MIRACLE

Voimassaoloaika: 01.10.2023- 30.09.2027

Projektin status: Käynnissä

Rahoittajat: EU

Projektin kokonaisrahoitus: 350 000€ - 400 000€

Projektin ulkoiset kumppanit: Itä-Suomen Yliopisto, Universitair Medisch Centrum Utrecht, Deutsches Herzzentrun Munchen, Ludwig-Maximilians-Universität (LMU), München, Klinikum der universitat München, KAROLINSKA INSTITUTET, Universitetet i Bergen

Monitieteiset teemat: Terveys, diagnostiikka ja lääkekehitys

YK:n kestävän kehityksen tavoite: Terveyttä ja hyvinvointia

Tiivistelmä: Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death worldwide. Aside from asymptomatic manifestations, the first sign of clinically significant ASCVD is often a severe clinical event, such as stroke or myocardial infarction (MI). Thus, identification of people at high risk is central to battle the deadly consequences of ASCVD. The usefulness of current risk prediction models, typically built on traditional risk factors, such as SCORE2, is limited, most likely because traditional risk factors do not capture all facets of mechanisms and intermediary phenotypes leading to ASCVD. Especially, genetic risk factors acting already early in life and diverse longitudinal exposures accumulating during the lifetime of a person, lead to disturbance of gene regulatory networks (GRNs) which are not considered in the current risk models. In addition, the current models predict the combined risk of CAD, PAD and ischemic stroke despite mounting evidence of ASCVD heterogeneity. To capture these missing aspects of current ASCVD risk scores, MIRACLE project brings together unique data resources and expertise to provide novel multiomics based prediction models of ASCVD. We aim to (1) Integrate the globally largest CAD, PAD, and stroke GWAS information to identify genetic loci that differ between or are shared by these diseases and their subtypes, (2) Identify sex-specific subtypes of ASCVD patients using transcriptomic phenotyping of plaques and circulating biomarkers, (3) Generate functionally informed polygenic risk scores by combining experimental fine-mapping and gene prioritization approaches with integrative GRN and deep learning modelling. (4) Derive novel risk prediction models incorporating polygenic risk and circulating biomarkers. Providing a new gold standard for prediction models to accurately risk stratify stroke and MI represents a technological breakthrough allowing for earlier diagnoses and treatments of ASCVD