Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes in the risk of coronary artery disease
Bibliographic record
Abstract
INTRODUCTION: Type 2 diabetes is a strong risk factor for coronary artery disease (CAD). However, the absence of a clear reduction in CAD by intensive glucose lowering in randomized controlled trials has fuelled uncertainty regarding the causal role of dysglycaemia and CAD. OBJECTIVE: To assess whether Mendelian randomization supports a causal role of dysglycaemia and diabetes for risk of CAD. METHODS: Effect size estimates of common genetic variants associated with fasting glucose (FG), glycated haemoglobin (HbA1c), and diabetes were obtained from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium and Diabetes Genetics Replication and Meta-Analysis consortia. The corresponding effect estimates of these single nucleotide polymorphisms (SNPs) on the risk of CAD were then evaluated in CARDIOGRAMplusC4D. RESULTS: SNPs associated with HbA1c and diabetes were associated with an increased risk of CAD. Using information from 59 genetic variants associated with diabetes, the causal effect of diabetes on the risk of CAD was estimated at an odds ratio (OR) of 1.63 (95% Confidence Interval (CI): 1.23-2.07; P = 0.002). On the other hand, nine genetic variants associated with HbA1c were associated with an OR of 1.53 per 1% HbA1c increase (95% CI: 1.14-2.05; P = 0.023) in the risk of CAD while this effect was non-significant among 30 genetic variants associated with FG per mmol/L (OR: 1.18, 95% CI: 0.97-1.42; P = 0.102). No significant differences were observed when categorizing genetic loci according to their effect on either β-cell dysfunction or insulin resistance. CONCLUSIONS: These Mendelian randomization analyses support a causal role for diabetes and its associated high glucose levels on CAD, and suggest that long-term glucose lowering may reduce CAD events.
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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 | 0.127 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".