Soluble CD14 and CD14 Polymorphisms in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Soluble CD14 (sCD14) is involved in innate immune responses and has been implicated to play a pathogenic role in inflammatory diseases including rheumatoid arthritis (RA). No studies have identified the specific factors that influence sCD14 expression in RA. We used cross-sectional data to evaluate the relationship of sCD14 concentrations in RA with measures of disease activity and severity. We hypothesized that sCD14 concentrations would be elevated in subjects with greater RA disease severity and markers of disease activity, compared to subjects with lower disease activity. We also examined whether well-defined polymorphisms in CD14 are associated with sCD14 expression in RA. METHODS: Soluble CD14 concentrations were measured using banked serum from patients with RA (n = 1270) and controls (n = 186). Associations of patient factors including demographics, measures of RA disease activity/severity, and select CD14 single-nucleotide polymorphisms (SNP) with sCD14 concentration were examined in patients with RA using ordinal logistic regression. RESULTS: Circulating concentrations of sCD14 were higher in patients with RA compared to controls (p < 0.0001). Factors significantly and independently associated with higher sCD14 levels in patients with RA included older age, being white (vs African American), lower body mass index, elevated high sensitivity C-reactive protein, and higher levels of disease activity based on the Disease Activity Score (DAS28). There were no significant associations of CD14 tagging SNP with sCD14 level in either univariate or multivariable analyses. CONCLUSION: Circulating levels of sCD14 are increased in RA and are highest in patients with increased levels of RA disease activity. In the context of RA, sCD14 concentrations also appear to be strongly influenced by specific patient factors including older age and race but not by genetic variation in CD14.
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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 | 0.000 |
| 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) | 0.002 | 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".