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. 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) Magnetic Resonance Imaging (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. <b>Baseline characteristics and sample collection</b>We collected demographic information and medical histories from all participants by face-to-face interview. The etiology of ischemic stroke was classified by the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria. Biochemical parameters including serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), fasting glucose, glycated hemoglobin, blood urea nitrogen (BUN), serum creatinine (Scr) and uric acid (UA) were collected after overnight fasting within 24 hours of admission and 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. It was calculated using the following formula: fasting glucose/glycated hemoglobin ratio. 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. Patients were divided into two groups: minor stroke, who had admission NIHSS score ≤ 3, and non-minor stroke with admission NIHSS score &gt; 3. Sterile fecal containers and instructions were distributed to each study participant on admission. Approximately 2 g of fresh fecal samples were collected from each participant within 24 hours after admission and immediately (within 1 hour) stored at -80℃ until analysis. <b>Functional outcomes</b>Functional outcomes were quantified using the modified Rankin scale (mRS) score at 30 days and 1 year through routine telephone interview. Poor functional outcome was defined as mRS score &gt; 2. <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>The unique 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. Within-individual (α) diversity (including observed species, Chao 1, ACE, Shannon, Simpson, and Coverage index) was used to measure the richness or evenness of taxa within each sample, and was analyzed by Mothur. Between-individual (β) diversity was provided for comparison of the taxonomic profiles between microbial communities. 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. We also used BugBase to predict potential microbiome phenotypes, including aerobic, anaerobic, containing mobile elements, facultatively anaerobic, biofilm forming, gram-negative, gram-positive, potentially pathogenic, and stress tolerant. The missing values of TC (1.5%), HDL (1.5%), LDL (1.5%), fasting glucose (3.7%), glycated hemoglobin ratio (3.7%), and UA (5.9%) 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 scores obtained at different timepoints and functional outcomes. We used linear mixed-effects models with random intercepts and slopes to test whether the relative abundance of discriminative taxa (e.g., genus <i>Roseburia</i>) or<i> Firmicutes</i> to <i>Bacteroidetes</i> ratio (F/B ratio) or gram-negative/gram-positive 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 centering for continuous covariates with 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 functional outcomes at 30 days and 1 year. 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.