Dual Layer Association of the C-Reactive Protein Triglyceride Glucose Index with Cardiovascular–Kidney–Metabolic Syndrome among Older Chinese Adults

Dual Layer Association of the C-Reactive Protein Triglyceride Glucose Index with Cardiovascular–Kidney–Metabolic Syndrome among Older Chinese Adults | 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 Dual Layer Association of the C-Reactive Protein Triglyceride Glucose Index with Cardiovascular–Kidney–Metabolic Syndrome among Older Chinese Adults JiaHao Shi, Anuchit Phanumartwiwath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7628351/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: The cardiovascular–kidney–metabolic (CKM) syndrome reconceptualizes multimorbidity as a progressive, multisystem disorder. Yet, existing research focuses mainly on disease staging, neglecting the distinction between optimal health and any CKM risk burden. The C-reactive protein–triglyceride–glucose (CTI) index reflects both inflammation and insulin resistance; however, its significance in CKM has not been rigorously evaluated. Methods: We examined data from 10,316 persons aged 45 years and older in the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort. We evaluated the association between CTI and (1) CKM presence (CKM vs. no CKM), and (2) stage-specific severity. Binary logistic, ordinal, multi-level binary logistic, and multinomial regression models were developed, controlling for an extensive array of covariates. A thorough series of sensitivity and robustness studies were conducted, encompassing E-value computation to evaluate the potential impact of unmeasured confounding, outlier-trimmed models, CTI tertile specification, and several propensity score methodologies (IPTW and 1:1 matching). Model diagnostics encompassed evaluations of multicollinearity, model fit (McFadden’s pseudo R²), and the proportional odds assumption using the Brant test. Robustness was additionally corroborated by convergence across several modeling approaches and studies stratified by geographic regions (East, Central, West China). Results: CTI had a positive and consistent association with CKM syndrome across all models. In fully adjusted binary logistic regression, each unit increase in CTI corresponded to significantly elevated odds of CKM (OR = 2.57; 95% CI: 2.02–3.27; p < 0.001). Tertile-based studies revealed a dose–response gradient, with the highest CTI tertile linked to a 15.02-fold increase in CKM chances relative to the lowest tertile. In ordinal and multi-level binary logistic models, CTI consistently shown a significant association with escalating CKM stage severity. Multinomial regression indicated no significant association with Stage 1 (isolated adiposity), but demonstrated robust relationships with Stage 2 (OR = 3.60; 95% CI: 2.91–4.44; p < 0.001), Stage 3 (OR = 4.07; 95% CI: 3.29–5.04; p < 0.001), and Stage 4 (OR = 4.19; 95% CI: 3.39–5.19; p < 0.001). Model diagnostics indicated the absence of multicollinearity and demonstrated a satisfactory model fit. The E-value analysis (E = 4.58) indicates that unmeasured variables must have an exceptionally strong correlation with both CTI and CKM to completely account for the observed association. The results remained strong after excluding CTI outliers, employing tertile-based categorization, and utilizing both inverse probability weighting and 1:1 propensity score matching. Regional stratification demonstrated consistent relationships in the eastern (OR = 2.76), central (OR = 2.97), and western (OR = 2.17) regions, with overlapping confidence ranges, so affirming geographic generalizability. The findings remained consistent across several modeling methodologies, risk classifications, and sensitivity analyses. Conclusion: This study provides the first nationally representative evidence of a dual-layer association between the C-reactive protein–triglyceride–glucose (CTI) index and cardiovascular–kidney–metabolic (CKM) syndrome—linking CTI both to the presence of any CKM risk and to stratified stage severity. Crucially, CTI was not associated with isolated adiposity (Stage 1), but demonstrated strong associations with advanced stages (Stages 2–4), highlighting its specificity for systemic metabolic-inflammatory dysfunction rather than general adiposity. These findings position CTI as a cost-effective, stage-sensitive biomarker for syndromic risk detection and stratification in aging populations. CKM Syndrome C-reactive Protein Triglyceride Glucose Index Syndromic Biomarker Integration Population-Based Precision Screening Older Adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction The advent of Cardiovascular–Kidney–Metabolic (CKM) syndrome signifies a transformative change in the comprehension and management of multimorbidity in aging populations. In 2023, the American Heart Association presented an integrative paradigm that recognizes the intricate pathophysiological connections among cardiovascular disease, chronic renal disease, diabetes, and obesity—conditions that have historically been treated in isolation (April-Sanders, 2024; Massy & Drueke, 2024). CKM syndrome reframes these problems not as isolated endpoints but as a progressive multisystem continuum, employing a five-stage classification system that spans from ideal health (Stage 0) to overt cardiovascular disease accompanied by metabolic comorbidities (Stage 4). In swiftly aging societies like China, where more than 75% of elderly individuals exhibit clustered cardiometabolic risk markers, the CKM construct holds substantial implications for population risk classification, early intervention, and systemic resource allocation (April‐Sanders, 2024; Y. Ding et al., 2024). As the global burden of interconnected chronic disease grows, the need for scalable tools that capture cross-organ dysfunction—has never been more urgent (Hu et al., 2025; Javaid et al., 2025; W. Li et al., 2024; J. Tang et al., 2024; Yim et al., 2025). Although there is growing agreement on cardiovascular–kidney–metabolic (CKM) syndrome as a cohesive pathophysiological entity, existing risk assessment methods continue to rely on organ-specific indicators that fail to accurately represent its systemic characteristics. Traditional indicators—such as fasting glucose for glycemic regulation, serum creatinine for renal function, and blood pressure for cardiovascular strain—are analyzed in isolation, concealing the synergistic biological interactions that contribute to CKM-related decline (Fong, Sia, & See, 2025; Hassanein & Shafi, 2022; Mancianti et al., 2025; Massy & Drueke, 2024; H. Zhang et al., 2025). Although the American Heart Association has recently proposed a CKM staging framework that integrates excess adiposity, metabolic risk, kidney impairment, and cardiovascular burden into a progressive classification, real-world application of this model remains constrained by the lack of integrative biomarkers capable of capturing multisystem dysfunction within a single measure (Huang, Li, & Cho, 2023; Javaid et al., 2025; Li & Wei, 2025; W. Li et al., 2024; Rysz et al., 2017; J. Tang et al., 2024). The disjunction between conceptual syndromic models and fragmented biomarker application generates blind spots, especially in recognizing individuals who may already display early multisystem risk despite presenting with borderline results in conventional measurements (W. Ding et al., 2024; Javaid et al., 2025; Sung et al.; Szabóová et al., 2021; Zhou et al., 2024). Moreover, the majority of clinical or epidemiological evaluations concentrate specifically on assessing severity among individuals already identified as at risk, rather than addressing the fundamental necessity to differentiate between those with any CKM burden and those possessing optimal systemic health—a vital distinction for comprehensive population risk surveillance (Dong et al., 2025; Lu et al., 2025). Additionally, current epidemiological studies frequently concentrate solely on the progression of CKM "stages," neglecting a vital distinction: the boundary between the absence of CKM-related risk and the onset of any developing danger. Effectively differentiating persons with optimal health from those on the CKM spectrum could enhance public health policies and clinical triage. To our knowledge, no previous study has employed an integrated analytic methodology that concurrently assesses the presence of CKM risk and its eventual severity. An effective biomarker must have applicability throughout the majority of stages (Dong et al., 2025; Gao et al., 2024; Hu et al., 2025; W. Li et al., 2024; Oleske, 2010; Tain & Hsu, 2024; H. Zhang et al., 2025). One promising candidate is the C-reactive protein–triglyceride–glucose (CTI) index, a composite marker that integrates low-grade systemic inflammation (CRP) with metabolic stress and insulin resistance (TyG index) (S. Tang et al., 2024). CRP serves as a recognized marker of vascular inflammation and endothelial damage, whilst the TyG index has been corroborated as a proxy for insulin resistance and lipotoxicity (Erdoğan et al., 2023; J. Li et al., 2024; Rizo-Téllez, Sekheri, & Filep, 2023). By integrating these two dimensions, CTI may provide a more comprehensive representation of the multisystem stress inherent in CKM syndrome. Significantly, although CTI has been linked to diabetes, stroke, and cardiovascular outcomes, its association with CKM syndrome—a framework that explicitly integrates cardiovascular, renal, and metabolic domains—remains unexplored (Huo et al., 2025; Shan, Liu, & Gao, 2025; Xu et al., 2024). To address this disparity, we utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort of middle-aged and elderly individuals (China Center for Economic Research, n.d.). Unlike prior studies that isolate CKM stage or specific endpoints, our study is the first to systematically assess the association between CTI and CKM syndrome across two complementary dimensions: (1) the presence of any CKM-related risk burden, and (2) the severity gradient spanning from early adiposity to clinical cardiovascular disease. This dual-layered framework allows us to ascertain not only if CTI correlates with CKM risk, but also if it can distinguish between stages of escalating complexity and clinical danger. Additionally, we investigate whether the association between CTI and CKM persists across the varied geographic regions of China—East, Central, and West. This stratified method improves the generalizability and public health significance of our results, especially in guiding regional efforts for risk assessment and prevention in resource-variable environments. In doing so, this study establishes an integrated, population-level framework that assesses CTI as a biomarker of systemic metabolic-inflammation burden and redefines the operationalization of cardio-kidney-metabolic risk within a syndromic paradigm. In contrast to current methodologies that analyze CKM phases separately, we incorporate a dual-risk framework that encompasses both the existence and intensity of CKM inside a unified analytical model. This signifies a key transition from isolated, organ-specific measurements to a multidimensional stratification instrument relevant to both clinical and public health sectors. Despite its initial development not being intended for CKM, our findings suggest that CTI serves as a cost-effective, scalable option for syndromic risk profiling in aging populations—facilitating earlier detection, more precise risk stratification, and improved allocation of preventive measures within health systems. 2. Methods 2.1. Study population The data for this investigation were obtained from the third wave (2015) of the China Health and Retirement Longitudinal investigation (CHARLS), a nationally representative survey of individuals aged 45 and older in mainland China. Wave 3 was chosen due to its provision of the most extensive biomarker data necessary for constructing the C-reactive protein–triglyceride–glucose index, the principal exposure variable in this investigation. Moreover, Wave 3 encompasses the requisite markers to delineate Cardiovascular-Kidney-Metabolic (CKM) syndrome, the principal outcome of interest. CHARLS employed a multistage, stratified, probability-proportional-to-size (PPS) sampling methodology to guarantee regional and demographic representativeness across 28 provinces. Comprehensive information concerning study protocols, data quality assurance, and sampling techniques may be found on the official CHARLS website ( https://charls.pku.edu.cn/ ). The CHARLS project received ethical approval from the Institutional Review Board of Peking University (IRB00001052–11015 and IRB00001052–11014), and written informed permission was secured from all participants before participation. The research complied with the guidelines established in the Declaration of Helsinki (Chen, 2019; China Center for Economic Research, n.d.; Zhao, 2023; Zhao, 2014; Zhao, 2020, 2013). In this study, we initially identified 21,095 individuals from CHARLS 2015 as the baseline sample. Participants were excluded if they were younger than 45 years, given the study’s focus on midlife and older adults, who are more vulnerable (Demetriou et al., 2024; Lyu et al., 2024), or if they had missing values for any of the following key analytic variables: age, CTI index, CKM syndrome classification, or province-level geographic identifiers. These variables were considered crucial for the following reasons: age determined eligibility, CTI and CKM functioned as the exposure and outcome, respectively, and province identifiers facilitated the examination of regional heterogeneity and distribution mapping. Province information was essential for the intended regional heterogeneity studies spanning eastern, central, and western China, contingent upon the observation of significant connections in the primary analysis. A total of 10,779 individuals were removed according to these criteria, yielding a final analytic sample of 10,316 participants (Fig. 1 ). To mitigate potential bias from incomplete data in non-exclusion variables, we employed multiple imputations by chained equations (MICE) with five iterations. The first imputed dataset was utilized for statistical analysis. This technique guaranteed data completeness and preserved the integrity of the final analytic sample for multivariable modeling. 2.2. Calculation of the C-Reactive Protein-Triglyceride-Glucose Index (CTI) The C-reactive protein-triglyceride-glucose index (CTI) was calculated to evaluate the combined burden of systemic inflammation and insulin resistance. The method for calculating CTI was adopted from a previously published study by S. Tang et al. (2024). The CTI was computed using the following formula: CTI was defined as 0.412* Ln (CRP [mg/L]) + Ln (TG [mg/dl] × FBG [mg/dl])/2 Where: CRP denotes C-reactive protein, a nonspecific biomarker of inflammation. TG denotes triglycerides. FBG denotes fasting blood glucose. ln represents the natural logarithm. All laboratory values were derived from fasting blood samples, with units standardized to mg/L for CRP and mg/dL for both TG and FBG before calculation. This composite score incorporates CRP as an indicator of systemic inflammation and the triglyceride-glucose product (TyG index), a recognized surrogate measure for insulin resistance. The calculation was performed for each participant to provide a unified measure reflecting both inflammatory status and insulin resistance, as per the methodology validated in S. Tang et al. (2024). 2.3. Definition of CKM syndrome Cardiovascular–Kidney–Metabolic (CKM) syndrome was defined in accordance with the American Heart Association (AHA) Presidential Advisory Statement (Ndumele, Rangaswami, et al., 2023). The AHA paradigm outlines five stages of CKM syndrome progression, encompassing the continuum of excessive adiposity, metabolic dysfunction, renal involvement, and cardiovascular disease (CVD). For applicability within the Chinese population, these parameters were modified utilizing locally relevant thresholds, including Asian-specific BMI and waist circumference cutoffs. Participants were assigned to one of five mutually exclusive CKM stages as follows: Stage 0 (No CKM Health Risk Factors): Individuals with normal BMI (< 23 kg/m²), waist circumference (< 80 cm for women or < 90 cm for men), normoglycemia, normotension, normal lipids, and no evidence of chronic kidney disease (CKD) or clinical/subclinical CVD. Stage 1 (Excess or Dysfunctional Adiposity Only): Individuals with overweight/obesity (BMI ≥ 23 kg/m²) or abdominal obesity, but no additional metabolic, renal, or cardiovascular abnormalities. Stage 2 (Metabolic Risk Factors and/or CKD): Individuals exhibiting metabolic disorders (e.g., type 2 diabetes, hypertension, hypertriglyceridemia ≥ 135 mg/dL, or metabolic syndrome) or diagnosed CKD. Stage 3 (Subclinical CVD with Coexisting CKM Features): Individuals with subclinical CVD in the presence of adiposity, metabolic risk, or CKD, including those at high atherosclerotic risk per AHA-PREVENT or KDIGO guidelines. Stage 4 (Clinical CVD with Coexisting CKM Features): Participants with overt clinical CVD alongside metabolic dysfunction, CKD, or obesity. In the primary analysis, CKM syndrome was operationalized as a binary outcome to enhance statistical power and simplify interpretation. Participants classified as Stage 1–4 were grouped as having CKM syndrome (“Yes”), while those in Stage 0 were designated as “No CKM”. This binary classification facilitates pragmatic risk stratification for public health interventions and preliminary clinical decision-making. The comprehensive five-level staging (Stage 0–4) was preserved in the secondary analysis to evaluate the correlation between CTI and CKM stage severity, facilitating the examination of stage-specific relationships and the gradient predictive significance of the CTI index. This multi-tiered strategy offers supplementary insights into the range of CKM burdens and augments the therapeutic significance of our findings. This dual-definition strategy not only encapsulates the essence of CKM but also facilitates mutual validation: the binary approach assesses initial overall risk, while the staging delineates stratified risk gradients—collectively enhancing the robustness of our analytical framework. 2.4. Assessment of covariates Potential confounders, including various Demographic and Socioeconomic, Body measurements and lifestyle factors, were sourced from the CHARLS database. The selection of appropriate confounding factors was based on previous literature (Dong et al., 2025; Lin et al., 2025; Tan et al., 2025; Tian et al., 2025). Demographic and socioeconomic variables included age (measured in years as a continuous variable), sex (“Female” and “Male”), residence (“Rural” and “Urban”), marital status (“Married and living with a spouse”, “Married but living without a spouse”, and “Single, divorced, and widowed”), and education status (categorized as “Elementary school or below” and “Middle school or above”). Body measurements and lifestyle factors included body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters), smoking status (“Smoker” and “Non-Smoker”), and drinking status (“Non-drinker”, “Drink but less than once a month”, and “Drink more than once a month”). All covariates were included in multivariable logistic regression models to adjust for potential confounding effects. 2.5. Statistical analysis Descriptive statistics were used to summarize the baseline characteristics of the study population. Continuous variables were first tested for normality using the Kolmogorov–Smirnov (K-S) test. Since the number of participants included in this study is very large, it is more appropriate to choose K-S test as the normality test. Variables not following a normal distribution were presented as medians with interquartile ranges (IQRs), while normally distributed variables were reported as means with standard deviations (SDs). Categorical variables were expressed as frequencies and percentages. Comparisons between participants with and without CKM syndrome were conducted using the independent t-test or Mann–Whitney U test for continuous variables, depending on their distribution. The chi-square test was used to compare categorical variables. For categorical variables with expected cell counts < 5, Fisher’s exact test was applied to ensure statistical robustness. To evaluate the association between the C-reactive protein–triglyceride–glucose index (CTI) and the presence of cardiovascular–kidney–metabolic (CKM) syndrome, binary logistic regression models were constructed using CKM syndrome as the dichotomous outcome variable (coded as 1 / CKM for Stages 1–4 and 0 / No CKM for Stage 0). CTI was entered as a continuous independent variable. Three hierarchical models were constructed: Model 1 was an uncorrected univariate logistic regression, analyzing the raw association between CTI and CKM syndrome. Models 2 and 3 were multivariate logistic regression analyses, controlling for potential confounding variables. Specifically, Model 2 adjusted for key Demographic and Socioeconomic covariates including age, sex, residence, education status, and marital status, while Model 3 was further adjusted for additional Body measurements and lifestyle factors including body mass index (BMI), smoking status, and drinking status. Odds ratios (ORs) and their respective 95% confidence intervals (CIs) were calculated by exponentiating the regression coefficients. To improve clarity, a forest plot was created to illustrate the odds ratios and 95% confidence intervals for CTI across the three models, along with the associated p-values. All models were computed utilizing the generalized linear model (glm) function in R version 4.4.1, employing a binomial family and logit link, with two-sided p-values < 0.05 being statistically significant. Generalized variance inflation factors (GVIFs) were calculated to evaluate potential multicollinearity among the independent variables in the fully adjusted model (Model 3), which included CTI and a comprehensive array of covariates. Variables with GVIF values over 5 were deemed indicative of significant multicollinearity issues. A graphical representation of GVIF values was created to visually evaluate the impact of each variable on collinearity. McFadden’s pseudo R² was employed to evaluate the overall goodness-of-fit of the fully adjusted logistic regression model (Model 3). This metric was calculated utilizing the pR2() function from the pscl package in R. The McFadden's pseudo R² indicates the relative enhancement in model log-likelihood compared to a null model and is commonly utilized as an indicator of model adequacy in logistic regression. The resultant value was compiled into a summary table utilizing the flextable and officer packages for reporting purposes. E-values were calculated to evaluate the strength of the observed relationship between CTI and CKM syndrome against potential unmeasured confounding, utilizing effect estimates from the fully adjusted model (Model 3). The E-value measures the minimal degree of connection an unmeasured confounder must possess with both the exposure and the outcome, beyond the assessed covariates, to entirely account for the observed association. This sensitivity analysis assists in determining if the observed correlation may reasonably be ascribed to residual confounding instead of a genuine effect. This methodology establishes a conservative and stringent benchmark and has been progressively embraced in high-caliber epidemiological research and premier journals, including The Lancet, JAMA, and BMJ Medicine (Ahmadi et al., 2023; Choi et al., 2024; Haneuse, VanderWeele, & Arterburn, 2019; Tobias et al., 2023; VanderWeele & Ding, 2017); given that our models already adjusted for a comprehensive set of known confounders, the use of E-value serves to further ensure the robustness of association inference without requiring additional model expansion. Moreover, several additional sensitivity analyses were conducted to assess the robustness of the association between CTI and CKM syndrome. First, CTI was categorized into tertiles based on its distribution in the study population. Participants were classified into three equal-sized groups—Low, Medium, and High CTI levels. Multivariable logistic regression models were then conducted, using the lowest tertile as the reference category, to evaluate the risk of CKM syndrome across increasing levels of CTI after adjusting for relevant covariates. Second, to assess the influence of outliers, a trimmed analysis was performed by excluding individuals with CTI values in the top and bottom 1% of the distribution. The regression model was then re-estimated on the restricted sample. Third, inverse probability of treatment weighting (IPTW) was employed to reduce residual confounding. Propensity scores were estimated via logistic regression using all covariates included in the main model 3, and stabilized weights were applied to construct a pseudo-population with balanced baseline characteristics. One-to-one nearest neighbor propensity score matching (PSM) with a caliper of 0.2 was conducted to replicate the association in a matched cohort. Covariate balance before and after matching was evaluated using standardized mean differences and visualized with Love plots. Across all sensitivity analyses, logistic regression models were refitted and odds ratios with 95% confidence intervals were extracted to compare the consistency of effect estimates. A regional heterogeneity analysis was conducted to assess whether the relationship between CTI and CKM syndrome varies across geographic locations. Participants were classified into three primary regions of China—eastern, central, and western—according to provincial administrative boundaries delineated in previous studies (Han et al., 2022). Specifically, Eastern region included: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. Central region included: Shanxi, Inner Mongolia, Anhui, Jiangxi, Henan, Hubei, Hunan, and Guangxi. Western region included: Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Heilongjiang, and Jilin. Multivariable logistic regression models were stratified by geographic region to estimate region-specific associations between CTI and CKM syndrome. Recognizing regional variability may uncover contextual variations in environmental exposures, healthcare infrastructure, or metabolic risk clustering, hence facilitating the creation of spatially customized public health interventions. To investigate whether the association between CTI and CKM syndrome varies by severity of disease stage, participants were categorized into five ordinal stages (Stage 0 to Stage 4) according to the adapted CKM classification criteria (Ndumele, Rangaswami, et al., 2023). An ordinal logistic regression model was first fitted using the polr() function from the MASS package, with CKM stage as an ordered outcome and CTI as the primary predictor. Three hierarchical models were constructed, ranging from crude to fully adjusted, including covariates such as age, sex, BMI, residence, education status, marital status, smoking, and alcohol use. Ordinal modeling facilitated the efficient estimation of a singular odds ratio under the proportional odds assumption, so maintaining statistical power and parsimony while utilizing the ordinal characteristics of the result. The Brant test was conducted utilizing the brant program to determine compliance with the proportionate odds assumption necessary for ordinal regression. In the event of an assumption violation, different modeling methodologies were employed. Specifically, three binary logistic models were fitted to contrast adjacent CKM stage groupings: (1) No CKM vs. any CKM (Stage 1–4), (2) early-stage (Stage 1) vs. more advanced stages (Stage 2–4), and (3) Stage 1–2 vs. Stage 3–4. These contrasts allowed for finer-grained risk differentiation and correspond to real-world public health screening and staging decisions. In addition, a multinomial logistic regression model was employed to examine the association between CTI and each CKM stage (Stage 1 through Stage 4), using Stage 0 (No CKM) as the reference. This model completely relaxed the proportionate odds assumption and permitted the emergence of non-monotonic risk patterns. These contrasting modeling methodologies ensured the validity, flexibility, and interpretability of the stage-specific studies, enabling us to systematically quantify CTI's association with the whole CKM severity spectrum under both limited and unconstrained assumptions. All models were calibrated for the identical factors included in the primary analysis. Results were expressed as odds ratios (ORs) accompanied by 95% confidence intervals and were visually represented in forest plots. 