Microbiome and well-being: a meta-analysis
Bibliographic record
Abstract
The human microbiome may play a significant role in both health and disease. However, most studies to date have focused on the microbiome's role in pathogenesis, while its potential role in promoting well-being remains underexplored. We conducted the first meta-analysis synthesizing empirical evidence on associations between the human microbiome and psychological well-being. Based on eight analyzed studies (N = 2526 participants), we found that both microbial diversity and taxonomic abundance were positively associated with psychological well-being, with diversity emerging as the stronger predictor. Notably, these associations appeared consistent across sex and age. This study provides preliminary evidence that microbiome composition may support salutogenic processes and offers a foundation for future integration of microbiome science into psychological and clinical interventions. However, given the small number of empirical studies included in the meta-analysis, the generalizability of these findings remains limited. Further research is required to strengthen and refine our understanding of the microbiome-well-being relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.027 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".