Effects of Exenatide Alone and in Combination With Daclizumab on β-Cell Function in Long-Standing Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: In patients with long-standing type 1 diabetes, we investigated whether improved beta-cell function can be achieved by combining intensive insulin therapy with agents that may 1) promote beta-cell growth and/or limit beta-cell apoptosis and 2) weaken the anti-beta-cell autoimmunity. RESEARCH DESIGN AND METHODS: For this study, 20 individuals (mean age 39.5 +/- 11.1 years) with long-standing type 1 diabetes (21.3 +/- 10.7 years) were enrolled in this prospective open-label crossover trial. After achieving optimal blood glucose control, 16 subjects were randomized to exenatide with or without daclizumab. Endogenous insulin production was determined by repeatedly measuring serum C-peptide. RESULTS: In 85% of individuals with long-standing type 1 diabetes who were screened for participation in this trial, C-peptide levels >or=0.05 ng/ml (0.02 nmol/l) were found. Residual beta-cells responded to physiological (mixed-meal) and pharmacological (arginine) stimuli. During exenatide treatment, patients lost 4.1 +/- 2.9 kg body wt and insulin requirements declined significantly (total daily dose on exenatide 0.48 +/- 0.11 vs. 0.55 +/- 0.13 units x kg(-1) x day(-1) without exenatide; P = 0.0062). No signs of further activation of the underlying autoimmune disease were observed. Exenatide delayed gastric emptying, suppressed endogenous incretin levels, but did not increase C-peptide secretion. CONCLUSIONS: In long-standing type 1 diabetes, which remains an active autoimmune disease even decades after its onset, surviving beta-cells secrete insulin in a physiologically regulated manner. However, the combination of intensified insulin therapy, exenatide, and daclizumab did not induce improved function of these remaining beta-cells.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".