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

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.

Figure 1

Associations between metabolites and between metabolites and MetS and its components. a: The associations between 20 MetS-associated metabolites and MetS components in the internal validation dataset (N=1157). The cells in blank signify that the correlation was non-significant (P>0.05); b: The associations between the 20 MetS-associated metabolites in the internal validation dataset (N=1157). The cells in blank signify that the correlation was non-significant (P>0.05); c: The associations of metabolites with MetS and its components in the external validation dataset (N=402). The OR was adjusted for age and sex. MetS, metabolic syndrome; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; FPG, fasting plasma glucose; HOMA-IR, homeostasis model assessment of insulin resistance; OR, odds ratio; CI, confidence interval.

Figure 2

Associations of quartiles of metabolite risk score (MRS) with MetS and its components in the external validation dataset (N=402). The OR was adjusted for age and sex. BP, blood pressure; FPG, fasting plasma glucose; OR, odds ratio; CI, confidence interval.

Figure 3

ROCs of the metabolite risk score on MetS and its components in the model construction dataset (N=402) and model validation dataset (N=1157). a: ROC for metabolic syndrome; b: ROC for overweight/obesity; c: ROC for dyslipidaemia; d: ROC for high FPG; e: ROC for high HP. Data were shown as AUC (95%CI). BP, blood pressure; FPG, fasting plasma glucose; ROC, receiver operating characteristic curve; AUC, area under curve; CI, confidence interval.

Figure 4

MR analysis between LysoPC(15:0) and MetS and metabolic abnormalities. The OR was adjusted for age and sex. MetS, metabolic syndrome; BP, blood pressure; UA, uric acid; FBG, fasting blood glucose; HOMA-IR, homeostasis model assessment of insulin resistance; MR, mendelian randomization. The cutoff value for high UA, high HOMA-IR, and high insulin was based on their respective 75^th percentile.