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Comparison of the Effects of Glucagon-Like Peptide Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors for Prevention of Major Adverse Cardiovascular and Renal Outcomes in Type 2 Diabetes Mellitus

2019· review· en· W2916296804 on OpenAlexaff
Thomas A. Zelniker, Stephen D. Wiviott, Itamar Raz, KyungAh Im, Erica L. Goodrich, Remo H.M. Furtado, Marc P. Bonaca, Ofri Mosenzon, Eri Kato, Avivit Cahn, Deepak L. Bhatt, Lawrence A. Leiter, Darren K. McGuire, John Wilding, Marc S. Sabatine

Bibliographic record

VenueCirculation · 2019
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMaceInternal medicineHazard ratioDiabetes mellitusMyocardial infarctionType 2 diabetesHeart failureAdverse effectStroke (engine)Type 2 Diabetes MellitusEndocrinologyConfidence intervalPercutaneous coronary intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Glucagon-like peptide 1 receptor agonists (GLP1-RA) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) have emerged as 2 new classes of antihyperglycemic agents that also reduce cardiovascular risk. The relative benefits in patients with and without established atherosclerotic cardiovascular disease for different outcomes with these classes of drugs remain undefined. METHODS: We performed a systematic review and trial-level meta-analysis of GLP1-RA and SGLT2i cardiovascular outcomes trials using the PubMed and EMBASE databases (Excerpta Medica Database). The primary outcomes were the composite of myocardial infarction, stroke, and cardiovascular death (MACE); hospitalization for heart failure; and progression of kidney disease. RESULTS: In total, data from 8 trials and 77 242 patients, 42 920 (55.6%) in GLP1-RA trials, and 34 322 (44.4%) in SGLT2i trials, were included. Both drug classes reduced MACE in a similar magnitude with GLP1-RA reducing the risk by 12% (hazard ratio [HR], 0.88; 95% CI, 0.84-0.94; P<0.001) and SGLT2i by 11% (HR, 0.89; 95% CI, 0.83-0.96; P=0.001). For both drug classes, this treatment effect was restricted to a 14% reduction in those with established atherosclerotic cardiovascular disease (HR, 0.86; 95% CI, 0.80-0.93; P=0.002), whereas no effect was seen in patients without established atherosclerotic cardiovascular disease (HR, 1.01; 95% CI, 0.87-1.19; P=0.81; P interaction, 0.028). SGLT2i reduced hospitalization for heart failure by 31% (HR, 0.69; 95% CI, 0.61-0.79; P<0.001), whereas GLP1-RA did not have a significant effect (HR, 0.93; 95% CI, 0.83-1.04; P=0.20). Both GLP1-RA (HR, 0.82; 95% CI, 0.75-0.89; P<0.001) and SGLT2i (HR, 0.62; 95% CI, 0.58-0.67; P<0.001) reduced the risk of progression of kidney disease including macroalbuminuria, but only SGLT2i reduced the risk of worsening estimated glomerular filtration rate, end-stage kidney disease, or renal death (HR, 0.55; 95% CI, 0.48-0.64; P<0.001). CONCLUSIONS: In trials reported to date, GLP1-RA and SGLT2i reduce atherosclerotic MACE to a similar degree in patients with established atherosclerotic cardiovascular disease, whereas SGLT2i have a more marked effect on preventing hospitalization for heart failure and progression of kidney disease. Their distinct clinical benefit profiles should be considered in the decision-making process when treating patients with type 2 diabetes mellitus.

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)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.024
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.025
GPT teacher head0.300
Teacher spread0.275 · 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 designSystematic review
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

Citations781
Published2019
Admission routes1
Has abstractyes

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