Depletion of proBNP1-108 in Patients with Heart Failure Prevents Cross-Reactivity with Natriuretic Peptides
Bibliographic record
Abstract
BACKGROUND: After synthesis by cardiomyocytes, precursor proBNP1-108 is cleaved into NT-proBNP and BNP. Recently, cross-reactivity between these assays was discussed. The aim of this study was to characterize the cross-reactivities, through a new biochemical innovative approach consisting in the total depletion of the circulating proBNP1-108 in patients with heart failure (HF). METHODS: This prospective study included 180 patients with chronic HF. BNP and NT-proBNP were dosed with commercial kits. ProBNP1-108 was determined using an ELISA research assay specific to the precursor. ProBNP1-108 depletion was performed by immunocapture with a specific antibody targeting exclusively the ProBNP1-108 hinge region. ProBNP1-108, BNP and NT-proBNP levels were determined before and after depletion using this process in HF patients. RESULTS: Mean age was 74.34 +/-12.5 y, and 69% of patients were males. NYHA classes II and III were the most frequent (32% and 45% respectively). Before depletion, ProBNP1-108, NT-proBNP and BNP levels were 316.8+/-265.9 pg/ml; 6,054.0+/-11,539 pg/ml and 684.3+/-82.1 pg/ml respectively, and were closely correlated with NHYA classes. After immuno-depletion, proBNP1-108 was decreased in mean by 96% (p<0.0001), BNP by 53% (p<0.0001) and NT-proBNP by 5%. The relationship between BNP or NT-proBNP and NHYA classes remained unchanged. CONCLUSION: Current BNP and NT-proBNP assays measured as well proBNP molecule. This cross reactivity percentage has been controversial. Thanks to the removal of circulating proBNP1-108 with our immunodepletion process, we are now able to assess the remaining "true" BNP and NT-proBNP molecules and further evaluate their clinical relevance.
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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 | 0.000 |
| 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) | 0.001 | 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".