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Record W4416017605 · doi:10.1001/jama.2025.20834

SGLT2 Inhibitors and Kidney Outcomes by Glomerular Filtration Rate and Albuminuria

2025· article· en· W4416017605 on OpenAlexaff
Brendon L. Neuen, Robert A. Fletcher, Stefan D. Anker, Deepak L. Bhatt, Javed Butler, David Z.I. Cherney, Kieran F. Docherty, Silvio E. Inzucchi, Meg Jardine, Kenneth W. Mahaffey, Finnian R. Mc Causland, Darren K. McGuire, John J.V. McMurray, Bruce Neal, Milton Packer, Siddharth M. Patel, Vlado Perkovic, Marc S. Sabatine, Rebecca J. Sardell, Scott D. Solomon, Muthiah Vaduganathan, C. Wanner, David C. Wheeler, Faïez Zannad, Richard Haynes, Natalie Staplin, William G. Herrington, Hiddo J.L. Heerspink, Hiddo JL Heerspink, Stefan D. Anker, Adrian F. Hernandez, Stefan James, Vlado Perkovic, Scott Solomon, Muthiah Vaduganathan, Christoph Wanner, Faiez Zannad

Bibliographic record

VenueJAMA · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAlbuminuriaRenal functionKidney diseaseKidneyDiabetes mellitusStage (stratigraphy)

Abstract

fetched live from OpenAlex

Importance: Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce chronic kidney disease (CKD) progression in individuals with type 2 diabetes, CKD, or heart failure. However, their effects in those with stage 4 CKD or little to no albuminuria remain uncertain. Objective: To assess whether estimated glomerular filtration rate (eGFR) or degree of albuminuria, measured by urinary albumin to creatinine ratio (UACR), modifies the effects of SGLT2 inhibitors on kidney outcomes. Data Sources: SGLT2 inhibitor trials participating in the SGLT2 Inhibitor Meta-Analysis Cardio-Renal Trialists' Consortium (SMART-C). Study Selection: Randomized, double-blind, placebo-controlled trials within SMART-C evaluating an SGLT2 inhibitor with label indications for reducing CKD progression including at least 500 participants in each group with at least 6 months of follow-up. Data Extraction and Synthesis: Treatment effects in individual trials were pooled using inverse variance-weighted meta-analysis. Main Outcomes and Measures: CKD progression, defined as kidney failure, at least 50% reduction in eGFR, or death due to kidney failure. Other outcomes included annual rate of eGFR decline and kidney failure. Results: Among 70 361 participants (mean [SD] age, 64.8 [8.7] years; 24 595 [35.0%] females) in 10 randomized trials, 2314 (3.3%) experienced CKD progression and 988 (1.4%) reached kidney failure. SGLT2 inhibitors reduced the risk of CKD progression (25.4 vs 40.3 events per 1000 patient-years; hazard ratio [HR], 0.62 [95% CI, 0.57-0.68]), irrespective of baseline eGFR (HR of 0.61 [95% CI, 0.52-0.71] for eGFR ≥60 mL/min/1.73 m2; 0.57 [95% CI, 0.47-0.70] for eGFR of 45 to <60 mL/min/1.73 m2; 0.64 [95% CI, 0.54-0.75] for eGFR of 30 to <45 mL/min/1.73 m2; and 0.71 [95% CI, 0.60-0.83] for eGFR <30 mL/min/1.73 m2; P for trend = .16) and baseline albuminuria (HR of 0.58 [95% CI, 0.44-0.76] for albuminuria ≤30 mg/g; 0.74 [95% CI, 0.57-0.96] for >30-300 mg/g; and 0.57 [95% CI, 0.52-0.64] for more than 300 mg/g; P for trend = .49). Although the magnitude of protection varied, SGLT2 inhibitors reduced the annual rate of eGFR decline across all eGFR and UACR subgroups, including when participants with and without diabetes were analyzed separately. SGLT2 inhibitors also reduced the risk of kidney failure alone (HR, 0.66 [95% CI, 0.58-0.75]). Conclusions and Relevance: In this meta-analysis, SGLT2 inhibitors were found to lower the risk of CKD progression regardless of baseline eGFR or albuminuria, including in patients with stage 4 CKD or minimal albuminuria, supporting their routine use to improve kidney outcomes across the full spectrum of kidney function among patients with type 2 diabetes, CKD, or heart failure.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.005
GPT teacher head0.250
Teacher spread0.245 · 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 designNot applicable
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

Citations39
Published2025
Admission routes1
Has abstractyes

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