Long-Term Insulin Independence and Improvement in Insulin Secretion After Supplemental Islet Infusion Under Exenatide and Etanercept
Bibliographic record
Abstract
BACKGROUND: Progressive graft dysfunction (GDF) and loss of insulin independence (II) have been invariably observed in islet transplant recipients under the "Edmonton protocol." To reestablish II, we performed supplemental islet infusions (SI) in recipients of allogeneic islet transplant alone, displaying GDF. To improve the engraftment and long-term graft function of SI, exenatide (EXN) and etanercept treatment at islet infusion, and long-term EXN treatment were tested in a non-randomized pilot clinical trial. METHODS: Patients with GDF received SI under Edmonton-like immunosuppression with daclizumab induction, either without interventions (SI-control; n=5) or with EXN and etanercept treatment (SI-EXN; n=4). Clinical and metabolic profiles were assessed during 18-month follow-up. RESULTS: Long-term II (18 months) was observed in 100% of SI-EXN and in 20% of SI-control (P=0.04). SI-EXN subjects demonstrated restoration of function better than that seen after initial islet infusions. Comparison of SI-EXN and SI-control groups demonstrated better responses in SI-EXN subjects at 3 months post-SI. During the 18 months of follow-up, function was sustained in the SI-EXN subjects better than in SI-controls. Acute effects of EXN during mixed meal tolerance test and intravenous glucose tolerance test results in improved first and second phase insulin release in response to intravenous glucose tolerance test and suppressed postprandial hyperglucagonemia after mixed meal tolerance test. CONCLUSION: These results suggest that the combination of EXN and etanercept improve engraftment and long-term islet survival and function in subjects undergoing SI. This data, however, must be interpreted with some caution because of small sample size, lack of randomization, and sequential comparison with historical controls.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".