Comprehensive Association of C-reactive Protein-Triglyceride Glucose Index with 14 New-Onset Chronic Diseases: Evidence from the China Health and Retirement Longitudinal Study

Comprehensive Association of C-reactive Protein-Triglyceride Glucose Index with 14 New-Onset Chronic Diseases: Evidence from the China Health and Retirement Longitudinal Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive Association of C-reactive Protein-Triglyceride Glucose Index with 14 New-Onset Chronic Diseases: Evidence from the China Health and Retirement Longitudinal Study Guanghai Li, Hao Liang, Mingxi Chen, Songzhan Yuan, Zhongwei Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8279156/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The rising burden of multimorbidity in aging populations underscores the need for efficient screening tools. While the Triglyceride-Glucose (TyG) index and Cardiometabolic Index (CMI) are established markers for metabolic risk, they fail to capture chronic low-grade inflammation, a pivotal pathological driver. We aimed to evaluate the C-reactive Protein-Triglyceride Glucose Index (CTI)—a novel composite marker integrating inflammation and metabolic status—and assess its prospective association with 14 new-onset chronic diseases. Methods This prospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). A total of 9,194 participants aged ≥ 45 years with complete baseline biomarker data were included. The CTI was calculated as 0.412 × ln(CRP [mg/L]) + ln(TG [mg/dL] × FPG [mg/dL]) / 2. We employed multivariable Cox proportional hazards models and restricted cubic splines (RCS) to estimate hazard ratios (HRs) and dose-response relationships for 14 incident diseases. Robustness was verified via sensitivity analyses (2-year lag) and subgroup stratifications. Results During the 9-year follow-up, elevated baseline CTI was independently associated with an increased risk of diabetes (HR 1.86, 95% CI 1.67–2.06), stroke (HR 1.42, 95% CI 1.23–1.64), dyslipidemia (HR 1.36, 95% CI 1.25–1.48), and liver disease (HR 1.16, 95% CI 1.01–1.33) after full adjustment. Notably, CTI demonstrated superior predictive value for stroke compared to traditional metabolic indices. These associations remained robust in lag analyses. Subgroup analyses revealed that the predictive value was more pronounced in individuals aged < 60 years and females. Crucially, CTI showed a stronger association with stroke risk in non-obese participants (BMI < 24 kg/m²; HR 1.59) compared to the obese population (HR 1.35). No significant associations were found for non-metabolic conditions (e.g., cancer, arthritis), indicating biological specificity. Conclusion The CTI serves as a robust and accessible biomarker capturing the dual burden of immunometabolic dysregulation. It effectively predicts risks for diabetes, dyslipidemia, liver disease, and particularly stroke . Our findings highlight the utility of CTI in identifying "hidden" cardiovascular risks in non-obese individuals, supporting its incorporation into routine health screenings for older adults. CTI Inflammation Multimorbidity Stroke Metabolically Obese Normal Weight CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Background The rapid acceleration of global population aging has positioned chronic non-communicable diseases (NCDs) as the predominant threat to public health. According to the Global Burden of Disease Study , NCDs account for over 70% of all deaths worldwide, with China facing particularly severe challenges due to its massive elderly demographic[1, 2]. A critical concern accompanying aging is multimorbidity—the coexistence of two or more chronic conditions—which significantly strains healthcare systems and diminishes quality of life[3]. Consequently, identifying cost-effective and accessible biomarkers for the early stratification of high-risk populations is a priority for primary prevention and healthy aging strategies. Limitations of Current Indices Insulin resistance (IR) and dyslipidemia are well-recognized pathological cornerstones of cardiovascular disease (CVD), diabetes, and metabolic dysfunction-associated fatty liver disease (MAFLD)[4, 5]. Recently, surrogate markers derived from routine lipid and glucose profiles, such as the Triglyceride-Glucose (TyG) index and the Cardiometabolic Index (CMI), have gained prominence for their ability to proxy IR[6-8]. However, a substantial limitation of these lipid-centric indices is their failure to encompass chronic low-grade inflammation . Accumulating evidence suggests that subclinical inflammation acts as a "common soil" linking obesity and metabolic syndrome to a broad spectrum of diseases, including atherosclerosis and neurodegeneration. Relying solely on metabolic parameters may overlook individuals with a "metabolically unhealthy but normal weight" phenotype or those driven primarily by inflammatory pathways[9, 10]. The Theoretical Advantage of CTI C-reactive protein (CRP) serves as the gold-standard biomarker for systemic inflammation, with elevated levels predicting risks for CVD, diabetes, and malignancy[11, 12]. To bridge the gap between metabolic and inflammatory assessments, the C-reactive Protein-Triglyceride Glucose Index (CTI) was proposed. By mathematically integrating CRP (inflammation) with the TyG index (glucolipotoxicity), CTI theoretically captures a more comprehensive pathophysiological profile than either marker alone[13]. While small-scale studies have hinted at the utility of combining inflammatory and metabolic markers[14], no large-scale prospective study has systematically evaluated the longitudinal association between CTI and a broad spectrum of chronic diseases beyond the cardiovascular system. Study Objectives To address this knowledge gap, we leveraged data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort. This study aimed to: (1) investigate the independent associations between baseline CTI and the incidence of 14 distinct chronic diseases over a 9-year follow-up; and (2) explore potential heterogeneity in these associations across age, sex, and BMI strata. We hypothesized that CTI, as a dual-domain marker, would provide superior risk stratification, particularly for vascular events like stroke and in populations where traditional obesity metrics may be less sensitive. Methods 2.1 Study Population and Design Data were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of Chinese residents aged 45 years and older. The cohort employs a multi-stage stratified probability sampling strategy, covering 450 villages and urban communities across 28 provinces[15]. The current study utilized the 2011 national baseline survey (Wave 1) as the starting point, with follow-up assessments conducted in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). Participants were included if they: (1) were ≥ 45 years of age at baseline; and (2) provided blood samples with complete data for C-reactive protein (CRP), triglycerides (TG), and fasting plasma glucose (FPG). Exclusion criteria were as follows: (1) loss to follow-up without death records; (2) extreme baseline CTI values; and (3) missing data on key covariates (e.g., BMI) or biologically implausible values. To minimize the influence of outliers and measurement errors, and specifically to exclude participants with potential acute infections (indicated by extremely high CRP levels) or extreme metabolic derangements not representative of the general population, baseline CTI values were trimmed at the 1st and 99th percentiles. The final analytic cohort consisted of 9,194 participants ( Fig. 1 ). Figure 1: 2.2 Exposure Assessment: CTI Venous blood samples were collected by trained nurses at baseline (2011), separated into plasma and buffy coat, and transported cold to the Chinese Center for Disease Control and Prevention (CDC) for standardized analysis[16]. The CTI was calculated using the following formula[17], integrating systemic inflammation and metabolic status: CTI = 0.412 × ln(CRP [mg/L]) + ln(TG [mg/dL] × FPG [mg/dL]) / 2 In statistical models, CTI was analyzed both as a continuous variable (per standard deviation increase) and as a categorical variable divided into quartiles (Q1–Q4). 