Association Analysis of Gut Microbiota and Prognosis of Patients with Acute Ischemic Stroke in Basal Ganglia Region.

Previous studies have implied the potential impact of gut microbiota on acute ischemic stroke (AIS), but the relationships of gut microbiota with basal ganglia region infarction (BGRI) and the predictive power of gut microbiota in BGRI prognosis is unclear. The aim of this study was to ascertain characteristic taxa of BGRI patients with different functional outcomes and identify their predictive value. Fecal samples of 65 BGRI patients were collected at admission and analyzed with 16s rRNA gene sequencing. Three-month functional outcomes of BGRI were evaluated using modified Rankin Scale (mRS), and patients with mRS score of 0-1 were assigned to good-BGRI group while others were assigned to poor-BGRI group. We further identified characteristic microbiota using linear discriminant analysis effect size, and receiver operating characteristic (ROC) curve was used to determine the predictive value of differential bacteria. According to the mRS score assessed after 3 months of stroke onset, 22 patients were assigned to poor-BGRI group, while 43 patients were assigned to good-BGRI group. Short chain fatty acids-producing bacteria, Romboutsia and Fusicatenibacter, were characteristic microbiota of the good-BGRI group, while pro-inflammatory taxa, Acetanaerobacterium, were characteristic microbiota of the poor-BGRI group. Furthermore, the differential bacteria showed extensive associations with clinical indices. ROC curves, separately plotted based on Romboutsia and Fusicatenibacter, achieved area under the curve values of 0.7193 and 0.6839, respectively. This study identified the efficient discriminative power of characteristic microbiota in BGRI patients with different outcomes and provided novel insights into the associations of gut microbiota with related risk factors.

Association analysis of the gut microbiota in predicting outcomes for patients with acute ischemic stroke and H-type hypertension.

Introduction: H-type hypertension (HHTN) is a subtype of hypertension that tends to worsen the prognosis of acute ischemic stroke (AIS). Recent studies have highlighted the vital role of gut microbiota in both hypertension and AIS, but there is little available data on the relationship between gut microbiota and the progression of AIS patients with HHTN. In this study, we investigated the microbial signature of AIS patients with HHTN and identified characteristic bacteria as biomarkers for predicting prognosis. Methods: AIS patients with HHTN (n = 150) and without HHTN (n = 50) were enrolled. All patients received a modified Rankin Scale (mRS) assessment at 3 months after discharge. Fecal samples were collected from the participants upon admission, including 150 AIS patients with HHTN, 50 AIS patients with non-HHTN, and 90 healthy subjects with HHTN. These samples were analyzed using 16S rRNA sequencing to characterize the bacterial taxa, predict functions, and conduct correlation analysis between specific taxa and clinical features. Results: Our results showed that the composition of the gut microbiota in HHTN patients differed significantly from that in non-HHTN patients. The abundance of the genera Bacteroides, Escherichia-Shigella, Lactobacillus, Bifidobacterium, and Prevotella in AIS patients with HHTN was significantly increased compared to AIS patients without HHTN, while the genus Streptococcus, Faecalibacterium, and Klebsiella were significantly decreased. Moreover, Bacteroides, Lactobacillus, Bifidobacterium, and Klebsiella in AIS patients with HHTN were more abundant than healthy subjects with HHTN, while Escherichia-Shigella, Blautia, and Faecalibacterium were less abundant. Moreover, the genera Butyricicoccus, Rothia, and Family_XIII_UCG-001 were negatively connected with the NIHSS score, and the genera Butyricicoccus and Rothia were observed to be negatively associated with the mRS score. The genera Butyricicoccus, Romboutsia, and Terrisporobacter were associated with a poor prognosis, whereas the increase in Butyricimonas and Odoribacter was correlated with good outcomes. Generated by eight genera and clinical indexes, the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve achieved 0.739 to effectively predict the prognosis of AIS patients with HHTN. Conclusion: These findings revealed the microbial signature of AIS patients with HHTN and further provided potential microbial biomarkers for the clinical diagnosis of AIS patients with HHTN.

The Diagnostic Value of Gut Microbiota Analysis for Post-Stroke Sleep Disorders.

BACKGROUND: Gut microbiota have been associated with many psychiatric disorders. However, the changes in the composition of gut microbiota in patients with post-stroke sleep disorders (PSSDs) remain unclear. Here, we determined the gut microbial signature of PSSD patients. METHODS: Fecal samples of 205 patients with ischemic stroke were collected within 24 h of admission and were further analyzed using 16 s RNA gene sequencing followed by bioinformatic analysis. The diversity, community composition, and differential microbes of gut microbiota were assessed. The outcome of sleep disorders was determined by the Pittsburgh Sleep Quality Index (PSQI) at 3 months after admission. The diagnostic performance of microbial characteristics in predicting PSSDs was assessed by receiver operating characteristic (ROC) curves. RESULTS: Our results showed that the composition and structure of microbiota in patients with PSSDs were different from those without sleep disorders (PSNSDs). Moreover, the linear discriminant analysis effect size (LEfSe) showed significant differences in gut-associated bacteria, such as species of Streptococcus, Granulicatella, Dielma, Blautia, Paeniclostridium, and Sutterella. We further managed to identify the optimal microbiota signature and revealed that the predictive model with eight operational-taxonomic-unit-based biomarkers achieved a high accuracy in PSSD prediction (AUC = 0.768). Blautia and Streptococcus were considered to be the key microbiome signatures for patients with PSSD. CONCLUSIONS: These findings indicated that a specific gut microbial signature was an important predictor of PSSDs, which highlighted the potential of microbiota as a promising biomarker for detecting PSSD patients.

