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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".