The association between T174M polymorphism of angiotensinogen gene and essential hypertension risk remains controversial. We herein performed a meta-analysis to achieve a reliable estimation of their relationship. All the studies published up to May 2013 on the association between T174M polymorphism and essential hypertension risk were identified by searching the electronic repositories PubMed, MEDLINE and EMBASE, Springer, Elsevier Science Direct, Cochrane Library and Google Scholar. Data were extracted and pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated. Ultimately, nine eligible studies, including 2188 essential hypertension cases and 2459 controls, were enrolled in this meta-analysis. No significant associations were found under the overall ORs for M-allele comparison (M vs. T, pooled OR 0.92, 95% CI 0.62–1.37), MM vs. TT (pooled OR 0.86, 95% CI 0.29–2.51), TM vs. TT n (pooled OR 0.91, 95% CI 0.63–1.32), recessive model (MM vs. TT+TM, pooled OR 0.89, 95% CI 0.35–2.30), dominant model (MM+TM vs. TT, pooled OR 0.91, 95% CI 0.60–1.38) between T174M polymorphism and risk for essential hypertension. This meta-analysis suggested that the T174M polymorphism of the angiotensinogen gene might not be associated with the susceptibility of essential hypertension in Asian or European populations.
Many seizure-free patients who consider withdrawing from antiepileptic drugs (AEDs) hope to discontinue treatment to avoid adverse effects. However, withdrawal has certain risks that are difficult to predict. In this study, we performed a literature review, summarized the causes of significant variability in the risk of postwithdrawal recurrent seizures, and reviewed study data on the age at onset, cause, types of seizures, epilepsy syndrome, magnetic resonance imaging (MRI) abnormalities, epilepsy surgery, and withdrawal outcomes of patients with epilepsy. Many factors are associated with recurrent seizures after AED withdrawal. For patients who are seizure-free after treatment, the role of an electroencephalogram (EEG) alone in ensuring safe withdrawal is limited. A series of prediction models for the postwithdrawal recurrence risk have incorporated various potentially important factors in a comprehensive analysis. We focused on the populations of studies investigating five risk prediction models and analyzed the predictive variables and recommended applications of each model, aiming to provide a reference for personalized withdrawal for patients with epilepsy in clinical practice.
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