A Comparison Between IgG- and IgA-class Antibodies to Cyclic Citrullinated Peptides and to Modified Citrullinated Vimentin in Early Rheumatoid Arthritis and Very Early Arthritis
Bibliographic record
Abstract
OBJECTIVE: Because of their slightly higher sensitivity, it has been argued that antibodies to modified citrullinated vimentin (anti-MCV) are superior to antibodies to cyclic citrullinated peptides (anti-CCP), while others claim that anti-CCP is preferable because of higher diagnostic specificity for rheumatoid arthritis (RA). We evaluated IgG- and IgA-class anti-MCV and anti-CCP as diagnostic and prognostic markers in early arthritis. METHODS: Two Swedish arthritis populations were examined: 215 patients with early RA (≤ 12 months' duration) from the Swedish TIRA-1 cohort, and 69 patients with very early arthritis (≤ 3 months' duration) from the Kronoberg Arthritis Incidence cohort, in which 22% were diagnosed with RA. IgG anti-CCP and anti-MCV antibodies were analyzed with commercial kits. These tests were modified for IgA-class antibody detection. Results were related to disease course, smoking habits, and shared epitope status. RESULTS: In the TIRA-1 cohort, occurrence of IgG anti-MCV and IgG anti-CCP showed a 93% overlap, although IgG anti-MCV had higher diagnostic sensitivity. Twenty-four percent tested positive for IgA anti-MCV compared to 29% for IgA anti-CCP. In the Kronoberg Arthritis Incidence cohort, 15% tested positive for IgG anti-MCV and 6% for IgA anti-MCV, compared to 10% positive for IgG anti-CCP and 3% positive for IgA anti-CCP, revealing that anti-CCP had higher diagnostic specificity for RA. As previously reported for IgA anti-CCP, IgA anti-MCV antibodies occurred in a small proportion of high-level IgG antibody-positive sera and were associated with a more aggressive disease course. Smokers were more often positive for antibodies to citrullinated proteins, most strikingly among the patients who were IgA anti-MCV-positive. CONCLUSION: The occurrences of IgG-class anti-MCV and anti-CCP in early RA largely overlap. The sensitivity of anti-MCV is slightly higher, while the diagnostic specificity is higher for anti-CCP. In both instances a positive test predicts an unfavorable disease course, possibly slightly more so for anti-MCV. Although associated with a more active disease over time, IgA-class anti-CCP or anti-MCV do not add any diagnostic advantage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".