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

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 disease...

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Autores principales:	Qiong Wu, Jiankang Li, Xiaohui Sun, Di He, Zongxue Cheng, Jun Li, Xuhui Zhang, Yongming Xie, Yimin Zhu, Maode Lai
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Metabolic syndrome Metabolomics mGWAS Biomarkers Zhejiang Metabolic Syndrome Cohort Medicine R Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/21253ba40e1f4d57aa9656e1865a5273
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spelling	oai:doaj.org-article:21253ba40e1f4d57aa9656e1865a52732021-11-20T05:07:00ZMulti-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants2352-396410.1016/j.ebiom.2021.103707https://doaj.org/article/21253ba40e1f4d57aa9656e1865a52732021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352396421005016https://doaj.org/toc/2352-3964Background: 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).Qiong WuJiankang LiXiaohui SunDi HeZongxue ChengJun LiXuhui ZhangYongming XieYimin ZhuMaode LaiElsevierarticleMetabolic syndromeMetabolomicsmGWASBiomarkersZhejiang Metabolic Syndrome CohortMedicineRMedicine (General)R5-920ENEBioMedicine, Vol 74, Iss , Pp 103707- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Metabolic syndrome Metabolomics mGWAS Biomarkers Zhejiang Metabolic Syndrome Cohort Medicine R Medicine (General) R5-920
spellingShingle	Metabolic syndrome Metabolomics mGWAS Biomarkers Zhejiang Metabolic Syndrome Cohort Medicine R Medicine (General) R5-920 Qiong Wu Jiankang Li Xiaohui Sun Di He Zongxue Cheng Jun Li Xuhui Zhang Yongming Xie Yimin Zhu Maode Lai Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
description	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).
format	article
author	Qiong Wu Jiankang Li Xiaohui Sun Di He Zongxue Cheng Jun Li Xuhui Zhang Yongming Xie Yimin Zhu Maode Lai
author_facet	Qiong Wu Jiankang Li Xiaohui Sun Di He Zongxue Cheng Jun Li Xuhui Zhang Yongming Xie Yimin Zhu Maode Lai
author_sort	Qiong Wu
title	Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
title_short	Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
title_full	Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
title_fullStr	Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
title_full_unstemmed	Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
title_sort	multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/21253ba40e1f4d57aa9656e1865a5273
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Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

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