Glycaemic control and hypoglycaemia benefits with insulin glargine 300 U/mL extend to people with type 2 diabetes and mild‐to‐moderate renal impairment
Bibliographic record
Abstract
Aim To investigate the impact of renal function on the safety and efficacy of insulin glargine 300 U/mL (Gla‐300) and insulin glargine 100 U/mL (Gla‐100). Materials and Methods A meta‐analysis was performed using pooled 6‐month data from the EDITION 1, 2 and 3 trials ( N = 2496). Eligible participants, aged ≥18 years with a diagnosis of type 2 diabetes (T2DM), were randomized to receive once‐daily evening injections of Gla‐300 or Gla‐100. Pooled results were assessed by two renal function subgroups: estimated glomerular filtration rate (eGFR) <60 and ≥60 mL/min/1.73 m 2 . Results The decrease in glycated haemoglobin (HbA1c) after 6 months and the proportion of individuals with T2DM achieving HbA1c targets were similar in the Gla‐300 and Gla‐100 groups, for both renal function subgroups. There was a reduced risk of nocturnal (12:00‐5:59 am ) confirmed (≤3.9 mmol/L [≤70 mg/dL]) or severe hypoglycaemia with Gla‐300 in both renal function subgroups (eGFR <60 mL/min/1.73 m 2 : relative risk [RR] 0.76 [95% confidence interval {CI} 0.62‐0.94] and eGFR ≥60 mL/min/1.73 m 2 : RR 0.75 [95% CI 0.67‐0.85]). For confirmed (≤70 mg/dL [≤3.9 mmol/L]) or severe hypoglycaemia at any time of day (24 hours) the hypoglycaemia risk was lower with Gla‐300 vs Gla‐100 in both the lower (RR 0.94 [95% CI 0.86‐1.03]) and higher (RR 0.90 [95% CI 0.85‐0.95]) eGFR subgroups. Conclusions Gla‐300 provided similar glycaemic control to Gla‐100, while indicating a reduced overall risk of confirmed (≤3.9 and <3.0 mmol/L [≤70 and <54 mg/dL]) or severe hypoglycaemia, with no significant difference between renal function subgroups.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".