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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".