Roseburia abundance associates with severity, evolution and outcome of acute ischemic stroke

<b>MATERIALS AND METHODS</b><b>Study participants</b>This is a prospective observational cohort study conducted in Nanjing First Hospital. Patients with acute ischemic stroke were consecutively recruited from May 2018 to June 2019 with the following inclusion criteria: 1) aged 50 years or older; 2) local residents for over 6 months; 3) MRI-confirmed ischemic stroke in the anterior circulation within 3 days of symptom onset; 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). The patients provided their written informed consent to participate in this study. <b>Baseline characteristics and sample collection</b>We collected demographic information and medical histories from all participants by face-to-face interview. Biochemical parameters including serum levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL), fasting glucose and glycated hemoglobinwere collected after overnight fasting within 24 hours of admissionand measured at the hospital central laboratory with laboratory staff blinded to clinical data. Stress hyperglycemia (SHG)was also included as a better biomarker of critical illness than absolute hyperglycemia (Roberts et al., 2015). It was calculated using the following formula: fasting glucose/glycated hemoglobin ratio. Stroke severity was assessed by experienced neurologists (H.S and Z.L) on admission using the National Institute of Health Stroke Scale (NIHSS) score and retested at 24 hours, 3 days and 7 days. Patients were divided into two groups: minor stroke, who had admission NIHSS score ≤ 3 (Wang et al., 2013), and non-minor stroke with admission NIHSS score &gt; 3. Short-termfunctional outcome was quantified using the modified Rankin scale (mRS)score at 30 days through a routine telephone interview. Poor functional outcome was defined as mRSscore &gt; 2. The fecal samples were collected within 24 hours after admission and temporarily stored in aseptic tubes at -80℃until analysis. <b>DNA extraction and high throughput sequencing</b>DNA extraction and sequencing were supported by the Shanghai Genesky Biotechnology Company (Shanghai, China) not knowing group assignment. 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 a 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 <i>t</i>test when appropriate. Categorical variables were expressed as number (percentage) and compared by Pearson’s chi-square test. The missing values of TC, LDL, fasting glucose, and glycated hemoglobin were interpolated with the median. Propensity score-matched (PSM) analysis was used to obtain matched pairs of samples from the minor stroke group and the 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 (CHD) as covariates. Spearman’s rank correlation coefficient was used to explore the correlation of different genera with biochemical parameters,NIHSS scoresobtained at different timepoints and one-month functional outcome. We used linear mixed-effects models with random intercepts and slopes to test if the relative abundance of discriminative taxa (e.g., genus <i>Roseburia</i>) or<i>Firmicutes</i>to <i>Bacteroidetes</i>ratio(F/B ratio) account for the evolution of NIHSS scores through the first 7 days of hospitalization.Since the NIHSS score was highly skewed, the natural logarithm transformation [ln (NIHSS + 1)] was applied. Grand-mean centeringfor continuous covariateswith meaningless 0 values (such as age) was performed. Multivariable logistic regression analyses were also used to evaluate the associations between the relative abundance of discriminative taxa and one-month functional outcome. The resulting <i>p</i>values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction. Two-sided <i>p</i>value &lt; 0.05 was considered significant.<br>