Predicting therapeutic response through biomarker analysis in psoriatic arthritis, an example of precision medicine

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.

Dawn of Precision Medicine in Psoriatic Arthritis

The establishment of precision medicine is considered particularly important in heterogeneous autoimmune diseases (e.g., psoriatic arthritis, systemic lupus erythematosus), which reveal clinical and molecular heterogeneity. The selection of optimal treatment strategies for individual patients may be more important and complex in autoimmune diseases than in other diseases. Two factors are important in precision medicine: patient stratification and use of targeted. When both factors work, patients are likely to have good outcomes. However, research into precision medicine and its practice in systemic autoimmune diseases is lacking. In contrast, the usefulness of peripheral immune cell phenotyping in the evaluation of immunological characteristics and stratification into subgroups of individual patients with systemic autoimmune diseases such as immunoglobulin 4-related disease, systemic lupus erythematosus, and anti-neutrophil cytoplasmic antibody-related vasculitis was reported. Furthermore, the potential of precision medicine using biological disease-modifying antirheumatic drugs based on peripheral immune cell phenotyping was recently demonstrated for psoriatic arthritis in the clinical setting. Precision medicine has not yet been sufficiently investigated in real world clinical settings. However, a dawn of precision medicine has emerged. We should shed further light on precision medicine in PsA and other autoimmune diseases. Here, we first review the usefulness of peripheral immune cell phenotyping in systemic autoimmune diseases and the potential of precision medicine in PsA based on this method.

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.