The Effects of Aging on Gut Microbiome Composition and Association With Age-Related Disease States: A Literature Review
Bibliographic record
Abstract
Introduction: The gut microbiome is the collection of microbial species residing in the gastrointestinal tract that play an important role in metabolism and immune function. A reduction in microbial diversity and/or an altered composition of microbiota results in dysbiosis, which is speculated to place individuals at greater risk for neurological, metabolic, and physical disorders. The purpose of this study is to provide a review of the literature describing alterations in composition and species richness that occur during aging in the gut microbiome whilst identifying how these changes are linked to age-related diseases. Methods: A review of the current literature was conducted by searching for applicable keywords using scientific, electronic databases. Keywords used to search for articles included (“gut microbiome” OR “gut microbiota” OR “bacteria flora”) AND (“aging” OR “ageing” OR “old age”) AND (“age-related disease” OR “disease”). Articles were screened and chosen for analysis based on the quality and relevance of the study. Results: There are many changes that occur in the gut microbiome with aging, such as reduced short-chain fatty acid production, lack of overall diversity and increase in pathobionts from phyla Proteobacteria and Enterobacteriaceae. Many age-related diseases display distinct changes in microbiome composition which have been shown to be implicated in disease onset or progression. In cases of extreme longevity, the microbiome displays specific signatures associated with youthfulness and health such as stability, resilience, and taxonomic diversity. Discussion: The alterations observed in the gut microbiome during aging are likely due to a concurrent deterioration of the immune system and reduction in intestinal function and motility. Microbial dysbiosis promotes a pro-inflammatory state in the gut which has implications for disease. Additionally, many microbial signatures of aging coincided with alterations attributed to diseased states, further supporting that dysbiosis in later years of life may accelerate or promote pathology. In contrast, the microbiome of centenarians and extremely long-lived individuals demonstrate a model for healthy aging and longevity. Conclusion: The findings from this study highlight the importance of the microbiome in age-related diseases and proposes the microbiome as a potential target for the mitigation and treatment of disease in elderly populations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".