A precision medicine approach to coronary artery disease risk prediction and mitigation in people with type 2 diabetes
Bibliographic record
Abstract
Abstract Type 2 diabetes (T2D) predisposes to cardiovascular disease (CVD), but it is still unclear why some individuals with T2D are at disproportionately higher or lower risk. In this study, we employed a genetic stratification method to investigate the main clinical features that differ between two diabetogenic profiles associated concordantly with susceptibility for CVD or discordantly with protection against CVD. Quantifying concordant and discordant genetic predispositions improved CVD risk prediction, especially in men, correctly reassigning higher predicted risk to 5.4% of new male cases of MACE in UK Biobank. Moreover, higher genetically determined discordance reduced the risk associated with MACE in men. In-depth comparisons across a wide spectrum of phenotypes uncovered significant disparities between these two profiles. Subsequent causal inference analyses highlighted critical features of very-low-density lipoprotein particles influencing the discordance between T2D and CVD. We prioritized 8 distinct discordant genomic loci with potential protective effects traits against CVD in individuals with T2D. These findings provide clinically relevant valuable insights for personalized approaches to prevent and treat CVD in individuals with T2D.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".