The gut microbial signatures of patients with lacunar cerebral infarction.

BACKGROUND: Emerging evidence revealed that gut microbial dysbiosis is involved in the pathogenesis of multiple neurological diseases, but there is little available data on the relationship between gut microbiota and lacunar cerebral infarction (LCI). METHODS: Fecal samples from acute LCI patients (n = 65) and matched healthy controls (n = 65) were collected. The compositions and potential functions of the gut microbiota were estimated. RESULTS: The results showed that there were significant gut microbial differences between LCI and control groups. Patients with LCI had higher abundances of genus Lactobacillus, Streptococcus, Veillonella, Acidaminococcus, Bacillus, Peptoclostridium, Intestinibacter, Alloscardovia and Cloacibacillus but lower proportions of genus Agathobacter and Lachnospiraceae_UCG-004. Investigating further these microbes such as Lactobacillus and Veillonella were correlated with clinical signs. Moreover, we found that 9 gene functions of gut microbiota were different between LCI patients and controls, which were associated with amino acid metabolism and inflammatory signal transduction. Notably, four optimal microbial markers were determined, and the combination of Streptococcus, Lactobacillus, Agathobacter, Lachnospiraceae_UCG-004 and the three risk factors achieved an area under the curve (AUC) value of 0.854 to distinguish LCI from controls. CONCLUSION: These findings revealed the characterizing of gut microbiota in LCI patients and provided potential microbial biomarkers for clinical diagnosis of LCI.

Predictive microbial feature analysis in patients with depression after acute ischemic stroke.

Introduction: Post-stroke depression (PSD) is the most common emotional problem following a stroke, which requires early diagnosis to improve the prognosis. Gut microbiota plays important role in the pathological mechanisms of acute ischemic stroke and influences the outcome of patients. However, the relationship between PSD and gut microbiota remains unknown. Here, we explored whether the microbial signatures of gut microbiota in the patients with stroke could be an appropriate predictor of PSD. Methods: Fecal samples were collected from 232 acute ischemic stroke patients and determined by 16s rRNA sequencing. All patients then received 17-Hamilton Depression Rating Scale (HAMD-17) assessment 3 months after discharge, and were further divided into PSD group and non-PSD group. We analyzed the differences of gut microbiota between these groups. To identify gut microbial biomarkers, we then established microbial biomarker model. Results: Our results showed that the composition of gut microbiota in the PSD patients differed significantly from that in non-PSD patients. The genus Streptococcus, Akkermansia, and Barnesiella were significantly increased in PSD patients compared to non-PSD, while the genus Escherichia-Shigella, Butyricicoccus, and Holdemanella were significantly decreased. Correlation analyses displayed that Akkermansia, Barnesiella, and Pyramidobacter were positively correlated with HAMD score, while Holdemanella was negatively correlated with HAMD score. The optimal microbial markers were determined, and the combination achieved an area under the curve (AUC) value of 0.705 to distinguish PSD from non-PSD. Conclusions: Our findings suggest that PSD patients had distinct gut microbiota compared to non-PSD patients, and explore the potential of microbial markers, which might provide clinical decision-making in PSD.

Gut microbiota alterations are associated with functional outcomes in patients of acute ischemic stroke with non-alcoholic fatty liver disease.

Introduction: Patients with acute ischemic stroke (AIS) with non-alcoholic fatty liver disease (NAFLD) frequently have poor prognosis. Many evidences suggested that the changes in gut microbiota may play an important role in the occurrence and development of AIS patients with NAFLD. The purpose of this study was to explore microbial characteristics in patients of AIS with NAFLD, and the correlation between gut microbiota and functional outcomes. Methods: The patients of AIS were recruited and divided into NAFLD group and non-NAFLD group. The stool samples and clinical information were collected. 16 s rRNA sequencing was used to analyze the characteristics of gut microbiota. The patients of AIS with NAFLD were followed-up to evaluate the functional outcomes of disease. The adverse outcomes were determined by modified Rankin scale (mRS) scores at 3 months after stroke. The diagnostic performance of microbial marker in predicting adverse outcomes was assessed by recipient operating characteristic (ROC) curves. Results: Our results showed that the composition of gut microbiota between non-NAFLD group and NAFLD group were different. The characteristic bacteria in the patients of AIS with NAFLD was that the relative abundance of Dorea, Dialister, Intestinibacter and Flavonifractor were decreased, while the relative abundance of Enorma was increased. Moreover, the characteristic microbiota was correlated with many clinical parameters, such as mRS scores, mean arterial pressure and fasting blood glucose level. In addition, ROC models based on the characteristic microbiota or the combination of characteristic microbiota with independent risk factors could distinguish functional dependence patients and functional independence patients in AIS with NAFLD (area under curve is 0.765 and 0.882 respectively). Conclusion: These findings revealed the microbial characteristics in patients of AIS with NAFLD, and further demonstrated the predictive capability of characteristic microbiota for adverse outcomes in patients of AIS with NAFLD.