026-02-13 12:26:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":459017,"visible":true,"origin":"","legend":"\u003cp\u003eStudy population flow chart.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/3592931b522371c1ffbcdd77.png"},{"id":102597496,"identity":"412e2365-4eec-44ed-b739-b30799e079db","added_by":"auto","created_at":"2026-02-13 12:25:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":778422,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of CTI and CHG trajectory patterns with incident hypertension risk. (A, D) Scatter plots depicting cluster distributions for CTI (A) and CHG (D), identifying three distinct subgroups via K-means clustering; (B, E) Temporal evolution of the three trajectory patterns from Wave 1 to Wave 3; (C, F) Bar charts displaying the incidence of hypertension stratified by trajectory group.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/d7ffb9e61521c81782aac4ad.png"},{"id":102597768,"identity":"44e21ca3-8cee-49d5-9208-d1fe7539cbbe","added_by":"auto","created_at":"2026-02-13 12:26:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":281074,"visible":true,"origin":"","legend":"\u003cp\u003eRCS curves depicting the adjusted association of cuCTI and cuCHG with incident hypertension. (A) Dose-response association of the cuCTI index with the risk of incident hypertension; (B) Dose-response association of the cuCHG index with the risk of incident hypertension. Analyses were derived from the fully adjusted Cox proportional hazards model (Model 3). Solid lines denote estimated HRs, while shaded areas indicate 95% CIs. Background histograms show the percentage distribution of the cumulative indices in the study population.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/ffdb54f1c646f8178efda0b4.png"},{"id":102597542,"identity":"d4949542-75cf-4c8b-9ca5-1a6fc86d5661","added_by":"auto","created_at":"2026-02-13 12:25:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":319441,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves comparing the predictive performance of cuCTI and cuCHG for incident hypertension at 7-year (A) and 9-year (B) follow-up.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/60afb13da6111088d327020a.png"},{"id":102597577,"identity":"0bac477c-ba43-4341-a7ca-4d1ef77e225e","added_by":"auto","created_at":"2026-02-13 12:25:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":360897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratified analysis of the link between cumulative CTI and incident hypertension. Hazard ratios represent the risk per 1-unit increment derived from the fully adjusted Model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/62a7da78700f1e7c70e8d0c2.png"},{"id":102597570,"identity":"c601a23c-b370-4efb-9e70-7007496e5ac6","added_by":"auto","created_at":"2026-02-13 12:25:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":164758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses of the association between cumulative CHG and the risk of incident hypertension. Hazard ratios are calculated per 1-unit increase in the index based on the fully adjusted Model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/b0b7f050ac26e25e7beaf827.png"},{"id":106809662,"identity":"e43f359f-5ad1-4732-a7f4-a3df9c533d54","added_by":"auto","created_at":"2026-04-13 16:12:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4398877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/8e03e2d5-3b43-4a00-8b4f-62c0659abf3c.pdf"},{"id":102597500,"identity":"02306c0a-8e58-401a-b188-137e1a41202f","added_by":"auto","created_at":"2026-02-13 12:25:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13757,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-8819257/v1/dea571010ebd8788bc8ae315.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index versus the Cholesterol, high-density lipoprotein, and glucose index for incident hypertension prediction: a national cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCurrently, hypertension is increasingly prevalent and remains a major threat to global cardiovascular health, with recent estimates exceeding 1.4\u0026nbsp;billion affected individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, 44.7% of adults aged 35\u0026ndash;75 had hypertension in 2017 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Without adequate blood pressure control, permanent organ damage can occur and the risks of stroke, heart disease, and kidney disease increase markedly [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, cost-effective biomarkers that capture metabolic or inflammatory status are valuable for early risk assessment. Prior studies have shown that traditional risk factors such as cigarette smoking and alcohol consumption are closely linked to hypertension risk. Yet these conventional exposures often provide limited insight into the hemodynamic dysregulation underlying the hypertensive state [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Hypertension is now often viewed as more than a simple consequence of altered hemodynamics; rather, it reflects an interaction between metabolic disruption and chronic inflammation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and it is closely connected with insulin resistance (IR) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose (CHG) index has been proposed as a novel surrogate for IR. Developed initially to assess diabetes and IR [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], CHG has later been associated with cardiovascular disease [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and incident hypertension [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Notably, metabolic disturbance alone does not fully explain hypertension pathogenesis, and inflammation is a key contributing component [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this setting, the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose (CTI) index extends IR related information by adding an inflammatory dimension. CTI has been reported to aid prognostic assessment in malignancies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and to predict cardiac event risk [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, its association with new-onset hypertension has not been systematically examined. Therefore, this study assessed CTI in relation to incident hypertension and compared its predictive performance with the CHG index to improve identification of individuals at elevated risk.