Project description
Challenging the limitations of classical risk factor approaches
Cardiovascular disease (CVD) remains a major global health challenge, and understanding its intricate underlying mechanisms has proven elusive. However, recent developments in the field of epigenetics and the use of Big Data have opened up exciting possibilities for uncovering critical insights. Specifically, epigenetic mechanisms involving changes in DNA methylation have been implicated in the regulation of biological processes linked to CVD development. With the support of the Marie Skłodowska-Curie Actions programme, the EpiBigDatainWomen project aims to leverage the power of Big Data analytics to bridge the gap between environmental and lifestyle factors and CVD in women. By integrating vast amounts of individualised data with cutting-edge data science techniques, researchers hope to revolutionise CVD prediction and prevention strategies.
Objective
Epigenetic mechanisms might be involved in linking environmental and lifestyle factors and CVD development. Several studies suggest that changes in DNA methylation contribute to the regulation of biological processes underlying CVD, such as atherosclerosis, hypertension and inflammation. The recent increased digitalization, collection and storage of vast quantities of data in combination with advances in data science, has opened up a new era of big data. Although these approaches are gradually implemented in a number of clinical settings, they still lack the integration with environmental individual data, strongly affecting several multifactorial diseases such as cardiovascular disease (CVD). Classical risk factor approaches still fail in correctly estimating CVD risk in women compared to men, therefore there is a need for novel strategies to identify signs of reversible early disease or disease risk factors in this population.
We plan to generate and analyse epigenetic data in the context of a very large number of environmental and lifestyle variables (big data) in a group of women and men with traditional CVD risk factors (and age-matched controls) selected from the MOLI-SANI cohort. With this approach we hope to shed light into the controversial aspects of CVD prediction and prevention in women, independently of traditional CV risk factors.
Fields of science
Programme(s)
Funding Scheme
MSCA-IF-EF-SE - Society and Enterprise panelCoordinator
86077 Pozzilli Is
Italy