Background: Rheumatoid factor (RF) autoantibodies are highly prevalent, yet the molecular determinants of RF development and its progression to rheumatoid arthritis (RA) remain poorly understood. Here, we define the genetic, phenotypic, and molecular architecture of RF and its progression to RA. Methods: 469,036 UK Biobank participants with RF testing and 76 ALTRA cohort individuals were studied. Phenome-wide (PheWAS), genome-wide (GWAS), and proteome-wide association studies compared RF-positive individuals without autoimmune disease to RF-negative controls. Single-cell RNA sequencing enabled pseudobulk differential expression and cytokine signature enrichment analyses. Results: RF seroprevalence was 9.3% and longitudinally stable in 94.5% of individuals. PheWAS identified 48 significant associations, led by chronic viral hepatitis (OR 4.8), hypersensitivity pneumonitis (OR 3.6), bronchiectasis (OR 1.9), and COPD (OR 1.4). GWAS of 24,216 RF-positive individuals revealed 29 independent loci; the strongest signal was in the extended HLA region (OR 1.45, P-value=5.4×10-221). Non-HLA loci converged on B cell homeostasis genes (ETS1, BACH2, PAX5, TNFRSF13B, FCGR2A). RF-positive individuals did not carry elevated RA polygenic risk. Proteomic profiling identified 153 differentially abundant proteins enriched for humoral immunity and interferon-induced chemokines, with 79% showing dose-response relationships across titers. Progression to RA involved a shift toward activating tissue-damaging inflammatory pathways rather than amplification of the RF signature. Single-cell transcriptomics of RF-positive individuals without RA localized dysregulation to memory B cells, with downregulation of inhibitory genes (FCGR2B, BACH2, FOXP1) and upregulation of activation markers. Conclusion: RF production is governed by HLA class II and B cell regulatory loci, associated with mucosal inflammation, and is genetically and molecularly distinct from RA.
Mehmet Hocaoglu, Amr H. Sawalha
Usage data is cumulative from June 2026 through July 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 729 | 0 |
| 293 | 0 | |
| Supplemental data | 117 | 0 |
| Citation downloads | 70 | 0 |
| Totals | 1,209 | 0 |
| Total Views | 1,209 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.