\u003c/p\u003e \u003cp\u003eMany earlier studies have depended on a single baseline measurement, and this approach limits the ability to capture within-person changes over time or to reflect the cumulative harm associated with long-term exposure. Available evidence indicates that an accumulated burden often predicts chronic disease more effectively than a one-time measure [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, longitudinal evidence on cumulative CTI (cuCTI) and cumulative CHG (cuCHG) in relation to incident hypertension among middle aged and older Chinese adults remains limited. This study aimed to evaluate the associations of cuCTI and cuCHG, together with their dynamic trajectory patterns, with the risk of incident hypertension. In addition, the predictive performance of cuCTI and cuCHG was further compared, with the goal of offering additional tools and perspectives for hypertension prevention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used data from the China Health and Retirement Longitudinal Study (CHARLS). This is a nationwide cohort managed by Peking University to track aging trends in China. The 2011 baseline survey used multi-stage probability sampling. It covered 150 counties and 450 villages in 28 provinces, including 17708 participants from about 10,000 households. Follow-up surveys are done every two years, and data from five waves (2011、2013、2015、2018、2020) are currently available. The data includes sociodemographics, health status, biochemical markers, and 13 physical exam indicators. Trained staff collected the data through face-to-face interviews. The study was approved by the Institutional Review Board of Peking University (No. 00001005-11,015), and all participants gave written informed consent. More details are provided by Zhao et al. [21] and on the official website (http://charls.pku.edu.cn/en).\u003c/p\u003e\n\u003cp\u003eWe initially included 17708 participants from Wave 1 (2011) and subsequently excluded participants based on the following criteria: (1) missing lab data in Wave 1 or Wave 3 (n=10227); (2) missing baseline demographics (n=5); (3) non-fasting blood samples (n=97); (4) loss to follow-up in Waves 4 and 5 (n=434); (5) age under 45 at baseline (n=173); and (6) hypertension diagnosed before Wave 4 (n=3971). After this screening, the final sample included 2801 participants (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of cuCHG and cuCTI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe calculation of CHG and CTI indices involved five key biomarkers: total cholesterol (TC), fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C), C-reactive protein (CRP), and triglycerides (TG). These two indices were determined using the following formulas: CHG =ln[TC (mg/dL) \u0026times; FPG (mg/dL) / 2 \u0026times; HDL-C (mg/dL)] [13]. CTI =0.412\u0026times;ln(CRP [mg/L])+ln(TG [mg/dL] \u0026times; FPG [mg/dL])/2 [18]. We derived cumulative indices by averaging values from 2012 and 2015, multiplied by the time elapsed between assessments: cumulative Index = (Index2012 + Index2015) / 2 \u0026times;\u0026nbsp;time interval (2015\u0026minus;2012). This method follows the approach for cumulative metabolic indices used in previous CHARLS studies by Yang [22] and Ma [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAscertainment of Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypertension was defined as the presence of at least one of the following indicators: (1) a self-reported history of a physician diagnosis; (2) current use of blood pressure-lowering medication; or (3) physical examination findings showing systolic blood pressure (SBP) \u0026ge;140 mmHg or diastolic blood pressure (DBP) \u0026ge;90 mmHg in two or more measurements. Participants who met any of these criteria during the 2018 or 2020 follow-up visits were classified as having new-onset hypertension [24，25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Variables and Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline covariates were gathered through structured interviews conducted by trained personnel. Sociodemographic and lifestyle variables included age, sex, urban or rural residence, education, marital status, and health behaviors (smoking/alcohol consumption). Anthropometric assessments followed standardized protocols to record height, weight, and waist circumference (WC). Medical history was ascertained via self-report, covering physician-diagnosed comorbidities such as diabetes and dyslipidemia, as well as concurrent medication regimens. Finally, venous blood samples were analyzed for key biomarkers, including FPG, HbA1c, CRP, and lipid profiles (TC, TG, HDL-C, LDL-C). Diabetes was defined as a self-reported diagnosis, use of antidiabetic medication, or an FPG \u0026ge; 7.0 mmol/L; other comorbidities relied on self-reported history or ongoing treatment. For subgroup analyses, the baseline cohort was stratified into a normal blood pressure group as well as an elevated blood pressure group. Elevated blood pressure was defined as SBP 120\u0026ndash;139 mmHg or DBP 70\u0026ndash;89 mmHg [26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were summarized using standard descriptive statistics. Continuous variables that followed an approximately normal distribution are presented as mean (SD), and group differences were assessed using Student\u0026rsquo;s t-test. Continuous variables showing a skewed distribution are presented as the median with interquartile range (IQR) and were examined using the Mann\u0026ndash;Whitney U test. Categorical variables are reported as counts with percentages and were compared using Pearson\u0026rsquo;s chi-square test. All analyses were carried out in Python version 3.12 and R version 4.3.0, and two-sided p \u0026lt; 0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariable and multivariable Cox proportional hazards models were used to assess how the cuCTI and cuCHG indices relate to hypertension risk, with findings expressed as hazard ratios (HRs) together with 95% confidence intervals (CIs). Both indices were entered into the models in two ways, namely as continuous measures and after being treated as categorical variables. To assess multicollinearity, variance inflation factors (VIFs) were computed. All VIFs remained below 5, indicating no evidence of multicollinearity (Table S1). Three nested models were fitted: Model 1 used no adjustment, Model 2 took age, sex, residence, education, marital status, smoking, alcohol consumption, and body mass index (BMI) into account, and Model 3 was fully adjusted by additionally including all comorbidities beyond Model 2. To keep the sample size as large as possible, missing values were handled by applying multiple imputation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor trajectory analysis, participants were assigned to groups by relying on