Deep phenotyping as a contribution to personalized depression therapy: the GEParD and DaCFail protocols.

Depressive patients suffer from a complex of symptoms of varying intensity compromising their mood, emotions, self-concept, neurocognition, and somatic function. Due to a mosaic of aetiologies involved in developing depression, such as somatic, neurobiological, (epi-)genetic factors, or adverse life events, patients often experience recurrent depressive episodes. About 20-30% of these patients develop difficult-to-treat depression. Here, we describe the design of the GEParD (Genetics and Epigenetics of Pharmaco- and Psychotherapy in acute and recurrent Depression) cohort and the DaCFail (Depression-associated Cardiac Failure) case-control protocol. Both protocols intended to investigate the incremental utility of multimodal biomarkers including cardiovascular and (epi-)genetic markers, functional brain and heart imaging when evaluating the response to antidepressive therapy using comprehensive psychometry. From 2012 to 2020, 346 depressed patients (mean age 45 years) were recruited to the prospective, observational GEParD cohort protocol. Between 2016 and 2020, the DaCFail case-control protocol was initiated integrating four study subgroups to focus on heart-brain interactions and stress systems in patients > 50 years with depression and heart failure, respectively. For DaCFail, 120 depressed patients (mean age 60 years, group 1 + 2), of which 115 also completed GEParD, and 95 non-depressed controls (mean age 66 years) were recruited. The latter comprised 47 patients with heart failure (group 3) and 48 healthy subjects (group 4) of a population-based control group derived from the Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression (STAAB) cohort study. Our hypothesis-driven, exploratory study design may serve as an exemplary roadmap for a standardized, reproducible investigation of personalized antidepressant therapy in an inpatient setting with focus on heart comorbidities in future multicentre studies.

Extent of cortisol suppression at baseline predicts improvement in HPA axis function during antidepressant treatment.

BACKGROUND: A dysregulation in the hypothalamic-pituitary-adrenal (HPA)-axis function has been repeatedly observed in major depressive disorders (MDD). Normalization of this dysregulation, i.e. of cortisol suppression after glucocorticoid receptor (GR)-stimulation, may be mandatory for clinical remission in some patient subgroups. However, there are no biological measures applied in the clinical setting to identify patient subgroups with HPA axis alterations. OBJECTIVE: We aimed to define a suppression index of cortisol concentrations before and after GR stimulation with dexamethasone to predict the variability in improvement of HPA axis activity during antidepressant treatment. METHODS: A modified dexamethasone suppression test (mDST) was performed with blood withdrawal for cortisol and ACTH measurement before and 3 h after 1.5 mg dexamethasone intake at 18:00 in two cohorts of depressed patients treated in a naturalistic setting. The discovery sample consisted of 106 patients, the replication sample of 117 patients. The suppression index was defined as cCORTpreDEXcCORTpostDEX. RESULTS: The baseline suppression index explained 27.4 % of the variance in changes of HPA axis activity before and after treatment with antidepressants. Age, cCORTpreDEXcACTHpreDEX at baseline and sex explained further variance up to 56.2 % (stepwise linear regression, p = 7.8e-8). A threshold of the suppression index at baseline was determined by ROC analysis and revealed, that only patients with a maximum index of 2.32 achieved a normalization of the HPA axis activity after antidepressant treatment. In the replication sample, the threshold was 2.86. However, the estimated suppression index was not associated with treatment response. CONCLUSION: For the first time, by establishing a short-term suppression index of cortisol before and after GR-stimulation a threshold could be identified to predict improvement of HPA axis activity during antidepressant therapy. After replication in further studies this index may help to identify patients who benefit from a specific treatment that targets components of the HPA axis in the future.