Abstract 4118762: Large-scale multinational trends in the use of cardioprotective antihyperglycemic agents as first-line therapy in patients with type 2 diabetes and cardiovascular disease: a LEGEND-T2DM study
Bibliographic record
Abstract
Background: US guidelines for type 2 diabetes (T2DM) recommend metformin as first-line anti-hyperglycemic therapy (AHT), but prioritizing the use of AHTs that reduce cardiovascular disease (CVD) risk – GLP1-RAs and SGLT2is – is increasingly suggested for people with CVD. We examine the multinational use patterns of GLP1-RAs, SGLT2is, DPP4is, and sulfonylureas (SUs) as first-line therapies for patients with CVD. Methods: In 10 US and 6 non-US databases between 2016 and 2021 mapped to a common data model [A], we measured yearly initiation of GLP1-RAs, SGLT2is, DPP4is, and SUs as first-line AHTs in patients with T2DM with and without CVD [B]. We also compared the initiation of the drug classes as second-line after metformin. We calculated the annual rate of change in proportional initiation of each drug class as first-line AHT and assessed differences in trends as group x time interactions in linear mixed models. Results: Across all data sources, 3.3 million patients with T2DM used GLP1-RAs, SGLT2is, DPP4is, or SUs as first-line AHTs, and 3.9m initiated these drug classes as second-line AHT after metformin. Within the first-line cohort, 1.4m (41%) had established CVD compared with 1.2m (31%) in the second-line cohort. For GLP1-RAs and SGLT2is, the annual rate of their relative use as first-line agents increased among patients with T2DM with CVD (GLP1RAs: ranging 1.3-5.4% in US [C] and 0.3-0.8% in non-US [D], SGLT2is: 2.1-12.9% in US [E] and 8.1-10.6% in non-US databases [F]). There was a larger relative increase in the use of SGLT2i and a smaller increase in GLP1RA use in CVD vs non-CVD cohorts (P interaction <.001). While there was a larger increase in GLP1RAs uptake in the US cohorts, SGLT2i use increased more in non-US cohorts ([C-F], P interaction <.001 for all, except .01 for SGLT2i). Of note, across cohorts, the uptake of GLP1RA and SGLT2i as first-line was higher than second-line use among those with CVD (P interaction <.001). In contrast, DPP4i and SU use as first-line was lower than GLP1RA and SGLT2is and decreased over time (P<.001). Conclusions: In a large-scale multinational, federated cohort of patients with T2DM, the use of cardioprotective agents increased as first-line AHT, with variation in SGLT2i and GLP1RAs uptake across study cohorts.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".