Unlocking the potential of biomarkers: The promise of adrenomedullin and its precursors in diagnosing and assessing heart failure
Bibliographic record
Abstract
BACKGROUND: Several studies have examined the potential of adrenomedullin (ADM), pro-adrenomedullin (Pro-ADM), and mid-regional-pro-ADM (MR-Pro-ADM) as biomarkers for diagnosing and assessing the severity of heart failure (HF), with conflicting results. We aimed to investigate their diagnostic utility and their correlation with HF severity based on the New York Heart Association (NYHA) classification. METHODS: We searched PubMed, EMBASE, and Scopus using a predefined search string. The quality assessment of included studies was conducted using the Newcastle Ottawa Scale (NOS), and the primary outcome was the mean difference (MD) in serum levels of ADM, Pro-ADM, and MR-Pro-ADM, in addition to the area under the curve (AUC). RESULTS: A total of 28 articles fulfilled our inclusion criteria and were included in our qualitative and quantitative synthesis, with a total of 15,405 subjects. Significant MD in ADM levels in HF patients vs. controls (6.024 [95 % CI 1.691, 10.356]), NYHA I vs. controls (-1.202 [95 % CI -2.111, -0.292]), NYHA IV vs. controls (-7.536 [95 % CI -12.680, -2.393]), NYHA III vs. NYHA IV (-4.438 [95 % CI -7.612, -1.263]), and NYHA I-II vs NYHA III-IV (-2.351 [95 % CI -4.361, -0.341]) were observed. Moreover, a significant MD was observed in pro-ADM levels in NYHA I-II patients vs. controls (-0.960 [95 % CI -1.479, -0.440]), NYHA III-IV vs. controls (-1.979 [95 % CI -2.958, -1.000]), and NYHA I-II vs. NYHA III-IV (-0.966 [95 % CI -1.407, -0.526]). Furthermore, MR-Pro-ADM levels were significantly different in NYHA I-II vs. NYHA III-IV (-0.428 [95 % CI -0.492, -0.365]). MR-Pro-ADM predicted HF with an AUC of 0.781 (95 % CI 0.755, 0.806). CONCLUSIONS: Among HF patients, there was a significant increase in ADM levels compared to control subjects, and these levels increased with the progression of NYHA classes. Similarly, both Pro-ADM and MR-Pro-ADM displayed higher concentrations in NYHA class III-IV HF patients compared to those in NYHA class I-II. However, MR-Pro-ADM exhibited lower accuracy in predicting HF compared to established biomarkers.
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 | 0.084 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".