In two independent samples, one of 600 subjects free of cardiovascular disease (control group) and the other of 600 subjects with a history of ischemic heart disease (established heart disease group) eating habits will be evaluated using Food Frequency Questionnaire, based on those repeatedly used in epidemiological studies carried out in Spain, as well as other nutritional evaluation tools. Each participant will undergo a complete plasma metabolomics profile with identification of > 300 metabolites and a complete genome-wide association analysis. Artificial intelligence models and elastic net regression self-learning algorithms will be applied to select the metabolomics fingerprint of eating patterns.~The objectives are:~To assess whether the relationship between diet and metabolic footprint is similar in those with established Cardio Vascular Disease or not. This will allow evaluating the usefulness of metabolic fingerprint to monitor the metabolic response to diet in patients with Cardio Vascular Disease and thus help to individualize their pharmacological and non-pharmacological treatment (diet and lifestyle)~If there are sociodemographic characteristics - including the gender perspective-, clinical or genetic characteristics that help identify people who present a greater metabolic response to diet both in people without and in people with Cardio Vascular Disease.~If the possible limitations of the metabolic footprint of different dietary patterns are reduced by using different tools for the collection of nutritional information. Self -completed Food Frequency Questionnaire a, Food Frequency Questionnaire b completed with telephone interviewer, brief assessment tools, and 4 dietary records of 3 days each distributed throughout the year will be used.
The objective of this project is to validate, through high-throughput advanced metabolomics techniques, the nutritional assessment tools to be used in the IMPaCT cohort. At the same time, the ability of metabolomics fingerprints of nutritional patterns to discriminate between patients with and without major cardiovascular disease will be identified. Finally, the modification of these predictions will be evaluated based on the genetic profiles. The main hypothesis holds that the integration of metabolomics, genomic and nutritional information will serve to personalize the approach to cardiovascular disease, both in prevention and treatment, and that these tools, in turn, will be valid enough to be applied systematically and efficiently in the IMPaCT cohort.