3. Results 3.1. Geographic Distribution, CKM Syndrome prevalence and Baseline Characteristics of Participants The final analytical sample comprised 10,316 people aged 45 and above from the nationally representative CHARLS 2015 dataset. Figure 2 illustrates the geographic dispersion of participants across several Chinese provinces. The sample was extensively disseminated, with the highest amounts originating from Shandong (10.3%), Henan (8.8%), and Sichuan (8.0%). Provinces including Hainan, Tibet, and Ningxia yielded no qualifying participants, indicating regional disparities in survey coverage and data integrity. The eastern, central, and western regions comprised 34.5%, 37.5%, and 28.0% of the sample, respectively. The incidence of cardiovascular–kidney–metabolic (CKM) syndrome in this cohort was notably high, with 98.2% of participants fulfilling the criteria for Stages 1–4 of CKM, whereas merely 1.8% were categorized as having no CKM-related risk (Stage 0), as illustrated in Fig. 3 . This distribution underscores the significant burden of metabolic, renal, and cardiovascular risks among midlife and older persons in China. Table 1 delineates the baseline characteristics of subjects, categorized by CKM status. Participants with CKM were older (median: 61.0 vs. 55.0 years, p < 0.001), had a higher body mass index (23.7 vs. 20.3 kg/m², p < 0.001), and a greater CTI score (8.8 vs. 8.1, p < 0.001) compared to those without CKM. In the absence of CKM, females constituted the predominant majority (91.9%), while the sex distribution in the CKM group was more equitable (52.6% female, 47.4% male). Table 1 Baseline Characteristics of Study Participants Stratified by CKM Variable Overall N = 10,316 1 No CKM (n = 185) 1 CKM (n = 10131) 1 p-value 2 Age (years) 61.0 [53.0, 67.0] 55.0 [50.0, 60.0] 61.0 [54.0, 68.0] < 0.001 Body Mass Index 23.6 [21.3, 26.3] 20.3 [19.1, 21.5] 23.7 [21.4, 26.3] < 0.001 CTI 8.8 [8.3, 9.4] 8.1 [7.6, 8.4] 8.8 [8.3, 9.4] < 0.001 Sex < 0.001 Female 5,496.0 (53.3%) 170.0 (91.9%) 5,326.0 (52.6%) Male 4,820.0 (46.7%) 15.0 (8.1%) 4,805.0 (47.4%) Residence 0.798 Rural 6,571.0 (63.7%) 120.0 (64.9%) 6,451.0 (63.7%) Urban 3,745.0 (36.3%) 65.0 (35.1%) 3,680.0 (36.3%) Marital Status 0.254 Married and living with a spouse 8,498.0 (82.4%) 158.0 (85.4%) 8,340.0 (82.3%) Married but living without a spouse 408.0 (4.0%) 9.0 (4.9%) 399.0 (3.9%) Single, divorced, and widowed 1,410.0 (13.7%) 18.0 (9.7%) 1,392.0 (13.7%) Education Status 0.485 Elementary school or below 7,020.0 (68.0%) 121.0 (65.4%) 6,899.0 (68.1%) Middle school or above 3,296.0 (32.0%) 64.0 (34.6%) 3,232.0 (31.9%) Regional Category 0.765 East 3,564.0 (34.5%) 63.0 (34.1%) 3,501.0 (34.6%) Midland 3,867.0 (37.5%) 66.0 (35.7%) 3,801.0 (37.5%) West 2,885.0 (28.0%) 56.0 (30.3%) 2,829.0 (27.9%) Smoking Status < 0.001 Non-smoker 5,798.0 (56.2%) 170.0 (91.9%) 5,628.0 (55.6%) Smoker 4,518.0 (43.8%) 15.0 (8.1%) 4,503.0 (44.4%) Drinking Status < 0.001 Drink but less than once a month 900.0 (8.7%) 9.0 (4.9%) 891.0 (8.8%) Drink more than once a month 2,665.0 (25.8%) 27.0 (14.6%) 2,638.0 (26.0%) Non-drinker 6,751.0 (65.4%) 149.0 (80.5%) 6,602.0 (65.2%) Province 0.014 Shanghai 29.0 (0.3%) 0.0 (0.0%) 29.0 (0.3%) Yunnan 651.0 (6.3%) 11.0 (5.9%) 640.0 (6.3%) Inner Mongolia 452.0 (4.4%) 5.0 (2.7%) 447.0 (4.4%) Beijing 9.0 (0.1%) 0.0 (0.0%) 9.0 (0.1%) Jilin 228.0 (2.2%) 3.0 (1.6%) 225.0 (2.2%) Sichuan 830.0 (8.0%) 21.0 (11.4%) 809.0 (8.0%) Tianjin 62.0 (0.6%) 2.0 (1.1%) 60.0 (0.6%) Anhui 578.0 (5.6%) 10.0 (5.4%) 568.0 (5.6%) Shandong 1,067.0 (10.3%) 11.0 (5.9%) 1,056.0 (10.4%) Shanxi 338.0 (3.3%) 3.0 (1.6%) 335.0 (3.3%) Guangdong 362.0 (3.5%) 8.0 (4.3%) 354.0 (3.5%) Guangxi 351.0 (3.4%) 10.0 (5.4%) 341.0 (3.4%) Xinjiang 49.0 (0.5%) 0.0 (0.0%) 49.0 (0.5%) Jiangsu 490.0 (4.7%) 5.0 (2.7%) 485.0 (4.8%) Jiangxi 518.0 (5.0%) 18.0 (9.7%) 500.0 (4.9%) Hebei 480.0 (4.7%) 4.0 (2.2%) 476.0 (4.7%) Henan 904.0 (8.8%) 11.0 (5.9%) 893.0 (8.8%) Zhejiang 465.0 (4.5%) 14.0 (7.6%) 451.0 (4.5%) Hubei 270.0 (2.6%) 4.0 (2.2%) 266.0 (2.6%) Hunan 456.0 (4.4%) 5.0 (2.7%) 451.0 (4.5%) Gansu 266.0 (2.6%) 6.0 (3.2%) 260.0 (2.6%) Fujian 291.0 (2.8%) 7.0 (3.8%) 284.0 (2.8%) Guizhou 91.0 (0.9%) 3.0 (1.6%) 88.0 (0.9%) Liaoning 309.0 (3.0%) 12.0 (6.5%) 297.0 (2.9%) Chongqing 113.0 (1.1%) 2.0 (1.1%) 111.0 (1.1%) Shaanxi 363.0 (3.5%) 7.0 (3.8%) 356.0 (3.5%) Qinghai 102.0 (1.0%) 0.0 (0.0%) 102.0 (1.0%) Heilongjiang 192.0 (1.9%) 3.0 (1.6%) 189.0 (1.9%) 1 Values are presented as mean ± SD or median [IQR], determined by the Kolmogorov-Smirnov test for normality. Continuous variables were compared using the t-test or Mann-Whitney U test, as appropriate. Categorical variables were compared using the Chi-square test or Fisher's exact test when cell counts were < 5. Overall, No CKM, and CKM sample sizes are indicated in the column headers. 2 Wilcoxon rank sum test; Pearson's Chi-squared test Lifestyle-related characteristics shown significant disparities between groups. Smoking and alcohol consumption were significantly less prevalent in the No CKM group: merely 8.1% were current smokers, 14.6% reported drinking more than once a month, and 4.9% consumed alcohol less than once a month, resulting in a total of 19.5% drinkers, in contrast to 44.4% and 34.8%, respectively, in the CKM group (p 0.05 for all). As a whole, these foundational data indicate that individuals with CKM syndrome are generally older, possess a greater metabolic burden, and display more detrimental behavioral risk profiles. 3.2. Primary Association Between CTI and CKM Syndrome The association between the C-reactive protein triglyceride glucose index (CTI) and the existence of cardiovascular–kidney–metabolic (CKM) syndrome was evaluated by three hierarchical binary logistic regression models, with findings illustrated in Fig. 4 . In the unadjusted model (Model 1), each unit increase in CTI corresponded to significantly elevated risks of CKM syndrome (OR = 4.25, 95% CI: 3.42–5.28, p < 0.001). After adjusting for key demographic and socioeconomic covariates, including age, sex, residence, education status, and marital status (Model 2), the association remained robust and slightly strengthened (OR = 4.48, 95% CI: 3.57–5.63, p < 0.001). In the fully adjusted model (Model 3), which further accounted for body measurements and lifestyle factors (body mass index, smoking status, and drinking status), the association was attenuated but remained statistically significant (OR = 2.57, 95% CI: 2.02–3.27, p < 0.001). The data indicate that systemic inflammation and insulin resistance, as measured by the composite CTI score, are independently linked to increased CKM risk, even when accounting for conventional risk variables. The diminishing amplitude of the odds ratio throughout the models underscores the impact of behavioral and metabolic variables while also affirming the independent role of CTI in CKM risk stratification among older people. 3.3. Establishing Methodological Rigor: Multicollinearity Diagnostics, Model Fit Evaluation, and Sensitivity to Unmeasured Confounding 3.3.1. Multicollinearity Diagnostics via Generalized Variance Inflation Factor (GVIF) To assess potential multicollinearity among the independent variables in the fully adjusted logistic regression model (Model 3), we computed the Generalized Variance Inflation Factor (GVIF) for each independent variable. Figure 5 illustrates that all GVIF values were far below the widely recognized threshold of 5, indicating an absence of considerable collinearity among the variables considered. The GVIFs varied from 1.02 (for CTI and marital status) to 1.33 (for sex), signifying negligible shared variance among the variables. Significantly, essential factors including age (GVIF = 1.13), BMI (1.10), smoking status (1.29), and education level (1.11) remained within acceptable limits. The CTI variable, the main exposure of interest, exhibited a GVIF of merely 1.02, further substantiating its independent associative role in the multivariable model. These results affirm that the fully adjusted model is not compromised by multicollinearity, thereby supporting the stability and interpretability of the regression estimates. 3.3.2. Model Goodness-of-Fit Assessment Using McFadden’s Pseudo R² Table 2 McFadden's Pseudo R² for Model 3 (Fully Adjusted) Model McFadden_R2 Model 3 0.372 Note: McFadden’s pseudo R² was used to assess the goodness-of-fit of the fully adjusted logistic regression model (Model 3). A value of 0.372 indicates a substantial improvement in model likelihood compared to the null model, reflecting strong explanatory power. Pseudo R² values between 0.2 and 0.4 are generally considered indicative of an excellent model fit in GLM models. To evaluate the explanatory power of the fully adjusted logistic regression model (Model 3), we computed McFadden’s Pseudo R², a widely used indicator of model fit in generalized linear models. As shown in Table 2 , the pseudo R² value for Model 3 was 0.372. According to conventional benchmarks, values between 0.2 and 0.4 are generally interpreted as indicating excellent model performance in logistic regression settings (Brunton-Martin, Wood, & Gaskett, 2024; Hauber et al., 2016). The results indicate that the model significantly enhanced likelihood compared to the null model and effectively accounted for the variance in CKM syndrome status described by CTI and the covariates included. This finding reinforces the validity of Model 3 and affirms the suitability of the chosen variables in elucidating the binary outcome. 3.3.3. E-value Analysis to Address Unmeasured Confounding To assess the potential influence of unmeasured confounding on the observed relationship between CTI and CKM syndrome, we computed the E-value for both the point estimate and the lower limit of the 95% confidence interval from the fully adjusted model (Model 3) (Table 3 ). The E-value for the point estimate (OR = 2.57) was 4.58, whereas the E-value for the lower bound (OR = 2.02) was 3.46. Table 3 E-values for the Association between CTI and CKM (Model 3, Fully Adjusted) Model OR (95% CI) E-value (Point Estimate) E-value (Lower 95% CI) Model 3 (Fully Adjusted) 2.57 (2.02–3.27) 4.58 3.46 Note: E-values indicate the minimum strength of unmeasured confounding required to explain away the observed association between CTI and CKM syndrome. The E-values indicate the minimal strength of association required for an unmeasured confounder, independent of those already accounted for, to simultaneously correlate with both CTI and CKM syndrome (on the risk ratio scale) in order to entirely account for the observed link. To diminish the observed odds ratio of 2.57 to null, a confounder necessitating a risk ratio of no less than 4.58 would be required for both the exposure (CTI) and the outcome (CKM syndrome). A confounder would require a risk ratio of 3.46 with both CTI and CKM to reduce the bottom bound of the 95% confidence interval (2.02) to null. Associations of this magnitude are exceptionally rare in real-world epidemiologic settings, as consistently demonstrated in sensitivity analyses across high-quality observational studies (Chao et al., 2023; Li et al., 2020; Pepe et al., 2004). Given that Model 3 already adjusts for a comprehensive set of confounders, including age, sex, residence, marital status, education status, BMI, smoking status, and drinking status, the presence of such a strong, independent, and unmeasured confounder is usually highly implausible. In addition, none of the covariates in the model demonstrated variance inflation factors nearing concerning thresholds (as illustrated in Fig. 5 ), so eliminating residual collinearity that might potentially hide the impacts of uncontrolled variables. This E-value analysis enhances the inferential validity of our findings by quantitatively illustrating that the observed association remains resilient against significant unmeasured confounding. It eliminates the possibility that the exclusion of supplementary factors, unless they possess implausibly high influence and are significantly associated with both CTI and CKM, will invalidate the observed association. 3.4. Additional Sensitivity Analyses 3.4.1. CTI Tertile Categorization and Risk Gradient To enhance the sensitivity analysis framework and evaluate the robustness and interpretability of the CTI–CKM connection, we classified CTI values into tertiles: Low, Medium, and High, according to their distribution among the research population. This stratification sought to evaluate the consistency of the correlation across different exposure levels and to assess a potential dose–response relationship, so reinforcing the claim for a causal connection. Figure 6 illustrates that participants in the medium CTI tertile exhibited a significantly increased risk of CKM syndrome (OR = 2.36, 95% CI: 1.60–3.47, p < 0.001), whereas those in the highest CTI tertile displayed an even more pronounced association (OR = 15.02, 95% CI: 5.48–41.19, p < 0.001) relative to the reference group. The expanding confidence intervals at elevated CTI levels indicate a greater variability of extreme biomarker values, however, do not reduce the size or relevance of the effect. This research demonstrates a distinct and strong dose–response relationship, suggesting that even slight increases in CTI correlate with substantially higher odds of CKM syndrome, whereas people with the highest CTI burden face a markedly elevated risk. These results not only validate the robustness of the primary findings under an alternative exposure specification but also furnish persuasive evidence for a dose–response connection between CTI and CKM syndrome. Moreover, results offer robust empirical evidence for a monotonic biological gradient and underscore the therapeutic significance of CTI as a risk classification instrument for CKM syndrome. 3.4.2. Outlier-Trimmed Analysis (Exclusion of Top and Bottom 1%) To test the sensitivity of our findings to extreme biomarker values and ensure that the association between CTI and CKM syndrome was not driven by outliers, we conducted a trimmed analysis by excluding individuals in the top and bottom 1% of the CTI distribution. The fully adjusted logistic regression model (Model 3) was re-evaluated on this constrained sample, resulting in a new trimmed model. Figure 7 illustrates that the removal of outliers exerted negligible influence on the direction or strength of the connection. The odds ratio in the trimmed model (OR = 2.94, 95% CI: 2.24–3.87, p < 0.001) was similar to that of the original fully adjusted model (OR = 2.57, 95% CI: 2.02–3.27, p < 0.001). The point estimate somewhat rose post-trimming, accompanied by narrower confidence intervals, signifying improved precision. These findings validate that the identified association is neither a product of biased data or the impact of outliers. The consistency of results in both the complete and reduced datasets reinforces the internal validity of the model and enhances confidence in the relationship between systemic inflammation–insulin resistance (CTI) and CKM syndrome. 3.4.3. Covariate Balance Assessment and Weighted Association Using IPTW To further account for potential residual confounding, we applied inverse probability of treatment weighting (IPTW) based on propensity scores derived from all covariates in the fully adjusted model (Model 3). Stabilized weights were used to create a pseudo-population in which covariate distributions were balanced between exposure levels, allowing estimation of the marginal effect of CTI on CKM syndrome. The assessment of covariate balance prior to and following weighting was conducted using absolute standardized mean differences (SMDs) and illustrated using a Love plot (Fig. 8 ). Before weighing, some factors displayed significant imbalance, especially age, sex, BMI, and smoking status. Following the use of IPTW, all covariates attained exceptional balance, with SMDs well below the standard threshold of 0.1, so validating effective covariate harmonization between exposure groups. Thereafter, logistic regression was performed on the IPTW-weighted sample to assess the impact of CTI on CKM syndrome. Figure 9 illustrates that CTI maintained a solid association with CKM risk in the IPTW model (OR = 4.35, 95% CI: 3.33–5.68, p < 0.001). The extent of the effect strengthens the reliability of the association across various modeling approaches. Collectively, these findings indicate that the association between CTI and CKM syndrome remains significant even with stringent statistical adjustments for confounding variables, hence reinforcing the validity of our results within an emulated pseudo-randomized context. 3.5. Regional Heterogeneity Analysis To investigate any geographic variation in the relationship between CTI and CKM syndrome, we performed stratified logistic regression analyses by region—East, Midland, and West—based on established administrative classifications. This investigation sought to evaluate if contextual factors, like environmental exposures, socioeconomic situations, or healthcare access, could influence the strength of the observed link. Figure 10 illustrates that CTI consistently is associated with elevated risks of CKM syndrome in all three areas. The association strength was greatest in the Midland region (OR = 2.97, 95% CI: 1.95–4.53, p < 0.001), followed by the East (OR = 2.76, 95% CI: 1.82–4.20, p < 0.001) and the West (OR = 2.17, 95% CI: 1.40–3.38, p < 0.001). Despite the overlap of confidence intervals, the point estimates indicate a tendency towards marginally stronger relationships in more urbanized or metabolically taxed areas. These regional variations may indicate fundamental discrepancies in chronic illness patterns, metabolic risk aggregation, or environmental stressors such pollution and urbanization. The results emphasize the necessity of region-specific strategies for CKM prevention and intervention. The persistent and significant associations identified throughout the three principal Chinese regions—East, Midland, and West—demonstrate that the relationship between CTI and CKM syndrome is regionally resilient. Notwithstanding regional variations in demographics, metabolic profiles, and healthcare systems, the direction and degree of the connection remained consistent. The cross-regional consistency improves the external validity and generalizability of our findings, indicating that the CTI index may function as a widely applicable indicator for CKM risk stratification across various populations in China and possibly other aging societies undergoing similar cardiometabolic transitions. 3.6. Stage-Specific Association Between CTI and CKM Severity While the primary binary logistic regression analysis provides a clear and actionable summary of the overall association between CTI and CKM syndrome, useful for broad public health initial screening and risk flagging, the binary classification (presence vs. absence of CKM) does not capture the clinical heterogeneity inherent in precise staging cardiometabolic–renal dysfunction. To facilitate more nuanced disease monitoring, individualized intervention planning, and precision risk stratification, we further adopted the 5-stage framework for CKM syndrome (Stages 0 to 4) proposed by the American Heart Association (AHA) and investigated the stage-specific relationship between CTI and CKM severity (Ndumele, Rangaswami, et al., 2023). This section delineates various modeling tools, including ordinal logistic regression and multinomial comparisons, to assess the relationship between CTI and both the existence and stages of CKM syndrome. These models enable the evaluation of whether elevated CTI levels are associated with increased probabilities of progressing to advanced CKM stages, hence enhancing the public health significance of CTI for precision clinical risk stratification or monitoring. 3.6.1. Distribution of Participants Across CKM Stages 0–4 Participants were classified into five mutually exclusive stages of cardiovascular–kidney–metabolic (CKM) syndrome following the American Heart Association (AHA) clinical framework. This staging system distinguishes individuals from Stage 0 (no identifiable CKM risk factors) to Stage 4 (clinical cardiovascular disease with metabolic or renal comorbidity), capturing a spectrum of CKM-related burden (Ndumele, Rangaswami, et al., 2023). The distribution of participants across these stages is summarized in Table 4 . Table 4 Distribution of Participants Across CKM Stages 0 to 4 CKM Stage n Percentage (%) Stage 0 (No CKM) 185 1.79 Stage 1 788 7.64 Stage 2 2,094 20.30 Stage 3 5,050 48.95 Stage 4 2,199 21.32 Total (N) 10,316 100.00 Note: Distribution of participants (N = 10,316) across CKM syndrome stages based on the American Heart Association classification. Stages 1–4 indicate increasing severity of metabolic, renal, or cardiovascular involvement; Stage 0 represents individuals without CKM-related risk. In the current sample of 10,316 adults aged 45 and older, 1.79% were assigned to Stage 0, and 7.64% to Stage 1. The majority of individuals fell into Stages 2 (20.30%), 3 (48.95%), or 4 (21.32%), reflecting substantial clinical heterogeneity within the population. While our primary binary logistic analysis (CKM: yes / no) provides a valuable and scalable foundation for early screening and population-level risk flagging, particularly suited for public health deployment, this stage-based classification enables a more refined understanding of differential risk across clinical strata. In this context, stage-specific modeling serves as a complementary strategy, allowing for more granular patient profiling, risk stratification, and the development of precision medicine approaches tailored to disease severity. 