Identifying Novel Biomarkers for Cardiovascular Events or Death in People With Dysglycemia
Bibliographic record
Abstract
BACKGROUND: Serum biomarkers may identify people at risk for cardiovascular (CV) outcomes. Biobanked serum samples from 8494 participants with dysglycemia in the completed Outcome Reduction With Initial Glargine Intervention trial were assayed for 284 biomarkers to identify those that could identify people at risk for a CV outcome or death when added to clinical measurements. METHODS AND RESULTS: A multiplex analysis measured a panel of cardiometabolic biomarkers in 1 mL of stored frozen serum from every participant who provided biobanked blood. After eliminating undetectable or unanalyzable biomarkers, 8401 participants who each had a set of 237 biomarkers were analyzed. Forward-selection Cox regression models were used to identify biomarkers that were each independent determinants of 3 different incident outcomes: (1) the composite of myocardial infarction, stroke, or CV death; (2) these plus heart failure hospitalization or revascularization; and (3) all-cause death. When added to clinical variables, 10 biomarkers were independent determinants of the 1405 CV composite outcomes observed during follow-up; 9 biomarkers (including 8 of these 10) were independent determinants of the 2435 expanded composite outcomes; and 15 (including the 10 CV composite biomarkers) were independent determinants of the 1340 deaths. Adjusted C statistics increased from 0.64 for the clinical variables to 0.71 and 0.68 for the 2 CV composite outcomes, respectively, with the greatest increase to 0.75 for death (P<0.001 for the change). CONCLUSIONS: A systematic hypothesis-free approach identified combinations of up to 15 cardiometabolic biomarkers as independent determinants of CV outcomes or death in people with dysglycemia. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00069784.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".