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：BMC neurology

发表类型: Original Article

影响因子(IF): 2.474

PMID: 35196998 Pubmed原文链接 全文下载

The incidence of post stroke cognitive impairment (PSCI) is increasing. In recent years, studies on imaging and laboratory biomarkers have brought about many advances in the diagnosis and treatment of PSCI. Previous studies have pointed out that elevated serum γ-glutamyl transferase (GGT) may be an important predictive factor for the occurrence of cognitive impairment after stroke. In analyses adjusted for age and established risk factors, researchers observed an approximately log-linear association of GGT with the incidence rate of vascular cognitive impairment and cognitive decline. What’s more, in a recent study predicting the development of cognitive dysfunction in diabetics, scientists found that GGT and its fluctuations are directly involved in oxidative damage and progression of arterial plaques, and further associated with decreased cognitive function, suggested the serum GGT concentration may be a marker for oxidative stress in cognitive decline. Furthermore, a study has found that serum glutamine is significantly different in patients with post-stroke non-cognitive dysfunction (PSNCI) and PSCI, and has a high specificity in predicting the occurrence of PSCI. Therefore, serum metabolic biomarker like GGT detection combined with Montreal Cognitive Assessment (MoCA) assessment may contribute to the early identification of PSCI. However, GGT activity is still currently mainly used to assess liver function, and there is a lack of relevant evidence in the population with PSCI, that means prospective studies are necessary.

Firstly, 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 PSCI 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 risk of PSCI. Finally, we considered interaction factors to examine by subgroup analysis.