2.3 Outcome Ascertainment The primary outcomes were incident cases of 14 chronic diseases identified during the follow-up period (2013–2020)[3]. These conditions included hypertension, diabetes, dyslipidemia, heart disease (including myocardial infarction and coronary heart disease), stroke, cancer, chronic lung disease, liver disease, kidney disease, digestive disease, arthritis/rheumatism, asthma, psychiatric disease, and memory-related disease[18–20]. Disease status was ascertained via self-reported physician diagnosis ("Have you been diagnosed with [condition] by a doctor?"). To ensure the analysis of incident events, we constructed separate sub-cohorts for each disease by excluding participants who reported the respective condition at baseline (2011). The time to event was defined as the midpoint between the last interview without the disease and the first interview reporting the diagnosis[21]. 2.4 Covariates Potential confounders were selected a priori based on the literature. Adjusted covariates included: Demographic factors : Age (continuous), sex (male/female), Household registration status (agricultural/non-agricultural), marital status, and education level. Lifestyle factors : Smoking status (never/former/current) and alcohol consumption. Clinical metrics: Body mass index (BMI, calculated as weight in kg divided by height in m 2 ), systolic blood pressure (SBP), diastolic blood pressure (DBP), and low-density lipoprotein cholesterol (LDL-C). All covariates were measured at the 2011 baseline. 2.5 Statistical Analysis Baseline characteristics were compared across CTI quartiles using the Kruskal-Wallis test for continuous variables and the Chi-square test for categorical variables. We estimated Hazard Ratios (HRs) and 95% Confidence Intervals (CIs) using Cox proportional hazards models. Two models were constructed: Model 1 : Unadjusted. Model 2 : Adjusted for age, sex, BMI, SBP, DBP, LDL-C, smoking, and drinking status. To examine the potential non-linear association between CTI and the risk of chronic diseases, we employed restricted cubic spline (RCS) regression models with 3 knots placed at the 10th, 50th, and 90th percentiles[22, 23]. The median value of CTI was used as the reference. Robustness was evaluated via: (1) sensitivity analysis (Lag-2): Excluding participants who developed the outcome within the first 2 years of follow-up to minimize reverse causality; and (2) subgroup analysis: Stratifying by age (<60 vs. ≥60 years), sex, and BMI (<24 vs. ≥24 kg/m 2 ) for diseases showing significant associations[24]. Data cleaning and preprocessing were initially performed using Stata software (Version 17.0, StataCorp LLC, College Station, TX, USA). Subsequent statistical analyses and visualization were conducted using Python (version 3.x) within the Spyder IDE, utilizing the lifelines , pandas , and matplotlib libraries.[25] A two-sided P -value < 0.05 was considered statistically significant. Results 3.1 Baseline Characteristics A total of 9,194 participants were included in the final analysis. The study population was stratified into four groups based on baseline CTI quartiles (Q1–Q4). As detailed in Table 1 , the mean age was 59.6 ± 9.36 years, and 53.3% were female. Participants in the highest CTI quartile (Q4) exhibited a distinct phenotype characterized by metabolic dysfunction and systemic inflammation. Compared to the lowest quartile (Q1), those in Q4 had significantly higher body mass index (BMI), systolic and diastolic blood pressure, and serum uric acid levels (all P < 0.001). Consistent with the CTI formula, levels of C-reactive protein (CRP), triglycerides, and fasting plasma glucose increased progressively from Q1 to Q4, while high-density lipoprotein cholesterol (HDL-C) decreased ( P < 0.001). Sociodemographically, a higher proportion of participants with non-agricultural Household registration status (indicative of urban residence) was observed in the highest CTI quartile (20.4% vs. 13.6% in Q1). Notably, while females comprised a slight majority of the total cohort, the distribution of sex did not differ significantly across CTI quartiles in this at-risk population ( P = 0.073). 3.2 Association Between CTI and New-Onset Chronic Diseases During the 9-year follow-up, we systematically evaluated the associations between baseline CTI and the incidence of 14 chronic diseases. The results of the unadjusted (Model 1) and fully adjusted (Model 2) Cox proportional hazards models are presented in Table 2 and Figure 2 . After fully adjusting for confounders, CTI emerged as a significant independent predictor for four specific conditions: diabetes (HR 1.86, 95% CI 1.67–2.06, P < 0.001), stroke (HR 1.42, 95% CI 1.23–1.64, P < 0.001), dyslipidemia (HR 1.36, 95% CI 1.25–1.48, P < 0.001), and liver disease (HR 1.16, 95% CI 1.01–1.33, P = 0.036). In contrast, no significant associations were found for non-metabolic conditions such as cancer, arthritis, psychiatric disorders, or memory-related diseases (all P > 0.05). Furthermore, while CTI was associated with hypertension, asthma, and kidney disease in unadjusted analyses, these associations were attenuated to non-significance after adjustment, suggesting that the effects of CTI on these conditions may be mediated by other metabolic covariates. Dose-Response Relationships We further explored the dose-response relationship between CTI and the four significantly associated diseases using RCS analyses ( Figure 3 ). A continuous, positive, and approximately linear association was observed for diabetes , stroke , and dyslipidemia , where the risk increased steadily with higher CTI levels ( P for non-linearity > 0.05). Interestingly, for liver disease , the curve exhibited a J-shaped pattern, with risk increasing sharply only after CTI exceeded the median level. Figure2 Figure3 3.3 Subgroup Analysis To explore population-specific risks, we stratified the analysis for the three most robust outcomes (diabetes, stroke, and dyslipidemia) by age, sex, and BMI ( Table 3 and Figure 4 ). Age: The predictive value of CTI was consistently stronger in younger participants (<60 years) compared to older adults (≥60 years) across all three diseases. For instance, the HR for diabetes was 2.13 in the younger group versus 1.55 in the older group, highlighting the utility of CTI as an early warning signal. Sex : Females exhibited a higher susceptibility to CTI-associated risks for diabetes (HR 1.99 vs. 1.70 in males) and stroke (HR 1.51 vs. 1.33 in males). BMI (The "Stroke Paradox"): A striking interaction was observed for stroke risk. Contrary to traditional expectations, the association between CTI and stroke was stronger in non-obese participants (BMI < 24 kg/m²; HR 1.59) compared to overweight/obese participants (HR 1.35). This suggests that CTI is particularly effective at identifying hidden vascular risks in individuals with a normal body weight. Table3 Figure 4: 3.4 Sensitivity Analysis To mitigate reverse causality (where preclinical disease elevates baseline biomarkers), we conducted a lag analysis excluding participants who developed outcomes within the first 2 years of follow-up ( Supplementary Table 1 and Supplementary Figure 1 ). The associations for diabetes (HR 1.79), stroke (HR 1.38), and dyslipidemia (HR 1.30) remained highly significant ( P < 0.001), underscoring the long-term predictive robustness of CTI. However, the association with liver disease became non-significant (HR 1.13, P = 0.099), implying that the link between CTI and liver disease might be driven by