Clinical Considerations When Initiating and Titrating Insulin Degludec/Liraglutide (IDegLira) in People with Type 2 Diabetes
Bibliographic record
Abstract
Therapeutic inertia is a substantial obstacle to the initiation of insulin therapy in people with uncontrolled type 2 diabetes (T2D). This effect has in part been perpetuated by concerns over the impact of a burdensome regimen and the increased risk of hypoglycemia and body weight gain often associated with insulin use. An effective, yet simple, less burdensome regimen with a lower risk of body weight gain and hypoglycemia compared with an insulin-only regimen, may help to address these concerns more effectively. We review the available clinical and real-world data on IDegLira, a once-daily, injectable, fixed-ratio combination of insulin degludec (degludec) and the glucagon-like peptide-1 receptor agonist (GLP-1RA) liraglutide, in people with T2D. Evidence from the comprehensive DUAL clinical trial program suggests an advantage of IDegLira over traditional insulin therapies in a number of clinical outcomes, including maintenance of glycemic control, achievement of glycemic targets, reducing the risk of hypoglycemia, and body weight loss. These findings were demonstrated in participants with T2D irrespective of prior GLP-1RA and insulin use. Furthermore, the individual components of IDegLira have confirmed safety (degludec) or significant benefit in terms of improvement of cardiovascular risk (liraglutide). As an injectable therapy that is simple to titrate, IDegLira has the potential to optimize the ability to achieve relevant glycemic targets, and offers a suitable treatment option for people with T2D requiring insulin therapy who are at risk of hypoglycemia or weight gain.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".