Patient Perceptions of Microbiome-Based Therapies as Novel Treatments for Mood Disorders: A Mixed Methods Study: Perceptions des patients sur les thérapies basées sur le microbiome pour les troubles de l’humeur : une étude à méthodes mixtes
Bibliographic record
Abstract
OBJECTIVE: Medications are critical for treating major depressive disorder (MDD) and bipolar disorder (BD). Unfortunately, 30% to 40% of individuals do not respond well to current pharmacotherapy. Given the compelling growing body of research on the gut-brain axis, this study aims to assess patient perspectives regarding microbiome-based therapies (MBT) such as probiotics, prebiotics, dietary changes, or fecal microbiota transplantation (FMT) in the management of MDD and BD. METHODS: This single-centred observational study used quantitative and qualitative assessments to examine patient perceptions of MBT. Participants diagnosed with MDD or BD completed an anonymous questionnaire obtaining demographics, prior medication history, and symptom burden. Self-assessment questionnaires specific to each diagnosis were also used: Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR), Altman Self-Rating Mania Scale (ASRM), and General Anxiety Disorder Questionnaire (GAD-7). A logistic regression model analysed the association of MBT acceptance with disorder type, QIDS-SR, and GAD-7 scores. A bootstrap method assessed the proportion of MBT acceptance. The qualitative assessment consisted of 30-minute interviews to elicit perceptions and attitudes towards MBT. RESULTS: = 43) showed probiotics (37.2%) as the top choice, followed by FMT (32.6%), dietary change (25.6%), and prebiotics (4.6%). A majority of participants (72.1%) expressed willingness to try MBT for their mood disorder, however, logistic regression analysis did not identify statistically significant predictors for MBT acceptance among disorder type, QIDS-SR, and GAD-7. CONCLUSION: There is an increased focus on the gut microbiota's role in mood disorders' etiology and treatment. Promising research and patient interest underscore the necessity for exploring and educating on patient perspectives and the factors influencing attitudes towards MBT.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".