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

doi:10.1016/j.ebiom.2021.103707

Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

EBioMedicine. 2021 Dec:74:103707. doi: 10.1016/j.ebiom.2021.103707. Epub 2021 Nov 18.

Authors

Qiong Wu¹, Jiankang Li², Xiaohui Sun³, Di He¹, Zongxue Cheng¹, Jun Li¹, Xuhui Zhang⁴, Yongming Xie⁵, Yimin Zhu⁶, Maode Lai⁷

Affiliations

¹ Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China.
² Institute of Medical Research, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shanxi 710072, China.
³ Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, China.
⁴ 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.
⁵ Shanghai Applied Protein Technology Co., Ltd.
⁶ 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. Electronic address: zhuym@zju.edu.cn.
⁷ 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. Electronic address: lmd@cpu.edu.cn.

Abstract

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).

Keywords: Biomarkers; Metabolic syndrome; Metabolomics; Zhejiang Metabolic Syndrome Cohort; mGWAS.

MeSH terms

Case-Control Studies
Early Diagnosis
Female
Genome-Wide Association Study
Humans
Linear Models
Lysophosphatidylcholines / blood*
Male
Mendelian Randomization Analysis
Metabolic Syndrome / diagnosis*
Metabolic Syndrome / genetics
Metabolic Syndrome / metabolism
Metabolomics / methods*
Middle Aged
Polymorphism, Single Nucleotide*

Substances

Lysophosphatidylcholines