Microbiome manipulation modifies sex-specific risk for autoimmunity
Bibliographic record
Abstract
Despite growing evidence for a causal role of environmental factors in autoimmune diseases including the rise in disease frequencies over the past several decades we lack an understanding of how particular environmental exposures modify disease risk. In addition, many autoimmune diseases display sex-biased incidence, with females being disproportionately affected but the mechanisms underlying this sex bias remain elusive. Emerging evidence suggests that both host metabolism and immune function is crucially regulated by the intestinal microbiome. Recently, we showed that in the non-obese diabetic (NOD) mouse model of Type 1 Diabetes (T1D), the gut commensal microbial community strongly impacts the pronounced sex bias in T1D risk by controlling serum testosterone and metabolic phenotypes (1). Here we present new data in the NOD model that explores the correlations between microbial phylogeny, testosterone levels, and metabolic phenotypes, and discuss the future of microbiome-centered analysis and microbe-based therapeutic approaches in autoimmune diseases.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".