This study is the first to investigate the relationship between the CTI and CVD risk among individuals with CKM syndrome stage 0–3. The results revealed a significant positive association, suggesting that the CTI may serve as an important predictor for future cardiovascular events in this population. Cox regression analyses indicated that, even after adjusting for multiple potential confounders, each one-unit increase in the CTI was associated with a 16% higher risk of CVD (HR = 1.16, 95% CI: 1.06–1.27), underscoring its value as an independent risk predictor in CVD risk assessment. Furthermore, quartile analyses of the CTI revealed a clear dose–response relationship. According to the fully adjusted model, individuals in the highest CTI quartile (Q4) had a 42% greater risk of CVD than those in the lowest quartile (Q1) did (HR = 1.42, 95% CI: 1.20–1.68). Kaplan–Meier curves further confirmed a significantly greater CVD incidence in the higher CTI group (log-rank test P < 0.001). RCS analysis revealed a nonlinear association between the CTI and CVD risk, with the hazard ratio rising sharply beyond a certain CTI threshold (CTI = 8.72). Identifying such a threshold may have important clinical implications, providing a basis for more precise risk stratification and highlighting the necessity for early initiation of primary prevention—such as adopting a healthy diet, increasing physical activity, quitting smoking, and controlling blood pressure, blood glucose and blood lipids—for patients with CTI values above this threshold. ROC curve analysis showed that CTI had greater predictive value for CVD risk than CRP or TyG. In addition, subgroup and interaction analyses revealed no significant interactions among the ten demographic subgroups examined (interaction p > 0.05), suggesting that the relationship between the CTI and CVD risk is consistent across different demographic groups. Although previous epidemiological studies have generally shown greater CVD risk in males26, our study revealed that the hazard ratio was slightly greater in females (HR = 1.25, 95% CI: 1.16, 1.35) than in males (HR = 1.23, 95% CI: 1.13, 1.33). This phenomenon may be closely related to the age structure of our study population, as all participants were aged 45 years or older, with the vast majority of female being perimenopausal or postmenopausal. Research has demonstrated that estrogen exerts multiple protective effects on the cardiovascular system, including promoting endothelial nitric oxide (NO) production to increase vasodilation, exerting anti-inflammatory and antioxidant effects, regulating lipid metabolism, and inhibiting thrombosis27. Following menopause, estrogen levels decrease markedly, resulting in the loss of these protective effects and a sharp increase in cardiovascular risk28. In a study involving 302,632 Chinese women, Ling Yang et al. found that postmenopausal women had a 49% higher CVD risk compared with premenopausal women (HR = 1.49, 95% CI:1.32–1.68)29. Therefore, clinicians should intensify their attention to cardiovascular health among perimenopausal and postmenopausal female. Furthermore, this study revealed that individuals with sleep problems had greater CVD risk than those without such problems. In recent years, the prevalence of sleep problems among middle-aged and elderly people has increased significantly. Substantial evidence suggested that inadequate sleep can induce chronic low-grade inflammation and excessive activation of the sympathetic nervous system, increase proinflammatory cytokine levels, and promote the development of IR30. Therefore, early clinical interventions to improve sleep quality should be considered important targets for reducing CVD risk and improving cardiovascular outcomes.
In recent years, the TyG, a surrogate marker for IR, has attracted increasing attention in the field of CVD. In a nested case‒control study including 3,745 patients with stable coronary artery disease (CAD), Jin et al. reported that each one-unit increase in the TyG was associated with a 36% increased risk of cardiovascular events (CVEs) (HR = 1.364, 95% CI: 1.100–1.691, P = 0.005)31. Similarly, a large prospective cohort study involving 96,541 Chinese adults conducted by Liu et al. demonstrated a significant positive association between the TyG and cardiovascular event risk, with participants in the highest TyG quartile showing a 34% higher risk of incident CVD than those in the lowest quartile (HR = 1.34, 95% CI: 1.23–1.45)32. These findings indicated that the TyG is an effective predictor of CVD risk. Nonetheless, as the TyG primarily reflects the state of IR, it does not provide a comprehensive evaluation of other major pathophysiological processes of CVD, such as chronic low-grade inflammation. In contrast, the CTI incorporates both the TyG and the CRP, thereby simultaneously capturing metabolic and inflammatory abnormalities to offer a more holistic assessment. The ROC curve analysis further demonstrated that the CTI had superior predictive value for CVD risk compared with the TyG alone. Furthermore, our results were consistent with two recent large-scale studies that reported a strong positive association between CTI and CVD risk. Xu et al. analysed data from 19,451 adult participants and reported a linear correlation between the CTI and the incidence of CHD, with those in the highest CTI quartile exhibiting an approximately 80.7% higher risk than those in the lowest quartile (HR = 1.807, 95% CI: 1.314–2.484, P < 0.001); importantly, the CTI demonstrated better predictive performance than CRP or TyG alone17. Similarly, Huo et al. analysed data from 10,443 participants and revealed a significant positive relationship between the CTI and stroke risk, particularly among individuals with normoglycemia and prediabetes, with hazard ratios of 1.33 and 1.20, respectively33. In comparison, our study specifically focused on individuals with CKM syndrome stage 0–3, a high-risk population commonly characterized by the coexistence of cardiovascular, renal, and metabolic risk factors, which may interact synergistically to exacerbate disease progression. Thus, early identification and accurate risk stratification in this high-risk group are crucial for implementing individualized interventions and effectively preventing adverse cardiovascular events.
