Type 2 diabetes is associated with suppression of autophagy and lipid accumulation in β‐cells
Bibliographic record
Abstract
Both type 2 diabetes (T2D) and obesity are characterized by excessive hyperlipidaemia and subsequent lipid droplet (LD) accumulation in adipose tissue. To investigate whether LDs also accumulate in β-cells of T2D patients, we assessed the expression of PLIN2, a LD-associated protein, in non-diabetic (ND) and T2D pancreata. We observed an up-regulation of PLIN2 mRNA and protein in β-cells of T2D patients, along with significant changes in the expression of lipid metabolism, apoptosis and oxidative stress genes. The increased LD buildup in T2D β-cells was accompanied by inhibition of nuclear translocation of TFEB, a master regulator of autophagy and by down-regulation of lysosomal biomarker LAMP2. To investigate whether LD accumulation and autophagy were influenced by diabetic conditions, we used rat INS-1 cells to model the effects of hyperglycaemia and hyperlipidaemia on autophagy and metabolic gene expression. Consistent with human tissue, both LD formation and PLIN2 expression were enhanced in INS-1 cells under hyperglycaemia, whereas TFEB activation and autophagy gene expression were significantly reduced. Collectively, these results suggest that lipid clearance and overall homeostasis is markedly disrupted in β-cells under hyperglycaemic conditions and interventions ameliorating lipid clearance could be beneficial in reducing functional impairments in islets caused by glucolipotoxicity.
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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 | 0.001 |
| 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".