The role of the beta cell in type 2 diabetes: new findings from the last 5 years
Bibliographic record
Abstract
Recent advances in genome-wide approaches, the availability of isolated human islets for research and the evaluation of novel incretin mimetics in large clinical trials have brought about remarkable progress in our understanding of the role of the pancreatic beta cell in type 2 diabetes. Here, we review key developments in type 2 diabetes initiation, progression and remission, focusing mostly on human studies published in the last 5 years. Progress in multi-omics technologies has enabled researchers to identify links between type 2 diabetes risk variants and gene regulatory networks in islet endocrine cells that control beta cell development, function and stress resilience. These studies support the notion that early abnormalities in insulin secretion, rather than a reduction in beta cell mass, play a fundamental and primary role in early type 2 diabetes pathogenesis. Contributing to these intrinsic beta cell defects are various pathogenic signals from other (endocrine and non-endocrine) islet cells, the exocrine pancreas, the gut and insulin-sensitive tissues. It has also become apparent that beta cells comprise a heterogeneous population that responds differently to stress situations and that sex-related differences in beta cell responses should not be underestimated. Finally, human clinical trials have clearly demonstrated that diabetes remission can be achieved using glucose-lowering therapies and particularly strategies focused on weight loss, including bariatric surgery and, more recently, the use of highly efficient new drugs targeting the incretin system. While progress in the last 5 years has been significant, much remains to be uncovered to bring these advances to the clinic and thereby alleviate the dramatic consequences of type 2 diabetes complications for the hundreds of millions of people who live with this disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".