The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics

Abstract

Background Hyperuricemia and gout are a group of disorders of purine metabolism. In recent years, the incidence of hyperuricemia and gout has been increasing, which is a severe threat to people’s health. Several studies on hyperuricemia and gout in proteomics and metabolomics have been conducted recently. Some literature has identified biomarkers that distinguish asymptomatic hyperuricemia from acute gout or remission of gout. We summarize the physiological processes in which these biomarkers may be involved and their role in disease progression. Methodology We used professional databases including PubMed, Web of Science to conduct the literature review. This review addresses the current landscape of hyperuricemia and gout biomarkers with a focus on proteomics and metabolomics. Results Proteomic methods are used to identify differentially expressed proteins to find specific biomarkers. These findings may be suggestive for the diagnosis and treatment of hyperuricemia and gout to explore the disease pathogenesis. The identified biomarkers may be mediators of the link between hyperuricemia, gout and kidney disease, metabolic syndrome, diabetes and hypertriglyceridemia. Metabolomics reveals the main influential pathways through small molecule metabolites, such as amino acid metabolism, lipid metabolism, or other characteristic metabolic pathways. These studies have contributed to the discovery of Chinese medicine. Some traditional Chinese medicine compounds can improve the metabolic disorders of the disease. Conclusions We suggest some possible relationships of potential biomarkers with inflammatory episodes, complement activation, and metabolic pathways. These biomarkers are able to distinguish between different stages of disease development. However, there are relatively few proteomic as well as metabolomic studies on hyperuricemia and gout, and some experiments are only primary screening tests, which need further in-depth study.

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

“…Zhao Q et al demonstrated a nonlinear relationship between serum UA and TyG index in children with short stature, which remained nonlinear after gender stratification [ 41 ]. Hyperuricemia is an important influence in causing gout [ 42 ], thus supporting our findings.…”

Comorbidities confound metabolomics studies of human disease

“…We are not aware of any untargeted metabolomics studies investigating risk factors of gout. Nevertheless, previous research on prevalent gout has established connections with altered amino acid levels, perturbations in purine, glycerophospholipid, sphingolipid, and carbohydrate metabolism [35]. These ndings were also partially replicated in our study.…”

Design, synthesis and evaluation of a myricetin and nobiletin hybrid compound for alleviating hyperuricemia based on metabolomics and gut microbiota

“…Studies have conrmed that glycerophospholipid metabolism, arachidonic acid metabolism and alanine, aspartate and glutamate metabolism may be related to the pathogenesis of hyperuricemia and gout. 10 In the pathway of glycerophospholipid metabolism, the levels of glycerylphosphorylcholine, glycerol 3-phosphate and LysoPC(20:1(11Z)/0:0) in MD group were signicantly increased, while the level of PE(16:0/20:3(5Z,8Z,11Z)) was decreased. Aer the treatment of MNH, the levels of glycerylphosphorylcholine, glycerol 3-phosphate, LysoPC(20:1(11Z)/0:0) and PE(16:0/ 20:3(5Z,8Z,11Z)) were signicantly adjusted.…”

“…With its comprehensive and dynamic properties, metabolomics has exhibited significant advantages in the entire understanding of the efficacy mechanism of drug. 10 The gut microbiota plays an important role in cultivating host immunity, digesting food, regulating intestinal endocrine function and nerve signals, altering drug action and metabolism, removing toxins, and producing a variety of compounds that affect the host. 11 Furthermore, an increasing number of studies have combined 16S rRNA amplicon sequencing and metabolomic methods to investigate the relationship between gut microbiota and circulating metabolites.…”