Prognostic Factors for Radiographic Progression in Patients with Seronegative Rheumatoid Arthritis

The Role of Autophagy as a Trigger of Post-Translational Modifications of Proteins and Extracellular Vesicles in the Pathogenesis of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease, characterized by persistent joint inflammation, leading to cartilage and bone destruction. Autoantibody production is directed to post-translational modified (PTM) proteins, i.e., citrullinated or carbamylated. Autophagy may be the common feature in several types of stress (smoking, joint injury, and infections) and may be involved in post-translational modifications (PTMs) in proteins and the generation of citrullinated and carbamylated peptides recognized by the immune system in RA patients, with a consequent breakage of tolerance. Interestingly, autophagy actively provides information to neighboring cells via a process called secretory autophagy. Secretory autophagy combines the autophagy machinery with the secretion of cellular content via extracellular vesicles (EVs). A role for exosomes in RA pathogenesis has been recently demonstrated. Exosomes are involved in intercellular communications, and upregulated proteins and RNAs may contribute to the development of inflammatory arthritis and the progression of RA. In RA, most of the exosomes are produced by leukocytes and synoviocytes, which are loaded with PTM proteins, mainly citrullinated proteins, inflammatory molecules, and enzymes that are implicated in RA pathogenesis. Microvesicles derived from cell plasma membrane may also be loaded with PTM proteins, playing a role in the immunopathogenesis of RA. An analysis of changes in EV profiles, including PTM proteins, could be a useful tool for the prevention of inflammation in RA patients and help in the discovery of personalized medicine.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

INTRODUCTION

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA.

AREAS COVERED

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

EXPERT OPINION

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

Analysis of Serum Immune Markers in Seropositive and Seronegative Rheumatoid Arthritis Among Sudanese Patients and the Relation Between the Serotype and Joint Involvement: A Cohort Study

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation of the synovial membrane. RA is classified as seropositive or seronegative, according to the absence or presence of primarily IgM RF, RF, and/or ACPA. The aim of this study is to identify the relationship between the serotype of rheumatoid arthritis and the level of ESR.

Methods and Materials

This is a descriptive, cross-sectional study done in Omdurman military hospital, Khartoum, Sudan. Conducted with 60 patients with RA, data were collected through a designated questionnaire which included demographic, age, gender, duration of the disease, laboratory finding. All the patients in the study were treated with conventional DMARDs and diagnosed according to the 2010 ACR/EULAR criteria; their disease activity status was assessed by DAS28/ESR. Data were analyzed using SPSS version 23.

Results

The study found that 91.7% of the patients were females, patients of age group between 36 and 50 years had the highest percentage at 38.3% followed by those between 51 and 70 years and the least age group between 20 and 35 years, 36.7% and 15%, respectively. Of all the patients 61.7% were found to be SPRA, while the remaining 38.3% were seronegative (SNRA). Altogether 55% of the patients had moderate disease activity, followed by 16.7% who had a remission, 15% had high disease activity and the remaining 13.3% had low disease activity. The metacarpophalangeal (MCP) joint was found to be the only joint that was significantly associated with DAS28 and its involvement was greater among seropositive patients. The most affected joints were found to be shoulders, knees, wrist, MCP, PIP and elbow, in that order.

Conclusion

Females, middle-age group and shoulder joint were the most affected. Most RA was found to be SPRA, and the seropositive group was found to be more associated with high disease activity, while the seronegative group was associated with remission and low disease activity.