Approved Research

Use of protein biomarkers to estimate the morbidity and mortality risk of cardiovascular diseases

Lay summary

Cardiovascular diseases pose a significant global health burden, contributing to substantial morbidity and mortality rates. However, existing biomarkers have limitations in their ability to provide early and accurate diagnoses for these conditions. To address this challenge, our project aims to identify and explore novel biomarkers, specifically Olink-quantified plasma proteins, that may have a causal relationship with cardiovascular diseases.

The primary objectives of this research are twofold: (1) we seek to investigate the associations between blood protein levels (proteomics) and cardiovascular diseases through observational analyses and mendelian randomization methods, this will enable us to explore potential causal relationships; (2) we aim to develop and validate a statistical model using machine learning techniques, this model will incorporate basic demographic variables such as age and sex, along with Olink proteomics data, to predict the morbidity and mortality risk associated with cardiovascular diseases. The successful development of this model has the potential to significantly enhance early diagnosis and risk assessment for cardiovascular diseases.

Ultimately, 3-years project strives to produce clinical decision support tools that can assist healthcare professionals in diagnosing and managing patients with cardiovascular diseases. By leveraging novel biomarkers and machine learning algorithms, we aim to improve the accuracy and efficiency of cardiovascular disease diagnosis.