3.6.2. Ordinal Logistic Regression Across CKM Stages To further examine whether higher CTI levels are associated with more advanced stages of CKM syndrome, we applied ordinal logistic regression using the CKM staging variable (Stage 0 to Stage 4) as the ordered outcome. This modeling framework assumes proportional odds across stage transitions and enables a single effect estimate reflecting the cumulative odds of being in a higher CKM category. Figure 11 illustrates a positive and consistent association between CTI and elevated CKM stages across all models. In the unadjusted model (Model 1), each unit increase in CTI corresponded to a 36% increase in the odds of being categorized into a more severe CKM stage (OR = 1.36, 95% CI: 1.31–1.42, p < 0.001). The association strengthened after adjusting for age, sex, residence, education status, and marital status in Model 2 (OR = 1.56, 95% CI: 1.49–1.62, p < 0.001), and remained robust in the fully adjusted model (Model 3), which also accounted for BMI, smoking, and alcohol consumption (OR = 1.54, 95% CI: 1.47–1.60, p < 0.001). These data suggest that CTI has association with the existence of CKM syndrome and is associated with increased likelihood of being classified into more severe phases. The findings endorse the prospective application of CTI in precise risk stratification, providing a scalable biomarker for evaluating stage-specific risk of cardiometabolic-renal dysfunction severity. 3.6.3. Assumption Testing Using the Brant Test Table 5 Brant Test Results for the Proportional Odds Assumption Variable Chi-squared df p -value Assumption Status CTI 326.64 3 < 0.01 Violated Age 512.55 3 < 0.01 Violated BMI 225.65 3 < 0.01 Violated Sex (Male) 627.31 3 < 0.01 Violated Residence (Urban) 8.09 3 0.04 Violated Education: Middle school or above 8.72 3 0.03 Violated Married but living without spouse 0.56 3 0.90 Not Violated Single/Divorced/Widowed 2.18 3 0.54 Not Violated Smoker 96.01 3 < 0.01 Violated Drink more than once a month 5.95 3 0.11 Not Violated Non-drinker 2.70 3 0.44 Not Violated Note: The Brant test evaluates the proportional odds assumption for ordinal logistic regression. A p-value < 0.05 indicates a violation of the assumption. To assess the validity of the proportional odds assumption underlying the ordinal logistic regression model, we conducted the Brant test for each covariate in the fully adjusted model. Results are summarized in Table 5 . The test revealed significant violations of the proportional odds assumption for several key variables, including CTI (χ 2 = 326.64, p < 0.001), age (χ 2 = 512.55, p < 0.001), BMI (χ 2 = 225.65, p < 0.001), and sex (χ 2 = 627.31, p < 0.001). Additional violations were observed for residence and education level, although the magnitude was smaller. In contrast, variables such as marital status, drinking status, and smoking showed mixed results, with some subcategories meeting the assumption criteria. The data indicates that the proportionate odds assumption is not entirely maintained for several key predictors, including the primary exposure variable (CTI). Consequently, the interpretation of the ordinal logistic model necessitates caution, and alternate modeling methodologies that mitigate this assumption should be considered. This further emphasizes the importance of performing stage-specific modeling to guarantee reliable inference across all degrees of CKM severity. 3.6.4. Multi-Level Binary Logistic Contrasts Between CKM Stage Groups Due to substantial breaches of the proportionate odds assumption in the ordinal logistic regression model, we utilized a multi-level binary logistic regression approach to explore the relationship between CTI and clinically pertinent distinctions throughout CKM syndrome phases. This methodology facilitates organized, sequential comparisons and circumvents the limiting assumptions of ordinal models, therefore providing a versatile and comprehensible framework consistent with practical clinical decision criteria. As shown in Fig. 12 , three multi-level binary models were constructed. In the first contrast (Model 1: CKM [Stages 1–4] vs. No CKM [Stage 0]), CTI demonstrated a strong association with CKM presence (OR = 4.12, 95% CI: 3.28–5.21, p < 0.001). In the second model (Model 2: Stages 3–4 vs. Stages 1–2), CTI remained robustly associated with more severe CKM burden (OR = 2.90, 95% CI: 2.60–3.25, p < 0.001). The third model (Model 3: Stages 3–4 vs. Stages 1–2, restricted to participants with CKM) confirmed that higher CTI was still associated with higher severity, albeit with attenuated effect size (OR = 1.22, 95% CI: 1.15–1.29, p < 0.001). Together, these multi-level binary contrasts reinforce the stage-responsiveness of CTI across the CKM spectrum and highlight its utility in both broad public health screening and precision risk stratification settings. This method facilitates customized interpretation by analyzing relationships across clinically specified transitions, without depending on a standardized proportional odds framework. While both Fig. 4 and Model 1 in Fig. 12 analyzing the analogous binary contrast—CKM (Stages 1–4) vs No CKM (Stage 0)—utilizing the same imputed dataset and covariate collection, the calculated odds ratios vary (2.57 vs. 4.12). This disagreement arises not from data or adjustment inconsistencies, but from variations in estimation approach. Figure 4 illustrates a sequential hierarchical model, in which covariates are progressively incorporated, resulting in increased coefficient shrinkage and a more conservative effect size. Conversely, Model 1 in Fig. 12 employs a fully adjusted model in a singular step. This structural disparity in modeling design inherently influences coefficient magnitude. Both models consistently demonstrate a coherent direction, exhibit substantial statistical significance, and bolster the stability of the CTI–CKM connection across various specifications. 3.6.5. Multinomial Logistic Regression for Stage-Specific Odds To offer a flexible, assumption-free alternative to the ordinal logistic regression model and to measure the independent association between CTI and each stage of CKM syndrome, we conducted a multinomial logistic regression analysis using Stage 0 (no CKM) as the reference group. This method facilitates the estimate of stage-specific odds ratios without supposing proportionality among outcome levels. As shown in Fig. 13 , CTI was not significantly associated with Stage 1 compared to Stage 0 (OR = 0.96, 95% CI: 0.76–1.20, p = 0.704). This is likely to reflect the absence of systemic inflammation or insulin resistance in isolated adiposity. This underscores that CTI's specificity does not overreact to low-risk profiles but rather accurately identifies metabolically active or progressed CKM, hence augmenting its effectiveness in precision screening. Conversely, CTI exhibited robust and statistically significant associations with elevated CKM levels. The likelihood of being in Stage 2 escalated over thrice with each unit rise in CTI (OR = 3.60, 95% CI: 2.91–4.44, p < 0.001). More pronounced relationships were noted for Stage 3 (OR = 4.07, 95% CI: 3.29–5.04, p < 0.001) and Stage 4 (OR = 4.19, 95% CI: 3.39–5.19, p < 0.001). These data collectively underscore a distinct stage-specific gradient in the relationship between CTI and cardiometabolic–renal load. The lack of significant association at Stage 1 indicates that CTI may not solely represent obesity but instead encompasses more complex systemic metabolic disorders and inflammation. This underscores the potential of CTI as a biomarker for clinically significant CKM abnormalities necessitating vigilant monitoring or intervention. 4. Discussion 4.1. From Multisystem Burden to New Biomarker Opportunity in China’s Ageing Population China's swiftly ageing population, with over 20% aged 60 and above, confronts an escalating public health challenge from cardiovascular–kidney–metabolic (CKM) syndrome, a multifaceted disorder marked by the intersection of cardiovascular disease, chronic kidney disease, and metabolic dysfunction. This combined decline impacts not only individual organs but also signifies a continuum of increasing, interconnected hazards (Ndumele, Neeland, et al., 2023; Qiong et al., 2023; Tu, Zeng, & Liu, 2022). Recent data indicate a significant aggregation of cardiometabolic risk: 87.1% of older persons are hypertensive, 47.6% display dyslipidemia, 45.5% are overweight or obese, and 75% possess at least two modifiable cardiovascular risk factors (X. Zhang et al., 2025). Meanwhile, CVD prevalence reaches 31.2%, with considerable regional variation (Qiong et al., 2023). Notwithstanding evident trends, existing screening methodologies continue to depend on traditional panels that overlook cross-system signals, resulting in a diagnostic void at the critical juncture where intervention is most effective (Xu, 2024; X. Zhang et al., 2025). Emerging biomarkers like TyG-BMI and HGI offer promise but often lack simplicity, low-cost, scalability, or validation in diverse Chinese aging populations (W. Li et al., 2024; Lin et al., 2025; Liu et al., 2025; Qiong et al., 2023). This highlights the necessity for a pragmatic, cost-effective indicator—one that may function in both public health and clinical settings to identify CKM risk early and provide data to potentially facilitate stratified care throughout disease stages. This study presents the inaugural nationwide examination of the C-reactive protein–triglyceride–glucose index (CTI) as a comprehensive marker of CKM syndrome. Utilizing a dual-layer model that sequentially evaluates its correlation with both presence (binary outcome) and severity (staging outcome), we seek to assess CTI’s potential as a “biological bridge,” connecting early community screening with precise risk stratification (rather than prediction) in China’s aging population, thereby facilitating decision-making at a population level. This enhances the translational potential of our findings for practical clinical and public health applications. 4.2. Core Findings and Multidimensional Validation of the CTI–CKM Association: Mapping a Risk Terrain for Precision Stratification Our data indicate that the C-reactive protein triglyceride glucose index is strongly and consistently linked to cardiovascular–kidney–metabolic (CKM) syndrome across many analytical parameters, including exposure intensity, regional variation, and illness severity. Moreover, these relationships are not only statistically significant but also methodologically robust, having weathered a series of sensitivity analyses and modeling frameworks intended to mitigate typical sources of bias and analytical errors. CTI exhibited a clear dose-dependent association with CKM risk. In the fully adjusted model, each unit increase in CTI was associated with 2.57-fold higher odds of having CKM syndrome (95% CI: 2.02–3.27, p < 0.001). When CTI was categorized into tertiles, participants in the highest tertile demonstrated a striking 15-fold increase in CKM risk compared to those in the lowest tertile (OR = 15.02, 95% CI: 5.48–41.19, p < 0.001), forming a discernible “risk staircase.” This gradient supports CTI’s potential utility for risk stratification, particularly in early identification of high-burden subpopulations. Stratified analyses by geographic region demonstrated that the CTI–CKM association was directionally consistent and statistically significant across all three major regions in China. The strongest association was observed in the central region (OR = 2.97, 95% CI: 1.95–4.53, p < 0.001), followed by the eastern (OR = 2.76, p < 0.001) and western regions (OR = 2.17, p < 0.001). These findings suggest that CTI is not context-dependent, but rather a generalizable biomarker candidate applicable across varied environmental, socioeconomic, and healthcare settings. The cross-regional robustness also strengthens its potential for nationwide public health deployment. Beyond overall risk differentiation, CTI demonstrated stage-sensitive associations with CKM severity. Ordinal logistic regression revealed a graded relationship between higher CTI values and more advanced CKM stages (OR = 1.54, 95% CI: 1.47–1.60, p < 0.001). However, recognizing a violation of the proportional odds assumption (Brant test), we applied two alternative approaches: (1) In multi-level binary contrasts, CTI was significantly associated with advanced disease stages (Stage 3–4 vs. Stage 1–2: OR = 2.90, p < 0.001). (2) In multinomial logistic regression, CTI was independently associated with Stage 2 (OR = 3.60), Stage 3 (OR = 4.07), and Stage 4 (OR = 4.19), all p < 0.001, but showed no association with Stage 1 (OR = 0.96, 95% CI: 0.76–1.20, p = 0.704). This pattern suggests that CTI specifically reflects inflammation- and insulin resistance–driven multisystem dysfunction, rather than isolated adiposity, underscoring its relevance in later-stage disease surveillance and targeted clinical management. The exciting thing is that these findings are truly credible and robust. We tested CTI as both a continuous and categorical exposure, and the direction and strength of association remained consistent. Outlier-trimmed analyses (excluding top and bottom 1%) yielded even stronger associations (OR = 2.94, p < 0.001), refuting the concern that extreme values skewed results. Using the E-value, we found that an unmeasured confounder would need to be associated with both CTI and CKM syndrome with a risk ratio of ≥ 4.58 to completely explain away the observed association. Given the comprehensive adjustment for demographic, socioeconomic, behavioral, and anthropometric variables, such a confounder is highly implausible in real-world settings, reinforcing the robustness of the observed association of our findings under conservative assumptions. To further mitigate residual confounding, we applied propensity score matching and inverse probability of treatment weighting. Post-weighting diagnostics showed excellent covariate balance (all SMDs < 0.1), and CTI remained strongly associated with CKM syndrome in the IPTW-weighted sample (OR = 4.35, 95% CI: 3.33–5.68, p < 0.001), providing an additional layer of association validation. Besides, importantly, rather than relying on a single statistical framework, we implemented a progressive, assumption-aware modeling strategy: Binary logistic regression to capture overall risk; Ordinal regression to assess gradient transitions; Brant test diagnostics to confirm assumption validity; multi-level binary contrasts and multinomial regression for granular, stage-specific differentiation. This methodological cascade not only increases interpretability for both public health and clinical applications but also serves to pre-empt common critiques regarding model misspecification, confounding, and overfitting. Overall, CTI is consistently and robustly associated with CKM syndrome across varying severity levels, geographic areas, and analytical approaches. Its sensitivity to illness prevalence, robustness against statistical assumptions, and consistency among models indicate it could function as a significant biomarker for risk stratification and focused intervention, especially in aging populations with increasingly intricate multimorbidity scenarios. 4.3. Mechanistic and Results Insights: Why Might CTI Function as a Biological Sensor for CKM? Although our findings are observational in nature, the biological plausibility of CTI as a marker for CKM syndrome is supported by its constituent components, C-reactive protein (CRP) and the triglyceride–glucose index (TyG), both of which are mechanistically linked to multisystem dysfunction spanning the cardiovascular, renal, and metabolic axes (S. Tang et al., 2024). 4.3.1. Chronic Inflammation as a Multisystem Accelerator C-reactive protein (CRP) is a pivotal mediator in the pathogenesis of cardiovascular–kidney–metabolic (CKM) syndrome, functioning through the convergence of inflammatory, oxidative, and metabolic pathways. Increased CRP levels induce endothelial dysfunction chiefly by activating the nuclear factor kappa B (NF-κB) pathway, which enhances reactive oxygen species (ROS) production through NADPH oxidase, diminishes nitric oxide bioavailability, and ultimately results in vascular stiffness and microvascular damage (Bekyarova et al., 2023; Huang et al., 2024; Lorenzo et al., 2021; Xu et al., 2016; Zhao & He, 2021). In atherosclerotic conditions, CRP correlates with plaque vulnerability via enhancing matrix metalloproteinase (MMP) activity and diminishing collagen production, hence compromising fibrous cap integrity and promoting plaque rupture (Franeková et al., 2015; Huang et al., 2024; Mohamed Abulnasr et al., 2024). From a metabolic perspective, CRP intensifies insulin resistance by disrupting adipokine signaling, notably by enhancing pro-inflammatory cytokines like TNF-α and IL-6, which hinder downstream insulin signaling pathways (Caturano et al., 2021; Lorenzo et al., 2021). These inflammatory cascades further enhance hepatic lipogenesis, particularly in non-alcoholic fatty liver disease (NAFLD), where NF-κB activation by free fatty acids promotes excessive lipid buildup in hepatocytes (Lorenzo et al., 2021; Milić, Lulić, & Štimac, 2014). Although direct renal histopathology data connecting CRP to CKM-associated glomerular injury is scarce, chronic low-grade inflammation is widely acknowledged as a significant factor in oxidative damage and filtration barrier impairment in the kidney (Bekyarova et al., 2023; Stoian et al., 2024). Novel therapeutic investigations focusing on CRP-related pathways offer further translational validation. Pharmacological therapies, including statins, have demonstrated efficacy in lowering CRP levels and accompanying vascular inflammation by inhibiting lipoprotein-associated phospholipase A2 (Lp-PLA2) activity. Natural substances like curcumin block NF-κB and ROS pathways, mitigating inflammation-induced vascular damage in preclinical investigations, whereas vitamin D administration has shown anti-inflammatory properties and diminished neuropathic consequences in diabetic animals (Jafari-Hafshejani et al., 2023; Karakas, Haase, & Zeller, 2018; Li et al., 2018; Stoian et al., 2024). These findings reinforce CRP’s multifaceted role in CKM pathogenesis and its value as a mechanistically grounded component of composite biomarkers such as CTI. 4.3.2. Insulin resistance as a convergence points of metabolic toxicity The triglyceride–glucose (TyG) index, calculated from fasting triglyceride and glucose concentrations, is an established surrogate indicator of insulin resistance and lipotoxic stress. The rise signifies not just hyperglycemia and lipid dysregulation but also a wider sequence of pathophysiological mechanisms that contribute to the emergence of cardiovascular–kidney–metabolic (CKM) syndrome (Devaraj, Krishnan, & Chen, 2025; Lu et al., 2025; Zhao et al., 2021). Insulin resistance enables the excess of circulating lipids to infiltrate non-adipose organs, particularly hepatocytes and cardiomyocytes, where abnormal fat deposition hinders mitochondrial oxidative capability and exacerbates oxidative stress. These alterations lead to cellular senescence and apoptosis, resulting in hepatic steatosis, myocardial metabolic dysfunction, and vascular damage. In experimental models, animals exposed to high-sugar/high-fat diets alongside renal stress (e.g., unilateral nephrectomy) exhibited systemic metabolic disturbances, including glucose intolerance, adipocyte hypertrophy, and increased blood pressure, which collectively exacerbated cardiac and renal remodeling (Carvalho et al., 2024; Kang et al., 2017; Zhao et al., 2021). The metabolic abnormalities indicated by TyG also initiate inflammatory and fibrotic signaling pathways. Hyperglycemia and dyslipidemia induce macrophage infiltration in adipose and vascular tissues, triggering pro-inflammatory cytokine cascades (e.g., IL-6, TNF-α). In mice models, these alterations were associated with increased production of TGF-β, VEGF, and collagen accumulation, hence expediting both atherogenesis and renal fibrosis. Bone marrow-derived mesenchymal stromal cells (BM-MSCs) exhibited enhanced anti-inflammatory effects in metabolically stressed mice relative to adipose- or lung-derived MSCs, highlighting the systemic aspect of TyG-associated inflammatory burden (Abreu et al., 2017; Carvalho et al., 2024). Prolonged exposure to TyG-elevating dietary regimens in animal models has been shown to induce left ventricular hypertrophy, proteinuria, and glomerulosclerosis—mirroring hallmark features of human CKM syndrome (Carvalho et al., 2024). Moreover, in pediatric cohorts, increased TyG indices exhibit a robust correlation with HOMA-IR and visceral obesity, indicating that the metabolic stress indicated by TyG may emerge early in life and persist into adulthood (Devaraj, Krishnan, & Chen, 2025). This supports the concept that the TyG index encompasses various dimensions of metabolic risk, including insulin resistance, lipid toxicity, and organ-specific inflammatory remodeling, rendering it a likely factor in the CTI's sensitivity to detecting CKM-related dysfunction. 4.3.3. Why does CTI remain unresponsive in Stage 1 CKM? A stage-dependent adipose biology hypothesis The lack of a notable association between CTI and Stage 1 CKM, characterized by isolated adiposity without apparent metabolic impairment, may indicate the variable, stage-dependent functions of adipose tissue in cardiometabolic health. In the initial phases, adipose tissue development is predominantly facilitated by adipocyte hyperplasia, a mechanism that enables the secure storage of surplus energy without inducing systemic metabolic disturbances. In this phase, adipose tissue mostly acts as a metabolically inactive buffer, capable of storing lipids and preventing ectopic accumulation and lipotoxicity (Baldelli et al., 2024; Clemente-Postigo et al., 2020; Markina et al., 2024). The transition to later CKM stages (Stage 2–4), however, marks a pathophysiological shift. Adipocytes become hypertrophic, outstripping their storage capacity, which induces hypoxia, endoplasmic reticulum stress, and reactive oxygen species (ROS) production. These changes initiate a cascade of macrophage infiltration and adipocyte–immune crosstalk, amplifying the secretion of pro-inflammatory cytokines such as TNF-α, IL-6, and MCP-1 (Cho et al., 2023; Juman et al., 2012; Sandoval-Bórquez et al., 2024; Simons et al., 2007; Yomlar, Trisat, & Limpeanchob, 2024). Simultaneously, the endocrine function of adipose tissue declines, characterized by diminished secretion of beneficial adipokines (e.g., adiponectin) and elevated levels of leptin and other mediators associated with insulin resistance and systemic inflammation (Cho et al., 2023; Ge et al., 2024; Simons et al., 2007; Yomlar, Trisat, & Limpeanchob, 2024). This transition signifies a biological barrier, beyond which CTI responsively detects molecular disturbances, including lipid remodeling and sphingolipid release that compromises vascular function. Redox imbalance, characterized by oxidative stress, leads to endothelial damage and disruptions in metabolic signaling (Cho et al., 2023; Clemente-Postigo et al., 2020; Ge et al., 2024; Ilieva et al., 2017; Lei et al., 2019; Sandoval-Bórquez et al., 2024). Thus, CTI’s unresponsiveness in Stage 1 is not a statistical artifact, but a reflection of its specificity for detecting pathological, inflammation-driven adiposity—a threshold that has yet to be crossed in early-stage CKM. 4.3.4. Emerging experimental and cross-population evidence Even though longitudinal evidence directly connecting CTI to CKM progression is limited experimental and epidemiological research provide mechanistic support for its two fundamental components—CRP and the TyG index—across several species and populations. In animal models, monomeric CRP (mCRP) has been demonstrated to exacerbate cardiac remodeling following myocardial infarction by facilitating macrophage polarization towards a pro-inflammatory M1 phenotype through the JNK signaling pathway, hence intensifying heart fibrosis and dysfunction (Zha et al., 2021). High-fat diet models, on the other hand, recapitulate key features of TyG-related metabolic toxicity, including insulin resistance, hepatic steatosis, and vascular endothelial injury (Rocchiccioli et al., 2022; Wan et al., 2025). These experimental results highlight the biological validity of CTI as an indicator of systemic metabolic-inflammation load. Observational data from many populations in human studies underscores the therapeutic significance of these pathways. In extensive cohorts like the UK Biobank, metrics that include TyG (e.g., TyG-BMI) have independently forecasted the emergence of cardiometabolic and renal multimorbidity from an initial state of apparent health (Tang et al., 2025). Among statin-treated European patients, elevated TyG and CRP levels have been associated with more severe coronary artery disease, suggesting residual cardiometabolic risk beyond standard lipid control (Rocchiccioli et al., 2022). Moreover, in Latin American populations, elevated CRP has been linked to life course psychosocial stressors such as racial discrimination, highlighting inflammation’s broader sociobiological relevance (Harris et al., 2024). Notably, Chinese studies have demonstrated that high TyG and hsCRP levels can synergistically increase the risk of cardiometabolic multimorbidity in Asian adults (Wan et al., 2025). Collectively, these findings indicate that, despite CTI being a composite metric, its underlying biological components, systemic inflammation and insulin resistance, are recognized contributors to CKM-related pathology. Although careful interpretation is necessary, the uniformity of these processes across several species and contexts enhances confidence in the biological significance and potential applicability of CTI outside the specific study population. In combination, these mechanistic and outcome discoveries underscore that CTI transcends mere statistical concept. Its vulnerability to inflammatory and metabolic pathways, stage-selective sensitivity, and alignment with established CKM pathophysiology indicate it may serve as a biological conduit, connecting upstream dysregulation to downstream organ damage. Although additional experimental and longterm research are necessary, these findings offer a robust theoretical framework that substantiates our observational data. This study does not intend to construct or validate a predictive model. All findings are analyzed within an associative framework to enhance population-level risk classification. 