Abstract 16139: A Targeted Proteomic Approach to Identify Circulating Biomarkers of Heart Failure Risk in Patients With Type 2 Diabetes Mellitus in DECLARE-TIMI 58
Bibliographic record
Abstract
Background: Patients (pts) with type 2 diabetes mellitus (T2DM) are at increased risk of heart failure (HF); however, the underlying mechanisms by which T2DM contributes to HF are incompletely understood. Hypothesis: We aimed to identify biological pathways associated with risk of hospitalization for HF (HHF) in a well-characterized cohort with T2DM followed for a median of 4.2 yrs. Methods: DECLARE-TIMI 58 was a randomized trial of dapagliflozin in pts with T2DM. We performed a nested case-control study of 184 candidate biomarkers (Olink CV II and CV III) in pts hospitalized for HF (n=432) and controls matched on age, sex, prior HF, prior CV disease, and f/u time (n=432). We evaluated associations between baseline biomarkers and HHF using logistic regression with a stringent threshold for significance (Bonferroni). Biomarkers were ranked according to Wald χ 2 values. ORs for the top 10 biomarkers were further adjusted for the components of the TIMI Risk Score for HF in Diabetes (AF, UACR, eGFR, CAD). ORs are per 1-SD. Results: 45 biomarkers were significantly associated with HHF. The 10 strongest associations were seen with N-terminal pro-B type natriuretic peptide (NT-proBNP), B type natriuretic peptide (BNP), spondin-1 (SPON1), insulin-like growth factor-binding protein 7 (IGFBP7), interleukin-6 (IL-6), fibroblast growth factor-23 (FGF-23), transferrin receptor protein 1 (TR), metalloproteinase inhibitor 4 (TIMP4), matrix metalloproteinase-2 (MMP-2), C-X-C motif chemokine 16 (CXCL16) ( Fig ). All 10 biomarkers were significantly associated with HHF both in pts with and without a history of HF. These proteins represent pathobiological axes implicated in hemodynamic stress, inflammation, myocardial hypertrophy, and cellular senescence, among others. Conclusions: A targeted proteomic approach identified established (NT-proBNP, BNP), investigational (IGFBP7, FGF-23, IL-6, TR, TIMP4, MMP-2, CXCL16), and novel (SPON1) biomarkers of HHF in pts with T2DM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".