C-reactive protein-triglyceride-glucose index and all-cause mortality among Chinese adults with hypertension: a retrospective cohort analysis of CHARLS

Introduction

Hypertension stands as one of the most prevalent chronic conditions, with projections indicating that the total number of individuals affected by this ailment will rise from approximately 1.13 billion in 2015 (1) to an estimated 1.56 billion by 2025 (2). Hypertension affects more than 60% of adults aged >60 years (3). This condition is recognized as a primary risk factor for cardiovascular diseases and related mortality, contributing to 13% of global deaths (4), thus imposing a significant burden on both society and families. Therefore, identifying additional prognostic factors associated with adverse outcomes in hypertension may improve risk stratification and prevention strategies.

A growing body of evidence indicates that dysregulation of inflammation plays a significant role in the pathogenesis of hypertension. An extensive analysis of genome-wide association studies (GWAS) has revealed that certain single nucleotide polymorphisms (SNPs) associated with hypertension are involved, either directly or indirectly, in inflammatory processes (5). Furthermore, a secondary analysis of the Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS) has suggested that particular anti-inflammatory therapies may mitigate the incidence of adverse cardiovascular events in individuals with hypertension (6). In recent years, extensive attention has been paid to the relationship between C-reactive protein (CRP) and hypertension. Prospective studies have shown that blood pressure is positively correlated with CRP levels, and that both are independently associated with cardiovascular events (7,8). CRP may impair vascular function by reducing nitric oxide and promoting vasoconstriction, while renin-angiotensin system activation exacerbates inflammation and endothelial dysfunction (9,10). However, CRP alone cannot fully reflect metabolic abnormalities such as insulin resistance and dyslipidemia, which are also critical contributors to cardiovascular risk in hypertensive patients (11). The triglyceride-glucose (TyG) index, calculated from fasting triglyceride and glucose levels, has emerged as a reliable surrogate marker of insulin resistance and metabolic dysfunction, with demonstrated associations with hypertension, diabetes, and cardiovascular risk (12,13). Yet, neither CRP nor TyG alone can comprehensively capture the combined inflammatory and metabolic disturbances that characterize high-risk hypertensive patients.

To address this gap, the C-reactive protein-triglyceride-glucose index (CTI), first proposed by Ruan et al. (14), integrates biomarkers of systemic inflammation and insulin resistance and has been increasingly used in clinical research. Prior studies have linked CTI to adverse outcomes in several settings, including cancer cachexia (15,16) and recurrent cardiovascular events among hypertensive patients undergoing percutaneous coronary intervention (17). More recently, analyses from China Health and Retirement Longitudinal Study (CHARLS) and other Chinese cohorts reported positive associations between CTI and all-cause mortality, with hypertension status showing heterogeneity in subgroup analyses (18). However, among middle-aged and older adults with established hypertension, it remains unclear whether CTI exhibits a consistent dose-response relationship and whether the association is robust to potential selection bias arising from missing biomarker data. In addition, the comparative discriminative performance of CTI versus its individual components (CRP and TyG) in this population has not been well described. Therefore, using CHARLS data, we evaluated the association between CTI and all-cause mortality among adults with established hypertension at baseline, characterized the dose-response shape, compared the discriminative performance of CTI with CRP and TyG, and assessed robustness to potential selection bias related to biomarker missingness. This article is presented in accordance with the STROBE reporting checklist (available at https://jphe.amegroups.com/article/view/10.21037/jphe-2025-1-54/rc).

Methods

Study population

Data for this study were obtained from the CHARLS, a longitudinal survey of middle-aged and older adults in China (19). The dataset includes baseline and follow-up data from standardized questionnaires and clinical evaluations, focusing on social, demographic, health, and behavioral aspects. The original CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent at the time of data collection. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Additional information regarding CHARLS can be found on its official website (http://charls.pku.edu.cn/en). This study involved secondary analysis of de-identified publicly available data and was therefore exempt from additional ethical review.

