Leveraging the microbiome to understand clinical heterogeneity in depression: findings from the T-RAD study
Bibliographic record
Abstract
Alterations in the gut microbiome have been linked to a variety of mental illnesses including anxiety and depression. This study utilized advanced bioinformatics tools that integrated both the compositional and community nature of gut microbiota to investigate how gut microbiota influence clinical symptoms in a sample of participants with depression. Gut microbiota of 179 participants with major depressive disorder (MDD) in the Texas Resilience Against Depression (T-RAD) study were analyzed by 16S rRNA gene sequencing of stool samples. Severity of anxiety, depression, and anhedonia symptoms were assessed with General Anxiety Disorder - 7 item scale, Patient Health 9-item Questionnaire, and Dimensional Anhedonia Rating Scale, respectively. Using weighted correlation network analysis, a data-driven approach, three co-occurrence networks of bacterial taxa were identified. One of these co-occurrence networks was significantly associated with clinical features including depression and anxiety. The hub taxa associated with this co-occurrence module -one Ruminococcaceae family taxon, one Clostridiales vadinBB60 group family taxon, and one Christencenellaceae family taxon- were connected to several additional butyrate-producing bacteria suggesting that deficits in butyrate production may contribute to clinical symptoms. Therefore, by considering the community nature of the gut microbiome in a real world clinical sample, this study identified a gut microbial co-occurrence network that was significantly associated with clinical anxiety in a cohort of depressed individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".