Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
J Lipid Res. 2024 Sep;65(9):100625. doi: 10.1016/j.jlr.2024.100625. Epub 2024 Sep 19.
Dyslipidemia is one of the cardiometabolic risk factors that influences mortality globally. Unraveling the causality between blood lipids and metabolites and the complex networks connecting lipids, metabolites, and other cardiometabolic traits can help to more accurately reflect the body's metabolic disorders and even cardiometabolic diseases. We conducted targeted metabolomics of 248 metabolites in 437 twins from the Chinese National Twin Registry. Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) analysis was used for causal inference between metabolites and lipid parameters. Bidirectional mediation analysis was performed to explore the linkages between blood lipids, metabolites, and other seven cardiometabolic traits. We identified 44, 1, and 31 metabolites associated with triglyceride (TG), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C), most of which were gut microbiota-derived metabolites. There were 9, 1, and 14 metabolites that showed novel associations with TG, TC, and HDL-C, respectively. ICE FALCON analysis found that TG and HDL-C may have a predicted causal effect on 23 and six metabolites, respectively, and one metabolite may have a predicted causal effect on TG. Mediation analysis discovered 14 linkages connecting blood lipids, metabolites, and other cardiometabolic traits. Our study highlights the significance of gut microbiota-derived metabolites in lipid metabolism. Most of the identified cross-sectional associations may be due to the lipids having a predicted causal effect on metabolites, but not vice versa, nor are they due to family confounding. These findings shed new light on lipid metabolism and personalized management of cardiometabolic diseases.
血脂异常是影响全球死亡率的心血管代谢危险因素之一。阐明血脂与代谢物之间的因果关系，以及连接脂质、代谢物和其他心血管代谢特征的复杂网络，可以帮助更准确地反映身体的代谢紊乱，甚至心血管代谢疾病。我们对来自中国国家双胞胎登记处的 437 对双胞胎的 248 种代谢物进行了靶向代谢组学研究。通过检查家族混杂因素（ICE FALCON）分析推断因果关系，用于代谢物与脂质参数之间的因果推断。进行了双向中介分析，以探讨血脂、代谢物与其他七种心血管代谢特征之间的联系。我们确定了 44、1 和 31 种与甘油三酯 (TG)、总胆固醇 (TC) 和高密度脂蛋白胆固醇 (HDL-C) 相关的代谢物，其中大多数是肠道微生物群衍生的代谢物。有 9、1 和 14 种代谢物分别与 TG、TC 和 HDL-C 显示出新颖的关联。ICE FALCON 分析发现 TG 和 HDL-C 可能分别对 23 和 6 种代谢物具有预测的因果效应，而一种代谢物可能对 TG 具有预测的因果效应。中介分析发现了 14 种连接血脂、代谢物和其他心血管代谢特征的联系。我们的研究强调了肠道微生物群衍生代谢物在脂质代谢中的重要性。大多数鉴定的横断面关联可能是由于脂质对代谢物具有预测的因果效应，而不是相反，也不是由于家族混杂。这些发现为脂质代谢和心血管代谢疾病的个性化管理提供了新的思路。
