Challenges and Opportunities: Using Omics to Generate Testable Insights Into Pathogenic Mechanisms in Preclinical Seropositive Rheumatoid Arthritis

Metabolomics of human umbilical vein endothelial cell-based analysis of the relationship between hyperuricemia and dyslipidemia

BACKGROUND AND AIMS

Hyperuricemia frequently accompanies dyslipidemia, yet the precise mechanism remains elusive. Leveraging cellular metabolomics analyses, this research probes the potential mechanisms wherein hyperuricemia provokes endothelial cell abnormalities, inducing disordered bile metabolism and resultant lipid anomalies.

METHODS AND RESULTS

We aimed to identify the differential metabolite associated with lipid metabolism through adopting metabolomics approach, and thereafter adequately validating its protective function on HUVECs by using diverse assays to measure cellular viability, reactive oxygen species, migration potential, apoptosis and gene and protein levels of inflammatory factors. Taurochenodeoxycholic acid (TCDCA) (the differential metabolite of HUVECs) and the TCDCA-involved primary bile acid synthesis pathway were found to be negatively correlated with high UA levels based on the results of metabolomics analysis. It was noted that compared to the outcomes observed in UA-treated HUVECs, TCDCA could protect against UA-induced cellular damage and oxidative stress, increase proliferation as well as migration, and decreases apoptosis. In addition, it was observed that TCDCA might protect HUVECs by inhibiting UA-induced p38 mitogen-activated protein kinase/nuclear factor kappa-B p65 (p38MAPK/NF-κB p65) pathway gene and protein levels, as well as the levels of downstream inflammatory factors.

CONCLUSION

The pathogenesis of hyperuricemia accompanying dyslipidemia may involve high uric acid levels eliciting inflammatory reactions and cellular damage in human umbilical vein endothelial cells (HUVECs), mediated through the p38MAPK/NF-κB signaling pathway, subsequently impinging on cellular bile acid synthesis and reducing bile acid production.

Metabolomic analysis for asymptomatic hyperuricemia and gout based on a combination of dried blood spot sampling and mass spectrometry technology

BACKGROUND

Gout is the most common inflammatory arthritis and closely related to metabolic syndrome, leading to excruciating pain and the decline in quality of patients' life. However, the pathogenesis of gout is still unclear, and novel biomarkers are demanded for the early prediction and diagnosis of gout.

OBJECTIVE

This study aimed at profiling the dysregulated metabolic pathways in asymptomatic hyperuricemia (AHU) and gout and elucidating the associations between AHU, gout and metabolomics, which may aid in performing gout screening.

METHODS

A total of 300 participants, including 114 healthy controls, 92 patients with AHU, and 94 patients with gout, were analyzed by using a combination of dried blood spot (DBS) sampling and mass spectrometry (MS) technology. Multiple algorithms were applied to characterize altered metabolic profiles in AHU and gout. The mainly altered metabolites were identified by random forest analysis.

RESULTS

There were significant differences in AHU and gout compared with control group. The altered metabolites were involved in oxidation of fatty acids, carnitine synthesis, urea cycle, and amino acid metabolism in AHU and gout. Random forest classification of 16 metabolites yielded 3 important features to distinguish gout from AHU.

CONCLUSIONS

Distinct metabolomic signatures were observed in AHU and gout. The selected metabolites may have the potential to improve the early detection of gout.

Serum Metabolomics Identifies Dysregulated Pathways and Potential Metabolic Biomarkers for Hyperuricemia and Gout

OBJECTIVE

To systematically profile metabolic alterations and dysregulated metabolic pathways in hyperuricemia and gout, and to identify potential metabolite biomarkers to discriminate gout from asymptomatic hyperuricemia.

METHODS

Serum samples from 330 participants, including 109 with gout, 102 with asymptomatic hyperuricemia, and 119 normouricemic controls, were analyzed by high-resolution mass spectrometry-based metabolomics. Multivariate principal components analysis and orthogonal partial least squares discriminant analysis were performed to explore differential metabolites and pathways. A multivariate methods with Unbiased Variable selection in R (MUVR) algorithm was performed to identify potential biomarkers and build multivariate diagnostic models using 3 machine learning algorithms: random forest, support vector machine, and logistic regression.

RESULTS

Univariate analysis demonstrated that there was a greater difference between the metabolic profiles of patients with gout and normouricemic controls than between the metabolic profiles of individuals with hyperuricemia and normouricemic controls, while gout and hyperuricemia showed clear metabolomic differences. Pathway enrichment analysis found diverse significantly dysregulated pathways in individuals with hyperuricemia and patients with gout compared to normouricemic controls, among which arginine metabolism appeared to play a critical role. The multivariate diagnostic model using MUVR found 13 metabolites as potential biomarkers to differentiate hyperuricemia and gout from normouricemia. Two-thirds of the samples were randomly selected as a training set, and the remainder were used as a validation set. Receiver operating characteristic analysis of 7 metabolites yielded an area under the curve of 0.83-0.87 in the training set and 0.78-0.84 in the validation set for distinguishing gout from asymptomatic hyperuricemia by 3 machine learning algorithms.

CONCLUSION

Gout and hyperuricemia have distinct serum metabolomic signatures. This diagnostic model has the potential to improve current gout care through early detection or prediction of progression to gout from hyperuricemia.