Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardiovascular health. We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). Machine learning was employed to assess 66 factors that associated with NT-proBNP levels, including demographic, anthropometric, lifestyle, biochemical, blood, metabolic, and disease characteristics. The predictive power of the model was assessed using five-fold cross-validation. The optimal features predicting NT-proBNP levels were identified using univariate and step-forward multivariate models. Weighted least squares regression (WLS) was applied for supplementary analysis. Finally, the relationship between the corresponding features and NT-proBNP was validated using weighted and adjusted generalized additive models (GAM). We included 12, 526 participants without cardiovascular diseases. In the univariate model, age exhibited the highest association with NT-proBNP levels (the coefficient of determination (R) = 36.91%). The multivariate models revealed that age, gender, red blood cell count, race/ethnicity, systolic blood pressure, and total protein level were the top six predictors of NT-proBNP. GAM demonstrated a noteworthy non-linear association between NT-proBNP and age, red blood cell count, systolic blood pressure, and total protein. Our study contributes to explaining the biological mechanisms of NT-proBNP and will facilitate the design of relevant cohort studies. We underscore the significance of assessing various population subgroups when employing NT-proBNP as a biomarker, and the need for developing innovative clinical algorithms to establish personalized levels.