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Developing tools for the PREDICTion, at individual level, of the CArdiometabolic REsponse to the consumption of dietary (poly)phenols
Funder: European CommissionProject code: 950050 Call for proposal: ERC-2020-STG
Funded under: H2020 | ERC | ERC-STG Overall Budget: 2,309,520 EURFunder Contribution: 2,309,520 EUR
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The increased burden of cardiometabolic diseases is a major societal challenge worldwide. Plant-based diets, rich in bioactive compounds such as (poly)phenols, may promote cardiometabolic health. However, the preventive effects of these bioactives depend on the individual capacity to produce, and respond to, (poly)phenol metabolites. This heterogeneity in the individual response to the consumption of (poly)phenols is the main hindrance to exploit their potential for the prevention of cardiometabolic diseases through effective dietary strategies. I aim to implement integrative tools for the prediction, at individual level, of the cardiometabolic response to the consumption of dietary (poly)phenols, taking into account inter-individual differences in both metabolism and health effects of these plant food bioactives. My vision is understanding the determinants leading to individual variability in the production of phenolic metabolites and driving cardiometabolic responsiveness to (poly)phenol consumption. I will be identifying comprehensive metabolic phenotypes (metabotypes) for main dietary (poly)phenols and the factors associated with their formation. Then, I will demonstrate the association between phenolic metabotypes and cardiometabolic health. Last, I will develop an integrative, high-throughput platform to identify phenolic metabotypes and to predict cardiometabolic responses to the consumption of dietary (poly)phenols considering individual’s makeup. PREDICT-CARE will develop new concepts, new methodologies and a new analytical platform. It relies on the integration of factors determining inter-individual variability, the deployment of translatable nutrition interventions, and the application of predictive modelling. PREDICT-CARE will lead to long-lasting breakthroughs and will build a new scenario in preventive, evidence-based, personalised nutrition strategies with these major dietary plant bioactives.

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