The findings of this study underscore the intricate link between the CMI and arteriosclerosis in patients with T2DM. Our data revealed a significant positive correlation between the CMI and the development of arteriosclerosis, which was robust even after adjusting for traditional risk factors. This association aligns with previous studies that have revealed the CMI to be a predictor of cardiovascular events in various populations [10, 11]. However, our research extends this knowledge by specifically elucidating the relationship within the T2DM cohort, a group at an inherently higher risk of cardiovascular complications.

Based on the CMI, the patients were categorized into three groups. The C3 group (CMI > 1.355) exhibited greater diastolic blood pressure, systolic blood pressure, BMI, hip circumference, glucose (0 min), insulin (120 min), TC and baPWV values. These findings suggest that the CMI serves as an effective marker for distinguishing T2DM patients with arteriosclerosis from those without arteriosclerosis.

An increased CMI poses a risk for individuals with T2DM and is recognized as a detrimental factor. To effectively prevent the onset of T2DM, appropriate measures should be implemented to mitigate the escalation of the CMI from low to high values [15, 16]. Additionally, the CMI exhibited positive correlations with diastolic blood pressure, systolic blood pressure, BMI, hip circumference, glucose (at 0 min), insulin (at 0 min), insulin (at 120 min), HOMA-IR, TC and PWV values. These findings suggest that the CMI may exert diverse influences on various cardiovascular disease risk factors. Specifically, in obese patients, the CMI might be more directly associated with weight and insulin resistance [17, 18]. In hypertensive patients, the CMI may be related to vascular function [19]. In patients with diabetes, the CMI may be associated with insulin resistance [20]. The group with the highest CMI, referred to as Group C3, exhibited greater diastolic blood pressure, systolic blood pressure, BMI, hip circumference, fasting glucose, 120-minute postload insulin, TC, and baPWV values. These findings suggest that the CMI is an effective marker for distinguishing T2DM patients with arteriosclerosis from those without arteriosclerosis.

Further analysis of the different subgroups revealed a significant correlation between the CMI and an increased incidence of arteriosclerosis in each subgroup. As the CMI increased, so did the prevalence of arteriosclerosis, with an apparent trend across the patient groups. These findings indicate that the CMI could be a key marker for predicting the risk of arteriosclerosis. Clinically, this means that by monitoring changes in the CMI, doctors can detect and act to prevent arteriosclerosis. It is also necessary to create specific prevention and treatment strategies based on the CMIs of different patient subgroups to mitigate the risk of arteriosclerosis. We constructed three models for multivariate regression analysis. In the analysis stratified by the CMI, after adjusting for other variables, the risk of arteriosclerosis in the C2 group was 4.6 times greater than that in the C3 group, and the risk was 5.1 times greater than that in the C1 group. These results suggest that CMI stratification is a valuable predictor of arteriosclerosis risk. Moreover, our study examined the impact of other covariates on arteriosclerosis risk. In Model 3, factors such as age, sex, hypertension, and hyperlipidemia were strongly associated with arteriosclerosis risk.

The biological plausibility of the CMI as a marker for arteriosclerosis can be understood through the lens of insulin resistance [18] and hyperglycemia [21], which are hallmarks of T2DM. These metabolic abnormalities contribute to the endothelial dysfunction [22], oxidative stress [23], and inflammatory processes[24] that are fundamental to the development of arteriosclerosis. Furthermore, the components of the CMI, which include measures of obesity and dyslipidemia, are known contributors to vascular pathology. Hence, our findings suggest that the CMI captures the cumulative effect of these metabolic derangements on arterial health.

However, our research is not without limitations. The design of the study precludes causal inferences. While we demonstrated a correlation, longitudinal studies are necessary to confirm whether changes in the CMI precede the progression of arteriosclerosis. Additionally, our study population may not be representative of all patients with T2DM, and our findings may not be generalizable to other cohorts, particularly those with varying durations of diabetes or levels of glycemic control. To delve deeper into the relationship between the CMI and arteriosclerosis, additional research and experimentation are necessary. We could investigate how the CMI is related to the mechanism of arteriosclerosis or examine the outcomes in patients with varying CMIs undergoing identical treatments.

In conclusion, our findings strongly support the use of CMI stratification as an important predictor of arteriosclerosis risk. These findings also highlight the need to consider other covariates for accurate risk assessment, which has significant implications for the prevention and treatment of arteriosclerosis. Further longitudinal studies are required to establish the prognostic value of the CMI in predicting cardiovascular events in patients with T2DM.