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

G. Perna*, A. Alciati, S. Dacco, M. Grassi, D. Caldirola

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

15 Citations (Web of Science)

Abstract

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 ]Le., 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 (Le., 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.
Original languageEnglish
Pages (from-to)193-206
Number of pages14
JournalPsychiatry Investigation
Volume17
Issue number3
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • antidepressant treatment
  • antidepressants
  • atypical features
  • body-mass index
  • clinical markers
  • depression
  • double-blind
  • drug targeting
  • major depression
  • melancholic features
  • personalized medicine
  • star-asterisk-d
  • treatment outcome
  • treatment-resistant depression
  • tricyclic antidepressants
  • venlafaxine extended-release
  • Treatment outcome
  • ATYPICAL FEATURES
  • MELANCHOLIC FEATURES
  • Antidepressants
  • Drug targeting
  • Clinical markers
  • TREATMENT-RESISTANT DEPRESSION
  • STAR-ASTERISK-D
  • Depression
  • ANTIDEPRESSANT TREATMENT
  • MAJOR DEPRESSION
  • Personalized medicine
  • TRICYCLIC ANTIDEPRESSANTS
  • VENLAFAXINE EXTENDED-RELEASE
  • DOUBLE-BLIND
  • BODY-MASS INDEX

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