4.4. Limitations This study has certain unavoidable limitations, which were methodologically addressed where possible to safeguard inference quality. First, its cross-sectional design restricts causal interpretation. However, the absence of association at early CKM stages, together with E-values, IPTW, and multi-model validation, minimizes reverse causality concerns. Second, CTI and CKM were measured once, but standardized fasting protocols and consistent results across continuous, categorical, and trimmed models reduce concerns over random biological fluctuation. Third, some unmeasured factors were not available; nonetheless, E-value thresholds suggest that only implausibly strong confounders could fully explain away the observed associations. Fourth, the high CKM prevalence (98.2%) reflects real-world risk clustering; to enhance interpretability, we incorporated stage-specific and multi-level binary models to mitigate ceiling effects. Fifth, Brant test results indicated proportional odds violations in ordinal regression; thus, complementary multi-level binary and multinomial models were applied to ensure valid stage-specific interpretation. Sixth, given the number of analytic layers, overfitting is theoretically possible; however, model diagnostics (e.g., GVIFs < 5, Macfadden pseudo R² = 0.372) support internal consistency. Seventh, the CHARLS sample comprises middle-aged and older adults only; generalization to younger populations requires external validation. Eighth, regional heterogeneity analysis was exploratory and not powered for formal interaction testing; however, consistent association directions across regions support geographic robustness. Ninth, CTI’s performance should be further validated in other populations, healthcare systems, and ethnic groups. Tenth, while CTI integrates inflammation and insulin resistance, it may not capture other CKM mechanisms. Eleventh, due to data constraints, negative control exposures or outcomes were not available for implementation. However, high E-values and extensive covariate adjustment mitigate this limitation. Finally, while CTI is not proposed as a diagnostic marker, its simplicity, reproducibility, and biological responsiveness support its use as a scalable stratification tool, especially where full clinical profiling is not feasible. 4.5. Implications and Recommendations The results of this study suggest that the C-reactive protein–triglyceride–glucose index (CTI), a low-cost composite marker derived from routine biomarkers (CRP, triglycerides, fasting glucose) (S. Tang et al., 2024), holds practical value for population-level risk stratification of CKM syndrome, particularly in aging societies with rising multimorbidity burdens. Through the use of a dual-layer analytical approach, binary logistic regression for overall CKM presence and multi-level binary and multinomial models for staging demonstrated that CTI effectively captured both general risk gradients and stage-specific burden changes. CTI is particularly advantageous in healthcare systems where resource optimization and early intervention are critical. Region-specific findings yield useful insights. In central provinces, where the CTI–CKM association was most pronounced, CTI-based stratification might be incorporated into community chronic illness surveillance, potentially in conjunction with environmental interventions like air quality enhancement. In under-resourced western regions, CTI could guide cardiometabolic resource allocation by identifying high-risk populations in the absence of sophisticated diagnostics. Importantly, CTI is not designed as a diagnostic or predictive instrument; instead, it serves as a supportive supplement to existing frameworks, improving the accuracy of risk classification and action planning. Its simplicity, reproducibility, and biological basis render it a theoretically robust and operationally scalable instrument for CKM preventive measures in swiftly aging populations. Subsequent research ought to assess the predictive validity of CTI over time, its generalizability, and its integration across health systems. This study's cross-sectional methodology and absence of repeated biomarker measurements precluded the evaluation of temporal risk or illness progression. Though not mechanistic, CTI's foundation in inflammation and insulin resistance underpins its biological validity. These findings necessitate prospective validation to enhance scalable CKM risk categorization. Mitigating negative control exposures in subsequent research will enhance methodological rigor and clinical significance (Austin, 2011; Braun et al., 2015; Fujikawa & Haruta, 2024; Hoyniak et al., 2025; Kumar et al., 2022; Lipsitch, Tchetgen Tchetgen, & Cohen, 2010; Qin et al., 2023; Takefuji, 2025; Tingulstad et al., 2023). 5. Conclusion This nationally representative cross-sectional study of older Chinese adults reveals a robust and consistent association between the C-reactive protein–triglyceride–glucose (CTI) index—a composite indicator of systemic inflammation and insulin resistance—and cardiovascular–kidney–metabolic (CKM) syndrome, encompassing both its presence and stage-defined severity. In contrast to previous studies that concentrated exclusively on illness staging, our dual-layered analytical paradigm incorporates the differentiation between any CKM risk and optimal health alongside stratified stage sensitivity, providing a more comprehensive and scalable method for syndromic risk profiling. The strength of this correlation across several geographic regions, exposure parameters, and modeling approaches underscores CTI's promise as a cost-effective, physiologically based indication of multisystem failure. Although causal inference is constrained by the cross-sectional design, these results offer essential evidence for the integration of biomarkers into CKM surveillance strategies and endorse the repositioning of CTI as a potential instrument for multidimensional risk stratification in aging populations. Future longitudinal studies and mechanistic research are necessary to clarify the prognostic validity and therapeutic significance of CTI throughout the CKM continuum. Declarations Acknowledgments The authors acknowledge the China Health and Retirement Longitudinal Study (CHARLS) research team and Peking University for providing access to the 2015 CHARLS dataset. Conflict of Interest The authors confirm there are no conflicts of interest. Data Availability Statement The data used in this study are publicly available from the China Health and Retirement Longitudinal Study (CHARLS), administered by the National School of Development at Peking University. Researchers may apply for data access at the CHARLS official website (https://charls.pku.edu.cn/). Ethics declarations This study was reviewed and approved by the Institutional Review Board (IRB) of Peking University (IRB00001052-11015; IRB00001052-11014). Written informed consent was obtained from all participants prior to participation. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Contributions A.P. contributed to methodology review, validation, and writing–review and editing. J.S. contributed to conceptualization, methodology, investigation, data curation, analysis, visualization, writing–original draft preparation, writing–review and editing, and project administration. All authors have read and agreed to the final version of the manuscript. The ORCID of the authors Anuchit Phanumartwiwath: https://orcid.org/0000-0001-8731-3875 JiaHao Shi: https://orcid.org/0009-0006-6136-7895 References Abreu, S. 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Zhou, C., Zhou, Y., Shuai, N., Zhou, J., & Kuang, X. (2024). The nonlinear relationship between estimated glomerular filtration rate and cardiovascular disease in US adults: a cross-sectional study from NHANES 2007–2018 [Original Research]. Frontiers in Cardiovascular Medicine , Volume 11–2024 . https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2024.1417926 Additional Declarations No competing interests reported. Supplementary Files CTICKMGraphicalAbstractHD.jpg Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers invited by journal 18 Oct, 2025 Editor invited by journal 22 Sep, 2025 Editor assigned by journal 20 Sep, 2025 Submission checks completed at journal 20 Sep, 2025 First submitted to journal 16 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7628351","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515817302,"identity":"f8efe8cf-3ed0-4917-b19a-39f66ef6fcc1","order_by":0,"name":"JiaHao Shi","email":"data:image/png;base64,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","orcid":"","institution":"MIVA Open University","correspondingAuthor":true,"prefix":"","firstName":"JiaHao","middleName":"","lastName":"Shi","suffix":""},{"id":515817303,"identity":"281c6acf-6c4c-474f-bfff-a8380ee36169","order_by":1,"name":"Anuchit Phanumartwiwath","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Anuchit","middleName":"","lastName":"Phanumartwiwath","suffix":""}],"badges":[],"createdAt":"2025-09-16 09:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7628351/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7628351/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91595051,"identity":"5da28263-9652-4329-ac72-9cff08cf7285","added_by":"auto","created_at":"2025-09-18 07:25:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28483,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection from the CHARLS 2015 wave.\u003c/p\u003e\n\u003cp\u003eLegend: Of the 21,095 participants in the 2015 wave (Wave 3) of CHARLS, individuals aged \u0026lt;45 or with missing data on age, CTI index, CKM classification, or province were excluded. The final analytic sample included 10,316 participants.\u003c/p\u003e","description":"","filename":"Figure1.Flowchartofparticipantsselection.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/70379b6b8ecbf380601ff32a.jpg"},{"id":91595054,"identity":"0256093f-56a3-47e5-8906-f569fb5505ab","added_by":"auto","created_at":"2025-09-18 07:25:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50425,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of study participants by province (% of total sample)\u003c/p\u003e\n\u003cp\u003eLegend: The map presents the proportion of participants (N = 10,316) from each province in the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). The largest contributions were from Shandong (10.3%), Henan (8.8%), and Sichuan (8.0%).\u003c/p\u003e","description":"","filename":"Figure2.Geographicdistribution.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/00796ecbdd79dccce67f1d39.jpg"},{"id":91595331,"identity":"57fcb9ec-3ca1-4f76-85fd-1898be38696f","added_by":"auto","created_at":"2025-09-18 07:33:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53410,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of participants with and without CKM syndrome (CHARLS 2015; N = 10,316). 98.2% were classified as CKM positive.\u003c/p\u003e","description":"","filename":"Figure3.Proportionofparticipantswithorwithoutckm.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/a1c74020a94b544d2ad450b0.jpg"},{"id":91596669,"identity":"2c1242aa-7923-4078-a89e-6462e7b68c14","added_by":"auto","created_at":"2025-09-18 07:49:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21619,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between CTI and CKM syndrome in three logistic regression models\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot displays odds ratios (ORs) with 95% confidence intervals for the association between CTI and CKM syndrome (CKM vs. No CKM). Model 1 is unadjusted; Model 2 adjusts for demographic and socioeconomic factors; Model 3 further adjusts for body and lifestyle variables. All results were statistically significant (p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure4.PrimaryassociationbetweenCTIandCKM.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/55ec298db88f683d5990d3b8.jpg"},{"id":91595332,"identity":"bd782831-99c6-4419-962e-8c511130d530","added_by":"auto","created_at":"2025-09-18 07:33:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46271,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized variance inflation factor (GVIF) for multicollinearity diagnosis in Model 3\u003c/p\u003e\n\u003cp\u003eLegend: Bar plot displaying GVIF values for all covariates included in the fully adjusted logistic regression model (Model 3). All values were well below the common threshold of 5, indicating no evidence of problematic multicollinearity.\u003c/p\u003e","description":"","filename":"Figure5.GVIFformulticollinearitydiagnosis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/d98cdbf904830493ccb6a6ce.jpg"},{"id":91595334,"identity":"2204f0ba-b785-4fa9-ada1-4bb075adaf2c","added_by":"auto","created_at":"2025-09-18 07:33:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41189,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation Between CTI Tertiles and CKM Syndrome\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot showing adjusted odds ratios (ORs) with 95% confidence intervals for CKM syndrome across CTI tertiles, using the lowest tertile as reference. Results adjusted for demographic, socioeconomic, anthropometric, and behavioral covariates (Model 3). A significant dose–response gradient was observed.\u003c/p\u003e","description":"","filename":"Figure6.CTITertilesandCKM.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/f0dd88e049973f226cb600da.jpg"},{"id":91595056,"identity":"e5bfe1ef-cb7c-4a6d-949f-0ace27e1849a","added_by":"auto","created_at":"2025-09-18 07:25:52","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":57063,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis of the association between CTI and CKM syndrome after excluding 1% extreme CTI values\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot showing odds ratios (ORs) and 95% confidence intervals (CIs) from logistic regression models examining the association between CTI and CKM syndrome. Model 3 (Trimmed CTI) excludes the top and bottom 1% of CTI values. The association remains significant and directionally consistent, indicating robustness to outliers.\u003c/p\u003e","description":"","filename":"Figure7.ExcludingCTIoutliers.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/727aebd96da1f090c163aeeb.jpg"},{"id":91596437,"identity":"74f02fba-57ac-4076-8005-105b9d6b96d6","added_by":"auto","created_at":"2025-09-18 07:41:52","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":61815,"visible":true,"origin":"","legend":"\u003cp\u003eCovariate Balance Before and After IPTW Weighting\u003c/p\u003e\n\u003cp\u003eLegend: Love plot showing absolute standardized mean differences for all covariates included in the propensity score model, before (red) and after (blue) IPTW weighting. IPTW achieved excellent covariate balance, with all post-weighting SMDs \u0026lt; 0.1.\u003c/p\u003e","description":"","filename":"Figure8.CovariateBalance.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/350336942a376cfc5f7a3eeb.jpg"},{"id":91595337,"identity":"383bd553-90b9-4718-996b-63eed116b5e8","added_by":"auto","created_at":"2025-09-18 07:33:52","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":37388,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of CTI on CKM Syndrome in IPTW-Weighted Sample\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot showing odds ratio and 95% confidence interval for the association between CTI and CKM syndrome after applying IPTW. The association remained statistically significant (OR = 4.35, 95% CI: 3.33–5.68, p \u0026lt; 0.001), confirming robustness to confounding.\u003c/p\u003e","description":"","filename":"Figure9.IPTWWeightedEffect.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/35bf5393c51ab4cf55a91c91.jpg"},{"id":91595339,"identity":"bebf087c-1ce7-4a82-8830-9aaa83691374","added_by":"auto","created_at":"2025-09-18 07:33:52","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":19279,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation Between CTI and CKM Syndrome by Geographic Region\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot showing region-specific odds ratios and 95% confidence intervals for the association between CTI and CKM syndrome in the East, Midland, and West regions of China. All associations were statistically significant (p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure10.RegionsepcificOddsratios.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/de00ba522cfcc4758b8bde93.jpg"},{"id":91595343,"identity":"712a16c5-7b1b-492c-a38a-0f625304efcc","added_by":"auto","created_at":"2025-09-18 07:33:53","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":23437,"visible":true,"origin":"","legend":"\u003cp\u003eOrdinal Logistic Regression of CTI on CKM Stage Severity\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot showing proportional odds ratios (and 95% confidence intervals) for the association between CTI and ordered CKM stages (0–4) across three models: crude, adjusted, and fully adjusted. Higher CTI levels were significantly associated with increased odds of more severe CKM staging.\u003c/p\u003e","description":"","filename":"Figure11.OrdinalLogisticregression.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/b7bfc701182ccc59aec2b657.jpg"},{"id":91595061,"identity":"2dbf248c-12c0-4087-8ba3-99640d0812de","added_by":"auto","created_at":"2025-09-18 07:25:52","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":40938,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-Level Binary Logistic Regression Models Comparing CKM Stage Groupings\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot showing odds ratios and 95% confidence intervals for the association between CTI and progressively defined binary contrasts: (1) CKM (Stages 1–4) vs. No CKM (Stage 0); (2) Stages 3–4 vs. 1–2; and (3) Stages 3–4 vs. 1–2 among individuals with CKM. All models adjusted for covariates and showed statistically significant associations (p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure12.MultilevelBinarylogistic.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/09e3b20a91aad5f56cdef69b.jpg"},{"id":91595075,"identity":"24507b17-f038-4870-9733-004f2f2c1cfa","added_by":"auto","created_at":"2025-09-18 07:25:53","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":48970,"visible":true,"origin":"","legend":"\u003cp\u003eMultinomial Logistic Regression of CTI on CKM Stages (Stage 0 as Reference)\u003c/p\u003e\n\u003cp\u003eLegend: Forest plot showing stage-specific odds ratios and 95% confidence intervals for the association between CTI and each CKM stage, using Stage 0 (no CKM) as the reference. CTI was significantly associated with higher odds of being in Stage 2, Stage 3, or Stage 4, but not Stage 1.\u003c/p\u003e","description":"","filename":"Figure13.MultinominalLogisticregression.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/41b008388416479245f103d4.jpg"},{"id":91597652,"identity":"74f22746-29b2-4d1f-90a5-9e6b59016c23","added_by":"auto","created_at":"2025-09-18 07:57:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2440035,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/b23ecbe0-1980-41bd-88f8-b45d68a314e5.pdf"},{"id":91596439,"identity":"171bdb13-7b78-458f-94ea-9db371a59746","added_by":"auto","created_at":"2025-09-18 07:41:52","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1108624,"visible":true,"origin":"","legend":"","description":"","filename":"CTICKMGraphicalAbstractHD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7628351/v1/0a0ed797ac19ae283b92ce01.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual Layer Association of the C-Reactive Protein Triglyceride Glucose Index with Cardiovascular–Kidney–Metabolic Syndrome among Older Chinese Adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe advent of Cardiovascular\u0026ndash;Kidney\u0026ndash;Metabolic (CKM) syndrome signifies a transformative change in the comprehension and management of multimorbidity in aging populations. In 2023, the American Heart Association presented an integrative paradigm that recognizes the intricate pathophysiological connections among cardiovascular disease, chronic renal disease, diabetes, and obesity\u0026mdash;conditions that have historically been treated in isolation (April-Sanders, 2024; Massy \u0026amp; Drueke, 2024). CKM syndrome reframes these problems not as isolated endpoints but as a progressive multisystem continuum, employing a five-stage classification system that spans from ideal health (Stage 0) to overt cardiovascular disease accompanied by metabolic comorbidities (Stage 4). In swiftly aging societies like China, where more than 75% of elderly individuals exhibit clustered cardiometabolic risk markers, the CKM construct holds substantial implications for population risk classification, early intervention, and systemic resource allocation (April‐Sanders, 2024; Y. Ding et al., 2024). As the global burden of interconnected chronic disease grows, the need for scalable tools that capture cross-organ dysfunction\u0026mdash;has never been more urgent (Hu et al., 2025; Javaid et al., 2025; W. Li et al., 2024; J. Tang et al., 2024; Yim et al., 2025).\u003c/p\u003e\u003cp\u003eAlthough there is growing agreement on cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome as a cohesive pathophysiological entity, existing risk assessment methods continue to rely on organ-specific indicators that fail to accurately represent its systemic characteristics. Traditional indicators\u0026mdash;such as fasting glucose for glycemic regulation, serum creatinine for renal function, and blood pressure for cardiovascular strain\u0026mdash;are analyzed in isolation, concealing the synergistic biological interactions that contribute to CKM-related decline (Fong, Sia, \u0026amp; See, 2025; Hassanein \u0026amp; Shafi, 2022; Mancianti et al., 2025; Massy \u0026amp; Drueke, 2024; H. Zhang et al., 2025). Although the American Heart Association has recently proposed a CKM staging framework that integrates excess adiposity, metabolic risk, kidney impairment, and cardiovascular burden into a progressive classification, real-world application of this model remains constrained by the lack of integrative biomarkers capable of capturing multisystem dysfunction within a single measure (Huang, Li, \u0026amp; Cho, 2023; Javaid et al., 2025; Li \u0026amp; Wei, 2025; W. Li et al., 2024; Rysz et al., 2017; J. Tang et al., 2024). The disjunction between conceptual syndromic models and fragmented biomarker application generates blind spots, especially in recognizing individuals who may already display early multisystem risk despite presenting with borderline results in conventional measurements (W. Ding et al., 2024; Javaid et al., 2025; Sung et al.; Szab\u0026oacute;ov\u0026aacute; et al., 2021; Zhou et al., 2024). Moreover, the majority of clinical or epidemiological evaluations concentrate specifically on assessing severity among individuals already identified as at risk, rather than addressing the fundamental necessity to differentiate between those with any CKM burden and those possessing optimal systemic health\u0026mdash;a vital distinction for comprehensive population risk surveillance (Dong et al., 2025; Lu et al., 2025).\u003c/p\u003e\u003cp\u003eAdditionally, current epidemiological studies frequently concentrate solely on the progression of CKM \"stages,\" neglecting a vital distinction: the boundary between the absence of CKM-related risk and the onset of any developing danger. Effectively differentiating persons with optimal health from those on the CKM spectrum could enhance public health policies and clinical triage. To our knowledge, no previous study has employed an integrated analytic methodology that concurrently assesses the presence of CKM risk and its eventual severity. An effective biomarker must have applicability throughout the majority of stages (Dong et al., 2025; Gao et al., 2024; Hu et al., 2025; W. Li et al., 2024; Oleske, 2010; Tain \u0026amp; Hsu, 2024; H. Zhang et al., 2025).\u003c/p\u003e\u003cp\u003eOne promising candidate is the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose (CTI) index, a composite marker that integrates low-grade systemic inflammation (CRP) with metabolic stress and insulin resistance (TyG index) (S. Tang et al., 2024). CRP serves as a recognized marker of vascular inflammation and endothelial damage, whilst the TyG index has been corroborated as a proxy for insulin resistance and lipotoxicity (Erdoğan et al., 2023; J. Li et al., 2024; Rizo-T\u0026eacute;llez, Sekheri, \u0026amp; Filep, 2023). By integrating these two dimensions, CTI may provide a more comprehensive representation of the multisystem stress inherent in CKM syndrome. Significantly, although CTI has been linked to diabetes, stroke, and cardiovascular outcomes, its association with CKM syndrome\u0026mdash;a framework that explicitly integrates cardiovascular, renal, and metabolic domains\u0026mdash;remains unexplored (Huo et al., 2025; Shan, Liu, \u0026amp; Gao, 2025; Xu et al., 2024).