Table 2.

A non-exhaustive list of multi -omic IBD studies. .

Study	Disease context	Study context	Omic layers	Source of validation dataseta	Control/ non-IBD samples?
[Lloyd-Price et al., 2019]4	CD, UC	Identification of multi-omic signatures associated with IBD patients	Faecal proteomics Host mucosal transcriptomics 16S faecal microbiome profiling WGS faecal metagenomics Faecal viromics Faecal metabolomics Serology Faecal metatranscriptomics	No validation	Yes
[Borren et al., 2020]111	CD, UC	Prediction of biomarkers associated with disease relapse	Faecal proteomics Faecal metabolomics WGS faecal metagenomics	Internal dataseta	No
[Suskind et al., 2020]120	CD	Investigating the effect of different diets on disease symptoms and inflammatory burden	Faecal metabolomics WGS faecal metagenomics Faecal proteomics	No validation	No
[Le et al., 2020]121	CD, UC	Prediction of metabolite abundances from microbial abundances	Faecal metabolomics WGS faecal metagenomics	Internal dataseta	No
[Dai et al., 2019]122	CD	Identification and characterisation of important drivers of CD pathogenesis	Host genetics TWAS Host mucosal transcriptomics Methylomics	No validation	Yes
[Liu et al., 2021]123	CD, UC	Role of microbiota in oxalate metabolism in IBD patients	WGS faecal metagenomics Faecal metatranscriptomics	Experimental validation	No
[Revilla et al., 2021]124	CD	Interdependent host genes and microbial genera in CD	Host mucosal transcriptomics 16S gut microbiome profiling	No validation	No
[Jin et al., 2019]125	CD, UC	Dysregulated genes and pathways in CD/UC pathogenesis	Host mucosal transcriptomics Host mucosal proteomics	No validation	No
[Sudhakar et al., 2020]22	CD	Drivers of clinical heterogeneity in CD	PBMC gene expression CD4 gene expression Host genetics	No validation	No
[Nusbaum et al., 2018]126	UC	Influence of FMT on gut microbial and metabolic activity in paediatric UC patients	16S faecal microbiome profiling WGS faecal metagenomics Faecal viromics Faecal metabolomics	No validation	No
[Metwaly et al., 2020]127	CD	Integrative analysis of metabolic and microbial profiles in CD	16S faecal microbiome profiling WGS faecal metagenomics faecal metabolomics	Validation in mouse model	No
[Douglas et al., 2018]113	CD	Prediction of treatment response	16S gut microbiome profiling WGS gut metagenomics	No validation	Yes
1000IBD dataset	CD, UC, IBDU	Discover molecular sub-types of IBD	Host genetics 16S faecal microbiome profiling 16S gut microbiome profiling WGS faecal metagenomics Single cell RNA sequencing from biopsies	NAa	No
[Franzosa et al., 2019]60	CD, UC	Investigation of microbiome and metabolic activity in IBD	Faecal metabolomics WGS faecal metagenomics	Independent validation cohort	Yes

IBD, inflammatory bowel disease; UC, ulcerative colitis; CD, Crohn’s disease; FMT, faecal microbiota transplanation; NA, not available; PMBC, peripheral blood mononuclear cells; TWAS, transcriptome-wide association study; WGS, whole genome sequencing;

^aInternal independent dataset: defined as a dataset which is derived by ring-fencing a particular proportion of the test cohort for validation. Only published studies related to IBD and which integrate at least two different -omic datatypes were included. Publications based on original research were retrieved from PubMed using the co-occurrence of the search term ‘multi -omics’ with ‘IBD’, ‘Inflammatory Bowel Disease’, ‘Ulcerative colitis’, ‘Crohns disease’, or ‘Crohn’s disease’