162-LB: Cardiovascular Outcomes in People with Type 2 Diabetes and Acute Coronary Syndrome—The ELIXA Biomarker Study
Bibliographic record
Abstract
Adding novel protein biomarkers to routine clinical risk factors may identify people with type 2 diabetes and acute coronary syndrome who are at highest risk for cardiovascular (CV) outcomes and death. Methods: Bio-banked baseline serum from 5128 of 6069 ELIXA (Evaluating Lixisenatide in Acute Coronary Syndrome) trial (NCT 01147250) participants was analyzed to identify independent risk factors for incident major adverse CV events (MACE, defined as a nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) , and death. A multiplex analysis of 1.8 ml of serum measured the concentration of 49 proteins. Forward-selection Cox models that identified proteins that independently predicted these outcomes were compared to previously validated biomarkers identified in the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) trial data (NCT00069784) . Results: Forty-two proteins were analyzed in 4957 participants who had 630 (12.7%) MACE outcomes and 349 (7.0%) deaths during a median follow-up period of 2.1 years. When added to clinical risk factors, the independent hazard ratios (HR; 95% confidence intervals) of MACE per standard deviation (SD) were NT-proBNP (1.54; 1,41, 1.68) , osteoprotegerin (1.18; 1.08, 1.30) and trefoil factor 3 (1.18, 1.08, 1.29) . HRs per SD for death were NT-proBNP (2.01; 1.78, 2.29) , osteoprotegerin (1.34; 1.18, 2.52) and angiopoietin-2 (1.28; 1.15, 1.44) . C statistics for MACE and death were 0.70 (0,68, 0.72) and 0.79 (0.76, 0.81) respectively compared to 0.63 (0.61, 0.65) and 0.66 (0.63, 0.69) for clinical variables alone. These proteins had all been previously identified and validated in ORIGIN. Notably, NT-proBNP alone plus clinical risk factors yielded C statistics of 0.69 (0.67, 0.71) and 0.78 (0.75, 0.80) for MACE and death respectively. Conclusion: NT-proBNP and other proteins independently predict CV outcomes in people with type 2 diabetes following acute coronary syndrome. Adding other biomarkers only marginally increased NT-proBNP's prognostic value. Disclosure H. C. Gerstein: Advisory Panel; Abbott, Eli Lilly and Company, Hanmi Pharm. Co., Ltd., Novo Nordisk, Pfizer Inc., Sanofi, Viatris Inc., Consultant; Kowa Company, Ltd., Other Relationship; DKSH, Eli Lilly and Company, Sanofi, Zuellig Pharma Holdings Pte. Ltd., Research Support; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Sanofi. S. Hess: Employee; Sanofi. B. Claggett: Consultant; Amgen Inc., Biogen, Cardurion, Corvia, MyoKardia, Novartis AG. J. Tardif: Consultant; AstraZeneca, DalCor Pharmaceuticals, HLS Therapeutics Inc., Pendopharm, Other Relationship; DalCor Pharmaceuticals, Research Support; Amarin Corporation, AstraZeneca, Ceapro Inc., DalCor Pharmaceuticals, ESPERION Therapeutics, Inc., Ionis Pharmaceuticals, Novartis Pharmaceuticals Corporation, Pfizer Inc., REGENXBIO Inc., Sanofi. M. A. Pfeffer: Consultant; AstraZeneca, Boehringer Ingelheim and Eli Lilly Alliance, Corvidia Therapeutics, GlaxoSmithKline plc., Lexicon Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Peerbridge, Sanofi, Other Relationship; DalCor Pharmaceuticals, National Heart, Lung, and Blood Institute, Research Support; Novartis Pharmaceuticals Corporation. Funding Sanofi
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".