IL-1 Blockade Attenuates Islet Amyloid Polypeptide-Induced Proinflammatory Cytokine Release and Pancreatic Islet Graft Dysfunction
Bibliographic record
Abstract
Islets from patients with type 2 diabetes exhibit β cell dysfunction, amyloid deposition, macrophage infiltration, and increased expression of proinflammatory cytokines and chemokines. We sought to determine whether human islet amyloid polypeptide (hIAPP), the main component of islet amyloid, might contribute to islet inflammation by recruiting and activating macrophages. Early aggregates of hIAPP, but not nonamyloidogenic rodent islet amyloid polypeptide, caused release of CCL2 and CXCL1 by islets and induced secretion of TNF-α, IL-1α, IL-1β, CCL2, CCL3, CXCL1, CXCL2, and CXCL10 by C57BL/6 bone marrow-derived macrophages. hIAPP-induced TNF-α secretion was markedly diminished in MyD88-, but not TLR2- or TLR4-deficient macrophages, and in cells treated with the IL-1R antagonist (IL-1Ra) anakinra. To determine the significance of IL-1 signaling in hIAPP-induced pancreatic islet dysfunction, islets from wild-type or hIAPP-expressing transgenic mice were transplanted into diabetic NOD/SCID recipients implanted with mini-osmotic pumps containing IL-1Ra (50 mg/kg/d) or saline. IL-1Ra significantly improved the impairment in glucose tolerance observed in recipients of transgenic grafts 8 wk following transplantation. Islet grafts expressing hIAPP contained amyloid deposits in close association with F4/80-expressing macrophages. Transgenic grafts contained 50% more macrophages than wild-type grafts, an effect that was inhibited by IL-1Ra. Our results suggest that hIAPP-induced islet chemokine secretion promotes macrophage recruitment and that IL-1R/MyD88, but not TLR2 or TLR4 signaling is required for maximal macrophage responsiveness to prefibrillar hIAPP. These data raise the possibility that islet amyloid-induced inflammation contributes to β cell dysfunction in type 2 diabetes and islet transplantation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".