Mental health conditions and cardiovascular diseases: association and risk stratification based on genetics, lifestyle, and biomarkers

Keywords: Mental health; cardiovascular; risk stratification; epidemiology

Algorithms for predicting cardiovascular disease (CVD) often focus on traditional risk factors (e.g. smoking) and biomarkers (e.g. cholesterol) even though epidemiological and Mendelian randomisation studies have shown mental health conditions, including major depressive disorder, to be causal of CVD. However, the relationship is not well understood with several important remaining questions, e.g. Is the association specific to atherosclerotic CVD, or does it extend to other CVDs, such as atrial fibrillation and heart failure? Is anxiety disorder, another common mental health condition, also predictive of CVDs? What are the underlying pathways between depression, anxiety and CVD? Perhaps more importantly, the reasons why some people with mental health conditions developed CVD but some do not is also elusive. This presents a unique opportunity for risk stratifying people with mental health conditions for CVD, as well as to the potential inclusion of mental health in the general CVD prediction algorithms.
Aims: The overarching aim of this PhD would be to investigate the association between mental health and CVD using existing big data (UK Biobank and primary care data) with the following objectives:
1. To study the association between anxiety disorder and types of CVD (ASCVD, AF, HF, PAD), as well as any additional prediction utility of adding anxiety to existing CVD prediction models (e.g. AHA/ACC or SCORE)
2. To explore the mechanistic pathways (mediators) connecting mental health conditions (including depression and anxiety) and CVD
3. To identify any interactions between risk factors (genetics, lifestyle, biomarkers) and mental health in relation to the risk of CVD