ArticleScientific reports2025
Prediction of irritable bowel syndrome by integrating urine metabolites and gut microbiota.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Pathophysiological and Therapeutic Association between Brain-Gut Axis and Irritable Bowel Syndrome: A Systematic Review.Pakistan journal of medical sciences · 2026Review
- Review
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Authors and funding
8 authors.
Funding
Abstract
Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder characterized by recurrent abdominal pain and altered bowel habits. While gut microbiota alterations and metabolic disturbances have been implicated in IBS, their potential role in classification and patient stratification remains unclear. This study aimed to evaluate the potential of integrating gut microbiota profiling and urinary metabolomics for improved IBS classification. Fifty-four participants (27 healthy controls, 27 IBS patients) were recruited for gut microbiota and urinary metabolite analysis. Gut microbiota composition was assessed via 16S rRNA gene sequencing, and urinary metabolites were profiled using gas chromatography-mass spectrometry (GC-MS). Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of gut microbiota, urinary metabolites, and their combined model. LEfSe analysis identified that Clostridia and Prevotella as enriched in IBS patients, whereas Bacteroidales and Faecalitalea were predominant in healthy individuals. Urinary metabolite analysis revealed significant alterations in metabolite profiles, with IBS patients exhibiting elevated fructose levels and trends of increased serine, mannose, and galactose. ROC curve analysis demonstrated that urinary metabolomics (AUC = 0.65) outperformed gut microbiota profiling (AUC = 0.54), while a combined approach integrating both datasets achieved the highest predictive accuracy (AUC = 0.74). These findings indicate that integrating urinary metabolomics with gut microbiota profiling may provide preliminary insights into IBS classification. Given the small sample size, potential risk of overfitting, absence of external validation, and possible dietary or medication confounding, the observed performance of the combined approach should be regarded as hypothesis-generating rather than confirmatory. Nevertheless, the results highlight the potential utility of integrative omics strategies in IBS research, underscoring the need for validation in larger, sex-balanced, and clinically diverse cohorts to establish their robustness and broader relevance.
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Registered trials
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