Soluble Vascular Biomarkers in Rheumatoid Arthritis and Ankylosing Spondylitis: Effects of 1-year Antitumor Necrosis Factor-α Therapy
Bibliographic record
Abstract
Objective. Rheumatoid arthritis (RA) and ankylosing spondylitis (AS) have been associated with cardiovascular disease. The treatment of arthritis by tumor necrosis factor-α (TNF-α) inhibitors may decrease the serum concentrations of vascular biomarkers. We determined circulating levels of oxidized low-density lipoprotein (oxLDL)/β2 glycoprotein I (β2-GPI) complexes, antibodies to 60 kDa heat shock protein (anti-Hsp60), soluble urokinase plasminogen activator receptor (suPAR), and B-type natriuretic peptide (BNP) fragment in sera of RA and AS patients undergoing anti-TNF treatment. Methods. Fifty-three patients with RA/AS were treated with etanercept or certolizumab pegol for 1 year. Circulating oxLDL/β2-GPI complex (AtherOx), anti-Hsp60 IgG, and BNP8-29 fragment levels were assessed by ELISA. suPAR levels were determined by suPARnostic Quick Triage test. Flow-mediated vasodilation (FMD), carotid intima-media thickness (CIMT), and arterial pulse wave velocity (PWV) were determined by ultrasound. Results. One-year anti-TNF treatment significantly decreased oxLDL/β2-GPI levels, as well as suPAR levels in patients with critically high suPAR levels at baseline. In RA, BNP levels were higher in seropositive vs seronegative patients. Serum levels of these vascular biomarkers variably correlated with lipids, anticitrullinated protein antibodies, rheumatoid factor, and C-reactive protein. CIMT positively correlated with BNP, and PWV with suPAR and anti-Hsp60, whereas FMD inversely associated with anti-Hsp60. In repeated measures ANOVA analysis, disease activity supported the effects of anti-TNF treatment on 12-month changes in oxLDL/β2-GPI. CIMT supported the effects of therapy on changes in anti-Hsp60 and suPAR. Conclusion. These biomarkers may be involved in the pathogenesis of atherosclerosis underlying RA/AS. TNF inhibition variably affects the serum levels of oxLDL/β2-GPI, suPAR, and BNP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".