Examining the causes and consequences of sub-types of depression across the life course

Depression is a complex and multi-faceted disorder, associated with concurrent and long-lasting social, psychological, and physiological impairments. It is critical to identify individuals in whom active management or effective prevention strategies could mitigate their symptoms and improve health and wellbeing. However, depression rates continue to rise.

Currently, our understanding of depression is limited by several interrelated challenges that must be addressed to improve mitigation strategies for depression. First, depression is highly heterogeneous and not the same thing for everyone. For example, research is beginning to show that there are key sub-types of depression, which vary by the number and combination of symptoms, duration of symptoms, age of onset and severity.

This makes treatment and prevention challenging as different sub-types may have more circumscribed aetiologies or differential outcomes that are not fully captured by a one size fits all approach. Secondly, different sub-types of depression are likely to reflect varying aetiologies comprised of multiple genetic and environmental antecedents. Untangling this requires large datasets combined with innovative approaches.

Furthermore, the causal role of these antecedents for depression sub types, either in isolation, or in combination is far from understood. There is also no definitive answer as to which risk factors (or combinations of) are most important and thus we do not know what treatments to prioritise for what sub-type of depression. Finally, depression does not necessarily mean the same thing across the life course.

Different sub-types of depression may manifest differentially and have varying aetiologies at different timings throughout development. What may underpin depression in youth may be very different to what underpins depression in later life. Therefore, it is important to identify timing specific sub-types of depression and their antecedents to ensure we know what may help the right person at the right time.

Aims:

To use newly available large datasets to identify sub-types of depression, and examine how a combination of genetic risk, environmental risk factors and biomarker data could underpin different sub-types of depression. The data included are UK Biobank (UKB, n=500K), Generation Scotland (GS, n=20K) and the Avon Longitudinal Study of Parents and Children (ALSPAC; n=30K).

Key aims include:

1. Stratify depression into clinically meaningful sub-types using cross-sectional and longitudinal data (using data driven approaches such as latent class analysis and growth mixture modelling)

2. Explore associations between these sub-types of depression and a range of genetic, environmental, biomarker and imaging data using traditional (regression and correlational) analysis and non-traditional (machine learning, prediction) analysis.

3. Conduct genome-wide association studies (GWAS) on the different subtypes of depression and explore genetic correlations and causal relationships (using Mendelian Randomization) with other disorders/traits.

4. Explore how this varies across the life-course by examining age effects of sub-types of depression and leveraging methods and results from the aims above.