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Record W4399782338 · doi:10.1152/physiol.00008.2024

Extrarenal Benefits of SGLT2 Inhibitors in the Treatment of Cardiomyopathies

2024· review· en· W4399782338 on OpenAlexaff
Veera Ganesh Yerra, Kim A. Connelly

Bibliographic record

VenuePhysiology · 2024
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsPharmacologyCardiomyopathyChemistryMedicineInternal medicineHeart failure

Abstract

fetched live from OpenAlex

Sodium-glucose cotransporter 2 (SGLT2) inhibitors have emerged as pivotal medications for heart failure, demonstrating remarkable cardiovascular benefits extending beyond their glucose-lowering effects. The unexpected cardiovascular advantages have intrigued and prompted the scientific community to delve into the mechanistic underpinnings of these novel actions. Preclinical studies have generated many mechanistic theories, ranging from their renal and extrarenal effects to potential direct actions on cardiac muscle cells, to elucidate the mechanisms linking these drugs to clinical cardiovascular outcomes. Despite the strengths and limitations of each theory, many await validation in human studies. Furthermore, whether SGLT2 inhibitors confer therapeutic benefits in specific subsets of cardiomyopathies akin to their efficacy in other heart failure populations remains unclear. By examining the shared pathological features between heart failure resulting from vascular diseases and other causes of cardiomyopathy, certain specific molecular actions of SGLT2 inhibitors (particularly those targeting cardiomyocytes) would support the concept that these medications will yield therapeutic benefits across a broad range of cardiomyopathies. This article aims to discuss the important mechanisms of SGLT2 inhibitors and their implications in hypertrophic and dilated cardiomyopathies. Furthermore, we offer insights into future research directions for SGLT2 inhibitor studies, which hold the potential to further elucidate the proposed biological mechanisms in greater detail.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.335
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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