# Integration of multiview microbiome data for deciphering microbiome-metabolome-disease pathways

**Authors:** Lei Fang, Yue Wang, Chenglong Ye  
**arXiv:** [2402.08222](https://arxiv.org/abs/2402.08222)  
**Date:** 2024-02-13  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2402.08222v2/

## Key points

- Study explores relationship between host organisms and gut microbiota in disease pathogenesis
- Introduces structural equation model to connect microbiome, metabolome, and disease processes
- Proposes integrative approach to address challenges from hidden confounders
- Validates methodologies through simulation studies and empirical application to inflammatory bowel disease (IBD)
- Discusses target-only model for multiview microbiome data and need for integrative method
- Emphasizes integrating informative external data sets into analysis framework
- Demonstrates data-driven procedure to quantify informativeness of external data sets
- Outlines future research directions and potential extensions of proposed framework
- Highlights leveraging multiple external cohorts with effective weighting for enhanced efficiency and accuracy

## Summary

In this study, we explore the intricate relationship between host organisms and their gut microbiota in disease pathogenesis through the lens of the microbiome, metabolome, and disease processes. We introduce a structural equation model to delineate the pathways connecting these elements using target multiview microbiome data. To address challenges posed by hidden confounders, we propose an integrative approach that incorporates data from an external microbiome cohort. This method not only supports the identification of disease-specific and microbiome-associated metabolites missing in the target cohort but also provides theoretical underpinnings for estimation consistency and asymptotic normality. Through comprehensive simulation studies and an empirical application to inflammatory bowel disease (IBD), we validate the effectiveness of these methodologies in unraveling complex relationships between the microbiome, metabolome, and disease. We further delve into discussing a target-only model for multiview microbiome data and emphasize the need for an integrative method to enhance statistical power and account for hidden confounders. Additionally, we introduce a discussion on integrating informative external data sets into our analysis framework. We demonstrate how this approach can improve understanding even when external data sets are not fully informative by presenting a data-driven procedure to quantify informativeness. This allows for a more robust analysis of microbiome-metabolome-disease pathways. The flexibility of l-1 penalized models enables easy extension to include observed covariates in our analysis. Furthermore, we outline future research directions and suggest potential extensions of our proposed framework to simultaneously analyze multiple metabolites within the same metabolic pathway. By leveraging multiple external cohorts with effective weighting based on informativeness, researchers can enhance the efficiency and accuracy of their analyses. In summary, this study contributes valuable insights into unraveling complex interactions within the microbiome-metabolome-disease pathway and paves the way for further advancements in understanding disease pathogenesis at a molecular level.