P6350Impact of diabetes on serum biomarkers in heart failure with preserved ejection fraction: insights from the spironolactone for heart failure with preserved ejection fraction (TOPCAT) trial
Bibliographic record
Abstract
Abstract Background and purpose Diabetes mellitus (DM) is common in heart failure with preserved ejection fraction (HFpEF). Patients with DM and HF with reduced ejection fraction have higher levels of prognostic biomarkers relative to non-diabetics. We sought to examine differences in biomarkers at baseline and over time in patients with HFpEF with and without DM (non-DM). Methods The Americas cohort of the TOPCAT trial included 248 subjects with baseline measurements of serum biomarkers and follow-up measures 12 months later. Subjects were identified as non-DM or DM at baseline. Baseline values were compared using non-parametric tests and 12-month changes were compared via linear regression after log-transformation and adjustment for baseline biomarker value, age, gender, randomization strata, and randomized treatment. Results At baseline, DM patients had significantly lower eGFR and higher hsCRP, PIIINP, TIMP1, and Gal-3 levels versus non-DM patients (Table). In addition, there was a significantly larger increase over time in levels of hs-TnT, a marker of myocyte death, in DM vs. non-DM patients (p=0.016). Baseline and 12-Month Biomarkers Baseline % Change at 12 Months Non-DM (n=132) DM (n=116) p Non-DM (n=110) DM (n=94) p eGFR (mL/min/1.73m2) 67 [57, 77] 57 [46, 73] 0.003 −14% (−17, −10) −14% (−18, −10) 0.34 hsCRP (mg/L) 2.4 [1.1, 5.6] 3.1 [1.6, 7.5] 0.046 1% (−5, 3) −11% (−27, 7) 0.54 NT-proBNP (pg/mL) 624 [338, 1235] 629 [278, 1429] 0.80 −2% (−12, 10) −5% (−21, 13) 0.48 hs-TNT (ng/mL) 5.7 [3.1, 12.4] 7.1 [3.7, 14.2] 0.17 −1% (−14, 13) 11% (−3, 27) 0.016 Soluble ST2 (ng/mL) 28 [22, 32] 28 [21, 35] 0.36 −1% (−7, 5) −4% (−9, 2) 0.60 Aldosterone (ng/L) 149 [120, 202] 142 [113, 174] 0.09 17% (9, 26) 23% (14, 33) 0.76 PICP (ng/mL) 137 [101, 169] 127 [102, 155] 0.29 5% (−5, 17) 1% (−9, 12) 0.07 CITP (ng/mL) 1.6 [1.0, 2.8] 1.6 [0.9, 3.0] 0.93 −25% (−37, 10) −23% (−36, −8) 0.87 PIIINP (ng/mL) 23 [16, 30] 28 [21, 36] <0.001 5% (−4, 15) 5% (−3, 14) 0.12 TIMP-1 (ng/mL) 188 [170, 212] 212 [183, 245] <0.001 −1% (−4, 2) −2% (−5, 2) 0.59 Galectin-3 (ng/mL) 20 [16, 23] 22 [18, 28] <0.001 6% (3, 10) 9% (4, 14) 0.52 eGFR, estimated glomerular filtration rate; hsCRP, high-sensitivity CRP; NT-proBNP, N-terminal pro-B-type natriuretic peptide; hs-TnT, high-sensitivity troponin T; PICP, pro-collagen type I carboxy-terminal peptide; CITP, collage type I; PIIINP=pro-collagen type III amino-terminal peptide; TIMP-1=tissue inhibitor of MMP-1. Conclusions In comparison to patients without DM, those with DM had poorer renal function and higher baseline levels of markers of myocardial stretch, myocyte death, and pro-fibrotic biomarkers. Further, hs-TnT increased over 12 months only in patients with DM.
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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) | 0.001 | 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 | 0.000 |
| 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".