Introduction:
Aim: To develop and validate non-invasive predictive models for detecting metabolic syndrome in young adults using NHANES 2011–2018 data to enable effective screening without laboratory testing.

Material and Methods:
Using data from the National Health and Nutrition Examination Survey (NHANES 2011–2018), we established a homogeneous cohort of Non-Hispanic White individuals (N=2,911). Gender-specific multivariate logistic regression models were developed to predict MetS risk using strictly non-invasive anthropometric and clinical parameters, including age, waist-to-height ratio (WHtR), and blood pressure.

Results:
The resulting algorithms demonstrated robust discriminatory power, achieving an area under the ROC curve (AUC) of 0.87 for males and 0.84 for females. WHtR emerged as the most significant independent predictor across both genders (Adjusted OR 1.10 per 0.01 unit increment; p < 0.001). Notably, while chronological age was significantly associated with risk in males (OR 1.07), it lacked statistical significance in the female population (p = 0.904). This divergence suggests a dominant role of phenotypic features over chronological aging in shaping MetS risk among young women.

Conclusions:
The developed nomograms and risk heatmaps enable precise cardiometabolic risk stratification in primary care without requiring laboratory resources. This non-invasive framework provides a scalable and practical tool for early intervention and personalized health management in young adults.