Improving glycaemic control in type 2 diabetes: <scp>S</scp> timulate insulin secretion or provide beta‐cell rest?
Bibliographic record
Abstract
Type 2 diabetes (T2D) is characterized by a gradual decline in pancreatic beta cell function that determines the progressive course of the disease. While beta-cell failure is an important contributor to hyperglycaemia, chronic hyperglycaemia itself is also detrimental for beta-cell function, probably by inducing prolonged secretory stress on the beta cell as well as through direct glucotoxic mechanisms that have not been fully defined. For years, research has been carried out in search of therapies targeting hyperglycaemia that preserve long-term beta-cell function in T2D, a quest that is still ongoing. Current strategies aim to improve glycaemic control, either by promoting endogenous insulin secretion, such as sulfonylureas, or by mechanisms that may impact the beta cell indirectly, for example, providing beta-cell rest through insulin treatment. Although overall long-term success is limited with currently available interventions, in this review we argue that strategies that induce beta-cell rest have considerable potential to preserve long-term beta-cell function. This is based on laboratory-based studies involving human islets as well as clinical studies employing intensive insulin therapy, thiazolidinediones, bariatric surgery, short-acting glucagon-like peptide (GLP)-1 receptor agonists and a promising new class of diabetes drugs, sodium-glucose-linked transporter (SGLT)-2 inhibitors. Nevertheless, a lack of long-term clinical studies that focus on beta-cell function for the newer glucose-lowering agents, as well as commonly used combination therapies, preclude a straightforward conclusion; this gap in our knowledge should be a focus of future studies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".