Healthy, sustainable and personalised precision nutrition

WP 4.3 addressed a central shift in nutritional science: the transition from general dietary recommendations to models capable of accounting for inter-individual variability.

The work was grounded in the recognition that responses to food are not uniform but depend on the interaction between anthropometric, metabolic, genetic and psychosocial factors. Personalised nutrition was therefore investigated as a tool to enhance the effectiveness of dietary interventions while maintaining coherence with public health and sustainability goals.

Over the three-year period, the research progressed from the definition of conceptual models to their experimental validation. A first line of activity examined the interactions between bioactive food components and digestive processes. In vitro and in silico studies demonstrated that polyphenols and other compounds can modulate enzymatic activity and influence nutrient absorption, providing mechanistic explanations for differences in metabolic responses. These findings reinforced the concept that the physiological effects of foods depend not only on composition but also on how they are processed within the organism.

A second research axis focused on the gut microbiota as a key mediator between diet and health. Experimental activities evaluated the impact of nutrient and non-nutrient components on microbiota composition and functionality. The results showed that even limited variations in microbial profiles may translate into distinct physiological responses, supporting the view of the microbiota as a dynamic system central to personalised nutritional effects.

Building on these findings, WP 4.3 developed predictive models integrating genetic, metabolic and lifestyle data. The objective was not to generate abstract individual profiles but to create operational tools capable of guiding adaptable and verifiable nutritional interventions. The integration of multi-omics data enabled the identification of nutritional phenotypes useful for predicting responses to specific foods or dietary strategies and for testing targeted intervention protocols with potential clinical and preventive applications.

From a methodological perspective, the work strengthened result transferability through the combined use of ex vivo and in vivo models to assess nutrient bioavailability, alongside the adoption of shared protocols for data collection and analysis. This framework supports replicability and scalability, helping to ensure that precision nutrition approaches move beyond experimental settings and can be integrated into clinical, industrial and policy contexts.