Albuminuria and Vascular Health in Females with CKD
Bibliographic record
Abstract
Background: Cardiovascular (CV) disease is the leading cause of death in chronic kidney disease (CKD), and elevated CV risk occurs earlier in the CKD disease course in females compared to males. Albuminuria, a marker of CKD severity, has been independently associated with CV disease in females, however the mechanism of this association is not yet understood. This study aimed to determine the association between albuminuria and markers of vascular health, including mean arterial pressure (MAP) and arterial stiffness, as measured by aortic augmentation index (AIx) and pulse-wave velocity (PWV), in females with CKD. Methods: An exploratory cross-sectional study recruited 54 females with CKD from nephrology clinics in Calgary, Alberta. Albuminuria was quantified by collection of a midstream urine and measurement of urine albumin-to-creatine ratio (ACR). Using standardized protocols, blood pressure and arterial stiffness were measured. Multiple linear regression analysis was used to estimate the association between urine ACR and each marker of vascular health. Results: Albuminuria was significantly associated with MAP (R2 = 0.20; p = 0.001) and AIx (R2 = 0.21; p = 0.006), but was not associated with PWV (R2 = 0.11; p = 0.127). Conclusions: Disruptions in vascular health, specifically related to elevated blood pressure and increased peripheral wave reflection, may represent the pathophysiologic mechanism by which albuminuria is associated with increased CV risk in females with CKD. These risk factors may represent modifiable risk factors that serve as treatment targets for CV risk reduction in this important population. Funding: Government Support - Non-U.S.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".