Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
Qiong Wu,
Jiankang Li,
Xiaohui Sun,
Di He,
Zongxue Cheng,
Jun Li,
Xuhui Zhang,
Yongming Xie,
Yimin Zhu,
Maode Lai
Affiliations
Qiong Wu
Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China
Jiankang Li
Institute of Medical Research, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shanxi 710072, China
Xiaohui Sun
Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, China
Di He
Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China
Zongxue Cheng
Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China
Jun Li
Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China
Xuhui Zhang
Hangzhou Center for Disease Control and Prevention, Hangzhou 310051, Zhejiang, China; Affiliated Hangzhou Center of Disease Control and Prevention, School of Public Health, Zhejiang University, Hangzhou 310051, Zhejiang, China
Yongming Xie
Shanghai Applied Protein Technology Co., Ltd
Yimin Zhu
Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China; Department of Respiratory Diseases, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310060, China; Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China; Address for correspondence: Yimin Zhu, Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China; Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China. Telephone: 0086-571-88208194
Maode Lai
Key Laboratory of Disease Proteomics of Zhejiang Province and Department of Pathology, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China; State Key Laboratory of Natural Medicines, School of Basic Medical Sciences and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, Jiangsu, China; Maode Lai, Key Laboratory of Disease Proteomics of Zhejiang Province and Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China; State Key Laboratory of Natural Medicines, School of Basic Medical Sciences and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, Jiangsu, China; Fax: 0086-571-88208198
Background: Metabolic syndrome (MetS) is a cluster of multiple cardiometabolic risk factors that increase the risk of type 2 diabetes and cardiovascular diseases. Identifying novel biomarkers of MetS and their genetic associations could provide insights into the mechanisms of cardiometabolic diseases. Methods: Potential MetS-associated metabolites were screened and internally validated by untargeted metabolomics analyses among 693 patients with MetS and 705 controls. External validation was conducted using two well-established targeted metabolomic methods among 149 patients with MetS and 253 controls. The genetic associations of metabolites were determined by linear regression using our previous genome-wide SNP data. Causal relationships were assessed using a one-sample Mendelian Randomization (MR) approach. Findings: Nine metabolites were ultimately found to be associated with MetS or its components. Five metabolites, including LysoPC(14:0), LysoPC(15:0), propionyl carnitine, phenylalanine, and docosapentaenoic acid (DPA) were selected to construct a metabolite risk score (MRS), which was found to have a dose-response relationship with MetS and metabolic abnormalities. Moreover, MRS displayed a good ability to differentiate MetS and metabolic abnormalities. Three SNPs (rs11635491, rs7067822, and rs1952458) were associated with LysoPC(15:0). Two SNPs, rs1952458 and rs11635491 were found to be marginally correlated with several MetS components. MR analyses showed that a higher LysoPC(15:0) level was causally associated with the risk of overweight/obesity, dyslipidaemia, high uric acid, high insulin and high HOMA-IR. Interpretation: We identified five metabolite biomarkers of MetS and three SNPs associated with LysoPC(15:0). MR analyses revealed that abnormal LysoPC metabolism may be causally linked the metabolic risk. Funding: This work was supported by grants from the National Key Research and Development Program of China (2017YFC0907004).
