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.