The CHARLS nationwide baseline survey occurred from June 2011 to March 2012, with biannual face-to-face follow-up interviews conducted by trained interviewers using computer-assisted techniques; individuals interviewed during this period were part of the baseline cohort, and follow-up data was collected in 2013, 2015, 2018, and 2020. In this study, individuals interviewed between 2011 and 2012 were classified as part of the baseline cohort, with follow-up data obtained in 2013, 2015, 2018, and 2020. Of 17,708 baseline respondents, we excluded those aged <45 years or pregnant (n=508), those without death status information or without follow-up data (n=3,118), and those without hypertension at baseline (n=10,110). Among baseline hypertensive participants (n=3,972), we further excluded individuals with insufficient data to calculate CTI due to missing/non-numeric/non-positive CRP, triglycerides (TG), or fasting plasma glucose (FPG) (n=1,387), and those with missing baseline covariates (dyslipidemia, smoking status, diabetes, age, heart disease, or drinking status; n=131). A flowchart is shown in Figure 1. The final analytic sample included 2,454 participants. Baseline characteristics differed between included and excluded hypertensive participants, suggesting that analytic inclusion was not fully random (Table S1). Biomarker availability was substantially lower among excluded participants (each ~25% for CRP, TG, and glucose), and only 8.6% had sufficient data to derive CTI (Table S2).

Figure 1 Flowchart of sample selection process. CHARLS, China Health and Retirement Longitudinal Study; CRP, C-reactive protein; CTI, C-reactive protein-triglyceride-glucose index.

Calculation of CTI

The CTI was calculated according to the established formula (18): CTI =0.412× ln[CRP (mg/L)] + TyG, where TyG = ln[TG (mg/dL) × FPG (mg/dL)/2].

Diagnostic criteria of hypertension

Hypertension at baseline was defined as any of the following: (I) mean systolic blood pressure (SBP) ≥140 mmHg and/or mean diastolic blood pressure (DBP) ≥90 mmHg based on standardized blood pressure measurements obtained by trained staff (with repeated measurements and the average value used); (II) self-reported physician diagnosis of hypertension; or (III) current use of antihypertensive medication. This definition has been widely used in prior CHARLS-based studies (20).

Ascertainment of outcomes

All-cause mortality was the primary outcome for hypertensive individuals, with data obtained from death certificates, medical records, or family interviews during waves 2 to 5, though precise death dates were available only in waves 2 and 5, and the duration until the event was calculated from the baseline to the final interview wave. Participants were followed from baseline until death or the last available follow-up interview (censoring), using the available wave-based timing information in CHARLS.

Covariates

The CHARLS researchers collected variables based on previously established criteria. This study used the following sociodemographic and health data on baseline: Sociodemographic information included sex, age, educational level, marital condition. Lifestyle information included smoking and drinking status. Diabetes, dyslipidemia, and cardiovascular disease were defined based on self-reported physician diagnosis in the CHARLS questionnaire.

Statistical analysis

Continuous variables are presented as median (interquartile range), and categorical variables are presented as counts (percentages). For comparisons across CTI tertiles, continuous variables were compared using the Kruskal-Wallis test and categorical variables using the chi-square test, as appropriate. Participants were categorized into tertiles of CTI, and CTI was also modeled as a continuous variable.

Associations between CTI and all-cause mortality were evaluated using Cox proportional hazards models, reporting hazard ratios (HRs) and 95% confidence intervals (CIs). We fitted sequential models: an unadjusted model; Model 1 adjusted for age and sex; Model 2 further adjusted for education level, marital status, cardiovascular disease, dyslipidemia, and diabetes; and Model 3 additionally adjusted for drinking and smoking status. The proportional hazards assumption was assessed using Schoenfeld residuals. Potential non-linear associations were examined using restricted cubic splines (RCS). Kaplan-Meier curves with log-rank tests were used to compare survival across CTI tertiles.

To evaluate discrimination, receiver operating characteristic (ROC) analyses were conducted using Cox model-derived risk scores, and area under the curves (AUCs) (with 95% CIs) were compared between CTI, CRP, and TyG using DeLong’s test. Subgroup analyses were performed across prespecified covariates, and effect modification was assessed using interaction terms. To address potential selection bias due to missing biomarker data, baseline characteristics of included versus excluded hypertensive participants were compared, and inverse probability weighting (IPW) was applied as a sensitivity analysis.

