Impact of Diabetes on Serum Biomarkers in Heart Failure with Preserved Ejection Fraction: Insights from the TOPCAT Trial
Bibliographic record
Abstract
AIMS: Diabetes mellitus (DM) is common in heart failure with preserved ejection fraction (HFpEF). Patients with DM and heart failure with reduced ejection fraction have higher levels of cardiac, profibrotic, and proinflammatory biomarkers relative to non-diabetics. Limited data are available regarding the biomarker profiles of HFpEF patients with diabetes (DM) vs. no diabetes (non-DM) and the impact of spironolactone on these biomarkers. This study aims to address such gaps in the literature. METHODS AND RESULTS: Biomarkers were measured at randomization and at 12 months in 248 patients enrolled in Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist's North American cohort. At baseline, DM patients had significantly lower estimated glomerular filtration rate and higher high-sensitivity C-reactive protein, pro-collagen type III amino-terminal peptide, tissue inhibitor of metalloproteinase 1 (TIMP-1), and galectin-3 levels than those without diabetes. There was a significantly larger 12 month increase in levels of high-sensitivity troponin T (hs-TnT), a marker of myocyte death, in DM patients. Elevated pro-collagen type III amino-terminal peptide and galectin-3 levels were associated with an increased risk of the primary outcome (cardiovascular mortality, aborted cardiac arrest, or heart failure hospitalization) in DM patients, but not in those without diabetes. A statistically significant interaction between spironolactone and diabetes status was observed for hs-TnT and for TIMP-1, with greater biomarker reductions among those with diabetes treated with spironolactone. CONCLUSIONS: The presence of diabetes is associated with higher levels of cardiac, profibrotic, and proinflammatory biomarkers in HFpEF. Spironolactone appears to alter the determinants of extracellular matrix remodelling in an anti-fibrotic fashion in patients with diabetes, reflected by changes in hs-TnT and TIMP-1 levels over time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".