Division of Preventive Medicine, Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Harvard Data Science Initiative, The Broad Institute, Harvard Medical School, Boston, USA.
Metabolomics. 2024 Jul 7;20(4):71. doi: 10.1007/s11306-024-02141-y.
Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline.
We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites.
We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet.
Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.
基于血液的小分子代谢物具有易于获取的特点，对于深入了解健康过程、生活方式的影响、遗传变异与疾病的关系具有重要意义，为精准预防风险提供了可能。本研究采用前瞻性设计，记录心力衰竭（HF）的发病情况，对基线时无 HF 的个体进行代谢组学特征分析。
采用数据驱动和因果网络方法，结合多基因因素，揭示代谢物之间的相互关系。通过对网络的分析，我们确定了代谢物的广播器、接收器、调节剂和与代谢物功能类别相对应的子网络，并深入了解代谢组学结构与健康调控之间的关系。我们将网络结构纳入与 HF 相关的代谢物的识别中，以控制混杂代谢物的影响。
我们确定了与 HF 发生风险升高或降低相关的代谢物，如甘氨酸、脲基丙酸和甘胆酸以及 LPC 18:2。这些关联不受其他代谢物的混杂影响，因为我们揭示了代谢物之间的连接，并对混杂代谢物进行了调整。例如，天冬酰胺对甘氨酸具有直接影响，这两种物质均与 HF 呈负相关。这两种代谢物受多基因因素的影响，而必需氨基酸不能在人体内合成，只能直接从饮食中获得。
代谢物可能在将遗传背景和生活方式因素与 HF 发病联系起来方面发挥关键作用。揭示与 HF 相关的代谢物的潜在关联有助于增强研究结果，并促进对 HF 等复杂疾病的研究。
