Microbial Medicine: Prebiotic and Probiotic Functional Foods to Target Obesity and Metabolic Syndrome
Bibliographic record
Abstract
Obesity has become a global epidemic and a public health crisis in the Western World, experiencing a threefold increase in prevalence since 1975. High-caloric diets and sedentary lifestyles have been identified as significant contributors to this widespread issue, although the role of genetic, social, and environmental factors in obesity's pathogenesis remain incompletely understood. In recent years, much attention has been drawn to the contribution of the gut microbiota in the development of obesity. Indeed, research has shown that in contrast to their healthier counterparts the microbiomes of obese individuals are structurally and functionally distinct, strongly suggesting microbiome as a potential target for obesity therapeutics. In particular, pre and probiotics have emerged as effective and integrative means of modulating the microbiome, in order to reverse the microbial dysbiosis associated with an obese phenotype. The following review brings forth animal and human research supporting the myriad of mechanisms by which the microbiome affects obesity, as well as the strengths and limitations of probiotic or prebiotic supplementation for the prevention and treatment of obesity. Finally, we set forth a roadmap for the comprehensive development of functional food solutions in combatting obesity, to capitalize on the potential of pre/probiotic therapies in optimizing host health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".