IAPP and type 1 diabetes: implications for immunity, metabolism and islet transplants
Bibliographic record
Abstract
Islet amyloid polypeptide (IAPP), the main component of islet amyloid in type 2 diabetes and islet transplants, is now recognized as a contributor to beta cell dysfunction. Increasingly, evidence warrants its investigation in type 1 diabetes owing to both its immunomodulatory and metabolic actions. Autoreactive T cells to IAPP-derived epitopes have been described in humans, suggesting that IAPP is an islet autoantigen in type 1 diabetes. In addition, although aggregates of IAPP have not been implicated in type 1 diabetes, they are potent pro-inflammatory stimuli to innate immune cells, and thus, could influence autoimmunity. IAPP aggregates also occur rapidly in transplanted islets and likely contribute to islet transplant failure in type 1 diabetes through sterile inflammation. In addition, since type 1 diabetes is a disease of both insulin and IAPP deficiency, clinical trials have examined the potential benefits of IAPP replacement in type 1 diabetes with the injectable IAPP analogue, pramlintide. Pramlintide limits postprandial hyperglycemia by delaying gastric emptying and suppressing hyperglucagonemia, underlining the possible role of IAPP in postprandial glucose metabolism. Here, we review IAPP in the context of type 1 diabetes: from its potential involvement in type 1 diabetes pathogenesis, through its role in glucose metabolism and use of IAPP analogues as therapeutics, to its potential role in clinical islet transplant failure and considerations in this regard for future beta cell replacement strategies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.002 |
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".