measurements from Wave 1 and Wave 3. In practice, K-means clustering was paired with the elbow method to decide the number of groups, leading to three clusters: Cluster 1 (stable low level), Cluster 2 (moderate level), and Cluster 3 (stable high level). With Cluster 1 taken as the reference category, the associations between trajectory groups and incident hypertension were evaluated by applying the fully adjusted Cox model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo check whether the associations might deviate from linearity, restricted cubic splines (RCS) were fitted within the fully adjusted model. In light of the Bayesian information criterion, the preferred specification used four knots placed at the 5th, 35th, 65th, and 95th percentiles, and evidence for non-linearity was tested using a likelihood ratio test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate predictive performance, time-dependent receiver operating characteristic (ROC) curves were generated for 7-year and 9-year follow-up, and areas under the curve (AUCs) were compared by applying the DeLong test. To further contrast the predictive ability of cuCTI versus cuCHG, the restricted Harrell\u0026rsquo;s C-index, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also calculated. Differences were assessed via bootstrap resampling within each imputed dataset, and estimates were then pooled using Rubin\u0026rsquo;s rules.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, subgroup analyses were performed based on the fully adjusted model to examine potential heterogeneity, with stratification by sex, age, residence, BMI, lifestyle factors, history of diabetes and baseline blood pressure status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of the findings, five sensitivity analyses were conducted. First, participants with extreme cuCTI or cuCHG values, defined as outliers beyond mean \u0026plusmn; 3 SD, were excluded and the analysis was repeated. Second, a complete-case analysis was performed by including only participants with full data. Third, the results were verified using a fully adjusted logistic regression model. Then, with all-cause death treated as a competing event, we performed a sensitivity analysis using the Fine\u0026ndash;Gray subdistribution hazards model to assess the associations between cuCTI and cuCHG and incident hypertension. Finally, the E-value was calculated to estimate the potential impact of unmeasured confounding: E = RR + \u0026radic;[RR \u0026times; (RR \u0026minus; 1)] [27].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the baseline characteristics of the study population stratified by incident hypertension. Out of 2801 participants, 437 (15.6%) developed hypertension during follow-up. The median age was 56 years, with an interquartile range (IQR) of 50\u0026ndash;61 years, and females accounted for 55.8% of the sample. In comparison with the non-hypertension group, participants who developed hypertension were older (median: 57 vs. 56 years, P = 0.001), and they were more likely to have no formal education (54.2% vs. 46.3%) and to live in rural areas (72.8% vs. 66.5%; all P \u0026lt; 0.05). Participants in this group also exhibited higher baseline BMI, WC, and blood pressure levels (systolic and diastolic) (all P \u0026lt; 0.001). Similarly, higher TG, FPG, HbA1c, and CRP levels were observed (all P \u0026le; 0.005). The cuCTI index was significantly higher among those who developed hypertension, at 14.40 (IQR: 13.35\u0026ndash;15.39), compared with 13.80 (12.96\u0026ndash;14.76) in those without hypertension (P \u0026lt; 0.001), and a similar trend was observed for the cuCHG index (15.69 [15.10\u0026ndash;16.35] vs. 15.50 [14.94\u0026ndash;16.14], P \u0026lt; 0.001). However, no significant differences were observed for sex, marital status, lifestyle habits such as smoking and drinking, other lipid parameters including HDL-C, LDL-C, and TC, or comorbidities such as diabetes, heart disease, stroke, and kidney disease (all P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCox proportional hazards models evaluating the associations of cuCTI and cuCHG indices with the risk of incident hypertension.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the cuCTI index, a 1-unit increase was linked to a 21% higher risk of hypertension in Model 1 (HR=1.21, 95% CI 1.14\u0026ndash;1.28, P\u0026lt;0.001). This association remained significant in the fully adjusted Model 3 (HR=1.22, 95% CI 1.14\u0026ndash;1.29, P\u0026lt;0.001). Similarly, we found that a 1-unit increase in cuCHG corresponded to a 19% risk increase in the unadjusted model (HR=1.19, 95% CI 1.09\u0026ndash;1.30, P\u0026lt;0.001). Models 2 and 3 supported this, both showing an HR of 1.20 (P\u0026lt;0.001). In categorical analyses, we observed a significant positive trend for both indices (P for trend \u0026lt; 0.001). Specifically, participants in the highest quartile (Q4) had a much higher risk compared to the lowest quartile for both cuCTI (HR=2.04, 95% CI 1.55\u0026ndash;2.68, P\u0026lt;0.001) and cuCHG (HR=1.58, 95% CI 1.20\u0026ndash;2.06, P\u0026lt;0.001) in the fully adjusted models (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations of CTI and CHG exposure trajectories with the risk of incident hypertension\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree trajectory patterns were identified for both CTI and CHG, namely low-stable, moderate, and high-stable. Hypertension incidence rose across CTI trajectories (11.5%, 15.7%, 23.6%) and likewise across CHG trajectories (13.2%, 16.1%, 20.5%). After full adjustment in Cox models, the moderate and high-stable groups showed significantly higher hazards than the low-stable reference, with a larger magnitude of association for CTI (HR = 1.39 and 2.24) than for CHG (HR = 1.28 and 1.68; all P \u0026lt; 0.05). Overall, this graded pattern suggests a dose\u0026ndash;response relationship, and the high-stable trajectory showed the highest hypertension risk (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDose-response associations of cuCTI and cuCHG with incident hypertension: An RCS analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant positive associations were observed for both cuCTI and cuCHG with hypertension risk (P for overall association \u0026lt; 0.001). For cuCTI, no evidence of nonlinearity was observed (P for nonlinearity = 0.452), indicating a continuous increase in risk without an evident threshold. cuCHG showed a similar linear trend, with P for nonlinearity = 0.382. Overall, each index is independently associated with hypertension risk across the continuous range, thus lowering cumulative exposure across that range could support primary prevention, as illustrated in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative predictive performance of cuCTI and cuCHG indices for incident hypertension.