Early postnatal overnutrition accelerates aging-associated epigenetic drift in pancreatic islets
Bibliographic record
Abstract
Abstract Pancreatic islets of type 2 diabetes patients have altered DNA methylation, contributing to islet dysfunction and the onset of type 2 diabetes. The cause of these epigenetic alterations is largely unknown. We set out to test whether (i) islet DNA methylation would change with aging and (ii) early postnatal overnutrition would persistently alter DNA methylation. We performed genome-scale DNA methylation profiling in islets from postnatally over-nourished (suckled in a small litter) and control male mice at both postnatal day 21 and postnatal day 180. DNA methylation differences were validated using quantitative bisulfite pyrosequencing, and associations with expression were assessed by RT-PCR. We discovered that genomic regions that are hypermethylated in exocrine relative to endocrine pancreas tend to gain methylation in islets during aging (R2 = 0.33, P < 0.0001). These methylation differences were inversely correlated with mRNA expression of genes relevant to β cell function [including Rab3b (Ras-related protein Rab-3B), Cacnb3 (voltage-dependent L-type calcium channel subunit 3), Atp2a3 (sarcoplasmic/endoplasmic reticulum calcium ATPase 3) and Ins2 (insulin 2)]. Relative to control, small litter islets showed DNA methylation differences directly after weaning and in adulthood, but few of these were present at both ages. Surprisingly, we found substantial overlap of methylated loci caused by aging and small litter feeding, suggesting that the age-associated gain of DNA methylation happened much earlier in small litter islets than control islets. Our results provide the novel insights that aging-associated DNA methylation increases reflect an epigenetic drift toward the exocrine pancreas epigenome, and that early postnatal overnutrition may accelerate this process.
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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 | 0.000 | 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 | 0.000 |
| 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".