Compounds that extend longevity are protective in neurodegenerative diseases and provide a novel treatment strategy for these devastating disorders
Bibliographic record
Abstract
While aging is the greatest risk factor for the development of neurodegenerative disease, the role of aging in these diseases is poorly understood. In the inherited forms of these diseases, the disease-causing mutation is present from birth but symptoms appear decades later. This indicates that these mutations are well tolerated in younger individuals but not in older adults. Based on this observation, we hypothesized that changes taking place during normal aging make the cells in the brain (and elsewhere) susceptible to the disease-causing mutations. If so, then delaying some of these age-related changes may be beneficial in the treatment of neurodegenerative disease. In this review, we examine the effects of five compounds that have been shown to extend longevity (metformin, rapamycin, resveratrol, N-acetyl-l-cysteine, curcumin) in four of the most common neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis). While not all investigations observe a beneficial effect of these compounds, there are multiple studies that show a protective effect of each of these lifespan-extending compounds in animal models of neurodegenerative disease. Combined with genetic studies, this suggests the possibility that targeting the aging process may be an effective strategy to treat neurodegenerative disease.
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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) | 0.001 | 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) | 0.005 | 0.002 |
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".