ArticleRenal failure2026
Gut microbiota biomarkers of chronic kidney disease progression identified by 16S rDNA sequencing and machine learning.
Article in Renal failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic kidney disease (CKD) is a global health concern characterized by high prevalence and mortality rates, yet its underlying pathogenesis remains inadequately understood. This study aimed to investigate microbial biomarkers associated with CKD progression across various stages by employing 16S rDNA sequencing, complemented by machine learning techniques including Lasso regression, the Boruta algorithm, and K-fold cross-validation, alongside microbial network analysis. Fecal samples were collected from a cohort consisting of six patients with stage II CKD, five with stage III CKD, seven with stage IV CKD, and nine healthy controls. Following quality control of the sequencing data, we analyzed microbial composition, richness, and diversity, revealing significant differences among the groups. Ten differential microbial taxa were identified, with
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.