The correlation between gut microbiota and stroke severity in acute ischemic stroke

<b>Materials and Methods</b><b>Study participants</b>This study is an observational cohort conducted in Nanjing First Hospital. Patients with acute ischemic stroke were consecutively recruited from May 2018 to June 2019. Patients were included if they satisfy the following criteria: 1) Chinese Han ethnicity; 2) 50 years or older; 3) diagnosed as acute anterior ischemic stroke and confirmed by head MRI that there was cerebral infarction consistent with clinical symptoms; and 4) signed written informed consents. Exclusion criteria included: 1) cerebral hemorrhagic stroke; 2) a history of chronic inflammatory or immune diseases (e.g., rheumatoid arthritis, systemic lupus erythematosus, or inflammatory bowel disease); 3) a history of severe liver or kidney dysfunction, hematological diseases and malignancies; 4) administration of probiotics, antibiotics, corticosteroids or immunosuppressants within the past 1 months; and 5) insufficient collection of fecal or blood samples. The study was approved by the Ethical Review Board of Nanjing First Hospital (Nanjing, China). <b>Baseline characteristics and sample collection</b>We collected demographic information and medical histories from all participants by face-to-face interview. Serum levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL), glucose and glycated hemoglobinwere collected after overnight fasting within 24 hours of admissionand measured by the clinical laboratory using standard techniques. Stress hyperglycemia (SHG)is a better biomarker of critical illness than absolute hyperglycemia(38)and was calculated using the following formula: fasting glucose/glycated hemoglobin. Stroke severity was assessed by experienced neurologists on admission using the National Institute of Health Stroke Scale (NIHSS) score and retested at 24 hours, 3 days and 7 days. Minor stroke was defined as admission NIHSS score ≤ 3(39). The fecal samples were collected within 24 hours after admission and temporarily stored in aseptic tubes at -80℃until analysis.<b>DNA extraction and </b><b>high throughput sequencing</b>DNA extraction and sequencing were supported by the Shanghai Genesky Biotechnology Company (Shanghai, China). According to the instructions, fecal genomic DNA was extracted from the fecal samples using the QIAamp® DNA Stool Mini Kit (Qiagen, Hilden, Germany). The V3–V4 hypervariable regions of the bacterial 16S rRNA gene were amplified by polymerase chain reaction (PCR) with the forward primer (5-CCTACGGGNGGCWGCAG-3) and the reverse primer (5-GACTACHVGGGTATCTAATCC-3). High throughput sequencing was performed on the Illumina Miseq platform using the 2×250 bp paired-end read protocol. <b>Bioinformatics and statistical analysis</b>After several steps of quality filtering, the raw reads were clustered into operational taxonomic units (OTUs) by UPARSE with 97% similarity cutoff. All OTUs were classified based on Ribosomal Database Project (RDP) Release 9 by Mothur. Alpha diversities (including Chao 1, ACE, Shannon, Simpson and Coverage index) were analyzed by Mothur. Unweighted and weighted UniFrac principal coordinate analysis (PCoA) based on OTUs were performed by R version 3.4.3(Vegan package). Permutational multivariate analysis of variance (PERMANOVA; Adonis function) was carried out to examine whether there were statistical differences in bacterial community composition (β-diversity) between groups. Metastats analysis and linear discriminant analysis (LDA) effect size (LEfSe) were used to determine the significantly discriminative taxa between groups. Bacteria with significant differences (absolute value of logarithmic LDA score &gt; 2) between the two groups were plotted on taxonomic bar plots. All statistical analyses were performed with R version 3.4.3 (R Development Core Team, Vienna, Austria). Continuous variables were expressed as median (interquartile range) or mean ± standard deviation (SD) and compared with Wilcoxon rank sum test or student t test when appropriate. Categorical variables were expressed as number (frequency) and compared by Pearson’s chi-square test. The missing values of TC, LDL, glucose and glycated hemoglobin were interpolated with the median. Propensity score-matched (PSM) analysis was used to obtain matched pairs of samples from minor stroke group and non-minor stroke group. In the PSM algorithm, the corresponding propensity score of the grouping variable (minor or non-minor) was calculated for each patient with a 1:1 nearest-neighbor matching algorithm with a caliper width of 0.2 of the propensity score, with age, sex and coronary heart disease as covariates. Spearman’s rank correlation coefficient was used to explore the association between the gut microbiota and biochemical parametersor NIHSS scores. We used a linear mixed-effects model with random intercepts and slopes to model the relationship between F/B ratio and the NIHSS score during the 7 days of follow-up.The resulting p values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction. Two-sided p value &lt; 0.05 was considered significant.