Exploring the Clinical Utility of Osteoprotegerin in Heart Failure—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Osteoprotegerin (OPG) is a glycoprotein involved in bone metabolism and cardiovascular health, with emerging evidence suggesting its role in heart failure (HF). Despite its potential as a biomarker, the association between circulating OPG levels and HF severity remains unclear. This systematic review and meta-analysis aimed to evaluate OPG levels in HF patients and their relationship with disease severity according to the New York Heart Association (NYHA) classification. A comprehensive search of PubMed, EMBASE, and Scopus was conducted to identify observational studies assessing OPG levels in HF patients. Studies were included if they reported OPG levels in HF patients and controls, with subgroup analyses according to NYHA classification when available. Risk of bias assessment was performed using the Newcastle-Ottawa Scale (NOS). The principal outcome was the mean difference (MD) in circulating OPG levels between HF patients and controls. Random-effects meta-analysis models were used to pool the data. Thirteen studies with a total of 1387 participants were included in the quantitative and qualitative synthesis. Overall, OPG levels were significantly elevated in HF patients compared to healthy controls (2.490 [95% CI 0.531, 4.449]). Subgroup analysis showed a significant decrease in OPG levels in controls versus NYHA II patients (-1.503 [95% CI -2.402, -0.604]). However, no statistically significant difference was found when comparing OPG levels between the combined NYHA II/III group and controls (-1.019 [95% CI -2.451, 0.412]). OPG levels are significantly elevated in HF patients compared to controls, with a progressive increase in NYHA II patients. However, the lack of significance in the NYHA II/III group highlights the need for further studies with a more comprehensive NYHA classification breakdown.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".