Associations of dietary copper intake with cardiovascular disease and mortality: findings from the Chinese Perspective Urban and Rural Epidemiology (PURE-China) Study
Bibliographic record
Abstract
BACKGROUND: Previous in vitro and animal experiments have shown that copper plays an important role in cardiovascular health. Dietary copper is the main source of copper in the human body and the association between dietary copper and cardiovascular disease remains unclear. Our study aimed to investigate the associations of dietary copper intake with the risk of major cardiovascular disease incidence, cardiovascular disease mortality, and all-cause mortality in Chinese adults. METHODS: Our study is based on Prospective Urban Rural Epidemiology China (PURE-China), a large prospective cohort study of 47 931 individuals aged 35-70 years from 12 provinces in China. Dietary intake was recorded using a validated semi-quantitative food frequency questionnaire designed specifically for the Chinese population. The daily intake of copper was obtained by multiplying the daily food intake with the nutrient content provided in the Chinese Food Composition Table (2002). Cox frailty proportional hazards models were developed to evaluate the association between dietary copper intake with mortality, major cardiovascular disease events, and their composite. RESULTS: A total of 45 101 participants (mean age: 51.1 ± 9.7 years old) with complete information were included in the current study. The mean dietary copper intake was 2.6 ± 1.1 mg/d. During the 482 833 person-years of follow-up, 2 644(5.9%) participants died, 4 012(8.9%) developed new cardiovascular diseases, and 5 608(12.4%) participants experienced the composite endpoint. Compared with those in the first and second quartile of dietary copper intake, individuals in the third and fourth quantile had higher risk of composite outcomes, all-cause death, cardiovascular disease death, major cardiovascular disease and stroke occurrences. The associations remained similar in the subgroup and sensitivity analyses. CONCLUSIONS: Our findings demonstrated that excessive dietary copper intake was associated with higher risks of death and cardiovascular diseases in Chinese adults. Further studies in populations with different dietary characteristics are needed to obtain dose-response relationships and to refine global dietary recommendations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".