Food supply toxicants and additives alter the gut microbiota and risk of metabolic disease
Bibliographic record
Abstract
Metabolic disease is rising along with both global industrialization and the use of new commercial, agricultural, and industrial chemicals and food additives. Exposure to these compounds may contribute to aspects of metabolic diseases such as obesity, diabetes, and fatty liver disease. Ingesting compounds in the food supply is a key route of human exposure, resulting in the interaction between toxicants or additives and the intestinal microbiota. Toxicants can influence the composition and function of the gut microbiota, and these microbes can metabolize and transform toxicants and food additives. Microbe-toxicant interactions in the intestine can alter host mucosal barrier function, immunity, and metabolism, which may contribute to the risk or severity of metabolic disease development. Targeting the connection between toxicants, food, and immunity in the gut using strategies such as fermentable fiber (i.e., inulin) may mitigate some of the effects of these compounds on host metabolism. Understanding causative factors in the microbe-host relationship that promote toxicant-induced dysmetabolism is an important goal. This review highlights the role of common toxicants (i.e., persistent organic pollutants, pesticides, and fungicides) and food additives (emulsifiers and artificial sweeteners) found in our food supply that alter the gut microbiota and promote metabolic disease development.
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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 | 0.001 | 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) | 0.003 | 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".