\u003c/p\u003e\u003cp\u003eTo address this disparity, we utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort of middle-aged and elderly individuals (China Center for Economic Research, n.d.). Unlike prior studies that isolate CKM stage or specific endpoints, our study is the first to systematically assess the association between CTI and CKM syndrome across two complementary dimensions: (1) the presence of any CKM-related risk burden, and (2) the severity gradient spanning from early adiposity to clinical cardiovascular disease. This dual-layered framework allows us to ascertain not only if CTI correlates with CKM risk, but also if it can distinguish between stages of escalating complexity and clinical danger. Additionally, we investigate whether the association between CTI and CKM persists across the varied geographic regions of China\u0026mdash;East, Central, and West. This stratified method improves the generalizability and public health significance of our results, especially in guiding regional efforts for risk assessment and prevention in resource-variable environments.\u003c/p\u003e\u003cp\u003eIn doing so, this study establishes an integrated, population-level framework that assesses CTI as a biomarker of systemic metabolic-inflammation burden and redefines the operationalization of cardio-kidney-metabolic risk within a syndromic paradigm. In contrast to current methodologies that analyze CKM phases separately, we incorporate a dual-risk framework that encompasses both the existence and intensity of CKM inside a unified analytical model. This signifies a key transition from isolated, organ-specific measurements to a multidimensional stratification instrument relevant to both clinical and public health sectors. Despite its initial development not being intended for CKM, our findings suggest that CTI serves as a cost-effective, scalable option for syndromic risk profiling in aging populations\u0026mdash;facilitating earlier detection, more precise risk stratification, and improved allocation of preventive measures within health systems.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Study population\u003c/h2\u003e\n\u003cp\u003eThe data for this investigation were obtained from the third wave (2015) of the China Health and Retirement Longitudinal investigation (CHARLS), a nationally representative survey of individuals aged 45 and older in mainland China. Wave 3 was chosen due to its provision of the most extensive biomarker data necessary for constructing the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index, the principal exposure variable in this investigation. Moreover, Wave 3 encompasses the requisite markers to delineate Cardiovascular-Kidney-Metabolic (CKM) syndrome, the principal outcome of interest. CHARLS employed a multistage, stratified, probability-proportional-to-size (PPS) sampling methodology to guarantee regional and demographic representativeness across 28 provinces. Comprehensive information concerning study protocols, data quality assurance, and sampling techniques may be found on the official CHARLS website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://charls.pku.edu.cn/\u003c/span\u003e\u003c/span\u003e). The CHARLS project received ethical approval from the Institutional Review Board of Peking University (IRB00001052\u0026ndash;11015 and IRB00001052\u0026ndash;11014), and written informed permission was secured from all participants before participation. The research complied with the guidelines established in the Declaration of Helsinki (Chen, 2019; China Center for Economic Research, n.d.; Zhao, 2023; Zhao, 2014; Zhao, 2020, 2013).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we initially identified 21,095 individuals from CHARLS 2015 as the baseline sample. Participants were excluded if they were younger than 45 years, given the study\u0026rsquo;s focus on midlife and older adults, who are more vulnerable (Demetriou et al., 2024; Lyu et al., 2024), or if they had missing values for any of the following key analytic variables: age, CTI index, CKM syndrome classification, or province-level geographic identifiers. These variables were considered crucial for the following reasons: age determined eligibility, CTI and CKM functioned as the exposure and outcome, respectively, and province identifiers facilitated the examination of regional heterogeneity and distribution mapping. Province information was essential for the intended regional heterogeneity studies spanning eastern, central, and western China, contingent upon the observation of significant connections in the primary analysis. A total of 10,779 individuals were removed according to these criteria, yielding a final analytic sample of 10,316 participants (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo mitigate potential bias from incomplete data in non-exclusion variables, we employed multiple imputations by chained equations (MICE) with five iterations. The first imputed dataset was utilized for statistical analysis. This technique guaranteed data completeness and preserved the integrity of the final analytic sample for multivariable modeling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Calculation of the C-Reactive Protein-Triglyceride-Glucose Index (CTI)\u003c/h2\u003e\n\u003cp\u003eThe C-reactive protein-triglyceride-glucose index (CTI) was calculated to evaluate the combined burden of systemic inflammation and insulin resistance. The method for calculating CTI was adopted from a previously published study by S. Tang et al. (2024).\u003c/p\u003e\n\u003cp\u003eThe CTI was computed using the following formula:\u003c/p\u003e\n\u003cp\u003eCTI was defined as 0.412* Ln (CRP [mg/L])\u0026thinsp;+\u0026thinsp;Ln (TG [mg/dl] \u0026times; FBG [mg/dl])/2\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCRP denotes C-reactive protein, a nonspecific biomarker of inflammation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTG denotes triglycerides.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFBG denotes fasting blood glucose.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eln represents the natural logarithm.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll laboratory values were derived from fasting blood samples, with units standardized to mg/L for CRP and mg/dL for both TG and FBG before calculation. This composite score incorporates CRP as an indicator of systemic inflammation and the triglyceride-glucose product (TyG index), a recognized surrogate measure for insulin resistance. The calculation was performed for each participant to provide a unified measure reflecting both inflammatory status and insulin resistance, as per the methodology validated in S. Tang et al. (2024).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Definition of CKM syndrome\u003c/h2\u003e\n\u003cp\u003eCardiovascular\u0026ndash;Kidney\u0026ndash;Metabolic (CKM) syndrome was defined in accordance with the American Heart Association (AHA) Presidential Advisory Statement (Ndumele, Rangaswami, et al., 2023). The AHA paradigm outlines five stages of CKM syndrome progression, encompassing the continuum of excessive adiposity, metabolic dysfunction, renal involvement, and cardiovascular disease (CVD). For applicability within the Chinese population, these parameters were modified utilizing locally relevant thresholds, including Asian-specific BMI and waist circumference cutoffs.\u003c/p\u003e\n\u003cp\u003eParticipants were assigned to one of five mutually exclusive CKM stages as follows:\u003c/p\u003e\n\u003cp\u003eStage 0 (No CKM Health Risk Factors): Individuals with normal BMI (\u0026lt;\u0026thinsp;23 kg/m\u0026sup2;), waist circumference (\u0026lt;\u0026thinsp;80 cm for women or \u0026lt;\u0026thinsp;90 cm for men), normoglycemia, normotension, normal lipids, and no evidence of chronic kidney disease (CKD) or clinical/subclinical CVD. Stage 1 (Excess or Dysfunctional Adiposity Only): Individuals with overweight/obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;23 kg/m\u0026sup2;) or abdominal obesity, but no additional metabolic, renal, or cardiovascular abnormalities. Stage 2 (Metabolic Risk Factors and/or CKD): Individuals exhibiting metabolic disorders (e.g., type 2 diabetes, hypertension, hypertriglyceridemia\u0026thinsp;\u0026ge;\u0026thinsp;135 mg/dL, or metabolic syndrome) or diagnosed CKD. Stage 3 (Subclinical CVD with Coexisting CKM Features): Individuals with subclinical CVD in the presence of adiposity, metabolic risk, or CKD, including those at high atherosclerotic risk per AHA-PREVENT or KDIGO guidelines. Stage 4 (Clinical CVD with Coexisting CKM Features): Participants with overt clinical CVD alongside metabolic dysfunction, CKD, or obesity.\u003c/p\u003e\n\u003cp\u003eIn the primary analysis, CKM syndrome was operationalized as a binary outcome to enhance statistical power and simplify interpretation. Participants classified as Stage 1\u0026ndash;4 were grouped as having CKM syndrome (\u0026ldquo;Yes\u0026rdquo;), while those in Stage 0 were designated as \u0026ldquo;No CKM\u0026rdquo;. This binary classification facilitates pragmatic risk stratification for public health interventions and preliminary clinical decision-making. The comprehensive five-level staging (Stage 0\u0026ndash;4) was preserved in the secondary analysis to evaluate the correlation between CTI and CKM stage severity, facilitating the examination of stage-specific relationships and the gradient predictive significance of the CTI index. This multi-tiered strategy offers supplementary insights into the range of CKM burdens and augments the therapeutic significance of our findings. This dual-definition strategy not only encapsulates the essence of CKM but also facilitates mutual validation: the binary approach assesses initial overall risk, while the staging delineates stratified risk gradients\u0026mdash;collectively enhancing the robustness of our analytical framework.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4. Assessment of covariates\u003c/h2\u003e\n\u003cp\u003ePotential confounders, including various Demographic and Socioeconomic, Body measurements and lifestyle factors, were sourced from the CHARLS database. The selection of appropriate confounding factors was based on previous literature (Dong et al., 2025; Lin et al., 2025; Tan et al., 2025; Tian et al., 2025). Demographic and socioeconomic variables included age (measured in years as a continuous variable), sex (\u0026ldquo;Female\u0026rdquo; and \u0026ldquo;Male\u0026rdquo;), residence (\u0026ldquo;Rural\u0026rdquo; and \u0026ldquo;Urban\u0026rdquo;), marital status (\u0026ldquo;Married and living with a spouse\u0026rdquo;, \u0026ldquo;Married but living without a spouse\u0026rdquo;, and \u0026ldquo;Single, divorced, and widowed\u0026rdquo;), and education status (categorized as \u0026ldquo;Elementary school or below\u0026rdquo; and \u0026ldquo;Middle school or above\u0026rdquo;). Body measurements and lifestyle factors included body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters), smoking status (\u0026ldquo;Smoker\u0026rdquo; and \u0026ldquo;Non-Smoker\u0026rdquo;), and drinking status (\u0026ldquo;Non-drinker\u0026rdquo;, \u0026ldquo;Drink but less than once a month\u0026rdquo;, and \u0026ldquo;Drink more than once a month\u0026rdquo;). All covariates were included in multivariable logistic regression models to adjust for potential confounding effects.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\n\u003cp\u003eDescriptive statistics were used to summarize the baseline characteristics of the study population. Continuous variables were first tested for normality using the Kolmogorov\u0026ndash;Smirnov (K-S) test. Since the number of participants included in this study is very large, it is more appropriate to choose K-S test as the normality test. Variables not following a normal distribution were presented as medians with interquartile ranges (IQRs), while normally distributed variables were reported as means with standard deviations (SDs). Categorical variables were expressed as frequencies and percentages. Comparisons between participants with and without CKM syndrome were conducted using the independent t-test or Mann\u0026ndash;Whitney U test for continuous variables, depending on their distribution. The chi-square test was used to compare categorical variables. For categorical variables with expected cell counts\u0026thinsp;\u0026lt;\u0026thinsp;5, Fisher\u0026rsquo;s exact test was applied to ensure statistical robustness.\u003c/p\u003e\n\u003cp\u003eTo evaluate the association between the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index (CTI) and the presence of cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome, binary logistic regression models were constructed using CKM syndrome as the dichotomous outcome variable (coded as 1 / CKM for Stages 1\u0026ndash;4 and 0 / No CKM for Stage 0). CTI was entered as a continuous independent variable. Three hierarchical models were constructed: Model 1 was an uncorrected univariate logistic regression, analyzing the raw association between CTI and CKM syndrome. Models 2 and 3 were multivariate logistic regression analyses, controlling for potential confounding variables. Specifically, Model 2 adjusted for key Demographic and Socioeconomic covariates including age, sex, residence, education status, and marital status, while Model 3 was further adjusted for additional Body measurements and lifestyle factors including body mass index (BMI), smoking status, and drinking status. Odds ratios (ORs) and their respective 95% confidence intervals (CIs) were calculated by exponentiating the regression coefficients. To improve clarity, a forest plot was created to illustrate the odds ratios and 95% confidence intervals for CTI across the three models, along with the associated p-values. All models were computed utilizing the generalized linear model (glm) function in R version 4.4.1, employing a binomial family and logit link, with two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 being statistically significant.\u003c/p\u003e\n\u003cp\u003eGeneralized variance inflation factors (GVIFs) were calculated to evaluate potential multicollinearity among the independent variables in the fully adjusted model (Model 3), which included CTI and a comprehensive array of covariates. Variables with GVIF values over 5 were deemed indicative of significant multicollinearity issues. A graphical representation of GVIF values was created to visually evaluate the impact of each variable on collinearity.\u003c/p\u003e\n\u003cp\u003eMcFadden\u0026rsquo;s pseudo R\u0026sup2; was employed to evaluate the overall goodness-of-fit of the fully adjusted logistic regression model (Model 3). This metric was calculated utilizing the pR2() function from the pscl package in R. The McFadden's pseudo R\u0026sup2; indicates the relative enhancement in model log-likelihood compared to a null model and is commonly utilized as an indicator of model adequacy in logistic regression. The resultant value was compiled into a summary table utilizing the flextable and officer packages for reporting purposes.\u003c/p\u003e\n\u003cp\u003eE-values were calculated to evaluate the strength of the observed relationship between CTI and CKM syndrome against potential unmeasured confounding, utilizing effect estimates from the fully adjusted model (Model 3). The E-value measures the minimal degree of connection an unmeasured confounder must possess with both the exposure and the outcome, beyond the assessed covariates, to entirely account for the observed association. This sensitivity analysis assists in determining if the observed correlation may reasonably be ascribed to residual confounding instead of a genuine effect. This methodology establishes a conservative and stringent benchmark and has been progressively embraced in high-caliber epidemiological research and premier journals, including The Lancet, JAMA, and BMJ Medicine (Ahmadi et al., 2023; Choi et al., 2024; Haneuse, VanderWeele, \u0026amp; Arterburn, 2019; Tobias et al., 2023; VanderWeele \u0026amp; Ding, 2017); given that our models already adjusted for a comprehensive set of known confounders, the use of E-value serves to further ensure the robustness of association inference without requiring additional model expansion.\u003c/p\u003e\n\u003cp\u003eMoreover, several additional sensitivity analyses were conducted to assess the robustness of the association between CTI and CKM syndrome. First, CTI was categorized into tertiles based on its distribution in the study population. Participants were classified into three equal-sized groups\u0026mdash;Low, Medium, and High CTI levels. Multivariable logistic regression models were then conducted, using the lowest tertile as the reference category, to evaluate the risk of CKM syndrome across increasing levels of CTI after adjusting for relevant covariates. Second, to assess the influence of outliers, a trimmed analysis was performed by excluding individuals with CTI values in the top and bottom 1% of the distribution. The regression model was then re-estimated on the restricted sample. Third, inverse probability of treatment weighting (IPTW) was employed to reduce residual confounding. Propensity scores were estimated via logistic regression using all covariates included in the main model 3, and stabilized weights were applied to construct a pseudo-population with balanced baseline characteristics. One-to-one nearest neighbor propensity score matching (PSM) with a caliper of 0.2 was conducted to replicate the association in a matched cohort. Covariate balance before and after matching was evaluated using standardized mean differences and visualized with Love plots. Across all sensitivity analyses, logistic regression models were refitted and odds ratios with 95% confidence intervals were extracted to compare the consistency of effect estimates.\u003c/p\u003e\n\u003cp\u003eA regional heterogeneity analysis was conducted to assess whether the relationship between CTI and CKM syndrome varies across geographic locations. Participants were classified into three primary regions of China\u0026mdash;eastern, central, and western\u0026mdash;according to provincial administrative boundaries delineated in previous studies (Han et al., 2022). Specifically, Eastern region included: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. Central region included: Shanxi, Inner Mongolia, Anhui, Jiangxi, Henan, Hubei, Hunan, and Guangxi. Western region included: Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Heilongjiang, and Jilin. Multivariable logistic regression models were stratified by geographic region to estimate region-specific associations between CTI and CKM syndrome. Recognizing regional variability may uncover contextual variations in environmental exposures, healthcare infrastructure, or metabolic risk clustering, hence facilitating the creation of spatially customized public health interventions.\u003c/p\u003e\n\u003cp\u003eTo investigate whether the association between CTI and CKM syndrome varies by severity of disease stage, participants were categorized into five ordinal stages (Stage 0 to Stage 4) according to the adapted CKM classification criteria (Ndumele, Rangaswami, et al., 2023). An ordinal logistic regression model was first fitted using the polr() function from the MASS package, with CKM stage as an ordered outcome and CTI as the primary predictor. Three hierarchical models were constructed, ranging from crude to fully adjusted, including covariates such as age, sex, BMI, residence, education status, marital status, smoking, and alcohol use.\u003c/p\u003e\n\u003cp\u003eOrdinal modeling facilitated the efficient estimation of a singular odds ratio under the proportional odds assumption, so maintaining statistical power and parsimony while utilizing the ordinal characteristics of the result. The Brant test was conducted utilizing the brant program to determine compliance with the proportionate odds assumption necessary for ordinal regression. In the event of an assumption violation, different modeling methodologies were employed. Specifically, three binary logistic models were fitted to contrast adjacent CKM stage groupings: (1) No CKM vs. any CKM (Stage 1\u0026ndash;4), (2) early-stage (Stage 1) vs. more advanced stages (Stage 2\u0026ndash;4), and (3) Stage 1\u0026ndash;2 vs. Stage 3\u0026ndash;4. These contrasts allowed for finer-grained risk differentiation and correspond to real-world public health screening and staging decisions. In addition, a multinomial logistic regression model was employed to examine the association between CTI and each CKM stage (Stage 1 through Stage 4), using Stage 0 (No CKM) as the reference. This model completely relaxed the proportionate odds assumption and permitted the emergence of non-monotonic risk patterns. These contrasting modeling methodologies ensured the validity, flexibility, and interpretability of the stage-specific studies, enabling us to systematically quantify CTI's association with the whole CKM severity spectrum under both limited and unconstrained assumptions. All models were calibrated for the identical factors included in the primary analysis. Results were expressed as odds ratios (ORs) accompanied by 95% confidence intervals and were visually represented in forest plots.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Geographic Distribution, CKM Syndrome prevalence and Baseline Characteristics of Participants\u003c/h2\u003e\n\u003cp\u003eThe final analytical sample comprised 10,316 people aged 45 and above from the nationally representative CHARLS 2015 dataset. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the geographic dispersion of participants across several Chinese provinces. The sample was extensively disseminated, with the highest amounts originating from Shandong (10.3%), Henan (8.8%), and Sichuan (8.0%). Provinces including Hainan, Tibet, and Ningxia yielded no qualifying participants, indicating regional disparities in survey coverage and data integrity. The eastern, central, and western regions comprised 34.5%, 37.5%, and 28.0% of the sample, respectively.\u003c/p\u003e\n\u003cp\u003eThe incidence of cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome in this cohort was notably high, with 98.2% of participants fulfilling the criteria for Stages 1\u0026ndash;4 of CKM, whereas merely 1.8% were categorized as having no CKM-related risk (Stage 0), as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. This distribution underscores the significant burden of metabolic, renal, and cardiovascular risks among midlife and older persons in China. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e delineates the baseline characteristics of subjects, categorized by CKM status. Participants with CKM were older (median: 61.0 vs. 55.0 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had a higher body mass index (23.7 vs. 20.3 kg/m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a greater CTI score (8.8 vs. 8.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those without CKM. In the absence of CKM, females constituted the predominant majority (91.9%), while the sex distribution in the CKM group was more equitable (52.6% female, 47.4% male).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline Characteristics of Study Participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eStratified by CKM\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;10,316\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo CKM\u003c/strong\u003e (n\u0026thinsp;=\u0026thinsp;185)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCKM\u003c/strong\u003e (n\u0026thinsp;=\u0026thinsp;10131)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.0 [53.0, 67.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.0 [50.0, 60.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.0 [54.0, 68.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBody Mass Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.6 [21.3, 26.3]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.3 [19.1, 21.5]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.7 [21.4, 26.3]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.8 [8.3, 9.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1 [7.6, 8.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.8 [8.3, 9.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,496.0 (53.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170.0 (91.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,326.0 (52.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,820.0 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.0 (8.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,805.0 (47.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidence\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,571.0 (63.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120.0 (64.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,451.0 (63.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,745.0 (36.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.0 (35.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,680.0 (36.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarital Status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.254\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried and living with a spouse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,498.0 (82.