Results

Baseline characteristics

A total of 2,454 hypertensive participants were included and stratified into tertiles according to CTI. Baseline demographic characteristics, including sex, age, education level, marital status, smoking status, and drinking status, were generally comparable across CTI tertiles (Table 1). Participants in higher CTI tertiles had a significantly higher prevalence of cardiovascular disease, diabetes, and dyslipidemia. Mean survival time showed a decreasing trend across CTI tertiles (Q1: 8.33±1.76 years; Q2: 8.30±1.80 years; Q3: 8.14±2.00 years), although the difference was not statistically significant (P=0.08); the overall mean survival time was 8.24±1.87 years. During follow-up, 314 deaths occurred in the overall cohort (12.80%), including 60 (10.51%), 112 (12.24%), and 142 (14.67%) deaths in Q1, Q2, and Q3, respectively.

Association of CTI with all-cause mortality in hypertensive participants

Cox regression analysis demonstrated a consistent association between CTI and all-cause mortality across multiple adjustment models (Table 2). As a continuous variable, each unit increase in CTI conferred approximately 4% elevated mortality risk across crude model (HR =1.04, 95% CI: 1.00–1.07, P=0.03), Model 1 (HR =1.04, 95% CI: 1.01–1.07, P=0.02), Model 2 (HR =1.04, 95% CI: 1.01–1.07, P=0.02), and Model 3 (HR =1.04, 95% CI: 1.01–1.07, P=0.02). Detailed characteristics of covariates included in each model are provided in supplementary material (Table S3).

In tertile analysis, participants in Q3 exhibited significantly increased mortality risk compared to Q1, with consistent associations across all models: crude model (HR =1.43, 95% CI: 1.06–1.94, P=0.02), Model 1 (HR =1.41, 95% CI: 1.04–1.90, P=0.03), Model 2 (HR =1.42, 95% CI: 1.05–1.93, P=0.02), and Model 3 (HR =1.43, 95% CI: 1.05–1.94, P=0.02). Notably, the absolute mortality risk increased from 10.51% in Q1 to 14.67% in Q3 over the 9-year follow-up period, yielding an absolute risk difference of 4.16% and translating to approximately 5 additional deaths per 1,000 person-years. Tests for linear trend remained significant across all models (all P<0.05), providing evidence of a dose-response association. The stability of these associations after sequential adjustment supports CTI as an independent prognostic factor associated with all-cause mortality in hypertensive patients.

Restricted cubic spline analysis revealed a statistically significant association between CTI and all-cause mortality across all models (Figure 2). The overall association was significant in the crude model (P overall =0.04), Model 1 (P overall =0.04), Model 2 (P overall =0.03), and Model 3 (P overall =0.03). The relationship remained predominantly linear across all models (all P-non-linear >0.05), with HRs progressively increasing with higher CTI values.

Figure 2 Restricted cubic spline curves illustrating the association between CTI and HR of all-cause mortality in hypertension participants. (A) Crude model: unadjusted for covariates. (B) Model 1: adjusted for age, gender. (C) Model 2: adjusted for age, gender, marital status, educational level, dyslipidemia, diabetes. (D) Model 3: adjusted for age, gender, marital status, educational level, dyslipidemia, diabetes, drinking and smoking status. Solid lines represent the estimated HR, and shaded areas indicate 95% CIs. The reference value was set at the median CTI. Dashed horizontal lines represent HR =1.0. CI, confidence interval; CTI, C-reactive protein-triglyceride-glucose index; HR, hazard ratio.

Kaplan-Meier survival curves

The Kaplan-Meier survival analysis demonstrated significant differential mortality across CTI tertiles during the follow-up period (P=0.047, Figure 3). The survival curves showed a pattern of gradual separation over time, with the highest CTI tertile (Q3) exhibiting slightly lower survival probability compared to Q1 and Q2, particularly in the later follow-up period. At 9 years, the number at risk remained substantial across all groups (Q1: 482, Q2: 765, Q3: 772), indicating adequate follow-up duration.

Figure 3 Kaplan-Meier survival curves comparing patients stratified by tertiles of the CTI index: Q1 (lowest tertile), Q2 (middle tertile), and Q3 (highest tertile). CTI, C-reactive protein-triglyceride-glucose index; CTIQ, computed tomography image quality.

