[PP.37.09] IMPACT OF INFLAMMATOIRY CYTOKINES ON AORTIC STIFFNESS IN PATIENTS WITH CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Objective: Cardiovascular diseases are the primary cause of morbidity and mortality in patients with chronic kidney disease (CKD). Aortic stiffness is a non-traditional risk factor in these patients. Using an animal model of CKD with vascular calcification, we reported that inflammation is involved in the development of aortic calcification and stiffness. Hence, increased vascular production of IL-1, IL-6 and TNF was associated with aortic calcification. Therefore, we investigated the impact of the latter cytokines on aortic stiffness and determined the profile of inflammatory cytokines in a cohort of CKD patients. Design and method: This is a transversal study involving 196 CKD patients on dialysis, in which aortic stiffness was determined non-invasively by the assessment of carotid-femoral pulse wave velocity (cf-PWV). The profile of inflammatory cytokines (IFN, IL-1, IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12 and TNF) was determined in plasma by ELISA using a Multiplex. Results: Mean cf-PWV of the cohort was 12.8 ± 3.9 m/s. Median plasma levels of IL-1, IL-6 and TNF were 1.01 pg/ml, 4.26 pg/ml and 3.33 pg/ml, respectively. IL-6 levels positively correlated with cf-PWV (= 0.218, P = 0.006, R = 0.129), suggesting a role in aortic stiffness. In contrast, no correlation between PWV and plasma levels of IL-1 or TNF was established. Conclusions: This study reveals a relationship between an inflammatory cytokine, Il-6, and aortic stiffness in patients with CKD. Our results, together with our previous findings in an experimental animal model, indicate that IL-6 may represent a novel therapeutic target of cardiovascular diseases in CKD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.477 | 0.286 |
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".