The Safety and Efficacy of Second-Generation Basal Insulin Analogues in Adults with Type 2 Diabetes at Risk of Hypoglycemia and Use in Other Special Populations: A Narrative Review
Bibliographic record
Abstract
Hypoglycemia is a major barrier impeding glycemic control in persons with type 2 diabetes mellitus and creates a substantial burden on the healthcare system. Certain populations that require special attention, such as older adults and individuals with renal impairment, a longer duration of diabetes or those who have experienced prior hypoglycemia, may be at a higher risk of hypoglycemia, particularly with insulin treatment. Second-generation basal insulin analogues (insulin glargine 300 U/mL and degludec) have demonstrated reductions in hypoglycemia compared with insulin glargine 100 U/mL although evidence of this benefit across specific populations is less clear. In this review we summarize the literature with respect to the efficacy and safety data for second-generation basal insulin analogues in adults with type 2 diabetes mellitus who are at risk of hypoglycemia or who require special attention. Randomized controlled trials, meta-analyses and real-world evidence demonstrate that the use of second-generation basal insulin analogues is associated with less hypoglycemia compared with insulin glargine 100 U/mL without compromising glycated hemoglobin control. A reduced risk of hypoglycemia with second-generation basal insulin analogues was evident in older adults and in individuals with obesity, renal impairment, a history of cardiovascular disease or a long duration of insulin use. Further studies are needed in other populations, including those with more severe renal impairment or hepatic dysfunction, the hospitalized population and those with cognitive impairment. Overall, less hypoglycemia associated with second-generation basal insulin analogues may help reduce barriers for insulin use, improve adherence and offset the costs of hypoglycemia-related healthcare resource utilization.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".