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The cardioprotective effect of inhibitor sodium glucose transporter in patients undergoing chemotherapy: a systematic review and meta-analysis

2025· article· en· W7127998088 on OpenAlexaff
R Huntermann, M E Molinari, J P Oliveira, M Y Sato, R F Gomes, E S Melo, C Fischer Bacca

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsHeart failureObservational studyHazard ratioRandomized controlled trialGlycemicMeta-analysisAcute kidney injuryConfidence intervalRelative risk

Abstract

fetched live from OpenAlex

Abstract Background Sodium-glucose co-transporter 2 (SGLT2) inhibitors are well known for the countless benefits in glycemic control and cardiovascular outcomes in patients with heart failure (HF), significantly reducing the risk of hospitalizations for HF and cardiovascular death, in addition to kidney protection. However the benefit of SGLT2 inhibitors in HF due cancer therapy-related cardiac dysfunction (CTRCD) is yet to be established. Purpose This systematic review and meta-analysis aimed to evaluate the cardioprotective effects of SGLT2 inhibitors in patients undergoing chemotherapy who are at risk of developing CTRCD. Methods We conduct a comprehensive search of PubMed, Embase and the Cochrane Central Register of controlled trials to identify randomized controlled trials (RCTs) and observational cohorts comparing the use of SGLT2 inhibitors versus control, in patients undergoing chemotherapy and reporting the following outcomes (1) Heart failure; (2) Heart failure hospitalizations; (3) All-cause mortality and as a safety outcome (4) acute kidney injury and (5) urinary tract infection. The statistical analysis was performed using RStudio (Version 4.2.2). Data were pooled and analyzed as risk ratio (RR) with 95% confidence interval (CI). Heterogeneity was assessed using the I² statistic. Heterogeneity was assessed using the I² statistical. Results We included 27,165 patients from 1 RCTs and 11 observational cohorts, of whom 44.31% were in the SGLT2 inhibitor group. The patients mean age was 64.7 ± 10.9 years and 49.43% were male. The new HF events were significantly lower in patients using SGLT2 inhibitors compared to control group (RR: 0.26; 95% CI 0.13 to 0.53; p<0.001; Fig 1a). Therefore, SGLT2 inhibitors statistically decreased the number of HF hospitalizations (RR: 0.46 ; 95% CI 0.30 to 0.72; p<0.001; Fig 1b). Consequently, there was also a reduction in all-cause mortality events (RR: 0.47; 95% CI 0.34 to 0.63; p<0.001; Fig 1c). In terms of safety outcomes, there was a renal protection with SGLT2 inhibitor, showed by a reduction in acute kidney injury (RR: 0.71; 95% CI 0.56 to 0.91; p<0.007; Fig 2a) and a reduction in urinary tract infection (RR:0.54; 95% CI 0.40 to 0.71; p<001; Fig 2b). Conclusion SGLT2 inhibitors reduced the risks of new-onset HF, HF hospitalizations, and all-cause mortality in CTRCD-risk patients, without worsening renal function or increasing urinary tract infections, suggesting a potential management strategy during chemotherapy.Main Outcomes Safety Outcomes

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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0180.028
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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 designMeta-analysis
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

Citations0
Published2025
Admission routes1
Has abstractyes

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