Beware of Antibodies to Dietary Proteins in “Antigen-specific” Immunoassays! Falsely Positive Anticytokine Antibody Tests Due to Reactivity with Bovine Serum Albumin in Rheumatoid Arthritis (The Swedish TIRA Project)
Bibliographic record
Abstract
OBJECTIVE: To evaluate (1) to what extent sera from healthy subjects and patients with rheumatoid arthritis (RA) contain antibodies to bovine serum albumin (BSA); and (2) if anti-BSA antibodies interfere with results of enzyme-linked immunoassays (ELISA) containing BSA. METHODS: The ELISA used was a previously developed in-house assay of autoantibodies to tumor necrosis factor (TNF). Anti-TNF and anti-BSA antibodies were analyzed by ELISA in 189 patients with early RA and 186 healthy blood donors. TNF preparations containing either BSA or human serum albumin (HSA) as carrier proteins were used as antigens in the anti-TNF assay. The presence and levels of antibodies were analyzed in relation to disease course and to the presence/absence of rheumatoid factor (RF). RESULTS: In patients with RA, anti-TNF/BSA levels strongly correlated with anti-BSA levels (r = 0.81, p < 0.001), whereas anti-TNF/HSA did not (r = -0.09). Neither the presence nor the levels of anti-BSA in RA patients were associated with disease progression, and antibody levels were not significantly altered compared to controls (p = 0.11). IgG reactivity with TNF/HSA was neglible. In paired sera, preincubation with BSA abolished the anti-TNF/BSA reactivity. There were no indications of RF interference with anti-BSA or anti-TNF reactivity. CONCLUSION: Antibodies to BSA are common in patients with RA as well as in healthy individuals. Their presence does not seem to be associated with RA disease activity or disease course, but may severely interfere with ELISA containing BSA. The use of BSA as a "blocking agent" or carrier protein in immunoassays should therefore be avoided.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".