Stem Cell–Derived Islet Transplantation in Patients With Type 2 Diabetes: Can Diabetes Subtypes Guide Implementation?
Bibliographic record
Abstract
Historically, only patients with brittle diabetes or severe recurrent hypoglycemia have been considered for islet transplantation (ITx). This population has been selected to optimize the risk-benefit profile, considering risks of long-term immunosuppression and limited organ supply. However, with the advent of stem cell (SC)-derived ITx and the potential for immunosuppression-free ITx, consideration of a broader recipient cohort may soon be justified. Simultaneously, the classical categorization of diabetes is being challenged by growing evidence in support of a clustering of disease subtypes that can be better categorized by the All New Diabetics in Scania (ANDIS) classification system. Using the ANDIS classification, 5 subtypes of diabetes have been described, each with unique causes and consequences. We evaluate consideration for ITx in the context of this broader patient population and the new classification of diabetes subtypes. In this review, we evaluate considerations for ITx based on novel diabetes subtypes, including their limitations, and we elaborate on unique transplant features that should now be considered to enable ITx in these "unconventional" patient cohorts. Based on evidence from those receiving whole pancreas transplant and our more than 20-year experience with ITx, we offer recommendations and potential research avenues to justify implementation of SC-derived ITx in broader populations of patients with all types of diabetes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".