A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels.
A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels.
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DOI：
10.1007/s11306-016-1045-2
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发表时间：
2016
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影响因子：
--
通讯作者：
Boerwinkle E
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文献类型：
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作者：
Yazdani A;Yazdani A;Saniei A;Boerwinkle E
血浆甘油三酯水平是冠心病的危险因素。甘油三酯代谢的特点很好，但挑战仍然存在，以确定新的途径，以降低水平。代谢组学分析可能有助于识别这种新的途径，因此，提供有关新的药物靶点的提示。在一项观察性研究中，考虑了粒度代谢组学水平的因果关系，以区分对血浆甘油三酯水平具有直接影响的代谢物和途径与仅与甘油三酯相关或对甘油三酯具有间接影响的代谢物和途径。该分析首先利用来自基因组粒度水平的接近完整的信息，使用GDAG算法在较高粒度水平上识别超过122种代谢物的稳健因果网络。了解代谢组学因果关系后，我们在模型中输入甘油三酯变量，以识别对血浆甘油三酯水平有直接影响的代谢物。我们对24年来5次不同访视测量的甘油三酯进行了相同的分析。在考虑的122种代谢物中，有9种代谢物直接影响血浆甘油三酯水平。鉴于这9种代谢物，在显著性水平α = 0.001下，研究中的其余代谢物对甘油三酯水平无显著影响。因此，对于甘油三酯水平的进一步分析和解释，应重点关注研究中122种代谢产物中的这9种代谢产物。基线访视时作用最强的代谢物是花生四烯酸和肉毒碱，其次是9-羟基十八烯酸和棕榈酰甘油磷酸肌醇。花生四烯酸对甘油三酯水平的影响即使在基线访视后10年的第四次访视时仍然显着。这些结果证明了在粒度框架中整合多组学数据以识别降低风险因素水平的新候选途径的实用性。本文的在线版本（doi：10.1007/s11306-016-1045-2）包含补充材料，可供授权用户使用。
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. 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. 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. 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. These results demonstrate the utility of integrating multi-omics data in a granularity framework to identify novel candidate pathways to lower risk factor levels. The online version of this article (doi:10.1007/s11306-016-1045-2) contains supplementary material, which is available to authorized users.