Comparison of Efficacy and Safety of Commercially Available Fixed-Ratio Combinations of Insulin Degludec/Liraglutide and Insulin Glargine/Lixisenatide: A Network Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Our aim in this study was to compare the efficacy and safety of commercially available fixed-ratio combinations (FRCs) of glucagon-like peptide-1 receptor agonists (GLP-1RAs) and basal insulins by a network meta-analysis of randomized controlled trials (RCTs) of people with type 2 diabetes. METHODS: We present a systematic review and network meta-analyses of RCTs of individuals with type 2 diabetes randomized to FRCs or to their components for ≥24 weeks. All reports were obtained from PubMed or ClinicalTrials.gov up to February 28, 2022. The primary outcome was glycated hemoglobin (A1C) level attained. Secondary outcomes included fasting plasma glucose, change in body weight, and incident hypoglycemia. Treatment effects were estimated as mean difference (MD) and standard error (SE), or as odds ratio (OR) with 95% confidence interval (CI) using the fixed combination of insulin glargine 100 IU/mL and lixisenatide (iGlarLixi) as reference. RESULTS: We included 29 RCTs from among the 1,404 articles identified. No direct comparisons between FRCs were found. After excluding some insulin-capped trials to reach model consistency, both FRCs were more efficacious regarding A1C than their components, but no difference between FRCs was found (MD, -0.10%; SE, 0.10%). The effect of the fixed combination of insulin degludec and liraglutide (IDegLira) (MD, -0.47 mmol/L; SE, 0.24 mmol/L) and basal insulins was similar to that of iGlarLixi (reference) on fasting glucose, whereas GLP-1RAs had lower efficacy than iGlarLixi. Weight gain was lower with GLP-1RAs and IDegLira (MD, -0.72 kg; SE, 0.32 kg) than with iGlarLixi (reference) and higher with basal insulins. Incident hypoglycemia (based on different definitions) was least frequent with GLP-1RAs, followed by IDegLira (OR, 0.78; 95% CI, 0.39 to 1.57), iGlarLixi (reference), and basal insulins. CONCLUSIONS: For A1C, both FRCs were more efficacious over their individual components, with similar efficacies of the 2 FRCs.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".