Kidney Week

Abstract: SA-PO798

Blood Microbiome Profile Characteristics in Patients with CKD

Session Information

• CKD: Epidemiology, Risk Factors, Prevention - III
  October 27, 2018 | Location: Exhibit Hall, San Diego Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 1901 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Shah, Neal B., Massachusetts General Hospital, Boston, Massachusetts, United States

Neal B. Shah,

• Lelouvier, Benjamin, Vaiomer, LABEGE, France

Benjamin Lelouvier,

• Allegretti, Andrew S., Massachusetts General Hospital, Boston, Massachusetts, United States

Andrew S. Allegretti,

• Thadhani, Ravi I., Cedars-Sinai, Los Angeles, California, United States

Ravi I. Thadhani,

• Fasano, Alessio, Massachusetts General Hospital, Boston, Massachusetts, United States

Alessio Fasano,

Background

Association between gut dysbiosis, increased intestinal permeability and endotoxemia mediated inflammation has been well established in Chronic Kidney Disease (CKD). Microbiome data in circulating blood is lacking. Our pilot study’s aim was to compare blood microbiome 16S ribosomal DNA (rDNA) levels and metagenomic profiles between CKD patients and healthy controls.

Methods

A case control study of 20 non-diabetic CKD and age and sex matched 20 healthy controls was done. Blood bacterial DNA was studied in buffy coat samples both quantitatively by 16S PCR and qualitatively by 16S targeted metagenomic sequencing using a molecular pipeline specifically optimized for blood samples. Patients with inflammatory bowel disease, WBC>11x10⁹/L or taking antibiotics at enrollment were excluded.

Results

Median 16S rDNA levels did not significantly differ between CKD and healthy groups (117 vs 122 copies/ng DNA, p=0.38). 16S Metagenomic sequencing revealed a significant decrease in alpha diversity (Chao1 index) in CKD group compared to healthy (p=0.0483). Linear discriminant analysis effect size (LEfSe, Fig 1) displays numerous bacterial taxa with proportions significantly modified in CKD group. There was significant increase in proteobacteria phylum, gammaproteobacterial class and enterobacteriacea, pseudomonadaceae and legionellaceae families in the CKD group compared to healthy group. At deeper taxonomic levels, there were other striking differences between bacterial profiles. No significant correlation was found between glomerular filtration rate and 16S rDNA levels (r= -0.002, p=0.98).

Conclusion

Our study demonstrates reduced alpha diversity and significant variations in blood microbial profile in CKD patients with numerous bacterial taxa impacted. Studying larger populations of CKD from various etiologies may help identify microbiome patterns as predictive and diagnostic biomarkers.

Taxonomic Cladogram using LEfSe analysis