Consumption of Acidic Water Alters the Gut Microbiome and Decreases the Risk of Diabetes in NOD Mice
Bibliographic record
Abstract
Infant formula and breastfeeding are environmental factors that influence the incidence of Type 1 Diabetes (T1D) as well as the acidity of newborn diets. To determine if altering the intestinal microbiome is one mechanism through which an acidic liquid plays a role in T1D, we placed non-obese diabetic (NOD)/ShiLtJt mice on neutral (N) or acidified H2O and monitored the impact on microbial composition and diabetes incidence. NOD-N mice showed an increased development of diabetes, while exhibiting a decrease in Firmicutes and an increase in Bacteroidetes, Actinobacteria, and Proteobacteria from as early as 2 weeks of age. NOD-N mice had a decrease in the levels of Foxp3 expression in CD4(+)Foxp3(+) cells, as well as decreased CD4(+)IL17(+) cells, and a lower ratio of IL17/IFNγ CD4+ T-cells. Our data clearly indicates that a change in the acidity of liquids consumed dramatically alters the intestinal microbiome, the presence of protective Th17 and Treg cells, and the incidence of diabetes. This data suggests that early dietary manipulation of intestinal microbiota may be a novel mechanism to delay T1D onset in genetically pre-disposed individuals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".