Establishment of a decision tree model for diagnosis of early rheumatoid arthritis by proteomic fingerprinting

A serum metabolic biomarker panel for early rheumatoid arthritis

Objective

There is an urgent need for novel biomarkers to improve the early diagnosis of rheumatoid arthritis (ERA). Current serum biomarkers used in the management of ERA, including rheumatoid factor and anti-cyclic citrullinated peptide (ACPA), show limited specificity and sensitivity. Here, we used metabolomics to uncover new serum biomarkers of ERA.

Methods

We applied an untargeted metabolomics approach including gas chromatography time-of-flight mass spectrometry in serum samples from an ERA cohort (n=32) and healthy controls (n=19). Metabolite set enrichment analysis was performed to explore potentially important biological pathways. Partial least squares discriminant analysis and variable importance in projection analysis were performed to construct an ERA biomarker panel.

Results

Significant differences in the content of 11/81 serum metabolites were identified in patients with ERA. Receiver operating characteristic (ROC) analysis showed that a panel of only three metabolites (glyceric acid, lactic acid, and 3-hydroxisovaleric acid) could correctly classify 96.7% of patients with ERA, with an area under the ROC curve of 0.963 and with 94.4% specificity and 93.5% sensitivity, outperforming ACPA-based diagnosis by 2.9% and, thus, improving the preclinical detection of ERA. Aminoacyl-tRNA biosynthesis and serine, glycine, and phenylalanine metabolism were the most significant dysregulated pathways in patients with ERA.

Conclusion

A metabolomics serum-based biomarker panel composed of glyceric acid, lactic acid, and 3-hydroxisovaleric acid offers potential for the early clinical diagnosis of RA.

Development and assessment of a predictive model for early diagnosis of rheumatoid arthritis in southwest China: A new nomogram

Rheumatoid arthritis (RA) is a disease complicated with inflammatory synovitis, which seriously affects the life quality of patients. Early diagnosis is important for prognosis of RA. Here, we aimed to develop and assess a model for early diagnosis of RA in southwest China. A nomogram including 44 patients with an early diagnosis of RA was developed. Variables were filtered by least absolute contraction selection operator and multiple logistic regression. The efficiency and clinical application range were evaluated. This nomogram showed that rheumatoid factor, erythrocyte sedimentation rate, RA33, facet joint and knee joint had high positive predictive value for RA. The area under curve was 0.920 [95% confidence interval (CI): 0.865-0.975]. In the validation model, area under curve was 0.942 (95% CI: 0.893-0.991). Calibration and decision curve suggested that this nomogram was helpful within the threshold probability range of 0.02 to 1.00. Using this nomogram will help clinicians in the early diagnosis of RA. Laboratory indicators such as rheumatoid factor, erythrocyte sedimentation rate, RA33, and clinical symptoms such as morning stiffness, facet joint and knee joint are very important, which deserves the attention of clinicians.

Serum Peptidomic Profile as a Novel Biomarker for Rheumatoid Arthritis

Over the last decades, there has been an increasing need to discover new diagnostic RA biomarkers, other than the current serologic biomarkers, which can assist early diagnosis and response to treatment. The purpose of this study was to analyze the serum peptidomic profile in patients with rheumatoid arthritis (RA) by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The study included 35 patients with rheumatoid arthritis (RA), 35 patients with primary osteoarthritis (OA) as the disease control (DC), and 35 healthy controls (HC). All participants were subjected to serum peptidomic profile analysis using magnetic bead (MB) separation (MALDI-TOF-MS). The trial showed 113 peaks that discriminated RA from OA and 101 peaks that discriminated RA from HC. Moreover, 95 peaks were identified and discriminated OA from HC; 38 were significant (p < 0.05) and 57 nonsignificant. The genetic algorithm (GA) model showed the best sensitivity and specificity in the three trials (RA versus HC, OA versus HC, and RA versus OA). The present data suggested that the peptidomic pattern is of value for differentiating individuals with RA from OA and healthy controls. We concluded that MALDI-TOF-MS combined with MB is an effective technique to identify novel serum protein biomarkers related to RA.

In search of pathobiological endotypes: a systems approach to early rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease. Early referral and treatment are key to the effective management of the disease. This makes imperative the identification of biomarkers and of pathobiological endotypes.

AREAS COVERED

This review describes recent efforts to integrate large-scale datasets for the identification of disease endotypes for precision medicine in early, seropositive RA. We conducted a search for systems and multi-omics papers in early RA patients through to 1 January 2020. We reviewed investigations of multiple technologies such as transcriptomic, proteomic and metabolomic platforms as well as extensive clinical datasets. We outline progress made and describe some of the advantages and limitations of current computational and statistical methods.

EXPERT OPINION

The search for pathobiological endotypes in early RA is rapidly developing. While currently, studies tend to be small, reliant upon new technologies and unproven analytical tools, as the technology becomes cheaper and more reliable, and the properties of analytical tools for the integration of cross-platform biology become better understood, it seems likely that better biomarkers of disease, remission and response to individual therapies will emerge.

Defining the proteomic landscape of rheumatoid arthritis: progress and prospective clinical applications

INTRODUCTION

The heterogeneity of Rheumatoid Arthritis (RA) and the absence of clinical tests accurate enough to identify the early stages of this disease have hampered its management. Therefore, proteomics research is increasingly focused on the discovery of novel biological markers, which would not only be able make an early diagnosis, but also to gain insight into the different pathological mechanisms underlying the heterogeneity of RA and also to stratify patients, which is critical to enabling effective treatments. Areas covered: The proteomic approaches that have been utilised to provide knowledge about RA pathogenesis, and to identify biomarkers for RA diagnosis, prognosis, disease monitoring and prediction of response to therapy, are summarized. Expert commentary: Although each proteomic study is unique in its design, all of them have contributed to the understanding of RA pathogenesis and the discovery of promising biomarkers for patient stratification, which would improve clinical care of RA patients. Still, efforts need to be made to validate these findings and translate them into clinical practice.