# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 4 Discussion
Although the HOMA-IR level is used as a valuable indicator for evaluating insulin resistance worldwide, the cutoff values should be adjusted based on race and metabolic factors. In addition, previous studies exploring effective methods for predicting MetS using simple indicators, such as HOMA-IR and BMI, have highlighted the importance of incorporating various indicators in predicting and managing the risk of MetS. These findings are consistent with the results of this study [25; 26; 27]. Such simple indicators can serve as essential tools for the early diagnosis and management of MetS. These findings support the utility of the prediction model used in this study, which demonstrates the cost-effective capabilities for predicting MetS. Notably, the use of low-cost indicators such as HOMA-IR enables efficient resource allocation in public health, providing essential basic data for developing tailored prevention programs and strategies for the early detection and management of MetS [28]. Previous studies have reported that incorporating additional variables, such as the neutrophil to high-density lipoprotein ratio (NHR), an inflammatory biomarker, in addition to HOMA-IR, could enhance the predictive accuracy of MetS. Integrating the NHR with conventional predictive variables, such as HOMA-IR levels, gender, age, and DM status used in this study, may significantly improve the predictive power. These findings align with the results of this study, underscore the importance of adopting a multivariate approach for the early diagnosis of MetS [29; 30; 31]. In conclusion, a multidimensional approach that considers complex factors such as HOMA-IR levels, age, gender, and DM status is essential for predicting MetS. When predicting MetS in older adults, additional indicators or supplemental models that reflect age-related characteristics are required. This approach provides essential baseline data for improving predictive accuracy and developing tailored prevention and management strategies for MetS. The prediction model developed in this study showed potential as a cost-effective diagnostic tool that can contribute to the early diagnosis and management of MetS by incorporating various factors such as HOMA-IR levels, gender, age, and DM status. It provides baseline data necessary for establishing public health policies and developing customized prevention programs according to age and gender while also laying a foundation for personalized prevention and management strategies for MetS through the regular monitoring of biomarkers such as HOMA-IR levels. However, this study has several limitations. Because it is cross-sectional in nature, we cannot draw definitive causal inferences about whether HOMA-IR directly contributes to MetS risk. Although our findings suggest that elevated HOMA-IR is associated with MetS, it may function primarily as an associated marker rather than a direct cause. Future cohort or longitudinal studies that track HOMA-IR changes over time are warranted to clarify whether insulin resistance drives MetS progression or merely correlates with it. Additionally, our dataset was limited to a Korean population, which may restrict the generalizability of these results to other ethnic or regional groups. Lastly, certain potential confounders (e.g., physical activity, dietary habits, medication use, and family history) were not consistently available in the 2019 KNHANES dataset and thus were not included in the regression model. Their omission could bias or partially confound the relationship between HOMA-IR and MetS risk; however, this does not invalidate our core approach, which focused on key metabolic and demographic predictors. Despite these limitations, the present study offers valuable baseline data and serves as a foundation for establishing more sophisticated MetS prediction models.