Multi-omics analyses for personalized nutrition

Dietary factors have been extensively investigated for the primary risk factors for non-communicable diseases. There is convincing evidence that a high intake of plant-based foods rich in fibre, moderate consumption of dairy foods (or other sources of calcium), together with limited consumption of (processed) meat and alcohol may positively impact the risk of cardio-metabolic disorders. More sophisticated approaches able to evaluate more comprehensively one’s diet shows that “healthy” and “prudent” dietary patterns, such as the Mediterranean diet (characterized by consumption of fruits and vegetables, cereals, legumes, olive oil, fish, white meat and dairy products, and a moderate consumption of wine and red meats) may be substantially beneficial while “Western-type” diets rich in energy-rich, nutrient-poor foods have been demonstrated to have a profound impact on the global burden of non-communicable diseases. Most existing evidence has been provided by pooled analyses from observational studies, but the underlying mechanisms can be only hypothesized and derived from mechanistic studies, and no cause-effect relation can be proved. The implementation of “omics” sciences has been suggested to potentially fill the gap between the observational associations of food intake and health outcomes, while providing insights of intermediate pathways and lay the ground for personalized approach based on unique characteristics of individuals.

Data on background characteristics and dietary habits will be collected in patients at high cardiovascular risk attending in-patient clinics. Biological specimens (blood, urine, feces) will be collected and data retrieved from microbiome and metabolome will be related to food frequency questionnaire data. Biomarkers of dietary consumption and patterns of food intake, differences in gut microbiota profiles, as well as biomarkers of disease gravity will be analyzed as predictors of outcome.

We expect to identify characteristics that may affect the individual’s response to food intake (i.e., specific gut microbiota profile), as well as a relation with health status. Specifically, it may be expected a correlation between gut microbiota profile, metabolome, and inflammasome, with some specific relation potentially providing evidence of the pro- and anti-inflammatory effects of certain foods/dietary patterns and mechanistic rationale for their role on disease risk.