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.

BACKGROUND AND PURPOSE: Migraine has been shown to increase cerebral excitability, promote rapid infarct expansion into tissue with perfusion deficits, and result in larger infarcts in animal models of focal cerebral ischemia. Whether these effects occur in humans has never been properly investigated. METHODS: In a series of consecutive patients with acute ischemic stroke, enrolled in the setting of the Italian Project on Stroke at Young Age, we assessed acute as well as chronic infarct volumes by volumetric magnetic resonance imaging, and compared these among different subgroups identified by migraine status. RESULTS: A cohort of 591 patients (male, 53.8%; mean age, 37.5±6.4 years) qualified for the analysis. Migraineurs had larger acute infarcts than non-migraineurs (median, 5.9 cm³ [interquartile range (IQR), 1.4 to 15.5] vs. 2.6 cm³ [IQR, 0.8 to 10.1], P<0.001), and the largest volumes were observed in patients with migraine with aura (median, 9.0 cm³ [IQR, 3.4 to 16.6]). In a linear regression model, migraine was an independent predictor of increased log (acute infarct volumes) (median ratio [MR], 1.64; 95% confidence interval [CI], 1.22 to 2.20), an effect that was more prominent for migraine with aura (MR, 2.92; 95% CI, 1.88 to 4.54). CONCLUSIONS: These findings reinforce the experimental observation of larger acute cerebral infarcts in migraineurs, extend animal data to human disease, and support the hypothesis of increased vulnerability to ischemic brain injury in people suffering migraine.
Animals ; Brain Injuries ; Brain Ischemia ; Brain ; Cohort Studies ; Cortical Spreading Depression ; Humans ; Linear Models ; Magnetic Resonance Imaging ; Migraine Disorders ; Migraine with Aura ; Models, Animal ; Perfusion ; Risk Factors ; Stroke