The association of <scp>long‐acting</scp> insulin analogue use versus neutral protamine Hagedorn insulin use and the risk of major adverse cardiovascular events among individuals with type 2 diabetes: A <scp>population‐based</scp> cohort study
Bibliographic record
Abstract
AIMS: To compare the risk of cardiovascular outcomes associated with long-acting insulin analogues versus neutral protamine Hagedorn (NPH) insulin among patients with type 2 diabetes. MATERIALS AND METHODS: We conducted a population-based retrospective cohort study using the UK Clinical Practice Research Datalink Aurum, linked with hospitalization and vital statistics data. Patients with type 2 diabetes who initiated basal insulin treatment between 2002 and 2018 were included in the study. Exposure was defined as current use of long-acting insulin analogues or NPH insulin, defined using a time-varying approach. The primary outcome was major adverse cardiovascular events (MACE; a composite endpoint of myocardial infarction, ischaemic stroke and cardiovascular death). We used a marginal structural Cox proportional hazards model to estimate the hazard ratio (HR) and 95% confidence interval (CI) for MACE with current use of long-acting insulin analogues versus NPH insulin, and in secondary analyses, by long-acting insulin molecule. RESULTS: Our cohort included 57 334 patients. A total of 3494 MACE occurred over a mean follow-up of 1.6 years (incidence rate 37.4, 95% CI 36.2 to 38.7 per 1000 person-years). Long-acting insulin analogues were associated with a decreased risk of MACE compared to NPH insulin (HR 0.89, 95% CI 0.83 to 0.96). CONCLUSIONS: Current use of long-acting insulin analogues is associated with a modestly reduced risk of MACE compared to current use of NPH insulin among patients with type 2 diabetes. This study could have important implications for drug plan managers and guideline-writing committees for recommendations of insulin treatment for type 2 diabetes.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".