A molecular pathway analysis of the glutamatergic-monoaminergic interplay serves to investigate the number of depressive records during citalopram treatment.

The efficacy of current antidepressant (AD) drugs for the treatment of major depressive disorder (MDD) lays behind expectations. The correct genetic differentiation between severe and less severe cases before treatment may pave the way to the most correct clinical choices in clinical practice. Genetics may pave the way such identification, which in turns may provide perspectives for the synthesis of new ADs by correcting the molecular unbalances that differentiate severe and less severe depressive patients. We investigated 1,903 MDD patients from the STAR*D study. Outcome was the number of severe depressive records, defined as a Quick Inventory of Depressive Symptomatology (QIDS)-Clinician rated (C) total score >15, corrected for the number of observations for each patient during the first 14 weeks of citalopram treatment. Predictors were the genetic variations harbored by genes involved in the glutamatergic-monoaminergic interplay as defined in a previous work published by our group. Clinical and socio-demographic stratification factor analyses were taken in cases and controls. Covariated linear regression was the statistical model for the analysis. SNPs were analyzed in groups (molecular pathway analysis) testing the hypothesis that the distribution of significant (p < 0.05) associations between SNPs and the outcome segregates within each pathway/gene subset. The best associated results are relative to two signle SNPs, (rs7744492 in AKAP12 p = 0.0004 and rs17046113 in CAMK2D p = 0.0006) and a molecular pathway (cAMP biosynthetic process p = 0.005). After correction for multitesting, none of them resulted to be significantly associated. These results are consistent with previous findings in literature and further stress that the molecular mechanisms targeted by current ADs may not be the key biological variables that differentiate severe from mild depression.