Determinants of change in arterial stiffness over 5 years in early chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Arterial stiffness (AS) is an established and potentially modifiable risk factor for cardiovascular disease associated with chronic kidney disease (CKD). There have been few studies to evaluate the progression of AS over time or factors that contribute to this, particularly in early CKD. We therefore investigated AS over 5 years in an elderly population with CKD Stage 3 cared for in primary care. METHODS: A total of 1741 persons with an estimated glomerular filtration rate of 30-59 mL/min/1.73 m2 underwent detailed clinical and biochemical assessment at baseline and Years 1 and 5. Carotid to femoral pulse wave velocity (PWV) was measured to assess AS using a Vicorder device. RESULTS: 970 participants had PWV assessments at baseline and 5 years. PWV increased significantly by a mean of 1.1 m/s (from 9.7 ± 1.9 to 10.8 ± 2.1 m/s). Multivariable linear regression analysis identified the following independent determinants of ΔPWV at Year 5: baseline age, diabetes status, baseline systolic blood pressure (SBP) and diastolic blood pressure, baseline PWV, ΔPWV at 1 year, ΔSBP over 5 years and Δserum bicarbonate over 5 years (R2 = 0.38 for the equation). CONCLUSIONS: We observed a clinically significant increase in PWV over 5 years in a cohort with early CKD despite reasonably well-controlled hypertension. Measures of BP were identified as the most important modifiable determinant of ΔPWV, suggesting that interventions to prevent arterial disease should focus on improved control of BP, particularly in those who evidence an early increase in PWV. These hypotheses should now be tested in prospective trials.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".