Vitamin and Mineral Intakes in Adults with Mood Disorders: Comparisons to Nutrition Standards and Associations with Sociodemographic and Clinical Variables
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the nutrient intakes of people with mood disorders. METHOD: A cross-sectional survey using 3-day food records was carried out in 97 adults with bipolar or major depressive disorder to compare nutrient intakes with Dietary Reference Intakes and British Columbia Nutrition Survey (BCNS) data. Blood levels of selected nutrients were compared to reference ranges. Bivariate and multivariate analyses examined the effects of sociodemographic and clinical variables on nutrient intakes. RESULTS: The average age of respondents was 46 (±13) years; most were women (n = 69) who had less than a university degree (n = 60) and whose incomes were in the government-defined lower range (n = 39). Compared with the BCNS, a larger proportion of the sample was below the estimated average requirement for thiamin (26% vs 8%), riboflavin (21% vs 4%), folate (64% vs 27%), phosphorous (12% vs 1%), and zinc (39% vs 15%; all P < 0.0001), as well as vitamin B(6) (25% vs 16%) and vitamin B(12) (27% vs 8%; both P < 0.05). Combined intakes of food and supplements helped reduce the prevalence of inadequacy; however, with supplementation, the proportion of participants exceeding the tolerable upper intake levels for niacin, vitamin B(6), folate, vitamin C, calcium, magnesium, iron, and zinc ranged from 1%-8%. Income, relationship status, age, gender, and caloric intake were associated with intakes of many nutrients. Types of medications were associated with nutrient intakes, as lower intakes of thiamin and phosphorous (P < 0.05) were found with antidepressant use, higher calcium and iron intakes (P < 0.05) were associated with antianxiety medication use, and magnesium intakes were increased with mood stabilizers (regression coefficient = 52.61, P < 0.05, 95% confidence interval = 0.74 to 104.48). CONCLUSIONS: Adults with mood disorders are at risk for many nutrient inadequacies, as well as occasional excesses; social, demographic, and clinical factors may affect their nutrient intakes.
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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".