EpiBigDatainWomen · Epigenetic Data Integrated in a Big Data Approach to Unravel Novel Pathzays of CV Risk independent of classical CVD Risk Factors in Women.
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2019-04-01 → 2021-09-01
- EU contribution
- €168,277
- Participants
- 1
- Scheme
- MSCA-IF-EF-SE
Lines connect the coordinator with its partners.
Results in brief
Epigenetic Data Integrated in a Big Data Approach to Unravel Novel Pathzays of CV Risk independent of classical CVD Risk Factors in Women.
Both cardiovascular disease (CVD) diagnosis and risk assessment in women are often not recognized in a timely manner. This is mainly dependent on two aspects: first, women often show atypical symptoms and non-specific response to cardiac stress that limit their prognosis; second, the use of traditional risk factors such as hypertension, hyperlipidemia and diabetes, still keep a larger group of women than men non-protected, based on their CVD risk estimation. CVD is the most prevalent cause of morbidity and mortality among women (and men) worldwide. Impacting on CVD prevention in this population can help identifying specific disease risk factors and signs of reversible early disease, in turs improving healthcare with important repercussions on public health costs. Machine learning techniques have been developed to address prediction of health-related outcomes, however, multifactorial diseases such as CVD, still lack the integration of environmental data with the clinical information of the patients. The project EpiBigDataInWomen aimed at identifying novel CVD-related gender-specific risk factors by combining genome-wide DNA methylation data with clinical, environmental, socio-economic information through machine learning statistical approaches in women and men with different CVD risk profiles.
Data: CORDIS, © European Union
Project 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.
Original text from CORDIS.
Participants
- INSTITUTO NEUROLOGICO MEDITERRANEO NEUROMED SOCIETA PER AZIONI · POZZILLI ISCoordinatorItaly
Links
Data: CORDIS, © European Union