eLife Assessment: Social and environmental predictors of gut microbiome age in wild baboons
Bibliographic record
Abstract
As we age, our bodies undergo a variety of physical changes. However, the pace at which these changes occur (known as our biological age) often does not reflect the number of years we’ve lived (known as our chronological age). Various markers have been proposed to predict biological age, including the composition of bacteria living in the gut. Which bacterial species reside in the gut is influenced by multiple factors, such as diet, living conditions and social interactions. This makes the microbiome unique to each individual, and potentially a rich indicator of age-related processes. To explore this idea, Dasari et al. studied a large dataset containing thousands of gut microbiome samples from almost 500 wild baboons, collected over 14 years. Several machine learning algorithms were applied to the data to estimate the ‘microbiome age’ of each individual. Dasari et al. found that these estimates correlated well with the baboons’ chronological ages, and mirrored known patterns of biological aging, such as male baboons aging faster than females. Environmental and social factors – such as a baboon’s social rank within a group – also influenced the relationship between chronological and biological age. During the dry season, for instance, female baboons had a higher microbiome age compared to their actual age, and baboons with low social status had a lower microbiome age than expected. Although life expectancy has steadily increased over the last century, our healthspan (the period of life spent in good health) has not kept pace with it. Understanding how our bodies age is key to prolonging healthspan. The findings of Dasari et al. suggest that the gut microbiome is a good predictor of biological age, and future work investigating this relationship could provide valuable clues for slowing down the aging process.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".