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Effect of Empagliflozin on Left Ventricular Mass in Patients With Type 2 Diabetes Mellitus and Coronary Artery Disease

2019· article· en· W2989579906 on OpenAlexaff
Subodh Verma, C. David Mazer, Andrew T. Yan, Vinay Garg, Hwee Teoh, Fei Zuo, Adrian Quan, Michael E. Farkouh, David Fitchett, Shaun G. Goodman, Ronald Goldenberg, Mohammed Al‐Omran, Richard E. Gilbert, Deepak L. Bhatt, Lawrence A. Leiter, Peter Jüni, Bernard Zinman, Kim A. Connelly

Bibliographic record

VenueCirculation · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsInstitute of Health Services and Policy ResearchLMC Diabetes & Endocrinology (Canada)Canadian Heart Research CentreLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalCanadian VIGOUR CentreHeart and Stroke FoundationUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineEmpagliflozinCardiologyEjection fractionInternal medicineBody surface areaCoronary artery diseaseType 2 Diabetes MellitusDiabetes mellitusGlycated hemoglobinType 2 diabetesBody mass indexHematocritHeart failureEndocrinology

Abstract

fetched live from OpenAlex

Background: SGLT2 (sodium-glucose cotransporter 2) inhibitors lower cardiovascular events in type 2 diabetes mellitus but whether they promote direct cardiac effects remains unknown. We sought to determine if empagliflozin causes a decrease in left ventricular (LV) mass in people with type 2 diabetes mellitus and coronary artery disease. Methods: Between November 2016 and April 2018, we recruited 97 individuals ≥40 and ≤80 years old with glycated hemoglobin 6.5% to 10.0%, known coronary artery disease, and estimated glomerular filtration rate ≥60mL/min/1.73m 2 . The participants were randomized to empagliflozin (10 mg/day, n=49) or placebo (n=48) for 6 months, in addition to standard of care. The primary outcome was the 6-month change in LV mass indexed to body surface area from baseline as measured by cardiac magnetic resonance imaging. Other measures included 6-month changes in LV end-diastolic and -systolic volumes indexed to body surface area, ejection fraction, 24-hour ambulatory blood pressure, hematocrit, and NT-proBNP (N-terminal pro b-type natriuretic peptide). Results: Among the 97 participants (90 men [93%], mean [standard deviation] age 62.8 [9.0] years, type 2 diabetes mellitus duration 11.0 [8.2] years, estimated glomerular filtration rate 88.4 [16.9] mL/min/1.73m 2 , LV mass indexed to body surface area 60.7 [11.9] g/m 2 ), 90 had evaluable imaging at follow-up. Mean LV mass indexed to body surface area regression over 6 months was 2.6 g/m 2 and 0.01 g/m 2 for those assigned empagliflozin and placebo, respectively (adjusted difference −3.35 g/m 2 ; 95% CI, −5.9 to −0.81g/m 2 , P =0.01). In the empagliflozin-allocated group, there was significant lowering of overall ambulatory systolic blood pressure (adjusted difference −6.8mmHg, 95% CI −11.2 to −2.3mmHg, P =0.003), diastolic blood pressure (adjusted difference −3.2mmHg; 95% CI, −5.8 to −0.6mmHg, P =0.02) and elevation of hematocrit ( P =0.0003). Conclusions: Among people with type 2 diabetes mellitus and coronary artery disease, SGLT2 inhibition with empagliflozin was associated with significant reduction in LV mass indexed to body surface area after 6 months, which may account in part for the beneficial cardiovascular outcomes observed in the EMPA-REG OUTCOME (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients) trial. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02998970.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.200
Teacher spread0.197 · 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 designRandomized trial
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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Citations578
Published2019
Admission routes1
Has abstractyes

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