Combining biological and non-biological markers to develop a model predictive of treatment response for individuals with depression

The student conducted a study investigating whether inflammatory biomarkers and clinical markers would predict treatment response to psychological therapy in depression and anxiety as part of their MRes rotation project. The study's findings suggested that higher rates of both childhood trauma and use of psychotropic medications were predictive of treatment non-response for both anxiety and depression. Thus, the study concluded that further research is needed in order to identify patients who are at higher risk for treatment non-response and pre-emptively allocate them to different or more intense treatments.
Both supervisors have substantial expertise in translational research into major depressive disorder and affective disorders more broadly. More specifically, Professor Cleare has conducted research examining clinical predictors of response, particularly markers of treatment resistance or severity (Fekadu et al., 2009) and various biomarker predictors of clinical outcomes. These include inflammatory proteins (Strawbridge et al., 2015; 2019), growth factors (Pisoni et al., 2018) and genetic polymorphisms (Fischer et al., 2018). Both Professor Cleare and Dr Juruena have examined patients' history of childhood trauma and biomarkers of hypothalamic-pituitary-adrenal (HPA) axis predicting future severity of remission of depression function (e.g. Fischer et al., 2018; Tunnard et al., 2014; Juruena et al., 2009; 2010; 2013).
This information may also be used by the Centre for Doctoral Studies for the electronic submission of Doctoral Training Grants.
The first part of the project constitutes a systematic review (and if appropriate, with random-effects meta-analysis) of published studies recruiting a sample of individuals undertaking a psychological therapy and combined psychological and pharmacological treatments for depression which measure potential non-biological (clinical, demographic or psychosocial) and biological (e.g. inflammatory and endocrine biomarkers) pre-treatment predictors of subsequent therapy response. Recently, comprehensive systematic reviews have examined biological and non-biological predictors of antidepressant treatment response (Perlman et al., 2019) and biomarker predictors of psychological therapy response (Cristea et al., 2019). These conclude that response predictors are, overall, not specific to individual treatments assessed and may represent generalised risk factors for poor depression prognosis. However, psychological therapies are an area of relative neglect and no recent review has assessed clinical predictors of psychotherapy response. Undertaking this systematic review as the first part of the project will allow the student to determine which factors have the most potential to be tested during a predictive model development stage. One year is allocated for the entire review process, which covers literature scoping, finalising the review question and specific methodology (October - December; protocol to be registered by January), running the systematic search and screening studies for inclusion (January - February), finalising the eligibility of studies for inclusion (March - April), extracting data in preparation for review results and undertaking quality assessment (May - June), completing results (including any data analyses; July - August), interpreting the meaning of results and writing up a manuscript for publication (September - October). During the first year the student will also attend various workshops organised by the biostatistics department which will develop their skills and understanding in longitudinal prediction modelling and statistical programming (see below section; Training). The second year will be focused on: (1) model development, in response to the results of the above review; (2) contributing to the completion of data collection for the second cohort (the LQD study); and (3) preparing all data for analyses.