Alpha-Glucosidase Inhibitors in Aging and Aging-Related Diseases: Clinical Applications and Relevant Mechanisms
Bibliographic record
Abstract
Aging is a complex and universal process marked by gradual functional declines at the cellular and tissue levels, often leading to a range of aging-related diseases such as diabetes, cardiovascular diseases, and cancer. Delaying the aging process can help prevent, slow down, and alleviate the severity of these various conditions, enhancing overall health and well-being. Alpha-glucosidase inhibitors (AGIs) are a class of widely used antidiabetic drugs that inhibit alpha-glucosidase in the small intestinal mucosa, delaying carbohydrate absorption and reducing postprandial hyperglycemia. Beyond their roles in diabetes treatment, AGIs have shown potential in extending lifespan and effectively treating aging-related diseases by modulating oxidative stress, gut microbiota, inflammatory responses, and nutrient-sensing pathways. This review summarizes recent advancements in the application of AGIs for preventing and treating aging and aging-related diseases, with a focus on their mechanisms and roles in these processes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".