Outcomes in New User Cohorts of SGLT2 Inhibitors or GLP-1 Receptor Agonists with Type 2 Diabetes and Chronic Kidney Disease
Bibliographic record
Abstract
People with chronic kidney disease (CKD) and type 2 diabetes (T2D) have an increased risk of kidney failure and cardiovascular disease. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) have shown cardiorenal protective effects. The objective of this multinational, multidatabase study was to describe the incidence of kidney and cardiovascular outcomes in separate, non–mutually exclusive cohorts of patients with CKD and T2D who initiated either an SGLT2i or a GLP-1 RA. Data describing adults (≥ 18 years) with T2D and CKD who were new users of either SGLT2i or GLP-1 RA from 2012 to 2019 were assessed from population-based Danish National Health Registers (DNHR) and Valencia Health System Integrated Database (VID), hospital-based Japan Chronic Kidney Disease Database Extension (J-CKD-DB-Ex), and US Optum ® de-identified Electronic Health Record dataset (Optum ® EHR). Crude incidence rates (IRs) and 95% confidence intervals (CIs) for primary outcomes (kidney failure, acute coronary syndrome, stroke, new-onset congestive heart failure, new-onset atrial fibrillation) and cumulative incidence by follow-up time for primary and secondary outcomes (laboratory measurements of kidney function) were estimated. SGLT2i cohorts comprised 12,501 patients in DNHR, 22,404 in VID, 811 in J-CKD-DB-Ex, and 54,308 in Optum ® EHR. GLP-1 RA cohorts comprised 10,696 in DNHR, 8317 in VID, 219 in J-CKD-DB-Ex, and 78,934 in Optum ® EHR. Baseline clinical profile differences were observed for GLP-1 RA and SGLT2i new users, and crude IRs of kidney and heart failure tended to be higher in the GLP-1 RA cohorts than in the SGLT2i cohorts across data sources. Understanding the incidence of kidney failure and cardiovascular outcomes in people receiving antidiabetic medications with cardiorenal protective effects is important for future studies aiming to compare the incidence of kidney and cardiovascular outcomes related to new and existing CKD treatments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".