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt 7 years, cuCTI yielded an AUC that was modestly higher than that of cuCHG (0.605 vs. 0.583), yet this difference did not reach statistical significance (P = 0.086). At 9 years, the DeLong test identified a significant difference, and cuCTI maintained a higher AUC (CTI: 0.622, 95% CI 0.593\u0026ndash;0.651 vs. CHG: 0.595, 95% CI 0.565\u0026ndash;0.625, P = 0.010). Overall, this pattern suggests that cuCTI has greater prognostic value for long-term risk assessment (Figure 4). In the fully adjusted model, adding cuCTI increased the C-index from 0.564 to 0.605 at 7 years (\u0026Delta;C-index=0.041, P=0.006), whereas adding cuCHG increased it to 0.582 (\u0026Delta;C-index=0.018, P=0.064). At the 9-year mark, cuCTI increased the C-index from 0.578 to 0.615 (\u0026Delta;C-index=0.037, P=0.002), but the change for cuCHG was not significant (\u0026Delta;C-index=0.012, P=0.129). Although both indices showed better reclassification and discrimination, cuCTI had larger NRI/IDI gains (7-year: 0.317/0.110 vs. 0.140/0.054; 9-year: 0.318/0.107 vs. 0.152/0.046) and significantly greater improvements in NRI and IDI at both time points (all P\u0026lt;0.05; Tables 3 and 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses continued to demonstrate consistent associations between higher cumulative indices and hypertension risk across groups defined by sex, age, BMI, and lifestyle, and no significant interactions were observed (P for interaction \u0026gt; 0.05). A similar pattern was observed in both the \u0026lt;60-year and \u0026ge;60-year cohorts. Upon stratification by baseline blood pressure, the association was notably stronger among those starting with normal blood pressure: HRs were 1.30 with 95% CI 1.17\u0026ndash;1.45 for cuCTI and 1.20 with 95% CI 1.02\u0026ndash;1.42 for cuCHG. For participants with elevated baseline blood pressure, the association was smaller but still significant (cuCTI: HR 1.15, 95% CI 1.06\u0026ndash;1.24; cuCHG: HR 1.15, 95% CI 1.03\u0026ndash;1.30), suggesting a more important role during the early stages of hemodynamic disturbance, as shown in Figures 5 and 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analyses supported the robustness of the findings (Table 5). Results remained consistent with the primary analysis after removing outliers, restricting analyses to complete cases, and using a fully adjusted logistic regression model (HRs and ORs per 1-unit increase ranged from 1.20\u0026ndash;1.24 for cuCTI and 1.17\u0026ndash;1.22 for cuCHG). Competing-risk analyses treating all-cause death as a competing event showed similar associations in full adjusted Fine\u0026ndash;Gray models (cuCTI: SHR = 1.19, 95% CI 1.12\u0026ndash;1.27; cuCHG: SHR = 1.16, 95% CI 1.05\u0026ndash;1.27). E-values of 1.73 for cuCTI and 1.69 for cuCHG indicated that relatively strong unmeasured confounding would be required to fully explain these associations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings align with earlier work showing that metabolic factors as well as inflammatory factors are associated with hypertension risk [\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this analysis, the predictive ability of cuCTI and cuCHG was assessed in middle-aged and older Chinese adults. After adjustment for lifestyle variables and comorbid conditions, both cumulative indices remained approximately linearly associated with incident hypertension, indicating that higher long-term exposure was associated with a higher risk of developing hypertension. The estimated associations also remained robust across Models 1\u0026ndash;3, with minimal HR fluctuation (\u0026lt;\u0026thinsp;0.02), suggesting that these cumulative measures retain predictive value that does not simply reflect traditional risk factors. The contributions of diabetes, heart disease, and stroke appeared limited, which is likely attributable to the low prevalence of these conditions in the cohort (1.0%\u0026ndash;7.9%) and by the fact that they were not strongly associated with the indices. When the two indices were contrasted directly, cuCTI repeatedly provided better discrimination than cuCHG, as reflected by larger improvements in NRI and IDI at both follow-up points. The trajectory analysis further indicated that participants who maintained persistently high index levels over time carried the greatest risk [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, subgroup analyses suggested that cumulative exposure exerted a stronger effect among participants with normal baseline blood pressure than among those who already had elevated blood pressure. Consistent with this pattern, cuCTI showed a stronger association than cuCHG (HR: 1.30 vs. 1.20), suggesting that inflammatory\u0026ndash;metabolic burden may contribute to the early onset of hypertension rather than merely aggravating an already elevated state, which in turn underscores the value of early intervention.\u003c/p\u003e \u003cp\u003eThe connection between CTI and cardiovascular disease has been well documented [\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and single-time-point CHG has likewise been linked to hypertension [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. K-means clustering was employed to characterize longer-term patterns, and several distinct trajectory groups were identified. However, the trajectories showed no clear temporal trend, suggesting that risk differences mainly reflect baseline levels rather than substantial changes over time. Future work would therefore benefit from examining populations in which these levels show greater fluctuation across follow-up.