Xu Ke, Ren Zhe, Zhao Shuang, Ren Yi, Wang Jiaolin, Wu Wentao, Hu Zicheng, He Fei, Tu Dianji, Zhong Qi, Chen Jianjun, Xie Peng
Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Department of Neurology, National Health Commission Key Laboratory of Diagnosis and Treatment On Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
BMC Microbiol. 2025 Aug 20;25(1):525. doi: 10.1186/s12866-025-04217-8.
There is growing research on the relationship between gut bacteria and various forms of strokes. This study aimed to investigate the relationship between fecal metabolites and ischemic stroke, providing a new perspective on predicting the latter.
Stool samples were taken from 60 patients with ischemic stroke and 60 healthy individuals, and non-targeted metabolomic analysis was used. The generalized boosted linear model was utilized for co-occurrence analysis to ascertain the noteworthy variation in fecal metabolites. The important differential metabolites were identified by the random forest algorithm, a prediction panel was developed to distinguish ischemic stroke patients from healthy individuals. Specifically, six differential metabolites (Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA(2:0/PGF1alpha), and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol) were identified as potential independent stroke-associated metabolites. A prediction panel consisting of these six metabolites could yield an area under the curve of 0.989 in training set and 0.973 in testing set. There was a substantial correlation between all six independent stroke-associated metabolites and the severity of ischemic stroke, but it was not affected by depression or anxiety.
These six differential metabolites were independent stroke-associated metabolites, and the panel consisting of these metabolites could serve as a potential prediction panel for ischemic stroke. However, future external validation in multi-ethnic cohorts is necessary to confirm broader generalizability.
关于肠道细菌与各种类型中风之间关系的研究日益增多。本研究旨在探讨粪便代谢物与缺血性中风之间的关系,为预测缺血性中风提供新的视角。
采集了60例缺血性中风患者和60例健康个体的粪便样本,并进行了非靶向代谢组学分析。采用广义增强线性模型进行共现分析,以确定粪便代谢物的显著变化。通过随机森林算法识别重要的差异代谢物,开发了一个预测模型来区分缺血性中风患者和健康个体。具体而言,六种差异代谢物(灵芝酸θ、果糖赖氨酸、五甘醇、2-氯十八烷酸、PA(2:0/PGF1α)和4-[(E)-5,6-二氢-2,3'-联吡啶-3(4H)-亚基甲基]-3-甲氧基苯酚)被确定为潜在的独立中风相关代谢物。由这六种代谢物组成的预测模型在训练集中的曲线下面积为0.989,在测试集中为0.973。所有六种独立的中风相关代谢物与缺血性中风的严重程度之间存在显著相关性,但不受抑郁或焦虑的影响。
这六种差异代谢物是独立的中风相关代谢物,由这些代谢物组成的模型可作为缺血性中风的潜在预测模型。然而,未来需要在多民族队列中进行外部验证,以确认其更广泛的通用性。