Succession of the Gut Microbiome in the Tibetan Population of the Minjiang River Basin
Bibliographic record
Abstract
Abstract Background: Tibetans are one of the oldest ethnic groups in China and South Asia. Tibetans have a unique lifestyle and a long history, which leads to the particularity of the composition and function of their gut microflora. Tibetans in the Minjiang River basin have gradually increased their migration to the Chengdu Plain in recent years. Based on the analysis of 1059 Tibetans in the Minjiang River basin at an altitude of 500-4001 m, we analyzed the characteristics of the gut microbiome and further elaborated the main factors affecting the succession of the gut microbiome in the Tibetan population.Results: Our study found that the dominant phyla of the Tibetan population were Bacteroidetes and Firmicutes, and the main genera were Prevotella and Bacteroides. To further study the factors affecting the gut microbial composition of the Tibetan population, 115 total parameters of 7 categories were evaluated. The results showed that altitude was the most important factor affecting the variation in the microbial community in the Tibetan population, and the change in altitude promoted the succession of the gut microbial community. In the process of migration from high altitudes to the plain, the gut microbial composition of late immigrants was similar to that of plateau aborigines, while that of early immigrants was similar to that of plain aborigines. Migration to Tibet is related to the loss of indigenous gut microbial community species. In addition, from low altitude to high altitude, the similarity of the microbial community with the high-altitude population increased with the reproduction of offspring after marriage. Changes in these microbials will affect the metabolism, disease incidence and cell function of the Tibetan population. The other two sets (AGP and Z208) of altitude data also showed the impact of altitude on the microbial community.Conclusions: This is the first large-scale study on the factors influencing the gut microflora in a Tibetan population. Our study confirmed that altitude change is the most important factor affecting the distribution of the microflora in the Tibetan population and provided abundant and unique data to explore the interaction of impact parameter-gut microbiome-host function and disease.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".