Diet standardization reduces intra-individual microbiome variation
Bibliographic record
Abstract
The human gut microbiota is highly heterogenous between individuals and also exhibits considerable day-to-day variation within individuals. We hypothesized that diet contributed to such inter- and/or intra-individual variance. Hence, we investigated the extent to which diet normalization impacted microbiota heterogeneity. We leveraged the control arm of our recently reported controlled-feeding study in which nine healthy individuals consumed a standardized additive-free diet for 10 days. Diet normalization did not impact inter-individual differences but reduced the extent of intra-individual day-to-day variation in fecal microbiota composition. Such decreased heterogeneity reflected individual-specific enrichment and depletion of an array of taxa microbiota members and was paralleled by a trend toward reduced intra-individual variance in fecal LPS and flagellin, which, collectively, reflect microbiota's pro-inflammatory potential. Yet, the microbiota of some subjects did not change significantly over the course of the study, suggesting heterogeneity in microbiota resilience to dietary stress or that baseline diets of some subjects were perhaps similar to our study's standardized diet. Collectively, our results indicate that short-term diet heterogeneity contributes to day-to-day intra-individual microbiota composition variance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".