This study explores the relationship between three NVOIs (LAP, VAI, and METS-VF) and 10-year CVD risk in patients with T2DM. The significant association between LAP, VAI, and METS-VF and 10-year CVD risk supports our hypothesis. These NVOIs reflect the roles of demographic (sex, age), metabolic (FBG, TG, HDL-C), and anthropometric (BMI, WC, WhtR) factors, which had a significant association with CVD risk in univariate analysis. Limited research has explored the association between NVOI and CVD risk in T2DM patients, with most studies focusing on “CVD events” rather than risk prediction [19, 20]. While an initial study in China found no significant link between LAP, VAI, and CVD events in the diabetic population [19], a subsequent prospective study showed a stronger connection between these NVOIs and future CVD events [20]. Additionally, a recent cohort study conducted in China found a significant association between METS-VF and the increased risk of CVD events among populations with diabetes [18]. Our study, utilizing a predictive modeling approach, extends these findings by demonstrating a significant association between these indices and 10-year CVD risk in patients with T2DM.

Although our Bonferroni-corrected analysis revealed significant differences in LAP and VAI medians between the low and high and moderate and high CVD risk groups, no significant differences were observed between the low and moderate groups among males. A study by Zheng et al. [17] reported significant differences in LAP and VAI means between CVD risk groups using univariate analysis. However, the use of only two CVD risk categories and the absence of sex stratification in their study likely limited their ability to capture nuanced differences between groups. Additionally, the studies employed different CVD risk prediction tools, which assess risk based on different populations and risk factors, potentially influencing the classification and prediction of CVD risk and limiting direct comparisons between the two studies.

Multivariate analysis of our study results revealed that LAP had the lowest odds of high CVD risk and was not statistically significant in both sexes. In contrast, METS-VF and VAI showed higher odds of high CVD risk, with METS-VF demonstrating superior predictive ability, especially in females. A study on a Ghanaian population with T2DM [33] identified LAP as a significant predictor of 10-year CVD risk using the Framingham general cardiovascular risk profile. This discrepancy underscores the importance of considering CVD risk assessment tools and population-specific factors that could impact how TG and waist circumference (the components of LAP) relate to CVD risk.

Previous studies on general populations in Brazil [34] and the USA [35] identified VAI as a significant predictor of high CVD risk, which aligns with our findings. However, LAP’s role in predicting CVD risk remains inconsistent across studies potentially due to variations in population characteristics and metabolic profiles. The superior performance of VAI and METS-VF over LAP can be attributed to their inclusion of additional cardio-metabolic risk factors, such as HDL-C and FBG, which offer a more comprehensive evaluation of metabolic syndrome, a key risk factor for CVD [35]. Moreover, factors such as BMI, age, and WhtR, which are components of these indices, are independently recognized as risk factors for both metabolic syndrome and CVD [36,37,38]. Given the well-established association between metabolic syndrome and CVD [35], along with prior findings linking VAI and METS-VF to metabolic syndrome [8, 39], our results suggest that these indices may serve as effective predictors of CVD risk, partly through their association with metabolic dysfunction.

Unlike previous studies in the general population, where VAI showed a stronger association with CVD risk [17] and incidence of CVD events in males [40], our results indicate that diabetic females have a higher CVD risk per unit increase in VAI compared to males (4.16 vs. 3.18, respectively). This difference may be explained by males’ generally higher baseline CVD risk [41], reducing the additional impact of VAI. In contrast, females, with a typically lower baseline risk, experience a more pronounced increase in CVD risk with higher VAI. Differences in study populations may also contribute to the observed variation in sex-specific associations.

The superior predictive ability of METS-VF for CVD risk is likely due to its inclusion of METS-IR, which provides a comprehensive assessment of IR [32, 42] and metabolic syndrome [43, 44], both are key risk factors for CVD. Insulin resistance has been recognized as one of the most critical factors contributing to coronary artery disease [45]. No studies have examined sex differences in METS-VF’s ability to predict CVD risk in T2DM populations. A cohort study by Zhu et. al. [18] in China concluded that METS-VF could serve as a predictive index for CVD events, but sex-stratified analysis was not conducted, limiting comparison with our study. However, our results suggest that METS-VF's predictive power extends beyond IR and metabolic syndrome to encompass the impact of visceral fat accumulation on CVD risk.

