Circulating senescence-associated secretory phenotype factors across the stages of type 1 diabetes in a cross-sectional cohort
Bibliographic record
Abstract
Type 1 diabetes (T1D) results from a chronic autoimmune disease that leads to pancreatic beta cell death and states of dysfunction such as senescence. Cellular senescence is a programmed stress response involving cell cycle arrest, apoptosis resistance and secretion of immunogenic molecules referred to as the senescence-associated secretory phenotype (SASP). Histologic evidence indicates the accumulation of senescent beta cells in T1D, however, there are no biomarkers to noninvasively detect senescent beta cells. Circulating SASP factors have been used as a biomarker for senescent cell accumulation in age-related diseases, but a similar approach has not been explored in T1D. Here, we measured a panel of 7 previously identified human islet-secreted SASP factors (GDF15, CXCL1, CXCL5, CXCL8, CCL20, IGFBP4 and TNFRSF10C) in a blinded cohort of pediatric and young adult plasma samples from TrialNet including autoantibody-negative controls, single autoantibody-positive, and clinical stages of T1D progression (n = 20 donors per group). SASP factor concentrations were higher in stages 1, 2 and 3 recent onset T1D donors versus controls and effectively discriminated stages 2 and 3 disease status. SASP factor concentration did not associate with the extent of beta cell dysfunction, autoantibody titre or donor age. Analysis of matched plasma and pancreas samples from an independent cohort of control donors supported a relationship between senescent beta cells and circulating SASP markers. These results suggest that senescent beta cell burden may be reflected by the circulating levels of specific islet-associated SASP factors and could represent a novel biomarker for senescence in T1D.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".