A Metabonomic Study to Explore Potential Markers of Asymptomatic Hyperuricemia and Acute Gouty Arthritis

Overview

Journal J Orthop Surg Res

Publisher Biomed Central

Specialty Orthopedics

Date 2023 Feb 14

PMID 36782295

Authors

Wei Wang

Jun Kou

Mingmei Zhang

Tao Wang

Wei Li

Yamen Wang

Qingyun Xie

Meng Wei

Affiliations

Soon will be listed here.

Abstract

Background: Acute gouty arthritis (AGA) is a metabolic disease with acute arthritis as its main manifestation. However, the pathogenesis of asymptomatic hyperuricemia (HUA) to AGA is still unclear, and metabolic markers are needed to early predict and diagnose. In this study, gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to reveal the changes of serum metabolites from healthy people to HUA and then to AGA, and to find the pathophysiological mechanism and biological markers.

Methods: Fifty samples were included in AGA, HUA, and healthy control group, respectively. The metabolites in serum samples were detected by GC/LC-MS. According to the statistics of pairwise grouping, the statistically significant differential metabolites were obtained by the combination of multidimensional analysis and one-dimensional analysis. Search the selected metabolites in KEGG database, determine the involved metabolic pathways, and draw the metabolic pathway map in combination with relevant literature.

Results: Using metabonomics technology, 23 different serum metabolic markers related to AGA and HUA were found, mainly related to uric acid metabolism and inflammatory response caused by HUA/AGA. Three of them are completely different from the previous gout studies, nine metabolites with different trends from conventional inflammation.

Conclusions: In conclusion, we analyzed 150 serum samples from AGA, HUA, and healthy control group by GC/LC-MS to explore the changes of these differential metabolites and metabolic pathways, suggesting that the disease progression may involve the changes of biomarkers, which may provide a basis for disease risk prediction and early diagnosis.

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