国家神经系统疾病临床医学研究中心数据管理与共享系统

发表杂志：CNS Neuroscience & Therapeutics

发表类型: Original Contributions

影响因子(IF): 7.035

PMID: 35789538 Pubmed原文链接 全文下载

Previous studies have investigated the association between gama-glutamyl transferase(GGT) is and cardiovascular disease (CVD) events or cardiac mortality, but conflicting results were reported. A retrospective study in Korea has found that healthy people with higher GGT levels are more likely to develop stroke after a period of follow-up. What’s more, scientists found that GGT are directly involved in oxidative damage and progression of arterial plaques, and further associated with risk of stroke and all-cause mortality after stroke, suggested the serum elevated levels of GGT may be a prognostic marker for stroke and its poor outcomes. However, limited data suggested adding GGT to conventional model is unlikely to improve stroke risk prediction, especially when predicting adverse outcomes in stroke patients. That means its predictive value remains uncertain. Thus, in this context, the relationship between GGT and adverse outcomes of stroke remains to be further elucidated. In this study, we aimed to explore the association between the measurement of serum metabolic biomarker-GGT concentrations and outcomes in patients with transient ischemic attack (TIA) and ischemic stroke. In addition, we will further analyze the interaction between GGT and different characteristics like age and sex, to find that whether GGT has different predictive effects in different populations.

To collect information of participants characteristics, continuous variables were expressed as mean ± standard deviation (s) and were compared using Kruskal–Wallis or ANOVA test. Categorical variables were presented as numbers (percentages) and were compared using χ2 tests. Patients in this study were categorized into 4 groups according to the quartile of serum GGT which assessed at baseline. Then, the association between GGT and stroke adverse outcomes were investigated with the use of multivariable logistic regression analysis, after adjusted for potential covariates. Thirdly, we used the restricted cubic spline analysis to address the association between GGT and the clinical outcomes. Finally, we considered interaction factors to examine by subgroup analysis.