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      Effects of AMPD1 gene C34T polymorphism on cardiac index, blood pressure and prognosis in patients with cardiovascular diseases: a meta-analysis

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          Abstract

          Background

          The meta-analysis was aimed to evaluate the effects of AMPD1 gene C34T polymorphism on cardiac function indexes, blood pressure and prognosis in patients with cardiovascular diseases (CVD).

          Methods

          Eligible studies were retrieved through a comprehensive search of electronic databases and manual search. Then the high-quality studies met the rigorous inclusion and exclusion criteria, as well as related to the subject was selected for the study. Comprehensive data analyses were conducted using STATA software 12.0.

          Results

          The study results revealed that CVD patients with CT + TT genotype of AMPD1 C34T polymorphism presented elevated left ventricular ejection fraction (LVEF) (%) and reduced left ventricular end diastolic dimension (LVEDD) (mm) as compared with CC genotype, moreover, the subgroup analysis found that the LVEF (%) was markedly higher in heart failure (HF) patients carrying CT + TT genotype than CC genotype. Besides, the systolic blood pressure (SBP) (mmHg) in CVD patients with CT + TT genotype was obviously decreased in contrast with the CC genotype. Patients suffered from HF with different genotypes (CT + TT and CC) of AMPD1 C34T polymorphism exhibited no significant differences in total survival rate and cardiac survival rate.

          Conclusions

          Our current meta-analysis indicated that the T allele of AMPD1 gene C34T polymorphism may be correlated with LVEF, LVEDD and SBP, which plays a protective role in the cardiac functions and blood pressure in CVD patients, but had no effects on total survival rate and cardiac survival rate for HF.

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          Most cited references14

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          Heterogeneity testing in meta-analysis of genome searches.

          Genome searches for identifying susceptibility loci for the same complex disease often give inconclusive or inconsistent results. Genome Search Meta-analysis (GSMA) is an established non-parametric method to identify genetic regions that rank high on average in terms of linkage statistics (e.g., lod scores) across studies. Meta-analysis typically aims not only to obtain average estimates, but also to quantify heterogeneity. However, heterogeneity testing between studies included in GSMA has not been developed yet. Heterogeneity may be produced by differences in study designs, study populations, and chance, and the extent of heterogeneity might influence the conclusions of a meta-analysis. Here, we propose and explore metrics that indicate the extent of heterogeneity for specific loci in GSMA based on Monte Carlo permutation tests. We have also developed software that performs both the GSMA and the heterogeneity testing. To illustrate the concept, the proposed methodology was applied to published data from meta-analyses of rheumatoid arthritis (4 scans) and schizophrenia (20 scans). In the first meta-analysis, we identified 11 bins with statistically low heterogeneity and 8 with statistically high heterogeneity. The respective numbers were 9 and 6 for the schizophrenia meta-analysis. For rheumatoid arthritis, bins 6.2 (the HLA region that is a well-documented susceptibility locus for the disease) and 16.3 (16q12.2-q23.1) had both high average ranks and low between-study heterogeneity. For schizophrenia, this was seen for bin 3.2 (3p25.3-p22.1) and heterogeneity was still significantly low after adjusting for its high average rank. Concordance was high between the proposed metrics and between weighted and unweighted analyses. Data from genome searches should be synthesized and interpreted considering both average ranks and heterogeneity between studies. 2004 Wiley-Liss, Inc.
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            Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

            Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I 2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R 2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I 2, which we call . We also provide a multivariate H 2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I 2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd.
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              MiR-222 in Cardiovascular Diseases: Physiology and Pathology

              MicroRNAs (miRNAs and miRs) are endogenous 19–22 nucleotide, small noncoding RNAs with highly conservative and tissue specific expression. They can negatively modulate target gene expressions through decreasing transcription or posttranscriptional inducing mRNA decay. Increasing evidence suggests that deregulated miRNAs play an important role in the genesis of cardiovascular diseases. Additionally, circulating miRNAs can be biomarkers for cardiovascular diseases. MiR-222 has been reported to play important roles in a variety of physiological and pathological processes in the heart. Here we reviewed the recent studies about the roles of miR-222 in cardiovascular diseases. MiR-222 may be a potential cardiovascular biomarker and a new therapeutic target in cardiovascular diseases.
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                Author and article information

                Contributors
                fengaifang2017@sina.com
                zhonghuiliu0010@sina.cn
                zsl_zsl_zsl@sina.cn
                syz_20170505@126.com
                zhuyanxin_zhu@126.com
                0536-3277731 , whx_xin@126.com
                Journal
                BMC Cardiovasc Disord
                BMC Cardiovasc Disord
                BMC Cardiovascular Disorders
                BioMed Central (London )
                1471-2261
                3 July 2017
                3 July 2017
                2017
                : 17
                : 174
                Affiliations
                Department of Emergency, Weifang Yidu Central Hospital, No. 4138, Linglongshan Southern Road, Weifang, 262500 People’s Republic of China
                Author information
                http://orcid.org/0000-0001-5815-3649
                Article
                608
                10.1186/s12872-017-0608-0
                5496365
                39f69ab6-a997-4c85-8074-8ce3bd3cb289
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 March 2017
                : 22 June 2017
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Cardiovascular Medicine
                ampd1,cardiovascular diseases,c34t,polymorphism,cardiac indexes,blood pressure,prognosis

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