Real‐world impact of adding a glucagon‐like peptide‐1 receptor agonist compared with basal insulin on metabolic targets in adults living with type 2 diabetes and chronic kidney disease already treated with a sodium‐glucose co‐transporter‐2 inhibitor: The Impact GLP‐1 CKD study
Bibliographic record
Abstract
Abstract Aim To compare the effectiveness of adding a glucagon‐like peptide‐1 receptor agonist (GLP‐1 RA) with adding basal insulin among adults with type 2 diabetes (T2D) and chronic kidney disease (CKD) already treated with a sodium‐glucose co‐transporter‐2 inhibitor (SGLT2i) and not reaching their glycaemic control targets. Methods A retrospective analysis of the Canadian LMC Diabetes Registry was conducted. Adults who initiated a GLP‐1 RA were matched 1:1 to adults who initiated basal insulin in a T2D and CKD population. Changes in metabolic outcomes were evaluated at 26‐52 weeks following the therapy start date. Results Propensity score matching was used to match participants who initiated a GLP‐1 RA to participants who initiated basal insulin ( n = 153/cohort). A significantly greater reduction in HbA1c at 26‐52 weeks of follow‐up was observed in the GLP‐1 RA cohort compared with the basal insulin cohort (−1.3% ± 1.4% vs. −1.1% ± 1.4%, P = .03). Weight was significantly reduced (−3.4 ± 3.7 vs. 2.6 ± 4.5 kg, P < .001), and the estimated glomerular filtration rate decline slowed significantly (−0.3 ± 8.2 vs. −2.4 ± 10.4 mL/min/1.73m 2 , P = .02), but the change in albuminuria was not significantly different (−5.7 ± 38.1 vs. −0.5 ± 38.3 mg/mmol, P = .47) at follow‐up in the GLP‐1 RA group compared with the basal insulin group. No differences in self‐reported hypoglycaemic events per week and therapy discontinuations were reported between the cohorts. Conclusions The study shows the real‐world effectiveness of GLP‐1 RA therapy for T2D and CKD. GLP‐1 RAs provided superior reductions in HbA1c and weight, and greater kidney protection, compared with basal insulin among adults with T2D and CKD already treated with an SGLT2i.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".