Association of serum fetuin-A levels with heart valve calcification and other biomarkers of inflammation among persons with acute coronary syndrome
Bibliographic record
Abstract
PURPOSE: Fetuin-A is a multifunctional hepatic secretory protein that inhibits dystrophic vascular and valvular calcification. Our aim was to evaluate the relationship among fetuin-A levels, heart valve calcification and other biomarkers of inflammation in patients with acute coronary syndrome (ACS). METHODS: The associations among serum fetuin-A concentrations, mitral annular (MAC) and aortic valve calcification and other biomarkers of inflammation (hs-CRP, ferritin, fibrinogen, white blood cell count (WBC), erythrocyte sedimentation rate (ESR), albumin levels) were evaluated in ACS patients and healthy controls. The study included 95 patients (mean age 61.8 ± 12.10 years) and 81 healthy controls (mean age 48.33 ± 9.19 years). RESULTS: Fetuin-A levels were significantly lower in patients with ACS than in healthy controls (0.76 ± 0.23 and 1.10 ± 0.45 g/L, respectively; p < 0.001). Fetuin-A was lower in patients with mitral annular calcification (p = 0.007) and aortic (p = 0.001) valve calcification. In patients with ACS, there was a negative correlation among serum urea (r = -0.377; p < 0.001) and creatinine (r = -0.232; p = 0.024) levels and fetuin-A, and a negative correlation among WBC (r = -0.156; p = 0,132), ESR (r = -0.214; p = 0.037), hs-CRP (r = -0.220; p = 0.032) levels and fetuin-A. A positive correlation was seen between albumin and fetuin-A (r = 0.362; p < 0.001). Multivariate logistic regression analysis revealed that fetuin-A was the variable that had a significant effect on ACS (p = 0.020 OR = .015; (95% CI)(0.000-0.520). CONCLUSION: Fetuin-A levels decrease in patients with acute coronary syndromes, independent of heart valve calcification. Fetuin-A may therefore act as a negative acute phase protein after myocardial infarction.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".