No abstract

No abstract

Our results suggest that the investigated polymorphisms are not major susceptibility factors in the etiology of MDD with suicidality. However, the results must be verified in larger samples and different ethnicities.

Objective:To explore the possible relationship between six single nucleotide polymorphisms (SNPs) (rs6311 and rs6305 of 5-HT2A, rs5443 of Gb3, rs2230739 of ACDY9, rs1549870 of PDE1A and rs255163 of CREB1, which are all related with 5-HT2A the signal transduction pathway) and the response efficacy to selective serotonin reuptake inhibitor (SSRI) treatments in major depressive disorder (MDD) Chinese. Methods: This study included 194 depressed patients to investigate the influence of 6 polymorphisms in 5-HT2A signal transduction-related genes on the efficacy of SSRIs assessed over 1 year. The efficacies of SSRIs on 194 MDD patients were evaluated in an 8-week open-trial study. Over 1 year, a follow-up study was completed for 174 of them to observe the long-term efficacy of SSRIs. The optimal-scaling regression analysis was used for testing the relationship between the different genotypes of five SNPs and the efficacy in MDD. Results: It showed that the patients with rs5443TT and rs2230739GG have a relatively good efficacy in response to short-term SSRIs. We also found that good efficacy appeared in depressed patients with rs2230739GG in response to long-term SSRIs. Conclusions: It suggested that different genotypes of rs5443 and rs2230739 might influence the signal transduction pathways of second message and affect therapeutic efficacy.

Background: A large proportion of major depressive patients will experience recurring episodes. Many patients still do not response to available antidepressants. In order to meaningfully predict who will not respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Methods: Eight hundred fifty-seven patients with recurrent major depressive disorder who were followed up 3-10 years involved 32 variables including socio-demographic, clinical features, and SSRIs treatment features when they received the first treatment. Also, 34 tagSNPs related to 5-HT signaling pathway, were detected by using mass spectrometry analysis. The training samples which had 12 clinical variables and four tagSNPs with statistical differences were learned repeatedly to establish prediction models based on support vector machine (SVM). Results: Twelve clinical features (psychomotor retardation, psychotic symptoms, suicidality, weight loss, SSRIs average dose, first-course treatment response, sleep disturbance, residual symptoms, personality, onset age, frequency of episode, and duration) were found significantly difference (P< 0.05) between 302 SSRI-resistance and 304 SSRI non-resistance group. Ten SSRI-resistance predicting models were finally selected by using support vector machine, and our study found that mutations in tagSNPs increased the accuracy of these models to a certain degree. Conclusion: Using a data-driven machine learning method, we found 10 predictive models by mining existing clinical data, which might enable prospective identification of patients who are likely to resistance to SSRIs antidepressant.