Trajectories of anxiety and depression across development and treatment

Lead Supervisor
Professor Thalia Eley
Professor of Developmental Behavioural Genetics
SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London
thalia.eley@kcl.ac.uk

Co-supervisor
Dr Kimberley Goldsmith
King’s College London

Industrial Partner
Ieso Digital Health

Project Details

Anxiety and depressive disorders begin early, show considerable continuity into adulthood, and are associated with a high degree of impairment. However, there is considerable heterogeneity in the presentation of individuals with these conditions, which is at present poorly understood. Importantly, psychological treatments such as cognitive-behavioural therapy (CBT) are becoming more widely available than ever before through the Improving Access to Psychological Treatments (IAPT) service. Psychological forms of treatment are often preferred by patients compared to medication, but we still do not know which treatment works for whom and why. Prior findings by researchers from our industrial partner IESO, have identified two subgroups within patients with depression reflecting cognitive and somatic subtypes with distinct profiles. Work from within our group has shown that patients engaging in IAPT treatments within the South London and Maudsley NHS Trust (SLaM) follow different trajectories of symptom change across treatment. Notably, preliminary evidence from IESO researchers suggests that patients from the cognitive depression subgroup respond better to CBT than the somatic subgroup.

In this project the student will drive forwards our understanding of heterogeneity of anxiety and depression, including their influence on outcomes following CBT.

The student will access two large datasets. First is GLAD, the Genetic Links to Anxiety and Depression. This is a large study using online recruitment, to which over 20,000 individuals with lifetime anxiety and/or depression diagnoses have signed up. The student will analyse the two standard measures of depression and anxiety symptoms used in IAPT services to replicate and extend findings from the IESO analyses. Furthermore, the student will have access to data collected by our industrial partner IESO, who deliver online cognitive-behaviour therapy within IAPT. The student will be able to build trajectories of response similar to those built with data from SLaM and explore the extent to which specific profiles of symptoms at the outset predict response to treatment.

The student will learn to undertake trajectory analyses using methods such as: repeated measures latent class analysis, growth mixture modelling and latent Markov models, and will triangulate findings from the different methods. The student will also explore these analyses for some natural comparison groups, e.g. higher/lower depression/anxiety at baseline or presence/absence of specific lifetime diagnoses.

  • Aim 1. Use the GLAD sample to replicate IESO depression subgroups identified and extend to anxiety. Explore the extent to which clinical, demographic and genetic factors are associated with subgroup membership.
  • Aim 2. Build trajectories of anxiety/depression symptoms over the course of treatment using IESO data.
  • Aim 3. Identify the extent to which clinical descriptors (such as depression/anxiety subgroup) predict anxiety/depression trajectories across treatment.

Datasets

The student will primarily utilise pre-existing data from GLAD and IESO. GLAD is a cohort of individuals with lifetime anxiety (or depression) diagnoses, it is a REC approved project, and the governance is through KCL and the NIHR BioResource. The first supervisor jointly leads GLAD and can guarantee access to the dataset. The IESO dataset is governed by a data protection officer and is held in accordance with internationally recognised standards for information security (ISO 27001; https://www.iesohealth.com/en-gb/legal/iso-certificates). IESO operates through the IAPT programme.

Keywords

Heterogeneity, trajectories, treatment outcomes, depression, anxiety