Multilayer Network of Cardiovascular Diseases and Depression via Multipartite Projection

TL;DR

This study introduces a multipartite network projection method based on mutual information to explore complex biological relationships between cardiovascular diseases and depression, identifying mediating biomarkers and key risk factors.

Contribution

The paper presents a novel multipartite projection approach that integrates multiple omics layers to analyze disease comorbidities at a system level.

Findings

Identified potential mediating biomarkers linking CVD and depression.

Highlighted sex and BMI as significant risk factors.

Demonstrated scalability of the method to multiple omics layers.

Abstract

Cardiovascular diseases (CVD) and depression exhibit significant comorbidity, which is highly predictive of poor clinical outcomes. Yet, the underlying biological pathways remain challenging to decipher, presumably due to the non-linear associations across multiple mechanisms. In this study, we introduced a multipartite projection method based on mutual information correlations to construct multilayer disease networks as a novel approach to explore such intricate relationships. We applied this method to a cross-sectional dataset from a wave of the Young Finns Study, which includes data on CVD and depression, along with related risk factors and two omics of biomarkers: metabolites and lipids. Rather than directly correlating CVD-related phenotypes and depressive symptoms, we extended the notion of bipartite networks to create a multipartite network, linking these phenotypes and symptoms to intermediate biological variables. Projecting from these intermediate variables results in a weighted multilayer network, where each link between CVD and depression variables is marked by its layer (i.e., metabolome or lipidome). Applying this projection method, we identified potential mediating biomarkers that connect CVD with depression. These biomarkers may therefore play significant roles in the biological pathways underlying CVD-depression comorbidity. Additionally, the network projection highlighted sex and BMI as key risk factors, or confounders, in this comorbidity. Our method is scalable to incorporate any number of omics layers and various disease phenotypes, offering a comprehensive, system-level perspective on the biological pathways contributing to comorbidity.