Red and White Meat Intake in Relation to Mental Disorders in Iranian Adults
Bibliographic record
Abstract
Background: The association between meat consumption and mental disorders is less investigated in Iranian population. We examined the association between meat consumption and prevalence of symptoms of depression, anxiety, and psychological distress in Iranian adults. Methods: This cross-sectional study included 3,362 participants aged 18–55 years old. A dish-based 106-item semiquantitative food frequency questionnaire (FFQ) was used to assess usual dietary intake of study population. Hospital Anxiety and Depression Scale (HADS) and General Health Questionnaire (GHQ), all validated in Iranian population, were applied to collect data on symptoms of anxiety, depression, and psychological distress, respectively. Results: The prevalence of symptoms of depression, anxiety, and psychological distress in the study population was 28.6, 13.6, and 22.6%, respectively. After considering potential confounders, individuals in the top quartile of red meat intake had 43% increased risk of depression symptoms [odds ratio (OR) = 1.43; 95% CI: 1.09–1.89] compared to those in the first quartile. No significant relation was observed between red meat intake and anxiety or psychological distress symptoms. White meat consumption was not associated with mental disorders. Stratified analysis by sex showed that male participants in the highest quartile of red meat intake had 92% greater risk of depression symptoms (95% CI: 1.17–3.15) than those individuals in the lowest category. Red and white meat intake was not associated with mental disorders in women. In overweight or obese individuals, despite lack of any association between red meat intake and mental disorders, high intake of white meat was associated with a lower odds of psychological distress symptoms (OR = 0.64; 95% CI: 0.42–0.99) and a lower risk of depression symptoms (OR = 0.68; 95% CI: 0.45–1.00). In normal-weight participants, those in the highest quartile of red meat intake had greater odds for depression symptoms than those in the lowest quartile (OR = 1.66; 95% CI: 1.14–2.42). Conclusions: We found that red meat consumption was associated with increased risk of depression symptoms, especially in men, and normal-weight participants. In overweight or obese participants, white meat intake was inversely associated with psychological distress symptoms.
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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 | 0.001 |
| 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) | 0.001 | 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".