Nutrient Intakes are Correlated with Overall Psychiatric Functioning in Adults with Mood Disorders
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relation between nutrient intake and psychiatric functioning in adults with confirmed mood disorders. METHOD: A cross-sectional study was conducted of the intake of major (that is, carbohydrates, fat, and protein) and minor (that is, vitamins and minerals) nutrients (from 3-day food records and a Food Frequency Questionnaire), Global Assessment of Functioning (GAF) scores, and symptoms of depression and mania (the Hamilton Depression Rating Scale and the Young Mania Rating Scale) in 97 community-based adults with mood disorders whose diagnoses were confirmed with structured interviews. RESULTS: Significant correlations were found between GAF scores and energy (kilocalories), carbohydrates, fibre, total fat, linoleic acid, riboflavin, niacin, folate, vitamin B6, vitamin B12, pantothenic acid, calcium, phosphorus, potassium, and iron (all P values < 0.05), as well as magnesium (r = 0.41, P < 0.001) and zinc (r = 0.35, P < 0.001). Though modest in magnitude, the pattern of correlations was consistent, indicating higher levels of mental function associated with a higher intake of nutrients. Depression and mania scores, which were generally mild or moderate, did not individually show consistent patterns. When dietary supplement use was added to nutrient intakes from food, GAF scores remained positively correlated (P < 0.05) with all dietary minerals. CONCLUSION: This detailed analysis in a clinically diagnosed sample was consistent with prior epidemiologic surveys, revealing an association between higher levels of nutrient intakes and better mental health. Nutrient intakes warrant further consideration in the treatment of people with mood disorders.
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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 | 0.003 |
| 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) | 0.001 | 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".