A261 MICROBIOTA PROFILES OF PATIENTS WITH MENTAL DISODERS DIFFER FROM THOSE OF HEALTHY CONTROLS
Bibliographic record
Abstract
Abstract Background Mental disorders are the leading cause of disabilities worldwide, with depression and anxiety among the most common ones, affecting up to 1/3 of the worldwide population at least once in their lifetime. In both preclinical models and clinical studies, gut microbiota has been associated with altered behavior and anxiety or depression, respectively. Aims To investigate 1) whether the microbial profiles of patients with generalized anxiety disorder (GAD) and major depression disorder (MDD) differ from those of healthy controls (HC), and 2) whether specific bacterial taxa associate with GAD or MDD. Methods 118 patients with primary GAD (n=82, 83.3 % female) or MDD (n=36, 62.9 % female) and 99 matched HC (66.6 % female) were recruited through the Anxiety Treatment and Research Centre. Anxiety, depression and stress levels were assessed by DASS-21 questionnaire. Stool samples were collected anaerobically and analysed for 16S rRNA gene sequencing through Illumina technique. The data was divided in 4 groups: 1) mental health disorder (MHD) combining GAD and MDD, 2) GAD, 3) MDD, and 4) HC. The data was analyzed following the pipelines of dada2 and QIIME2. RandomForest plugin for QIIME2 was used to investigate predictive characteristics of MHD, GAD or MDD microbiota. SPSS software v.23 was used to perform Spearman correlations and logistic regressions between microbial taxa and clinical scores. Results The mean anxiety score was 16.2 (severe anxiety) for GAD patients and 9.8 (moderate anxiety) for MDD patients; the mean depression score was 19.2 (moderate depression) for MDD patients and 16.0 (moderate depression) for GAD patients, while healthy controls averaged only 1.5 (normal anxiety) and 1.7 (normal depression) for anxiety and depression, respectively. The microbiota profile of the MHD group was predictive of the patients’ disease state with an 83.3% accuracy. In particular, increased relative abundance of Bacteroides ovatus and Bacteroides spp. and decreased relative abundance of Dialister spp. (Veilonellaceae), Haemophilus parainfluenzae and Bifidobacterium adolescentis, were predictive of MHD. Neither the GAD or MDD group microbiota profiles alone were accurate in the prediction of the patients’ disease state. There was a positive correlation between the relative abundance of Bacteroides spp. and a negative correlation between the relative abundance of Clostridium sensu stricto spp. and Sutterella, and the clinical scores of combined MDH and HC groups. Conclusions Our data suggest that patients with mental health disorders have different microbiota profiles compared to healthy controls. We have identified specific bacterial signatures that will inform mechanistic studies in gnotobiotic mouse models to investigate further the role of microbiome in mental disorders. Funding Agencies NIH
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".