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Depression is highly prevalent and a leading cause of disease burden worldwide. Lifestyle interventions and antidepressant treatments are effective in only around two-thirds of depressed patients. These disappointing results have been ascribed to a one-size fits all depression diagnosis and treatment. Depression is a highly heterogeneous disease with very diverse symptom profiles and many contributing biological mechanisms, often with small effect sizes. Therefore, success rates of depression treatment can be improved by a personalized care approach: identifying clinically meaningful groups of patients with more homogeneous depression pathology, and finding the right treatment that targets this pathology. In current clinical practice a personalized approach based on symptoms and/or biomarkers is lacking. In this project we aim to take the first steps in developing personalized care for depression.


Our previous work has shown that approximately 25% of depressed patients exhibits a combination of atypical symptoms (e.g. increased appetite/weight, hypersomnia, extreme fatigue, leaden paralysis during a depressive episode) and immuno-metabolic (IM) dysregulations (increased body mass index (BMI), and dysregulated levels of leptin, insulin, and other metabolic and inflammatory markers). This suggests that the IM dysregulations reflect a more homogeneous depression pathology which may need specific treatment such as lifestyle interventions. However, it is still to be determined how to best characterize IM depression and which treatment is best to target IM dysregulations, therefore this project will 1) Improve IM depression profiling in terms of clinical characteristics and biomarkers, 2) Identify personalized treatment options for IM depression and 3) Include the patient perspective in personalized medicine for IM depression.


Aim 1: Improve IM depression profiling in terms of clinical characteristics and biomarkers.

To identify the driving clinical characteristics and biological mechanisms of IM depression, we will use questionnaire data on clinical symptoms and behaviour (e.g. appetite and weight increase or decrease, physical activity, sleep), and corresponding data on metabolic health (BMI, metabolic syndrome, inflammation, metabolome) and genetic data (genome, transcriptome and methylome) in already existing, unique and comprehensive datasets unprecedented both in terms of sample size (N>75K questionnaires) and available characteristics. We will also propose a representative set of biomarkers and/or symptoms that are reasonable to assess in a visit to the clinic, in order to identify patients with IM depression in clinical practice.


Aim 2: Identify personalized treatment modalities for IM depression.

We will compare differential treatment response across depressed persons with and without IM depression, and investigate whether specific IM features predict treatment response. We have the unique opportunity to use data from four existing intervention studies comparing: running therapy vs antidepressants, nutritional strategies vs placebo, active vs placebo light therapy and SSRI vs TCA antidepressants. Given preliminary earlier findings we expect that patients with IM depression will respond better to lifestyle interventions, but worse to antidepressants compared to patients without IM depression. Furthermore, as proof of concept we will longitudinally assess change in IM characteristics and IM biomarkers, to confirm that responders also show reduced IM dysregulations after treatment.


Aim 3: Include the patient perspective in personalized medicine for IM depression.

We will examine reasons for patients to accept or reject lifestyle interventions, based on existing quantitative data from the 4 used intervention studies. Based on this, and in collaboration with patient researchers, we will discuss with focus groups of patients their preferences for treatment modalities (antidepressant versus lifestyle interventions), how to increase acceptability of IM depression personalized treatment, and how patients are best informed on IM depression and suitable treatment options. Final results are combined in a set of patient recommendations.


Finally, we will integrate results from all three work packages and propose recommendations for clinical profiling and treatment of IM depression, facilitating personalized medicine and fuelling future clinical guidelines. Given the state-of-the-art evidence, we believe that patients with IM features are the most promising group to be considered for personalized medicine in depression. It is now time to valorise research findings and translate knowledge to clinical recommendations.

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