ArticlemSystems2021
Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models.
Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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
24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- A population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases.NPJ biofilms and microbiomes · 2024Pooled it
- Cross-cohort single-nucleotide-variant profiling of gut microbiota suggests a novel gut-health assessment approach.mSystems · 2023Pooled it
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Individual variability shapes ex vivo responses to resistant starch in inflammatory bowel disease derived microbiomes.NPJ biofilms and microbiomes · 2026Article
- GMrepo v3: a curated human gut microbiome database with expanded disease coverage and enhanced cross-dataset biomarker analysis.Nucleic acids research · 2026Article
- Best practices for developing microbiome-based disease diagnostic classifiers through machine learning.Gut microbes · 2025Article
- In vivo systematic analysis of microbiota-prebiotic crosstalk reveals a synbiotic that effectively ameliorates DSS-induced colitis in mice.Gut microbes · 2025Article
- Gut microbiota analysis reveals microbial signature for multi-autoimmune diseases based on machine learning model.Frontiers in microbiology · 2025Article
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.Briefings in bioinformatics · 2024Article
- Disease-associated microbiome signature species in the gut.PNAS nexus · 2024Article
- Article
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Gut microbiota signatures in inflammatory bowel disease.United European gastroenterology journal · 2024Article
- Analyzing lung cancer risks in patients with impaired pulmonary function through characterization of gut microbiome and metabolites.BMC pulmonary medicine · 2024Article
- Article
- Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways.iScience · 2023Article
- mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis.Genome biology · 2022Article
- GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison.Nucleic acids research · 2022Article
- mBodyMap: a curated database for microbes across human body and their associations with health and diseases.Nucleic acids research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Common intestinal diseases such as Crohn's disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data sets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple data sets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and disease-common clusters and revealed their distinct phylogenetic relationships; species in the CD-specific cluster were phylogenetically related, while those in the CRC-specific cluster were more distant. Strikingly, UC-specific species were phylogenetically closer to CRC, likely because UC patients have higher risk of CRC. Consistent with their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. A portion of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human-derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection on gut microbes. With the marker species and pathways, we constructed four high-performance (including multidisease) models with an area under the receiver operating characteristic curve (AUROC) of 0.87 and true-positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multidisease diagnosis.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.