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Record W4229023293 · doi:10.1093/ndt/gfac070.076

MO462: Change in KDIGO Kidney Risk Category With Semaglutide Treatment—A <i>Post Hoc</i> Analysis of the Sustain 6 Trial

2022· article· en· W4229023293 on OpenAlexaff
Katherine R. Tuttle, Stephen C. Bain, David Cherney, Jack Lawson, Søren Rasmussen, Blaz Vrhnjak, Kamlesh Khunti

Bibliographic record

VenueNephrology Dialysis Transplantation · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSemaglutideMedicineKidney diseasePlaceboPost-hoc analysisAlbuminuriaPopulationInternal medicineOdds ratioRenal functionType 2 diabetesDiabetes mellitusEndocrinologyLiraglutideEnvironmental healthPathology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND AND AIMS Glucagon-like peptide-1 receptor agonists, such as semaglutide, have been associated with reductions in albuminuria, and may preserve estimated glomerular filtration rate (eGFR) in people with type 2 diabetes (T2D). However, it has not been explored whether treatment with semaglutide affects a person's chronic kidney disease (CKD) risk category. The Kidney Disease: Improving Global Outcomes (KDIGO) risk category classification is a validated approach for defining the likelihood of CKD and cardiovascular (CV) disease progression, based on eGFR and urinary albumin-to-creatinine ratio (UACR). The aim of this analysis was to determine whether treatment with once-weekly (OW) semaglutide resulted in improvements in KDIGO risk category compared with placebo. METHOD The proportion of subjects with T2D moving to a lower KDIGO risk category, remaining in the same category or moving to a higher risk category between baseline and 2 years on treatment with OW subcutaneous semaglutide versus placebo was assessed post hoc using SUSTAIN 6 (NCT01720446) CV outcomes trial data. The endpoints were assessed for the overall population and by baseline KDIGO risk category (low, moderate, high and very high). Sensitivity analyses were conducted to investigate the contributions of UACR and eGFR to change in risk category. RESULTS Data from 2804 of the 3297 subjects randomized in SUSTAIN 6 were available for this post hoc analysis. At 2 years, in the overall population, subjects receiving OW semaglutide were more likely to move to a lower risk category (n = 183; 13.0%) than subjects receiving placebo (n = 114; 8.2%); odds ratio (OR; semaglutide versus placebo) 1.69 [95% confidence interval (CI) 1.32; 2.16, P < 0.0001) (Figure). Conversely, subjects receiving OW semaglutide were less likely to move to a higher risk category (n = 254; 18.1%) than those receiving placebo (n = 330; 23.6%); OR 0.71 (95% CI 0.59;0.86, P = 0.0003) (Figure). When the data were stratified by baseline KDIGO risk categories, across all categories, greater proportions of subjects receiving semaglutide than placebo moved to a lower risk category and lower proportions of subjects receiving semaglutide than placebo moved to a higher risk category (Figure). The effects of semaglutide on UACR and eGFR both contributed to the favourable change-in-risk-category profile compared with placebo (Table). CONCLUSION Subjects receiving semaglutide versus placebo were more likely to move to a lower KDIGO risk category and less likely to move to a higher risk category, both in the overall population and across KDIGO risk category subgroups. The potential kidney protective effects of semaglutide and the mechanisms underlying these are being investigated in subjects with T2D and CKD in the FLOW and REMODEL trials.

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)0.008
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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".

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Citations2
Published2022
Admission routes1
Has abstractyes

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