An integrative analysis using 16S rDNA sequencing and machine learning identified stage-specific gut microbial biomarkers and functional pathways implicated in CKD progression, supporting the mechanistic role of the gut-kidney axis and the potential for microbiota-based interventions and early diagnostic approaches.
Significant differences in microbial composition, richness, and diversity were found among CKD stage groups and healthy controls.
Ten differential microbial taxa were identified across CKD stages, with Actinobacteriota and Bifidobacterium showing the highest relative abundances.
Machine learning methods identified six microbial biomarkers capable of distinguishing CKD patients from healthy individuals, all with AUC values exceeding 0.7.
Species driving force analysis revealed increasing numbers of microbial interaction relationships with advancing CKD stage, with 26, 27, and 39 interactions identified in stage II, III, and IV CKD compared to controls, respectively.
The study identified stage-specific gut microbial biomarkers and functional pathways implicated in CKD progression, supporting the mechanistic role of the gut-kidney axis.
This research suggests that the bacteria living in the gut change in specific ways as chronic kidney disease (CKD) progresses through its stages. By analyzing stool samples from 27 people — including patients with stage II, III, and IV CKD and healthy individuals — researchers used genetic sequencing to identify which gut bacteria were present and how their communities differed between groups. They found significant shifts in the diversity and makeup of gut bacteria across CKD stages, with certain bacteria like Actinobacteriota and Bifidobacterium standing out as particularly notable. Using artificial intelligence and statistical tools (machine learning), the researchers then identified six specific bacterial markers that could distinguish CKD patients from healthy people with reasonable accuracy (all with AUC scores above 0.7, where 1.0 would be perfect). Two of these were Eubacterium eligens and Lactococcus. The study also found that as CKD worsened, the interactions between different bacterial species became more complex, rising from 26 interaction relationships in stage II to 39 in stage IV. This research suggests that gut bacteria may play a role in how CKD develops and worsens — a concept known as the 'gut-kidney axis.' The identified bacterial markers could potentially be developed into non-invasive tools to detect or monitor CKD at earlier stages. The findings also raise the possibility that targeting gut bacteria through diet, probiotics, or other interventions might one day help slow kidney disease progression, though further research in larger populations is needed before any clinical applications could be considered.
Zhang Y, Zhang M, Liu S, Li X. (2026). Gut microbiota biomarkers of chronic kidney disease progression identified by 16S rDNA sequencing and machine learning.. Renal failure. https://doi.org/10.1080/0886022X.2026.2667649