short-term progression or concurrent inflammatory states rather than long-term causality. Discussion In this large-scale, national prospective cohort study, we conducted the first comprehensive evaluation of the longitudinal association between the C-reactive Protein-Triglyceride Glucose Index (CTI) and the risk of 14 new-onset chronic diseases. Over a 9-year follow-up, elevated CTI was independently associated with an increased risk of diabetes , stroke , dyslipidemia , and liver disease . Notably, CTI demonstrated superior predictive efficacy for stroke compared to traditional metabolic indices, particularly among non-obese individuals and younger adults. Our findings suggest that CTI, by integrating inflammatory and metabolic dimensions, serves as a simple yet powerful tool for the early stratification of chronic disease risk. 4.1 CTI vs. Traditional Metabolic Indices: The Value of Inflammation Prior research has largely focused on the Triglyceride-Glucose (TyG) index or the Cardiometabolic Index (CMI) in isolation as surrogates for insulin resistance [26, 27]. While a recent study by Zhuo et al. using CHARLS data investigated CMI, they found that the association between CMI and stroke became non-significant after adjusting for confounders (HR 1.02, P = 0.054) [28]. In stark contrast, our study reveals a robust and independent association between CTI and new-onset stroke (HR 1.42, P < 0.001). This discrepancy underscores the critical role of the inflammatory component. CMI relies solely on lipids and waist-to-height ratio, whereas CTI incorporates C-reactive protein (CRP). Chronic low-grade inflammation is a well-established driver of endothelial dysfunction, atherosclerotic plaque instability, and rupture [29-31]. Purely metabolic indices may fail to capture this residual inflammatory burden on the vascular endothelium. By integrating CRP, CTI provides a more holistic view of the "immunometabolic" risk profile driving cerebrovascular events [32]. Similarly, the predictive strength of CTI for diabetes (HR 1.86) substantially exceeded that reported for CMI (HR 1.08), likely because CTI directly incorporates fasting glucose, rendering it inherently more sensitive to glucotoxicity [33]. 4.2 Identifying Hidden Risks: The "Lean Stroke" Phenomenon A novel finding of this study is the stronger predictive value of CTI for stroke in non-obese participants (BMI < 24 kg/m²) compared to their overweight/obese counterparts. This observation aligns with the "Metabolically Obese Normal Weight" (MONW) phenotype described in literature [34]. Individuals with MONW possess a normal BMI but harbor substantial visceral adiposity, insulin resistance, and subclinical inflammation—pathologies that are frequently overlooked in routine screenings relying solely on anthropometry [35, 36]. Furthermore, previous studies indicate that Asian populations are genetically predisposed to visceral fat accumulation at lower BMI thresholds compared to Western populations, making them particularly susceptible to metabolic risks despite a "healthy" weight [37]. Our results suggest that CTI serves as a sensitive tool to unveil this latent vascular risk obscured by normal body weight, highlighting a critical window for primary prevention in a population traditionally deemed "healthy." 4.3 Early Warning in Younger Populations Our subgroup analysis indicated that the risks of diabetes and stroke associated with elevated CTI were significantly more pronounced in participants under 60 years of age. This observation aligns with the "cumulative exposure" hypothesis, suggesting that metabolic and inflammatory insults exert a time-dependent deleterious effect, and biomarkers often show higher predictive sensitivity in the earlier stages of pathogenesis before irreversible organ damage manifests[38, 39]. In contrast, among older adults, the presence of multiple comorbidities and age-related physiological decline may dilute the specific contribution of metabolic inflammation to disease risk, a phenomenon known as "risk factor attenuation" in the elderly[40]. Therefore, CTI screening in the 45–60 age group represents a strategic opportunity for cost-effective primary prevention[41]. 4.4 Specificity of CTI Importantly, CTI was not associated with digestive diseases, cancer, arthritis, or memory-related disorders. This null finding reinforces the specificity of CTI as a marker for metabolic and vascular pathologies rather than a generalized marker of ill health[42]. In contrast to Zhuo et al., who utilized a broad classification, our analysis specifically examined digestive diseases (excluding liver pathology) and found no association. However, we confirmed a link with liver disease (HR 1.16), which is mechanistically consistent with the role of "glucolipotoxicity" in the progression of metabolic dysfunction-associated fatty liver disease (MAFLD) [43]. The attenuation of this link in our lag analysis suggests that elevated CTI may reflect acute inflammatory activity in active liver disease or a short-term prodromal phase rather than a long-term causal driver[44]. 4.5 Strengths and Limitations The strengths of this study include its prospective design, large nationally representative sample, and long follow-up duration. Furthermore, by rigorously excluding baseline prevalent cases and performing lag analyses, we minimized the potential for reverse causality bias. Several limitations should be acknowledged. First , chronic diseases were ascertained via self-reported physician diagnosis, which may introduce recall bias. However, the concordance between self-reported conditions and clinical diagnoses in the CHARLS cohort has been validated in previous studies [45]. Second , our primary analysis relied on a single baseline measurement of CTI (2011). Although we verified the stability of CTI using 2015 data in supplementary analyses, the lack of repeated measures prevented us from accounting for time-varying exposure, potentially leading to regression dilution bias. Finally , as an observational study, residual confounding from unmeasured factors (e.g., detailed dietary patterns or genetic susceptibility) cannot be entirely ruled out, and causal inferences should be drawn with caution [46]. Conclusions In summary, the CTI is a robust and accessible biomarker that independently predicts the risk of diabetes, stroke, dyslipidemia, and liver disease. By fusing inflammatory and metabolic signals, CTI offers superior prognostic value for stroke , particularly among non-obese individuals who might otherwise be missed by traditional screening. These findings support the integration of CTI into routine health assessments for middle-aged and older adults to facilitate the precision prevention of cardiometabolic multimorbidity. Declarations Ethics approval and consent to participate Clinical trial number: Not applicable. The CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (Approval No.: IRB00001052-11015). The study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before participating in the survey. Consent for publication Not applicable. This study is a secondary analysis of the publicly available data from the China Health and Retirement Longitudinal Study (CHARLS). All data were de-identified, and the manuscript does not contain any individual person’s data in any form (including individual details, images, or videos). Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: The China Health and Retirement Longitudinal Study can be publicly accessed at https://charls.pku.edu.cn/. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding ： The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author contributions ： GL and HL conceived and designed the study. SY and ZZ performed the statistical analysis. MC verified the underlying data. GL drafted the original manuscript. JC critically reviewed and edited the manuscript. All authors have read and approved the final manuscript. 