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

Holers, V. Michael

doi:10.1002/art.41479

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Hyperuricemia is associated with many comorbidities, such as chronic kidney disease, diabetes mellitus, hypertension, hyperlipidemia, nonalcoholic fatty liver disease, and cardiovascular diseases (1,8). In these metabolic diseases, systematic metabolic alterations have often been observed (9,10). As such, metabolomic analysis of biologic fluids may provide an organism‐wide view at a systems biology level of the dysregulated metabolic pathways and identify potential biomarkers for the progression from hyperuricemia to gout.…”

Section: Introductionmentioning

confidence: 99%

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

Shen

Wang

Liang

et al. 2021

Arthritis & Rheumatology

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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.

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Section: Introductionmentioning

confidence: 99%

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

Shen

Wang

Liang

et al. 2021

Arthritis & Rheumatology

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“…Hyperuricemia and gout are closely related to metabolic syndrome, kidney injury, and cardiovascular disease [ 7 – 9 ]. Systematic metabolic changes were observed in both of the two diseases [ 10 , 11 ]. However, it is still unsure what are the differences in metabolism of hyperuricemia and gout and whether these differences could affect acute gouty attacks.…”

Section: Introductionmentioning

confidence: 99%

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

Liu,

et al. 2023

J Orthop Surg Res

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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.

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

Huang,

Zhang,

Qiu

et al. 2024

Nutrition, Metabolism and Cardiovascular Diseases

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

Cited by 3 publications

References 15 publications

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

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

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

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

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