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Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.
We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.
This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.
This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.
This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.
Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design.
This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934).
Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups.
The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine.
The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.
Our previous genome-wide association study (CONVERGE sample) identified significant association between single nucleotide polymorphisms (SNPs) near the SIRT1 gene and major depressive disorder (MDD) in Chinese populations.
To investigate whether SNPs across the SIRT1 gene locus affect regional grey matter density in the Han Chinese population.
T1-weighted structural magnetic resonance imaging was conducted on 92 healthy participants from Eastern China. Grey matter was segmented from the image, which consisted of voxel-wise grey matter density. The effect of SIRT1 SNPs on grey matter density was determined by a multiple linear regression framework.
SNP rs4746720 was significantly associated with grey matter density in two brain cortical regions: the orbital part of the right inferior frontal gyrus and the orbital part of the left inferior frontal gyrus (family-wise error-corrected P < 0.05; voxel-wise P < 0.001). Also, rs4746720 exceeded genome-wide significance in association with MDD in our CONVERGE sample (P = 3.32 × 10−08, odds ratio 1.161).
Our results provided evidence for a potential role of the SIRT1 gene in the brain, implying a possible pathophysiological mechanism underlying susceptibility to MDD.
Brain-derived neurotrophic factor (BDNF) plays an important role in neural survival and was proposed to be related to psychiatric disorders. Val66Met (also known as rs6265 or G196A), the only known functional polymorphism of the BDNF gene, has been widely studied and considered to be associated with risk of some psychiatric disorders such as bipolar disorder and schizophrenia. However, studies evaluating its association with obsessive–compulsive disorder (OCD) obtained inconsistent results. The purpose of this study was to derive a more precise estimation of the association between BDNF Val66Met polymorphism and OCD susceptibility by a meta-analysis.
We carried a structured literature search in PubMed, Embase, PsycINFO and Chinese Biomedical Database up to December 2014; and retrieved all eligible case–control studies according to the including criteria. Meta-analysis was performed for four genetic models: allelic model: Met versus Val; additive model: Met/Met versus Val/Val; recessive model: Met/Met versus Val/Val+Val/Met; and dominant model: Val/Met+Met/Met versus Val/Val. Stratified analyses were performed by ethnicity and gender where appropriate.
A total of eight articles with nine studies including 1632 OCD cases and 2417 controls were identified. No significant association was detected in any comparison when the whole data were pooled together or stratified by ethnicity or gender in all four genetic models (p>0.05 for each comparison).
Despite some limitations, our meta-analysis suggests that no significant association exists between the BDNF Val66Met polymorphism and OCD susceptibility.
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