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Verbeek JH, Whaley P, Morgan RL, Taylor KW, Rooney AA, Schwingshackl L, Hoving JL, Vittal Katikireddi S, Shea B, Mustafa RA et al : An approach to quantifying the potential importance of residual confounding in systematic reviews of observational studies: A GRADE concept paper . Environ Int 2021, 157 :106868. Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2CoxModelResults.xlsx Table3.xlsx SupplementaryTable1.xlsx sFigure1.jpg Supplementary Figure 1. Robustness check comparing Hazard Ratios (HRs) from the main analysis and the 2-year lag sensitivity analysis.Red circles represent the main analysis, and blue squares represent the sensitivity analysis excluding events within the first 2 years. All estimates were derived from multivariable Cox regression models adjusted for age, sex, BMI, systolic blood pressure, diastolic blood pressure, LDL-C, smoking status, and alcohol consumption. The consistency between the two estimates supports the robustness of the findings. Cite Share Download PDF Status: Posted Version 1 posted 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-8279156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557709124,"identity":"1815be39-b042-4f3c-924b-663a995a286d","order_by":0,"name":"Guanghai Li","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guanghai","middleName":"","lastName":"Li","suffix":""},{"id":557709125,"identity":"0654ce76-77cd-4d2f-929c-176a164ac2a3","order_by":1,"name":"Hao Liang","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical 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1","display":"","copyAsset":false,"role":"figure","size":1058264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of study participant selection.\u003c/strong\u003e Data were derived from the China Health and Retirement Longitudinal Study (CHARLS). The baseline survey was conducted in 2011, with follow-up assessments in 2013, 2015, 2018, and 2020. Participants were sequentially excluded based on age criteria (\u0026lt;45 years), missing blood biomarker data for CTI calculation, loss to follow-up, extreme CTI values (trimmed at the 1st and 99th percentiles), and missing or abnormal key covariates. The final analytic cohort included 9,194 participants. For each of the 14 chronic diseases, a specific sub-cohort was established by further excluding participants with prevalent disease at baseline. \u003cstrong\u003eAbbreviations:\u003c/strong\u003eCTI, C-reactive Protein-Triglyceride Glucose Index; CRP, C-reactive protein; TG, triglycerides; FPG, fasting plasma glucose; BMI, body mass index.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/bef12aeacae474c437503085.jpg"},{"id":97932586,"identity":"c4c91eff-3799-4944-a0c0-86aeda0d611c","added_by":"auto","created_at":"2025-12-11 00:45:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1192195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of the associations between baseline CTI and risk of 14 incident chronic diseases. \u003c/strong\u003eThe red squares represent multivariable-adjusted hazard ratios (HRs) from Model 2, and the horizontal lines indicate 95% confidence intervals (CIs). Model 2 was adjusted for age, sex, BMI, systolic blood pressure, diastolic blood pressure, LDL-C, smoking status, and alcohol consumption. \u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e (shown in bold red) indicates statistical significance.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/7ef3002caedb4164f760281e.jpg"},{"id":98421473,"identity":"b5abfa6b-9aaf-46e2-97c8-f3da196b51b5","added_by":"auto","created_at":"2025-12-17 16:27:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2643060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline analysis of the association between baseline CTI and risk of incident chronic diseases.\u003c/strong\u003e The solid red lines represent multivariable-adjusted hazard ratios (HRs), and the shaded areas indicate the 95% confidence intervals. The models were adjusted for age, sex, BMI, blood pressure, LDL-C, smoking, and drinking status. The reference point (HR=1) was set at the median value of CTI.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/624a450118db6eb834d18f70.jpg"},{"id":97932596,"identity":"9ee2b043-0a9c-4c48-8f0f-84a4e7917d03","added_by":"auto","created_at":"2025-12-11 00:45:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1025712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the association between baseline CTI and risk of diabetes, stroke, and dyslipidemia.\u003c/strong\u003e The forest plots display the multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) in subgroups stratified by age (\u0026lt;60 vs. ≥60 years), sex (male vs. female), and BMI (\u0026lt;24 vs. ≥24 kg/m²). The models were adjusted for \u003cstrong\u003eage, sex, BMI, systolic blood pressure, diastolic blood pressure, LDL-C, smoking status, and alcohol consumption\u003c/strong\u003e, excluding the stratification variable in each respective subgroup (e.g., age was omitted from the adjustment in age-stratified analysis).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/79eb31c8fba8ca063ef87515.jpg"},{"id":99314443,"identity":"ce3ca346-1597-4526-889a-c0602fc7aaea","added_by":"auto","created_at":"2025-12-31 16:21:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9290935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/3616ca82-493d-440f-a4b4-cd441ec7b434.pdf"},{"id":98421622,"identity":"1000d1ae-da62-48f0-9f0e-8e6205433099","added_by":"auto","created_at":"2025-12-17 16:28:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21435,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/cdb4fe7eaedd658b89e334a9.docx"},{"id":98421970,"identity":"5ef7e703-3082-498d-bb1e-4c1b6c2c8867","added_by":"auto","created_at":"2025-12-17 16:30:03","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11034,"visible":true,"origin":"","legend":"","description":"","filename":"Table2CoxModelResults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/b00f98f08544bce17a68c0f8.xlsx"},{"id":97932594,"identity":"b0dfb6bb-9bb7-48c4-a194-48635ce32adf","added_by":"auto","created_at":"2025-12-11 00:45:30","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12286,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/9c508c94bad707d530c978f1.xlsx"},{"id":98421705,"identity":"15a9cef0-63e3-419f-bb81-b85dc4ceb4f7","added_by":"auto","created_at":"2025-12-17 16:29:02","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10290,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/1ae3f49ebb04999fec62073b.xlsx"},{"id":97932608,"identity":"7178ccad-0021-41cd-9d63-12ca16beffcd","added_by":"auto","created_at":"2025-12-11 00:45:30","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":360629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e \u003cstrong\u003eRobustness check comparing Hazard Ratios (HRs) from the main analysis and the 2-year lag sensitivity analysis.\u003c/strong\u003eRed circles represent the main analysis, and blue squares represent the sensitivity analysis excluding events within the first 2 years. All estimates were derived from multivariable Cox regression models adjusted for \u003cstrong\u003eage, sex, BMI, systolic blood pressure, diastolic blood pressure, LDL-C, smoking status, and alcohol consumption\u003c/strong\u003e. The consistency between the two estimates supports the robustness of the findings.\u003c/p\u003e","description":"","filename":"sFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8279156/v1/ed204787f0d24974178721e7.