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Record W2142593697 · doi:10.1177/1479164114559852

SGLT-2 inhibitors and cardiovascular risk: Proposed pathways and review of ongoing outcome trials

2015· review· en· W2142593697 on OpenAlexaff
Silvio E. Inzucchi, Bernard Zinman, Christoph Wanner, Roberto Ferrari, David Fitchett, Stefan Hantel, Rosa-Maria Espadero, H.J. Woerle, Uli C. Broedl, Odd Erik Johansen

Bibliographic record

VenueDiabetes and Vascular Disease Research · 2015
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's HospitalLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineDapagliflozinEmpagliflozinRenal glucose reabsorptionType 2 Diabetes MellitusAlbuminuriaDiabetes mellitusInternal medicineEndocrinologyArterial stiffnessType 2 diabetesBlood pressureBioinformaticsCardiology

Abstract

fetched live from OpenAlex

Given the multi-faceted pathogenesis of atherosclerosis in type 2 diabetes mellitus (T2DM), it is likely that interventions to mitigate this risk must address cardiovascular (CV) risk factors beyond glucose itself. Sodium glucose cotransporter-2 (SGLT-2) inhibitors are newer antihyperglycaemic agents with apparent multiple effects. Inherent in their mode of action to decrease glucose reabsorption by the kidneys by increasing urinary glucose excretion, these agents improve glycaemic control independent of insulin secretion with a low risk of hypoglycaemia. In this review, we outline those CV risk factors that this class appears to influence and provide the design features and trial characteristics of six ongoing outcome trials involving more than 41,000 individuals with T2DM. Those risk factors beyond glucose that can potentially be modulated positively with SGLT-2 inhibitors include blood pressure, weight, visceral adiposity, hyperinsulinaemia, arterial stiffness, albuminuria, circulating uric acid levels and oxidative stress. On the other hand, small increases in low-density lipoprotein (LDL)-cholesterol levels have also been observed for the class, which theoretically might offset some of these benefits. The potential translational impact of these effects is being tested with outcome trials, also reviewed in this article, powered to assess both macrovascular as well as certain microvascular outcomes in T2DM. These are expected to begin to report in late 2015.

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)0.017
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.218
GPT teacher head0.426
Teacher spread0.208 · 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.

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

Citations405
Published2015
Admission routes1
Has abstractyes

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