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Record W4395446333 · doi:10.1136/bmj-2023-078242

Effect of combination treatment with glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors on incidence of cardiovascular and serious renal events: population based cohort study

2024· article· en· W4395446333 on OpenAlexafffund
Nikita Simms-Williams, Nir Treves, Hui Yin, Sally Lu, Oriana Hoi Yun Yu, Richeek Pradhan, Christel Renoux, Samy Suissa, Laurent Azoulay

Bibliographic record

VenueBMJ · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsMedicineType 2 diabetesAdverse effectDiabetes mellitusGlucagon-like peptide-1Internal medicineDrug classEndocrinologyPopulationGlucagon-like peptide 1 receptorIncidence (geometry)CohortHeart failurePharmacologyDrugReceptorAgonist

Abstract

fetched live from OpenAlex

Abstract Objective To determine whether the combined use of glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT-2) inhibitors is associated with a decreased risk of major adverse cardiovascular events and serious renal events compared with either drug class alone among patients with type 2 diabetes, and to assess the effect of the combination on the individual components of major adverse cardiovascular events, heart failure, and all cause mortality. Design Population based cohort study using a prevalent new-user design, emulating a trial. Setting UK Clinical Practice Research Datalink linked to Hospital Episode Statistics Admitted Patient Care and Office for National Statistics databases. Participants Two prevalent new-user cohorts were assembled between January 2013 and December 2020, with follow-up until the end of March 2021. The first cohort included 6696 patients who started GLP-1 receptor agonists and added on SGLT-2 inhibitors, and the second included 8942 patients who started SGLT-2 inhibitors and added on GLP-1 receptor agonists. Combination users were matched, in a 1:1 ratio, to patients prescribed the same background drug, duration of background drug, and time conditional propensity score. Main outcome measures Cox proportional hazards models were fitted to estimate the hazard ratios and 95% confidence intervals of major adverse cardiovascular events and serious renal events, separately, comparing the GLP-1 receptor agonist-SGLT-2 inhibitor combination with the background drug, either GLP-1 receptor agonists or SGLT-2 inhibitors, depending on the cohort. Secondary outcomes included associations with the individual components of major adverse cardiovascular events (myocardial infarction, ischaemic stroke, cardiovascular mortality), heart failure, and all cause mortality. Results Compared with GLP-1 receptor agonists, the SGLT-2 inhibitor-GLP-1 receptor agonist combination was associated with a 30% lower risk of major adverse cardiovascular events (7.0 v 10.3 events per 1000 person years; hazard ratio 0.70, 95% confidence interval 0.49 to 0.99) and a 57% lower risk of serious renal events (2.0 v 4.6 events per 1000 person years; hazard ratio 0.43, 0.23 to 0.80). Compared with SGLT-2 inhibitors, the GLP-1 receptor agonist-SGLT-2 inhibitor combination was associated with a 29% lower risk of major adverse cardiovascular events (7.6 v 10.7 events per 1000 person years; hazard ratio 0.71, 0.52 to 0.98), whereas serious renal events generated a wide confidence interval (1.4 v 2.0 events per 1000 person years; hazard ratio 0.67, 0.32 to 1.41). Secondary outcomes generated similar results but with wider confidence intervals. Conclusions In this cohort study, the GLP-1 receptor agonist-SGLT-2 inhibitor combination was associated with a lower risk of major adverse cardiovascular events and serious renal events compared with either drug class alone.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.256
Teacher spread0.250 · 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 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".

Quick stats

Citations113
Published2024
Admission routes2
Has abstractyes

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