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Association of C-reactive Protein-Triglyceride Glucose Index with 14 New-Onset Chronic Diseases: Evidence from the China Health and Retirement Longitudinal Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rapid acceleration of global population aging has positioned chronic non-communicable diseases (NCDs) as the predominant threat to public health. According to the \u003cem\u003eGlobal Burden of Disease Study\u003c/em\u003e, NCDs account for over 70% of all deaths worldwide, with China facing particularly severe challenges due to its massive elderly demographic[1, 2]. A critical concern accompanying aging is multimorbidity—the coexistence of two or more chronic conditions—which significantly strains healthcare systems and diminishes quality of life[3]. Consequently, identifying cost-effective and accessible biomarkers for the early stratification of high-risk populations is a priority for primary prevention and healthy aging strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations of Current Indices\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsulin resistance (IR) and dyslipidemia are well-recognized pathological cornerstones of cardiovascular disease (CVD), diabetes, and metabolic dysfunction-associated fatty liver disease (MAFLD)[4, 5]. Recently, surrogate markers derived from routine lipid and glucose profiles, such as the Triglyceride-Glucose (TyG) index and the Cardiometabolic Index (CMI), have gained prominence for their ability to proxy IR[6-8]. However, a substantial limitation of these lipid-centric indices is their failure to encompass \u003cstrong\u003echronic low-grade inflammation\u003c/strong\u003e. Accumulating evidence suggests that subclinical inflammation acts as a \"common soil\" linking obesity and metabolic syndrome to a broad spectrum of diseases, including atherosclerosis and neurodegeneration. Relying solely on metabolic parameters may overlook individuals with a \"metabolically unhealthy but normal weight\" phenotype or those driven primarily by inflammatory pathways[9, 10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Theoretical Advantage of CTI\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC-reactive protein (CRP) serves as the gold-standard biomarker for systemic inflammation, with elevated levels predicting risks for CVD, diabetes, and malignancy[11, 12]. To bridge the gap between metabolic and inflammatory assessments, the \u003cstrong\u003eC-reactive Protein-Triglyceride Glucose Index (CTI)\u003c/strong\u003e was proposed. By mathematically integrating CRP (inflammation) with the TyG index (glucolipotoxicity), CTI theoretically captures a more comprehensive pathophysiological profile than either marker alone[13]. While small-scale studies have hinted at the utility of combining inflammatory and metabolic markers[14], no large-scale prospective study has systematically evaluated the longitudinal association between CTI and a broad spectrum of chronic diseases beyond the cardiovascular system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Objectives\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address this knowledge gap, we leveraged data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort. This study aimed to: (1) investigate the independent associations between baseline CTI and the incidence of 14 distinct chronic diseases over a 9-year follow-up; and (2) explore potential heterogeneity in these associations across age, sex, and BMI strata. We hypothesized that CTI, as a dual-domain marker, would provide superior risk stratification, particularly for vascular events like stroke and in populations where traditional obesity metrics may be less sensitive.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Population and Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of Chinese residents aged 45 years and older. The cohort employs a multi-stage stratified probability sampling strategy, covering 450 villages and urban communities across 28 provinces[15]. The current study utilized the 2011 national baseline survey (Wave 1) as the starting point, with follow-up assessments conducted in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5).\u003c/p\u003e\n\u003cp\u003eParticipants were included if they: (1) were \u0026ge;\u0026thinsp;45 years of age at baseline; and (2) provided blood samples with complete data for C-reactive protein (CRP), triglycerides (TG), and fasting plasma glucose (FPG). Exclusion criteria were as follows: (1) loss to follow-up without death records; (2) extreme baseline CTI values; and (3) missing data on key covariates (e.g., BMI) or biologically implausible values. To minimize the influence of outliers and measurement errors, and specifically to exclude participants with potential acute infections (indicated by extremely high CRP levels) or extreme metabolic derangements not representative of the general population, baseline CTI values were trimmed at the 1st and 99th percentiles. The final analytic cohort consisted of 9,194 participants \u003cstrong\u003e(\u003c/strong\u003eFig. 1\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Exposure Assessment: CTI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous blood samples were collected by trained nurses at baseline (2011), separated into plasma and buffy coat, and transported cold to the Chinese Center for Disease Control and Prevention (CDC) for standardized analysis[16]. The CTI was calculated using the following formula[17], integrating systemic inflammation and metabolic status:\u003c/p\u003e\n\u003cp\u003eCTI\u0026thinsp;=\u0026thinsp;0.412 \u0026times; ln(CRP [mg/L])\u0026thinsp;+\u0026thinsp;ln(TG [mg/dL] \u0026times; FPG [mg/dL]) / 2\u003c/p\u003e\n\u003cp\u003eIn statistical models, CTI was analyzed both as a continuous variable (per standard deviation increase) and as a categorical variable divided into quartiles (Q1\u0026ndash;Q4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Outcome Ascertainment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcomes were incident cases of 14 chronic diseases identified during the follow-up period (2013\u0026ndash;2020)[3]. These conditions included hypertension, diabetes, dyslipidemia, heart disease (including myocardial infarction and coronary heart disease), stroke, cancer, chronic lung disease, liver disease, kidney disease, digestive disease, arthritis/rheumatism, asthma, psychiatric disease, and memory-related disease[18\u0026ndash;20]. Disease status was ascertained via self-reported physician diagnosis (\u0026quot;Have you been diagnosed with [condition] by a doctor?\u0026quot;). To ensure the analysis of \u003cem\u003eincident\u003c/em\u003e events, we constructed separate sub-cohorts for each disease by excluding participants who reported the respective condition at baseline (2011). The time to event was defined as the midpoint between the last interview without the disease and the first interview reporting the diagnosis[21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential confounders were selected a priori based on the literature. Adjusted covariates included:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eDemographic factors\u003c/strong\u003e: Age (continuous), sex (male/female), Household registration status (agricultural/non-agricultural), marital status, and education level.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLifestyle factors\u003c/strong\u003e: Smoking status (never/former/current) and alcohol consumption.\u003c/li\u003e\n \u003cli\u003eClinical metrics: Body mass index (BMI, calculated as weight in kg divided by height in m\u003csup\u003e2\u003c/sup\u003e), systolic blood pressure (SBP), diastolic blood pressure (DBP), and low-density lipoprotein cholesterol (LDL-C). All covariates were measured at the 2011 baseline.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were compared across CTI quartiles using the Kruskal-Wallis test for continuous variables and the Chi-square test for categorical variables. We estimated Hazard Ratios (HRs) and 95% Confidence Intervals (CIs) using Cox proportional hazards models. Two models were constructed:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e: Unadjusted.