Incretin-Based Drugs and the Risk of Congestive Heart Failure
Bibliographic record
Abstract
OBJECTIVE: To determine whether the use of incretin-based drugs, including GLP-1 analogs and dipeptidyl peptidase-4 inhibitors, is associated with an increased risk of congestive heart failure (CHF) among patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: The U.K. Clinical Practice Research Datalink, linked to the Hospital Episode Statistics database, was used to conduct a cohort study with a nested case-control analysis among patients newly prescribed antidiabetic drugs between 1 January 2007 and 31 March 2012 and no prior history of CHF. Case subjects were defined as patients hospitalized for a first CHF and matched with up to 20 control subjects on age, duration of treated diabetes, calendar year, and time since cohort entry. Conditional logistic regression was used to estimate odds ratios (ORs) with corresponding 95% CIs of incident CHF comparing current use of incretin-based drugs with current use of two or more oral antidiabetic drugs. RESULTS: The cohort consisted of 57,737 patients followed for a mean 2.4 years, during which time 1,118 incident cases of hospitalized CHF were identified (incidence rate 8.1/1,000 person-years). Current use of incretin-based drugs was not associated with an increased risk of CHF (adjusted OR 0.85 [95% CI 0.62-1.16]). Secondary analyses revealed no duration-response relationship (P trend = 0.39). CONCLUSIONS: In our population-based study, incretin-based drug use was not associated with an increased risk of CHF among patients with type 2 diabetes. These findings provide some reassurance, but will need to be replicated in other large-scale studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".