Probiotics, prebiotics, synbiotics and other microbiome-based innovative therapeutics to mitigate obesity and enhance longevity via the gut-brain axis
Bibliographic record
Abstract
The global prevalence of obesity currently exceeds 1 billion people and is accompanied by an increase in the aging population. Obesity and aging share many hallmarks and are leading risk factors for cardiometabolic disease and premature death. Current anti-obesity and pro-longevity pharmacotherapies are limited by side effects, warranting the development of novel therapies. The gut microbiota plays a major role in human health and disease, with a dysbiotic composition evident in obese and aged individuals. The bidirectional communication system between the gut and the central nervous system, known as the gut-brain axis, may link obesity to unhealthy aging. Modulating the gut with microbiome-targeted therapies, such as biotics, is a novel strategy to treat and/or manage obesity and promote longevity. Biotics represent material derived from living or once-living organisms, many of which have therapeutic effects. Pre-, pro-, syn- and post-biotics may beneficially modulate gut microbial composition and function to improve obesity and the aging process. However, the investigation of biotics as next-generation therapeutics has only just begun. Further research is needed to identify therapeutic biotics and understand their mechanisms of action. Investigating the function of the gut-brain axis in obesity and aging may lead to novel therapeutic strategies for obese, aged and comorbid (e.g., sarcopenic obese) patient populations. This review discusses the interrelationship between obesity and aging, with a particular emphasis on the gut microbiome, and presents biotics as novel therapeutic agents for obesity, aging and related disease states.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".