Measurement of Pro-Islet Amyloid Polypeptide (1–48) in Diabetes and Islet Transplants
Bibliographic record
Abstract
Context: Islet amyloid is a feature of β-cell failure in type 2 diabetes (T2D) and type 1 diabetes (T1D) recipients of islet transplants. Islet amyloid contains islet amyloid polypeptide (IAPP; amylin), a circulating peptide that is produced in β cells by processing of its precursor, proIAPP1-67, via an intermediate form, proIAPP1-48. Elevated proinsulin to C-peptide ratios in the plasma of persons with diabetes suggest defects in β-cell prohormone processing. Objective: Determine whether plasma levels of precursor forms of IAPP are elevated in diabetes. Design, Setting, and Patients: We developed an immunoassay to detect proIAPP1-48 in human plasma, and we determined the ratio of proIAPP1-48 to mature IAPP in subjects with T1D, T2D, recipients of islet transplants, and healthy controls. Results: The proIAPP1-48 immunoassay had a limit of detection of 0.18 ± 0.06 pM and cross-reactivity with intact proIAPP1-67 <15%. Healthy individuals had plasma concentrations of proIAPP1-48 immunoreactivity of 1.5 ± 0.2 pM and a proIAPP1-48 to total IAPP ratio of 0.28 ± 0.03. Plasma concentrations of proIAPP1-48 immunoreactivity were not significantly different in subjects with T2D but were markedly increased in T1D recipients of islet transplants. Children and adults with T1D had reduced mature IAPP levels relative to age-matched controls but an elevated ratio of proIAPP1-48 to total IAPP. Conclusion: The β cells in T1D and islet transplants have impaired processing of the proIAPP1-48 intermediate. The ratio of proIAPP1-48-to-IAPP immunoreactivity may have value as a biomarker of β-cell stress and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".