ArticleMicroorganisms2020
Fused Omics Data Models Reveal Gut Microbiome Signatures Specific of Inactive Stage of Juvenile Idiopathic Arthritis in Pediatric Patients.
Article in Microorganisms, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06992453 (Disentangling Microbiome Engraftment by Multi-omics of the Gut Ecosystem During Fecal Transplant), which is not on this map. Cited by 4 papers.
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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.
Disentangling Microbiome Engraftment by Multi-omics of the Gut Ecosystem During Fecal Transplant
Who cites it
4 citing papers in PubMed.
- An integrative multi-omic analysis defines gut microbiota, mycobiota, and metabolic fingerprints in ulcerative colitis patients.Frontiers in cellular and infection microbiology · 2024Article
- Gut Microbiota Ecological and Functional Modulation in Post-Stroke Recovery Patients: An Italian Study.Microorganisms · 2023Article
- Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.Frontiers in microbiology · 2023Article
- Metabolomic analysis in spondyloarthritis: A systematic review.Frontiers in microbiology · 2022Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in children. Herein, we evaluated the relationship between the gut microbiome (GM) and disease phenotype by an integrated omics fused approach. In a multicenter, observational cohort study, stools from Italian JIA patients were collected at baseline, active, and inactive disease stages, and their GM compared to healthy controls (CTRLs). The microbiota metabolome was analyzed to detect volatile- and non-volatile organic compounds (VOCs); the data were fused with operational taxonomic units (OTUs) from 16S RNA targeted-metagenomics and classified by chemometric models. Non-VOCs did not characterize JIA patients nor JIA activity stages compared to CTRLs. The core of VOCs, (Ethanol, Methyl-isobutyl-ketone, 2,6-Dimethyl-4-heptanone and Phenol) characterized patients at baseline and inactive disease stages, while the OTUs represented by Ruminococcaceae, Lachnospiraceae and Clostridiacea discriminated between JIA inactive stage and CTRLs. No differences were highlighted amongst JIA activity stages. Finally, the fused data discriminated inactive and baseline stages versus CTRLs, based on the contribution of the invariant core of VOCs while Ruminococcaceae concurred for the inactive stage versus CTRLs comparison. In conclusion, the GM signatures enabled to distinguish the inactive disease stage from CTRLs.
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