\u003c/p\u003e \u003cp\u003eThe biological basis linking CTI/CHG to incident hypertension has not been fully clarified, and it likely operates through multiple, partly overlapping pathways, including disrupted glucose and lipid metabolism, insulin resistance, inflammation, and oxidative stress [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The present results nevertheless illustrate how inflammatory status together with metabolic dysregulation can shape the likelihood of developing hypertension, supporting the view that hypertension onset may reflect the combined influence of inflammatory and metabolic abnormalities. Several limitations should also be recognized. First, many participants were excluded due to missing key laboratory measures at the first or third survey wave or because hypertension had already been diagnosed, and this exclusion process could have introduced selection bias. Second, although objective measurements (rather than self-reports) were used to improve ascertainment [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], some degree of underdiagnosis may still have occurred. Third, although the E-value analysis suggested that only a confounder of considerable strength could account for the observed associations, unmeasured confounding, such as salt intake and genetic predisposition, may still have influenced the magnitude of the observed risk effects linking the two cumulative indices to incident hypertension [\u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Finally, because the sample was restricted to Chinese adults aged 45 years and older, the generalizability of these findings to other populations remains uncertain.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBoth cuCTI and cuCHG indices showed a clear linear link to new-onset hypertension risk in middle-aged and older Chinese adults. Specifically, cuCTI includes an inflammatory marker, and it performed significantly better than cuCHG in predicting long-term risk. Trajectory analysis also indicated that the highest risk was seen in individuals who maintained high index levels. These results suggest that monitoring cumulative inflammatory and metabolic burden is valuable for early identification and prevention of hypertension.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC Area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI Body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHARLS China health and retirement longitudinal study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHG Cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI Confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCTI C-reactive protein-triglyceride-glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIR insulin resistance\u003c/p\u003e\n\u003cp\u003ecuCHG Cumulative cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ecuCTI Cumulative C-reactive protein-triglyceride-glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP Diastolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFPG Fasting plasma glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHbA1c Hemoglobin A1c\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHDL-C High-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR Hazard ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHTN Hypertension\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR Interquartile range\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL-C Low-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNRI Net reclassification improvement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR Odds ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCS Restricted cubic spline\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC-index concordance index\u003c/p\u003e\n\u003cp\u003eSBP Systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD Standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC Total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG Triglyceride RR Relative risk\u003c/p\u003e\n\u003cp\u003eVIF variance inflation factor\u003c/p\u003e\n\u003cp\u003eWC waist circumference\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the CHARLS team for their hard work and every participant who gave their time and data to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJD: Writing \u0026ndash; original draft, Formal analysis, Investigation, Methodology, Visualization. SZ: Formal analysis. HL: Data curation. DC: HL: Data curation. PZ: Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Funding acquisition, Project administration. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (Grant No. 2023YFC2506502) and the Shenzhen Science and Technology Program (Nos. JCYJ20220818103407015, GJHZ20240218113401003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study are available in the CHARLS database (http://charls.pku.edu.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS study was approved by the Institutional Review Board of Peking University (IRB00001052-11015 for the household survey and IRB00001052-11014 for blood samples). All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e State Key Laboratory for Innovation and Transformation of Luobing Theory; Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences; Department of Cardiology, Qilu Hospital of Shandong University, Jinan, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003eDepartment of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003eInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2023 Causes of Death Collaborators. \u0026ldquo;Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.\u0026rdquo; 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JMIR mHealth and uHealth vol. 9,10 e26233. 21 Oct. 2021, doi:10.2196/26233.\u003c/li\u003e\n\u003cli\u003eMani, Arya. \u0026ldquo;Update in genetic and epigenetic causes of hypertension.\u0026rdquo; Cellular and molecular life sciences : CMLS vol. 81,1 201. 30 Apr. 2024, doi:10.1007/s00018-024-05220-4.\u003c/li\u003e\n\u003cli\u003eZheng, Zhihao et al. \u0026ldquo;Sleep quality and incident hypertension.\u0026rdquo; Revista espanola de cardiologia (English ed.) vol. 78,7 (2025): 600-608. doi:10.1016/j.rec.2024.12.003.\u003c/li\u003e\n\u003cli\u003eXiao, Zhihao et al. \u0026ldquo;Night Shift Work, Genetic Risk, and Hypertension.\u0026rdquo; Mayo Clinic proceedings vol. 97,11 (2022): 2016-2027. doi:10.1016/j.mayocp.2022.04.007.\u003c/li\u003e\n\u003cli\u003eWeng, Zhenkun et al. \u0026ldquo;Associations of genetic risk factors and air pollution with incident hypertension among participants in the UK Biobank study.\u0026rdquo; Chemosphere vol. 299 (2022): 134398. doi:10.1016/j.chemosphere.2022.134398.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable. 1\u003c/strong\u003e Baseline characteristics of the study population. HTN, hypertension; WC, waist circumference; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; CRP, C-reactive protein; CTI, C-reactive protein-triglyceride-glucose; cuCTI, cumulative C-reactive protein-triglyceride-glucose; CHG, cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose; cuCHG, cumulative cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=2801)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HTN (n=2364)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHTN (n=437)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.00 (50.00-61.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.00 (50.00-61.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e57.00 (51.00-62.