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Record W4212882787 · doi:10.1093/jcag/gwab049.228

A229 GUT MICROBIOTA PROFILES, DIET AND SHORT-CHAIN FATTY ACIDS AS PREDICTORS OF GENERALIZED ANXIETY DISORDER

2022· article· en· W4212882787 on OpenAlexaff
V Rabbia, Giada De Palma, Jun Lü, Elena F. Verdú, Heather Armstrong, Stephen M. Collins, Rebecca Anglin, Michael G. Surette, Přemysl Berčík

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of AlbertaMcMaster University
Fundersnot available
KeywordsGeneralized anxiety disorderInternal medicineGut floraCalprotectinFecesGastroenterologyBody mass indexMedicineAnxietyMini-international neuropsychiatric interviewImmunologyBiologyPsychiatryDiseaseInflammatory bowel diseaseMicrobiology

Abstract

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Abstract Background Generalized anxiety disorder (GAD) is a debilitating chronic condition with a lifetime prevalence of 4–7% worldwide. Both diet and gut microbiota have been previously associated with anxiety. Aims To investigate whether bacterial taxa and/or nutrients associate with GAD, and whether they differ from those of healthy controls (HC). Methods Patients with GAD (n=82) and matched HC (n=97) were assessed by validated questionnaires for anxiety (DASS-21), gastrointestinal (GI) symptoms (Rome III, Short-Form Leeds Dyspepsia), and dietary profiles by the Dietary Questionnaire for Epidemiological Studies. We quantified several blood and stool biomarkers, including inflammatory and neuroactive metabolites, as well as short-chain fatty acids. Stool microbiota profiles were assessed by16S rRNA gene sequencing through Illumina. The data was then analyzed following the pipelines of dada2 and by multiple factor analysis (MFA), mean comparisons, correlation, LEfSe and XGBoost using R software (v.1.2.1335). Multiple comparison results were corrected allowing 5% of FDR. Results Using MFA to analyze all variables, we identified 3 clusters: one mainly composed of HC (n=99, 91% HC, GI symptoms in 25% of subjects), a second mixed cluster (n=30, 80% GAD, GI symptoms in 80%) and a third cluster mainly composed of GAD patients (n=50, 98% GAD, GI symptoms in 86%). When focusing only on the HCs of cluster 1 (n=90) and GADs of cluster 3 (n=49), we found higher GI symptoms, body mass index, serum C-reactive protein and stool calprotectin levels (adj. p=1.3x10-9, 0.001, 0.017 and 0.017, respectively) and lower concentrations of propionate, butyrate and acetate in GAD compared to HC. GADs also reported overall lower caloric intake (kJ/day; adj. p=1.7x10-4) in the food frequency questionnaire. Fibre (g/day) was the macronutrient most negatively associated with anxiety scores (R=-0.44; adj. p=4.2x10-5). Bacteroides was the only bacterial taxon significantly associated with GAD, as well as with anxiety scores (R=0.31, adj. p=0.003). Interestingly, Bacteroides/fiber ratio was strongly correlated to anxiety scores (R=0.58, adj. p=2.7x10-09). Furthermore, demographic, biomarkers and bacterial taxa data were predictive of the patients’ disease state with 92.8% accuracy. The features that aid the model to predict disease state were Bacteroides/fiber ratio, GI symptoms and stool acetate levels. Conclusions Our results suggest that most GAD patients differ in dietary and microbiota profiles from HCs, and that the Bacteroides/fiber ratio, stool acetate and GI symptoms might be good predictors of disease state. Furthermore, these data strongly support the role of microbiota-gut-brain axis in genesis of psychiatric diseases, and they will inform mechanistic studies in gnotobiotic mouse models. Funding Agencies NIH

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.004
GPT teacher head0.212
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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