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

Figures

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  Fig. 1. Flowchart of the study.

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  Fig. 2. SHAP plot showing the features influencing the NT pro-BNP model predictions. (a) SHAP plot of the top 20 most important variables affecting the NT pro-BNP model predictions, with different features in descending order based on the mean SHAP values (Y axis). The X-axis represented the SHAP values, categorized as positive or negative if a feature has a positive or negative relation to NT pro-BNP. Each point reflected the individual’s SHAP value with red and blue, indicating high and low values of the trait, respectively. (b) Heatmap showed the effects of other characteristics on NT pro-BNP across ages, with higher mean absolute SHAP values shown in deeper yellow). A total of 12526 participants were included.

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  Fig. 3. Univariate and multivariable models assessing the contributions of different features to NT pro-BNP. (a) Separate univariate models were constructed with each feature individually and cross-validated R² values were used to gauge predictive power. (b) Multivariable models used a stepwise forward selection, first selecting and sequentially adding features based on their contributions one by one to refine the model until it met the predefined criteria. The figure showed all the features that contributed more than 0, with those before the red line contributing more than 1%, and those before the blue line more than 0.5%. The size of the circle corresponds to the magnitude of the influence.

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  Fig. 4. Univariate and multivariate associations between candidate predictors and NT-proBNP levels. In univariate (a) and multivariate (b) weighted least squares (WLS) models, standardized beta coefficients were calculated to obtain estimates of effect size (95% CI), which were used to assess the direction of influence of a given predictor. A total of 12,526 participants were included.

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  Fig. 5. Generalized Additive Model (GAM) validated the relationship between the top features and NT-proBNP. (a) Adjusted relationship of age with logNT-proBNP varied by race and gender. NT pro-BNP values gradually increased with Age, more significant in male white people. (b) The adjusted relationship between red blood cell counts and logNT-proBNP showed that NT pro-BNP levels were negatively related to red blood cell count over a range. This trend was also found in (c) Higher systolic blood pressure correlated with elevated NT pro-BNP levels. (d) The relationship between total protein and NT-proBNP. A total of 12,526 participants were included.