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.356\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1562 (55.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1309 (55.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e253 (57.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1239 (44.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1055 (44.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e184 (42.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo formal education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1332 (47.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1095 (46.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e237 (54.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary school\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e577 (20.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e490 (20.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e87 (19.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle school\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e583 (20.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e509 (21.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e74 (16.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh school\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e309 (11.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e270 (11.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39 (8.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.771\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e331 (11.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e277 (11.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e54 (12.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarried\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2467 (88.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2084 (88.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e383 (87.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e912 (32.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e793 (33.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e119 (27.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1889 (67.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1571 (66.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e318 (72.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC, cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.00 (76.00-89.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.60 (75.80-88.40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.40 (78.50-91.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.50 (20.56-24.76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.38 (20.44-24.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.18 (21.25-25.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP, mmHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e114.00 (106.62-122.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e113.50 (106.00-121.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e119.00 (111.50-127.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP, mmHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.50 (63.00-75.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.50 (62.50-74.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.00 (65.00-77.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.852\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1941 (69.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1635 (69.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e306 (70.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e812 (29.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e687 (29.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e125 (28.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking status, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.997\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1867 (66.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1575 (66.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e292 (66.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e922 (32.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e777 (32.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e145 (33.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50.26 (41.37-60.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50.64 (41.75-60.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e49.10 (40.21-59.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.068\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e112.50 (93.17-134.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e112.11 (92.78-133.76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e114.43 (93.56-137.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e186.73 (165.08-209.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e185.57 (165.46-209.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e192.14 (164.69-214.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.095\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.35 (70.80-140.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.02 (69.92-138.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e107.97 (76.11-153.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFPG, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.80 (93.42-109.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.44 (93.24-109.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e102.42 (94.68-111.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.10 (4.90-5.40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.10 (4.90-5.40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.20 (4.90-5.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP, mg/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.81 (0.48-1.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.78 (0.47-1.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96 (0.54-2.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e97 (3.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e76 (3.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21 (4.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.131\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart disease, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e219 (7.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e174 (7.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45 (10.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.051\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29 (1.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23 (1.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 (1.