The role of biofactors in the prevention and treatment of age‐related diseases
Bibliographic record
Abstract
The present demographic changes toward an aging society caused a rise in the number of senior citizens and the incidence and burden of age-related diseases (such as cardiovascular diseases [CVD], cancer, nonalcoholic fatty liver disease [NAFLD], diabetes mellitus, and dementia), of which nearly half is attributable to the population ≥60 years of age. Deficiencies in individual nutrients have been associated with increased risks for age-related diseases and high intakes and/or blood concentrations with risk reduction. Nutrition in general and the dietary intake of essential and nonessential biofactors is a major determinant of human health, the risk to develop age-related diseases, and ultimately of mortality in the older population. These biofactors can be a cost-effective strategy to prevent or, in some cases, even treat age-related diseases. Examples reviewed herein include omega-3 fatty acids and dietary fiber for the prevention of CVD, α-tocopherol (vitamin E) for the treatment of biopsy-proven nonalcoholic steatohepatitis, vitamin D for the prevention of neurodegenerative diseases, thiamine and α-lipoic acid for the treatment of diabetic neuropathy, and the role of folate in cancer epigenetics. This list of potentially helpful biofactors in the prevention and treatment of age-related diseases, however, is not exhaustive and many more examples exist. Furthermore, since there is currently no generally accepted definition of the term biofactors, we here propose a definition that, when adopted by scientists, will enable a harmonization and consistent use of the term in the scientific literature.
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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".