Does epigenetic dysregulation of pancreatic islets contribute to impaired insulin secretion and type 2 diabetes?
Bibliographic record
Abstract
β cell dysfunction is central to the development and progression of type 2 diabetes (T2D). T2D develops when β cells are not able to compensate for the increasing demand for insulin caused by insulin resistance. Epigenetic modifications play an important role in establishing and maintaining β cell identity and function in physiological conditions. On the other hand, epigenetic dysregulation can cause a loss of β cell identity, which is characterized by reduced expression of genes that are important for β cell function, ectopic expression of genes that are not supposed to be expressed in β cells, and loss of genetic imprinting. Consequently, this may lead to β cell dysfunction and impaired insulin secretion. Risk factors that can cause epigenetic dysregulation include parental obesity, an adverse intrauterine environment, hyperglycemia, lipotoxicity, aging, physical inactivity, and mitochondrial dysfunction. These risk factors can affect the epigenome at different time points throughout the lifetime of an individual and even before an individual is conceived. The plasticity of the epigenome enables it to change in response to environmental factors such as diet and exercise, and also makes the epigenome a good target for epigenetic drugs that may be used to enhance insulin secretion and potentially treat diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".