Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Personalized Psychiatry and Depression:
The Role of Sociodemographic and Clinical Variables

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized
psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach
can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this
narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e.,
body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice
may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder.
We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations
were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits
with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in
our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there
are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational
resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/
clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient
with MDD.