Glycaemic control and hypoglycaemia risk with insulin glargine 300 U/mL and insulin degludec 100 U/mL in older participants in the BRIGHT trial
Bibliographic record
Abstract
AIM: To evaluate the efficacy and safety of insulin glargine 300 U/mL (Gla-300) versus insulin degludec 100 U/mL (IDeg-100) in predefined (</≥65 years) and post hoc (</≥70 years) age groups of people with type 2 diabetes (T2D) in the BRIGHT trial. MATERIALS AND METHODS: BRIGHT was the first head-to-head randomized trial comparing Gla-300 and Deg-100 in insulin-naïve adults with T2D. In this subanalysis, endpoints were studied by predefined (</≥65 years, N = 596/333) and post hoc (</≥70 years, N = 768/161) age groups. RESULTS: Heterogeneity of treatment effect was observed for HbA1c reductions across the </≥70 years subgroups, but not across the </≥ 65 years subgroups, with greater HbA1c reductions with Gla-300 versus IDeg-100 in those 70 years or older (least squares mean -0.34% [95% confidence interval: -0.589% to -0.100%]). There was no significant heterogeneity of treatment effect for incidence and rates of confirmed (≤3.9 mmol/L [≤70 mg/dL]) hypoglycaemia across any age subgroups over 24 weeks, but numerically lower incidence and rates were consistently observed for Gla-300 versus IDeg-100 in the 65 years or older and 70 years or older age groups in the initial 12 weeks. CONCLUSIONS: Gla-300 may be a suitable treatment option in the growing population of older people with T2D. Further investigation is required to determine Gla-300 glycaemic benefits in high-risk populations without increasing the risk of hypoglycaemia.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".