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.631\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKidney disease, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e158 (5.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e128 (5.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30 (6.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.285\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTI (Wave 1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.55 (4.24-4.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.52 (4.22-4.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.68 (4.35-5.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTI (Wave 3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.70 (4.35-5.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.68 (4.32-5.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.81 (4.48-5.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecuCTI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.90 (13.02-14.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.80 (12.96-14.76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.40 (13.35-15.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHG (Wave 1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.22 (5.00-5.48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.21 (4.99-5.47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.27 (5.04-5.55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHG (Wave 3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.12 (4.93-5.34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.11 (4.92-5.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.17 (4.98-5.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecuCHG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.53 (14.96-16.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.50 (14.94-16.14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.69 (15.10-16.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 2\u003c/strong\u003e Associations of cuCTI and cuCHG indices with incident hypertension. Model 1: Unadjusted for any covariates. Model 2: Adjusted for sociodemographic factors, including age, gender, residence, education level, marital status, smoking status, drinking status, and BMI. Model 3: Fully adjusted model, adjusting for age, gender, residence, education level, marital status, smoking status, drinking status, BMI, and comorbidities.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"701\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eEvents（n）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eCumulative CTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Per 1-unit increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.21 (1.14-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.22 (1.15-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.22 (1.14-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.03 (0.75-1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.02 (0.75-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.02 (0.75-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.63 (1.23-2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.63 (1.23-2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.62 (1.22-2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.99 (1.52-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2.07 (1.58-2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.04 (1.55-2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; P for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eCumulative CHG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Per 1-unit increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.19 (1.09-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.20 (1.10-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.20 (1.09-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.05 (0.79-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.06 (0.80-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.07 (0.80-1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.20 (0.91-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.22 (0.92-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.22 (0.92-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.51 (1.16-1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.59 (1.22-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.58 (1.20-2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp; P for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 3\u003c/strong\u003e Incremental predictive value of cuCTI and cuCHG at 7- and 9-year follow-up. cuCTI, cumulative C-reactive protein-triglyceride-glucose; cuCHG, cumulative cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose; NRI, net reclassification improvement; C-index, concordance index; IDI, integrated discrimination improvement; CI, confidence interval. Fully adjusted models included: age, gender, rural residency, education level, marital status, smoking status, drinking status, BMI, diabetes, heart disease, stroke, and kidney disease.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"127%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003eNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 32px;\"\u003e\n \u003cp\u003eIDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eEstimate (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eEstimate (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eEstimate (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7-year follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFully adjusted model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.564 (0.528\u0026ndash;0.600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFully adjusted model + cuCHG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.582 (0.545\u0026ndash;0.620)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.1400 (0.0137\u0026ndash;0.2663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.0541 (0.0241\u0026ndash;0.0840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFully adjusted model + cuCTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.605 (0.568\u0026ndash;0.641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.3174 (0.1943\u0026ndash;0.4405)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.1097 (0.0623\u0026ndash;0.1572)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e9-year follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFully adjusted model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.578 (0.550\u0026ndash;0.606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFully adjusted model + cuCHG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.590 (0.562\u0026ndash;0.619)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.1517 (0.0496\u0026ndash;0.2537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.0459 (0.0212\u0026ndash;0.0705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eFully adjusted model + cuCTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.615 (0.588\u0026ndash;0.643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.3180 (0.2172\u0026ndash;0.4188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.1069 (0.0680\u0026ndash;0.1458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 4\u003c/strong\u003e Comparisons of incremental predictive performance between cuCTI and cuCHG at 7- and 9-years. cuCTI, cumulative C-reactive protein-triglyceride-glucose; cuCHG, cumulative cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose; NRI, net reclassification improvement; C-index, concordance index; IDI, integrated discrimination improvement; CI, confidence interval. \u0026Delta;, the difference between cuCTI and cuCHG in their incremental improvement over the fully adjusted model.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"78%\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003ecuCTI vs cuCHG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026Delta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e7-year follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.023 (-0.002\u0026ndash;0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.1774 (0.0340\u0026ndash;0.3208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eIDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.0556 (0.0195\u0026ndash;0.0919)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e9-year follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.025 (0.006\u0026ndash;0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.1663 (0.0552\u0026ndash;0.2775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eIDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.0610 (0.0296\u0026ndash;0.0926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, C-reactive protein-triglyceride-glucose index, Cholesterol, high-density lipoprotein, glucose index, Cumulative exposure, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-8819257/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8819257/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile the C-reactive protein-triglyceride-glucose (CTI) and cholesterol\u0026ndash;high-density lipoprotein\u0026ndash;glucose (CHG) indices have emerged as potent surrogates for inflammatory-metabolic status, the long-term effects of their sustained accumulation are not yet clearly understood. Specifically, the predictive divergence between cumulative CTI (cuCTI) and CHG (cuCHG) in determining incident hypertension remains a critical knowledge gap in aging Chinese cohorts.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eLeveraging a nationwide longitudinal cohort from the China Health and Retirement Longitudinal Study (CHARLS), we modeled the cumulative burden of CTI and CHG by integrating temporal data from Wave 1 through Wave 3. We then used multivariable Cox proportional hazards models to assess associations and restricted cubic splines (RCS) for dose-response relationships. K-means clustering identified trajectory patterns. To gauge the predictive performance at 7 and 9 years, we analyzed time-dependent ROC curves, alongside the C-index, NRI, and IDI. Finally, our findings were further subjected to subgroup and sensitivity analyses to test their robustness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring follow-up, 437 (15.6%) participants developed hypertension. Both elevated cuCTI and cuCHG significantly increased hypertension risk. Multivariable Cox regression analysis unveiled a clear difference in risk magnitude: participants in the highest quartile of cuCTI faced a two-fold risk of hypertension (HR\u0026thinsp;=\u0026thinsp;2.04; 95% CI: 1.55\u0026ndash;2.68), surpassing the 58% increase seen with cuCHG (HR\u0026thinsp;=\u0026thinsp;1.58; 95% CI: 1.20\u0026ndash;2.06). Crucially, cuCTI demonstrated superior predictive accuracy in time-dependent ROC analysis (9-year DeLong P\u0026thinsp;=\u0026thinsp;0.010). Adding cuCTI to the fully adjusted model significantly improved the C-index at both 7 and 9 years, whereas cuCHG did not. Furthermore, cuCTI showed stronger gains in NRI and IDI compared to cuCHG (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subgroup and sensitivity analyses also showed consistent results.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlthough both indices serve as independent predictors, cuCTI offers superior predictive power, likely by capturing the synergistic detriment of systemic inflammation and insulin resistance. These findings substantiate the imperative of monitoring cumulative inflammatory-metabolic load for the early stratification of hypertension risk.\u003c/p\u003e","manuscriptTitle":"Cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index versus the Cholesterol, high-density lipoprotein, and glucose index for incident hypertension prediction: a national cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 12:23:20","doi":"10.21203/rs.3.rs-8819257/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-07T05:13:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T06:35:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64217648976427714176732858268857042455","date":"2026-02-13T09:54:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280671126640884142244001659212828403691","date":"2026-02-11T14:09:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T06:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67943871183394399606756422929799224042","date":"2026-02-10T02:08:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T20:24:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T20:17:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T15:58:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2026-02-08T05:09:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08f91063-eb22-43f1-8afb-b8e40cf2e075","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:08:47+00:00","versionOfRecord":{"articleIdentity":"rs-8819257","link":"https://doi.org/10.1186/s12933-026-03175-3","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2026-04-12 15:59:22","publishedOnDateReadable":"April 12th, 2026"},"versionCreatedAt":"2026-02-13 12:23:20","video":"","vorDoi":"10.1186/s12933-026-03175-3","vorDoiUrl":"https://doi.org/10.1186/s12933-026-03175-3","workflowStages":[]},"version":"v1","identity":"rs-8819257","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8819257","identity":"rs-8819257","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}