Integrative AI-Based Multiomic and Neurophysiological Profiling of Chronic Pain in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease in which pain remains the most prominent and burdensome symptom from the patient's perspective. Although the introduction of biological and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs) has significantly improved the control of inflammatory activity, an estimated 20-30% of patients continues to experience persistent moderate-to-severe pain despite achieving clinical remission. This discordance between objective inflammatory markers and subjective pain perception reflects an important unmet clinical need and suggests that chronic pain in RA may become partially or fully independent of peripheral inflammation, driven instead by central sensitization and nociplastic mechanisms. The RA-PAIN-AI study is a prospective, observational, case-control study integrating clinical assessment, patient-reported outcome measures, neurophysiological evaluation, and multiomic profiling, analyzed using artificial intelligence (AI)-based methods. Two groups of adult patients with RA are enrolled: a study group of patients with active disease qualifying for biological therapy under the Polish national drug program B.33, and a control group of patients in sustained clinical remission (at least 1.5 years) under biological therapy, without clinically significant chronic pain. All participants undergo clinical assessment, standardized questionnaires, peripheral blood collection at the baseline (Visit 1) and follow-up (Visit 2) visits. Electroencephalography (EEG) is offered as an optional procedure, at the discretion of the investigator. Biological samples are analyzed using advanced multiomic technologies, including whole genome sequencing (genomics), mRNA expression profiling (transcriptomics), and measurement of circulating proteins (secretomics). All clinical, biological, and neurophysiological data are then integrated using AI methods, including dimensionality reduction, clustering, and