With the improvement of global living standards, the impact of CVD on human health is increasing [1]-[3]. Over the past two decades, the number of cases has risen from 271 million to 523 million, nearly doubling, and the number of deaths from CVD has also increased significantly [2][4]. Despite significant advancements in medical technology, the incidence and mortality rates of CVD remain the highest globally, placing a substantial burden on public health systems. Therefore, early identification of populations at high risk for CVD is of great significance for controlling disease progression.

The cardiometabolic index (CMI) is a comprehensive metabolic indicator that reflects both visceral fat distribution and dyslipidemia. It was first proposed by Wakabayashi et al. [5], in 2015, calculated as the product of the triglyceride-to-high-density lipoprotein cholesterol ratio and the waist-to-height ratio, and is primarily used for the auxiliary diagnosis of diabetes and the assessment of visceral adipose tissue dysfunction. Recent studies have shown that CMI is closely associated with cardiovascular diseases, renal dysfunction, acute pancreatitis, and various metabolic abnormalities [6]-[8]. More recent research has further proposed the modified cardiometabolic index (MCMI), which incorporates fasting blood glucose on the basis of CMI, making it a comprehensive assessment tool that simultaneously covers insulin resistance, central obesity, and dyslipidemia [9]. Existing studies have indicated that MCMI is positively correlated with the risk of non-alcoholic fatty liver disease (NAFLD) and can effectively predict the occurrence of NAFLD and liver fibrosis [9]. Studies have shown that insulin resistance, obesity, and dyslipidemia are closely associated with the occurrence of CVD [10]-[14].

However, the association between MCMI and CVD risk in middle-aged and elderly populations with dysglycemia remains unclear. To address this research gap, this study, based on the CHARLS database, aims to investigate the relationship between MCMI levels and the risk of CVD in individuals aged 45 years and older with abnormal glucose metabolism.

This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative longitudinal survey of individuals aged 45 years and above in China. Using a multistage probability sampling method, the baseline survey was conducted between 2011 and 2012 (Wave 1). To date, multiple follow-up waves have been made publicly available, including those in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5) [15].

Initially, 11,847 participants who had undergone complete blood count testing were included. A total of 8427 participants were excluded based on the following criteria: 1) Age < 44 years; 2) Missing data on triglycerides, high-density lipoprotein cholesterol, fasting blood glucose, height, or waist circumference; 3) No follow-up data on cardiovascular disease from Wave 1 to Wave 5 years; 4) History of cardiovascular disease before 2011; 5) Normal glucose metabolism. Ultimately, 3420 participants were included in the analysis (Figure 1).

Figure 1.Flow diagram of study participants.

Table 1. Baseline characteristics of the study participants.

Abbreviations: Glu, Fasting Blood Glucose; Tg, Triglycerides; HDL-C, High-Density Lipoprotein Cho, Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; HbA1c, Glycated Hemoglobin A1c; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; WC, Waist Circumference; BMI, Body Mass Index; SD, Standard Deviation. P value estimated using chi-square for categorical variable and t-tests for continuous variables.

Table 1 shows a total of 3420 participants were included in this study, among whom 705 (20.61%) developed new-onset cardiovascular disease during the follow-up period. The mean age of the participants was 58.63 ± 8.37 years, and 1537 (44.98%) were male. Inter-group comparisons showed statistically significant differences (P < 0.05) in the distribution of the following variables: blood glucose, lowdensity lipoprotein cholesterol, glycated hemoglobin, systolic blood pressure, diastolic blood pressure, waist circumference, body mass index, drinking status, smoking status, hypertension status, new-onset heart disease, new-onset stroke, and new-onset cardiovascular disease.

This study included 3450 middle-aged and elderly patients with dysglycemia. After 10 years of follow-up, 705 participants developed cardiovascular disease (CVD), with a cumulative incidence of 20.61%. Survival analysis indicated that as MCMI levels increased, the incidence of CVD showed an upward trend. In the fully adjusted model (Model 3), compared with the lowest quartile (Q1), the highest quartile (Q4) had a significantly increased risk of CVD, with a hazard ratio (HR) of 1.40 (95% CI: 1.12 - 1.77, P = 0.004).