Subgroup analyses

Subgroup analyses using both tertile-based CTI models (Table 3) and continuous CTI models (Figure 4) showed that the CTI-mortality association appeared more evident in some strata, but most interaction tests were not statistically significant. Significant associations were observed in participants aged ≥60 years and in those with cardiovascular disease in both models, whereas no significant interaction was detected for age or cardiovascular disease. In contrast, dyslipidemia showed significant interaction in both models (Table 3: P for interaction =0.04; Figure 4: P for interaction =0.04), with significant associations mainly observed among participants without dyslipidemia, suggesting potential heterogeneity by dyslipidemia status.

Figure 4 Subgroup analysis of HRs (95% CIs) for all-cause mortality of CTI. CI, confidence interval; CTI, C-reactive protein-triglyceride-glucose index; HR, hazard ratio.

AUC and ROC

ROC analysis based on Cox-derived risk scores showed modest discrimination for all-cause mortality. CTI yielded an AUC of 0.704 (95% CI: 0.674–0.734), comparable to CRP (AUC 0.702, 95% CI: 0.672–0.732) and TyG (AUC 0.701, 95% CI: 0.671–0.730). Pairwise DeLong tests indicated no significant differences between models (all P>0.05) (Figure 5).

Figure 5 ROC curves for CTI, CRP, and TyG in discriminating all-cause mortality (Cox-derived risk scores). AUCs were not significantly different by DeLong tests (CTI vs. CRP, P=0.38; CTI vs. TyG, P=0.36; CRP vs. TyG, P=0.19). AUC, area under the curve; CI, confidence interval; CRP, C-reactive protein; CTI, C-reactive protein-triglyceride-glucose index; ROC, receiver operating characteristic; TyG, triglyceride-glucose.

Sensitivity analysis

IPW was used to assess potential selection bias due to analytic sample inclusion (Table S4). The CTI-mortality association remained materially unchanged after weighting. In the fully adjusted IPW model, CTI was positively associated with all-cause mortality (HR 1.04, 95% CI 1.01–1.08; P=0.008). Compared with the lowest tertile, the highest CTI tertile showed elevated risk (HR 1.57, 95% CI 1.17–2.10; P=0.003), with evidence of a dose-response trend across tertiles (P for trend =0.003).

Discussion

Key findings

In this study of 2,454 hypertensive participants from the CHARLS cohort, we found that higher CTI was consistently associated with increased all-cause mortality. In the fully adjusted model, each 1-unit increase in CTI was associated with a 4% higher mortality risk (HR =1.040, 95% CI: 1.007–1.074, P=0.02), and participants in the highest CTI tertile had a higher risk of all-cause mortality than those in the lowest tertile (HR =1.425, 95% CI: 1.049–1.935, P=0.02), with significant linear trends across tertiles. Restricted cubic spline analyses supported a predominantly linear association, and Kaplan-Meier analyses showed significant differences in survival across CTI tertiles.

Subgroup analyses suggested that the CTI-mortality association appeared more evident in some strata, but most interaction tests were not statistically significant. A significant interaction was consistently observed for dyslipidemia status, with stronger associations mainly among participants without dyslipidemia. In addition, the association remained materially unchanged in IPW analyses, supporting the robustness of the findings despite concerns about analytic sample selection. ROC analyses showed modest discrimination (AUC for CTI =0.704), and CTI performed comparably to CRP and TyG, with no significant differences in pairwise DeLong tests. Overall, these findings support CTI as a prognostic risk marker associated with all-cause mortality in hypertensive populations, but not as a stand-alone prediction tool.

Explanations of findings

The association between CTI and mortality in hypertensive individuals is biologically plausible and may reflect the combined effects of chronic inflammation, insulin resistance, and metabolic dysregulation. In hypertension, low-grade inflammation and oxidative stress can contribute to endothelial dysfunction, vascular remodeling, and atherosclerotic progression (21-23). Meanwhile, insulin resistance is closely linked to dyslipidemia, impaired glucose metabolism, and blood pressure elevation, and may interact bidirectionally with inflammatory pathways (24,25). By integrating CRP, TG, and glucose into a single metric, CTI may capture this multidimensional cardiometabolic risk burden more comprehensively than any single component alone.

