Separating the Empirical Wheat From the Pseudoscientific Chaff: A Critical Review of the Literature Surrounding Glyphosate, Dysbiosis and Wheat-Sensitivity
Bibliographic record
Abstract
The prevalence of digestive disorders has increased globally, as countries have adopted a more "Westernized" diet pattern. A Western diet, characterized as high in fat and refined carbohydrates, can also be defined as a product of increased technology and industrialization. Modern farmers rely on agrochemicals to meet the needs of a growing population, and these chemicals have shifted the Western diet's chemical composition. While the number of individuals choosing to live a wheat-free lifestyle without a celiac disease diagnosis has increased, clinical trials have shown that gluten from wheat is not responsible for causing symptoms in healthy individuals suggesting that something else is inducing symptoms. The herbicide, glyphosate, is applied to wheat crops before harvest to encourage ripening resulting in higher glyphosate residues in commercial wheat products within North America. Glyphosate inhibits the shikimate pathway, a pathway exclusive to plants and bacteria. Glyphosate's effect on dysbiosis was not considered when making safety recommendations. Here, we evaluate the literature surrounding glyphosate's effects on the gut microbiome and conclude that glyphosate residues on food could cause dysbiosis, given that opportunistic pathogens are more resistant to glyphosate compared to commensal bacteria. However, research on glyphosate's effects on the microbiome suffers from numerous methodological weaknesses, and these limitations make it impossible to draw any definitive conclusions regarding glyphosate's influence on health through alterations in the gut microbiome. In this review, we critically evaluate the evidence currently known and discuss recommendations for future studies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".