# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 1 Introduction  
Metabolic syndrome (MetS) serves as a comprehensive framework for describing a cluster of clinical symptoms, including central obesity, dyslipidemia, impaired glycemic control, and elevated blood pressure. It is a critical tool for identifying individuals at increased risk for atherosclerotic cardiovascular disease (CVD) and type 2 Diabetes Mellitus (DM2) [1, 2, 3, 4]. The global prevalence of these factors has rapidly increased in recent decades [5, 6, 7], establishing MetS as an important risk factor for various cardiovascular and metabolic diseases. Insulin resistance, a major physiological feature of MetS, is associated with obesity, hypertension, cancer, autoimmune diseases, and DM2 [8, 9, 10]. Insulin resistance arises from metabolic, hemodynamic, and inflammatory changes driven by interactions between genetic and environmental factors [8, 9, 10]. In this context, the homeostasis model assessment of insulin resistance (HOMA-IR) level has proven to be a robust alternative indicator for evaluating insulin resistance and is widely used in clinical practice [11, 12]. Previous studies have demonstrated that the prevalence of cardiovascular metabolic diseases, such as Diabetes Mellitus and central obesity, increased with age and varied by gender [6, 7]. Furthermore, the HOMA-IR levels increased in those aged \(\geq\)50 years [13].  
These findings suggest that age, sex, and HOMA-IR levels may affect the accuracy of determining cardiometabolic risk.  
In addition to widely recognized MetS risk algorithms, such as the Framingham Risk Score or waist circumference/BMI-based strategies [14; 15], HOMA-IR offers a direct measure of insulin resistance a core pathophysiological factor in MetS. By incorporating a marker that reflects \(\beta\)-cell function and insulin sensitivity more closely, HOMA-IR-based models can potentially provide more targeted risk estimates. Nonetheless, other well-established risk factors for MetS, including physical activity, dietary habits, medication use (e.g., antihypertensive or lipid-lowering agents), and family history, also substantially influence MetS outcomes [16; 17]. Owing to limitations in the 2019 KNHANES dataset, these additional variables were not consistently measured or reported and thus could not be included in the present model. This omission may affect the full spectrum of predictive accuracy; however, examining HOMA-IR in concert with basic demographic variables remains a valuable approach for better understanding MetS risk in large-scale population studies.  
Early identification of individuals at risk for MetS through valid predictive models is of paramount importance, as it allows for timely intervention and more effective disease management. In particular, integrating key metabolic markers, such as HOMA-IR, with demographic factors (e.g., age, gender, and Diabetes Mellitus status) can substantially improve the precision and applicability of these models. Robust prediction tools not only facilitate clinicians' decision-making process but also guide resource allocation and policy development in public health.  
Therefore, this study developed and evaluated a multiple logistic regression model using HOMA-IR levels, gender, age, and DM status to predict the occurrence of MetS, with the goal of improving prevention and management strategies and verifying its clinical potential.