Association between copper levels and heart failure: a meta-analysis
Bibliographic record
Abstract
Background and Objectives: Copper dyshomeostasis can lead to many diseases, including cardiovascular disease. However, there are conflicting reports on the relationship between serum copper levels and heart failure (HF). To explore the relationship between serum copper levels and HF by performing a meta-analysis. Methods and Study Design: The PubMed and ScienceDirect databases until June 2019 were searched for reports on the association between serum copper levels and HF. Results: A total of thirteen studies including 1504 subjects were chosen for the meta-analysis. The pooled analysis indicated that patients with HF had higher copper levels than the control subjects [standardized mean difference (SMD), 0.982; 95% confidence interval (CI), (0.679, 1.285)]. Subgroup analysis stratified by different geographic locations found that HF patients had higher copper levels than the control subjects in Asia and Europe [Asia: SMD, 0.948 and 95% CI, (0.569, 1.327); Europe: SMD, 1.275 and 95% CI, (0.633, 1.917)], but not in America [America: SMD, 0.637 and 95% CI, (-0.109, 1.383)]. Additionally, subgroup analysis revealed that patients with ischemic cardiomyopathy (ICM) [SMD, 1.171; 95% CI, (0.717, 1.624)], idiopathic dilated cardiomyopathy (IDCM) [SMD, 0.569; 95% CI, (0.097, 1.042)] and other types of HF [SMD, 1.152; 95% CI, (0.594, 1.710)] all had higher copper levels than controls. Further subgroup analysis stratified by Newcastle-Ottawa Scale (NOS) scores also found higher serum copper levels in patients with HF than controls within each subgroup. Conclusions: Our meta-analysis identified a significant association between high copper levels and HF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".