Differential glycaemic control with basal insulin glargine 300 <scp>U/mL</scp> versus degludec 100 <scp>U/mL</scp> according to kidney function in type 2 diabetes: A subanalysis from the <scp>BRIGHT</scp> trial
Bibliographic record
Abstract
Abstract Aims Chronic kidney disease (CKD) challenges diabetes management and is associated with increased cardiovascular morbidity and mortality. We examined whether clinical outcomes with insulin glargine 300 U/mL (Gla‐300) and insulin degludec 100 U/mL (IDeg‐100) are affected by renal function in a prespecified subgroup analysis from the BRIGHT trial. Materials and methods BRIGHT (NCT02738151) was a multicentre, open‐label, randomized, active‐controlled, two‐arm, parallel‐group, 24‐week study in insulin‐naïve uncontrolled type 2 diabetes (T2D). Participants were randomized 1:1 to evening Gla‐300 (n = 466) or IDeg‐100 (n = 463) and stratified based on baseline estimated glomerular filtration rate (eGFR) for this analysis. Results Heterogeneity of treatment effect across renal function subgroups was observed ( P = .02), reflecting a greater mean glycated haemoglobin (HbA1c) reduction from baseline to week 24 with Gla‐300 versus IDeg‐100 in the eGFR <60 mL/min/1.73 m 2 subgroup (least squares mean difference: −0.43% [95% confidence interval: −0.74% to −0.12%]), while there were no differences in hypoglycaemia incidence or rates over 24 weeks in that subgroup. HbA1c reductions were similar between treatments in the other eGFR subgroups. However, heterogeneity was observed for annualized rates of anytime (24 hours) or nocturnal (00:00‐05:59 hours) confirmed hypoglycaemia (≤70 mg/dL [≤3.9 mmol/L]) over 24 weeks showing less hypoglycaemia with Gla‐300 versus IDeg‐100 in the ≥90 mL/min/1.73 m 2 . Conclusions Kidney function seems to affect the glucose‐lowering effects of Gla‐300 versus IDeg‐100 in insulin‐naïve T2D. Greater HbA1c reductions with Gla‐300 without increase in hypoglycaemia risk, were observed in patients with eGFR <60 mL/min/1.73 m 2 .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".