Understanding the role of the human gut microbiome in overweight and obesity
Bibliographic record
Abstract
The gut microbiome may be related to the prevalence of overweight and obesity, but high interindividual variability of the human microbiome complicates our understanding. Obesity often occurs concomitantly with micronutrient deficiencies that impair energy metabolism. Microbiota composition is affected by diet. Host-microbiota interactions are bidirectional. We propose three pathways whereby these interactions may modulate the gut microbiome and obesity: (1) ingested compounds or derivatives affecting small intestinal transit, endogenous secretions, digestion, absorption, microbiome balance, and gut barrier function directly affect host metabolism; (2) substrate availability affecting colonic microbial composition and contact with the gut barrier; and (3) microbial end products affecting host metabolism. The quantity/concentration, duration, and/or frequency (circadian rhythm) of changes in these pathways can alter the gut microbiome, disrupt the gut barrier, alter host immunity, and increase the risk of and progression to overweight and obesity. Host-specific characteristics (e.g., genetic variations) may further affect individual sensitivity and/or resilience to diet- and microbiome-associated perturbations in the colonic environment. In this narrative review, the effects of selected interventions, including fecal microbiota transplantation, dietary calorie restriction, dietary fibers and prebiotics, probiotics and synbiotics, vitamins, minerals, and fatty acids, on the gut microbiome, body weight, and/or adiposity are summarized to help identify mechanisms of action and research opportunities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".