TUM School of Medicine, Technical University of Munich (TUM), Munich, Germany.
Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Cardiovasc Diabetol. 2023 Jun 16;22(1):141. doi: 10.1186/s12933-023-01862-z.
Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways.
We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed.
We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism.
Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.
代谢综合征(MetS)的特征是存在多种风险因素,如腹部肥胖、高三酰甘油血症、低高密度脂蛋白胆固醇(HDL-C)、高血压和高血糖,这些因素会导致心血管疾病和 2 型糖尿病的发生。在这里,我们旨在确定代谢综合征及其相关风险因素的候选代谢生物标志物,以更好地了解潜在信号通路的复杂相互作用。
我们对 KORA F4 研究参与者的血清样本(N=2815)进行了定量分析,并分析了 121 种代谢物。使用经临床和生活方式协变量调整的多元回归模型来识别与 MetS 呈 Bonferroni 显著相关的代谢物。在 SHIP-TREND-0 研究(N=988)中对这些发现进行了复制,并进一步分析了复制的代谢物与 MetS 的五个组成部分的相关性。还构建了鉴定代谢物及其相互作用酶的数据库驱动网络。
我们鉴定并复制了 56 种 MetS 特异性代谢物:13 种呈正相关(例如 Val、Leu/Ile、Phe 和 Tyr),43 种呈负相关(例如 Gly、Ser 和 40 种脂质)。此外,大多数(89%)和少数(23%)MetS 特异性代谢物分别与低 HDL-C 和高血压相关。一种脂质,即 lysoPC a C18:2,与 MetS 及其五个组成部分均呈负相关,表明与相应对照相比,患有 MetS 及其每个风险因素的个体的 lysoPC a C18:2 浓度较低。我们的代谢网络通过揭示支链和芳香族氨基酸代谢的破坏以及 Gly 代谢的加速,阐明了这些观察结果。
我们鉴定的候选代谢生物标志物与 MetS 及其风险因素的病理生理学有关。它们可能有助于开发预防 2 型糖尿病和心血管疾病的治疗策略。例如,lysoPC a C18:2 水平升高可能会保护 MetS 及其五个风险成分。需要更深入的研究来确定 MetS 病理生理学中关键代谢物的机制。