Mental illness, substance use, and cardiovascular disease: Unravelling causal relationships

Project Information

UNRAVEL-CAUSALITY

Grant agreement ID: 101076686

DOI

10.3030/101076686

EC signature date 23 January 2023

Start date 1 April 2023

End date 31 March 2028

Funded under

• European Research Council (ERC)

Total cost

€ 1 500 000,00

EU contribution

€ 1 500 000,00

Coordinated by

STICHTING AMSTERDAM UMC
Netherlands

Periodic Reporting for period 1 - UNRAVEL-CAUSALITY (Mental illness, substance use, and cardiovascular disease: Unravelling causal relationships)

Reporting period: 2023-04-01 to 2025-09-30

Summary of the context and overall objectives of the project

Serious mental illness is among the leading causes of disability worldwide. The most prevalent is depression (264 million current cases), with other major disorders such as bipolar and psychotic disorder being less common (45 and 20 million).On top of the burden posed by its symptoms, mental illness is also associated with comorbid health problems. My project focuses on the two most important ‘comorbidities’ of mental illness, given their driving role in decreasing quality and duration of life: substance (mis)use & cardiovascular disease. Exactly why these comorbidities arise is poorly understood. One potential explanation is that there are shared – environmental or genetic – risk factors. A more relevant question is if there are causal effect as well as shared risk factors? The direction of causality is also uncertain: does mental illness lead to comorbidities, and / or do comorbidities increase the risk of (more severe) mental illness? Because randomized controlled trials (RCT) are practically and ethically unfeasible for the long-term effects under study, these questions have been left unanswered. This lack of understanding is causing patients with mental illness to be insufficiently screened and not effectively treated for substance use and CVD. More reliable causal knowledge would also aid the development of more effective preventive efforts and public health messages for the population at large, which are currently mostly benefiting people without mental health problems. In this project, I will bring together sophisticated epidemiological methods and novel genetic methods to fully unravel the causal nature of relationships between mental illness and its comorbidities.

In this ambitious project, I will bring together innovative epidemiological and genetic approaches to unravel the relationships of mental illness with substance (mis)use and cardiovascular disease. My aims are to: 1) assess bidirectional relationships between mental illness and its comorbidities by conducting longitudinal analyses in (multi-ancestry) prospective cohort studies, 2) distinguish bidirectional relationships from shared genetic liability by jointly modelling the complete genetic architecture of mental illness and its comorbidities (genomic structural equation modelling), 3) establish causality by using only highly significant genetic variants as instruments for one variable and testing causal effects on another (Mendelian randomization), 4) fully unravel the nature of relationships between mental illness and its comorbidities by ‘triangulating’ evidence from aims 1 to 3, and 5) assess how informing medical doctors about the outcomes of aim 4 influences their clinical decisions in a randomized online experiment. This interdisciplinary project sets the stage for more effective prevention and treatment of mental illness, across ancestry groups.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

So far, my team and I have already made major steps in aims 1 to 4, both with regards to the comorbidity between mental illness and cardiovascular disease and the comorbidity between mental illness and substance use. I will describe the main achievements, separately for those two topics, below:

So far, I have (co-)published 5 papers on mental illness and cardiovascular disease. First, an important overview article, which I was invited to write for the prestigious European Heart Journal and in which I set out the main aims, methods, and considerations for the ERC project. Next, I published an empirical study on the link between schizophrenia and cardiological (specifically arhythmic) traits as first author, an empirical paper on the link between PTSD and a range of cardiovascular traits, and I was involved as a co-author in a major collaborative effort to investigate the link between major depressive disorder and cardiovascular disease. In all three of these empirical papers, I triangulated multiple advanced genetic methodologies to elucidate underlying mechanisms, including genomic SEM (gSEM) and Mendelian randomization (MR). Besides these empirical papers, I also senior-authored an important systematic review study in the specialized journal Heart Rhythm.

So far, I have (co-)published 5 papers on mental illness and substance use. First, a review on the causal nature of the link between alcohol use and mental health problems and how Mendelian randomization can help obtain reliable evidence. Second, a very extensive review of the role that gray matter volume changes might have in the (causal) effects of smoking on depression, summarizing neuro-imaging studies. Third, a review I worked on with fellow leading researchers in the field of substance use genetics, wherein we provide an overview of recent developments in genetic research on substance use. In addition, I was invited to write a commentary on a new study that looked at the link between smoking and obesity. Finally, one of the key empirical papers planned for this project was recently accepted for publication at Psychological Medicine. This paper describes extensive advanced genetic analyses to assess (gSEM and multivariable MR) whether subcortical brain volume measures mediate the effects of smoking on developing serious mental illness.

Finally, published an important guide on evidence triangulation in psychiatric epidemiology.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The research I conduct revolves around thorough, systematic, and robust analysis of rich and powerful datasets, combined through evidence triangulation, so that we can answer important research questions about causality. The breakthroughs that result from my work, are usually in the form of robust causal evidence. So far, a couple of breakthrough findings and/or advances beyond state-of-the-art have been:

• By triangulating genetic advanced methods, I was able to convincingly show that PTSD shows robust causal effects on developing serious mental illness, and this is considerably mediated by smoking, alcohol use, and inflammation markers.3 Particularly innovative in this study, was the use of MR in combination with gSEM, which has never before been done applied to a complex causal question like this one.

• There are distinct biological pathways that connect the rare disorder of schizophrenia with the even rarer disorder of Brugada syndrome (an arrhythmic trait).2 The link between these two disorders is of great clinical interest (because it hugely increases the risk of sudden cardiac death), but because of the very low prevalence research so far had not been able to disentangle their connections. With my study, I was able to circumvent these issues holding back other research, by linking large GWAS databases together allowing a range of sophisticated follow-up analyses.

• Besides applying triangulation in empirical studies, a major break-through/moving beyond the state of the art, is my work on improving triangulation as a research approach and developing more systematic frameworks and ways to do so.10 Due to my work with the ERC project, I am now in a leading position in pushing triangulation forward, being involved in continuing discussions and collaborations with experts from around the world around these topics. I have a particularly unique position, as I connect different disciplines (psychology, psychiatry, epidemiology, genetics) and methods (quantitative, qualitative, different datasets, population studies, clinical studies).