Combining GLP-1 Receptor Agonists and Basal Insulin in Older Adults with Type 2 Diabetes: Focus on Lixisenatide and Insulin Glargine
Bibliographic record
Abstract
Estimates suggest that there are currently 122.8 million adults 65-99 years of age living with diabetes, of whom 90-95% are diagnosed with type 2 diabetes (T2D). Over the past two decades, a greater understanding of the complex and multifactorial pathogenesis of T2D has resulted in the development and introduction of new-generation classes of glucose-lowering therapies, which are now extensively endorsed by prevailing guidelines and are increasingly being used worldwide. These newer agents may further assist in the effective pharmacological management of T2D through the provision of patient-centered care that acknowledges multimorbidity and is respectful of and responsive to individual patient preferences and barriers. Given these considerations, the therapeutic approach in older patients with T2D is complex, particularly in those who have functional dependence, frailty, dementia, or who are at end-of-life. It is currently too early to draw conclusions on the long-term use of newer glucose-lowering agents in this population, as their efficacy and safety in older adults remains largely unknown. In this review, we will discuss considerations for the use of glucose-lowering treatments in older adults, with particular focus on the use of basal insulin and glucagon-like peptide-1 receptor agonists, and the rationale for the use of combination therapy comprising these agents. Finally, we will review clinical data from studies of the fixed-ratio combination of insulin glargine and lixisenatide in older patients with T2D. FUNDING: Sanofi US, Inc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".