Peripheral Levels of the Brain-Derived Neurotrophic Factor in Coronary Artery Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Among its functions, brain-derived neurotrophic factor (BDNF) regulates endothelial and macrophage activation, possibly playing a role in atherosclerotic plaque pathophysiology. Given contradicting reports, this study sought to investigate whether blood levels of BDNF differed between patients with coronary heart disease (CHD) and controls. Methods: We explored PubMed, Embase, Web of Science, and Cochrane Library for studies comparing BDNF blood levels in patients with CHD and controls. Random-effect meta-analysis was conducted to calculate the standardized mean differences (SMD) and 95% confidence intervals (CI). The Newcastle-Ottawa scale was used to evaluate the quality of included articles, and statistical analyses were conducted using R version 4.0.4. Results: The final analysis comprised 12 investigations covering 1422 CHD cases and 929 controls with mean ages of 59.66±13.56 and 53.78±13.61 years, respectively. The initial analyses revealed a tendency toward low levels of BDNF in the CHD group compared with the control group (SMD= -0.41; 95% CI, -1.12 to 0.30; P=0.26). After the removal of outliers, the difference achieved statistical difference (SMD= -0.56; 95% CI, -0.93 to -0.19; P<0.01). Subgroup analysis demonstrated no significant difference between serum and plasma BDNF levels (P=0.54); however, subgroup analyses of studies investigating plasma BDNF showed that patients with CHD had significantly lower BDNF levels. Conclusion: Serum and plasma BDNF concentrations were considerably lower in patients with CHD than in healthy controls. Further studies of higher quality are required on the potential role of BDNF as a biomarker of CHD pathophysiology and severity.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".