DOI: 10.5061/dryad.k3j9kd55d
title: Gut dysbiosis and mortality in hemodialysis patients

description: Gut dysbiosis, characterized by decreased microbial diversity, promotes
 inflammation. Persistent inflammation plays a pathogenic role in
 complications of chronic kidney disease (CKD). However, little is known
 about the relationship between gut dysbiosis and adverse outcomes in
 patients with CKD. First, we examined the association of microbial
 diversity with all-cause mortality in CKD patients receiving hemodialysis
 (n=109). The microbial composition of fecal samples was profiled by means
 of 16S ribosomal RNA gene sequencing. Microbial diversity was calculated
 using the Simpson index. Participants were stratified into higher- (above
 the median) and lower-diversity (below the median) groups and were
 followed up for a median of 2.1 years. Kaplan-Meier analyses revealed a
 significant association between higher diversity and a lower risk of death
 (log-rank P=0.015). After adjustment for patient characteristics and
 comorbid conditions, the risk of death among patients with higher
 diversity was 74% lower than that among patients with lower diversity
 (hazard ratio, 0.26; 95% CI, 0.07 to 0.95). Next, in a matched
 case-control study, we compared the microbial composition between
 nonsurvivors and survivors who were matched 1:4 for age and sex. We
 observed significantly lower values of microbial diversity and higher
 levels of proinflammatory cytokines among nonsurvivors (n=14) than
 survivors (n=56). Specifically, the relative abundance of Succinivibrio
 and Anaerostipes, two short-chain fatty acid-producing bacteria, was
 markedly reduced in nonsurvivors compared with survivors. In conclusion, a
 unique gut microbial composition is associated with an increased risk of
 mortality among hemodialysis patients and may be used to identify subjects
 with a poor prognosis.
description: <b>Fecal
 sample collection</b> Fecal samples were
 obtained at home using a specimen collection kit and delivered to the
 laboratory (Germark Biotechnology, Taichung, Taiwan) within 24 hours by
 refrigerated (4°C) transportation. The samples were subsequently
 aliquoted, and a 200-mg subsample was immediately kept in InhibitEx buffer
 (Qiagen, Valencia, CA). DNA was extracted using the Qiagen DNA Mini Kit
 (Qiagen, Valencia, CA). The bacterial DNA concentration was measured with
 a NanoDrop ND-1000 (Thermo Scientific, Wilmington,
 DE). <b>16S
 ribosomal RNA gene sequencing and data
 processing</b> Amplification of
 genomic DNA was performed using bar-coded primers (341F and 805R) that
 targeted the V3–V4 regions of the bacterial 16S rRNA gene. A paired-end
 library (insert size of 465 bp for each sample) was constructed with the
 TruSeq Nano DNA Library Prep kit (Illumina, San Diego, CA). Amplicons were
 sequenced on an Illumina MiSeq 2000 sequencer using a MiSeq Reagent Kit v3
 (Illumina). To minimize batch effects, all samples were sequenced at the
 same time in the same research laboratory (Germark Biotechnology,
 Taichung, Taiwan). On a per-sample basis, paired-end reads were merged
 using USEARCH (v8.0.1623), setting 8 bp as the minimum overlap of read
 pairs. Merged sequences were quality trimmed using Mothur (v1.35.1). Those
 reads that did not meet the quality criteria of a minimum quality score of
 27 and sequence length shorter than 400 bp or longer than 550 bp for 16S
 amplicon reads were removed. Chimeric sequences were identified and
 deleted by USEARCH (reference mode and 3% minimum divergence). Clustering
 of sequence reads into operational taxonomical units (OTUs) at 97%
 identity level was achieved using the UPARSE pipeline, and identified
 taxonomy was then aligned using the Greengenes reference
 database.