Association of a Serum Protein Signature With Rheumatoid Arthritis Development
Bibliographic record
Abstract
OBJECTIVE: The pathophysiologic events that precede the onset of rheumatoid arthritis (RA) remain incompletely understood. This study was undertaken to identify changes in the serum proteome that precede the onset of RA, with the aim of providing new insights into the pathogenic mechanisms that lead to its development. METHODS: In a cohort of first-degree relatives of Indigenous North American RA patients, the SomaScan proteomics platform was used to determine the levels of 1,307 proteins in multiple longitudinal serum samples from 17 individuals who were followed up prospectively to the time of disease onset. Proteomic signatures from this group of individuals (designated the progressor group) were compared to those in a group of individuals who were considered at risk of developing RA, stratified as either positive (n = 63) or negative (n = 47) for anti-citrullinated protein antibodies (ACPAs) (designated the at-risk group). Machine learning was used to identify a protein signature that could accurately classify those individuals at highest risk of future RA development. RESULTS: A preclinical proteomic signature that differentiated RA progressors from at-risk individuals, irrespective of ACPA status, was identified (area under the curve 0.913, accuracy 91.2%). Importantly, the predictive preclinical proteomic signature was present not only in serum samples obtained close to the onset of RA, but also in serum samples obtained a median of 30.9 months prior to onset. Network analysis implicated the activation of Toll-like receptor 2 and production of tumor necrosis factor and interleukin-1 as key events that precede RA progression. CONCLUSION: Alterations in the serum proteome in the preclinical phase of RA can emerge years prior to the onset of disease. Our findings suggest that the serum proteome provides a rich source of proteins serving both to classify at-risk individuals and to identify molecular pathways involved in the development of clinically detectable RA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".