Using Precision Medicine to Tailor Antidepressant Choices
A patient’s body mass index (BMI), sex, and symptom profile can be used to tailor antidepressant choices and improve outcomes, according to a study in Personalized Medicine in Psychiatry.
“We are in the midst of a paradigm shift in the field of psychiatry, to find specific clinical and biological signals that help clinicians and patients decide what is the best treatment,” said study lead investigator Leanne Williams, PhD, VA Palo Alto Health Care System, Livermore, California, and Stanford University School of Medicine, California.
“Our study adds new knowledge to this effort, and does so for 2 commonly associated chronic conditions, clinical depression and obesity, that need new treatment approaches. Our results have the potential for a significant impact on the majority of patients suffering from depression who are seen in primary care and community settings.”
Researchers looked at data for 659 adults with clinical depression randomly assigned to 8 weeks of treatment with escitalopram, sertraline, or venlafaxine extended-release (venlafaxine-XR). Using the 17-item Hamilton Rating Scale for Depression, researchers defined participants as remitters if they no longer experienced clinical symptoms at the study’s end.
According to the study, men and women with a higher BMI than normal-weight participants were more likely to remit on venlafaxine-XR. The improvement was due to a reduction in physical symptoms, such as sleep disturbance, somatic anxiety, and appetite.
The study also found that women with a higher BMI (but not men) were more likely to remit regardless of the antidepressant used. The effect was related to a change in cognitive symptoms, such as suicidal ideation, guilt, and psychomotor changes.
“Although these findings require replication, they are ready for ‘prime time’ translation into clinical practice where there are currently no indicators and algorithms available for guiding treatment choice for patients with both depression and obesity,” said lead author Erin Green, PhD, VA Palo Alto Health Care System and Stanford University School of Medicine.
Green E, Goldstein-Piekarski AN, Schatzberg AF, Rush AJ, Ma J, Williams L. Personalizing antidepressant choice by sex, body mass index, and symptom profile: an iSPOT-D report. Personalized Medicine in Psychiatry. 2017;1-2:65-73.