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. 2025 Dec;17(1):2517828.
doi: 10.1080/19490976.2025.2517828. Epub 2025 Jun 12.

Fecal gut microbiota and amino acids as noninvasive diagnostic biomarkers of Pediatric inflammatory bowel disease

Eva Vermeer  1   2   3 , Jasmijn Z Jagt  1   2   3 , Eline M Lap  4 , Eduard A Struys  5 , Andries E Budding  6 , Nanda M Verhoeven-Duif  7 , Marjolein Bosma  7 , Johan E van Limbergen  1   2   3 , Bart G P Koot  1   2   3 , Robert de Jonge  5 , Marc A Benninga  1   2   3 , Animesh Acharjee  8   9 , Nanne K H de Boer  2   10 , Tim G J de Meij  1   2   3
Affiliations

Fecal gut microbiota and amino acids as noninvasive diagnostic biomarkers of Pediatric inflammatory bowel disease

Eva Vermeer et al. Gut Microbes. 2025 Dec.

Abstract

Background and aims: Fecal calprotectin (FCP) has limited specificity as diagnostic biomarker of pediatric inflammatory bowel disease (IBD), leading to unnecessary invasive endoscopies. This study aimed to develop and validate a fecal microbiota and amino acid (AA)-based diagnostic model.

Methods: Fecal samples from a discovery cohort (de novo IBD and healthy controls [HC]) were used to develop the diagnostic model. This model was applied in a validation cohort (de novo IBD and controls with gastrointestinal symptoms [CGI]). Microbiota and AAs were analyzed using interspace profiling and liquid chromatography-mass spectrometry techniques, respectively. Machine learning techniques were used to build the diagnostic model.

Results: In the discovery cohort (58 IBD, 59 hC), two microbial species (Escherichia coli and Alistipes finegoldii) and four AAs (leucine, ornithine, taurine, and alpha-aminoadipic acid [AAD]) combined allowed for discrimination between both subgroups (AUC 0.94, 95% CI [0.89, 0.98]). In the validation cohort (43 IBD, 38 CGI), this panel of six markers could differentiate patients with IBD from CGI with an AUC of 0.84, 95% CI [0.67, 0.95]). Leucine showed the best diagnostic performance (AUC 0.89, 95% CI [0.81, 0.95]).

Conclusions: Leucine might serve as adjuvant noninvasive biomarker in the diagnostic work-up of pediatric IBD. Future research should investigate whether the combination of leucine with FCP could improve specificity and may help tailor the course of diagnostics.

Keywords: Paediatric inflammatory bowel disease; amino acids; gut microbiota.

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Conflict of interest statement

JvL reports consulting, travel and/or speaker fees and research support from AbbVie, Janssen, Nestlé Health Science, Novalac, Pfizer, Merck, P&G, GSK, Illumina, Otsuka. NdB has served as a speaker for AbbVie and MSD and has served as consultant and/or principal investigator for TEVA Pharma BV and Takeda. He has received a (unrestricted) research grant from Dr. Falk, TEVA Pharma BV, MLDS and Takeda. This was all outside the submitted work. EV, JJ, EL, ES, AB, NVD, MB, BK, RdJ, MAB, AA, and TdM certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter of materials discussed in the manuscript.

Figures

Figure 1.
Figure 1.
Flow chart of the discovery and validation cohort study design.
Figure 2.
Figure 2.
Receiver operating characteristic (ROC) curve with an area under the curve (AUC) related to the performance of the model using alpha-aminoadipic acid (AAD), ornithine, leucine, taurine, E. coli, and A. finegoldii. The light blue indicating the confidence interval of the estimates. (a) the AUC of the discovery cohort is 0.96, 95% CI [0.90, 1] (b) the AUC of the validation cohort is 0.84, 95% CI [0.67, 0.95].
Figure 3.
Figure 3.
(a) Principal component analysis (PCA) score plot performed using six selected markers on children with IBD and controls with gastrointestinal symptoms (CGI), which shows clustering of subjects within, and variation between, groups. Dots represent samples and are colored according to the subject cohort. Ellipse represents 95% confidence. Results are plotted according to the PC1 and PC2 scores, with the percent variation explained by the respective axis. (b) box plots displaying the up and downregulated six markers in patients with IBD and CGI. A statistical significance noted from t-test at p-value (<0.05). (c) heat map visualizing selected markers within the validation cohort. The X-axis represents study participants classified into green (IBD) or red (controls), whereas the Y-axis represents different features. Blue and red colored blocks represent down- and upregulation of markers, respectively. Hierarchical clustering was applied to the rows (dendrograms) and similarly expressed markers were presented in the proximity.
Figure 4.
Figure 4.
Receiver operating characteristic (ROC) curves of the six selected features within the validation cohort (a) leucine (b) ornithine (c) taurine (d) alpha-aminoadipic acid (e). A. finegoldii (f) E. coli.
Figure 5.
Figure 5.
Receiver operating characteristic (ROC) curve with an area under the curve (AUC) related to the performance of the model in a subgroup of the validation cohort who underwent endoscopy, leucine. The AUC is 0.89, 95% CI [0.81, 0.95].
See this image and copyright information in PMC

References

    1. Baumgart DC, Carding SR.. Inflammatory bowel disease: cause and immunobiology. Lancet. 2007;369(9573):1627–14. doi: 10.1016/S0140-6736(07)60750-8. - DOI - PubMed
    1. Coward S, Clement F, Benchimol EI, Bernstein CN, Avina-Zubieta JA, Bitton A, Carroll MW, Hazlewood G, Jacobson K, Jelinski S, et al. Past and future burden of inflammatory bowel diseases based on modeling of population-based data. Gastroenterology. 2019;156(5):1345–53.e4. doi: 10.1053/j.gastro.2019.01.002. - DOI - PubMed
    1. Ghione S, Sarter H, Fumery M, Armengol-Debeir L, Savoye G, Ley D, Spyckerelle C, Pariente B, Peyrin-Biroulet L, Turck D, et al. Dramatic increase in incidence of ulcerative colitis and Crohn’s disease (1988–2011): a population-based study of French adolescents. Off J Am Coll Gastroenterol | ACG. 2018;113(2):265–272. doi: 10.1038/ajg.2017.228. - DOI - PubMed
    1. Lophaven SN, Lynge E, Burisch J. The incidence of inflammatory bowel disease in Denmark 1980–2013: a nationwide cohort study. Alimentary Pharmacol & Ther. 2017;45(7):961–972. doi: 10.1111/apt.13971. - DOI - PubMed
    1. Levine A, Koletzko S, Turner D, Escher JC, Cucchiara S, de Ridder L, Kolho K-L, Veres G, Russell RK, Paerregaard A, et al. ESPGHAN revised porto criteria for the diagnosis of inflammatory bowel disease in children and adolescents. J Pediatr Gastroenterol Nutr. 2014;58(6):795–806. doi: 10.1097/MPG.0000000000000239. - DOI - PubMed

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