Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.

The present study proposes a methodology to emulate an interventional trial by employing machine-learning (ML) models. A maqui-citrus beverage is used as a case study, exploiting empirical data to assess the performance of multiple ML algorithms, to further build regression models. Those models predicted the effect of consuming the beverage for 60 days, sweetened with different sweeteners, on flavanones and their metabolites and anthocyanin metabolites present in plasma and urine. To guarantee the reliability of the predictions, a comprehensive data analysis and preprocessing was carried out, followed by a hyperparameter tuning using Bayesian optimization. The models were benchmarked, yielding a goodness of fit R2 of approximately 89% and reaching error rates (mean absolute error and root-mean-squared error) of about 2% and 10%, respectively. This study demonstrates the reliability of ML tools in simulating interventional trials, providing results without the need to expose participants to the intervention.