Gut microbiota as a potential predictor of therapeutic response in adults with Crohn’s disease: A systematic review

Table 5 Bacterial function characteristics and baseline predictive models for predicting response to therapy

Ref.	Bacterial function and predictive models
Studies evaluating combined predictive models
Ananthakrishnan et al[57], 2017	Baseline enrichment of 13 microbial pathways noted in treatment responsive CD patients. Neural network model (Vedonet): Combination of clinical, taxonomic and metabolic pathway data predicted clinical remission at week 14
Doherty et al[9], 2018	Combination of clinical and baseline fecal microbiome data had an AUC of 0.844 for predicting response to therapy
Lee et al[63], 2021	120 bacterial enzyme pathways were mostly differentially abundant at baseline in responders to anti-cytokine therapy. Combination of clinical, metagenomic, metabolomic and proteomic markers had AUC of 0.96 in predicting response to anti-cytokine therapy
Zhou et al[10], 2018	Gut microbiota alone predicted responses to therapy with to 86.5% accuracy. Combination of gut microbiota, fecal calprotectin and CDAI improve the accuracy of prediction to 93.8%
Caenepeel et al[69], 2024	Combination of clinical data, microbial load in stool, bacterial enterotype Bacteroides2 in stool, fecal moisture, and fecal calprotectin level predicted response to therapy with AUC of 0.74
Studies evaluating microbial function (without a combined model)
Effenberger et al[61], 2021	Higher butyrate production capacity was noted in responders compared to non-responders
Aden et al[56], 2019	Intercellular exchange of butyrate was significantly reduced in non-responders compared to responders

CD: Crohn’s disease; AUC: Area under the curve; CDAI: Crohn’s disease activity index.