ArticleMetabolomics : Official journal of the Metabolomic Society
A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels.
Article in Metabolomics : Official journal of the Metabolomic Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 21 citations in OpenAlex.
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- Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.Metabolomics : Official journal of the Metabolomic Society · 2024Article
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- A fat- and sucrose-enriched diet causes metabolic alterations in mdx mice.American journal of physiology. Regulatory, integrative and comparative physiology · 2023Article
- Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data.Genetic epidemiology · 2023Article
- Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems.GigaScience · 2022Article
- Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics.BMC genomics · 2019Article
- Comparison of metabolite networks from four German population-based studies.International journal of epidemiology · 2018Article
- Arachidonic acid as a target for treating hypertriglyceridemia reproduced by a causal network analysis and an intervention study.Metabolomics : Official journal of the Metabolomic Society · 2018Article
- A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements.Genetic epidemiology · 2017Article
- A Causal Network Analysis of the Fatty Acid Metabolome in African-Americans Reveals a Critical Role for Palmitoleate and Margarate.Omics : a journal of integrative biology · 2016Article
- Bounded Fuzzy Possibilistic Method Reveals Information about Lung Cancer through Analysis of Metabolomics.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
4 authors at 2 institutions in 2 countries.
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Abstract
introductionPlasma 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.
objectivesIn 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.
methodThe 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.
resultNine 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.
conclusionThese results demonstrate the utility of integrating multi-omics data in a granularity framework to identify novel candidate pathways to lower risk factor levels.
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