Deciphering the Causal Relationships between Blood Metabolites and Coronary Atherosclerosis: A Comprehensive Mendelian Randomization Study
Bibliographic record
Abstract
Objective: This study aims to investigate the causal relationships between specific blood metabolites and the risk of coronary atherosclerosis using a two-sample Mendelian Randomization (MR) approach. Methods: We utilized genome-wide association summary statistics from 8,299 participants in the Canadian Longitudinal Study on Aging (CLSA) and 456,348 participants from the UK Biobank to examine 1091 blood metabolites and 309 metabolite ratios. Instrumental variables (IVs) were identified based on genetic variants strongly associated with these metabolites and ratios, and a robust set of IVs was employed to ensure the validity of the MR analyses. Various MR methods, including Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median approaches, were used to address different assumptions and potential biases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".