Our spline and tertile analyses suggested a graded, predominantly linear increase in risk across the CTI spectrum, which is consistent with cumulative cardiometabolic burden rather than an abrupt threshold effect. Potential mechanisms include inflammation-related plaque instability and vascular remodeling (26), as well as insulin resistance-related oxidative stress and vascular injury via the advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) axis (27).

Subgroup findings should be interpreted cautiously. Although associations appeared more evident in some strata, most formal interaction tests were not statistically significant. In contrast, dyslipidemia showed a consistent interaction, with stronger associations mainly among participants without dyslipidemia. A plausible explanation is treatment-related modification, as lipid-lowering therapy (especially statins) may reduce both lipid burden and inflammation, potentially attenuating CTI-associated risk in participants with diagnosed dyslipidemia (28). This interpretation is broadly consistent with evidence from some hypertensive trial populations showing long-term cardiovascular mortality benefit with statin-based strategies, although results have not been uniform across all trials (29,30). Alternatively, CTI may capture early metabolic-inflammatory risk before overt dyslipidemia is clinically recognized. These explanations remain hypothesis-generating.

Comparison with similar research

Recent studies have supported CTI as an inflammatory-metabolic marker associated with adverse outcomes, including cardiovascular disease incidence (31), mortality risk across CKM [chronic kidney disease-cardiovascular-metabolic (syndrome)] syndrome stages (32), and stroke in non-diabetic adults (33). Importantly, Sun et al. reported an association between elevated CTI and all-cause mortality in a joint analysis of the CHARLS cohort and a hospital-based Chinese cohort, and also explored subgroup differences by hypertension status (18). Therefore, the CTI-mortality association is not entirely uncharacterized in Chinese populations.

Against this background, our study extends prior evidence by focusing specifically on participants with established hypertension at baseline, characterizing the dose-response shape using spline models, directly comparing CTI with CRP and TyG in discrimination analyses, and addressing potential selection bias related to analytic sample inclusion through included-versus-excluded comparisons and IPW sensitivity analyses. At the same time, the comparable AUCs for CTI, CRP, and TyG and the non-significant DeLong tests indicate that CTI should be interpreted primarily as a prognostic risk marker rather than a stand-alone prediction tool.

Strengths and limitations

This study has several strengths, including use of a nationwide cohort (CHARLS), focus on a clinically important hypertensive population, and complementary analytical strategies (continuous and tertile-based Cox models, spline analyses, Kaplan-Meier analysis, and IPW sensitivity analysis). In particular, the included-versus-excluded baseline comparisons and IPW analyses strengthened evaluation of potential selection bias related to biomarker completeness.

Several limitations should be acknowledged. First, this was an observational analysis and causality cannot be inferred. Second, CTI was measured only at baseline, and longitudinal changes were unavailable. Third, the outcome was all-cause mortality, without cause-specific analyses. Fourth, detailed medication information (including class and adherence, especially lipid-lowering therapy) was limited, which may affect subgroup interpretation. Fifth, substantial exclusions were required to construct the analytic sample; although baseline comparisons and IPW analyses were performed, residual selection bias and limited generalizability remain possible.

Implications and actions needed

CTI may serve as a practical prognostic risk marker for mortality assessment in hypertensive populations because it integrates routinely available inflammatory and metabolic indicators. However, given its modest discrimination and lack of superiority over CRP or TyG alone in ROC comparisons, CTI should be considered an adjunctive marker within broader clinical risk assessment rather than a stand-alone basis for intervention decisions.

Future studies should validate these findings in external cohorts, assess incremental predictive value beyond conventional risk models (e.g., reclassification metrics), clarify the mechanisms underlying the dyslipidemia interaction, and evaluate whether longitudinal changes in CTI are associated with changes in mortality risk.

Conclusions

In this CHARLS-based retrospective cohort analysis, higher CTI was associated with increased all-cause mortality in Chinese adults with hypertension and may serve as a prognostic risk marker. However, given its modest discrimination and comparable ROC performance to CRP and TyG, CTI should be considered an adjunctive marker rather than a stand-alone prediction tool, and the findings should be interpreted with caution in light of potential selection bias and single-time biomarker measurement.

