A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels.

ABSTRACT:

Introduction

Plasma triglyceride levels are a risk factor for coronary heart disease. Triglyceride metabolism is well characterized, but challenges remain to identify novel paths to lower levels. A metabolomics analysis may help identify such novel pathways and, therefore, provide hints about new drug targets.

Objectives

In an observational study, causal relationships in the metabolomics level of granularity are taken into account to distinguish metabolites and pathways having a direct effect on plasma triglyceride levels from those which are only associated with or have indirect effect on triglyceride.

Method

The analysis began by leveraging near-complete information from the genome level of granularity using the GDAG algorithm to identify a robust causal network ove

SUBMITTER: Yazdani A 

PROVIDER: S-EPMC4869741 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature