11.6 THE AORTIC-TO-BRACHIAL STIFFNESS GRADIENT AND AORTIC RESERVOIR-EXCESS PRESSURE IN A DIALYSIS POPULATION
Bibliographic record
Abstract
Introduction: 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-1b, IL-6 and TNFa 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.Methods: 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) using Complior SP (Artech Medical, Pantin, France).The profile of inflammatory cytokines (IFNv, IL-1a, IL-1b, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12 and TNF a) was determined in plasma by ELISA using a Multiplex (Aushon, Maine, USA).Results: Mean cf-PWV of the cohort was 12.8AE3.9m/s.Median plasma levels of IL-1b, IL-6 and TNFa were 1.01 pg/ml, 4.26 pg/ml and 3.33 pg/ml, respectively.IL-6 levels positively correlated with cf-PWV (b Z 0.218, P Z 0.006, R Z 0.129), suggesting a role in aortic stiffness.In contrast, no correlation between PWV and plasma levels of IL-1b or TNFa was established. Conclusion: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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".