The Brain-Gut-Microbiome Axis Across the Life Continuum and the Role of Microbes in Maintaining the Balance of Health
Bibliographic record
Abstract
There is a growing body of evidence that the interaction between various microbial organisms and the human host can affect various physical and even mental health conditions. Bidirectional communication occurs between the brain and the gut microbiome, referred to as the brain-gut-microbiome axis. During aging, changes occur to the gut microbiome due to various events and factors such as the mode of delivery at birth, exposure to medications (e.g., antibiotics), environmental exposures, diet, and host genetics. Connections to the brain-gut-microbiome axis through different systems also change during aging, leading to the development of chronic diseases. Disruption of the gut microbiome, known as dysbiosis, can lead to a reduction in beneficial bacteria and a corresponding increase in more harmful or even pathogenic bacteria. This imbalance may predispose or contribute to the development of various health conditions and illnesses. Targeted treatment of the gut microbiome and the brain-gut-microbiome axis may assist in the overall management of these various ailments. The purpose of this review is to describe the changes that occur in the gut microbiome throughout life, and to highlight the risk factors for microbial dysbiosis. We discuss the different health conditions experienced at various stages of life, and how dysbiosis may contribute to the clinical presentation of these diseases. Modulation of the gut microbiome and the brain-gut-microbiome axis may therefore be beneficial in the management of various ailments. This review also explores how various therapeutics may be used to target the gut microbiome. Gut biotics and microbial metabolites such as short chain fatty acids may serve as additional forms of treatment. Overall, the targeting of gut health may be an important strategy in the treatment of different medical conditions, with nutritional modulation of the brain-gut-microbiome axis also representing a novel strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".