The vascular function effects of adding exenatide or meal insulin to basal insulin therapy in early type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: Basal insulin glargine has a neutral effect on cardiovascular risk in type 2 diabetes (T2DM). In practice, basal insulin is often paired with a glucagon-like peptide-1 receptor agonist (GLP1-RA) or meal insulin; however, the cardiovascular implications of these combinations have not been fully elucidated. In this context, we sought to evaluate the vascular function effects of adding the GLP1-RA exenatide or meal insulin lispro to basal glargine therapy in early T2DM. METHODS: In this 20-week trial, adults with T2DM of < 7-years duration were randomized to 8-weeks treatment with (i) insulin glargine (Glar), (ii) glargine + thrice-daily lispro (Glar/Lispro), or (iii) glargine + twice-daily exenatide (Glar/Exenatide), followed by 12-weeks washout. At baseline, 8-weeks, and washout, fasting endothelial function was assessed with reactive hyperemia index (RHI) measurement by peripheral arterial tonometry. RESULTS: At baseline, there were no differences in blood pressure (BP), heart rate (HR) or RHI between participants randomized to Glar (n = 24), Glar/Lispro (n = 24), and Glar/Exenatide (n = 25). At 8-weeks, Glar/Exenatide decreased systolic BP (mean - 8.1 mmHg [95%CI - 13.9 to - 2.4], p = 0.008) and diastolic BP (mean - 5.1 mmHg [- 9.0 to - 1.3], p = 0.012) compared to baseline, with no significant changes in HR or RHI. Notably, baseline-adjusted RHI (mean ± SE) did not differ between the groups at 8-weeks (Glar 2.07 ± 0.10; Glar/Lispro 2.00 ± 0.10; Glar/Exenatide 1.81 ± 0.10; p = 0.19), nor did baseline-adjusted BP or HR. There were no differences between the groups in baseline-adjusted RHI, BP or HR after 12-weeks washout. CONCLUSION: Adding either exenatide or lispro to basal insulin therapy does not appear to affect fasting endothelial function in early T2DM. TRIAL REGISTRATION: ClinicalTrials.Gov NCT02194595.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".