Integrated Multivariate Predictive Model of Body Composition and Lipid Profile for Cardiovascular Risk Assessment

(1) Background/Objectives: Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality globally, necessitating effective risk prediction strategies. This study was aimed at developing and validating a multivariate predictive model integrating body composition and lipid profile to assess cardiovascular risk in an adult population. (2) Methods: A cross-sectional analysis of 90 participants from the general Spanish population was conducted. Participants were classified into cardiovascular risk groups (low, medium, high) based on systolic blood pressure. (3) Results: Descriptive and multinomial logistic regression analyses revealed significant associations between cardiovascular risk and specific parameters, such as visceral fat, glucose levels, and waist-to-hip ratio. Visceral adiposity emerged as a strong predictor of high cardiovascular risk, highlighting its critical role in cardiovascular health. Glucose levels were also significantly associated with increased risk, underscoring the importance of metabolic health in cardiovascular outcomes. Contrary to expectations, lipid markers like cholesterol and triglycerides did not show significant variations across risk categories, suggesting that traditional lipid profiles may not fully capture cardiovascular risk in the study group. Waist-to-hip ratio showed significant associations with cardiovascular risk transitions, particularly between low and medium risk, emphasizing the importance of fat distribution patterns. (4) Conclusions: These findings suggest that body composition, particularly visceral fat, is a crucial determinant of cardiovascular risk, necessitating more personalized risk assessment approaches that move beyond traditional lipid markers.