Integrating Artificial Intelligence into metabolomics for predicting diseases

Early diagnosis of disease is essential for timely intervention and improved clinical outcomes. Metabolomics offers a powerful approach to predict early clinical events such as disease onset by detecting subtle metabolic changes before clinical symptoms appear. Through real-time metabolic profiling, it provides unique insights into disease initiation, progression, and the underlying pathophysiological mechanisms. This makes metabolomics particularly valuable for identifying early biomarkers in complex and devastating conditions, including various cancers, diabetes and its complications, as well as disorders affecting the brain, heart, liver, kidneys and other essential organs. However, the inherently high-dimensional and complex nature of metabolomics data poses significant analytical challenges particularly when aiming to harness its full potential for predictive and personalized medicine. Extracting meaningful insights from thousands of metabolites across diverse biological contexts requires advanced computational strategies capable of managing data heterogeneity, noise, and non-linear relationships. In this chapter, we present in-depth overview of how Artificial Intelligence (AI)-particularly, machine learning (ML) and deep learning (DL) approaches-has been integrated into metabolomics workflows to overcome these limitations. We discuss both technical and conceptual frameworks essential for state-of-the-art disease prediction, illustrated through recent case studies and methodological advances.