The gut microbiome: Potential innovations for the understanding and treatment of psychopathology.
Bibliographic record
Abstract
Biological models for psychiatric disorders, such as anxiety and depression, emphasize the role of neurochemical changes in the development and maintenance of the disorders. The past decade, however, has shown increased evidence for the role of the microbiota-gut-brain axis in psychiatric disorders. Although much of this research is still in its preclinical stages and has largely been based on animal models, the findings in this area have potential implications for the conceptualization and treatment of psychiatric disorders. A number of excellent in-depth systematic and narrative reviews have been published on the topic of the microbiota-gutbrain axis and psychopathology recently (Collins, Surette & Bercik, 2012; De Angelis et al., 2015; Foster & Neufeld, 2013; Montiel-Castro et al., 2013; Wang & Kasper, 2014), many of which have been written by our research group at McMaster University. However, one of the limitations of these reviews is that they are published in basic science journals and tend to provide a very in-depth, detailed, and technical review of the basic science literature as it relates to the microbiota-gut-brain axis with limited focus on clinical implications. Therefore, the goal of the current article is to increase the visibility and accessibility of this research by providing an introduction to the topic with a strong focus on theoretical and clinical implications. Moreover, this article brings together ideas on the topic from a multidisciplinary team, including clinical psychologists, psychiatrists, gastroenterologists, and primary researchers, which highlights an interdisciplinary perspective on the topic with ideas for future research.The Gut MicrobiomeThe human body consists of a number of microbial environments that are predominantly made up of bacteria but also includes archaea, fungi, protists, and viruses. The intestinal or gut microbiome is the largest with approximately 100 trillion bacteria, most of which are located in the distal gut (Gill et al., 2006; Qin et al., 2010). The gut microbiome consists of over 1,000 distinct bacterial species (Qin et al., 2010) and its genetic material outnumbers human DNA by 10-fold. Although a core microbiome is shared by all humans, there is variability and fluctuation in the microbiota throughout the life span. This variability and fluctuation is influenced by factors such as diet, stress, infections, and antibiotic use (Cryan & O'Mahony, 2011; Forsythe, Sudo, Dinan, Taylor, & Bienenstock, 2010; Turnbaugh, Ridaura, Faith, Rey, Knight, & Gordon, 2009; Wu & Hui, 2011). The gut microbiome changes rapidly during the first 2 years of life and is influenced by a number of factors including genetics, method of birth delivery, maternal characteristics, nutrition, infections, use of antibiotics, and stress (Collado et al., 2010; Dominguez-Bello et al., 2010; Harmsen et al., 2000; Palmer et al., 2007; Penders et al., 2006).The Microbiota-Gut-Brain AxisThe microbiota-gut-brain axis is defined by the bidirectional communication between the digestive system and the central nervous system. The microbiota-gut-brain axis involves the central nervous system, the autonomic nervous system, and the enteric nervous system. The enteric nervous system consists of approximately 100 million neurons that line the gastrointestinal tract and is often referred to as the second brain because it can function autonomously (Pocock & Richards, 2006). Early research focused on the role of the gut-brain axis in digestion, metabolism, and immune functioning (Konturek, Konturek, Pawlik, & Brzozowski, 2004; Tache, Vale, Rivier, & Brown, 1980), with a strong focus on the role of the central nervous system in regulating these functions (i.e., a top-down model). More recently, there is growing appreciation for the bidirectional communication between the central nervous and digestive systems. The gut is not only influenced by the brain, but also influences the brain. …
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".