A critical analysis of eating disorders and the gut microbiome
Bibliographic record
Abstract
The gut microbiota, also known as our "second brain" is an exciting frontier of research across a multitude of health domains. Gut microbes have been implicated in feeding behaviour and obesity, as well as mental health disorders including anxiety and depression, however their role in the development and maintenance of eating disorders (EDs) has only recently been considered. EDs are complex mental health conditions, shaped by a complicated interplay of factors. Perhaps due to an incomplete understanding of the etiology of EDs, treatment remains inadequate with affected individuals likely to face many relapses. The gut microbiota may be a missing piece in understanding the etiology of eating disorders, however more robust scientific inquiry is needed in the field before concrete conclusions can be made. In this spotlight paper, we critically evaluate what is known about the bi-directional relationship between gut microbes and biological processes that are implicated in the development and maintenance of EDs, including physiological functioning, hormones, neurotransmitters, the central nervous system, and the immune system. We outline limitations of current research, propose concrete steps to move the field forward and, hypothesize potential clinical implications of this research. Our gut is inhabited by millions of bacteria which have more recently been referred to as "our second brain". In fact, these microbes are thought to play a role in ED behaviour, associated anxiety and depression, and even affect our weight. Recent research has dove into this field with promising findings that have the potential to be applied clinically to improve ED recovery. The present paper discusses what is known about the gut microbiome in relation to EDs and the promising implications that leveraging this knowledge, through fecal microbiome transplants, probiotics, and microbiome-directed supplemental foods, could have on ED treatment.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".