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e: Adjusted for age, sex, BMI, SBP, DBP, LDL-C, smoking, and drinking status.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo examine the potential non-linear association between CTI and the risk of chronic diseases, we employed restricted cubic spline (RCS) regression models with 3 knots placed at the 10th, 50th, and 90th percentiles[22, 23]. The median value of CTI was used as the reference.\u0026nbsp;Robustness was evaluated via: (1)\u0026nbsp;sensitivity analysis (Lag-2): Excluding participants who developed the outcome within the first 2 years of follow-up to minimize reverse causality; and (2)\u0026nbsp;subgroup analysis: Stratifying by age (\u0026lt;60 vs. \u0026ge;60 years), sex, and BMI (\u0026lt;24 vs. \u0026ge;24 kg/m\u003csup\u003e2\u003c/sup\u003e) for diseases showing significant associations[24]. Data cleaning and preprocessing were initially performed using Stata software (Version 17.0, StataCorp LLC, College Station, TX, USA). Subsequent statistical analyses and visualization were conducted using Python (version 3.x) within the Spyder IDE, utilizing the \u003ccode\u003elifelines\u003c/code\u003e, \u003ccode\u003epandas\u003c/code\u003e, and \u003ccode\u003ematplotlib\u003c/code\u003e libraries.[25] A two-sided\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05 was considered statistically significant. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 9,194 participants were included in the final analysis. The study population was stratified into four groups based on baseline CTI quartiles (Q1\u0026ndash;Q4). As detailed in \u003cstrong\u003eTable 1\u003c/strong\u003e, the mean age was 59.6 \u0026plusmn; 9.36 years, and 53.3% were female. Participants in the highest CTI quartile (Q4) exhibited a distinct phenotype characterized by metabolic dysfunction and systemic inflammation. Compared to the lowest quartile (Q1), those in Q4 had significantly higher body mass index (BMI), systolic and diastolic blood pressure, and serum uric acid levels (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Consistent with the CTI formula, levels of C-reactive protein (CRP), triglycerides, and fasting plasma glucose increased progressively from Q1 to Q4, while high-density lipoprotein cholesterol (HDL-C) decreased (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Sociodemographically, a higher proportion of participants with non-agricultural Household registration status (indicative of urban residence) was observed in the highest CTI quartile (20.4% vs. 13.6% in Q1). Notably, while females comprised a slight majority of the total cohort, the distribution of sex did not differ significantly across CTI quartiles in this at-risk population (\u003cem\u003eP\u003c/em\u003e = 0.073).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Association Between CTI and New-Onset Chronic Diseases\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the 9-year follow-up, we systematically evaluated the associations between baseline CTI and the incidence of 14 chronic diseases. The results of the unadjusted (Model 1) and fully adjusted (Model 2) Cox proportional hazards models are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAfter fully adjusting for confounders, CTI emerged as a significant independent predictor for four specific conditions: \u003cstrong\u003ediabetes\u003c/strong\u003e (HR 1.86, 95% CI 1.67\u0026ndash;2.06, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), \u003cstrong\u003estroke\u003c/strong\u003e (HR 1.42, 95% CI 1.23\u0026ndash;1.64, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), \u003cstrong\u003edyslipidemia\u003c/strong\u003e (HR 1.36, 95% CI 1.25\u0026ndash;1.48, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and \u003cstrong\u003eliver disease\u003c/strong\u003e (HR 1.16, 95% CI 1.01\u0026ndash;1.33, \u003cem\u003eP\u003c/em\u003e = 0.036).\u003c/p\u003e\n\u003cp\u003eIn contrast, no significant associations were found for non-metabolic conditions such as cancer, arthritis, psychiatric disorders, or memory-related diseases (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Furthermore, while CTI was associated with hypertension, asthma, and kidney disease in unadjusted analyses, these associations were attenuated to non-significance after adjustment, suggesting that the effects of CTI on these conditions may be mediated by other metabolic covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDose-Response Relationships\u003c/strong\u003e We further explored the dose-response relationship between CTI and the four significantly associated diseases using RCS analyses (\u003cstrong\u003eFigure 3\u003c/strong\u003e). A continuous, positive, and approximately linear association was observed for \u003cstrong\u003ediabetes\u003c/strong\u003e, \u003cstrong\u003estroke\u003c/strong\u003e, and \u003cstrong\u003edyslipidemia\u003c/strong\u003e, where the risk increased steadily with higher CTI levels (\u003cem\u003eP\u003c/em\u003e for non-linearity \u0026gt; 0.05). Interestingly, for \u003cstrong\u003eliver disease\u003c/strong\u003e, the curve exhibited a J-shaped pattern, with risk increasing sharply only after CTI exceeded the median level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Subgroup Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo explore population-specific risks, we stratified the analysis for the three most robust outcomes (diabetes, stroke, and dyslipidemia) by age, sex, and BMI (\u003cstrong\u003eTable 3\u003c/strong\u003e and \u003cstrong\u003eFigure 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAge:\u003c/strong\u003e The predictive value of CTI was consistently stronger in younger participants (\u0026lt;60 years) compared to older adults (\u0026ge;60 years) across all three diseases. For instance, the HR for diabetes was 2.13 in the younger group versus 1.55 in the older group, highlighting the utility of CTI as an early warning signal.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Females exhibited a higher susceptibility to CTI-associated risks for diabetes (HR 1.99 vs. 1.70 in males) and stroke (HR 1.51 vs. 1.33 in males).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBMI (The \u0026quot;Stroke Paradox\u0026quot;):\u003c/strong\u003e A striking interaction was observed for stroke risk. Contrary to traditional expectations, the association between CTI and stroke was stronger in \u003cstrong\u003enon-obese participants\u003c/strong\u003e (BMI \u0026lt; 24 kg/m\u0026sup2;; HR 1.59) compared to overweight/obese participants (HR 1.35). This suggests that CTI is particularly effective at identifying hidden vascular risks in individuals with a normal body weight.