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158.0 (85.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,340.0 (82.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried but living without a spouse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e408.0 (4.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0 (4.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e399.0 (3.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSingle, divorced, and widowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,410.0 (13.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.0 (9.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,392.0 (13.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation Status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.485\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElementary school or below\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,020.0 (68.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121.0 (65.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,899.0 (68.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle school or above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,296.0 (32.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.0 (34.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,232.0 (31.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegional Category\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.765\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEast\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,564.0 (34.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.0 (34.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,501.0 (34.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMidland\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,867.0 (37.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.0 (35.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,801.0 (37.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,885.0 (28.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.0 (30.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,829.0 (27.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking Status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,798.0 (56.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170.0 (91.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,628.0 (55.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,518.0 (43.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.0 (8.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,503.0 (44.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking Status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrink but less than once a month\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e900.0 (8.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0 (4.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e891.0 (8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrink more than once a month\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,665.0 (25.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.0 (14.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,638.0 (26.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-drinker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,751.0 (65.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149.0 (80.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,602.0 (65.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProvince\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanghai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.0 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.0 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYunnan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e651.0 (6.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.0 (5.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e640.0 (6.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInner Mongolia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e452.0 (4.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.0 (2.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e447.0 (4.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeijing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJilin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e228.0 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e225.0 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSichuan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e830.0 (8.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.0 (11.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e809.0 (8.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTianjin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.0 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.0 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnhui\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e578.0 (5.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.0 (5.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e568.0 (5.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShandong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,067.0 (10.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.0 (5.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,056.0 (10.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShanxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e338.0 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e335.0 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuangdong\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e362.0 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.0 (4.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e354.0 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuangxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e351.0 (3.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.0 (5.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e341.0 (3.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXinjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.0 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.0 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangsu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e490.0 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.0 (2.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e485.0 (4.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJiangxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e518.0 (5.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.0 (9.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e500.0 (4.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHebei\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e480.0 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.0 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e476.0 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHenan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e904.0 (8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.0 (5.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e893.0 (8.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZhejiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e465.0 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.0 (7.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e451.0 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHubei\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e270.0 (2.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.0 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e266.0 (2.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHunan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e456.0 (4.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.0 (2.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e451.0 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGansu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e266.0 (2.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.0 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e260.0 (2.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFujian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e291.0 (2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.0 (3.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284.0 (2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGuizhou\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.0 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.0 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiaoning\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e309.0 (3.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.0 (6.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e297.0 (2.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChongqing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113.0 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111.0 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShaanxi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e363.0 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.0 (3.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356.0 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQinghai\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.0 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0 (0.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.0 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeilongjiang\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e192.0 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189.0 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median [IQR], determined by the Kolmogorov-Smirnov test for normality. Continuous variables were compared using the t-test or Mann-Whitney U test, as appropriate. Categorical variables were compared using the Chi-square test or Fisher's exact test when cell counts were \u0026lt;\u0026thinsp;5. Overall, No CKM, and CKM sample sizes are indicated in the column headers.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWilcoxon rank sum test; Pearson's Chi-squared test\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\u003eLifestyle-related characteristics shown significant disparities between groups. Smoking and alcohol consumption were significantly less prevalent in the No CKM group: merely 8.1% were current smokers, 14.6% reported drinking more than once a month, and 4.9% consumed alcohol less than once a month, resulting in a total of 19.5% drinkers, in contrast to 44.4% and 34.8%, respectively, in the CKM group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both comparisons). No notable variations were detected across groups regarding domicile (urban vs. rural), marital status, educational achievement, or regional classification (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all). As a whole, these foundational data indicate that individuals with CKM syndrome are generally older, possess a greater metabolic burden, and display more detrimental behavioral risk profiles.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Primary Association Between CTI and CKM Syndrome\u003c/h2\u003e\n\u003cp\u003eThe association between the C-reactive protein triglyceride glucose index (CTI) and the existence of cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome was evaluated by three hierarchical binary logistic regression models, with findings illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In the unadjusted model (Model 1), each unit increase in CTI corresponded to significantly elevated risks of CKM syndrome (OR\u0026thinsp;=\u0026thinsp;4.25, 95% CI: 3.42\u0026ndash;5.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for key demographic and socioeconomic covariates, including age, sex, residence, education status, and marital status (Model 2), the association remained robust and slightly strengthened (OR\u0026thinsp;=\u0026thinsp;4.48, 95% CI: 3.57\u0026ndash;5.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eIn the fully adjusted model (Model 3), which further accounted for body measurements and lifestyle factors (body mass index, smoking status, and drinking status), the association was attenuated but remained statistically significant (OR\u0026thinsp;=\u0026thinsp;2.57, 95% CI: 2.02\u0026ndash;3.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The data indicate that systemic inflammation and insulin resistance, as measured by the composite CTI score, are independently linked to increased CKM risk, even when accounting for conventional risk variables. The diminishing amplitude of the odds ratio throughout the models underscores the impact of behavioral and metabolic variables while also affirming the independent role of CTI in CKM risk stratification among older people.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Establishing Methodological Rigor: Multicollinearity Diagnostics, Model Fit Evaluation, and Sensitivity to Unmeasured Confounding\u003c/h2\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.1. Multicollinearity Diagnostics via Generalized Variance Inflation Factor (GVIF)\u003c/h2\u003e\n\u003cp\u003eTo assess potential multicollinearity among the independent variables in the fully adjusted logistic regression model (Model 3), we computed the Generalized Variance Inflation Factor (GVIF) for each independent variable. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates that all GVIF values were far below the widely recognized threshold of 5, indicating an absence of considerable collinearity among the variables considered. The GVIFs varied from 1.02 (for CTI and marital status) to 1.33 (for sex), signifying negligible shared variance among the variables. Significantly, essential factors including age (GVIF\u0026thinsp;=\u0026thinsp;1.13), BMI (1.10), smoking status (1.29), and education level (1.11) remained within acceptable limits. The CTI variable, the main exposure of interest, exhibited a GVIF of merely 1.02, further substantiating its independent associative role in the multivariable model.\u003c/p\u003e\n\u003cp\u003eThese results affirm that the fully adjusted model is not compromised by multicollinearity, thereby supporting the stability and interpretability of the regression estimates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.2. Model Goodness-of-Fit Assessment Using McFadden\u0026rsquo;s Pseudo R\u0026sup2;\u003c/h2\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMcFadden's Pseudo R\u0026sup2; for Model 3 (Fully Adjusted)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMcFadden_R2\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.372\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eNote: McFadden\u0026rsquo;s pseudo R\u0026sup2; was used to assess the goodness-of-fit of the fully adjusted logistic regression model (Model 3). A value of 0.372 indicates a substantial improvement in model likelihood compared to the null model, reflecting strong explanatory power. Pseudo R\u0026sup2; values between 0.2 and 0.4 are generally considered indicative of an excellent model fit in GLM models.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo evaluate the explanatory power of the fully adjusted logistic regression model (Model 3), we computed McFadden\u0026rsquo;s Pseudo R\u0026sup2;, a widely used indicator of model fit in generalized linear models. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the pseudo R\u0026sup2; value for Model 3 was 0.372. According to conventional benchmarks, values between 0.2 and 0.4 are generally interpreted as indicating excellent model performance in logistic regression settings (Brunton-Martin, Wood, \u0026amp; Gaskett, 2024; Hauber et al., 2016). The results indicate that the model significantly enhanced likelihood compared to the null model and effectively accounted for the variance in CKM syndrome status described by CTI and the covariates included. This finding reinforces the validity of Model 3 and affirms the suitability of the chosen variables in elucidating the binary outcome.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.3. E-value Analysis to Address Unmeasured Confounding\u003c/h2\u003e\n\u003cp\u003eTo assess the potential influence of unmeasured confounding on the observed relationship between CTI and CKM syndrome, we computed the E-value for both the point estimate and the lower limit of the 95% confidence interval from the fully adjusted model (Model 3) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The E-value for the point estimate (OR\u0026thinsp;=\u0026thinsp;2.57) was 4.58, whereas the E-value for the lower bound (OR\u0026thinsp;=\u0026thinsp;2.02) was 3.46.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eE-values for the Association between CTI and CKM (Model 3, Fully Adjusted)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eE-value (Point Estimate)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eE-value (Lower 95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3 (Fully Adjusted)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.57 (2.02\u0026ndash;3.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eNote: E-values indicate the minimum strength of unmeasured confounding required to explain away the observed association between CTI and CKM syndrome.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe E-values indicate the minimal strength of association required for an unmeasured confounder, independent of those already accounted for, to simultaneously correlate with both CTI and CKM syndrome (on the risk ratio scale) in order to entirely account for the observed link. To diminish the observed odds ratio of 2.57 to null, a confounder necessitating a risk ratio of no less than 4.58 would be required for both the exposure (CTI) and the outcome (CKM syndrome). A confounder would require a risk ratio of 3.46 with both CTI and CKM to reduce the bottom bound of the 95% confidence interval (2.02) to null. Associations of this magnitude are exceptionally rare in real-world epidemiologic settings, as consistently demonstrated in sensitivity analyses across high-quality observational studies (Chao et al., 2023; Li et al., 2020; Pepe et al., 2004). Given that Model 3 already adjusts for a comprehensive set of confounders, including age, sex, residence, marital status, education status, BMI, smoking status, and drinking status, the presence of such a strong, independent, and unmeasured confounder is usually highly implausible. In addition, none of the covariates in the model demonstrated variance inflation factors nearing concerning thresholds (as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), so eliminating residual collinearity that might potentially hide the impacts of uncontrolled variables.\u003c/p\u003e\n\u003cp\u003eThis E-value analysis enhances the inferential validity of our findings by quantitatively illustrating that the observed association remains resilient against significant unmeasured confounding. It eliminates the possibility that the exclusion of supplementary factors, unless they possess implausibly high influence and are significantly associated with both CTI and CKM, will invalidate the observed association.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Additional Sensitivity Analyses\u003c/h2\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.1. CTI Tertile Categorization and Risk Gradient\u003c/h2\u003e\n\u003cp\u003eTo enhance the sensitivity analysis framework and evaluate the robustness and interpretability of the CTI\u0026ndash;CKM connection, we classified CTI values into tertiles: Low, Medium, and High, according to their distribution among the research population. This stratification sought to evaluate the consistency of the correlation across different exposure levels and to assess a potential dose\u0026ndash;response relationship, so reinforcing the claim for a causal connection.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates that participants in the medium CTI tertile exhibited a significantly increased risk of CKM syndrome (OR\u0026thinsp;=\u0026thinsp;2.36, 95% CI: 1.60\u0026ndash;3.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas those in the highest CTI tertile displayed an even more pronounced association (OR\u0026thinsp;=\u0026thinsp;15.02, 95% CI: 5.48\u0026ndash;41.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) relative to the reference group. The expanding confidence intervals at elevated CTI levels indicate a greater variability of extreme biomarker values, however, do not reduce the size or relevance of the effect. This research demonstrates a distinct and strong dose\u0026ndash;response relationship, suggesting that even slight increases in CTI correlate with substantially higher odds of CKM syndrome, whereas people with the highest CTI burden face a markedly elevated risk. These results not only validate the robustness of the primary findings under an alternative exposure specification but also furnish persuasive evidence for a dose\u0026ndash;response connection between CTI and CKM syndrome. Moreover, results offer robust empirical evidence for a monotonic biological gradient and underscore the therapeutic significance of CTI as a risk classification instrument for CKM syndrome.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.2. Outlier-Trimmed Analysis (Exclusion of Top and Bottom 1%)\u003c/h2\u003e\n\u003cp\u003eTo test the sensitivity of our findings to extreme biomarker values and ensure that the association between CTI and CKM syndrome was not driven by outliers, we conducted a trimmed analysis by excluding individuals in the top and bottom 1% of the CTI distribution. The fully adjusted logistic regression model (Model 3) was re-evaluated on this constrained sample, resulting in a new trimmed model. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates that the removal of outliers exerted negligible influence on the direction or strength of the connection. The odds ratio in the trimmed model (OR\u0026thinsp;=\u0026thinsp;2.94, 95% CI: 2.24\u0026ndash;3.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was similar to that of the original fully adjusted model (OR\u0026thinsp;=\u0026thinsp;2.57, 95% CI: 2.02\u0026ndash;3.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The point estimate somewhat rose post-trimming, accompanied by narrower confidence intervals, signifying improved precision.\u003c/p\u003e\n\u003cp\u003eThese findings validate that the identified association is neither a product of biased data or the impact of outliers. The consistency of results in both the complete and reduced datasets reinforces the internal validity of the model and enhances confidence in the relationship between systemic inflammation\u0026ndash;insulin resistance (CTI) and CKM syndrome.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.3. Covariate Balance Assessment and Weighted Association Using IPTW\u003c/h2\u003e\n\u003cp\u003eTo further account for potential residual confounding, we applied inverse probability of treatment weighting (IPTW) based on propensity scores derived from all covariates in the fully adjusted model (Model 3). Stabilized weights were used to create a pseudo-population in which covariate distributions were balanced between exposure levels, allowing estimation of the marginal effect of CTI on CKM syndrome.\u003c/p\u003e\n\u003cp\u003eThe assessment of covariate balance prior to and following weighting was conducted using absolute standardized mean differences (SMDs) and illustrated using a Love plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Before weighing, some factors displayed significant imbalance, especially age, sex, BMI, and smoking status. Following the use of IPTW, all covariates attained exceptional balance, with SMDs well below the standard threshold of 0.1, so validating effective covariate harmonization between exposure groups.\u003c/p\u003e\n\u003cp\u003eThereafter, logistic regression was performed on the IPTW-weighted sample to assess the impact of CTI on CKM syndrome. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates that CTI maintained a solid association with CKM risk in the IPTW model (OR\u0026thinsp;=\u0026thinsp;4.35, 95% CI: 3.33\u0026ndash;5.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The extent of the effect strengthens the reliability of the association across various modeling approaches. Collectively, these findings indicate that the association between CTI and CKM syndrome remains significant even with stringent statistical adjustments for confounding variables, hence reinforcing the validity of our results within an emulated pseudo-randomized context.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5. Regional Heterogeneity Analysis\u003c/h2\u003e\n\u003cp\u003eTo investigate any geographic variation in the relationship between CTI and CKM syndrome, we performed stratified logistic regression analyses by region\u0026mdash;East, Midland, and West\u0026mdash;based on established administrative classifications. This investigation sought to evaluate if contextual factors, like environmental exposures, socioeconomic situations, or healthcare access, could influence the strength of the observed link.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e illustrates that CTI consistently is associated with elevated risks of CKM syndrome in all three areas. The association strength was greatest in the Midland region (OR\u0026thinsp;=\u0026thinsp;2.97, 95% CI: 1.95\u0026ndash;4.