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. 2022 Aug 17;12(1):13980.
doi: 10.1038/s41598-022-18361-y.

Investigation of pathogenesis of hyperuricemia based on untargeted and targeted metabolomics

Nankun Qin #  1 , Ming Qin #  1 , Wenjun Shi  1 , Lingbo Kong  2 , Liting Wang  1 , Guang Xu  1 , Yuying Guo  1 , Jiayu Zhang  3 , Qun Ma  4
Affiliations

Investigation of pathogenesis of hyperuricemia based on untargeted and targeted metabolomics

Nankun Qin et al. Sci Rep. .

Abstract

Hyperuricemia (HUA) seriously harms human health but the exact etiology and pathogenesis of HUA are not fully understood. Therefore, it is still of great significance to find effective biomarkers and explore the pathogenesis of HUA. Metabolomics reflects the influence of internal and external factors on system metabolism, explains the changes in metabolite levels during the development of diseases, and reveals the molecular mechanism of pathogenesis. Metabolomics is divided into untargeted metabolomics and targeted metabolomics according to different research modes. Each other's advantages can be fully utilized by combining the two so that the results of metabolomics research can be consummated. 20 HUA patients and 20 healthy individuals participated in the experiment, and untargeted metabolomics was employed to find 50 differential metabolites in HUA serum samples. Twelve candidate biomarkers were screened based on literature research and ROC Curve analysis for subsequent verification. Based on the UPLC-TQ-MS analysis platform, the targeted metabolomics detection methods were established and the content of 12 candidate biomarkers was precisely quantified. Compare with the results of untargeted metabolomics, the targeted metabolomics results were considered more reliable.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
In ESI+ mode and ESI- mode, PCA score, OPLS-DA score, and permutation test of serum samples from the control group and HUA group were performed (C control group, H HUA group).
Figure 2
Figure 2
Metabolic pathway analysis of 138 differential metabolites. (1) Glycerophospholipid metabolism; (2) phenylalanine, tyrosine, and tryptophan biosynthesis; (3) phenylalanine metabolism; (4) linoleic acid metabolism; (5) α-linolenic acid metabolism; (6) arachidonic acid metabolism; (7) sphingolipid metabolism.
Figure 3
Figure 3
ROC Curve analysis results of candidate biomarkers.
Figure 4
Figure 4
Determination results of serum samples of patients with HUA and normal people (n = 20) [(A) polar biomarkers; (B) lipid biomarkers] (note: compared with the normal group, *P < 0.01).
See this image and copyright information in PMC

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