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Record W2981454313 · doi:10.1093/eurheartj/ehz746.0946

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

2019· article· en· W2981454313 on OpenAlexaff
Corrado De Marco, Brian Claggett, Simon de Denus, Thao Huynh, Akshay S. Desai, Martin G. Sirois, Petr Jarolı́m, Scott D. Solomon, Bertram Pitt, Jean L. Rouleau, Marc A. Pfeffer, Eileen O’Meara

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMcGill University Health CentreMontreal Heart Institute
Fundersnot available
KeywordsMedicineEjection fractionInternal medicineHeart failureHeart failure with preserved ejection fractionCardiologyDiabetes mellitusBiomarkerSpironolactoneValsartanEndocrinologyBlood pressure

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.261
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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