Modified rheumatoid arthritis impact of disease (RAID) score, a potential tool for depression and anxiety screening for rheumatoid arthritis

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Increased Serum Levels of Brain-Derived Neurotrophic Factor Contribute to Inflammatory Responses in Patients with Rheumatoid Arthritis

The aim of this study is to investigate the role of brain-derived neurotrophic factor (BDNF) in the inflammatory responses in patients with rheumatoid arthritis (RA). Serum levels of BDNF and the precursor form of BDNF (proBDNF) from 625 RA patients and 40 controls were analyzed using enzyme-linked immunosorbent assay. Effects of BDNF on the mitogen-activated protein kinase pathway were analyzed by Western blotting. Microarray analysis was conducted to search BDNF regulated gene expression in Jurkat cells, and the differentially expressed genes were validated using T cells from patients with RA and controls. Serum BDNF levels were significantly elevated in patients with RA compared with the controls. Low serum BDNF levels were found in RA patients with anxiety or receiving biologics treatment. BDNF (20 ng/mL) enhanced the phosphorylation of ERK, JNK, and c-Jun, but suppressed the phosphorylation of p38, whereas BDNF (200 ng/mL) enhanced the phosphorylation of ERK and p38. After validation, the expression of CAMK2A, MASP2, GNG13, and MUC5AC, regulated by BDNF and one of its receptors, NGFR, was increased in RA T cells. BDNF increased the IL-2, IL-17, and IFN-γ expression in Jurkat cells and IL-2 and IFN-γ secretion in activated peripheral blood mononuclear cells.