SOURCE-LINKED INTELLIGENCE

Proteomics for Heart disease risk prediction

CORDIS · observation · Publication date unknown

in-based risk score (ProtRS); 2) develop an inflammation-specific ProtRS (i-ProtRS); and 3) estimate the clinical utility of these scores when modelled with clinical risk scores and PGS. Cutting-edge machine learning algorithms will be used to establish target proteins for CVD prediction in 100,000 individuals across four European biobanks. ProtectHearts brings together a physician-scientist Supervisor with proteomics expertise and a Fellow with experience modelling CVD risk prediction using PGS in biobanks. Through a secondment and non-academic placement, the Fellow will gain intersectoral experience. Activities of the training and dissemination work packages will build her competencies in machine learning, advanced statistics and science communication. ProtectHearts will engage clinicians in exploitation of the new sex-specific, comprehensive CVD risk prediction model. ProtectHearts has the potential to move CVD risk prediction beyond state-of-the-art by improving risk prediction accuracy, specifically in women, using protein-based markers for CVD and CVD-related inflammation.

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recordType

award

status

SIGNED

region

EU

value

263638.8

unit

EUR