Gut dysbiosis and mortality in hemodialysis patients
Lin, Ting-Yun; Wu, Ping-Hsun; Lin, Yi-Ting; Hung, Szu-Chun (2021), Gut dysbiosis and mortality in hemodialysis patients, Dryad, Dataset, https://doi.org/10.5061/dryad.k3j9kd55d
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.
Fecal sample collection
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).
16S ribosomal RNA gene sequencing and data processing
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.