Exposure of the Host-Associated Microbiome to Nutrient-Rich Conditions May Lead to Dysbiosis and Disease Development—an Evolutionary Perspective
Bibliographic record
Abstract
Inflammatory diseases, such as inflammatory bowel diseases, are dramatically increasing worldwide, but an understanding of the underlying factors is lacking. We here present an ecoevolutionary perspective on the emergence of inflammatory diseases. We propose that adaptation has led to fine-tuned host-microbe interactions, which are maintained by secreted host metabolites nourishing the associated microbes. A constant elevation of nutrients in the gut environment leads to an increased activity and changed functionality of the microbiota, thus severely disturbing host-microbe interactions and leading to dysbiosis and disease development. In the past, starvation and pathogen infections, causing diarrhea, were common incidences that reset the gut bacterial community to its "human-specific-baseline." However, these natural clearing mechanisms have been virtually eradicated in developed countries, allowing a constant uncontrolled growth of bacteria. This leads to an increase of bacterial products that stimulate the immune system and ultimately might initiate inflammatory reactions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".