Elevations in plasma proinsulin predict the development of diabetes in NOD mice
Bibliographic record
Abstract
Abstract The global incidence of type 1 diabetes (T1D) continues to rise, yet reliable biomarkers for predicting disease onset remain limited. Studies have demonstrated persistent proinsulin secretion in individuals living with T1D, suggesting a processing impairment. Proinsulin is processed into mature active insulin by the prohormone convertases PC1/3, PC2, and carboxypeptidase E. We hypothesized that elevated circulating proinsulin-to-C-peptide (PI:C) ratios precede the onset of diabetes and are associated with reduced expression of PC1/3 in pancreatic beta cells. Non-obese diabetic (NOD) mice were monitored for changes in plasma proinsulin, C-peptide, and beta cell Pc1/3 levels prior to diabetes onset. Female NOD mice that progressed to diabetes exhibited increased plasma proinsulin and PI:C ratios several weeks before the onset of diabetes compared to mice that remained normoglycemic. Plasma proinsulin levels were predictive of diabetes onset, with earlier elevations observed in mice that progressed to disease more rapidly. These increases in plasma proinsulin and PI:C ratios correlated with reduced beta cell Pc1/3 expression. These findings support the potential of plasma proinsulin and PI:C ratios as predictive biomarkers for T1D development and implicate diminished Pc1/3 expression as a possible mechanism underlying impaired proinsulin processing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".