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

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 over 122 metabolites in an upper level of granularity. Knowing the metabolomics causal relationships, we enter the triglyceride variable in the model to identify metabolites with direct effect on plasma triglyceride levels. We carried out the same analysis on triglycerides measured over five different visits spanning 24 years.

RESULT: Nine metabolites out of 122 metabolites under consideration influenced directly plasma triglyceride levels. Given these nine metabolites, the rest of metabolites in the study do not have a significant effect on triglyceride levels at significance level alpha = 0.001. Therefore, for the further analysis and interpretations about triglyceride levels, the focus should be on these nine metabolites out of 122 metabolites in the study. The metabolites with the strongest effects at the baseline visit were arachidonate and carnitine, followed by 9-hydroxy-octadecadenoic acid and palmitoylglycerophosphoinositol. The influence of arachidonate on triglyceride levels remained significant even at the fourth visit, which was 10 years after the baseline visit.

CONCLUSION: These results demonstrate the utility of integrating multi-omics data in a granularity framework to identify novel candidate pathways to lower risk factor levels.