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. 2021 Mar 23;5(4):2000088.
doi: 10.1002/gch2.202000088. eCollection 2021 Apr.

Metabolome-Genome-Wide Association Study (mGWAS) Reveals Novel Metabolites Associated with Future Type 2 Diabetes Risk and Susceptibility Loci in a Case-Control Study in a Chinese Prospective Cohort

Yang Ouyang  1   2 , Gaokun Qiu  3 , Xinjie Zhao  1 , Benzhe Su  4 , Disheng Feng  1   2 , Wangjie Lv  1   2 , Qiuhui Xuan  1   2 , Lichao Wang  1   2 , Di Yu  1   2 , Qingqing Wang  1   2 , Xiaohui Lin  4 , Tangchun Wu  3 , Guowang Xu  1   2
Affiliations

Metabolome-Genome-Wide Association Study (mGWAS) Reveals Novel Metabolites Associated with Future Type 2 Diabetes Risk and Susceptibility Loci in a Case-Control Study in a Chinese Prospective Cohort

Yang Ouyang et al. Glob Chall. .

Abstract

In a Chinese prospective cohort, 500 patients with new-onset type 2 diabetes (T2D) within 4.61 years and 500 matched healthy participants are selected as case and control groups, and randomized into discovery and validation sets to discover the metabolite changes before T2D onset and the related diabetogenic loci. A serum metabolomics analysis reveals that 81 metabolites changed significantly before T2D onset. Based on binary logistic regression, eight metabolites are defined as a biomarker panel for T2D prediction. Pipecolinic acid, carnitine C14:0, epinephrine and phosphatidylethanolamine 34:2 are first found associated with future T2D. The addition of the biomarker panel to the clinical markers (BMI, triglycerides, and fasting glucose) significantly improves the predictive ability in the discovery and validation sets, respectively. By associating metabolomics with genomics, a significant correlation (p < 5.0 × 10-8) between eicosatetraenoic acid and the FADS1 (rs174559) gene is observed, and suggestive correlations (p < 5.0 × 10-6) between pipecolinic acid and CHRM3 (rs535514), and leucine/isoleucine and WWOX (rs72487966) are discovered. Elevated leucine/isoleucine levels increased the risk of T2D. In conclusion, multiple metabolic dysregulations are observed to occur before T2D onset, and the new biomarker panel can help to predict T2D risk.

Keywords: genomics; mGWAS; nested case‐control study; type 2 diabetes; untargeted metabolomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A) Scatter plot of significantly changed metabolites whose p values were below 0.05 and FDR values were below 0.1 in the discovery set. The diameter of the circles indicates the degree of metabolite changes. Significantly changed metabolites with an increased level in the case group are marked in red. Significantly changed metabolites with a decreased level in the case group are marked in green. B) Significantly changed metabolites and the risk of diabetes in the discovery set. Unadjusted ORs per SD increment and multivariable‐adjusted ORs per SD increment are shown (*: p < 0.05; **: p < 0.01). The multivariable‐adjusted ORs were adjusted by age, sex, BMI, smoking status, drinking status, and physical activity. C) Pathway analysis based on significantly changed metabolites before diabetes onset. D) ORs per SD increment in predictive model scores of T2D. ROC curves of the discovery set E) and validation set F). CMB consisted of eight metabolites, and CCB consisted of BMI, TG and FG. The combination was composed of CMB and CCB.
Figure 2
Figure 2
A) Manhattan plot of FFA 20:4. B) Regional plot showing LD (r 2) and p values of FFA 20:4‐related SNPs near the FADS1 gene. C) Manhattan plot of pipecolinic acid, D) Regional plot showing LD (r 2) and p values of pipecolinic acid‐related SNPs near the CHRM3 gene. E) Manhattan plot of leucine/isoleucine. F) Regional plot showing LD (r 2) and p values of leucine/isoleucine‐related SNPs near WWOX gene. The blue line and red line indicate the suggestive (p < 5.0 × 10−6) and significant (p < 5.0 × 10−8) genome‐wide thresholds, respectively.
Figure 3
Figure 3
Flowchart of the study.
See this image and copyright information in PMC

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