As an integrated indicator reflecting both IR and inflammatory status, the CTI is associated with CVD risk through several mechanisms. Firstly, IR can inhibit the PI3K/Akt signaling pathway. This decreases endothelial Nitric Oxide Synthase (eNOS) activity and nitric oxide (NO) production, impairs vasodilation, and ultimately leads to endothelial dysfunction34. Secondly, IR is characterized by consistently elevated expression of proinflammatory cytokines, which aggravates chronic low-grade inflammation, promotes inflammatory cell infiltration, causes further structural damage to the vascular wall, and leads to the instability of atherosclerotic plaques35. Moreover, IR is often accompanied by lipid metabolism disorders, including increased release of free fatty acids (FFAs), elevated plasma triglyceride levels, reduced HDL-C, and accelerated formation of LDL particles, thereby facilitating lipid deposition in the arterial wall and related inflammatory responses36. On the other hand, CRP, a critical biomarker of inflammation, has been shown to play a significant role in the development and progression of CVD. Specifically, CRP can upregulate the expression of adhesion molecules (such as VCAM-1 and ICAM-1) on endothelial cells, increase leukocyte adhesion and transmigration, and accelerate atherosclerotic plaque formation37. Furthermore, CRP can bind to oxidized low-density lipoprotein (ox-LDL) and damaged cell membranes, thereby activating the classical complement pathway. This in turn induces a stronger local inflammatory response and promotes the progression and instability of atherosclerotic plaques38.
Furthermore, we explored the potential mechanisms underlying the nonlinear association between CTI and CVD risk. Firstly, when the CTI remains within a certain range, the body is able to maintain homeostasis. However, once CTI exceeds a critical threshold, homeostasis is disrupted and the CVD risk increases sharply. Secondly, inflammation induces IR, which in turn aggravates inflammatory processes. This bidirectional interplay establishes a vicious positive feedback loop, substantially increasing the CVD risk39,40.
This study has several notable strengths. Firstly, it is the first to explore the association between CTI and CVD risk, specifically in individuals with CKM syndrome stage 0–3. Secondly, this research is a prospective, nationwide longitudinal cohort study involving a relatively large sample of middle-aged and older adults with a balanced age distribution, which enhances the reliability of the findings. Additionally, we systematically adjusted for potential confounders and performed subgroup analyses to assess the consistency of associations across different demographic and clinical subpopulations, further strengthening the clinical applicability and external validity of the results.
Nevertheless, several limitations should be acknowledged. Firstly, the definition of subclinical CVD in this study was based on the Framingham 10-year cardiovascular risk score rather than the latest PREVENT equations. Secondly, although multiple known confounders were adjusted for, the possibility of residual unmeasured confounding influencing the results cannot be entirely excluded. Thirdly, since the CRP and TyG were assessed only once at baseline, dynamic changes in these biomarkers and their potential impact on CVD risk over time could not be evaluated. Fourthly, This study utilized data from the CHARLS, and thus the results are primarily applicable to middle-aged and older adults in China. Consequently, the generalizability of these findings to other populations may be limited by differences in ethnicity, culture, healthcare system, and socioeconomic context. Finally, CVD were identified based on self-reported information provided by participants. It is important to acknowledge that self-reported data are subject to misclassification bias, which may includes both under-reporting (failure to report existing CVD) and over-reporting (reporting CVD in the absence of a confirmed diagnosis). Such misclassification can arise from participants’ limited understanding of medical conditions or recall bias.