Enriched taxa were found among the gut microbiota of centenarians in East China
Bibliographic record
Abstract
BACKGROUND: Gut microbiota is closely related to age. Studies from Europe and the U.S. identified featured microbiota in different age groups for the elderly. Asian studies mainly focused on people living in longevity areas. Featured microbiota for the elderly people of different age groups, especially in the centenarian in the general population, has not been well investigated in China. METHOD: We conducted a comparative study by including 198 subjects of three age groups (65-70, 90-99, and 100+ years) in East China. Information regarding age, sex, height, weight, waist circumference, hip circumference, food preference, smoking status and alcohol consumption were collected by using a structured questionnaire. Fecal samples for each participant were collected as well. 16S rRNA gene sequencing were employed to analyze the gut microbiota composition. Logistic regression with LASSO feature selection was used to identify featured taxa in different age groups and to assess their potential interactions with other factors such as lifestyle. RESULT: The gut microbiota of the 90-99 year and 100+ year age groups showed more diversity, robustness, and richness compared with the 65-70 year age group. PCoA analysis showed a clear separation between the 65-70 and 100+ year age groups. At the species level, Bacteroides fragilis, Parabacteroides merdae, Ruminococcus gnavus, Coprococcus and Clostridium perfringens increased, but Bacteroides vulgatus, Ruminococcus sp.5139BFAA and Clostridium sp.AT5 decreased in the 90-99 year age group. The age differences in gut microbiota were similar across the strata of smoking, alcohol consumption status and food preference. CONCLUSION: Our study demonstrated age differences in many aspects of gut microbiota, such as overall diversity, microbiota structure, and relative abundance of key taxa. Moreover, the gut microbiota of centenarian was significantly different from those of younger age groups of the elderly.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".