Gut Microbiota and Dietary Strategies for Age‐Related Diseases
Bibliographic record
Abstract
The gut microbiota plays a vital role in the aging process and the onset of age-related diseases, offering promising targets for dietary interventions to support healthy aging. This diverse microbial community influences metabolism, immune function, and gut homeostasis, all of which are impacted by diet. Nutrients such as dietary fiber, polyphenols, plant-based proteins, and fermented foods promote beneficial microbes and metabolites like short-chain fatty acids (SCFAs), which help reduce inflammation and protect against chronic conditions, including cardiovascular disease, diabetes, and neurodegenerative disorders. However, aging is often accompanied by reduced microbial diversity and dysbiosis, contributing to chronic low-grade inflammation or, "inflammaging." Dietary strategies incorporating prebiotics, probiotics, and postbiotics may help restore microbial balance and mitigate age-related decline. Despite advances, challenges remain in translating microbiota research to clinical practice due to individual variability, limited human trials, and issues of accessibility. This review highlights the potential of microbiota-focused diets in managing age-related diseases and promoting longevity.
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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".