The CMI is a metabolic indicator that combines features of dyslipidemia and abdominal obesity [5]. Since its proposal a decade ago, multiple studies have confirmed its close association with diseases such as NAFLD, CVD, and infertility [6]-[8]. Recently, Guo et al. further proposed the MCMI based on CMI [9]. This index adds fasting blood glucose parameters to CMI, thereby integrating multiple metabolic risk factors including insulin resistance, dyslipidemia, and obesity. Existing studies have demonstrated a significant correlation between MCMI and NAFLD as well as liver fibrosis.

This study is the first to reveal a significant positive correlation between the MCMI and the risk of CVD in middle-aged and elderly populations with dysglycemia. Although the exact pathophysiological mechanisms remain to be further elucidated, existing theories on metabolism and cardiovascular disease may provide potential explanatory pathways for this phenomenon: insulin resistance reduces the uptake and utilization efficiency of glucose in peripheral tissues, leading to a chronic hyperglycemic state and secondary dyslipidemia (such as elevated triglycerides and decreased high-density lipoprotein cholesterol). These metabolic abnormalities collectively act on the vascular system and can accelerate the formation and progression of atherosclerotic plaques through multiple pathways, including promoting oxidative stress, low-grade inflammatory responses, and endothelial dysfunction, ultimately increasing the risk of CVD events [18]-[20]. Furthermore, obesity, as a key driver of insulin resistance, is often associated with chronic low-grade inflammation, abnormal adipokine secretion, and elevated free fatty acid levels [21]-[24]. These changes can collectively exacerbate vascular endothelial dysfunction, promote plaque destabilization, and accelerate the progression of atherosclerosis, thereby further increasing the risk of cardiovascular disease.

Future research could focus on the following directions to systematically elucidate the pathophysiological link between MCMI and CVD: at the metabolic regulation level, investigating whether MCMI contributes to CVD development by affecting key pathways in glucose and lipid metabolism (such as insulin signal transduction and fatty acid oxidation); in terms of inflammatory mechanisms, analyzing the association between MCMI and systemic inflammatory markers (e.g., IL-6, TNF-α, CRP) as well as local vascular inflammatory responses; and from the perspective of vascular function, further exploring the impact of MCMI on vascular endothelial function, arterial elasticity, and microcirculatory status. Through integrated multidimensional mechanistic studies, a more robust theoretical foundation may be established for MCMI as a predictive and interventional target for CVD.

This study has several limitations. First, due to the limited sample size, the sampling error is relatively large, which may affect the accuracy and statistical stability of the estimated association between MCMI and incident cardiovascular disease in populations with dysglycemia. Second, all study participants were from China; although the findings may have broader population-level relevance, their applicability to other ethnic groups and regions requires validation through cross-population, multicenter prospective studies before they can inform revisions to global clinical guidelines. Furthermore, the determination of cardiovascular disease events relied primarily on self-reported physician diagnoses, lacking a unified standard for re-evaluating reported events, and recall bias could lead to an underestimation of CVD incidence, which might mean the observed association is even stronger than reported. This may introduce information bias and compromise the precision of outcome definitions. Future research could enhance the reliability of the conclusions by expanding the sample size, conducting multinational cohort comparisons, and employing objective clinical and imaging indicators for endpoint verification.

Higher levels of the modified cardiometabolic index exhibit an independent and dose-response positive correlation with the risk of cardiovascular disease incidence. These findings suggest that the modified cardiometabolic index can serve not only as an effective predictor of cardiovascular disease risk in middle-aged and elderly populations but also indicate that systematic monitoring and proactive intervention targeting this index hold significant clinical and public health importance for reducing the occurrence and progression of cardiovascular disease in this demographic. Therefore, when formulating cardiovascular disease prevention strategies for middle-aged and elderly populations, it is recommended to incorporate the modified cardiometabolic index into routine risk assessments, given that it can be calculated from simple, commonly available clinical measurements and represents a cost-effective screening tool. Effective management of this index should be achieved through lifestyle interventions and metabolic regulation, thereby providing a robust foundation for early prevention and control of cardiovascular disease.

*Corresponding author.