Animal and plant protein intake association with mental health, tryptophan metabolites pathways, and gut microbiota in healthy women: a cross-sectional study
Bibliographic record
Abstract
Mental health is affected by tryptophane (TRP) metabolism regulation. Diet-influenced gut microbiome regulates TRP metabolism. Thus, the present study aimed to explore the relationship between type of dietary protein intake, gut microbiota, TRP metabolites homeostasis, and mental well-being in healthy women. 91 healthy females aged 18-50 were recruited based on the study protocol. Validate and reliable questionnaires assessed dietary intake and mental health. Biochemical tests and gut microbiota composition were analyzed following the manufacturer's instructions for each enzyme-linked immune sorbent assay (ELISA) kit and Real-time quantitative polymerase chain reaction (qPCR) methods respectively. Regression methods were used to estimate the considered associations. The results show that in the fully adjusted model, plant protein consumption was partially inversely associated with depression risk (OR = 0.27; 95% CI: 0.06, 1.09; P = 0.06). Higher dietary animal protein intake was marginally associated with psychological distress (OR = 2.59; 95% CI: 0.91, 7.34; P = 0.07). KYN to serotonin ratio was inversely associated with animal protein consumption (ß = 1.10; 95% CI: -0.13, 2.33; P = 0.07). Firmicutes/Bacteriodetes ratio (β = -1.27 × 103, SE = 5.99 × 102, P = 0.03) was lower in the top tertile of plant protein. A partially negative correlation was found between dietary animal protein and Prevotella abundance (β = -9.20 × 1018, SE = 5.04 × 1018, P = 0.06). Overall, significant inverse associations were found between a diet high in plant protein with mental disorders, KYN levels, and Firmicutes to Bacteroidetes ratio while adhering to higher animal protein could predispose women to psychological stress.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".