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by the East (OR\u0026thinsp;=\u0026thinsp;2.76, 95% CI: 1.82\u0026ndash;4.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the West (OR\u0026thinsp;=\u0026thinsp;2.17, 95% CI: 1.40\u0026ndash;3.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Despite the overlap of confidence intervals, the point estimates indicate a tendency towards marginally stronger relationships in more urbanized or metabolically taxed areas.\u003c/p\u003e\n\u003cp\u003eThese regional variations may indicate fundamental discrepancies in chronic illness patterns, metabolic risk aggregation, or environmental stressors such pollution and urbanization. The results emphasize the necessity of region-specific strategies for CKM prevention and intervention. The persistent and significant associations identified throughout the three principal Chinese regions\u0026mdash;East, Midland, and West\u0026mdash;demonstrate that the relationship between CTI and CKM syndrome is regionally resilient. Notwithstanding regional variations in demographics, metabolic profiles, and healthcare systems, the direction and degree of the connection remained consistent. The cross-regional consistency improves the external validity and generalizability of our findings, indicating that the CTI index may function as a widely applicable indicator for CKM risk stratification across various populations in China and possibly other aging societies undergoing similar cardiometabolic transitions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e3.6. Stage-Specific Association Between CTI and CKM Severity\u003c/h2\u003e\n\u003cp\u003eWhile the primary binary logistic regression analysis provides a clear and actionable summary of the overall association between CTI and CKM syndrome, useful for broad public health initial screening and risk flagging, the binary classification (presence vs. absence of CKM) does not capture the clinical heterogeneity inherent in precise staging cardiometabolic\u0026ndash;renal dysfunction. To facilitate more nuanced disease monitoring, individualized intervention planning, and precision risk stratification, we further adopted the 5-stage framework for CKM syndrome (Stages 0 to 4) proposed by the American Heart Association (AHA) and investigated the stage-specific relationship between CTI and CKM severity (Ndumele, Rangaswami, et al., 2023). This section delineates various modeling tools, including ordinal logistic regression and multinomial comparisons, to assess the relationship between CTI and both the existence and stages of CKM syndrome. These models enable the evaluation of whether elevated CTI levels are associated with increased probabilities of progressing to advanced CKM stages, hence enhancing the public health significance of CTI for precision clinical risk stratification or monitoring.\u003c/p\u003e\n\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n\u003ch2\u003e3.6.1. Distribution of Participants Across CKM Stages 0\u0026ndash;4\u003c/h2\u003e\n\u003cp\u003eParticipants were classified into five mutually exclusive stages of cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome following the American Heart Association (AHA) clinical framework. This staging system distinguishes individuals from Stage 0 (no identifiable CKM risk factors) to Stage 4 (clinical cardiovascular disease with metabolic or renal comorbidity), capturing a spectrum of CKM-related burden (Ndumele, Rangaswami, et al., 2023). The distribution of participants across these stages is summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistribution of Participants Across CKM Stages 0 to 4\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCKM Stage\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercentage (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 0 (No CKM)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e788\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal (N)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e10,316\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eNote: Distribution of participants (N\u0026thinsp;=\u0026thinsp;10,316) across CKM syndrome stages based on the American Heart Association classification. Stages 1\u0026ndash;4 indicate increasing severity of metabolic, renal, or cardiovascular involvement; Stage 0 represents individuals without CKM-related risk.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the current sample of 10,316 adults aged 45 and older, 1.79% were assigned to Stage 0, and 7.64% to Stage 1. The majority of individuals fell into Stages 2 (20.30%), 3 (48.95%), or 4 (21.32%), reflecting substantial clinical heterogeneity within the population. While our primary binary logistic analysis (CKM: yes / no) provides a valuable and scalable foundation for early screening and population-level risk flagging, particularly suited for public health deployment, this stage-based classification enables a more refined understanding of differential risk across clinical strata. In this context, stage-specific modeling serves as a complementary strategy, allowing for more granular patient profiling, risk stratification, and the development of precision medicine approaches tailored to disease severity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n\u003ch2\u003e3.6.2. Ordinal Logistic Regression Across CKM Stages\u003c/h2\u003e\n\u003cp\u003eTo further examine whether higher CTI levels are associated with more advanced stages of CKM syndrome, we applied ordinal logistic regression using the CKM staging variable (Stage 0 to Stage 4) as the ordered outcome. This modeling framework assumes proportional odds across stage transitions and enables a single effect estimate reflecting the cumulative odds of being in a higher CKM category.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e illustrates a positive and consistent association between CTI and elevated CKM stages across all models. In the unadjusted model (Model 1), each unit increase in CTI corresponded to a 36% increase in the odds of being categorized into a more severe CKM stage (OR\u0026thinsp;=\u0026thinsp;1.36, 95% CI: 1.31\u0026ndash;1.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The association strengthened after adjusting for age, sex, residence, education status, and marital status in Model 2 (OR\u0026thinsp;=\u0026thinsp;1.56, 95% CI: 1.49\u0026ndash;1.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and remained robust in the fully adjusted model (Model 3), which also accounted for BMI, smoking, and alcohol consumption (OR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.47\u0026ndash;1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These data suggest that CTI has association with the existence of CKM syndrome and is associated with increased likelihood of being classified into more severe phases. The findings endorse the prospective application of CTI in precise risk stratification, providing a scalable biomarker for evaluating stage-specific risk of cardiometabolic-renal dysfunction severity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n\u003ch2\u003e3.6.3. Assumption Testing Using the Brant Test\u003c/h2\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBrant Test Results for the Proportional Odds Assumption\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eChi-squared\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003edf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAssumption Status\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e326.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViolated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e512.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViolated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e225.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViolated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex (Male)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e627.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViolated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidence (Urban)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViolated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation: Middle school or above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViolated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried but living without spouse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot Violated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSingle/Divorced/Widowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot Violated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViolated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrink more than once a month\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot Violated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-drinker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot Violated\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: The Brant test evaluates the proportional odds assumption for ordinal logistic regression. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a violation of the assumption.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo assess the validity of the proportional odds assumption underlying the ordinal logistic regression model, we conducted the Brant test for each covariate in the fully adjusted model. Results are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The test revealed significant violations of the proportional odds assumption for several key variables, including CTI (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;326.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;512.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;225.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and sex (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;627.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additional violations were observed for residence and education level, although the magnitude was smaller. In contrast, variables such as marital status, drinking status, and smoking showed mixed results, with some subcategories meeting the assumption criteria.\u003c/p\u003e\n\u003cp\u003eThe data indicates that the proportionate odds assumption is not entirely maintained for several key predictors, including the primary exposure variable (CTI). Consequently, the interpretation of the ordinal logistic model necessitates caution, and alternate modeling methodologies that mitigate this assumption should be considered. This further emphasizes the importance of performing stage-specific modeling to guarantee reliable inference across all degrees of CKM severity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n\u003ch2\u003e3.6.4. Multi-Level Binary Logistic Contrasts Between CKM Stage Groups\u003c/h2\u003e\n\u003cp\u003eDue to substantial breaches of the proportionate odds assumption in the ordinal logistic regression model, we utilized a multi-level binary logistic regression approach to explore the relationship between CTI and clinically pertinent distinctions throughout CKM syndrome phases. This methodology facilitates organized, sequential comparisons and circumvents the limiting assumptions of ordinal models, therefore providing a versatile and comprehensible framework consistent with practical clinical decision criteria.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e, three multi-level binary models were constructed. In the first contrast (Model 1: CKM [Stages 1\u0026ndash;4] vs. No CKM [Stage 0]), CTI demonstrated a strong association with CKM presence (OR\u0026thinsp;=\u0026thinsp;4.12, 95% CI: 3.28\u0026ndash;5.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the second model (Model 2: Stages 3\u0026ndash;4 vs. Stages 1\u0026ndash;2), CTI remained robustly associated with more severe CKM burden (OR\u0026thinsp;=\u0026thinsp;2.90, 95% CI: 2.60\u0026ndash;3.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The third model (Model 3: Stages 3\u0026ndash;4 vs. Stages 1\u0026ndash;2, restricted to participants with CKM) confirmed that higher CTI was still associated with higher severity, albeit with attenuated effect size (OR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.15\u0026ndash;1.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eTogether, these multi-level binary contrasts reinforce the stage-responsiveness of CTI across the CKM spectrum and highlight its utility in both broad public health screening and precision risk stratification settings. This method facilitates customized interpretation by analyzing relationships across clinically specified transitions, without depending on a standardized proportional odds framework.\u003c/p\u003e\n\u003cp\u003eWhile both Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Model 1 in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e analyzing the analogous binary contrast\u0026mdash;CKM (Stages 1\u0026ndash;4) vs No CKM (Stage 0)\u0026mdash;utilizing the same imputed dataset and covariate collection, the calculated odds ratios vary (2.57 vs. 4.12). This disagreement arises not from data or adjustment inconsistencies, but from variations in estimation approach. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates a sequential hierarchical model, in which covariates are progressively incorporated, resulting in increased coefficient shrinkage and a more conservative effect size. Conversely, Model 1 in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e employs a fully adjusted model in a singular step. This structural disparity in modeling design inherently influences coefficient magnitude. Both models consistently demonstrate a coherent direction, exhibit substantial statistical significance, and bolster the stability of the CTI\u0026ndash;CKM connection across various specifications.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n\u003ch2\u003e3.6.5. Multinomial Logistic Regression for Stage-Specific Odds\u003c/h2\u003e\n\u003cp\u003eTo offer a flexible, assumption-free alternative to the ordinal logistic regression model and to measure the independent association between CTI and each stage of CKM syndrome, we conducted a multinomial logistic regression analysis using Stage 0 (no CKM) as the reference group. This method facilitates the estimate of stage-specific odds ratios without supposing proportionality among outcome levels.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e, CTI was not significantly associated with Stage 1 compared to Stage 0 (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.76\u0026ndash;1.20, p\u0026thinsp;=\u0026thinsp;0.704). This is likely to reflect the absence of systemic inflammation or insulin resistance in isolated adiposity. This underscores that CTI's specificity does not overreact to low-risk profiles but rather accurately identifies metabolically active or progressed CKM, hence augmenting its effectiveness in precision screening. Conversely, CTI exhibited robust and statistically significant associations with elevated CKM levels. The likelihood of being in Stage 2 escalated over thrice with each unit rise in CTI (OR\u0026thinsp;=\u0026thinsp;3.60, 95% CI: 2.91\u0026ndash;4.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). More pronounced relationships were noted for Stage 3 (OR\u0026thinsp;=\u0026thinsp;4.07, 95% CI: 3.29\u0026ndash;5.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Stage 4 (OR\u0026thinsp;=\u0026thinsp;4.19, 95% CI: 3.39\u0026ndash;5.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These data collectively underscore a distinct stage-specific gradient in the relationship between CTI and cardiometabolic\u0026ndash;renal load. The lack of significant association at Stage 1 indicates that CTI may not solely represent obesity but instead encompasses more complex systemic metabolic disorders and inflammation. This underscores the potential of CTI as a biomarker for clinically significant CKM abnormalities necessitating vigilant monitoring or intervention.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.1. From Multisystem Burden to New Biomarker Opportunity in China\u0026rsquo;s Ageing Population\u003c/h2\u003e\u003cp\u003eChina's swiftly ageing population, with over 20% aged 60 and above, confronts an escalating public health challenge from cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome, a multifaceted disorder marked by the intersection of cardiovascular disease, chronic kidney disease, and metabolic dysfunction. This combined decline impacts not only individual organs but also signifies a continuum of increasing, interconnected hazards (Ndumele, Neeland, et al., 2023; Qiong et al., 2023; Tu, Zeng, \u0026amp; Liu, 2022). Recent data indicate a significant aggregation of cardiometabolic risk: 87.1% of older persons are hypertensive, 47.6% display dyslipidemia, 45.5% are overweight or obese, and 75% possess at least two modifiable cardiovascular risk factors (X. Zhang et al., 2025). Meanwhile, CVD prevalence reaches 31.2%, with considerable regional variation (Qiong et al., 2023). Notwithstanding evident trends, existing screening methodologies continue to depend on traditional panels that overlook cross-system signals, resulting in a diagnostic void at the critical juncture where intervention is most effective (Xu, 2024; X. Zhang et al., 2025). Emerging biomarkers like TyG-BMI and HGI offer promise but often lack simplicity, low-cost, scalability, or validation in diverse Chinese aging populations (W. Li et al., 2024; Lin et al., 2025; Liu et al., 2025; Qiong et al., 2023). This highlights the necessity for a pragmatic, cost-effective indicator\u0026mdash;one that may function in both public health and clinical settings to identify CKM risk early and provide data to potentially facilitate stratified care throughout disease stages.\u003c/p\u003e\u003cp\u003eThis study presents the inaugural nationwide examination of the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index (CTI) as a comprehensive marker of CKM syndrome. Utilizing a dual-layer model that sequentially evaluates its correlation with both presence (binary outcome) and severity (staging outcome), we seek to assess CTI\u0026rsquo;s potential as a \u0026ldquo;biological bridge,\u0026rdquo; connecting early community screening with precise risk stratification (rather than prediction) in China\u0026rsquo;s aging population, thereby facilitating decision-making at a population level. This enhances the translational potential of our findings for practical clinical and public health applications.\u003c/p\u003e\u003cp\u003e4.2. Core Findings and Multidimensional Validation of the CTI\u0026ndash;CKM Association: Mapping a Risk Terrain for Precision Stratification\u003c/p\u003e\u003cp\u003eOur data indicate that the C-reactive protein triglyceride glucose index is strongly and consistently linked to cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome across many analytical parameters, including exposure intensity, regional variation, and illness severity. Moreover, these relationships are not only statistically significant but also methodologically robust, having weathered a series of sensitivity analyses and modeling frameworks intended to mitigate typical sources of bias and analytical errors.\u003c/p\u003e\u003cp\u003eCTI exhibited a clear dose-dependent association with CKM risk. In the fully adjusted model, each unit increase in CTI was associated with 2.57-fold higher odds of having CKM syndrome (95% CI: 2.02\u0026ndash;3.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When CTI was categorized into tertiles, participants in the highest tertile demonstrated a striking 15-fold increase in CKM risk compared to those in the lowest tertile (OR\u0026thinsp;=\u0026thinsp;15.02, 95% CI: 5.48\u0026ndash;41.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), forming a discernible \u0026ldquo;risk staircase.\u0026rdquo; This gradient supports CTI\u0026rsquo;s potential utility for risk stratification, particularly in early identification of high-burden subpopulations. Stratified analyses by geographic region demonstrated that the CTI\u0026ndash;CKM association was directionally consistent and statistically significant across all three major regions in China. The strongest association was observed in the central region (OR\u0026thinsp;=\u0026thinsp;2.97, 95% CI: 1.95\u0026ndash;4.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by the eastern (OR\u0026thinsp;=\u0026thinsp;2.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and western regions (OR\u0026thinsp;=\u0026thinsp;2.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that CTI is not context-dependent, but rather a generalizable biomarker candidate applicable across varied environmental, socioeconomic, and healthcare settings. The cross-regional robustness also strengthens its potential for nationwide public health deployment.\u003c/p\u003e\u003cp\u003eBeyond overall risk differentiation, CTI demonstrated stage-sensitive associations with CKM severity. Ordinal logistic regression revealed a graded relationship between higher CTI values and more advanced CKM stages (OR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.47\u0026ndash;1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, recognizing a violation of the proportional odds assumption (Brant test), we applied two alternative approaches: (1) In multi-level binary contrasts, CTI was significantly associated with advanced disease stages (Stage 3\u0026ndash;4 vs. Stage 1\u0026ndash;2: OR\u0026thinsp;=\u0026thinsp;2.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (2) In multinomial logistic regression, CTI was independently associated with Stage 2 (OR\u0026thinsp;=\u0026thinsp;3.60), Stage 3 (OR\u0026thinsp;=\u0026thinsp;4.07), and Stage 4 (OR\u0026thinsp;=\u0026thinsp;4.19), all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, but showed no association with Stage 1 (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.76\u0026ndash;1.20, p\u0026thinsp;=\u0026thinsp;0.704). This pattern suggests that CTI specifically reflects inflammation- and insulin resistance\u0026ndash;driven multisystem dysfunction, rather than isolated adiposity, underscoring its relevance in later-stage disease surveillance and targeted clinical management.\u003c/p\u003e\u003cp\u003eThe exciting thing is that these findings are truly credible and robust. We tested CTI as both a continuous and categorical exposure, and the direction and strength of association remained consistent. Outlier-trimmed analyses (excluding top and bottom 1%) yielded even stronger associations (OR\u0026thinsp;=\u0026thinsp;2.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), refuting the concern that extreme values skewed results. Using the E-value, we found that an unmeasured confounder would need to be associated with both CTI and CKM syndrome with a risk ratio of \u0026ge;\u0026thinsp;4.58 to completely explain away the observed association. Given the comprehensive adjustment for demographic, socioeconomic, behavioral, and anthropometric variables, such a confounder is highly implausible in real-world settings, reinforcing the robustness of the observed association of our findings under conservative assumptions. To further mitigate residual confounding, we applied propensity score matching and inverse probability of treatment weighting. Post-weighting diagnostics showed excellent covariate balance (all SMDs\u0026thinsp;\u0026lt;\u0026thinsp;0.1), and CTI remained strongly associated with CKM syndrome in the IPTW-weighted sample (OR\u0026thinsp;=\u0026thinsp;4.35, 95% CI: 3.33\u0026ndash;5.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), providing an additional layer of association validation. Besides, importantly, rather than relying on a single statistical framework, we implemented a progressive, assumption-aware modeling strategy: Binary logistic regression to capture overall risk; Ordinal regression to assess gradient transitions; Brant test diagnostics to confirm assumption validity; multi-level binary contrasts and multinomial regression for granular, stage-specific differentiation. This methodological cascade not only increases interpretability for both public health and clinical applications but also serves to pre-empt common critiques regarding model misspecification, confounding, and overfitting.\u003c/p\u003e\u003cp\u003eOverall, CTI is consistently and robustly associated with CKM syndrome across varying severity levels, geographic areas, and analytical approaches. Its sensitivity to illness prevalence, robustness against statistical assumptions, and consistency among models indicate it could function as a significant biomarker for risk stratification and focused intervention, especially in aging populations with increasingly intricate multimorbidity scenarios.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Mechanistic and Results Insights: Why Might CTI Function as a Biological Sensor for CKM?\u003c/h2\u003e\u003cp\u003eAlthough our findings are observational in nature, the biological plausibility of CTI as a marker for CKM syndrome is supported by its constituent components, C-reactive protein (CRP) and the triglyceride\u0026ndash;glucose index (TyG), both of which are mechanistically linked to multisystem dysfunction spanning the cardiovascular, renal, and metabolic axes (S. Tang et al., 2024).