- 著者
- Takashima Shin-ichiro Usui Soichiro Kurokawa Keisuke Kitano Teppei Kato Takeshi Murai Hisayoshi Furusho Hiroshi Oda Hiroyuki Maruyama Michiro Nagata Yoshiki Usuda Kazuo Kubota Koji Takeshita Yumie Sakai Yoshio Honda Masao Kaneko Shuichi Takamura Masayuki
- 出版者
- BMJ Publishing Group
- 雑誌
- Open Heart (ISSN:20533624)
- 巻号頁・発行日
- vol.3, no.1, 2016-06-01
- 被引用文献数
- 4
Objective: Comprehensive profiling of gene expression in peripheral blood leucocytes (PBLs) in patients with acute coronary syndrome (ACS) as a prognosticator is needed. We explored the specific profile of gene expression in PBLs in ACS for long-term risk stratification. Methods: 30 patients with ACS who underwent primary percutaneous coronary intervention (PCI) and 15 age-matched adults who participated in medical check-ups were enrolled from three centres. Peripheral blood samples were collected to extract RNA for microarray analyses. Results: During the 5-year follow-up, 36% of this cohort developed the expected non-fatal coronary events (NFEs) of target lesion revascularisation (TLR) and PCI for a de novo lesion. Class comparison analysis (p<0.005) demonstrated that 83 genes among 7785 prefiltered genes (41 upregulated vs 42 downregulated genes) were extracted to classify the patients according to the occurrence of NFE. Pathway analysis based on gene ontology revealed that the NFEs were associated with altered gene expression regarding the T-cell receptor signalling pathway in ACS. Univariate t test showed that the expression level of death-associated protein kinase1 (DAPK1), known to regulate inflammation, was the most significantly negatively regulated gene in the event group (0.61-fold, p<0.0005). Kaplan-Meier curve analysis and multivariate analysis adjusted for baseline characteristics or clinical biomarkers demonstrated that lower DAPK1 expression in PBL emerged as an independent risk factor for the NFEs (HR: 8.73; CI 1.05 to 72.8, p=0.045). Conclusions: Altered gene expression in T-cell receptor signalling in PBL in ACS could be a prognosticator for secondary coronary events. © Published by the BMJ Publishing Group Limited.
- 著者
- Furukawa Toshi A Salanti Georgia Atkinson Lauren Z Leucht Stefan Ruhe Henricus G Turner Erick H Chaimani Anna Ogawa Yusuke Takeshima Nozomi Hayasaka Yu Imai Hissei Shinohara Kiyomi Suganuma Aya Watanabe Norio Stockton Sarah Geddes John R Cipriani Andrea
- 出版者
- BMJ Publishing Group
- 雑誌
- BMJ Open (ISSN:20446055)
- 巻号頁・発行日
- vol.6, no.7, 2016-07-08
- 被引用文献数
- 134
[Introduction] Many antidepressants are indicated for the treatment of major depression. Two network meta-analyses have provided the most comprehensive assessments to date, accounting for both direct and indirect comparisons; however, these reported conflicting interpretation of results. Here, we present a protocol for a systematic review and network meta-analysis aimed at updating the evidence base and comparing all second-generation as well as selected first-generation antidepressants in terms of efficacy and acceptability in the acute treatment of major depression. [Methods and analysis] We will include all randomised controlled trials reported as double-blind and comparing one active drug with another or with placebo in the acute phase treatment of major depression in adults. We are interested in comparing the following active agents: agomelatine, amitriptyline, bupropion, citalopram, clomipramine, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, milnacipran, mirtazapine, nefazodone, paroxetine, reboxetine, sertraline, trazodone, venlafaxine, vilazodone and vortioxetine. The main outcomes will be the proportion of patients who responded to or dropped out of the allocated treatment. Published and unpublished studies will be sought through relevant database searches, trial registries and websites; all reference selection and data extraction will be conducted by at least two independent reviewers. We will conduct a random effects network meta-analysis to synthesise all evidence for each outcome and obtain a comprehensive ranking of all treatments. To rank the various treatments for each outcome, we will use the surface under the cumulative ranking curve and the mean ranks. We will employ local as well as global methods to evaluate consistency. We will fit our model in a Bayesian framework using OpenBUGS, and produce results and various checks in Stata and R. We will also assess the quality of evidence contributing to network estimates of the main outcomes with the GRADE framewor