Proenkephalin, an Opioid System Surrogate, as a Novel Comprehensive Renal Marker in Heart Failure
Bibliographic record
Abstract
BACKGROUND: PENK (proenkephalin) is a stable surrogate for enkephalins, endogenous opioid peptides, which exert cardiodepressive effects and improve renal function. PENK has been associated with heart failure (HF) severity and renal dysfunction. We therefore hypothesized that PENK could be associated with deterioration of kidney function and could have a role as a novel renal marker in HF. METHODS AND RESULTS: In 2180 patients with HF of a large multicenter cohort (BIOSTAT-CHF [A Systems Biology Study to Tailored Treatment in Chronic Heart Failure]), the relationship between PENK and clinical variables, plasma and urinary biomarkers, and clinical end points was established. Data were validated in a separate cohort of 1703 patients with HF. PENK was elevated (>80 pmol/L, 99th percentile) in 1245 (57%) patients. Higher PENK was associated with more advanced HF and glomerular and tubular dysfunction. The strongest independent predictor of PENK was estimated glomerular filtration rate. Others were plasma NGAL (neutrophil gelatinase-associated lipocalin) and NT-proBNP (N-terminal pro-B-type natriuretic peptide; all P<0.001). Using correlation heatmaps and hierarchical cluster analyses, PENK clustered with estimated glomerular filtration rate, creatinine, NGAL, galectin-3, and urea. Higher PENK was independently associated with increased risk of deterioration of kidney function between baseline and 9 months (odds ratio, 1.29 [1.02-1.65] per PENK doubling; P=0.038; defined as >25% decrease in estimated glomerular filtration rate) and mortality (hazard ratio, 1.23 [1.07-1.43] per doubling; P=0.004). Analyses in the validation cohort yielded comparable findings. CONCLUSIONS: Higher PENK levels are associated with more severe HF, with glomerular and tubular renal dysfunction, with incidence of a deterioration of kidney function, and with mortality. These findings suggest that the opioid system might be involved in deteriorating kidney function in HF.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".