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

## TL;DR

This study uses machine learning to predict how a new healthy beverage affects metabolism, avoiding the need for human trials.

## Contribution

A novel ML-based approach to simulate interventional trials using real-world data for predicting metabolic responses.

## Key findings

- ML models predicted the metabolic effects of a maqui-citrus beverage with an R² of ~89%.
- Error rates (MAE and RMSE) were approximately 2% and 10%, respectively.
- Bayesian optimization improved model reliability and performance.

## Abstract

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 R
2 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.