Changes in Cardiovascular Biomarkers Associated With the Sodium–Glucose Cotransporter 2 (SGLT2) Inhibitor Ertugliflozin in Patients With Chronic Kidney Disease and Type 2 Diabetes
Bibliographic record
Abstract
Patients with type 2 diabetes are at high risk of developing renal and cardiovascular complications. Sodium–glucose cotransporter 2 (SGLT2) inhibitors have garnered interest due to their glucose-independent cardiorenal protective effects, as reported in trials including participants with and without diabetes, such as Dapagliflozin And Prevention of Adverse outcomes in Chronic Kidney Disease (DAPA-CKD) (1,2). These trials have demonstrated that SGLT2 inhibitors reduce cardiovascular disease (CVD) risk, especially hospitalization for heart failure (1,2). Despite these clinical benefits, the underlying physiological mechanisms of SGLT2 inhibitors are incompletely understood, particularly in patients with chronic kidney disease (CKD). Accordingly, this analysis examined the impact of treatment with an SGLT2 inhibitor, ertugliflozin, on markers of plasma volume contraction and myocardial strain in participants with type 2 diabetes and moderate CKD. We performed a post hoc exploratory analysis in a subset of 231 participants from the eValuation of ERTugliflozin efficacy and Safety (VERTIS) RENAL trial (clinical trial reg. no. NCT01986855, ClinicalTrials.gov) with type 2 diabetes and stage 3 CKD (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m2) who were randomized to SGLT2 inhibitor therapy with ertugliflozin (5 mg or 15 mg daily; pooled herein) or placebo (3). Clinical and biomarker measurements were obtained at baseline and 26 weeks and 52 weeks postrandomization. Biomarkers were quantified with Luminex xMAP (cardiac troponin, renin, and N-terminal pro B-type natriuretic peptide [NT-proBNP]) or ELISA (atrial natriuretic peptide [ANP], human erythropoietin [EPO], ACE, and ACE2). Aldosterone was quantified by DiaSorin LIAISON XL Analyzer based on competitive chemiluminescent immunoassay. Differences in longitudinal changes in biomarkers among participants receiving either …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".