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

Table 1 Characteristics of the studies included in the systematic review

Ref.	Study design patient details	Sample and microbiota assessment technique	Therapy given, definition of response, duration of follow-up
Studies using TNF inhibitors
Chen et al[58], 2022, China	Retrospective study. n = 8. Age > 18 years	Stool sample. 16S rRNA gene sequencing	Adalimumab. Remission: CDAI < 150. 12 weeks
Park et al[64], 2022, Korea	Prospective study. n = 10. Mean age 31 years	Stool and saliva sample. 16S rRNA gene sequencing	Infliximab. Adalimumab. Response: Decrease in CDAI > 70 points or Remission: CDAI < 150 points. 3 months
Sanchis-Artero et al[66], 2021, Spain	Prospective multicenter study. n = 27. Mean age 41.4 ± 17.4 years. Men 55.6%	Stool sample. 16S rRNA gene sequencing	Infliximab. Adalimumab. Response assessment by Clinical, laboratory, stool tests. 3 months and 6 months
Yilmaz et al[68], 2019, Switzerland	Retrospective study. n = 270	Mucosal tissue sample. 16S rRNA gene sequencing	Anti TNF drugs. Response: Clinical and biochemical. Up to 9 years
Aden et al[56], 2019, Germany	Prospective study. n = 18	Stool sample. 16S rRNA gene sequencing	Anti TNF drugs. Response: HBI decrease ≥ 2. Remission: HBI ≤ 4. 2, 6, 14, and 30 weeks
Ding et al[59], 2020, United Kingdom	Prospective study. n = 76. Mean age 38.49 ± 14.6 years. Males 47%	Stool sample. 16S rRNA gene sequencing	Infliximab. Adalimumab. Response: Clinical indices and objective markers including FC, CRP, and radiological or endoscopic improvement. 3 monthly follow-up to 16 months
Guo et al[62], 2024, China	Prospective study. n = 28	Stool sample. 16S rRNA gene and ITS2 gene sequencing	Infliximab. Remission based on symptoms and HBI. 14 weeks
Dovrolis et al[60], 2020, Greece	Prospective study. n = 10	Mucosal tissue sample. 16S rRNA gene sequencing	Infliximab. Complete responder: HBI < 4, normal CRP and mucosal healing. 12-20 weeks
Ribaldone et al[65], 2019, Italy	Prospective study. n = 20. Median age 52.5 (26–69) years. Males: 12 (60%)	Stool sample. 16S rRNA gene sequencing	Adalimumab. Response: Decrease in the HBI score > 2 or HBI 4, 6 months
Ventin-Holmberg et al[67], 2021, Finland	Prospective study. CD-25	Stool sample. 16S rRNA gene and ITS1 gene sequencing	Infliximab. 12 weeks
Zhou et al[10], 2018, China	Prospective study. CD-16	Stool sample. 16S rRNA gene sequencing	Infliximab. Clinical response: ≥ 70-point reduction in CDAI. Clinical remission: CDAI < 150. 30 weeks
Study using integrin inhibitors
Ananthakrishnan et al[57], 2017, United States	Prospective. n = 47	Stool sample. Shotgun metagenome sequencing	Vedolizumab. Response: HBI < 4 or reduction in HBI by ≥ 3 points. 14 weeks
Study using interleukin inhibitor
Doherty et al[9], 2018, United States	Prospective study. n = 232. Mean age 38 ± 13 years. Males 36.6%	Stool sample. 16S rRNA gene sequencing	Ustekinumab. Response: Decrease in CDAI > 100 points or Remission: CDAI < 150 points, 6 weeks
Study using drugs from multiple class
Effenberger et al[61], 2021, Austria and Germany	Prospective study. n = 43	Stool sample. 16S rRNA gene sequencing	Azathioprine. Anti-TNF inhibitors. Remission: CDAI < 150. 12 and 30 weeks
Lee et al[63], 2021, United States	Prospective study. n = 108	Stool sample. Metagenomic sequencing	Anti cytokine (Anti TNF, Ustekinumab). Anti integrin (Vedolizumab). Response: Clinical and endoscopic, 14 weeks and 52 weeks
Caenepeel et al[69], 2024, Belgium	Prospective study. CD-203	Stool sample. 16S rRNA gene sequencing	Infliximab, Adalimumab, Golimumab, Vedolizumab, Ustekinumab. Response: Endoscopic remission SES CD < 3 or absence of ulceration, 24 weeks

TNF: Tumour necrosis factor; CD: Crohn’s disease; CDAI: Crohn’s disease activity index; HBI: Harvey Bradshaw index; FC: Fecal calprotectin; CRP: C-reactive protein; ITS: Internal transcribed spacer; SES: Simple endoscopic score.

