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Record W3034294121 · doi:10.2337/db20-356-or

356-OR: Effect of Dulaglutide on Kidney Function–Related Outcomes in Type 2 Diabetes: Post Hoc Analysis from the REWIND Trial

2020· article· en· W3034294121 on OpenAlexaboutno aff
Jonathan E. Shaw, Fady T. Botros, Raleigh Malik, Charles Atisso, Helen M. Colhoun, Hertzel C. Gerstein

Bibliographic record

VenueDiabetes · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPost-hoc analysisMedicineRenal functionDulaglutideType 2 diabetesInternal medicineKidney diseasePlaceboClinical endpointDiabetes mellitusRandomized controlled trialEndocrinologyLiraglutidePathology

Abstract

fetched live from OpenAlex

In participants with type 2 diabetes (T2D) in the REWIND trial, dulaglutide (DU) use for median follow-up of 5.4 years was associated with reduced composite renal outcomes, defined as the first occurrence of new macroalbuminuria, sustained decline in estimated glomerular filtration rate (eGFR) of ≥30%, or chronic renal replacement therapy. The objective of this post-hoc analysis was to evaluate the effect of dulaglutide on renal outcomes related to kidney function that are typically used in renal outcomes studies, defined as the composite endpoint of sustained eGFR decline ≥40%, end-stage renal disease (ESRD), or all-cause death. Participants with T2D at cardiovascular (CV) risk were randomized (1:1) to DU 1.5 mg once-weekly or placebo. This post-hoc analysis used Cox proportional hazards modeling for time to first event to determine the risk of renal outcomes. Sensitivity analyses were conducted by replacing the all-cause death component with CV or renal death, or renal death. At baseline, treatment groups had similar eGFR (mean±SD: DU=77.2±22.7; placebo=76.6±22.8). The incidence rate of the composite endpoint was significantly lower for the DU group compared with placebo (Table). Treatment with DU 1.5 mg was associated with a 17% risk reduction in kidney function-related outcomes, suggesting potential delay in progression of diabetic kidney disease in patients with T2D at CV risk. Disclosure J.E. Shaw: Advisory Panel; Self; AstraZeneca, Merck Sharp & Dohme Corp., Mylan, Sanofi. Research Support; Self; AstraZeneca. Speaker’s Bureau; Self; Eli Lilly and Company, Mylan. F.T. Botros: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. R. Malik: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. C. Atisso: Employee; Self; Eli Lilly and Company. H.M. Colhoun: Advisory Panel; Self; AstraZeneca, Eli Lilly and Company, Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Regeneron Pharmaceuticals, Sanofi-Aventis. Research Support; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk Inc., Novo Nordisk Inc., Pfizer Inc., Regeneron Pharmaceuticals, Sanofi-Aventis. Speaker’s Bureau; Self; Eli Lilly and Company, Regeneron Pharmaceuticals, Sanofi. Stock/Shareholder; Self; Bayer AG, Roche Pharma. Other Relationship; Self; Eli Lilly and Company, Sanofi. H.C. Gerstein: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Kowa Pharmaceuticals America, Inc. Research Support; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Other Relationship; Self; Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Sanofi. Funding Eli Lilly and Company

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.012
GPT teacher head0.248
Teacher spread0.235 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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