\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1. Chronic Inflammation as a Multisystem Accelerator\u003c/h2\u003e\u003cp\u003eC-reactive protein (CRP) is a pivotal mediator in the pathogenesis of cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome, functioning through the convergence of inflammatory, oxidative, and metabolic pathways. Increased CRP levels induce endothelial dysfunction chiefly by activating the nuclear factor kappa B (NF-κB) pathway, which enhances reactive oxygen species (ROS) production through NADPH oxidase, diminishes nitric oxide bioavailability, and ultimately results in vascular stiffness and microvascular damage (Bekyarova et al., 2023; Huang et al., 2024; Lorenzo et al., 2021; Xu et al., 2016; Zhao \u0026amp; He, 2021). In atherosclerotic conditions, CRP correlates with plaque vulnerability via enhancing matrix metalloproteinase (MMP) activity and diminishing collagen production, hence compromising fibrous cap integrity and promoting plaque rupture (Franekov\u0026aacute; et al., 2015; Huang et al., 2024; Mohamed Abulnasr et al., 2024). From a metabolic perspective, CRP intensifies insulin resistance by disrupting adipokine signaling, notably by enhancing pro-inflammatory cytokines like TNF-α and IL-6, which hinder downstream insulin signaling pathways (Caturano et al., 2021; Lorenzo et al., 2021). These inflammatory cascades further enhance hepatic lipogenesis, particularly in non-alcoholic fatty liver disease (NAFLD), where NF-κB activation by free fatty acids promotes excessive lipid buildup in hepatocytes (Lorenzo et al., 2021; Milić, Lulić, \u0026amp; Štimac, 2014). Although direct renal histopathology data connecting CRP to CKM-associated glomerular injury is scarce, chronic low-grade inflammation is widely acknowledged as a significant factor in oxidative damage and filtration barrier impairment in the kidney (Bekyarova et al., 2023; Stoian et al., 2024). Novel therapeutic investigations focusing on CRP-related pathways offer further translational validation. Pharmacological therapies, including statins, have demonstrated efficacy in lowering CRP levels and accompanying vascular inflammation by inhibiting lipoprotein-associated phospholipase A2 (Lp-PLA2) activity. Natural substances like curcumin block NF-κB and ROS pathways, mitigating inflammation-induced vascular damage in preclinical investigations, whereas vitamin D administration has shown anti-inflammatory properties and diminished neuropathic consequences in diabetic animals (Jafari-Hafshejani et al., 2023; Karakas, Haase, \u0026amp; Zeller, 2018; Li et al., 2018; Stoian et al., 2024). These findings reinforce CRP\u0026rsquo;s multifaceted role in CKM pathogenesis and its value as a mechanistically grounded component of composite biomarkers such as CTI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2. Insulin resistance as a convergence points of metabolic toxicity\u003c/h2\u003e\u003cp\u003eThe triglyceride\u0026ndash;glucose (TyG) index, calculated from fasting triglyceride and glucose concentrations, is an established surrogate indicator of insulin resistance and lipotoxic stress. The rise signifies not just hyperglycemia and lipid dysregulation but also a wider sequence of pathophysiological mechanisms that contribute to the emergence of cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome (Devaraj, Krishnan, \u0026amp; Chen, 2025; Lu et al., 2025; Zhao et al., 2021). Insulin resistance enables the excess of circulating lipids to infiltrate non-adipose organs, particularly hepatocytes and cardiomyocytes, where abnormal fat deposition hinders mitochondrial oxidative capability and exacerbates oxidative stress. These alterations lead to cellular senescence and apoptosis, resulting in hepatic steatosis, myocardial metabolic dysfunction, and vascular damage. In experimental models, animals exposed to high-sugar/high-fat diets alongside renal stress (e.g., unilateral nephrectomy) exhibited systemic metabolic disturbances, including glucose intolerance, adipocyte hypertrophy, and increased blood pressure, which collectively exacerbated cardiac and renal remodeling (Carvalho et al., 2024; Kang et al., 2017; Zhao et al., 2021). The metabolic abnormalities indicated by TyG also initiate inflammatory and fibrotic signaling pathways. Hyperglycemia and dyslipidemia induce macrophage infiltration in adipose and vascular tissues, triggering pro-inflammatory cytokine cascades (e.g., IL-6, TNF-α). In mice models, these alterations were associated with increased production of TGF-β, VEGF, and collagen accumulation, hence expediting both atherogenesis and renal fibrosis. Bone marrow-derived mesenchymal stromal cells (BM-MSCs) exhibited enhanced anti-inflammatory effects in metabolically stressed mice relative to adipose- or lung-derived MSCs, highlighting the systemic aspect of TyG-associated inflammatory burden (Abreu et al., 2017; Carvalho et al., 2024). Prolonged exposure to TyG-elevating dietary regimens in animal models has been shown to induce left ventricular hypertrophy, proteinuria, and glomerulosclerosis\u0026mdash;mirroring hallmark features of human CKM syndrome (Carvalho et al., 2024). Moreover, in pediatric cohorts, increased TyG indices exhibit a robust correlation with HOMA-IR and visceral obesity, indicating that the metabolic stress indicated by TyG may emerge early in life and persist into adulthood (Devaraj, Krishnan, \u0026amp; Chen, 2025). This supports the concept that the TyG index encompasses various dimensions of metabolic risk, including insulin resistance, lipid toxicity, and organ-specific inflammatory remodeling, rendering it a likely factor in the CTI's sensitivity to detecting CKM-related dysfunction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3. Why does CTI remain unresponsive in Stage 1 CKM? A stage-dependent adipose biology hypothesis\u003c/h2\u003e\u003cp\u003eThe lack of a notable association between CTI and Stage 1 CKM, characterized by isolated adiposity without apparent metabolic impairment, may indicate the variable, stage-dependent functions of adipose tissue in cardiometabolic health. In the initial phases, adipose tissue development is predominantly facilitated by adipocyte hyperplasia, a mechanism that enables the secure storage of surplus energy without inducing systemic metabolic disturbances. In this phase, adipose tissue mostly acts as a metabolically inactive buffer, capable of storing lipids and preventing ectopic accumulation and lipotoxicity (Baldelli et al., 2024; Clemente-Postigo et al., 2020; Markina et al., 2024). The transition to later CKM stages (Stage 2\u0026ndash;4), however, marks a pathophysiological shift. Adipocytes become hypertrophic, outstripping their storage capacity, which induces hypoxia, endoplasmic reticulum stress, and reactive oxygen species (ROS) production. These changes initiate a cascade of macrophage infiltration and adipocyte\u0026ndash;immune crosstalk, amplifying the secretion of pro-inflammatory cytokines such as TNF-α, IL-6, and MCP-1 (Cho et al., 2023; Juman et al., 2012; Sandoval-B\u0026oacute;rquez et al., 2024; Simons et al., 2007; Yomlar, Trisat, \u0026amp; Limpeanchob, 2024).\u003c/p\u003e\u003cp\u003eSimultaneously, the endocrine function of adipose tissue declines, characterized by diminished secretion of beneficial adipokines (e.g., adiponectin) and elevated levels of leptin and other mediators associated with insulin resistance and systemic inflammation (Cho et al., 2023; Ge et al., 2024; Simons et al., 2007; Yomlar, Trisat, \u0026amp; Limpeanchob, 2024). This transition signifies a biological barrier, beyond which CTI responsively detects molecular disturbances, including lipid remodeling and sphingolipid release that compromises vascular function. Redox imbalance, characterized by oxidative stress, leads to endothelial damage and disruptions in metabolic signaling (Cho et al., 2023; Clemente-Postigo et al., 2020; Ge et al., 2024; Ilieva et al., 2017; Lei et al., 2019; Sandoval-B\u0026oacute;rquez et al., 2024). Thus, CTI\u0026rsquo;s unresponsiveness in Stage 1 is not a statistical artifact, but a reflection of its specificity for detecting pathological, inflammation-driven adiposity\u0026mdash;a threshold that has yet to be crossed in early-stage CKM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\u003ch2\u003e4.3.4. Emerging experimental and cross-population evidence\u003c/h2\u003e\u003cp\u003eEven though longitudinal evidence directly connecting CTI to CKM progression is limited experimental and epidemiological research provide mechanistic support for its two fundamental components\u0026mdash;CRP and the TyG index\u0026mdash;across several species and populations. In animal models, monomeric CRP (mCRP) has been demonstrated to exacerbate cardiac remodeling following myocardial infarction by facilitating macrophage polarization towards a pro-inflammatory M1 phenotype through the JNK signaling pathway, hence intensifying heart fibrosis and dysfunction (Zha et al., 2021). High-fat diet models, on the other hand, recapitulate key features of TyG-related metabolic toxicity, including insulin resistance, hepatic steatosis, and vascular endothelial injury (Rocchiccioli et al., 2022; Wan et al., 2025). These experimental results highlight the biological validity of CTI as an indicator of systemic metabolic-inflammation load. Observational data from many populations in human studies underscores the therapeutic significance of these pathways. In extensive cohorts like the UK Biobank, metrics that include TyG (e.g., TyG-BMI) have independently forecasted the emergence of cardiometabolic and renal multimorbidity from an initial state of apparent health (Tang et al., 2025). Among statin-treated European patients, elevated TyG and CRP levels have been associated with more severe coronary artery disease, suggesting residual cardiometabolic risk beyond standard lipid control (Rocchiccioli et al., 2022). Moreover, in Latin American populations, elevated CRP has been linked to life course psychosocial stressors such as racial discrimination, highlighting inflammation\u0026rsquo;s broader sociobiological relevance (Harris et al., 2024). Notably, Chinese studies have demonstrated that high TyG and hsCRP levels can synergistically increase the risk of cardiometabolic multimorbidity in Asian adults (Wan et al., 2025). Collectively, these findings indicate that, despite CTI being a composite metric, its underlying biological components, systemic inflammation and insulin resistance, are recognized contributors to CKM-related pathology. Although careful interpretation is necessary, the uniformity of these processes across several species and contexts enhances confidence in the biological significance and potential applicability of CTI outside the specific study population.\u003c/p\u003e\u003cp\u003eIn combination, these mechanistic and outcome discoveries underscore that CTI transcends mere statistical concept. Its vulnerability to inflammatory and metabolic pathways, stage-selective sensitivity, and alignment with established CKM pathophysiology indicate it may serve as a biological conduit, connecting upstream dysregulation to downstream organ damage. Although additional experimental and longterm research are necessary, these findings offer a robust theoretical framework that substantiates our observational data. This study does not intend to construct or validate a predictive model. All findings are analyzed within an associative framework to enhance population-level risk classification.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Limitations\u003c/h2\u003e\u003cp\u003eThis study has certain unavoidable limitations, which were methodologically addressed where possible to safeguard inference quality. First, its cross-sectional design restricts causal interpretation. However, the absence of association at early CKM stages, together with E-values, IPTW, and multi-model validation, minimizes reverse causality concerns. Second, CTI and CKM were measured once, but standardized fasting protocols and consistent results across continuous, categorical, and trimmed models reduce concerns over random biological fluctuation. Third, some unmeasured factors were not available; nonetheless, E-value thresholds suggest that only implausibly strong confounders could fully explain away the observed associations. Fourth, the high CKM prevalence (98.2%) reflects real-world risk clustering; to enhance interpretability, we incorporated stage-specific and multi-level binary models to mitigate ceiling effects. Fifth, Brant test results indicated proportional odds violations in ordinal regression; thus, complementary multi-level binary and multinomial models were applied to ensure valid stage-specific interpretation. Sixth, given the number of analytic layers, overfitting is theoretically possible; however, model diagnostics (e.g., GVIFs\u0026thinsp;\u0026lt;\u0026thinsp;5, Macfadden pseudo R\u0026sup2; = 0.372) support internal consistency. Seventh, the CHARLS sample comprises middle-aged and older adults only; generalization to younger populations requires external validation. Eighth, regional heterogeneity analysis was exploratory and not powered for formal interaction testing; however, consistent association directions across regions support geographic robustness. Ninth, CTI\u0026rsquo;s performance should be further validated in other populations, healthcare systems, and ethnic groups. Tenth, while CTI integrates inflammation and insulin resistance, it may not capture other CKM mechanisms. Eleventh, due to data constraints, negative control exposures or outcomes were not available for implementation. However, high E-values and extensive covariate adjustment mitigate this limitation. Finally, while CTI is not proposed as a diagnostic marker, its simplicity, reproducibility, and biological responsiveness support its use as a scalable stratification tool, especially where full clinical profiling is not feasible.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Implications and Recommendations\u003c/h2\u003e\u003cp\u003eThe results of this study suggest that the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index (CTI), a low-cost composite marker derived from routine biomarkers (CRP, triglycerides, fasting glucose) (S. Tang et al., 2024), holds practical value for population-level risk stratification of CKM syndrome, particularly in aging societies with rising multimorbidity burdens. Through the use of a dual-layer analytical approach, binary logistic regression for overall CKM presence and multi-level binary and multinomial models for staging demonstrated that CTI effectively captured both general risk gradients and stage-specific burden changes. CTI is particularly advantageous in healthcare systems where resource optimization and early intervention are critical. Region-specific findings yield useful insights. In central provinces, where the CTI\u0026ndash;CKM association was most pronounced, CTI-based stratification might be incorporated into community chronic illness surveillance, potentially in conjunction with environmental interventions like air quality enhancement. In under-resourced western regions, CTI could guide cardiometabolic resource allocation by identifying high-risk populations in the absence of sophisticated diagnostics. Importantly, CTI is not designed as a diagnostic or predictive instrument; instead, it serves as a supportive supplement to existing frameworks, improving the accuracy of risk classification and action planning. Its simplicity, reproducibility, and biological basis render it a theoretically robust and operationally scalable instrument for CKM preventive measures in swiftly aging populations. Subsequent research ought to assess the predictive validity of CTI over time, its generalizability, and its integration across health systems. This study's cross-sectional methodology and absence of repeated biomarker measurements precluded the evaluation of temporal risk or illness progression. Though not mechanistic, CTI's foundation in inflammation and insulin resistance underpins its biological validity. These findings necessitate prospective validation to enhance scalable CKM risk categorization. Mitigating negative control exposures in subsequent research will enhance methodological rigor and clinical significance (Austin, 2011; Braun et al., 2015; Fujikawa \u0026amp; Haruta, 2024; Hoyniak et al., 2025; Kumar et al., 2022; Lipsitch, Tchetgen Tchetgen, \u0026amp; Cohen, 2010; Qin et al., 2023; Takefuji, 2025; Tingulstad et al., 2023).\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis nationally representative cross-sectional study of older Chinese adults reveals a robust and consistent association between the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose (CTI) index\u0026mdash;a composite indicator of systemic inflammation and insulin resistance\u0026mdash;and cardiovascular\u0026ndash;kidney\u0026ndash;metabolic (CKM) syndrome, encompassing both its presence and stage-defined severity. In contrast to previous studies that concentrated exclusively on illness staging, our dual-layered analytical paradigm incorporates the differentiation between any CKM risk and optimal health alongside stratified stage sensitivity, providing a more comprehensive and scalable method for syndromic risk profiling. The strength of this correlation across several geographic regions, exposure parameters, and modeling approaches underscores CTI's promise as a cost-effective, physiologically based indication of multisystem failure. Although causal inference is constrained by the cross-sectional design, these results offer essential evidence for the integration of biomarkers into CKM surveillance strategies and endorse the repositioning of CTI as a potential instrument for multidimensional risk stratification in aging populations. Future longitudinal studies and mechanistic research are necessary to clarify the prognostic validity and therapeutic significance of CTI throughout the CKM continuum.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the China Health and Retirement Longitudinal Study (CHARLS) research team and Peking University for providing access to the 2015 CHARLS dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available from the China Health and Retirement Longitudinal Study (CHARLS), administered by the National School of Development at Peking University. Researchers may apply for data access at the CHARLS official website (https://charls.pku.edu.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Institutional Review Board (IRB) of Peking University (IRB00001052-11015; IRB00001052-11014). Written informed consent was obtained from all participants prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.P. contributed to methodology review, validation, and writing\u0026ndash;review and editing. J.S. contributed to conceptualization, methodology, investigation, data curation, analysis, visualization, writing\u0026ndash;original draft preparation, writing\u0026ndash;review and editing, and project administration. All authors have read and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe ORCID of the authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnuchit Phanumartwiwath: https://orcid.org/0000-0001-8731-3875\u003c/p\u003e\n\u003cp\u003eJiaHao Shi: https://orcid.org/0009-0006-6136-7895\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbreu, S. C., Antunes, M. A., Xisto, D. G., Cruz, F. F., Branco, V. C., Bandeira, E., Zola Kitoko, J., de Ara\u0026uacute;jo, A. F., Dellatorre-Texeira, L., Olsen, P. C., Weiss, D. J., Diaz, B. L., Morales, M. M., \u0026amp; Rocco, P. R. M. (2017). 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National School of Development.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, C., Zhou, Y., Shuai, N., Zhou, J., \u0026amp; Kuang, X. (2024). The nonlinear relationship between estimated glomerular filtration rate and cardiovascular disease in US adults: a cross-sectional study from NHANES 2007\u0026ndash;2018 [Original Research]. \u003cem\u003eFrontiers in Cardiovascular Medicine\u003c/em\u003e, \u003cem\u003eVolume 11\u0026ndash;2024\u003c/em\u003e. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2024.1417926\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CKM Syndrome, C-reactive Protein Triglyceride Glucose Index, Syndromic Biomarker Integration, Population-Based Precision Screening, Older Adults","lastPublishedDoi":"10.21203/rs.3.rs-7628351/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7628351/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The cardiovascular–kidney–metabolic (CKM) syndrome reconceptualizes multimorbidity as a progressive, multisystem disorder. Yet, existing research focuses mainly on disease staging, neglecting the distinction between optimal health and any CKM risk burden. The C-reactive protein–triglyceride–glucose (CTI) index reflects both inflammation and insulin resistance; however, its significance in CKM has not been rigorously evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We examined data from 10,316 persons aged 45 years and older in the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort. We evaluated the association between CTI and (1) CKM presence (CKM vs. no CKM), and (2) stage-specific severity. Binary logistic, ordinal, multi-level binary logistic, and multinomial regression models were developed, controlling for an extensive array of covariates. A thorough series of sensitivity and robustness studies were conducted, encompassing E-value computation to evaluate the potential impact of unmeasured confounding, outlier-trimmed models, CTI tertile specification, and several propensity score methodologies (IPTW and 1:1 matching). Model diagnostics encompassed evaluations of multicollinearity, model fit (McFadden’s pseudo R²), and the proportional odds assumption using the Brant test. Robustness was additionally corroborated by convergence across several modeling approaches and studies stratified by geographic regions (East, Central, West China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e CTI had a positive and consistent association with CKM syndrome across all models. In fully adjusted binary logistic regression, each unit increase in CTI corresponded to significantly elevated odds of CKM (OR = 2.57; 95% CI: 2.02–3.27; p \u0026lt; 0.001). Tertile-based studies revealed a dose–response gradient, with the highest CTI tertile linked to a 15.02-fold increase in CKM chances relative to the lowest tertile. In ordinal and multi-level binary logistic models, CTI consistently shown a significant association with escalating CKM stage severity. Multinomial regression indicated no significant association with Stage 1 (isolated adiposity), but demonstrated robust relationships with Stage 2 (OR = 3.60; 95% CI: 2.91–4.44; p \u0026lt; 0.001), Stage 3 (OR = 4.07; 95% CI: 3.29–5.04; p \u0026lt; 0.001), and Stage 4 (OR = 4.19; 95% CI: 3.39–5.19; p \u0026lt; 0.001). Model diagnostics indicated the absence of multicollinearity and demonstrated a satisfactory model fit. The E-value analysis (E = 4.58) indicates that unmeasured variables must have an exceptionally strong correlation with both CTI and CKM to completely account for the observed association. The results remained strong after excluding CTI outliers, employing tertile-based categorization, and utilizing both inverse probability weighting and 1:1 propensity score matching. Regional stratification demonstrated consistent relationships in the eastern (OR = 2.76), central (OR = 2.97), and western (OR = 2.17) regions, with overlapping confidence ranges, so affirming geographic generalizability. The findings remained consistent across several modeling methodologies, risk classifications, and sensitivity analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study provides the first nationally representative evidence of a dual-layer association between the C-reactive protein–triglyceride–glucose (CTI) index and cardiovascular–kidney–metabolic (CKM) syndrome—linking CTI both to the presence of any CKM risk and to stratified stage severity. Crucially, CTI was not associated with isolated adiposity (Stage 1), but demonstrated strong associations with advanced stages (Stages 2–4), highlighting its specificity for systemic metabolic-inflammatory dysfunction rather than general adiposity. These findings position CTI as a cost-effective, stage-sensitive biomarker for syndromic risk detection and stratification in aging populations.\u003c/p\u003e","manuscriptTitle":"Dual Layer Association of the C-Reactive Protein Triglyceride Glucose Index with Cardiovascular–Kidney–Metabolic Syndrome among Older Chinese Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 07:25:47","doi":"10.21203/rs.3.rs-7628351/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-10T03:01:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15241802208161547742323115440906597070","date":"2025-10-19T13:37:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-18T09:43:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-22T05:42:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-20T09:57:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T09:57:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-09-16T09:06:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac1e4e2e-7fb8-4045-9140-51f4b8a8c205","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-18T09:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 07:25:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7628351","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7628351","identity":"rs-7628351","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}