Yearly changes in the composition of gut microbiota in the elderly, and the effect of lactobacilli intake on these changes
Bibliographic record
Abstract
The onset and worsening of some diseases are related to the variation and instability of gut microbiota. However, studies examining the personal variation of gut microbiota in detail are limited. Here, we evaluated the yearly variation of individual gut microbiota in 218 Japanese subjects aged 66-91 years, using Jensen-Shannon distance (JSD) metrics. Approximately 9% of the subjects showed a substantial change, as their formerly predominant bacterial families were replaced over the year. These subjects consumed fermented milk products less frequently than their peers. The relationship between the intake frequencies of fermented milk products containing Lactocaseibacillus paracasei strain Shirota (LcS) and JSD values was also investigated. The intra-individual JSD of subjects ingesting LcS products ≥ 3 days/week over the past 10 years was statistically lower than the < 3 days/week group (P = 0.045). Focusing on subjects with substantial gut microbiota changes, only 1.7% of the subjects were included in the LcS intake ≥ 3 days/week group whereas 11.3% were found in the < 3 days/week group (P = 0.029). These results suggest that about one-tenth of the elderly Japanese could experience a substantial change in their gut microbiota during a 1-year period, and that the habitual intake of probiotics may stabilize their gut microbiota.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".