Investigating the relationship between depression and cardiovascular diseases in three cohort studies
Creators: name: Prigge, Regina
Publisher: name: The University of Edinburgh
Year: 2020
Subjects: depression
cardiovascular disease
public health
epidemiology

description: BACKGROUND: Depression and cardiovascular diseases (CVD) are significant public health concerns. Whilst CVD are recognised as the leading cause of death worldwide, depression is responsible for a substantial non-fatal disease burden globally. A bidirectional association between depression and CVD has been reported in primary research and systematic reviews. However, there is an ongoing debate whether the evidence base is sufficient to acknowledge depression as an independent risk factor for subsequent CVD. Multiple potential mechanisms have been proposed but the exact nature of the association remains poorly understood. METHODS: I conducted a systematic review and meta-analysis to identify and critically appraise existing studies of the association between clinical depression or depressive symptoms and risk of major cardiovascular events (MCVE). To overcome some of the methodological shortcomings of existing studies I carried out quantitative analyses of three cohort studies. I performed a retrospective cohort study using Cox proportional hazard models to estimate the risk of MCVE among UK Biobank participants with depression, antidepressant use, hospital admission with depression, and self-reported depression relative to participants without the exposure of interest. Furthermore, I investigated the role of selected comorbidities and socioeconomic factors as potential effect-modifying factors. Using data from the Longitudinal Study on Women’s Health (ALSWH), I used latent process mixed modelling to identify subgroups of participants with similar patterns of depressive symptoms over time and explored cardiovascular risk factor profiles at baseline and end of follow-up. Lastly, using data from the Whitehall II study, I compared psychological distress trajectories of individuals prior to diagnosis of cardiovascular events to psychological distress trajectories of individuals free from cardiovascular events over the same time period. RESULTS: The meta-analysis of 51 studies suggested that the risk of MCVE was higher among individuals with clinical depression or depressive symptoms relative to non-exposed individuals. However, results were influenced by methodological shortcomings of existing studies. For example, the role of covariates as potential confounding or mediating factors was unclear and studies were prone to information bias since they relied on a single measure of clinical depression or depressive symptoms at baseline. Using data from the UK Biobank, different measures of depression were associated with increased risk of MCVE even after adjustment for a wide range of potential confounding factors. The increased risk was particularly high among individuals with comorbidities and among individuals from lower socioeconomic backgrounds. Using data from the ALSWH, three subgroups of women with distinct depressive symptom trajectories were identified. There were differences between groups with regards to profiles of key cardiovascular risk factors at baseline and end of follow-up. Women with stable moderate and fluctuating depressive symptoms had lower educational attainment, found it harder to manage on their income, reported more adverse health behaviours and medical conditions and gained weight more rapidly than women with stable low depressive symptoms. Using data from the Whitehall II study, there was some evidence of small differences between mean predicted psychological distress scores among those with and without cardiovascular events in the time prior to diagnosis, death, or end-of follow-up but no clear evidence for differences in the patterns of psychological distress over time. CONCLUSIONS: This project addressed a number of shortcomings of the existing evidence base. The results of the UK Biobank analysis suggest that it is unlikely that residual confounding due to unmeasured covariates alone explained the observed associations between different measure of depression and MCVE. In addition, there was some evidence that the risk of MCVE might differ between subgroups of individuals with clinical depression or depressive symptoms. The analysis of ALSWH data highlighted the importance of considering repeat assessments of depressive symptoms. Furthermore, it illustrated that latent process mixed modelling might be a useful tool to identify potentially clinically meaningful groups with different patterns of depressive symptoms over time. The results of the Whitehall II analysis suggest that it is unlikely that reverse causation or overlap of symptoms between subclinical CVD and somatic symptoms of depression explained the observed association. Whilst a number of shortcomings of the existing evidence base could be addressed, some alternative explanations of the observed association remain to be investigated. For example, iatrogenic effects of psychotropic medications might partly explain the observed association between depression and CVD and there might be common risk factors including genetic predisposition for depression and CVD. Considering the substantial public health burden of both depression and CVD, it should remain a public health priority to further advance our understanding of the relationship between depression and subsequent CVD in future research.