Acknowledgments

This study used data from the China Health and Retirement Longitudinal Study (CHARLS), which was conducted by the National School of Development at Peking University. We thank the CHARLS research team and all respondents for their contributions to the survey. The content is solely the responsibility of the authors and does not represent the official views of the CHARLS team.

Footnote

Reporting Checklist: The author has completed the STROBE reporting checklist. Available at https://jphe.amegroups.com/article/view/10.21037/jphe-2025-1-54/rc

Data Sharing Statement: Available at https://jphe.amegroups.com/article/view/10.21037/jphe-2025-1-54/dss

Peer Review File: Available at https://jphe.amegroups.com/article/view/10.21037/jphe-2025-1-54/prf

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://jphe.amegroups.com/article/view/10.21037/jphe-2025-1-54/coif). The author has no conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The original CHARLS study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided written informed consent at the time of data collection. This study involved secondary analysis of de-identified publicly available data and was therefore exempt from additional ethical review.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Zhou B, Perel P, Mensah GA, et al. Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. Nat Rev Cardiol 2021;18:785-802. [Crossref] [PubMed]
2. Lee DE, Cooper RS. Recommendations for global hypertension monitoring and prevention. Curr Hypertens Rep 2009;11:444-9. [Crossref] [PubMed]
3. Marfany A, Sierra C, Camafort M, et al. High blood pressure, Alzheimer disease and antihypertensive treatment. Panminerva Med 2018;60:8-16. [Crossref] [PubMed]
4. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol 2014;2:634-47. [Crossref] [PubMed]
5. Hiura Y, Tabara Y, Kokubo Y, et al. A genome-wide association study of hypertension-related phenotypes in a Japanese population. Circ J 2010;74:2353-9. [Crossref] [PubMed]
6. Rothman AM, MacFadyen J, Thuren T, et al. Effects of Interleukin-1β Inhibition on Blood Pressure, Incident Hypertension, and Residual Inflammatory Risk: A Secondary Analysis of CANTOS. Hypertension 2020;75:477-82. [Crossref] [PubMed]
7. Liu Y, He W, Ji Y, et al. A linear positive association between high-sensitivity C-reactive protein and the prevalence of cardiovascular disease among individuals with diabetes. BMC Cardiovasc Disord 2024;24:411. [Crossref] [PubMed]
8. Hage FG. C-reactive protein and hypertension. J Hum Hypertens 2014;28:410-5. [Crossref] [PubMed]
9. Lin HF, Huang LC, Chen CH, et al. Age and sex differences in the effect of parental stroke on the progression of carotid intima-media thickness. Atherosclerosis 2015;241:229-33. [Crossref] [PubMed]
10. Ghanem FA, Movahed A. Inflammation in high blood pressure: a clinician perspective. J Am Soc Hypertens 2007;1:113-9. [Crossref] [PubMed]
11. Püschel GP, Klauder J, Henkel J. Macrophages, Low-Grade Inflammation, Insulin Resistance and Hyperinsulinemia: A Mutual Ambiguous Relationship in the Development of Metabolic Diseases. J Clin Med 2022;11:4358. [Crossref] [PubMed]
12. Zhao Y, Sun H, Zhang W, et al. Elevated triglyceride-glucose index predicts risk of incident ischaemic stroke: The Rural Chinese cohort study. Diabetes Metab 2021;47:101246. [Crossref] [PubMed]
13. Gounden V, Devaraj S, Jialal I. The role of the triglyceride-glucose index as a biomarker of cardio-metabolic syndromes. Lipids Health Dis 2024;23:416. [Crossref] [PubMed]
14. Ruan GT, Xie HL, Zhang HY, et al. A Novel Inflammation and Insulin Resistance Related Indicator to Predict the Survival of Patients With Cancer. Front Endocrinol (Lausanne) 2022;13:905266. [Crossref] [PubMed]
15. Ruan GT, Deng L, Xie HL, et al. Systemic inflammation and insulin resistance-related indicator predicts poor outcome in patients with cancer cachexia. Cancer Metab 2024;12:3. [Crossref] [PubMed]