Table 2 Microbial diversity at baseline and prediction of response to therapy

Ref.	Alpha diversity1	Beta diversity1
Ananthakrishnan et al[57], 2017	Alpha diversity (Fisher’s alpha) higher in those achieving remission	Lower beta diversity in remission group (Bray Curtis dissimilarity)
Lee et al[63], 2021	Responders to anti-cytokine therapy had significantly higher indices of microbial richness at baseline	NA
Doherty et al[9], 2018	No significant difference in alpha diversity (Inverse Simpson index)	Baseline beta diversity was significantly different by response (P = 0.018)
Effenberger et al[61], 2021	No significant difference in alpha diversity (Shannon index)	No significant difference in beta diversity (paired Bray Curtis distance)
Yilmaz et al[68], 2019	No significant difference in alpha diversity	NA
Guo et al[62], 2024	No significant difference in alpha diversity (microbial richness and evenness)	NA
Dovrolis et al[60], 2020	NA	Distinct enterotype of bacteria noted in the two groups

¹Comparison between responders and non-responders to therapy.

NA: Not available.

Table 3 Bacterial taxonomy at baseline before starting treatment and prediction of response to therapy

Ref.	Bacterial taxonomy
Ananthakrishnan et al[57], 2017	Treatment responders had higher abundance of Burkholderiales species and Roseburia inulinivorans and butyrate producers than non-responders. Microbial dysbiosis index showed no significant difference between the two groups
Doherty et al[9], 2018	Treatment responders had higher abundance of Bacteroides (P = 0.022) and Faecalibacterium (P = 0.003) than non-responders
Park et al[64], 2022	Stool samples: Actinobacteria, Dorea, Agathobaculum, and Blautia levels were higher and Proteobacteria, Enterobacteriaceae, Odoribacter and Ruminococcus gnavus were lower in anti-TNF-α responders. Saliva samples: Levels of Abiotrophia defective-species, and FJ976422_s-species were higher and Ralstonia were lower in responders than in non-responders
Yilmaz et al[68], 2019	Higher abundance of Bifidobacterium, Collinsella, Lachnospira, Lachnospiraceae, Roseburia, Eggerthella taxa and lower abundance of Phascolarctobacterium noted in treatment responders than non-responders to anti TNF therapy. No differences noted between the responders and non-responders to steroid treatment
Guo et al[62], 2024	Lower abundance of Ruminococcus, Lachnoclostridium, Akkermansia in bacterial community and lower abundance of Asterotremella and Wallemia in fungal community noted in non-responder group compared to responders
Dovrolis et al[60], 2020	Parvimonas, Hungatella, Roseburia, Ruminococcus and Stenotrophomonas were associated with responders, and Negativibacillus, Faecalibacterium, Eubacterium_hallii_group, Blautia, Ruminococcus_gnavus_group were associated with non responders
Lee et al[63], 2021	Clostridium citronae, Agathobaculum butyriciproducens associated with remission after anti-cytokine therapy, B stercoris with anti-integrin therapy and B. caccae and B. ovatus with both types of therapies
Caenepeel et al[69], 2024	Enterotype Bacteroides 2 was negatively associated with remission after treatment with Vedolizumab. No association was found with Ustekinumab or anti-TNF agents
Sanchis-Artero et al[66], 2021	Significant difference in Faecalibacterium prausnitzii/Escherichia coli (F/E) ratio noted between responders and non-responders (P < 0.01)
Effenberger et al[61], 2021	No significant difference at genus level between responders and non-responders
Ding et al[59], 2020	No significant difference in micorbial profiles of responders and non-responders
Ribaldone et al[65], 2019	No significant difference in concentration of all taxa between responder and non-responder

TNF: Tumour necrosis factor.

Table 4 Microbial taxonomic associations with response to therapy in studies using a single class of drug to treat Crohn’s disease

Microbial taxa	Park et al[64], 2022, CDAI	Guo et al[62], 2024, HBI	Dovrolis et al[60], 2020, HBI	Yilmaz et al[68], 2019, C and B	Ananthakrishnan et al[57], 2017, HBI	Doherty et al[9], 2018, CDAI
	TNF inhibitor				Integrin inhibitor	Interleukin inhibitor
Actinobacteria	P
Dorea	P
Agathobaculum	P
Blautia	P		N
Proteobacteria	N
Enterobacteriaceae	N
Odoribacter	N
Ruminococcus gnavus	N		N
Lachnoclostridium		P
Akkermansia		P
Ruminococcus		P	P
Parvimonas			P
Hungatella			P
Roseburia			P	P
Stenotrophomonas			P
Negativibacillus			N
Faecalibacterium			N			P
Eubacterium_hallii_group			N
Bifidobacterium				P
Collinsella				P
Lachnospiraceae				P
Eggerthella				P
Phascolarctobacterium				N
Bacteroides						P
Burkholderiales					P
Roseburia inulinivorans					P

P: Positive association; N: Negative association; TNF: Tumour necrosis factor; CDAI: Crohn’s disease activity index; HBI: Harvey Bradshaw index; C and B: Clinical and biochemical.

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.