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Sensitivity Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo mitigate reverse causality (where preclinical disease elevates baseline biomarkers), we conducted a lag analysis excluding participants who developed outcomes within the first 2 years of follow-up (\u003cstrong\u003eSupplementary Table 1\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e). The associations for \u003cstrong\u003ediabetes\u003c/strong\u003e (HR 1.79), \u003cstrong\u003estroke\u003c/strong\u003e (HR 1.38), and \u003cstrong\u003edyslipidemia\u003c/strong\u003e (HR 1.30) remained highly significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), underscoring the long-term predictive robustness of CTI. However, the association with liver disease became non-significant (HR 1.13, \u003cem\u003eP\u003c/em\u003e = 0.099), implying that the link between CTI and liver disease might be driven by short-term progression or concurrent inflammatory states rather than long-term causality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale, national prospective cohort study, we conducted the first comprehensive evaluation of the longitudinal association between the C-reactive Protein-Triglyceride Glucose Index (CTI) and the risk of 14 new-onset chronic diseases. Over a 9-year follow-up, elevated CTI was independently associated with an increased risk of \u003cstrong\u003ediabetes\u003c/strong\u003e, \u003cstrong\u003estroke\u003c/strong\u003e, \u003cstrong\u003edyslipidemia\u003c/strong\u003e, and \u003cstrong\u003eliver disease\u003c/strong\u003e. Notably, CTI demonstrated superior predictive efficacy for stroke compared to traditional metabolic indices, particularly among non-obese individuals and younger adults. Our findings suggest that CTI, by integrating inflammatory and metabolic dimensions, serves as a simple yet powerful tool for the early stratification of chronic disease risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 CTI vs. Traditional Metabolic Indices: The Value of Inflammation\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior research has largely focused on the Triglyceride-Glucose (TyG) index or the Cardiometabolic Index (CMI) in isolation as surrogates for insulin resistance [26, 27]. While a recent study by Zhuo et al. using CHARLS data investigated CMI, they found that the association between CMI and stroke became non-significant after adjusting for confounders (HR 1.02, \u003cem\u003eP\u003c/em\u003e = 0.054) [28]. In stark contrast, our study reveals a robust and independent association between CTI and new-onset stroke (HR 1.42, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eThis discrepancy underscores the critical role of the inflammatory component. CMI relies solely on lipids and waist-to-height ratio, whereas CTI incorporates C-reactive protein (CRP). Chronic low-grade inflammation is a well-established driver of endothelial dysfunction, atherosclerotic plaque instability, and rupture [29-31]. Purely metabolic indices may fail to capture this residual inflammatory burden on the vascular endothelium. By integrating CRP, CTI provides a more holistic view of the \"immunometabolic\" risk profile driving cerebrovascular events [32]. Similarly, the predictive strength of CTI for diabetes (HR 1.86) substantially exceeded that reported for CMI (HR 1.08), likely because CTI directly incorporates fasting glucose, rendering it inherently more sensitive to glucotoxicity [33].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Identifying Hidden Risks: The \"Lean Stroke\" Phenomenon\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA novel finding of this study is the stronger predictive value of CTI for stroke in \u003cstrong\u003enon-obese participants (BMI \u0026lt; 24 kg/m²)\u003c/strong\u003e compared to their overweight/obese counterparts. This observation aligns with the \"Metabolically Obese Normal Weight\" (MONW) phenotype described in literature [34]. Individuals with MONW possess a normal BMI but harbor substantial visceral adiposity, insulin resistance, and subclinical inflammation—pathologies that are frequently overlooked in routine screenings relying solely on anthropometry [35, 36]. Furthermore, previous studies indicate that Asian populations are genetically predisposed to visceral fat accumulation at lower BMI thresholds compared to Western populations, making them particularly susceptible to metabolic risks despite a \"healthy\" weight [37]. Our results suggest that CTI serves as a sensitive tool to unveil this latent vascular risk obscured by normal body weight, highlighting a critical window for primary prevention in a population traditionally deemed \"healthy.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Early Warning in Younger Populations\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur subgroup analysis indicated that the risks of diabetes and stroke associated with elevated CTI were significantly more pronounced in participants under 60 years of age. This observation aligns with the \"cumulative exposure\" hypothesis, suggesting that metabolic and inflammatory insults exert a time-dependent deleterious effect, and biomarkers often show higher predictive sensitivity in the earlier stages of pathogenesis before irreversible organ damage manifests[38, 39]. In contrast, among older adults, the presence of multiple comorbidities and age-related physiological decline may dilute the specific contribution of metabolic inflammation to disease risk, a phenomenon known as \"risk factor attenuation\" in the elderly[40]. Therefore, CTI screening in the 45–60 age group represents a strategic opportunity for cost-effective primary prevention[41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Specificity of CTI\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, CTI was not associated with digestive diseases, cancer, arthritis, or memory-related disorders. This null finding reinforces the specificity of CTI as a marker for metabolic and vascular pathologies rather than a generalized marker of ill health[42]. In contrast to Zhuo et al., who utilized a broad classification, our analysis specifically examined digestive diseases (excluding liver pathology) and found no association. However, we confirmed a link with \u003cstrong\u003eliver disease\u003c/strong\u003e (HR 1.16), which is mechanistically consistent with the role of \"glucolipotoxicity\" in the progression of metabolic dysfunction-associated fatty liver disease (MAFLD) [43]. The attenuation of this link in our lag analysis suggests that elevated CTI may reflect acute inflammatory activity in active liver disease or a short-term prodromal phase rather than a long-term causal driver[44].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Strengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe strengths of this study include its prospective design, large nationally representative sample, and long follow-up duration. Furthermore, by rigorously excluding baseline prevalent cases and performing lag analyses, we minimized the potential for reverse causality bias.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. \u003cstrong\u003eFirst\u003c/strong\u003e, chronic diseases were ascertained via self-reported physician diagnosis, which may introduce recall bias. However, the concordance between self-reported conditions and clinical diagnoses in the CHARLS cohort has been validated in previous studies [45]. \u003cstrong\u003eSecond\u003c/strong\u003e, our primary analysis relied on a single baseline measurement of CTI (2011). Although we verified the stability of CTI using 2015 data in supplementary analyses, the lack of repeated measures prevented us from accounting for time-varying exposure, potentially leading to regression dilution bias. \u003cstrong\u003eFinally\u003c/strong\u003e, as an observational study, residual confounding from unmeasured factors (e.g., detailed dietary patterns or genetic susceptibility) cannot be entirely ruled out, and causal inferences should be drawn with caution [46].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the CTI is a robust and accessible biomarker that independently predicts the risk of diabetes, stroke, dyslipidemia, and liver disease. By fusing inflammatory and metabolic signals, CTI offers superior prognostic value for \u003cstrong\u003estroke\u003c/strong\u003e, particularly among \u003cstrong\u003enon-obese individuals\u003c/strong\u003e who might otherwise be missed by traditional screening. These findings support the integration of CTI into routine health assessments for middle-aged and older adults to facilitate the precision prevention of cardiometabolic multimorbidity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cstrong\u003eClinical trial number: Not applicable.