Effect of <scp>iGlarLixi</scp> on continuous glucose monitoring—measured time in range in insulin‐naive adults with suboptimally controlled type 2 diabetes
Bibliographic record
Abstract
AIMS: People with type 2 diabetes (T2D) and glycated haemoglobin (HbA1c) ≥9% may benefit from fixed-ratio combination therapies such as iGlarLixi (insulin glargine 100 U/mL and lixisenatide 33 μg/mL). Use of continuous glucose monitoring (CGM) is recommended, but data are lacking to assess the impact of iGlarLixi in individuals with HbA1c ≥9%. MATERIALS AND METHODS: Soli-CGM (NCT05114590) was a 16-week, multicentre, open-label study evaluating the efficacy of once-daily iGlarLixi using blinded CGM-based metrics in insulin-naive adults with HbA1c ≥9%-13% who were receiving ≥2 oral antihyperglycaemic agents (OADs) ± glucagon-like peptide-1 receptor agonists (GLP-1 RAs). The primary outcome was the change from baseline to week 16 in percent time in range (TIR; 70-180 mg/dL). Secondary outcomes included change in mean daily blood glucose (BG), maximum postprandial glucose 4 h post-breakfast (PPG-4 h), and time above range (TAR; >180 mg/dL). On-treatment hypoglycaemia was assessed. RESULTS: The study enrolled 124 participants (mean age, 55.6 years; HbA1c, 10.2%). Sixteen weeks of treatment with iGlarLixi improved TIR (+26.2%), mean BG (-52.5 mg/dL), maximum PPG-4 h (-73.7 mg/dL), and TAR (-28.7%); all p < 0.001. Rates of American Diabetes Association level 1 (BG <70 but ≥54 mg/dL) and level 2 (BG <54 mg/dL) hypoglycaemia were reported as 1.4 and 0.6 events per person-year, respectively. No level 3 events (requiring assistance) were reported. CONCLUSIONS: In people with T2D suboptimally controlled on ≥2 OADs ± GLP-1 RAs, 16 weeks of treatment with iGlarLixi significantly improved TIR and reduced TAR without severe 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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".