Resident Macrophages Mediate Islet Amyloid Polypeptide–Induced Islet IL-1β Production and β-Cell Dysfunction
Bibliographic record
Abstract
Islet amyloid polypeptide (IAPP) aggregates to form amyloid fibrils in patients with type 2 diabetes and acts as a potent stimulus for interleukin (IL)-1β secretion by bone marrow-derived macrophages. We sought to determine the contribution of resident islet macrophages to IAPP-induced inflammation and β-cell dysfunction. In cultured islets, macrophages (F4/80(+)CD11b(+)CD11c(+) cells) were required for IAPP-induced mRNA expression of the proinflammatory cytokines IL-1β, tumor necrosis factor-α, and IL-6 and the anti-inflammatory cytokines IL-10 and IL-1 receptor antagonist. Moreover, IAPP-induced IL-1β synthesis and caspase-1 activation were detected in macrophages but not other islet cell types. Transgenic mice with β-cell human IAPP (hIAPP) expression had impaired glucose tolerance, elevated islet Il1b mRNA, and decreased Il10 and Il1rn expression following high-fat feeding. Islet macrophages were the major source of these transcripts and expressed increased cell surface Ly6C and CD11c in hIAPP transgenic mice. Clodronate liposome-mediated depletion of islet macrophages improved glucose tolerance and blocked proinflammatory gene expression in hIAPP-expressing mice, despite increasing the amount of islet amyloid. These data provide the first evidence that IAPP aggregates skew resident islet macrophages toward a proinflammatory phenotype and suggest a mechanism by which anti-inflammatory therapies may protect β-cells from IAPP-induced islet dysfunction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".