\u003c/strong\u003e The CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (Approval No.: IRB00001052-11015). The study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before participating in the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study is a secondary analysis of the publicly available data from the China Health and Retirement Longitudinal Study (CHARLS). All data were de-identified, and the manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: The China Health and Retirement Longitudinal Study can be publicly accessed at https://charls.pku.edu.cn/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGL\u003c/strong\u003e and \u003cstrong\u003eHL\u003c/strong\u003e conceived and designed the study. \u003cstrong\u003eSY\u003c/strong\u003e and \u003cstrong\u003eZZ\u003c/strong\u003e performed the statistical analysis. \u003cstrong\u003eMC\u003c/strong\u003e verified the underlying data. \u003cstrong\u003eGL\u003c/strong\u003e drafted the original manuscript. \u003cstrong\u003eJC\u003c/strong\u003e critically reviewed and edited the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to the CHARLS research team for providing high-quality data that formed the foundation of this study. We also extend our deepest appreciation to all CHARLS participants for their invaluable and selfless contributions, which made this research possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eNon-communicable diseases: what now?\u003c/strong\u003e \u003cem\u003eLancet \u003c/em\u003e2022, \u003cstrong\u003e399\u003c/strong\u003e(10331):1201.\u003c/li\u003e\n\u003cli\u003eCenter For Cardiovascular Diseases The Writing Committee Of The Report On Cardiovascular H, Diseases In China N: \u003cstrong\u003eReport on Cardiovascular Health and Diseases in China 2023: An Updated Summary\u003c/strong\u003e. \u003cem\u003eBiomed Environ Sci \u003c/em\u003e2024, \u003cstrong\u003e37\u003c/strong\u003e(9):949\u0026ndash;992.\u003c/li\u003e\n\u003cli\u003eYao SS, Cao GY, Han L, Chen ZS, Huang ZT, Gong P, Hu Y, Xu B: \u003cstrong\u003ePrevalence and Patterns of Multimorbidity in a Nationally Representative Sample of Older Chinese: Results From the China Health and Retirement Longitudinal Study\u003c/strong\u003e. \u003cem\u003eJ Gerontol A Biol Sci Med Sci \u003c/em\u003e2020, \u003cstrong\u003e75\u003c/strong\u003e(10):1974\u0026ndash;1980.\u003c/li\u003e\n\u003cli\u003ePetersen MC, Shulman GI: \u003cstrong\u003eMechanisms of Insulin Action and Insulin Resistance\u003c/strong\u003e. \u003cem\u003ePhysiol Rev \u003c/em\u003e2018, \u003cstrong\u003e98\u003c/strong\u003e(4):2133\u0026ndash;2223.\u003c/li\u003e\n\u003cli\u003eEslam M, Sanyal AJ, George J: \u003cstrong\u003eMAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease\u003c/strong\u003e. \u003cem\u003eGastroenterology \u003c/em\u003e2020, \u003cstrong\u003e158\u003c/strong\u003e(7):1999\u0026ndash;2014.e1991.\u003c/li\u003e\n\u003cli\u003eGuerrero-Romero F, Simental-Mend\u0026iacute;a LE, Gonz\u0026aacute;lez-Ortiz M, Mart\u0026iacute;nez-Abundis E, Ramos-Zavala MG, Hern\u0026aacute;ndez-Gonz\u0026aacute;lez SO, Jacques-Camarena O, Rodr\u0026iacute;guez-Mor\u0026aacute;n M: \u003cstrong\u003eThe product of triglycerides and glucose, a simple measure of insulin sensitivity. 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objectively measured hypertension diagnosis and control: evidence from a nationally representative sample of community-dwelling middle-aged and older adults in China\u003c/strong\u003e. \u003cem\u003eArch Public Health \u003c/em\u003e2024, \u003cstrong\u003e82\u003c/strong\u003e(1):245.\u003c/li\u003e\n\u003cli\u003eVerbeek JH, Whaley P, Morgan RL, Taylor KW, Rooney AA, Schwingshackl L, Hoving JL, Vittal Katikireddi S, Shea B, Mustafa RA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAn approach to quantifying the potential importance of residual confounding in systematic reviews of observational studies: A GRADE concept paper\u003c/strong\u003e. \u003cem\u003eEnviron Int \u003c/em\u003e2021, \u003cstrong\u003e157\u003c/strong\u003e:106868.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CTI, Inflammation, Multimorbidity, Stroke, Metabolically Obese Normal Weight, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-8279156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8279156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe rising burden of multimorbidity in aging populations underscores the need for efficient screening tools. While the Triglyceride-Glucose (TyG) index and Cardiometabolic Index (CMI) are established markers for metabolic risk, they fail to capture chronic low-grade inflammation, a pivotal pathological driver. We aimed to evaluate the C-reactive Protein-Triglyceride Glucose Index (CTI)\u0026mdash;a novel composite marker integrating inflammation and metabolic status\u0026mdash;and assess its prospective association with 14 new-onset chronic diseases.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis prospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS, 2011\u0026ndash;2020). A total of 9,194 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years with complete baseline biomarker data were included. The CTI was calculated as 0.412 \u0026times; ln(CRP [mg/L])\u0026thinsp;+\u0026thinsp;ln(TG [mg/dL] \u0026times; FPG [mg/dL]) / 2. We employed multivariable Cox proportional hazards models and restricted cubic splines (RCS) to estimate hazard ratios (HRs) and dose-response relationships for 14 incident diseases. Robustness was verified via sensitivity analyses (2-year lag) and subgroup stratifications.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDuring the 9-year follow-up, elevated baseline CTI was independently associated with an increased risk of \u003cb\u003ediabetes\u003c/b\u003e (HR 1.86, 95% CI 1.67\u0026ndash;2.06), \u003cb\u003estroke\u003c/b\u003e (HR 1.42, 95% CI 1.23\u0026ndash;1.64), \u003cb\u003edyslipidemia\u003c/b\u003e (HR 1.36, 95% CI 1.25\u0026ndash;1.48), and \u003cb\u003eliver disease\u003c/b\u003e (HR 1.16, 95% CI 1.01\u0026ndash;1.33) after full adjustment. Notably, CTI demonstrated superior predictive value for stroke compared to traditional metabolic indices. These associations remained robust in lag analyses. Subgroup analyses revealed that the predictive value was more pronounced in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and females. Crucially, CTI showed a stronger association with stroke risk in \u003cb\u003enon-obese participants\u003c/b\u003e (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;; HR 1.59) compared to the obese population (HR 1.35). No significant associations were found for non-metabolic conditions (e.g., cancer, arthritis), indicating biological specificity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe CTI serves as a robust and accessible biomarker capturing the dual burden of immunometabolic dysregulation. It effectively predicts risks for diabetes, dyslipidemia, liver disease, and particularly \u003cb\u003estroke\u003c/b\u003e. Our findings highlight the utility of CTI in identifying \"hidden\" cardiovascular risks in non-obese individuals, supporting its incorporation into routine health screenings for older adults.\u003c/p\u003e","manuscriptTitle":"Comprehensive Association of C-reactive Protein-Triglyceride Glucose Index with 14 New-Onset Chronic Diseases: Evidence from the China Health and Retirement Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 00:45:21","doi":"10.21203/rs.3.rs-8279156/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5e26feb-639a-4785-a811-196048b9c1d4","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-28T16:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 00:45:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8279156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8279156","identity":"rs-8279156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}