Glycaemic control and hypoglycaemia risk with insulin glargine 300 U/mL versus glargine 100 U/mL: A patient-level meta-analysis examining older and younger adults with type 2 diabetes
Bibliographic record
Abstract
AIM: Older people with type 2 diabetes (T2DM) are at an increased risk of hypoglycaemia and its consequences. However, efficacy and safety data for basal insulin therapy are limited in these individuals. This patient-level meta-analysis assessed the treatment effects of insulin glargine 300 U/mL (Gla-300) versus glargine 100 U/mL (Gla-100) in people with T2DM ≥ 65 years old. METHODS: Data were pooled for patients randomised to receive Gla-300 or Gla-100 in the Phase 3a, treat-to-target EDITION 1, 2 and 3 trials. Glycaemic efficacy, hypoglycaemia, changes in body weight and insulin dosage and adverse events were examined over 6 months' treatment with Gla-300 versus Gla-100 for participants aged ≥ 65 and < 65 years. RESULTS: change from baseline to month 6: 0.00 [-0.14 to 0.15] %; 0.00 [-1.53 to 1.64] mmol/mol) and < 65 years (0.00 [-0.09 to 0.08] %; 0.00 [-0.98 to 0.87] mmol/mol). Fewer participants receiving Gla-300 versus Gla-100 experienced nocturnal confirmed (≤ 3.9 mmol/L [≤ 70 mg/dL]) or severe hypoglycaemia (relative risk: ≥ 65 years: 0.70 [0.57 to 0.85]; < 65 years: 0.77 [0.68 to 0.87]). Annualised rates of nocturnal confirmed or severe hypoglycaemia were lower with Gla-300 than Gla-100 for both age groups. CONCLUSION: Gla-300 was associated with a reduced risk of nocturnal hypoglycaemia versus Gla-100, accompanied by comparable glycaemic improvement, for people aged ≥ 65 and < 65 years with T2DM.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.051 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".