Studies that examined the performance of NVOI in predicting high CVD risk in T2DM are limited. The C-statistic results in our study demonstrate the strong predictive value of these indices in identifying individuals at high CVD risk in both sexes (AUC = 0.79–0.866), highlighting their clinical usability [46]. In particular, the superior predictive performance of METS-VF in females may be attributed not only to its inclusion of METS-IR, which a recent Chinese study found to have the strongest predictive power for major adverse cardiac events in individuals with diabetes, outperforming other IR indices [47], but also to the stronger impact of insulin resistance (IR) on CVD risk in females. Previous research found a significant association between cytokines and METS-IR in females, with this effect being mediated by BMI [48]. Females in both studies had a higher BMI, likely amplifying the role of metabolic and inflammatory pathways in CVD risk. Despite lower METS-VF values in females, its stronger predictive power highlights the greater influence of IR and inflammation on cardiovascular risk in females.

VAI also demonstrated higher predictive performance than LAP in both sexes, with slightly better values in females. This contrasts with a Brazilian study that found LAP, not VAI, had significant predictive value for CVD risk using the Framingham risk score [49]. The differences between our study and the Brazilian study could be explained by variations in the study populations (less than 25% of participants in the Brazilian study had T2DM), the tools used for CVD risk assessment, and the lack of sex-stratified analysis in the Brazilian study, which may have missed important sex-specific differences. These findings, along with DeLong’s test, highlight the complexity of predicting CVD risk using LAP, VAI, and METS-VF, particularly regarding sex differences. The overlap in predictive components among the indices likely accounts for the similarities in performance across the indices, particularly in males and most comparisons in females. However, in females, VAI's performance was improved over LAP due to additional factors such as HDL-C and BMI, suggesting these indices may operate differently by sex.

In terms of cut-off points, the higher values for LAP and METS-VF in males and VAI in females reflect the sex-specific baseline differences. Females had higher sensitivity, meaning the indices were better at identifying high-risk females, while males showed higher specificity, making the indices more effective at excluding low-risk males. VAI showed the highest sensitivity in males, while METS-VF was most sensitive in females, further emphasizing sex differences in predicting high CVD risk. Youden’s index for LAP and VAI (0.56–0.58) in our study was higher than that reported in a healthy Iranian population [50], underscoring their greater predictive power in individuals with T2DM, likely due to their higher baseline risk. The indices showed higher negative predictive values (NPVs) in females (88.9–93%), meaning they are more effective at reassuring females when the test is negative. In contrast, higher positive predictive values (PPVs) in males (77–92.5%) provided stronger confirmation of high risk when the test was positive. Likelihood ratios (LR) also confirmed the indices’ effectiveness, with higher LR+ in males and lower LR− in females, reinforcing the importance of using sex-specific strategies when applying these indices for CVD risk prediction.

Although this study explored the association between NVOI indices and 10-year CVD risk, as well as their predictive performance in adults with T2DM, it has several limitations. First, its cross-sectional design prevents causal inferences between NVOI and CVD risk. Longitudinal studies are needed to evaluate these indices’ predictive ability over time. Second, the study was conducted in a single region of Egypt, limiting generalizability. Future research should include larger, multi-regional cohorts to enhance external validity. Third, the 2019 WHO/ISH risk prediction charts do not incorporate clinical event data, potentially reducing risk estimation precision. Investigating NVOIs alongside clinical event data could strengthen their predictive utility. Fourth, these charts exclude patients under 40 years old, limiting representation of younger individuals. Fifth, despite adjusting for major confounders, residual confounding from unmeasured factors such as dietary intake, stress levels, and genetic predisposition cannot be ruled out. Future studies should integrate more comprehensive lifestyle and genetic risk factors for a holistic understanding of CVD risk. Finally, reliance on self-reported data may have introduced recall and social desirability biases, affecting accuracy. While confidentiality measures helped mitigate these biases, future research should incorporate objective assessments for validation.