Comparison Between Copeptin and Vasopressin in a Population From the Community and in People With Chronic Kidney Disease
Bibliographic record
Abstract
CONTEXT: Vasopressin plays a central role in water homeostasis but it has also been recognized to be associated with adverse effects in several chronic diseases. Recently, copeptin has been increasingly used as a surrogate for vasopressin, as they are co-secreted, and copeptin is easier to measure. However, the relationship between plasma concentrations of copeptin (P(cop)) and vasopressin (P(vp)) has only been studied in relatively small numbers of selected people. OBJECTIVE: This study sought to evaluate the relationship between P(vp) and P(cop) in a community-based population and in people with chronic kidney disease (CKD). DESIGN, SETTING, AND PARTICIPANTS: P(vp), P(cop), and urinary osmolarity (Uosm) were compared in 500 participants of the DESIR study, and in 83 ambulatory people with CKD. RESULTS: Median [interquartile range] of P(cop) and P(vp) in the DESIR study were 4.13 [3.58] pmol/L and 0.92 [1.93] pmol/L, respectively. Log-transformed P(cop) and P(vp) concentrations correlated significantly and positively (r = 0.686, P < .001) and they correlated inversely with estimated U(osm) (P < .001). Copeptin explained only approximately half of the vasopressin variation. In CKD, P(cop) and P(vp) both increased with decreasing estimated glomerular filtration rate (eGFR), but P(cop) increased much faster than P(vp). The P(cop)/P(vp) ratios in the lower and upper quintile groups of eGFR were 14.3 [18.3] and 5.3 [4.5], P < .001, respectively. CONCLUSIONS: This study in a normal population, the largest ever with measurements of both peptides, shows that copeptin and vasopressin concentrations correlated well. But their relationship is distorted in CKD, suggesting that the peptide clearances differ when the renal function is impaired.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".