Dose-Response Association Between Bilirubin and Cardiovascular Disease: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
The association between bilirubin (BIL) and cardiovascular disease (CVD) remains controversial. We performed a meta-analysis of prospective studies to evaluate this association in the general population. We searched PubMed, EMBASE, Web of Science, Cochrane, and Scopus databases through to September 2021. The Newcastle-Ottawa Quality Assessment Scale was used to assess study quality. The pooled effect estimate was calculated by the fixed-effect model or random-effect model. We included 12 prospective studies (368 567 participants). The pooled risk ratio of CVD for the lowest vs highest groups of BIL levels was .75 (95% CI: .58-.97) with high heterogeneity (I 2 = 87.5%, P < .001). Similar associations were observed for coronary heart disease and stroke. We further performed a “dose-response” meta-analysis, and a significant U-shaped relationship between circulating (most values were serum bilirubin, but a few were plasma bilirubin) BIL and CVD ( P < .01) was observed. The lowest risk of CVD events was observed in participants with a BIL of 17-20 µmol/L in serum and/or plasma. In conclusion, there was a U-shaped dose-response relationship between BIL and CVD, especially for men. Further studies are needed to confirm our findings and identify the mechanisms involved as well as any prognostic or therapeutic potential.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.005 | 0.007 |
| 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".