# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 4 Discussion  
Using various variables such as HOMA-IR levels, gender, age, and DM status, we developed and evaluated a multiple logistic regression model for predicting the occurrence of MetS among 6134 participants in the KNHANES with available data on fasting blood glucose and HOMA-IR levels. The results showed that DM status was the strongest predictor of MetS occurrence, and HOMA-IR levels, gender, and age in order had a significant effect on the prediction model. These results align with those of previous studies, confirming the significant role of DM status and insulin resistance in predicting MetS [6; 7; 13]. When the optimal threshold was set to 0.2408, the model demonstrated a sensitivity of 80% and a specificity of 68.9%, with an AUC of 0.819, indicating high predictive accuracy for MetS. The additional ROC comparison revealed that incorporating gender, age, and DM status alongside HOMA-IR significantly enhanced model performance compared with using HOMA-IR alone (AUC: 0.819 vs. 0.761). Notably, the model's sensitivity and specificity varied across age groups. It demonstrated the highest predictive accuracy in the 20-29 years group, while accuracy decreased among individuals aged 50 years or older. The AUC values in individuals aged 50-59 years and those aged \(\geq\) 60 years were 0.805 and 0.737, respectively, indicating a gradual decline in predictive power. This decline may stem from the increasing physiological complexity and comorbidities associated with older age, suggesting that a single prediction model may not fully account for these factors. Consequently, caution is warranted when applying this model to adults \(\geq\) 50 years, and additional or age-specific variables may be needed to improve accuracy in older populations. Age and gender significantly influence the predictive accuracy of HOMA-IR-based MetS models. Similar to the results of this study, other previous studies have reported that the optimal HOMA-IR cutoff value differed in  
Figure 2: Receiver operating characteristic curve of the regression model with covariates. women without Diabetes Mellitus by age group. In particular, the predictive power of HOMA-IR levels in women aged \(\geq\) 50 years was affected by the decreased estrogen levels and increased visceral fat associated with menopause [19; 20]. In addition, the results of this study are consistent with those of previous studies indicating that HOMA-IR might have limited utility in assessing the risk of MetS in women aged \(\geq\)70 years [21]. Such observations suggest that HOMA-IR values may vary with age and may be more useful for predicting cardiovascular and metabolic risks in certain age groups. Additionally, the optimal HOMA-IR cutoff values appear to vary across different populations, which may be due to the variations in metabolic characteristics by region and physiological factors between different populations. For example, the HOMA-IR cutoff value in the Spanish population was reduced to 2.05 when accounting for the MetS components. This differs from the cutoff values reported in studies conducted in the United States and Iran [20; 22; 23]. Such differences highlight the need to develop HOMA-IR-based MetS prediction models tailored to specific regions and population characteristics. Similar to the results of previous studies, our findings indicated that the MetS prediction criteria tailored to specific age groups and gender are needed. They further support the effectiveness of using differentiated HOMA-IR cutoff values based on population characteristics for the prediction of MetS [19; 24]. These findings underscore the importance of a multivariate approach to improve the predictive accuracy of MetS by age and gender. These findings align with the results of a previous study [21], which suggested that the clinical utility of HOMA-IR levels can be improved by adopting the criteria linked to the individual components of MetS rather than setting population-based cutoff values.