Abstract
Treatment resistant depression is challenging because patients who fail their initial treatments often do not respond to subsequent trials and their course of illness is frequently marked by chronic depression. Repetitive transcranial magnetic stimulation (rTMS) is a well-established treatment alternative, but there are several limitations that decreases accessibility. Identifying biomarkers that can help clinicians to reliably predict response to rTMS is therefore necessary. Allostatic load (AL), which represents the 'wear and tear' on the body and brain which accumulates as an individual is exposed to chronic stress could be an interesting staging model for TRD and help predict rTMS treatment response. We propose an open study which aims to test whether patients with a lower pre-treatment AL will have a stronger antidepressant response to 4 week-rTMS treatment. We will also assess the relation between healthy lifestyle behaviors, AL, and rTMS treatment response. Blood samples for AL parameters will be collected before the treatment. The AL indices will summarize neuroendocrine (cortisol, Dehydroepiandrosterone), immune (CRP, fibrinogen, ferritin), metabolic (glycosylated hemoglobin, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, uric acid, body mass index, waist circumference), and cardiovascular (heart rate, systolic and diastolic blood pressure) functioning. Mood assessment (Montgomery-Åsberg Depression Rating Scale and Inventory of Depressive symptomatology) will be measured before the treatment and at two-week intervals up to 4 weeks. With the help of different lifestyle questionnaires, a healthy lifestyle index (i.e., a single score based on lifestyle factors) will be created. We will use linear and logistic regressions to assess AL in relation to changes in mood score. Hierarchical regression will be done in order to assess the association between AL, healthy lifestyle index and mood score. Long-lasting and unsuccessful antidepressant trials may increase the chance of not responding to future trials of antidepressants and it can therefore increase treatment resistance. It is essential to identify reliable biomarkers that can predict treatment responses.