Blood Monocyte Heterogeneity and Markers of Endothelial Activation in Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Ankylosing spondylitis (AS) is associated with excessive cardiovascular (CV) morbidity. Interactions between activated endothelium and monocytes precede atherosclerotic plaques. Our aim was to quantify blood monocyte subsets in relation to endothelial activation and inflammatory activity in subjects with AS who were free of clinical atherosclerotic CV disease. METHODS: Markers of inflammation and endothelial activation were measured in 47 patients with AS receiving no disease-modifying antirheumatic drugs, and 22 healthy controls. Exclusion criteria included atherosclerotic CV disease and traditional risk factors. Flow cytometry was used to identify monocyte subsets: classical CD14(++)CD16(-), intermediate CD14(++)CD16(+), and nonclassical CD14(+)CD16(++) monocytes and to evaluate their expression of CD11b and CD11c. RESULTS: Traditional risk factors were comparable among the groups, except for lower high-density lipoprotein cholesterol in AS (p = 0.007). Relative to controls, in subjects with AS counts of classical monocytes were higher (84.3 ± 5.4 vs 78.9 ± 5.3% of blood monocytes, p < 0.001) and nonclassical monocytes lower (2.9 ± 2.2 vs 5.5 ± 2.3%, p < 0.001). In AS we observed increased soluble intercellular adhesion molecule-1 [251 (224-293) vs 202 (187-230) ng/ml, p = 0.002], an endothelial ligand for monocytic β2-integrin CD11b/CD18. CD11b expression on all 3 monocyte subsets was elevated in 21 AS subjects with a Bath Ankylosing Spondylitis Disease Activity Index score ≥ 4 versus the remaining patients (p = 0.005-0.03). C-reactive protein, interleukin 6 (IL-6), and pentraxin-3 were increased in AS, in contrast to tumor necrosis factor-α and IL-18. IL-6 correlated with classical monocytes numbers in AS (r = 0.56, p < 0.0001) but not in the controls (r = 0.10, p = 0.65). CONCLUSION: Our findings suggest a contribution of immune dysregulation to enhanced monocyte-endothelial interactions in AS, especially in patients with active disease, which possibly can accelerate atherogenesis on a longterm basis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".