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

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