Poly-omic risk scores predict inflammatory bowel disease diagnosis

Inflammatory Bowel Disease (IBD) is characterized by complex etiology and a disrupted gut microbiota. The substantial non-genetic variance for Crohn’s disease and ulcerative colitis (≥25%) suggests that both genetic and environmental factors contribute to IBD development. We aim to extend the framework of genomic studies by examining gut characteristics that are affected by both genetic and environmental factors. Specifically, we train models and validate their accuracy using data that quantifies the microbiota, their transcripts, and the metabolites present in the gut. The IBD Multi-omics Database from the Human Microbiome Project 2 provided 1,785 repeated samples for 131 individuals (103 cases, 27 controls) across multiple -omics layers including metagenomics, metatranscriptomics, viromics, and metabolomics. After splitting the subjects into training and validation groups, we used mixed effects least absolute shrinkage and selection operator (LASSO) regression to determine the most relevant features for each -omic layer. These features, along with demographic covariates, were incorporated into a polygenic risk score framework to generate four separate -omic-level prediction scores. All four -omic-level scores were then combined into a final regression to assess the relative importance of individual -omics and the added benefits when considered together. Our models identified several species, pathways, and metabolites known to be associated with IBD risk. Individually, metabolomics and viromics based scores were more predictive than metagenomics or metatranscriptomics based scores, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke’s R2 of 0.46 and an AUC of 0.80 [95% CI: 0.63, 0.98].ImportanceThe health burden of inflammatory bowel disease (IBD) among affected individuals is large, and its complex etiology has been studied using high throughput -omics technology. We applied a prediction framework across multiple -omics from the gut microbiome (metagenomics, metatranscriptomics, metabolomics, and viromics) to predict diagnoses of Crohn’s disease and ulcerative colitis. The predicted scores from our models illustrated key features and allowed us to compare the relative utility of each -omic data type when used individually versus when combined in a multi-omics model. The individual and combined models performed well and emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics predictive scores for IBD. The better predictive capability of metabolomics and viromics is likely because these -omics also serve as markers of lifestyle factors such as diet. This study shows the utility of combining multiple -omic data types to disentangle complex disease etiologies and biological signatures.