Correlates of Osteoprotegerin and Association with Aortic Pulse Wave Velocity in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Osteoprotegerin (OPG), a cytokine that regulates bone resorption, has been implicated in the process of vascular calcification and stiffness. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Serum OPG was measured in 351 participants with chronic kidney disease (CKD) from one site of the Chronic Renal Insufficiency Cohort Study. Cortical bone mineral content (BMC) was measured by quantitative computed tomography in the tibia. Multivariable linear regression was used to test the association between serum OPG and traditional cardiovascular risk factors, measures of abnormal bone and mineral metabolism, and pulse wave velocity. RESULTS: Higher serum OPG levels were associated with older age, female gender, greater systolic BP, lower estimated GFR, and lower serum albumin. OPG was not associated with measures of abnormal bone or mineral metabolism including serum phosphorus, albumin-corrected serum calcium, intact parathyroid hormone, bone-specific alkaline phosphatase, or cortical BMC. Among 226 participants with concurrent aortic pulse wave velocity measurements, increasing tertiles of serum OPG were associated with higher aortic pulse wave velocity after adjustment for demographics, traditional vascular risk factors, and nontraditional risk factors such as estimated GFR, albuminuria, serum phosphate, corrected serum calcium, presence of secondary hyperparathyroidism, serum albumin, and C-reactive protein or after additional adjustment for cortical BMC in a subset (n = 161). CONCLUSIONS: These data support a strong relationship between serum OPG and arterial stiffness independent of many potential confounders including traditional cardiovascular risk factors, abnormal bone and mineral metabolism, and inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".