Unlike other systems such as plants, microorganisms or fungi, human cells are not proficient in eliciting the production of defense compounds in response to external stresses and threats. Human metabolism is essentially based on a set of primary metabolites that participate in the various regulatory events of cells and tissues. The challenge is therefore to maintain homeostasis and allow the survival of the individual through the modulation of existing endogenous metabolic pathways with a relatively stable set of ubiquitous compounds. Since these complex regulatory phenomena are potentially subject to multiple influences, assessing their overall variability, as achieved by most conventional approaches, is not sufficiently informative. The experimental evaluation of several factors acting simultaneously on the metabolome is paramount. Because metabolomics involves the characterization of multivariate metabolic phenotypes, such a methodology requires specific data analysis tools to fully exploit the relevant information considering the different factors, as well as their respective impact on metabolite levels. The investigation of high-dimensional multifactorial data in metabolomics opens new challenges and requires the development of innovative experimental strategies involving structured designs of experiments to assess cause-effect associations and offer deeper insight into relevant biological information. In the future, key outputs should not only consider lists of metabolites, but also include their specific variation related to each effect that can be identified and/or quantified, thus allowing accurate biochemical and functional relationships to be highlighted.