Specific gut microbiome signatures predict the risk of acute ischemic stroke - Yu, Shicheng; Shi, Jiayu; Yu, Gaojie; Xu, Jin; Dong, Yiyao; Lin, Yan; Xie, Huijia; Liu, Jiaming; Sun, Jing (Frontiers in Aging Neuroscience, 2024)
IntroductionNumerous studies have reported alterations in the composition of gut microbiota in patients with acute ischemic stroke (AIS), with changes becoming more pronounced as the disease progresses. However, the association between the progression of transient ischemic attack (TIA) and AIS remains unclear. This study aims to elucidate the microbial differences among TIA, AIS, and healthy controls (HC) while exploring the associations between disease progression and gut microbiota.MethodsFecal samples were collected from acute TIA patients (n = 28), AIS patients (n = 235), and healthy controls (n = 75) and analyzed using 16 s rRNA gene sequencing. We determined characteristic microbiota through linear discriminant analysis effect size and used the receiver operating characteristic (ROC) curve to assess their predictive value as diagnostic biomarkers.ResultsOur results showed significant gut microbial differences among the TIA, AIS, and HC groups. Patients with AIS exhibited higher abundances of Lactobacillus and Streptococcus, along with lower abundances of Butyricicoccaceae and Lachnospiraceae_UCG-004. Further analysis revealed that the abundance of characteristic bacteria, such as Lactobacillus and Streptococcus, was negatively correlated with HDL levels, while Lactobacillus was positively correlated with risk factors such as homocysteine (Hcy). In contrast, the abundance of Lachnospiraceae_UCG-004 was negatively correlated with both Hcy and D-dimer levels. ROC models based on the characteristic bacteria Streptococcus and Lactobacillus effectively distinguished TIA from AIS, yielding areas under the curve of 0.699 and 0.626, respectively.ConclusionWe identified distinct changes in gut bacteria associated with the progression from TIA to AIS and highlighted specific characteristic bacteria as predictive biomarkers. Overall, our findings may promote the development of microbiome-oriented diagnostic methods for the early detection of AIS.