Dataset Information

Integrated multi-omics of feces, plasm aand urine can describe and differentiate pediatric active Crohn’s Disease from remission

ABSTRACT:

Background

This study aimed to obtain a holistic view of remission in pediatric Crohn’s Disease (CD) by integrating six omics datasets from three anatomical compartments.

Methods

Patients with fecal calprotectin below 250 mg/kg were considered in remission (n = 27), and those above 250 mg/kg as having active disease (n = 31). Proteome and microbiomes (fungi and bacteria) were analyzed in feces. Metabolomes were analyzed in feces, urine, and plasma. Datasets were integrated into a multi-omics model.

Results

The use of individual datasets shows multiple differences between remission and active disease. Integration yielded a good model (AUC of 0.8) for predicting remission. The most important features in this model are fecal bacteria (40%), fecal metabolites (22%), fecal proteins (16%), plasma metabolites (12%), fecal fungi (6%), and urine metabolites (4%). The interactome reveals Ruminococcaceae and Faecalibacterium as key players, with a correlation between antifungal urine hydroxyphenyllactic acid and fecal fungi. Pathway analysis shows an association of purine metabolism with remission, independent of thiopurine use. Changes in purine metabolism are confirmed in a pediatric CD public dataset.

Conclusion

The pathways and correlations identified as playing a role in remission may remain undetectable if individual omics datasets or single anatomical compartments are used, highlighting the need for a holistic approach that integrates multiple datasets from multiple anatomical compartments.

INSTRUMENT(S): Liquid Chromatography MS - positive - hilic, Liquid Chromatography MS - negative - hilic

PROVIDER: MTBLS9877 | MetaboLights | 2025-07-09

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	21P0120_Fecal_Water_NEG_01_19361.mzML	Mzml
	21P0120_Fecal_Water_NEG_02_19381.mzML	Mzml
	21P0120_Fecal_Water_NEG_03_19411.mzML	Mzml
	21P0120_Fecal_Water_NEG_04_19395.mzML	Mzml
	21P0120_Fecal_Water_NEG_05_19348.mzML	Mzml

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Publications

Integrated multi-omics of feces, plasma and urine can describe and differentiate pediatric active Crohn's Disease from remission.

Koopman Nienke N Jaspers Yorrick Y van Leeuwen Pim T PT Chronas Konstantinos K Li Yim Andrew Y F AYF Diederen Kay K Te Velde Anje A AA Roseboom Winfried W Kindermann Angelika A Benninga Marc A MA Kramer Gertjan G de Jonge Wouter J WJ Brul Stanley S Levin Evgeni E Kemp Stephan S Seppen Jurgen J

Communications medicine 20250708 1

<h4>Background</h4>This study aimed to obtain a holistic view of remission in pediatric Crohn's Disease (CD) by integrating six omics datasets from three anatomical compartments.<h4>Methods</h4>Patients with fecal calprotectin below 250 mg/kg were considered in remission (n = 27), above 250 mg/kg as having active disease (n = 31). Proteome and microbiomes (fungi and bacteria) were analyzed in feces. Metabolomes in feces, urine, and plasma. Datasets were integrated into a multi-omics model.<h4>Re ...[more]

PMID: 40629054

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Project description:Introduction Monitoring disease activity in inflammatory bowel disease (IBD) is essential for guiding therapy and preventing irreversible tissue damage. Colonoscopy, although the gold standard, is invasive and unsuitable for frequent monitoring, while fecal calprotectin lacks accuracy within its diagnostic gray zone (fecal calprotectin 100–250 µg/g). Stool proteomics offers a non-invasive alternative by directly capturing molecular signatures of intestinal inflammation. We conducted a proof-of-concept study to determine whether stool-derived peptides can accurately classify IBD activity (Active vs Remission) using a fully unbiased and reproducible nested cross-validation machine-learning framework. Methods A total of 174 stool samples from IBD patients were collected and profiled using SWATH-DIA mass spectrometry. Feature selection was performed within the training loops only (Boruta, LASSO, RFE) across repeated subsampling, retaining peptides consistently identified in ≥70 % of runs. Stable features were used to train four classifiers (GLMNet, SVM-Radial, SVM-Linear, Naïve Bayes) under inner 5-fold tuning. Outer test folds provided fully unseen evaluation, and model performance was additionally assessed exclusively on gray zone samples extracted from the outer test splits to quantify diagnostic resolution in this clinically challenging subgroup. Results Nested cross-validation identified a consensus panel of nine stool-derived peptides from five proteins. Across candidate classifiers, performance was broadly similar, with GLMNet consistently achieving the best trade-off between metrics. For GLMNet, outer-fold mean AUC was 0.93 and balanced accuracy 0.88, with specificity 0.94, sensitivity 0.82, and F1-score 0.85; close agreement between inner- and outer-fold metrics indicated minimal overfitting. Approximately half of the misclassified test patients were shared with the other models, suggesting intrinsically ambiguous cases rather than GLMNet-specific errors. Within the calprotectin gray zone subgroup (n = 34), GLMNet maintained good performance (accuracy 0.76, balanced accuracy 0.78, F1 0.79, AUC 0.80), confirming that the peptide signature remains informative in this diagnostically challenging range. SHAP and correlation analyses confirmed directionally consistent, largely non-redundant peptide contributions, and network analysis showed that the underlying proteins participate in related but distinct inflammatory pathways. Conclusions A stool-based multi-peptide signature, evaluated with a rigorously nested, leakage-free machine-learning framework, can reliably classify IBD activity and retain discriminative power within the gray zone. This biologically interpretable five-protein panel provides a strong basis for targeted mass-spectrometry assay development and prospective validation as a non-invasive tool for personalized IBD monitoring.