16. Zhao DF. Value of C-Reactive Protein-Triglyceride Glucose Index in Predicting Cancer Mortality in the General Population: Results from National Health and Nutrition Examination Survey. Nutr Cancer 2023;75:1934-44. [Crossref] [PubMed]
17. Gao A, Peng B, Gao Y, et al. Evaluation and comparison of inflammatory and insulin resistance indicators on recurrent cardiovascular events in patients undergoing percutaneous coronary intervention: a single center retrospective observational study. Diabetol Metab Syndr 2025;17:157. [Crossref] [PubMed]
18. Sun T, Zhang M, Liu J, et al. Association between C-reactive protein-triglyceride glucose index and all-cause mortality and premature death: a joint analysis based on case data from the Central Hospital of Shaoyang and CHARLS database. Front Med (Lausanne) 2025;12:1656187. [Crossref] [PubMed]
19. Wei JM, Li S, Claytor L, et al. Prevalence and predictors of malnutrition in elderly Chinese adults: results from the China Health and Retirement Longitudinal Study. Public Health Nutr 2018;21:3129-34. [Crossref] [PubMed]
20. Imazu M, Sumida K, Yamabe T, et al. A comparison of the prevalence and risk factors of high blood pressure among Japanese living in Japan, Hawaii, and Los Angeles. Public Health Rep 1996;111:59-61.
21. Farah R, Khamisy-Farah R, Shurtz-Swirski R. Calcium channel blocker effect on insulin resistance and inflammatory markers in essential hypertension patients. Int Angiol 2013;32:85-93.
22. Farah R, Shurtz-Swirski R, Khamisy-Farah R. Lercanidipine effect on polymorphonuclear leukocyte-related inflammation and insulin resistance in essential hypertension patients. Cardiol Ther 2012;1:4. [Crossref] [PubMed]
23. Sinha S, Haque M. Insulin Resistance Is Cheerfully Hitched with Hypertension. Life (Basel) 2022;12:564. [Crossref] [PubMed]
24. Natali A, Ferrannini E. Hypertension, insulin resistance, and the metabolic syndrome. Endocrinol Metab Clin North Am 2004;33:417-29. [Crossref] [PubMed]
25. Rana JS, Nieuwdorp M, Jukema JW, et al. Cardiovascular metabolic syndrome - an interplay of, obesity, inflammation, diabetes and coronary heart disease. Diabetes Obes Metab 2007;9:218-32. [Crossref] [PubMed]
26. Peng H, Xian D, Liu J, et al. Regulating the Polarization of Macrophages: A Promising Approach to Vascular Dermatosis. J Immunol Res 2020;2020:8148272. [Crossref] [PubMed]
27. Twarda-Clapa A, Olczak A, Białkowska AM, et al. Advanced Glycation End-Products (AGEs): Formation, Chemistry, Classification, Receptors, and Diseases Related to AGEs. Cells 2022;11:1312. [Crossref] [PubMed]
28. Kandelouei T, Abbasifard M, Imani D, et al. Effect of Statins on Serum level of hs-CRP and CRP in Patients with Cardiovascular Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Mediators Inflamm 2022;2022:8732360. [Crossref] [PubMed]
29. ALLHAT Officers and Coordinators for the ALLHAT Collaborative Research Group. Major outcomes in moderately hypercholesterolemic, hypertensive patients randomized to pravastatin vs usual care: The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT). JAMA 2002;288:2998-3007. [Crossref] [PubMed]
30. Gupta A, Mackay J, Whitehouse A, et al. Long-term mortality after blood pressure-lowering and lipid-lowering treatment in patients with hypertension in the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT) Legacy study: 16-year follow-up results of a randomised factorial trial. Lancet 2018;392:1127-37. [Crossref] [PubMed]
31. Ma X, Ma X, Wang Y, et al. Associations of cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index with incident cardiovascular disease in middle-aged and older Chinese adults: a nationwide cohort study. Cardiovasc Diabetol 2025;24:303. [Crossref] [PubMed]
32. Ou H, Wei M, Li X, et al. C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0-3 of cardiovascular-kidney-metabolic syndrome: a nationwide prospective cohort study. Cardiovasc Diabetol 2025;24:296. [Crossref] [PubMed]
33. Li J, Tian X, Zhao D, et al. Change in C-reactive protein-triglyceride glucose index and risk of stroke among middle-aged and older adults free of diabetes: A national cohort study. Nutr Metab Cardiovasc Dis 2025;35:104372. [Crossref] [PubMed]